vlfeat/0000755000175000017500000000000012237042517010750 5ustar dimadimavlfeat/src/0000755000175000017500000000000012237035722011537 5ustar dimadimavlfeat/src/mser.10000644000175000017500000000771112237004736012576 0ustar dimadima.TH MSER 1 "" "VLFeat" "VLFeat" .\" ------------------------------------------------------------------ .SH NAME .\" ------------------------------------------------------------------ mser \- Maximally Stable Extremal Regions .\" ------------------------------------------------------------------ .SH SYNOPSIS .\" ------------------------------------------------------------------ .B mser .RI [ options ] FILE .\|.\|. .\" ------------------------------------------------------------------ .SH OPTIONS .\" ------------------------------------------------------------------ .TP .B \-v\fR,\fP \-\^\-verbose Increase verbosity level (may be repeated). .TP .B \-h\fR,\fP \-\^\-help Show options and version. .TP .BI \-\^\-seeds \fR[=\fPFILESPEC\fR]\fP Enable/specify seeds output. .TP .BI \-\^\-frames \fR[=\fPFILESPEC\fR]\fP Enable/specify frames output. .TP .BI \-\^\-meta \fR[=\fPFILESPEC\fR]\fP Enable/specify meta output. .TP .BI \-d " REAL" "\fR,\fP \-\^\-delta" "=REAL" Specify delta parameter. .TP .BI \-\^\-min-area \fR=\fPREAL Specify minimum region area. .TP .BI \-\^\-max-area \fR=\fPREAL Specify maximum region area. .TP .BI \-\^\-max-variation \fR=\fPREAL Specify maximum region variation. .TP .BI \-\^\-min-diversity \fR=\fPREAL Specify minimum region diversity. .\" ------------------------------------------------------------------ .SH DESCRIPTION .\" ------------------------------------------------------------------ .B mser computes the .I Maximally Stable Extremal Regions (MSER) \. In the simplest case, .B mser reads an image file (in PGM format), computes the MSERs, and writes them to a file of region seeds. Alternatively, the .B --frames option can be used to compute elliptical frames instead of region seeds. It is also possible to use .B --frames and .B --seeds in combination to save both seeds and frames. . .P A .IR region\ seed is and index pointing to one of the image pixels (pixels are enumerated in row major order, with 0 corresponding to the upper-left corner of the image). An MSER containing a given seed is the largest connect subset of pixels whose value is not smaller than the seed value. . .P A .IR frame is an ellipse approximating an MSER and is specified by 5 floating point numbers: coordinate of the center .I x and .I y, and elements .IR S11 , .IR S12 , .IR S22 of the co-variance matrix. .P Both seeds and frames can be saved either in ascii or binary format, based on the specified .IR FILESPEC s (see also .BR vlfeat (7)): . .TP Ascii format . Each seed is a decimal number, one per line of text, and each frame is a list of 6 floating point numbers, one per line of text. . .TP Binary format . The binary format is similar to the ascii format, except that each seed is stored as a unsigned integer (four bytes) and each frame component is stored as an IEEE double (eight bytes). The data is written in little endian order. . .P .B mser can process multiple images. In this case the names of the corresponding output files are calculated based on .IR FILESPEC s. .\" ------------------------------------------------------------------ .SH EXAMPLES .\" ------------------------------------------------------------------ . .TP mser test.pgm -d 5 Computes the MSERs of image .I test.pgm setting the parameter .I delta to 5 and save the result as a sequence of region seeds to .IR test.mser . .TP mser test.pgm \-\^\-frames Computes the MSERs of image .IR test.pgm , fits elliptical frames to them and saves the frames to .IR test.frame . . .TP mser test.pgm \-\^\-frames=bin:///tmp/%.frame \-\^\-seeds Computes the MSERs of image .IR test.pgm , fits elliptical frames to them and saves the frames to .I /tmp/test.frame and the region seeds to .IR test.mser . . .\" ------------------------------------------------------------------ .SH SEE ALSO .\" ------------------------------------------------------------------ .P .BR vlfeat (7) . .P J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In .IR "Proc. BMVC" , 2002. . vlfeat/src/test_host.c0000644000175000017500000000064312237004736013723 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include int main(int argc VL_UNUSED, char ** argv VL_UNUSED) { char * string = vl_configuration_to_string_copy() ; assert (string) ; VL_PRINTF(string) ; vl_free(string) ; return 0 ; } vlfeat/src/test_stringop.c0000644000175000017500000000741512237004736014617 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "check.h" #include #include int main (int argc VL_UNUSED, char *argv[] VL_UNUSED) { vl_size q ; int err, protocol ; char const *repl = "vision" ; char const *str = "*: * '\\*' *" ; char const *subst = "vision: vision '*' vision" ; char const *t1 = "a/b/c/d.tar.gz" ; char const *t3 = "t" ; char const *next ; enum { buf_len = 1024 } ; char buf [buf_len] ; enum { sml_len = 2 } ; char sml [sml_len] ; /* -------------------------------------------------------------- */ /* t3 fits in the destination buffer */ q = vl_string_copy (sml, sml_len, t3) ; err = (q >= sml_len) ; check (! err, "vl_string_copy") ; check (q == strlen(t3), "vl_string_copy") ; /* t1 does not fit in the destination buffer */ q = vl_string_copy (sml, sml_len, t1) ; err = (q >= sml_len) ; check (err, "vl_string_copy") ; check (q == strlen(t1), "vl_string_copy") ; /* -------------------------------------------------------------- */ /* check substitution with widcard */ q = vl_string_replace_wildcard (buf, buf_len, str, '*', '\\', repl) ; err = (q >= buf_len) ; check (! err, "vl_string_replace_wildcard") ; check (strcmp(buf, subst) == 0, "vl_string_replace_wildcard") ; check (strlen(subst) == q, "vl_string_replace_wildcard") ; /* and the case in which the destination buffer is too small */ q = vl_string_replace_wildcard (sml, sml_len, str, '*', '\\', repl) ; err = (q >= sml_len) ; check (err, "vl_string_replace_wildcard") ; check (strlen(subst) == q, "vl_string_replace_wildcard") ; /* and without output string */ q = vl_string_replace_wildcard (0,0, str, '*', '\\', repl) ; err = (q >= sml_len) ; check (err, "vl_string_replace_wildcard") ; check (strlen(subst) == q, "vl_string_replace_wildcard") ; /* -------------------------------------------------------------- */ q = vl_string_basename (buf, buf_len, "a/b/c/d.tar.gz", 0) ; err = (q >= buf_len) ; check (! err, "vl_string_basename") ; check (strcmp(buf, "d.tar.gz") == 0, "vl_string_basename") ; q = vl_string_basename (buf, buf_len, "a/b/c/d.tar.gz", 1) ; err = (q >= buf_len) ; check (! err, "vl_string_basename") ; check (strcmp(buf, "d.tar") == 0, "vl_string_basename") ; q = vl_string_basename (buf, buf_len, "a/b/c/d.tar.gz", 2) ; err = (q >= buf_len) ; check (! err, "vl_string_basename") ; check (strcmp(buf, "d") == 0, "vl_string_basename") ; q = vl_string_basename (buf, buf_len, "a/b/c/d.tar.gz", 3) ; err = (q >= buf_len) ; check (! err, "vl_string_basename") ; check (strcmp(buf, "d") == 0, "vl_string_basename") ; /* -------------------------------------------------------------- */ next = "ascii://bin://ascii://bin://unkown1://unknown2://bin:///which remains/" ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_ASCII,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_BINARY,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_ASCII,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_BINARY,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_UNKNOWN,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_UNKNOWN,) ; next = vl_string_parse_protocol(next, &protocol) ; check (protocol == VL_PROT_BINARY,) ; check (strcmp(next, "/which remains/") == 0,"%s",next) ; check_signoff() ; return 0 ; } vlfeat/src/generic-driver.h0000644000175000017500000001435112237004745014622 0ustar dimadima/** @author Andrea Vedaldi ** @brief Support for command line drivers - Definition. ** @internal ** ** This file contains support code which is shared by the command ** line drivers. **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. Copyright (C) 2013 Andrea Vedaldi. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #ifndef VL_GENERIC_DRIVER #define VL_GENERIC_DRIVER #include #include #include #include /** @brief File meta information **/ struct _VlFileMeta { vl_bool active ; /**< Is the file active? */ char pattern [1024] ; /**< File name pattern */ int protocol ; /**< File protocol */ char name [1024] ; /**< Current file name */ FILE * file ; /**< Current file stream */ } ; /** @brief File meta information type ** @see ::_VlFileMeta **/ typedef struct _VlFileMeta VlFileMeta ; /* ----------------------------------------------------------------- */ /** @brief Parse argument for file meta information ** ** @param optarg argument to parse. ** @param self structure to initalize. ** ** The function parses the string @a optarg to fill the structure @a ** self. @a optarg is supposed to be composed of two parts: a file ** protocol specification and a file pattern. Then the function: ** ** - Sets VlFileMeta::active to true. ** - Sets VlFileMeta::protocol to the file protocol id (if any). ** - Sets VlFileMeta::pattern to the file pattern (if any). ** ** @return error code. The funciton may fail either because the file ** protocol is not recognized (::VL_ERR_BAD_ARG) or because the file ** pattern is too long to be stored (::VL_ERR_OVERFLOW). **/ static int vl_file_meta_parse (VlFileMeta * self, char const * optarg) { vl_size q ; self->active = 1 ; if (optarg) { int protocol ; char const * arg = vl_string_parse_protocol (optarg, &protocol) ; /* parse the (optional) protocol part */ switch (protocol) { case VL_PROT_UNKNOWN : return VL_ERR_BAD_ARG ; case VL_PROT_ASCII : case VL_PROT_BINARY : self->protocol = protocol ; break ; case VL_PROT_NONE : break ; } if (vl_string_length (arg) > 0) { q = vl_string_copy (self->pattern, sizeof (self->pattern), arg) ; if (q >= sizeof(self->pattern)) { return VL_ERR_OVERFLOW ; } } } return VL_ERR_OK ; } /* ----------------------------------------------------------------- */ /** @brief Open the file associated to meta information ** ** @param self File meta information. ** @param basename Basename. ** @param mode Opening mode (as in @c fopen). ** ** @return error code. The error may be either either ** ::VL_ERR_OVERFLOW if the file name is too long or to ::VL_ERR_IO ** if the file cannot be opened. **/ static int vl_file_meta_open (VlFileMeta * self, char const * basename, char const * mode) { vl_size q ; if (! self->active) { return VL_ERR_OK ; } q = vl_string_replace_wildcard (self->name, sizeof(self->name), self -> pattern, '%', '\0', basename) ; if (q >= sizeof(self->name)) { return vl_set_last_error (VL_ERR_OVERFLOW, NULL) ; } if (self->active) { self->file = fopen (self->name, mode) ; if (! self->file) { return vl_set_last_error(VL_ERR_IO, NULL) ; } } return 0 ; } /* ----------------------------------------------------------------- */ /** @brief Close the file associated to meta information ** ** @param self File meta information. **/ static void vl_file_meta_close (VlFileMeta * self) { if (self -> file) { fclose (self -> file) ; self -> file = 0 ; } } /* ----------------------------------------------------------------- */ /** @brief Write double to file ** ** @param self File meta information. ** @param x Datum to write. ** ** @return error code. The function returns ::VL_ERR_ALLOC if the ** datum cannot be written. **/ VL_INLINE int vl_file_meta_put_double (VlFileMeta * self, double x) { int err ; size_t n ; double y ; switch (self -> protocol) { case VL_PROT_ASCII : err = fprintf (self -> file, "%g ", x) ; break ; case VL_PROT_BINARY : vl_swap_host_big_endianness_8 (&y, &x) ; n = fwrite (&y, sizeof(double), 1, self -> file) ; err = n < 1 ; break ; default : abort() ; } return err ? VL_ERR_ALLOC : VL_ERR_OK ; } /* ----------------------------------------------------------------- */ /** @brief Write uint8 to file ** ** @param self File meta information. ** @param x Datum to write. ** ** @return error code. The function returns ::VL_ERR_ALLOC if the ** datum cannot be written. **/ VL_INLINE int vl_file_meta_put_uint8 (VlFileMeta *self, vl_uint8 x) { size_t n ; int err ; switch (self -> protocol) { case VL_PROT_ASCII : err = fprintf (self -> file, "%d ", x) ; if (err) return VL_ERR_ALLOC ; break ; case VL_PROT_BINARY : n = fwrite (&x, sizeof(vl_uint8), 1, self -> file) ; if (n < 1) return VL_ERR_ALLOC ; break ; default : abort() ; } return VL_ERR_OK ; } /* ----------------------------------------------------------------- */ /** @brief Read double from file ** ** @param self File meta information. ** @param x Datum read. ** ** @return error code. The function returns ::VL_ERR_EOF if the ** end-of-file is reached and ::VL_ERR_BAD_ARG if the file is ** malformed. **/ VL_INLINE int vl_file_meta_get_double (VlFileMeta *self, double *x) { int err ; size_t n ; double y ; switch (self -> protocol) { case VL_PROT_ASCII : err = fscanf (self -> file, " ") ; if (err == EOF) return VL_ERR_EOF ; err = fscanf (self -> file, "%lg", x) ; if (err == EOF) return VL_ERR_EOF ; if (err < 1 ) return VL_ERR_BAD_ARG ; break ; case VL_PROT_BINARY : n = fread (&y, sizeof(double), 1, self -> file) ; if (n < 1) return VL_ERR_BAD_ARG ; vl_swap_host_big_endianness_8 (x, &y) ; break ; default : abort() ; } return VL_ERR_OK ; } /* VL_GENERIC_DRIVER */ #endif vlfeat/src/test_getopt_long.c0000644000175000017500000000420212237004736015262 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include int daggerset; /* options descriptor */ static struct option longopts[] = { { "help", no_argument, 0, 'h' }, { "buffy", no_argument, 0, 'b' }, { "fluoride", required_argument, 0, 'f' }, { "daggerset", no_argument, &daggerset, 1000 }, { "daggerunset", no_argument, &daggerset, 1001 }, { "spike", optional_argument, 0, 1002 }, { NULL, 0, 0, 0 } }; int main (int argc, char *argv[]) { int ch ; printf("*** parsing options: start\n") ; while ((ch = getopt_long(argc, argv, "hbf:", longopts, 0)) != -1) { switch (ch) { case 'h': printf("test_getopt_long: test VLFeat implementation of this function.\n" " --help -h this message\n" " --buffy -b option with no arguments\n" " --fluoride -f option with required argument\n" " --daggerset auto option with no arguments\n" " --daggerrunset auto option with no argument\n" " --spike long option with optional argument\n") ; break; case 'b': printf("option `b' or `buffy'\n") ; break; case 'f': printf("option `f' or `flouride' with arg `%s'\n", optarg) ; break ; case 1002 : printf("option `spike' with arg `%s'\n", optarg) ; break ; case 0: printf("automatic longoption (daggerset=%d)\n", daggerset) ; break ; case '?' : printf("illegal option or missing argument\n") ; break ; case ':' : printf("illegal option\n") ; break ; default: abort() ; } } printf("*** parsing options: end\n"); { int i ; for(i = optind ; i < argc ; ++i) printf("non-option: '%s'\n", argv[i]) ; } return 0 ; } vlfeat/src/vlfeat.70000644000175000017500000000547412237004736013123 0ustar dimadima.TH VLFEAT 1 "" "VLFeat" "VLFeat" .\" ------------------------------------------------------------------ .SH NAME .\" ------------------------------------------------------------------ VLFeat \- VisionLab Features Library .\" ------------------------------------------------------------------ .SH DESCRIPTION .\" ------------------------------------------------------------------ .B VLFeat is a collection of computer vision algorithms with a special focus on image features such as SIFT and MSER. .P The functionalities offered by the library can be accessed either through its API, or by means of command line and MATLAB programs. See the .B SEE ALSO section for a list of the related command line programs and for a pointer to the general documentation of the library. . .P . .\" ------------------------------------------------------------------ .SH "FILE SPECIFIERS (FILESPEC)" .\" ------------------------------------------------------------------ Some commands in .B VLFeat make use of `file specifiers' to associate to one or more input files the appropriate output/auxiliary files. A file specifier is a string such as `ascii://%.txt' and contains two pieces of information: .IP \(bu 4 The .B file format (in this case .IR ascii ). The supported formats depend on the particular application. Two common formats are .I ascii and .IR bin (ary). . .IP \(bu 4 A .B name transformation rule (in this case .IR %.txt ). This is used to transform the name an input file into the name of the corresponding output/auxiliary file. The transformed name is comput by extracting the basename of the input file substituting it to all occurences of the wildcard characer `%' in the transformation rule (`%%') passes the wildcard character untouched. .P Recall that the basename of a file name is obtained by stripping the directory prefix (everything from the beginning up to the last `/' or `\\' character) and extension (everything from the last `.' characeter (excluding the prefix) to the end). .P For instance, the filespec `ascii://%.txt' and the input file `test.data' specify a file named `test.txt' in ascii (text) format. . .SS "Partial file specifiers" . Normally, a program provides default values for its file specifiers. In this case, partial file specifier (with only the file format or the name transformation rule are acceptable). . .SS "File specifiers equivalent to a file name" . A partial specifier such as `file.txt' is in effect equivalent to the file name `file.txt'. If the specifier is used in conjunction with multiple input files, this causes the same file to be accessed multile times. .\" ------------------------------------------------------------------ .SH SEE ALSO .\" ------------------------------------------------------------------ .BR sift (1), .BR mser (1) .P A. Vedaldi and B. Fulkerson. VLFeat Library. .IR http://www.vlfeat.org/ vlfeat/src/test_mathop.c0000644000175000017500000000303612237004736014235 0ustar dimadima#include #include #include #include int main (int argc VL_UNUSED, char** argv VL_UNUSED) { int error = 0 ; /* ----------------------------------------------------------------- * vl_fast_resqrt_* * -------------------------------------------------------------- */ VL_PRINTF ("%20s %10s %10s %10s\n", "func", "elaps [s]", "eval/s", "chksum") ; #define SFX f #define SFX2 FLT #define T float #define SQRT sqrtf #define ABS fabsf #define ONE 1.0F #include "test_mathop_fast_resqrt.tc" #undef ONE #undef ABS #undef SQRT #undef T #undef SFX2 #undef SFX #define SFX d #define SFX2 DBL #define T float #define SQRT sqrt #define ABS fabs #define ONE 1.0 #include "test_mathop_fast_resqrt.tc" #undef ONE #undef ABS #undef SQRT #undef T #undef SFX2 #undef SFX VL_PRINTF("\n") ; /* ----------------------------------------------------------------- * vl_fast_sqrt_ui* * -------------------------------------------------------------- */ VL_PRINTF ("%20s %10s %10s %10s\n", "func", "elaps [s]", "eval/s", "chksum") ; #undef SFX #undef T #undef STEP #define SFX 32 #define T vl_uint32 #define STEP 7 #include "test_mathop_fast_sqrt_ui.tc" #undef SFX #undef T #undef STEP #define SFX 16 #define T vl_uint16 #define STEP 0 #include "test_mathop_fast_sqrt_ui.tc" #undef SFX #undef T #undef STEP #define SFX 8 #define T vl_uint8 #define STEP 0 #include "test_mathop_fast_sqrt_ui.tc" return error ; } vlfeat/src/test_threads.c0000644000175000017500000000435612237004745014405 0ustar dimadima/** @file test_threads.c ** @author Andrea Vedaldi ** @brief Test threaded VLFeat client **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. Copyright (C) 2013 Andrea Vedaldi. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #if defined(VL_THREADS_POSIX) #include #endif #if defined(VL_THREADS_WIN) #include #endif #if defined(VL_THREADS_POSIX) void * testThread(void * args) { int j ; int id = *(int*)args ; vl_tic() ; for (j = 0 ; j < 10 ; ++j) { printf("Thread %5d: %d\n", id, vl_rand_int31(vl_get_rand())) ; fflush(stdout) ; } printf("Thread %5d: elapsed time: %.2f s\n", id, vl_toc()) ; return NULL ; } #elif defined(VL_THREADS_WIN) DWORD WINAPI testThread(LPVOID args) { int j ; int id = *(DWORD*)args ; vl_tic() ; for (j = 0 ; j < 10 ; ++j) { printf("Thread %5d: %d\n", id, vl_rand_int31(vl_get_rand())) ; fflush(stdout) ; } printf("Thread %5d: elapsed time: %.2f s\n", id, vl_toc()) ; return 0 ; } #endif int main(int argc VL_UNUSED, char** argv VL_UNUSED) { #ifndef VL_DISABLE_THREADS #if defined(VL_THREADS_POSIX) vl_uindex i ; pthread_t threads [5] ; int threadIds [5] ; for (i = 0 ; i < sizeof(threads) / sizeof(threads[0]) ; ++i) { threadIds [i] = i ; pthread_create (threads + i, NULL, testThread, threadIds + i) ; } for (i = 0 ; i < sizeof(threads) / sizeof(threads[0]) ; ++i) { pthread_join (threads[i], NULL) ; } #elif defined(VL_THREADS_WIN) int i ; DWORD threadIds [5] ; HANDLE threadHandles [5] ; for (i = 0 ; i < sizeof(threadHandles) / sizeof(threadHandles[0]) ; ++i) { threadHandles [i] = CreateThread ( NULL, 0, testThread, threadIds + i, 0, threadIds + i) ; } for (i = 0 ; i < sizeof(threadHandles) / sizeof(threadHandles[0]) ; ++i) { WaitForSingleObject (threadHandles[i], INFINITE); } #endif /* VL_DISABLE_THREADS */ #else printf("VLFeat was compiled without threading support\n") ; #endif { char * string = vl_configuration_to_string_copy() ; printf("%s", string) ; vl_free(string) ; } return 0 ; } vlfeat/src/test_kmeans.c0000644000175000017500000000320312237004745014217 0ustar dimadima/** @file test_kmeans.c ** @brief K-means test ** @author David Novotny **/ #include #include #include //#include int main(int argc VL_UNUSED, char ** argv VL_UNUSED) { VlRand rand ; vl_size numData = 100000; vl_size dimension = 256; vl_size numCenters = 400; vl_size maxiter = 10; vl_size maxComp = 100; vl_size maxrep = 1; vl_size ntrees = 1; double * data; vl_size dataIdx, d; //VlKMeansAlgorithm algorithm = VlKMeansANN ; VlKMeansAlgorithm algorithm = VlKMeansLloyd ; //VlKMeansAlgorithm algorithm = VlKMeansElkan ; VlVectorComparisonType distance = VlDistanceL2 ; VlKMeans * kmeans = vl_kmeans_new (VL_TYPE_DOUBLE,distance) ; vl_rand_init (&rand) ; vl_rand_seed (&rand, 1000) ; data = vl_malloc(sizeof(double) * dimension * numData); for(dataIdx = 0; dataIdx < numData; dataIdx++) { for(d = 0; d < dimension; d++) { double randomNum = (double)vl_rand_real3(&rand)+1; data[dataIdx*dimension+d] = randomNum; } } vl_kmeans_set_verbosity (kmeans,1); vl_kmeans_set_max_num_iterations (kmeans, maxiter) ; vl_kmeans_set_max_num_comparisons (kmeans, maxComp) ; vl_kmeans_set_num_repetitions (kmeans, maxrep) ; vl_kmeans_set_num_trees (kmeans, ntrees); vl_kmeans_set_algorithm (kmeans, algorithm); //struct timeval t1,t2; //gettimeofday(&t1, NULL); vl_kmeans_cluster(kmeans,data,dimension,numData,numCenters); //gettimeofday(&t2, NULL); //VL_PRINT("elapsed vlfeat: %f s\n",(double)(t2.tv_sec - t1.tv_sec) + ((double)(t2.tv_usec - t1.tv_usec))/1000000.); vl_kmeans_delete(kmeans); vl_free(data); return 0 ; } vlfeat/src/test_heap-def.c0000644000175000017500000000766212237004736014427 0ustar dimadima/** @file test_heap-def.c ** @brief Test heap-def.h ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #define VL_HEAP_prefix vl_heap_float #define VL_HEAP_type float #include #include typedef struct _S { int x ; } S ; int s_cmp (S const * v, vl_uindex a, vl_uindex b) { return v[a].x - v[b].x ; } void s_swap (S * v, vl_uindex a, vl_uindex b) { S t = v[a] ; v[a] = v[b] ; v[b] = t ; printf("Swapping %" VL_FMT_UINDEX "x with %" VL_FMT_UINDEX "\n", a, b) ; } #define VL_HEAP_prefix s_heap #define VL_HEAP_type S #define VL_HEAP_cmp s_cmp #include #define VL_HEAP_prefix track_s_heap #define VL_HEAP_type S #define VL_HEAP_cmp s_cmp #define VL_HEAP_swap s_swap #include typedef struct _H { vl_size numNodes ; int* array ; } H ; int h_cmp (H const * h, vl_uindex a, vl_uindex b) { return h->array[a] - h->array[b] ; } void h_swap (H * h, vl_uindex a, vl_uindex b) { int t = h->array[a] ; h->array[a] = h->array[b] ; h->array[b] = t ; } #define VL_HEAP_prefix h_heap #define VL_HEAP_array H* #define VL_HEAP_array_const H const* #define VL_HEAP_swap h_swap #define VL_HEAP_cmp h_cmp #include int main (int argc VL_UNUSED, char** argv VL_UNUSED) { vl_uindex i ; vl_size numNodes = 0 ; float data [] = {1.01, 5.02, 8, 0.1, 100, 3, 9, 4, 1.02} ; S data_s [] = {{5}, {7}, {9}, {1}} ; S data_s_track [] = {{5}, {7}, {9}, {1}} ; int data_h [] = {5, 7, 9, 1} ; H h ; h.numNodes = 0 ; h.array = data_h ; printf("Pushing heap\n") ; for (i = 0 ; i < sizeof(data) / sizeof(data[0]) ; ++i) { printf ("%5" VL_FMT_UINDEX ": %f\n", i, data[i]) ; vl_heap_float_push (data, &numNodes) ; } printf("Popping heap\n") ; for (i = 0 ; i < sizeof(data) / sizeof(data[0]) ; ++i) { printf ("%" VL_FMT_UINDEX ": %f\n", i, data[vl_heap_float_pop (data, &numNodes)]) ; } printf("Refilling, updating fourth element, and popping again\n") ; for (i = 0 ; i < sizeof(data) / sizeof(data[0]) ; ++i) { vl_heap_float_push (data, &numNodes) ; } printf("%f -> %f\n", data[3], 9.01) ; data [3] = 9.01 ; vl_heap_float_update (data, numNodes, 3) ; for (i = 0 ; i < sizeof(data) / sizeof(data[0]) ; ++i) { printf ("%" VL_FMT_UINDEX ": %f\n", i, data[vl_heap_float_pop (data, &numNodes)]) ; } printf("Pushing heap of structures\n") ; numNodes = 0 ; for (i = 0 ; i < sizeof(data_s) / sizeof(data_s[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, data_s[i].x) ; s_heap_push (data_s, &numNodes) ; } printf("Popping heap of structures\n") ; for (i = 0 ; i < sizeof(data_s) / sizeof(data_s[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, data_s[s_heap_pop (data_s, &numNodes)].x) ; } printf("Pushing heap of structures with custom swap\n") ; numNodes = 0 ; for (i = 0 ; i < sizeof(data_s) / sizeof(data_s[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, data_s_track[i].x) ; track_s_heap_push (data_s_track, &numNodes) ; } printf("Popping heap of structures with custom swap\n") ; for (i = 0 ; i < sizeof(data_s) / sizeof(data_s[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, data_s_track [track_s_heap_pop (data_s_track, &numNodes)].x) ; } printf("Pushing heap of structures with custom container\n") ; numNodes = 0 ; for (i = 0 ; i < sizeof(data_h) / sizeof(data_h[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, h.array[i]) ; h_heap_push (&h, &h.numNodes) ; } printf("Popping heap of structures with custom container\n") ; for (i = 0 ; i < sizeof(data_h) / sizeof(data_h[0]) ; ++i) { printf ("s[%" VL_FMT_UINDEX "].x = %d\n", i, h.array [h_heap_pop (&h, &h.numNodes)]) ; } return 0 ; } vlfeat/src/test_liop.c0000644000175000017500000000132212237004745013704 0ustar dimadima#include #include #include #include #include #include int main(){ vl_int i ; VlLiopDesc * liop ; vl_size size = 11*11 ; float mat[] = { 6,6,6,6,6,6,6,6,6,6,6, 6,6,6,5,4,4,4,5,6,6,6, 6,6,5,4,3,3,3,4,5,6,6, 6,5,4,3,2,2,2,3,4,5,6, 6,4,3,2,2,1,2,2,3,4,6, 6,4,3,2,1,1,1,2,3,4,6, 6,4,3,2,2,1,2,2,3,4,6, 6,5,4,3,2,2,2,3,4,5,6, 6,6,5,4,3,3,3,4,5,6,6, 6,6,6,5,4,4,4,5,6,6,6, 6,6,6,6,6,6,6,6,6,6,6}; float * patch = vl_malloc(sizeof(float)*size); for(i = 0; i < (signed)size; i++){ patch[i] = mat[i]; } liop = vl_liopdesc_new(4,6,2,11); vl_liopdesc_delete(liop) ; vl_free(patch) ; } vlfeat/src/test_gauss_elimination.c0000644000175000017500000000077112237004736016462 0ustar dimadima/** file: test_gauss_elimination.cpp */ #include #include #include int main(int argc VL_UNUSED, char**argv VL_UNUSED) { double A [] = { 1, 12, 3, 4 }; double b [] = { 1, -2 } ; double x [2] ; double c [2] ; int i ; vl_solve_linear_system_2 (x, A, b) ; c[0] = A[0] * x[0] + A[2] * x[1] ; c[1] = A[1] * x[0] + A[3] * x[1] ; for (i = 0 ; i < 2 ; ++i) { VL_PRINTF("[A x]_%-4d = %4g, b_%-4d = %4g\n", i+1, c[i], i+1, b[i]) ; } return 0 ; } vlfeat/src/test_rand.c0000644000175000017500000000132212237004736013665 0ustar dimadima/** @file test_rand.c ** @author Andrea Vedaldi ** @breif Test vl/random.h **/ #include #include #include int main (int argc VL_UNUSED, char *argv[] VL_UNUSED) { int i ; vl_uint32 init [4] = {0x123, 0x234, 0x345, 0x456} ; VlRand rand ; vl_rand_init (&rand) ; vl_rand_seed_by_array (&rand, init, sizeof(init)/sizeof(init[0])) ; printf("1000 outputs of vl_rand_uint32()\n"); for (i=0; i<1000; i++) { printf("%10" VL_FL_INT32 "u ", vl_rand_uint32(&rand)); if (i%5==4) printf("\n"); } printf("\n1000 outputs of vl_rand_real2()\n"); for (i=0; i<1000; i++) { printf("%10.8f ", vl_rand_real2(&rand)); if (i%5==4) printf("\n"); } return 0; } vlfeat/src/test_gmm.c0000644000175000017500000001724312237004745013532 0ustar dimadima/** @file test_gmm.c ** @brief GMM test ** @author David Novotny **/ #include #include #include #include #include #include //#include //#define TYPE double //#define VL_F_TYPE VL_TYPE_DOUBLE #define TYPE float #define VL_F_TYPE VL_TYPE_FLOAT void saveResults(const char * dataFileData, const char * dataFileResults, VlGMM * gmm, void * data, vl_size numData); int main(int argc VL_UNUSED, char ** argv VL_UNUSED) { VlKMeans * kmeans = 0; VlRand rand ; vl_size dataIdx, d, cIdx; VlGMM * gmm; double sigmaLowerBound = 0.000001; vl_size numData = 1000; vl_size dimension = 3; vl_size numClusters = 20; vl_size maxiter = 5; vl_size maxrep = 1; vl_size maxiterKM = 5; vl_size ntrees = 3; vl_size maxComp = 20; typedef enum _init { KMeans, Rand, Custom } Init ; vl_bool computeFisher = VL_TRUE; vl_bool computeVlad = VL_FALSE; Init init = KMeans; //char * dataFileResults = "/home/dave/vlfeat/data/gmm/gmm-results.mat"; //char * dataFileData = "/home/dave/vlfeat/data/gmm/gmm-data.mat"; TYPE * data = vl_malloc(sizeof(TYPE)*numData*dimension); TYPE * enc = vl_malloc(sizeof(TYPE)*2*dimension*numClusters); vl_uint32 * assign; vl_set_num_threads(0) ; /* use the default number of threads */ vl_rand_init (&rand) ; vl_rand_seed (&rand, 49000) ; for(dataIdx = 0; dataIdx < numData; dataIdx++) { for(d = 0; d < dimension; d++) { data[dataIdx*dimension+d] = (TYPE)vl_rand_real3(&rand); //VL_PRINT("%f ",data[dataIdx*dimension+d]); } //VL_PRINT("\n"); } gmm = vl_gmm_new (VL_F_TYPE, dimension, numClusters) ; switch(init) { case KMeans: kmeans = vl_kmeans_new(VL_F_TYPE,VlDistanceL2); vl_kmeans_set_verbosity (kmeans,1); vl_kmeans_set_max_num_iterations (kmeans, maxiterKM) ; vl_kmeans_set_max_num_comparisons (kmeans, maxComp) ; vl_kmeans_set_num_trees (kmeans, ntrees); vl_kmeans_set_algorithm (kmeans, VlKMeansANN); vl_kmeans_set_initialization(kmeans, VlKMeansRandomSelection); vl_gmm_set_initialization (gmm,VlGMMKMeans); vl_gmm_set_kmeans_init_object(gmm,kmeans); break; case Rand: vl_gmm_set_initialization (gmm,VlGMMRand); break; case Custom: { TYPE * initSigmas; TYPE * initMeans; TYPE * initWeights; initSigmas = vl_malloc(sizeof(TYPE) * numClusters * dimension); initWeights = vl_malloc(sizeof(TYPE) * numClusters); initMeans = vl_malloc(sizeof(TYPE) * numClusters * dimension); vl_gmm_set_initialization (gmm,VlGMMCustom); for(cIdx = 0; cIdx < numClusters; cIdx++) { for(d = 0; d < dimension; d++) { initMeans[cIdx*dimension+d] = (TYPE)vl_rand_real3(&rand); initSigmas[cIdx*dimension+d] = (TYPE)vl_rand_real3(&rand); } initWeights[cIdx] = (TYPE)vl_rand_real3(&rand); } vl_gmm_set_priors(gmm,initWeights); vl_gmm_set_covariances(gmm,initSigmas); vl_gmm_set_means(gmm,initMeans); break; } default: abort(); } vl_gmm_set_max_num_iterations (gmm, maxiter) ; vl_gmm_set_num_repetitions(gmm, maxrep); vl_gmm_set_verbosity(gmm,1); vl_gmm_set_covariance_lower_bound (gmm,sigmaLowerBound); //struct timeval t1,t2; //gettimeofday(&t1, NULL); vl_gmm_cluster (gmm, data, numData); //gettimeofday(&t2, NULL); //VL_PRINT("elapsed vlfeat: %f s\n",(double)(t2.tv_sec - t1.tv_sec) + ((double)(t2.tv_usec - t1.tv_usec))/1000000.); // VL_PRINT("posterior:\n"); // for(cIdx = 0; cIdx < clusterNum; cIdx++){ // for(dataIdx = 0; dataIdx < Ndata; dataIdx++){ // VL_PRINT("%f ",((float*)posteriors)[cIdx*Ndata+dataIdx]); // } // VL_PRINT("\n"); // } // VL_PRINT("mean:\n"); // for(cIdx = 0; cIdx < numClusters; cIdx++) { // for(d = 0; d < dimension; d++) { // VL_PRINT("%f ",((TYPE*)means)[cIdx*dimension+d]); // } // VL_PRINT("\n"); // } // // VL_PRINT("sigma:\n"); // for(cIdx = 0; cIdx < numClusters; cIdx++) { // for(d = 0; d < dimension; d++) { // VL_PRINT("%f ",((TYPE*)sigmas)[cIdx*dimension+d]); // } // VL_PRINT("\n"); // } // // VL_PRINT("w:\n"); // for(cIdx = 0; cIdx < numClusters; cIdx++) { // VL_PRINT("%f ",((TYPE*)weights)[cIdx]); // VL_PRINT("\n"); // } //saveResults(dataFileData,dataFileResults,gmm,(void*) data, numData); // VL_PRINT("fisher:\n"); // for(cIdx = 0; cIdx < clusterNum; cIdx++) { // for(d = 0; d < dimension*2; d++) { // VL_PRINT("%f ",enc[cIdx*dimension*2+d]); // } // VL_PRINT("\n"); // } vl_free(data); numData = 2000; data = vl_malloc(numData*dimension*sizeof(TYPE)); for(dataIdx = 0; dataIdx < numData; dataIdx++) { for(d = 0; d < dimension; d++) { data[dataIdx*dimension+d] = (TYPE)vl_rand_real3(&rand); } } if(computeFisher) { vl_fisher_encode (enc, VL_F_TYPE, vl_gmm_get_means(gmm), dimension, numClusters, vl_gmm_get_covariances(gmm), vl_gmm_get_priors(gmm), data, numData, VL_FISHER_FLAG_IMPROVED ) ; } assign = vl_malloc(numData*numClusters*sizeof(vl_uint32)); for(dataIdx = 0; dataIdx < numData; dataIdx++) { for(cIdx = 0; cIdx < numClusters; cIdx++) { assign[cIdx*numData+dataIdx] = (vl_uint32)vl_rand_real3(&rand); } } if(computeVlad) { vl_free(enc); enc = vl_malloc(sizeof(TYPE)*dimension*numClusters); vl_vlad_encode (enc, VL_F_TYPE, vl_gmm_get_means(gmm), dimension, numClusters, data, numData, assign, 0) ; } vl_gmm_delete(gmm); vl_free(data); if(enc){ vl_free(enc); } if(kmeans) { vl_kmeans_delete(kmeans); } return 0 ; } void saveResults(const char * dataFileData, const char * dataFileResults, VlGMM * gmm, void * data, vl_size numData) { char *mode = "w"; FILE * ofp; vl_size d, cIdx; vl_uindex i_d; vl_size dimension = vl_gmm_get_dimension(gmm) ; vl_size numClusters = vl_gmm_get_num_clusters(gmm) ; vl_type dataType = vl_gmm_get_data_type(gmm) ; double const * sigmas = vl_gmm_get_covariances(gmm) ; double const * means = vl_gmm_get_means(gmm) ; double const * weights = vl_gmm_get_priors(gmm) ; double const * posteriors = vl_gmm_get_posteriors(gmm) ; ofp = fopen(dataFileData, mode); for(i_d = 0; i_d < numData; i_d++) { if(vl_gmm_get_data_type(gmm) == VL_TYPE_DOUBLE) { for(d = 0; d < vl_gmm_get_dimension(gmm) ; d++) { fprintf(ofp, "%f ", ((double*)data)[i_d * vl_gmm_get_dimension(gmm) + d]); } } else { for(d = 0; d < vl_gmm_get_dimension(gmm); d++) { fprintf(ofp, "%f ", ((float*) data)[i_d * vl_gmm_get_dimension(gmm) + d]); } } fprintf(ofp, "\n"); } fclose (ofp); ofp = fopen(dataFileResults, mode); for(cIdx = 0; cIdx < numClusters; cIdx++) { if(dataType == VL_TYPE_DOUBLE) { for(d = 0; d < vl_gmm_get_dimension(gmm); d++) { fprintf(ofp, "%f ", ((double*)means)[cIdx*dimension+d]); } for(d = 0; d < dimension; d++) { fprintf(ofp, "%f ", ((double*)sigmas)[cIdx*dimension+d]); } fprintf(ofp, "%f ", ((double*)weights)[cIdx]); for(i_d = 0; i_d < numData; i_d++) { fprintf(ofp, "%f ", ((double*)posteriors)[cIdx*numData + i_d]); } fprintf(ofp, "\n"); } else { for(d = 0; d < dimension; d++) { fprintf(ofp, "%f ", ((float*)means)[cIdx*dimension+d]); } for(d = 0; d < dimension; d++) { fprintf(ofp, "%f ", ((float*)sigmas)[cIdx*dimension+d]); } fprintf(ofp, "%f ", ((float*)weights)[cIdx]); for(i_d = 0; i_d < numData; i_d++) { fprintf(ofp, "%f ", ((float*)posteriors)[cIdx*numData + i_d]); } fprintf(ofp, "\n"); } } fclose (ofp); } vlfeat/src/test_mathop_fast_resqrt.tc0000644000175000017500000000252312237004736017036 0ustar dimadima#define F_EPS VL_XCAT(SFX2, _EPSILON) #define F_MAX VL_XCAT(SFX2, _MAX) #define F_MIN VL_XCAT(SFX2, _MIN) { T x ; T dx ; T maxErr = 0 ; T acc ; int numEval = 0 ; double elaps ; char const* funcName = VL_XSTRINGIFY(VL_XCAT(vl_fast_resqrt_, SFX)) ; for (x = F_MIN, dx = F_EPS ; x < F_MAX ; x += dx, dx *= 1 + 1e-6 ) { T y = VL_XCAT(vl_fast_resqrt_, SFX)(x) ; T y_ = ONE / SQRT (x) ; T err = ABS (y_ - y) / y * 100 ; maxErr = VL_MAX(maxErr, err) ; } VL_PRINTF ("%20s (max relative error: %.6f%%)\n", funcName, maxErr) ; if (maxErr > 0.001) { VL_PRINTF ("error: %s: relative error is too large!\n", funcName) ; error = 1 ; } vl_tic() ; numEval = 0 ; acc = 0 ; for (x = F_MIN, dx = F_EPS ; x < F_MAX ; x += dx, dx *= 1 + 1e-6 ) { T y = VL_XCAT(vl_fast_resqrt_, SFX)(x) ; acc += y ; numEval += 1 ; } elaps = vl_toc() ; VL_PRINTF ("%20s %10.2g %10.2g\n", funcName, elaps, numEval / elaps) ; vl_tic() ; numEval = 0 ; acc = 0 ; for (x = F_MIN, dx = F_EPS ; x < F_MAX ; x += dx, dx *= 1 + 1e-6 ) { T y = ONE / SQRT (x) ; acc += y ; numEval += 1 ; } elaps = vl_toc() ; VL_PRINTF ("%20s %10.2g %10.2g\n", "baseline", elaps, numEval / elaps) ; } #undef F_MIN #undef F_MAX #undef F_EPS vlfeat/src/test_svd2.c0000644000175000017500000000374612237004736013633 0ustar dimadima/** @file test_svd2.c ** @brief Test SVD on 2x2 matrices (@ref mathop.h) ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include void print_matrix(char const * name, double *M) { printf("%s = \n[ %10f %10f ]\n[ %10f %10f ]\n", name, M[0], M[2], M[1], M[3]) ; } void prod2 (double R [4], double A [4], double B [4]) { R[0] = A[0]*B[0] + A[2]*B[1] ; R[1] = A[1]*B[0] + A[3]*B[1] ; R[2] = A[0]*B[2] + A[2]*B[3] ; R[3] = A[1]*B[2] + A[3]*B[3] ; } void transp2 (double R [4], double A [4]) { R[0] = A[0] ; R[1] = A[2] ; R[2] = A[1] ; R[3] = A[3] ; } double det2 (double A [4]) { return A[0]*A[3] - A[1]*A[2]; } void check_svd (double *M , double * U, double * S, double *V) { double T1 [4] ; double T2 [4] ; print_matrix("M",M) ; print_matrix("U",U) ; print_matrix("S",S) ; print_matrix("V",V) ; transp2(T1, V) ; prod2(T2, S, T1) ; prod2(T1, U, T2) ; print_matrix("USV'",T1) ; transp2(T1, U) ; prod2(T2, T1, U) ; print_matrix("U'U",T2) ; transp2(T1, V) ; prod2(T2, T1, V) ; print_matrix("V'V",T2) ; printf("det(M) = %f\n", det2(M)) ; printf("det(U) = %f\n", det2(U)) ; printf("det(V) = %f\n", det2(V)) ; printf("det(S) = %f\n", det2(S)) ; printf("\n") ; } int main (int argc VL_UNUSED, char ** argv VL_UNUSED) { double M [] = { 0.864397318249258, 0.094202610858281, -0.851909224508774, 0.873504449150106 } ; double S [4] ; double U [4] ; double V [4] ; vl_svd2(S, U, V, M) ; check_svd(M, U, S, V) ; M[1] = 0 ; vl_svd2(S, U, V, M) ; check_svd(M, U, S, V) ; vl_lapack_dlasv2(S+3, S, V+1, V, U+1, U, M[0], M[2], M[3]) ; V[2] = -V[1] ; V[3] = V[0] ; U[2] = -U[1] ; U[3] = U[0] ; check_svd(M, U, S, V) ; return 0 ; } vlfeat/src/test_mathop_fast_sqrt_ui.tc0000644000175000017500000000163412237004736017206 0ustar dimadima#define FL VL_XCAT(VL_FL_INT, SFX) { T x, acc, acc_ ; double elaps ; int neval ; vl_tic() ; neval = 0 ; x = 0 ; acc = 0 ; do { T y = VL_XCAT(vl_fast_sqrt_ui, SFX) (x) ; acc += y ; x += 1 << STEP ; neval += 1 ; } while (x > (1 << STEP) - 1) ; elaps = vl_toc() ; VL_PRINTF ("%20s", "vl_fast_sqrt_ui" VL_XSTRINGIFY(SFX)) ; VL_PRINTF (" %10.2f %10.2g %010" FL "x\n", elaps, (double) neval / elaps, acc) ; vl_tic() ; neval = 0 ; x = 0 ; acc_ = 0 ; do { vl_uint32 y = floor(sqrt((double) x)); acc_ += y ; x += 1 << STEP ; neval += 1 ; } while (x > (1 << STEP) - 1) ; elaps = vl_toc() ; VL_PRINTF ("%20s", "baseline") ; VL_PRINTF (" %10.2f %10.2g %010" FL "x\n", elaps, (double) neval / elaps, acc) ; if (acc != acc_) { VL_PRINTF ("error: vl_fast_sqrt_ui" VL_XSTRINGIFY(SFX) " != floor(sqrt((double) x))\n") ; error = 1 ; } } #undef FL vlfeat/src/test_nan.c0000644000175000017500000000146712237004736013527 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include int main(int argc VL_UNUSED, char** argv VL_UNUSED) { VL_PRINTF ("Double NaN : `%g'\n", VL_NAN_D ) ; VL_PRINTF ("Double Inf : `%g'\n", VL_INFINITY_D) ; VL_PRINTF ("Double - Inf : `%g'\n", - VL_INFINITY_D) ; VL_PRINTF ("Single NaN : `%g'\n", VL_NAN_F ) ; VL_PRINTF ("Single Inf : `%g'\n", VL_INFINITY_F) ; VL_PRINTF ("Single - Inf : `%g'\n", - VL_INFINITY_F) ; VL_PRINTF ("Double: 0.0 < VL_INFINITY_D: %d\n", 0.0 < VL_INFINITY_D) ; VL_PRINTF ("Double: 0.0 > - VL_INFINITY_D: %d\n", 0.0 > - VL_INFINITY_D) ; return 0 ; } vlfeat/src/check.h0000644000175000017500000000133012237004736012763 0ustar dimadima/** @file check.h ** @brief Unit test utilties ** @author Andrea Vedaldi **/ #ifndef __CHECK_H__ #define __CHECK_H__ #include #include #include static void check_impl (int condition, const char * file, int line, const char * format, ...) { va_list args; va_start(args, format) ; if (! condition) { fprintf(stderr, "%s:%d: check failed: ", file, line) ; vfprintf(stderr, format, args) ; fprintf(stderr, "\n") ; exit (1) ; } va_end(args) ; } #define check(condition, ...) \ check_impl(condition, __FILE__, __LINE__, "" __VA_ARGS__) #define check_signoff() \ fprintf(stdout, "%s passed\n", __FILE__) /* __CHECK_H__ */ #endif vlfeat/src/test_imopv.c0000644000175000017500000000512212237004736014075 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include int main (int argc, char** argv) { int width = 256 ; int height = 256 ; int const W = 7 ; float * image ; float * dest ; float * dest2 ; float * filt ; int x, y ; if (argc < 2) { image = vl_malloc (sizeof(float) * width * height) ; for (y = 0 ; y < height ; ++y) { for (x = 0 ; x < width ; ++x) { image [x + width * y] = 128.0f * (((x % 16) < 8) ^ ((y % 16) < 8)) ; } } } else { VlPgmImage im ; int err ; err = vl_pgm_read_new_f (argv[1], &im, &image) ; if (err) { VL_PRINTF("test_imopv: error: %s (%d)\n", vl_get_last_error_message(), vl_get_last_error()) ; return -1 ; } width = im.width ; height = im.height ; } VL_PRINTF("test_imopv: width: %d, height: %d\n", width, height); dest = vl_malloc (sizeof(float) * width * height) ; dest2 = vl_malloc (sizeof(float) * width * height) ; filt = vl_malloc (sizeof(float) * (2*W+1)) ; for (x = 0 ; x < 2*W+1 ; ++ x) { filt [x] = 1.0f / (2*W+1) ; } #if 1 vl_set_simd_enabled (0) ; vl_tic() ; { int trial = 0 ; for (trial = 0 ; trial < 1000 ; ++trial) vl_imconvcol_vf (dest, height, image, width, height, width, filt, -W, W, 1, VL_TRANSPOSE|VL_PAD_BY_CONTINUITY) ; } VL_PRINTF ("Elapsed time no SIMD: %f [s]\n", vl_toc()) ; vl_set_simd_enabled (1) ; vl_tic() ; { int trial = 0 ; for (trial = 0 ; trial < 1000 ; ++trial) vl_imconvcol_vf (dest2, height, image, width, height, width, filt, -W, W, 1, VL_TRANSPOSE|VL_PAD_BY_CONTINUITY) ; } VL_PRINTF ("Elapsed time with SIMD: %f [s]\n", vl_toc()) ; #else vl_imconvcoltri_vf (dest, height, image, width, height, width, 5, 1, VL_TRANSPOSE|VL_PAD_BY_CONTINUITY) ; vl_imconvcoltri_vf (dest2, height, image, width, height, width, 5, 1, VL_TRANSPOSE|VL_PAD_BY_ZERO) ; #endif vl_pgm_write_f("/tmp/src.pgm", image, width, height) ; vl_pgm_write_f("/tmp/test.pgm", dest, height, width) ; vl_pgm_write_f("/tmp/test2.pgm", dest2, height, width) ; vl_free(image) ; vl_free(filt) ; vl_free(dest) ; vl_free(dest2) ; return 0 ; } vlfeat/src/test_mathop_abs.c0000644000175000017500000000204012237004736015054 0ustar dimadima/* * test_mathop_abs.c * vlfeat * * Created by Andrea Vedaldi on 16/07/2009. * Copyright 2009 UCLA. All rights reserved. * */ #include #include #include int main(int argc VL_UNUSED, char**argv VL_UNUSED) { VlRand * rand = vl_get_rand() ; vl_size numData = 100000000 ; vl_size i ; float * X = vl_malloc(sizeof(float) * numData) ; float * Y = vl_malloc(sizeof(float) * numData) ; float * X_end = X + numData ; for (i = 0 ; i < numData ; ++ i) { X[i] = vl_rand_real1(rand) - .5 ; Y[i] = 0 ; } { float * Xi = X ; float * Yi = Y ; vl_tic() ; while (Xi < X_end) { *Yi++ = fabsf (*Xi++) ; } VL_PRINTF("fabsf: %.3f\n", vl_toc()) ; } for (i = 0 ; i < numData ; ++ i) { X[i] = vl_rand_real1(rand) - .5 ; X[i] = 0 ; } { float * Xi = X ; float * Yi = Y ; vl_tic() ; while (Xi < X_end) { *Yi++ = vl_abs_f (*Xi++) ; } VL_PRINTF("vl_abs_f: %.3f\n", vl_toc()) ; } vl_free (Y) ; vl_free (X) ; return 0 ; } vlfeat/src/aib.c0000644000175000017500000000244612237004736012445 0ustar dimadima/* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include int main() { vl_uint32 nrows = 10; vl_uint32 ncols = 3; double Pic[3*10] = { 0.6813, 0.3028, 0.8216, 0.3795, 0.5417, 0.6449, 0.8318, 0.1509, 0.8180, 0.5028, 0.6979, 0.6602, 0.7095, 0.3784, 0.3420, 0.4289, 0.8600, 0.2897, 0.3046, 0.8537, 0.3412, 0.1897, 0.5936, 0.5341, 0.1934, 0.4966, 0.7271, 0.6822, 0.8998, 0.3093, }; vl_uint32 r,c; VlAIB * aib; vl_uint * parents; printf("Pic = ["); for(r=0; r %d\n", r, parents[r]); vl_aib_delete(aib); } /* free(Pic); */ printf("IB done\n"); return 0; } vlfeat/src/test_qsort-def.c0000644000175000017500000000142112237004736014645 0ustar dimadima/** @file test_heap-def.c ** @brief Test heap-def.h ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #define VL_QSORT_prefix my_qsort #define VL_QSORT_type float #include #include int main (int argc VL_UNUSED, char ** argv VL_UNUSED) { float values [] = {.4, 12.3, 44.0, 1.2, 3.4, 5.6} ; vl_size n = sizeof(values) / sizeof(values[0]) ; vl_uindex i ; for (i = 0 ; i < n ; ++i) printf("%5.2f ", values [i]) ; printf("\n") ; my_qsort_sort (values, n) ; for (i = 0 ; i < n ; ++i) printf("%5.2f ", values [i]) ; printf("\n") ; return 0 ; } vlfeat/src/test_vec_comp.c0000644000175000017500000000325412237004745014542 0ustar dimadima/** @file test_vec_comp.c ** @brief Test vector computations ** @author Andrea Vedaldi **/ /* Copyright (C) 2008-11 Andrea Vedaldi. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include void init_data (vl_size numDimensions, vl_size numSamples, float ** X, float ** Y) { VlRand * rand = vl_get_rand() ; vl_uindex i ; float * Xi = *X = vl_malloc(sizeof(float) * numDimensions * numSamples) ; float * Yi = *Y = vl_malloc(sizeof(float) * numDimensions * numSamples) ; for (i = 0 ; i < numDimensions * numSamples ; ++ i) { *Xi++ = vl_rand_real1(rand) ; *Yi++ = vl_rand_real1(rand) ; } } int main (int argc VL_UNUSED, char** argv VL_UNUSED) { float * X ; float * Y ; vl_size numDimensions = 1000 ; vl_size numSamples = 2000 ; float * result = vl_malloc (sizeof(float) * numSamples * numSamples) ; VlFloatVectorComparisonFunction f ; init_data (numDimensions, numSamples, &X, &Y) ; X+=1 ; Y+=1 ; vl_set_simd_enabled (VL_FALSE) ; f = vl_get_vector_comparison_function_f (VlDistanceL2) ; vl_tic () ; vl_eval_vector_comparison_on_all_pairs_f (result, numDimensions, X, numSamples, Y, numSamples, f) ; VL_PRINTF("Float L2 distnace: %.3f s\n", vl_toc ()) ; vl_set_simd_enabled (VL_TRUE) ; f = vl_get_vector_comparison_function_f (VlDistanceL2) ; vl_tic () ; vl_eval_vector_comparison_on_all_pairs_f (result, numDimensions, X, numSamples, Y, numSamples, f) ; VL_PRINTF("Float L2 distance (SIMD): %.3f s\n", vl_toc ()) ; X-- ; Y-- ; vl_free (X) ; vl_free (Y) ; vl_free (result) ; return 0 ; } vlfeat/src/mser.c0000644000175000017500000004165212237004736012662 0ustar dimadima/** @file mser-driver.c ** @author Andrea Vedaldi ** @brief MSER command line driver - Definition ** @internal **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #define VL_MSER_DRIVER_VERSION 0.2 #include "generic-driver.h" #include #include #include #include #include #include #include #include /* ----------------------------------------------------------------- */ /* help message */ char const help_message [] = "Usage: %s [options] files ...\n" "\n" "Options include:\n" " --verbose -v Be verbose\n" " --help -h Print this help message\n" " --seeds Specify seeds file\n" " --frames Specify frames file\n" " --meta Specify meta file\n" " --delta -d Specify MSER delta paramter\n" " --epsilon -e Specify MSER epsilon parameter\n" " --no-dups Remove duplicate\n" " --dups Keep duplicates\n" " --max-area Specify maximum region (relative) area\n" " --min-area Specify minimum region (relative) area\n" " --max-variation Specify maximum absolute region stability\n" " --bright-on-dark Enable or disable bright-on-dark regions (default 1)\n" " --dark-on-bright Enable or disable dark-on-bright regions (default 1)\n" "\n" ; /* ----------------------------------------------------------------- */ /* long options codes */ enum { opt_seed = 1000, opt_frame, opt_meta, opt_max_area, opt_min_area, opt_max_variation, opt_min_diversity, opt_bright, opt_dark } ; /* short options */ char const opts [] = "vhd:" ; /* long options */ struct option const longopts [] = { { "verbose", no_argument, 0, 'v' }, { "help", no_argument, 0, 'h' }, { "delta", required_argument, 0, 'd' }, { "seeds", optional_argument, 0, opt_seed }, { "frames", optional_argument, 0, opt_frame }, { "meta", optional_argument, 0, opt_meta }, { "max-area", required_argument, 0, opt_max_area }, { "min-area", required_argument, 0, opt_min_area }, { "max-variation", required_argument, 0, opt_max_variation }, { "min-diversity", required_argument, 0, opt_min_diversity }, { "bright-on-dark", required_argument, 0, opt_bright }, { "dark-on-bright", required_argument, 0, opt_dark }, { 0, 0, 0, 0 } } ; /* ----------------------------------------------------------------- */ /** @brief MSER driver entry point **/ int main(int argc, char **argv) { /* algorithm parameters */ double delta = -1 ; double max_area = -1 ; double min_area = -1 ; double max_variation = -1 ; double min_diversity = -1 ; int bright_on_dark = 1 ; int dark_on_bright = 1 ; vl_bool err = VL_ERR_OK ; char err_msg [1024] ; int n ; int exit_code = 0 ; int verbose = 0 ; VlFileMeta frm = {0, "%.frame", VL_PROT_ASCII, "", 0} ; VlFileMeta piv = {0, "%.mser", VL_PROT_ASCII, "", 0} ; VlFileMeta met = {0, "%.meta", VL_PROT_ASCII, "", 0} ; #define ERRF(msg, arg) { \ err = VL_ERR_BAD_ARG ; \ snprintf(err_msg, sizeof(err_msg), msg, arg) ; \ break ; \ } #define ERR(msg) { \ err = VL_ERR_BAD_ARG ; \ snprintf(err_msg, sizeof(err_msg), msg) ; \ break ; \ } /* ------------------------------------------------------------------ * Parse options * --------------------------------------------------------------- */ while (!err) { int ch = getopt_long(argc, argv, opts, longopts, 0) ; /* If there are no files passed as input, print the help and settings */ if (ch == -1 && argc - optind == 0) ch = 'h'; /* end of option list? */ if (ch == -1) break; /* process options */ switch (ch) { /* .......................................................... */ case '?' : ERRF("Invalid option '%s'.", argv [optind - 1]) ; break ; case ':' : ERRF("Missing mandatory argument for option '%s'.", argv [optind - 1]) ; break ; case 'h' : printf (help_message, argv [0]) ; printf ("MSERs filespec: `%s'\n", piv.pattern) ; printf ("Frames filespec: `%s'\n", frm.pattern) ; printf ("Meta filespec: `%s'\n", met.pattern) ; printf ("Version: driver %s; libvl %s\n", VL_XSTRINGIFY(VL_MSER_DRIVER_VERSION), vl_get_version_string()) ; exit (0) ; break ; case 'v' : ++ verbose ; break ; /* .......................................................... */ case 'd' : n = sscanf (optarg, "%lf", &delta) ; if (n == 0 || delta < 0) ERRF("The argument of '%s' must be a non-negative number.", argv [optind - 1]) ; break ; /* ........................................................... */ case opt_max_area : n = sscanf (optarg, "%lf", &max_area) ; if (n == 0 || max_area < 0 || max_area > 1) ERR("max-area argument must be in the [0,1] range.") ; break ; case opt_min_area : n = sscanf (optarg, "%lf", &min_area) ; if (n == 0 || min_area < 0 || min_area > 1) ERR("min-area argument must be in the [0,1] range.") ; break ; case opt_max_variation : n = sscanf (optarg, "%lf", &max_variation) ; if (n == 0 || max_variation < 0) ERR("max-variation argument must be non-negative.") ; break ; case opt_min_diversity : n = sscanf (optarg, "%lf", &min_diversity) ; if (n == 0 || min_diversity < 0 || min_diversity > 1) ERR("min-diversity argument must be in the [0,1] range.") ; break ; /* ........................................................... */ case opt_frame : err = vl_file_meta_parse (&frm, optarg) ; if (err) ERRF("The arguments of '%s' is invalid.", argv [optind - 1]) ; break ; case opt_seed : err = vl_file_meta_parse (&piv, optarg) ; if (err) ERRF("The arguments of '%s' is invalid.", argv [optind - 1]) ; break ; case opt_meta : err = vl_file_meta_parse (&met, optarg) ; if (err) ERRF("The arguments of '%s' is invalid.", argv [optind - 1]) ; if (met.protocol != VL_PROT_ASCII) ERR("meta file supports only ASCII protocol") ; break ; case opt_bright : n = sscanf (optarg, "%d", &bright_on_dark) ; if (n == 0 || (bright_on_dark != 0 && bright_on_dark != 1)) ERR("bright_on_dark must be 0 or 1.") ; break ; case opt_dark : n = sscanf (optarg, "%d", &dark_on_bright) ; if (n == 0 || (dark_on_bright != 0 && dark_on_bright != 1)) ERR("dark_on_bright must be 0 or 1.") ; break ; /* .......................................................... */ case 0 : default : abort() ; } } /* check for parsing errors */ if (err) { fprintf(stderr, "%s: error: %s (%d)\n", argv [0], err_msg, err) ; exit (1) ; } /* parse other arguments (filenames) */ argc -= optind ; argv += optind ; /* make sure at least one file */ if (piv.active == 0 && frm.active == 0) { frm.active = 1 ; } if (verbose > 1) { printf("mser: frames output\n") ; printf("mser: active %d\n", frm.active ) ; printf("mser: pattern %s\n", frm.pattern) ; printf("mser: protocol %s\n", vl_string_protocol_name (frm.protocol)) ; printf("mser: seeds output\n") ; printf("mser: active %d\n", piv.active ) ; printf("mser: pattern %s\n", piv.pattern) ; printf("mser: protocol %s\n", vl_string_protocol_name (piv.protocol)) ; printf("mser: meta output\n") ; printf("mser: active %d\n", met.active ) ; printf("mser: pattern %s\n", met.pattern) ; printf("mser: protocol %s\n", vl_string_protocol_name (met.protocol)) ; } /* ------------------------------------------------------------------ * Process one image per time * --------------------------------------------------------------- */ while (argc--) { char basename [1024] ; char const *name = *argv++ ; VlMserFilt *filt = 0 ; VlMserFilt *filtinv = 0 ; vl_uint8 *data = 0 ; vl_uint8 *datainv = 0 ; VlPgmImage pim ; vl_uint const *regions ; vl_uint const *regionsinv ; float const *frames ; float const *framesinv ; enum {ndims = 2} ; int dims [ndims] ; int nregions = 0, nregionsinv = 0, nframes = 0, nframesinv =0; int i, j, dof ; vl_size q ; FILE *in = 0 ; /* Open files ------------------------------------------------ */ /* get basenmae from filename */ q = vl_string_basename (basename, sizeof(basename), name, 1) ; err = (q >= sizeof(basename)) ; if (err) { snprintf(err_msg, sizeof(err_msg), "Basename of '%s' is too long", name); err = VL_ERR_OVERFLOW ; goto done ; } if (verbose) { printf("mser: processing '%s'\n", name) ; } if (verbose > 1) { printf("mser: basename is '%s'\n", basename) ; } #define WERR(name) \ if (err == VL_ERR_OVERFLOW) { \ snprintf(err_msg, sizeof(err_msg), \ "Output file name too long.") ; \ goto done ; \ } else if (err) { \ snprintf(err_msg, sizeof(err_msg), \ "Could not open '%s' for writing.", name) ; \ goto done ; \ } /* open input file */ in = fopen (name, "rb") ; if (!in) { err = VL_ERR_IO ; snprintf(err_msg, sizeof(err_msg), "Could not open '%s' for reading.", name) ; goto done ; } /* open output files */ err = vl_file_meta_open (&piv, basename, "w") ; WERR(piv.name) ; err = vl_file_meta_open (&frm, basename, "w") ; WERR(frm.name) ; err = vl_file_meta_open (&met, basename, "w") ; WERR(met.name) ; if (verbose > 1) { if (piv.active) printf("mser: writing seeds to '%s'\n", piv.name); if (frm.active) printf("mser: writing frames to '%s'\n", frm.name); if (met.active) printf("mser: writing meta to '%s'\n", met.name); } /* Read image data -------------------------------------------- */ /* read source image header */ err = vl_pgm_extract_head (in, &pim) ; if (err) { err = VL_ERR_IO ; snprintf(err_msg, sizeof(err_msg), "PGM header corrputed.") ; goto done ; } if (verbose) { printf("mser: image is %" VL_FMT_SIZE " by %" VL_FMT_SIZE " pixels\n", pim. width, pim. height) ; } /* allocate buffer */ data = malloc(vl_pgm_get_npixels (&pim) * vl_pgm_get_bpp (&pim)) ; if (!data) { err = VL_ERR_ALLOC ; snprintf(err_msg, sizeof(err_msg), "Could not allocate enough memory.") ; goto done ; } /* read PGM */ err = vl_pgm_extract_data (in, &pim, data) ; if (err) { snprintf(err_msg, sizeof(err_msg), "PGM body corrputed.") ; goto done ; } /* Process data ---------------------------------------------- */ dims[0] = pim.width ; dims[1] = pim.height ; filt = vl_mser_new (ndims, dims) ; filtinv = vl_mser_new (ndims, dims) ; if (!filt || !filtinv) { snprintf(err_msg, sizeof(err_msg), "Could not create an MSER filter.") ; goto done ; } if (delta >= 0) vl_mser_set_delta (filt, (vl_mser_pix) delta) ; if (max_area >= 0) vl_mser_set_max_area (filt, max_area) ; if (min_area >= 0) vl_mser_set_min_area (filt, min_area) ; if (max_variation >= 0) vl_mser_set_max_variation (filt, max_variation) ; if (min_diversity >= 0) vl_mser_set_min_diversity (filt, min_diversity) ; if (delta >= 0) vl_mser_set_delta (filtinv, (vl_mser_pix) delta) ; if (max_area >= 0) vl_mser_set_max_area (filtinv, max_area) ; if (min_area >= 0) vl_mser_set_min_area (filtinv, min_area) ; if (max_variation >= 0) vl_mser_set_max_variation (filtinv, max_variation) ; if (min_diversity >= 0) vl_mser_set_min_diversity (filtinv, min_diversity) ; if (verbose) { printf("mser: parameters:\n") ; printf("mser: delta = %d\n", vl_mser_get_delta (filt)) ; printf("mser: max_area = %g\n", vl_mser_get_max_area (filt)) ; printf("mser: min_area = %g\n", vl_mser_get_min_area (filt)) ; printf("mser: max_variation = %g\n", vl_mser_get_max_variation (filt)) ; printf("mser: min_diversity = %g\n", vl_mser_get_min_diversity (filt)) ; } if (dark_on_bright) { vl_mser_process (filt, (vl_mser_pix*) data) ; /* Save result ----------------------------------------------- */ nregions = vl_mser_get_regions_num (filt) ; regions = vl_mser_get_regions (filt) ; if (piv.active) { for (i = 0 ; i < nregions ; ++i) { fprintf(piv.file, "%d ", regions [i]) ; } } if (frm.active) { vl_mser_ell_fit (filt) ; nframes = vl_mser_get_ell_num (filt) ; dof = vl_mser_get_ell_dof (filt) ; frames = vl_mser_get_ell (filt) ; for (i = 0 ; i < nframes ; ++i) { for (j = 0 ; j < dof ; ++j) { fprintf(frm.file, "%f ", *frames++) ; } fprintf(frm.file, "\n") ; } } } if (bright_on_dark) { /* allocate buffer */ datainv = malloc(vl_pgm_get_npixels (&pim) * vl_pgm_get_bpp (&pim)) ; for (i = 0; i < (signed)vl_pgm_get_npixels (&pim); i++) { datainv[i] = ~data[i]; /* 255 - data[i] */ } if (!datainv) { err = VL_ERR_ALLOC ; snprintf(err_msg, sizeof(err_msg), "Could not allocate enough memory.") ; goto done ; } vl_mser_process (filtinv, (vl_mser_pix*) datainv) ; /* Save result ----------------------------------------------- */ nregionsinv = vl_mser_get_regions_num (filtinv) ; regionsinv = vl_mser_get_regions (filtinv) ; if (piv.active) { for (i = 0 ; i < nregionsinv ; ++i) { fprintf(piv.file, "%d ", -regionsinv [i]) ; } } if (frm.active) { vl_mser_ell_fit (filtinv) ; nframesinv = vl_mser_get_ell_num (filtinv) ; dof = vl_mser_get_ell_dof (filtinv) ; framesinv = vl_mser_get_ell (filtinv) ; for (i = 0 ; i < nframesinv ; ++i) { for (j = 0 ; j < dof ; ++j) { fprintf(frm.file, "%f ", *framesinv++) ; } fprintf(frm.file, "\n") ; } } } if (met.active) { fprintf(met.file, "\n") ; } /* Next guy ----------------------------------------------- */ done : /* release filter */ if (filt) { vl_mser_delete (filt) ; filt = 0 ; } if (filtinv) { vl_mser_delete (filtinv) ; filtinv = 0 ; } /* release image data */ if (data) { free (data) ; data = 0 ; } if (datainv) { free (datainv) ; datainv = 0 ; } /* close files */ if (in) { fclose (in) ; in = 0 ; } vl_file_meta_close (&frm) ; vl_file_meta_close (&piv) ; vl_file_meta_close (&met) ; /* if bad print error message */ if (err) { fprintf (stderr, "mser: err: %s (%d)\n", err_msg, err) ; exit_code = 1 ; } } /* quit */ return exit_code ; } vlfeat/.gitignore0000644000175000017500000000124412237004745012742 0ustar dimadima# Mac OS X Finder .DS_Store # MEX files *.mexglx *.mexmac *.mexmaci *.mexmaci64 *.mexa64 *.mex # Python *.pyc # Other generated files and directories VERSION docsrc/version.html bin results doc build toolbox/mexw32 toolbox/mexmaci toolbox/mexmaci64 toolbox/mexmac toolbox/mexglx toolbox/mexa64 toolbox/mexw64 toolbox/mex toolbox/noprefix # Xcode vlfeat.xcodeproj/*.mode1 vlfeat.xcodeproj/*.mode1v3 vlfeat.xcodeproj/*.mode2v3 vlfeat.xcodeproj/*.pbxuser vlfeat.xcodeproj/*.perspective* vlfeat.xcodeproj/*.xcworkspace vlfeat.xcodeproj/xcuserdata # Visual C++ vlfeat.suo vlfeat.ncb vlfeat.vcproj.*.user vc/* # Emacs backups *~ # Vim swap files .*.swp # Opt directory opt/*vlfeat/vlfeat.xcodeproj/0000755000175000017500000000000012237004745014226 5ustar dimadimavlfeat/vlfeat.xcodeproj/project.pbxproj0000644000175000017500000045152112237004745017312 0ustar dimadima// !$*UTF8*$! { archiveVersion = 1; classes = { }; 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"#%$%$+,./3/:8:>::;?BBCCKLPRPSVY]_ce^ebb`^[_\]UXZ\_`__[Z_abaeehggmmpootrqqmnswswvyy}{zy||€z{|~~‚}~€‚ƒ‚€~‚„‡ƒ…~}|}|z}|}…~€yzy~{|€}z}€‚ƒ~}}€†„‚……‡ˆ‚„„†ƒ‚‡ˆƒ„‚‚€~€~}~}€€€„~~||z|ƒƒ„ƒ…„…|€………„ƒ‚ƒƒƒ‚€|||wvnnjc]XVSMD@6)'"&                        ##!   !$ #%&!&(**+..///0/.--,*+,+*('&&# "'#!%#%$"#                        vlfeat/toolbox/0000755000175000017500000000000012237042512012431 5ustar dimadimavlfeat/toolbox/geometry/0000755000175000017500000000000012237042171014265 5ustar dimadimavlfeat/toolbox/geometry/vl_rodr.c0000644000175000017500000000413412237004736016107 0ustar dimadima/* file: rodr.mex.c ** author: Andrea Vedaldi ** description: Rodrigues formula **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include enum { IN_OM = 0 } ; enum { OUT_R=0, OUT_DR } ; /* ------------------------------------------------------------------- ** Driver ** ---------------------------------------------------------------- */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { int k,K ; double const * om_pt ; double* R_pt ; double* dR_pt ; if(nin != 1) { mexErrMsgTxt("Exactly one argument required.") ; } if(!vlmxIsMatrix(in[IN_OM],-1,-1)) { mexErrMsgTxt("OM must be a DOUBLE array") ; } K = mxGetNumberOfElements(in[IN_OM]) ; if(K % 3 || K < 3) { mexErrMsgTxt("The number of elements of OM must be a multiple of 3") ; } K /= 3 ; om_pt = mxGetPr(in[IN_OM]) ; /* space for output (R) */ if( K == 1 ) { out[OUT_R] = mxCreateDoubleMatrix(3,3,mxREAL) ; } else { mwSize dims [3] ; dims[0] = 3 ; dims[1] = 3 ; dims[2] = K ; out[OUT_R] = mxCreateNumericArray(3,dims,mxDOUBLE_CLASS,mxREAL) ; } R_pt = mxGetPr(out[OUT_R]) ; /* space for optional output (dR) */ dR_pt = NULL ; if( nout > 1 ) { if( K == 1 ) { out[OUT_DR] = mxCreateDoubleMatrix(9,3,mxREAL) ; } else { mwSize dims [3] ; dims[0] = 9 ; dims[1] = 3 ; dims[2] = K ; out[OUT_DR] = mxCreateNumericArray(3,dims,mxDOUBLE_CLASS,mxREAL) ; } dR_pt = mxGetPr(out[OUT_DR]) ; } /* ----------------------------------------------------------------- ** Process ** -------------------------------------------------------------- */ for(k = 0 ; k < K ; ++k) { vl_rodrigues(R_pt, dR_pt, om_pt) ; om_pt += 3 ; R_pt += 3*3 ; dR_pt += 9*3 ; } } vlfeat/toolbox/geometry/vl_rodr.m0000644000175000017500000000154712237004736016126 0ustar dimadima% VL_RODR Rodrigues' formula % R = VL_RODR(OM) where OM a 3-dimensional column vector computes the % Rodrigues' formula of OM, returning the rotation matrix R = % expm(vl_hat(OM)). % % [R,DR] = VL_RODR(OM) computes also the derivative of the Rodrigues % formula. In matrix notation this is the expression % % d(vec expm(vl_hat(OM)) ) % dR = ----------------------. % d om^T % % [R,DR]=VL_RODR(OM) when OM is a 3xK matrix repeats the operation for % each column (or equivalently matrix with 3*K elements). In this % case R and DR are arrays with K slices, one per rotation. % % See also: VL_IRODR(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/geometry/vl_ihat.m0000644000175000017500000000067312237004736016104 0ustar dimadimafunction om = vl_ihat( H ) % VL_IHAT Inverse vl_hat operator % OM = VL_IHAT(H) returns a vector OM such that VL_HAT(OM) = H. % H hast to be 3x3 skew-symmetric. % % See also: VL_HAT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). om = [H(3,2);H(1,3);H(2,1)] ; vlfeat/toolbox/geometry/vl_irodr.m0000644000175000017500000000157712237004736016302 0ustar dimadima% VL_IRODR Inverse Rodrigues' formula % OM = VL_IRODR(R) where R is a rotation matrix computes the the % inverse Rodrigues' formula of om, returning the rotation matrix R % = dehat(Logm(OM)). % % [OM,DOM] = VL_IRODR(R) computes also the derivative of the Rodrigues' % formula. In matrix notation this is the expression % % d( dehat logm(vl_hat(R)) ) % dom = ----------------------. % d(vec R)^T % % [OM,DOM] = VL_IRODR(R) when R is a 9xK matrix repeats the operation % for each column (or equivalently matrix with 9*K elements). In % this case OM and DOM are arrays with K slices, one per rotation. % % See also: VL_RODR(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/geometry/vl_irodr.c0000644000175000017500000000417112237004736016261 0ustar dimadima/* file: irodr.mex.c ** author: Andrea Vedaldi ** description: Inverse rodrigues formula **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include enum { IN_R = 0 } ; enum { OUT_OM=0, OUT_DOM } ; /* ------------------------------------------------------------------- ** Driver ** ----------------------------------------------------------------- */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { int k,K ; double* om_pt ; double* dom_pt ; double const * R_pt ; /* ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if(nin != 1) { mexErrMsgTxt("Exactly one argument required.") ; } if(!vlmxIsMatrix(in[IN_R],-1,-1)) { mexErrMsgTxt("R must be a DOUBLE array") ; } K = mxGetNumberOfElements(in[IN_R]) ; if(K % 9 || K < 9) { mexErrMsgTxt("The elements of R must be a multiple of 9.") ; } K /= 9 ; R_pt = mxGetPr(in[IN_R]) ; /* space for output (OM) */ out[OUT_OM] = mxCreateDoubleMatrix(3,1,mxREAL) ; om_pt = mxGetPr(out[OUT_OM]) ; /* space for optional output (dR) */ dom_pt = NULL ; if( nout > 1 ) { if( K == 1 ) { out[OUT_DOM] = mxCreateDoubleMatrix(3,9,mxREAL) ; } else { mwSize dims [3] ; dims[0] = 3 ; dims[1] = 9 ; dims[2] = K ; out[OUT_DOM] = mxCreateNumericArray(3,dims,mxDOUBLE_CLASS,mxREAL) ; } dom_pt = mxGetPr(out[OUT_DOM]) ; } /* ----------------------------------------------------------------- ** Process ** -------------------------------------------------------------- */ for(k = 0 ; k < K ; ++k) { vl_irodrigues(om_pt, dom_pt, R_pt) ; om_pt += 3 ; dom_pt += 3*9 ; R_pt += 3*3 ; } } vlfeat/toolbox/geometry/vl_hat.m0000644000175000017500000000074112237004736015727 0ustar dimadimafunction H = vl_hat(om) % VL_HAT Hat operator % H = VL_HAT(OM) returns the skew symmetric matrix by taking the "hat" % of the 3D vector OM. % % See also: VL_IHAT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). H = [0 -om(3) om(2) ; om(3) 0 -om(1) ; -om(2) om(1) 0 ] ; vlfeat/toolbox/quickshift/0000755000175000017500000000000012237042171014604 5ustar dimadimavlfeat/toolbox/quickshift/vl_imseg.m0000644000175000017500000000125712237004736016601 0ustar dimadimafunction Q = vl_imseg(I,labels) % VL_IMSEG Color an image based on the segmentation % ISEG = VL_IMSEG(I,LABELS) labels ISEG with the average color from I of % each cluster indicated by LABELS. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [M,N,K] = size(I) ; Q = 0*I ; for k=1:size(I,3) acc = zeros(M,N) ; nrm = zeros(M,N) ; acc = vl_binsum(acc, I(:,:,k), labels) ; nrm = vl_binsum(nrm, ones(M,N), labels) ; acc = acc ./ (nrm+eps) ; Q(:,:,k) = acc(labels) ; end Q = min(1,Q); vlfeat/toolbox/quickshift/vl_quickseg.m0000644000175000017500000000531012237004736017302 0ustar dimadimafunction [Iseg labels map gaps E] = vl_quickseg(I, ratio, kernelsize, maxdist) % VL_QUICKSEG Produce a quickshift segmentation of a grayscale or color image % [ISEG LABELS MAPS GAPS E] = VL_QUICKSEG(I, RATIO, KERNELSIZE, MAXDIST) % % Produces a Quickshift segmentation of an image. See VL_QUICKSHIFT for more % details. % % Inputs: % I Input image, may be RGB or Grayscale. RGB images are first % converted to LAB. % RATIO Tradeoff between spatial consistency and color consistency. % Small ratio gives more importance to the spatial component. % Note that distance calculations happen in unnormalized image % coordinates, so RATIO should be adjusted to compensate for % larger images. % KERNELSIZE The standard deviation of the parzen window density estimator. % MAXDIST The maximum distance between nodes in the quickshift tree. Used % to cut links in the tree to form the segmentation. % % Outputs: % ISEG A color image where each pixel is labeled by the mean color in its % region. % LABELS A labeled image where the number corresponds to the cluster identity % MAP MAP as returned by VL_QUICKSHIFT: For each pixel, the pointer to the % nearest pixel which increases the estimate of the density % GAPS GAPS as returned by VL_QUICKSHIFT: For each pixel, the distance to % the nearest pixel which increases the estimate of the density % E E as returned by VL_QUICKSHIFT: The estimate of the density % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). I = im2double(I); % Add less than one pixel noise to break ties caused by constant regions in an % arbitrary fashon I = I + rand(size(I))/2550; if size(I,3) == 1 Ix = ratio * I; else Ix = ratio * vl_xyz2lab(vl_rgb2xyz(I)); %Ix = Ix(:,:,2:3); % Throw away L end % Perform quickshift to obtain the segmentation tree, which is already cut by % maxdist. If a pixel has no nearest neighbor which increases the density, its % parent in the tree is itself, and gaps is inf. [map,gaps,E] = vl_quickshift(Ix, kernelsize, maxdist) ; % Follow the parents of the tree until we have reached the root nodes % mapped: a labeled segmentation where the labels are the indicies of the modes % in the original image. % labels: mapped after having been renumbered 1:nclusters and reshaped into a % vector [mapped labels] = vl_flatmap(map) ; labels = reshape(labels, size(map)); % imseg builds an average description of the region by color Iseg = vl_imseg(I, labels); vlfeat/toolbox/quickshift/vl_flatmap.m0000644000175000017500000000137012237004736017115 0ustar dimadimafunction [map, C] = vl_flatmap(map) % VL_FLATMAP Flatten a tree, assigning the label of the root to each node % [LABELS CLUSTERS] = VL_FLATMAP(MAP) labels each tree of the forest contained % in MAP. LABELS contains the linear index of the root node in MAP, CLUSTERS % instead contains a label between 1 and the number of clusters. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % follow the parents list to the root nodes (where nothing changes) while 1 map_ = map(map) ; if isequal(map_,map) ; break ; end map = map_ ; end [drop,drop,C] = unique(map) ; vlfeat/toolbox/quickshift/vl_quickvis.m0000644000175000017500000000716012237004736017332 0ustar dimadimafunction [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts) % VL_QUICKVIS Create an edge image from a Quickshift segmentation. % IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge % stability image from a Quickshift segmentation. RATIO controls the tradeoff % between color consistency and spatial consistency (See VL_QUICKSEG) and % KERNELSIZE controls the bandwidth of the density estimator (See VL_QUICKSEG, % VL_QUICKSHIFT). MAXDIST is the maximum distance between neighbors which % increase the density. % % VL_QUICKVIS takes at most MAXCUTS thresholds less than MAXDIST, forming at % most MAXCUTS segmentations. The edges between regions in each of these % segmentations are labeled in IEDGE, where the label corresponds to the % largest DIST which preserves the edge. % % [IEDGE,DISTS] = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) also % returns the DIST thresholds that were chosen. % % IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, DISTS) will use the DISTS % specified % % [IEDGE,DISTS,MAP,GAPS] = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) % also returns the MAP and GAPS from VL_QUICKSHIFT. % % See Also: VL_QUICKSHIFT(), VL_QUICKSEG(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin == 4 dists = maxdist; maxdist = max(dists); [Iseg labels map gaps E] = vl_quickseg(I, ratio, kernelsize, maxdist); else [Iseg labels map gaps E] = vl_quickseg(I, ratio, kernelsize, maxdist); dists = unique(floor(gaps(:))); dists = dists(2:end-1); % remove the inf thresh and the lowest level thresh if length(dists) > maxcuts ind = round(linspace(1,length(dists), maxcuts)); dists = dists(ind); end end [Iedge dists] = mapvis(map, gaps, dists); function [Iedge dists] = mapvis(map, gaps, maxdist, maxcuts) % MAPVIS Create an edge image from a Quickshift segmentation. % IEDGE = MAPVIS(MAP, GAPS, MAXDIST, MAXCUTS) creates an edge % stability image from a Quickshift segmentation. MAXDIST is the maximum % distance between neighbors which increase the density. % % MAPVIS takes at most MAXCUTS thresholds less than MAXDIST, forming at most % MAXCUTS segmentations. The edges between regions in each of these % segmentations are labeled in IEDGE, where the label corresponds to the % largest DIST which preserves the edge. % % [IEDGE,DISTS] = MAPVIS(MAP, GAPS, MAXDIST, MAXCUTS) also returns the DIST % thresholds that were chosen. % % IEDGE = MAPVIS(MAP, GAPS, DISTS) will use the DISTS specified % % See Also: VL_QUICKVIS, VL_QUICKSHIFT, VL_QUICKSEG if nargin == 3 dists = maxdist; maxdist = max(dists); else dists = unique(floor(gaps(:))); dists = dists(2:end-1); % remove the inf thresh and the lowest level thresh % throw away min region size instead of maxdist? ind = find(dists < maxdist); dists = dists(ind); if length(dists) > maxcuts ind = round(linspace(1,length(dists), maxcuts)); dists = dists(ind); end end Iedge = zeros(size(map)); for i = 1:length(dists) s = find(gaps >= dists(i)); mapdist = map; mapdist(s) = s; [mapped labels] = vl_flatmap(mapdist); fprintf('%d/%d %d regions\n', i, length(dists), length(unique(mapped))) borders = getborders(mapped); Iedge(borders) = dists(i); %Iedge(borders) = Iedge(borders) + 1; %Iedge(borders) = i; end %%%%%%%%% GETBORDERS function borders = getborders(map) dx = conv2(map, [-1 1], 'same'); dy = conv2(map, [-1 1]', 'same'); borders = find(dx ~= 0 | dy ~= 0); vlfeat/toolbox/quickshift/vl_quickshift.m0000644000175000017500000000343512237004736017647 0ustar dimadima% VL_QUICKSHIFT Quick shift image segmentation % Quick shift is a mode seeking algorithm which links each pixel to % its nearest neighbor which has an increase in the estimate of the % density. These links form a tree, where the root of the tree is % the pixel which correspond to the highest mode in the image. % % [MAP,GAPS] = VL_QUICKSHIFT(I, KERNELSIZE, MAXDIST) computes quick shift on the % image I. KERNELSIZE is the bandwidth of the Parzen window estimator of % the density. Since searching over all pixels for the nearest % neighbor which increases the density would be prohibitively % expensive, MAXDIST controls the maximum L2 distance between neighbors % that should be linked. MAP and GAP represent the resulting forest % of trees. They are array of the same size of I. Each element % (pixel) of MAP is and index to the parent elemen in the forest and % GAP contains the corresponding branch length. Pixels which are at % the root of their respective tree have MAP(x) = x and GAPS(x) = % inf. % % [MAP,GAPS,E] = VL_QUICKSHIFT(I, KERNELSIZE, MAXDIST) also returns the estimate % of the density E. % % [MAP,GAPS] = VL_QUICKSHIFT(I, KERNELSIZE) uses a default MAXDIST of 3 * KERNELSIZE. % % Notes:: % The distance between pixels is always measured in image % coordinates (not normalized), so the importance of the color % component should be weighted accordingly before calling this % function. % % Options: % % Verbose:: % Toggles verbose output. % % Medoid:: % Run medoid shift instead of quick shift. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/quickshift/vl_quickshift.c0000644000175000017500000000745112237004745017637 0ustar dimadima/** @internal ** @file: quickshift.c ** @author: Andrea Vedaldi ** @author: Brian Fulkerson ** @brief: Quickshift MEX driver **/ /* Copyright (C) 2009-11 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include enum { opt_medoid, opt_verbose } ; vlmxOption options [] = { {"Medoid", 0, opt_medoid }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; /** ------------------------------------------------------------------ ** @brief MEX entry point **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { IN_I=0, /* Input image */ IN_KERNEL_SIZE, /* The bandwidth parameter for density estimation */ IN_MAX_DIST, /* The maximum distance to a neighbor which increases the density */ IN_END } ; enum { OUT_PARENTS=0, /* parents (same size as I) */ OUT_DISTS, /* dists (same size as I) */ OUT_DENSITY /* density (same size as I) */ } ; int verb = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; double const *I ; double *parents, *dists, *density ; int *parentsi; double sigma ; double tau ; int K,N1,N2; int medoid = 0 ; mwSize const *dims ; int ndims ; int i; VlQS * q; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check arguments * -------------------------------------------------------------- */ if (nin < 2) { mexErrMsgTxt("At least two arguments.") ; } if (nout > 3) { mexErrMsgTxt("At most three output arguments.") ; } ndims = mxGetNumberOfDimensions(in[IN_I]) ; dims = mxGetDimensions(in[IN_I]) ; if (ndims > 3) { mexErrMsgTxt("I must have at most 3 dimensions.") ; } if (mxGetClassID(in[IN_I]) != mxDOUBLE_CLASS) { mexErrMsgTxt("I must be DOUBLE.") ; } N1 = dims [0] ; N2 = dims [1] ; K = (ndims == 3) ? dims [2] : 1 ; I = mxGetPr (in[IN_I]) ; sigma = *mxGetPr (in[IN_KERNEL_SIZE]) ; tau = 3*sigma; if (nin > 2) tau = *mxGetPr (in[IN_MAX_DIST]) ; while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_medoid: /* Do medoid shift instead of mean shift */ medoid = 1 ; break ; case opt_verbose : ++ verb ; break ; } } /* while opts */ /* Create outputs */ out[OUT_PARENTS] = mxCreateDoubleMatrix(N1, N2, mxREAL) ; parents = mxGetPr (out[OUT_PARENTS]) ; out[OUT_DISTS] = mxCreateDoubleMatrix(N1, N2, mxREAL) ; dists = mxGetPr (out[OUT_DISTS]) ; out[OUT_DENSITY] = mxCreateDoubleMatrix(N1, N2, mxREAL) ; density = mxGetPr (out[OUT_DENSITY]) ; if (verb) { mexPrintf("quickshift: [N1,N2,K]: [%d,%d,%d]\n", N1,N2,K) ; mexPrintf("quickshift: type: %s\n", medoid ? "medoid" : "quick"); mexPrintf("quickshift: kernel size: %g\n", sigma) ; mexPrintf("quickshift: maximum gap: %g\n", tau) ; } /* Do job */ q = vl_quickshift_new(I, N1, N2, K); vl_quickshift_set_kernel_size (q, sigma) ; vl_quickshift_set_max_dist (q, tau) ; vl_quickshift_set_medoid (q, medoid) ; vl_quickshift_process(q); parentsi = vl_quickshift_get_parents(q); /* Copy results */ for(i = 0; i < N1*N2; i++) parents[i] = parentsi[i] + 1; memcpy(dists, vl_quickshift_get_dists(q), sizeof(double)*N1*N2); memcpy(density, vl_quickshift_get_density(q), sizeof(double)*N1*N2); /* Delete quick shift object */ vl_quickshift_delete(q); } vlfeat/toolbox/special/0000755000175000017500000000000012237004736014057 5ustar dimadimavlfeat/toolbox/special/vl_ddgaussian.m0000644000175000017500000000120012237004736017051 0ustar dimadimafunction y = vl_ddgaussian(x) % VL_DDGAUSSIAN Second derivative of the Gaussian density function % Y=VL_DDGAUSSIAN(X) computes the second derivative of the standard % Gaussian density. % % To obtain the second derivative of the Gaussian density of % standard deviation S, do % % Y = 1/S^3 * VL_DDGAUSSIAN(X/S) . % % See also: VL_GAUSSIAN(), VL_DGAUSSIAN(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). y = (x.^2 - 1)/sqrt(2*pi) .* exp(-0.5*x.^2) ; vlfeat/toolbox/special/vl_gaussian.m0000644000175000017500000000110012237004736016540 0ustar dimadimafunction y = vl_gaussian(x) % VL_GAUSSIAN Standard Gaussian density function % Y=VL_GAUSSIAN(X) computes the standard (zero mean, unit variance) % Gaussian density. % % To obtain the Gaussian density of standard deviation S do % % Y = 1/S * VL_GAUSSIAN(X/S). % % See also: VL_DGAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). y = 1/sqrt(2*pi)*exp(-0.5*x.^2) ; vlfeat/toolbox/special/vl_sigmoid.m0000644000175000017500000000117112237004736016371 0ustar dimadimafunction y = vl_sigmoid(x) % VL_SIGMOID Sigmoid function % Y = VL_SIGMOID(X) returns % % Y = 1 ./ (1 + EXP(X)) ; % % Remark:: % Useful properties of the sigmoid function are: % % - 1 - VL_SIGMOID(X) = VL_SIGMOID(-X) % - Centered sigmoid: 2 * VL_SIGMOID(X) - 1 ; % - VL_SIGMOID(X) = (EXP(X/2) - EXP(X/2)) / (EXP(X/2) + EXP(X/2)) % % See also: VL_DSIGMOID(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). y = 1 ./ (1 + exp(-x)) ; vlfeat/toolbox/special/vl_rcos.m0000644000175000017500000000100212237004736015675 0ustar dimadimafunction y = vl_rcos(r,x) % VL_RCOS RCOS function % Y = VL_RCOS(R,X) computes the RCOS function with roll-off R. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). x = abs(x) ; if(r > 0) y = (x < (1 - r)) + ... 0.5 * (1 + cos((1 - r - x)/r*pi)) .* ... (x <= 1) .* (x >= 1 - r) ; else y = (abs(x) <= 1) ; end vlfeat/toolbox/special/vl_dgaussian.m0000644000175000017500000000111412237004736016711 0ustar dimadimafunction y = vl_dgaussian(x) % VL_DGAUSSIAN Derivative of the Gaussian density function % Y=VL_DGAUSSIAN(X) evaluates the derivative of the standard Gaussian % density. % % To obtain the Gaussian density of standard deviation S, do % % Y = 1/S^2 * VL_DGAUSSIAN(X/S) . % % See also: VL_GAUSSIAN(), VL_DDGAUSSIAN(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). y = -x/sqrt(2*pi) .* exp(-0.5*x.^2) ; vlfeat/toolbox/special/vl_dsigmoid.m0000644000175000017500000000076412237004736016544 0ustar dimadimafunction y = vl_dsigmoid(x) % VL_DSIGMOID Derivative of the sigmoid function % Y = VL_DSIGMOID(X) returns the derivative of VL_SIGMOID(X). This is % calculated as - VL_SIGMOID(X) * (1 - VL_SIGMOID(X)). % % See also: VL_SIGMOID(X), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). t = vl_sigmoid(x) ; y = t .* (1 - t) ; vlfeat/toolbox/vl_root.m0000644000175000017500000000077012237004736014305 0ustar dimadimafunction path = vl_root % VL_ROOT Obtain VLFeat root path % PATH = VL_ROOT() returns the path to the VLFeat installation. % % See also: VL_SETUP(), VL_HELP(). % Authors: Andrea Vedaldi and Brian Fulkerson % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [a,b,c] = fileparts(mfilename('fullpath')) ; [a,b,c] = fileparts(a) ; path = a ; vlfeat/toolbox/vl_help.m0000644000175000017500000000311012237004745014241 0ustar dimadimafunction vl_help(topic, varargin) % VL_HELP VLFeat toolbox builtin help % VL_HELP('doc') displays the HTML documentation in MATLAB % web-browser. VL_HELP('vl_function') displays the HTML % documentation relative to the specified funtion. VL_HELP('topic') % displays the HTML documentation relative to a particular 'topic'. % % VL_HELP(..., 'ONLINE', true) uses the online documentation instead of % the local copy. % % Example:: % To get information about the library, a topic such as Fisher vectors, % or a function such as VL_FISHER(), use: % vl_help doc % vl_help fisher % vl_help vl_fisher % % See also the core VLFeat % documentation. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin == 0 help vl_help ; return ; end opts.online = false ; opts = vl_argparse(opts, varargin) ; local = fullfile(vl_root, 'doc') ; if opts.online prefix = 'http://www.vlfeat.org/doc' ; else prefix = local ; end if strcmp(topic, 'doc') asFunction = 'index.html' ; asTopic = 'index.html' ; else asFunction = fullfile('mdoc', [lower(topic) '.html']) ; asTopic = fullfile('api', [lower(topic) '.html']) ; end if exist(fullfile(local, asFunction), 'file') web(fullfile(prefix, asFunction)) ; elseif exist(fullfile(local, asTopic), 'file') web(fullfile(prefix, asTopic)) ; else error('''%s'' is not a valid function or topic.', topic) ; end vlfeat/toolbox/vl_setup.m0000644000175000017500000000552012237004745014460 0ustar dimadimafunction path = vl_setup(varargin) % VL_SETUP Add VLFeat Toolbox to the path % PATH = VL_SETUP() adds the VLFeat Toolbox to MATLAB path and % returns the path PATH to the VLFeat package. % % VL_SETUP('NOPREFIX') adds aliases to each function that do not % contain the VL_ prefix. For example, with this option it is % possible to use SIFT() instead of VL_SIFT(). % % VL_SETUP('TEST') or VL_SETUP('XTEST') adds VLFeat unit test % function suite. See also VL_TEST(). % % VL_SETUP('QUIET') does not print the greeting message. % % See also: VL_ROOT(), VL_HELP(). % Authors: Andrea Vedaldi and Brian Fulkerson % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). noprefix = false ; quiet = true ; xtest = false ; demo = false ; for ai=1:length(varargin) opt = varargin{ai} ; switch lower(opt) case {'noprefix', 'usingvl'} noprefix = true ; case {'test', 'xtest'} xtest = true ; case {'demo'} demo = true ; case {'quiet'} quiet = true ; case {'verbose'} quiet = false ; otherwise error('Unknown option ''%s''.', opt) ; end end % Do not use vl_root() to avoid conflicts with other VLFeat % installations. [a,b,c] = fileparts(mfilename('fullpath')) ; [a,b,c] = fileparts(a) ; root = a ; addpath(fullfile(root,'toolbox' )) ; addpath(fullfile(root,'toolbox','aib' )) ; addpath(fullfile(root,'toolbox','geometry' )) ; addpath(fullfile(root,'toolbox','imop' )) ; addpath(fullfile(root,'toolbox','kmeans' )) ; addpath(fullfile(root,'toolbox','misc' )) ; addpath(fullfile(root,'toolbox','mser' )) ; addpath(fullfile(root,'toolbox','plotop' )) ; addpath(fullfile(root,'toolbox','quickshift')) ; addpath(fullfile(root,'toolbox','sift' )) ; addpath(fullfile(root,'toolbox','special' )) ; addpath(fullfile(root,'toolbox','slic' )) ; addpath(fullfile(root,'toolbox','gmm' )) ; addpath(fullfile(root,'toolbox','vlad' )) ; addpath(fullfile(root,'toolbox','fisher' )) ; if vl_isoctave() addpath(genpath(fullfile(root,'toolbox','mex','octave'))) ; warning('off', 'Octave:possible-matlab-short-circuit-operator') ; pkg load image ; else bindir = mexext ; if strcmp(bindir, 'dll'), bindir = 'mexw32' ; end addpath(fullfile(root,'toolbox','mex',bindir)) ; end if noprefix addpath(fullfile(root,'toolbox','noprefix')) ; end if xtest addpath(fullfile(root,'toolbox','xtest')) ; end if demo addpath(fullfile(root,'toolbox','demo')) ; end if ~quiet if exist('vl_version') == 3 fprintf('VLFeat %s ready.\n', vl_version) ; else warning('VLFeat does not seem to be installed correctly. Make sure that the MEX files are compiled.') ; end end if nargout == 0 clear path ; end vlfeat/toolbox/xtest/0000755000175000017500000000000012237035722013605 5ustar dimadimavlfeat/toolbox/xtest/vl_test_kdtree.m0000644000175000017500000000462112237004745017005 0ustar dimadimafunction results = vl_test_kdtree(varargin) % VL_TEST_KDTREE vl_test_init ; function s = setup() randn('state',0) ; s.X = single(randn(10, 1000)) ; s.Q = single(randn(10, 10)) ; function test_nearest(s) for tmethod = {'median', 'mean'} for type = {@single, @double} conv = type{1} ; tmethod = char(tmethod) ; X = conv(s.X) ; Q = conv(s.Q) ; tree = vl_kdtreebuild(X,'ThresholdMethod', tmethod) ; [nn, d2] = vl_kdtreequery(tree, X, Q) ; D2 = vl_alldist2(X, Q, 'l2') ; [d2_, nn_] = min(D2) ; vl_assert_equal(... nn,uint32(nn_),... 'incorrect nns: type=%s th. method=%s', func2str(conv), tmethod) ; vl_assert_almost_equal(... d2,d2_,... 'incorrect distances: type=%s th. method=%s', func2str(conv), tmethod) ; end end function test_nearests(s) numNeighbors = 7 ; tree = vl_kdtreebuild(s.X) ; [nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ... 'numNeighbors', numNeighbors) ; D2 = vl_alldist2(s.X, s.Q, 'l2') ; [d2_, nn_] = sort(D2) ; d2_ = d2_(1:numNeighbors, :) ; nn_ = nn_(1:numNeighbors, :) ; vl_assert_equal(nn,uint32(nn_)) ; vl_assert_almost_equal(d2,d2_) ; function test_ann(s) vl_twister('state', 1) ; numNeighbors = 7 ; maxComparisons = numNeighbors * 50 ; tree = vl_kdtreebuild(s.X) ; [nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ... 'numNeighbors', numNeighbors, ... 'maxComparisons', maxComparisons) ; D2 = vl_alldist2(s.X, s.Q, 'l2') ; [d2_, nn_] = sort(D2) ; d2_ = d2_(1:numNeighbors, :) ; nn_ = nn_(1:numNeighbors, :) ; for i=1:size(s.Q,2) overlap = numel(intersect(nn(:,i), nn_(:,i))) / ... numel(union(nn(:,i), nn_(:,i))) ; assert(overlap > 0.6, 'ANN did not return enough correct nearest neighbors') ; end function test_ann_forest(s) vl_twister('state', 1) ; numNeighbors = 7 ; maxComparisons = numNeighbors * 25 ; numTrees = 5 ; tree = vl_kdtreebuild(s.X, 'numTrees', 5) ; [nn, d2] = vl_kdtreequery(tree, s.X, s.Q, ... 'numNeighbors', numNeighbors, ... 'maxComparisons', maxComparisons) ; D2 = vl_alldist2(s.X, s.Q, 'l2') ; [d2_, nn_] = sort(D2) ; d2_ = d2_(1:numNeighbors, :) ; nn_ = nn_(1:numNeighbors, :) ; for i=1:size(s.Q,2) overlap = numel(intersect(nn(:,i), nn_(:,i))) / ... numel(union(nn(:,i), nn_(:,i))) ; assert(overlap > 0.6, 'ANN did not return enough correct nearest neighbors') ; end vlfeat/toolbox/xtest/vl_test_aib.m0000644000175000017500000000237512237004736016266 0ustar dimadimafunction results = vl_test_aib(varargin) % VL_TEST_AIB vl_test_init ; function s = setup() s = [] ; function test_basic(s) Pcx = [.3 .3 0 0 0 0 .2 .2] ; % This results in the AIB tree % % 1 - \ % 5 - \ % 2 - / \ % - 7 % 3 - \ / % 6 - / % 4 - / % % coded by the map [5 5 6 6 7 1] (1 denotes the root). [parents,cost] = vl_aib(Pcx) ; vl_assert_equal(parents, [5 5 6 6 7 7 1]) ; vl_assert_almost_equal(mi(Pcx)*[1 1 1], cost(1:3), 1e-3) ; [cut,map,short] = vl_aibcut(parents,2) ; vl_assert_equal(cut, [5 6]) ; vl_assert_equal(map, [1 1 2 2 1 2 0]) ; vl_assert_equal(short, [5 5 6 6 5 6 7]) ; function test_cluster_null(s) Pcx = [.5 .5 0 0 0 0 0 0] ; % This results in the AIB tree % % 1 - \ % 5 % 2 - / % % 3 x % % 4 x % % If ClusterNull is specified, the values 3 and 4 % which have zero probability are merged first % % 1 ----------\ % 7 % 2 ----- \ / % 6-/ % 3 -\ / % 5 -/ % 4 -/ parents1 = vl_aib(Pcx) ; parents2 = vl_aib(Pcx,'ClusterNull') ; vl_assert_equal(parents1, [5 5 0 0 1 0 0]) ; vl_assert_equal(parents2(3), parents2(4)) ; function x = mi(P) % mutual information P1 = sum(P,1) ; P2 = sum(P,2) ; x = sum(sum(P .* log(max(P,1e-10) ./ (P2*P1)))) ; vlfeat/toolbox/xtest/vl_test_mser.m0000644000175000017500000000036212237004736016473 0ustar dimadimafunction results = vl_test_mser(varargin) % VL_TEST_MSER vl_test_init ; function s = setup() s.im = im2uint8(rgb2gray(vl_impattern('roofs1'))) ; function test_mser(s) [regions,frames] = vl_mser(s.im) ; mask = vl_erfill(s.im, regions(1)) ; vlfeat/toolbox/xtest/vl_test.m0000644000175000017500000000345412237004745015452 0ustar dimadimafunction results = vl_test(suite, test) % VL_TEST Run test suite % RESULTS = VL_TEST() runs all VLFeat test suites. The tests % verify that VLFeat is working correctly. % % RESULTS is a structure listing the result of each test. % It has fileds: % % TESTNAME: name of the test % SUCCEDED: a boolean flag indicating whether the test succeded % EXCEPTION: the exception generated if the test failed % % VL_TEST(SUITE) runs only the specified SUITE. % % VL_TEST(SUITE, TEST) runs the specified SUITE/TEST without catching % the potential exception. Useful to DBSTOP to debug. % Author: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % Copyright (C) 2013 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). clear functions ; testRoot = fileparts(mfilename('fullpath')) ; if nargin == 0 files = dir(fullfile(testRoot, 'vl_test_*.m')) ; elseif nargin == 1 files.name = fullfile(['vl_test_' suite '.m']) ; end if nargin < 2 results = {} ; for i = 1:length(files) testCommand = files(i).name(1:end-2) ; if strcmp(testCommand, 'vl_test_init'), continue ; end fprintf('vl_test: running %s\n', testCommand) ; results{i} = feval(str2func(testCommand)) ; end results = cat(2, results{:}) ; for i = 1:length(results) if results(i).succeded fprintf('vl_test: %-35s ... passed\n', results(i).testName) ; else fprintf('vl_test: %-35s ... failed\n', results(i).testName) ; fprintf('%s:%d: %s\n', ... results(i).exception.stack(1).name, ... results(i).exception.stack(1).line, ... results(i).exception.message) ; end end else feval(str2func(sprintf('vl_test_%s', suite)), test) ; end vlfeat/toolbox/xtest/vl_test_argparse.m0000644000175000017500000000143312237004736017331 0ustar dimadimafunction results = vl_test_argparse(varargin) % VL_TEST_ARGPARSE vl_test_init ; function test_basic() opts.field1 = 1 ; opts.field2 = 2 ; opts.field3 = 3 ; opts_ = opts ; opts_.field1 = 3 ; opts_.field2 = 10 ; opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ; assert(isequal(opts, opts_)) ; opts_.field1 = 9 ; opts = vl_argparse(opts, {'field1', 4, 'field1', 9}) ; assert(isequal(opts, opts_)) ; function test_error() opts.field1 = 1 ; try opts = vl_argparse(opts, {'field2', 5}) ; catch e return ; end assert(false) ; function test_leftovers() opts1.field1 = 1 ; opts2.field2 = 1 ; opts1_.field1 = 2 ; opts2_.field2 = 2 ; [opts1,args] = vl_argparse(opts1, {'field1', 2, 'field2', 2}) ; opts2 = vl_argparse(opts2, args) ; assert(isequal(opts1,opts1_), isequal(opts2,opts2_)) ; vlfeat/toolbox/xtest/vl_test_imwbackward.m0000644000175000017500000000100212237004736020010 0ustar dimadimafunction results = vl_test_imwbackward(varargin) % VL_TEST_IMWBACKWARD vl_test_init ; function s = setup() s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ; function test_identity(s) xr = 1:size(s.I,2) ; yr = 1:size(s.I,1) ; [x,y] = meshgrid(xr,yr) ; vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,x,y)) ; function test_invalid_args(s) xr = 1:size(s.I,2) ; yr = 1:size(s.I,1) ; [x,y] = meshgrid(xr,yr) ; vl_assert_exception(@() vl_imwbackward(xr,yr,single(s.I),x,y), 'vl:invalidArgument') ; vlfeat/toolbox/xtest/vl_assert_exception.m0000644000175000017500000000101512237004736020041 0ustar dimadimafunction vl_assert_exception(func, errorId, errorMessage) % VL_ASSERT_EXCEPTION try func() ; catch e = lasterror ; if nargin >= 2 assert(isequal(e.identifier, errorId), ... 'Exception ID ''%s'' is not ''%s''.', ... e.identifier, errorId) ; end if nargin >= 3 assert(isequal(e.message, errorMessage), ... 'Exception message ''%s'' is not ''%s''.', ... e.message, errorMessage) ; end return ; end assert(0, 'The function did not generate an exception.') ; end vlfeat/toolbox/xtest/vl_test_vlad.m0000644000175000017500000000367112237004745016461 0ustar dimadimafunction results = vl_test_vlad(varargin) % VL_TEST_VLAD vl_test_init ; function s = setup() randn('state',0) ; s.x = randn(128,256) ; s.mu = randn(128,16) ; assignments = rand(16, 256) ; s.assignments = bsxfun(@times, assignments, 1 ./ sum(assignments,1)) ; function test_basic (s) x = [1, 2, 3] ; mu = [0, 0, 0] ; assignments = eye(3) ; phi = vl_vlad(x, mu, assignments, 'unnormalized') ; vl_assert_equal(phi, [1 2 3]') ; mu = [0, 1, 2] ; phi = vl_vlad(x, mu, assignments, 'unnormalized') ; vl_assert_equal(phi, [1 1 1]') ; phi = vl_vlad([x x], mu, [assignments assignments], 'unnormalized') ; vl_assert_equal(phi, [2 2 2]') ; function test_rand (s) phi_ = simple_vlad(s.x, s.mu, s.assignments) ; phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized') ; vl_assert_equal(phi, phi_) ; function test_norm (s) phi_ = simple_vlad(s.x, s.mu, s.assignments) ; phi_ = phi_ / norm(phi_) ; phi = vl_vlad(s.x, s.mu, s.assignments) ; vl_assert_almost_equal(phi, phi_, 1e-4) ; function test_sqrt (s) phi_ = simple_vlad(s.x, s.mu, s.assignments) ; phi_ = sign(phi_) .* sqrt(abs(phi_)) ; phi_ = phi_ / norm(phi_) ; phi = vl_vlad(s.x, s.mu, s.assignments, 'squareroot') ; vl_assert_almost_equal(phi, phi_, 1e-4) ; function test_individual (s) phi_ = simple_vlad(s.x, s.mu, s.assignments) ; phi_ = reshape(phi_, size(s.x,1), []) ; phi_ = bsxfun(@times, phi_, 1 ./ sqrt(sum(phi_.^2))) ; phi_ = phi_(:) ; phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized', 'normalizecomponents') ; vl_assert_almost_equal(phi, phi_, 1e-4) ; function test_mass (s) phi_ = simple_vlad(s.x, s.mu, s.assignments) ; phi_ = reshape(phi_, size(s.x,1), []) ; phi_ = bsxfun(@times, phi_, 1 ./ sum(s.assignments,2)') ; phi_ = phi_(:) ; phi = vl_vlad(s.x, s.mu, s.assignments, 'unnormalized', 'normalizemass') ; vl_assert_almost_equal(phi, phi_, 1e-4) ; function enc = simple_vlad(x, mu, assign) for i = 1:size(assign,1) enc{i} = x * assign(i,:)' - sum(assign(i,:)) * mu(:,i) ; end enc = cat(1, enc{:}) ; vlfeat/toolbox/xtest/vl_test_kmeans.m0000644000175000017500000000706012237004745017005 0ustar dimadimafunction results = vl_test_kmeans(varargin) % VL_TEST_KMEANS % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vl_test_init ; function s = setup() randn('state',0) ; s.X = randn(128, 100) ; function test_basic(s) [centers, assignments, en] = vl_kmeans(s.X, 10, 'NumRepetitions', 10) ; [centers_, assignments_, en_] = simpleKMeans(s.X, 10) ; assert(en_ <= 1.1 * en, 'vl_kmeans did not optimize enough') ; function test_algorithms(s) distances = {'l1', 'l2'} ; dataTypes = {'single','double'} ; for dataType = dataTypes for distance = distances distance = char(distance) ; conversion = str2func(char(dataType)) ; X = conversion(s.X) ; vl_twister('state',0) ; [centers, assignments, en] = vl_kmeans(X, 10, ... 'NumRepetitions', 1, ... 'MaxNumIterations', 10, ... 'Algorithm', 'Lloyd', ... 'Distance', distance) ; vl_twister('state',0) ; [centers_, assignments_, en_] = vl_kmeans(X, 10, ... 'NumRepetitions', 1, ... 'MaxNumIterations', 10, ... 'Algorithm', 'Elkan', ... 'Distance', distance) ; vl_twister('state',0) ; [centers__, assignments__, en__] = vl_kmeans(X, 10, ... 'NumRepetitions', 1, ... 'MaxNumIterations', 10, ... 'Algorithm', 'ANN', ... 'Distance', distance, ... 'NumTrees', 3, ... 'MaxNumComparisons',0) ; vl_assert_almost_equal(centers, centers_, 1e-5) ; vl_assert_almost_equal(assignments, assignments_, 1e-5) ; vl_assert_almost_equal(en, en_, 1e-5) ; vl_assert_almost_equal(centers, centers__, 1e-5) ; vl_assert_almost_equal(assignments, assignments__, 1e-5) ; vl_assert_almost_equal(en, en__, 1e-5) ; vl_assert_almost_equal(centers_, centers__, 1e-5) ; vl_assert_almost_equal(assignments_, assignments__, 1e-5) ; vl_assert_almost_equal(en_, en__, 1e-5) ; end end function test_patterns(s) distances = {'l1', 'l2'} ; dataTypes = {'single','double'} ; for dataType = dataTypes for distance = distances distance = char(distance) ; conversion = str2func(char(dataType)) ; data = [1 1 0 0 ; 1 0 1 0] ; data = conversion(data) ; [centers, assignments, en] = vl_kmeans(data, 4, ... 'NumRepetitions', 100, ... 'Distance', distance) ; assert(isempty(setdiff(data', centers', 'rows'))) ; end end function [centers, assignments, en] = simpleKMeans(X, numCenters) [dimension, numData] = size(X) ; centers = randn(dimension, numCenters) ; for iter = 1:10 [dists, assignments] = min(vl_alldist(centers, X)) ; en = sum(dists) ; centers = [zeros(dimension, numCenters) ; ones(1, numCenters)] ; centers = vl_binsum(centers, ... [X ; ones(1,numData)], ... repmat(assignments, dimension+1, 1), 2) ; centers = centers(1:end-1, :) ./ repmat(centers(end,:), dimension, 1) ; end vlfeat/toolbox/xtest/vl_test_homkermap.m0000644000175000017500000000355712237004736017521 0ustar dimadimafunction results = vl_test_homkermap(varargin) % VL_TEST_HOMKERMAP vl_test_init ; function check_ker(ker, n, window, period) args = {n, ker, 'window', window} ; if nargin > 3 args = {args{:}, 'period', period} ; end x = [-1 -.5 0 .5 1] ; y = linspace(0,2,100) ; for conv = {@single, @double} x = feval(conv{1}, x) ; y = feval(conv{1}, y) ; sx = sign(x) ; sy = sign(y) ; psix = vl_homkermap(x, args{:}) ; psiy = vl_homkermap(y, args{:}) ; k = vl_alldist(psix,psiy,'kl2') ; k_ = (sx'*sy) .* vl_alldist(sx.*x,sy.*y,ker) ; vl_assert_almost_equal(k, k_, 2e-2) ; end function test_uniform_kchi2(), check_ker('kchi2', 3, 'uniform', 15) ; function test_uniform_kjs(), check_ker('kjs', 3, 'uniform', 15) ; function test_uniform_kl1(), check_ker('kl1', 29, 'uniform', 15) ; function test_rect_kchi2(), check_ker('kchi2', 3, 'rectangular', 15) ; function test_rect_kjs(), check_ker('kjs', 3, 'rectangular', 15) ; function test_rect_kl1(), check_ker('kl1', 29, 'rectangular', 10) ; function test_auto_uniform_kchi2(),check_ker('kchi2', 3, 'uniform') ; function test_auto_uniform_kjs(), check_ker('kjs', 3, 'uniform') ; function test_auto_uniform_kl1(), check_ker('kl1', 25, 'uniform') ; function test_auto_rect_kchi2(), check_ker('kchi2', 3, 'rectangular') ; function test_auto_rect_kjs(), check_ker('kjs', 3, 'rectangular') ; function test_auto_rect_kl1(), check_ker('kl1', 25, 'rectangular') ; function test_gamma() x = linspace(0,1,20) ; for gamma = linspace(.2,2,10) k = vl_alldist(x, 'kchi2') .* (x'*x + 1e-12).^((gamma-1)/2) ; psix = vl_homkermap(x, 3, 'kchi2', 'gamma', gamma) ; assert(norm(k - psix'*psix) < 1e-2) ; end function test_negative() x = linspace(-1,1,20) ; k = vl_alldist(abs(x), 'kchi2') .* (sign(x)'*sign(x)) ; psix = vl_homkermap(x, 3, 'kchi2') ; assert(norm(k - psix'*psix) < 1e-2) ; vlfeat/toolbox/xtest/vl_test_ihashsum.m0000644000175000017500000000110512237004736017342 0ustar dimadimafunction results = vl_test_ihashsum(varargin) % VL_TEST_IHASHSUM vl_test_init ; function s = setup() rand('state',0) ; s.data = uint8(round(16*rand(2,100))) ; sel = find(all(s.data==0)) ; s.data(1,sel)=1 ; function test_hash(s) D = size(s.data,1) ; K = 5 ; h = zeros(1,K,'uint32') ; id = zeros(D,K,'uint8'); next = zeros(1,K,'uint32') ; [h,id,next] = vl_ihashsum(h,id,next,K,s.data) ; sel = vl_ihashfind(id,next,K,s.data) ; count = double(h(sel)) ; [drop,i,j] = unique(s.data','rows') ; for k=1:size(s.data,2) count_(k) = sum(j == j(k)) ; end vl_assert_equal(count,count_) ; vlfeat/toolbox/xtest/vl_test_twister.m0000644000175000017500000000234312237004745017227 0ustar dimadimafunction results = vl_test_twister(varargin) % VL_TEST_TWISTER vl_test_init ; function test_illegal_args() vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ; vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ; vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ; function test_seed_by_scalar() rand('twister',1) ; a = rand ; vl_twister('state',1) ; b = vl_twister ; vl_assert_equal(a,b,'seed by scalar + VL_TWISTER()') ; function test_get_set_state() rand('twister',1) ; a = rand('twister') ; vl_twister('state',1) ; b = vl_twister('state') ; vl_assert_equal(a,b,'read state') ; a(1) = a(1) + 1 ; vl_twister('state',a) ; b = vl_twister('state') ; vl_assert_equal(a,b,'set state') ; function test_multi_dimensions() b = rand('twister') ; rand('twister',b) ; vl_twister('state',b) ; a=rand([1 2 3 4 5]) ; b=vl_twister([1 2 3 4 5]) ; vl_assert_equal(a,b,'VL_TWISTER([M N P ...])') ; function test_multi_multi_args() rand('twister',1) ; a=rand(1, 2, 3, 4, 5) ; vl_twister('state',1) ; b=vl_twister(1, 2, 3, 4, 5) ; vl_assert_equal(a,b,'VL_TWISTER(M, N, P, ...)') ; function test_square() rand('twister',1) ; a=rand(10) ; vl_twister('state',1) ; b=vl_twister(10) ; vl_assert_equal(a,b,'VL_TWISTER(N)') ; vlfeat/toolbox/xtest/vl_test_phow.m0000644000175000017500000000104512237004736016501 0ustar dimadimafunction results = vl_test_phow(varargin) % VL_TEST_PHOPW vl_test_init ; function s = setup() s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ; s.I = single(s.I) ; function test_gray(s) [f,d] = vl_phow(s.I, 'color', 'gray') ; assert(size(d,1) == 128) ; function test_rgb(s) [f,d] = vl_phow(s.I, 'color', 'rgb') ; assert(size(d,1) == 128*3) ; function test_hsv(s) [f,d] = vl_phow(s.I, 'color', 'hsv') ; assert(size(d,1) == 128*3) ; function test_opponent(s) [f,d] = vl_phow(s.I, 'color', 'opponent') ; assert(size(d,1) == 128*3) ; vlfeat/toolbox/xtest/vl_test_init.m0000644000175000017500000000532412237004745016473 0ustar dimadima% VL_TEST_INIT Intialize unit test file % This is a VLFeat internal function used to initialize an .m file % containing a unit test. % % See also: VL_TEST(), VL_HELP(). % Author:: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % Copyright (C) 2013 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % Obtain the path to the m-file that called VL_TEST_INIT() stack = dbstack(1) ; mfilePath = stack(1).file ; mfileName = stack(1).name ; fid = fopen(mfilePath, 'r') ; % Parse the source code of the caller to get the names of its % subfunctions. One could use which('-subfun', stack(1).name) here, % but this feature is undocumented. setupFunc = [] ; teardownFunc = [] ; tests = {} ; l = 0 ; t = 0 ; while 1 tline = fgetl(fid) ; l = l + 1 ; if ~ischar(tline), break ; end mo = regexp(tline, '^\s*function\s+(\w\s*=\s*)?test_(?[\w_]+)\s*\(.*\).*$', ... 'names') ; if length(mo) > 0 t = t + 1 ; tests{t}.name = mo(1).name ; tests{t}.func = str2func(['test_' mo(1).name]) ; tests{t}.lineNumber = l ; end mo = regexp(tline, '^\s*function\s+(\w+\s*=\s*)?(?setup|teardown)\s*\(.*\).*$', ... 'names') ; if length(mo) > 0 switch mo(1).name case 'setup' if isempty(setupFunc) setupFunc = @setup ; else warning('%s:%d: ignoring redundant definition of the setup function', ... mfilePath) ; end case 'teardown' if isempty(teardownFunc) teardownFunc = @teardown ; else warning('%s:%d: ignoring redundant definition of the setup function', ... mfilePath) ; end otherwise assert(0) ; end end end if ~isempty(setupFunc), state = setupFunc() ; end if length(varargin) > 0 testFunc = str2func(sprintf('test_%s', varargin{1})) ; if exist('state','var') testFunc(state) ; else testFunc() ; end if ~isempty(teardownFunc), teardownFunc(state) ; end return ; end % Run all tests collecting the results. if length(tests) == 0 error('No tests found in ''%s''', mfileName) ; end for t = 1:length(tests) subTestName = sprintf('test_%s', tests{t}.name) ; results{t}.testName = sprintf('%s:%s', mfileName(9:end), tests{t}.name) ; results{t}.succeded = true ; results{t}.exception = [] ; try if exist('state', 'var') tests{t}.func(state) ; else tests{t}.func() ; end catch exception results{t}.succeded = false ; results{t}.exception = exception ; end end if ~isempty(teardownFunc), teardownFunc(state) ; end results = cat(2, results{:}) ; vlfeat/toolbox/xtest/vl_test_gmm.m0000644000175000017500000000246412237004745016312 0ustar dimadimafunction results = vl_test_gmm(varargin) % VL_TEST_GMM % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vl_test_init ; end function s = setup() randn('state',0) ; s.X = randn(128, 1000) ; end function test_multithreading(s) dataTypes = {'single','double'} ; for dataType = dataTypes conversion = str2func(char(dataType)) ; X = conversion(s.X) ; vl_twister('state',0) ; vl_threads(0) ; [means, covariances, priors, ll, posteriors] = ... vl_gmm(X, 10, ... 'NumRepetitions', 1, ... 'MaxNumIterations', 10, ... 'Initialization', 'rand') ; vl_twister('state',0) ; vl_threads(1) ; [means_, covariances_, priors_, ll_, posteriors_] = ... vl_gmm(X, 10, ... 'NumRepetitions', 1, ... 'MaxNumIterations', 10, ... 'Initialization', 'rand') ; vl_assert_almost_equal(means, means_, 1e-2) ; vl_assert_almost_equal(covariances, covariances_, 1e-2) ; vl_assert_almost_equal(priors, priors_, 1e-2) ; vl_assert_almost_equal(ll, ll_, 1e-2 * abs(ll)) ; vl_assert_almost_equal(posteriors, posteriors_, 1e-2) ; end end vlfeat/toolbox/xtest/vl_test_inthist.m0000644000175000017500000000145312237004736017211 0ustar dimadimafunction results = vl_test_inthist(varargin) % VL_TEST_INTHIST vl_test_init ; function s = setup() rand('state',0) ; s.labels = uint32(8*rand(123, 76, 3)) ; function test_basic(s) l = 10 ; hist = vl_inthist(s.labels, 'numlabels', l) ; hist_ = inthist_slow(s.labels, l) ; vl_assert_equal(double(hist),hist_) ; function test_sample(s) rand('state',0) ; boxes = 10 * rand(4,20) + .5 ; boxes(3:4,:) = boxes(3:4,:) + boxes(1:2,:) ; boxes = min(boxes, 10) ; boxes = uint32(boxes) ; inthist = vl_inthist(s.labels) ; hist = vl_sampleinthist(inthist, boxes) ; function hist = inthist_slow(labels, numLabels) m = size(labels,1) ; n = size(labels,2) ; l = numLabels ; b = zeros(m*n,l) ; b = vl_binsum(b, 1, reshape(labels,m*n,[]), 2) ; b = reshape(b,m,n,l) ; for k=1:l hist(:,:,k) = cumsum(cumsum(b(:,:,k)')') ; end vlfeat/toolbox/xtest/vl_test_cummax.m0000644000175000017500000000137212237004736017021 0ustar dimadimafunction results = vl_test_cummax(varargin) % VL_TEST_CUMMAX vl_test_init ; function test_basic() vl_assert_almost_equal(... vl_cummax(1), 1) ; vl_assert_almost_equal(... vl_cummax([1 2 3 4], 2), [1 2 3 4]) ; function test_multidim() a = [1 2 3 4 3 2 1] ; b = [1 2 3 4 4 4 4] ; for k=1:6 dims = ones(1,6) ; dims(k) = numel(a) ; a = reshape(a, dims) ; b = reshape(b, dims) ; vl_assert_almost_equal(... vl_cummax(a, k), b) ; end function test_storage_classes() types = {@double, @single, @int64, @uint64, ... @int32, @uint32, @int16, @uint16, ... @int8, @uint8} ; for a = types a = a{1} ; for b = types b = b{1} ; vl_assert_almost_equal(... vl_cummax(a(eye(3))), a(toeplitz([1 1 1], [1 0 0 ]))) ; end end vlfeat/toolbox/xtest/vl_test_whistc.m0000644000175000017500000000255012237004736017027 0ustar dimadimafunction results = vl_test_whistc(varargin) % VL_TEST_WHISTC vl_test_init ; function test_acc() x = ones(1, 10) ; e = 1 ; o = 1:10 ; vl_assert_equal(vl_whistc(x, o, e), 55) ; function test_basic() x = 1:10 ; e = 1:10 ; o = ones(1, 10) ; vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ; x = linspace(-1,11,100) ; o = ones(size(x)) ; vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ; function test_multidim() x = rand(10, 20, 30) ; e = linspace(0,1,10) ; o = ones(size(x)) ; vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ; vl_assert_equal(histc(x, e, 1), vl_whistc(x, o, e, 1)) ; vl_assert_equal(histc(x, e, 2), vl_whistc(x, o, e, 2)) ; vl_assert_equal(histc(x, e, 3), vl_whistc(x, o, e, 3)) ; function test_nan() x = rand(10, 20, 30) ; e = linspace(0,1,10) ; o = ones(size(x)) ; x(1:7:end) = NaN ; vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ; vl_assert_equal(histc(x, e, 1), vl_whistc(x, o, e, 1)) ; vl_assert_equal(histc(x, e, 2), vl_whistc(x, o, e, 2)) ; vl_assert_equal(histc(x, e, 3), vl_whistc(x, o, e, 3)) ; function test_no_edges() x = rand(10, 20, 30) ; o = ones(size(x)) ; vl_assert_equal(histc(1, []), vl_whistc(1, 1, [])) ; vl_assert_equal(histc(x, []), vl_whistc(x, o, [])) ; vl_assert_equal(histc(x, [], 1), vl_whistc(x, o, [], 1)) ; vl_assert_equal(histc(x, [], 2), vl_whistc(x, o, [], 2)) ; vl_assert_equal(histc(x, [], 3), vl_whistc(x, o, [], 3)) ; vlfeat/toolbox/xtest/vl_test_binsum.m0000644000175000017500000000242512237004736017024 0ustar dimadimafunction results = vl_test_binsum(varargin) % VL_TEST_BINSUM vl_test_init ; function test_three_args() vl_assert_almost_equal(... vl_binsum([0 0], 1, 2), [0 1]) ; vl_assert_almost_equal(... vl_binsum([1 7], -1, 1), [0 7]) ; vl_assert_almost_equal(... vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ; function test_four_args() vl_assert_almost_equal(... vl_binsum(eye(3), [1 1 1], [1 2 3], 1), 2*eye(3)) ; vl_assert_almost_equal(... vl_binsum(eye(3), [1 1 1]', [1 2 3]', 2), 2*eye(3)) ; vl_assert_almost_equal(... vl_binsum(eye(3), 1, [1 2 3], 1), 2*eye(3)) ; vl_assert_almost_equal(... vl_binsum(eye(3), 1, [1 2 3]', 2), 2*eye(3)) ; function test_3d_one() Z = zeros(3,3,3) ; B = 3*ones(3,1,3) ; R = Z ; R(:,3,:) = 17 ; vl_assert_almost_equal(... vl_binsum(Z, 17, B, 2), R) ; function test_3d_two() Z = zeros(3,3,3) ; B = 3*ones(3,3,1) ; X = zeros(3,3,1) ; X(:,:,1) = 17 ; R = Z ; R(:,:,3) = 17 ; vl_assert_almost_equal(... vl_binsum(Z, X, B, 3), R) ; function test_storage_classes() types = {@double, @single, @int64, @uint64, ... @int32, @uint32, @int16, @uint16, ... @int8, @uint8} ; for a = types a = a{1} ; for b = types b = b{1} ; vl_assert_almost_equal(... vl_binsum(a(eye(3)), a([1 1 1]), b([1 2 3]), 1), a(2*eye(3))) ; end end vlfeat/toolbox/xtest/vl_test_printsize.m0000644000175000017500000000264712237004745017564 0ustar dimadimafunction results = vl_test_printsize(varargin) % VL_TEST_PRINTSIZE vl_test_init ; function s = setup() s.fig = figure(1) ; s.usletter = [8.5, 11] ; % inches s.a4 = [8.26772, 11.6929] ; clf(s.fig) ; plot(1:10) ; function teardown(s) close(s.fig) ; function test_basic(s) for sigma = [1 0.5 0.2] vl_printsize(s.fig, sigma) ; set(1, 'PaperUnits', 'inches') ; siz = get(1, 'PaperSize') ; pos = get(1, 'PaperPosition') ; vl_assert_almost_equal(siz(1), sigma*s.usletter(1), 1e-4) ; vl_assert_almost_equal(pos(1), 0, 1e-4) ; vl_assert_almost_equal(pos(3), sigma*s.usletter(1), 1e-4) ; end function test_papertype(s) vl_printsize(s.fig, 1, 'papertype', 'a4') ; set(1, 'PaperUnits', 'inches') ; siz = get(1, 'PaperSize') ; pos = get(1, 'PaperPosition') ; vl_assert_almost_equal(siz(1), s.a4(1), 1e-4) ; function test_margin(s) m = 0.5 ; vl_printsize(s.fig, 1, 'margin', m) ; set(1, 'PaperUnits', 'inches') ; siz = get(1, 'PaperSize') ; pos = get(1, 'PaperPosition') ; vl_assert_almost_equal(siz(1), s.usletter(1) * (1 + 2*m), 1e-4) ; vl_assert_almost_equal(pos(1), s.usletter(1) * m, 1e-4) ; function test_reference(s) sigma = 1 ; vl_printsize(s.fig, 1, 'reference', 'vertical') ; set(1, 'PaperUnits', 'inches') ; siz = get(1, 'PaperSize') ; pos = get(1, 'PaperPosition') ; vl_assert_almost_equal(siz(2), sigma*s.usletter(2), 1e-4) ; vl_assert_almost_equal(pos(2), 0, 1e-4) ; vl_assert_almost_equal(pos(4), sigma*s.usletter(2), 1e-4) ; vlfeat/toolbox/xtest/vl_test_plotbox.m0000644000175000017500000000063612237004745017220 0ustar dimadimafunction results = vl_test_plotbox(varargin) % VL_TEST_PLOTBOX vl_test_init ; function test_basic(s) figure(1) ; clf ; vl_plotbox([-1 -1 1 1]') ; xlim([-2 2]) ; ylim([-2 2]) ; close(1) ; function test_multiple(s) figure(1) ; clf ; randn('state', 0) ; vl_plotbox(randn(4,10)) ; close(1) ; function test_style(s) figure(1) ; clf ; randn('state', 0) ; vl_plotbox(randn(4,10), 'r-.', 'LineWidth', 3) ; close(1) ; vlfeat/toolbox/xtest/vl_test_lbp.m0000644000175000017500000000204012237004736016275 0ustar dimadimafunction results = vl_test_lbp(varargin) % VL_TEST_TWISTER vl_test_init ; function test_one_on() I = {} ; I{1} = [0 0 0 ; 0 0 1 ; 0 0 0] ; I{2} = [0 0 0 ; 0 0 0 ; 0 0 1] ; I{3} = [0 0 0 ; 0 0 0 ; 0 1 0] ; I{4} = [0 0 0 ; 0 0 0 ; 1 0 0] ; I{5} = [0 0 0 ; 1 0 0 ; 0 0 0] ; I{6} = [1 0 0 ; 0 0 0 ; 0 0 0] ; I{7} = [0 1 0 ; 0 0 0 ; 0 0 0] ; I{8} = [0 0 1 ; 0 0 0 ; 0 0 0] ; for j=0:7 h = vl_lbp(single(I{j+1}), 3) ; h = find(squeeze(h)) ; vl_assert_equal(h, j * 7 + 1) ; end function test_two_on() I = {} ; I{1} = [0 0 0 ; 0 0 1 ; 0 0 1] ; I{2} = [0 0 0 ; 0 0 0 ; 0 1 1] ; I{3} = [0 0 0 ; 0 0 0 ; 1 1 0] ; I{4} = [0 0 0 ; 1 0 0 ; 1 0 0] ; I{5} = [1 0 0 ; 1 0 0 ; 0 0 0] ; I{6} = [1 1 0 ; 0 0 0 ; 0 0 0] ; I{7} = [0 1 1 ; 0 0 0 ; 0 0 0] ; I{8} = [0 0 1 ; 0 0 1 ; 0 0 0] ; for j=0:7 h = vl_lbp(single(I{j+1}), 3) ; h = find(squeeze(h)) ; vl_assert_equal(h, j * 7 + 2) ; end function test_fliplr() randn('state',0) ; I = randn(256,256,1,'single') ; f = vl_lbp(fliplr(I), 8) ; f_ = vl_lbpfliplr(vl_lbp(I, 8)) ; vl_assert_almost_equal(f,f_,1e-3) ; vlfeat/toolbox/xtest/vl_test_hikmeans.m0000644000175000017500000000071712237004736017330 0ustar dimadimafunction results = vl_test_hikmeans(varargin) % VL_TEST_IKMEANS vl_test_init ; function s = setup() rand('state',0) ; s.data = uint8(rand(2,1000) * 255) ; function test_basic(s) [tree, assign] = vl_hikmeans(s.data,3,100) ; assign_ = vl_hikmeanspush(tree, s.data) ; vl_assert_equal(assign,assign_) ; function test_elkan(s) [tree, assign] = vl_hikmeans(s.data,3,100,'method','elkan') ; assign_ = vl_hikmeanspush(tree, s.data) ; vl_assert_equal(assign,assign_) ; vlfeat/toolbox/xtest/vl_test_imarray.m0000644000175000017500000000143312237004736017171 0ustar dimadimafunction results = vl_test_imarray(varargin) % VL_TEST_IMARRAY vl_test_init ; function test_movie_rgb(s) A = rand(23,15,3,4) ; B = vl_imarray(A,'movie',true) ; function test_movie_indexed(s) cmap = get(0,'DefaultFigureColormap') ; A = uint8(size(cmap,1)*rand(23,15,4)) ; A = min(A,size(cmap,1)-1) ; B = vl_imarray(A,'movie',true) ; function test_movie_gray_indexed(s) A = uint8(255*rand(23,15,4)) ; B = vl_imarray(A,'movie',true,'cmap',gray(256)) ; for k=1:size(A,3) vl_assert_equal(squeeze(A(:,:,k)), ... frame2im(B(k))) ; end function test_basic(s) M = 3 ; N = 4 ; width = 32 ; height = 15 ; for i=1:M for j=1:N A{i,j} = rand(width,height) ; end end A1 = A'; A1 = cat(3,A1{:}) ; A2 = cell2mat(A) ; B = vl_imarray(A1, 'layout', [M N]) ; vl_assert_equal(A2,B) ; vlfeat/toolbox/xtest/vl_assert_equal.m0000644000175000017500000000013712237004736017156 0ustar dimadimafunction vl_assert_equal(x, y, varargin) assert(isequalwithequalnans(x,y),varargin{:}) ; end vlfeat/toolbox/xtest/vl_test_roc.m0000644000175000017500000000177312237004736016317 0ustar dimadimafunction results = vl_test_roc(varargin) % VL_TEST_ROC vl_test_init ; function s = setup() s.scores0 = [5 4 3 2 1] ; s.scores1 = [5 3 4 2 1] ; s.labels = [1 1 -1 -1 -1] ; function test_perfect_tptn(s) [tpr,tnr] = vl_roc(s.labels,s.scores0) ; vl_assert_almost_equal(tpr, [0 1 2 2 2 2] / 2) ; vl_assert_almost_equal(tnr, [3 3 3 2 1 0] / 3) ; function test_perfect_metrics(s) [tpr,tnr,info] = vl_roc(s.labels,s.scores0) ; vl_assert_almost_equal(info.eer, 0) ; vl_assert_almost_equal(info.auc, 1) ; function test_swap1_tptn(s) [tpr,tnr] = vl_roc(s.labels,s.scores1) ; vl_assert_almost_equal(tpr, [0 1 1 2 2 2] / 2) ; vl_assert_almost_equal(tnr, [3 3 2 2 1 0] / 3) ; function test_swap1_tptn_stable(s) [tpr,tnr] = vl_roc(s.labels,s.scores1,'stable',true) ; vl_assert_almost_equal(tpr, [1 2 1 2 2] / 2) ; vl_assert_almost_equal(tnr, [3 2 2 1 0] / 3) ; function test_swap1_metrics(s) [tpr,tnr,info] = vl_roc(s.labels,s.scores1) ; vl_assert_almost_equal(info.eer, 1/3) ; vl_assert_almost_equal(info.auc, 1 - 1/(2*3)) ; vlfeat/toolbox/xtest/vl_test_ikmeans.m0000644000175000017500000000072212237004736017154 0ustar dimadimafunction results = vl_test_ikmeans(varargin) % VL_TEST_IKMEANS vl_test_init ; function s = setup() rand('state',0) ; s.data = uint8(rand(2,1000) * 255) ; function test_basic(s) [centers, assign] = vl_ikmeans(s.data,100) ; assign_ = vl_ikmeanspush(s.data, centers) ; vl_assert_equal(assign,assign_) ; function test_elkan(s) [centers, assign] = vl_ikmeans(s.data,100,'method','elkan') ; assign_ = vl_ikmeanspush(s.data, centers) ; vl_assert_equal(assign,assign_) ; vlfeat/toolbox/xtest/vl_test_imdisttf.m0000644000175000017500000000353512237004736017355 0ustar dimadimafunction results = vl_test_imdisttf(varargin) % VL_TEST_DISTTF vl_test_init ; function test_basic() for conv = {@single, @double} conv = conv{1} ; I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ; D = vl_imdisttf(I); assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ; I(2,2) = -3 ; [D,map] = vl_imdisttf(I) ; assert(isequal(D, conv(-1 - [0 1 0 ; 1 2 1 ; 0 1 0]))) ; assert(isequal(map, 5 * ones(3))) ; end function test_1x1() assert(isequal(1, vl_imdisttf(1))) ; function test_rand() I = rand(13,31) ; for t=1:4 param = [rand randn rand randn] ; [D0,map0] = imdisttf_equiv(I,param) ; [D,map] = vl_imdisttf(I,param) ; vl_assert_almost_equal(D,D0,1e-10) assert(isequal(map,map0)) ; end function test_param() I = zeros(3,4) ; I(1,1) = -1 ; [D,map] = vl_imdisttf(I,[1 0 1 0]); assert(isequal(-[1 0 0 0 ; 0 0 0 0 ; 0 0 0 0 ;], D)) ; D0 = -[1 .9 .6 .1 ; 0 0 0 0 ; 0 0 0 0 ;] ; [D,map] = vl_imdisttf(I,[.1 0 1 0]); vl_assert_almost_equal(D,D0,1e-10); D0 = -[1 .9 .6 .1 ; .9 .8 .5 0 ; .6 .5 .2 0 ;] ; [D,map] = vl_imdisttf(I,[.1 0 .1 0]); vl_assert_almost_equal(D,D0,1e-10); D0 = -[.9 1 .9 .6 ; .8 .9 .8 .5 ; .5 .6 .5 .2 ; ] ; [D,map] = vl_imdisttf(I,[.1 1 .1 0]); vl_assert_almost_equal(D,D0,1e-10); function test_special() I = rand(13,31) -.5 ; D = vl_imdisttf(I, [0 0 1e5 0]) ; vl_assert_almost_equal(D(:,1),min(I,[],2),1e-10); D = vl_imdisttf(I, [1e5 0 0 0]) ; vl_assert_almost_equal(D(1,:),min(I,[],1),1e-10); function [D,map]=imdisttf_equiv(I,param) D = inf + zeros(size(I)) ; map = zeros(size(I)) ; ur = 1:size(D,2) ; vr = 1:size(D,1) ; [u,v] = meshgrid(ur,vr) ; for v_=vr for u_=ur E = I(v_,u_) + ... param(1) * (u - u_ - param(2)).^2 + ... param(3) * (v - v_ - param(4)).^2 ; map(E < D) = sub2ind(size(I),v_,u_) ; D = min(D,E) ; end end vlfeat/toolbox/xtest/vl_assert_almost_equal.m0000644000175000017500000000123212237004745020532 0ustar dimadimafunction vl_assert_almost_equal(x, y, varargin) epsilon = 1e-6 ; if length(varargin) > 0 if isnumeric(varargin{1}) epsilon = varargin{1} ; varargin(1) = [] ; end end assert(isequal(class(x), class(y)), varargin{:}) ; assert(isequal(size(x), size(y)), varargin{:}) ; if isstruct(x) fx = fieldnames(x) ; fy = fieldnames(y) ; assert(isequal(fx,fy), varargin{:}) ; for i=1:numel(fx) vl_assert_almost_equal(x.(fx{i}), y.(fy{i}), epsilon, varargin{:}) ; end else i = isnan(x) ; j = isnan(y) ; assert(isequal(i,j), varargin{:}) ; assert(max(abs(x(~i) - y(~i))) < epsilon, varargin{:}) ; end end vlfeat/toolbox/xtest/vl_test_pr.m0000644000175000017500000000726312237004745016155 0ustar dimadimafunction results = vl_test_pr(varargin) % VL_TEST_PR vl_test_init ; function s = setup() s.scores0 = [5 4 3 2 1] ; s.scores1 = [5 3 4 2 1] ; s.labels = [1 1 -1 -1 -1] ; function test_perfect_tptn(s) [rc,pr] = vl_pr(s.labels,s.scores0) ; vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ; vl_assert_almost_equal(rc, [0 1 2 2 2 2] / 2) ; function test_perfect_metrics(s) [rc,pr,info] = vl_pr(s.labels,s.scores0) ; vl_assert_almost_equal(info.auc, 1) ; vl_assert_almost_equal(info.ap, 1) ; vl_assert_almost_equal(info.ap_interp_11, 1) ; function test_swap1_tptn(s) [rc,pr] = vl_pr(s.labels,s.scores1) ; vl_assert_almost_equal(pr, [1 1/1 1/2 2/3 2/4 2/5]) ; vl_assert_almost_equal(rc, [0 1 1 2 2 2] / 2) ; function test_swap1_tptn_stable(s) [rc,pr] = vl_pr(s.labels,s.scores1,'stable',true) ; vl_assert_almost_equal(pr, [1/1 2/3 1/2 2/4 2/5]) ; vl_assert_almost_equal(rc, [1 2 1 2 2] / 2) ; function test_swap1_metrics(s) [rc,pr,info] = vl_pr(s.labels,s.scores1) ; clf; vl_pr(s.labels,s.scores1) ; vl_assert_almost_equal(info.auc, [.5 + .5 * (.5 + 2/3)/2]) ; vl_assert_almost_equal(info.ap, [1/1 + 2/3]/2) ; vl_assert_almost_equal(info.ap_interp_11, mean([1 1 1 1 1 1 2/3 2/3 2/3 2/3 2/3])) ; function test_inf(s) scores = [1 -inf -1 -1 -1 -1] ; labels = [1 1 -1 -1 -1 -1] ; [rc1,pr1,info1] = vl_pr(labels, scores, 'includeInf', true) ; [rc2,pr2,info2] = vl_pr(labels, scores, 'includeInf', false) ; vl_assert_equal(numel(rc1), numel(rc2) + 1) ; vl_assert_almost_equal(info1.auc, [1 * .5 + (1/5 + 2/6)/2 * .5]) ; vl_assert_almost_equal(info1.ap, [1 * .5 + 2/6 * .5]) ; vl_assert_almost_equal(info1.ap_interp_11, [1 * 6/11 + 2/6 * 5/11]) ; vl_assert_almost_equal(info2.auc, 0.5) ; vl_assert_almost_equal(info2.ap, 0.5) ; vl_assert_almost_equal(info2.ap_interp_11, 1 * 6 / 11) ; function test_inf_stable(s) scores = [-1 -1 -1 -1 -inf +1] ; labels = [-1 -1 -1 -1 +1 +1] ; [rc1,pr1,info1] = vl_pr(labels, scores, 'includeInf', true, 'stable', true) ; [rc2,pr2,info2] = vl_pr(labels, scores, 'includeInf', false, 'stable', true) ; [rc1_,pr1_,info1_] = vl_pr(labels, scores, 'includeInf', true, 'stable', false) ; [rc2_,pr2_,info2_] = vl_pr(labels, scores, 'includeInf', false, 'stable', false) ; % stability does not change scores vl_assert_almost_equal(info1,info1_) ; vl_assert_almost_equal(info2,info2_) ; % unstable with inf (first point (0,1) is conventional) vl_assert_almost_equal(rc1_, [0 .5 .5 .5 .5 .5 1]) vl_assert_almost_equal(pr1_, [1 1 1/2 1/3 1/4 1/5 2/6]) % unstable without inf vl_assert_almost_equal(rc2_, [0 .5 .5 .5 .5 .5]) vl_assert_almost_equal(pr2_, [1 1 1/2 1/3 1/4 1/5]) % stable with inf (no conventional point here) vl_assert_almost_equal(rc1, [.5 .5 .5 .5 1 .5]) ; vl_assert_almost_equal(pr1, [1/2 1/3 1/4 1/5 2/6 1]) ; % stable without inf (no conventional point and -inf are NaN) vl_assert_almost_equal(rc2, [.5 .5 .5 .5 NaN .5]) ; vl_assert_almost_equal(pr2, [1/2 1/3 1/4 1/5 NaN 1]) ; function test_normalised_pr(s) scores = [+1 +2] ; labels = [+1 -1] ; [rc1,pr1,info1] = vl_pr(labels,scores) ; [rc2,pr2,info2] = vl_pr(labels,scores,'normalizePrior',.5) ; vl_assert_almost_equal(pr1, pr2) ; vl_assert_almost_equal(rc1, rc2) ; scores_ = [+1 +2 +2 +2] ; labels_ = [+1 -1 -1 -1] ; [rc3,pr3,info3] = vl_pr(labels_,scores_) ; [rc4,pr4,info4] = vl_pr(labels,scores,'normalizePrior',1/4) ; vl_assert_almost_equal(info3, info4) ; function test_normalised_pr_corner_cases(s) scores = 1:10 ; labels = ones(1,10) ; [rc1,pr1,info1] = vl_pr(labels,scores) ; vl_assert_almost_equal(rc1, (0:10)/10) ; vl_assert_almost_equal(pr1, ones(1,11)) ; scores = 1:10 ; labels = zeros(1,10) ; [rc2,pr2,info2] = vl_pr(labels,scores) ; vl_assert_almost_equal(rc2, zeros(1,11)) ; vl_assert_almost_equal(pr2, ones(1,11)) ; vlfeat/toolbox/xtest/vl_test_alldist.m0000644000175000017500000000450512237004736017164 0ustar dimadimafunction results = vl_test_alldist(varargin) % VL_TEST_ALLDIST vl_test_init ; function s = setup() vl_twister('state', 0) ; s.X = 3.1 * vl_twister(10,10) ; s.Y = 4.7 * vl_twister(10,7) ; function test_null_args(s) vl_assert_equal(... vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ... zeros(12,0)) ; vl_assert_equal(... vl_alldist(zeros(15,0), zeros(15,0), 'kl2'), ... zeros(0,0)) ; vl_assert_equal(... vl_alldist(zeros(15,0), zeros(15,12), 'kl2'), ... zeros(0,12)) ; vl_assert_equal(... vl_alldist(zeros(0,15), zeros(0,12), 'kl2'), ... zeros(15,12)) ; function test_self(s) vl_assert_almost_equal(... vl_alldist(s.X, 'kl2'), ... makedist(@(x,y) x*y, s.X, s.X), ... 1e-6) ; function test_distances(s) dists = {'chi2', 'l2', 'l1', 'hell', 'js', ... 'kchi2', 'kl2', 'kl1', 'khell', 'kjs'} ; distsEquiv = { ... @(x,y) (x-y)^2 / (x + y), ... @(x,y) (x-y)^2, ... @(x,y) abs(x-y), ... @(x,y) (sqrt(x) - sqrt(y))^2, ... @(x,y) x - x .* log2(1 + y/x) + y - y .* log2(1 + x/y), ... @(x,y) 2 * (x*y) / (x + y), ... @(x,y) x*y, ... @(x,y) min(x,y), ... @(x,y) sqrt(x.*y), ... @(x,y) .5 * (x .* log2(1 + y/x) + y .* log2(1 + x/y))} ; types = {'single', 'double'} ; for simd = [0 1] for d = 1:length(dists) for t = 1:length(types) vl_simdctrl(simd) ; X = feval(str2func(types{t}), s.X) ; Y = feval(str2func(types{t}), s.Y) ; vl_assert_almost_equal(... vl_alldist(X,Y,dists{d}), ... makedist(distsEquiv{d},X,Y), ... 1e-4, ... 'alldist failed for dist=%s type=%s simd=%d', ... dists{d}, ... types{t}, ... simd) ; end end end function test_distance_kernel_pairs(s) dists = {'chi2', 'l2', 'l1', 'hell', 'js'} ; for d = 1:length(dists) dist = char(dists{d}) ; X = s.X ; Y = s.Y ; ker = ['k' dist] ; kxx = vl_alldist(X,X,ker) ; kyy = vl_alldist(Y,Y,ker) ; kxy = vl_alldist(X,Y,ker) ; kxx = repmat(diag(kxx), 1, size(s.Y,2)) ; kyy = repmat(diag(kyy), 1, size(s.X,1))' ; d2 = vl_alldist(X,Y,dist) ; vl_assert_almost_equal(d2, kxx + kyy - 2 * kxy, '1e-6') ; end function D = makedist(cmp,X,Y) [d,m] = size(X) ; [d,n] = size(Y) ; D = zeros(m,n) ; for i = 1:m for j = 1:n acc = 0 ; for k = 1:d acc = acc + cmp(X(k,i),Y(k,j)) ; end D(i,j) = acc ; end end conv = str2func(class(X)) ; D = conv(D) ; vlfeat/toolbox/xtest/vl_test_grad.m0000644000175000017500000000066212237004736016445 0ustar dimadimafunction results = vl_test_grad(varargin) % VL_TEST_GRAD vl_test_init ; function s = setup() s.I = rand(150,253) ; s.I_small = rand(2,2) ; function test_equiv(s) vl_assert_equal(gradient(s.I), vl_grad(s.I)) ; function test_equiv_small(s) vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ; function test_equiv_forward(s) Ix = diff(s.I,2,1) ; Iy = diff(s.I,2,1) ; vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ; vlfeat/toolbox/xtest/vl_test_imintegral.m0000644000175000017500000000262512237004736017664 0ustar dimadimafunction results = vl_test_imintegral(varargin) % VL_TEST_IMINTEGRAL vl_test_init ; function state = setup() state.I = ones(5,6) ; state.correct = [ 1 2 3 4 5 6 ; 2 4 6 8 10 12 ; 3 6 9 12 15 18 ; 4 8 12 16 20 24 ; 5 10 15 20 25 30 ; ] ; function test_matlab_equivalent(s) vl_assert_equal(slow_imintegral(s.I), s.correct) ; function test_basic(s) vl_assert_equal(vl_imintegral(s.I), s.correct) ; function test_multi_dimensional(s) vl_assert_equal(vl_imintegral(repmat(s.I, [1 1 3])), ... repmat(s.correct, [1 1 3])) ; function test_random(s) numTests = 50 ; for i = 1:numTests I = rand(5) ; vl_assert_almost_equal(vl_imintegral(s.I), ... slow_imintegral(s.I)) ; end function test_datatypes(s) vl_assert_equal(single(vl_imintegral(s.I)), single(s.correct)) ; vl_assert_equal(double(vl_imintegral(s.I)), double(s.correct)) ; vl_assert_equal(uint32(vl_imintegral(s.I)), uint32(s.correct)) ; vl_assert_equal(int32(vl_imintegral(s.I)), int32(s.correct)) ; vl_assert_equal(int32(vl_imintegral(-s.I)), -int32(s.correct)) ; function integral = slow_imintegral(I) integral = zeros(size(I)); for k = 1:size(I,3) for r = 1:size(I,1) for c = 1:size(I,2) integral(r,c,k) = sum(sum(I(1:r,1:c,k))); end end end vlfeat/toolbox/xtest/vl_test_alphanum.m0000644000175000017500000000313012237004736017326 0ustar dimadimafunction results = vl_test_alphanum(varargin) % VL_TEST_ALPHANUM vl_test_init ; function s = setup() s.strings = ... {'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Allegia 51 Clasteron','Allegia 6R Clasteron','Alpha 100','Alpha 2','Alpha 200','Alpha 2A','Alpha 2A-8000','Alpha 2A-900','Callisto Morphamax','Callisto Morphamax 500','Callisto Morphamax 5000','Callisto Morphamax 600','Callisto Morphamax 6000 SE','Callisto Morphamax 6000 SE2','Callisto Morphamax 700','Callisto Morphamax 7000','Xiph Xlater 10000','Xiph Xlater 2000','Xiph Xlater 300','Xiph Xlater 40','Xiph Xlater 5','Xiph Xlater 50','Xiph Xlater 500','Xiph Xlater 5000','Xiph Xlater 58'} ; s.sortedStrings = ... {'10X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','200X Radonius','1000X Radonius Maximus','Allegia 6R Clasteron','Allegia 50 Clasteron','Allegia 50B Clasteron','Allegia 51 Clasteron','Allegia 500 Clasteron','Alpha 2','Alpha 2A','Alpha 2A-900','Alpha 2A-8000','Alpha 100','Alpha 200','Callisto Morphamax','Callisto Morphamax 500','Callisto Morphamax 600','Callisto Morphamax 700','Callisto Morphamax 5000','Callisto Morphamax 6000 SE','Callisto Morphamax 6000 SE2','Callisto Morphamax 7000','Xiph Xlater 5','Xiph Xlater 40','Xiph Xlater 50','Xiph Xlater 58','Xiph Xlater 300','Xiph Xlater 500','Xiph Xlater 2000','Xiph Xlater 5000','Xiph Xlater 10000'} ; function test_basic(s) sorted = vl_alphanum(s.strings) ; assert(isequal(sorted,s.sortedStrings)) ; vlfeat/toolbox/xtest/vl_test_colsubset.m0000644000175000017500000000147412237004736017535 0ustar dimadimafunction results = vl_test_colsubset(varargin) % VL_TEST_COLSUBSET vl_test_init ; function s = setup() s.x = [5 2 3 6 4 7 1 9 8 0] ; function test_beginning(s) vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ; vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ; function test_ending(s) vl_assert_equal(6:10, vl_colsubset(1:10, 5, 'ending')) ; vl_assert_equal(6:10, vl_colsubset(1:10, .5, 'ending')) ; function test_largest(s) vl_assert_equal([5 6 7 9 8], vl_colsubset(s.x, 5, 'largest')) ; vl_assert_equal([5 6 7 9 8], vl_colsubset(s.x, .5, 'largest')) ; function test_smallest(s) vl_assert_equal([2 3 4 1 0], vl_colsubset(s.x, 5, 'smallest')) ; vl_assert_equal([2 3 4 1 0], vl_colsubset(s.x, .5, 'smallest')) ; function test_random(s) assert(numel(intersect(s.x, vl_colsubset(s.x, 5, 'random'))) == 5) ; vlfeat/toolbox/xtest/vl_test_hog.m0000644000175000017500000000302312237004736016277 0ustar dimadimafunction results = vl_test_hog(varargin) % VL_TEST_HOG vl_test_init ; function s = setup() s.im = im2single(vl_impattern('roofs1')) ; [x,y]= meshgrid(linspace(-1,1,128)) ; s.round = single(x.^2+y.^2); s.imSmall = s.im(1:128,1:128,:) ; s.imSmall = s.im ; s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ; function test_basic_call(s) cellSize = 8 ; hog = vl_hog(s.im, cellSize) ; function test_bilinear_orientations(s) cellSize = 8 ; vl_hog(s.im, cellSize, 'bilinearOrientations') ; function test_variants_and_flipping(s) variants = {'uoctti', 'dalaltriggs'} ; numOrientationsRange = 3:9 ; cellSize = 8 ; for cellSize = [4 8 16] for i=1:numel(variants) for j=1:numel(numOrientationsRange) args = {'bilinearOrientations', ... 'variant', variants{i}, ... 'numOrientations', numOrientationsRange(j)} ; hog = vl_hog(s.imSmall, cellSize, args{:}) ; perm = vl_hog('permutation', args{:}) ; hog1 = vl_hog(s.imSmallFlipped, cellSize, args{:}) ; hog2 = hog(:,end:-1:1,perm) ; %norm(hog1(:)-hog2(:)) vl_assert_almost_equal(hog1,hog2,1e-3) ; end end end function test_polar(s) cellSize = 8 ; im = s.round ; for b = [0 1] if b args = {'bilinearOrientations'} ; else args = {} ; end hog1 = vl_hog(im, cellSize, args{:}) ; [ix,iy] = vl_grad(im) ; m = sqrt(ix.^2 + iy.^2) ; a = atan2(iy,ix) ; m(:,[1 end]) = 0 ; m([1 end],:) = 0 ; hog2 = vl_hog(cat(3,m,a), cellSize, 'DirectedPolarField', args{:}) ; vl_assert_almost_equal(hog1,hog2,norm(hog1(:))/1000) ; end vlfeat/toolbox/xtest/vl_test_svmtrain.m0000644000175000017500000001026512237004745017373 0ustar dimadimafunction results = vl_test_svmtrain(varargin) % VL_TEST_SVMTRAIN vl_test_init ; end function s = setup() randn('state',0) ; Np = 10 ; Nn = 10 ; xp = diag([1 3])*randn(2, Np) ; xn = diag([1 3])*randn(2, Nn) ; xp(1,:) = xp(1,:) + 2 + 1 ; xn(1,:) = xn(1,:) - 2 + 1 ; s.x = [xp xn] ; s.y = [ones(1,Np) -ones(1,Nn)] ; s.lambda = 0.01 ; s.biasMultiplier = 10 ; if 0 figure(1) ; clf; vl_plotframe(xp, 'g') ; hold on ; vl_plotframe(xn, 'r') ; axis equal ; grid on ; end % Run LibSVM as an accuate solver to compare results with. Note that % LibSVM optimizes a slightly different cost function due to the way % the bias is handled. % [s.w, s.b] = accurate_solver(s.x, s.y, s.lambda, s.biasMultiplier) ; s.w = [1.180762951236242; 0.098366470721632] ; s.b = -1.540018443946204 ; s.obj = obj(s, s.w, s.b) ; end function test_sgd_basic(s) for conv = {@single, @double} conv = conv{1} ; vl_twister('state',0) ; [w b info] = vl_svmtrain(s.x, s.y, s.lambda, ... 'Solver', 'sgd', ... 'BiasMultiplier', s.biasMultiplier, ... 'BiasLearningRate', 1/s.biasMultiplier, ... 'MaxNumIterations', 1e5, ... 'Epsilon', 1e-3) ; % there are no absolute guarantees on the objective gap, but % the heuristic SGD uses as stopping criterion seems reasonable % within a factor 10 at least. o = obj(s, w, b) ; gap = o - s.obj ; vl_assert_almost_equal(conv([w; b]), conv([s.w; s.b]), 0.1) ; assert(gap <= 1e-2) ; end end function test_sdca_basic(s) for conv = {@single, @double} conv = conv{1} ; vl_twister('state',0) ; [w b info] = vl_svmtrain(s.x, s.y, s.lambda, ... 'Solver', 'sdca', ... 'BiasMultiplier', s.biasMultiplier, ... 'MaxNumIterations', 1e5, ... 'Epsilon', 1e-3) ; % the gap with the accurate solver cannot be % greater than the duality gap. o = obj(s, w, b) ; gap = o - s.obj ; vl_assert_almost_equal(conv([w; b]), conv([s.w; s.b]), 0.1) ; assert(gap <= 1e-3) ; end end function test_weights(s) for algo = {'sgd', 'sdca'} for conv = {@single, @double} conv = conv{1} ; vl_twister('state',0) ; numRepeats = 10 ; pos = find(s.y > 0) ; neg = find(s.y < 0) ; weights = ones(1, numel(s.y)) ; weights(pos) = numRepeats ; % simulate weighting by repeating positives [w b info] = vl_svmtrain(... s.x(:, [repmat(pos,1,numRepeats) neg]), ... s.y(:, [repmat(pos,1,numRepeats) neg]), ... s.lambda / (numel(pos) *numRepeats + numel(neg)) / (numel(pos) + numel(neg)), ... 'Solver', 'sdca', ... 'BiasMultiplier', s.biasMultiplier, ... 'MaxNumIterations', 1e6, ... 'Epsilon', 1e-4) ; % apply weigthing [w_ b_ info_] = vl_svmtrain(... s.x, ... s.y, ... s.lambda, ... 'Solver', char(algo), ... 'BiasMultiplier', s.biasMultiplier, ... 'MaxNumIterations', 1e6, ... 'Epsilon', 1e-4, ... 'Weights', weights) ; vl_assert_almost_equal(conv([w; b]), conv([w_; b_]), 0.05) ; end end end function test_homkermap(s) for solver = {'sgd', 'sdca'} for conv = {@single,@double} conv = conv{1} ; dataset = vl_svmdataset(conv(s.x), 'homkermap', struct('order',1)) ; vl_twister('state',0) ; [w_ b_] = vl_svmtrain(dataset, s.y, s.lambda) ; x_hom = vl_homkermap(conv(s.x), 1) ; vl_twister('state',0) ; [w b] = vl_svmtrain(x_hom, s.y, s.lambda) ; vl_assert_almost_equal([w; b],[w_; b_], 1e-7) ; end end end function [w,b] = accurate_solver(X, y, lambda, biasMultiplier) addpath opt/libsvm/matlab/ N = size(X,2) ; model = svmtrain(y', [(1:N)' X'*X], sprintf(' -c %f -t 4 -e 0.00001 ', 1/(lambda*N))) ; w = X(:,model.SVs) * model.sv_coef ; b = - model.rho ; format long ; disp('model w:') disp(w) disp('bias b:') disp(b) end function o = obj(s, w, b) o = (sum(w.*w) + b*b) * s.lambda / 2 + mean(max(0, 1 - s.y .* (w'*s.x + b))) ; end vlfeat/toolbox/xtest/vl_test_fisher.m0000644000175000017500000000324712237004745017012 0ustar dimadimafunction results = vl_test_fisher(varargin) % VL_TEST_FISHER vl_test_init ; function s = setup() randn('state',0) ; dimension = 5 ; numData = 21 ; numComponents = 3 ; s.x = randn(dimension,numData) ; s.mu = randn(dimension,numComponents) ; s.sigma2 = ones(dimension,numComponents) ; s.prior = ones(1,numComponents) ; s.prior = s.prior / sum(s.prior) ; function test_basic(s) phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ; phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior) ; vl_assert_almost_equal(phi, phi_, 1e-10) ; function test_norm(s) phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ; phi_ = phi_ / norm(phi_) ; phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'normalized') ; vl_assert_almost_equal(phi, phi_, 1e-10) ; function test_sqrt(s) phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ; phi_ = sign(phi_) .* sqrt(abs(phi_)) ; phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'squareroot') ; vl_assert_almost_equal(phi, phi_, 1e-10) ; function test_improved(s) phi_ = simple_fisher(s.x, s.mu, s.sigma2, s.prior) ; phi_ = sign(phi_) .* sqrt(abs(phi_)) ; phi_ = phi_ / norm(phi_) ; phi = vl_fisher(s.x, s.mu, s.sigma2, s.prior, 'improved') ; vl_assert_almost_equal(phi, phi_, 1e-10) ; function enc = simple_fisher(x, mu, sigma2, pri) sigma = sqrt(sigma2) ; for i = 1:size(mu,2) delta{i} = bsxfun(@times, bsxfun(@minus, x, mu(:,i)), 1./sigma(:,i)) ; q(i,:) = log(pri(i)) - 0.5 * log(sigma2(i)) - 0.5 * sum(delta{i}.^2,1) ; end q = exp(bsxfun(@minus, q, max(q,[],1))) ; q = bsxfun(@times, q, 1 ./ sum(q,1)) ; n = size(x,2) ; for i = 1:size(mu,2) u{i} = delta{i} * q(i,:)' / n / sqrt(pri(i)) ; v{i} = (delta{i}.^2 - 1) * q(i,:)' / n / sqrt(2*pri(i)) ; end enc = cat(1, u{:}, v{:}) ; vlfeat/toolbox/xtest/vl_test_alldist2.m0000644000175000017500000000435412237004736017250 0ustar dimadimafunction results = vl_test_alldist2(varargin) % VL_TEST_ALLDIST vl_test_init ; % TODO: test integer classes function s = setup() vl_twister('state', 0) ; s.X = 3.1 * vl_twister(10,10) ; s.Y = 4.7 * vl_twister(10,7) ; function test_null_args(s) vl_assert_equal(... vl_alldist2(zeros(15,12), zeros(15,0), 'kl2'), ... zeros(12,0)) ; vl_assert_equal(... vl_alldist2(zeros(15,0), zeros(15,0), 'kl2'), ... zeros(0,0)) ; vl_assert_equal(... vl_alldist2(zeros(15,0), zeros(15,12), 'kl2'), ... zeros(0,12)) ; vl_assert_equal(... vl_alldist2(zeros(0,15), zeros(0,12), 'kl2'), ... zeros(15,12)) ; function test_self(s) vl_assert_almost_equal(... vl_alldist2(s.X, 'kl2'), ... makedist(@(x,y) x*y, s.X, s.X), ... 1e-6) ; function test_distances(s) dists = {'chi2', 'l2', 'l1', 'hell', ... 'kchi2', 'kl2', 'kl1', 'khell'} ; distsEquiv = { ... @(x,y) (x-y)^2 / (x + y), ... @(x,y) (x-y)^2, ... @(x,y) abs(x-y), ... @(x,y) (sqrt(x) - sqrt(y))^2, ... @(x,y) 2 * (x*y) / (x + y), ... @(x,y) x*y, ... @(x,y) min(x,y), ... @(x,y) sqrt(x.*y)}; types = {'single', 'double', 'sparse'} ; for simd = [0 1] for d = 1:length(dists) for t = 1:length(types) vl_simdctrl(simd) ; X = feval(str2func(types{t}), s.X) ; Y = feval(str2func(types{t}), s.Y) ; a = vl_alldist2(X,Y,dists{d}) ; b = makedist(distsEquiv{d},X,Y) ; vl_assert_almost_equal(a,b, ... 1e-4, ... 'alldist failed for dist=%s type=%s simd=%d', ... dists{d}, ... types{t}, ... simd) ; end end end function test_distance_kernel_pairs(s) dists = {'chi2', 'l2', 'l1', 'hell'} ; for d = 1:length(dists) dist = char(dists{d}) ; X = s.X ; Y = s.Y ; ker = ['k' dist] ; kxx = vl_alldist2(X,X,ker) ; kyy = vl_alldist2(Y,Y,ker) ; kxy = vl_alldist2(X,Y,ker) ; kxx = repmat(diag(kxx), 1, size(s.Y,2)) ; kyy = repmat(diag(kyy), 1, size(s.X,1))' ; d2 = vl_alldist2(X,Y,dist) ; vl_assert_almost_equal(d2, kxx + kyy - 2 * kxy, '1e-6') ; end function D = makedist(cmp,X,Y) [d,m] = size(X) ; [d,n] = size(Y) ; D = zeros(m,n) ; for i = 1:m for j = 1:n acc = 0 ; for k = 1:d acc = acc + cmp(X(k,i),Y(k,j)) ; end D(i,j) = acc ; end end conv = str2func(class(X)) ; D = conv(D) ; vlfeat/toolbox/xtest/vl_test_liop.m0000644000175000017500000000177712237004745016503 0ustar dimadimafunction results = vl_test_liop(varargin) % VL_TEST_SIFT vl_test_init ; function s = setup() randn('state',0) ; s.patch = randn(65,'single') ; xr = -32:32 ; [x,y] = meshgrid(xr) ; s.blob = - single(x.^2+y.^2) ; function test_basic(s) d = vl_liop(s.patch) ; function test_blob(s) % with a blob, all local intensity order pattern are equal. In % particular, if the blob intensity decreases away from the center, % then all local intensities sampled in a neighbourhood of 2 elements % are already sorted (see LIOP details). d = vl_liop(s.blob, ... 'IntensityThreshold', 0, ... 'NumNeighbours', 2, ... 'NumSpatialBins', 1) ; assert(isequal(d, single([1;0]))) ; function test_neighbours(s) for n=2:5 for p=1:3 d = vl_liop(s.patch, 'NumNeighbours', n, 'NumSpatialBins', p) ; assert(numel(d) == p * factorial(n)) ; end end function test_multiple(s) x = randn(31,31,3, 'single') ; d = vl_liop(x) ; for i=1:3 d_(:,i) = vl_liop(squeeze(x(:,:,i))) ; end assert(isequal(d,d_)) ; vlfeat/toolbox/xtest/vl_test_slic.m0000644000175000017500000000031012237004745016450 0ustar dimadimafunction results = vl_test_slic(varargin) % VL_TEST_SLIC vl_test_init ; function s = setup() s.im = im2single(vl_impattern('roofs1')) ; function test_slic(s) segmentation = vl_slic(s.im, 10, 0.1) ; vlfeat/toolbox/xtest/vl_test_imsmooth.m0000644000175000017500000000345512237004736017372 0ustar dimadimafunction results = vl_test_imsmooth(varargin) % VL_TEST_IMSMOOTH vl_test_init ; function s = setup() I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ; I = max(min(vl_imdown(I),1),0) ; s.I = single(I) ; function test_pad_by_continuity(s) % Convolving a constant signal padded with continuity does not change % the signal. I = ones(3) ; for ker = {'triangular', 'gaussian'} ker = char(ker) ; J = vl_imsmooth(I, 2, ... 'kernel', ker, ... 'padding', 'continuity') ; vl_assert_almost_equal(J, I, 1e-4, ... 'padding by continutiy with kernel = %s', ker) ; end function test_kernels(s) for ker = {'triangular', 'gaussian'} ker = char(ker) ; for type = {@single, @double} for simd = [0 1] for sigma = [1 2 7] for step = [1 2 3] vl_simdctrl(simd) ; conv = type{1} ; g = equivalent_kernel(ker, sigma) ; J = vl_imsmooth(conv(s.I), sigma, ... 'kernel', ker, ... 'padding', 'zero', ... 'subsample', step) ; J_ = conv(convolve(s.I, g, step)) ; vl_assert_almost_equal(J, J_, 1e-4, ... 'kernel=%s sigma=%f step=%d simd=%d', ... ker, sigma, step, simd) ; end end end end end function g = equivalent_kernel(ker, sigma) switch ker case 'gaussian' W = ceil(4*sigma) ; g = exp(-.5*((-W:W)/(sigma+eps)).^2) ; case 'triangular' W = max(round(sigma),1) ; g = W - abs(-W+1:W-1) ; end g = g / sum(g) ; function I = convolve(I, g, step) if strcmp(class(I),'single') g = single(g) ; else g = double(g) ; end for k=1:size(I,3) I(:,:,k) = conv2(g,g,I(:,:,k),'same'); end I = I(1:step:end,1:step:end,:) ; vlfeat/toolbox/xtest/vl_test_binsearch.m0000644000175000017500000000247312237004736017470 0ustar dimadimafunction results = vl_test_binsearch(varargin) % VL_TEST_BINSEARCH vl_test_init ; function test_inf_bins() x = [-inf -1 0 1 +inf] ; vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ; vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ; vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ; vl_assert_equal(vl_binsearch([-inf +inf], x), [1 1 1 1 2]) ; function test_empty() vl_assert_equal(vl_binsearch([], []), []) ; function test_bnd() vl_assert_equal(vl_binsearch([], [1]), [0]) ; vl_assert_equal(vl_binsearch([], [-inf]), [0]) ; vl_assert_equal(vl_binsearch([], [+inf]), [0]) ; vl_assert_equal(vl_binsearch([1], [.9]), [0]) ; vl_assert_equal(vl_binsearch([1], [1]), [1]) ; vl_assert_equal(vl_binsearch([1], [-inf]), [0]) ; vl_assert_equal(vl_binsearch([1], [+inf]), [1]) ; function test_basic() vl_assert_equal(vl_binsearch(-10:10, -10:10), 1:21) ; vl_assert_equal(vl_binsearch(-10:10, -11:10), 0:21) ; vl_assert_equal(vl_binsearch(-10:10, [-inf, -11:10, +inf]), [0 0:21 21]) ; function test_frac() vl_assert_equal(vl_binsearch(1:10, 1:.5:10), floor(1:.5:10)) vl_assert_equal(vl_binsearch(1:10, fliplr(1:.5:10)), ... fliplr(floor(1:.5:10))) ; function test_array() a = reshape(1:100,10,10) ; b = reshape(1:.5:100.5, 2, []) ; c = floor(b) ; vl_assert_equal(vl_binsearch(a,b), c) ; vlfeat/toolbox/demo/0000755000175000017500000000000012237035722013362 5ustar dimadimavlfeat/toolbox/demo/vl_demo_gmm_2d_twist.m0000644000175000017500000000501612237004745017647 0ustar dimadima%% data init numClusters = 30; dimension = 2; cc=hsv(numClusters); X = []; a = 0.9; t = linspace(0,4*pi,numClusters); x = a*(t.*cos(t)); y = a*(t.*sin(t)); mu = [x;y]; Ns = linspace(1,numClusters,numClusters) * 20; for i=1:numClusters add = randn(dimension,Ns(i)) + repmat(mu(:,i),1,Ns(i)); X = [X, add*50]; end X = single(X); %% KMeans elapsedKMEANS = tic; [initMeans, assignments] = vl_kmeans(X, numClusters, ... 'Verbose', ... 'Algorithm','ann', ... 'MaxNumIterations',5, ... 'Distance','l2', ... 'NumTrees',3, ... 'MaxNumComparisons', 20); elapsedKMEANS = toc(elapsedKMEANS); fprintf('\n ---------- \n KMeans time - %f \n ---------- \n\n',elapsedKMEANS); initSigmas = zeros(dimension,numClusters); initWeights = zeros(1,numClusters); figure(1) ; clf ; subplot(3,1,1) ; hold on ; for i=1:numClusters Xk = X(:,assignments==i); initWeights(i) = size(Xk,2) / numClusters; plot(Xk(1,:),Xk(2,:),'.','color',cc(i,:)); if size(Xk,1) == 0 || size(Xk,2) == 0 initSigmas(:,i) = diag(cov(X')); else initSigmas(:,i) = diag(cov(Xk')); end end axis equal set(gca,'xtick',[],'ytick',[]); axis off title('GMM: KMeans intialization'); initSigmas = single(initSigmas); initWeights = single(initWeights); %% GMM kmeans elapsedGMM = tic ; [means,sigmas,weights,ll,posteriors] = vl_gmm(X, numClusters, ... 'Initialization','custom', ... 'InitMeans',initMeans, ... 'InitCovariances',initSigmas, ... 'InitPriors',initWeights, ... 'verbose', ... 'MaxNumIterations', 20); elapsedGMM = toc(elapsedGMM); fprintf('\n ---------- \n GMM time - %f \n ---------- \n',elapsedGMM); subplot(3,1,2) ; hold on ; [~,idx] = max(posteriors,[],1); for i=1:numClusters plot(X(1,idx == i),X(2,idx == i),'.','color',cc(i,:)); vl_plotframe([means(:,i)' sigmas(1,i) 0 sigmas(2,i)]); end title('GMM: Gaussian mixture - kmeans init'); set(gca,'xtick',[],'ytick',[]); axis off axis equal %% GMM random elapsedGMM = tic; [means,sigmas,weights,ll,posteriors] = vl_gmm(X, numClusters, ... 'Initialization','rand', ... 'Verbose', ... 'MaxNumIterations', 100); elapsedGMM = toc(elapsedGMM); fprintf('\n ---------- \n GMM time - %f \n ---------- \n',elapsedGMM); subplot(3,1,3) ; hold on ; [~,idx] = max(posteriors,[],1); for i=1:numClusters plot(X(1,idx == i),X(2,idx == i),'.','color',cc(i,:)); vl_plotframe([means(:,i)' sigmas(1,i) 0 sigmas(2,i)]); end title('GMM: Gaussian mixture - random init'); axis equal set(gca,'xtick',[],'ytick',[]); axis off vl_demo_print('gmm_2d_shell',1.2); vlfeat/toolbox/demo/vl_demo_gmm_convergence.m0000644000175000017500000000373512237004745020414 0ustar dimadimadimension = 128; numData = 10000; numClusters = 100; numIterations = 5; numIterationsKM = 3; X = rand(dimension,numData); numTrials = 2; lls = zeros(numTrials,2); for trial = 1:numTrials % kmeans initialization startKMEANS = tic; [initMeans, assignments] = vl_kmeans(X, numClusters, ... 'verbose', ... 'multithreading', 'parallel', ... 'algorithm','ann', ... 'MaxNumIterations',numIterationsKM, ... 'distance','l2', ... 'NumTrees',3, ... 'MaxNumComparisons', 5); elapsedKMEANS = toc(startKMEANS); fprintf('\n ---------- \n KMeans time - %f \n ---------- \n\n',elapsedKMEANS); % compute initial gaussian parameters initWeights = zeros(1,numClusters); initSigmas = zeros(dimension,numClusters); for i=1:numClusters Xk = X(:,assignments==i); initWeights(i) = size(Xk,2) / numClusters; if size(Xk,1) == 0 || size(Xk,2) == 0 initSigmas(:,i) = diag(cov(X')); else initSigmas(:,i) = diag(cov(Xk')); end end % gmm with kmeans init [means,sigmas,weights,ll,posteriors] = vl_gmm(X, numClusters, ... 'initialization','custom', ... 'InitMeans',initMeans, ... 'InitSigmas',initSigmas, ... 'InitWeights',initWeights, ... 'verbose', ... 'multithreading', 'parallel', ... 'MaxNumIterations', numIterations); elapsedGMM = toc(startKMEANS); fprintf('\n ---------- \n GMM kmeans init time - %f \n ---------- \n\n',elapsedGMM); lls(trial,1) = ll; % gmm with random init elapsedGMM = tic; [means,sigmas,weights,ll,posteriors] = vl_gmm(X, numClusters, ... 'initialization','rand', ... 'verbose', ... 'multithreading', 'parallel', ... 'MaxNumIterations', numIterations); elapsedGMM = toc(elapsedGMM); fprintf('\n ---------- \n GMM random init time - %f \n ---------- \n\n',elapsedGMM); lls(trial,2) = ll; end lls = sum(lls,1)/numTrials; inits = {'KMeans','Random'}; disp(lls) figure bar(lls); set(gca,'xticklabel',inits); xlabel('Initialization type'); ylabel('LogLikelyhood'); title('GMM convergence test') vlfeat/toolbox/demo/vl_demo_kdtree.m0000644000175000017500000000306112237004736016524 0ustar dimadimafunction vl_demo_kdtree % VL_DEMO_KDTREE % Demonstrates the construction of a kd-tree for uniformly % distributed 2-D data and data distributed on a circle. Both the % median and mean threshold selection methods are illustrated. randn('state',0) ; rand('state',0) ; % uniform 2D points Q = single(rand(2,1)) ; X = single(rand(2, 100)) ; % kd-tree with median and mean thresholds figure(1) ; clf ; do('thresholdmethod', 'mean') ; figure(2) ; clf ; do('thresholdmethod', 'median') ; % 2D points on a circle thr = 2*pi*rand(1,100) ; X = single(.4*[cos(thr); sin(thr)] + .5) ; Q = single(Q) ; % kd-tree with median and mean thresholds figure(3) ; clf ; do('thresholdmethod', 'mean') ; figure(4) ; clf ; do('thresholdmethod', 'median') ; vl_demo_print(1, 'kdtree_uniform_mean') ; vl_demo_print(2, 'kdtree_uniform_median') ; vl_demo_print(3, 'kdtree_circle_mean') ; vl_demo_print(4, 'kdtree_circle_median') ; function do(varargin) % build the forest kdforest = vl_kdtreebuild (X, 'verbose', varargin{:}) ; % query the kd-tree [i, d] = vl_kdtreequery (kdforest, X, Q, 'numneighbors', 10, 'verbose') ; vl_plotframe(X, 'ro') ; hold on ; xl = [0, +1] ; yl = [0, +1] ; axis equal ; xlim(xl) ; ylim(yl) ; vl_demo_kdtree_plot(kdforest.trees(1), 1, xl, yl) ; % plot query point and returns vl_plotframe(Q,'b*') ; for k=1:length(i) vl_plotframe([Q ; sqrt(d(k))],'b:','linewidth',1) ; vl_plotframe(X(:, i(k)), 'bx') ; end set(gca,'xtick',[],'ytick',[]) ; end end vlfeat/toolbox/demo/vl_demo_kmeans_vs_builtin.m0000644000175000017500000000205112237004736020760 0ustar dimadima% VL_DEMO_KMEANS_VS_BUILTIN clear elaps energy ; dimension = 128 ; numData = 1000 ; numCenters = 10 ; numTrials = 10 ; for trial = 1:numTrials X = rand(dimension, numData) ; tic ; [idx, C, e] = kmeans(X', numCenters) ; e = sum(e) ; elaps.builtin(trial) = toc ; energy.builtin(trial) = e ; tic ; [idx_, C_, e] = vl_kmeans(X, numCenters, 'verbose') ; elaps.vl(trial) = toc ; energy.vl(trial) = e ; tic ; [idx_, C_, e] = vl_kmeans(X, numCenters, 'initialization', 'plusplus') ; elaps.vlpp(trial) = toc ; energy.vlpp(trial) = e ; end figure(1) ; clf ; subplot(1,2,1) ; title('Energy') ; mu = [mean(elaps.builtin) mean(elaps.vl) mean(elaps.vlpp)] ; st = [std(elaps.builtin) std(elaps.vl) std(elaps.vlpp)] ; bar(mu) ; hold on ; errorbar(mu, st, 'linestyle', 'none', 'color', 'r', 'linewidth', 4) ; subplot(1,2,2) ; mu = [mean(energy.builtin) mean(energy.vl) mean(energy.vlpp)] ; st = [std(energy.builtin) std(energy.vl) std(energy.vlpp)] ; bar(mu) ; hold on ; errorbar(mu, st, 'linestyle', 'none', 'color', 'r', 'linewidth', 4) ; vlfeat/toolbox/demo/vl_demo_quickshift.m0000644000175000017500000000227212237004736017423 0ustar dimadima% VL_DEMO_QUICKSHIFT Demo: Quick shift: basic functionality pfx = fullfile(vl_root,'figures','demo') ; randn('state',0) ; rand('state',0) ; figure(1) ; clf ; % -------------------------------------------------------------------- % Load a figure % -------------------------------------------------------------------- I = imread(fullfile(vl_root,'data','roofs1.jpg')) ; image(I) ; axis equal off tight ; vl_demo_print('quickshift_image') ; % -------------------------------------------------------------------- % Create a quickshift segmentation % -------------------------------------------------------------------- ratio = 0.5; kernelsize = 2; maxdist = 10; Iseg = vl_quickseg(I, ratio, kernelsize, maxdist); image(Iseg); axis equal off tight; vl_demo_print('quickshift_qseg_1') ; kernelsize = 2; maxdist = 20; Iseg = vl_quickseg(I, ratio, kernelsize, maxdist); image(Iseg); axis equal off tight; vl_demo_print('quickshift_qseg_2') ; maxdist = 50; ndists = 10; Iedge = vl_quickvis(I, ratio, kernelsize, maxdist, ndists); imagesc(Iedge); axis equal off tight; colormap gray; vl_demo_print('quickshift_qvis') ; vlfeat/toolbox/demo/vl_demo_mser_delta.m0000644000175000017500000000153412237004736017370 0ustar dimadima% VL_DEMO_MSER_DELTA Demo: MSER: delta parameter randn('state',0) ; rand('state',0) ; I = zeros(100,500) + 255 ; for i=1:5 I((1:50) + 25 - 1, ... (1:50) + 25 - 1 + 100 * (i-1)) = ... 255 - 32 * i ; end I = uint8(I) ; figure(1) ; clf ; axes('position',[0.05 0.05 1-.1 2*1/5-.1]) ; plot(I(end/2,:),'linewidth',3) ; hold on ; vl_demo_print('mser_delta_0') ; figure(2) ; clf ; imagesc(I) ; axis off ; axis equal ; axis tight ; colormap(gray(256)) ; set(gca,'fontsize',20) ; deltar = [1 32 159 160] ; clear h ; for delta=deltar [r,f] = vl_mser(I, 'Delta', delta, 'BrightOnDark', 0, 'verbose') ; if exist('h','var'), delete(h) ; end h = vl_plotframe(vl_ertr(f)) ; if ~isempty(h), set(h,'color','y','linewidth',1) ; end title(sprintf('delta = %g', delta)) ; drawnow ; vl_demo_print(sprintf('mser_delta_%d',find(delta==deltar))) ; end vlfeat/toolbox/demo/vl_demo_covdet.m0000644000175000017500000001042312237004745016532 0ustar dimadima% VL_DEMO_COVDET Demo: VL_COVDET() % -------------------------------------------------------------------- % Basics % -------------------------------------------------------------------- im = vl_impattern('roofs1') ; im = im(end-128:end,128:320,:) ; figure(1) ; clf ; image(im) ; axis image off ; vl_demo_print('covdet_basic_image') ; imgs = im2single(rgb2gray(im)) ; frames = vl_covdet(imgs, 'verbose') ; hold on ; vl_plotframe(frames) ; vl_demo_print('covdet_basic_frames') ; % -------------------------------------------------------------------- % Affine adaptation % -------------------------------------------------------------------- frames = vl_covdet(imgs, 'estimateAffineShape', true, 'verbose') ; figure(2) ; clf ; image(im) ; axis image off ; hold on ; vl_plotframe(frames) ; vl_demo_print('covdet_affine_frames',.8) ; % -------------------------------------------------------------------- % Estimating orientations % -------------------------------------------------------------------- frames = vl_covdet(imgs, 'estimateOrientation', true, 'verbose') ; figure(3) ; clf ; image(im) ; axis image off ; hold on ; vl_plotframe(frames) ; vl_demo_print('covdet_oriented_frames',.8) ; % -------------------------------------------------------------------- % Extracting patches % -------------------------------------------------------------------- [frames, patches] = vl_covdet(imgs, 'descriptor', 'patch') ; figure(4) ; clf ; w = sqrt(size(patches,1)) ; vl_imarraysc(reshape(patches(:,1:10*10), w,w,[])) ; axis image off ; hold on ; colormap gray ; vl_demo_print('covdet_patches') ; [frames, patches] = vl_covdet(imgs, ... 'descriptor', 'patch' ,... 'estimateAffineShape', true, ... 'estimateOrientation', false) ; figure(5) ; clf ; w = sqrt(size(patches,1)) ; vl_imarraysc(reshape(patches(:,1:10*10), w,w,[])) ; axis image off ; hold on ; colormap gray ; vl_demo_print('covdet_affine_patches') ; % -------------------------------------------------------------------- % Different detectors % -------------------------------------------------------------------- names = {'DoG', 'Hessian', ... 'HarrisLaplace', 'HessianLaplace', ... 'MultiscaleHarris', 'MultiscaleHessian'} ; figure(6) ; clf ; for i = 1:numel(names) frames = vl_covdet(imgs, 'method', names{i}) ; vl_tightsubplot(3,2,i, 'margintop',0.025, 'marginright', 0.01) ; imagesc(im) ; axis image off ; hold on ; vl_plotframe(frames) ; title(names{i}) ; end vl_figaspect(3/4) ; vl_demo_print('covdet_detectors',.9) ; % -------------------------------------------------------------------- % Custom frames % -------------------------------------------------------------------- delta = 15 ; xr = delta:delta:size(im,2)-delta+1 ; yr = delta:delta:size(im,1)-delta+1 ; [x,y] = meshgrid(xr,yr) ; frames = [x(:)'; y(:)'] ; frames(end+1,:) = delta/2 ; [frames, patches] = vl_covdet(imgs, ... 'frames', frames, ... 'estimateAffineShape', true, ... 'estimateOrientation', true) ; figure(7) ; clf ; imagesc(im) ; axis image off ; hold on ; colormap gray ; vl_plotframe(frames) ; vl_demo_print('covdet_custom_frames',.8) ; % -------------------------------------------------------------------- % Scale spaces % -------------------------------------------------------------------- [frames, descrs, info] = vl_covdet(imgs) ; figure(8) ; clf ; vl_plotss(info.gss) ; colormap gray ; vl_figaspect(2) ; vl_demo_print('covdet_gss',.8) ; figure(9) ; clf ; vl_plotss(info.css) ; colormap gray ; vl_figaspect(2) ; vl_demo_print('covdet_css',.8) ; figure(10) ; clf ; subplot(1,2,1) ; hist([info.peakScores],10) ; xlabel('Peak Score') ; ylabel('Occurences') ; grid on ; subplot(1,2,2) ; hist([info.edgeScores],10) ; xlabel('Edge Score') ; ylabel('Occurences') ; grid on ; vl_figaspect(2) ; vl_demo_print('covdet_scores',.9) ; vlfeat/toolbox/demo/vl_demo_kmeans_init.m0000644000175000017500000000267712237004736017563 0ustar dimadima% VL_DEMO_KMEANS_INIT if 0 I = vl_imtest('box') ; [f, d] = vl_dsift(single(I) / 255, 'step', 1, 'size', 3) ; X = double(d) ; numCenters = 300 ; else X = [] ; numCenters = 40 ; for i=1:numCenters X_ = randn(10,100) ; X_(i) = X_(i) + 100 ; X = [X X_] ; end numCenters = numCenters * 2 ; end numData = size(X, 2) ; dimension = size(X, 1) ; clear elaps energy ; dimension = 128 ; numData = 1000 ; numCenters = 100 ; numTrials = 5 ; numIters = 100 ; vl_twister(0) ; for trial = 1:numTrials tic ; [C, idx, e] = vl_kmeans(X, numCenters, 'Verbose', ... 'Initialization', 'randsel', ... 'MaxNumIterations', numIters) ; elaps.vlrs(trial) = toc ; energy.vlrs(trial) = e ; tic ; [C_, idx_, e_] = vl_kmeans(X, numCenters, 'Verbose', ... 'Initialization', 'plusplus', ... 'MaxNumIterations', numIters) ; elaps.vlpp(trial) = toc ; energy.vlpp(trial) = e_ ; end figure(1) ; clf ; subplot(1,2,1) ; title('Energy') ; mu = [mean(elaps.vlrs) mean(elaps.vlpp)] ; st = [std(elaps.vlrs) std(elaps.vlpp)] ; bar(mu) ; hold on ; errorbar(mu, st, 'linestyle', 'none', 'color', 'r', 'linewidth', 4) ; title('Time') ; subplot(1,2,2) ; mu = [mean(energy.vlrs) mean(energy.vlpp)] ; st = [std(energy.vlrs) std(energy.vlpp)] ; bar(mu) ; hold on ; errorbar(mu, st, 'linestyle', 'none', 'color', 'r', 'linewidth', 4) ; title('Energy') ; vlfeat/toolbox/demo/vl_demo_hog.m0000644000175000017500000000653512237004736016034 0ustar dimadima% VL_DEMO_HOG Demo: HOG features % Author: Andrea Vedaldi % AUTORIGHTS prefix = fullfile(vl_root,'figures','demo') ; randn('state',0) ; rand('state',0) ; figure(1) ; clf ; % -------------------------------------------------------------------- % Load a figure % -------------------------------------------------------------------- im = imread(fullfile(vl_root,'data','roofs1.jpg')) ; im = im2single(im) ; im = im(1:128,end-128+1:end,:) ; figure(1) ; clf ; image(im) ; axis equal off tight ; vl_demo_print('hog_image') ; % -------------------------------------------------------------------- % Compute HOG features % -------------------------------------------------------------------- % Standard features cellSize = 8 ; hog = vl_hog(im, cellSize, 'verbose') ; imhog = vl_hog('render', hog, 'verbose') ; figure(2) ; clf ; imagesc(imhog) ; axis image off ; colormap gray ; vl_demo_print('hog_features') ; % Dalal-Triggs variant cellSize = 8 ; hog = vl_hog(im, cellSize, 'verbose', 'variant', 'dalaltriggs') ; imhog = vl_hog('render', hog, 'verbose', 'variant', 'dalaltriggs') ; figure(3) ; clf ; imagesc(imhog) ; axis image off ; colormap gray ; vl_demo_print('hog_features_dalal_triggs') ; % -------------------------------------------------------------------- % Flip HOG features % -------------------------------------------------------------------- hog = vl_hog(im, cellSize) ; hogFromFlippedImage = vl_hog(im(:,end:-1:1,:), cellSize) ; perm = vl_hog('permutation') ; flippedHog = hog(:,end:-1:1,perm) ; imHog = vl_hog('render', hog) ; imHogFromFlippedImage = vl_hog('render', hogFromFlippedImage) ; imFlippedHog = vl_hog('render', flippedHog) ; figure(4) ; clf ; subplot(1,3,1) ; imagesc(imHog) ; axis image off ; title('HOG features') ; subplot(1,3,2) ; imagesc(imHogFromFlippedImage) ; axis image off ; title('Flipping the image') ; subplot(1,3,3) ; imagesc(imFlippedHog) ; axis image off ; title('Flipping the features') ; colormap gray ; vl_demo_print('hog_flipping',1) ; % -------------------------------------------------------------------- % Other HOG parameters % -------------------------------------------------------------------- figure(5) ; clf ; numOrientationsRange = [3, 4, 5, 9, 21] ; for i = 1:numel(numOrientationsRange) vl_tightsubplot(1, numel(numOrientationsRange), i) ; o = numOrientationsRange(i) ; hog = vl_hog(im, cellSize, 'verbose', 'numOrientations', o) ; imhog = vl_hog('render', hog, 'verbose', 'numOrientations', o) ; imagesc(imhog) ; axis image off ; colormap gray ; end vl_demo_print('hog_num_orientations',.9) ; figure(6) ; clf ; [x,y] = meshgrid(linspace(-1,1,128)) ; im = single(sqrt(x.^2+y.^2)) ; hog1 = vl_hog(im,cellSize,'numOrientations', 4) ; hog2 = vl_hog(im,cellSize,'numOrientations', 4,'bilinearOrientations') ; imhog1 = vl_hog('render',hog1,'numOrientations', 4) ; imhog2 = vl_hog('render',hog2,'numOrientations', 4) ; subplot(1,3,1) ; imagesc(im) ; axis image off ; title('Input image') ; subplot(1,3,2) ; imagesc(imhog1) ; axis image off ; title('Hard orientation assignments') ; subplot(1,3,3) ; imagesc(imhog2) ; axis image off ; title('Soft orientation assignments') ; colormap gray ; vl_demo_print('hog_bilinear_orientations',1) ; vlfeat/toolbox/demo/vl_demo_kdtree_plot.m0000644000175000017500000000201512237004736017560 0ustar dimadimafunction h = vl_demo_kdtree_plot(kdtree, nodeIndex, xlim, ylim) % VL_DEMO_KDTREE_PLOT % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). dim = kdtree.nodes.splitDimension(nodeIndex) ; thresh = kdtree.nodes.splitThreshold(nodeIndex) ; lowerChild = kdtree.nodes.lowerChild(nodeIndex) ; upperChild = kdtree.nodes.upperChild(nodeIndex) ; if dim == 1 xs = [thresh, thresh] ; ys = ylim ; ylimLower = ylim ; ylimUpper = ylim ; xlimLower = [xlim(1) thresh] ; xlimUpper = [thresh xlim(2)] ; else xs = xlim ; ys = [thresh, thresh] ; xlimLower = xlim ; xlimUpper = xlim ; ylimLower = [ylim(1) thresh] ; ylimUpper = [thresh ylim(2)] ; end h = line(xs,ys) ; if lowerChild > 1 h = [h vl_demo_kdtree_plot(kdtree, lowerChild, xlimLower, ylimLower)] ; end if upperChild > 1 h = [h vl_demo_kdtree_plot(kdtree, upperChild, xlimUpper, ylimUpper)] ; end vlfeat/toolbox/demo/vl_demo_kdtree_self.m0000644000175000017500000000156312237004736017542 0ustar dimadimafunction vl_demo_kdtree_self % VL_DEMO_KDTREE_SELF % Demonstrates using a kd-tree to find the pairwise nearest % neighbors in a set of points. % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). randn('state',0) ; rand('state',0) ; % a set of 2D pints X = single(rand(2, 100)) ; % build the kdtree kdtree = vl_kdtreebuild(X) ; % query the kdtree with X itself [i, d] = vl_kdtreequery(kdtree, X, X, 'numneighbors', 2, 'verbose') ; % plot figure(1) ; clf ; vl_plotframe(X, 'ro') ; hold on ; xl = [0, +1] ; yl = [0, +1] ; axis equal ; xlim(xl) ; ylim(yl) ; vl_demo_kdtree_plot(kdtree.trees(1), 1, xl, yl) ; line([X(1,:) ; X(1,i(2,:))], ... [X(2,:) ; X(2,i(2,:))], 'linewidth', 2) ; vl_demo_print('kdtree_self') ; vlfeat/toolbox/demo/vl_demo_svm.m0000644000175000017500000000232312237004745016053 0ustar dimadima% VL_DEMO_SVM Demo: SVM: 2D linear learning function vl_demo_svm y=[];X=[]; % Load training data X and their labels y load('vl_demo_svm_data.mat') Xp = X(:,y==1); Xn = X(:,y==-1); figure plot(Xn(1,:),Xn(2,:),'*r') hold on plot(Xp(1,:),Xp(2,:),'*b') axis equal ; vl_demo_print('svm_training') ; % Parameters lambda = 0.01 ; % Regularization parameter maxIter = 1000 ; % Maximum number of iterations energy = [] ; % Diagnostic function function diagnostics(svm) energy = [energy [svm.objective ; svm.dualObjective ; svm.dualityGap ] ] ; end % Training the SVM energy = [] ; [w b info] = vl_svmtrain(X, y, lambda,... 'MaxNumIterations',maxIter,... 'DiagnosticFunction',@diagnostics,... 'DiagnosticFrequency',1) % Visualisation eq = [num2str(w(1)) '*x+' num2str(w(2)) '*y+' num2str(b)]; line = ezplot(eq, [-0.9 0.9 -0.9 0.9]); set(line, 'Color', [0 0.8 0],'linewidth', 2); vl_demo_print('svm_training_result') ; figure hold on plot(energy(1,:),'--b') ; plot(energy(2,:),'-.g') ; plot(energy(3,:),'r') ; legend('Primal objective','Dual objective','Duality gap') xlabel('Diagnostics iteration') ylabel('Energy') vl_demo_print('svm_energy') ; endvlfeat/toolbox/demo/vl_demo_print.m0000644000175000017500000000355712237004745016414 0ustar dimadimafunction vl_demo_print(varargin) % VL_DEMO_PRINT % VL_DEMO_PRINT(NAME) prints the current figure to the documentation % directory with the specified filename, assuming that the global % variable VL_DEMO_PRINT is defined and non-empty when MATLAB is % started (our using SETENV() from MATLAB). Otherwise the function % flushes the displays and returns. % % VL_DEMO_PRINT(NAME, R) specifies a magnification factor R, setting % the figure width relatively to the page width. If not specified, R % is assumed to be 1/2. % % Remarks:: The figure paper type is set to letter, that has size 8.5 x % 11 inches. When converted for web viewing, images are rasterized % at either 75 or 95 DPI, The documentation system converts images % to bitmap with a resolution of 75 DPI, which makes a letter size % page 637 or 808 pixels large, repsectively. % % In MATLAB font sizes are usually expressed in points, where a % point is a 1/72 inch. Thus a 12pt font sampled at 75 DPI is % about 12.5 pixels high. % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if isempty(getenv('VL_DEMO_PRINT')) drawnow ; return ; end if isa(varargin{1}, 'double') fig = varargin{1} ; varargin(1) = [] ; else fig = gcf ; end name = varargin{1} ; if length(varargin) < 2 figurePaperSize = 0.5 ; else figurePaperSize = varargin{2} ; end vl_printsize(fig, figurePaperSize) ; figDir = fullfile(vl_root,'doc','demo') ; if ~ exist(figDir, 'dir') mkdir(figDir) ; end if 0 filePath = fullfile(figDir, [name '.eps']) ; print(fig, '-depsc2', filePath) ; else filePath = fullfile(figDir, [name '.jpg']) ; print(fig, '-djpeg95', filePath, '-r95') ; end fprintf('%s: wrote file ''%s''\n', mfilename, filePath) ; vlfeat/toolbox/demo/vl_demo_mser_cmd.m0000644000175000017500000000246512237004736017046 0ustar dimadima% VL_DEMO_MSER_CMD Demo: MSER: test command line utility pfx = fullfile(vl_root,'results') ; if ~ exist(pfx, 'dir') mkdir(pfx) ; end p_img = fullfile(vl_root,'data','spots.jpg') ; p_pgm = fullfile(pfx,'spots.pgm') ; p_sed = fullfile(pfx,'spots.mser') ; p_frm = fullfile(pfx,'spots.frame') ; p_tmp = fullfile(pfx,'spots.tmp') ; I = imread(p_img) ; I = uint8(rgb2gray(I)) ; imwrite(I,p_pgm,'pgm') ; % -------------------------------------------------------------------- % Test cmd % -------------------------------------------------------------------- [err,msg] = vl_demo_cmd('mser',[p_pgm ... ' --seeds=ascii://' p_sed ... ' --frames=ascii://' p_frm ... ' --max-variation=0.2 ' ... ' --min-diversity=0.7 ' ... ' --delta=10 ' ... ' --verbose '] ) ; r_ = load(p_sed,'-ASCII')'; % note: bright-on-dark region seeds are negative r_ = r_ + sign(r_); % add one for matlab indexing f_ = load(p_frm,'-ASCII')' ; f_(1:2,:) = f_(1:2,:) + 1 ; [r,f] = vl_mser(I','MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ; if(any(r ~= r_) | any(any(abs(f - f_)>1e-5))) warning('demo_mser_cmd: failed consistency test of commmand line and MATLAB drivers.') ; end vlfeat/toolbox/demo/vl_demo_slic.m0000644000175000017500000000301712237004736016201 0ustar dimadima% VL_DEMO_SLICT Demo: SLIC superpixels prefix = fullfile(vl_root,'figures','demo') ; randn('state',0) ; rand('state',0) ; figure(1) ; clf ; % -------------------------------------------------------------------- % Load a figure % -------------------------------------------------------------------- im = imread(fullfile(vl_root,'data','roofs1.jpg')) ; im = im2single(im) ; im = im(1:128,end-128+1:end,:) ; figure(1) ; clf ; image(im) ; axis equal off tight ; vl_demo_print('slic_image') ; % -------------------------------------------------------------------- % Create various SLIC segmentations % -------------------------------------------------------------------- regionSizes = [10 30] ; regularizers = [0.01 0.1 1] ; figure(2) ; clf ; for i = 1:numel(regionSizes) for j = 1:numel(regularizers) regionSize = regionSizes(i) ; regularizer = regularizers(j) ; segments = vl_slic(im, regionSize, regularizer, 'verbose') ; % overaly segmentation [sx,sy]=vl_grad(double(segments), 'type', 'forward') ; s = find(sx | sy) ; imp = im ; imp([s s+numel(im(:,:,1)) s+2*numel(im(:,:,1))]) = 0 ; vl_tightsubplot(numel(regionSizes),numel(regularizers), (i-1)*numel(regularizers) + j) ; imagesc(imp) ; axis image off ; hold on ; text(5,5,sprintf('regionSize:%.2g\nregularizer:%.2g', regionSize, regularizer), ... 'Background', 'white','VerticalAlignment','top') end end vl_demo_print('slic_segmentation') ; vlfeat/toolbox/demo/vl_demo_cmd.m0000644000175000017500000000103412237004736016007 0ustar dimadimafunction [err,msg] = vl_demo_cmd(name, args) % DEMO_CMD: Demo: Command line utility ext = '' ; switch mexext case 'mexmac' arch = 'mac' ; case 'mexmaci' arch = 'maci' ; case 'mexmaci64' arch = 'maci64'; case 'mexglx' arch = 'glnx86' ; case 'mexa64' arch = 'glnxa64' ; case 'dll' arch = 'win32' ; ext = '.exe' ; case 'mexw32' arch = 'win32' ; ext ='.exe' ; case 'mexw64' arch = 'win64' ; ext ='.exe' ; end cmd = fullfile(vl_root,'bin',arch,[name ext]) ; str = ['"' cmd '" ' args] ; disp(str); [err,msg] = system(str) ; vlfeat/toolbox/demo/vl_demo_imdisttf.m0000644000175000017500000000205512237004736017073 0ustar dimadima% VL_DEMO_IMDISTTF Demonstrates image distance transform % load image im = imread(fullfile(vl_root, 'data', 'roofs1.jpg')) ; im = im(100:200,100:200,:) ; imSize = [size(im,1) size(im,2)] ; % creates an edge map edges = zeros(imSize) + inf; edges(edge(rgb2gray(im), 'canny')) = 0 ; % compute distance transform [distanceTransform, neighbors] = vl_imdisttf(single(edges)) ; % plot [u,v] = meshgrid(1:imSize(2),1:imSize(1)) ; [v_,u_] = ind2sub(imSize, neighbors) ; % avoid cluttering the plot too much u = u(1:3:end,1:3:end) ; v = v(1:3:end,1:3:end) ; u_ = u_(1:3:end,1:3:end) ; v_ = v_(1:3:end,1:3:end) ; figure(1) ; clf ; imagesc(im) ; axis off image ; figure(2) ; clf ; imagesc(edges) ; axis off image ; figure(3) ; clf ; imagesc(edges) ; axis off image ; hold on ; h = quiver(u,v,u_-u,v_-v,0) ; colormap gray ; figure(4) ; clf ; imagesc(sqrt(distanceTransform)) ; axis off image ; figure(1) ; vl_demo_print('imdisttf_src') ; figure(2) ; vl_demo_print('imdisttf_edge') ; figure(3) ; vl_demo_print('imdisttf_neigh') ; figure(4) ; vl_demo_print('imdisttf_dist') ; vlfeat/toolbox/demo/vl_demo_plots_rank.m0000644000175000017500000000136012237004736017422 0ustar dimadima%VL_DEMO_PLOTS_RANK Demonstates VL_ROC, VL_DET, VL_PR randn('state',0) ; numPos = 20 ; numNeg = 100 ; labels = [ones(1, numPos) -ones(1,numNeg)] ; scores = randn(size(labels)) + labels ; figure(1) ; clf ; vl_roc(labels,scores) ; vl_demo_print(1,'plots_rank_roc', 0.5) ; figure(2) ; clf ; subplot(2,2,1) ; vl_roc(labels,scores, 'plot', 'tntp') ; subplot(2,2,2) ; vl_roc(labels,scores, 'plot', 'tptn') ; subplot(2,2,3) ; vl_roc(labels,scores, 'plot', 'fptp') ; subplot(2,2,4) ; vl_roc(labels,scores, 'plot', 'fpfn') ; vl_figaspect(1) ; vl_demo_print(2,'plots_rank_roc_variants', 1) ; figure(3) ; clf ; vl_det(labels,scores) ; vl_demo_print(3,'plots_rank_det', 0.5) ; figure(4) ; clf ; vl_pr(labels,scores) ; vl_demo_print(4,'plots_rank_pr', 0.5) ; vlfeat/toolbox/demo/vl_demo_svm_data.mat0000644000175000017500000000151712237004745017375 0ustar dimadimaMATLAB 5.0 MAT-file, Platform: GLNX86, Created on: Mon Mar 11 14:05:30 2013 IM2xœãc``è`d``Ò X¡| Ãj0ÍÈP ¤™8ŒÂðÿ Ë,­ãxœeV=ˆÔ@‚¢[Xm­íY[KJë³²ØÚÚú¬¯¾Z[« WšjàR {,,š\r¹ìîql£õÍdæ=™—Y–áñÍ÷¾ï{óóåE’$õ,IžÚùYâg7žüØþ¿Ûÿ#ûû`ççöa×ÿúéÆùúÅ2ýb7âo„_ìe੉ý~mD¯5±¯w%þëF=±=œ'Ñ ]øs#ü’O\G™Æ:Àž Î&õÄz—r ±çRÏ!àC8/÷ĸÀ8ßÀ8¯À~„¯¾NøZák„¯ÿ¬_K¼•ð/…¯ÿRÖbÏgáœÝ§P0è;t‚?t?tB|è„üˆCþÄ¡>âP?qÐGøjYß+þÄë…o¾­ð“:Ä8=x^©³øáûäQÏØoI =…GÎEE yo&ñå}z–ÄØ/øígþ›õígñ‡õû÷¿¦¿ï5õÃþB_ß_®©¿ïG×¢GK»ïg-ý}¿kÉïûaÇø¾_vÌçù‡þ"ú0?ƒ|â{ÕOîI¼?[ÉOŒ{ïïŽõÅ8#F}b7âOŒúb{Éw!ÎïÎAÞƒA¾À¨õC˜¯¾NøZák„¯ÿ¬_K¼•ð/…¯ÿRÖbÏgÒ‡ô; ÷ßYèÿ¿›‚Nò]€>T‡ü‰C}Äè/Àè?1_-ëáoÅ¿“x½ð ·%~¡o™_vlfeat/toolbox/demo/vl_demo_kmeans_2d.m0000644000175000017500000000057212237004745017115 0ustar dimadimaN = 10000 ; dimension = 2 ; data = rand(dimension,N) ; numClusters = 20 ; cc=hsv(numClusters); [centers, assignments] = vl_kmeans(data, numClusters); figure hold on for i=1:numClusters plot(data(1,assignments == i),data(2,assignments == i),'.','color',cc(i,:)); end plot(centers(1,:),centers(2,:),'k.','MarkerSize',20) axis off vl_demo_print('kmeans_2d_rand',0.6);vlfeat/toolbox/demo/vl_demo_kdtree_ann.m0000644000175000017500000000254412237004736017365 0ustar dimadimafunction vl_demo_kdtree_ann % VL_DEMO_KDTREE % Demonstrates the use of a kd-tree for approximate nearest neighbor % (ANN) queries. % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). randn('state',0) ; rand('state',0) ; % Generate some 2D data and a query point X = rand(2, 100) ; Q = rand(2,1) ; % Buld a kd-tree kdtree = vl_kdtreebuild(X) ; % Query with increasing accuracy maxNumComparisonRange = [1 10 20 30] ; for t = [1 2 3 4] figure(t) ; clf ; vl_plotframe(X, 'ro') ; hold on ; xl = [.2, .8] ; yl = [.1, .7] ; xlim(xl) ; ylim(yl) ; % vl_demo_kdtree_plot(kdtree, 1, xl, yl) ; [i, d] = vl_kdtreequery (kdtree, X, Q, ... 'NumNeighbors', 10, ... 'MaxComparisons', maxNumComparisonRange(t), ... 'Verbose') ; vl_plotframe(Q,'b*','markersize',10) ; for k=1:length(i) if i(k) == 0, continue ; end vl_plotframe([Q ; sqrt(d(k))],'b-','linewidth',1) ; vl_plotframe(X(:, i(k)), 'bx','markersize',15) ; end title(sprintf('10 ANNs with at most %d comparisions', maxNumComparisonRange(t))) ; axis square ; set(gca,'xtick',[],'ytick',[]) ; vl_demo_print(t, sprintf('kdtree_ann_%d', t)) ; end vlfeat/toolbox/demo/vl_demo_aib.m0000644000175000017500000000556012237004736016007 0ustar dimadimafunction vl_demo_aib % VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB) D = 4 ; K = 20 ; randn('state',0) ; rand('state',0) ; X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ; X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ; X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ; figure(1) ; clf ; hold on ; vl_plotframe(X1,'color','r') ; vl_plotframe(X2,'color','g') ; vl_plotframe(X3,'color','b') ; axis equal ; xlim([-4 4]); ylim([-4 4]); axis off ; rectangle('position',D*[-1 -1 2 2]) vl_demo_print('aib_basic_data', .6) ; C = 1:K*K ; Pcx = zeros(3,K*K) ; f1 = quantize(X1,D,K) ; f2 = quantize(X2,D,K) ; f3 = quantize(X3,D,K) ; Pcx(1,:) = vl_binsum(Pcx(1,:), ones(size(f1)), f1) ; Pcx(2,:) = vl_binsum(Pcx(2,:), ones(size(f2)), f2) ; Pcx(3,:) = vl_binsum(Pcx(3,:), ones(size(f3)), f3) ; Pcx = Pcx / sum(Pcx(:)) ; [parents, cost] = vl_aib(Pcx) ; cutsize = [K*K, 10, 3, 2, 1] ; for i=1:length(cutsize) [cut,map,short] = vl_aibcut(parents, cutsize(i)) ; parents_cut(short > 0) = parents(short(short > 0)) ; C = short(1:K*K+1) ; [drop1,drop2,C] = unique(C) ; figure(i+1) ; clf ; plotquantization(D,K,C) ; hold on ; %plottree(D,K,parents_cut) ; axis equal ; axis off ; title(sprintf('%d clusters', cutsize(i))) ; vl_demo_print(sprintf('aib_basic_clust_%d',i),.6) ; end % -------------------------------------------------------------------- function f = quantize(X,D,K) % -------------------------------------------------------------------- d = 2*D / K ; j = round((X(1,:) + D) / d) ; i = round((X(2,:) + D) / d) ; j = max(min(j,K),1) ; i = max(min(i,K),1) ; f = sub2ind([K K],i,j) ; % -------------------------------------------------------------------- function [i,j] = plotquantization(D,K,C) % -------------------------------------------------------------------- hold on ; cl = [[.3 .3 .3] ; .5*hsv(max(C)-1)+.5] ; d = 2*D / K ; for i=0:K-1 for j=0:K-1 patch(d*(j+[0 1 1 0])-D, ... d*(i+[0 0 1 1])-D, ... cl(C(j*K+i+1),:)) ; end end % -------------------------------------------------------------------- function h = plottree(D,K,parents) % -------------------------------------------------------------------- d = 2*D / K ; C = zeros(2,2*K*K-1)+NaN ; N = zeros(1,2*K*K-1) ; for i=0:K-1 for j=0:K-1 C(:,j*K+i+1) = [d*j-D; d*i-D]+d/2 ; N(:,j*K+i+1) = 1 ; end end for i=1:length(parents) p = parents(i) ; if p==0, continue ; end; if all(isnan(C(:,i))), continue; end if all(isnan(C(:,p))) C(:,p) = C(:,i) / N(i) ; else C(:,p) = C(:,p) + C(:,i) / N(i) ; end N(p) = N(p) + 1 ; end C(1,:) = C(1,:) ./ N ; C(2,:) = C(2,:) ./ N ; xt = zeros(3, 2*length(parents)-1)+NaN ; yt = zeros(3, 2*length(parents)-1)+NaN ; for i=1:length(parents) p = parents(i) ; if p==0, continue ; end; xt(1,i) = C(1,i) ; xt(2,i) = C(1,p) ; yt(1,i) = C(2,i) ; yt(2,i) = C(2,p) ; end h=line(xt(:),yt(:),'linestyle','-','marker','.','linewidth',3) ; vlfeat/toolbox/demo/vl_demo_alldist.m0000644000175000017500000001252412237004736016706 0ustar dimadimafunction vl_demo_alldist % numRepetitions = 3 ; numDimensions = 1000 ; numSamplesRange = [300] ; settingsRange = {{'alldist2', 'double', 'l2', }, ... {'alldist', 'double', 'l2', 'nosimd'}, ... {'alldist', 'double', 'l2' }, ... {'alldist2', 'single', 'l2', }, ... {'alldist', 'single', 'l2', 'nosimd'}, ... {'alldist', 'single', 'l2' }, ... {'alldist2', 'double', 'l1', }, ... {'alldist', 'double', 'l1', 'nosimd'}, ... {'alldist', 'double', 'l1' }, ... {'alldist2', 'single', 'l1', }, ... {'alldist', 'single', 'l1', 'nosimd'}, ... {'alldist', 'single', 'l1' }, ... {'alldist2', 'double', 'chi2', }, ... {'alldist', 'double', 'chi2', 'nosimd'}, ... {'alldist', 'double', 'chi2' }, ... {'alldist2', 'single', 'chi2', }, ... {'alldist', 'single', 'chi2', 'nosimd'}, ... {'alldist', 'single', 'chi2' }, ... {'alldist2', 'double', 'hell', }, ... {'alldist', 'double', 'hell', 'nosimd'}, ... {'alldist', 'double', 'hell' }, ... {'alldist2', 'single', 'hell', }, ... {'alldist', 'single', 'hell', 'nosimd'}, ... {'alldist', 'single', 'hell' }, ... {'alldist2', 'double', 'kl2', }, ... {'alldist', 'double', 'kl2', 'nosimd'}, ... {'alldist', 'double', 'kl2' }, ... {'alldist2', 'single', 'kl2', }, ... {'alldist', 'single', 'kl2', 'nosimd'}, ... {'alldist', 'single', 'kl2' }, ... {'alldist2', 'double', 'kl1', }, ... {'alldist', 'double', 'kl1', 'nosimd'}, ... {'alldist', 'double', 'kl1' }, ... {'alldist2', 'single', 'kl1', }, ... {'alldist', 'single', 'kl1', 'nosimd'}, ... {'alldist', 'single', 'kl1' }, ... {'alldist2', 'double', 'kchi2', }, ... {'alldist', 'double', 'kchi2', 'nosimd'}, ... {'alldist', 'double', 'kchi2' }, ... {'alldist2', 'single', 'kchi2', }, ... {'alldist', 'single', 'kchi2', 'nosimd'}, ... {'alldist', 'single', 'kchi2' }, ... {'alldist2', 'double', 'khell', }, ... {'alldist', 'double', 'khell', 'nosimd'}, ... {'alldist', 'double', 'khell' }, ... {'alldist2', 'single', 'khell', }, ... {'alldist', 'single', 'khell', 'nosimd'}, ... {'alldist', 'single', 'khell' }, ... } ; %settingsRange = settingsRange(end-5:end) ; styles = {} ; for marker={'x','+','.','*','o'} for color={'r','g','b','k','y'} styles{end+1} = {'color', char(color), 'marker', char(marker)} ; end end for ni=1:length(numSamplesRange) for ti=1:length(settingsRange) tocs = [] ; for ri=1:numRepetitions rand('state',ri) ; randn('state',ri) ; numSamples = numSamplesRange(ni) ; settings = settingsRange{ti} ; [tocs(end+1), D] = run_experiment(numDimensions, ... numSamples, ... settings) ; end means(ni,ti) = mean(tocs) ; stds(ni,ti) = std(tocs) ; if mod(ti-1,3) == 0 D0 = D ; else err = max(abs(D(:)-D0(:))) ; fprintf('err %f\n', err) ; if err > 1, keyboard ; end end end end if 0 figure(1) ; clf ; hold on ; numStyles = length(styles) ; for ti=1:length(settingsRange) si = mod(ti - 1, numStyles) + 1 ; h(ti) = plot(numSamplesRange, means(:,ti), styles{si}{:}) ; leg{ti} = sprintf('%s ', settingsRange{ti}{:}) ; errorbar(numSamplesRange, means(:,ti), stds(:,ti), 'linestyle', 'none') ; end end for ti=1:length(settingsRange) leg{ti} = sprintf('%s ', settingsRange{ti}{:}) ; end figure(1) ; clf ; barh(means(end,:)) ; set(gca,'ytick', 1:length(leg), 'yticklabel', leg,'ydir','reverse') ; xlabel('Time [s]') ; function [elaps, D] = run_experiment(numDimensions, numSamples, settings) distType = 'l2' ; algType = 'alldist' ; classType = 'double' ; useSimd = true ; for si=1:length(settings) arg = settings{si} ; switch arg case {'l1', 'l2', 'chi2', 'hell', 'kl2', 'kl1', 'kchi2', 'khell'} distType = arg ; case {'alldist', 'alldist2'} algType = arg ; case {'single', 'double'} classType = arg ; case 'simd' useSimd = true ; case 'nosimd' useSimd = false ; otherwise assert(false) ; end end X = rand(numDimensions, numSamples) ; X(X < .3) = 0 ; switch classType case 'double' case 'single' X = single(X) ; end vl_simdctrl(double(useSimd)) ; switch algType case 'alldist' tic ; D = vl_alldist(X, distType) ; elaps = toc ; case 'alldist2' tic ; D = vl_alldist2(X, distType) ; elaps = toc ; end vlfeat/toolbox/demo/vl_demo_kmeans_ann_speed.m0000644000175000017500000000400312237004745020535 0ustar dimadima% VL_DEMO_KMEANS_ANN_SPEED Compares Lloyd's, Elkan, and ANN k-means numCenters = 200 ; numTrials = 3 ; maxNumIterations = 10 ; initialization = 'randsel' ; %initialization = 'plusplus'; distance = 'l2' ; %% Create an example dataset dimension = 32 ; numData = 50000 ; X = randn(dimension,numData); %% Run various k-means algorithms on the data algorithms = {'Lloyd','Elkan','ANN 1/4','ANN 1/10','ANN 1/50' } ; options = {{'Algorithm', 'Lloyd'}, ... {'Algorithm', 'Elkan'}, ... {'Algorithm', 'ANN', 'MaxNumComparisons', ceil(numCenters / 4)}, ... {'Algorithm', 'ANN', 'MaxNumComparisons', ceil(numCenters / 10)}, ... {'Algorithm', 'ANN', 'MaxNumComparisons', ceil(numCenters / 50)}} ; numCpus = [1 0] ; clear time energy ; for n = 1:2 for a = 1:numel(algorithms) for t = 1:numTrials vl_threads(numCpus(n)) ; start = tic ; [C, A, E] = vl_kmeans(X, ... numCenters, 'Verbose', ... 'Distance', distance, ... 'MaxNumIterations', maxNumIterations, ... options{a}{:}) ; if vl_isoctave() time(t,a,n) = (tic() - start) / 1e6 ; else time(t,a,n) = toc(start) ; end energy(t,a,n) = E ; end end end % average over tirals time = squeeze(mean(time,1)) ; energy = squeeze(mean(energy,1)) ; figure(1) ; clf ; for n=1:2 if n == 1 str = 'Serial' ; else str = 'Parallel' ; end subplot(3,2,(n-1)+1) ; bar(time(:,n)) ; set(gca,'XTickLabel',algorithms); set(gca,'FontSize',8), xlabel('Algorithm'); ylabel('Time [s]'); title(str) ; subplot(3,2,(n-1)+3) ; bar(energy(:,n)); set(gca,'XTickLabel',algorithms); set(gca,'FontSize',8), xlabel('Algorithm'); ylabel('Energy'); title(str) ; subplot(3,2,(n-1)+5) ; bar(time(1,1)./time(:,n)) ; set(gca,'XTickLabel',algorithms); set(gca,'FontSize',8), xlabel('Algorithm'); ylabel('Speedup'); title(str) ; end vl_demo_print('kmeans_speed',1); vlfeat/toolbox/demo/vl_demo_gmm_2d_rand.m0000644000175000017500000000132712237004745017422 0ustar dimadima% VL_DEMO_GMM_2D_RAND Demonstrate clustering points with a GMM %% Create a random set of points numPoints = 5000 ; dimension = 2 ; numClusters = 20 ; data = rand(dimension, numPoints) ; %% Learn a GMM: fit the points at maximum likelihood vl_twister('state',0) ; [means, covariances, priors] = ... vl_gmm(data, numClusters, ... 'MaxNumIterations', 1000, ... 'Verbose') ; figure(1) ; clf ; hold on plot(data(1,:),data(2,:),'r.'); for i=1:numClusters vl_plotframe([means(:,i)' covariances(1,i) 0 covariances(2,i)], ... 'Color','blue','LineWidth',2); end title('GMM: Gaussian mixture initialized choosing random points') ; axis equal ; axis off ; vl_demo_print('gmm_2d_rand',0.6); vlfeat/toolbox/demo/vl_demo_gmm_3d.m0000644000175000017500000000470012237004745016415 0ustar dimadimafunction vl_demo_gmm_3d % VL_DEMO_GMM_3D Show how to initialize GMM learning with a custom set of modes %% Initialize points clustered in 3D numClusters = 5 ; dimension = 3 ; cc = hsv(numClusters) ; randn('state',0) ; X = [] ; for i=1:numClusters delta = 5*[i;0;0] ; %num = ceil(1000 / i) ; num = 500 ; s = diag([1,i, 1]) ; X = [X, bsxfun(@plus, s*randn(dimension, num), delta)] ; end %% Cluster using KMeans (ANN algorithm) elapsedKMEANS = tic ; vl_twister('state',0) ; [initMeans, assignments] = vl_kmeans(X, numClusters, ... 'Verbose', ... 'Algorithm', 'ann', ... 'MaxNumIterations', 10, ... 'Distance', 'l2', ... 'NumTrees', 3, ... 'MaxNumComparisons', 10); elapsedKMEANS = toc(elapsedKMEANS) ; fprintf('\n ---------- \n KMeans time - %f \n ---------- \n\n',elapsedKMEANS) ; %% Create initial GMM modes from KMeans clusters initSigmas = zeros(dimension,numClusters); initWeights = zeros(1,numClusters); figure(1) ; clf ; subplot(1,2,1) ; hold on ; for i=1:numClusters Xk = X(:,assignments==i) ; initWeights(i) = size(Xk,2) / numClusters ; plot3(Xk(1,:),Xk(2,:),Xk(3,:),'.','color',cc(i,:)) ; if size(Xk,1) == 0 || size(Xk,2) == 0 initSigmas(:,i) = diag(cov(X')); else initSigmas(:,i) = diag(cov(Xk')); end end axis equal ; xlabel('x') ; ylabel('y') ; zlabel('z') ; title('GMM: KMeans intialization') ; %% Fit a GMM model to the maximum likelihood elapsedGMM = tic ; [means,sigmas,weights,ll,posteriors] = vl_gmm(X, numClusters, ... 'Initialization','custom', ... 'InitMeans',initMeans, ... 'InitCovariances',initSigmas, ... 'InitPriors',initWeights, ... 'Verbose', ... 'MaxNumIterations', 100) ; elapsedGMM = toc(elapsedGMM); fprintf('\n ---------- \n GMM time - %f \n ---------- \n',elapsedGMM) ; subplot(1,2,2) ; hold on ; [~,idx] = max(posteriors,[],1); for i=1:numClusters plot3(X(1,idx == i),X(2,idx == i),X(3,idx == i),'.','color',cc(i,:)) ; end axis equal ; xlabel('x') ; ylabel('y') ; zlabel('z') ; title('GMM: Estimated Gaussian mixture') ; vlfeat/toolbox/demo/vl_demo_mser_basic.m0000644000175000017500000000507212237004745017361 0ustar dimadima% VL_DEMO_MSER_BASIC Demo: MSER: basic functionality pfx = fullfile(vl_root,'figures','demo') ; randn('state',0) ; rand('state',0) ; figure(1) ; clf ; % -------------------------------------------------------------------- % Load a figure % -------------------------------------------------------------------- I = imread(fullfile(vl_root,'data','spots.jpg')) ; image(I) ; colormap gray ; axis equal ; axis off ; axis tight ; vl_demo_print('mser_basic_0') ; % -------------------------------------------------------------------- % Convert the to required format % -------------------------------------------------------------------- I = uint8(rgb2gray(I)) ; clf ; imagesc(I) ; colormap(gray(256)) ; axis equal ; axis off ; axis tight ; % -------------------------------------------------------------------- % Run VL_MSER % -------------------------------------------------------------------- [r,f] = vl_mser(I,'MinDiversity',0.7,'MaxVariation',0.2,'Delta',10) ; % compute regions mask M = zeros(size(I)) ; for x=r' s = vl_erfill(I,x) ; M(s) = M(s) + 1; end % adjust convention f = vl_ertr(f) ; figure(1) ; hold on ; h1 = vl_plotframe(f) ; set(h1,'color','y','linewidth',3) ; h2 = vl_plotframe(f) ; set(h2,'color','k','linewidth',1) ; vl_demo_print('mser_basic_frames') ; figure(2) ; clf ; imagesc(I) ; hold on ; colormap(gray(256)) ; if vl_isoctave() [c,h]=contour(M,(0:max(M(:)))+.5,'y','linewidth',3) ; else [c,h]=contour(M,(0:max(M(:)))+.5) ; set(h,'color','y','linewidth',3) ; end axis equal ; axis off ; vl_demo_print('mser_basic_contours') ; % -------------------------------------------------------------------- % Run VL_MSER again % -------------------------------------------------------------------- [r,f] = vl_mser(I,'MinDiversity',0.7,'MaxVariation',0.2,... 'Delta',10,'BrightOnDark',1,'DarkOnBright',0) ; % adjust convention f = vl_ertr(f) ; % compute regions mask M = zeros(size(I)) ; for x=r' s = vl_erfill(I,x) ; M(s) = M(s) + 1; end figure(1) ; hold on ; h1 = vl_plotframe(f) ; set(h1,'color','g','linewidth',3) ; h2 = vl_plotframe(f) ; set(h2,'color','k','linewidth',1) ; vl_demo_print('mser_basic_frames_both') ; figure(2) ; if vl_isoctave() [c,h]=contour(M,(0:max(M(:)))+.5,'y','linewidth',3) ; else [c,h]=contour(M,(0:max(M(:)))+.5) ; set(h,'color','g','linewidth',3) ; end hold on ; axis equal ; axis off ; vl_demo_print('mser_basic_contours_both') ; vlfeat/toolbox/demo/vl_demo_kdtree_forest.m0000644000175000017500000000276212237004736020115 0ustar dimadimafunction vl_demo_kdtree_forest % VL_DEMO_KDTREE % Demonstrates the construction of a forest of randomized % kd-trees. % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % numer of trees in the forest numTrees = 4 ; randn('state',0) ; rand('state',0) ; % 2D data X and a query point Q = single(rand(2,1)) ; X = single(rand(2, 100)) ; % build the forest kdforest = vl_kdtreebuild (X, ... 'verbose', ... 'numtrees', numTrees) ; % plot each tree of the forest for t=1:numTrees figure(t) ; clf ; vl_plotframe(X, 'ro') ; hold on ; xl = [0, +1] ; yl = [0, +1] ; xlim(xl) ; ylim(yl) ; axis square ; h = vl_demo_kdtree_plot(kdforest.trees(t), 1, xl, yl) ; title(sprintf('Tree number %d', t)) ; set(gca,'xtick',[],'ytick',[]) ; vl_demo_print(t,sprintf('kdtree_forest_tree_%d', t)) ; end % query [i, d] = vl_kdtreequery (kdforest, X, Q, 'numneighbors', 10, 'verbose') ; % show query and return figure(5) ; clf ; vl_plotframe(X, 'ro') ; hold on ; vl_plotframe(Q,'b*') ; for k=1:length(i) vl_plotframe([Q ; sqrt(d(k))],'b:','linewidth',1) ; vl_plotframe(X(:, i(k)), 'bx') ; end xlim([0 1]) ; ylim([0 1]) ; axis square ; set(gca,'xtick',[],'ytick',[]) ; vl_demo_print(5,'kdtree_forest_query') ; end vlfeat/toolbox/mser/0000755000175000017500000000000012237042171013400 5ustar dimadimavlfeat/toolbox/mser/vl_ertr.m0000644000175000017500000000074312237004736015244 0ustar dimadimafunction f = vl_ertr(f) ; % VL_ERTR Transpose exremal regions frames % F = VL_ERTR(F) transposes the frames F as returned by VL_MSER(). This % conversion is required as the VL_MSER algorithm considers the column % index I as the frist image index, while according standard image % convention the first coordinate is the abscissa X. % % See also: VL_MSER(), VL_HELP(). if size(f,1) ~= 5 error('F is not in the right format') ; end % adjust convention f = f([2 1 5 4 3],:) ; vlfeat/toolbox/mser/vl_mser.m0000644000175000017500000000554012237004736015236 0ustar dimadima% VL_MSER Maximally Stable Extremal Regions % R=VL_MSER(I) computes the Maximally Stable Extremal Regions (MSER) % [1] of image I with stability threshold DELTA. I is any array of % class UINT8. R is a vector of region seeds. % % A (maximally stable) extremal region is just a connected component % of one of the level sets of the image I. An extremal region can % be recovered from a seed X as the connected component of the level % set {Y: I(Y) <= I(X)} which contains the pixel o index X. % % The function supports images of arbitrary dimension D. % % [R,F]=VL_MSER(...) also returns ellipsoids F fitted to the regions. % Each column of F describes an ellipsoid; F(1:D,i) is the center of % the elliposid and F(D:end,i) are the independent elements of the % co-variance matrix of the ellipsoid. % % Ellipsoids are computed according to the same reference frame of I % seen as a matrix. This means that the first coordinate spans the % first dimension of I. % % Notice that for 2-D images usually the opposite convention is used % (i.e. the first coordinate is the x-axis, which corresponds to the % column index). Thus, if the function VL_PLOTFRAME() is used to plot % the ellipses, the frames F should be `transposed' as in F = F([2 % 1 5 4 3],:). VL_ERTR() exists for this purpose. % % VL_MSER(I,'Option'[,Value]...) accepts the following options % % Delta:: [5] % Set the DELTA parameter of the VL_MSER algorithm. Roughly % speaking, the stability of a region is the relative variation % of the region area when the intensity is changed of +/- % Delta/2. % % MaxArea:: [0.75] % Set the maximum area (volume) of the regions relative to % the image domain area (volume). % % MinArea:: [3 / numPixels] % Set the minimum area (volume) of the regions relative to % the image domain area (volume). % % MaxVariation:: [0.25] % Set the maximum variation (absolute stability score) of the % regions. % % MinDiversity:: [0.2] % Set the minimum diversity of the region. When the relative % area variation of two nested regions is below this threshold, % then only the most stable one is selected. % % BrightOnDark:: [1] % Detect bright-on-dark MSERs. This corresponds to MSERs of the % inverted image. % % DarkOnBright:: [1] % Detect dark-on-bright MSERs. This corresponds to MSERs of the % original image. % % Verbose:: % Be verbose. % % REFERENCES:: % [1] J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust wide % baseline stereo from maximally stable extremal regions," in % Proc. BMVC, 2002. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/mser/vl_mser.c0000644000175000017500000002235012237004736015222 0ustar dimadima/** @file mser.c ** @author Andrea Vedaldi ** @brief MSER MEX driver **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include enum { opt_delta = 0, opt_max_area, opt_min_area, opt_max_variation, opt_min_diversity, opt_bright_on_dark, opt_dark_on_bright, opt_verbose } ; vlmxOption options [] = { {"Delta", 1, opt_delta }, {"MaxArea", 1, opt_max_area }, {"MinArea", 1, opt_min_area }, {"MaxVariation", 1, opt_max_variation }, {"MinDiversity", 1, opt_min_diversity }, {"BrightOnDark", 1, opt_bright_on_dark }, {"DarkOnBright", 1, opt_dark_on_bright }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; /** @brief MEX entry point */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_I = 0, IN_END } ; enum {OUT_SEEDS = 0, OUT_FRAMES } ; int verbose = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; /* algorithm parameters */ double delta = -1 ; double max_area = -1 ; double min_area = -1 ; double max_variation = -1 ; double min_diversity = -1 ; int bright_on_dark = 1 ; int dark_on_bright = 1 ; int nel ; int ndims ; mwSize const* dims ; vl_mser_pix const *data ; vl_mser_pix *datainv = 0; VlMserFilt *filt, *filtinv ; vl_uint const *regions = 0 ; vl_uint const *regionsinv = 0 ; float const *frames = 0; float const *framesinv = 0 ; int nregions = 0, nregionsinv = 0; int nframes = 0, nframesinv = 0; int i, j, dof = 0 ; mwSize odims [2] ; double *pt ; VL_USE_MATLAB_ENV ; /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (nin < 1) { mexErrMsgTxt("At least one input argument is required.") ; } if (nout > 2) { mexErrMsgTxt("Too many output arguments."); } if(mxGetClassID(in[IN_I]) != mxUINT8_CLASS) { mexErrMsgTxt("I must be of class UINT8") ; } /* get dimensions */ nel = mxGetNumberOfElements(in[IN_I]) ; ndims = mxGetNumberOfDimensions(in[IN_I]) ; dims = mxGetDimensions(in[IN_I]) ; data = mxGetData(in[IN_I]) ; while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbose ; break ; case opt_delta : if (!vlmxIsPlainScalar(optarg) || (delta = *mxGetPr(optarg)) < 0) { mexErrMsgTxt("'Delta' must be non-negative.") ; } break ; case opt_max_area : if (!vlmxIsPlainScalar(optarg) || (max_area = *mxGetPr(optarg)) < 0 || max_area > 1) { mexErrMsgTxt("'MaxArea' must be in the range [0,1].") ; } break ; case opt_min_area : if (!vlmxIsPlainScalar(optarg) || (min_area = *mxGetPr(optarg)) < 0 || min_area > 1) { mexErrMsgTxt("'MinArea' must be in the range [0,1].") ; } break ; case opt_max_variation : if (!vlmxIsPlainScalar(optarg) || (max_variation = *mxGetPr(optarg)) < 0) { mexErrMsgTxt("'MaxVariation' must be non negative.") ; } break ; case opt_min_diversity : if (!vlmxIsPlainScalar(optarg) || (min_diversity = *mxGetPr(optarg)) < 0 || min_diversity > 1.0) { mexErrMsgTxt("'MinDiversity' must be in the [0,1] range.") ; } break ; case opt_bright_on_dark : if (!vlmxIsPlainScalar(optarg) || ((bright_on_dark = *mxGetPr(optarg)) != 0 && bright_on_dark != 1)) { mexErrMsgTxt("'BrightOnDark' must be in 0 or 1.") ; } break ; case opt_dark_on_bright : if (!vlmxIsPlainScalar(optarg) || ((dark_on_bright = *mxGetPr(optarg)) != 0 && dark_on_bright != 1)) { mexErrMsgTxt("'DarkOnBright' must be in 0 or 1.") ; } break ; default : abort() ; } } /* ----------------------------------------------------------------- * Run algorithm * -------------------------------------------------------------- */ /* new filter */ { int * vlDims = mxMalloc(sizeof(int) * ndims) ; for (i = 0 ; i < ndims ; ++i) vlDims [i] = dims [i] ; filt = vl_mser_new (ndims, vlDims) ; filtinv = vl_mser_new (ndims, vlDims) ; mxFree(vlDims) ; } if (!filt || !filtinv) { mexErrMsgTxt("Could not create an MSER filter.") ; } if (delta >= 0) vl_mser_set_delta (filt, (vl_mser_pix) delta) ; if (max_area >= 0) vl_mser_set_max_area (filt, max_area) ; if (min_area >= 0) vl_mser_set_min_area (filt, min_area) ; if (max_variation >= 0) vl_mser_set_max_variation (filt, max_variation) ; if (min_diversity >= 0) vl_mser_set_min_diversity (filt, min_diversity) ; if (delta >= 0) vl_mser_set_delta (filtinv, (vl_mser_pix) delta) ; if (max_area >= 0) vl_mser_set_max_area (filtinv, max_area) ; if (min_area >= 0) vl_mser_set_min_area (filtinv, min_area) ; if (max_variation >= 0) vl_mser_set_max_variation (filtinv, max_variation) ; if (min_diversity >= 0) vl_mser_set_min_diversity (filtinv, min_diversity) ; if (verbose) { mexPrintf("mser: parameters:\n") ; mexPrintf("mser: delta = %d\n", vl_mser_get_delta (filt)) ; mexPrintf("mser: max_area = %g\n", vl_mser_get_max_area (filt)) ; mexPrintf("mser: min_area = %g\n", vl_mser_get_min_area (filt)) ; mexPrintf("mser: max_variation = %g\n", vl_mser_get_max_variation (filt)) ; mexPrintf("mser: min_diversity = %g\n", vl_mser_get_min_diversity (filt)) ; } if (dark_on_bright) { /* process the image */ vl_mser_process (filt, data) ; /* save regions back to array */ nregions = vl_mser_get_regions_num (filt) ; regions = vl_mser_get_regions (filt) ; if (nout > 1) { vl_mser_ell_fit (filt) ; dof = vl_mser_get_ell_dof (filt) ; nframes = vl_mser_get_ell_num (filt) ; frames = vl_mser_get_ell (filt) ; } } if (bright_on_dark) { datainv = mxMalloc(sizeof(vl_uint)*nel) ; for(i=0; i 1) { vl_mser_ell_fit (filtinv) ; dof = vl_mser_get_ell_dof (filtinv) ; nframesinv = vl_mser_get_ell_num (filtinv) ; framesinv = vl_mser_get_ell (filtinv) ; } } odims [0] = nregions + nregionsinv ; out [OUT_SEEDS] = mxCreateNumericArray (1, odims, mxDOUBLE_CLASS,mxREAL) ; pt = mxGetPr (out [OUT_SEEDS]) ; for (i = 0 ; i < nregions ; ++i) pt [i] = (int)regions [i] + 1 ; for (i = nregions; i < nregions + nregionsinv; ++i) pt [i] = -((int)regionsinv [i-nregions] + 1) ; /* Inverted seed means dark on bright */ /* optionally compute and save ellipsoids */ if (nout > 1) { odims [0] = dof ; odims [1] = nframes + nframesinv; out [OUT_FRAMES] = mxCreateNumericArray (2, odims, mxDOUBLE_CLASS, mxREAL) ; pt = mxGetPr (out [OUT_FRAMES]) ; for (i = 0 ; i < nframes ; ++i) { for (j = 0 ; j < dof ; ++j) { pt [i * dof + j] = frames [i * dof + j] + ((j < ndims)?1.0:0.0) ; } } for (i = nframes ; i < nframes + nframesinv ; ++i) { for (j = 0 ; j < dof ; ++j) { pt [i * dof + j] = framesinv [(i-nframes) * dof + j] + ((j < ndims)?1.0:0.0) ; } } } if (verbose) { VlMserStats const* s = vl_mser_get_stats (filt) ; VlMserStats const* sinv = vl_mser_get_stats (filtinv) ; int tot = s-> num_extremal + sinv-> num_extremal ; mexPrintf("mser: statistics:\n") ; mexPrintf("mser: %d extremal regions of which\n", tot) ; #define REMAIN(test,num) \ mexPrintf("mser: %5d (%7.3g %% of previous) " test "\n", \ tot-(num),100.0*(double)(tot-(num))/(tot+VL_EPSILON_D)) ; \ tot -= (num) ; REMAIN("maximally stable,", s-> num_unstable + sinv-> num_unstable ) ; REMAIN("stable enough,", s-> num_abs_unstable + sinv-> num_abs_unstable ) ; REMAIN("small enough,", s-> num_too_big + sinv->num_too_big ) ; REMAIN("big enough,", s-> num_too_small + sinv->num_too_small ) ; REMAIN("diverse enough.", s-> num_duplicates + sinv->num_duplicates ) ; } /* cleanup */ if (datainv) mxFree(datainv); vl_mser_delete (filt) ; vl_mser_delete (filtinv) ; } vlfeat/toolbox/mser/vl_erfill.c0000644000175000017500000001302612237004736015531 0ustar dimadima/* file: erfill.mex.c ** description: Extremal Regions filling ** author: Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ /** @file ** @brief Maximally Stable Extremal Regions - MEX implementation **/ #include #include #include #include #include #include #define MIN(x,y) (((x)<(y))?(x):(y)) #define MAX(x,y) (((x)>(y))?(x):(y)) typedef char unsigned val_t ; typedef int idx_t ; typedef vl_uint64 acc_t ; /* advance N-dimensional subscript */ void adv(mwSize const* dims, int ndims, int* subs_pt) { int d = 0 ; while(d < ndims) { if( ++subs_pt[d] < (signed) dims[d] ) return ; subs_pt[d++] = 0 ; } } /* driver */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_I=0, IN_ER} ; enum {OUT_MEMBERS} ; idx_t i ; int k, nel, ndims ; mwSize const * dims ; val_t const * I_pt ; int last = 0 ; int last_expanded = 0 ; val_t value = 0 ; double const * er_pt ; int* subs_pt ; /* N-dimensional subscript */ int* nsubs_pt ; /* diff-subscript to point to neigh. */ idx_t* strides_pt ; /* strides to move in image array */ val_t* visited_pt ; /* flag */ idx_t* members_pt ; /* region members */ bool invert = VL_FALSE ; /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (nin != 2) { mexErrMsgTxt("Two arguments required.") ; } else if (nout > 4) { mexErrMsgTxt("Too many output arguments."); } if(mxGetClassID(in[IN_I]) != mxUINT8_CLASS) { mexErrMsgTxt("I must be of class UINT8.") ; } if(!vlmxIsPlainScalar(in[IN_ER])) { mexErrMsgTxt("ER must be a DOUBLE scalar.") ; } /* get dimensions */ nel = mxGetNumberOfElements(in[IN_I]) ; ndims = mxGetNumberOfDimensions(in[IN_I]) ; dims = mxGetDimensions(in[IN_I]) ; I_pt = mxGetData(in[IN_I]) ; /* allocate stuff */ subs_pt = mxMalloc( sizeof(int) * ndims ) ; nsubs_pt = mxMalloc( sizeof(int) * ndims ) ; strides_pt = mxMalloc( sizeof(idx_t) * ndims ) ; visited_pt = mxMalloc( sizeof(val_t) * nel ) ; members_pt = mxMalloc( sizeof(idx_t) * nel ) ; er_pt = mxGetPr(in[IN_ER]) ; /* compute strides to move into the N-dimensional image array */ strides_pt [0] = 1 ; for(k = 1 ; k < ndims ; ++k) { strides_pt [k] = strides_pt [k-1] * dims [k-1] ; } /* load first pixel */ memset(visited_pt, 0, sizeof(val_t) * nel) ; { idx_t idx = (idx_t) *er_pt ; if (idx < 0) { idx = -idx; invert = VL_TRUE ; } if( idx < 1 || idx > nel ) { char buff[80] ; snprintf(buff,80,"ER=%d out of range [1,%d]",idx,nel) ; mexErrMsgTxt(buff) ; } members_pt [last++] = idx - 1 ; } value = I_pt[ members_pt[0] ] ; /* ----------------------------------------------------------------- * Fill region * -------------------------------------------------------------- */ while(last_expanded < last) { /* pop next node xi */ idx_t index = members_pt[last_expanded++] ; /* convert index into a subscript sub; also initialize nsubs to (-1,-1,...,-1) */ { idx_t temp = index ; for(k = ndims-1 ; k >=0 ; --k) { nsubs_pt [k] = -1 ; subs_pt [k] = temp / strides_pt [k] ; temp = temp % strides_pt [k] ; } } /* process neighbors of xi */ while(VL_TRUE) { int good = VL_TRUE ; idx_t nindex = 0 ; /* compute NSUBS+SUB, the correspoinding neighbor index NINDEX and check that the pixel is within image boundaries. */ for(k = 0 ; k < ndims && good ; ++k) { int temp = nsubs_pt [k] + subs_pt [k] ; good &= 0 <= temp && temp < (signed) dims[k] ; nindex += temp * strides_pt [k] ; } /* process neighbor 1 - the pixel is within image boundaries; 2 - the pixel is indeed different from the current node (this happens when nsub=(0,0,...,0)); 3 - the pixel has value not greather than val is a pixel older than xi 4 - the pixel has not been visited yet */ if(good && nindex != index && ((!invert && I_pt [nindex] <= value) || ( invert && I_pt [nindex] >= value)) && ! visited_pt [nindex] ) { /* mark as visited */ visited_pt [nindex] = 1 ; /* add to list */ members_pt [last++] = nindex ; } /* move to next neighbor */ k = 0 ; while(++ nsubs_pt [k] > 1) { nsubs_pt [k++] = -1 ; if(k == ndims) goto done_all_neighbors ; } } /* next neighbor */ done_all_neighbors : ; } /* goto pop next member */ /* * Save results */ { mwSize dims[2] ; int unsigned * pt ; dims[0] = last ; out[OUT_MEMBERS] = mxCreateNumericArray(1,dims,mxUINT32_CLASS,mxREAL); pt = mxGetData(out[OUT_MEMBERS]) ; for (i = 0 ; i < last ; ++i) { *pt++ = members_pt[i] + 1 ; } } /* free stuff */ mxFree( members_pt ) ; mxFree( visited_pt ) ; mxFree( strides_pt ) ; mxFree( nsubs_pt ) ; mxFree( subs_pt ) ; } vlfeat/toolbox/mser/vl_erfill.m0000644000175000017500000000113412237004736015540 0ustar dimadima% VL_ERFILL Fill extremal region % MEMBERS=VL_ERFILL(I,ER) returns the list MEMBERS of the pixels which % belongs to the extremal region represented by the pixel ER. % % The selected region is the one that contains pixel ER and of % intensity I(ER). % % I must be of class UINT8 and ER must be a (scalar) index of the % region representative point. % % See also: VL_MSER(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/mexutils.h0000644000175000017500000006451112237004745014471 0ustar dimadima/** @file mexutils.h ** @brief MEX utilities ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #ifndef MEXUTILS_H #define MEXUTILS_H #include"mex.h" #include #include #include #include #include #include #include #ifdef VL_COMPILER_MSC #define snprintf _snprintf #define vsnprintf _vsnprintf #endif #if (! defined(MX_API_VER) || (MX_API_VER < 0x07030000)) && \ (! defined(HAVE_OCTAVE)) typedef vl_uint32 mwSize ; typedef vl_int32 mwIndex ; #endif /** @brief Access MEX input argument */ #undef IN #define IN(x) (in[IN_ ## x]) /** @brief Access MEX output argument */ #undef OUT #define OUT(x) (out[OUT_ ## x]) #ifdef HAVE_OCTAVE static void * mxReallocOctaveWorkaround(void * ptr, size_t size) { /* mexPrintf("fixed realloc\n") ; */ if (ptr) { return mxRealloc(ptr, size) ; } else { return mxMalloc(size) ; } } #define mxRealloc mxReallocOctaveWorkaround static int mxSetDimensionsOctaveWorkaround(mxArray * array, const mwSize *dims, int ndims) { mwSize * dims_ = mxMalloc(sizeof(mwSize)*ndims) ; int i ; for (i = 0 ; i < ndims ; ++i) dims_[i] = dims[i] ; mxSetDimensions(array,dims_,ndims) ; return 0 ; } #define mxSetDimensions mxSetDimensionsOctaveWorkaround #endif /** @brief Setup VLFeat to be used in a MEX file ** ** This makes VLFeat use MATLAB version of the memory allocation and ** logging functions. **/ #define VL_USE_MATLAB_ENV \ vl_set_alloc_func (mxMalloc, mxRealloc, mxCalloc, mxFree) ; \ vl_set_printf_func ((printf_func_t)mexPrintf) ; /** @file mexutils.h This header file provides helper functions for writing MATLAB MEX files. - @ref mexutils-env "VLFeat environment" - @ref mexutils-array-test "Array tests" - @ref mexutils-options "Parsing options" @section mexutils-env VLFeat environment When the VLFeat DLL is linked to a MATLAB MEX files, at run time the MEX file must configure VLFeat to use MATLAB memory allocation and logging functions. This can be obtained by calling the macro ::VL_USE_MATLAB_ENV as the first line of each MEX file which is linked to the VLFeat DLL. @section mexutils-array-test Array tests MATLAB supports a variety of array types. Most MEX file arguments are restricted to a few types and must be properly checked at run time. @ref mexutils.h provides some helper functions to make it simpler to check such arguments. MATLAB basic array types are: - Numeric array: @c mxDOUBLE_CLASS, @c mxSINGLE_CLASS, @c mxINT8_CLASS, @c mxUINT8_CLASS, @c mxINT16_CLASS, @c mxUINT16_CLASS, @c mxINT32_CLASS, @c mxUINT32_CLASS. Moreover: - all such types have a @e real component - all such types may have a @e imaginary component - @c mxDOUBLE_LCASS arrays with two dimensions can be @e sparse. - Logical array (@c mxLOGICAL_CLASS). - Character array (@c mxCHAR_CLASS). The other MATLAB array types are: - Struct array (@c mxSTRUCT_CLASS). - Cell array (@c mxCELL_CLASS). - Custom class array (@c mxCLASS_CLASS). - Unkown type array (@c mxUNKNOWN_CLASS). VLFeat defines a number of common classes of arrays and corresponding tests. - Scalar array is a non-sparse array with exactly one element. Note that the array may have an arbitrary number of dimensions, and be of any numeric or other type. All dimensions are singleton (which is implied by having exactly one element). Use ::vlmxIsScalar to test if an array is scalar. - Vector array is a non-sparse array which is either empty (empty vector) or has at most one non-singleton dimension. The array can be of any numeric or other type. The elements of such a MATLAB array are stored as a plain C array with a number of elements equal to the number of elements in the array (obtained with @c mxGetNumberOfElements). Use ::vlmxIsVector to test whether an array is a vector. - Matrix array is a non-sparse array for which all dimensions beyond the first two are singleton, or a non-sparse array which is empty and for which at least one of the first two dimensions is zero. The array can be of any numeric or other type. The non-singleton dimensions can be zero (empty matrix), one, or more. The element of such a MATLAB array are stored as a C array in column major order and its dimensions can be obtained by @c mxGetM and @c mxGetN. Use ::vlmxIsMatrix to test if an array is a matrix. - Real array is a numeric array (as for @c mxIsNumeric) without a complex component. Use ::vlmxIsReal to check if an array is real. - Use ::vlmxIsOfClass to check if an array is of a prescribed (storage) class, such as @c mxDOUBLE_CLASS. - Plain scalar, vector, and matrix are a scalar, vector, and matrix arrays which are real and of class @c mxDOUBLE_CLASS. Use ::vlmxIsPlainScalar, ::vlmxIsPlainVector and ::vlmxIsPlainMatrix to check this. @section mexutils-options Parsing options It is common to pass optional arguments to a MEX file as option type-value pairs. Here type is a string identifying the option and value is a MATLAB array specifing its value. The function ::vlmxNextOption can be used to simplify parsing a list of such arguments (similar to UNIX @c getopt). The functions ::vlmxError and ::vlmxWarning are shortcuts to specify VLFeat formatted errors. **/ /* these attributes suppress undefined symbols warning with GCC */ #ifdef VL_COMPILER_GNUC #if (! defined(HAVE_OCTAVE)) EXTERN_C void __attribute__((noreturn)) mexErrMsgIdAndTxt (const char * identifier, const char * err_msg, ...) ; #else extern void __attribute__((noreturn)) mexErrMsgIdAndTxt (const char *id, const char *s, ...); #endif #endif #define MEXUTILS_RAISE_HELPER_A \ char const * errorString ; \ char formattedErrorId [512] ; \ char formattedErrorMessage [1024] ; \ \ switch (errorId) { \ case vlmxErrAlloc : errorString = "outOfMemory" ; break ; \ case vlmxErrInvalidArgument : errorString = "invalidArgument" ; break ; \ case vlmxErrNotEnoughInputArguments : errorString = "notEnoughInputArguments" ; break ; \ case vlmxErrTooManyInputArguments : errorString = "tooManyInputArguments" ; break ; \ case vlmxErrNotEnoughOutputArguments : errorString = "notEnoughOutputArguments" ; break ; \ case vlmxErrTooManyOutputArguments : errorString = "tooManyOutputArguments" ; break ; \ case vlmxErrInvalidOption : errorString = "invalidOption" ; break ; \ case vlmxErrInconsistentData : errorString = "inconsistentData" ; break ; \ default : errorString = "undefinedError" ; break ; \ } \ \ if (! errorMessage) { \ switch (errorId) { \ case vlmxErrAlloc: errorMessage = "Out of memory." ; break ; \ case vlmxErrInvalidArgument: errorMessage = "Invalid argument." ; break ; \ case vlmxErrNotEnoughInputArguments: errorMessage = "Not enough input arguments." ; break ; \ case vlmxErrTooManyInputArguments: errorMessage = "Too many input arguments." ; break ; \ case vlmxErrNotEnoughOutputArguments: errorMessage = "Not enough output arguments." ; break ; \ case vlmxErrTooManyOutputArguments: errorMessage = "Too many output arguments." ; break ; \ case vlmxErrInconsistentData: errorMessage = "Inconsistent data." ; break ; \ case vlmxErrInvalidOption: errorMessage = "Invalid option." ; break ; \ default: errorMessage = "Undefined error message." ; \ } \ } #ifdef VL_COMPILER_LCC #define MEXUTILS_RAISE_HELPER_B \ { \ va_list args ; \ va_start(args, errorMessage) ; \ sprintf(formattedErrorId, \ "vl:%s", errorString) ; \ vsprintf(formattedErrorMessage, \ errorMessage, args) ; \ va_end(args) ; \ } #else #define MEXUTILS_RAISE_HELPER_B \ { \ va_list args ; \ va_start(args, errorMessage) ; \ snprintf(formattedErrorId, \ sizeof(formattedErrorId)/sizeof(char), \ "vl:%s", errorString) ; \ vsnprintf(formattedErrorMessage, \ sizeof(formattedErrorMessage)/sizeof(char), \ errorMessage, args) ; \ va_end(args) ; \ } #endif #define MEXUTILS_RAISE_HELPER MEXUTILS_RAISE_HELPER_A MEXUTILS_RAISE_HELPER_B /** @{ ** @name Error handling **/ /** @brief VLFeat MEX errors */ typedef enum _VlmxErrorId { vlmxErrAlloc = 1, vlmxErrInvalidArgument, vlmxErrNotEnoughInputArguments, vlmxErrTooManyInputArguments, vlmxErrNotEnoughOutputArguments, vlmxErrTooManyOutputArguments, vlmxErrInvalidOption, vlmxErrInconsistentData } VlmxErrorId ; /** @brief Raise a MEX error with VLFeat format ** @param errorId error ID string. ** @param errorMessage error message C-style format string. ** @param ... format string arguments. ** ** The function internally calls @c mxErrMsgTxtAndId, which causes ** the MEX file to abort. **/ #if defined(VL_COMPILER_GNUC) & ! defined(__DOXYGEN__) static void __attribute__((noreturn)) #else static void #endif vlmxError (VlmxErrorId errorId, char const * errorMessage, ...) { MEXUTILS_RAISE_HELPER ; mexErrMsgIdAndTxt (formattedErrorId, formattedErrorMessage) ; } /** @brief Raise a MEX warning with VLFeat format ** @param errorId error ID string. ** @param errorMessage error message C-style format string. ** @param ... format string arguments. ** ** The function internally calls @c mxWarnMsgTxtAndId. **/ static void vlmxWarning (VlmxErrorId errorId, char const * errorMessage, ...) { MEXUTILS_RAISE_HELPER ; mexWarnMsgIdAndTxt (formattedErrorId, formattedErrorMessage) ; } /** @} */ /** @name Check for array attributes ** @{ */ /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is of a prescribed class ** @param array MATLAB array. ** @param classId prescribed class of the array. ** @return ::VL_TRUE if the class is of the array is of the prescribed class. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsOfClass (mxArray const * array, mxClassID classId) { return mxGetClassID (array) == classId ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is real ** @param array MATLAB array. ** @return ::VL_TRUE if the array is real. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsReal (mxArray const * array) { return mxIsNumeric (array) && ! mxIsComplex (array) ; } /** @} */ /** @name Check for scalar, vector and matrix arrays ** @{ */ /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is scalar ** @param array MATLAB array. ** @return ::VL_TRUE if the array is scalar. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsScalar (mxArray const * array) { return (! mxIsSparse (array)) && (mxGetNumberOfElements (array) == 1) ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is a vector. ** @param array MATLAB array. ** @param numElements number of elements (negative for any). ** @return ::VL_TRUE if the array is a vecotr of the prescribed size. ** @sa @ref mexutils-array-test **/ static vl_bool vlmxIsVector (mxArray const * array, vl_index numElements) { vl_size numDimensions = (unsigned) mxGetNumberOfDimensions (array) ; mwSize const * dimensions = mxGetDimensions (array) ; vl_uindex di ; /* check that it is not sparse */ if (mxIsSparse (array)) { return VL_FALSE ; } /* check that the number of elements is the prescribed one */ if ((numElements >= 0) && ((unsigned) mxGetNumberOfElements (array) != (unsigned) numElements)) { return VL_FALSE ; } /* check that all but at most one dimension is singleton */ for (di = 0 ; di < numDimensions ; ++ di) { if (dimensions[di] != 1) break ; } for (++ di ; di < numDimensions ; ++di) { if (dimensions[di] != 1) return VL_FALSE ; } return VL_TRUE ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is a matrix. ** @param array MATLAB array. ** @param M number of rows (negative for any). ** @param N number of columns (negative for any). ** @return ::VL_TRUE if the array is a matrix of the prescribed size. ** @sa @ref mexutils-array-test **/ static vl_bool vlmxIsMatrix (mxArray const * array, vl_index M, vl_index N) { vl_size numDimensions = (unsigned) mxGetNumberOfDimensions (array) ; mwSize const * dimensions = mxGetDimensions (array) ; vl_uindex di ; /* check that it is not sparse */ if (mxIsSparse (array)) { return VL_FALSE ; } /* check that the number of elements is the prescribed one */ if ((M >= 0) && ((unsigned) mxGetM (array) != (unsigned) M)) { return VL_FALSE; } if ((N >= 0) && ((unsigned) mxGetN (array) != (unsigned) N)) { return VL_FALSE; } /* ok if empty and either M = 0 or N = 0 */ if ((mxGetNumberOfElements (array) == 0) && (mxGetM (array) == 0 || mxGetN (array) == 0)) { return VL_TRUE ; } /* ok if any dimension beyond the first two is singleton */ for (di = 2 ; ((unsigned)dimensions[di] == 1) && di < numDimensions ; ++ di) ; return di == numDimensions ; } /** ------------------------------------------------------------------ ** @brief Check if the MATLAB array has the specified dimensions. ** @param array array to check. ** @param numDimensions number of dimensions. ** @param dimensions dimensions. ** @return true the test succeeds. ** ** The test is true if @a numDimensions < 0. If not, it is false if ** the array has not @a numDimensions. Otherwise it is true is @a ** dimensions is @c NULL or if each entry of @a dimensions is ** either negative or equal to the corresponding array dimension. **/ static vl_bool vlmxIsArray (mxArray const * array, vl_index numDimensions, vl_index* dimensions) { if (numDimensions >= 0) { vl_index d ; mwSize const * actualDimensions = mxGetDimensions (array) ; if ((unsigned) mxGetNumberOfDimensions (array) != (unsigned) numDimensions) { return VL_FALSE ; } if(dimensions != NULL) { for(d = 0 ; d < numDimensions ; ++d) { if (dimensions[d] >= 0 && (unsigned) dimensions[d] != (unsigned) actualDimensions[d]) return VL_FALSE ; } } } return VL_TRUE ; } /** @} */ /** @name Check for plain arrays ** @{ */ /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is plain ** @param array MATLAB array. ** @return ::VL_TRUE if the array is plain. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsPlain (mxArray const * array) { return vlmxIsReal (array) && vlmxIsOfClass (array, mxDOUBLE_CLASS) ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is plain scalar ** @param array MATLAB array. ** @return ::VL_TRUE if the array is plain scalar. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsPlainScalar (mxArray const * array) { return vlmxIsPlain (array) && vlmxIsScalar (array) ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is a plain vector. ** @param array MATLAB array. ** @param numElements number of elements (negative for any). ** @return ::VL_TRUE if the array is a plain vecotr of the prescribed size. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsPlainVector (mxArray const * array, vl_index numElements) { return vlmxIsPlain (array) && vlmxIsVector (array, numElements) ; } /** ------------------------------------------------------------------ ** @brief Check if a MATLAB array is a plain matrix. ** @param array MATLAB array. ** @param M number of rows (negative for any). ** @param N number of columns (negative for any). ** @return ::VL_TRUE if the array is a plain matrix of the prescribed size. ** @sa @ref mexutils-array-test **/ VL_INLINE vl_bool vlmxIsPlainMatrix (mxArray const * array, vl_index M, vl_index N) { return vlmxIsPlain (array) && vlmxIsMatrix (array, M, N) ; } /** ------------------------------------------------------------------ ** @brief Check if the array is a string ** @param array array to test. ** @param length string length. ** @return true if the array is a string of the specified length ** ** The array @a array satisfies the test if: ** - its storage class is CHAR; ** - it has two dimensions but only one row; ** - @a length < 0 or the array has @a length columns. **/ static int vlmxIsString (const mxArray* array, vl_index length) { mwSize M = (mwSize) mxGetM (array) ; mwSize N = (mwSize) mxGetN (array) ; return mxIsChar(array) && mxGetNumberOfDimensions(array) == 2 && (M == 1 || (M == 0 && N == 0)) && (length < 0 || (signed)N == length) ; } /** @} */ /** ------------------------------------------------------------------ ** @brief Create a MATLAB array which is a plain scalar ** @param x scalar value. ** @return the new array. **/ static mxArray * vlmxCreatePlainScalar (double x) { mxArray * array = mxCreateDoubleMatrix (1,1,mxREAL) ; *mxGetPr(array) = x ; return array ; } /** ------------------------------------------------------------------ ** @brief Create a MATLAB array from a VlArray ** @param x VlArray instance. ** @return the new array. **/ static mxArray * vlmxCreateArrayFromVlArray (VlArray const * x) { mwSize dimensions [VL_ARRAY_MAX_NUM_DIMENSIONS] ; mxArray * array = NULL ; mxClassID classId = (mxClassID)0 ; vl_uindex d ; vl_size numElements = vl_array_get_num_elements(x) ; vl_size numDimensions = vl_array_get_num_dimensions(x) ; vl_size const * xdimensions = vl_array_get_dimensions(x) ; vl_type type = vl_array_get_data_type(x) ; vl_size typeSize = vl_get_type_size(type) ; for (d = 0 ; d < numDimensions ; ++d) { dimensions[d] = (mwSize) xdimensions[d] ; } switch (type) { case VL_TYPE_FLOAT : classId = mxSINGLE_CLASS ; break ; case VL_TYPE_DOUBLE : classId = mxDOUBLE_CLASS ; break ; case VL_TYPE_INT8 : classId = mxINT8_CLASS ; break ; case VL_TYPE_INT16 : classId = mxINT16_CLASS ; break ; case VL_TYPE_INT32 : classId = mxINT32_CLASS ; break ; case VL_TYPE_INT64 : classId = mxINT64_CLASS ; break ; case VL_TYPE_UINT8 : classId = mxUINT8_CLASS ; break ; case VL_TYPE_UINT16 : classId = mxUINT16_CLASS ; break ; case VL_TYPE_UINT32 : classId = mxUINT32_CLASS ; break ; case VL_TYPE_UINT64 : classId = mxUINT64_CLASS ; break ; default: assert(VL_FALSE) ; } array = mxCreateNumericArray(numDimensions, dimensions, classId, mxREAL) ; if (array == NULL) return NULL ; memcpy(mxGetData(array), vl_array_get_data(x), typeSize * numElements) ; return array ; } /** ------------------------------------------------------------------ ** @brief Envelope a MATLAB array in a VlArray instance ** @param v VlArray instance (out) ** @param x MATALB array. ** @return @c v. **/ static VlArray * vlmxEnvelopeArrayInVlArray (VlArray * v, mxArray * x) { vl_size numDimensions = mxGetNumberOfDimensions(x) ; mwSize const * dimensions = mxGetDimensions(x) ; mxClassID classId = mxGetClassID(x) ; vl_size vdimensions [VL_ARRAY_MAX_NUM_DIMENSIONS] ; vl_type type ; vl_uindex d ; for (d = 0 ; d < numDimensions ; ++d) { vdimensions[d] = dimensions[d] ; } switch (classId) { case mxSINGLE_CLASS: type = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS: type = VL_TYPE_DOUBLE ; break ; case mxINT8_CLASS : type = VL_TYPE_INT8 ; break ; case mxINT16_CLASS : type = VL_TYPE_INT16 ; break ; case mxINT32_CLASS : type = VL_TYPE_INT32 ; break ; case mxINT64_CLASS : type = VL_TYPE_INT64 ; break ; case mxUINT8_CLASS : type = VL_TYPE_UINT8 ; break ; case mxUINT16_CLASS: type = VL_TYPE_UINT16 ; break ; case mxUINT32_CLASS: type = VL_TYPE_UINT32 ; break ; case mxUINT64_CLASS: type = VL_TYPE_UINT64 ; break ; default: assert(VL_FALSE) ; } vl_array_init_envelope(v, mxGetData(x), type, numDimensions, vdimensions) ; return v ; } /** ------------------------------------------------------------------ ** @brief Case insensitive string comparison ** @param s1 first string. ** @param s2 second string. ** @return comparison result. ** ** The comparison result is equal to 0 if the strings are equal, >0 ** if the first string is greater than the second (in lexicographical ** order), and <0 otherwise. **/ static int vlmxCompareStringsI(const char *s1, const char *s2) { /* Since tolower has an int argument, characters must be unsigned otherwise will be sign-extended when converted to int. */ while (tolower((unsigned char)*s1) == tolower((unsigned char)*s2)) { if (*s1 == 0) return 0 ; /* implies *s2 == 0 */ s1++; s2++; } return tolower((unsigned char)*s1) - tolower((unsigned char)*s2) ; } /** ------------------------------------------------------------------ ** @brief Case insensitive string comparison with array ** @param array first string (as a MATLAB array). ** @param string second string. ** @return comparison result. ** ** The comparison result is equal to 0 if the strings are equal, >0 ** if the first string is greater than the second (in lexicographical ** order), and <0 otherwise. **/ static int vlmxCompareToStringI(mxArray const * array, char const * string) { mxChar const * s1 = (mxChar const *) mxGetData(array) ; char unsigned const * s2 = (char unsigned const*) string ; vl_size n = mxGetNumberOfElements(array) ; /* Since tolower has an int argument, characters must be unsigned otherwise will be sign-extended when converted to int. */ while (n && tolower((unsigned)*s1) == tolower(*s2)) { if (*s2 == 0) return 1 ; /* s2 terminated on 0, but s1 did not terminate yet */ s1 ++ ; s2 ++ ; n -- ; } return tolower(n ? (unsigned)*s1 : 0) - tolower(*s2) ; } /** ------------------------------------------------------------------ ** @brief Case insensitive string equality test with array ** @param array first string (as a MATLAB array). ** @param string second string. ** @return true if the strings are equal. **/ static int vlmxIsEqualToStringI(mxArray const * array, char const * string) { return vlmxCompareToStringI(array, string) == 0 ; } /* ---------------------------------------------------------------- */ /* Options handling */ /* ---------------------------------------------------------------- */ /** @brief MEX option */ struct _vlmxOption { const char *name ; /**< option name */ int has_arg ; /**< has argument? */ int val ; /**< value to return */ } ; /** @brief MEX option type */ typedef struct _vlmxOption vlmxOption ; /** ------------------------------------------------------------------ ** @brief Parse the next option ** @param args MEX argument array. ** @param nargs MEX argument array length. ** @param options List of option definitions. ** @param next Pointer to the next option (input and output). ** @param optarg Pointer to the option optional argument (output). ** @return the code of the next option, or -1 if there are no more options. ** ** The function parses the array @a args for options. @a args is ** expected to be a sequence alternating option names and option ** values, in the form of @a nargs instances of @c mxArray. The ** function then scans the option starting at position @a next in the ** array. The option name is matched (case insensitive) to the table ** of options @a options, a pointer to the option value is stored in ** @a optarg, @a next is advanced to the next option, and the option ** code is returned. ** ** The function is typically used in a loop to parse all the available ** options. @a next is initialized to zero, and then the function ** is called until the special code -1 is returned. ** ** If the option name cannot be matched to the available options, ** either because the option name is not a string array or because ** the name is unknown, the function exits the MEX file with an ** error. **/ static int vlmxNextOption (mxArray const *args[], int nargs, vlmxOption const *options, int *next, mxArray const **optarg) { char name [1024] ; int opt = -1, i; if (*next >= nargs) { return opt ; } /* check the array is a string */ if (! vlmxIsString (args [*next], -1)) { vlmxError (vlmxErrInvalidOption, "The option name is not a string (argument number %d)", *next + 1) ; } /* retrieve option name */ if (mxGetString (args [*next], name, sizeof(name))) { vlmxError (vlmxErrInvalidOption, "The option name is too long (argument number %d)", *next + 1) ; } /* advance argument list */ ++ (*next) ; /* now lookup the string in the option table */ for (i = 0 ; options[i].name != 0 ; ++i) { if (vlmxCompareStringsI(name, options[i].name) == 0) { opt = options[i].val ; break ; } } /* unknown argument */ if (opt < 0) { vlmxError (vlmxErrInvalidOption, "Unknown option '%s'.", name) ; } /* no argument */ if (! options [i].has_arg) { if (optarg) *optarg = 0 ; return opt ; } /* argument */ if (*next >= nargs) { vlmxError(vlmxErrInvalidOption, "Option '%s' requires an argument.", options[i].name) ; } if (optarg) *optarg = args [*next] ; ++ (*next) ; return opt ; } /** @brief Get an emumeration member by name ** @param enumeration the enumeration to decode. ** @param name_array member name as a MATLAB string array. ** @param caseInsensitive if @c true match the string case-insensitive. ** @return the corresponding enumeration member, or @c NULL if any. **/ static VlEnumerator * vlmxDecodeEnumeration (mxArray const *name_array, VlEnumerator const *enumeration, vl_bool caseInsensitive) { char name [1024] ; /* check the array is a string */ if (! vlmxIsString (name_array, -1)) { vlmxError (vlmxErrInvalidArgument, "The array is not a string.") ; } /* retrieve option name */ if (mxGetString (name_array, name, sizeof(name))) { vlmxError (vlmxErrInvalidArgument, "The string array is too long.") ; } if (caseInsensitive) { return vl_enumeration_get_casei(enumeration, name) ; } else { return vl_enumeration_get(enumeration, name) ; } } /* MEXUTILS_H */ #endif vlfeat/toolbox/vlad/0000755000175000017500000000000012237042170013357 5ustar dimadimavlfeat/toolbox/vlad/vl_vlad.m0000644000175000017500000000357712237004745015206 0ustar dimadima% VL_VLAD VLAD feature encoding % ENC = VL_VLAD(X, MEANS, ASSIGNMENTS) computes the VLAD % encoding of the vectors X relative to cluster centers MEANS and % vector-to-cluster soft assignments ASSIGNMENTS. % % X has one column per data vector (e.g. a SIFT descriptor), and % MEANS has one column per component. Usually one has one component % per KMeans cluster and MEANS are the KMeans centers. X and MEANS % have the same number of rows and the data class, which can be % either SINGLE or DOUBLE. % % ASSIGNMENTS has as many rows as clusters and as many columns as % X. Its columns are non-negative and should sum to one, % representing the soft assignment of the corresponding vector in X % to each of the clusters. It is of the same class as X. % % ENC is a vector of the same class of X of size equal to the % product of the data dimension and the number of clusters. % % By default, ENC is L2 normalized. VL_VLAD() accepts the following % options: % % Unnormalized:: % If specified, no overall normalization is applied to ENC. % % NormalizeComponents:: % If specified, the part of the encoding corresponding to each % cluster is individually normalized. % % NormalizeMass:: % If specified, each component is re-normalized by the mass % of data vectors assigned to it. If NormalizedComponents is % also selected, this has no effect. % % SquareRoot:: % If specified, the signed square root function is applied to % ENC before normalization. % % Verbose:: % Increase the verbosity level (may be specified multiple times). % % See: VLAD, VL_HELP(). % Authors: David Novotny and Andrea Vedaldi % Copyright (C) 2013 David Novotny and Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/vlad/vl_vlad.c0000644000175000017500000001150212237004745015157 0ustar dimadima/** @file vl_vlad.c ** @brief vl_vlad MEX definition. ** @author David Novotny **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include enum { opt_verbose, opt_normalize_components, opt_unnormalized, opt_square_root, opt_normalize_mass } ; vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"Unnormalized", 0, opt_unnormalized }, {"NormalizeComponents", 0, opt_normalize_components }, {"NormalizeMass", 0, opt_normalize_mass }, {"SquareRoot", 0, opt_square_root } } ; /* driver */ void mexFunction (int nout VL_UNUSED, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_DATA = 0, IN_MEANS, IN_ASSIGNMENTS, IN_END} ; enum {OUT_ENC} ; int opt ; int next = IN_END ; mxArray const *optarg ; mxArray const * means_array = in[IN_MEANS] ; mxArray const * data_array = in[IN_DATA] ; mxArray const * assign_array = in[IN_ASSIGNMENTS] ; vl_size numClusters ; vl_size dimension ; vl_size numData ; int flags = 0 ; void const * means = NULL; void const * assignments = NULL; void const * data = NULL ; int verbosity = 0 ; vl_type dataType ; mxClassID classID ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 3) { vlmxError (vlmxErrInvalidArgument, "At least three arguments required."); } if (nout > 1) { vlmxError (vlmxErrInvalidArgument, "At most one output argument."); } if (!vlmxIsMatrix(IN(DATA),-1,-1)) { vlmxError (vlmxErrInvalidArgument, "DATA is not a dense matrix.") ; } classID = mxGetClassID (IN(DATA)) ; switch (classID) { case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError (vlmxErrInvalidArgument, "DATA is neither of class SINGLE or DOUBLE.") ; } if (mxGetClassID (IN(MEANS)) != classID) { vlmxError(vlmxErrInvalidArgument, "MEANS is not of the same class as DATA.") ; } if (mxGetClassID (IN(ASSIGNMENTS)) != classID) { vlmxError(vlmxErrInvalidArgument, "ASSIGNMENTS is not of the same class as DATA.") ; } dimension = mxGetM (IN(DATA)) ; numData = mxGetN (IN(DATA)) ; numClusters = mxGetN (IN(MEANS)) ; if (dimension == 0) { vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero.") ; } if (!vlmxIsMatrix(IN(MEANS), dimension, -1)) { vlmxError (vlmxErrInvalidArgument, "MEANS is not a matrix or does not have the right size.") ; } if (!vlmxIsMatrix(IN(ASSIGNMENTS), numClusters, -1)) { vlmxError (vlmxErrInvalidArgument, "ASSIGNMENTS is not a matrix or does not have the right size.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbosity ; break ; case opt_unnormalized: flags |= VL_VLAD_FLAG_UNNORMALIZED ; break ; case opt_normalize_components: flags |= VL_VLAD_FLAG_NORMALIZE_COMPONENTS ; break ; case opt_normalize_mass: flags |= VL_VLAD_FLAG_NORMALIZE_MASS ; break ; case opt_square_root: flags |= VL_VLAD_FLAG_SQUARE_ROOT ; break ; default : abort() ; break ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ data = mxGetPr(data_array); means = mxGetPr(means_array); assignments = mxGetData(assign_array); if (verbosity) { mexPrintf("vl_vlad: num data: %d\n", numData) ; mexPrintf("vl_vlad: num clusters: %d\n", numClusters) ; mexPrintf("vl_vlad: data dimension: %d\n", dimension) ; mexPrintf("vl_vlad: code dimension: %d\n", numClusters * dimension) ; mexPrintf("vl_vlad: unnormalized: %s\n", VL_YESNO(flags & VL_VLAD_FLAG_UNNORMALIZED)) ; mexPrintf("vl_vlad: normalize mass: %s\n", VL_YESNO(flags & VL_VLAD_FLAG_NORMALIZE_MASS)) ; mexPrintf("vl_vlad: normalize components: %s\n", VL_YESNO(flags & VL_VLAD_FLAG_NORMALIZE_COMPONENTS)) ; mexPrintf("vl_vlad: square root: %s\n", VL_YESNO(flags & VL_VLAD_FLAG_SQUARE_ROOT)) ; } OUT(ENC) = mxCreateNumericMatrix (dimension * numClusters, 1, classID, mxREAL) ; vl_vlad_encode (mxGetPr(OUT(ENC)), dataType, means, dimension, numClusters, data, numData, assignments, flags) ; } vlfeat/toolbox/vl_compile.m0000644000175000017500000001170412237004736014751 0ustar dimadimafunction vl_compile(compiler) % VL_COMPILE Compile VLFeat MEX files % VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command % works only under Windows and is used to re-build problematic % binaries. The preferred method of compiling VLFeat on both UNIX % and Windows is through the provided Makefiles. % % VL_COMPILE() only compiles the MEX files and assumes that the % VLFeat DLL (i.e. the file VLFEATROOT/bin/win{32,64}/vl.dll) has % already been built. This file is built by the Makefiles. % % By default VL_COMPILE() assumes that Visual C++ is the active % MATLAB compiler. VL_COMPILE('lcc') assumes that the active % compiler is LCC instead (see MEX -SETUP). Unfortunately LCC does % not seem to be able to compile the latest versions of VLFeat due % to bugs in the support of 64-bit integers. Therefore it is % recommended to use Visual C++ instead. % % See also: VL_NOPREFIX(), VL_HELP(). % Authors: Andrea Vedadli, Jonghyun Choi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 1, compiler = 'visualc' ; end switch lower(compiler) case 'visualc' fprintf('%s: assuming that Visual C++ is the active compiler\n', mfilename) ; useLcc = false ; case 'lcc' fprintf('%s: assuming that LCC is the active compiler\n', mfilename) ; warning('LCC may fail to compile VLFeat. See help vl_compile.') ; useLcc = true ; otherwise error('Unknown compiler ''%s''.', compiler) end vlDir = vl_root ; toolboxDir = fullfile(vlDir, 'toolbox') ; switch computer case 'PCWIN' fprintf('%s: compiling for PCWIN (32 bit)\n', mfilename); mexwDir = fullfile(toolboxDir, 'mex', 'mexw32') ; binwDir = fullfile(vlDir, 'bin', 'win32') ; case 'PCWIN64' fprintf('%s: compiling for PCWIN64 (64 bit)\n', mfilename); mexwDir = fullfile(toolboxDir, 'mex', 'mexw64') ; binwDir = fullfile(vlDir, 'bin', 'win64') ; otherwise error('The architecture is neither PCWIN nor PCWIN64. See help vl_compile.') ; end impLibPath = fullfile(binwDir, 'vl.lib') ; libDir = fullfile(binwDir, 'vl.dll') ; mkd(mexwDir) ; % find the subdirectories of toolbox that we should process subDirs = dir(toolboxDir) ; subDirs = subDirs([subDirs.isdir]) ; discard = regexp({subDirs.name}, '^(.|..|noprefix|mex.*)$', 'start') ; keep = cellfun('isempty', discard) ; subDirs = subDirs(keep) ; subDirs = {subDirs.name} ; % Copy support files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if ~exist(fullfile(binwDir, 'vl.dll')) error('The VLFeat DLL (%s) could not be found. See help vl_compile.', ... fullfile(binwDir, 'vl.dll')) ; end tmp = dir(fullfile(binwDir, '*.dll')) ; supportFileNames = {tmp.name} ; for fi = 1:length(supportFileNames) name = supportFileNames{fi} ; cp(fullfile(binwDir, name), ... fullfile(mexwDir, name) ) ; end % Ensure implib for LCC ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if useLcc lccImpLibDir = fullfile(mexwDir, 'lcc') ; lccImpLibPath = fullfile(lccImpLibDir, 'VL.lib') ; lccRoot = fullfile(matlabroot, 'sys', 'lcc', 'bin') ; lccImpExePath = fullfile(lccRoot, 'lcc_implib.exe') ; mkd(lccImpLibDir) ; cp(fullfile(binwDir, 'vl.dll'), fullfile(lccImpLibDir, 'vl.dll')) ; cmd = ['"' lccImpExePath '"', ' -u ', '"' fullfile(lccImpLibDir, 'vl.dll') '"'] ; fprintf('Running:\n> %s\n', cmd) ; curPath = pwd ; try cd(lccImpLibDir) ; [d,w] = system(cmd) ; if d, error(w); end cd(curPath) ; catch cd(curPath) ; error(lasterr) ; end end % Compile each mex file ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ for i = 1:length(subDirs) thisDir = fullfile(toolboxDir, subDirs{i}) ; fileNames = ls(fullfile(thisDir, '*.c')); for f = 1:size(fileNames,1) fileName = fileNames(f, :) ; sp = strfind(fileName, ' '); if length(sp) > 0, fileName = fileName(1:sp-1); end filePath = fullfile(thisDir, fileName); fprintf('MEX %s\n', filePath); dot = strfind(fileName, '.'); mexFile = fullfile(mexwDir, [fileName(1:dot) 'dll']); if exist(mexFile) delete(mexFile) end cmd = {['-I' toolboxDir], ... ['-I' vlDir], ... '-O', ... '-outdir', mexwDir, ... filePath } ; if useLcc cmd{end+1} = lccImpLibPath ; else cmd{end+1} = impLibPath ; end mex(cmd{:}) ; end end % -------------------------------------------------------------------- function cp(src,dst) % -------------------------------------------------------------------- if ~exist(dst,'file') fprintf('Copying ''%s'' to ''%s''.\n', src,dst) ; copyfile(src,dst) ; end % -------------------------------------------------------------------- function mkd(dst) % -------------------------------------------------------------------- if ~exist(dst, 'dir') fprintf('Creating directory ''%s''.', dst) ; mkdir(dst) ; end vlfeat/toolbox/info.xml0000644000175000017500000000066512237004736014123 0ustar dimadima 2012a VLFeat toolbox ../doc/images/vl_blue.png ../doc/mdoc/ ../doc/images/vl_blue.png vlfeat/toolbox/misc/0000755000175000017500000000000012237042170013364 5ustar dimadimavlfeat/toolbox/misc/inthist.tc0000644000175000017500000000230612237004736015405 0ustar dimadima/** @file inthist.tc ** @brief vl_intist.c helper ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #define JOIN_(a,b) a ## b #define JOIN(a,b) JOIN_(a,b) #undef INTEGRAL #define INTEGRAL JOIN(integral_, SFX) /* This function is 'transpose safe': The integral histogram SRC can be either column major or row major, as long as WIDTH is the fastest varying dimension. */ void INTEGRAL (T* dst, int dstStride, T const* src, int srcWidth, int srcHeight, int srcStride) { int x, y ; *dst = *src ; dst += 1 ; src += 1 ; for (x = 1 ; x < srcWidth ; ++ x) { *dst = *(dst - 1) + *src ; dst += 1 ; src += 1 ; } for (y = 1 ; y < srcHeight ; ++ y) { dst += dstStride - srcWidth ; src += srcStride - srcWidth ; *dst = *(dst - dstStride) + *src ; dst += 1 ; src += 1 ; for (x = 1 ; x < srcWidth ; ++ x) { *dst = *(dst - dstStride) - *(dst - dstStride - 1) + *(dst - 1) + *src ; dst += 1 ; src += 1 ; } } } vlfeat/toolbox/misc/vl_inthist.m0000644000175000017500000000205612237004736015736 0ustar dimadima% VL_INTHIST Calculate Integral Histogram % INTHIST = VL_INTHIST(LABELS) computes the integral histogram for % the label map(s) LABELS. % % LABELS is a [M,N,L] UINT32 array containing L label maps (one for % each MxN layer). Each label map associates to each of the MxN % pixels one of K labels (a value of 0 denotes no association) with % unitary mass. % % INTHIST is the [M,N,K] integral histogram obtained by integrating % the label maps (an integral histogram is just an array of K % integral images, one for each of the K labels). All layers of % labels are accumulated to the same integral histogram. % % The total number of labels K is obtained as the maximum value of % LABELS. VL_INTHIST(..., 'NUMLABELS', K) specifies the number of % labels explicitly. % % VL_INTHIST(..., 'MASS', MASSES) specifies a mass MASSES for each % entry of LABELS. MASSES can be either of class UINT32 or DOUBLE, % and the class of INTHIST varies accordingly. % % See also: VL_IMINTEGRAL(), VL_SAMPLEINTHIST(), VL_HELP(). % Authors: Andrea Vedaldi vlfeat/toolbox/misc/vl_numder2.m0000644000175000017500000000152212237004736015625 0ustar dimadimafunction d = vl_numder2(func, x, varargin) % VL_NUMDER2 Numerical second derivative % D = VL_NUMDER2(FUNC, X) computes the numerical second derivative of % the function FUNC at point X. % % D = VL_NUMDER2(FUNC, X, ARG1, ARG2, ...) allow to pass extra % parameters to the function FUNC. % % See also: VL_NUMDER(), VL_HELP(). N=length(x(:)) ; f00 = feval(func, x, varargin{:}) ; d=zeros(length(f00),N,N) ; D=1e-5 ; D2=D*D ; for n=1:N en=zeros(size(x)) ; en(n)=1 ; for m=1:N em=zeros(size(x)) ; em(m)=1 ; if( n ~= m ) f10 = feval(func, x+en*D, varargin{:}) ; f01 = feval(func, x+em*D, varargin{:}) ; f11 = feval(func, x+en*D+em*D, varargin{:}) ; d(:,n,m) = (f11-f01+f00-f10)/D2 ; else fp = feval(func, x+en*D, varargin{:}) ; fm = feval(func, x-en*D, varargin{:}) ; d(:,n,m) = (fm+fp-2*f00)/D2 ; end end end vlfeat/toolbox/misc/vl_localmax.m0000644000175000017500000000363312237004736016056 0ustar dimadima% VL_LOCALMAX Find local maximizers % SEL=VL_LOCALMAX(F) returns the indexes of the local maximizers of % the Q-dimensional array F. % % A local maximizer is an array element whose value is larger than % the value of all its neighbors. The neighbors of an element % i1...iQ have subscripts j1...jQ such that iq-1 <= jq <= iq % (excluding i1...iQ itself). For example, if Q=1 the neighbors of % an element are its predecessor and successor in the linear order; % if Q=2, its neighbors are the elements immediately to its north, % south, west, est, north-west, north-est, south-west and south-est % (8-neighborhood). % % Points on the boundary of F are ignored (and never selected as % local maximizers). % % SEL=VL_LOCALMAX(F,THRESH) accepts an element as a mazimizer only % if it is not smaller than THRESH. % % SEL=VL_LOCALMAX(F,THRESH,P) looks for neighbors only in the first % P dimensions of the Q-dimensional array F. This allows to process % F in ``slices''. % % Remark:: % Matrices (2-array) with a singleton dimension are % interpreted as vectors (1-array). So for example VL_LOCALMAX([0 1 % 0]) and VL_LOCALMAX([0 1 0]') both return 2 as an aswer. However, % if [0 1 0] is to be interpreted as a 1x2 matrix, then the % correct answer is the empty set, as all elements are on the % boundary. Unfortunately MATLAB does not distinguish between % vectors and 2-matrices with a singleton dimension. To forece % the interpretation of all matrices as 2-arrays, use % VL_LOCALMAX(F,TRESH,2) (but note that in this case the result is % always empty!). % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % TODO: Companion REFINELOCALMAX % TODO: Do something smarter at boundaries vlfeat/toolbox/misc/vl_sampleinthist.m0000644000175000017500000000135712237004736017143 0ustar dimadima% VL_SAMPLINTHIST Sample integral histogram % HISTS = VL_SAMPLINTHIST(INTHIST, BOXES) samples the integral % histogram INTHIST to obtain the histograms of the specified % BOXES. % % INTHIST is a MxNxK array, where M x N are ``spatial'' dimensions, % and K is the number of histogram bins. INTHIST may be of class % UINT32 or DOUBLE. % % Each box is a four dimensional vector [IMIN JMIN IMAX JMAX]' of % class UINT32 and correspond to the index set [IMIN, IMAX] x [JMIN, % JMAX]. To specify an empty box, let IMIN > IMAX. % % HISTS stores one histogram per column (one for each box) and has K % rows, one for each histogram bin. HIST is of the same class of % INTHIST. % % See also: VL_INTHIST(), VL_IMINTEGRAL(), VL_HELP(). vlfeat/toolbox/misc/vl_binsum.def0000644000175000017500000001755612237004736016066 0ustar dimadima/** @file vl_binsum.def ** @author Andrea Vedaldi ** @brief vl_binsum.def - vl_binsum template **/ #ifndef ITERATE_1 # define ITERATE_1 1 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 2 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 3 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 4 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 5 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 6 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 7 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 8 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 9 # include "vl_binsum.def" # undef ITERATE_1 # define ITERATE_1 10 # include "vl_binsum.def" # undef ITERATE_1 #else # ifndef ITERATE_2 # define ITERATE_2 1 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 2 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 3 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 4 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 5 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 6 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 7 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 8 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 9 # include "vl_binsum.def" # undef ITERATE_2 # define ITERATE_2 10 # include "vl_binsum.def" # undef ITERATE_2 # endif #endif #if defined(ITERATE_1) & defined(ITERATE_2) # define __VALUE_T__ TYPE(ITERATE_1) # define __INDEX_T__ TYPE(ITERATE_2) static void VL_XCAT4(_vl_binsum_,__VALUE_T__,_,__INDEX_T__) (mxArray * H_array, mxArray const * X_array, mxArray const * B_array, vl_index dim) { vl_size KH, KX, KB ; vl_index j ; __VALUE_T__ * H_pt ; __VALUE_T__ const * X_pt ; __INDEX_T__ const * B_pt ; __INDEX_T__ const * B_end ; KH = mxGetNumberOfElements(H_array) ; /* accumulator */ KX = mxGetNumberOfElements(X_array) ; /* values */ KB = mxGetNumberOfElements(B_array) ; /* accumulator indeces */ H_pt = (__VALUE_T__ *) mxGetData(H_array) ; X_pt = (__VALUE_T__ const *) mxGetData(X_array) ; B_pt = (__INDEX_T__ const *) mxGetData(B_array) ; B_end = B_pt + KB ; if ((KX != KB) && (KX > 1)) { vlmxError(vlmxErrInvalidArgument, "VALUES and INDEXES do not have the same number of elements nor VALUES is a scalar.") ; } /* All dimensions mode ------------------------------------------- */ if (dim <= 0) { while (B_pt < B_end) { /* next bin to accumulate */ j = (vl_index)(*B_pt) - 1 ; /* bin index out of bounds ?*/ if(j < -1 || j >= (signed) KH) { vlmxError(vlmxErrInconsistentData, "The index INDEXES(%" VL_FMT_INDEX ") = %" VL_FMT_INDEX " is out of bound.", (vl_index)(B_pt - (__INDEX_T__ const *)mxGetData(B_array) + 1), j + 1) ; } /* accumulate (but skip null indeces) */ if (j >= 0) { H_pt[j] += *X_pt ; } /* except for the scalar X case, keep X and B synchronized */ ++ B_pt ; if (KX > 1) ++ X_pt ; } } /* One dimension mode -------------------------------------------- */ else { vl_index k ; vl_size d = dim - 1 ; mwSize HD = mxGetNumberOfDimensions(H_array) ; mwSize XD = mxGetNumberOfDimensions(X_array) ; mwSize BD = mxGetNumberOfDimensions(B_array) ; mwSize const * Hdims = mxGetDimensions(H_array) ; mwSize const * Xdims = mxGetDimensions(X_array) ; mwSize const * Bdims = mxGetDimensions(B_array) ; __INDEX_T__ const * B_brk ; __INDEX_T__ const * B_nbrk ; vl_size srd ; /* We need to check a few more details about the matrices */ if (d >= HD) { vlmxError(vlmxErrInconsistentData, "DIM is smaller than one or larger than the number of dimensions of ACCUMULATOR.") ; } /* Here either B has the same number of dimensions of H, or B has exactly one dimension less and DIM=end. The latter is a special case due to the fact that MATLAB deletes singleton dimensions at the ends of arrays, so it would be impossible to operate with DIM=end and size(B,end)=1, which is a logically acceptable case. */ if (HD != BD) { if (! d == HD - 1 && BD == HD - 1) { vlmxError(vlmxErrInconsistentData, "ACCUMULATOR and INDEXES do not have the same number of dimensions.") ; } } if ((BD != XD) && (KX > 1)) { vlmxError(vlmxErrInconsistentData, "VALUES is not a scalar nor has the same number of dimensions of INDEXES.") ; } /* This will contain the stride required to advance to the next * element along dimension DIM. This is the product of all * dimensions < d. */ srd = 1 ; for(k = 0 ; k < (signed) XD ; ++k) { if (KX > 1 && Xdims[k] != Bdims[k]) { vlmxError(vlmxErrInconsistentData, "VALUES and INDEXES have incompatible dimensions.") ; } if (k != (signed) d && (Bdims[k] != Hdims[k])) { vlmxError(vlmxErrInconsistentData, "VALUES and ACCUMULATOR have incompatible dimensions.") ; } if (k < (signed) d) { srd = srd * Bdims[k] ; } } /* We sum efficiently by a single pass of B_pt in memory order. * This however makes the algorithm a bit more involved. How the * sum is performed is easeir understood by an example. Consider * the case d = 3 and BD = 5. So elements in B are indexed as * * B[i0, i1, id, i4, i5] (note that id=i3) * * where the indexes (i0,i1,id,i4,i5) are scanned in column-major * orer. For each of such elements, we acces the element * * R[i0, i1, B[i0,i1,id,i4,i5], i4, i5] * * For greater efficiency, we do not compute the multi-indexes * explicity; instead we advance B_pt at each iteration and we * keep R_pt properly synchronized. * * In particular, at each iteration we want R_pt to point to * * R[i0, i1, 0, i4, i5] * * Therefore, whenever advancing B_pt correspnds to advancing * * a - i0, i1 : we advance R_pt * b - id : we advance R_pt and then we subtract srd to move id back * one position; * c - i4,i5 : we do the same as before, but then we need to add * srd * Hdims[d] * elements to move R_pt up one place * after dimension d. * * We can easily keep track of the three cases without computing * explicitly the indexs. In fact, case (a) occurs srd-1 * consecutive steps and case (b) at the srd step. Similarly, case * (c) accurs at step srd * Bdims[d] steps. The pattern repeats * regularly. */ KH = Hdims[d] ; B_brk = B_pt + srd ; /* next time case (b) */ B_nbrk = B_pt + srd * Bdims [d] ; /* next time case (c) */ while (B_pt < B_end) { /* next bin to accumulate */ j = (vl_index)(*B_pt) - 1; /* index out of bounds? */ if(j < -1 || j >= (signed) KH) { vlmxError(vlmxErrInconsistentData, "The index INDEXES(%" VL_FMT_INDEX ") = %" VL_FMT_INDEX " is out of bounds.", (vl_index)(B_pt - (__INDEX_T__ * const)mxGetData(B_array)) + 1, j + 1) ; } /* accumulate (but skip null indeces) */ if (j >= 0) { H_pt [j * srd] += *X_pt ; } /* next element */ if (KX > 1) X_pt++ ; B_pt ++ ; H_pt ++ ; if (B_pt == B_brk) { /* case (b) occurs */ B_brk += srd ; /* next time case (b) */ H_pt -= srd ; if (B_pt == B_nbrk) { /* case (c) occurs */ B_nbrk += srd * Bdims[d] ; /* next time case (c) */ H_pt += srd * Hdims[d] ; } } } } } #undef __VALUE_T__ #undef __INDEX_T__ #endif vlfeat/toolbox/misc/vl_histmarg.m0000644000175000017500000000162012237004736016066 0ustar dimadimafunction H = vl_histmarg(H, dims) % VL_HISTMARG Marginal of histogram % H = VL_HISTMARG(H, DIMS) marginalizes the historgram H w.r.t the % dimensions DIMS. This is done by summing out all dimensions not % listed in DIMS and deleting them. % % Remark:: % If DIMS lists only one dimension, the returned histogram H is a % column vector. Notice that this way of deleting dimensions is % not always consistent with the SQUEEZE function. % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). sz = size(H) ; for d=setdiff(1:length(sz), dims(:)) H = sum(H, d) ; end % Squeeze out marginalized dimensions sz = sz(dims(:)) ; sz = [sz ones(1,2-length(dims(:)))] ; H = reshape(H, sz) ; vlfeat/toolbox/misc/samplinthist.tc0000644000175000017500000000343512237004736016446 0ustar dimadima#define JOIN_(a,b) a ## b #define JOIN(a,b) JOIN_(a,b) #undef SAMPLE #define SAMPLE JOIN(sample_, SFX) VL_INLINE void SAMPLE (T* histPt, T const* intHistPt, vl_size width, vl_size height, vl_size numLabels, vl_uint32 const* boxesPt, vl_size numBoxes) { vl_uindex bi, k ; for (bi = 0 ; bi < numBoxes ; ++ bi) { vl_uint32 x1 = *boxesPt++ ; vl_uint32 y1 = *boxesPt++ ; vl_uint32 x2 = *boxesPt++ ; vl_uint32 y2 = *boxesPt++ ; vl_uint32 stride = width ; vl_uint32 labelStride = width * height ; T const* iter = 0 ; /* empty box case */ if (x1 > x2 || y1 > y2) { for (k = 0 ; k < numLabels ; ++k) { histPt [k] = 0 ; } histPt += numLabels ; continue ; } if (x1 < 1 || y1 < 1) { vlmxError(vlmxErrInconsistentData, "Box %d is out of bounds.", bi + 1) ; } if (x2 > width || y2 > height) { vlmxError(vlmxErrInconsistentData, "Box %d is out of bounds.", bi + 1) ; } -- x1 ; -- x2 ; -- y1 ; -- y2 ; iter = intHistPt + x2 + y2 * stride ; for (k = 0 ; k < numLabels ; ++k) { histPt [k] = *iter ; iter += labelStride ; } if (x1 > 0) { iter = intHistPt + (x1 - 1) + y2 * stride ; for (k = 0 ; k < numLabels ; ++k) { histPt [k] -= *iter ; iter += labelStride ; } } if (x1 > 0 && y1 > 0) { iter = intHistPt + (x1 - 1) + (y1 - 1) * stride ; for (k = 0 ; k < numLabels ; ++k) { histPt [k] += *iter ; iter += labelStride ; } } if (y1 > 0) { iter = intHistPt + x2 + (y1 - 1) * stride ; for (k = 0 ; k < numLabels ; ++k) { histPt [k] -= *iter ; iter += labelStride ; } } histPt += numLabels ; } } vlfeat/toolbox/misc/vl_threads.m0000644000175000017500000000111712237004745015703 0ustar dimadima%VL_THREADS Control VLFeat computational threads % [NUM,MAXNUM] = VL_THREADS() returns the current number of % computational threads NUM and the maximum possible number MAXNUM. % % VL_THREADS(NUM) sets the current number of threads to the % specified value. NUM = VL_THREADS(NUM) does the same, but returns % the *previous* number of computational threads as well. % % See also: VL_HELP(). % Copyright (C) 2013 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_cummax.m0000644000175000017500000000130112237004736015536 0ustar dimadima% VL_CUMMAX Cumulative maximum % If X is a vector, VL_CUMMAX(X) is the vector with the cumulative % maximum of X. If X is a matrix, VL_CUMMAX(X) is the matrix of the % cumulative maximum of the columns of X. If X is a multi-dimensional % array, VL_CUMMAX(X) operates along the first non-singleton % dimension. % % The function is analogous to CUMSUM() and CUMPROD(), but it % computes the cumulative maximum rather than the cumulative sum or % product. % % The array X can be of any numeric class. % % Example:: % The cumulative maximum VL_CUMMAX([1 2 3 2 1]) is the vector % [1 2 3 3 3]. % % See also: CUMSUM(), CUMPROD(), VL_HELP(). % Author: Andrea Vedaldi % Copyright vlfeat/toolbox/misc/vl_kdtreequery.c0000644000175000017500000001074012237004745016605 0ustar dimadima/** @internal ** @file vl_kdtreequery.c ** @brief vl_kdtreequery - MEX implementation ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include "kdtree.h" /* option codes */ enum { opt_verbose, opt_num_neighs, opt_max_num_comparisons, opt_multithreading } ; /* options */ vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"NumNeighbors", 1, opt_num_neighs }, {"MaxComparisons", 1, opt_max_num_comparisons }, {"MaxNumComparisons", 1, opt_max_num_comparisons }, {0, 0, 0 } } ; /** ------------------------------------------------------------------ ** @brief MEX entry point **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_FOREST = 0, IN_DATA, IN_QUERY, IN_END} ; enum {OUT_INDEX = 0, OUT_DISTANCE} ; int verbose = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; VlKDForest * forest ; mxArray const * forest_array = in[IN_FOREST] ; mxArray const * data_array = in[IN_DATA] ; mxArray const * query_array = in[IN_QUERY] ; void * query ; vl_uint32 * index ; void * distance ; vl_size numNeighbors = 1 ; vl_size numQueries ; unsigned int numComparisons = 0 ; unsigned int maxNumComparisons = 0 ; mxClassID dataClass ; vl_index i ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 3) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nout > 2) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } forest = new_kdforest_from_array (forest_array, data_array) ; dataClass = mxGetClassID (data_array) ; if (mxGetClassID (query_array) != dataClass) { vlmxError(vlmxErrInvalidArgument, "QUERY must have the same storage class as DATA.") ; } if (! vlmxIsReal (query_array)) { vlmxError(vlmxErrInvalidArgument, "QUERY must be real.") ; } if (! vlmxIsMatrix (query_array, forest->dimension, -1)) { vlmxError(vlmxErrInvalidArgument, "QUERY must be a matrix with TREE.NUMDIMENSIONS rows.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_num_neighs : if (! vlmxIsScalar(optarg) || (numNeighbors = mxGetScalar(optarg)) < 1) { vlmxError(vlmxErrInvalidArgument, "NUMNEIGHBORS must be a scalar not smaller than one.") ; } break; case opt_max_num_comparisons : if (! vlmxIsScalar(optarg)) { vlmxError(vlmxErrInvalidArgument, "MAXNUMCOMPARISONS must be a scalar.") ; } maxNumComparisons = mxGetScalar(optarg) ; break; case opt_verbose : ++ verbose ; break ; } } vl_kdforest_set_max_num_comparisons (forest, maxNumComparisons) ; query = mxGetData (query_array) ; numQueries = mxGetN (query_array) ; out[OUT_INDEX] = mxCreateNumericMatrix (numNeighbors, numQueries, mxUINT32_CLASS, mxREAL) ; out[OUT_DISTANCE] = mxCreateNumericMatrix (numNeighbors, numQueries, dataClass, mxREAL) ; index = mxGetData (out[OUT_INDEX]) ; distance = mxGetData (out[OUT_DISTANCE]) ; if (verbose) { VL_PRINTF ("vl_kdforestquery: number of queries: %d\n", numQueries) ; VL_PRINTF ("vl_kdforestquery: number of neighbors per query: %d\n", numNeighbors) ; VL_PRINTF ("vl_kdforestquery: max num of comparisons per query: %d\n", vl_kdforest_get_max_num_comparisons (forest)) ; } numComparisons = vl_kdforest_query_with_array (forest, index, numNeighbors, numQueries, distance, query) ; vl_kdforest_delete(forest) ; /* adjust for MATLAB indexing */ for (i = 0 ; i < (signed) (numNeighbors * numQueries) ; ++i) { index[i] ++ ; } if (verbose) { VL_PRINTF ("vl_kdforestquery: number of comparisons per query: %.3f\n", ((double) numComparisons) / numQueries) ; VL_PRINTF ("vl_kdforestquery: number of comparisons per neighbor: %.3f\n", ((double) numComparisons) / (numQueries * numNeighbors)) ; } } vlfeat/toolbox/misc/vl_twister.m0000644000175000017500000000231012237004736015746 0ustar dimadima% VL_TWISTER Random number generator % VL_TWISTER() is essentially equivalent to MATLAB native RAND() % when using the Twister random number generator. VL_TWISTER(), % VL_TWISTER(M,N,P,...) and VL_TWISTER([M N P ...]) are equivalent % to RAND(), RAND(M,N,P,...) and RAND([M N P ...]) respectively. % % The state of the random generator can be seeded by % VL_TWISTER('STATE', X), where X is a DOUBLE scalar (this is % equivalent to RAND('TWISTER', X)). The state can be read by % VL_TWISTER('STATE') (equivalent to RAND('TWISTER')) and set by % VL_TWISTER('STATE', STATE) (equivalent to RAND('TWISTER', % STATE)). Here STATE is a vector of 625 elements of class % UINT32. Finally VL_TWISTER('STATE',KEY) seeds the generator by a % vector of DOUBLE of length not greater than 624. % % VL_TWISTER() is slightly faster than RAND(). Moreover it can be % used to control the state of the random number generator used by % all VLFEAT functions. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/svms_common.h0000644000175000017500000002000312237004745016076 0ustar dimadima/** @internal ** @file svms_common.h ** @brief Common functions for SVMs in MATLAB ** @author Milan Sulc ** @author Daniele Perrone ** @author Andrea Vedaldi **/ /* Copyright (C) 2013 Milan Sulc. Copyright (C) 2012 Daniele Perrone. Copyright (C) 2011-12 Andrea Vedaldi. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mex.h" #include #include /** ------------------------------------------------------------------ ** @internal ** @brief Set value of scalar double to mxArray ** @param array scalar mxArray. ** @param value new value. **/ void setDoubleValue(mxArray* array, double value) { double* temp = (double*) mxGetData(array) ; *temp = value ; } /** ------------------------------------------------------------------ ** @internal ** @brief Set value of scalar integer to mxArray ** ** @param array scalar mxArray. ** @param value new value. ** **/ void setUintValue(mxArray* array, vl_uint32 value) { vl_uint32 * temp = (vl_uint32*) mxGetData(array) ; *temp = value ; } /** ------------------------------------------------------------------ ** @internal ** @brief Create a Matlab struct with diagnostics informations. ** ** @param svm SVM status. **/ mxArray * createInfoStruct(VlSvm* svm) { mwSize dims[] = {1 , 1} ; mxArray *model, *bias; mxArray *output, *dimension, *iterations, *maxIterations, *epsilon ; mxArray *lambda, *biasMultiplier ; mxArray *biasPreconditioner, *energyFrequency, *elapsedTime ; if (svm->type == VL_SVM_SGD) { const char* names [17] = {"model","bias","dimension", "iterations","maxIterations", "epsilon", "lambda", "biasMultiplier", "elapsedTime","energy","regularizerTerm", "lossPos", "lossNeg", "hardLossPos", "hardLossNeg", "biasPreconditioner", "energyFrequency" }; output = mxCreateStructArray(1, dims, 17, names); biasPreconditioner = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(biasPreconditioner,svm->biasPreconditioner) ; mxSetField(output, 0, "biasPreconditioner", biasPreconditioner) ; energyFrequency = mxCreateNumericMatrix(1, 1,mxUINT32_CLASS, mxREAL) ; mxSetField(output, 0, "energyFrequency", energyFrequency) ; setUintValue(energyFrequency,svm->energyFrequency) ; } else { const char* names [15] = {"model","bias","dimension", "iterations","maxIterations", "epsilon", "lambda", "biasMultiplier", "elapsedTime","energy","regularizerTerm", "lossPos", "lossNeg", "hardLossPos", "hardLossNeg"}; output = mxCreateStructArray(1, dims, 15, names); } /*--MODEL--*/ mwSize mdims[2] ; double * tempBuffer ; mdims[0] = svm->dimension ; mdims[1] = 1 ; model = mxCreateNumericArray(2, mdims, mxDOUBLE_CLASS, mxREAL) ; tempBuffer = (double*) mxGetData(model) ; memcpy(tempBuffer,svm->model,svm->dimension * sizeof(double)) ; mxSetField(output, 0, "model", model) ; /*-- --*/ bias = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(bias,svm->bias) ; mxSetField(output, 0, "bias", bias) ; dimension = mxCreateNumericMatrix(1, 1,mxUINT32_CLASS, mxREAL) ; setUintValue(dimension,svm->dimension) ; mxSetField(output, 0, "dimension", dimension) ; iterations = mxCreateNumericMatrix(1, 1,mxUINT32_CLASS, mxREAL) ; setUintValue(iterations,svm->iterations) ; mxSetField(output, 0, "iterations", iterations) ; maxIterations = mxCreateNumericMatrix(1, 1,mxUINT32_CLASS, mxREAL) ; setUintValue(maxIterations,svm->maxIterations) ; mxSetField(output, 0, "maxIterations", maxIterations) ; epsilon = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(epsilon,svm->epsilon) ; mxSetField(output, 0, "epsilon", epsilon) ; lambda = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(lambda,svm->lambda) ; mxSetField(output, 0, "lambda", lambda) ; biasMultiplier = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(biasMultiplier,svm->biasMultiplier) ; mxSetField(output, 0, "biasMultiplier", biasMultiplier) ; elapsedTime = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(elapsedTime,svm->elapsedTime) ; mxSetField(output, 0, "elapsedTime", elapsedTime) ; if (svm->objective) { mxArray * energy, *regularizerTerm, *lossPos, *lossNeg, *hardLossPos, *hardLossNeg ; energy = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(energy,svm->objective->energy) ; regularizerTerm = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(regularizerTerm,svm->objective->regularizer) ; lossPos = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(lossPos,svm->objective->lossPos) ; lossNeg = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(lossNeg,svm->objective->lossNeg) ; hardLossPos = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(hardLossPos,svm->objective->hardLossPos) ; hardLossNeg = mxCreateNumericMatrix(1, 1,mxDOUBLE_CLASS, mxREAL) ; setDoubleValue(hardLossNeg,svm->objective->hardLossNeg) ; mxSetField(output, 0, "energy", energy) ; mxSetField(output, 0, "regularizerTerm", regularizerTerm) ; mxSetField(output, 0, "lossPos", lossPos) ; mxSetField(output, 0, "lossNeg", lossNeg) ; mxSetField(output, 0, "hardLossPos", hardLossPos) ; mxSetField(output, 0, "hardLossNeg", hardLossNeg) ; } return output ; } VL_INLINE void diagnosticDispatcher(VlSvm* svm) { if (svm->diagnosticFunction) { mxArray *rhs[2] ; rhs[0] = (mxArray*) svm->diagnosticFunction ; rhs[1] = createInfoStruct(svm) ; if( mxIsClass( rhs[0] , "function_handle")) { mexCallMATLAB(0,NULL,2,rhs,"feval"); } mxDestroyArray(rhs[1]) ; } } /** ------------------------------------------------------------------ ** @internal ** @brief Get training data from training dataset struct ** ** @param trainingData Matlab training dataset struct. ** @param data pointer to variable where to save the data pointer. ** @param dataDimension pointer to variable where to save data dimension. ** @param dataType pointer to variable where to save the data type. ** @param numSamples pointer to variable where to save the number of samples. ** @param labels pointer to variable where to save the set of labels. ** ** The function extracts the information provided in the Matlab ** training set struct. ** **/ void getTrainingData(const mxArray* trainingData, void** data, vl_size* dataDimension, vl_type* dataType, vl_size* numSamples, vl_int8** labels) { mxClassID dataClass ; mxArray* field = NULL ; if (! mxIsStruct(trainingData)) vlmxError(vlmxErrInvalidArgument,"DATA must be a valid TRAINING SET Struct.") ; /* Get data */ field = mxGetField(trainingData, 0, "data") ; if (field == NULL) vlmxError(vlmxErrInvalidArgument, "DATA array missing in TRAINING SET Struct.") ; *data = mxGetData (field) ; *dataDimension = mxGetM(field) ; *numSamples = mxGetN(field) ; dataClass = mxGetClassID(field) ; switch (dataClass) { case mxSINGLE_CLASS : *dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS : *dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError(vlmxErrInvalidArgument, "DATA must be either SINGLE or DOUBLE.") ; } /* Get labels */ field = mxGetField(trainingData, 0, "labels") ; if (field == NULL) vlmxError(vlmxErrInvalidArgument, "DATA array missing in TRAINING SET Struct.") ; if (mxGetClassID(field) != mxINT8_CLASS) vlmxError(vlmxErrInvalidArgument, "LABELS must be INT8.") ; if (! vlmxIsVector(field, *numSamples)) { vlmxError(vlmxErrInvalidArgument, "LABELS is not a vector of dimension compatible with DATA.") ; } *labels = (vl_int8*) mxGetData(field) ; } vlfeat/toolbox/misc/vl_binsum.m0000644000175000017500000000350412237004736015550 0ustar dimadima% VL_BINSUM Binned summation % ACCUMULATOR = VL_BINSUM(ACCUMULATOR,VALUES,INDEXES) adds the % elements of the array VALUES to the elements of the array % ACCUMULATOR indexed by INDEXES. VALUES and INDEXES must have the % same dimensions, and the elements of INDEXES must be valid indexes % for the array ACCUMULATOR (except for null indexes, which are % silently skipped). An application is the calculation of a % histogram ACCUMULATOR, where INDEXES are the bin occurences and % VALUES are the occurence weights. % % VL_BINSUM(...,DIM) operates only along the specified dimension % DIM. In this case, ACCUMULATOR, VALUES and INDEXES are array of % the same dimensions, except for the dimension DIM of ACCUMULATOR, % which may differ, and INDEXES is an array of subscripts of the % DIM-th dimension of ACCUMULATOR. A typical application is the % calculation of multiple histograms, where each histogram is a % 1-dimensional slice of the array ACCUMULATOR along the dimension % DIM. % % VALUES can also be a scalar. In this case, the value of VALUES is % summed to all the specified bins. % % ACCUMULATOR and VALUES must have the same storage % class. ACCUMULATOR, VALUES, and INDEXES can be either DOUBLE, % SINGLE, UINT64, INT64, UINT32, INT32, UINT16, INT16, UINT8, % INT8. Integer math uses modulo-2 arithmetic (most MATLAB functions % saturate instead). % % Example:: % The following relations illustrate VL_BINSUM() behavior: % VL_BINSUM([0 0], 1, 2) = [0 1] % VL_BINSUM([1 7], -1, 1) = [0 7] % VL_BINSUM(EYE(3), [1 1 1], [1 2 3], 1) = 2*EYE(3) % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_getpid.c0000644000175000017500000000152412237004736015515 0ustar dimadima/** @internal ** @file vl_getpid.c ** @author Andrea Vedaldi ** @brief MEX implementation of VL_GETPID() **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #ifdef VL_OS_WIN #include #else #include #endif #include void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[] VL_UNUSED) { double pid ; if (nin > 0) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } #ifdef VL_OS_WIN pid = (double) GetCurrentProcessId() ; #else pid = (double) getpid() ; #endif out[0] = vlmxCreatePlainScalar (pid) ; } vlfeat/toolbox/misc/vl_ihashsum.c0000644000175000017500000001435212237004736016065 0ustar dimadima/** @internal ** @file ihashsum.c ** @author Andrea Vedaldi ** @brief BINSUM - MEX **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include /* hash function */ unsigned int fnv_hash (void const *key, int len) { unsigned char const *p = key; unsigned int h = 2166136261U ; int i; for ( i = 0; i < len; i++ ) h = ( h * 16777619 ) ^ p[i]; return h; } int is_null (vl_uint8 const * x, int n) { int i ; for (i = 0 ; i < n ; ++i) { if (x[i]) return 0 ; } return 1 ; } int is_equal (vl_uint8 const * x, vl_uint8 const * y, int n) { int i ; for (i = 0 ; i < n ; ++i) { if (x[i] != y[i]) return 0 ; } return 1 ; } void cpy (vl_uint8 * x, vl_uint8 const * y, int n) { int i ; for (i = 0 ; i < n ; ++i){ /* mexPrintf("cpy:%d %d\n",x[i],y[i]);*/ x[i] = y[i] ; } } /** @brief Driver. ** ** @param nount number of output arguments. ** @param out output arguments. ** @param nin number of input arguments. ** @param in input arguments. **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { IN_H, IN_ID, IN_NEXT, IN_K, IN_X } ; enum { OUT_H, OUT_ID, OUT_NEXT} ; mxArray *h_, *id_, *next_ ; vl_uint32 * h ; vl_uint32 * next ; vl_uint8 * id ; vl_uint8 const * x ; unsigned int K, i, N, res, last, ndims ; /* ----------------------------------------------------------------- * Check arguments * -------------------------------------------------------------- */ if( nin != 5 ) { mexErrMsgTxt("Five arguments required") ; } else if (nout > 3) { mexErrMsgTxt("At most three output argument.") ; } if(! mxIsNumeric(in[IN_H]) || mxGetClassID(in[IN_H] )!= mxUINT32_CLASS || ! mxIsNumeric(in[IN_NEXT])|| mxGetClassID(in[IN_NEXT])!= mxUINT32_CLASS) { mexErrMsgTxt("H, NEXT must be UINT32.") ; } if(! mxIsNumeric(in[IN_X]) || mxGetClassID(in[IN_X]) != mxUINT8_CLASS) { mexErrMsgTxt("X must be UINT8") ; } if (mxGetM(in[IN_H]) != 1 || mxGetM(in[IN_NEXT]) != 1) { mexErrMsgTxt("H, NEXT must be row vectors") ; } if(! mxIsNumeric(in[IN_ID]) || mxGetClassID(in[IN_ID])!= mxUINT8_CLASS) { mexErrMsgTxt("ID must be UINT8.") ; } ndims = mxGetM(in[IN_ID]) ; res = mxGetN(in[IN_H]) ; if(res != mxGetN(in[IN_ID]) || res != mxGetN(in[IN_NEXT])) { mexErrMsgTxt("H, ID, NEXT must have the same number of columns") ; } if(ndims != mxGetM(in[IN_X])) { mexErrMsgTxt("ID and X must havethe same number of rows") ; } if(! vlmxIsPlainScalar(in[IN_K])) { mexErrMsgTxt("K must be a scalar") ; } K = (unsigned int) *mxGetPr(in[IN_K]) ; h_ = mxDuplicateArray(in[IN_H]) ; id_ = mxDuplicateArray(in[IN_ID]) ; next_ = mxDuplicateArray(in[IN_NEXT]) ; N = mxGetN(in[IN_X]) ; h = mxGetData(h_ ) ; id = mxGetData(id_ ) ; next = mxGetData(next_) ; x = mxGetData(in[IN_X]) ; /* Temporary remove mxArray pointers to these buffer as we will mxRealloc them and if the user presses Ctrl-C matlab will attempt to free unvalid memory */ mxSetData(h_, 0) ; mxSetData(id_, 0) ; mxSetData(next_, 0) ; /* search for last occupied slot */ last = res ; for (i = 0 ; i < res ; ++i) last = VL_MAX(last, next [i]) ; /* REMARK: last and next are 1 based */ if (K > res) { mexErrMsgTxt("K cannot be larger then the size of H") ; } if (last > res) { mexErrMsgTxt("An element of NEXT is greater than the size of the table") ; } /* mexPrintf("last:%d\n",last) ;*/ /* ----------------------------------------------------------------- * Do job * -------------------------------------------------------------- */ for (i = 0 ; i < N ; ++i) { /* hash */ unsigned int h1, h2 ; unsigned int j, p = 0 ; /* cannot hash null labels */ if (is_null (x + i * ndims, ndims)) { vlmxError(vlmxErrInvalidArgument, "The %d column of X is null.", i+1) ; continue ; } h1 = fnv_hash(x + i * ndims, ndims) % K ; h2 = h1 | 0x1 ; /* this needs to be odd */ /* search first free or matching position */ p = h1 % K ; for (j = 0 ; j < K ; ++j) { if (is_null (id + p * ndims, ndims) || is_equal(id + p * ndims, x + i * ndims, ndims)) break ; h1 += h2 ; p = h1 % K ; } /* if after scanning the K elements in the hash table an empty/matching bucket is still not found, start using next to go into the overflow table */ while (! is_null (id + p * ndims, ndims) && ! is_equal(id + p * ndims, x + i * ndims, ndims)) { if (next [p] > res) { mexErrMsgTxt("An element of NEXT is greater than the size of the table") ; } /* append */ if (next [p] == 0) { if (last >= res) { size_t res_ = res + VL_MAX(res / 2, 2) ; h = mxRealloc(h, res_ * sizeof(vl_uint32) ) ; next = mxRealloc(next, res_ * sizeof(vl_uint32) ) ; id = mxRealloc(id, res_ * sizeof(vl_uint8) * ndims) ; memset (h + res, 0, (res_ - res) * sizeof(vl_uint32) ) ; memset (next + res, 0, (res_ - res) * sizeof(vl_uint32) ) ; memset (id + res * ndims, 0, (res_ - res) * sizeof(vl_uint8) * ndims) ; res = res_ ; } next [p] = ++ last ; } p = next [p] - 1 ; } /* accumulate */ h [p] += 1 ; /* mexPrintf("p %d dims %d i %d N %d\n ", p, ndims, i, N) ;*/ cpy(id + p * ndims, x + i * ndims, ndims) ; } mxSetData(h_, mxRealloc(h, last * sizeof(vl_uint32) )) ; mxSetData(next_, mxRealloc(next, last * sizeof(vl_uint32) )) ; mxSetData(id_, mxRealloc(id, last * sizeof(vl_uint8 ) * ndims)) ; mxSetN(h_, last) ; mxSetN(id_, last) ; mxSetN(next_, last) ; mxSetM(h_, 1) ; mxSetM(next_, 1) ; mxSetM(id_, ndims) ; out[OUT_H] = h_ ; out[OUT_ID] = id_ ; out[OUT_NEXT] = next_ ; } vlfeat/toolbox/misc/vl_homkermap.m0000644000175000017500000000515712237004745016244 0ustar dimadima% VL_HOMKERMAP Homogeneous kernel map % V = VL_HOMKERMAP(X, N) computes a 2*N+1 dimensional approximated % kernel map for the Chi2 kernel. X is an array of data points. Each % point is expanded into a vector of dimension 2*N+1 and saved to % the output V. The expanded feature vectors are stacked along the % first dimension, so that the output array V has the same % dimensions of the input array X except for the first one, which is % 2*N+1 times larger. % % The function accepts the following options: % % Kernel:: KCHI2 % One of KCHI2 (Chi2 kernel), KINTERS (intersection kernel), KJS % (Jensen-Shannon kernel). The 'Kernel' option name can be omitted, % i.e. VL_HOMKERMAP(..., 'kernel', 'kchi2') has the same effect of % VL_HOMKERMAP(..., 'kchi2'). % % Period:: [automatically tuned] % Set the period of the kernel specturm. The approximation is % based on periodicizing the kernel specturm. If not specified, % the period is automatically set based on the heuristic described % in [2]. % % Window:: [RECTANGULAR] % Set the window used to truncate the spectrum before The window % can be either RECTANGULAR or UNIFORM window. See [2] and the API % documentation for details. % % Gamma:: [1] % Set the homogeneity degree of the kernel. The standard kernels % are 1-homogeneous, but sometimes smaller values perform better % in applications. See [2] for details. % % Example:: % The following code results in approximatively the same % similarities matrices between points X and Y: % % x = rand(10,1) ; % y = rand(10,100) ; % psix = vl_homkermap(x, 3) ; % psiy = vl_homkermap(y, 3) ; % figure(1) ; clf ; % ker = vl_alldist(x, y, 'kchi2') ; % ker_ = psix' * psiy ; % plot([ker ; ker_]') ; % % Note:: % The homogeneous kernels K(X,Y) are normally defined for % non-negative data only. VL_HOMKERMAP defines them for both % positive and negative data by using the definition % SIGN(X)SIGN(Y)K(ABS(X),ABS(Y)) -- note that other extensions are % possible as well (see [2]). % % REFERENCES:: % [1] A. Vedaldi and A. Zisserman % `Efficient Additive Kernels via Explicit Feature Maps', % Proc. CVPR, 2010. % % [2] A. Vedaldi and A. Zisserman % `Efficient Additive Kernels via Explicit Feature Maps', % PAMI, 2011 (submitted). % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_lbp.c0000644000175000017500000000447012237004736015021 0ustar dimadima/** @internal ** @file imintegral.c ** @author Brian Fulkerson ** @brief Create an integral image - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { float * image ; vl_size width, height ; vl_size cellSize = 16 ; enum {IN_I = 0, IN_CELLSIZE} ; enum {OUT_FEATURES = 0} ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin > 2) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nin < 2) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (! mxIsNumeric(IN(I)) || ! vlmxIsReal(IN(I)) || ! vlmxIsMatrix(IN(I), -1, -1)) { vlmxError(vlmxErrInvalidArgument, "I is not a numeric matrix.") ; } if (mxGetClassID(IN(I)) != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "I is not of class SINGLE.") ; } if (! vlmxIsPlainScalar(IN(CELLSIZE))) { vlmxError(vlmxErrInvalidArgument, "CELLSIZE is not a plain scalar.") ; } if (mxGetScalar(IN(CELLSIZE)) < 1.0) { vlmxError(vlmxErrInvalidArgument, "CELLSIZE is less than 1.") ; } cellSize = (vl_size) mxGetScalar(IN(CELLSIZE)) ; image = mxGetData(IN(I)) ; width = mxGetN(IN(I)) ; height = mxGetM(IN(I)) ; /* do job */ { /* recall that MATLAB images are transposed */ mwSize dimensions [3] ; /* get LBP object */ VlLbp * lbp = vl_lbp_new (VlLbpUniform, VL_TRUE) ; if (lbp == NULL) { vlmxError(vlmxErrAlloc, NULL) ; } /* get output buffer */ dimensions[0] = height / cellSize ; dimensions[1] = width / cellSize ; dimensions[2] = vl_lbp_get_dimension(lbp) ; OUT(FEATURES) = mxCreateNumericArray(3, dimensions, mxSINGLE_CLASS, mxREAL) ; vl_lbp_process(lbp, mxGetData(OUT(FEATURES)), image, height, width, cellSize) ; vl_lbp_delete(lbp) ; } } vlfeat/toolbox/misc/vl_inthist.c0000644000175000017500000001421712237004736015726 0ustar dimadima/** @internal ** @file inthist.c ** @author Andrea Vedaldi ** @brief Calculate an integral histogram - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include enum {opt_mass = 1, opt_numLabels, opt_verbose} ; /* options */ vlmxOption options [] = { {"Mass", 1, opt_mass, }, {"NumLabels", 1, opt_numLabels }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; #undef T #undef SFX #define T vl_int32 #define SFX i #include "inthist.tc" #undef T #undef SFX #define T vl_uint32 #define SFX ui #include "inthist.tc" #undef T #undef SFX #define T double #define SFX d #include "inthist.tc" #undef T #undef SFX #define T float #define SFX f #include "inthist.tc" void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { mwSize dims [3] ; vl_size numDims ; mwSize const * dimsPt = 0 ; vl_uint32* labelsPt = 0 ; void* massPt = 0 ; void* histPt = 0 ; vl_uint32 numLabels = 0 ; mxClassID dataClass = mxUINT32_CLASS ; vl_size width, height, numMaps ; vl_uindex k, q ; enum {IN_LABELS = 0, IN_END} ; enum {OUT_HIST = 0} ; int opt ; int nextOpt = IN_END ; mxArray const *optArg ; int verb = 0 ; /* ------------------------------------------------------------------ ** Check the arguments ** --------------------------------------------------------------- */ if (nin < 1) { mexErrMsgTxt("At least one input argument is required.") ; } else if (nout > 1) { mexErrMsgTxt("Too many output arguments."); } if (mxGetClassID(IN(LABELS)) != mxUINT32_CLASS) { mexErrMsgTxt("LABELS must be of class UINT32.") ; } labelsPt = mxGetData(IN(LABELS)) ; numDims = mxGetNumberOfDimensions(IN(LABELS)) ; if (numDims > 3) { mexErrMsgTxt("LABELS must be a MxNxK array.") ; } labelsPt = mxGetData(IN(LABELS)) ; dimsPt = mxGetDimensions(IN(LABELS)) ; height = dimsPt [0] ; width = dimsPt [1] ; if (numDims > 2) { numMaps = dimsPt [2] ; } else { numMaps = 1 ; } while ((opt = vlmxNextOption (in, nin, options, &nextOpt, &optArg)) >= 0) { switch (opt) { case opt_mass : { massPt = mxGetData(optArg) ; dimsPt = mxGetDimensions(optArg) ; if (mxGetNumberOfDimensions(optArg) != numDims || height != dimsPt [0] || width != dimsPt [1] || ((numDims > 2) && numMaps < dimsPt[2])) { mexErrMsgTxt("MASS must have the same dimensions of LABELS.") ; } /* the data is DOUBLE or UINT32 depending on the class of MASS */ dataClass = mxGetClassID(optArg) ; if (dataClass != mxDOUBLE_CLASS && dataClass != mxUINT32_CLASS) { mexErrMsgTxt("MASS must be of either class DOUBLE or UINT32.") ; } break ; } case opt_numLabels : if (!vlmxIsPlainScalar(optArg)) { mexErrMsgTxt("NUMLABELS must be a real scalar.") ; } numLabels = *mxGetPr(optArg) ; break ; case opt_verbose : ++ verb ; break ; default: abort() ; } } /* If numLabels is not specified, guess it from LABELS */ if (numLabels == 0) { for (k = 0 ; k < width*height ; ++k) { numLabels = VL_MAX(numLabels, labelsPt [k]) ; } } else { for (k = 0 ; k < width*height ; ++k) { if (labelsPt [k] > numLabels) { mexErrMsgTxt("LABELS contains an element greater than NUMLABELS.") ; } } } /* Allocate space for the integral histogram */ dims [0] = height ; dims [1] = width ; dims [2] = numLabels ; OUT(HIST) = mxCreateNumericArray(3, dims, dataClass, mxREAL) ; histPt = mxGetData(OUT(HIST)) ; if (verb) { mexPrintf("inthist: integrating %d x %d label map with %d labels\n", width, height, numLabels) ; mexPrintf(" custom mass map: %s\n", VL_YESNO(massPt)) ; } /* ------------------------------------------------------------------ * Distribute data * --------------------------------------------------------------- */ #define PROCESS(T, INTEGRAL) \ size_t const K = width*height ; \ T* dataPt = histPt ; \ if (massPt == 0) { \ for (q = 0 ; q < numMaps ; ++ q) { \ for (k = 0 ; k < K ; ++ k) { \ size_t label = *labelsPt++ ; \ if (label) dataPt [k + (label - 1) * K] += (T) 1 ; \ } \ } \ } else { \ for (q = 0 ; q < numMaps ; ++q) { \ for (k = 0 ; k < K ; ++ k) { \ size_t label = *labelsPt++ ; \ if (label) dataPt [k + (label - 1) * K] \ += ((T*)massPt) [k] ; \ } \ } \ } \ for (k = 0 ; k < numLabels ; ++k) { \ INTEGRAL (dataPt + k*K, height, \ dataPt + k*K, height, width, height) ; \ } switch (dataClass) { case mxUINT32_CLASS: { PROCESS(vl_uint32, integral_ui) } ; break ; case mxDOUBLE_CLASS: { PROCESS(double, integral_d) } ; break ; default : abort() ; } } vlfeat/toolbox/misc/vl_numder.m0000644000175000017500000000125412237004736015545 0ustar dimadimafunction d = vl_numder(func, x, varargin) % VL_NUMDER Numerical derivative % D = VL_NUMDER(FUNC, X) computes the numerical derivative of the % function FUNC at point X. X is a real array and is passed as first % argument of FUNC. % % D = VL_NUMDER(FUNC, X, ARG1, ARG2, ...) passes ARG1, ARG2, ... as % additional arguments to the function FUNC. % % See also: VL_NUMDER2(), VL_HELP(). % TODO: uniform sacaling of axis is not a good idea dx = 1e-7 ; N = numel(x) ; fx = feval(func, x, varargin{:}) ; d = zeros(length(fx(:)),N) ; for n=1:N e = zeros(size(x)) ; e(n) = 1 ; fxn = feval(func, x+dx*e, varargin{:}) ; di = (fxn - fx) / dx ; d(:,n) = di(:) ; end vlfeat/toolbox/misc/vl_localmax.c0000644000175000017500000001507712237004736016051 0ustar dimadima/** file: localmax.c ** author: Andrea Vedaldi ** description: Find local maximizer of multi-dimensional array. **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include /** Matlab driver. **/ #define greater(a,b) ((a) > (b)+threshold) void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { int M, N ; const double* F_pt ; int ndims ; int pdims = -1 ; int* offsets ; int* midx ; int* neighbors ; int nneighbors ; int* dims ; enum {F=0,THRESHOLD,P} ; enum {MAXIMA=0} ; double threshold = - mxGetInf() ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { mexErrMsgTxt("At least one input argument is required."); } else if (nin > 3) { mexErrMsgTxt("At most three arguments are allowed.") ; } else if (nout > 1) { mexErrMsgTxt("Too many output arguments"); } /* The input must be a real matrix. */ if (!mxIsDouble(in[F]) || mxIsComplex(in[F])) { mexErrMsgTxt("Input must be real matrix."); } if(nin > 1) { if(!vlmxIsPlainScalar(in[THRESHOLD])) { mexErrMsgTxt("THRESHOLD must be a real scalar.") ; } threshold = *mxGetPr(in[THRESHOLD]) ; } if(nin > 2) { if(!vlmxIsPlainScalar(in[P])) mexErrMsgTxt("P must be a non-negative integer") ; pdims = (int) *mxGetPr(in[P]) ; if(pdims < 0) mexErrMsgTxt("P must be a non-negative integer") ; } ndims = mxGetNumberOfDimensions(in[F]) ; { /* We need to make a copy because in one special case (see below) we need to adjust dims[]. */ int d ; mwSize const * const_dims = mxGetDimensions(in[F]) ; dims = mxMalloc(sizeof(int)*ndims) ; for(d=0 ; d < ndims ; ++d) dims[d] = const_dims[d] ; } M = dims[0] ; N = dims[1] ; F_pt = mxGetPr(in[F]) ; /* If there are only two dimensions and if one is singleton, then assume that a vector has been provided as input (and treat this as a COLUMN matrix with p=1). We do this because Matlab does not distinguish between vectors and 1xN or Mx1 matrices and because the cases 1xN and Mx1 are trivial (the result is alway empty). */ if((ndims == 2) && (pdims < 0) && (M == 1 || N == 1)) { pdims = 1 ; M = (M>N)?M:N ; N = 1 ; dims[0]=M ; dims[1]=N ; } /* search the local maxima along the first p dimensions only */ if(pdims < 0) pdims = ndims ; if(pdims > ndims) { mxFree(dims) ; mexErrMsgTxt("P must not be greater than the number of dimensions") ; } /* ------------------------------------------------------------------ * Do the job * --------------------------------------------------------------- */ { int maxima_size = M*N ; int* maxima_start = mxMalloc(sizeof(int) * maxima_size) ; int* maxima_iterator = maxima_start ; int* maxima_end = maxima_start + maxima_size ; int i,h,o ; const double* pt = F_pt ; /* Compute the offsets between dimensions. */ offsets = mxMalloc(sizeof(int) * ndims) ; offsets[0] = 1 ; for(h = 1 ; h < ndims ; ++h) offsets[h] = offsets[h-1]*dims[h-1] ; /* Multi-index. */ midx = mxMalloc(sizeof(int) * ndims) ; for(h = 0 ; h < ndims ; ++h) midx[h] = 1 ; /* Neighbors. */ nneighbors = 1 ; o=0 ; for(h = 0 ; h < pdims ; ++h) { nneighbors *= 3 ; midx[h] = -1 ; o -= offsets[h] ; } nneighbors -= 1 ; neighbors = mxMalloc(sizeof(int) * nneighbors) ; i = 0 ; while(VL_TRUE) { if(o != 0 ) neighbors[i++] = o ; h = 0 ; while( o += offsets[h], (++midx[h]) > 1 ) { o -= 3*offsets[h] ; midx[h] = -1 ; if(++h >= pdims) goto stop ; } } stop: ; /* Starts at the corner (1,1,...,1,0,0,...0) */ for(h = 0 ; h < pdims ; ++h) { midx[h] = 1 ; pt += offsets[h] ; } for(h = pdims ; h < ndims ; ++h) { midx[h] = 0 ; } /* --------------------------------------------------------------- * Loop * ------------------------------------------------------------ */ /* If any dimension in the first P is less than 3 elements wide then just return the empty matrix (if we proceed without doing anything we break the carry reporting algorithm below). */ for(h=0 ; h < pdims ; ++h) if(dims[h] < 3) goto end ; while(VL_TRUE) { double v ; bool is_greater; /* Propagate carry along multi index midx */ h = 0 ; while((midx[h]) >= dims[h] - 1) { pt += 2*offsets[h] ; /* skip first and last el. */ midx[h] = 1 ; if(++h >= pdims) goto next_layer ; ++midx[h] ; } /* for(h = 0 ; h < ndims ; ++h ) mexPrintf("%d ", midx[h]) ; mexPrintf(" -- %d -- pdims %d \n", pt - F_pt,pdims) ; */ /* Scan neighbors */ v = *pt ; is_greater = (v >= threshold) ; i = 0 ; while(is_greater && i < nneighbors) is_greater &= v > *(pt + neighbors[i++]) ; /* Add the local maximum */ if(is_greater) { /* Need more space? */ if(maxima_iterator == maxima_end) { maxima_size += M*N ; maxima_start = mxRealloc(maxima_start, maxima_size*sizeof(int)) ; maxima_end = maxima_start + maxima_size ; maxima_iterator = maxima_end - M*N ; } *maxima_iterator++ = pt - F_pt + 1 ; } /* Go to next element */ pt += 1 ; ++midx[0] ; continue ; next_layer: ; if( h >= ndims ) goto end ; while((++midx[h]) >= dims[h]) { midx[h] = 0 ; if(++h >= ndims) goto end ; } } end:; /* Return. */ { double* M_pt ; out[MAXIMA] = mxCreateDoubleMatrix (1, maxima_iterator-maxima_start, mxREAL) ; maxima_end = maxima_iterator ; maxima_iterator = maxima_start ; M_pt = mxGetPr(out[MAXIMA]) ; while(maxima_iterator != maxima_end) { *M_pt++ = *maxima_iterator++ ; } } /* Release space. */ mxFree(offsets) ; mxFree(neighbors) ; mxFree(midx) ; mxFree(maxima_start) ; } mxFree(dims) ; } vlfeat/toolbox/misc/vl_sampleinthist.c0000644000175000017500000000652512237004736017133 0ustar dimadima/** @internal ** @file samplinthist.c ** @author Andrea Vedaldi ** @brief Sample an integral histogram - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #undef T #undef SFX #define T vl_int32 #define SFX i #include "samplinthist.tc" #undef T #undef SFX #define T vl_uint32 #define SFX ui #include "samplinthist.tc" #undef T #undef SFX #define T double #define SFX d #include "samplinthist.tc" #undef T #undef SFX #define T float #define SFX f #include "samplinthist.tc" void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { mwSize dims [3] ; int numDims ; mwSize const * dimsPt = 0 ; void* histPt = 0 ; void const* intHistPt = 0 ; vl_uint32 const* boxesPt = 0 ; size_t numLabels = 0 ; mxClassID histClass = mxUINT32_CLASS ; size_t numBoxes = 0 ; int width, height ; enum {IN_INTHIST = 0, IN_BOXES, IN_END} ; enum {OUT_HIST = 0} ; /* ------------------------------------------------------------------ ** Check the arguments ** --------------------------------------------------------------- */ if (nin != 2) { vlmxError(vlmxErrInvalidArgument, "Two arguments required.") ; } else if (nout > 1) { vlmxError(vlmxErrInvalidArgument, "Too many output arguments."); } histClass = mxGetClassID(in[IN_INTHIST]) ; if (histClass != mxDOUBLE_CLASS && histClass != mxUINT32_CLASS) { vlmxError(vlmxErrInvalidArgument, "INTHIST must be of either class DOUBLE or UINT32.") ; } numDims = mxGetNumberOfDimensions(in[IN_INTHIST]) ; if (numDims > 3) { vlmxError(vlmxErrInvalidArgument, "INTHIST must be a MxNxK array.") ; } intHistPt = mxGetData(in[IN_INTHIST]) ; dimsPt = mxGetDimensions(in[IN_INTHIST]) ; height = dimsPt [0] ; width = dimsPt [1] ; numLabels = (numDims >= 3) ? dimsPt [2] : 1 ; numBoxes = mxGetNumberOfElements(in[IN_BOXES]) ; if (numBoxes % 4 != 0) { vlmxError(vlmxErrInvalidArgument, "The number of elements of BOXES must be a multiple of four.") ; } numBoxes /= 4 ; if (mxGetClassID(in[IN_BOXES]) != mxUINT32_CLASS) { vlmxError(vlmxErrInvalidArgument, "BOXES must be of class UINT32.") ; } boxesPt = (vl_uint32*) mxGetData(in[IN_BOXES]) ; /* Allocate space for the result */ dims [0] = numLabels ; dims [1] = numBoxes ; out [OUT_HIST] = mxCreateNumericArray(2, dims, histClass, mxREAL) ; histPt = mxGetData(out[OUT_HIST]) ; /* ------------------------------------------------------------------ * Sample integral histogram * --------------------------------------------------------------- */ #define PROCESS(SAMPLE, T) \ SAMPLE (histPt, \ intHistPt, height, width, numLabels, \ boxesPt, numBoxes) ; switch (histClass) { case mxDOUBLE_CLASS: { PROCESS(sample_d, double) } ; break ; case mxUINT32_CLASS: { PROCESS(sample_ui, vl_uint32) } ; break ; default: abort() ; } } vlfeat/toolbox/misc/vl_homkermap.c0000644000175000017500000001324012237004745016222 0ustar dimadima/** @file vl_homkmap.c ** @brief vl_homkmap - MEX implementation ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mexutils.h" #include #include #include enum { opt_KCHI2, opt_KL1, opt_KJS, opt_KINTERS, opt_kernel, opt_gamma, opt_period, opt_window } ; vlmxOption options [] = { {"kl1", 0, opt_KL1 }, {"kchi2", 0, opt_KCHI2 }, {"kjs", 0, opt_KJS }, {"kinters", 0, opt_KINTERS }, {"kernel", 1, opt_kernel }, {"gamma", 1, opt_gamma }, {"period", 1, opt_period }, {"window", 1, opt_window }, {0, 0, 0 } } ; /* ------------------------------------------------------------------- * Driver * ---------------------------------------------------------------- */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { typedef int unsigned data_t ; enum {IN_X = 0, IN_N, IN_END} ; enum {OUT_V = 0} ; VlHomogeneousKernelType kernelType = VlHomogeneousKernelChi2 ; VlHomogeneousKernelMapWindowType windowType = VlHomogeneousKernelMapWindowRectangular ; mwSize numDimensions ; mwSize const * dimensions ; mxClassID dataClassId ; double gamma = 1.0 ; int n ; double period = -1 ; /* for option parsing */ int opt ; int next = IN_END ; mxArray const *optarg ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (nin < IN_END) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } dataClassId = mxGetClassID(IN(X)) ; if (dataClassId != mxDOUBLE_CLASS && dataClassId != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "X is neither DOUBLE nor SINGLE.") ; } numDimensions = mxGetNumberOfDimensions(IN(X)) ; dimensions = mxGetDimensions(IN(X)) ; if (! vlmxIsPlainScalar(IN(N))) { vlmxError(vlmxErrInvalidArgument, "N is not a scalar.") ; } n = *mxGetPr(IN(N)) ; if (n < 0) { vlmxError(vlmxErrInvalidArgument, "N is negative.") ; } while ((opt = vlmxNextOption(in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_KINTERS: case opt_KL1: kernelType = VlHomogeneousKernelIntersection ; break ; case opt_KCHI2: kernelType = VlHomogeneousKernelChi2 ; break ; case opt_KJS: kernelType = VlHomogeneousKernelJS ; break ; case opt_kernel: /* make the kernel option argument to be the next option, and hence processed by the code above */ next-- ; break ; case opt_period: if (! vlmxIsPlainScalar(optarg)){ vlmxError(vlmxErrInvalidArgument, "PERIOD is not a scalar.") ; } period = *mxGetPr(optarg) ; if (period <= 0) { vlmxError(vlmxErrInvalidArgument, "PERIOD is not positive.") ; } break ; case opt_gamma: if (! vlmxIsPlainScalar(optarg)){ vlmxError(vlmxErrInvalidArgument, "GAMMA is not a scalar.") ; } gamma = *mxGetPr(optarg) ; if (gamma <= 0) { vlmxError(vlmxErrInvalidArgument, "GAMMA is not positive.") ; } break ; case opt_window: if (! vlmxIsString(optarg,-1)){ vlmxError(vlmxErrInvalidArgument, "WINDOW is not a string.") ; } else { char buffer [1024] ; mxGetString(optarg, buffer, sizeof(buffer) / sizeof(char)) ; if (vl_string_casei_cmp("uniform", buffer) == 0) { windowType = VlHomogeneousKernelMapWindowUniform ; } else if (vl_string_casei_cmp("rectangular", buffer) == 0) { windowType = VlHomogeneousKernelMapWindowRectangular ; } else { vlmxError(vlmxErrInvalidArgument, "WINDOW=%s is not recognized.", buffer) ; } } break ; default: abort() ; } } /* ----------------------------------------------------------------- * Evaluate kernel map * -------------------------------------------------------------- */ { vl_uindex j ; vl_size numElements = mxGetNumberOfElements(IN(X)) ; VlHomogeneousKernelMap * map = vl_homogeneouskernelmap_new (kernelType, gamma, n, period, windowType) ; mwSize extDimensions [20] ; for (j = 0 ; j < numDimensions ; ++j) extDimensions[j] = dimensions[j] ; extDimensions[0] *= 2*n+1 ; OUT(V) = mxCreateNumericArray(numDimensions, extDimensions, dataClassId, mxREAL) ; switch (dataClassId) { case mxDOUBLE_CLASS : { double * X = mxGetData(IN(X)) ; double * V = mxGetData(OUT(V)) ; for (j = 0 ; j < numElements ; ++j) { vl_homogeneouskernelmap_evaluate_d(map, V, 1, *X++) ; V += 2*n+1 ; } break ; } case mxSINGLE_CLASS : { float * X = mxGetData(IN(X)) ; float * V = mxGetData(OUT(V)) ; for (j = 0 ; j < numElements ; ++j) { vl_homogeneouskernelmap_evaluate_f(map, V, 1, *X++) ; V += 2*n+1 ; } break ; } default: abort() ; } vl_homogeneouskernelmap_delete (map) ; } } vlfeat/toolbox/misc/vl_svmpegasos.m0000644000175000017500000000223212237004745016437 0ustar dimadima% VL_SVMPEGASOS [deprecated] % VL_SVMPEGASOS is deprecated. Please use VL_SVMTRAIN() instead. function [w b info] = vl_svmpegasos(DATA,LAMBDA, varargin) % Verbose not supported if (sum(strcmpi('Verbose',varargin))) varargin(find(strcmpi('Verbose',varargin),1))=[]; fprintf('Option VERBOSE is no longer supported.\n'); end % DiagnosticCallRef not supported if (sum(strcmpi('DiagnosticCallRef',varargin))) varargin(find(strcmpi('DiagnosticCallRef',varargin),1)+1)=[]; varargin(find(strcmpi('DiagnosticCallRef',varargin),1))=[]; fprintf('Option DIAGNOSTICCALLREF is no longer supported.\n Please follow the VLFeat tutorial on SVMs for more information on diagnostics\n'); end % different default value for MaxIterations if (sum(strcmpi('MaxIterations',varargin)) == 0) varargin{end+1} = 'MaxIterations'; varargin{end+1} = ceil(10/LAMBDA); end % different default value for BiasMultiplier if (sum(strcmpi('BiasMultiplier',varargin)) == 0) varargin{end+1} = 'BiasMultiplier'; varargin{end+1} = 0; end [w b info] = vl_svmtrain(DATA,LAMBDA,varargin{:}); fprintf('\n vl_svmpegasos is DEPRECATED. Please use vl_svmtrain instead. \n\n'); end vlfeat/toolbox/misc/vl_getpid.m0000644000175000017500000000045212237004736015526 0ustar dimadima% VL_GETPID Get MATLAB process ID % VL_GETPID() returns MATLAB process ID. % % Algorithm:: % VL_GETPID() returns the result of the getpid() system call on % UNIX-like operating systems and of GetCurrentProcessID() on % Windows. % % See also: VL_HELP(). % Authors: Andrea Vedaldi vlfeat/toolbox/misc/vl_kdtreebuild.c0000644000175000017500000001274612237004745016547 0ustar dimadima/** @internal ** @file vl_kdetreebuild.c ** @brief vl_KDForestbuild MEX implementation ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include "kdtree.h" /* option codes */ enum { opt_verbose, opt_threshold_method, opt_num_trees, opt_distance } ; /* options */ vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"ThresholdMethod", 1, opt_threshold_method }, {"NumTrees", 1, opt_num_trees }, {"Distance", 1, opt_distance }, {0, 0, 0 } } ; /** ------------------------------------------------------------------ ** @brief MEX entry point **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_DATA = 0, IN_END} ; enum {OUT_TREE = 0} ; int verbose = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; VlKDForest * forest ; void * data ; vl_size numData ; vl_size dimension ; mxClassID dataClass ; vl_type dataType ; int thresholdingMethod = VL_KDTREE_MEDIAN ; VlVectorComparisonType distance = VlDistanceL2; vl_size numTrees = 1 ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { vlmxError(vlmxErrInvalidArgument, "At least one argument required") ; } else if (nout > 2) { vlmxError(vlmxErrInvalidArgument, "Too many output arguments"); } dataClass = mxGetClassID(IN(DATA)) ; if (! vlmxIsMatrix (IN(DATA), -1, -1) || ! vlmxIsReal (IN(DATA))) { vlmxError(vlmxErrInvalidArgument, "DATA must be a real matrix ") ; } switch (dataClass) { case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError(vlmxErrInvalidArgument, "DATA must be either SINGLE or DOUBLE") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buffer [1024] ; switch (opt) { case opt_threshold_method : mxGetString (optarg, buffer, sizeof(buffer)/sizeof(buffer[0])) ; if (! vlmxIsString(optarg, -1)) { vlmxError(vlmxErrInvalidOption, "THRESHOLDMETHOD must be a string") ; } if (vl_string_casei_cmp(buffer, "median") == 0) { thresholdingMethod = VL_KDTREE_MEDIAN ; } else if (vl_string_casei_cmp(buffer, "mean") == 0) { thresholdingMethod = VL_KDTREE_MEAN ; } else { vlmxError(vlmxErrInvalidOption, "Unknown thresholding method %s", buffer) ; } break ; case opt_num_trees : if (! vlmxIsScalar(optarg) || (numTrees = mxGetScalar(optarg)) < 1) { vlmxError(vlmxErrInvalidOption, "NUMTREES must be not smaller than one") ; } break ; case opt_verbose : ++ verbose ; break ; case opt_distance : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "DISTANCE must be a string.") ; } if (mxGetString (optarg, buffer, sizeof(buffer))) { vlmxError (vlmxErrInvalidArgument, "DISTANCE argument too long.") ; } if (vlmxCompareStringsI("l2", buffer) == 0) { distance = VlDistanceL2 ; } else if (vlmxCompareStringsI("l1", buffer) == 0) { distance = VlDistanceL1 ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for DISTANCE", buffer) ; } break ; } } data = mxGetData (IN(DATA)) ; numData = mxGetN (IN(DATA)) ; dimension = mxGetM (IN(DATA)) ; forest = vl_kdforest_new (dataType, dimension, numTrees, distance) ; vl_kdforest_set_thresholding_method (forest, thresholdingMethod) ; if (verbose) { char const * str = 0 ; mexPrintf("vl_kdforestbuild: data %s [%d x %d]\n", vl_get_type_name (dataType), dimension, numData) ; switch (vl_kdforest_get_thresholding_method(forest)) { case VL_KDTREE_MEAN : str = "mean" ; break ; case VL_KDTREE_MEDIAN : str = "median" ; break ; default: abort() ; } mexPrintf("vl_kdforestbuild: threshold selection method: %s\n", str) ; mexPrintf("vl_kdforestbuild: number of trees: %d\n", vl_kdforest_get_num_trees(forest)) ; } /* ----------------------------------------------------------------- * Do job * -------------------------------------------------------------- */ vl_kdforest_build (forest, numData, data) ; if (verbose) { vl_uindex ti ; for (ti = 0 ; ti < vl_kdforest_get_num_trees(forest) ; ++ ti) { mexPrintf("vl_kdforestbuild: tree %d: depth %d, num nodes %d\n", ti, vl_kdforest_get_depth_of_tree(forest, ti), vl_kdforest_get_num_nodes_of_tree(forest, ti)) ; } } out[OUT_TREE] = new_array_from_kdforest (forest) ; vl_kdforest_delete (forest) ; } vlfeat/toolbox/misc/vl_whistc.m0000644000175000017500000000220412237004736015550 0ustar dimadimafunction h = vl_whistc(x, w, edges, dim) % VL_WHISTC Weighted histogram % H = VL_WHISTC(X,W,EDGES) behaves exactly like HISTC(X,EDGES), but % weights the samples X by W. Samples that have NaN weight are % skipped. % % See also: HITSC(), VL_HELP(). % Authors: Andrea Vedladi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). numEdges = numel(edges) ; % map x to the bins defined by edges binIndexes = vl_binsearch(edges, x) ; % binsearch last bin is [edges(end) +inf] but for histc it is the % signleton [edges(end)] if numEdges > 0 binIndexes(binIndexes == numEdges & x > edges(end)) = 0 ; end % NaNs must be also removed binIndexes(isnan(x)) = 0 ; % find operating dimension dimensions = size(x) ; if nargin < 4 nonSingletonDims = find(dimensions > 1) ; if ~ isempty(nonSingletonDims) dim = nonSingletonDims(1) ; else dim = 2 ; % assume row vector end end % accumulate dimensions(dim) = numEdges ; h = zeros(dimensions) ; h = vl_binsum(h, w, binIndexes, dim) ; vlfeat/toolbox/misc/vl_hog.c0000644000175000017500000002370712237004745015025 0ustar dimadima/** @file vl_hog.c ** @brief vl_hog MEX definition. ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include /* option codes */ enum { opt_verbose, opt_variant, opt_num_orientations, opt_directed_polar_field, opt_undirected_polar_field, opt_bilinear_orientations } ; /* options */ vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"Variant", 1, opt_variant }, {"NumOrientations", 1, opt_num_orientations }, {"DirectedPolarField", 0, opt_directed_polar_field }, {"UndirectedPolarField", 0, opt_undirected_polar_field }, {"BilinearOrientations", 0, opt_bilinear_orientations }, {0, 0, 0 } } ; enum Mode {ExtractFeatures, Render, GetPermutation} ; enum InputType {Image, DirectedPolarField, UndirectedPolarField} ; static char const * inputTypeNames [] = { "Image", "DirectedPolarField", "UndirectedPolarField" } ; void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { mxArray const * descriptor_array ; float const * descriptor = NULL ; float const * image = NULL ; vl_size width = 0, height = 0, numChannels = 0 ; vl_size cellSize = 16 ; vl_size numOrientations = 9 ; vl_bool bilinearOrientations = VL_FALSE ; VlHogVariant variant = VlHogVariantUoctti ; char const * variantName ; enum {IN_I = 0, IN_CELLSIZE, IN_END} ; enum {OUT_FEATURES = 0} ; vl_bool verbose = 0 ; int opt, next ; mxArray const *optarg ; enum Mode mode = ExtractFeatures ; enum InputType inputType = Image ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (vlmxIsString(IN(I), -1)) { mxArray const * string_array = IN(I) ; if (vlmxIsEqualToStringI(string_array, "permutation")) { /* perm = vl_hog('permutation') */ mode = GetPermutation ; next = 1 ; } else if (vlmxIsEqualToStringI(string_array, "render")) { /* image = vl_hog('render', descriptor) */ mode = Render ; descriptor_array = IN(CELLSIZE) ; next = 2 ; if (! mxIsNumeric(descriptor_array) || ! vlmxIsReal(descriptor_array) || mxGetClassID(descriptor_array) != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "DESCRIPTOR is not a real numeric array of class SINGLE.") ; } if (mxGetNumberOfDimensions(descriptor_array) > 3) { vlmxError(vlmxErrInvalidArgument, "DESCRIPTOR has more than three dimensions.") ; } if (mxGetNumberOfDimensions(descriptor_array) == 3) { numChannels = mxGetDimensions(descriptor_array)[2] ; } else { numChannels = 1 ; } descriptor = mxGetData(descriptor_array) ; height = mxGetDimensions(descriptor_array)[0] ; width = mxGetDimensions(descriptor_array)[1] ; } else { vlmxError(vlmxErrInvalidArgument, "The first argument is neither an image nor a recognized command.") ; } } else { /* descriptor = vl_hog(image, cellSize) */ mode = ExtractFeatures ; next = 2 ; if (nin < 2) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (! mxIsNumeric(IN(I)) || ! vlmxIsReal(IN(I)) || mxGetClassID(IN(I)) != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "I is not a real numeric array of class SINGLE.") ; } if (mxGetNumberOfDimensions(IN(I)) > 3) { vlmxError(vlmxErrInvalidArgument, "I has more than three dimensions.") ; } if (mxGetNumberOfDimensions(IN(I)) == 3) { numChannels = mxGetDimensions(IN(I))[2] ; } else { numChannels = 1 ; } if (! vlmxIsPlainScalar(IN(CELLSIZE))) { vlmxError(vlmxErrInvalidArgument, "CELLSIZE is not a plain scalar.") ; } if (mxGetScalar(IN(CELLSIZE)) < 1.0) { vlmxError(vlmxErrInvalidArgument, "CELLSIZE is less than 1.") ; } cellSize = (vl_size) mxGetScalar(IN(CELLSIZE)) ; image = mxGetData(IN(I)) ; height = mxGetDimensions(IN(I))[0] ; width = mxGetDimensions(IN(I))[1] ; } /* parse the options */ while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_variant : if (! vlmxIsString(optarg, -1)) { vlmxError(vlmxErrInvalidArgument, "VARIANT must be a string") ; } if (vlmxIsEqualToStringI(optarg, "uoctti")) { variant = VlHogVariantUoctti ; } else if (vlmxIsEqualToStringI(optarg, "dalaltriggs")) { variant = VlHogVariantDalalTriggs ; } else { vlmxError(vlmxErrInvalidArgument, "The option VARIANT has an unknown value.") ; } break ; case opt_num_orientations : if (! vlmxIsPlainScalar(optarg)) { vlmxError(vlmxErrInvalidArgument, "NUMORIENTATIONS is not a plain scalar.") ; } if (mxGetScalar(optarg) < 1) { vlmxError(vlmxErrInvalidArgument, "NUMORIENTATIONS is smaller than one.") ; } numOrientations = mxGetScalar(optarg) ; break; case opt_directed_polar_field : inputType = DirectedPolarField ; break ; case opt_undirected_polar_field : inputType = UndirectedPolarField ; break ; case opt_bilinear_orientations : bilinearOrientations = VL_TRUE ; break ; case opt_verbose : ++ verbose ; break ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ switch (variant) { case VlHogVariantUoctti : variantName = "UOCTTI" ; break ; case VlHogVariantDalalTriggs : variantName = "DalalTriggs" ; break ; default: abort() ; break ; } switch (mode) { case ExtractFeatures : { /* recall that MATLAB images are transposed */ VlHog * hog = vl_hog_new (variant, numOrientations, VL_TRUE) ; mwSize dimensions [3] ; vl_hog_set_use_bilinear_orientation_assignments (hog, bilinearOrientations) ; if ((inputType == DirectedPolarField || inputType == UndirectedPolarField) && numChannels != 2) { vlmxError(vlmxErrInvalidArgument, "NUMCHANNELS=%d is not equal to two with input of type %s.", numChannels, inputTypeNames[inputType]) ; } switch (inputType) { case Image: vl_hog_put_image(hog, image, height, width, numChannels, cellSize) ; break ; case DirectedPolarField: case UndirectedPolarField: vl_hog_put_polar_field(hog, image, image + height*width, inputType == DirectedPolarField, height, width, cellSize) ; break ; default: abort() ; } dimensions[0] = vl_hog_get_width(hog) ; dimensions[1] = vl_hog_get_height(hog) ; dimensions[2] = vl_hog_get_dimension(hog) ; if (verbose) { mexPrintf("vl_hog: image: [%d x %d x %d]\n", height, width, numChannels) ; mexPrintf("vl_hog: descriptor: [%d x %d x %d]\n", dimensions[0], dimensions[1], dimensions[2]) ; mexPrintf("vl_hog: number of orientations: %d\n", numOrientations) ; mexPrintf("vl_hog: bilinear orientation assignments: %s\n", VL_YESNO(vl_hog_get_use_bilinear_orientation_assignments(hog))) ; mexPrintf("vl_hog: variant: %s\n", variantName) ; mexPrintf("vl_hog: input type: %s\n", inputTypeNames[inputType]) ; } OUT(FEATURES) = mxCreateNumericArray(3, dimensions, mxSINGLE_CLASS, mxREAL) ; vl_hog_extract (hog, mxGetData(OUT(FEATURES))) ; vl_hog_delete(hog) ; break ; } case GetPermutation : { VlHog * hog = vl_hog_new(variant, numOrientations, VL_TRUE) ; vl_size dimension = vl_hog_get_dimension(hog) ; vl_uindex k ; vl_uint32 * permutationOut ; vl_index const * permutation ; if (verbose) { mexPrintf("vl_hog: number of orientations: %d\n", numOrientations) ; mexPrintf("vl_hog: variant: %s\n", variantName) ; } OUT(FEATURES) = mxCreateNumericMatrix(dimension, 1, mxUINT32_CLASS, mxREAL) ; permutationOut = mxGetData(OUT(FEATURES)) ; permutation = vl_hog_get_permutation(hog) ; for (k = 0 ; k < dimension ; ++k) { permutationOut[k] = permutation[k] + 1 ; } break ; } case Render : { VlHog * hog = vl_hog_new(variant, numOrientations, VL_TRUE) ; vl_size glyphSize = vl_hog_get_glyph_size(hog) ; vl_size imageHeight = glyphSize * height ; vl_size imageWidth = glyphSize * width ; if (numChannels != vl_hog_get_dimension(hog)) { vlmxError(vlmxErrInvalidArgument, "The third dimension of DESCRIPTOR is not equal to the dimension of a HOG descriptor."); } if (verbose) { mexPrintf("vl_hog: descriptor: [%d x %d x %d]\n", height, width, numChannels) ; mexPrintf("vl_hog: glyph image: [%d x %d]\n", imageHeight, imageWidth) ; mexPrintf("vl_hog: number of orientations: %d\n", numOrientations) ; mexPrintf("vl_hog: variant: %s\n", variantName) ; } OUT(FEATURES) = mxCreateNumericMatrix(imageHeight, imageWidth, mxSINGLE_CLASS, mxREAL) ; vl_hog_render(hog, mxGetData(OUT(FEATURES)), descriptor, height, width) ; break ; } } } vlfeat/toolbox/misc/vl_svmtrain.m0000644000175000017500000001165712237004745016126 0ustar dimadima% VL_SVMTRAIN Train a Support Vector Machine % [W B] = VL_SVMTRAIN(X, Y, LAMBDA) trains a linear Support Vector % Machine (SVM) from the data vectors X and the labels Y. X is a D % by N matrix, with one column per example and D feature dimensions % (SINGLE or DOUBLE). Y is a DOUBLE vector with N elements with a % binary (-1 or +1) label for each training point. To a first order % approximation, the function computes a weight vector W and offset % B such that the score W'*X(:,i)+B has the same sign of LABELS(i) % for all i. % % VL_SVMTRAIN(DATASET, LABELS, LAMBDA) takes as input a DATASET % structure, which allows more sophisticated input formats to be % supported (see VL_SVMDATASET()). % % [W, B, INFO] = VL_SVMTRAIN(...) additionally returns a structure % INFO with the following fields: % % iteration:: % Number of iterations performed. % % epoch:: % Number of iterations over number of training data points. % % elapsedTime:: % Time elapsed since the start of training. % % objective:: % SVM objective value. % % regularizer:: % Regularizer value. % % loss:: % Loss value. % % scoreVariation:: [SGD only] % Mean square root of the difference between the last two % values of the SVM scores for each point. % % dualObjective:: [SDCA only] % Dual objective value. % % dualLoss:: [SDCA only] % Dual loss value:: % % dualityGap:: [SDCA only] % Difference between the objective and the dual objective. % % [W, B, INFO, SCORES] = VL_SVMTRAIN(X, Y, LABMDA) returns a row % vector of the SVM score for each training point. This can be used % in combination with the options SOLVER, MODEL, and BIAS to % evaluate an existing SVM on new data points. Furthermore INFO will % contain the corresponding SVM loss, regularizer, and objective % function value. If this information is not of interest, it is % possible to pass a null vector Y instead of the actual labels as % well as a null regularizer. % % VL_SVMTRAIN() accepts the following options: % % Verbose:: % Specify one or multiple times to increase the verbosity level. % Given only once, produces messages at the beginning and end of % the learning. Verbosity of at least 2 prints information at % every diagnostic step. % % Epsilon:: 1e-3 % Tolerance for the stopping criterion. % % MaxNumIterations:: 10/LAMBDA % Maximum number of iterations. % % BiasMultiplier:: 1 % Value of the constant B0 used as bias term (see below). % % BiasLearningRate:: 0.5 % Learning rate for the bias (SGD solver only). % % DiagnosticFunction:: [] % Diagnostic function callback. The callback takes the INFO % structure as only argument. To trace energies and plot graphs, % the callback can update a global variable or, preferably, be % defined as a nested function and update a local variable in the % parent function. % % DiagnosticFrequency:: Number of data points % After how many iteration the diagnostic is run. This step check % for convergence, and is done rarely, typically after each epoch % (pass over the data). It also calls the DiangosticFunction, % if any is specified. % % Loss:: HINGE % Loss function. One of HINGE, HINGE2, L1, L2, LOGISTIC. % % Solver:: SDCA % One of SGD (stochastic gradient descent [1]), SDCA (stochastic % dual coordinate ascent [2,3]), or NONE (no training). The % last option can be used in combination with the options MODEL % and BIAS to evaluate an existing SVM. % % Model:: null vector % Specifies the initial value for the weight vector W (SGD only). % % Bias:: 0 % Specifies the initial value of the bias term (SGD only). % % Weights:: [] % Specifies a weight vector to assign a different non-negative % weight to each data point. An application is to rebalance % unbalanced datasets. % % FORMULATION % % VL_SVMTRAIN() minimizes the objective function of the form: % % LAMBDA/2 |W|^2 + 1/N SUM_i LOSS(W' X(:,i), Y(i)) % % where LOSS(W' Xi,Yi) is the loss (hinge by default) for i-th % data point. The bias is incorporated by extending each data % point X with a feature of constant value B0, such that the % objective becomes % % LAMBDA/2 (|W|^2 + WB^2) 1/N SUM_i LOSS(W' X(:,i) + WB B0, Y(i)) % % Note that this causes the learned bias B = WB B0 to shrink % towards the origin. % % Example:: % Learn a linear SVM from data X and labels Y using 0.1 % as regularization coefficient: % % [w, b] = vl_svmtrain(x, y, 0.1) ; % % The SVM can be evaluated on new data XTEST with: % % scores = w'*xtest + b ; % % Alternatively, VL_SVMTRAIN() can be used for evaluation too: % % [~,~,~, scores] = vl_svmtrain(xtest, y, 0, 'model', w, 'bias', b, 'solver', 'none') ; % % The latter form is particularly useful when X is a DATASET structure. % % See also: SVM fundamentals, % VL_SVMDATASET(), VL_HELP(). % AUTHORIGHTS vlfeat/toolbox/misc/vl_argparse.m0000644000175000017500000000411412237004745016055 0ustar dimadimafunction [conf, args] = vl_argparse(conf, args, varargin) % VL_ARGPARSE Parse list of parameter-value pairs % CONF = VL_ARGPARSE(CONF, ARGS) updates the structure CONF based on % the specified parameter-value pairs ARGS={PAR1, VAL1, ... PARN, % VALN}. The function produces an error if an unknown parameter name % is passed in. % % [CONF, ARGS] = VL_ARGPARSE(CONF, ARGS) copies any parameter in % ARGS that does not match CONF back to ARGS instead of producing an % error. % % Example:: % The function can be used to parse a list of arguments % passed to a MATLAB functions: % % function myFunction(x,y,z,varargin) % conf.parameterName = defaultValue ; % conf = vl_argparse(conf, varargin) % % If only a subset of the options should be parsed, for example % because the other options are interpreted by a subroutine, then % use the form % % [conf, varargin] = vl_argparse(conf, varargin) % % that copies back to VARARGIN any unknown parameter. % % See also: VL_OVERRIDE(), VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if ~isstruct(conf), error('CONF must be a structure') ; end if length(varargin) > 0, args = {args, varargin{:}} ; end remainingArgs = {} ; names = fieldnames(conf) ; if mod(length(args),2) == 1 error('Parameter-value pair expected (missing value?).') ; end for ai = 1:2:length(args) paramName = args{ai} ; if ~ischar(paramName) error('The name of the parameter number %d is not a string.', (ai-1)/2+1) ; end value = args{ai+1} ; if isfield(conf,paramName) conf.(paramName) = value ; else % try case-insensitive i = find(strcmpi(paramName, names)) ; if isempty(i) if nargout < 2 error('Unknown parameter ''%s''.', paramName) ; else remainingArgs(end+1:end+2) = args(ai:ai+1) ; end else conf.(names{i}) = value ; end end end args = remainingArgs ; vlfeat/toolbox/misc/kdtree.h0000644000175000017500000003717112237004745015032 0ustar dimadima/** @internal ** @file KDForest.c ** @brief KDForest MEX utilities ** @author Andrea Vedaldi **/ #include "mex.h" #include #include /** ------------------------------------------------------------------ ** @interan@brief Hepler function ** @param tree KDForest object to process ** @param nodeIndex index of the KDForest node to start from ** @param numNodesToVisit total number of nodes to visit. ** ** The function visits in depth first order the tree's nodes starting ** from the root, restoring the node parent pointers. ** ** It also chekcs for tree consistency, aborting the MEX file in case ** of inconsistencies. Loops are detected by counting how many nodes ** have been visited so far compared to the total number of nodes in ** the tree. **/ void restore_parent_recursively (VlKDTree * tree, int nodeIndex, int * numNodesToVisit) { VlKDTreeNode * node = tree->nodes + nodeIndex ; int lowerChild = node->lowerChild ; int upperChild = node->upperChild ; if (*numNodesToVisit == 0) { vlmxError (vlmxErrInconsistentData, "FOREST.TREES has an inconsitsent tree structure.") ; } *numNodesToVisit -= 1 ; if (lowerChild >= 0) { VlKDTreeNode * child = tree->nodes + lowerChild ; child->parent = nodeIndex ; restore_parent_recursively (tree, lowerChild, numNodesToVisit) ; } if (upperChild >= 0) { VlKDTreeNode * child = tree->nodes + upperChild ; child->parent = nodeIndex ; restore_parent_recursively (tree, upperChild, numNodesToVisit) ; } } /** ------------------------------------------------------------------ ** @internal @brief Builds a MEX array representing a VlKDForest object ** @param tree object to convert. ** @return MEX representation of the tree. ** ** The KDForest object returned encapsulates the data (no copies are made). ** Recall that a KDForest object by design does not own the data. ** ** In case of error, the function aborts by calling ::mxErrMsgTxt. **/ static mxArray * new_array_from_kdforest (VlKDForest const * forest) { vl_uindex ti ; mwSize dims [] = {1,1} ; mwSize treeDims [] = {1,0} ; char const * fieldNames [] = { "dimension", "numData", "trees", "distance" } ; char const * treeFieldNames [] = { "nodes", "dataIndex" } ; char const * nodesFieldNames [] = { "lowerChild", "upperChild", "splitDimension", "splitThreshold", "lowerBound" , "upperBound" } ; mxArray * forest_array ; mxArray * trees_array ; treeDims [0] = 1 ; treeDims [1] = forest->numTrees ; trees_array = mxCreateStructArray (2, treeDims, sizeof(treeFieldNames) / sizeof(treeFieldNames[0]), treeFieldNames) ; /* FOREST.NUMDIMENSIONS FOREST.NUMDATA */ forest_array = mxCreateStructArray (2, dims, sizeof(fieldNames) / sizeof(fieldNames[0]), fieldNames) ; mxSetField (forest_array, 0, "dimension", vlmxCreatePlainScalar (forest->dimension)) ; mxSetField (forest_array, 0, "numData", vlmxCreatePlainScalar (forest->numData)) ; mxSetField (forest_array, 0, "trees", trees_array) ; switch(forest->distance){ case VlDistanceL1: mxSetField (forest_array, 0, "distance", mxCreateString("l1")); break; case VlDistanceL2: mxSetField (forest_array, 0, "distance", mxCreateString("l2")) ; break; default: abort(); } for (ti = 0 ; ti < forest->numTrees ; ++ ti) { VlKDTree * tree = forest->trees[ti] ; mxArray * nodes_array = mxCreateStructArray (2, dims, sizeof(nodesFieldNames) / sizeof(nodesFieldNames[0]), nodesFieldNames) ; mxArray * dataIndex_array = mxCreateNumericMatrix (1, forest->numData, mxUINT32_CLASS, mxREAL) ; mxSetField (trees_array, ti, "nodes", nodes_array) ; mxSetField (trees_array, ti, "dataIndex", dataIndex_array) ; /* FOREST.TREES.NODES.LOWERCHILD FOREST.TREES.NODES.UPPERCHILD FOREST.TREES.NODES.SPLITDIMENSION FOREST.TREES.NODES.SPLITTHRESHOLD */ { vl_uindex ni ; mxArray * lowerChild_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxINT32_CLASS, mxREAL) ; mxArray * upperChild_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxINT32_CLASS, mxREAL) ; mxArray * splitDimension_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxUINT32_CLASS, mxREAL) ; mxArray * splitThreshold_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxDOUBLE_CLASS, mxREAL) ; mxArray * lowerBound_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxDOUBLE_CLASS, mxREAL) ; mxArray * upperBound_array = mxCreateNumericMatrix (1, tree->numUsedNodes, mxDOUBLE_CLASS, mxREAL) ; vl_uint32 * upperChild = mxGetData (upperChild_array) ; vl_uint32 * lowerChild = mxGetData (lowerChild_array) ; vl_uint32 * splitDimension = mxGetData (splitDimension_array) ; double * splitThreshold = mxGetData (splitThreshold_array) ; double * lowerBound = mxGetData (lowerBound_array) ; double * upperBound = mxGetData (upperBound_array) ; for (ni = 0 ; ni < tree -> numUsedNodes ; ++ ni) { VlKDTreeNode const * node = tree -> nodes + ni ; int a = node->upperChild ; int b = node->lowerChild ; upperChild [ni] = (a>=0) ? a + 1 : a ; lowerChild [ni] = (b>=0) ? b + 1 : b ; splitDimension [ni] = node->splitDimension + 1 ; splitThreshold [ni] = node->splitThreshold ; lowerBound [ni] = node->lowerBound ; upperBound [ni] = node->upperBound ; } mxSetField (nodes_array, 0, "lowerChild", lowerChild_array) ; mxSetField (nodes_array, 0, "upperChild", upperChild_array) ; mxSetField (nodes_array, 0, "splitDimension", splitDimension_array) ; mxSetField (nodes_array, 0, "splitThreshold", splitThreshold_array) ; mxSetField (nodes_array, 0, "lowerBound", lowerBound_array) ; mxSetField (nodes_array, 0, "upperBound", upperBound_array) ; } /* FOREST.TREEE.DATAINDEX */ { vl_uint32 * dataIndex = mxGetData (dataIndex_array) ; vl_uindex di ; for (di = 0 ; di < forest->numData ; ++ di) { dataIndex [di] = forest->trees[ti]->dataIndex[di].index + 1 ; } } } return forest_array ; } /** ------------------------------------------------------------------ ** @internal @brief Builds a VlKDForest from MEX parameters ** @param forest_array MEX array representing the kd-tree ** @param data_array MEX array representing the kd-tree data ** @return KDForest object instance. ** ** The KDForest object returned encapsulates the data (no copies are made). ** Recall that a KDForest object by design does not own the data. ** ** In case of error, the function aborts by calling ::mxErrMsgTxt. **/ static VlKDForest * new_kdforest_from_array (mxArray const * forest_array, mxArray const * data_array) { VlKDForest * forest ; mxArray const * distance_array ; mxArray const * dimension_array ; mxArray const * numData_array ; mxArray const * trees_array ; mxArray const * nodes_array ; mxArray const * dataIndex_array ; mxArray const * lowerChild_array ; mxArray const * upperChild_array ; mxArray const * splitDimension_array ; mxArray const * splitThreshold_array ; mxArray const * lowerBound_array; mxArray const * upperBound_array; vl_int32 const * lowerChild ; vl_int32 const * upperChild ; vl_uint32 const * splitDimension ; double const * splitThreshold ; double const * upperBound ; double const * lowerBound ; vl_uindex ti ; int unsigned dimension ; VlVectorComparisonType distance; vl_size numData ; vl_size numUsedNodes ; vl_size numTrees ; vl_size maxNumNodes = 0; vl_type dataType ; /* FOREST.NUMDIMENSIONS FOREST.NUMDATA FOREST.DATA FOREST.TREES */ distance_array = mxGetField (forest_array, 0, "distance") ; if(distance_array && vlmxIsString (distance_array, -1)){ if (vlmxCompareToStringI(distance_array, "l1") == 0) { distance = VlDistanceL1 ; } else if (vlmxCompareToStringI(distance_array, "l2") == 0) { distance = VlDistanceL2 ; } else { vlmxError(vlmxErrInconsistentData, "FOREST.DISTANCE must be either 'l1' or 'l2'.") ; } } else { vlmxError(vlmxErrInconsistentData, "FOREST.DISTANCE must be a string.") ; } if (! mxIsStruct (forest_array) || mxGetNumberOfElements (forest_array) != 1) { vlmxError (vlmxErrInconsistentData, "FOREST must be a 1 x 1 structure.") ; } dimension_array = mxGetField (forest_array, 0, "dimension") ; if (! dimension_array || ! vlmxIsPlainScalar (dimension_array) || (dimension = mxGetScalar (dimension_array)) < 1) { vlmxError(vlmxErrInconsistentData, "FOREST.NUMDIMENSIONS must be a poisitve integer.") ; } numData_array = mxGetField (forest_array, 0, "numData") ; if (! numData_array || ! vlmxIsPlainScalar (numData_array) || (numData = mxGetScalar (numData_array)) < 1) { vlmxError(vlmxErrInconsistentData, "FOREST.NUMDATA must be a poisitve integer.") ; } trees_array = mxGetField (forest_array, 0, "trees") ; if (! mxIsStruct (trees_array)) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES must be a structure array.") ; } numTrees = mxGetNumberOfElements (trees_array) ; if (numTrees < 1) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES must have at least one element.") ; } if (! vlmxIsMatrix (data_array, dimension, numData)) { vlmxError(vlmxErrInconsistentData, "DATA dimensions are not compatible with TREE.") ; } if (! vlmxIsReal (data_array)) { vlmxError(vlmxErrInvalidArgument, "DATA must be real.") ; } switch (mxGetClassID (data_array)) { case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ; default : vlmxError(vlmxErrInvalidArgument, "DATA must be either SINGLE or DOUBLE.") ; } forest = vl_kdforest_new (dataType, dimension, numTrees, distance) ; forest->numData = numData ; forest->trees = vl_malloc (sizeof(VlKDTree*) * numTrees) ; forest->data = mxGetData (data_array) ; /* FOREST.TREES.NODES FOREST.TREES.DATAINDEX */ for (ti = 0 ; ti < numTrees ; ++ ti) { VlKDTree * tree = vl_malloc (sizeof(VlKDTree)) ; nodes_array = mxGetField (trees_array, ti, "nodes") ; dataIndex_array = mxGetField (trees_array, ti, "dataIndex") ; if (! nodes_array || ! mxIsStruct (nodes_array)) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES must be a struct array.", ti+1) ; } /* FOREST.TREES.NODES.LOWERCHILD FOREST.TREES.NODES.UPPERCHILD FOREST.TREES.NODES.SPLITTHRESHOLD FOREST.TREES.NODES.SPLITDIMENSION */ lowerChild_array = mxGetField (nodes_array, 0, "lowerChild") ; upperChild_array = mxGetField (nodes_array, 0, "upperChild") ; splitDimension_array = mxGetField (nodes_array, 0, "splitDimension") ; splitThreshold_array = mxGetField (nodes_array, 0, "splitThreshold") ; lowerBound_array = mxGetField (nodes_array, 0, "lowerBound") ; upperBound_array = mxGetField (nodes_array, 0, "upperBound") ; numUsedNodes = mxGetN (lowerChild_array) ; maxNumNodes += numUsedNodes ; if (! lowerChild_array || ! vlmxIsMatrix (lowerChild_array, 1, numUsedNodes) || mxGetClassID (lowerChild_array) != mxINT32_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.LOWERCHILD must be a 1 x NUMNODES INT32 array.",ti+1) ; } if (! upperChild_array || ! vlmxIsMatrix (upperChild_array, 1, numUsedNodes) || mxGetClassID (upperChild_array) != mxINT32_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.UPPERCHILD must be a 1 x NUMNODES INT32 array.",ti+1) ; } if (! splitDimension_array || ! vlmxIsMatrix (splitDimension_array, 1, numUsedNodes) || mxGetClassID (splitDimension_array) != mxUINT32_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.SPLITDIMENSION must be a 1 x NUMNODES UINT32 array",ti+1) ; } if (! splitThreshold_array || ! vlmxIsMatrix (splitThreshold_array, 1, numUsedNodes) || mxGetClassID (splitThreshold_array) != mxDOUBLE_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.SPLITTHRESHOLD must be a 1 x NUMNODES DOUBLE array",ti+1) ; } if (! splitThreshold_array || ! vlmxIsMatrix (lowerBound_array, 1, numUsedNodes) || mxGetClassID (lowerBound_array) != mxDOUBLE_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.LOWERBOUND must be a 1 x NUMNODES DOUBLE array",ti+1) ; } if (! splitThreshold_array || ! vlmxIsMatrix (upperBound_array, 1, numUsedNodes) || mxGetClassID (upperBound_array) != mxDOUBLE_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).NODES.UPPERBOUND must be a 1 x NUMNODES DOUBLE array",ti+1) ; } lowerChild = (vl_int32*) mxGetData (lowerChild_array) ; upperChild = (vl_int32*) mxGetData (upperChild_array) ; splitDimension = (vl_uint32*) mxGetData (splitDimension_array) ; splitThreshold = (double*) mxGetData (splitThreshold_array) ; lowerBound = (double*) mxGetData (lowerBound_array) ; upperBound = (double*) mxGetData (upperBound_array) ; if (! dataIndex_array || ! vlmxIsMatrix (dataIndex_array, 1, numData) || mxGetClassID (dataIndex_array) != mxUINT32_CLASS) { vlmxError(vlmxErrInconsistentData, "FOREST.TREES(%d).DATAINDEX must be a 1 x NUMDATA array of class UINT32.",ti+1) ; } tree->numAllocatedNodes = numUsedNodes ; tree->numUsedNodes = numUsedNodes ; tree->nodes = vl_malloc (sizeof(VlKDTreeNode) * numUsedNodes) ; tree->dataIndex = vl_malloc (sizeof(VlKDTreeDataIndexEntry) * numData) ; { vl_uindex ni ; for (ni = 0 ; ni < numUsedNodes ; ++ ni) { vl_int32 lc = lowerChild [ni] ; vl_int32 uc = upperChild [ni] ; vl_uint32 d = splitDimension [ni] ; if (uc < - (signed)numData - 1 || uc > (signed)numUsedNodes) { vlmxError (vlmxErrInconsistentData, "TREE.NODES.UPPERCHILD(%d)=%d out of bounds", ni+1,uc) ; } if (lc < - (signed)numData || lc > (signed)numUsedNodes) { vlmxError (vlmxErrInconsistentData, "TREE.NODES.LOWERCHILD(%d)=%d out of bounds", ni+1,lc) ; } if (d > dimension) { vlmxError (vlmxErrInconsistentData, "TREE.NODES.SPLITDIMENSION(%d)=%d out of bounds", ni+1,d) ; } tree->nodes[ni].parent = 0 ; tree->nodes[ni].upperChild = (uc >= 1) ? uc-1 : uc ; tree->nodes[ni].lowerChild = (lc >= 1) ? lc-1 : lc ; tree->nodes[ni].splitDimension = d - 1 ; tree->nodes[ni].splitThreshold = splitThreshold[ni] ; tree->nodes[ni].lowerBound = lowerBound[ni] ; tree->nodes[ni].upperBound = upperBound[ni] ; } } { vl_uindex di ; vl_uint32 * dataIndex = mxGetData (dataIndex_array) ; for (di = 0 ; di < numData ; ++ di) { tree->dataIndex[di].index = dataIndex [di] - 1 ; } } { int numNodesToVisit = tree->numUsedNodes ; restore_parent_recursively (tree, 0, &numNodesToVisit) ; if (numNodesToVisit != 0) { vlmxError (vlmxErrInconsistentData, "TREE has an inconsitsent tree structure.") ; } } forest->trees[ti] = tree ; } forest->maxNumNodes = maxNumNodes; return forest ; } vlfeat/toolbox/misc/vl_grad.m0000644000175000017500000000327612237004736015176 0ustar dimadimafunction [Ix,Iy] = vl_grad(I,varargin) ; % VL_GRAD Compute the gradient of an image % [IX,IY] = VL_GRAD(I) returns the gradient components IX,IY of the % 2-D discrete function I. I must be a two-dimensional % matrix. VL_GRAD() computes the gradient by using finite % differences; specifically, it uses central differences for all but % the boundary pixels, for which it uses forward/backward % differences as appropriate. % % Remark:: % VL_GRAD() is similar to the MATLAB built-in GRADIENT() function, % excepts that it supports different gradient approximations. % % VL_GRAD() accepts the following options: % % Type:: central % Specify which type of finite differences to use for all but the % boundary samples. TYPE can be one of 'central', 'forward', or % 'backward'. % % See also: GRADIENT(), VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.type = 'central' ; opts = vl_argparse(opts, varargin) ; switch lower(opts.type) case 'central' Ix = [ I(:,2)-I(:,1) , ... 0.5*(I(:,3:end)-I(:,1:end-2)) , I(:,end)-I(:,end-1) ] ; Iy = [ I(2,:)-I(1,:) ; ... 0.5*(I(3:end,:)-I(1:end-2,:)) ; I(end,:)-I(end-1,:) ] ; case 'forward' Ix = [ I(:,2:end)-I(:,1:end-1) , I(:,end)-I(:,end-1) ] ; Iy = [ I(2:end,:)-I(1:end-1,:) ; I(end,:)-I(end-1,:) ] ; case 'backward' Ix = [ I(:,2)-I(:,1) , I(:,2:end)-I(:,1:end-1)] ; Iy = [ I(2,:)-I(1,:) ; I(2:end,:)-I(1:end-1,:)] ; otherwise error('Difference type ''%s'' unknown.', opts.type) ; end vlfeat/toolbox/misc/vl_kdtreebuild.m0000644000175000017500000000464612237004745016561 0ustar dimadima% VL_KDTREEBUILD Build randomized kd-tree % FOREST = VL_KDTREEBUILD(X) returns a structure FOREST containing % the kd-tree indexing the data X. X is a M x N dimensional matrix % of class DOUBLE or SINGLE with one data point per column. Use % VL_KDTREEQUERY() to query the index. % % VL_KDETREEBUILD() accepts the following options: % % NumTrees:: [1] % Specifies the number of semi-randomized KD-trees to add to the % forest. Multiple trees can be used to improve approximate % nearest-neighbors searches with VL_KDTREEQUERY(). % % ThresholdMethod:: [MEDIAN] % Specifies the thresholding method used to split the data. Two % methods are supported: split around the median (MEDIAN) or split % around the mean (MEAN). % % Verbose:: % Increases the verbosity level (can be repeated). % % Distance:: [L2] % Specifies the type of norm which the KD-Tree should use (L1 or L2) % % The FOREST structure has the following fields: % % FOREST.TREES:: % A structure array with one element per tree. % % FOREST.DIMENSION:: % Dimensionality of the indexed data. % % FOREST.NUMDATA:: % Number of indexed data points. % % The TREE structure has the following fields: % % TREE.NODES:: % A structure array representing the nodes of the tree. % % TREE.DATAINDEX:: % A 1 x NUMDATA vector of class UINT32 representing a permutation % of the data. % % Nodes are numbered from 1 to NUMNODES. The NODES structure array % has the following fields: % % NODES.LOWERCHILD and NODES.UPPERCHILD:: % 1 x NUMNODES vectors of class INT32. A positive value is the % index of the lower/upper child node. A negative value denotes a % leaf and is (after negation) is the first or last element plus % one of a range of entries in the permutation TREE.DATAINDEX. % Such entries are in turn indexes of the data points that belong % to that leaf. Typically there is one point per leaf. % % NODES.SPLITDIMENSION and NODES.SPLITTHRESHOLD:: % 1 x NUMNODES vector of class UINT32 and DOUBLE, respectively, % with the index of the splitting dimension and the threshold for % each node. % % See also: VL_KDTREEQUERY(), VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_svmtrain.c0000644000175000017500000005455112237004745016114 0ustar dimadima/** @file vl_svmtrain.c ** @brief vl_svmtrain MEX definition ** @author Daniele Perrone ** @author Milan Sulc ** @author Andrea Vedaldi **/ /* Copyright (C) 2012 Daniele Perrone. Copyright (C) 2013 Milan Sulc Copyright (C) 2011-13 Andrea Vedaldi. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include /* option codes */ enum { // common opt_epsilon, opt_max_num_iterations, opt_bias_multiplier, opt_diagnostic_function, opt_diagnostic_frequency, opt_validation_subset, opt_loss, opt_model, opt_bias, opt_weights, // switching to SDCA opt_verbose, opt_solver, // SGD specific opt_starting_iteration, opt_bias_learning_rate // DCA specific: }; /* options */ vlmxOption options [] = { {"Epsilon", 1, opt_epsilon }, {"MaxNumIterations", 1, opt_max_num_iterations }, {"BiasMultiplier", 1, opt_bias_multiplier }, {"DiagnosticFunction", 1, opt_diagnostic_function }, {"DiagnosticFrequency", 1, opt_diagnostic_frequency}, {"ValidationSubset", 1, opt_validation_subset }, {"Loss", 1, opt_loss }, {"Verbose", 0, opt_verbose }, {"Solver", 1, opt_solver }, {"Model", 1, opt_model }, {"Bias", 1, opt_bias }, {"Weights", 1, opt_weights }, // SGD specific {"StartingIteration", 1, opt_starting_iteration }, {"BiasLearningRate", 1, opt_bias_learning_rate }, // DCA specific: {0, 0, 0 } } ; mxArray * createScalarStructArray(void const **fields) { void const **iter ; char const **niter ; char const **names ; vl_size numFields = 0 ; mxArray * s ; mwSize dims [] = {1, 1} ; for (iter = fields ; *iter ; iter += 2) numFields++ ; names = vl_calloc(numFields, sizeof(char const*)) ; for (iter = fields, niter = names ; *iter ; iter += 2, niter++) { *niter = *iter ; } s = mxCreateStructArray(sizeof(dims)/sizeof(dims[0]), dims, (int)numFields, names) ; for (iter = fields, niter = names ; *iter; iter += 2, niter++) { mxSetField(s, 0, *niter, (mxArray*)(*(iter+1))) ; } return s ; } /* ---------------------------------------------------------------- */ /* Parsing datasets */ /* ---------------------------------------------------------------- */ VlSvmDataset * parseDataset(const mxArray * dataset_array) { VlSvmDataset * dataset ; { mxArray * data_array ; mxClassID dataClass ; vl_size dimension ; vl_size numData ; vl_type dataType ; if (! mxIsStruct(dataset_array)) { vlmxError(vlmxErrInvalidArgument, "DATASET is not a structure.") ; } if (mxGetNumberOfElements(dataset_array) != 1) { vlmxError(vlmxErrInvalidArgument, "DATASET is not a singleton.") ; } data_array = mxGetField(dataset_array, 0, "data") ; if (data_array == NULL) { vlmxError(vlmxErrInvalidArgument, "DATASET is missing the DATA field.") ; } if (!vlmxIsMatrix(data_array,-1,-1)) { vlmxError(vlmxErrInvalidArgument,"DATASET.DATA is not a matrix.") ; } dimension = mxGetM (data_array) ; numData = mxGetN (data_array) ; dataClass = mxGetClassID (data_array) ; if (dimension == 0 || numData == 0) { vlmxError(vlmxErrInvalidArgument, "DATASET.DATA is empty.") ; } switch (dataClass) { case mxSINGLE_CLASS : dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS : dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError(vlmxErrInvalidArgument, "DATASET.DATA is neither either SINGLE or DOUBLE.") ; } dataset = vl_svmdataset_new(dataType, mxGetData(data_array), dimension, numData) ; } /* homogeneous kernel map support */ { VlHomogeneousKernelType kernelType = VlHomogeneousKernelChi2 ; VlHomogeneousKernelMapWindowType windowType = VlHomogeneousKernelMapWindowRectangular ; double gamma = 1.0 ; double period = -1 ; int n = 1 ; VlHomogeneousKernelMap * hom = NULL ; mxArray * hom_array ; mxArray * field ; hom_array = mxGetField(dataset_array, 0, "homkermap") ; if (hom_array != NULL) { if (!mxIsStruct(hom_array)) { vlmxError(vlmxErrInvalidArgument, "DATASET.HOMKERMAP is not a structure") ; } field = mxGetField(hom_array, 0, "order") ; if (field != NULL) { if (! vlmxIsPlainScalar(field)) { vlmxError(vlmxErrInvalidArgument, "DATASET.HOMKERMAP.ORDER is not a scalar.") ; } n = *mxGetPr(field) ; if (n < 0) { vlmxError(vlmxErrInvalidArgument, "DATASET.HOMKERMAP.ORDER is negative.") ; } } field = mxGetField(hom_array, 0, "kernel") ; if (field != NULL) { char buffer [1024] ; mxGetString(field, buffer, sizeof(buffer) / sizeof(char)) ; if (vl_string_casei_cmp("kl1", buffer) == 0) { kernelType = VlHomogeneousKernelIntersection ; } else if (vl_string_casei_cmp("kchi2", buffer) == 0) { kernelType = VlHomogeneousKernelChi2 ; } else if (vl_string_casei_cmp("kjs", buffer) == 0) { kernelType = VlHomogeneousKernelJS ; } else if (vl_string_casei_cmp("kinters", buffer) == 0) { kernelType = VlHomogeneousKernelIntersection ; } else { vlmxError(vlmxErrInvalidArgument, "DATASET.HOMKERMAP.KERNEL is not a recognized kernel type.") ; } } field = mxGetField(hom_array, 0, "window") ; if (field != NULL) { char buffer [1024] ; mxGetString(field, buffer, sizeof(buffer) / sizeof(char)) ; if (vl_string_casei_cmp("uniform", buffer) == 0) { windowType = VlHomogeneousKernelMapWindowUniform ; } else if (vl_string_casei_cmp("rectangular", buffer) == 0) { windowType = VlHomogeneousKernelMapWindowRectangular; } else { vlmxError(vlmxErrInvalidArgument, "DATASET.HOMKERMAP.WINDOW is not a recognized window type.") ; } } field = mxGetField(hom_array, 0, "gamma") ; if (field != NULL) { if (! vlmxIsPlainScalar(field)) { vlmxError(vlmxErrInvalidArgument, "GAMMA is not a scalar.") ; } gamma = *mxGetPr(field) ; if (gamma <= 0) { vlmxError(vlmxErrInvalidArgument, "GAMMA is not positive.") ; } } field = mxGetField(hom_array, 0, "period") ; if (field != NULL) { if (! vlmxIsPlainScalar(field)) { vlmxError(vlmxErrInvalidArgument, "PERIOD is not a scalar.") ; } period = *mxGetPr(field) ; if (period <= 0) { vlmxError(vlmxErrInvalidArgument, "PERIOD is not positive.") ; } } hom = vl_homogeneouskernelmap_new (kernelType, gamma, n, period, windowType) ; vl_svmdataset_set_homogeneous_kernel_map (dataset, hom) ; } } return dataset ; } /* ---------------------------------------------------------------- */ /* Diagnostic helpers */ /* ---------------------------------------------------------------- */ mxArray * makeInfoStruct (VlSvm* svm) { VlSvmStatistics const * s = vl_svm_get_statistics(svm) ; mxArray * info = 0 ; switch (vl_svm_get_solver(svm)) { case VlSvmSolverSdca: { void const * fields [] = { "solver", mxCreateString("sdca"), "lambda", vlmxCreatePlainScalar(vl_svm_get_lambda(svm)), "biasMultiplier", vlmxCreatePlainScalar(vl_svm_get_bias_multiplier(svm)), "bias", vlmxCreatePlainScalar(vl_svm_get_bias(svm)), "objective", vlmxCreatePlainScalar(s->objective), "regularizer", vlmxCreatePlainScalar(s->regularizer), "loss", vlmxCreatePlainScalar(s->loss), "dualObjective", vlmxCreatePlainScalar(s->dualObjective), "dualLoss", vlmxCreatePlainScalar(s->dualLoss), "dualityGap", vlmxCreatePlainScalar(s->dualityGap), "iteration", vlmxCreatePlainScalar(s->iteration), "epoch", vlmxCreatePlainScalar(s->epoch), "elapsedTime", vlmxCreatePlainScalar(s->elapsedTime), 0, 0 } ; info = createScalarStructArray(fields) ; break ; } case VlSvmSolverSgd: { void const * fields [] = { "solver", mxCreateString("sgd"), "lambda", vlmxCreatePlainScalar(vl_svm_get_lambda(svm)), "biasMultiplier", vlmxCreatePlainScalar(vl_svm_get_bias_multiplier(svm)), "bias", vlmxCreatePlainScalar(vl_svm_get_bias(svm)), "objective", vlmxCreatePlainScalar(s->objective), "regularizer", vlmxCreatePlainScalar(s->regularizer), "loss", vlmxCreatePlainScalar(s->loss), "scoreVariation", vlmxCreatePlainScalar(s->scoresVariation), "iteration", vlmxCreatePlainScalar(s->iteration), "epoch", vlmxCreatePlainScalar(s->epoch), "elapsedTime", vlmxCreatePlainScalar(s->elapsedTime), 0, 0 } ; info = createScalarStructArray(fields) ; break ; } case VlSvmSolverNone : { void const * fields [] = { "solver", mxCreateString("none"), "lambda", vlmxCreatePlainScalar(vl_svm_get_lambda(svm)), "biasMultiplier", vlmxCreatePlainScalar(vl_svm_get_bias_multiplier(svm)), "bias", vlmxCreatePlainScalar(vl_svm_get_bias(svm)), "objective", vlmxCreatePlainScalar(s->objective), "regularizer", vlmxCreatePlainScalar(s->regularizer), "loss", vlmxCreatePlainScalar(s->loss), "elapsedTime", vlmxCreatePlainScalar(s->elapsedTime), 0, 0 } ; info = createScalarStructArray(fields) ; break ; } default: assert(0) ; } return info ; } /* ---------------------------------------------------------------- */ /* SVM diagnostic callback */ /* ---------------------------------------------------------------- */ typedef struct DiagnsoticOpts_ { vl_bool verbose ; mxArray const * matlabDiagonsticFunctionHandle ; } DiagnosticOpts ; void diagnostic (VlSvm * svm, DiagnosticOpts * opts) { VlSvmStatistics const * s = vl_svm_get_statistics(svm) ; if ((opts->verbose && s->status != VlSvmStatusTraining) || (opts->verbose > 1)) { const char * statusName = 0 ; switch (s->status) { case VlSvmStatusTraining: statusName = "training" ; break ; case VlSvmStatusConverged: statusName = "converged" ; break ; case VlSvmStatusMaxNumIterationsReached: statusName = "max num iterations reached" ; break ; } mexPrintf("vl_svmtrain: iteration: %d (epoch: %d)\n", s->iteration+1, s->epoch+1) ; mexPrintf("\ttime elapsed: %f\n", s->elapsedTime) ; mexPrintf("\tobjective: %g (regul: %g, loss: %g)\n", s->objective, s->regularizer, s->loss) ; switch (vl_svm_get_solver(svm)) { case VlSvmSolverSgd: mexPrintf("\tscore variation: %f\n", s->scoresVariation) ; break; case VlSvmSolverSdca: mexPrintf("\tdual objective: %g (dual loss: %g)\n", s->dualObjective, s->dualLoss) ; mexPrintf("\tduality gap: %g\n", s->dualityGap) ; break; default: break; } mexPrintf("\tstatus: %s\n", statusName) ; } if (opts->matlabDiagonsticFunctionHandle) { mxArray *rhs[2] ; rhs[0] = (mxArray*) opts->matlabDiagonsticFunctionHandle ; rhs[1] = makeInfoStruct(svm) ; if (mxIsClass(rhs[0] , "function_handle")) { mexCallMATLAB(0,NULL,sizeof(rhs)/sizeof(rhs[0]),rhs,"feval") ; } mxDestroyArray(rhs[1]) ; } } /* ---------------------------------------------------------------- */ /* MEX entry point */ /* ---------------------------------------------------------------- */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_DATASET = 0, IN_LABELS, IN_LAMBDA, IN_END} ; enum {OUT_MODEL = 0, OUT_BIAS, OUT_INFO, OUT_SCORES, OUT_END} ; vl_int opt, next; mxArray const *optarg ; VlSvmSolverType solver = VlSvmSolverSdca ; VlSvmLossType loss = VlSvmLossHinge ; int verbose = 0 ; VlSvmDataset * dataset ; double * labels ; double * weights = NULL ; double lambda ; double epsilon = -1 ; double biasMultipler = -1 ; vl_index maxNumIterations = -1 ; vl_index diagnosticFrequency = -1 ; mxArray const * matlabDiagnosticFunctionHandle = NULL ; mxArray const * initialModel_array = NULL ; double initialBias = VL_NAN_D ; vl_index startingIteration = -1 ; /* SGD */ double sgdBiasLearningRate = -1 ; VL_USE_MATLAB_ENV ; if (nin < 3) { vlmxError(vlmxErrInvalidArgument, "At least three arguments are required.") ; } if (nout > OUT_END) { vlmxError(vlmxErrInvalidArgument, "Too many output arguments."); } #define GET_SCALAR(NAME, variable) \ if (!vlmxIsPlainScalar(optarg)) { \ vlmxError(vlmxErrInvalidArgument, VL_STRINGIFY(NAME) " is not a plain scalar.") ; \ } \ variable = (double) *mxGetPr(optarg); #define GET_NN_SCALAR(NAME, variable) GET_SCALAR(NAME, variable) \ if (variable < 0) { \ vlmxError(vlmxErrInvalidArgument, VL_STRINGIFY(NAME) " is negative.") ; \ } /* Mode 1: pass data, labels, lambda, and options */ if (mxIsNumeric(in[IN_DATASET])) { mxArray const* samples_array = in[IN_DATASET] ; vl_size dimension ; vl_size numSamples ; void * data ; vl_type dataType ; if (!vlmxIsMatrix(samples_array, -1, -1)) { vlmxError (vlmxErrInvalidArgument, "X is not a matrix.") ; } if (mxGetClassID(samples_array) == mxDOUBLE_CLASS) { dataType = VL_TYPE_DOUBLE ; } else if (mxGetClassID(samples_array) == mxSINGLE_CLASS) { dataType = VL_TYPE_FLOAT ; } else { vlmxError (vlmxErrInvalidArgument, "X is not of class SINGLE or DOUBLE.") ; } data = mxGetData(samples_array) ; dimension = mxGetM(samples_array) ; numSamples = mxGetN(samples_array) ; dataset = vl_svmdataset_new(dataType, data, dimension, numSamples) ; } /* Mode 2: pass dataset structure */ else { dataset = parseDataset(in[IN_DATASET]) ; } { mxArray const* labels_array = in[IN_LABELS] ; if (!vlmxIsPlainMatrix(labels_array, -1, -1)) { vlmxError (vlmxErrInvalidArgument, "Y is not a plain matrix.") ; } labels = mxGetPr(labels_array) ; if (mxGetNumberOfElements(labels_array) != vl_svmdataset_get_num_data(dataset)) { vlmxError (vlmxErrInvalidArgument, "The number of labels Y is not the same as the number of data samples X.") ; } optarg = in[IN_LAMBDA] ; GET_NN_SCALAR(LAMBDA, lambda) ; } /* Parse optional arguments */ next = 3 ; while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose: verbose ++ ; break ; case opt_epsilon: GET_NN_SCALAR(EPSLON, epsilon) ; break ; case opt_bias_multiplier: GET_NN_SCALAR(BIASMULTIPLIER, biasMultipler) ; break ; case opt_max_num_iterations: GET_NN_SCALAR(MAXNUMITERATIONS, maxNumIterations) ; break ; case opt_diagnostic_frequency: GET_NN_SCALAR(DIAGNOSTICFREQUENCY, diagnosticFrequency) ; break ; case opt_diagnostic_function: if (!mxIsClass(optarg ,"function_handle")) { mexErrMsgTxt("DIAGNOSTICSFUNCTION is not a function handle."); } matlabDiagnosticFunctionHandle = optarg ; break ; case opt_solver : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "SOLVER must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "SOLVER argument too long.") ; } if (vlmxCompareStringsI("sgd", buf) == 0) { solver = VlSvmSolverSgd ; } else if (vlmxCompareStringsI("sdca", buf) == 0) { solver = VlSvmSolverSdca ; } else if (vlmxCompareStringsI("none", buf) == 0) { solver = VlSvmSolverNone; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for SOLVER", buf) ; } break ; case opt_loss : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "LOSS must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "LOSS argument too long.") ; } if (vlmxCompareStringsI("hinge", buf) == 0) { loss = VlSvmLossHinge ; } else if (vlmxCompareStringsI("hinge2", buf) == 0) { loss = VlSvmLossHinge2 ; } else if (vlmxCompareStringsI("l1", buf) == 0) { loss = VlSvmLossL1 ; } else if (vlmxCompareStringsI("l2", buf) == 0) { loss = VlSvmLossL2 ; } else if (vlmxCompareStringsI("logistic", buf) == 0) { loss = VlSvmLossLogistic ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for LOSS", buf) ; } break ; case opt_model : if (!vlmxIsPlainVector(optarg, vl_svmdataset_get_dimension(dataset))) { vlmxError(vlmxErrInvalidArgument, "MODEL is not a plain vector of size equal to the data dimension.") ; } initialModel_array = optarg ; break ; case opt_bias: GET_SCALAR(BIAS, initialBias) ; break ; case opt_weights: if (!vlmxIsPlainVector(optarg, vl_svmdataset_get_num_data(dataset))) { vlmxError(vlmxErrInvalidArgument, "WEIGHTS is not a plain vector of size equal to the number of training samples.") ; } weights = mxGetPr(optarg) ; break ; /* SGD specific */ case opt_starting_iteration: GET_NN_SCALAR(STARTINGITERATION, startingIteration) ; break ; case opt_bias_learning_rate: GET_NN_SCALAR(BIASLEARNINGRATE, sgdBiasLearningRate) ; break ; /* DCA specific */ } /* choose option */ } /* next option */ { VlSvm * svm = vl_svm_new_with_dataset(solver, dataset, labels, lambda) ; DiagnosticOpts dopts ; if (initialModel_array) { if (solver != VlSvmSolverNone && solver != VlSvmSolverSgd) { vlmxError(vlmxErrInvalidArgument, "MODEL cannot be specified with this type of solver.") ; } if (mxGetNumberOfElements(initialModel_array) != vl_svm_get_dimension(svm)) { vlmxError(vlmxErrInvalidArgument, "MODEL has not the same dimension as the data.") ; } vl_svm_set_model(svm, mxGetPr(initialModel_array)) ; } if (! vl_is_nan_d(initialBias)) { if (solver != VlSvmSolverNone && solver != VlSvmSolverSgd) { vlmxError(vlmxErrInvalidArgument, "BIAS cannot be specified with this type of solver.") ; } vl_svm_set_bias(svm, initialBias) ; } if (epsilon >= 0) vl_svm_set_epsilon(svm, epsilon) ; if (maxNumIterations >= 0) vl_svm_set_max_num_iterations(svm, maxNumIterations) ; if (biasMultipler >= 0) vl_svm_set_bias_multiplier(svm, biasMultipler) ; if (sgdBiasLearningRate >= 0) vl_svm_set_bias_learning_rate(svm, sgdBiasLearningRate) ; if (diagnosticFrequency >= 0) vl_svm_set_diagnostic_frequency(svm, diagnosticFrequency) ; if (startingIteration >= 0) vl_svm_set_iteration_number(svm, (unsigned)startingIteration) ; if (weights) vl_svm_set_weights(svm, weights) ; vl_svm_set_loss (svm, loss) ; dopts.verbose = verbose ; dopts.matlabDiagonsticFunctionHandle = matlabDiagnosticFunctionHandle ; vl_svm_set_diagnostic_function (svm, (VlSvmDiagnosticFunction)diagnostic, &dopts) ; if (verbose) { double C = 1.0 / (vl_svm_get_lambda(svm) * vl_svm_get_num_data (svm)) ; char const * lossName = 0 ; switch (loss) { case VlSvmLossHinge: lossName = "hinge" ; break ; case VlSvmLossHinge2: lossName = "hinge2" ; break ; case VlSvmLossL1: lossName = "l1" ; break ; case VlSvmLossL2: lossName = "l2" ; break ; case VlSvmLossLogistic: lossName = "logistic" ; break ; } mexPrintf("vl_svmtrain: parameters (verbosity: %d)\n", verbose) ; mexPrintf("\tdata dimension: %d\n",vl_svmdataset_get_dimension(dataset)) ; mexPrintf("\tnum samples: %d\n", vl_svmdataset_get_num_data(dataset)) ; mexPrintf("\tlambda: %g (C equivalent: %g)\n", vl_svm_get_lambda(svm), C) ; mexPrintf("\tloss function: %s\n", lossName) ; mexPrintf("\tmax num iterations: %d\n", vl_svm_get_max_num_iterations(svm)) ; mexPrintf("\tepsilon: %g\n", vl_svm_get_epsilon(svm)) ; mexPrintf("\tdiagnostic frequency: %d\n", vl_svm_get_diagnostic_frequency(svm)) ; mexPrintf("\tusing custom weights: %s\n", VL_YESNO(weights)) ; mexPrintf("\tbias multiplier: %g\n", vl_svm_get_bias_multiplier(svm)) ; switch (vl_svm_get_solver(svm)) { case VlSvmSolverNone: mexPrintf("\tsolver: none (evaluation mode)\n") ; break ; case VlSvmSolverSgd: mexPrintf("\tsolver: sgd\n") ; mexPrintf("\tbias learning rate: %g\n", vl_svm_get_bias_learning_rate(svm)) ; break ; case VlSvmSolverSdca: mexPrintf("\tsolver: sdca\n") ; break ; } } vl_svm_train(svm) ; { mwSize dims[2] ; dims[0] = vl_svmdataset_get_dimension(dataset) ; dims[1] = 1 ; out[OUT_MODEL] = mxCreateNumericArray(2, dims, mxDOUBLE_CLASS, mxREAL) ; memcpy(mxGetPr(out[OUT_MODEL]), vl_svm_get_model(svm), vl_svm_get_dimension(svm) * sizeof(double)) ; } out[OUT_BIAS] = vlmxCreatePlainScalar(vl_svm_get_bias(svm)) ; if (nout >= 3) { out[OUT_INFO] = makeInfoStruct(svm) ; } if (nout >= 4) { mwSize dims[2] ; dims[0] = 1 ; dims[1] = vl_svmdataset_get_num_data(dataset) ; out[OUT_SCORES] = mxCreateNumericArray(2, dims, mxDOUBLE_CLASS, mxREAL) ; memcpy(mxGetPr(out[OUT_SCORES]), vl_svm_get_scores(svm), vl_svm_get_num_data(svm) * sizeof(double)) ; } vl_svm_delete(svm) ; if (vl_svmdataset_get_homogeneous_kernel_map(dataset)) { VlHomogeneousKernelMap * hom = vl_svmdataset_get_homogeneous_kernel_map(dataset) ; vl_svmdataset_set_homogeneous_kernel_map(dataset,0) ; vl_homogeneouskernelmap_delete(hom) ; } vl_svmdataset_delete(dataset) ; } } vlfeat/toolbox/misc/vl_xmkdir.m0000644000175000017500000000442312237004736015552 0ustar dimadimafunction vl_xmkdir(path, varargin) % VL_XMKDIR Create a directory recursively. % VL_XMKDIR(PATH) creates all directory specified by PATH if they % do not exist (existing directories are skipped). % % The function is meant as a silent replacement of MATLAB's builtin % MKDIR() function. It can also be used to show more clearly what % directories are or would be created by the command. % % The function accepts the following options: % % Pretend:: false % If TRUE the function does not create any directoty (implies % 'Verbose'). % % Verbose:: false % If TRUE the function prints the operations performed. % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.pretend = false ; opts.verbose = false ; opts = vl_argparse(opts,varargin) ; opts.verbose = opts.verbose | opts.pretend ; [subPath, name, ext] = fileparts(path) ; name = [name ext] ; % Paths are parsed as sequences of blocks % % ('/', 'DIR1/', 'DIR2/', ... , 'DIRK/') + DIRNAME % % So for example: % % '' -> () + '' % a -> () + 'a' % a/ -> ('a/') + '' % a/b -> ('a/') + 'b' % a/b/ -> ('a/', 'b/') + '' % / -> ('/') + '' % /a -> ('/') + 'a' % /a/ -> ('/', 'a/') + '' % /a/b -> ('/', 'a/') + 'b' % /a/b/ -> ('/, 'a/', 'b/) + '' % % DIRNAME is empty if PATH ends with / and is correctly extracted in % this way by FILEPARTS(). SUBDIR is always different from PATH except % for the base cases PATH='/' or PATH='', for which the recursion % stops. if ~strcmp(subPath, path) vl_xmkdir(subPath, varargin{:}) ; end % name is empty if path = '*/' if isempty(name) return ; end if opts.verbose fprintf('%s: directory ''%s'' in ''%s''', mfilename, name, subPath) ; end if ~exist(path, 'dir') if opts.pretend && opts.verbose fprintf(' does not exist, would create.\n') ; else [success,message,messageId] = mkdir(fullfile(subPath, name)) ; if opts.verbose fprintf(' did not exist, created.\n') ; end end else if opts.verbose if opts.pretend fprintf(' exists, would not create.\n') ; else fprintf(' exists, not creating.\n') ; end end end vlfeat/toolbox/misc/vl_alldist2.m0000644000175000017500000000433412237004736015773 0ustar dimadima% VL_ALLDIST2 Pairwise distances % D = VL_ALLDIST2(X,Y) returns the pairwise distance matrix D of the % columns of S1 and S2, yielding % % D(i,j) = sum (X(:,i) - Y(:,j)).^2 % % VL_ALLDIST2(X) returns the pairwise distance matrix fo the columns of % S, yielding % % D(i,j) = sum (X(:,i) - X(:,j)).^2 % % VL_ALLDIST2(...,'METRIC') changes the computed distance. Supported % values for METRIC are % % METRIC D(i,j) % -------------------------------------------------------- % LINF max |X - Y| % L2 sum (X - Y).^2 % L1 sum |X - Y| % L0 sum (X ~= Y) % CHI2 sum (X - Y).^2 ./ (X + Y) % HELL sum (X^.5 - Y^.5) .^ 2 % % (Notice that the standard definition of chi2 is half of what is % computed here). % % VL_ALLDIST2(...,'KERNEL') computes the following 'kernels' K: % % KERNEL K(i,j) % --------------------------------------------------------- % KL2 sum X .* Y % KL1 sum min (X, Y) % KCHI2 2 * sum (X .* Y) ./ (X + Y) % KHELL (X .* Y) .^ 0.5 % % The constant are chosen so that D(i,j) = K(i,i) + K(j,j) - 2 K(i,j) % where D is the metric corresponding to the kenrel (if the arguments % are non-negative vectors). Each kernel can be interpreted as the % inner product inducing the corresponding metric in an embedding of % the real space into an approrpiate reproducing Kenrel Hilbert % space. % % VL_ALLDIST2() supports several storage classes. X and Y must have the % same storage class. The sotrage class of D is promoted to reduce % the chance of overvlow, but this is not checked. % % X & Y class D class % --------------------------- % UINT8 UINT32 % INT8 INT32 % UINT16 UINT32 % INT16 INT32 % UINT32 UINT32 % INT32 INT32 % SINGLE SINGLE % DOUBLE DOUBLE % % Warning: Both chi2 and kchi2 use integer math when presented with % integer data types. This can easily result in zeros where you did % not expect them. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_alphanum.m0000644000175000017500000000223212237004736016055 0ustar dimadimafunction [strings, perm] = vl_alphanum(strings) % VL_ALPHANUM Sort strings using the Alphanum algorithm % STRINGS = VL_ALPHANUM(STRINGS) sorts the cell array of strings % STRINGS by using the Alphanum algorithm [1]. [STRINGS,PERM] = % VL_ALPHANUM(...) returns the corresponding permutation PERM as % well. % % Example:: % Alphanum sorts strings in a way that 'makes sense'. For instance % % strings = {'B1', 'B2', 'B12', 'A12', 'A1', 'A2'} ; % sorted = vl_alphanum(strings) ; % % produces the sorted array {'A1', 'A2', 'A12', 'B1', B2', % 'B12'}. By contrast, SORT() produces the array {'A1', 'A12', % 'A2', 'B1', B12', 'B2'} (note the position of the elements % 'A12', 'B12'). % % References: % [1] Dave Koelle, 'The Alphanum Algorithm', % http://www.davekoelle.com/alphanum.html chunks = regexp(strings, '(\d+|\D+)', 'tokens') ; for i = 1:length(strings) chunks{i} = [chunks{i}{:}] ; for j = 1:length(chunks{i}) if isstrprop(chunks{i}{j},'digit') chunks{i}{j} = sprintf('%020.0f',sscanf(chunks{i}{j},'%d')) ; end end chunks{i} = [chunks{i}{:}] ; end [dorp,perm] = sort(chunks) ; strings = strings(perm) ; vlfeat/toolbox/misc/vl_svmdataset.m0000644000175000017500000000324312237004745016426 0ustar dimadimafunction dataset = vl_svmdataset(x, varargin) % VL_SVMDATASET Construct advanced SVM dataset structure % DATASET = VL_SVMDATASET(X) wraps the matrix X into a dataset % structure. All the formats supported by VL_SVMTRAIN() are % suppported here as well. % % DATASET = VL_SVMDATASET(X, 'homkermap', HOM) where HOM is a % structure with zero or more of the following fields: HOM.kernel, % HOM.order, HOM.window, HOM.gamma, HOM.period, See VL_HOMKERMAP() % for a discussion of these parameters. The effect is to use the % homogeneous kernel map to expand the data on the fly. This is % equivalent (albeit somewhat slower) to running VL_HOMKERMAP() % prior to learning. The main advantage is the significant memory % saving as the expanded data needs not to be stored in memory. % % See:: VL_SVMTRAIN(), VL_HOMKERMAP(), VL_HELP(). % Author: Daniele Perrone and Andrea Vedaldi opts.homkermap = [] ; opts = vl_argparse(opts, varargin) ; if issparse(x) error('X is a sparse matrix (sparse data will support be added in a future version.)') ; end if ~isa(x,'single') && ~isa(x,'double') error('X is neither SINGLE nor DOUBLE.') ; end sz = size(x) ; if numel(sz) > 2 error('X has more than two dimensions.') ; end dataset.data = x ; if isstruct(opts.homkermap) if numel(opts.homkermap) ~= 1 error('HOMKERMAP is not a singleton.') ; end ok = all(ismember(fieldnames(opts.homkermap), ... {'kernel', 'order', 'window', 'gamma', 'period'})) ; if ~ok error('HOMKERMAP contains an unsupported field name.') ; end dataset.homkermap = opts.homkermap ; elseif ~isempty(opts.homkermap) error('HOMKERMAP is not a structure nor []') ; end vlfeat/toolbox/misc/vl_colsubset.m0000644000175000017500000000453112237004736016257 0ustar dimadimafunction [Y, sel] = vl_colsubset(X,n,varargin) % VL_COLSUBSET Select a given number of columns % Y = VL_COLSUBSET(X, N) returns a random subset Y of N columns of % X. The selection is order-preserving and without replacement. If N % is larger or equal to the number of columns of X (e.g. N = Inf), % then the function returns all the columns (i.e., Y = X). % % If 0 < N < 1, then the function returns a fraction N of the % columns (rounded to the closest integer). % % [Y, SEL] = VL_COLSUBSET(...) returns the indexes SEL of the % selected columns. % % The function accepts the following options: % % Beginning:: % Returns the fist N columns. % % Ending:: % Returns the last N columns. % % Random:: [default] % Returns N columns selected at random (using RANDPERM()). % % Uniform:: % Returns N uniformly spaced columns. % % Largest:: % Returns the N largest columns (using SORTROWS()). % % Smallest:: % Returns the N smallest columns (using SORTROWS()). % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 2, n = 1 ; end mode = 'random' ; i = 1 ; while i <= length(varargin) switch lower(varargin{i}) case {'beginning', ... 'ending', ... 'random', ... 'uniform', ... 'largest', ... 'smallest'} mode = lower(varargin{1}) ; i = i + 1 ; otherwise error('Unknown option ''%s''.', varargin{i}) ; end end m = size(X,2) ; if n < 0, error('N must not be smaller than 0.') ; end if n ~= round(n) if n > 1 error('N must be a natural number, +inf, or a fraction in 0 and 1.') ; end n = round(m * n) ; end n = min(m,n) ; switch mode case 'random' perm = randperm(m) ; sel = sort(perm(1:n)) ; case 'beginning' perm = 1:m ; sel = sort(perm(1:n)) ; case 'ending' perm = m:-1:1 ; sel = sort(perm(1:n)) ; case 'uniform' if n < 1 sel = [] ; else sel = round(linspace(1, m, min(m,n))) ; end case 'largest' [drop, perm] = sortrows(X') ; sel = sort(perm(end-n+1:end)) ; case 'smallest' [drop, perm] = sortrows(X') ; sel = sort(perm(1:n)) ; end Y = X(:, sel) ; vlfeat/toolbox/misc/vl_alldist2.c0000644000175000017500000004271012237004736015761 0ustar dimadima/* file: alldist2.c ** description: All pairwise distances ** author: Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include #include enum { opt_LINF, opt_L2, opt_L1, opt_L0, opt_CHI2, opt_HELL, opt_KL2, opt_KL1, opt_KCHI2, opt_KHELL, opt_MIN } ; vlmxOption options [] = { {"linf", 0, opt_LINF }, {"l2", 0, opt_L2 }, {"l1", 0, opt_L1 }, {"l0", 0, opt_L0 }, {"chi2", 0, opt_CHI2 }, {"hell", 0, opt_HELL }, {"kl2", 0, opt_KL2 }, {"kl1", 0, opt_KL1 }, {"kchi2", 0, opt_KCHI2 }, {"khell", 0, opt_KHELL }, {"min", 0, opt_MIN }, {0, 0, 0 } } ; #undef MIN #undef MAX #undef ABS #undef ABS_DIFF #undef CORE #define MIN(x,y) ((x) <= (y) ? (x) : (y)) #define MAX(x,y) ((x) >= (y) ? (x) : (y)) #define ABS(x) ((x) >= 0 ? (x) : -(x)) #define ABS_DIFF(x,y) ((x) >= (y) ? ((x) - (y)) : ((y) - (x))) #ifndef sqrtf #define sqrtf(x) ((float)sqrt(x)) #endif /* for L2 norm */ /*#define CMP(s1,s2) ((double)(s1*s2)) */ /* for L1 norm */ #define CMP(s1,s2) ((double)MIN(s1,s2)) #define UINT8_t vl_uint8 #define INT8_t vl_int8 #define UINT16_t vl_uint16 #define INT16_t vl_int16 #define UINT32_t vl_uint32 #define INT32_t vl_int32 #define SINGLE_t float #define DOUBLE_t double #define CORE(NORM,F,DC,AC) \ void \ dist ## NORM ## _ ## DC ## _ ## AC \ ( \ AC ## _t * pt, DC ## _t * s1_pt, \ DC ## _t * s2_pt, \ vl_size L, vl_size N1, vl_size N2, \ bool self) \ { \ vl_uindex j1,j2,l ; \ for(j2 = 0 ; j2 < N2 ; ++j2) { \ for(j1 = 0 ; j1 < N1 ; ++j1) { \ if(! self || j1>=j2) { \ AC ## _t acc = 0 ; \ DC ## _t * s1_it = s1_pt + L*j1 ; \ DC ## _t * s2_it = s2_pt + L*j2 ; \ for(l = 0 ; l < L ; ++l) { \ AC ## _t s1 = *s1_it++ ; \ AC ## _t s2 = *s2_it++ ; \ F(AC, s1, s2) \ } \ *pt = acc; \ } else { \ *pt = *(pt + (j1 - j2) * (N1 - 1)) ; \ } \ pt++ ; \ } \ } \ } \ #define CORE_SPARSE(NORM, F) \ { \ double const * s1_pt = mxGetPr(in[IN_S1]) ; \ mwIndex const * s1_ir = mxGetIr(in[IN_S1]) ; \ mwIndex const * s1_jc = mxGetJc(in[IN_S1]) ; \ double const * s2_pt = 0 ; \ mwIndex const * s2_ir = 0 ; \ mwIndex const * s2_jc = 0 ; \ double * pt = mxGetPr(out[OUT_D]) ; \ vl_uindex j1, j2 ; \ \ if (self) { \ s2_pt = s1_pt ; \ s2_ir = s1_ir ; \ s2_jc = s1_jc ; \ } else { \ s2_pt = mxGetPr(in[IN_S2]) ; \ s2_ir = mxGetIr(in[IN_S2]) ; \ s2_jc = mxGetJc(in[IN_S2]) ; \ } \ \ for (j2 = 0 ; j2 < N2 ; ++j2) { \ for (j1 = 0 ; j1 < N1 ; ++j1) { \ int nz1 = s1_jc [j1+1] - s1_jc [j1] ; \ int nz2 = s2_jc [j2+1] - s2_jc [j2] ; \ if(! self || j1 >= j2) { \ double acc = 0 ; \ double const * s1_it = s1_pt + s1_jc [j1] ; \ double const * s2_it = s2_pt + s2_jc [j2] ; \ mwIndex const * s1_ir_it = s1_ir + s1_jc [j1] ; \ mwIndex const * s2_ir_it = s2_ir + s2_jc [j2] ; \ mwIndex ir1 ; \ mwIndex ir2 ; \ while (nz1 || nz2) { \ if (nz2 == 0) { \ double a = *s1_it++ ; \ F(DOUBLE, a, 0) ; \ s1_ir_it ++ ; \ nz1 -- ; \ continue ; \ } \ if (nz1 == 0) { \ double b = *s2_it++ ; \ F(DOUBLE, 0, b) ; \ s2_ir_it ++ ; \ nz2 -- ; \ continue ; \ } \ ir1 = *s1_ir_it ; \ ir2 = *s2_ir_it ; \ if (ir1 < ir2) { \ double a = *s1_it++ ; \ F(DOUBLE, a, 0) ; \ s1_ir_it ++ ; \ nz1 -- ; \ continue ; \ } \ if (ir1 > ir2) { \ double b = *s2_it++ ; \ F(DOUBLE, 0, b) ; \ s2_ir_it ++ ; \ nz2 -- ; \ continue ; \ } \ { \ double a = *s1_it++ ; \ double b = *s2_it++ ; \ F(DOUBLE, a, b) ; \ s1_ir_it ++ ; \ s2_ir_it ++ ; \ nz1 -- ; \ nz2 -- ; \ } \ } \ *pt = acc; \ } else { \ *pt = *(pt + (j1 - j2) * (N1 - 1)) ; \ } \ pt++ ; \ } \ } \ } #define DEF_CLASS(NORM,F) \ CORE (NORM, F, INT8, INT32) \ CORE (NORM, F, UINT8, UINT32) \ CORE (NORM, F, INT16, INT32) \ CORE (NORM, F, UINT16, UINT32) \ CORE (NORM, F, INT32, INT32) \ CORE (NORM, F, UINT32, UINT32) \ CORE (NORM, F, SINGLE, SINGLE) \ CORE (NORM, F, DOUBLE, DOUBLE) #define F_L0(AC,x,y) { acc += (x) != (y) ; } #define F_L1(AC,x,y) { acc += ABS_DIFF(x,y) ; } #define F_L2(AC,x,y) { AC ## _t tmp = ABS_DIFF(x,y) ; acc += tmp * tmp ; } #define F_LINF(AC,x,y) { acc = MAX(acc, ABS_DIFF(x,y)) ; } #define F_CHI2(AC,x,y) \ { \ AC ## _t meant2 = ((x) + (y)) ; \ if (meant2 != 0) { \ AC ## _t tmp = ABS_DIFF(x,y) ; \ acc += tmp * tmp / meant2 ; \ } \ } #define F_HELL_SINGLE(x,y) { acc += x + y - 2 * sqrtf (x * y) ; } #define F_HELL_DOUBLE(x,y) { acc += x + y - 2 * sqrt (x * y) ; } #define F_HELL_UINT32(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL_INT32(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL_UINT16(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL_INT16(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL_UINT8(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL_INT8(x,y) { acc += x + y - 2 * vl_fast_sqrt_ui32 (x * y) ; } #define F_HELL(AC,x,y) F_HELL_ ## AC (x,y) #define F_KL2(AC,x,y) { acc += (x*y) ; } #define F_KL1(AC,x,y) { acc += MIN(x,y) ; } #define F_MIN(AC,x,y) { acc += MIN(x,y) ; } #define F_KCHI2(AC,x,y) \ { \ AC ## _t mean = ((x) + (y)) / 2 ; \ if (mean != 0) { \ AC ## _t tmp = (x) * (y) ; \ acc += tmp / mean ; \ } \ } #define F_KHELL_SINGLE(x,y) { acc += sqrtf (x * y) ; } #define F_KHELL_DOUBLE(x,y) { acc += sqrt (x * y) ; } #define F_KHELL_UINT32(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL_INT32(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL_UINT16(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL_INT16(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL_UINT8(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL_INT8(x,y) { acc += vl_fast_sqrt_ui32 (x * y) ; } #define F_KHELL(AC,x,y) F_KHELL_ ## AC (x,y) DEF_CLASS (LINF, F_LINF ) DEF_CLASS (L2, F_L2 ) DEF_CLASS (L1, F_L1 ) DEF_CLASS (L0, F_L0 ) DEF_CLASS (CHI2, F_CHI2 ) DEF_CLASS (HELL, F_HELL ) DEF_CLASS (KL2, F_KL2 ) DEF_CLASS (KL1, F_KL1 ) DEF_CLASS (KCHI2, F_KCHI2) DEF_CLASS (KHELL, F_KHELL) DEF_CLASS (MIN, F_MIN ) /* driver */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { typedef int unsigned data_t ; /* mxClassID data_class = mxINT8_CLASS ;*/ enum {IN_S1,IN_S2} ; enum {OUT_D=0} ; vl_size L,N1,N2 ; vl_bool sparse = 0 ; void const * s1_pt ; void const * s2_pt ; mxClassID data_class ; mxClassID acc_class ; mwSize dims [2] ; /* for option parsing */ bool self = 1 ; /* called with one numeric argument? */ int norm = opt_L2 ; /* type of norm to be computed */ int opt ; int next = 1 ; mxArray const *optarg ; /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (nout > 1) { mexErrMsgTxt("Too many output arguments."); } if (nin < 1) { mexErrMsgTxt("At leat one argument required.") ; } if(! mxIsNumeric(in[IN_S1])) { mexErrMsgTxt ("X must be numeric") ; } if (nin >= 2 && mxIsNumeric(in[IN_S2])) { self = 0 ; next = 2 ; } sparse = mxIsSparse(in[IN_S1]) ; if (sparse && nin >=2 && mxIsNumeric(in[IN_S2])) { if (! mxIsSparse(in[IN_S2])) { mexErrMsgTxt ("X and Y must be either both full or sparse.") ; } } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_LINF : case opt_L2 : case opt_L1 : case opt_L0 : case opt_CHI2 : case opt_HELL : case opt_KL2 : case opt_KL1 : case opt_KCHI2 : case opt_KHELL : case opt_MIN : norm = opt ; break ; default: abort() ; } } data_class = mxGetClassID(in[IN_S1]) ; if ((!self) && data_class != mxGetClassID(in[IN_S2])) { mexErrMsgTxt("X and Y must have the same numeric class") ; } assert ((! sparse) || (data_class == mxDOUBLE_CLASS)) ; L = mxGetM(in[IN_S1]) ; N1 = mxGetN(in[IN_S1]) ; N2 = self ? N1 : mxGetN(in[IN_S2]) ; dims[0] = N1 ; dims[1] = N2 ; if ((!self) && L != mxGetM(in[IN_S2])) { mexErrMsgTxt("X and Y must have the same number of rows") ; } s1_pt = mxGetData(in[IN_S1]) ; s2_pt = self ? s1_pt : mxGetData(in[IN_S2]) ; #define DISPATCH_CLASS(NORM, DC,AC) \ case mx ## DC ## _CLASS : \ acc_class = mx ## AC ## _CLASS ; \ out[OUT_D] = mxCreateNumericArray(2,dims,acc_class,mxREAL) ; \ dist ## NORM ## _ ## DC ## _ ## AC \ ( (AC ## _t *)mxGetData(out[OUT_D]), \ (DC ## _t *)s1_pt, \ (DC ## _t *)s2_pt, \ L, N1, N2, \ self ) ; \ break ; #define DISPATCH_NORM(NORM) \ case opt_ ## NORM : \ if (sparse) { \ out[OUT_D] = mxCreateNumericArray(2,dims,mxDOUBLE_CLASS,mxREAL) ; \ CORE_SPARSE(NORM, VL_XCAT(F_, NORM)) \ } else { \ switch (data_class) { \ DISPATCH_CLASS(NORM, UINT8 , UINT32) \ DISPATCH_CLASS(NORM, INT8 , INT32) \ DISPATCH_CLASS(NORM, UINT16, UINT32) \ DISPATCH_CLASS(NORM, INT16, INT32) \ DISPATCH_CLASS(NORM, UINT32, UINT32) \ DISPATCH_CLASS(NORM, INT32, INT32) \ DISPATCH_CLASS(NORM, SINGLE, SINGLE) \ DISPATCH_CLASS(NORM, DOUBLE,DOUBLE) \ default: \ mexErrMsgTxt("Data class not supported!") ; \ } \ } \ break ; switch (norm) { DISPATCH_NORM(LINF ) ; DISPATCH_NORM(L2 ) ; DISPATCH_NORM(L1 ) ; DISPATCH_NORM(L0 ) ; DISPATCH_NORM(CHI2 ) ; DISPATCH_NORM(HELL ) ; DISPATCH_NORM(KL2 ) ; DISPATCH_NORM(KL1 ) ; DISPATCH_NORM(KCHI2) ; DISPATCH_NORM(KHELL) ; DISPATCH_NORM(MIN ) ; default: abort() ; } } vlfeat/toolbox/misc/vl_kdtreequery.m0000644000175000017500000000346312237004736016623 0ustar dimadima% VL_KDTREEQUERY Query KD-tree % [INDEX, DIST] = VL_KDTREEQUERY(KDTREE, X, Y) computes the nearest % column of X to each column of Y (in Euclidean distance). KDTREE is % a forest of kd-trees build by VL_KDTREEBUILD(). X is a % NUMDIMENSIONS x NUMDATA data matrix of class SINGLE or DOUBLE with % the data indexed by the kd-trees (it must be the same data matrix % passed to VK_KDTREEBUILD() to build the trees). Y is the % NUMDIMENSIONS x NUMQUERIES matrix of query points and must have % the same class of X. INDEX is a 1 x NUMQUERIES matrix of class % UINT32 with the index of the nearest column of X for each column % of Y. DIST is a 1 x NUMQUERIES vector of class SINGLE or DOUBLE % (depending on the class of X and Y) with the corresponding squared % Euclidean distances. % % [INDEX, DIST] = VL_KDTREEQUERY(..., 'NUMNEIGHBORS', NN) can be % used to return the N nearest neighbors rather than just the % nearest one. In this case INDEX and DIST are NN x NUMQUERIES % matrices. Neighbors are returned by increasing distance. % % VL_KDTREEQUERY(..., 'MAXNUMCOMPARISONS', NCOMP) performs at most % NCOMP comparisons for each query point. In this case the result is % only approximate (i.e. approximated nearest-neighbors, or ANNs) % but the speed can be greatly improved. % % Options: % % NumNeighbors:: % Sets the number of neighbors to compute for each query point (by % default 1). % % MaxNumComparisons:: % Sets the maximum number of comparisons per query point. The % special value 0 means unbounded. The default is 0. % % See also: VL_KDTREEBUILD(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_alldist.c0000644000175000017500000001326712237004736015704 0ustar dimadima/** @file vl_alldist.c ** @brief vl_alldist - MEX implementation ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mexutils.h" #include enum { opt_LINF, opt_L2, opt_L1, opt_L0, opt_CHI2, opt_HELL, opt_JS, opt_KL2, opt_KL1, opt_KCHI2, opt_KHELL, opt_KJS } ; vlmxOption options [] = { {"linf", 0, opt_LINF }, {"l2", 0, opt_L2 }, {"l1", 0, opt_L1 }, {"l0", 0, opt_L0 }, {"chi2", 0, opt_CHI2 }, {"hell", 0, opt_HELL }, {"js", 0, opt_JS }, {"kl2", 0, opt_KL2 }, {"kl1", 0, opt_KL1 }, {"kchi2", 0, opt_KCHI2 }, {"khell", 0, opt_KHELL }, {"kjs", 0, opt_KJS }, {0, 0, 0 } } ; /* driver */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { typedef int unsigned data_t ; vl_bool autoComparison = VL_TRUE ; VlVectorComparisonType comparisonType = VlDistanceL2 ; enum {IN_X = 0, IN_Y} ; enum {OUT_D = 0} ; mwSize numDataX = 0 ; mwSize numDataY = 0 ; mwSize dimension ; mxClassID classId ; /* for option parsing */ int opt ; int next ; mxArray const *optarg ; VL_USE_MATLAB_ENV ; if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (nin < 1) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (! (vlmxIsMatrix (in[IN_X],-1,-1) && vlmxIsReal(in[IN_X]))) { vlmxError(vlmxErrInvalidArgument, "X must be a real matrix.") ; } next = 1 ; classId = mxGetClassID(in[IN_X]) ; dimension = mxGetM(in[IN_X]) ; numDataX = mxGetN(in[IN_X]) ; if (nin > 1 && vlmxIsMatrix (in[IN_Y],-1,-1) && vlmxIsReal(in[IN_Y])) { next = 2 ; autoComparison = VL_FALSE ; numDataY = mxGetN(in[IN_Y]) ; if (mxGetClassID(in[IN_Y]) != classId) { vlmxError(vlmxErrInvalidArgument, "X and Y must have the same class.") ; } if (dimension != mxGetM(in[IN_Y])) { vlmxError(vlmxErrInvalidArgument, "X and Y must have the same number of rows.") ; } } if (classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "X must be either of class SINGLE or DOUBLE."); } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_L2 : comparisonType = VlDistanceL2 ; break ; case opt_L1 : comparisonType = VlDistanceL1 ; break ; case opt_CHI2 : comparisonType = VlDistanceChi2 ; break ; case opt_HELL : comparisonType = VlDistanceHellinger ; break ; case opt_JS : comparisonType = VlDistanceJS ; break ; case opt_KL2 : comparisonType = VlKernelL2 ; break ; case opt_KL1 : comparisonType = VlKernelL1 ; break ; case opt_KCHI2 : comparisonType = VlKernelChi2 ; break ; case opt_KHELL : comparisonType = VlKernelHellinger ; break ; case opt_KJS : comparisonType = VlKernelJS ; break ; default: abort() ; } } /* allocate output */ { mwSize dims [2] ; dims[0] = numDataX ; dims[1] = autoComparison ? numDataX : numDataY ; out[OUT_D] = mxCreateNumericArray (2, dims, classId, mxREAL) ; } /* If either numDataX or numDataY are null, their data pointers are null as well. This may confuse vl_eval_vector_comparison_on_all_pairs_*, so we intercept this as a special case. The same is true if dimension is null. */ if (numDataX == 0 || (! autoComparison && numDataY == 0)) { return ; } if (dimension == 0) { return ; } /* make calculation */ switch (classId) { case mxSINGLE_CLASS: { VlFloatVectorComparisonFunction f = vl_get_vector_comparison_function_f (comparisonType) ; if (autoComparison) { vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]), dimension, (float*)mxGetData(in[IN_X]), numDataX, 0, 0, f) ; } else { vl_eval_vector_comparison_on_all_pairs_f ((float*)mxGetData(out[OUT_D]), dimension, (float*)mxGetData(in[IN_X]), numDataX, (float*)mxGetData(in[IN_Y]), numDataY, f) ; } } break ; case mxDOUBLE_CLASS: { VlDoubleVectorComparisonFunction f = vl_get_vector_comparison_function_d (comparisonType) ; if (autoComparison) { vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]), dimension, (double*)mxGetData(in[IN_X]), numDataX, 0, 0, f) ; } else { vl_eval_vector_comparison_on_all_pairs_d ((double*)mxGetData(out[OUT_D]), dimension, (double*)mxGetData(in[IN_X]), numDataX, (double*)mxGetData(in[IN_Y]), numDataY, f) ; } } break ; default: abort() ; } } vlfeat/toolbox/misc/vl_cummax.c0000644000175000017500000000743512237004736015542 0ustar dimadima/** @file vl_cummax.c ** @brief vl_cummax MEX defition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #define REPEAT1_1(m,p) m(1,p) #define REPEAT1_2(m,p) REPEAT1_1(m,p) m(2,p) #define REPEAT1_3(m,p) REPEAT1_2(m,p) m(3,p) #define REPEAT1_4(m,p) REPEAT1_3(m,p) m(4,p) #define REPEAT1_5(m,p) REPEAT1_4(m,p) m(5,p) #define REPEAT1_6(m,p) REPEAT1_5(m,p) m(6,p) #define REPEAT1_7(m,p) REPEAT1_6(m,p) m(7,p) #define REPEAT1_8(m,p) REPEAT1_7(m,p) m(8,p) #define REPEAT1_9(m,p) REPEAT1_8(m,p) m(9,p) #define REPEAT1_10(m,p) REPEAT1_9(m,p) m(10,p) #define TYPE_1 double #define TYPE_2 float #define TYPE_3 vl_int64 #define TYPE_4 vl_uint64 #define TYPE_5 vl_int32 #define TYPE_6 vl_uint32 #define TYPE_7 vl_int16 #define TYPE_8 vl_uint16 #define TYPE_9 vl_int8 #define TYPE_10 vl_uint8 #define TYPE_AUX(x) TYPE_ ## x #define TYPE(x) TYPE_AUX(x) #define CLASS_1 DOUBLE #define CLASS_2 SINGLE #define CLASS_3 INT64 #define CLASS_4 UINT64 #define CLASS_5 INT32 #define CLASS_6 UINT32 #define CLASS_7 INT16 #define CLASS_8 UINT16 #define CLASS_9 INT8 #define CLASS_10 UINT8 #define CLASS_AUX(x) CLASS_ ## x #define CLASS(x) CLASS_AUX(x) #include "vl_cummax.def" void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_X, IN_DIM, IN_END} ; enum {OUT_Y = 0} ; vl_index whichDimension = 0 ; vl_size dimension ; vl_size numDimensions ; mwSize const * dimensions ; mxClassID classId ; vl_size stride = 1 ; vl_index k ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } else if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } else if (nin > 2) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } /* get information on the data */ if (!mxIsNumeric(IN(X))) { vlmxError(vlmxErrInvalidArgument, "X is not a numeric array.") ; } classId = mxGetClassID(IN(X)) ; numDimensions = mxGetNumberOfDimensions(IN(X)) ; dimensions = mxGetDimensions(IN(X)) ; /* get the operating dimension */ if (nin == 2) { if (!vlmxIsPlainScalar(IN(DIM))) { vlmxError(vlmxErrInvalidArgument, "DIM is not a plain scalar.") ; } whichDimension = mxGetScalar(IN(DIM)) ; if (whichDimension < 1 || whichDimension > (signed)numDimensions) { vlmxError(vlmxErrInvalidArgument, "DIM=%d is not a valid dimension of X.", whichDimension) ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ OUT(Y) = mxCreateNumericArray(numDimensions, dimensions, classId, mxREAL) ; if (whichDimension >= 1) { /* Compute the stride to whichDimension. */ for (k = 0 ; k < whichDimension - 1 ; ++k) { stride *= dimensions[k] ; } } else { /* Pick the first non-singleton dimension, if any. Note that in this case the stride is equal to 1. */ for (k = 0 ; k < (signed)numDimensions-1 && dimensions[k] == 1 ; ++k) ; whichDimension = k + 1 ; } dimension = dimensions[whichDimension - 1] ; #define DISPATCH(I,V) \ case VL_XCAT3(mx,CLASS(I),_CLASS): \ VL_XCAT(_vl_cummax_,TYPE(I))(OUT(Y), IN(X), stride, dimension) ; break ; switch (classId) { REPEAT1_10(DISPATCH,_) ; default : vlmxError(vlmxErrInvalidArgument, "X is not of one of the supported storage classes.") ; } } vlfeat/toolbox/misc/vl_lbpfliplr.m0000644000175000017500000000154412237004736016243 0ustar dimadimafunction f = vl_lbpfliplr(f) % VL_LBPFLIPLR Flip LBP features left-right % F = VL_LBPFLIPLR(F) flips the LBP features F from left to % right. % % Example:: % The following instructions return results almost identical % assuming that the width of I is exactly divisible by the cell % size: % F = VL_LBP(FLIPLR(I), 8) % F_ = VL_LBPFLIPLR(VL_LBP(I), 8) % % See also: VL_LBP(), VL_HELP(). % construct permutation perm = zeros(1,58) ; perm(58) = 58 ; perm(57) = 57 ; % i is the start of the sequence of 1 and j its length % recall that i=0 -> E, i=1 -> SE, etc % the length j remains the same. % flipping the start gives the flipped end, and subtracting the length % gives the flipped start for i = 0:7 for j = 1:7 i_ = mod(4 - i - j + 1, 8) ; j_ = j ; perm(i * 7 + j) = i_ * 7 + j_ ; end end f = f(:, end:-1:1, perm) ; vlfeat/toolbox/misc/vl_override.m0000644000175000017500000001105612237004736016073 0ustar dimadimafunction config = vl_override(config,update,varargin) % VL_OVERRIDE Override structure subset % CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds % of the structure UPDATE to the corresponding fields of the % struture CONFIG. % % Usually CONFIG is interpreted as a list of paramters with their % default values and UPDATE as a list of new paramete values. % % VL_OVERRIDE(..., 'Warn') prints a warning message whenever: (i) % UPDATE has a field not found in CONFIG, or (ii) non-leaf values of % CONFIG are overwritten. % % VL_OVERRIDE(..., 'Skip') skips fields of UPDATE that are not found % in CONFIG instead of copying them. % % VL_OVERRIDE(..., 'CaseI') matches field names in a % case-insensitive manner. % % Remark:: % Fields are copied at the deepest possible level. For instance, % if CONFIG has fields A.B.C1=1 and A.B.C2=2, and if UPDATE is the % structure A.B.C1=3, then VL_OVERRIDE() returns a strucuture with % fields A.B.C1=3, A.B.C2=2. By contrast, if UPDATE is the % structure A.B=4, then the field A.B is copied, and VL_OVERRIDE() % returns the structure A.B=4 (specifying 'Warn' would warn about % the fact that the substructure B.C1, B.C2 is being deleted). % % Remark:: % Two fields are matched if they correspond exactly. Specifically, % two fileds A(IA).(FA) and B(IA).FB of two struct arrays A and B % match if, and only if, (i) A and B have the same dimensions, % (ii) IA == IB, and (iii) FA == FB. % % See also: VL_ARGPARSE(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). warn = false ; skip = false ; err = false ; casei = false ; if length(varargin) == 1 & ~ischar(varargin{1}) % legacy warn = 1 ; end if ~warn & length(varargin) > 0 for i=1:length(varargin) switch lower(varargin{i}) case 'warn' warn = true ; case 'skip' skip = true ; case 'err' err = true ; case 'argparse' argparse = true ; case 'casei' casei = true ; otherwise error(sprintf('Unknown option ''%s''.',varargin{i})) ; end end end % if CONFIG is not a struct array just copy UPDATE verbatim if ~isstruct(config) config = update ; return ; end % if CONFIG is a struct array but UPDATE is not, no match can be % established and we simply copy UPDATE verbatim if ~isstruct(update) config = update ; return ; end % if CONFIG and UPDATE are both struct arrays, but have different % dimensions then nom atch can be established and we simply copy % UPDATE verbatim if numel(update) ~= numel(config) config = update ; return ; end % if CONFIG and UPDATE are both struct arrays of the same % dimension, we override recursively each field for idx=1:numel(update) fields = fieldnames(update) ; for i = 1:length(fields) updateFieldName = fields{i} ; if casei configFieldName = findFieldI(config, updateFieldName) ; else configFieldName = findField(config, updateFieldName) ; end if ~isempty(configFieldName) config(idx).(configFieldName) = ... vl_override(config(idx).(configFieldName), ... update(idx).(updateFieldName)) ; else if warn warning(sprintf('copied field ''%s'' which is in UPDATE but not in CONFIG', ... updateFieldName)) ; end if err error(sprintf('The field ''%s'' is in UPDATE but not in CONFIG', ... updateFieldName)) ; end if skip if warn warning(sprintf('skipping field ''%s'' which is in UPDATE but not in CONFIG', ... updateFieldName)) ; end continue ; end config(idx).(updateFieldName) = update(idx).(updateFieldName) ; end end end % -------------------------------------------------------------------- function field = findFieldI(S, matchField) % -------------------------------------------------------------------- field = '' ; fieldNames = fieldnames(S) ; for fi=1:length(fieldNames) if strcmpi(fieldNames{fi}, matchField) field = fieldNames{fi} ; end end % -------------------------------------------------------------------- function field = findField(S, matchField) % -------------------------------------------------------------------- field = '' ; fieldNames = fieldnames(S) ; for fi=1:length(fieldNames) if strcmp(fieldNames{fi}, matchField) field = fieldNames{fi} ; end end vlfeat/toolbox/misc/vl_lbp.m0000644000175000017500000000117112237004745015026 0ustar dimadima% VL_LBP Local Binary Patterns % F = VL_LBP(IM, CELLSIZE) computes the Local Binary Pattern (LBP) % features for image I. % % IM is divided in cells of size CELLSIZE. F is a three-dimensional % array containing one histograms of quantized LBP features per % cell. The witdh of F is FLOOR(WIDTH/CELLSIZE), where WIDTH is the % width of the image. The same for the height. The third dimension % is 58. % % See also: VL_HELP(). % Copyright (C) 2010-11 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_hog.m0000644000175000017500000000331212237004745015025 0ustar dimadima% VL_HOG Compute HOG features % HOG = VL_HOG(IM, CELLSIZE) computes the HOG features for image IM % and the specified CELLSIZE. IM can be either grayscale or colour % in SINGLE storage class. HOG is an array of cells: its number % of columns is approximately the number of columns of IM divided % by CELLSIZE and the same for the number of rows. The third % dimension spans the feature compoents. % % PERM = VL_HOG('permutation') returns the left-right permutation % to apply to each HOG cell to flip it. % % IMAGE = VL_HOG('render', HOG) returns an IMAGE containing an % iconic representation of the array of cells HOG. % % Options: % % Variant:: 'UoCTTI' % Choose a HOG variant: 'UoCTTI' or 'DalalTriggs'. % % NumOrientations:: 9 % Choose a number of undirected orientations in the orientation % histograms. The angle [0,pi) is divided in to NumOrientation % equal parts. % % DirectedPolarField:: % By specifying this flag the image IM is interpreted as samples % from a 2D vector field specified by their argument IM(:,:,2) and % modulus IM(:,:,1). % % UndirectedPolarField:: % Same as above, but wraps angles in [0,pi). % % BilinearOrientations:: % This flags activates the use of bilinear interpolation to assign % orientations to bins. This produces a smoother feature, but is % not some other implementations (e.g. UoCTTI). % % Example:: computing and visualizing HOG features % hog = vl_hog(im2single(im)) ; % compute HOG features % Author: Andrea Vedaldi % Copyright (C) 2012-13 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_cummax.def0000644000175000017500000000464312237004736016054 0ustar dimadima/** @file vl_cummax.def ** @brief vl_cummax MEX helper template ** @author Andrea Vedaldi **/ /* Copyright */ /* include this file for different values of ITERATE_1 */ #ifndef ITERATE_1 # define ITERATE_1 1 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 2 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 3 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 4 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 5 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 6 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 7 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 8 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 9 # include "vl_cummax.def" # undef ITERATE_1 # define ITERATE_1 10 # include "vl_cummax.def" # undef ITERATE_1 #endif /* For each value of ITERATE_1, instantiate the algorithm for a different data type. */ #if defined(ITERATE_1) # define __VALUE_T__ TYPE(ITERATE_1) /* The algorithm scans in linear order the input data X. X indexed as X[i1,i2,i3,...,iD] where D is the number of dimensions. In order to operate on the d-th dimension, one defines the output Y as / MAX(Y[i1,i2,...,id - 1,...], X[i1,i2,...,id,...]), if id > 0 (OP), Y[i1,i2,...] = < \ X[i1,i2,...,id,...], if id = 0 (COPY). So by scanning X in column major, one has id = 0 while i1,i2,...,id-1 traverse the first STRIDE = D1*D2*...*D_{d-1} elements and performs a COPY operation. Then id is incremented by one and remains > 0 until D1*D2*...*D_{d-1}*Dd elemenets are visited overall, and performs OP operations (MAX in this case). Then id = 0 again and so on until all elements are visited. */ VL_INLINE void VL_XCAT(_vl_cummax_,__VALUE_T__) (mxArray * Y_array, mxArray const * X_array, vl_size stride, vl_size dimension) { __VALUE_T__ const * x = (__VALUE_T__*) mxGetData(X_array) ; __VALUE_T__ const * x_stop = x + mxGetNumberOfElements(X_array) ; __VALUE_T__ * y = (__VALUE_T__*) mxGetData(Y_array) ; while (x < x_stop) { __VALUE_T__ const * x_ops_stop = x + stride ; /* copy stride elements */ while (x < x_ops_stop) { *y++ = *x++ ; } /* op stride * (dimension - 1) */ x_ops_stop += stride * (dimension - 1) ; while (x < x_ops_stop) { *y = VL_MAX(*x, *(y - stride)) ; ++ y ; ++ x ; } } } #undef __VALUE_T__ #endif vlfeat/toolbox/misc/vl_binsum.c0000644000175000017500000000750212237004736015540 0ustar dimadima/** @internal ** @file binsum.c ** @author Andrea Vedaldi ** @brief vl_binsum.c - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #define REPEAT1_1(m,p) m(1,p) #define REPEAT1_2(m,p) REPEAT1_1(m,p) m(2,p) #define REPEAT1_3(m,p) REPEAT1_2(m,p) m(3,p) #define REPEAT1_4(m,p) REPEAT1_3(m,p) m(4,p) #define REPEAT1_5(m,p) REPEAT1_4(m,p) m(5,p) #define REPEAT1_6(m,p) REPEAT1_5(m,p) m(6,p) #define REPEAT1_7(m,p) REPEAT1_6(m,p) m(7,p) #define REPEAT1_8(m,p) REPEAT1_7(m,p) m(8,p) #define REPEAT1_9(m,p) REPEAT1_8(m,p) m(9,p) #define REPEAT1_10(m,p) REPEAT1_9(m,p) m(10,p) #define REPEAT2_1(m,p) m(1,p) #define REPEAT2_2(m,p) REPEAT2_1(m,p) m(2,p) #define REPEAT2_3(m,p) REPEAT2_2(m,p) m(3,p) #define REPEAT2_4(m,p) REPEAT2_3(m,p) m(4,p) #define REPEAT2_5(m,p) REPEAT2_4(m,p) m(5,p) #define REPEAT2_6(m,p) REPEAT2_5(m,p) m(6,p) #define REPEAT2_7(m,p) REPEAT2_6(m,p) m(7,p) #define REPEAT2_8(m,p) REPEAT2_7(m,p) m(8,p) #define REPEAT2_9(m,p) REPEAT2_8(m,p) m(9,p) #define REPEAT2_10(m,p) REPEAT2_9(m,p) m(10,p) #define TYPE_1 double #define TYPE_2 float #define TYPE_3 vl_int64 #define TYPE_4 vl_uint64 #define TYPE_5 vl_int32 #define TYPE_6 vl_uint32 #define TYPE_7 vl_int16 #define TYPE_8 vl_uint16 #define TYPE_9 vl_int8 #define TYPE_10 vl_uint8 #define TYPE_AUX(x) TYPE_ ## x #define TYPE(x) TYPE_AUX(x) #define CLASS_1 DOUBLE #define CLASS_2 SINGLE #define CLASS_3 INT64 #define CLASS_4 UINT64 #define CLASS_5 INT32 #define CLASS_6 UINT32 #define CLASS_7 INT16 #define CLASS_8 UINT16 #define CLASS_9 INT8 #define CLASS_10 UINT8 #define CLASS_AUX(x) CLASS_ ## x #define CLASS(x) CLASS_AUX(x) #include "vl_binsum.def" /* ---------------------------------------------------------------- */ /* */ /* ---------------------------------------------------------------- */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_ACCUMULATOR = 0, IN_VALUES, IN_INDEXES, IN_DIM} ; enum {OUT_ACCUMULATOR = 0} ; mxClassID valueClass ; mxClassID indexClass ; vl_index dim = 0 ; if (nin < 3) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nin > 4) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (!vlmxIsReal(IN(ACCUMULATOR))) { vlmxError(vlmxErrInvalidArgument, "ACCUMULATOR is not a numeric real array.") ; } if (!vlmxIsReal(IN(VALUES))) { vlmxError(vlmxErrInvalidArgument, "VALUES is not a numeric real array.") ; } if (!vlmxIsReal(IN(INDEXES))) { vlmxError(vlmxErrInvalidArgument, "INDEXES is not a numeric real array.") ; } indexClass = mxGetClassID(IN(INDEXES)) ; valueClass = mxGetClassID(IN(ACCUMULATOR)) ; if (valueClass != mxGetClassID(IN(VALUES))) { vlmxError(vlmxErrInvalidArgument, "ACCUMULATOR and VALUES do not have the same storage class.") ; } if (nin > 3) { if (!vlmxIsPlainScalar(IN(DIM))) { vlmxError(vlmxErrInvalidArgument, "DIM is not a plain scalar.") ; } dim = (vl_index)mxGetScalar(IN(DIM)) ; } out[0] = mxDuplicateArray(IN(ACCUMULATOR)) ; #define DISPATCH(I,V) \ case VL_XCAT3(mx,CLASS(I),_CLASS): \ VL_XCAT4(_vl_binsum_,TYPE(V),_,TYPE(I))(out[0], IN(VALUES), IN(INDEXES), dim) ; break ; #define DISPATCH_VALUE(V,_) \ case VL_XCAT3(mx,CLASS(V),_CLASS) : \ switch (indexClass) { \ REPEAT2_10(DISPATCH,V) ; \ default: \ vlmxError(vlmxErrInvalidArgument, "INDEXES is not of one of the supported storage classes.") ; \ } \ break ; switch (valueClass) { REPEAT1_10(DISPATCH_VALUE,_) ; default: vlmxError(vlmxErrInvalidArgument, "ACCUMULATOR is not of one of the supported storage classes.") ; } } vlfeat/toolbox/misc/vl_simdctrl.c0000644000175000017500000000173112237004736016062 0ustar dimadima/** @file vl_simdctlr.c ** @brief vl_simdctrl MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mexutils.h" void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_ENABLED = 0} ; enum {OUT_ENABLED = 0} ; vl_bool wasEnabled = vl_get_simd_enabled() ; if (nout > 1) { vlmxError(vlmxErrInvalidArgument, "at most one output argument") ; } OUT(ENABLED) = vlmxCreatePlainScalar (wasEnabled) ; if (nin == 0) { return ; } if (nin > 1) { vlmxError(vlmxErrInvalidArgument, "At most one argument") ; } if (!vlmxIsScalar(IN(ENABLED))) { vlmxError(vlmxErrInvalidArgument, "ENABLED must be a scalar") ; } vl_set_simd_enabled ((vl_bool) mxGetScalar(IN(ENABLED))) ; } vlfeat/toolbox/misc/vl_ihashsum.m0000644000175000017500000000454712237004745016104 0ustar dimadima% VL_IHASHSUM Accumulate integer labels into a hash table % [H,ID,NEXT] = VL_IHASHSUM(H,ID,NEXT,K,X) counts the number of % occurences of the columns of X, accumulating these to the hash % table represented by the tripled H,ID,NEXT. % % X is a D x N array of class UINT8 each row of which defines an D % dimensional label. Labels cannot be all zeros. % % H and NEXT are 1 x C arrays of class UINT32 and ID is a D x C % array of class UINT8. H is a vector of counts, ID stores, for each % element of H, the corresponding label, and NEXT is a vector of % indexes. % % Once constructed, the hash table can be searched by means of the % VL_IHASHFIND() function. % % The hash table uses double hashing [1] with an initial size equal % to K (so that C >= K). Given a label X, this is first hashed by % using the FNV algorithm [2] to one of K bucket. If this bucket is % free, it is assigned to label X and the count is incremented. If % the bucket is already assigned to the same label X, the count is % incremented. If the bucket is already assigned to a different % label, a second hash is used to scan (probe) the table for a free % bucket. % % If no free/matching bucket is found (because the hash table is % full) an overflow area containing extra buckets is used. This is % visited by reading off indexe from the NEXT vector, until a % matching bucket is found or the overflow area is enlarged. % % Example:: % The following example counts integer bi-dimensional label % occurences: % % K = 5 ; % h = zeros(1,K,'uint32') ; % id = zeros(2,K,'uint8'); % next = zeros(1,K,'uint32') ; % X = uint8([1 1 ; 1 2 ; 2 1 ; 1 1]') ; % [h,id,next] = vl_ihashsum(h,id,next,K,X) ; % % resulting in % % h = [1 0 1 2 0] % id = [1 0 2 1 0 % 2 0 1 1 0] % next = [0 0 0 0 0] % % For example, [1;2] has a count of 1 and [1;1] has a count of % 2. NEXT is zero because there have been no collisions. % % REFERENCES:: % [1] http://en.wikipedia.org/wiki/Double_hashing % [2] http://www.isthe.com/chongo/tech/comp/fnv % % See also: VL_IHASHFIND(). % Author: Andrea Vedaldi % Copyright (C) 2008-12 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_version.c0000644000175000017500000000317012237004736015725 0ustar dimadima/** @file vl_version.c ** @brief vl_version MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mexutils.h" #include #include /* option codes */ enum { opt_verbose } ; /* options */ vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { int verbose = 0 ; char buffer [1024] ; int unsigned const bufferSize = sizeof(buffer)/sizeof(buffer[0]) ; int opt ; int next = 0 ; mxArray const *optarg ; VL_USE_MATLAB_ENV ; if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbose ; break ; default: abort() ; } } if (verbose) { int offset = 0 ; char * string = vl_configuration_to_string_copy() ; offset = vl_string_copy(buffer, bufferSize, string) ; snprintf(buffer + offset, bufferSize - offset, " SIMD enabled: %s\n", VL_YESNO(vl_get_simd_enabled())) ; if(string) vl_free(string) ; } else { snprintf(buffer, sizeof(buffer)/sizeof(buffer[0]), "%s", VL_VERSION_STRING) ; } if (nout == 0) { mexPrintf("%s\n", buffer) ; } else { out[0] = mxCreateString(buffer) ; } } vlfeat/toolbox/misc/vl_binsearch.m0000644000175000017500000000151312237004736016207 0ustar dimadima% VL_BINSEARCH Maps data to bins % IDX = VL_BINSEARCH(B, X) maps the elements of X to the bins % defined by B. B is a non-decreasing vector of M real numbers % defining bins [-inf, B(1)), [B(1), B(2)), ... [B(M) +inf] (note % that the last bin contains +inf). The bins are numbered from 0 to % M. X is a real (plain) array and IDX is an array with the same % dimensions of X, specifying the bin correpsonding to each element % of X. % % If B(1) = -inf the bin number 0 is removed and the bin number 1 is % [-inf B(2)). If B(M) = +inf, the last bin M is the singleton +inf. % % See also: VL_BINSUM(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_twister.c0000644000175000017500000001200212237004736015733 0ustar dimadima/** @internal ** @file vl_twister.c ** @brief vl_twister - MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include /* driver */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { MANIP_STATE, RUN_GENERATOR } mode ; VlRand * rand ; VL_USE_MATLAB_ENV ; rand = vl_get_rand() ; /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (nout > 1) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nin > 0 && ! mxIsNumeric(in[0])) { mode = MANIP_STATE ; } else { mode = RUN_GENERATOR ; } switch (mode) { case RUN_GENERATOR: { enum { maxNumDimensions = 30 } ; vl_size numDimensions = 2, n ; vl_uindex k ; mwSize dimensions [maxNumDimensions] = {1, 1} ; double * x ; if (nin > 1) { /* TWISTER(N1 N2 ...) style */ if (nin >= maxNumDimensions) { vlmxError(vlmxErrTooManyInputArguments, "Too many dimensions specified.") ; } for (k = 0 ; k < (unsigned)nin ; ++k) { if (! vlmxIsPlainScalar(in[k])) { vlmxError(vlmxErrInvalidArgument, "The %d-th argument is not a plain scalar.", k + 1) ; } if (mxGetScalar(in[k]) < 0) { vlmxError(vlmxErrInvalidArgument, "The %d-th argument is negative.", k + 1) ; } dimensions[k] = mxGetScalar(in[k]) ; } numDimensions = k ; } else if (nin == 1) { /* TWISTER([N1 N2 ...]) style */ if (! vlmxIsPlainVector(in[0], -1)) { vlmxError(vlmxErrInvalidArgument, "The argument is not a plain vector.") ; } x = mxGetPr(in[0]) ; n = mxGetNumberOfElements(in[0]) ; numDimensions = VL_MAX(2, n) ; if (numDimensions > maxNumDimensions) { vlmxError(vlmxErrInvalidArgument, "Too many dimensions specified.") ; } if (n == 1) { if (*x < 0) { vlmxError(vlmxErrInvalidArgument, "The specified dimension is negative.") ; } dimensions[0] = dimensions[1] = *x ; } else { for (k = 0 ; k < n ; ++k) { if (x[k] < 0) { vlmxError(vlmxErrInvalidArgument, "One of the specified dimensions is negative.") ; } dimensions[k] = x[k] ; } } } out[0] = mxCreateNumericArray (numDimensions, dimensions, mxDOUBLE_CLASS, mxREAL) ; n = mxGetNumberOfElements (out[0]) ; x = mxGetPr (out[0]) ; for (k = 0 ; k < n ; ++k) { x[k] = vl_rand_res53(rand) ; } } break ; case MANIP_STATE: { enum { buff_size = 32 } ; char buff [buff_size] ; /* check for 'state' string */ if (! vlmxIsString(in[0], -1) || mxGetString(in[0], buff, buff_size) || vl_string_casei_cmp ("state", buff) != 0 ) { vlmxError(vlmxErrInvalidArgument, NULL) ; } /* TWISTER('state') */ if (nin == 1) { vl_uindex i ; vl_uint32 * data ; out[0] = mxCreateNumericMatrix (625, 1, mxUINT32_CLASS, mxREAL) ; data = mxGetData(out[0]) ; for (i = 0 ; i < 624 ; ++i) data[i] = rand->mt[i] ; data[624] = (vl_uint32) rand->mti ; } else { if (vlmxIsPlainScalar(in[1])) { /* TWISTER('state', X) */ vl_uint32 x = (vl_uint32) mxGetScalar(in[1]) ; vl_rand_seed (rand, x) ; } else if (mxIsNumeric(in[1]) && mxGetClassID(in[1]) == mxUINT32_CLASS && mxGetNumberOfElements(in[1]) == 624+1 && ((vl_uint32 const*)mxGetData(in[1]))[624] <= 624 ) { /* TWISTER('state', STATE) */ vl_uindex i ; vl_uint32 * data = mxGetData(in[1]) ; for (i = 0 ; i < 624 ; ++i) rand->mt[i] = data[i] ; rand->mti = data [624] ; } else if (mxIsNumeric(in[1]) && mxGetClassID(in[1]) == mxDOUBLE_CLASS && mxGetNumberOfElements(in[1]) <= 624) { /* TWISTER('state', KEY) */ vl_uint32 key [624] ; double const * x = mxGetPr(in[1]) ; vl_size n = mxGetNumberOfElements(in[1]) ; vl_uindex k ; for (k = 0 ; k < n ; ++k) { key [k] = x [k] ; } vl_rand_seed_by_array (rand, key, n) ; } } } break ; default: abort() ; } } vlfeat/toolbox/misc/vl_ihashfind.c0000644000175000017500000001065112237004736016177 0ustar dimadima/** @internal ** @file ihashfind.c ** @author Andrea Vedaldi ** @brief BINSUM - MEX **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include /* hash function */ unsigned int fnv_hash (void const *key, int len) { unsigned char const *p = key; unsigned int h = 2166136261U ; int i; for ( i = 0; i < len; i++ ) h = ( h * 16777619 ) ^ p[i]; return h; } int is_null (vl_uint8 const * x, int n) { int i ; for (i = 0 ; i < n ; ++i) { if (x[i]) return 0 ; } return 1 ; } int is_equal (vl_uint8 const * x, vl_uint8 const * y, int n) { int i ; for (i = 0 ; i < n ; ++i) { if (x[i] != y[i]) return 0 ; } return 1 ; } void cpy (vl_uint8 * x, vl_uint8 const * y, int n) { int i ; for (i = 0 ; i < n ; ++i){ /* mexPrintf("cpy:%d %d\n",x[i],y[i]);*/ x[i] = y[i] ; } } /** @brief Driver. ** ** @param nount number of output arguments. ** @param out output arguments. ** @param nin number of input arguments. ** @param in input arguments. **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { IN_ID, IN_NEXT, IN_K, IN_X } ; enum { OUT_SEL } ; vl_uint32 const * next ; vl_uint32 * sel ; vl_uint8 const * id ; vl_uint8 const * x ; unsigned int K, i, N, res, last, ndims ; /* ----------------------------------------------------------------- * Check arguments * -------------------------------------------------------------- */ if( nin != 4 ) { mexErrMsgTxt("Four arguments required") ; } else if (nout > 1) { mexErrMsgTxt("At most one output argument.") ; } if(! mxIsNumeric(in[IN_NEXT])|| mxGetClassID(in[IN_NEXT])!= mxUINT32_CLASS) { mexErrMsgTxt("NEXT must be UINT32.") ; } if(! mxIsNumeric(in[IN_X]) || mxGetClassID(in[IN_X])!= mxUINT8_CLASS) { mexErrMsgTxt("X must be UINT8") ; } if (mxGetM(in[IN_NEXT]) != 1) { mexErrMsgTxt("NEXT must be a row vector") ; } if(! mxIsNumeric(in[IN_ID]) || mxGetClassID(in[IN_ID])!= mxUINT8_CLASS) { mexErrMsgTxt("ID must be UINT8.") ; } ndims = mxGetM(in[IN_ID]) ; res = mxGetN(in[IN_ID]) ; if(res != mxGetN(in[IN_NEXT])) { mexErrMsgTxt("ID, NEXT must have the same number of columns") ; } if(ndims != mxGetM(in[IN_X])) { mexErrMsgTxt("ID and X must havethe same number of rows") ; } if(! vlmxIsPlainScalar(in[IN_K])) { mexErrMsgTxt("K must be a scalar") ; } K = (unsigned int) *mxGetPr(in[IN_K]) ; N = mxGetN(in[IN_X]) ; id = mxGetData(in[IN_ID]) ; next = mxGetData(in[IN_NEXT]) ; x = mxGetData(in[IN_X]) ; out[OUT_SEL] = mxCreateNumericMatrix (1, N, mxUINT32_CLASS, mxREAL) ; sel = mxGetData (out[OUT_SEL]) ; /* search for last occupied slot */ last = res ; for (i = 0 ; i < res ; ++i) last = VL_MAX(last, next [i]) ; /* REMARK: last and next are 1 based */ if (K > res) { mexErrMsgTxt("K cannot be larger then the size of H") ; } if (last > res) { mexErrMsgTxt("An element of NEXT is greater than the size of the table") ; } /* ----------------------------------------------------------------- * Do job * -------------------------------------------------------------- */ for (i = 0 ; i < N ; ++i) { /* hash */ unsigned int h1, h2 ; unsigned int j, p = 0 ; if (is_null (x + i * ndims, ndims)) { *sel++ = 0 ; continue ; } h1 = fnv_hash(x + i * ndims, ndims) % K ; h2 = h1 | 0x1 ; /* this needs to be odd */ /* search first free or matching position */ p = h1 % K ; for (j = 0 ; j < K ; ++j) { if (is_null (id + p * ndims, ndims) || is_equal(id + p * ndims, x + i * ndims, ndims)) break ; h1 += h2 ; p = h1 % K ; } /* handle extended table */ while (! is_null (id + p * ndims, ndims) && ! is_equal(id + p * ndims, x + i * ndims, ndims)) { if (next[p] == 0) break ; p = next [p] - 1 ; } /* found or not ? */ if (is_equal(id + p * ndims, x + i * ndims, ndims)) { /* found */ *sel++ = p + 1 ; } else { /* not found */ *sel++ = 0 ; } } /* next guy to search for */ } vlfeat/toolbox/misc/vl_threads.c0000644000175000017500000000174312237004745015676 0ustar dimadima/** @file vl_threads.c ** @brief vl_threads MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2013 Andrea Vedaldi All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include "mexutils.h" void mexFunction(int nout VL_UNUSED, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_NUM = 0} ; enum {OUT_NUM = 0} ; vl_size numThreads ; numThreads = vl_get_max_threads() ; if (nout > 1) { vlmxError(vlmxErrInvalidArgument, "More than one ouptut argumnets requested.") ; } if (nin > 1) { vlmxError(vlmxErrInvalidArgument, "More than one input argument specified.") ; } OUT(NUM) = vlmxCreatePlainScalar (numThreads) ; if (nin == 0) { return ; } if (!vlmxIsScalar(IN(NUM))) { vlmxError(vlmxErrInvalidArgument, "NUM is not a scalar.") ; } numThreads = (vl_size) mxGetScalar(IN(NUM)) ; vl_set_num_threads (numThreads) ; } vlfeat/toolbox/misc/vl_ihashfind.m0000644000175000017500000000102012237004745016177 0ustar dimadima% VL_IHASHFIND Find labels in an integer hash table % SEL = VL_IHASHFIND(ID, NEXT, K, X) returns a vector SEL of the % entires in the hash table ID,NEXT,K corresponding to the labels % stored as columns of X. % % The format is the same as for the functino VL_IHASHSUM(). % % See also: VL_IHASHSUM(). % Author: Andrea Vedaldi % Copyright (C) 2008-12 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_binsearch.c0000644000175000017500000000364712237004736016207 0ustar dimadima/** @internal ** @file vl_binsearch.c ** @brief vl_binsearch - MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { IN_B=0, IN_X, IN_END } ; enum { OUT_IDX=0 } ; vl_size numElementsX, numElementsB ; const double *X, *B ; double *IDX ; if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (nin != 2) { vlmxError(vlmxErrInvalidArgument, "Incorrect number of arguments.") ; } if (! vlmxIsPlain (IN(B)) || ! vlmxIsPlain (IN(X)) ) { vlmxError(vlmxErrInvalidArgument, "All arguments must be plain arrays.") ; } numElementsX = mxGetNumberOfElements(IN(X)) ; numElementsB = mxGetNumberOfElements(IN(B)) ; { mwSize const * dimensions = mxGetDimensions(IN(X)) ; vl_size numDimensions = mxGetNumberOfDimensions(IN(X)) ; OUT(IDX) = mxCreateNumericArray (numDimensions, dimensions, mxDOUBLE_CLASS, mxREAL) ; } /* if B is empty it defines only [-inf, +inf) */ if (numElementsB == 0) return ; IDX = mxGetPr(OUT(IDX)) ; X = mxGetPr(IN(X)) ; B = mxGetPr(IN(B)) ; { vl_uindex i ; for (i = 0 ; i < numElementsX ; ++i) { double x = X[i] ; vl_uindex blower = 0 ; vl_uindex bupper = numElementsB - 1 ; vl_uindex bsplit ; if (x < B[0]) { IDX [i] = 0 ; continue ; } if (x >= B[numElementsB - 1]) { IDX [i] = numElementsB ; continue ; } while (blower + 1 < bupper) { bsplit = (bupper + blower) / 2 ; if (x < B[bsplit]) bupper = bsplit ; else blower = bsplit ; } IDX [i] = blower + 1 ; } } } vlfeat/toolbox/misc/vl_version.m0000644000175000017500000000073612237004736015744 0ustar dimadima% VL_VERSION Obtain VLFeat version information % VL_VERSION() prints VLFeat version number. VL_VERSION('VERBOSE') % prints additional information. % % STR = VL_VERSION(...) returns the ouptut in a string rather than % printing it. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_simdctrl.m0000644000175000017500000000074012237004736016073 0ustar dimadima% VL_SIMDCTRL Toggle VLFeat SIMD optimizations % VL_SIMDCTRL() returns TRUE if VLFeat SIMD optimizations are enabled % and FALSE otherwise. % % VL_SIMDCTRL(TRUE) enables VLFeat SIMD optimizations. VL_SIMDCTRL(FALSE) % disables them. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/misc/vl_isoctave.m0000644000175000017500000000043212237004745016065 0ustar dimadimafunction x = vl_isoctave() % VL_ISOCTAVE Determines whether Octave is running % X = VL_ISOCTAVE() returns TRUE if the script is running in the % Octave environment (instead of MATLAB). persistent y ; if isempty(y) y = exist('OCTAVE_VERSION','builtin') ~= 0 ; end x = y ; vlfeat/toolbox/misc/vl_pegasos.m0000644000175000017500000000542512237004745015720 0ustar dimadima% VL_PEGASOS [deprecated] % VL_PEGASOS is deprecated. Please use VL_SVMTRAIN() instead. function [w b info] = vl_pegasos(X,Y,LAMBDA, varargin) % Verbose not supported if (sum(strcmpi('Verbose',varargin))) varargin(find(strcmpi('Verbose',varargin),1))=[]; fprintf('Option VERBOSE is no longer supported.\n'); end % DiagnosticCallRef not supported if (sum(strcmpi('DiagnosticCallRef',varargin))) varargin(find(strcmpi('DiagnosticCallRef',varargin),1)+1)=[]; varargin(find(strcmpi('DiagnosticCallRef',varargin),1))=[]; fprintf('Option DIAGNOSTICCALLREF is no longer supported.\n Please follow the VLFeat tutorial on SVMs for more information on diagnostics\n'); end % different default value for MaxIterations if (sum(strcmpi('MaxIterations',varargin)) == 0) varargin{end+1} = 'MaxIterations'; varargin{end+1} = ceil(10/LAMBDA); end % different default value for BiasMultiplier if (sum(strcmpi('BiasMultiplier',varargin)) == 0) varargin{end+1} = 'BiasMultiplier'; varargin{end+1} = 0; end % parameters for vl_maketrainingset setvarargin = {}; if (sum(strcmpi('HOMKERMAP',varargin))) setvarargin{end+1} = 'HOMKERMAP'; setvarargin{end+1} = varargin{find(strcmpi('HOMKERMAP',varargin),1)+1}; varargin(find(strcmpi('HOMKERMAP',varargin),1)+1)=[]; varargin(find(strcmpi('HOMKERMAP',varargin),1))=[]; end if (sum(strcmpi('KChi2',varargin))) setvarargin{end+1} = 'KChi2'; varargin(find(strcmpi('KChi2',varargin),1))=[]; end if (sum(strcmpi('KINTERS',varargin))) setvarargin{end+1} = 'KINTERS'; varargin(find(strcmpi('KINTERS',varargin),1))=[]; end if (sum(strcmpi('KL1',varargin))) setvarargin{end+1} = 'KL1'; varargin(find(strcmpi('KL1',varargin),1))=[]; end if (sum(strcmpi('KJS',varargin))) setvarargin{end+1} = 'KJS'; varargin(find(strcmpi('KJS',varargin),1))=[]; end if (sum(strcmpi('Period',varargin))) setvarargin{end+1} = 'Period'; setvarargin{end+1} = varargin{find(strcmpi('Period',varargin),1)+1}; varargin(find(strcmpi('Period',varargin),1)+1)=[]; varargin(find(strcmpi('Period',varargin),1))=[]; end if (sum(strcmpi('Window',varargin))) setvarargin{end+1} = 'Window'; setvarargin{end+1} = varargin{find(strcmpi('Window',varargin),1)+1}; varargin(find(strcmpi('Window',varargin),1)+1)=[]; varargin(find(strcmpi('Window',varargin),1))=[]; end if (sum(strcmpi('Gamma',varargin))) setvarargin{end+1} = 'Gamma'; setvarargin{end+1} = varargin{find(strcmpi('Gamma',varargin),1)+1}; varargin(find(strcmpi('Gamma',varargin),1)+1)=[]; varargin(find(strcmpi('Gamma',varargin),1))=[]; end setvarargin{:} DATA = vl_maketrainingset(double(X),int8(Y),setvarargin{:}); DATA [w b info] = vl_svmtrain(DATA,LAMBDA,varargin{:}); fprintf('\n vl_pegasos is DEPRECATED. Please use vl_svmtrain instead. \n\n'); end vlfeat/toolbox/plotop/0000755000175000017500000000000012237004745013754 5ustar dimadimavlfeat/toolbox/plotop/vl_plotframe.m0000644000175000017500000001046512237004745016632 0ustar dimadimafunction h = vl_plotframe(frames,varargin) % VL_PLOTFRAME Plot feature frame % VL_PLOTFRAME(FRAME) plots the frames FRAME. Frames are attributed % image regions (as, for example, extracted by a feature detector). A % frame is a vector of D=2,3,..,6 real numbers, depending on its % class. VL_PLOTFRAME() supports the following classes: % % Point:: % FRAME(1:2) are the x,y coordinates of the point % % Circle:: % FRAME(1:2) are the x,y coordinates of the center. FRAME(3) % is the circle radius.. % % Oriented circle:: % FRAME(1:2) are the x,y coordiantes of the center. FRAME(3) is the % radius. FRAME(4) is the orientation, expressed as a ppsitive % rotation (note that images use a left-handed system with the Y % axis pointing downwards). % % Ellipse:: % FRAME(1:2) are the x,y coordiantes of the center. FRAME(3:5) are % the element S11, S12, S22 of a 2x2 covariance matrix S (a positive % semidefinite matrix) defining the ellipse shape. The ellipse % is the set of points {x + T: x' inv(S) x = 1}, where T is the center. % % Oriented ellipse:: % FRAME(1:2) are the x,y coordiantes of the center. FRAME(3:6) is % the column-wise stacking of a 2x2 matrix A defining the ellipse % shape and orientation. The ellipse is obtaine by transforming % a unit circle by A as the set of points {A x + T : |x| = 1}, where % T is the center. % % All frames can be thought of as an affine transformation of the unit circle. % For unoriented frames, the affine transformation is selected so that % the positive Y direction (downwards, graviy vector) is preserved. % % H = VL_PLOTFRAME(...) returns the handle H of the graphical object % representing the frames. % % VL_PLOTFRAME(FRAMES) for a matrix of FRAMES plots multiple frames. % Using this call is much faster than calling VL_PLOTFRAME() for each frame. % % VL_PLOTFRAME(FRAMES,...) passes any extra argument to the % underlying plot function. The first optional argument can be a line % specification string such as the one used by PLOT(). % % See also: VL_FRAME2OELL(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % number of vertices drawn for each frame np = 40 ; lineprop = {} ; if length(varargin) > 0 lineprop = vl_linespec2prop(varargin{1}) ; lineprop = {lineprop{:}, varargin{2:end}} ; end % -------------------------------------------------------------------- % Handle various frame classes % -------------------------------------------------------------------- % if just a vector, make sure it is column if(min(size(frames))==1) frames = frames(:) ; end [D,K] = size(frames) ; zero_dimensional = D==2 ; % just points? if zero_dimensional h = plot(frames(1,:),frames(2,:),'g.',lineprop{:}) ; return ; end % reduce all other cases to ellipses/oriented ellipses frames = vl_frame2oell(frames) ; do_arrows = (D==4 || D==6) ; % -------------------------------------------------------------------- % Draw % -------------------------------------------------------------------- K = size(frames,2) ; thr = linspace(0,2*pi,np) ; % allx and ally are nan separated lists of the vertices describing the % boundary of the frames allx = nan*ones(1, np*K+(K-1)) ; ally = nan*ones(1, np*K+(K-1)) ; if do_arrows % allxf and allyf are nan separated lists of the vertices of the allxf = nan*ones(1, 3*K) ; allyf = nan*ones(1, 3*K) ; end % vertices around a unit circle Xp = [cos(thr) ; sin(thr) ;] ; for k=1:K % frame center xc = frames(1,k) ; yc = frames(2,k) ; % frame matrix A = reshape(frames(3:6,k),2,2) ; % vertices along the boundary X = A * Xp ; X(1,:) = X(1,:) + xc ; X(2,:) = X(2,:) + yc ; % store allx((k-1)*(np+1) + (1:np)) = X(1,:) ; ally((k-1)*(np+1) + (1:np)) = X(2,:) ; if do_arrows allxf((k-1)*3 + (1:2)) = xc + [0 A(1,2)] ; allyf((k-1)*3 + (1:2)) = yc + [0 A(2,2)] ; end end if do_arrows h = line([allx nan allxf], ... [ally nan allyf], ... 'Color','g','LineWidth',3, ... lineprop{:}) ; else h = line(allx, ally, ... 'Color','g','LineWidth',3, ... lineprop{:}) ; end vlfeat/toolbox/plotop/vl_printsize.m0000644000175000017500000001046412237004745016667 0ustar dimadimafunction vl_printsize(varargin) % VL_PRINTSIZE Set the printing size of a figure % VL_PRINTSIZE(R) adjusts the PaperPosition property of the current % figure to make the figure paper width equal to R times the width % of a 'uslsetter' page, while preserving the aspect ratio of the % figure. Then, it sets the PaperSize property to match tightly the % size of the figure. % % VL_PRINTSIZE(FIG,R) operates on the specified figure FIG. The % function accepts the following optional arguments: % % Aspect:: [] % Change the figure aspect ratio (width/height) to the specified % value. % % Reference:: 'horizontal' % If set to 'horizontal', VL_PRINTSIZE(R) makes the width of the % figure equal to a fraction R of the width of the page. If set to % 'vertical', the height is used instead. % % PaperType:: 'usletter' % Set the type of the reference page to the specified type. Any of % the paper types supported by MATLAB can be used (see % PRINT()). % % Margin:: 0 % If greater than zero, VL_PRINTSIZE(R) leaves the specified % margin around the figure rather than fitting the paper size % tightly around it. The margin is expressed as a fraction of the % figure paper width (or height if Reference is set to % 'vertical'). % % PRINTING FIGURES IN MATLAB % % The standard procedure to include a MATLAB figure in a publication % consists in printing the figure to an EPS or PDF file (by using % the PRINT() function) and then rescaling it to the desired size in % the publication. PDF printing has the additional inconvenience % that large white margins are generated, and requires cropping the % figure too. % % Unfortunately, rescalign a figure in this manner has the usually % unwanted effect of rescaling elements of the graphics such as font % sizes and line thicknesses. For example, a font which is 12pt in % the original MATLAB figure may become too large or too small after % rescaling. % % The `proper' way of printing a figure is to specify its desired % size on paper before calling the PRINT() function. VL_PRINTSIZE() % helps doing so. % % Margins are an additional nuances. When printing to certain % formats such as PDF, MATLAB prints in fact entire page (e.g. a % whole US Letter sheet), resulting in wide white margin all around % the figure. VL_PRINTSIZE() alleviates this problem by setting the % paper size to match the figure size tightly. This does not % eliminate margins completely, especially if the figure has % multiple subplots, but it is certainly a large improvement. % % See also: VL_HELP(). % Author: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % Copyright (C) 2013 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if length(varargin) >= 2 && isnumeric(varargin{2}) % called with two numeric arguments fig = varargin{1} ; varargin(1) = [] ; else fig = gcf ; end sizeRatio = varargin{1} ; varargin(1) = [] ; opts.aspectRatio = NaN ; opts.reference = 'horizontal' ; opts.paperType = 'usletter' ; opts.margin = 0 ; opts = vl_argparse(opts, varargin) ; opts.reference = lower(opts.reference) ; if ~ismember(opts.reference, {'horizontal', 'vertical'}) error('Invalid value ''%s'' for the REFERENCE option', opts.reference) ; end % set the paper size to the reference type set(fig, 'PaperType', opts.paperType) ; paperSize = get(fig, 'PaperSize') ; % get the current figure position to compute the current aspect ratio position = get(fig, 'PaperPosition') ; % if not specified, compute current aspect ratio if isnan(opts.aspectRatio) opts.aspectRatio = position(3) / position(4) ; end % resize the figure switch opts.reference case 'horizontal' s = paperSize(1) / position(3) * sizeRatio ; case 'vertical' s = paperSize(2) / position(4) * sizeRatio ; end position(3:4) = position(3) * s * [1 1/opts.aspectRatio] ; % add margin switch opts.reference case 'horizontal' position(1) = position(3) * opts.margin ; position(2) = position(3) * opts.margin ; case 'vertical' position(1) = position(4) * opts.margin ; position(2) = position(4) * opts.margin ; end set(fig, 'PaperPosition', position, ... 'PaperSize', 2 * position(1:2) + position(3:4)) ; end vlfeat/toolbox/plotop/vl_figaspect.m0000644000175000017500000000141512237004736016601 0ustar dimadimafunction vl_figaspect(a) % VL_FIGASPECT Set figure aspect ratio % VL_FIGASPECT(A) sets the current figure aspect ratio to A. It % leaves the length of the shortest side unaltered. Both the % screen and paper positions are affected. % % See also: VL_PRINTSIZE(), VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). pos = get(gcf, 'Position') ; if a >= 1 pos(3) = a * pos(4) ; else pos(4) = pos(3) / a ; end set(gcf,'Position',pos) ; pos = get(gcf, 'PaperPosition') ; if a >= 1 pos(3) = a * pos(4) ; else pos(4) = pos(3) / a ; end set(gcf,'PaperPosition', pos) ; vlfeat/toolbox/plotop/vl_roc.m0000644000175000017500000002105312237004745015417 0ustar dimadimafunction [tpr,tnr,info] = vl_roc(labels, scores, varargin) %VL_ROC ROC curve. % [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating % Characteristic (ROC) curve. LABELS are the ground truth labels, % greather than zero for a positive sample and smaller than zero for % a negative one. SCORES are the scores of the samples obtained from % a classifier, where lager scores should correspond to positive % labels. % % Samples are ranked by decreasing scores, starting from rank 1. % TPR(K) and TNR(K) are the true positive and true negative rates % when samples of rank smaller or equal to K-1 are predicted to be % positive. So for example TPR(3) is the true positive rate when the % two samples with largest score are predicted to be % positive. Similarly, TPR(1) is the true positive rate when no % samples are predicted to be positive, i.e. the constant 0. % % Set the zero the lables of samples that should be ignored in the % evaluation. Set to -INF the scores of samples which are not % retrieved. If there are samples with -INF score, then the ROC curve % may have maximum TPR and TNR smaller than 1. % % [TPR,TNR,INFO] = VL_ROC(...) returns an additional structure INFO % with the following fields: % % info.auc:: Area under the ROC curve (AUC). % The ROC curve has a `staircase shape' because for each sample % only TP or TN changes, but not both at the same time. Therefore % there is no approximation involved in the computation of the % area. % % info.eer:: Equal error rate (EER). % The equal error rate is the value of FPR (or FNR) when the ROC % curves intersects the line connecting (0,0) to (1,1). % % VL_ROC(...) with no output arguments plots the ROC curve in the % current axis. % % VL_ROC() acccepts the following options: % % Plot:: [] % Setting this option turns on plotting unconditionally. The % following plot variants are supported: % % tntp:: Plot TPR against TNR (standard ROC plot). % tptn:: Plot TNR against TPR (recall on the horizontal axis). % fptp:: Plot TPR against FPR. % fpfn:: Plot FNR against FPR (similar to DET curve). % % NumPositives:: [] % NumNegatives:: [] % If set to a number, pretend that LABELS contains this may % positive/negative labels. NUMPOSITIVES/NUMNEGATIVES cannot be % smaller than the actual number of positive/negative entrires in % LABELS. The additional positive/negative labels are appended to % the end of the sequence, as if they had -INF scores (not % retrieved). This is useful to evaluate large retrieval systems in % which one stores ony a handful of top results for efficiency % reasons. % % About the ROC curve:: % Consider a classifier that predicts as positive all samples whose % score is not smaller than a threshold S. The ROC curve represents % the performance of such classifier as the threshold S is % changed. Formally, define % % P = overall num. of positive samples, % N = overall num. of negative samples, % % and for each threshold S % % TP(S) = num. of samples that are correctly classified as positive, % TN(S) = num. of samples that are correctly classified as negative, % FP(S) = num. of samples that are incorrectly classified as positive, % FN(S) = num. of samples that are incorrectly classified as negative. % % Consider also the rates: % % TPR = TP(S) / P, FNR = FN(S) / P, % TNR = TN(S) / N, FPR = FP(S) / N, % % and notice that by definition % % P = TP(S) + FN(S) , N = TN(S) + FP(S), % 1 = TPR(S) + FNR(S), 1 = TNR(S) + FPR(S). % % The ROC curve is the parametric curve (TPR(S), TNR(S)) obtained % as the classifier threshold S is varied in the reals. The TPR is % also known as recall (see VL_PR()). % % The ROC curve is contained in the square with vertices (0,0) The % (average) ROC curve of a random classifier is a line which % connects (1,0) and (0,1). % % The ROC curve is independent of the prior probability of the % labels (i.e. of P/(P+N) and N/(P+N)). % % REFERENCES: % [1] http://en.wikipedia.org/wiki/Receiver_operating_characteristic % % See also: VL_PR(), VL_DET(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [tp, fp, p, n, perm, varargin] = vl_tpfp(labels, scores, varargin{:}) ; opts.plot = [] ; opts.stable = false ; opts = vl_argparse(opts,varargin) ; % compute the rates small = 1e-10 ; tpr = tp / max(p, small) ; fpr = fp / max(n, small) ; fnr = 1 - tpr ; tnr = 1 - fpr ; % -------------------------------------------------------------------- % Additional info % -------------------------------------------------------------------- if nargout > 2 || nargout == 0 % Area under the curve. Since the curve is a staircase (in the % sense that for each sample either tn is decremented by one % or tp is incremented by one but the other remains fixed), % the integral is particularly simple and exact. info.auc = sum(tnr .* diff([0 tpr])) ; % Equal error rate. One must find the index S for which there is a % crossing between TNR(S) and TPR(s). If such a crossing exists, % there are two cases: % % o tnr o % / \ % 1-eer = tnr o-x-o 1-eer = tpr o-x-o % / \ % tpr o o % % Moreover, if the maximum TPR is smaller than 1, then it is % possible that neither of the two cases realizes (then EER=NaN). s = max(find(tnr > tpr)) ; if s == length(tpr) info.eer = NaN ; else if tpr(s) == tpr(s+1) info.eer = 1 - tpr(s) ; else info.eer = 1 - tnr(s) ; end end end % -------------------------------------------------------------------- % Plot % -------------------------------------------------------------------- if ~isempty(opts.plot) || nargout == 0 if isempty(opts.plot), opts.plot = 'fptp' ; end cla ; hold on ; switch lower(opts.plot) case {'truenegatives', 'tn', 'tntp'} hroc = plot(tnr, tpr, 'b', 'linewidth', 2) ; hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ; spline([0 1], [0 1], 'k--', 'linewidth', 1) ; plot(1-info.eer, 1-info.eer, 'k*', 'linewidth', 1) ; xlabel('true negative rate') ; ylabel('true positive rate (recall)') ; loc = 'sw' ; case {'falsepositives', 'fp', 'fptp'} hroc = plot(fpr, tpr, 'b', 'linewidth', 2) ; hrand = spline([0 1], [0 1], 'r--', 'linewidth', 2) ; spline([1 0], [0 1], 'k--', 'linewidth', 1) ; plot(info.eer, 1-info.eer, 'k*', 'linewidth', 1) ; xlabel('false positive rate') ; ylabel('true positive rate (recall)') ; loc = 'se' ; case {'tptn'} hroc = plot(tpr, tnr, 'b', 'linewidth', 2) ; hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ; spline([0 1], [0 1], 'k--', 'linewidth', 1) ; plot(1-info.eer, 1-info.eer, 'k*', 'linewidth', 1) ; xlabel('true positive rate (recall)') ; ylabel('false positive rate') ; loc = 'sw' ; case {'fpfn'} hroc = plot(fpr, fnr, 'b', 'linewidth', 2) ; hrand = spline([0 1], [1 0], 'r--', 'linewidth', 2) ; spline([0 1], [0 1], 'k--', 'linewidth', 1) ; plot(info.eer, info.eer, 'k*', 'linewidth', 1) ; xlabel('false positive (false alarm) rate') ; ylabel('false negative (miss) rate') ; loc = 'ne' ; otherwise error('''%s'' is not a valid PLOT type.', opts.plot); end grid on ; xlim([0 1]) ; ylim([0 1]) ; axis square ; title(sprintf('ROC (AUC: %.2f%%, EER: %.2f%%)', info.auc * 100, info.eer * 100), ... 'interpreter', 'none') ; legend([hroc hrand], 'ROC', 'ROC rand.', 'location', loc) ; end % -------------------------------------------------------------------- % Stable output % -------------------------------------------------------------------- if opts.stable tpr(1) = [] ; tnr(1) = [] ; tpr_ = tpr ; tnr_ = tnr ; tpr = NaN(size(tpr)) ; tnr = NaN(size(tnr)) ; tpr(perm) = tpr_ ; tnr(perm) = tnr_ ; end % -------------------------------------------------------------------- function h = spline(x,y,spec,varargin) % -------------------------------------------------------------------- prop = vl_linespec2prop(spec) ; h = line(x,y,prop{:},varargin{:}) ; vlfeat/toolbox/plotop/vl_det.m0000644000175000017500000000332612237004736015413 0ustar dimadimafunction [missRate, falseAlarm] = vl_det(labels, scores, varargin) % VL_DET Compute DET curve % [MISSRATE, FALSEALRAM] = VL_DET(LABELS, SCORES) computes the % Detection Error Trade-off (DET) curve for the given set LABELS and % SCORES. The function is a simple wrapper to VL_ROC(LABELS, % SCORES). In fact, MISSRATE is simply the false positive rate and % FALSEARALM the false negative rate returned by VL_ROC(). % % VL_DET(...) without output arguments plots the curves in the % current axis. Note that the DET curves use logarithmic axis. % % VL_DET(..., OPT, ARG, ...) accepts the same options as VL_ROC(). % % REFERENCES: % [1] A. Martin, G. Doddington, T. Kamm, M. Ordowski, and % M. Przybocki. The DET curve in assessment of detection task % performance. In Proc. Conf. on Speech Communication and Technology, % 1997. [tp,tn] = vl_roc(labels, scores, varargin{:}) ; missRate = 1 - tp ; falseAlarm = 1 - tn ; if nargout == 0 cla ; loglog(falseAlarm,missRate,'linewidth',2) ; grid on ; axis square ; xlabel('false alaram (false positive) rate') ; ylabel('miss (false negative) rate') ; title('DET') ; end % %conf = vl_argparse(conf, varargin{:}) ; % for i = 1:2:length(varargin) % opt = varargin{i} ; % val = varargin{i+1} ; % switch lower(opt) % case 'numnegatives' % N = val ; % otherwise % error('Unknown option %s', opt) ; % end % end % P = sum(labels > 0) ; % if ~exist('N'), N = sum(labels < 0) ; end % if N < sum(labels < 0), warning('NumNegatives < number of provided negatives') ; end % N_=N-sum(labels<0) ; % [scores, perm] = sort(scores) ; % disp(sum(labels<0)/N) % tp = [0 cumsum(labels(fliplr(perm)) > 0)/P 1] ; % tn = [1 (fliplr(cumsum(labels(perm) < 0))+N_)/N 0] ; vlfeat/toolbox/plotop/vl_clickpoint.m0000644000175000017500000000371712237004736017002 0ustar dimadimafunction sel = vl_clickpoint(V,N,varargin) % VL_CLICKPOINT Select a point by clicking % SEL = VL_CLICKPOINT(V) let the user vl_click a point in the current % figure and returns the index v of the closest point (in Euclidean % norm) in the collection V. The 2xK matrix V has a a column for % each point. % % The user can abort the operation by pressing any key. In this case % the function returns the empty matrix. % % VL_CLICKPOINT(V,N) selects N points in a row. The user can stop the % selection at any time by pressing any key. In this case the % partial selection is returned. This can be used in combination % with N=inf to get an arbitrary number of points. % % VL_CLICKPOINT() accepts the following options: % % PlotMarker:: [0] % Put a marker as points are selected. The markers are deleted on % exiting the function. % % See also: VL_CLICK(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). plot_marker = 0 ; for k=1:2:length(varargin) switch lower(varargin{k}) case 'plotmarker' plot_marker = varargin{k+1} ; otherwise error(['Uknown option ''', varargin{k}, '''.']) ; end end if nargin < 2 N=1; end if size(V,1) ~= 2 error('Array V should be 2xK') ; end % -------------------------------------------------------------------- % Do job % -------------------------------------------------------------------- fig = gcf ; is_hold = ishold(fig) ; hold on ; sel = [] ; h = [] ; for n=1:N P=vl_click ; if ~isempty( P ) d = (V(1,:)-P(1)).^2 + (V(2,:)-P(2)).^2; [drop,v]=min(d(:)) ; if(plot_marker) h=[h plot(V(1,v),V(2,v),'go')] ; end sel = [sel v] ; else return ; end end if ~is_hold hold off ; end if( plot_marker ) pause(.1); delete(h) ; end vlfeat/toolbox/plotop/vl_plotgrid.m0000644000175000017500000000127312237004736016462 0ustar dimadimafunction h=vl_plotgrid(x,y,varargin) % VL_PLOTGRID Plot a 2-D grid % VL_PLOTGRID(X,Y) plots a grid with vertices (X,Y). X and Y are MxN % matrices, with one entry per vertex. % % H=VL_PLOTGRID(...) returns the handle to the grid object. % % See also: VL_HELP(). washold = ishold ; [M,N] = size(x) ; hold on ; xh = [x' ; nan*ones(1,M) ] ; yh = [y' ; nan*ones(1,M) ] ; xh = xh(:) ; yh = yh(:) ; xv = [x ; nan*ones(1,N) ] ; yv = [y ; nan*ones(1,N) ] ; xv = xv(:) ; yv = yv(:) ; lineprop = {} ; if length(varargin) > 0 lineprop = vl_linespec2prop(varargin{1}) ; lineprop = {lineprop{:}, varargin{2:end}} ; end h = line([xh' xv'], [yh' yv'],lineprop{:}) ; if ~washold hold off ; end vlfeat/toolbox/plotop/vl_linespec2prop.m0000644000175000017500000000230512237004745017420 0ustar dimadimafunction prop=vl_linespec2prop(spec) % VL_LINESPEC2PROP Convert PLOT style line specs to line properties % PROPR = VL_LINESPEC2PROP(SPEC) converts the string SPEC to a cell % array of properties PROPR. SPEC is in the format of PLOT(). % % If SPEC is not a line spec, the string SPEC is returned unaltered % as the only element of the cell array PROPR. % % See also: VL_PLOTFRAME(), PLOT(), VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). prop = {} ; if ~ ischar(spec) error('SPEC must be a string') ; end spec_ = spec ; switch spec(1:min(numel(spec),1)) case {'b' 'g' 'r' 'c' 'm' 'y' 'k' 'w'} prop = {prop{:}, 'Color', spec(1)} ; spec(1) = [] ; end switch spec(1:min(numel(spec),1)) case {'.' 'o' 'x' '+' '*' 's' 'd' 'v' '^' '<' '>' 'p' 'h'} prop = {prop{:}, 'Marker', spec(1)} ; spec(1) = [] ; end if isempty(spec) return ; end switch spec case {'-' ':' '-.' '--'} prop = {prop{:}, 'LineStyle', spec} ; otherwise prop = {spec_} ; end vlfeat/toolbox/plotop/vl_pr.m0000644000175000017500000002165712237004745015267 0ustar dimadimafunction [recall, precision, info] = vl_pr(labels, scores, varargin) %VL_PR Precision-recall curve. % [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the % precision-recall (PR) curve. LABELS are the ground truth labels, % greather than zero for a positive sample and smaller than zero for % a negative one. SCORES are the scores of the samples obtained from % a classifier, where lager scores should correspond to positive % samples. % % Samples are ranked by decreasing scores, starting from rank 1. % PRECISION(K) and RECALL(K) are the precison and recall when % samples of rank smaller or equal to K-1 are predicted to be % positive and the remaining to be negative. So for example % PRECISION(3) is the percentage of positive samples among the two % samples with largest score. PRECISION(1) is the precision when no % samples are predicted to be positive and is conventionally set to % the value 1. % % Set to zero the lables of samples that should be ignored in the % evaluation. Set to -INF the scores of samples which are not % retrieved. If there are samples with -INF score, then the PR curve % may have maximum recall smaller than 1, unless the INCLUDEINF % option is used (see below). The options NUMNEGATIVES and % NUMPOSITIVES can be used to add additional surrogate samples with % -INF score (see below). % % [RECALL, PRECISION, INFO] = VL_PR(...) returns an additional % structure INFO with the following fields: % % info.auc:: % The area under the precision-recall curve. If the INTERPOLATE % option is set to FALSE, then trapezoidal interpolation is used % to integrate the PR curve. If the INTERPOLATE option is set to % TRUE, then the curve is piecewise constant and no other % approximation is introduced in the calculation of the area. In % the latter case, INFO.AUC is the same as INFO.AP. % % info.ap:: % Average precision as defined by TREC. This is the average of the % precision observed each time a new positive sample is % recalled. In this calculation, any sample with -INF score % (unless INCLUDEINF is used) and any additional positive induced % by NUMPOSITIVES has precision equal to zero. If the INTERPOLATE % option is set to true, the AP is computed from the interpolated % precision and the result is the same as INFO.AUC. Note that AP % as defined by TREC normally does not use interpolation [1]. % % info.ap_interp_11:: % 11-points interpolated average precision as defined by TREC. % This is the average of the maximum precision for recall levels % greather than 0.0, 0.1, 0.2, ..., 1.0. This measure was used in % the PASCAL VOC challenge up to the 2008 edition. % % info.auc_pa08:: % Deprecated. It is the same of INFO.AP_INTERP_11. % % VL_PR(...) with no output arguments plots the PR curve in the % current axis. % % VL_PR() accepts the following options: % % Interpolate:: false % If set to true, use interpolated precision. The interpolated % precision is defined as the maximum precision for a given recall % level and onwards. Here it is implemented as the culumative % maximum from low to high scores of the precision. % % NumPositives:: [] % NumNegatives:: [] % If set to a number, pretend that LABELS contains this may % positive/negative labels. NUMPOSITIVES/NUMNEGATIVES cannot be % smaller than the actual number of positive/negative entrires in % LABELS. The additional positive/negative labels are appended to % the end of the sequence, as if they had -INF scores (not % retrieved). This is useful to evaluate large retrieval systems % for which one stores ony a handful of top results for efficiency % reasons. % % IncludeInf:: false % If set to true, data with -INF score SCORES is included in the % evaluation and the maximum recall is 1 even if -INF scores are % present. This option does not include any additional positive or % negative data introduced by specifying NUMPOSITIVES and % NUMNEGATIVES. % % Stable:: false % If set to true, RECALL and PRECISION are returned the same order % of LABELS and SCORES rather than being sorted by decreasing % score (increasing recall). Samples with -INF scores are assigned % RECALL and PRECISION equal to NaN. % % NormalizePrior:: [] % If set to a scalar, reweights positive and negative labels so % that the fraction of positive ones is equal to the specified % value. This computes the normalised PR curves of [2] % % About the PR curve:: % This section uses the same symbols used in the documentation of % the VL_ROC() function. In addition to those quantities, define: % % PRECISION(S) = TP(S) / (TP(S) + FP(S)) % RECALL(S) = TPR(S) = TP(S) / P % % The precision is the fraction of positivie predictions which are % correct, and the recall is the fraction of positive labels that % have been correctly classified (recalled). Notice that the recall % is also equal to the true positive rate for the ROC curve (see % VL_ROC()). % % REFERENCES: % [1] C. D. Manning, P. Raghavan, and H. Schutze. An Introduction to % Information Retrieval. Cambridge University Press, 2008. % [2] D. Hoiem, Y. Chodpathumwan, and Q. Dai. Diagnosing error in % object detectors. In Proc. ECCV, 2012. % % See also VL_ROC(), VL_HELP(). % Author: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % TP and FP are the vectors of true positie and false positve label % counts for decreasing scores, P and N are the total number of % positive and negative labels. Note that if certain options are used % some labels may actually not be stored explicitly by LABELS, so P+N % can be larger than the number of element of LABELS. [tp, fp, p, n, perm, varargin] = vl_tpfp(labels, scores, varargin{:}) ; opts.stable = false ; opts.interpolate = false ; opts.normalizePrior = [] ; opts = vl_argparse(opts,varargin) ; % compute precision and recall small = 1e-10 ; recall = tp / max(p, small) ; if isempty(opts.normalizePrior) precision = max(tp, small) ./ max(tp + fp, small) ; else a = opts.normalizePrior ; precision = max(tp * a/max(p,small), small) ./ ... max(tp * a/max(p,small) + fp * (1-a)/max(n,small), small) ; end % interpolate precision if needed if opts.interpolate precision = fliplr(vl_cummax(fliplr(precision))) ; end % -------------------------------------------------------------------- % Additional info % -------------------------------------------------------------------- if nargout > 2 || nargout == 0 % area under the curve using trapezoid interpolation if ~opts.interpolate info.auc = 0.5 * sum((precision(1:end-1) + precision(2:end)) .* diff(recall)) ; end % average precision (for each recalled positive sample) sel = find(diff(recall)) + 1 ; info.ap = sum(precision(sel)) / p ; if opts.interpolate info.auc = info.ap ; end % TREC 11 points average interpolated precision info.ap_interp_11 = 0.0 ; for rc = linspace(0,1,11) pr = max([0, precision(recall >= rc)]) ; info.ap_interp_11 = info.ap_interp_11 + pr / 11 ; end % legacy definition info.auc_pa08 = info.ap_interp_11 ; end % -------------------------------------------------------------------- % Plot % -------------------------------------------------------------------- if nargout == 0 cla ; hold on ; plot(recall,precision,'linewidth',2) ; if isempty(opts.normalizePrior) randomPrecision = p / (p + n) ; else randomPrecision = opts.normalizePrior ; end spline([0 1], [1 1] * randomPrecision, 'r--', 'linewidth', 2) ; axis square ; grid on ; xlim([0 1]) ; xlabel('recall') ; ylim([0 1]) ; ylabel('precision') ; title(sprintf('PR (AUC: %.2f%%, AP: %.2f%%, AP11: %.2f%%)', ... info.auc * 100, ... info.ap * 100, ... info.ap_interp_11 * 100)) ; if opts.interpolate legend('PR interp.', 'PR rand.', 'Location', 'SouthEast') ; else legend('PR', 'PR rand.', 'Location', 'SouthEast') ; end clear recall precision info ; end % -------------------------------------------------------------------- % Stable output % -------------------------------------------------------------------- if opts.stable precision(1) = [] ; recall(1) = [] ; precision_ = precision ; recall_ = recall ; precision = NaN(size(precision)) ; recall = NaN(size(recall)) ; precision(perm) = precision_ ; recall(perm) = recall_ ; end % -------------------------------------------------------------------- function h = spline(x,y,spec,varargin) % -------------------------------------------------------------------- prop = vl_linespec2prop(spec) ; h = line(x,y,prop{:},varargin{:}) ; vlfeat/toolbox/plotop/vl_click.m0000644000175000017500000000514512237004736015725 0ustar dimadimafunction P = vl_click(N,varargin) ; % VL_CLICK Click a point % P=VL_CLICK() let the user click a point in the current figure and % returns its coordinates in P. P is a two dimensiona vectors where % P(1) is the point X-coordinate and P(2) the point Y-coordinate. The % user can abort the operation by pressing any key, in which case the % empty matrix is returned. % % P=VL_CLICK(N) lets the user select N points in a row. The user can % stop inserting points by pressing any key, in which case the % partial list is returned. % % VL_CLICK() accepts the following options: % % PlotMarker:: [0] % Plot a marker as points are selected. The markers are deleted on % exiting the function. % % See also: VL_CLICKPOINT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). plot_marker = 0 ; for k=1:2:length(varargin) switch lower(varargin{k}) case 'plotmarker' plot_marker = varargin{k+1} ; otherwise error(['Uknown option ''', varargin{k}, '''.']) ; end end if nargin < 1 N=1; end % -------------------------------------------------------------------- % Do job % -------------------------------------------------------------------- fig = gcf ; is_hold = ishold ; hold on ; bhandler = get(fig,'WindowButtonDownFcn') ; khandler = get(fig,'KeyPressFcn') ; pointer = get(fig,'Pointer') ; set(fig,'WindowButtonDownFcn',@click_handler) ; set(fig,'KeyPressFcn',@key_handler) ; set(fig,'Pointer','crosshair') ; P=[] ; h=[] ; data.exit=0; guidata(fig,data) ; while size(P,2) < N uiwait(fig) ; data = guidata(fig) ; if(data.exit) break ; end P = [P data.P] ; if( plot_marker ) h=[h plot(data.P(1),data.P(2),'rx')] ; end end if ~is_hold hold off ; end if( plot_marker ) pause(.1); delete(h) ; end set(fig,'WindowButtonDownFcn',bhandler) ; set(fig,'KeyPressFcn',khandler) ; set(fig,'Pointer',pointer) ; % ==================================================================== function click_handler(obj,event) % -------------------------------------------------------------------- data = guidata(gcbo) ; P = get(gca, 'CurrentPoint') ; P = [P(1,1); P(1,2)] ; data.P = P ; guidata(obj,data) ; uiresume(gcbo) ; % ==================================================================== function key_handler(obj,event) % -------------------------------------------------------------------- data = guidata(gcbo) ; data.exit = 1 ; guidata(obj,data) ; uiresume(gcbo) ; vlfeat/toolbox/plotop/vl_tightsubplot.m0000644000175000017500000000702012237004736017362 0ustar dimadimafunction H = vl_tightsubplot(varargin) % VL_TIGHTSUBPLOT Tiles axes without wasting space % H = VL_TIGHTSUBPLOT(K,P) returns an handle to the P-th axis in a % regular grid of K axes. The K axes are numbered from left to right % and from top to bottom. The function operates similarly to % SUBPLOT(), but by default it does not put any margin between % axes. This is done by tiling the axes inner box rather than the % axes outer box. % % H = VL_TIGHTSUBPLOT(M,N,P) retursn an handle to the P-th axes in a % regular subdivision with M rows and N columns. % % VL_TIGHTSUBPLOT() accepts the following options: % % Margin:: [0] % MarginLeft:: [0] % MarginRight:: [0] % MarginTop:: [0] % MarginBottom:: [0] % Set the axes inner box margin, either uniformly in all % directions, or specifically to the left, right, top or bottom. % % Box:: [0] % If set to 'outer', tile the axes outer box, thus protecting % title and labels. Unfortunately this usually produces % unnecesarily large margins. % % Spacing:: [0] (legacy option) % Set extra spacing between axes. The space is added between the % inner or outer boxes, depending on the setting below. % % REMARK. While SUBPLOT kills any pre-existing axes that overalps a % new one, this function does not. % % See also: SUBPLOT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % margins ml = 0 ; mr = 0 ; mt = 0 ; mb = 0 ; use_outer=0 ; % -------------------------------------------------------------------- % Parse arguments % -------------------------------------------------------------------- K = varargin{1} ; p = varargin{2} ; N = ceil(sqrt(K)) ; M = ceil(K/N) ; a=3 ; NA = length(varargin) ; if NA > 2 if isa(varargin{3},'char') % Called with K and p else % Called with M,N and p a = 4 ; M = K ; N = p ; p = varargin{3} ; end end for a=a:2:NA opt=lower(varargin{a}) ; arg=varargin{a+1} ; switch opt case 'margin' mt = arg ; mb = arg ; ml = arg ; mr = arg ; case 'marginleft' ml = arg ; case 'marginright' mr = arg ; case 'margintop' mt = arg ; case 'marginbottom' mb = arg ; case 'spacing' mt = arg/2 ; mb = arg/2 ; ml = arg/2 ; mr = arg/2 ; case 'box' switch lower(arg) case 'inner' use_outer = 0 ; case 'outer' use_outer = 1 ; otherwise error(['Box is either ''inner'' or ''outer''']) ; end otherwise error(['Uknown parameter ''', varargin{a}, '''.']) ; end end % -------------------------------------------------------------------- % Check the arguments % -------------------------------------------------------------------- [j,i]=ind2sub([N M],p) ; i=i-1 ; j=j-1 ; pos = [ j * 1/N + ml,... 1 - i * 1/M - 1/M + mb,... 1/N - ml - mr, ... 1/M - mt - mb] ; switch use_outer case 0 H = findobj(gcf, 'Type', 'axes', 'Position', pos) ; if(isempty(H)) H = axes('Position', pos) ; else axes(H) ; end case 1 H = findobj(gcf, 'Type', 'axes', 'OuterPosition', pos) ; if(isempty(H)) H = axes('ActivePositionProperty', 'outerposition',... 'OuterPosition', pos) ; else axes(H) ; end end vlfeat/toolbox/plotop/vl_plotpoint.m0000644000175000017500000000202112237004736016656 0ustar dimadimafunction h=vl_plotpoint(V,varargin) % VL_PLOTPOINT Plot 2 or 3 dimensional points % VL_PLOTPOINT(V) plots the 2 or 3 dimensional points V. V is a 2xK or % 3xK array, with one point per column. % % H=VL_PLOTPOINT(...) returns the handle H of the plot. % % VL_PLOTPOINT() is a simple wrapper around the PLOT() and PLOT3() % functions. By default, VL_PLOTPOINT(V) plots the points with line % style '.'. VL_PLOTPOINT(V,...) does not use the default line style; % rather it passess any extra argument to the underlying plot % function. % % See also: PLOT(), PLOT3(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if length(varargin) == 0 varargin = {'.'}; end switch size(V,1) case 2 h=plot(V(1,:),V(2,:),varargin{:}) ; case 3 h=plot3(V(1,:),V(2,:),V(3,:),varargin{:}) ; otherwise error(['V must be either 2xK or 3xK.']) ; end vlfeat/toolbox/plotop/vl_tpfp.m0000644000175000017500000000321512237004736015605 0ustar dimadimafunction [tp, fp, p, n, perm, varargin] = vl_tpfp(labels, scores, varargin) % VL_TPFP Compute true positives and false positives % This is an helper function used by VL_PR(), VL_ROC(), VL_DET(). % % See also: VL_PR(), VL_ROC(), VL_DET(), VL_HELP(). % Author: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % extraNeg and extraPos depend on numNegatives and numPositives: % % [ labels | -1 +1 ] % [ +inf | finite scores | -inf | extraNeg extraPos] opts.includeInf = false ; opts.numNegatives = [] ; opts.numPositives = [] ; [opts, varargin] = vl_argparse(opts, varargin) ; % make row vectors labels = labels(:)' ; scores = scores(:)' ; % count labels p = sum(labels > 0) ; n = sum(labels < 0) ; if ~isempty(opts.numPositives) if opts.numPositives < p warning('NUMPOSITIVES is smaller than the number of positives in LABELS.') ; end p = opts.numPositives ; end if ~isempty(opts.numNegatives) if opts.numNegatives < n warning('NUMNEGATIVES is smaller than the number of negatives in LABELS.') ; end n = opts.numNegatives ; end % sort by descending scores [scores, perm] = sort(scores, 'descend') ; % assume that data with -INF score is never retrieved if opts.includeInf stop = length(scores) ; else stop = max(find(scores > -inf)) ; end perm = perm(1:stop) ; labels = labels(perm) ; % accumulate true positives and false positives by scores % in descending order tp = [0 cumsum(labels > 0)] ; fp = [0 cumsum(labels < 0)] ; vlfeat/toolbox/plotop/vl_plotbox.m0000644000175000017500000000427012237004745016325 0ustar dimadimafunction [h, t] = vl_plotbox(boxes, varargin) % PLOTBOX Plot boxes % PLOTBOX(BOX) plots a box specified by the 4-dimensional vector % BOXES = [XMIN YMIN XMAX YMAX]'. If BOXES is a 4 x N matrix, a box % for each of the N columns is plotted. % % H = PLOTBOX(BOXES) returns an handle to the line drawings % representing the boxes. For multiple boxes, H is a row vector with % one handle per box. % % PLOTBOX(BOXES, 'LABEL', LABEL) annotates the box with the string % LABEL. If BOXES contains multiple boxes, then LABEL can be a cell % array with one entry for each box. H is then a 2 x N array with % handles to boxes and labels. % % PLOTBOX(BOXES, ...) passes any extra argument to the underlying % plotting function. The first optional argument can be a line % specification string such as the one used by PLOT(). % % See also:: VL_PLOTFRAME(). % Author:: Andrea Vedaldi % Copyright (C) 2008-13 Andrea Vedaldi % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.label = {} ; % if the first optional argument is a linespec expand it if length(varargin) > 0 lineprop = vl_linespec2prop(varargin{1}) ; varargin = {lineprop{:}, varargin{2:end}} ; end % parse optional arguments [opts, varargin] = vl_argparse(opts, varargin) ; if ischar(opts.label) opts.label = {opts.label} ; end if size(boxes,2) == 0 h = [] ; return ; end if size(boxes,1) ~= 4 error('BOXES must be a 4 x N matrix') ; end Lx = [1 0 0 0 ; 0 0 1 0 ; 0 0 1 0 ; 1 0 0 0 ; 1 0 0 0 ] ; Ly = [0 1 0 0 ; 0 1 0 0 ; 0 0 0 1 ; 0 0 0 1 ; 0 1 0 0 ] ; fig = newplot ; h = line(Lx * boxes, Ly * boxes, varargin{:}) ; h = h' ; if ~isempty(opts.label) ish = ishold ; hold on ; t = zeros(1,length(opts.label)) ; for r = 1:size(boxes,2) cl = get(h(1,r), 'Color') ; q = mod(r - 1, length(opts.label)) + 1 ; h(2,r) = text(mean(boxes([1 3],r)), boxes(2,r), opts.label{q}, ... 'Background', cl, ... 'VerticalAlignment', 'bottom', ... 'HorizontalAlignment', 'center') ; end if ~ish, hold off ; end end if nargout == 0 clear h ; endvlfeat/toolbox/plotop/vl_clicksegment.m0000644000175000017500000000152312237004736017304 0ustar dimadimafunction [P1,P2] = vl_clicksegment % VL_CLICKSEGMENT Select a segment by clicking % [P1,P2] = VL_CLICKSEGMENT() lets the user select a segment in the % current figure and returns the starting and ending point P1 and P2. % % The user can abort the operation by pressing any key. In this case % the function returns the empty matrix. % % See also: VL_CLICK(), VL_CLICKPOINT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % Click first segment P1=vl_click ; if isempty(P1) P2=P1 ; return ; end was_hold = ishold(gca) ; hold on ; h=plot(P1(1),P1(2),'rx') ; % Click second segment P2=vl_click ; if isempty(P2) P1=P2 ; end delete(h) ; if ~was_hold, hold off ; end vlfeat/toolbox/plotop/vl_cf.m0000644000175000017500000000122212237004736015220 0ustar dimadimafunction h = vl_cf(h0) % VL_CF Creates a copy of a figure % VL_CF() creates a copy of the current figure and returns VL_CF(H0) % creates a copy of the figure(s) whose handle is H0. H = % VL_CF(...) returns the handles of the copies. % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 1 h0 = gcf ; end h=zeros(size(h0)) ; for i=1:numel(h0) h(i) = copyobj(h0(i),get(h0(i),'Parent')) ; end if nargout < 1 clear h ; end vlfeat/toolbox/plotop/vl_plotstyle.m0000644000175000017500000000404612237004736016676 0ustar dimadimafunction style = vl_plotstyle(index) % VL_PLOTSTYLES Get a plot style % STYLES = VL_PLOTSTYLE(INDEX) returns a cell array of PLOT() options % for the color/line style of index INDEX. The available styles are % defined the ColorOrder and LineStyleOrder properties of the % current axes and cycled first by color and then by line style. % % If there is no current axes can be found, then VL_PLOTSTYLE() uses % the DefaultAxesColorOrder and DefaultLineStyleOrder root % properties. % % If INDEXES has more than one element, then VL_PLOTSTYLE(INDEXES) % returns a cell array of styles of the same size of INDEXES. % % Example:: % The code fragment % % set(gca,'LineStyleOrder', {'-','-.'}, 'NextPlot', 'replacechildren') ; % plot([1 2], [1:20 ; 1:20]') ; % % and % % set(gca,'NextPlot', 'replacechildren') ; % set(gca,'LineStyleOrder', {'-','-.'}) ; % for i = 1:20 % style = vl_plotstyle(i) ; % plot([1 2], [i i], style{:}) ; % hold on ; % end % % produce visually similar results. % % See also: VL_HELP(). % Author: Andrea Vedaldi colors = [] ; lines = {} ; % try to get from current axes, if any fig = get(0, 'CurrentFigure') ; if ~isempty(fig) ax = get(fig, 'CurrentAxes') ; if ~isempty(ax) colors = get(gca, 'ColorOrder') ; lines = get(gca, 'LineStyleOrder') ; end end % if not, pick matlab defaults if isempty(colors) colors = get(0,'DefaultAxesColorOrder') ; lines = get(0,'DefaultAxesLineStyleOrder') ; end % make sure lines is a cell and not a char array if ~iscell(lines) lines_ = lines ; lines = {} ; for i = 1:size(lines_,1) lines{i} = deblank(lines_(i,:)) ; end end % if more than one style specified style = cell(size(index)) ; for i = 1:numel(index) ci = mod(index(i) - 1, size(colors,1)) + 1 ; li = mod(floor((index(i) - 1) / size(colors,1)), numel(lines)) + 1 ; style{i} = {'Color', colors(ci,:), 'LineStyle', lines{li}} ; end % remove one layer of cell array if single style requested if numel(index) == 1 style = [style{:}] ; end vlfeat/toolbox/vl_noprefix.m0000644000175000017500000000352312237004736015153 0ustar dimadimafunction vl_noprefix % VL_NOPREFIX Create a prefix-less version of VLFeat commands % VL_NOPREFIX() creats prefix-less stubs for VLFeat functions % (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs % are included in the VLFeat binary distribution anyways. Moreover, % on UNIX platforms, the stubs are generally constructed by the % Makefile. % % See also: VL_COMPILE(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). root = fileparts(which(mfilename)) ; list = listMFilesX(root); outDir = fullfile(root, 'noprefix') ; if ~exist(outDir, 'dir') mkdir(outDir) ; end for li = 1:length(list) name = list(li).name(1:end-2) ; % remove .m nname = name(4:end) ; % remove vl_ stubPath = fullfile(outDir, [nname '.m']) ; fout = fopen(stubPath, 'w') ; fprintf('Creating stub %s for %s\n', stubPath, nname) ; fprintf(fout, 'function varargout = %s(varargin)\n', nname) ; fprintf(fout, '%% %s Stub for %s\n', upper(nname), upper(name)) ; fprintf(fout, '[varargout{1:nargout}] = %s(varargin{:})\n', name) ; fclose(fout) ; end end function list = listMFilesX(root) list = struct('name', {}, 'path', {}) ; files = dir(root) ; for fi = 1:length(files) name = files(fi).name ; if files(fi).isdir if any(regexp(name, '^(\.|\.\.|noprefix)$')) continue ; else tmp = listMFilesX(fullfile(root, name)) ; list = [list, tmp] ; end end if any(regexp(name, '^vl_(demo|test).*m$')) continue ; elseif any(regexp(name, '^vl_(demo|setup|compile|help|root|noprefix)\.m$')) continue ; elseif any(regexp(name, '\.m$')) list(end+1) = struct(... 'name', {name}, ... 'path', {fullfile(root, name)}) ; end end end vlfeat/toolbox/aib/0000755000175000017500000000000012237042170013164 5ustar dimadimavlfeat/toolbox/aib/vl_aibcut.m0000644000175000017500000000674712237004736015336 0ustar dimadimafunction [cut, map, short] = vl_aibcut(parents, n) % VL_AIBCUT Cut VL_AIB tree % CUT = VL_AIBCUT(PARENTS, N) cuts the binary merge tree PARENTS and % returns a cut CUT of N nodes. The format of PARENTS is the same % used by the VL_AIB() function. % % A cut is a set of N nodes such that no node is a descendant of any % other node in the cut and such that all leaves descend from a node % in the cut. The vector CUT lists the nodes of the binary merge tree % PARENT that form the cut. % % Nodes with null parent (as defined by PARENTS) are included in the % cut if the other nodes are not enough to fill a cut of N elements. % % [CUT, MAP] = VL_AIBCUT(...) returns a vector MAP with the same size % as PARENTS. MAP assigns each node below or in the cut to the % corresponding element in the CUT vector (each element above the cut % or with null parent is mapped to 0). To get the index of the % corresponding cut nodes use CUT(MAP). MAP can be used to quantize % the leaves in a sequences of N contiguous indexes, starting from % one (see also VL_AIBCUTPUSH()). % % [CUT, MAP, SHORT] = VL_AIBCUT(...) returns also a vector SHORT that % represents a version of the PARENTS tree where nodes below the cut % are short-circuitied to link to the corresponding cut ancestors % directly. Null parents are left unchanged, except if the % corresponding node is in the cut (in which case the map-to-itself % rule has the precedence). % % See also: VL_HELP(), VL_AIB(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % -------------------------------------------------------------------- % Determine nodes in the cut % -------------------------------------------------------------------- if n > 1 root = max(parents) ; % count number of null nodes z = sum(parents(1:root) == 0) ; % determine number of leves nleaves = (root - z + 1) / 2 ; % find first node of the cut mu = root - min(n, nleaves) + 1 ; % correction for presence of null nodes nz = find(parents(1:mu) > 0) ; mu = nz(end) ; % find node belnoging to the cut cut = find(parents(1:mu) > mu) ; % In the presence of null nodes, the cut size might exceed nleaves, % which is the maximum cut size we can obtain with the specified % tree. The additional nodes have to be picked up from the null % nodes. if length(cut) < n sel_z = find(parents == 0) ; cut = [sel_z(1:n-length(cut)) cut] ; end % aesthetic reasons only cut = sort(cut) ; else mu = max(parents) ; cut = mu ; end % -------------------------------------------------------------------- % Short-circuit nodes to the cut % -------------------------------------------------------------------- stop = [cut find(parents == 0)] ; short = 1:length(parents) ; while 1 [drop,sel] = setdiff(short(1:mu), stop) ; sel = setdiff(sel, stop) ; if isempty(sel), break ; end short(sel) = parents(short(sel)) ; end short(setdiff(find(parents == 0), cut)) = 0 ; % -------------------------------------------------------------------- % Build quantizer map % -------------------------------------------------------------------- map = 1:numel(parents) ; map(cut) = 1:n ; map(short > 0) = map(short(short > 0)) ; map(short == 0) = 0 ; map(mu+1:end) = 0 ; vlfeat/toolbox/aib/vl_aibhist.c0000644000175000017500000001026412237004745015465 0ustar dimadima/** @internal ** @file aibhist.c ** @brief Push data down the AIB tree - MEX driver ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include /* ---------------------------------------------------------------- */ /** @brief MEX driver entry point **/ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_PARENTS = 0, IN_DATA, IN_OPT} ; enum {OUT_TREE} ; vl_uint32 const *parents ; vl_uint32 *tree ; double const *data ; int nnull = 0 ; int histmode = 0 ; vl_uint32 i, P, N ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if ((nin < 2) || (nin > 3)) { mexErrMsgTxt ("Two or three arguments required.") ; } if (nout > 1) { mexErrMsgTxt ("Too many output arguments.") ; } if (!vlmxIsMatrix(in[IN_DATA], -1, -1)) { mexErrMsgTxt ("DATA must be a matrix of DOUBLE"); } if (!vlmxIsVector(in[IN_PARENTS], -1)) { mexErrMsgTxt ("PARENTS must be a vector") ; } if (mxGetClassID(in[IN_PARENTS]) != mxUINT32_CLASS) { mexErrMsgTxt ("PARENTS must be UINT32") ; } N = mxGetNumberOfElements (in[IN_DATA]) ; data = mxGetPr (in[IN_DATA]) ; P = mxGetNumberOfElements (in[IN_PARENTS]) ; parents = mxGetData (in[IN_PARENTS]) ; if (nin > 2) { enum {buflen = 32} ; char buf [buflen] ; if (!vlmxIsString(in[IN_OPT], -1)) { mexErrMsgTxt("OPT must be a string") ; } mxGetString(in[IN_OPT], buf, buflen) ; buf [buflen - 1] = 0 ; if (!vlmxCompareStringsI("hist", buf)) { mexErrMsgTxt("OPT must be equal to 'hist'") ; } histmode = 1 ; } out[OUT_TREE] = mxCreateNumericMatrix(1, P,mxUINT32_CLASS, mxREAL) ; tree = mxGetData (out[OUT_TREE]) ; /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ { char buf [1024] ; vl_uint32 max_node = 0 ; vl_uint32 min_node = 0 ; vl_uint32 last_leaf = 0 ; /*vl_uint32 root = 0 ;*/ /* exhamine parents for errors and informations */ for (i = 0 ; i < P ; ++i) { vl_uint32 node = parents [i] ; if ((node != 0) & (node != 1)) { max_node = VL_MAX (node, max_node) ; min_node = VL_MIN (node, min_node) ; } /* check no node points outside the tree */ if (node > P) { snprintf(buf, sizeof(buf), "Out of bounds link PARENTS[%d] = %u > %u", i, node, P) ; mexErrMsgTxt (buf) ; } /* check node points to something above him */ if ((node != 0) & (node != 1) & (node < i)) { snprintf(buf, sizeof(buf), "Backward link PARENTS[%d] = %u < %d", i, node, i) ; mexErrMsgTxt (buf) ; } if (node == 0) ++ nnull ; } /* now * * min_node = first node which is not a leaf * max_node = root node * nnull = number of leaves pointing to the null node */ last_leaf = min_node - 1 ; /*root = max_node ;*/ /* process data */ for (i = 0 ; i < N ; ++i) { /*int w = 1 ;*/ vl_uint32 x = data [i] ; if (histmode) { /*w = x ;*/ x = i ; } if ((x < 1) | (x > last_leaf)) { if (histmode) { snprintf(buf, sizeof(buf), "DATA length exceeds number of AIB leaves") ; } else { snprintf(buf, sizeof(buf), "DATA [%u] = %u is not a leaf", i, x) ; } mexErrMsgTxt (buf) ; } while (VL_TRUE) { vl_uint32 x_ = parents [x -1] ; /* mexPrintf("%u : x_=%u, x=%u\n", i, x_, x) ; */ ++ tree [x - 1] ; if ((x_ == x) | (x_ == 0) | (x_ == 1)) break ; x = x_ ; } } } } vlfeat/toolbox/aib/vl_aib.m0000644000175000017500000000615212237004736014610 0ustar dimadima% VL_AIB Agglomerative Information Bottleneck % PARENTS = VL_AIB(PCX) runs Agglomerative Information Bottleneck % (AIB) on the class-feature co-occurrence matrix PCX and returns a % vector PARENTS representing the sequence of compressed AIB % alphabets. % % PCX is the joint probability of the occurrence of the class label % C and the feature value X. PCX has one row for each class label % and one column for each feature value, non negative entires and % sums to one. AIB iteratively merges the pair of feature values % that decreases the mutual information I(X,C) the least. This % compresses the alphabet of the discrete random variable X in such % a way that the new variable is still informative about C. % % Merge operations are represented by a binary tree. The nodes of % the tree correspond to the original feature values and any other % value obtained by merging. % % The vector PARENTS represents the merge tree. The nodes are % numbered in breadth-first order, starting from the leaves. The % numbers associated to the tree leaves correspond to the original % feature values (so the first leaf has number one and correspond to % the first feature value). In total there are 2*M-1 nodes, where M % is the number of feature values (the number of columns of % PCX). The internal nodes are numbered according to the order in % which AIB generates them. It is therefore possible to recover from % the tree the state of the AIB algorithm at each step (see also % VL_AIBCUT()). PARENTS is a UINT32 array with one element for each % tree node storing the index of the parent node. The root parent is % conventionally set to 1. % % Feature values with null probability (null columns of the PCX % matrix) are ignored by the AIB algorithm and the corresponding % entries in the PARENTS vectors are set to zero. Notice that this % causes the root of the tree to have index smaller of 2*M-1 % (PARENTS has still 2*M-1 entries, but the last portion is % zero-padded). % % Alternatively, the option ClusterNull can be used to assign the % null probability values to a special value. The result is similar % to pretending that the null probability nodes have indeed very % small probability, uniform across categories. % % [PARENTS, COST] = VL_AIB(...) returns the values COST of the cost % function being optimized by AIB (i.e. the mutual information % I(X,C)). COST has M column. The first column is the initial value % of the cost function. The others correspond to the cost after each % of the M-1 merges. If less than M-1 merges are performed, the rest % of the vector is filled with NaNs. % % VL_AIB() accepts the following options: % % Verbose:: % If specified, increase verbosity level. % % ClusterNull:: % If specified, do not signal null nodes; instead cluster them. % % See also: VL_AIBCUT(), VL_AIBHIST(), VL_AIBCUTHIST(), % VL_AIBCUTPUSH(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/aib/vl_aibcuthist.m0000644000175000017500000000354512237004736016217 0ustar dimadimafunction hist = vl_aibcuthist(map, x, varargin) % VL_AIBCUTHIST Compute a histogram by using an AIB compressed alphabet % HIST = VL_AIBCUTHIST(MAP, X) computes the histogram of the data X % using the compressed alphabet specified by a cut MAP of the AIB % tree (as returned by VL_AIBCUT()). Each element of HIST counts how % many elements of X are projected to the corresponding cut node % (encoding the compressed alphabet). % % Data are mapped to bins as specified by VL_AIBCUTPUSH(). Data mapped % to the null node are dropped. % % VL_AIBCUTHIST() accepts the following options: % % Nulls:: [drop] % What to do of null nodes: drop ('drop'), accumulate to an % extra bin at the end of HIST ('append'), or accumulate to % the first bin ('first') % % See also: VL_AIB(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). mode = 'drop' ; for k=1:2:length(varargin) opt=varargin{k} ; arg=varargin{k+1} ; switch lower(opt) case 'nulls' switch lower(arg) case 'drop' mode = 'drop' ; case 'append' mode = 'append' ; case 'first' mode = 'first' ; otherwise error(sprintf('Illegal argument ''%s'' for ''Nulls''', arg)) ; end otherwise error(sprintf('Unknown option ''%''', opt)) ; end end % determine cut size cut_size = max(map) ; % relabel data y = vl_aibcutpush(map, x) ; % null? if any(y == 0) switch mode case 'drop' y = y(y ~= 0) ; case 'append' cut_size = cut_size + 1 ; y(y == 0) = cut_size ; case 'first' y(y == 0) = 1 ; end end % Now we have the nodes of the cut. Accumulate. hist = zeros(1, cut_size) ; hist = vl_binsum(hist, ones(size(y)), y) ; vlfeat/toolbox/aib/vl_aibcutpush.m0000644000175000017500000000074212237004736016223 0ustar dimadimafunction y = vl_aibcutpush(map, x) % VL_AIBCUTPUSH Quantize based on VL_AIB cut % Y = VL_AIBCUTPUSH(MAP, X) maps the data X to elements of the AIB % cut specified by MAP. % % The function is equivalent to Y = MAP(X). % % See also: VL_HELP(), VL_AIB(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). y = map(x) ; vlfeat/toolbox/aib/vl_aib.c0000644000175000017500000001602112237004736014572 0ustar dimadima/** @internal ** @file aib.c ** @author Brian Fulkerson ** @author Andrea Vedaldi ** @brief AIB MEX driver **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include /* option codes */ enum { opt_cluster_null = 0, opt_verbose } ; /* options */ vlmxOption options [] = { {"ClusterNull",0, opt_cluster_null}, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; #define CLUSTER_NULL_NODES 1 /** ------------------------------------------------------------------ ** ** Null nodes are nodes with null probability and are not merged by AIB. ** It is convenient, however, to treat them as follows: ** ** - we pretend that AIB merged those nodes at the very beginning into a ** single cluster (as they, after all, yield zero information drop). ** ** - we attach this cluster to the root as the very last step (as we ** do not want to change other nodes. ** **/ static void cluster_null_nodes (vl_uint32* parents, vl_uint32 nvalues, double *cost, int verbosity) { vl_uint32 nnull = 0 ; vl_uint32 n ; vl_uint32 first ; vl_uint32 last_intermed ; vl_uint32 a, b, c, d, e ; vl_uint32 dp, ep ; /* count null nodes so far */ for (n = 0 ; n < nvalues ; ++ n) { if (parents[n] >= 2 * nvalues - 1) { ++ nnull ; } } if (nnull == 0) return ; /* = : leaves * 0 : null leaves * i : internal node * * : padding * x : intermediate nodes to link null nodes * * Input: * * ====== 0000 ======= iiiiiii **** * | | | * 0 dp ep * * * Output: * * ====== ==== ======= xxxx iiiiiii * | | || | | * 0 a bc d e */ a = nvalues ; b = nvalues + nnull - 1 - 1 ; c = b + 1 ; d = c + 1 ; e = 2 * nvalues - 2 ; dp = nvalues ; ep = 2 * nvalues - 2 - nnull ; if (verbosity > 1) { mexPrintf("vl_aib: a:%u b:%u c:%u d:%u e:%u dp:%u ep:%u\n", a,b,c,d,e,dp,ep) ; } /* search first leaf that has been merged */ { vl_uint32 first_parent = e ; first = 0 ; for (n = 0 ; n < nvalues ; ++ n) { if ((parents[n] <= e) & (parents[n] != 1)) { if (first_parent >= parents [n]) { first_parent = parents [n] ; first = n ; } } } } if (verbosity > 1) { mexPrintf("vl_aib: nnull:%u, nvalues:%u, first: %u\n", nnull,nvalues,first) ; } /* move internal node block [dp:ep] to [d:e] */ for (n = 0 ; n < e ; ++ n) { if ((parents [n] <= e) & (parents [n] != 0)) { parents [n] += (e - ep) ; } } for (n = e ; n >= d ; -- n) { parents [n] = parents [n - (e - ep)] ; } /* find first null node and connect it to a */ last_intermed = a ; for (n = 0 ; n < a ; ++ n) { if (parents[n] > e) { parents [n] = last_intermed ; break ; } } if (verbosity > 1) { mexPrintf("vl_aib:first null %u parent seto to last_intermed:%u\n", n, last_intermed) ; } /* chain rest of intermediate nodes */ for (; n < a ; ++ n) { if (parents[n] > e) { parents [n] = last_intermed ; parents [last_intermed] = last_intermed + 1 ; ++ last_intermed ; } } if (verbosity > 1) { mexPrintf("vl_aib: after chaining other nulls last_intermed:%u\n", last_intermed) ; } /* make last_intermed point to d */ parents [last_intermed] = d ; /* change parent of first to be last_intermed */ if (verbosity > 1) { mexPrintf("vl_aib: parent of %u (first) was %u\n", first, parents[first]) ; } parents [first] = last_intermed ; if (verbosity > 1) { mexPrintf("vl_aib: parent of %u (first) is now %u\n", first, parents[first]) ; } /* fix cost too (reall that the fist entry is the cost before any merge) */ if (cost) { cost -= nvalues - 1 ; for (n = e ; n >= d ; --n) { cost [n] = cost [n - (e - ep)] ; } for (n = c ; n >= a ; --n) { cost [n] = cost [d] ; } } } /** ------------------------------------------------------------------ ** @brief MEX entry point **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_PCX = 0, IN_END} ; enum {OUT_PARENTS = 0, OUT_COST} ; enum {INFORMATION, EC} ; int verbose = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; int cluster_null = 0 ; double *Pcx ; vl_uint32 nlabels ; vl_uint32 nvalues ; mxArray *Pcx_cpy ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { mexErrMsgTxt("One argument required.") ; } else if (nout > 2) { mexErrMsgTxt("Too many output arguments."); } if (!vlmxIsMatrix(in[IN_PCX], -1, -1)) { mexErrMsgTxt("PCX must be a real matrix.") ; } Pcx_cpy = mxDuplicateArray(in[IN_PCX]); Pcx = mxGetPr (Pcx_cpy) ; nlabels = mxGetM (in[IN_PCX]) ; nvalues = mxGetN (in[IN_PCX]) ; while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbose ; break ; case opt_cluster_null : cluster_null = 1 ; break ; } } if (verbose) { mexPrintf("vl_aib: clustering null probability variables: %s\n", VL_YESNO(cluster_null)) ; } /* ----------------------------------------------------------------- * Do job * -------------------------------------------------------------- */ { VlAIB *aib; double* acost = 0, *cost = 0 ; vl_uint32 *aparents = 0, *parents = 0 ; vl_uint32 n ; out[OUT_PARENTS] = mxCreateNumericMatrix(1, 2*nvalues - 1, mxUINT32_CLASS, mxREAL); parents = mxGetData(out[OUT_PARENTS]); if (nout > 1) { out[OUT_COST] = mxCreateNumericMatrix(1, nvalues, mxDOUBLE_CLASS, mxREAL); cost = mxGetPr(out[OUT_COST]); } aib = vl_aib_new (Pcx, nvalues, nlabels) ; vl_aib_set_verbosity (aib, verbose) ; vl_aib_process (aib); aparents = vl_aib_get_parents (aib); acost = vl_aib_get_costs (aib); memcpy(parents, aparents, sizeof(vl_uint32)*(2*nvalues-1)); if (nout > 1) memcpy(cost, acost, sizeof(double)*nvalues); vl_aib_delete(aib); if (cluster_null) { cluster_null_nodes (parents, nvalues, (nout == 0) ? 0 : cost, verbose) ; } /* save back parents */ for (n = 0 ; n < 2 * nvalues - 1 ; ++n) { if (parents [n] > 2 * nvalues - 1) { /* map ingored nodes to zero */ parents [n] = 0 ; } else { /* MATLAB starts counting from 1 */ ++ parents [n] ; } } } mxDestroyArray(Pcx_cpy); } vlfeat/toolbox/aib/vl_aibhist.m0000644000175000017500000000150712237004736015477 0ustar dimadima% VL_AIBHIST Compute histogram over VL_AIB tree % H = VL_AIBHIST(PARENTS, DATA) computes the histogram of the data % points DATA on the VL_AIB tree defined by PARENTS. Each element of % DATA indexes one of the leaves of the VL_AIB tree. % % H = VL_AIBHIST(PARENTS, DATA, 'HIST') treats DATA as an histograms. % In this case each compoment of DATA is the number of occurences of % the VL_AIB leaves corresponding to that component. % % H has the same dimension of parents and counts how many data points % are descendent of the corresponding node of the VL_AIB tree. % % See also: VL_HELP(), VL_AIB(), VL_AIBCUTPUSH(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/vl_demo.m0000644000175000017500000000175412237035727014255 0ustar dimadimafunction vl_demo % VL_DEMO Run VLFeat demos % VL_DEMO runs all VLFeat demos in a row, exporting figures for % the documentation. Edit this file to see a list of the vailable % demos, or look into the toolbox/demo directory. % Author: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if vl_isoctave() more off ; end root = vl_root ; demo_path = fullfile(root,'toolbox','demo') ; addpath(demo_path) ; vl_demo_covdet ; vl_demo_mser_basic ; vl_demo_mser_delta ; %vl_demo_mser_cmd ; vl_demo_aib ; vl_demo_quickshift ; vl_demo_slic ; vl_demo_hog ; vl_demo_svm ; vl_demo_kdtree ; vl_demo_kdtree_self ; vl_demo_kdtree_forest ; vl_demo_kdtree_ann ; vl_demo_kmeans_2d ; vl_demo_imdisttf ; vl_demo_plots_rank ; vl_demo_gmm_2d_rand; vl_demo_gmm_2d_twist; vl_demo_gmm_3d; vl_demo_kmeans_ann_speed; rmpath(demo_path); vlfeat/toolbox/kmeans/0000755000175000017500000000000012237042170013707 5ustar dimadimavlfeat/toolbox/kmeans/vl_hikmeanspush.c0000644000175000017500000001367412237004736017274 0ustar dimadima/** @internal ** @file hikmeanspush.c ** @brief vl_hikm_push - MEX driver ** @author Brian Fulkerson ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include #include enum { opt_method, opt_verbose } ; vlmxOption options [] = { {"Method", 1, opt_method }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; #define NFIELDS(field_names) (sizeof(field_names)/sizeof(*field_names)) /** ------------------------------------------------------------------ ** @internal ** @brief Convert MATLAB structure to HIKM node **/ static VlHIKMNode * xcreate (VlHIKMTree *tree, mxArray const *mnode, int i) { mxArray const *mcenters, *msub ; VlHIKMNode *node ; vl_size M, node_K ; vl_uindex k ; /* sanity checks */ mcenters = mxGetField(mnode, i, "centers") ; msub = mxGetField(mnode, i, "sub") ; if (!mcenters || mxGetClassID (mcenters) != mxINT32_CLASS || !vlmxIsMatrix (mcenters, -1, -1) ) { mexErrMsgTxt("NODE.CENTERS must be a INT32 matrix.") ; } M = mxGetM (mcenters) ; node_K = mxGetN (mcenters) ; if (node_K > (vl_size)tree->K) { mexErrMsgTxt("A node has more clusters than TREE.K.") ; } if (tree->M < 0) { tree->M = M ; } else if (M != (vl_size)tree->M) { mexErrMsgTxt("A node CENTERS field has inconsistent dimensionality.") ; } node = mxMalloc (sizeof(VlHIKMNode)) ; node->filter = vl_ikm_new (tree->method) ; node->children = 0 ; vl_ikm_init (node->filter, mxGetData(mcenters), M, node_K) ; /* has any childer? */ if (msub) { /* sanity checks */ if (mxGetClassID (msub) != mxSTRUCT_CLASS) { mexErrMsgTxt("NODE.SUB must be a MATLAB structure array.") ; } if (mxGetNumberOfElements (msub) != node_K) { mexErrMsgTxt("NODE.SUB size must correspond to NODE.CENTERS.") ; } node-> children = mxMalloc (sizeof(VlHIKMNode *) * node_K) ; for(k = 0 ; k < node_K ; ++ k) { node-> children [k] = xcreate (tree, msub, k) ; } } return node ; } /** ------------------------------------------------------------------ ** @internal ** @brief Convert MATLAB structure to HIKM tree **/ static VlHIKMTree* matlab_to_hikm (mxArray const *mtree, int method_type) { VlHIKMTree *tree ; mxArray *mK, *mdepth ; int K = 0, depth = 0; VL_USE_MATLAB_ENV ; if (mxGetClassID (mtree) != mxSTRUCT_CLASS) { mexErrMsgTxt("TREE must be a MATLAB structure.") ; } mK = mxGetField(mtree, 0, "K") ; mdepth = mxGetField(mtree, 0, "depth") ; if (!mK || !vlmxIsPlainScalar (mK) || (K = (int) *mxGetPr (mK)) < 1) { mexErrMsgTxt("TREE.K must be a DOUBLE not smaller than one.") ; } if (!mdepth || !vlmxIsPlainScalar (mdepth) || (depth = (int) *mxGetPr (mdepth)) < 1) { mexErrMsgTxt("TREE.DEPTH must be a DOUBLE not smaller than one.") ; } tree = mxMalloc (sizeof(VlHIKMTree)) ; tree-> depth = depth ; tree-> K = K ; tree-> M = -1 ; /* to be initialized later */ tree-> method= method_type ; tree-> root = xcreate (tree, mtree, 0) ; return tree ; } /* ---------------------------------------------------------------- */ /** @brief MEX driver entry point **/ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_TREE = 0, IN_DATA, IN_END} ; enum {OUT_ASGN = 0} ; vl_uint8 const *data; int opt ; int next = IN_END ; mxArray const *optarg ; int N = 0 ; int method_type = VL_IKM_LLOYD ; int verb = 0 ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 2) mexErrMsgTxt ("At least two arguments required."); else if (nout > 1) mexErrMsgTxt ("Too many output arguments."); if (mxGetClassID (in[IN_DATA]) != mxUINT8_CLASS) { mexErrMsgTxt ("DATA must be of class UINT8"); } N = mxGetN (in[IN_DATA]); /* n of elements */ data = (vl_uint8 *) mxGetPr (in[IN_DATA]); while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verb ; break ; case opt_method : if (!vlmxIsString (optarg, -1)) { mexErrMsgTxt("'Method' must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { mexErrMsgTxt("Option argument too long.") ; } if (strcmp("lloyd", buf) == 0) { method_type = VL_IKM_LLOYD ; } else if (strcmp("elkan", buf) == 0) { method_type = VL_IKM_ELKAN ; } else { mexErrMsgTxt("Unknown cost type.") ; } break ; default : abort() ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ { VlHIKMTree *tree ; vl_uint *ids ; int j; int depth ; tree = matlab_to_hikm (in[IN_TREE], method_type) ; depth = vl_hikm_get_depth (tree) ; if (verb) { mexPrintf("hikmeanspush: ndims: %d K: %d depth: %d\n", vl_hikm_get_ndims (tree), vl_hikm_get_K (tree), depth) ; } out[OUT_ASGN] = mxCreateNumericMatrix (depth, N, mxUINT32_CLASS, mxREAL) ; ids = mxGetData (out[OUT_ASGN]) ; vl_hikm_push (tree, ids, data, N) ; vl_hikm_delete (tree) ; for (j = 0 ; j < N*depth ; j++) ids [j] ++ ; } } vlfeat/toolbox/kmeans/vl_hikmeanshist.m0000644000175000017500000000345312237004736017270 0ustar dimadimafunction h = vl_hikmeanshist(tree,path) % VL_HIKMEANSHIST Compute histogram of quantized data % H = VL_HIKMEANSHIST(TREE,PATH) computes the histogram of the HIKM tree % nodes activated by the root-to-leaf paths PATH. PATH is usually % obtained by quantizing data by means of VL_HIKMEANSPUSH(). % % The histogram H has one bin for each node of the HIKM tree TREE. % The tree has K = TREE.K nodes and depth D = TREE.DEPTH. Therefore % there are M = (K^(D+1) - 1) / (K - 1) nodes in the tree (not % counting the root which carries no information). Nodes are stacked % into a vector of bins in breadth first order. % % Example:: % The following relations illustrate the structure of PATH: % H(1) = # of paths such that PATH(1,:) = 1 % H(K) = # of paths such that PATH(1,:) = K % H(K+1) = # of paths such that PATH(1:2,:) = [1 ; 1] % H(K+K) = # of paths such that PATH(1:2,:) = [1 ; K] % % See also: VL_HIKMEANS(), VL_HIKMEANSPUSH(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % PATH(:,k) is colum of subscripts i1 i2 ... id identifying a path in % the tree. In a bread first enumeration of the tree nodes (starting % from one and not counting the root), the node of subscripts % i1,i2,...id has index % % idx = i1 K^{d-1} + i2 K^{d-2} + ... + id % % where we assumed the indeces i1,i2,... start from 1. This formula % can be easily computed recursively. Since we also have a root % node, we need to add one. K = tree.K ; D = tree.depth ; M = (K^(D+1) - 1) / (K - 1) ; h = zeros(M, 1) ; p = zeros(1,size(path,2)) ; h(1) = size(path,2) ; for d=1:D p = p * K + double(path(d,:)) ; h = vl_binsum(h, 1, p + 1) ; end vlfeat/toolbox/kmeans/vl_hikmeanspush.m0000644000175000017500000000145112237004736017274 0ustar dimadima% VL_HIKMEANSPUSH Push data down an integer K-means tree % PATH = VL_HIKMEANSPUSH(TREE,X) quanizes the data X with the % hierachical integer K-means tree TREE. This is obtained by % calculating the path of each datum from the root of TREE down to a % leaf. Here X has a datum for each column and each column of PATH % represents the corresponding root-to-leaf path. % % Example:: % PATH(:,1) = [2 1 4] means that the first datum follows % the branches number 2, 1 and 4 starting from the root of the % HIKM tree ending to a leaf. % % See also: VL_HIKMEANS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/kmeans/vl_kmeans.m0000644000175000017500000000454312237004745016060 0ustar dimadima%VL_KMEANS Cluster data using k-means % [C, A] = VL_KMEANS(X, NUMCENTERS) clusters the columns of the % matrix X in NUMCENTERS centers C using k-means. X may be either % SINGLE or DOUBLE. C has the same number of rows of X and NUMCENTER % columns, with one column per center. A is a UINT32 row vector % specifying the assignments of the data X to the NUMCENTER % centers. % % [C, A, ENERGY] = VL_KMEANS(...) returns the energy of the solution % (or an upper bound for the ELKAN algorithm) as well. % % KMEANS() supports different initialization and optimization % methods and different clustering distances. Specifically, the % following options are supported: % % Verbose:: % Increase the verbosity level (may be specified multiple times). % % Distance:: [L2] % Use either L1 or L2 distance. % % Initialization:: % Use either random data points (RANDSEL) or k-means++ (PLUSPLUS) % to initialize the centers. % % Algorithm:: [LLOYD] % One of LLOYD, ELKAN, or ANN. LLOYD is the standard Lloyd % algorithm (similar to expectation maximisation). ELKAN is a % faster version of LLOYD using triangular inequalities to cut % down significantly the number of sample-to-center % comparisons. ANN is the same as Lloyd, but uses an approximated % nearest neighbours (ANN) algorithm to accelerate the % sample-to-center comparisons. The latter is particularly % suitable for very large problems. % % NumRepetitions:: [1] % Number of time to restart k-means. The solution with minimal % energy is returned. % % The following options tune the KD-Tree forest used for ANN % computations in the ANN algorithm (see also VL_KDTREEBUILD() % andVL_KDTREEQUERY()). % % NumTrees:: [3] % The number of trees int the randomized KD-Tree forest. % % MaxNumComparisons:: [100] % Maximum number of sample-to-center comparisons when searching % for the closest center. % % Example:: % VL_KMEANS(X, 10, 'verbose', 'distance', 'l1', 'algorithm', % 'elkan') clusters the data point X using 10 centers, l1 % distance, and the Elkan's algorithm. % % See also: VL_HELP(). % Authors: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/kmeans/vl_ikmeans.c0000644000175000017500000001061112237004736016210 0ustar dimadima/* file: ikmeans.c ** description: MEX weighted ikmeans function. ** author: Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include #include enum { opt_max_niters, opt_method, opt_verbose } ; vlmxOption options [] = { {"MaxIters", 1, opt_max_niters }, {"Method", 1, opt_method }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; /* driver */ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_X = 0, IN_K, IN_END} ; enum {OUT_C = 0, OUT_I} ; int opt ; int next = IN_END ; mxArray const *optarg ; int M, N, K = 0 ; int err = 0 ; vl_uint *asgn = 0 ; vl_ikm_acc *centers = 0 ; vl_uint8 *data ; int method_type = VL_IKM_LLOYD ; int max_niters = 200 ; int verb = 0 ; VlIKMFilt *ikmf ; VL_USE_MATLAB_ENV ; /* ------------------------------------------------------------------ * Check the arguments * --------------------------------------------------------------- */ if (nin < 2) { mexErrMsgTxt ("At least two arguments required."); } else if (nout > 2) { mexErrMsgTxt ("Too many output arguments."); } if (mxGetClassID (in[IN_X]) != mxUINT8_CLASS) { mexErrMsgTxt ("X must be of class uint8"); } M = mxGetM (in[IN_X]); /* n of components */ N = mxGetN (in[IN_X]); /* n of elements */ if (!vlmxIsPlainScalar (in[IN_K]) || (K = (int) *mxGetPr(in[IN_K])) < 1 || K > N ) { mexErrMsgTxt ("K must be a positive integer not greater than the number of data."); } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verb ; break ; case opt_max_niters : if (!vlmxIsPlainScalar(optarg) || (max_niters = (int) *mxGetPr(optarg)) < 1) { mexErrMsgTxt("MaxNiters must be not smaller than 1.") ; } break ; case opt_method : if (!vlmxIsString (optarg, -1)) { mexErrMsgTxt("'Method' must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { mexErrMsgTxt("Option argument too long.") ; } if (strcmp("lloyd", buf) == 0) { method_type = VL_IKM_LLOYD ; } else if (strcmp("elkan", buf) == 0) { method_type = VL_IKM_ELKAN ; } else { mexErrMsgTxt("Unknown method type.") ; } break ; default : abort() ; } } /* ------------------------------------------------------------------ * Do the job * --------------------------------------------------------------- */ if (verb) { char const * method_name = 0 ; switch (method_type) { case VL_IKM_LLOYD: method_name = "Lloyd" ; break ; case VL_IKM_ELKAN: method_name = "Elkan" ; break ; default : abort() ; } mexPrintf("ikmeans: MaxInters = %d\n", max_niters) ; mexPrintf("ikmeans: Method = %s\n", method_name) ; } data = (vl_uint8*) mxGetPr(in[IN_X]) ; ikmf = vl_ikm_new (method_type) ; vl_ikm_set_verbosity (ikmf, verb) ; vl_ikm_set_max_niters (ikmf, max_niters) ; vl_ikm_init_rand_data (ikmf, data, M, N, K) ; err = vl_ikm_train (ikmf, data, N) ; if (err) mexWarnMsgTxt("ikmeans: possible overflow!") ; /* ------------------------------------------------------------------ * Return results * --------------------------------------------------------------- */ { out[OUT_C] = mxCreateNumericMatrix (M, K, mxINT32_CLASS, mxREAL) ; centers = mxGetData (out[OUT_C]) ; memcpy (centers, vl_ikm_get_centers (ikmf), sizeof(vl_ikm_acc) * M * K) ; } if (nout > 1) { int j ; out[OUT_I] = mxCreateNumericMatrix (1, N, mxUINT32_CLASS, mxREAL) ; asgn = mxGetData (out[OUT_I]) ; vl_ikm_push (ikmf, asgn, data, N) ; for (j = 0 ; j < N ; ++j) ++ asgn [j] ; } vl_ikm_delete (ikmf) ; if (verb) { mexPrintf("ikmeans: done\n") ; } } vlfeat/toolbox/kmeans/vl_ikmeanspush.c0000644000175000017500000000714612237004736017121 0ustar dimadima/* file: ikmeanspush.c ** description: MEX weighted ikmeanspush function. ** author: Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include #include enum { opt_method, opt_verbose } ; vlmxOption options [] = { {"Method", 1, opt_method }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; /* driver */ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_X=0,IN_C,IN_END} ; enum {OUT_ASGN=0} ; vl_uint* asgn ; vl_ikm_acc* centers ; vl_uint8* data ; int M,N,j,K=0 ; int opt ; int next = IN_END ; mxArray const *optarg ; int method_type = VL_IKM_LLOYD ; int verb = 0 ; VlIKMFilt *ikmf ; VL_USE_MATLAB_ENV ; /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (nin < 2) { mexErrMsgTxt("At least two arguments required.") ; } else if (nout > 2) { mexErrMsgTxt("Too many output arguments.") ; } if(mxGetClassID(in[IN_X]) != mxUINT8_CLASS) { mexErrMsgTxt("X must be of class UINT8") ; } if(mxGetClassID(in[IN_C]) != mxINT32_CLASS) { mexErrMsgTxt("C must be of class INT32") ; } M = mxGetM(in[IN_X]) ; /* n of components */ N = mxGetN(in[IN_X]) ; /* n of elements */ K = mxGetN(in[IN_C]) ; /* n of centers */ if( (int) mxGetM(in[IN_C]) != M ) { mexErrMsgTxt("DATA and CENTERS must have the same number of columns.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verb ; break ; case opt_method : if (!vlmxIsString (optarg, -1)) { mexErrMsgTxt("'Method' must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { mexErrMsgTxt("Option argument too long.") ; } if (strcmp("lloyd", buf) == 0) { method_type = VL_IKM_LLOYD ; } else if (strcmp("elkan", buf) == 0) { method_type = VL_IKM_ELKAN ; } else { mexErrMsgTxt("Unknown cost type.") ; } break ; default : abort() ; } } /** ----------------------------------------------------------------- ** Check the arguments ** -------------------------------------------------------------- */ if (verb) { char const * method_name = 0 ; switch (method_type) { case VL_IKM_LLOYD: method_name = "Lloyd" ; break ; case VL_IKM_ELKAN: method_name = "Elkan" ; break ; default : abort() ; } mexPrintf("ikmeanspush: Method = %s\n", method_name) ; mexPrintf("ikmeanspush: ndata = %d\n", N) ; } out[OUT_ASGN] = mxCreateNumericMatrix (1, N, mxUINT32_CLASS, mxREAL) ; data = (vl_uint8*) mxGetData (in[IN_X]) ; centers = (vl_ikm_acc*) mxGetData (in[IN_C]) ; asgn = (vl_uint*) mxGetData (out[OUT_ASGN]) ; ikmf = vl_ikm_new (method_type) ; vl_ikm_set_verbosity (ikmf, verb) ; vl_ikm_init (ikmf, centers, M, K) ; vl_ikm_push (ikmf, asgn, data, N) ; /* adjust for MATLAB indexing */ for(j = 0 ; j < N ; ++j) ++ asgn[j] ; vl_ikm_delete (ikmf) ; } vlfeat/toolbox/kmeans/vl_kmeans.c0000644000175000017500000002462412237004745016050 0ustar dimadima/** @file vl_kmeans.c ** @brief vl_kmeans MEX definition. ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include enum { opt_max_num_iterations, opt_algorithm, opt_distance, opt_initialization, opt_num_repetitions, opt_verbose, opt_num_comparisons, opt_num_trees, opt_multithreading } ; enum { INIT_RANDSEL, INIT_PLUSPLUS } ; vlmxOption options [] = { {"MaxNumIterations", 1, opt_max_num_iterations }, {"Algorithm", 1, opt_algorithm }, {"Distance", 1, opt_distance }, {"Verbose", 0, opt_verbose }, {"NumRepetitions", 1, opt_num_repetitions, }, {"Initialization", 1, opt_initialization }, {"Initialisation", 1, opt_initialization }, /* UK spelling */ {"NumTrees", 1, opt_num_trees }, {"MaxNumComparisons", 1, opt_num_comparisons }, {0, 0, 0 } } ; /* driver */ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_DATA = 0, IN_NUMCENTERS, IN_END} ; enum {OUT_CENTERS = 0, OUT_ASSIGNMENTS, OUT_ENERGY} ; int opt ; int next = IN_END ; mxArray const *optarg ; vl_size numCenters ; vl_size dimension ; vl_size numData ; void const * data = NULL ; VlKMeansAlgorithm algorithm = VlKMeansLloyd ; VlVectorComparisonType distance = VlDistanceL2 ; vl_size maxNumIterations = 100 ; vl_size numRepetitions = 1 ; double energy ; int verbosity = 0 ; int initialization = INIT_PLUSPLUS ; vl_size maxNumComparisons = 100 ; vl_size numTrees = 3; vl_type dataType ; mxClassID classID ; VlKMeans * kmeans ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 2) { vlmxError (vlmxErrInvalidArgument, "At least two arguments required."); } else if (nout > 3) { vlmxError (vlmxErrInvalidArgument, "Too many output arguments."); } classID = mxGetClassID (IN(DATA)) ; switch (classID) { case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError (vlmxErrInvalidArgument, "DATA must be of class SINGLE or DOUBLE") ; abort() ; } dimension = mxGetM (IN(DATA)) ; numData = mxGetN (IN(DATA)) ; if (dimension == 0) { vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero") ; } if (!vlmxIsPlainScalar(IN(NUMCENTERS)) || (numCenters = (vl_size) mxGetScalar(IN(NUMCENTERS))) < 1 || numCenters > numData) { vlmxError (vlmxErrInvalidArgument, "NUMCENTERS must be a positive integer not greater " "than the number of data.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verbosity ; break ; case opt_max_num_iterations : if (!vlmxIsPlainScalar(optarg) || mxGetScalar(optarg) < 0) { vlmxError (vlmxErrInvalidArgument, "MAXNUMITERATIONS must be a non-negative integer scalar") ; } maxNumIterations = (vl_size) mxGetScalar(optarg) ; break ; case opt_algorithm : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "ALGORITHM must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "ALGORITHM argument too long.") ; } if (vlmxCompareStringsI("lloyd", buf) == 0) { algorithm = VlKMeansLloyd ; } else if (vlmxCompareStringsI("elkan", buf) == 0) { algorithm = VlKMeansElkan ; } else if (vlmxCompareStringsI("ann", buf) == 0) { algorithm = VlKMeansANN ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for ALGORITHM", buf) ; } break ; case opt_initialization : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "INITLAIZATION must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "INITIALIZATION argument too long.") ; } if (vlmxCompareStringsI("plusplus", buf) == 0 || vlmxCompareStringsI("++", buf) == 0) { initialization = VlKMeansPlusPlus ; } else if (vlmxCompareStringsI("randsel", buf) == 0) { initialization = VlKMeansRandomSelection ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for INITIALISATION.", buf) ; } break ; case opt_distance : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "DISTANCE must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "DISTANCE argument too long.") ; } if (vlmxCompareStringsI("l2", buf) == 0) { distance = VlDistanceL2 ; } else if (vlmxCompareStringsI("l1", buf) == 0) { distance = VlDistanceL1 ; } else if (vlmxCompareStringsI("chi2", buf) == 0) { distance = VlDistanceChi2 ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value %s for DISTANCE", buf) ; } break ; case opt_num_repetitions : if (!vlmxIsPlainScalar (optarg)) { vlmxError (vlmxErrInvalidArgument, "NUMREPETITIONS must be a scalar.") ; } if (mxGetScalar (optarg) < 1) { vlmxError (vlmxErrInvalidArgument, "NUMREPETITIONS must be larger than or equal to 1.") ; } numRepetitions = (vl_size) mxGetScalar (optarg) ; break ; case opt_num_trees : if (!vlmxIsPlainScalar (optarg)) { vlmxError (vlmxErrInvalidArgument, "NUMTREES must be a scalar.") ; } if (mxGetScalar (optarg) < 1) { vlmxError (vlmxErrInvalidArgument, "NUMTREES must be larger than or equal to 1.") ; } numTrees = (vl_size) mxGetScalar (optarg) ; break; case opt_num_comparisons : if (!vlmxIsPlainScalar (optarg)) { vlmxError (vlmxErrInvalidArgument, "NUMCOMPARISONS must be a scalar.") ; } if (mxGetScalar (optarg) < 0) { vlmxError (vlmxErrInvalidArgument, "NUMCOMPARISONS must be larger than or equal to 0.") ; } maxNumComparisons = (vl_size) mxGetScalar (optarg) ; break; default : abort() ; break ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ data = mxGetPr(IN(DATA)) ; kmeans = vl_kmeans_new (dataType, distance) ; vl_kmeans_set_verbosity (kmeans, verbosity) ; vl_kmeans_set_num_repetitions (kmeans, numRepetitions) ; vl_kmeans_set_algorithm (kmeans, algorithm) ; vl_kmeans_set_initialization (kmeans, initialization) ; vl_kmeans_set_max_num_iterations (kmeans, maxNumIterations) ; vl_kmeans_set_max_num_comparisons (kmeans, maxNumComparisons) ; vl_kmeans_set_num_trees (kmeans, numTrees); if (verbosity) { char const * algorithmName = 0 ; char const * initializationName = 0 ; switch (vl_kmeans_get_algorithm(kmeans)) { case VlKMeansLloyd: algorithmName = "Lloyd" ; break ; case VlKMeansElkan: algorithmName = "Elkan" ; break ; case VlKMeansANN: algorithmName = "ANN" ; break ; default : abort() ; } switch (vl_kmeans_get_initialization(kmeans)) { case VlKMeansPlusPlus : initializationName = "plusplus" ; break ; case VlKMeansRandomSelection : initializationName = "randsel" ; break ; default: abort() ; } mexPrintf("kmeans: Initialization = %s\n", initializationName) ; mexPrintf("kmeans: Algorithm = %s\n", algorithmName) ; mexPrintf("kmeans: MaxNumIterations = %d\n", vl_kmeans_get_max_num_iterations(kmeans)) ; mexPrintf("kmeans: NumRepetitions = %d\n", vl_kmeans_get_num_repetitions(kmeans)) ; mexPrintf("kmeans: data type = %s\n", vl_get_type_name(vl_kmeans_get_data_type(kmeans))) ; mexPrintf("kmeans: distance = %s\n", vl_get_vector_comparison_type_name(vl_kmeans_get_distance(kmeans))) ; mexPrintf("kmeans: data dimension = %d\n", dimension) ; mexPrintf("kmeans: num. data points = %d\n", numData) ; mexPrintf("kmeans: num. centers = %d\n", numCenters) ; mexPrintf("kmeans: max num. comparisons = %d\n", maxNumComparisons) ; mexPrintf("kmeans: num. trees = %d\n", numTrees) ; mexPrintf("\n") ; } /* -------------------------------------------------------------- */ /* Clustering and quantization */ /* -------------------------------------------------------------- */ energy = vl_kmeans_cluster(kmeans, data, dimension, numData, numCenters) ; /* copy centers */ OUT(CENTERS) = mxCreateNumericMatrix (dimension, numCenters, classID, mxREAL) ; memcpy (mxGetData(OUT(CENTERS)), vl_kmeans_get_centers (kmeans), vl_get_type_size (dataType) * dimension * vl_kmeans_get_num_centers(kmeans)) ; /* optionally qunatize */ if (nout > 1) { vl_uindex j ; vl_uint32 * assignments ; OUT(ASSIGNMENTS) = mxCreateNumericMatrix (1, numData, mxUINT32_CLASS, mxREAL) ; assignments = mxGetData (OUT(ASSIGNMENTS)) ; vl_kmeans_quantize (kmeans, assignments, NULL, data, numData) ; /* use MATLAB indexing convention */ for (j = 0 ; j < numData ; ++j) { assignments[j] += 1 ; } } /* optionally return energy */ if (nout > 2) { OUT(ENERGY) = vlmxCreatePlainScalar (energy) ; } vl_kmeans_delete (kmeans) ; } vlfeat/toolbox/kmeans/vl_ikmeanshist.m0000644000175000017500000000102012237004736017104 0ustar dimadimafunction h = vl_ikmeanshist(K,asgn) % VL_IKMEANSHIST Compute histogram of quantized data % H = VL_IKMEANSHIST(K,ASGN) computes the histogram of the IKM clusters % activated by cluster assignments ASGN. % % See also: VL_IKMEANS(), VL_IKMEANSPUSH(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). h = zeros(K,1) ; h = vl_binsum(h, 1, double(asgn)) ; vlfeat/toolbox/kmeans/vl_hikmeans.m0000644000175000017500000000272112237004736016375 0ustar dimadimafunction [tree,asgn] = vl_hikmeans(data,K,nleaves) % VL_HIKMEANS Hierachical integer K-means % [TREE,ASGN] = VL_HIKMEANS(DATA,K,NLEAVES) applies recursive integer % K-menas to cluster the data DATA, returing a structure TREE % representing the clusters and a vector ASGN with the data to % cluster assignments. The depth of the recursive partition is % computed so that at least NLEAVES are generated. % % VL_HIKMEANS() is built on top of VL_IKMEANS() and requires the data to % be of class UINT8. % % TREE is a structure representing the hierarchical clusters. Each % node of the tree is also a structure with fields: % % DEPTH:: % Depth of the tree (only at the root node) % % CENTERS:: % K cluster centers % % SUB:: % Array of K node structures representing subtrees % (this field is missing at leaves). % % ASGN is a matrix with one column per datum and height equal to the % depth of the tree. Each column encodes the branch of the tree that % correspond to each datum. % % Example:: % ASGN(:,7) = [1 5 3] means that the tree as depth equal to 3 and % that the datum X(:,7) corresponds to the branch % ROOT->SUB(1)->SUB(5)->SUB(3). % % See also: VL_HIKMEANSPUSH(), VL_HIKMEANSHIST(), VL_IKMEANS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/kmeans/vl_ikmeanspush.m0000644000175000017500000000067312237004736017131 0ustar dimadima% VL_IKMEANSPUSH Project data on integer K-means paritions % I = VL_IKMEANSPUSH(X,C) projects the data X to the integer K-meanns % clusters of centers C returning the cluster indeces I. % % See also: VL_IKMEANS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/kmeans/vl_hikmeans.c0000644000175000017500000001360712237004736016370 0ustar dimadima/** file: hikmeans.c ** description: MEX hierarchical ikmeans. ** author: Brian Fulkerson **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include #include #include #define NFIELDS(field_names) (sizeof(field_names)/sizeof(*field_names)) enum { opt_max_niters, opt_method, opt_verbose } ; vlmxOption options [] = { {"MaxIters", 1, opt_max_niters }, {"Method", 1, opt_method }, {"Verbose", 0, opt_verbose }, {0, 0, 0 } } ; /** ------------------------------------------------------------------ ** @internal ** @brief Copy HIKM tree node to a MATLAB structure **/ static void xcreate (mxArray *mnode, int i, VlHIKMNode *node) { int node_K = vl_ikm_get_K (node->filter) ; int M = vl_ikm_get_ndims (node->filter) ; vl_ikm_acc const *centers = vl_ikm_get_centers (node->filter) ; mxArray *mcenters ; mcenters = mxCreateNumericMatrix (M, node_K, mxINT32_CLASS, mxREAL); memcpy (mxGetPr(mcenters), centers, sizeof(vl_ikm_acc) * M * node_K) ; mxSetField (mnode, i, "centers", mcenters) ; if (node->children) { mxArray * msub ; const char * field_names[] = {"centers", "sub" } ; mwSize dims [2] ; int k ; dims[0] = 1 ; dims[1] = node_K ; msub = mxCreateStructArray (2, dims, 2, field_names) ; for (k = 0 ; k < node_K ; ++k) { xcreate (msub, k, node -> children [k]) ; } mxSetField (mnode, i, "sub", msub) ; } } /** ------------------------------------------------------------------ ** @internal ** @brief Copy HIKM tree to a MATLAB structure **/ mxArray * hikm_to_matlab (VlHIKMTree * tree) { int K = vl_hikm_get_K (tree) ; int depth = vl_hikm_get_depth (tree) ; mwSize dims [2] = {1, 1} ; mxArray *mtree ; const char *field_names[] = {"K", "depth", "centers", "sub"} ; /* Create the main struct array */ mtree = mxCreateStructArray (2, dims, NFIELDS(field_names), field_names) ; mxSetField (mtree, 0, "K", mxCreateDoubleScalar (K)) ; mxSetField (mtree, 0, "depth", mxCreateDoubleScalar (depth)) ; if (tree->root) xcreate (mtree, 0, tree->root) ; return mtree; } /** ------------------------------------------------------------------ ** @internal ** @brief MEX driver **/ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { vl_uint8 *data ; enum {IN_DATA = 0, IN_K, IN_NLEAVES, IN_END} ; enum {OUT_TREE = 0, OUT_ASGN} ; int M, N, K = 2, depth = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; int nleaves = 1 ; int method_type = VL_IKM_LLOYD ; int max_niters = 200 ; int verb = 0 ; VlHIKMTree* tree ; VL_USE_MATLAB_ENV ; /* ------------------------------------------------------------------ * Check the arguments * --------------------------------------------------------------- */ if (nin < 3) { mexErrMsgTxt ("At least three arguments required."); } else if (nout > 2) { mexErrMsgTxt ("Too many output arguments."); } if (mxGetClassID (in[IN_DATA]) != mxUINT8_CLASS) { mexErrMsgTxt ("DATA must be of class UINT8."); } if (! vlmxIsPlainScalar (in[IN_NLEAVES]) || (nleaves = (int) *mxGetPr (in[IN_NLEAVES])) < 1) { mexErrMsgTxt ("NLEAVES must be a scalar not smaller than 2.") ; } M = mxGetM (in[IN_DATA]); /* n of components */ N = mxGetN (in[IN_DATA]); /* n of elements */ if (! vlmxIsPlainScalar (in[IN_K]) || (K = (int) *mxGetPr (in[IN_K])) > N ) { mexErrMsgTxt ("Cannot have more clusters than data.") ; } data = (vl_uint8 *) mxGetPr (in[IN_DATA]) ; while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verb ; break ; case opt_max_niters : if (!vlmxIsPlainScalar(optarg) || (max_niters = (int) *mxGetPr(optarg)) < 1) { mexErrMsgTxt("MaxNiters must be not smaller than 1.") ; } break ; case opt_method : if (!vlmxIsString (optarg, -1)) { mexErrMsgTxt("'Method' must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { mexErrMsgTxt("Option argument too long.") ; } if (strcmp("lloyd", buf) == 0) { method_type = VL_IKM_LLOYD ; } else if (strcmp("elkan", buf) == 0) { method_type = VL_IKM_ELKAN ; } else { mexErrMsgTxt("Unknown cost type.") ; } break ; default : abort() ; break ; } } /* --------------------------------------------------------------- * Do the job * ------------------------------------------------------------ */ depth = VL_MAX(1, ceil (log (nleaves) / log(K))) ; tree = vl_hikm_new (method_type) ; if (verb) { mexPrintf("hikmeans: # dims: %d\n", M) ; mexPrintf("hikmeans: # data: %d\n", N) ; mexPrintf("hikmeans: K: %d\n", K) ; mexPrintf("hikmeans: depth: %d\n", depth) ; } vl_hikm_set_verbosity (tree, verb) ; vl_hikm_init (tree, M, K, depth) ; vl_hikm_train (tree, data, N) ; out[OUT_TREE] = hikm_to_matlab (tree) ; if (nout > 1) { vl_uint *asgn ; int j ; out [OUT_ASGN] = mxCreateNumericMatrix (vl_hikm_get_depth (tree), N, mxUINT32_CLASS, mxREAL) ; asgn = mxGetData(out[OUT_ASGN]) ; vl_hikm_push (tree, asgn, data, N) ; for (j = 0 ; j < N*depth ; ++ j) asgn [j] ++ ; } if (verb) { mexPrintf("hikmeans: done.\n") ; } /* vl_hikm_delete (tree) ; */ } vlfeat/toolbox/kmeans/vl_ikmeans.m0000644000175000017500000000157412237004736016232 0ustar dimadima% VL_IKMEANS Integer K-means % C = VL_IKMEANS(X,K) returns the centers of a K-means paritioning of % the data space X. X must be of class UINT8. C is of class UINT32. % % [C, I] = VL_IKMEANS(...) returns the cluster associations I of the % data as well. % % VL_IKMEANS() accepts the following options: % % MaxPasses:: 200 % Maximum number of iterations before giving up (the algorithm % stops as soon as there is no change in the data to cluster % associations). % % Method:: Lloyd % Algorithm to use ('Lloyd', 'Elkan'). % % Verbose:: % Increase the verbosity level. % % See also: VL_IKMEANSPUSH(), VL_IKMEANSHIST(), VL_HIKMEANS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/fisher/0000755000175000017500000000000012237042170013711 5ustar dimadimavlfeat/toolbox/fisher/vl_fisher.m0000644000175000017500000000260112237004745016055 0ustar dimadima% VL_FISHER Fisher vector feature encoding % ENC = VL_FISHER(X, MEANS, COVARIANCES, PRIORS) computes the % Fisher vector encoding of the vectors X relative to the Gaussian % mixture model with means MEANS, covariances COVARIANCES, and pror % mode probabilities PRIORS. % % X has one column per data vector (e.g. a SIFT descriptor), and % MEANS and COVARIANCES one column per GMM component (covariance % matrices are assumed diagonal). PRIORS has size equal to the % number of GMM components. All data must be of the smae class, % either SINGLE or DOUBLE. % % ENC is a vector of the same class of X of size equal to the % product of the data dimension and the number of components. % % By default, the standard Fisher vector is computed. VL_FISHER() % accepts the following options: % % Normalized:: % If specified, L2 normalize the Fisher vector. % % SquareRoot:: % If specified, the signed square root function is applied to % ENC before normalization. % % Verbose:: % Increase the verbosity level (may be specified multiple times). % % See: Fisher vectors, VL_HELP(). % Authors: David Novotny, Andrea Vedaldi % Copyright (C) 2013 David Novotny and Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/fisher/vl_fisher.c0000644000175000017500000001221412237004745016044 0ustar dimadima/** @file vl_fisher.c ** @brief vl_fisher MEX definition. ** @author Andrea Vedaldi ** @author David Novotny **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include enum { opt_verbose, opt_normalized, opt_square_root, opt_improved, opt_fast } ; vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"Normalized", 0, opt_normalized }, {"SquareRoot", 0, opt_square_root }, {"Improved", 0, opt_improved }, {"Fast", 0, opt_fast } } ; /* driver */ void mexFunction (int nout VL_UNUSED, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_DATA = 0, IN_MEANS, IN_COVARIANCES, IN_PRIORS, IN_END} ; enum {OUT_ENC} ; int opt ; int next = IN_END ; mxArray const *optarg ; vl_size numClusters = 10; vl_size dimension ; vl_size numData ; int flags = 0 ; void * covariances = NULL; void * means = NULL; void * priors = NULL; void * data = NULL ; int verbosity = 0 ; vl_type dataType ; mxClassID classID ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 4) { vlmxError (vlmxErrInvalidArgument, "At least four arguments required."); } if (nout > 1) { vlmxError (vlmxErrInvalidArgument, "At most one output argument."); } classID = mxGetClassID (IN(DATA)) ; switch (classID) { case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError (vlmxErrInvalidArgument, "DATA is neither of class SINGLE or DOUBLE.") ; } if (mxGetClassID (IN(MEANS)) != classID) { vlmxError(vlmxErrInvalidArgument, "MEANS is not of the same class as DATA.") ; } if (mxGetClassID (IN(COVARIANCES)) != classID) { vlmxError(vlmxErrInvalidArgument, "COVARIANCES is not of the same class as DATA.") ; } if (mxGetClassID (IN(PRIORS)) != classID) { vlmxError(vlmxErrInvalidArgument, "PRIORS is not of the same class as DATA.") ; } dimension = mxGetM (IN(DATA)) ; numData = mxGetN (IN(DATA)) ; numClusters = mxGetN (IN(MEANS)) ; if (dimension == 0) { vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero.") ; } if (!vlmxIsMatrix(IN(MEANS), dimension, numClusters)) { vlmxError (vlmxErrInvalidArgument, "MEANS is not a matrix or does not have the correct size.") ; } if (!vlmxIsMatrix(IN(COVARIANCES), dimension, numClusters)) { vlmxError (vlmxErrInvalidArgument, "COVARIANCES is not a matrix or does not have the correct size.") ; } if (!vlmxIsVector(IN(PRIORS), numClusters)) { vlmxError (vlmxErrInvalidArgument, "PRIORS is not a vector or does not have the correct size.") ; } if (!vlmxIsMatrix(IN(DATA), dimension, numData)) { vlmxError (vlmxErrInvalidArgument, "DATA is not a matrix or does not have the correct size.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbosity ; break ; case opt_normalized: flags |= VL_FISHER_FLAG_NORMALIZED ; break ; case opt_square_root: flags |= VL_FISHER_FLAG_SQUARE_ROOT ; break ; case opt_improved: flags |= VL_FISHER_FLAG_IMPROVED ; break ; case opt_fast: flags |= VL_FISHER_FLAG_FAST ; break ; default : abort() ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ data = mxGetPr(IN(DATA)) ; means = mxGetPr(IN(MEANS)) ; covariances = mxGetPr(IN(COVARIANCES)) ; priors = mxGetPr(IN(PRIORS)) ; if (verbosity) { mexPrintf("vl_fisher: num data: %d\n", numData) ; mexPrintf("vl_fisher: num clusters: %d\n", numClusters) ; mexPrintf("vl_fisher: data dimension: %d\n", dimension) ; mexPrintf("vl_fisher: code dimension: %d\n", numClusters * dimension) ; mexPrintf("vl_fisher: square root: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_SQUARE_ROOT)) ; mexPrintf("vl_fisher: normalized: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_NORMALIZED)) ; mexPrintf("vl_fisher: fast: %s\n", VL_YESNO(flags & VL_FISHER_FLAG_FAST)) ; } /* -------------------------------------------------------------- */ /* Encoding */ /* -------------------------------------------------------------- */ OUT(ENC) = mxCreateNumericMatrix (dimension * numClusters * 2, 1, classID, mxREAL) ; vl_fisher_encode (mxGetData(OUT(ENC)), dataType, means, dimension, numClusters, covariances, priors, data, numData, flags) ; } vlfeat/toolbox/gmm/0000755000175000017500000000000012237042170013211 5ustar dimadimavlfeat/toolbox/gmm/vl_gmm.c0000644000175000017500000002716312237004745014655 0ustar dimadima/** @file vl_gmm.c ** @brief vl_gmm MEX definition. ** @author David Novotny **/ /* Copyright (C) 2013 David Novotny. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include enum { opt_max_num_iterations, opt_distance, opt_initialization, opt_num_repetitions, opt_verbose, opt_means, opt_covariances, opt_priors, opt_covariance_bound } ; vlmxOption options [] = { {"MaxNumIterations", 1, opt_max_num_iterations }, {"Verbose", 0, opt_verbose }, {"NumRepetitions", 1, opt_num_repetitions, }, {"Initialization", 1, opt_initialization }, {"Initialisation", 1, opt_initialization }, /* UK spelling */ {"InitMeans", 1, opt_means }, {"InitCovariances", 1, opt_covariances }, {"InitPriors", 1, opt_priors }, {"CovarianceBound", 1, opt_covariance_bound }, {0, 0, 0 } } ; /* driver */ void mexFunction (int nout, mxArray * out[], int nin, const mxArray * in[]) { enum {IN_DATA = 0, IN_NUMCLUSTERS, IN_END} ; enum {OUT_MEANS, OUT_COVARIANCES, OUT_PRIORS, OUT_LL, OUT_POSTERIORS} ; int opt ; int next = IN_END ; mxArray const *optarg ; vl_size i; vl_size numClusters = 10; vl_size dimension ; vl_size numData ; void * initCovariances = 0 ; void * initMeans = 0 ; void * initPriors = 0 ; double covarianceScalarBound = VL_NAN_D ; double const * covarianceBound = NULL ; void const * data = NULL ; vl_size maxNumIterations = 100 ; vl_size numRepetitions = 1 ; double LL ; int verbosity = 0 ; VlGMMInitialization initialization = VlGMMRand ; vl_bool initializationSet = VL_FALSE ; vl_type dataType ; mxClassID classID ; VlGMM * gmm ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 2) { vlmxError (vlmxErrInvalidArgument, "At least two arguments required."); } else if (nout > 5) { vlmxError (vlmxErrInvalidArgument, "Too many output arguments."); } classID = mxGetClassID (IN(DATA)) ; switch (classID) { case mxSINGLE_CLASS: dataType = VL_TYPE_FLOAT ; break ; case mxDOUBLE_CLASS: dataType = VL_TYPE_DOUBLE ; break ; default: vlmxError (vlmxErrInvalidArgument, "DATA is neither of class SINGLE or DOUBLE.") ; abort() ; } dimension = mxGetM (IN(DATA)) ; numData = mxGetN (IN(DATA)) ; if (dimension == 0) { vlmxError (vlmxErrInvalidArgument, "SIZE(DATA,1) is zero.") ; } if (!vlmxIsPlainScalar(IN(NUMCLUSTERS)) || (numClusters = (vl_size) mxGetScalar(IN(NUMCLUSTERS))) < 1 || numClusters > numData) { vlmxError (vlmxErrInvalidArgument, "NUMCLUSTERS must be a positive integer not greater " "than the number of data.") ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { char buf [1024] ; switch (opt) { case opt_verbose : ++ verbosity ; break ; case opt_max_num_iterations : if (!vlmxIsPlainScalar(optarg) || mxGetScalar(optarg) < 0) { vlmxError (vlmxErrInvalidArgument, "MAXNUMITERATIONS must be a non-negative integer scalar") ; } maxNumIterations = (vl_size) mxGetScalar(optarg) ; break ; case opt_covariance_bound : if (vlmxIsPlainScalar(optarg)) { covarianceScalarBound = mxGetScalar(optarg) ; continue ; } if (!vlmxIsPlainVector(optarg,dimension)) { vlmxError (vlmxErrInvalidArgument, "COVARIANCEBOUND must be a DOUBLE vector of size " "equal to the dimension of the data X.") ; } covarianceBound = mxGetPr(optarg) ; break ; case opt_priors : { if (mxGetClassID (optarg) != mxGetClassID(IN(DATA))) { vlmxError (vlmxErrInvalidArgument, "INITPRIORS is not of the same class as the data X.") ; } if (! vlmxIsVector (optarg, numClusters) || ! vlmxIsReal (optarg)) { vlmxError(vlmxErrInvalidArgument, "INITPRIORS is not a real vector or does not have the correct size.") ; } initPriors = mxGetPr(optarg) ; break ; } case opt_means : { if (mxGetClassID (optarg) != mxGetClassID(IN(DATA))) { vlmxError (vlmxErrInvalidArgument, "INITMEANS is not of the same class as the data X.") ; } if (! vlmxIsMatrix (optarg, dimension, numClusters) || ! vlmxIsReal (optarg)) { vlmxError(vlmxErrInvalidArgument, "INITMEANS is not a real matrix or does not have the correct size.") ; } initMeans = mxGetPr(optarg) ; break; } case opt_covariances : { if (mxGetClassID (optarg) != mxGetClassID(IN(DATA))) { vlmxError (vlmxErrInvalidArgument, "INITCOVARIANCES is not of the same class as the data X.") ; } if (! vlmxIsMatrix (optarg, dimension, numClusters) || ! vlmxIsReal (optarg)) { vlmxError(vlmxErrInvalidArgument, "INITCOVARIANCES is not a real matrix or does not have the correct size.") ; } initCovariances = mxGetPr(optarg) ; break; } case opt_initialization : if (!vlmxIsString (optarg, -1)) { vlmxError (vlmxErrInvalidArgument, "INITLAIZATION must be a string.") ; } if (mxGetString (optarg, buf, sizeof(buf))) { vlmxError (vlmxErrInvalidArgument, "INITIALIZATION argument too long.") ; } if (vlmxCompareStringsI("rand", buf) == 0) { initialization = VlGMMRand ; } else if (vlmxCompareStringsI("custom", buf) == 0) { initialization = VlGMMCustom ; } else if (vlmxCompareStringsI("kmeans", buf) == 0) { initialization = VlGMMKMeans ; } else { vlmxError (vlmxErrInvalidArgument, "Invalid value '%s' for INITIALIZATION.", buf) ; } initializationSet = VL_TRUE ; break ; case opt_num_repetitions : if (!vlmxIsPlainScalar (optarg)) { vlmxError (vlmxErrInvalidArgument, "NUMREPETITIONS is not a scalar.") ; } if (mxGetScalar (optarg) < 1) { vlmxError (vlmxErrInvalidArgument, "NUMREPETITIONS is not larger than or equal to 1.") ; } numRepetitions = (vl_size) mxGetScalar (optarg) ; break ; default : abort() ; break ; } } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ data = mxGetPr(IN(DATA)) ; switch(dataType){ case VL_TYPE_DOUBLE: for(i = 0; i < numData*dimension; i++) { double datum = *((double*)data + i); if(!(datum < VL_INFINITY_D && datum > -VL_INFINITY_D)){ vlmxError (vlmxErrInvalidArgument, "DATA contains NaNs or Infs.") ; } } break; case VL_TYPE_FLOAT: for(i = 0; i < numData*dimension; i++) { float datum = *((float*)data + i); if(!(datum < VL_INFINITY_F && datum > -VL_INFINITY_F)){ vlmxError (vlmxErrInvalidArgument, "DATA contains NaNs or Infs.") ; } } break; default: abort(); break; } if (initPriors || initMeans || initCovariances) { if (!initPriors || !initMeans || !initCovariances) { vlmxError (vlmxErrInvalidArgument, "All or none of INITPRIORS, INITMEANS, " "INITCOVARIANCES must be set.") ; } if (initializationSet && initialization != VlGMMCustom) { vlmxError (vlmxErrInvalidArgument, "INITPRIORS, INITMEANS, and " "INITCOVARIANCES requires 'custom' INITALIZATION.") ; } initialization = VlGMMCustom ; } gmm = vl_gmm_new (dataType, dimension, numClusters) ; vl_gmm_set_verbosity (gmm, verbosity) ; vl_gmm_set_num_repetitions (gmm, numRepetitions) ; vl_gmm_set_max_num_iterations (gmm, maxNumIterations) ; vl_gmm_set_initialization (gmm, initialization) ; if (!vl_is_nan_d(covarianceScalarBound)) { vl_gmm_set_covariance_lower_bound (gmm, covarianceScalarBound) ; } if (covarianceBound) { vl_gmm_set_covariance_lower_bounds (gmm, covarianceBound) ; } if (initPriors) { vl_gmm_set_priors(gmm, initPriors) ; } if (initMeans) { vl_gmm_set_means(gmm, initMeans) ; } if (initCovariances) { vl_gmm_set_covariances(gmm, initCovariances) ; } if (verbosity) { char const * initializationName = 0 ; switch (vl_gmm_get_initialization(gmm)) { case VlGMMRand : initializationName = "rand" ; break ; case VlGMMKMeans : initializationName = "kmeans" ; break ; case VlGMMCustom : initializationName = "custom" ; break ; default: abort() ; } mexPrintf("vl_gmm: initialization = %s\n", initializationName) ; mexPrintf("vl_gmm: maxNumIterations = %d\n", vl_gmm_get_max_num_iterations(gmm)) ; mexPrintf("vl_gmm: numRepetitions = %d\n", vl_gmm_get_num_repetitions(gmm)) ; mexPrintf("vl_gmm: data type = %s\n", vl_get_type_name(vl_gmm_get_data_type(gmm))) ; mexPrintf("vl_gmm: data dimension = %d\n", dimension) ; mexPrintf("vl_gmm: num. data points = %d\n", numData) ; mexPrintf("vl_gmm: num. Gaussian modes = %d\n", numClusters) ; mexPrintf("vl_gmm: lower bound on covariance = [") ; if (dimension < 3) { for (i = 0 ; i < dimension ; ++i) { mexPrintf(" %f", vl_gmm_get_covariance_lower_bounds(gmm)[i]) ; } } else { mexPrintf(" %f %f ... %f", vl_gmm_get_covariance_lower_bounds(gmm)[0], vl_gmm_get_covariance_lower_bounds(gmm)[1], vl_gmm_get_covariance_lower_bounds(gmm)[dimension-1]) ; } mexPrintf("]\n") ; } /* -------------------------------------------------------------- */ /* Clustering */ /* -------------------------------------------------------------- */ LL = vl_gmm_cluster(gmm, data, numData) ; /* copy centers */ OUT(MEANS) = mxCreateNumericMatrix (dimension, numClusters, classID, mxREAL) ; OUT(COVARIANCES) = mxCreateNumericMatrix (dimension, numClusters, classID, mxREAL) ; OUT(PRIORS) = mxCreateNumericMatrix (numClusters, 1, classID, mxREAL) ; OUT(POSTERIORS) = mxCreateNumericMatrix (numClusters, numData, classID, mxREAL) ; memcpy (mxGetData(OUT(MEANS)), vl_gmm_get_means (gmm), vl_get_type_size (dataType) * dimension * vl_gmm_get_num_clusters(gmm)) ; memcpy (mxGetData(OUT(COVARIANCES)), vl_gmm_get_covariances (gmm), vl_get_type_size (dataType) * dimension * vl_gmm_get_num_clusters(gmm)) ; memcpy (mxGetData(OUT(PRIORS)), vl_gmm_get_priors (gmm), vl_get_type_size (dataType) * vl_gmm_get_num_clusters(gmm)) ; /* optionally return loglikelihood */ if (nout > 3) { OUT(LL) = vlmxCreatePlainScalar (LL) ; } /* optionally return posterior probabilities */ if (nout > 4) { memcpy (mxGetData(OUT(POSTERIORS)), vl_gmm_get_posteriors (gmm), vl_get_type_size (dataType) * numData * vl_gmm_get_num_clusters(gmm)) ; } vl_gmm_delete (gmm) ; } vlfeat/toolbox/gmm/vl_gmm.m0000644000175000017500000000470312237004745014662 0ustar dimadima% Vl_GMM Learn a Gaussian Mixture Model using EM % [MEANS, COVARIANCES, PRIORS] = VL_GMM(X, NUMCLUSTERS) fits a GMM with % NUMCLUSTERS components to the data X. Each column of X represent a % sample point. X may be either SINGLE or DOUBLE. MEANS, COVARIANCES, and % PRIORS are respectively the means, the diagonal covariances, and % the prior probabilities of the Guassian modes. MEANS and COVARIANCES % have the same number of rows as X and NUMCLUSTERS columns with one % column per mode. PRIORS is a row vector with NUMCLUSTER entries % summing to one. % % [MEANS, COVARIANCES, PRIORS, LL] = VL_GMM(...) returns the % loglikelihood (LL) of the model as well. % % [MEANS, COVARIANCES, PRIORS, LL, POSTERIORS] = VL_GMM(...) returns % the posterior probabilities POSTERIORS of the Gaussian modes given % each data point. The POSTERIORS matrix has NUMCLUSTERS rows and % NUMDATA columns. % % VL_GMM() supports different initialization and optimization % methods. Specifically, the following options are supported: % % Verbose:: % Increase the verbosity level (may be specified multiple times). % % Initialization:: RAND % RAND initializes the means as random data poitns and the % covaraince matrices as the covariance of X. CUSTOM allow % specifying the initial means, covariances, and prior % probabilities. % % InitMeans:: none % Specify the initial means (size(X,1)-by-NUMCLUSTERS matrix). % % InitPriors:: none % Specify the initial weights (a vector of dimension NUMCLUSTER). % % InitCovariances:: none % Specify the initial diagonal covariance matrices % % NumRepetitions:: 1 % Number of times to restart EM. The solution with maximum % loglikelihood is returned. % % CovarianceBound:: 10e-6 % Set the lower bound on the diagonal covariance values. % The bound can be either a scalar or a vector with one % entry per dimension. Using null bounds is possible, but % may yield degenerate solutions, including NaNs. % % Example:: % VL_GMM(X, 10, 'verbose', 'MaxNumIterations', 20) estimates the % mixture of 10 gaussians using at mosst 20 iterations. % % See also: GMMs, VL_KMEANS(), VL_HELP(). % Authors: David Novotny and Andrea Vedaldi % Copyright (C) 2013 David Novotny and Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/vl_harris.m0000644000175000017500000000733212237004736014613 0ustar dimadimafunction [H,details] = vl_harris(I,si,alpha) % VL_HARRIS Harris corner strength % H = VL_HARRIS(I,SI) computes the Harris corner strength of the image I % at ``integration'' scale SI. % % The Harris strength [1] of a pixel is a statistic of the gradient % of the image integrated in a neighborhood of that pixel. This % neighborhood is a Gaussian window of variance SI. % % In computing the Harris corner strength, there is a second scale % parameter, the ``derivation'' scale SD, which is the variance of % the Gaussian kernel used to pre-smooth the image I before computing % its gradient. SI and SD are independent parameters and VL_HARRIS(I,SI) % assumes that I is already smoothed at level SD. % % VL_HARRIS(I,SI) uses Noble's variation [2] of the Harris score. If % SIGMAP and SIGMAM are respectively the biggest and smallest % eigenvalue of the structure tensor at a pixel, the score is given % by (SIGMAP*SIGMAM) / (SIGMAP+SIGMAM/2). Let GAMMA = SIGMAM/SIGMAP % the ratio between the eigenvalues, which measures the degree of % anisotropy of the tensor and is always comprised in the range % [0,1]. Noble's score can be decomposed in two factors: the biggest % eigenvalue SIGMAP and the number % % RHO = (2 GAMMA) / (GAMMA + 1). % % RHO is another measure of isotropy that has value one for a % symmetric tensor and and zero for maximally anisotropic tensor. % [H,DETAILS] = VL_HARRIS(I,SIGMA) returns the additional structure % DETAILS with the following fields: % % DETAILS.SIGMAP % DETAILS.RHO % % VL_HARRIS(I,SI,ALPHA) uses Harris' original score [1], defined to be % SIGMAP*SIGMAM - ALPHA*(SIGMAP+SIGMAM)^2. This can be decomposed in % the factors SIGMAP^2 (note the square) and % % RHO = GAMMA - ALPHA (1+GAMMA)^2. % % Note that RHO is equal to -ALPHA for a maximally anisotropic % tensor. Typically ALPHA=0.04 and this is what is used by % VL_HARRIS(I,SI,[]). % % REMARK. The gradient of the image I, used to compute the structure % tensor, is computed using central differencies. This means that a % function line [+1,-1,+1,...] has null Harris' score. This is % generally assumed to be a sampling artifact, and might be % avoided by oversampling the image. % % EXAMPLE:: % To extacts Harris points from image I: % idx = vl_localmax( vl_harris( vl_imsmooth( I, sd ), si ) ) ; % [i,j] = ind2sub( size(I), idx ) % % REFERENCES:: % [1] C. Harris and M. Stephens, "A combined corner and edge detector," % in Proceedings of The Fourth Alvey Vision Conference, pp. 147-151, % 1988. % % [2] J. A. Noble, "Finding corners, "Image Vision Computing, vol. 6, % no. 2, pp. 121-128, 1988. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [Ix,Iy] = gradient(I) ; H11 = vl_imsmooth(Ix.*Ix, si) ; H12 = vl_imsmooth(Ix.*Iy, si) ; H22 = vl_imsmooth(Iy.*Iy, si) ; if nargin > 2 % Harris' original if isempty(alpha) alpha = 0.04 ; end H = (H11.*H22 - H12.^2) - alpha*(H11+H22).^2 ; else % Noble's variation H = 2 * (H11.*H22 - H12.^2)./(H11+H22+eps) ; end % ------------------------------------------------------------------- % Extract auxiliary info % ------------------------------------------------------------------- if nargout > 1 tr = H11 + H22 ; dt = H11.*H22 - H12.^2 ; Lm = 0.5 * (tr - sqrt(tr.^2 - 4*dt)); Lp = 0.5 * (tr + sqrt(tr.^2 - 4*dt)); Lm = real(Lm) ; Lp = real(Lp) ; gamma=sqrt(Lm./Lp) ; details.sigmap = Lp ; if nargin > 2 details.rho = gamma - alpha * (1+gamma).^2 ; else details.rho = 2*gamma ./ (1 + gamma) ; end end vlfeat/toolbox/imop/0000755000175000017500000000000012237042170013375 5ustar dimadimavlfeat/toolbox/imop/vl_imintegral.m0000644000175000017500000000131412237004736016414 0ustar dimadima% VL_IMINTEGRAL Compute integral image % J = VL_IMINTEGRAL(I) calculates the integral image J of the image % I. I must a matrix with DOUBLE, SINGLE, UINT32, or INT32 storage % class. J is given by % % J(i,j) = sum(I(1:i,1:j)). % % J has the same size as I and the same storage class. % % Example:: % The following identity holds: % VL_IMINTEGRAL(ONES(3)) = [ 1 2 3 ; % 2 4 6 ; % 3 6 9 ] % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/imop/vl_imreadbw.m0000644000175000017500000000121212237004736016050 0ustar dimadimafunction I = vl_imreadgray(file,varargin) % VL_IMREADGRAY Reads an image as gray-scale % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the % result to a gray scale image (DOUBLE storage class ranging in % [0,1]). % % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). % % See also: RGB2DOUBLE(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). I = imread(file) ; I = im2double(I) ; if(size(I,3) > 1) I = rgb2gray(I) ; end vlfeat/toolbox/imop/vl_rgb2xyz.m0000644000175000017500000000254212237004736015674 0ustar dimadimafunction J=vl_rgb2xyz(I,ws) % VL_RGB2XYZ Convert RGB color space to XYZ % J=VL_RGB2XYZ(I) converts the CIE RGB image I to the image J in % CIE XYZ format. CIE RGB has a white point of R=G=B=1.0 % % VL_RGB2XYZ(I,WS) uses the specified RGB working space WS. The % function supports the following RGB working spaces: % % * `CIE' E illuminant, gamma=2.2 % * `Adobe' D65 illuminant, gamma=2.2 % % The default workspace is CIE. % % See also: VL_XYZ2RGB(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [M,N,K] = size(I) ; if K~=3 error('I must be a MxNx3 array.') ; end I=im2double(I) ; if(nargin < 2) workspace = 'CIE' ; else workspace = ws ; end switch workspace case 'CIE' % CIE: E illuminant and 2.2 gamma A = [ 0.488718 0.176204 0.000000 0.310680 0.812985 0.0102048 0.200602 0.0108109 0.989795 ]' ; gamma = 2.2 ; case 'Adobe' % Adobe 1998: D65 illuminant and 2.2 gamma A = [ 0.576700 0.297361 0.0270328 0.185556 0.627355 0.0706879 0.188212 0.0752847 0.99124 ]' ; gamma = 2.2 ; end [M,N,K] = size(I) ; I = reshape(I.^gamma, M*N, K) ; J = A*I' ; J = reshape(J', M, N, K) ; vlfeat/toolbox/imop/vl_xyz2rgb.m0000644000175000017500000000221112237004736015665 0ustar dimadimafunction J=vl_xyz2rgb(I,ws) % VL_XYZ2RGB Convert XYZ to RGB % J = VL_XYZ2RGB(I) the XYZ image I in RGB format. % % VL_XYZ2RGB(I,WS) uses the RGB workspace WS. WS is a string in % % - CIE: E illuminant and 2.2 gamma % - Adobe: D65 illuminant and 2.2 gamma % % The default workspace is CIE. % % See also: VL_RGB2XYZ(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if(nargin < 2) workspace = 'CIE' ; else workspace = ws ; end switch workspace case 'CIE' % CIE: E illuminant and 2.2 gamma A = [ 0.488718 0.176204 0.000000 0.310680 0.812985 0.0102048 0.200602 0.0108109 0.989795 ]' ; gamma = 2.2 ; case 'Adobe' % Adobe 1998: D65 illuminant and 2.2 gamma A = [ 0.576700 0.297361 0.0270328 0.185556 0.627355 0.0706879 0.188212 0.0752847 0.99124 ]' ; gamma = 2.2 ; end [M,N,K] = size(I) ; I = reshape(I, M*N, K) ; J = inv(A)*I' ; J = reshape(J'.^(1/gamma), M, N, K) ; vlfeat/toolbox/imop/vl_imgrad.m0000644000175000017500000000304312237004736015525 0ustar dimadimafunction [Fx,Fy] = vl_imgrad(F,varargin) ; % VL_IMGRAD Image gradient % [Fx,Fy] = VL_GRAD(F) returns the finite differencies gradient of the % image F. The function uses central differencies and for all but % the boundaries pixels, for which it uses forward/backward % differencies as appropriate. % % VL_GRAD(F,'forward') uses only forward differencies (except on the % lower and right boundaries, where backward difference is used). % Similarly, VL_GRAD(F,'backward') uses only backward differencies. % % See also: GRADIENT(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). type = 'central' ; % forward, backward for k=1:2:length(varargin) switch varargin{k} case 'Type' type = varargin{k+1} ; otherwise error(['Parameter ''', varargin{k}, ''' unknown']) ; end end [M,N]=size( F ) ; switch type case 'central' Fx = [ F(:,2)-F(:,1) , ... 0.5*(F(:,3:end)-F(:,1:end-2)) , F(:,end)-F(:,end-1) ] ; Fy = [ F(2,:)-F(1,:) ; ... 0.5*(F(3:end,:)-F(1:end-2,:)) ; F(end,:)-F(end-1,:) ] ; case 'forward' Fx = [ F(:,2:end)-F(:,1:end-1) , F(:,end)-F(:,end-1) ] ; Fy = [ F(2:end,:)-F(1:end-1,:) ; F(end,:)-F(end-1,:) ] ; case 'backward' Fx = [ F(:,2)-F(:,1) , F(:,2:end)-F(:,1:end-1)] ; Fy = [ F(2,:)-F(1,:) ; F(2:end,:)-F(1:end-1,:)] ; otherwise error(['Difference type ''', type, ''' unknown']) ; end vlfeat/toolbox/imop/vl_imup.m0000644000175000017500000000142212237004736015233 0ustar dimadimafunction J = vl_imup(I) % VL_IMUP Upsample an image by two % J=VL_IMUP(I) doubles the resolution of the image I by using % bilinear interpolation. % % See also: VL_IMDOWN(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [M,N,K] = size(I) ; J = zeros(2*M,2*N,K) ; J(1:2:end,1:2:end,:) = I ; J(2:2:end,1:2:end,:) = 0.5*(I+[I(2:end,:,:);I(end,:,:)]) ; J(1:2:end,2:2:end,:) = 0.5*(I+[I(:,2:end,:),I(:,end,:)]) ; J(2:2:end,2:2:end,:) = ... 0.25*(... J(2:2:end,1:2:end-1,:)+... J(1:2:end-1,2:2:end,:)+... [J(2:2:end,3:2:end,:),J(2:2:end,end-1,:)]+... [J(3:2:end,2:2:end,:);J(end-1,2:2:end,:)]) ; vlfeat/toolbox/imop/vl_imwbackwardmx.c0000644000175000017500000001365712237004736017124 0ustar dimadima/* file: imwbackward.c ** author: Andrea Vedaldi ** description: Backward projection of an image. **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ /* TODO. * - Make a faster version for the uniform grid case. * - Rename variables. */ #include #include #include #include /** Matlab driver. **/ #define greater(a,b) (a) > (b) #define getM(arg) mxGetM(in[arg]) #define getN(arg) mxGetN(in[arg]) #define getPr(arg) mxGetPr(in[arg]) VL_INLINE int findNeighbor(double x, const double* X, int K) { int i = 0 ; int j = K - 1 ; int pivot = 0 ; double y = 0 ; if(x < X[i]) return i-1 ; if(x >= X[j]) return j ; while(i < j - 1) { pivot = (i+j) >> 1 ; y = X[pivot] ; /*mexPrintf("%d %d %d %f %f\n",i,j,pivot,x,y) ;*/ if(x < y) { j = pivot ; } else { i = pivot ; } } return i ; } void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { X=0,Y,I,iwXp,iwYp } ; enum { wI=0, wIx, wIy } ; int M, N, Mp, Np, ip, jp ; double *X_pt, *Y_pt, *I_pt, *iwXp_pt, *iwYp_pt, *wI_pt, *wIx_pt = 0, *wIy_pt = 0 ; double Xmin, Xmax, Ymin, Ymax ; const double NaN = mxGetNaN() ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 5) { vlmxError (vlmxErrNotEnoughInputArguments, NULL) ; } if (nin > 5) { vlmxError (vlmxErrTooManyOutputArguments, NULL) ; } if (nout > 3) { vlmxError (vlmxErrTooManyOutputArguments, NULL) ; } if (! vlmxIsPlainMatrix(in[I], -1, -1)) { vlmxError (vlmxErrInvalidArgument, "I is not a plain matrix.") ; } if (! vlmxIsPlainMatrix(in[iwXp], -1, -1)) { vlmxError(vlmxErrInvalidArgument, "iwXp is not a plain matrix.") ; } M = getM(I) ; N = getN(I) ; Mp = getM(iwXp) ; Np = getN(iwXp) ; if(!vlmxIsPlainMatrix(in[iwYp], Mp, Np)) { vlmxError(vlmxErrInvalidArgument, "iwXp is not a plain matrix of the same idmension of iwYp.") ; } if(!vlmxIsPlainVector(in[X],N) || !vlmxIsPlainVector(in[Y],M)) { vlmxError(vlmxErrInvalidArgument, "X and Y are not plain vectors with a length equal to the" " number of columns and rows of I.") ; } X_pt = getPr(X); Y_pt = getPr(Y) ; I_pt = getPr(I) ; iwXp_pt = getPr(iwXp) ; iwYp_pt = getPr(iwYp) ; /* Allocate the result. */ out[wI] = mxCreateDoubleMatrix(Mp, Np, mxREAL) ; wI_pt = mxGetPr(out[wI]) ; if (nout > 1) { out[wIx] = mxCreateDoubleMatrix(Mp, Np, mxREAL) ; out[wIy] = mxCreateDoubleMatrix(Mp, Np, mxREAL) ; wIx_pt = mxGetPr (out[wIx]) ; wIy_pt = mxGetPr (out[wIy]) ; } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ Xmin = X_pt [0] ; Xmax = X_pt [N - 1] ; Ymin = Y_pt [0] ; Ymax = Y_pt [M - 1] ; if (nout <= 1) { /* optimized for only image output */ for(jp = 0 ; jp < Np ; ++jp) { for(ip = 0 ; ip < Mp ; ++ip) { /* Search for the four neighbors of the backprojected point. */ double x = *iwXp_pt++ ; double y = *iwYp_pt++ ; double z = NaN ; /* This messy code allows the identity transformation * to be processed as expected. */ if(x >= Xmin && x <= Xmax && y >= Ymin && y <= Ymax) { int j = findNeighbor(x, X_pt, N) ; int i = findNeighbor(y, Y_pt, M) ; double* pt = I_pt + j*M + i ; /* Weights. */ double x0 = X_pt[j] ; double x1 = (j < N-1) ? X_pt[j+1] : x0 + 1; double y0 = Y_pt[i] ; double y1 = (i < M-1) ? Y_pt[i+1] : y0 + 1; double wx = (x-x0)/(x1-x0) ; double wy = (y-y0)/(y1-y0) ; /* Load all possible neighbors. */ double z00 = 0.0 ; double z10 = 0.0 ; double z01 = 0.0 ; double z11 = 0.0 ; if(j > -1) { if(i > -1 ) z00 = *pt ; pt++ ; if(i < M-1) z10 = *pt ; } else { pt++ ; } pt += M - 1; if(j < N-1) { if(i > -1 ) z01 = *pt ; pt++ ; if(i < M-1) z11 = *pt ; } /* Bilinear interpolation. */ z = (1 - wy) * ((1-wx) * z00 + wx * z01) + ( wy) * ((1-wx) * z10 + wx * z11) ; } *(wI_pt + jp*Mp + ip) = z ; } } } /* do also the derivatives */ else { /* optimized for only image output */ for(jp = 0 ; jp < Np ; ++jp) { for(ip = 0 ; ip < Mp ; ++ip) { /* Search for the four neighbors of the backprojected point. */ double x = *iwXp_pt++ ; double y = *iwYp_pt++ ; double z = NaN, zx = NaN, zy = NaN ; /* This messy code allows the identity transformation * to be processed as expected. */ if(x >= Xmin && x <= Xmax && y >= Ymin && y <= Ymax) { int j = findNeighbor(x, X_pt, N) ; int i = findNeighbor(y, Y_pt, M) ; double* pt = I_pt + j*M + i ; /* Weights. */ double x0 = X_pt[j] ; double x1 = X_pt[j+1] ; double y0 = Y_pt[i] ; double y1 = Y_pt[i+1] ; double wx = (x-x0)/(x1-x0) ; double wy = (y-y0)/(y1-y0) ; /* Load all possible neighbors. */ double z00 = 0.0 ; double z10 = 0.0 ; double z01 = 0.0 ; double z11 = 0.0 ; if(j > -1) { if(i > -1 ) z00 = *pt ; pt++ ; if(i < M-1) z10 = *pt ; } else { pt++ ; } pt += M - 1; if(j < N-1) { if(i > -1 ) z01 = *pt ; pt++ ; if(i < M-1) z11 = *pt ; } /* Bilinear interpolation. */ z = (1-wy)*( (1-wx) * z00 + wx * z01) + wy*( (1-wx) * z10 + wx * z11) ; zx = (1-wy) * (z01 - z00) + wy * (z11 - z10) ; zy = (1-wx) * (z10 - z00) + wx * (z11 - z01) ; } *(wI_pt + jp*Mp + ip) = z ; *(wIx_pt + jp*Mp + ip) = zx ; *(wIy_pt + jp*Mp + ip) = zy ; } } } } vlfeat/toolbox/imop/vl_imdisttf.m0000644000175000017500000000211412237004736016103 0ustar dimadima% VL_IMDISTTF Image distance transform % DT = VL_IMDISTTF(I) computes the distance transform of image I. % The distance transform is defined as % % DT(x,y) = min_{u,v} I(u,v) + (u - x)^2 + (v - y)^2. % % [D, INDEXES] = VL_IMDISTTF(...) returns a matrix INDEXES that % contains for each pixel (x,y) the index of the pixel (u,v) which % is the minimizer of the distance transform objective. % % VL_IMDSITTF(I, PARAM) uses differenet parameters for the % transform: % % DT(u,v) = min_{u,v} I(u,v) + PARAM(1) (u - x - PARAM(2))^2 % + PARAM(3) (v - y - PARAM(4))^2 % % The function uses the fast algorithm from [1]. % % REFERENCES:: % [1] P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms % of sampled functions. Technical report, Cornell University, 2004. % % See also: VL_HELP(). % Author:: Andrea Vedaldi % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/imop/vl_tps.m0000644000175000017500000000253112237004736015071 0ustar dimadimafunction [phi,S] = vl_tps(x1,x2,Y) % VL_TPS Compute the thin-plate spline basis % PHI=VL_TPS(X1,X2,Y) returns the basis PHI of a thin-plate spline % (TPS) defined on the domain X1,X2 with control points Y. % % X1 and X2 are MxN matrices specifying the grid vertices. When % warping images, these usually correspond to image pixels. % % Y is a 2xK matrix specifying the control points, one per % column. Ofthen Y is a subset of the domain X1,X2, but this is not % required. % % PHI is a (K+3)xNxM matrix, with one layer per basis element. Each % basis element is a function of the domain X1,X2. % % [PHI,S] = VL_TPS(X1,X2,Y) additionally returns the stiffness matrix S % of the TPS. % % See also: VL_WTPS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). X = [x1(:)';x2(:)'] ; K = size(Y,2) ; Q = size(X,2) ; U = vl_tpsu(Y,Y) ; L = [[ones(1,K); Y], zeros(3) ; U, ones(K,1), Y'] ; invL = inv(L) ; tmp = vl_tpsu(Y,X) ; phi = invL * [ ones(1,Q) ; X(1,:) ; X(2,:) ; tmp ] ; [M,N] = size(x1) ; phi = reshape(phi,K+3,M,N) ; if nargout > 1 % See Bookstein; note that here the terms are re-arranged a bit invLn = invL(1:K, end-K+1:end) ; S = invLn * U * invLn ; end vlfeat/toolbox/imop/vl_impattern.m0000644000175000017500000001533412237004745016273 0ustar dimadimafunction im = vl_impattern(varargin) % VL_IMPATTERN Generate an image from a stock pattern % IM=VLPATTERN(NAME) returns an instance of the specified % pattern. These stock patterns are useful for testing algoirthms. % % All generated patterns are returned as an image of class % DOUBLE. Both gray-scale and colour images have range in [0,1]. % % VL_IMPATTERN() without arguments shows a gallery of the stock % patterns. The following patterns are supported: % % Wedge:: % The image of a wedge. % % Cone:: % The image of a cone. % % SmoothChecker:: % A checkerboard with Gaussian filtering on top. Use the % option-value pair 'sigma', SIGMA to specify the standard % deviation of the smoothing and the pair 'step', STEP to specfity % the checker size in pixels. % % ThreeDotsSquare:: % A pattern with three small dots and two squares. % % UniformNoise:: % Random i.i.d. noise. % % Blobs: % Gaussian blobs of various sizes and anisotropies. % % Blobs1: % Gaussian blobs of various orientations and anisotropies. % % Blob: % One Gaussian blob. Use the option-value pairs 'sigma', % 'orientation', and 'anisotropy' to specify the respective % parameters. 'sigma' is the scalar standard deviation of an % isotropic blob (the image domain is the rectangle % [-1,1]^2). 'orientation' is the clockwise rotation (as the Y % axis points downards). 'anisotropy' (>= 1) is the ratio of the % the largest over the smallest axis of the blob (the smallest % axis length is set by 'sigma'). Set 'cut' to TRUE to cut half % half of the blob. % % A stock image:: % Any of 'box', 'roofs1', 'roofs2', 'river1', 'river2', 'spotted'. % % All pattern accept a SIZE parameter [WIDTH,HEIGHT]. For all but % the stock images, the default size is [128,128]. % Author: Andrea Vedaldi % Copyright (C) 2012 Andrea Vedaldi. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin > 0 pattern=varargin{1} ; varargin=varargin(2:end) ; else pattern = 'gallery' ; end patterns = {'wedge','cone','smoothChecker','threeDotsSquare', ... 'blob', 'blobs', 'blobs1', ... 'box', 'roofs1', 'roofs2', 'river1', 'river2'} ; % spooling switch lower(pattern) case 'wedge', im = wedge(varargin) ; case 'cone', im = cone(varargin) ; case 'smoothchecker', im = smoothChecker(varargin) ; case 'threedotssquare', im = threeDotSquare(varargin) ; case 'uniformnoise', im = uniformNoise(varargin) ; case 'blob', im = blob(varargin) ; case 'blobs', im = blobs(varargin) ; case 'blobs1', im = blobs1(varargin) ; case {'box','roofs1','roofs2','river1','river2','spots'} im = stockImage(pattern, varargin) ; case 'gallery' clf ; num = numel(patterns) ; for p = 1:num vl_tightsubplot(num,p,'box','outer') ; imagesc(vl_impattern(patterns{p}),[0 1]) ; axis image off ; title(patterns{p}) ; end colormap gray ; return ; otherwise error('Unknown patter ''%s''.', pattern) ; end if nargout == 0 clf ; imagesc(im) ; hold on ; colormap gray ; axis image off ; title(pattern) ; clear im ; end function [u,v,opts,args] = commonOpts(args) opts.size = [128 128] ; [opts,args] = vl_argparse(opts, args) ; ur = linspace(-1,1,opts.size(2)) ; vr = linspace(-1,1,opts.size(1)) ; [u,v] = meshgrid(ur,vr); function im = wedge(args) [u,v,opts,args] = commonOpts(args) ; im = abs(u) + abs(v) > (1/4) ; im(v < 0) = 0 ; function im = cone(args) [u,v,opts,args] = commonOpts(args) ; im = sqrt(u.^2+v.^2) ; im = im / max(im(:)) ; function im = smoothChecker(args) opts.size = [128 128] ; opts.step = 16 ; opts.sigma = 2 ; opts = vl_argparse(opts, args) ; [u,v] = meshgrid(0:opts.size(1)-1, 0:opts.size(2)-1) ; im = xor((mod(u,opts.step*2) < opts.step),... (mod(v,opts.step*2) < opts.step)) ; im = double(im) ; im = vl_imsmooth(im, opts.sigma) ; function im = threeDotSquare(args) [u,v,opts,args] = commonOpts(args) ; im = ones(size(u)) ; im(-2/3 0) ; end function im = blobs1(args) [u,v,opts,args] = commonOpts(args) ; opts.number = 5 ; opts.sigma = [] ; opts = vl_argparse(opts, args) ; im = zeros(size(u)) ; square = 2 / opts.number ; num = opts.number ; if isempty(opts.sigma) sigma = 1/6 * square ; else sigma = opts.sigma * square ; end rotations = linspace(0,pi,num+1) ; rotations(end) = [] ; skews = linspace(1,2,num) ; for i=1:num for j=1:num cy = (i-1) * square + square/2 - 1; cx = (j-1) * square + square/2 - 1; th = rotations(i) ; R = [cos(th) -sin(th); sin(th) cos(th)] ; A = sigma * R * diag([1 1/skews(j)]) ; C = inv(A*A') ; x = u - cx ; y = v - cy ; im = im + exp(-0.5 *(x.*x*C(1,1) + y.*y*C(2,2) + 2*x.*y*C(1,2))) ; end end im = im / max(im(:)) ; function im = uniformNoise(args) opts.size = [128 128] ; opts.seed = 1 ; opts = vl_argparse(opts, args) ; state = vl_twister('state') ; vl_twister('state',opts.seed) ; im = vl_twister(opts.size([2 1])) ; vl_twister('state',state) ; function im = stockImage(pattern,args) opts.size = [] ; opts = vl_argparse(opts, args) ; switch pattern case 'river1', path='river1.jpg' ; case 'river2', path='river2.jpg' ; case 'roofs1', path='roofs1.jpg' ; case 'roofs2', path='roofs2.jpg' ; case 'box', path='box.pgm' ; case 'spots', path='spots.jpg' ; end im = imread(fullfile(vl_root,'data',path)) ; im = im2double(im) ; if ~isempty(opts.size) im = imresize(im, opts.size) ; im = max(im,0) ; im = min(im,1) ; end vlfeat/toolbox/imop/vl_xyz2lab.m0000644000175000017500000000304212237004736015654 0ustar dimadimafunction J=vl_xyz2lab(I,il) % VL_XYZ2LAB Convert XYZ color space to LAB % J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format. % % VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55, % D65, D75, D93. The default illuminatn is E. % % See also: VL_XYZ2LUV(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 2 il='E' ; end switch lower(il) case 'a' xw = 0.4476 ; yw = 0.4074 ; case 'b' xw = 0.3324 ; yw = 0.3474 ; case 'c' xw = 0.3101 ; yw = 0.3162 ; case 'e' xw = 1/3 ; yw = 1/3 ; case 'd50' xw = 0.3457 ; yw = 0.3585 ; case 'd55' xw = 0.3324 ; yw = 0.3474 ; case 'd65' xw = 0.312713 ; yw = 0.329016 ; case 'd75' xw = 0.299 ; yw = 0.3149 ; case 'd93' xw = 0.2848 ; yw = 0.2932 ; end J=zeros(size(I)) ; % Reference white Yw = 1.0 ; Xw = xw/yw ; Zw = (1-xw-yw)/yw * Yw ; % XYZ components X = I(:,:,1) ; Y = I(:,:,2) ; Z = I(:,:,3) ; x = X/Xw ; y = Y/Yw ; z = Z/Zw ; L = 116 * f(y) - 16 ; a = 500*(f(x) - f(y)) ; b = 200*(f(y) - f(z)) ; J = cat(3,L,a,b) ; % -------------------------------------------------------------------- function b=f(a) % -------------------------------------------------------------------- sp = find(a > 0.00856) ; sm = find(a <= 0.00856) ; k = 903.3 ; b=zeros(size(a)) ; b(sp) = a(sp).^(1/3) ; b(sm) = (k*a(sm) + 16)/116 ; vlfeat/toolbox/imop/vl_imarray.m0000644000175000017500000000627112237004736015734 0ustar dimadimafunction J = vl_imarray(A,varargin) % VL_IMARRAY Flattens image array % J=VL_IMARRAY(A) creates a mosaic J from the array of images A. A % can be either a M*N*K array, storing one gray-scale image per % slice, or a M*N*3*K or M*N*K*3 array, storing a true color RGB % image per slice. The function returns an image J which is a tiling % of the images in the array. Tiles are filled from left to right % and top to bottom. % % VL_IMARRAY(...) displays the image J rather than returning it. % % VL_IMARRAY() accepts the following options: % % Spacing:: 0 % Separate the images by a border of the specified width (the % border is assigned 0 value, which usually corresponds to black). % % Layout:: empty % Specify a vector [TM TN] with the number of rows and columns of % the tiling. If equal to [] the layout is computed automatically. % % Movie:: false % Display or return a movie instead of generating a tiling. % % CMap:: [] % Specify a colormap to construct a movie when the input is an % indexed image array. If not specified, MATLAB default colormap % is used. % % Reverse:: true % Start filling the mosaic tiles from the bottom rather than from % the top. % % See also:VL_IMARRAYSC(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.reverse = false ; opts.spacing = 0 ; opts.layout = [] ; opts.movie = false ; opts.cmap = [] ; opts = vl_argparse(opts, varargin) ; swap3 = false ; % retrieve image dimensions if ndims(A) <= 3 numChannels = 1 ; [height,width,numImages] = size(A) ; else if ndims(A) == 4 && size(A,3) == 3 [height,width,numChannels,numImages] = size(A) ; elseif ndims(A) == 4 && size(A,4) == 3 ; swap3 = true ; [height,width,numImages,numChannels] = size(A) ; else error('A is neither M*N*K, nor M*N*3*K, nor M*N*K*3.') ; end end % compute layout if isempty(opts.layout) N = ceil(sqrt(numImages)) ; M = ceil(numImages / N) ; else M = opts.layout(1) ; N = opts.layout(2) ; numImages = min(numImages, M*N) ; end % make storage for composite image if ~ opts.movie cdata = zeros(height * M + opts.spacing * (M-1), ... width * N + opts.spacing * (N-1), ... numChannels, ... class(A)) ; end % add one image per time for k = 1:numImages % retrieve k-th image if numChannels == 1 tmp = A(:,:,k) ; else if swap3 tmp = A(:,:,k,:) ; else tmp = A(:,:,:,k) ; end end if ~ opts.movie p = k - 1 ; i = floor(p/N) ; if opts.reverse i = M - 1 - i ; end j = mod(p,N) ; ir = i * (height + opts.spacing) + (1:height) ; jr = j * (width + opts.spacing) + (1:width) ; cdata(ir,jr,:) = tmp ; else if isempty(opts.cmap) opts.cmap = get(0, 'DefaultFigureColormap') ; end MOV(k) = im2frame(tmp, opts.cmap) ; end end if ~ opts.movie if nargout == 0 image(cdata) ; colormap(opts.cmap) ; return ; else J = cdata ; end else if nargout == 0 movie(MOV) ; return ; else J = MOV ; end end vlfeat/toolbox/imop/vl_tpsumx.c0000644000175000017500000000371612237004736015617 0ustar dimadima/* file: tpsumx.c ** author: Andrea Vedaldi ** description: vl_tpsumx - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include /** Matlab driver. **/ #define getM(arg) mxGetM(in[arg]) #define getN(arg) mxGetN(in[arg]) #define getPr(arg) mxGetPr(in[arg]) void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum { X=0,Y } ; enum { U } ; int NP, NCP ; int i,j ; double *X_pt, *Y_pt, *U_pt ; #undef small const double small = 2.220446049250313e-16 ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin != 2) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } else if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if(!vlmxIsMatrix(in[X], 2, -1)) { mexErrMsgTxt("X must be a 2xNP real matrix") ; } if(!vlmxIsMatrix(in[Y], 2, -1)) { mexErrMsgTxt("Y must be a 2xNCP real matrix") ; } NP = getN(X) ; NCP = getN(Y) ; X_pt = getPr(X); Y_pt = getPr(Y) ; /* Allocate the result. */ out[U] = mxCreateDoubleMatrix(NP, NCP, mxREAL) ; U_pt = mxGetPr(out[U]) ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ for(j = 0 ; j < NCP ; ++j) { double xcp = *Y_pt++ ; double ycp = *Y_pt++ ; for(i = 0 ; i < NP ; ++i) { double dx = *X_pt++ - xcp ; double dy = *X_pt++ - ycp ; double r2 = dx*dx + dy*dy ; *U_pt++ = (r2 <= small) ? 0 : r2 * log (r2) ; } X_pt -= 2*NP ; } } vlfeat/toolbox/imop/vl_tpsu.m0000644000175000017500000000333312237004736015257 0ustar dimadimafunction [U,dU,delta]=vl_tpsu(X,Y) % VL_TPSU Compute the U matrix of a thin-plate spline transformation % U=VL_TPSU(X,Y) returns the matrix % % [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ] % [ ] % [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ] % % where X is a 2xM matrix and Y a 2xN matrix of points and U(r) is % the opposite -r^2 log(r^2) of the radial basis function of the % thin plate spline specified by X and Y. % % [U,dU]=vl_tpsu(x,y) returns the derivatives of the columns of U with % respect to the parameters Y. The derivatives are arranged in a % Mx2xN array, one layer per column of U. % % See also: VL_TPS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if exist('tpsumx') U = tpsumx(X,Y) ; else M=size(X,2) ; N=size(Y,2) ; % Faster than repmat, but still fairly slow r2 = ... (X( ones(N,1), :)' - Y( ones(1,M), :)).^2 + ... (X( 1+ones(N,1), :)' - Y(1+ones(1,M), :)).^2 ; U = - rb(r2) ; end if nargout > 1 M=size(X,2) ; N=size(Y,2) ; dx = X( ones(N,1), :)' - Y( ones(1,M), :) ; dy = X(1+ones(N,1), :)' - Y(1+ones(1,M), :) ; r2 = (dx.^2 + dy.^2) ; r = sqrt(r2) ; coeff = drb(r)./(r+eps) ; dU = reshape( [coeff .* dx ; coeff .* dy], M, 2, N) ; end % The radial basis function function y = rb(r2) y = zeros(size(r2)) ; sel = find(r2 ~= 0) ; y(sel) = - r2(sel) .* log(r2(sel)) ; % The derivative of the radial basis function function y = drb(r) y = zeros(size(r)) ; sel = find(r ~= 0) ; y(sel) = - 4 * r(sel) .* log(r(sel)) - 2 * r(sel) ; vlfeat/toolbox/imop/vl_imarraysc.m0000644000175000017500000000570112237004736016257 0ustar dimadimafunction J = vl_imarraysc(A, varargin) % VL_IMARRAYSC Scale and flattens image array % J=VL_IMARRAYSC(A) constructs an image mosaic similar to % J=VL_IMARRAY(A), but it rescales the range of each image in the % array. If A is an array of grayscale images, J will index all the % colors in the current colormap; if A is a true color image, J will % span the range [0,1]. % % If A is of an integer class, J will be of class single SINGLE class. % % VL_IMARRAYSC(...) displays the image J rather than returning it. % % VL_IMARRAYSC() accepts the options of VL_IMARRAY() and: % % CLim:: [] % Rescale the specified range of values rather than the actual % range of each image. % % Uniform:: [false] % Rescale the range of all the images together, rather than on % an image-by-image basis. % % CMap:: [] % Use the specified color map as a reference rather than the % current or default one. % % Algorithm:: % CLim is seet to the image range [m, M], where m is the minimum % value of an image and M is the maximum. The image range CLim is % then affinely mapped to the integers from 1 to C, where C is the % number of colors in the colormap, or to the range [0,1] for true % color images. The mapping is done so that the first color is % assigned the first subinterval of length C of the range [m,M] % and so on. % % See also: VL_IMARRAY(), VL_HELP(), IMAGE(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.clim = [] ; opts.cmap = colormap ; opts.uniform = false ; [opts, varargin] = vl_argparse(opts, varargin) ; numDimensions = ndims(A) ; if numDimensions <= 3 numChannels = 1 ; numImages = size(A,3) ; numLevels = size(opts.cmap,1) ; elseif numDimensions == 4 numChannels = size(A,3) ; numImages = size(A,4) ; numLevels = 1 ; if numChannels ~= 3 error('A has more than three dimensions and the third one is not equal to three.') ; end else error('A has neither 2, 3 or 4 dimensions.') ; end if opts.uniform & ~isempty(opts.clim) error('UNIFORM cannot be used in combination with CLIM.') ; end if isinteger(A) A = single(A) ; end if opts.uniform && numDimensions < 4 opts.clim = [min(A(:)) max(A(:))] ; end for k = 1:numImages if isempty(opts.clim) if numChannels == 1 tmp = A(:,:,k) ; else tmp = A(:,:,:,k) ; end dataMin = min(tmp(:)) ; dataMax = max(tmp(:)) ; else dataMin = opts.clim(1) ; dataMax = opts.clim(2) ; end a = numLevels / (dataMax - dataMin + eps) ; b = - dataMin * a ; if numChannels == 1 A(:,:,k) = max(min(floor(a * A(:,:,k) + b + 1),numLevels),1) ; else A(:,:,:,k) = max(min(a * A(:,:,:,k) + b,numLevels),0) ; end end if nargout == 0 vl_imarray(A,varargin{:}, 'cmap', opts.cmap) ; else J = vl_imarray(A,varargin{:}, 'cmap', opts.cmap) ; end vlfeat/toolbox/imop/vl_imsmooth.m0000644000175000017500000000227712237004736016131 0ustar dimadima% VL_IMSMOOTH Smooth image % J = VL_IMSMOOTH(I,SIGMA) convolves the image I by an isotropic % Gaussian kernel of standard deviation SIGMA. I must be an array % of doubles. IF the array is three dimensional, the third dimension % is assumed to span different channels (e.g. R,G,B). In this case, % each channel is convolved independently. % % VL_IMSMOOTH() accepts the following options: % % Kernel:: % Selects between GAUSSIAN and TRIANGULAR kernels. The triangular % kernel support has 2*SIGMA-1 sampels. Kernels have unit mass. % % Padding:: % Selects between ZERO or CONTINUITY padding method to handle the % image boundaries. ZERO extends the input image with zeroes % around the border, and CONTINUITY extends the image with % constant pixels. % % Step:: % Sets the subsampling step. A subsampling step of STEP pixels % causes J(1:STEPS:end, 1:STEPS:end, :) to be computed. This is % useful to downsample the image. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). vlfeat/toolbox/imop/vl_imwbackward.m0000644000175000017500000000347612237004736016567 0ustar dimadimafunction [wI,wIx,wIy] = vl_imwbackward(varargin) % VL_IMWBACKWARD Image backward warping % J = VL_IMWBACKWARD(I, X, Y) returns the values of image I at % locations X,Y. X and Y are real matrices of arbitrary but % identical dimensions. I is bilinearly interpolated between samples % and extended with NaNs to the whole real plane. % % [J,JX,JY] = VL_IMWBACKWARD(...) returns the warped derivatives JX and % JY too. % % By default, VL_IMWBACKWARD() assumes that the image I uses the standard % coordinate system. VL_IMWBACKWARD(XR,YR,I,X,Y) assumes instead that I % is defined on a rectangular grid specified by the vectors XR and % YR. % % VL_IMWBACKWARD() is less general than the MATLAB native function % INTERP2(), but it is significantly faster. % % See also: IMWFORWARD(), INTERP2(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 5 I = varargin{1} ; [M,N,K] = size(I) ; xr = 1:N ; yr = 1:M ; varargin = { varargin{2:end} } ; else xr = varargin{1} ; yr = varargin{2} ; I = varargin{3} ; [M,N,K] = size(I) ; varargin = { varargin{4:end} } ; end if K == 1 if nargout == 1 wI = vl_imwbackwardmx(xr, yr, I, varargin{:}) ; else [wI,wIx,wIy] = vl_imwbackwardmx(xr, yr, I, varargin{:}) ; end else [M,N] = size(varargin{1}) ; if nargout == 1 wI = zeros(M,N,K) ; for k=1:K wI(:,:,k) = vl_imwbackwardmx(xr, yr, squeeze(I(:,:,k)), varargin{:}) ; end else wI = zeros(M,N,K) ; wIx = zeros(M,N,K) ; wIy = zeros(M,N,K) ; for k=1:K [tmp1, tmp2, tmp3] = vl_imwbackwardmx(xr, yr, squeeze(I(:,:,k)), varargin{:}) ; wI(:,:,k) = tmp1; wIx(:,:,k) = tmp2; wIy(:,:,k) = tmp3 ; end end end vlfeat/toolbox/imop/vl_dwaffine.m0000644000175000017500000000133012237004736016042 0ustar dimadimafunction [dWx,dWy] = vl_dwaffine(x,y) % VL_DWAFFINE Derivative of an affine warp % [DWX,DWY]=VL_DWAFFINE(X,Y) returns the derivative of the 2-D affine % warp [WX; WY] = [A T] [X; Y] with respect to the parameters A,T % computed at points X,Y. % % See also: VL_WAFFINE(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % dW = [ kron(x',I) I ] % | % = [ x1 0 x2 0 1 0 ] % [ 0 x1 0 x2 0 1 ] z = zeros(length(x(:)),1) ; o = ones(length(x(:)),1) ; dWx = [ x(:) z y(:) z o z ] ; dWy = [ z x(:) z y(:) z o ] ; vlfeat/toolbox/imop/vl_waffine.m0000644000175000017500000000332212237004736015701 0ustar dimadimafunction varargout = vl_waffine(A,T,varargin) % VL_WAFFINE Apply affine transformation to points % Y = VL_WAFFINE(A,T,X) applies the affine transformatio (A,T) to points % X. X contains one point per column. % % [Y1,Y2,...] = VL_WAFFINE(A,T,X1,X2,...) applies the affine % transformation (A,T) to the points (X1,X2,...). Each array % X1,X2,... contains one of the coordinates of the points. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if numel(varargin)==1 X = varargin{1} ; [n,k] = size(X); if n == 2 Y(1,:) = A(1,1)*X(1,:) + A(1,2)*X(2,:) + T(1) ; Y(2,:) = A(2,1)*X(1,:) + A(2,2)*X(2,:) + T(2) ; elseif n == 3 Y(1,:) = A(1,1)*X(1,:) + A(1,2)*X(2,:) + A(1,3) * X(3,:) + T(1) ; Y(2,:) = A(2,1)*X(1,:) + A(2,2)*X(2,:) + A(2,3) * X(3,:) + T(2) ; Y(3,:) = A(3,1)*X(1,:) + A(3,2)*X(2,:) + A(3,3) * X(3,:) + T(3) ; else Y = A*X + repmat(T,1,k) ; end varargout{1} = Y ; else n = numel(varargin) ; if n == 2 varargout{1} = A(1,1)*varargin{1} + A(1,2)*varargin{2} + T(1) ; varargout{2} = A(2,1)*varargin{1} + A(2,2)*varargin{2} + T(2) ; elseif n == 3 varargout{1} = A(1,1)*varargin{1} + A(1,2)*varargin{2} + A(1,3)*varargin{3} + T(1) ; varargout{2} = A(2,1)*varargin{1} + A(2,2)*varargin{2} + A(2,3)*varargin{3} + T(2) ; varargout{3} = A(3,1)*varargin{1} + A(3,2)*varargin{2} + A(3,3)*varargin{3} + T(3) ; else for i=1:n varargout{i} = T(i) * ones(size(varargin{1})) ; for j=1:n varargout{i} = varargout{i} + A(i,j)*varargin{j} ; end end end end vlfeat/toolbox/imop/vl_imdisttf.c0000644000175000017500000000765012237004736016103 0ustar dimadima/** @internal ** @file vl_imdisttf.c ** @brief vl_imdisttf - MEX definition ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { vl_size M, N ; enum {IN_I = 0, IN_PARAM, IN_END} ; enum {OUT_DT = 0, OUT_INDEXES} ; vl_uindex * indexes = NULL ; mxClassID classId ; double const defaultParam [] = {1.0, 0.0, 1.0, 0.0} ; double const * param = defaultParam ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 1) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nin > 2) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nout > 2) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } classId = mxGetClassID(IN(I)) ; if (! vlmxIsMatrix(IN(I), -1, -1) || (classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS)) { vlmxError(vlmxErrInvalidArgument, "I is not a SINGLE or DOUBLE matrix.") ; } if (nin == 2) { if (! vlmxIsPlainVector(IN(PARAM), 4)) { vlmxError(vlmxErrInvalidArgument, "PARAM is not a 4-dimensional vector.") ; } param = mxGetPr (IN(PARAM)) ; if (param[0] < 0.0 || param[2] < 0.0) { vlmxError(vlmxErrInvalidArgument, "Either PARAM[0] or PARAM[2] is negative.") ; } } M = mxGetM (IN(I)) ; N = mxGetN (IN(I)) ; OUT(DT) = mxCreateNumericMatrix (M, N, classId, mxREAL) ; if (nout > 1) { vl_uindex i ; OUT(INDEXES) = mxCreateDoubleMatrix (M, N, mxREAL) ; indexes = mxMalloc(sizeof(vl_uindex) * M * N) ; for (i = 0 ; i < M * N ; ++i) indexes[i] = i + 1 ; } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ switch (classId) { case mxSINGLE_CLASS: vl_image_distance_transform_f((float const*)mxGetData(IN(I)), M, N, 1, M, (float*)mxGetPr(OUT(DT)), indexes, param[2], param[3]) ; vl_image_distance_transform_f((float*)mxGetPr(OUT(DT)), N, M, M, 1, (float*)mxGetPr(OUT(DT)), indexes, param[0], param[1]) ; break ; case mxDOUBLE_CLASS: vl_image_distance_transform_d((double const*)mxGetData(IN(I)), M, N, 1, M, (double*)mxGetPr(OUT(DT)), indexes, param[2], param[3]) ; vl_image_distance_transform_d((double*)mxGetPr(OUT(DT)), N, M, M, 1, (double*)mxGetPr(OUT(DT)), indexes, param[0], param[1]) ; break; default: abort() ; } if (indexes) { vl_uindex i ; double * pt = mxGetPr(OUT(INDEXES)) ; for (i = 0 ; i < M * N ; ++i) pt[i] = indexes[i] ; mxFree(indexes) ; } } vlfeat/toolbox/imop/vl_imwhiten.m0000644000175000017500000000242012237004736016104 0ustar dimadimafunction J=vl_imwhiten(I,alpha,cutoff) % VL_IMWHITEN Whiten image % J = VL_IMWHITEN(I,ALPHA) approximatively whitens the power spectrum % of the natural image I. The algorithm assumes that the modulus of % the spectrum decays as 1/f^ALPHA (f is the frequency). % % VL_IMWHITEN(I) uses ALPHA=1 (a typical value for natural images). % % VL_IMWHITEN(I,ALPHA,CUTOFF) also applies a low-pass filter with % cutoff frequency equal to CUTOFF x FN, where FN is the Nyquist % frequency (half of the sampling frequency). % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if ~exist('alpha'), alpha = 1 ; end if ~exist('cutoff'), cutoff = [] ; end [M,N]=size(I) ; % Frequency domain fn = 0.5 ; % Nyquist freq (=1/2T, T=1) fx_range=linspace(-fn, fn, N) ; fy_range=linspace(-fn, fn, M) ; [fx fy]=meshgrid(fx_range, fy_range) ; % Whitening filter rho=sqrt(fx.*fx+fy.*fy); filt=rho.^alpha ; % Low-pass filter if ~isempty(cutoff) fcut = cutoff * fn ; filt = filt .* exp(-(rho/fcut).^4); %filt = filt .* exp( - 0.5 * (rho / fcut) .^ 2); end % Apply filter J = real(ifft2(fft2(I).*fftshift(filt))) ; vlfeat/toolbox/imop/vl_xyz2luv.m0000644000175000017500000000313012237004736015722 0ustar dimadimafunction J=vl_xyz2luv(I,il) % VL_XYZ2LUV Convert XYZ color space to LUV % J = VL_XYZ2LUV(I) converts the image I in XYZ format to the image J % in Luv format. % % J = VL_XYZ2LUV(I,IL) uses the specified illuminant. The following % illuminant are supported: A, B, C, E, D50, D55, D65, D75, D93. IL % is the name of the illuminant. % % See also: VL_XYZ2LAB(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if nargin < 2 il='E' ; end switch lower(il) case 'a' xw = 0.4476 ; yw = 0.4074 ; case 'b' xw = 0.3324 ; yw = 0.3474 ; case 'c' xw = 0.3101 ; yw = 0.3162 ; case 'e' xw = 1/3 ; yw = 1/3 ; case 'd50' xw = 0.3457 ; yw = 0.3585 ; case 'd55' xw = 0.3324 ; yw = 0.3474 ; case 'd65' xw = 0.312713 ; yw = 0.329016 ; case 'd75' xw = 0.299 ; yw = 0.3149 ; case 'd93' xw = 0.2848 ; yw = 0.2932 ; end J=zeros(size(I)) ; % Reference white xw = 1/3 ; yw = 1/3 ; Yw = 1.0 ; Xw = xw/yw ; Zw = (1-xw-yw)/yw * Yw ; J=zeros(size(I)) ; X = I(:,:,1) ; Y = I(:,:,2) ; Z = I(:,:,3) ; upw = 4*Xw / (Xw + 15*Yw + 3*Zw) ; vpw = 9*Yw / (Xw + 15*Yw + 3*Zw) ; up = 4*X ./ (X + 15*Y + 3*Z) ; vp = 9*Y ./ (X + 15*Y + 3*Z) ; sp = find( Y / Yw > 0.008856) ; sm = find( Y / Yw <= 0.008856) ; L = zeros(size(Y)) ; L(sp) = 116*( Y(sp) / Yw ).^(1/3) - 16 ; L(sm) = 903.3*( Y(sm) / Yw ) ; u = 13 * L .* (up - upw) ; v = 13 * L .* (vp - vpw) ; J = cat(3,L,u,v) ; vlfeat/toolbox/imop/vl_imdown.m0000644000175000017500000000211712237004736015560 0ustar dimadimafunction J = vl_imdown(I, varargin) % VL_IMDOWN Downsample an image by two % J = VL_IMDOWN(I) downsamples the image I by half by discarding % each other pixel. % % VL_IMDOWN() accepts the following options: % % Method:: Sample % 'Sample' downsamples the image by discarding % pixels. 'Average' instead averages groups of 2x2 pixels. % % See also: VL_IMUP(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.method = 'sample' ; opts = vl_argparse(opts, varargin) ; switch lower(opts.method) case 'sample' J = I(1:2:floor(end-.5),1:2:floor(end-.5),:) ; case 'average' Iclass = class(I); I = double(I); J = ... I(1:2:end-1,1:2:end-1,:) + ... I(2:2:end,1:2:end-1,:) + ... I(1:2:end-1,2:2:end,:) + ... I(2:2:end,2:2:end,:) ; J = J / 4 ; eval(sprintf('J=%s(J);', Iclass)); otherwise error('Unknown downsampling method ''%s''.', method) ; end vlfeat/toolbox/imop/vl_imreadgray.m0000644000175000017500000000121212237004736016402 0ustar dimadimafunction I = vl_imreadgray(file,varargin) % VL_IMREADGRAY Reads an image as gray-scale % I=VL_IMREADGRAY(FILE) reads the image from file FILE and converts the % result to a gray scale image (DOUBLE storage class ranging in % [0,1]). % % VL_IMREADGRAY(FILE,FMT) specifies the file format FMT (see IMREAD()). % % See also: RGB2DOUBLE(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). I = imread(file) ; I = im2double(I) ; if(size(I,3) > 1) I = rgb2gray(I) ; end vlfeat/toolbox/imop/vl_imsc.m0000644000175000017500000000072412237004736015220 0ustar dimadimafunction J=vl_imsc(I) % VL_IMSC Scale image % J=VL_IMSC(I) scales the range of the gray-scale or color image I to % fit in the interval [0,1]. % % See also: VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if ~isfloat(I) I = im2double(I) ; end J = I - min(I(:)) ; J = J / max(J(:)) ; vlfeat/toolbox/imop/vl_witps.m0000644000175000017500000000265712237004736015442 0ustar dimadimafunction [x1,x2]=vl_witps(xp1,xp2,Y,Yp) % VL_WITPS Inverse thin-plate spline warping % [X1,X2]=VL_WITPS(XP1,XP2,Y,Yp) computes the inverse thin-plate spline % (TPS) warp of the points XP1,XP2. % % Remark:: % The inverse of a thin-plate spline in general is NOT a % thin-plate spline and some splines do not have an inverse. This % function uses Gauss-Newton to compute a set of points (X1,X2) % such that [XP1,XP2]=VL_WTPS(X1,X2,Y,Yp). % % See also: VL_WTPS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). % Initial guess by inverting the control points [x1,x2] = vl_wtps(vl_tps(xp1,xp2,Yp),Y) ; X = [x1(:)';x2(:)'] ; Xp = [xp1(:)',;xp2(:)'] ; % Gauss-Newton K = size(Y,2) ; N = size(X,2) ; U = vl_tpsu(Y,Y) ; L = [[ones(1,K); Y], zeros(3) ; U, ones(K,1), Y'] ; invL = inv(L) ; A = [Yp, zeros(2,3)] * invL ; for t=1:5 [U,dU] = vl_tpsu(Y,X); W = A * [repmat([0 0;1 0;0 1],1,N); reshape(dU, K, 2*N)] ; err = Xp - A * [ ones(1,N) ; X(1,:) ; X(2,:) ; U ] ; W = reshape(W,4,N) ; dets = W(1,:).*W(4,:) - W(3,:).*W(2,:) ; dX = [ ( W(4,:).*err(1,:) - W(3,:).*err(2,:) ) ./ dets ; ... (- W(2,:).*err(1,:) + W(1,:).*err(2,:) ) ./ dets ] ; X = X + dX ; end [M,N] = size(xp1) ; x1 = reshape(X(1,:),M,N) ; x2 = reshape(X(2,:),M,N) ; vlfeat/toolbox/imop/vl_wtps.m0000644000175000017500000000240612237004736015261 0ustar dimadimafunction [xp1,xp2]=vl_wtps(phi,Yp) % VL_WTPS Thin-plate spline warping % [XP1,XP2]=VL_WTPS(PHI,YP) computes the thin-plate spline (TPS) % specified by the basis PHI and the warped control point Yp. % % Yp is a 2xK matrix with one column per control point and the basis % PHI is calculated by means of the VL_TPS function. % % The thin-palte spline is defined on a domain X1,X2 and specified % by a set of points Y and their warp YP. The spline passes % interpolates exaclty the control points. % % The parameters X1,X2 and Y are used to compute the basis PHI. This % operation is fairily slow, but computing the spline for a given Yp % is then very quick, as the operation is just a linear combination % of the basis. % % Example:: % To calculate the warped grid [X1,X2] by moving the control points Y to % the control points YP use: % [xp1,xp2]=VL_WTPS(VL_TPS(x1,x2,Y),Yp). % % See also: VL_TPS(), VL_HELP(). % Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). [K,M,N] = size(phi) ; Xp=[Yp, zeros(2,3)]*reshape(phi,K,M*N) ; xp1 = reshape(Xp(1,:),M,N) ; xp2 = reshape(Xp(2,:),M,N) ; vlfeat/toolbox/imop/vl_imsmooth.c0000644000175000017500000002321112237004736016106 0ustar dimadima/** @internal ** @file imsmooth.c ** @author Andrea Vedaldi ** @brief Smooth an image - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #ifdef VL_IMSMOOTH_INSTANTIATING #include #include #if (FLT == VL_TYPE_FLOAT) #define IMCONVCOL vl_imconvcol_vf #define IMCONVCOLTRI vl_imconvcoltri_f #else #define IMCONVCOL vl_imconvcol_vd #define IMCONVCOLTRI vl_imconvcoltri_d #endif VL_INLINE void VL_XCAT(_vl_imsmooth_smooth_, SFX) (T * outputImage, vl_size numOutputRows, vl_size numOutputColumns, T const * inputImage, vl_size numRows, vl_size numColumns, vl_size numChannels, int kernel, double sigma, int step, int flags) { T * tempImage = (T*) mxMalloc (sizeof(T) * numRows * numOutputColumns) ; vl_uindex k ; vl_index j ; /* Note that MATLAB uses a column major ordering, while VLFeat a row major (standard) ordering for the image data. Effectively, VLFeat is operating on a transposed image, but this is fine since filters are symmetric. Therefore: input image width = numRows input image height = numColumns output image width = numOutputRows (downsamped rows) outout image height = numOutputColumns (downsampled columns) In addition a temporary buffer is used. This is an image that is obtained from the input image by convolving and downsampling along the height and saving the result transposed: temp image width = numOutputColumns temp image height = numRows */ switch (kernel) { case GAUSSIAN : { vl_size W = ceil (4.0 * sigma) ; T * filter = (T*) mxMalloc (sizeof(T) * (2 * W + 1)) ; T acc = 0 ; for (j = 0 ; j < (signed)(2 * W + 1) ; ++j) { T z = ( (T) j - W) / (sigma + VL_EPSILON_F) ; filter[j] = exp(- 0.5 * (z*z)) ; acc += filter[j] ; } for (j = 0 ; j < (signed)(2 * W + 1) ; ++j) { filter[j] /= acc ; } for (k = 0 ; k < numChannels ; ++k) { IMCONVCOL (tempImage, numOutputColumns, inputImage, numRows, numColumns, numRows, filter, -W, W, step, flags) ; IMCONVCOL (outputImage, numOutputRows, tempImage, numOutputColumns, numRows, numOutputColumns, filter, -W, W, step, flags) ; inputImage += numRows * numColumns ; outputImage += numOutputRows * numOutputColumns ; } mxFree (filter) ; break ; } case TRIANGULAR: { unsigned int W = VL_MAX((unsigned int) sigma, 1) ; for (k = 0 ; k < numChannels ; ++k) { IMCONVCOLTRI (tempImage, numOutputColumns, inputImage, numRows, numColumns, numRows, W, step, flags) ; IMCONVCOLTRI (outputImage, numOutputRows, tempImage, numOutputColumns, numRows, numOutputColumns, W, step, flags) ; inputImage += numRows * numColumns ; outputImage += numOutputRows * numOutputColumns ; } break ; } default: abort() ; } mxFree (tempImage) ; } #undef FLT #undef IMCONVCOLTRI #undef IMCONVCOL #undef VL_IMSMOOTH_INSTANTIATING /* ---------------------------------------------------------------- */ /* VL_IMSMOOTH_INSTANTIATING */ #else #include #include #include #include #include #include #include /* option codes */ enum { opt_padding = 0, opt_subsample, opt_kernel, opt_verbose } ; /* options */ vlmxOption options [] = { {"Padding", 1, opt_padding }, {"Verbose", 0, opt_verbose }, {"Subsample", 1, opt_subsample }, {"Kernel", 1, opt_kernel }, {0, 0, 0 } } ; enum {GAUSSIAN, TRIANGULAR} ; #define VL_IMSMOOTH_INSTANTIATING #define FLT VL_TYPE_FLOAT #include "vl_imsmooth.c" #define VL_IMSMOOTH_INSTANTIATING #define FLT VL_TYPE_DOUBLE #include "vl_imsmooth.c" void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_I = 0, IN_S, IN_END} ; enum {OUT_J = 0} ; int opt ; int next = IN_END ; mxArray const *optarg ; int padding = VL_PAD_BY_CONTINUITY ; int kernel = GAUSSIAN ; int flags ; vl_size step = 1 ; int verb = 0 ; double sigma ; mxClassID classid ; mwSize M, N, K, M_, N_, ndims ; mwSize dims_ [3] ; mwSize const * dims ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 2) { mexErrMsgTxt("At least two input arguments required."); } else if (nout > 1) { mexErrMsgTxt("Too many output arguments."); } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_padding : { enum {buflen = 32} ; char buf [buflen] ; if (!vlmxIsString(optarg, -1)) { vlmxError(vlmxErrInvalidArgument, "PADDING argument must be a string.") ; } mxGetString(optarg, buf, buflen) ; buf [buflen - 1] = 0 ; if (vlmxCompareStringsI("zero", buf) == 0) { padding = VL_PAD_BY_ZERO ; } else if (vlmxCompareStringsI("continuity", buf) == 0) { padding = VL_PAD_BY_CONTINUITY ; } else { vlmxError(vlmxErrInvalidArgument, "PADDING must be either ZERO or CONTINUITY, was '%s'.", buf) ; } break ; } case opt_subsample : if (!vlmxIsPlainScalar(optarg)) { vlmxError(vlmxErrInvalidArgument, "SUBSAMPLE must be a scalar.") ; } step = *mxGetPr(optarg) ; if (step < 1) { vlmxError(vlmxErrInvalidArgument, "SUBSAMPLE must be not less than one.") ; } break ; case opt_kernel : { enum {buflen = 32} ; char buf [buflen] ; if (!vlmxIsString(optarg, -1)) { vlmxError(vlmxErrInvalidArgument, "KERNEL argument must be a string.") ; } mxGetString(optarg, buf, buflen) ; buf [buflen - 1] = 0 ; if (vlmxCompareStringsI("gaussian", buf) == 0) { kernel = GAUSSIAN ; } else if (vlmxCompareStringsI("triangular", buf) == 0) { kernel = TRIANGULAR ; } else { vlmxError(vlmxErrInvalidArgument, "Unknown kernel type '%s'.", buf) ; } break ; } case opt_verbose : ++ verb ; break ; default: abort() ; } } if (! vlmxIsPlainScalar(IN(S))) { vlmxError(vlmxErrInvalidArgument, "S must be a real scalar.") ; } classid = mxGetClassID(IN(I)) ; if (classid != mxDOUBLE_CLASS && classid != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "I must be either DOUBLE or SINGLE.") ; } if (mxGetNumberOfDimensions(IN(I)) > 3) { vlmxError(vlmxErrInvalidArgument, "I must be either a two or three dimensional array.") ; } ndims = mxGetNumberOfDimensions(IN(I)) ; dims = mxGetDimensions(IN(I)) ; M = dims[0] ; N = dims[1] ; K = (ndims > 2) ? dims[2] : 1 ; sigma = * mxGetPr(IN(S)) ; if ((sigma < 0.01) && (step == 1)) { OUT(J) = mxDuplicateArray(IN(I)) ; return ; } M_ = (M - 1) / step + 1 ; N_ = (N - 1) / step + 1 ; dims_ [0] = M_ ; dims_ [1] = N_ ; if (ndims > 2) dims_ [2] = K ; OUT(J) = mxCreateNumericArray(ndims, dims_, classid, mxREAL) ; if (verb) { char const *classid_str = 0, *kernel_str = 0, *padding_str = 0 ; switch (padding) { case VL_PAD_BY_ZERO : padding_str = "with zeroes" ; break ; case VL_PAD_BY_CONTINUITY : padding_str = "by continuity" ; break ; default: abort() ; } switch (classid) { case mxDOUBLE_CLASS: classid_str = "DOUBLE" ; break ; case mxSINGLE_CLASS: classid_str = "SINGLE" ; break ; default: abort() ; } switch (kernel) { case GAUSSIAN: kernel_str = "Gaussian" ; break ; case TRIANGULAR: kernel_str = "triangular" ; break ; default: abort() ; } mexPrintf("vl_imsmooth: [%dx%dx%d] -> [%dx%dx%d] (%s, subsampling step %d)\n", N, M, K, N_, M_, K, classid_str, step) ; mexPrintf("vl_imsmooth: padding: %s\n", padding_str) ; mexPrintf("vl_imsmooth: kernel: %s\n", kernel_str) ; mexPrintf("vl_imsmooth: sigma: %g\n", sigma) ; mexPrintf("vl_imsmooth: SIMD enabled: %s\n", vl_get_simd_enabled() ? "yes" : "no") ; } /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ flags = padding ; flags |= VL_TRANSPOSE ; switch (classid) { case mxSINGLE_CLASS: _vl_imsmooth_smooth_f ((float*) mxGetPr(OUT(J)), M_, N_, (float const*) mxGetPr(IN(I)), M, N, K, kernel, sigma, step, flags) ; break ; case mxDOUBLE_CLASS: _vl_imsmooth_smooth_d ((double*) mxGetPr(OUT(J)), M_, N_, (double const*) mxGetPr(IN(I)), M, N, K, kernel, sigma, step, flags) ; break ; default: abort() ; } } /* VL_IMSMOOTH_INSTANTIATING */ #endif vlfeat/toolbox/imop/vl_imintegral.c0000644000175000017500000000541212237004736016405 0ustar dimadima/** @internal ** @file imintegral.c ** @author Brian Fulkerson ** @brief Create an integral image - MEX definition **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include #include #include void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { vl_size numDimensions, numChannels ; mwSize const *dimensions ; mxClassID classId ; void * integral ; void const * image ; vl_uindex k ; enum {IN_I=0} ; enum {OUT_J=0} ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin > 1) { vlmxError(vlmxErrTooManyInputArguments, NULL) ; } if (nin < 1) { vlmxError(vlmxErrNotEnoughInputArguments, NULL) ; } if (nout > 1) { vlmxError(vlmxErrTooManyOutputArguments, NULL) ; } if (! mxIsNumeric(IN(I))) { vlmxError(vlmxErrInvalidArgument, "I is not numeric.") ; } dimensions = mxGetDimensions(IN(I)) ; numDimensions = mxGetNumberOfDimensions(IN(I)) ; if (numDimensions > 3) { vlmxError(vlmxErrInvalidArgument, "I has more than 3 dimensions (%d).", numDimensions) ; } if (numDimensions > 2) { numChannels = dimensions [2] ; } else { numChannels = 1 ; } classId = mxGetClassID(IN(I)) ; if (classId != mxSINGLE_CLASS && classId != mxDOUBLE_CLASS && classId != mxUINT32_CLASS && classId != mxINT32_CLASS) { vlmxError(vlmxErrInvalidArgument, "I is not of a supported storage class.") ; } OUT(J) = mxCreateNumericArray(numDimensions, dimensions, classId, mxREAL) ; image = mxGetData(IN(I)) ; integral = mxGetData(OUT(J)) ; /* ----------------------------------------------------------------- * Do the job * -------------------------------------------------------------- */ #define DO(CLASS, T, SFX) \ case mx ## CLASS ## _CLASS : \ vl_imintegral_ ## SFX (integral, dimensions[0], \ image, dimensions[0], dimensions[1], dimensions[0]) ; \ integral = ((T *) integral) + dimensions[0]*dimensions[1] ; \ image = ((T const *) image) + dimensions[0]*dimensions[1] ; \ break for (k = 0 ; k < numChannels ; ++k) { switch (classId) { DO(SINGLE, float, f) ; DO(DOUBLE, double, d) ; DO(UINT32, vl_uint32, ui32) ; DO(INT32, vl_int32, i32) ; default: abort() ; } } } vlfeat/toolbox/slic/0000755000175000017500000000000012237042167013371 5ustar dimadimavlfeat/toolbox/slic/vl_slic.c0000644000175000017500000001046612237004736015177 0ustar dimadima/** @file vl_slic.c ** @brief vl_slic ** @author Andrea Vedaldi **/ /* Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. All rights reserved. This file is part of the VLFeat library and is made available under the terms of the BSD license (see the COPYING file). */ #include #include #include #include /* option codes */ enum { opt_verbose, opt_min_segment_size } ; /* options */ vlmxOption options [] = { {"Verbose", 0, opt_verbose }, {"MinRegionSize", 1, opt_min_segment_size }, {0, 0, 0 } } ; /** ------------------------------------------------------------------ ** @brief MEX entry point **/ void mexFunction(int nout, mxArray *out[], int nin, const mxArray *in[]) { enum {IN_IMAGE, IN_REGIONSIZE, IN_REGULARIZER, IN_END} ; enum {OUT_SEGMENTATION = 0} ; int verbose = 0 ; int opt ; int next = IN_END ; mxArray const *optarg ; float const * image ; vl_size width ; vl_size height ; vl_size numChannels ; vl_size regionSize ; double regularizer ; vl_uint32 * segmentation ; int minRegionSize = -1 ; VL_USE_MATLAB_ENV ; /* ----------------------------------------------------------------- * Check the arguments * -------------------------------------------------------------- */ if (nin < 3) { vlmxError(vlmxErrInvalidArgument, "At least three arguments are required.") ; } else if (nout > 1) { vlmxError(vlmxErrInvalidArgument, "Too many output arguments."); } image = mxGetData(IN(IMAGE)) ; if (!mxIsNumeric(IN(IMAGE)) || mxIsComplex(IN(IMAGE))) { vlmxError(vlmxErrInvalidArgument, "IMAGE is not a real matrix.") ; } if (mxGetClassID(IN(IMAGE)) != mxSINGLE_CLASS) { vlmxError(vlmxErrInvalidArgument, "IMAGE is not of class SINGLE.") ; } if (mxGetNumberOfDimensions(IN(IMAGE)) > 3) { vlmxError(vlmxErrInvalidArgument, "IMAGE has more than three dimensions.") ; } width = mxGetDimensions(IN(IMAGE))[1] ; height = mxGetDimensions(IN(IMAGE))[0] ; if (mxGetNumberOfDimensions(IN(IMAGE)) == 2) { numChannels = 1 ; } else { numChannels = mxGetDimensions(IN(IMAGE))[2] ; } if (!vlmxIsPlainScalar(IN(REGIONSIZE))) { vlmxError(vlmxErrInvalidArgument, "REGIONSIZE is not a plain scalar.") ; } regionSize = mxGetScalar(IN(REGIONSIZE)) ; if (regionSize < 1) { vlmxError(vlmxErrInvalidArgument, "REGIONSIZE=%d is smaller than one.", regionSize) ; } if (!vlmxIsPlainScalar(IN(REGULARIZER))) { vlmxError(vlmxErrInvalidArgument, "REGULARIZER is not a plain scalar.") ; } regularizer = mxGetScalar(IN(REGULARIZER)) ; if (regularizer < 0) { vlmxError(vlmxErrInvalidArgument, "REGULARIZER=%g is smaller than one.", regularizer) ; } while ((opt = vlmxNextOption (in, nin, options, &next, &optarg)) >= 0) { switch (opt) { case opt_verbose : ++ verbose ; break ; case opt_min_segment_size : if (!vlmxIsPlainScalar(optarg)) { vlmxError(vlmxErrInvalidArgument, "MINREGIONSIZE is not a plain scalar.") ; } minRegionSize = mxGetScalar(optarg) ; if (minRegionSize < 0) { vlmxError(vlmxErrInvalidArgument, "MINREGIONSIZE=%d is smaller than zero.", minRegionSize) ; } break ; } } if (minRegionSize < 0) { minRegionSize = (regionSize * regionSize) / (6*6) ; } if (verbose) { mexPrintf("vl_slic: image = [%d x %d x %d]\n", width, height, numChannels) ; mexPrintf("vl_slic: regionSize = %d\n", regionSize) ; mexPrintf("vl_slic: regularizer = %g\n", regularizer) ; mexPrintf("vl_slic: minRegionSize = %d\n", minRegionSize) ; } /* ----------------------------------------------------------------- * Do work * -------------------------------------------------------------- */ OUT(SEGMENTATION) = mxCreateNumericMatrix((mwSize)height, (mwSize)width, mxUINT32_CLASS, mxREAL) ; segmentation = mxGetData(OUT(SEGMENTATION)) ; vl_slic_segment(segmentation, image, height, width, numChannels, /* the image is transposed */ regionSize, regularizer, minRegionSize) ; } vlfeat/toolbox/slic/vl_slic.m0000644000175000017500000000211712237004736015203 0ustar dimadima% VL_SLIC SLIC superpixels % SEGMENTS = VL_SLIC(IM, REGIONSIZE, REGULARIZER) extracts the SLIC % superpixes [1] from image IM. REGIONSIZE is the starting size of % the superpixels and REGULARIZER is the trades-off appearance for % spatial regularity when clustering (a larger value results in more % spatial regularization). SEGMENTS is a UINT32 array containing the % superpixel identifier for each image pixel. % % The image IM is a SINGLE array with two or three dimensions. The % third dimension is arbitrary, but it is usually three for RGB or % LAB images. % % VL_SLIC() accepts the following options: % % Verbose:: % Be verbose. % % MinRegionSize:: (1/6 of REGIONSIZE)^2 % The minimum size (in pixel area) of the extracted % superpixels. % % Example:: % If IM contains an RGB images, the SLIC superpixels can be % extracted as: % % REFERENCES:: % [1] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and % S. Susstrunk. SLIC superpixels. Technical report, EPFL, 2010. % % See also:: http://www.vlfeat.org/doc/api/slic.html, VL_HELP(). vlfeat/vlfeat.vcproj0000644000175000017500000003011412237004745013456 0ustar dimadima vlfeat/apps/0000755000175000017500000000000012237035722011713 5ustar dimadimavlfeat/apps/recognition/0000755000175000017500000000000012237035722014233 5ustar dimadimavlfeat/apps/recognition/setupGeneric.m0000644000175000017500000000767012237004745017061 0ustar dimadimafunction imdb = setupGeneric(datasetDir, varargin) % SETUPGENERIC Setup a Caltech-101-like dataset % IMDB = SETUPGENERIC(DATASETDIR) initializes a IMDB structure % representing the dataset located at DATASETDIR. The dataset % is supposed to be organized similarly to Caltech-101, i.e. % to have one directory per image class, with a number of % image files in each directory. % % SETUPGENERIC(..., 'OPT', VAL, ...) accepts the following % options: % % Lite:: false % If set to TRUE, use at most 3 classes and at most 5 images % in each of TRAIN, VAL, and TEST. % % Seed:: 1 % The random seed used to generate the partitions. % % NumTrain:: 50 % Maximum number of training images per class. % % NumVal:: 0 % Maximum number of validation images per class. % % NumTest:: 50 % Maximum number of test images per class. % % ExpectedNumClasses:: [] % If set to a number, generate an error if the number % of classes found in DATASETDIR differs. This is only % used as a sanity check. % % IMDB is a structure with the following fields: % % IMDB.IMAGES:: % A structure of arrays representing the images in the % dataset. % % IMDB.IMAGES.ID:: % Vector of image numeric IDs. % % IMDB.IMAGES.NAME:: % Cell array with the image names. % % IMDB.IMAGES.SET:: % Vector of subset IDs for each image (train, val, test). % % IMDB.IMAGES.CLASS:: % Vector of class IDs for each image. % % IMDB.META:: % A substructure with meta information on the data. % % IMDB.META.CLASSES:: % Cell array of class names. % % IMDB.IMAGEDIR:: % Image directory. Join the image name to this directory to % access an image file. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.lite = false ; opts.numTrain = 50 ; opts.numVal = 0 ; opts.numTest = 50 ; opts.seed = 1 ; opts.expectedNumClasses = [] ; opts = vl_argparse(opts, varargin) ; % Construct image database imdb structure imdb.meta.sets = {'train', 'val', 'test'} ; names = dir(datasetDir) ; names = {names([names.isdir]).name} ; names = setdiff(names, {'.', '..'}) ; imdb.meta.classes = names ; names = {} ; classes = {} ; for c = 1:numel(imdb.meta.classes) class = imdb.meta.classes{c} ; tmp = dir(fullfile(datasetDir, [class filesep '*.jpg'])) ; names{c} = strcat([class filesep], {tmp.name}) ; classes{c} = repmat(c, 1, numel(names{c})) ; end names = cat(2,names{:}) ; classes = cat(2,classes{:}) ; sets = zeros(1,numel(names)) ; ids = 1:numel(names) ; numClasses = numel(imdb.meta.classes) ; if ~isempty(opts.expectedNumClasses) && numClasses ~= opts.expectedNumClasses error('Expected %d classes in image database at %s.', opts.expectedNumClasses, datasetDir) ; end for c = 1:numClasses sel = find(classes == c) ; randn('state', opts.seed) ; rand('state', opts.seed) ; selTrain = vl_colsubset(sel, opts.numTrain) ; selVal = vl_colsubset(setdiff(sel, selTrain), opts.numVal) ; selTest = vl_colsubset(setdiff(sel, [selTrain selVal]), opts.numTest) ; sets(selTrain) = 1 ; sets(selVal) = 2 ; sets(selTest) = 3 ; end ok = find(sets ~= 0) ; imdb.images.id = ids(ok) ; imdb.images.name = names(ok) ; imdb.images.set = sets(ok) ; imdb.images.class = classes(ok) ; imdb.imageDir = datasetDir ; if opts.lite ok = {} ; for c = 1:3 ok{end+1} = vl_colsubset(find(imdb.images.class == c & imdb.images.set == 1), 5) ; ok{end+1} = vl_colsubset(find(imdb.images.class == c & imdb.images.set == 2), 5) ; ok{end+1} = vl_colsubset(find(imdb.images.class == c & imdb.images.set == 3), 5) ; end ok = cat(2, ok{:}) ; imdb.meta.classes = imdb.meta.classes(1:3) ; imdb.images.id = imdb.images.id(ok) ; imdb.images.name = imdb.images.name(ok) ; imdb.images.set = imdb.images.set(ok) ; imdb.images.class = imdb.images.class(ok) ; end vlfeat/apps/recognition/setupVoc.m0000644000175000017500000001210512237004745016221 0ustar dimadimafunction imdb = setupVoc(datasetDir, varargin) % SETUPVOC Setup PASCAL VOC data % IMDB = SETUPVOC(DATASETDIR, 'EDITION', '2007') setups the % PASCAL VOC 2007 data. This is similar to SETUPGENERIC(), but adapted % to the peculiarities of this dataset. In particular, the % difficult image flag and the fact that multiple labels apply to % each image are supported. % % Note that only the PASCAL VOC 2007 data comes with test images % and labels. For the other editions, setting up the test images % cannot be automatized due to restrictions in the distribution. % % See also: SETUPGENERIC(). % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.edition = '2007' ; opts.autoDownload = true ; opts.lite = false ; opts = vl_argparse(opts, varargin) ; switch opts.edition case '2007' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtrainval_06-Nov-2007.tar', ... 'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2007/VOCtest_06-Nov-2007.tar'} ; case '2008' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2008/VOCtrainval_14-Jul-2008.tar'} ; case '2009' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2009/VOCtrainval_11-May-2009.tar'} ; case '2010' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2010/VOCtrainval_03-May-2010.tar'} ; case '2011' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2011/VOCtrainval_25-May-2011.tar'} ; case '2012' urls = {'http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/VOCtrainval_11-May-2012.tar'} ; otherwise error('Unknown PASCAL VOC edition ''%s''.', opts.edition) ; end % Download and unpack vl_xmkdir(datasetDir) ; if exist(fullfile(datasetDir, ['VOC' opts.edition], 'Annotations')) % ok elseif exist(fullfile(datasetDir, 'VOCdevkit', ['VOC' opts.edition], 'Annotations')) % ok datasetDir = fullfile(datasetDir, 'VOCdevkit') ; elseif opts.autoDownload for i = 1:length(urls) fprintf('Downloading VOC data ''%s'' to ''%s''. This will take a while.', urls{i}, datasetDir) ; untar(urls{i}, datasetDir) ; end datasetDir = fullfile(datasetDir, 'VOCdevkit') ; else error('VOC data not found in %s', datasetDir) ; end imdb.images.id = [] ; imdb.images.set = uint8([]) ; imdb.images.name = {} ; imdb.images.size = zeros(2,0) ; imdb.meta.sets = {'train', 'val', 'test'} ; imdb.meta.classes = {'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', ... 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', ... 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'} ; imdb.imageDir = fullfile(datasetDir, ['VOC', opts.edition], 'JPEGImages') ; % Get the list of images map = containers.Map() ; j = 0 ; for si = 1:numel(imdb.meta.sets) setName = imdb.meta.sets{si} ; annoPath = fullfile(datasetDir, ['VOC' opts.edition], ... 'ImageSets', 'Main', ... [setName '.txt']) ; fprintf('%s: reading %s\n', mfilename, annoPath) ; [names,labels] = textread(annoPath, '%s %f') ; for i=1:length(names) j = j + 1 ; map(names{i}) = j ; imdb.images.id(j) = j ; imdb.images.set(j) = si ; imdb.images.name{j} = [names{i} '.jpg'] ; %info = imfinfo(fullfile(imdb.imageDir, imdb.images.name{j})) ; %imdb.images.size(:,j) = [info.Width ; info.Height] ; %fprintf('\radded %s', imdb.images.name{j}) ; end end % Get class labels for ci = 1:length(imdb.meta.classes) imdb.classes.imageIds{ci} = [] ; imdb.classes.difficult{ci} = false(0) ; end for si = 1:numel(imdb.meta.sets) for ci = 1:length(imdb.meta.classes) setName = imdb.meta.sets{si} ; className = imdb.meta.classes{ci} ; annoPath = fullfile(datasetDir, ['VOC' opts.edition], ... 'ImageSets', 'Main', ... [className '_' setName '.txt']) ; fprintf('%s: reading %s\n', mfilename, annoPath) ; [names,labels] = textread(annoPath, '%s %f') ; for i = 1:numel(names) j = map(names{i}) ; if labels(i) >= 0 imdb.classes.imageIds{ci}(end+1) = j ; imdb.classes.difficult{ci}(end+1) = (labels(i) == 0) ; end end end end if opts.lite ok = {} ; for c = 1:3 trainIds = intersect(imdb.images.id(imdb.images.set == 1), imdb.classes.imageIds{c}) ; testIds = intersect(imdb.images.id(imdb.images.set == 3), imdb.classes.imageIds{c}) ; ok{end+1} = vl_colsubset(find(ismember(imdb.images.id, trainIds)), 5) ; ok{end+1} = vl_colsubset(find(ismember(imdb.images.id, testIds)), 5) ; end ok = unique(cat(2, ok{:})) ; imdb.meta.classes = imdb.meta.classes(1:3) ; imdb.classes.imageIds = imdb.classes.imageIds(1:3) ; imdb.classes.difficult = imdb.classes.difficult(1:3) ; imdb.images.id = imdb.images.id(ok) ; imdb.images.name = imdb.images.name(ok) ; imdb.images.set = imdb.images.set(ok) ; for c = 1:3 ok = ismember(imdb.classes.imageIds{c}, imdb.images.id) ; imdb.classes.imageIds{c} = imdb.classes.imageIds{c}(ok) ; imdb.classes.difficult{c} = imdb.classes.difficult{c}(ok) ; end end vlfeat/apps/recognition/trainEncoder.m0000644000175000017500000001412212237004745017027 0ustar dimadimafunction encoder = trainEncoder(images, varargin) % TRAINENCODER Train image encoder: BoVW, VLAD, FV % ENCODER = TRAINENCOER(IMAGES) trains a BoVW encoder from the % specified list of images IMAGES. % % TRAINENCODER(..., 'OPT', VAL, ...) accepts the following options: % % Type:: 'bovw' % Bag of visual words ('bovw'), VLAD ('vlad') or Fisher Vector % ('fv'). % % numPcaDimension:: +inf % Use PCA to reduce the descriptor dimensionality to this % dimension. Use +inf to deactivate PCA. % % Whitening:: false % Set to true to divide the principal components by the % corresponding standard deviation s_i. % % WhiteningRegul:: 0 % When using whitening, divide by s_max * WhiteningRegul + s_i % instead of s_i alone. % % Renormalize:: false % If true, descriptors are L2 normalized after PCA or % whitening. % % % Subdivisions:: [] % A list of spatial subdivisions. Each column is a rectangle % [XMIN YMIN XMAX YMAX]. The spatial subdivisions are % % Layouts:: {'1x1'} % A list of strings representing regular spatial subdivisions % in the format MxN, where M is the number of vertical % subdivisions and N the number of horizontal ones. For % example {'1x1', 2x2'} uses 5 partitions: the whole image and % four quadrants. The subdivisions are appended to the ones % specified by the SUBDIVISIONS option. % % ReadImageFn:: @readImage % The function used to load an image. % % ExtractorFn:: @getDenseSIFT % The function used to extract the feature frames and % descriptors from an image. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.type = 'bovw' ; opts.numWords = [] ; opts.seed = 1 ; opts.numPcaDimensions = +inf ; opts.whitening = false ; opts.whiteningRegul = 0 ; opts.numSamplesPerWord = [] ; opts.renormalize = false ; opts.layouts = {'1x1'} ; opts.geometricExtension = 'none' ; opts.subdivisions = zeros(4,0) ; opts.readImageFn = @readImage ; opts.extractorFn = @getDenseSIFT ; opts.lite = false ; opts = vl_argparse(opts, varargin) ; for i = 1:numel(opts.layouts) t = sscanf(opts.layouts{i},'%dx%d') ; m = t(1) ; n = t(2) ; [x,y] = meshgrid(... linspace(0,1,n+1), ... linspace(0,1,m+1)) ; x1 = x(1:end-1,1:end-1) ; y1 = y(1:end-1,1:end-1) ; x2 = x(2:end,2:end) ; y2 = y(2:end,2:end) ; opts.subdivisions = cat(2, opts.subdivisions, ... [x1(:)' ; y1(:)' ; x2(:)' ; y2(:)'] ) ; end if isempty(opts.numWords) switch opts.type case {'bovw'} opts.numWords = 1024 ; case {'fv'} opts.numWords = 64 ; opts.numPcaDimensions = 80 ; case {'vlad'} opts.numWords = 64 ; opts.numPcaDimensions = 100 ; opts.whitening = true ; opts.whiteninRegul = 0.01 ; otherwise assert(false) ; end end if isempty(opts.numSamplesPerWord) switch opts.type case {'bovw'} opts.numSamplesPerWord = 200 ; case {'vlad','fv'} opts.numSamplesPerWord = 1000 ; otherwise assert(false) ; end if opts.lite opts.numSamplesPerWord = 10 ; end end disp(opts) ; encoder.type = opts.type ; encoder.subdivisions = opts.subdivisions ; encoder.readImageFn = opts.readImageFn ; encoder.extractorFn = opts.extractorFn ; encoder.numWords = opts.numWords ; encoder.renormalize = opts.renormalize ; encoder.geometricExtension = opts.geometricExtension ; %% Step 0: obtain sample image descriptors numImages = numel(images) ; numDescrsPerImage = ceil(opts.numWords * opts.numSamplesPerWord / numImages) ; parfor i = 1:numImages fprintf('%s: reading: %s\n', mfilename, images{i}) ; im = encoder.readImageFn(images{i}) ; w = size(im,2) ; h = size(im,1) ; features = encoder.extractorFn(im) ; randn('state',0) ; rand('state',0) ; sel = vl_colsubset(1:size(features.descr,2), single(numDescrsPerImage)) ; descrs{i} = features.descr(:,sel) ; frames{i} = features.frame(:,sel) ; frames{i} = bsxfun(@times, bsxfun(@minus, frames{i}(1:2,:), [w;h]/2), 1./[w;h]) ; end descrs = cat(2, descrs{:}) ; frames = cat(2, frames{:}) ; %% Step 1 (optional): learn PCA projection if opts.numPcaDimensions < inf || opts.whitening fprintf('%s: learning PCA rotation/projection\n', mfilename) ; encoder.projectionCenter = mean(descrs,2) ; x = bsxfun(@minus, descrs, encoder.projectionCenter) ; X = x*x' / size(x,2) ; [V,D] = eig(X) ; d = diag(D) ; [d,perm] = sort(d,'descend') ; d = d + opts.whiteningRegul * max(d) ; m = min(opts.numPcaDimensions, size(descrs,1)) ; V = V(:,perm) ; if opts.whitening encoder.projection = diag(1./sqrt(d(1:m))) * V(:,1:m)' ; else encoder.projection = V(:,1:m)' ; end clear X V D d ; else encoder.projection = 1 ; encoder.projectionCenter = 0 ; end descrs = encoder.projection * bsxfun(@minus, descrs, encoder.projectionCenter) ; if encoder.renormalize descrs = bsxfun(@times, descrs, 1./max(1e-12, sqrt(sum(descrs.^2)))) ; end %% Step 2 (optional): geometrically augment the features descrs = extendDescriptorsWithGeometry(opts.geometricExtension, frames, descrs) ; %% Step 3: learn a VQ or GMM vocabulary dimension = size(descrs,1) ; numDescriptors = size(descrs,2) ; switch encoder.type case {'bovw', 'vlad'} vl_twister('state', opts.seed) ; encoder.words = vl_kmeans(descrs, opts.numWords, 'verbose', 'algorithm', 'elkan') ; encoder.kdtree = vl_kdtreebuild(encoder.words, 'numTrees', 2) ; case {'fv'} ; vl_twister('state', opts.seed) ; if 1 v = var(descrs')' ; [encoder.means, encoder.covariances, encoder.priors] = ... vl_gmm(descrs, opts.numWords, 'verbose', ... 'Initialization', 'kmeans', ... 'CovarianceBound', double(max(v)*0.0001), ... 'NumRepetitions', 1) ; else addpath lib/yael/matlab [a,b,c] = ... yael_gmm(descrs, opts.numWords, 'verbose', 2) ; encoder.priors = single(a) ; encoder.means = single(b) ; encoder.covariances = single(c) ; end end vlfeat/apps/recognition/experiments.m0000644000175000017500000001537112237004745016764 0ustar dimadimafunction experiments() % EXPERIMENTS Run image classification experiments % The experimens download a number of benchmark datasets in the % 'data/' subfolder. Make sure that there are several GBs of % space available. % % By default, experiments run with a lite option turned on. This % quickly runs all of them on tiny subsets of the actual data. % This is used only for testing; to run the actual experiments, % set the lite variable to false. % % Running all the experiments is a slow process. Using parallel % MATLAB and several cores/machiens is suggested. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). lite = true ; clear ex ; ex(1).prefix = 'fv-aug' ; ex(1).trainOpts = {'C', 10} ; ex(1).datasets = {'fmd', 'scene67'} ; ex(1).seed = 1 ; ex(1).opts = {... 'type', 'fv', ... 'numWords', 256, ... 'layouts', {'1x1'}, ... 'geometricExtension', 'xy', ... 'numPcaDimensions', 80, ... 'extractorFn', @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(1:-.5:-3))}; ex(2) = ex(1) ; ex(2).datasets = {'caltech101'} ; ex(2).opts{end} = @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(0:-.5:-3)) ; ex(3) = ex(1) ; ex(3).datasets = {'voc07'} ; ex(3).C = 1 ; ex(4) = ex(1) ; ex(4).prefix = 'vlad-aug' ; ex(4).opts = {... 'type', 'vlad', ... 'numWords', 256, ... 'layouts', {'1x1'}, ... 'geometricExtension', 'xy', ... 'numPcaDimensions', 100, ... 'whitening', true, ... 'whiteningRegul', 0.01, ... 'renormalize', true, ... 'extractorFn', @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(1:-.5:-3))}; ex(5) = ex(4) ; ex(5).datasets = {'caltech101'} ; ex(5).opts{end} = ex(2).opts{end} ; ex(6) = ex(4) ; ex(6).datasets = {'voc07'} ; ex(6).C = 1 ; ex(7) = ex(1) ; ex(7).prefix = 'bovw-aug' ; ex(7).opts = {... 'type', 'bovw', ... 'numWords', 4096, ... 'layouts', {'1x1'}, ... 'geometricExtension', 'xy', ... 'numPcaDimensions', 100, ... 'whitening', true, ... 'whiteningRegul', 0.01, ... 'renormalize', true, ... 'extractorFn', @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(1:-.5:-3))}; ex(8) = ex(7) ; ex(8).datasets = {'caltech101'} ; ex(8).opts{end} = ex(2).opts{end} ; ex(9) = ex(7) ; ex(9).datasets = {'voc07'} ; ex(9).C = 1 ; ex(10).prefix = 'fv' ; ex(10).trainOpts = {'C', 10} ; ex(10).datasets = {'fmd', 'scene67'} ; ex(10).seed = 1 ; ex(10).opts = {... 'type', 'fv', ... 'numWords', 256, ... 'layouts', {'1x1'}, ... 'geometricExtension', 'none', ... 'numPcaDimensions', 80, ... 'extractorFn', @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(1:-.5:-3))}; ex(11) = ex(10) ; ex(11).datasets = {'caltech101'} ; ex(11).opts{end} = @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(0:-.5:-3)) ; ex(12) = ex(10) ; ex(12).datasets = {'voc07'} ; ex(12).C = 1 ; ex(13).prefix = 'fv-sp' ; ex(13).trainOpts = {'C', 10} ; ex(13).datasets = {'fmd', 'scene67'} ; ex(13).seed = 1 ; ex(13).opts = {... 'type', 'fv', ... 'numWords', 256, ... 'layouts', {'1x1', '3x1'}, ... 'geometricExtension', 'none', ... 'numPcaDimensions', 80, ... 'extractorFn', @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(1:-.5:-3))}; ex(14) = ex(13) ; ex(14).datasets = {'caltech101'} ; ex(14).opts{6} = {'1x1', '2x2'} ; ex(14).opts{end} = @(x) getDenseSIFT(x, ... 'step', 4, ... 'scales', 2.^(0:-.5:-3)) ; ex(15) = ex(13) ; ex(15).datasets = {'voc07'} ; ex(15).C = 1 ; if lite, tag = 'lite' ; else, tag = 'ex' ; end for i=1:numel(ex) for j=1:numel(ex(i).datasets) dataset = ex(i).datasets{j} ; if ~isfield(ex(i), 'trainOpts') || ~iscell(ex(i).trainOpts) ex(i).trainOpts = {} ; end traintest(... 'prefix', [tag '-' dataset '-' ex(i).prefix], ... 'seed', ex(i).seed, ... 'dataset', char(dataset), ... 'datasetDir', fullfile('data', dataset), ... 'lite', lite, ... ex(i).trainOpts{:}, ... 'encoderParams', ex(i).opts) ; end end % print HTML table pf('\n') ; ph('method', 'VOC07', 'Caltech 101', 'Scene 67', 'FMD') ; pr('FV', ... ge([tag '-voc07-fv'],'ap11'), ... ge([tag '-caltech101-fv']), ... ge([tag '-scene67-fv']), ... ge([tag '-fmd-fv'])) ; pr('FV + aug.', ... ge([tag '-voc07-fv-aug'],'ap11'), ... ge([tag '-caltech101-fv-aug']), ... ge([tag '-scene67-fv-aug']), ... ge([tag '-fmd-fv-aug'])) ; pr('FV + s.p.', ... ge([tag '-voc07-fv-sp'],'ap11'), ... ge([tag '-caltech101-fv-sp']), ... ge([tag '-scene67-fv-sp']), ... ge([tag '-fmd-fv-sp'])) ; %pr('VLAD', ... % ge([tag '-voc07-vlad'],'ap11'), ... % ge([tag '-caltech101-vlad']), ... % ge([tag '-scene67-vlad']), ... % ge([tag '-fmd-vlad'])) ; pr('VLAD + aug.', ... ge([tag '-voc07-vlad-aug'],'ap11'), ... ge([tag '-caltech101-vlad-aug']), ... ge([tag '-scene67-vlad-aug']), ... ge([tag '-fmd-vlad-aug'])) ; %pr('VLAD+sp', ... % ge([tag '-voc07-vlad-sp'],'ap11'), ... % ge([tag '-caltech101-vlad-sp']), ... % ge([tag '-scene67-vlad-sp']), ... % ge([tag '-fmd-vlad-sp'])) ; %pr('BOVW', ... % ge([tag '-voc07-bovw'],'ap11'), ... % ge([tag '-caltech101-bovw']), ... % ge([tag '-scene67-bovw']), ... % ge([tag '-fmd-bovw'])) ; pr('BOVW + aug.', ... ge([tag '-voc07-bovw-aug'],'ap11'), ... ge([tag '-caltech101-bovw-aug']), ... ge([tag '-scene67-bovw-aug']), ... ge([tag '-fmd-bovw-aug'])) ; %pr('BOVW+sp', ... % ge([tag '-voc07-bovw-sp'],'ap11'), ... % ge([tag '-caltech101-bovw-sp']), ... % ge([tag '-scene67-bovw-sp']), ... % ge([tag '-fmd-bovw-sp'])) ; pf('
\n'); function pf(str) fprintf(str) ; function str = ge(name, format) if nargin == 1, format = 'acc'; end data = load(fullfile('data', name, 'result.mat')) ; switch format case 'acc' str = sprintf('%.2f%% Acc', mean(diag(data.confusion)) * 100) ; case 'ap11' str = sprintf('%.2f%% mAP', mean(data.ap11) * 100) ; end function pr(varargin) fprintf('') ; for i=1:numel(varargin), fprintf('%s',varargin{i}) ; end fprintf('\n') ; function ph(varargin) fprintf('') ; for i=1:numel(varargin), fprintf('%s',varargin{i}) ; end fprintf('\n') ; vlfeat/apps/recognition/setupScene67.m0000644000175000017500000000450012237004745016704 0ustar dimadimafunction imdb = setupScene67(datasetDir, varargin) % SETUPSCENE67 Setup MIT Scene 67 dataset % This is similar to SETUPGENERIC(), with modifications to setup % MIT Scene 67 according to the standard evaluation protocols. The % function supports only the LITE option. % % See: SETUPGENERIC(). % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.lite = false ; opts = vl_argparse(opts, varargin) ; % Download and unpack vl_xmkdir(datasetDir) ; if exist(fullfile(datasetDir, 'Images', 'airport_inside')) % ok else url = 'http://groups.csail.mit.edu/vision/LabelMe/NewImages/indoorCVPR_09.tar' ; trainImagesUrl = 'http://web.mit.edu/torralba/www/TrainImages.txt' ; testImagesUrl = 'http://web.mit.edu/torralba/www/TestImages.txt' ; fprintf('Downloading MIT Scene 67 data to ''%s''. This will take a while.', datasetDir) ; urlwrite(trainImagesUrl, fullfile(datasetDir, 'TrainImages.txt')) ; urlwrite(testImagesUrl, fullfile(datasetDir, 'TestImages.txt')) ; untar(url, datasetDir) ; end % Construct image database imdb structure imdb.meta.sets = {'train', 'val', 'test'} ; trainNames = textread(fullfile(datasetDir, 'TrainImages.txt'),'%s','delimiter','\n') ; testNames = textread(fullfile(datasetDir, 'TestImages.txt'),'%s','delimiter','\n') ; names = [trainNames; testNames]' ; sets = [ones(1,numel(trainNames)), 3*ones(1,numel(testNames))] ; imdb.images.id = 1:numel(names) ; [imdb.images.name,perm] = sort(names) ; imdb.images.set = sets(perm) ; a = regexp(imdb.images.name, '^([^/]+)*/.*$', 'tokens') ; for i = 1:numel(names) a{i} = a{i}{1}{1} ; end [imdb.meta.classes, ~, imdb.images.class] = unique(a) ; imdb.images.class = imdb.images.class(:)' ; imdb.imageDir = fullfile(datasetDir, 'Images') ; if opts.lite ok = {} ; for c = 1:3 ok{end+1} = vl_colsubset(find(imdb.images.class == c & imdb.images.set == 1), 5) ; ok{end+1} = vl_colsubset(find(imdb.images.class == c & imdb.images.set == 3), 5) ; end ok = cat(2, ok{:}) ; imdb.meta.classes = imdb.meta.classes(1:3) ; imdb.images.id = imdb.images.id(ok) ; imdb.images.name = imdb.images.name(ok) ; imdb.images.set = imdb.images.set(ok) ; imdb.images.class = imdb.images.class(ok) ; end vlfeat/apps/recognition/encodeImage.m0000644000175000017500000001223612237004745016616 0ustar dimadimafunction descrs = encodeImage(encoder, im, varargin) % ENCODEIMAGE Apply an encoder to an image % DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER % to image IM, returning a corresponding code vector PSI. % % IM can be an image, the path to an image, or a cell array of % the same, to operate on multiple images. % % ENCODEIMAGE(ENCODER, IM, CACHE) utilizes the specified CACHE % directory to store encodings for the given images. The cache % is used only if the images are specified as file names. % % See also: TRAINENCODER(). % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.cacheDir = [] ; opts.cacheChunkSize = 512 ; opts = vl_argparse(opts,varargin) ; if ~iscell(im), im = {im} ; end % break the computation into cached chunks startTime = tic ; descrs = cell(1, numel(im)) ; numChunks = ceil(numel(im) / opts.cacheChunkSize) ; for c = 1:numChunks n = min(opts.cacheChunkSize, numel(im) - (c-1)*opts.cacheChunkSize) ; chunkPath = fullfile(opts.cacheDir, sprintf('chunk-%03d.mat',c)) ; if ~isempty(opts.cacheDir) && exist(chunkPath) fprintf('%s: loading descriptors from %s\n', mfilename, chunkPath) ; load(chunkPath, 'data') ; else range = (c-1)*opts.cacheChunkSize + (1:n) ; fprintf('%s: processing a chunk of %d images (%3d of %3d, %5.1fs to go)\n', ... mfilename, numel(range), ... c, numChunks, toc(startTime) / (c - 1) * (numChunks - c + 1)) ; data = processChunk(encoder, im(range)) ; if ~isempty(opts.cacheDir) save(chunkPath, 'data') ; end end descrs{c} = data ; clear data ; end descrs = cat(2,descrs{:}) ; % -------------------------------------------------------------------- function psi = processChunk(encoder, im) % -------------------------------------------------------------------- psi = cell(1,numel(im)) ; if numel(im) > 1 & matlabpool('size') > 1 parfor i = 1:numel(im) psi{i} = encodeOne(encoder, im{i}) ; end else % avoiding parfor makes debugging easier for i = 1:numel(im) psi{i} = encodeOne(encoder, im{i}) ; end end psi = cat(2, psi{:}) ; % -------------------------------------------------------------------- function psi = encodeOne(encoder, im) % -------------------------------------------------------------------- im = encoder.readImageFn(im) ; features = encoder.extractorFn(im) ; imageSize = size(im) ; psi = {} ; for i = 1:size(encoder.subdivisions,2) minx = encoder.subdivisions(1,i) * imageSize(2) ; miny = encoder.subdivisions(2,i) * imageSize(1) ; maxx = encoder.subdivisions(3,i) * imageSize(2) ; maxy = encoder.subdivisions(4,i) * imageSize(1) ; ok = ... minx <= features.frame(1,:) & features.frame(1,:) < maxx & ... miny <= features.frame(2,:) & features.frame(2,:) < maxy ; descrs = encoder.projection * bsxfun(@minus, ... features.descr(:,ok), ... encoder.projectionCenter) ; if encoder.renormalize descrs = bsxfun(@times, descrs, 1./max(1e-12, sqrt(sum(descrs.^2)))) ; end w = size(im,2) ; h = size(im,1) ; frames = features.frame(1:2,:) ; frames = bsxfun(@times, bsxfun(@minus, frames, [w;h]/2), 1./[w;h]) ; descrs = extendDescriptorsWithGeometry(encoder.geometricExtension, frames, descrs) ; switch encoder.type case 'bovw' [words,distances] = vl_kdtreequery(encoder.kdtree, encoder.words, ... descrs, ... 'MaxComparisons', 100) ; z = vl_binsum(zeros(encoder.numWords,1), 1, double(words)) ; z = sqrt(z) ; case 'fv' z = vl_fisher(descrs, ... encoder.means, ... encoder.covariances, ... encoder.priors, ... 'Improved') ; case 'vlad' [words,distances] = vl_kdtreequery(encoder.kdtree, encoder.words, ... descrs, ... 'MaxComparisons', 15) ; assign = zeros(encoder.numWords, numel(words), 'single') ; assign(sub2ind(size(assign), double(words), 1:numel(words))) = 1 ; z = vl_vlad(descrs, ... encoder.words, ... assign, ... 'SquareRoot', ... 'NormalizeComponents') ; end z = z / max(sqrt(sum(z.^2)), 1e-12) ; psi{i} = z(:) ; end psi = cat(1, psi{:}) ; % -------------------------------------------------------------------- function psi = getFromCache(name, cache) % -------------------------------------------------------------------- [drop, name] = fileparts(name) ; cachePath = fullfile(cache, [name '.mat']) ; if exist(cachePath, 'file') data = load(cachePath) ; psi = data.psi ; else psi = [] ; end % -------------------------------------------------------------------- function storeToCache(name, cache, psi) % -------------------------------------------------------------------- [drop, name] = fileparts(name) ; cachePath = fullfile(cache, [name '.mat']) ; vl_xmkdir(cache) ; data.psi = psi ; save(cachePath, '-STRUCT', 'data') ; vlfeat/apps/recognition/readImage.m0000644000175000017500000000162712237004745016276 0ustar dimadimafunction [im, scale] = readImage(imagePath) % READIMAGE Read and standardize image % [IM, SCALE] = READIMAGE(IMAGEPATH) reads the specified image file, % converts the result to SINGLE class, and rescales the image % to have a maximum height of 480 pixels, returing the corresponding % scaling factor SCALE. % % READIMAGE(IM) where IM is already an image applies only the % standardization to it. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). if ischar(imagePath) try im = imread(imagePath) ; catch error('Corrupted image %s', imagePath) ; end else im = imagePath ; end im = im2single(im) ; scale = 1 ; if (size(im,1) > 480) scale = 480 / size(im,1) ; im = imresize(im, scale) ; im = min(max(im,0),1) ; end vlfeat/apps/recognition/setupCaltech256.m0000644000175000017500000000461412237004745017300 0ustar dimadimafunction imdb = setupCaltech256(datasetDir, varargin) % SETUPCALTECH256 Setup Caltech 256 and 101 datasets % This is similar to SETUPGENERIC(), with modifications to setup % Caltech-101 and Caltech-256 according to the standard % evaluation protocols. Specific options include: % % Variant:: 'caltech256' % Either 'caltech101' or 'caltech256'. % % AutoDownload:: true % Automatically download the data from the Internet if not % found at DATASETDIR. % % See:: SETUPGENERIC(). % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.lite = false ; opts.numTrain = 30 ; opts.numTest = 25 ; opts.seed = 1 ; opts.variant = 'caltech256' ; opts.autoDownload = true ; opts = vl_argparse(opts, varargin) ; % Download and unpack vl_xmkdir(datasetDir) ; switch opts.variant case 'caltech101' name = '101_ObjectCategories' ; checkClassName = 'airplanes' ; url = 'http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz' ; numClasses = 102 ; case 'caltech256' name = '256_ObjectCategories' ; checkClassName = '001.ak47' ; url = 'http://www.vision.caltech.edu/Image_Datasets/Caltech256/256_ObjectCategories.tar' ; numClasses = 257 ; otherwise error('Uknown dataset variant ''%s''.', opts.variant) ; end if exist(fullfile(datasetDir, checkClassName), 'file') % ok elseif exist(fullfile(datasetDir, name, checkClassName), 'file') datasetDir = fullfile(datasetDir, name) ; elseif opts.autoDownload fprintf('Downloading %s data to ''%s''. This will take a while.', opts.variant, datasetDir) ; untar(url, datasetDir) ; datasetDir = fullfile(datasetDir, name) ; else error('Could not find %s dataset in ''%s''', opts.variant, datasetDir) ; end % Read classes imdb = setupGeneric(datasetDir, ... 'numTrain', opts.numTrain, 'numVal', 0, 'numTest', opts.numTest, ... 'expectedNumClasses', numClasses, ... 'seed', opts.seed, 'lite', opts.lite) ; % In Caltech 256 background is not evaluated switch opts.variant case 'caltech101' case 'caltech256' imdb.images.sets(classes == 257) = 0 ; ok = find(imdb.images.sets ~= 0) ; imdb.images.id = ids(ok) ; imdb.images.name = names(ok) ; imdb.images.set = sets(ok) ; imdb.images.class = classes(ok) ; end vlfeat/apps/recognition/extendDescriptorsWithGeometry.m0000644000175000017500000000146612237004745022502 0ustar dimadimafunction descrs = extendDescriptorsWithGeometry(type, frames, descrs) % EXTENDDESCRIPTORSWITHGEOMETRY Extend feature descriptors with geometric components % DESCRS = EXTENDDESCRIPTORSWITHGEOMETRY(TYPE, FRAMES, DESCRS) % extends the descriptorss DESCRS with either nothing (TYPE = % 'none') or XY (TYPE = 'xy') from the FRAMES matrix. Note that, % for this to make sense, DESCRS and FRAMES should be properly normalized. % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). switch lower(type) case 'none' case 'xy' z = frames(1:2,:) ; descrs = cat(1, descrs, z) ; otherwise error('Uknown geometric extension ''%s''.', type) ; end vlfeat/apps/recognition/traintest.m0000644000175000017500000001372112237004745016433 0ustar dimadimafunction recognition_demo(varargin) % RECOGNITION_DEMO Demonstrates using VLFeat for image classification if ~exist('vl_version') run(fullfile(fileparts(which(mfilename)), ... '..', '..', 'toolbox', 'vl_setup.m')) ; end opts.dataset = 'caltech101' ; opts.prefix = 'bovw' ; opts.encoderParams = {'type', 'bovw'} ; opts.seed = 1 ; opts.lite = true ; opts.C = 1 ; opts.kernel = 'linear' ; opts.dataDir = 'data'; for pass = 1:2 opts.datasetDir = fullfile(opts.dataDir, opts.dataset) ; opts.resultDir = fullfile(opts.dataDir, opts.prefix) ; opts.imdbPath = fullfile(opts.resultDir, 'imdb.mat') ; opts.encoderPath = fullfile(opts.resultDir, 'encoder.mat') ; opts.modelPath = fullfile(opts.resultDir, 'model.mat') ; opts.diaryPath = fullfile(opts.resultDir, 'diary.txt') ; opts.cacheDir = fullfile(opts.resultDir, 'cache') ; opts = vl_argparse(opts,varargin) ; end % do not do anything if the result data already exist if exist(fullfile(opts.resultDir,'result.mat')), load(fullfile(opts.resultDir,'result.mat'), 'ap', 'confusion') ; fprintf('%35s mAP = %04.1f, mean acc = %04.1f\n', opts.prefix, ... 100*mean(ap), 100*mean(diag(confusion))) ; return ; end vl_xmkdir(opts.cacheDir) ; diary(opts.diaryPath) ; diary on ; disp('options:' ); disp(opts) ; % -------------------------------------------------------------------- % Get image database % -------------------------------------------------------------------- if exist(opts.imdbPath) imdb = load(opts.imdbPath); else switch opts.dataset case 'scene67', imdb = setupScene67(opts.datasetDir, 'lite', opts.lite) ; case 'caltech101', imdb = setupCaltech256(opts.datasetDir, 'lite', opts.lite, ... 'variant', 'caltech101', 'seed', opts.seed) ; case 'caltech256', imdb = setupCaltech256(opts.datasetDir, 'lite', opts.lite) ; case 'voc07', imdb = setupVoc(opts.datasetDir, 'lite', opts.lite, 'edition', '2007') ; case 'fmd', imdb = setupFMD(opts.datasetDir, 'lite', opts.lite) ; otherwise, error('Unknown dataset type.') ; end save(opts.imdbPath, '-struct', 'imdb') ; end % -------------------------------------------------------------------- % Train encoder and encode images % -------------------------------------------------------------------- if exist(opts.encoderPath) encoder = load(opts.encoderPath) ; else numTrain = 5000 ; if opts.lite, numTrain = 10 ; end train = vl_colsubset(find(imdb.images.set <= 2), numTrain, 'uniform') ; encoder = trainEncoder(fullfile(imdb.imageDir,imdb.images.name(train)), ... opts.encoderParams{:}, ... 'lite', opts.lite) ; save(opts.encoderPath, '-struct', 'encoder') ; diary off ; diary on ; end descrs = encodeImage(encoder, fullfile(imdb.imageDir, imdb.images.name), ... 'cacheDir', opts.cacheDir) ; diary off ; diary on ; % -------------------------------------------------------------------- % Train and evaluate models % -------------------------------------------------------------------- if isfield(imdb.images, 'class') classRange = unique(imdb.images.class) ; else classRange = 1:numel(imdb.classes.imageIds) ; end numClasses = numel(classRange) ; % apply kernel maps switch opts.kernel case 'linear' case 'hell' descrs = sign(descrs) .* sqrt(abs(descrs)) ; case 'chi2' descrs = vl_homkermap(descrs,1,'kchi2') ; otherwise assert(false) ; end descrs = bsxfun(@times, descrs, 1./sqrt(sum(descrs.^2))) ; % train and test train = find(imdb.images.set <= 2) ; test = find(imdb.images.set == 3) ; lambda = 1 / (opts.C*numel(train)) ; par = {'Solver', 'sdca', 'Verbose', ... 'BiasMultiplier', 1, ... 'Epsilon', 0.001, ... 'MaxNumIterations', 100 * numel(train)} ; scores = cell(1, numel(classRange)) ; ap = zeros(1, numel(classRange)) ; ap11 = zeros(1, numel(classRange)) ; w = cell(1, numel(classRange)) ; b = cell(1, numel(classRange)) ; for c = 1:numel(classRange) if isfield(imdb.images, 'class') y = 2 * (imdb.images.class == classRange(c)) - 1 ; else y = - ones(1, numel(imdb.images.id)) ; [~,loc] = ismember(imdb.classes.imageIds{classRange(c)}, imdb.images.id) ; y(loc) = 1 - imdb.classes.difficult{classRange(c)} ; end if all(y <= 0), continue ; end [w{c},b{c}] = vl_svmtrain(descrs(:,train), y(train), lambda, par{:}) ; scores{c} = w{c}' * descrs + b{c} ; [~,~,info] = vl_pr(y(test), scores{c}(test)) ; ap(c) = info.ap ; ap11(c) = info.ap_interp_11 ; fprintf('class %s AP %.2f; AP 11 %.2f\n', imdb.meta.classes{classRange(c)}, ... ap(c) * 100, ap11(c)*100) ; end scores = cat(1,scores{:}) ; diary off ; diary on ; % confusion matrix (can be computed only if each image has only one label) if isfield(imdb.images, 'class') [~,preds] = max(scores, [], 1) ; confusion = zeros(numClasses) ; for c = 1:numClasses sel = find(imdb.images.class == classRange(c) & imdb.images.set == 3) ; tmp = accumarray(preds(sel)', 1, [numClasses 1]) ; tmp = tmp / max(sum(tmp),1e-10) ; confusion(c,:) = tmp(:)' ; end else confusion = NaN ; end % save results save(opts.modelPath, 'w', 'b') ; save(fullfile(opts.resultDir,'result.mat'), ... 'scores', 'ap', 'ap11', 'confusion', 'classRange', 'opts') ; % figures meanAccuracy = sprintf('mean accuracy: %f\n', mean(diag(confusion))); mAP = sprintf('mAP: %.2f %%; mAP 11: %.2f', mean(ap) * 100, mean(ap11) * 100) ; figure(1) ; clf ; imagesc(confusion) ; axis square ; title([opts.prefix ' - ' meanAccuracy]) ; vl_printsize(1) ; print('-dpdf', fullfile(opts.resultDir, 'result-confusion.pdf')) ; print('-djpeg', fullfile(opts.resultDir, 'result-confusion.jpg')) ; figure(2) ; clf ; bar(ap * 100) ; title([opts.prefix ' - ' mAP]) ; ylabel('AP %%') ; xlabel('class') ; grid on ; vl_printsize(1) ; ylim([0 100]) ; print('-dpdf', fullfile(opts.resultDir,'result-ap.pdf')) ; disp(meanAccuracy) ; disp(mAP) ; diary off ; end vlfeat/apps/recognition/setupFMD.m0000644000175000017500000000225512237004745016105 0ustar dimadimafunction imdb = setupFMD(datasetDir, varargin) % SETUPSCENE67 Setup Flickr Material Dataset % This is similar to SETUPCALTECH101(), with modifications to setup % the Flickr Material Dataset accroding to the standard % evaluation protocols. % % See: SETUPCALTECH101(). % Author: Andrea Vedaldi % Copyright (C) 2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). opts.lite = false ; opts.seed = 1 ; opts.numTrain = 50 ; opts.numTest = 50 ; opts.autoDownload = true ; opts = vl_argparse(opts, varargin) ; % Download and unpack vl_xmkdir(datasetDir) ; if exist(fullfile(datasetDir, 'image', 'wood')) % ok elseif opts.autoDownload url = 'http://people.csail.mit.edu/celiu/CVPR2010/FMD/FMD.zip' ; fprintf('Downloading FMD data to ''%s''. This will take a while.', datasetDir) ; unzip(url, datasetDir) ; else error('FMD not found in %s', datasetDir) ; end imdb = setupGeneric(fullfile(datasetDir,'image'), ... 'numTrain', opts.numTrain, 'numVal', 0, 'numTest', opts.numTest, ... 'expectedNumClasses', 10, ... 'seed', opts.seed, 'lite', opts.lite) ; vlfeat/apps/phow_caltech101.m0000644000175000017500000002651312237004745014763 0ustar dimadimafunction phow_caltech101() % PHOW_CALTECH101 Image classification in the Caltech-101 dataset % This program demonstrates how to use VLFeat to construct an image % classifier on the Caltech-101 data. The classifier uses PHOW % features (dense SIFT), spatial histograms of visual words, and a % Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT, % kd-trees, and homogeneous kernel map. The program also % demonstrates VLFeat PEGASOS SVM solver, although for this small % dataset other solvers such as LIBLINEAR can be more efficient. % % By default 15 training images are used, which should result in % about 64% performance (a good performance considering that only a % single feature type is being used). % % Call PHOW_CALTECH101 to train and test a classifier on a small % subset of the Caltech-101 data. Note that the program % automatically downloads a copy of the Caltech-101 data from the % Internet if it cannot find a local copy. % % Edit the PHOW_CALTECH101 file to change the program configuration. % % To run on the entire dataset change CONF.TINYPROBLEM to FALSE. % % The Caltech-101 data is saved into CONF.CALDIR, which defaults to % 'data/caltech-101'. Change this path to the desired location, for % instance to point to an existing copy of the Caltech-101 data. % % The program can also be used to train a model on custom data by % pointing CONF.CALDIR to it. Just create a subdirectory for each % class and put the training images there. Make sure to adjust % CONF.NUMTRAIN accordingly. % % Intermediate files are stored in the directory CONF.DATADIR. All % such files begin with the prefix CONF.PREFIX, which can be changed % to test different parameter settings without overriding previous % results. % % The program saves the trained model in % /-model.mat. This model can be used to % test novel images independently of the Caltech data. % % load('data/baseline-model.mat') ; # change to the model path % label = model.classify(model, im) ; % % Author: Andrea Vedaldi % Copyright (C) 2011-2013 Andrea Vedaldi % All rights reserved. % % This file is part of the VLFeat library and is made available under % the terms of the BSD license (see the COPYING file). conf.calDir = 'data/caltech-101' ; conf.dataDir = 'data/' ; conf.autoDownloadData = true ; conf.numTrain = 15 ; conf.numTest = 15 ; conf.numClasses = 102 ; conf.numWords = 600 ; conf.numSpatialX = [2 4] ; conf.numSpatialY = [2 4] ; conf.quantizer = 'kdtree' ; conf.svm.C = 10 ; conf.svm.solver = 'sdca' ; %conf.svm.solver = 'sgd' ; %conf.svm.solver = 'liblinear' ; conf.svm.biasMultiplier = 1 ; conf.phowOpts = {'Step', 3} ; conf.clobber = false ; conf.tinyProblem = true ; conf.prefix = 'baseline' ; conf.randSeed = 1 ; if conf.tinyProblem conf.prefix = 'tiny' ; conf.numClasses = 5 ; conf.numSpatialX = 2 ; conf.numSpatialY = 2 ; conf.numWords = 300 ; conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ; end conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ; conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ; conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ; conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ; randn('state',conf.randSeed) ; rand('state',conf.randSeed) ; vl_twister('state',conf.randSeed) ; % -------------------------------------------------------------------- % Download Caltech-101 data % -------------------------------------------------------------------- if ~exist(conf.calDir, 'dir') || ... (~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ... ~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes'))) if ~conf.autoDownloadData error(... ['Caltech-101 data not found. ' ... 'Set conf.autoDownloadData=true to download the required data.']) ; end vl_xmkdir(conf.calDir) ; calUrl = ['http://www.vision.caltech.edu/Image_Datasets/' ... 'Caltech101/101_ObjectCategories.tar.gz'] ; fprintf('Downloading Caltech-101 data to ''%s''. This will take a while.', conf.calDir) ; untar(calUrl, conf.calDir) ; end if ~exist(fullfile(conf.calDir, 'airplanes'),'dir') conf.calDir = fullfile(conf.calDir, '101_ObjectCategories') ; end % -------------------------------------------------------------------- % Setup data % -------------------------------------------------------------------- classes = dir(conf.calDir) ; classes = classes([classes.isdir]) ; classes = {classes(3:conf.numClasses+2).name} ; images = {} ; imageClass = {} ; for ci = 1:length(classes) ims = dir(fullfile(conf.calDir, classes{ci}, '*.jpg'))' ; ims = vl_colsubset(ims, conf.numTrain + conf.numTest) ; ims = cellfun(@(x)fullfile(classes{ci},x),{ims.name},'UniformOutput',false) ; images = {images{:}, ims{:}} ; imageClass{end+1} = ci * ones(1,length(ims)) ; end selTrain = find(mod(0:length(images)-1, conf.numTrain+conf.numTest) < conf.numTrain) ; selTest = setdiff(1:length(images), selTrain) ; imageClass = cat(2, imageClass{:}) ; model.classes = classes ; model.phowOpts = conf.phowOpts ; model.numSpatialX = conf.numSpatialX ; model.numSpatialY = conf.numSpatialY ; model.quantizer = conf.quantizer ; model.vocab = [] ; model.w = [] ; model.b = [] ; model.classify = @classify ; % -------------------------------------------------------------------- % Train vocabulary % -------------------------------------------------------------------- if ~exist(conf.vocabPath) || conf.clobber % Get some PHOW descriptors to train the dictionary selTrainFeats = vl_colsubset(selTrain, 30) ; descrs = {} ; %for ii = 1:length(selTrainFeats) parfor ii = 1:length(selTrainFeats) im = imread(fullfile(conf.calDir, images{selTrainFeats(ii)})) ; im = standarizeImage(im) ; [drop, descrs{ii}] = vl_phow(im, model.phowOpts{:}) ; end descrs = vl_colsubset(cat(2, descrs{:}), 10e4) ; descrs = single(descrs) ; % Quantize the descriptors to get the visual words vocab = vl_kmeans(descrs, conf.numWords, 'verbose', 'algorithm', 'elkan', 'MaxNumIterations', 50) ; save(conf.vocabPath, 'vocab') ; else load(conf.vocabPath) ; end model.vocab = vocab ; if strcmp(model.quantizer, 'kdtree') model.kdtree = vl_kdtreebuild(vocab) ; end % -------------------------------------------------------------------- % Compute spatial histograms % -------------------------------------------------------------------- if ~exist(conf.histPath) || conf.clobber hists = {} ; parfor ii = 1:length(images) % for ii = 1:length(images) fprintf('Processing %s (%.2f %%)\n', images{ii}, 100 * ii / length(images)) ; im = imread(fullfile(conf.calDir, images{ii})) ; hists{ii} = getImageDescriptor(model, im); end hists = cat(2, hists{:}) ; save(conf.histPath, 'hists') ; else load(conf.histPath) ; end % -------------------------------------------------------------------- % Compute feature map % -------------------------------------------------------------------- psix = vl_homkermap(hists, 1, 'kchi2', 'gamma', .5) ; % -------------------------------------------------------------------- % Train SVM % -------------------------------------------------------------------- if ~exist(conf.modelPath) || conf.clobber switch conf.svm.solver case {'sgd', 'sdca'} lambda = 1 / (conf.svm.C * length(selTrain)) ; w = [] ; parfor ci = 1:length(classes) perm = randperm(length(selTrain)) ; fprintf('Training model for class %s\n', classes{ci}) ; y = 2 * (imageClass(selTrain) == ci) - 1 ; [w(:,ci) b(ci) info] = vl_svmtrain(psix(:, selTrain(perm)), y(perm), lambda, ... 'Solver', conf.svm.solver, ... 'MaxNumIterations', 50/lambda, ... 'BiasMultiplier', conf.svm.biasMultiplier, ... 'Epsilon', 1e-3); end case 'liblinear' svm = train(imageClass(selTrain)', ... sparse(double(psix(:,selTrain))), ... sprintf(' -s 3 -B %f -c %f', ... conf.svm.biasMultiplier, conf.svm.C), ... 'col') ; w = svm.w(:,1:end-1)' ; b = svm.w(:,end)' ; end model.b = conf.svm.biasMultiplier * b ; model.w = w ; save(conf.modelPath, 'model') ; else load(conf.modelPath) ; end % -------------------------------------------------------------------- % Test SVM and evaluate % -------------------------------------------------------------------- % Estimate the class of the test images scores = model.w' * psix + model.b' * ones(1,size(psix,2)) ; [drop, imageEstClass] = max(scores, [], 1) ; % Compute the confusion matrix idx = sub2ind([length(classes), length(classes)], ... imageClass(selTest), imageEstClass(selTest)) ; confus = zeros(length(classes)) ; confus = vl_binsum(confus, ones(size(idx)), idx) ; % Plots figure(1) ; clf; subplot(1,2,1) ; imagesc(scores(:,[selTrain selTest])) ; title('Scores') ; set(gca, 'ytick', 1:length(classes), 'yticklabel', classes) ; subplot(1,2,2) ; imagesc(confus) ; title(sprintf('Confusion matrix (%.2f %% accuracy)', ... 100 * mean(diag(confus)/conf.numTest) )) ; print('-depsc2', [conf.resultPath '.ps']) ; save([conf.resultPath '.mat'], 'confus', 'conf') ; % ------------------------------------------------------------------------- function im = standarizeImage(im) % ------------------------------------------------------------------------- im = im2single(im) ; if size(im,1) > 480, im = imresize(im, [480 NaN]) ; end % ------------------------------------------------------------------------- function hist = getImageDescriptor(model, im) % ------------------------------------------------------------------------- im = standarizeImage(im) ; width = size(im,2) ; height = size(im,1) ; numWords = size(model.vocab, 2) ; % get PHOW features [frames, descrs] = vl_phow(im, model.phowOpts{:}) ; % quantize local descriptors into visual words switch model.quantizer case 'vq' [drop, binsa] = min(vl_alldist(model.vocab, single(descrs)), [], 1) ; case 'kdtree' binsa = double(vl_kdtreequery(model.kdtree, model.vocab, ... single(descrs), ... 'MaxComparisons', 50)) ; end for i = 1:length(model.numSpatialX) binsx = vl_binsearch(linspace(1,width,model.numSpatialX(i)+1), frames(1,:)) ; binsy = vl_binsearch(linspace(1,height,model.numSpatialY(i)+1), frames(2,:)) ; % combined quantization bins = sub2ind([model.numSpatialY(i), model.numSpatialX(i), numWords], ... binsy,binsx,binsa) ; hist = zeros(model.numSpatialY(i) * model.numSpatialX(i) * numWords, 1) ; hist = vl_binsum(hist, ones(size(bins)), bins) ; hists{i} = single(hist / sum(hist)) ; end hist = cat(1,hists{:}) ; hist = hist / sum(hist) ; % ------------------------------------------------------------------------- function [className, score] = classify(model, im) % ------------------------------------------------------------------------- hist = getImageDescriptor(model, im) ; psix = vl_homkermap(hist, 1, 'kchi2', 'period', .7) ; scores = model.w' * psix + model.b' ; [score, best] = max(scores) ; className = model.classes{best} ; vlfeat/vlfeat.sln0000644000175000017500000000155512236612215012752 0ustar dimadima Microsoft Visual Studio Solution File, Format Version 10.00 # Visual Studio 2008 Project("{8BC9CEB8-8B4A-11D0-8D11-00A0C91BC942}") = "vlfeat", "vlfeat.vcproj", "{07367665-DCDA-4044-94CE-ABC01CC4119D}" EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Win32 = Debug|Win32 Release|Win32 = Release|Win32 EndGlobalSection GlobalSection(ProjectConfigurationPlatforms) = postSolution {07367665-DCDA-4044-94CE-ABC01CC4119D}.Debug|Win32.ActiveCfg = Debug|Win32 {07367665-DCDA-4044-94CE-ABC01CC4119D}.Debug|Win32.Build.0 = Debug|Win32 {07367665-DCDA-4044-94CE-ABC01CC4119D}.Release|Win32.ActiveCfg = Release|Win32 {07367665-DCDA-4044-94CE-ABC01CC4119D}.Release|Win32.Build.0 = Release|Win32 EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE EndGlobalSection EndGlobal vlfeat/make/0000755000175000017500000000000012237042314011660 5ustar dimadimavlfeat/make/dll.mak0000644000175000017500000001021412237042135013124 0ustar dimadima# file: dll.mak # description: Build VLFeat DLL # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). all: dll-all clean: dll-clean archclean: dll-archclean distclean: dll-distclean info: dll-info # -------------------------------------------------------------------- # Configuration # -------------------------------------------------------------------- DLL_NAME = vl DLL_CFLAGS = $(STD_CFLAGS) DLL_CFLAGS += -fvisibility=hidden -fPIC -DVL_BUILD_DLL -pthread DLL_CFLAGS += $(call if-like,%_sse2,$*, $(if $(DISABLE_SSE2),,-msse2)) DLL_CFLAGS += $(call if-like,%_avx,$*, $(if $(DISABLE_AVX),,-mavx)) DLL_CFLAGS += $(if $(DISABLE_OPENMP),,-fopenmp) DLL_LDFLAGS += -lm -lpthread BINDIR = bin/$(ARCH) # Mac OS X on Intel 32 bit processor ifeq ($(ARCH),maci) DLL_SUFFIX := dylib DLL_LDFLAGS += -m32 endif # Mac OS X on Intel 64 bit processor ifeq ($(ARCH),maci64) DLL_SUFFIX := dylib DLL_LDFLAGS += -m64 endif # Linux-32 ifeq ($(ARCH),glnx86) DLL_SUFFIX := so DLL_LDFLAGS += -m32 endif # Linux-64 ifeq ($(ARCH),glnxa64) DLL_SUFFIX := so DLL_LDFLAGS += -m64 endif # -------------------------------------------------------------------- # Build # -------------------------------------------------------------------- # On Mac OS X the library install_name is prefixed with @loader_path/. # At run time this causes the loader to search for a local copy of the # library for any binary which is linked against it. The install_name # can be modified later by install_name_tool. dll_tgt := $(BINDIR)/lib$(DLL_NAME).$(DLL_SUFFIX) dll_src := $(wildcard $(VLDIR)/vl/*.c) dll_hdr := $(wildcard $(VLDIR)/vl/*.h) dll_obj := $(addprefix $(BINDIR)/objs/, $(notdir $(dll_src:.c=.o))) dll_dep := $(dll_obj:.o=.d) arch_bins += $(dll_tgt) comm_bins += deps += $(dll_dep) .PHONY: dll .PHONY: dll-all, dll-clean, dll-archclean, dll-distclean .PHONY: dll-info no_dep_targets += dll-dir dll-clean dll-archclean dll-distclean no_dep_targets += dll-info dll-all: dll dll: $(dll_tgt) # generate the dll-dir target $(eval $(call gendir, dll, $(BINDIR) $(BINDIR)/objs)) $(BINDIR)/objs/%.o : $(VLDIR)/vl/%.c $(dll-dir) $(call C,CC) $(DLL_CFLAGS) \ -c "$(<)" -o "$(@)" $(BINDIR)/objs/%.d : $(VLDIR)/vl/%.c $(dll-dir) $(call C,CC) $(DLL_CFLAGS) \ -M -MT '$(BINDIR)/objs/$*.o $(BINDIR)/objs/$*.d' \ "$(<)" -MF "$(@)" $(BINDIR)/lib$(DLL_NAME).dylib : $(dll_obj) $(call C,CC) -m64 \ -dynamiclib \ -undefined suppress \ -flat_namespace \ -install_name @loader_path/lib$(DLL_NAME).dylib \ -compatibility_version $(VER) \ -current_version $(VER) \ -isysroot $(SDKROOT) \ -mmacosx_version_min=$(MACOSX_DEPLOYMENT_TARGET) \ $(DLL_LDFLAGS) \ $(if $(DISABLE_OPENMP),,-fopenmp) \ $^ \ -o $@ $(BINDIR)/lib$(DLL_NAME).so : $(dll_obj) $(call C,CC) -shared \ $(^) \ $(DLL_LDFLAGS) \ $(if $(DISABLE_OPENMP),,-fopenmp) \ -o $(@) dll-clean: rm -f $(dll_dep) $(dll_obj) dll-archclean: dll-clean rm -rf $(BINDIR) dll-distclean: rm -rf bin dll-info: $(call echo-title,VLFeat core library) $(call dump-var,dll_hdr) $(call dump-var,dll_src) $(call dump-var,dll_obj) $(call dump-var,dll_dep) $(call echo-var,BINDIR) $(call echo-var,DLL_NAME) $(call echo-var,DLL_CFLAGS) $(call echo-var,DLL_LDFLAGS) $(call echo-var,DLL_SUFFIX) $(call echo-var,LIBTOOL) @echo # Local variables: # mode: Makefile # End: vlfeat/make/update-mak.sh0000755000175000017500000000354512237004736014264 0ustar dimadima#!/bin/bash # file: update-mak.sh # description: Helper script to update Makefile.mak # author: Andrea Vedaldi # AUTORIGHTS # This script updates Makefile.mak based on the current source tree # and version in vl/generic.h. It saves the new version to # Makefile.mak.new. function make { # sed below removes the trailing '\' from the last entry ( printf '%s \\\n' "$1 =" find "$2" \ -name '*.c' \ -not -path 'vl/doc/*' \ | tr '/' '\' | while read -r x do printf ' %s \\\n' "$x" done ) | sed '$s/ *\\$//' echo } function sub { # Put substitution text at the beginning. Then sed puts the # beginning in its hold buffer, finds the paragraph starting with # "$2 =", deletes all but the last lines of it and substititue the # last line with the hold buffer. It also removes trailing # whitespaces. (make "$2" "$3" ; echo "$1") | \ sed -e '1{h;d;}' \ -e '2,/^$/{H;d;}' \ -e "/$2 =/,/^$/{/^$/!d;g;}" \ -e 's/ *$//' } function subv { # Substitutes VER = x.x.x with the new version string. echo "$1" | sed -e "/VER *=/s/\(\([0-9][0-9]*\.\{0,1\}\)\{3\}\)/$2/" } # source version string from vl/generic.h ver=$(cat vl/generic.h | sed -n \ 's/.*VL_VERSION_STRING.*\"\([0-9.]*\)\".*/\1/p') # source current nmake script a=$(cat Makefile.mak) # updaetes version string a=$(subv "$a" "$ver") # updates hard-wired list of source code files in the nmake script a=$(sub "$a" libsrc vl) a=$(sub "$a" cmdsrc src) a=$(sub "$a" mexsrc toolbox) echo "$a" > Makefile.mak.new diffs=$(diff Makefile.mak Makefile.mak.new) if [ -z "$diffs" ] then echo "Makefile.mak is up to date" rm Makefile.mak.new else echo "Makefile.mak should be updated to Makefile.mak.new" echo "Differences:" echo "$diffs" fi vlfeat/make/matlab.mak0000644000175000017500000002015612237042135013617 0ustar dimadima# file: matlab.mak # description: Build MATALB toolbox # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). # MATLAB support is enabled if $(MEX) is executable and if MATLAB root # can be deduced from the output of `$(MEX) -v`. Therefore setting MEX to # the empty string disables MATLAB support. MEX ?= mex MATLAB_PATH ?= $(strip $(shell test "$$(command -v '$(MEX)')" && \ $(MEX) -v 2>&1 | sed -n 's/.*MATLAB *= *\(.*\)/\1/gp')) MATLAB_EXE ?= "$(MATLAB_PATH)/bin/matlab" # if expand to empty string, set to empty string for use with ifdef ifeq ($(MATLAB_PATH),) MATLAB_PATH= endif ifdef MATLAB_PATH all: mex-all matlab-all clean: mex-clean matlab-clean archclean: mex-archclean matlab-archclean distclean: mex-distclean matlab-distclean endif info: mex-info matlab-info MEX_ARCH = $(ARCH) MEX_CFLAGS = -I$(VLDIR) -I$(VLDIR)/toolbox MEX_LDFLAGS = -L$(BINDIR) -lvl MEX_FLAGS = $(MEXFLAGS) MEX_FLAGS += -$(MEX_ARCH) MEX_FLAGS += $(if $(DEBUG), -g, -O) MEX_FLAGS += $(if $(PROFILE), -O -g,) MEX_FLAGS += CFLAGS='$$CFLAGS $(STD_CFLAGS)' # Mac OS X on Intel 32 bit processor ifeq ($(ARCH),maci) MEX_SUFFIX := mexmaci MEX_FLAGS += LDFLAGS='$$LDFLAGS $(STD_LDFLAGS)' MEX_FLAGS += CC='$(CC)' MEX_FLAGS += LD='$(CC)' endif # Mac OS X on Intel 64 bit processor ifeq ($(ARCH),maci64) MEX_SUFFIX := mexmaci64 MEX_FLAGS += -largeArrayDims MEX_FLAGS += LDFLAGS='$$LDFLAGS $(STD_LDFLAGS)' MEX_FLAGS += CC='$(CC)' MEX_FLAGS += LD='$(CC)' endif # Linux on 32 bit processor ifeq ($(ARCH),glnx86) MEX_SUFFIX := mexglx MEX_FLAGS += LDFLAGS='$$LDFLAGS $(STD_LDFLAGS) -Wl,--rpath,\\\$$ORIGIN/' endif # Linux on 64 bit processorm ifeq ($(ARCH),glnxa64) MEX_SUFFIX := mexa64 MEX_FLAGS += -largeArrayDims MEX_FLAGS += LDFLAGS='$$LDFLAGS $(STD_LDFLAGS) -Wl,--rpath,\\\$$ORIGIN/' endif MEX_BINDIR := toolbox/mex/$(MEX_SUFFIX) # -------------------------------------------------------------------- # Sanity check # -------------------------------------------------------------------- err_no_mex_suffix = $(shell echo "** Could not set MEX_SUFFIX for ARCH = $(ARCH)" 1>&2) err_no_mex_suffix +=no_mex_suffix ifeq ($(MEX_SUFFIX),) die:=$(error $(err_no_mex_suffix)) endif # -------------------------------------------------------------------- # Build # -------------------------------------------------------------------- .PHONY: mex-all, mex-dir, mex-info, mex-test .PHONY: mex-clean, mex-distclean, mex-archclean no_dep_targets += mex-dir mex-info mex-test no_dep_targets += mex-clean mex-distclean mex-archclean mex_src := $(shell find $(VLDIR)/toolbox -name "*.c") mex_tgt := $(addprefix $(MEX_BINDIR)/,\ $(notdir $(mex_src:.c=.$(MEX_SUFFIX)) ) ) mex_dep := $(mex_tgt:.$(MEX_SUFFIX)=.d) mex_dll := $(MEX_BINDIR)/lib$(DLL_NAME).$(DLL_SUFFIX) ifdef MATLAB_PATH arch_bins += $(mex_tgt) $(MEX_BINDIR)/lib$(DLL_NAME).$(DLL_SUFFIX) comm_bins += deps += $(mex_dep) endif vpath vl_%.c $(shell find $(VLDIR)/toolbox -type d) mex-all: $(mex_dll) $(mex_tgt) # generate the mex-dir target $(eval $(call gendir, mex, $(MEX_BINDIR))) # generate mex-dir target $(eval $(call gendir, mex, $(MEX_BINDIR))) $(MEX_BINDIR)/lib$(DLL_NAME).dylib : $(mex-dir) $(dll_obj) $(call C,CC) -m64 \ -dynamiclib \ -undefined suppress \ -flat_namespace \ -install_name @loader_path/lib$(DLL_NAME).dylib \ -compatibility_version $(VER) \ -current_version $(VER) \ -isysroot $(SDKROOT) \ -mmacosx_version_min=$(MACOSX_DEPLOYMENT_TARGET) \ $(DLL_LDFLAGS) \ $(if $(DISABLE_OPENMP),,-L$(MATLAB_PATH)/bin/$(ARCH)/) \ $(if $(DISABLE_OPENMP),,-L$(MATLAB_PATH)/sys/os/$(ARCH)/ -liomp5) \ $(dll_obj) \ -o $@ $(MEX_BINDIR)/lib$(DLL_NAME).so : $(mex-dir) $(dll_obj) $(call C,CC) -shared \ $(dll_obj) \ $(DLL_LDFLAGS) \ $(if $(DISABLE_OPENMP),,-L$(MATLAB_PATH)/bin/$(ARCH)/) \ $(if $(DISABLE_OPENMP),,-L$(MATLAB_PATH)/sys/os/$(ARCH)/ -liomp5) \ -o $(@) #$(mex_dll) : $(dll_tgt) $(mex-dir) # cp -v "$(<)" "$(@)" $(MEX_BINDIR)/%.d : %.c $(mex-dir) $(call C,CC) \ $(MEX_CFLAGS) \ -I"$(MATLAB_PATH)/extern/include" -M -MT \ '$(MEX_BINDIR)/$*.$(MEX_SUFFIX) $(MEX_BINDIR)/$*.d' \ "$(<)" -MF "$(@)" $(MEX_BINDIR)/%.$(MEX_SUFFIX) : %.c $(mex-dir) $(call C,MEX) \ $(MEX_FLAGS) \ $(MEX_CFLAGS) \ "$(<)" \ $(MEX_LDFLAGS) \ -outdir "$(dir $(@))" mex-info: $(call echo-title,MATLAB support) $(if $(MATLAB_PATH),\ @echo "MATLAB support enabled (MEX found)",\ @echo "MATLAB support disabled (MEX not found)") $(call dump-var,mex_src) $(call dump-var,mex_tgt) $(call dump-var,mex_dep) mex-clean: rm -f $(mex_dep) mex-archclean: mex-clean rm -rf $(MEX_BINDIR) mex-distclean: rm -rf "toolbox/mex" ; # -------------------------------------------------------------------- # Prefix-less M files # -------------------------------------------------------------------- # Populate the directory toolbox/noprefix with links to the MEX / M # files without the vl_ prefix. m_src := $(shell find $(VLDIR)/toolbox -name "vl_*.m") m_lnk := $(addprefix toolbox/noprefix/, \ $(filter-out setup.m, \ $(filter-out help.m, \ $(filter-out version.m, \ $(filter-out root.m, \ $(filter-out demo.m, \ $(filter-out compile.m, \ $(filter-out test_%, \ $(filter-out demo_%, \ $(filter-out tune_%, \ $(subst vl_,,$(notdir $(m_src))))))))))))) m_lnk += $(addprefix toolbox/noprefix/, \ $(subst, $(MEX_SUFFIX),.m,$(subst vl_,,$(notdir $(mex_tgt))))) vpath vl_%.m $(shell find $(VLDIR)/toolbox -type d) .PHONY: matlab-all, matlab-noprefix, matlab-info .PHONY: matlab-clean, matlab-archclean, matlab-distclean no_dep_targets += matlab-all matlab-noprefix matlab-info no_dep_targets += matlab-clean matlab-archclean matlab-distclean ifdef MATLAB_PATH arch_bins += comm_bins += $(m_lnk) deps += endif matlab-all: matlab-noprefix # generate matlab-noprefix-dir target $(eval $(call gendir, matlab-noprefix, toolbox/noprefix)) matlab-noprefix: $(matlab-noprefix-dir) $(m_lnk) toolbox/noprefix/%.m : vl_%.m @upperName=`echo "$*" | tr [a-z] [A-Z]` ; \ echo "function varargout = $*(varargin)" > "$@" ; \ cat "$<" | sed -n -e '/^function/b' -e '/^%.*$$/p' \ -e '/^%.*$$/b' -e q >> "$@" ; \ echo "[varargout{1:nargout}] = vl_$*(varargin{:});" >> "$@" ; matlab-test: @echo "Testing Matlab toolbox" ; \ cd toolbox ; \ RESULT=$$( \ $(MATLAB_EXE) -$(ARCH) -nodesktop -r \ "vl_setup('xtest','verbose') ; vl_test ; exit") ; \ echo "$$RESULT" ; \ if test -n "$$(echo \"$$RESULT\" | grep \"failed\")" ; \ then \ echo "Matlab toolbox test encountered an error!" ; \ exit 1 ; \ else \ echo "Matlab toolbox test completed successfully!" ; \ fi matlab-info: $(call dump-var,m_src) $(call dump-var,m_lnk) $(call echo-var,mex_dll) $(call echo-var,MATLAB_PATH) $(call echo-var,MATLAB_EXE) $(call echo-var,MEX) $(call echo-var,MEX_FLAGS) $(call echo-var,MEX_CFLAGS) $(call echo-var,MEX_LDFLAGS) @echo matlab-clean: matlab-archclean: matlab-clean matlab-distclean: rm -rf toolbox/noprefix # Emacs: # Local variables: # mode: Makefile # End: vlfeat/make/doc.mak0000644000175000017500000002602512237042135013125 0ustar dimadima# file: doc.mak # description: Makefile submodule to build the documentation # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). info: doc-info clean: doc-clean distclean: doc-distclean archclean: doc-archclean # -------------------------------------------------------------------- # Configuration # -------------------------------------------------------------------- DOXYGEN ?= doxygen PDFLATEX ?= pdflatex FIG2DEV ?= fig2dev INKSCAPE ?= inkscape CONVERT ?= convert PYTHON ?= python GROFF ?= groff TIDY ?= tidy MV ?= mv SED ?= sed # 95 DPI makes a letter page 808 pixels large screen_dpi := 95 doc: doc-api doc-man doc-matlab # generate doc-dir: target $(eval $(call gendir, doc, \ doc \ doc/build doc/build/api doc/build/man doc/build/figures \ doc/demo doc/figures doc/matlab doc/api)) # generate results-dir: target $(eval $(call gendir, results, results)) # -------------------------------------------------------------------- # MATLAB # -------------------------------------------------------------------- .PHONY: doc-deep ifdef MATLAB_PATH doc-matlab: doc/matlab/helpsearch/deletable endif # use MATLAB to create the figures for the tutorials doc-deep: all $(doc-dir) $(results-dir) cd toolbox ; \ VL_DEMO_PRINT=y $(MATLAB_EXE) \ -$(ARCH) -nodesktop -nosplash \ -r "clear mex;vl_setup demo;vl_demo;exit" @echo "Trimming images ..." find doc/demo -name "*.jpg" -exec $(CONVERT) -trim "{}" "{}" ";" @echo "Done trimming" $(MAKE) doc # make documentation searchable in MATLAB doc/matlab/helpsearch/deletable : doc/build/matlab/helpsearch/deletable $(doc-dir) cp -v doc/build/matlab/helptoc.xml doc/matlab/ cp -rv doc/build/matlab/helpsearch doc/matlab/ doc/build/matlab/helpsearch/deletable: doc/build/matlab/helptoc.xml $(MATLAB_EXE) -$(ARCH) -nodisplay -r "builddocsearchdb('doc/build/matlab/') ; exit" # -------------------------------------------------------------------- # MDoc # -------------------------------------------------------------------- doc-matlab: doc/build/matlab/mdoc.html # # Use mdoc.py to create the toolbox documentation that will be # embedded in the website. # doc/build/matlab/mdoc.html doc/build/matlab/helptoc.xml: \ $(m_src) \ docsrc/mdoc.py #make/doc.mak $(PYTHON) docsrc/mdoc.py toolbox doc/build/matlab \ --format=web \ --exclude='noprefix/.*' \ --exclude='xtune/.*' \ --exclude='xtest/.*' \ --exclude='.*/vl_test_.*' \ --exclude='.*/vl_demo_.*' \ --exclude='.*/vl_tune_.*' \ --helptoc \ --helptoc-toolbox-name VLFeat \ --verbose # -------------------------------------------------------------------- # Auto-include demo m-files # -------------------------------------------------------------------- m_demo_src=$(wildcard toolbox/demo/vl_demo_*.m) # Convert the various man pages doc/build/matlab/demo.xml : $(m_demo_src) $(doc-dir) make/doc.mak @echo "Indexing demo files -> $@" @( \ echo '' ; \ echo "" ; \ for fullName in $(m_demo_src) ; \ do \ fileName=$${fullName#toolbox/demo/} ; \ stem=$${fileName%.*} ; \ ( \ echo "" ; \ printf "

Source code for %s.m

" "$${stem}"; \ echo "

This file is located in the toolbox/demo folder in VLFeat package.

" ; \ echo "" ; \ echo "" ; \ echo "" ; \ echo "" \ ) ; \ done ; \ echo "" ; \ ) > "$@" # -------------------------------------------------------------------- # Man # -------------------------------------------------------------------- doc-man: doc/build/man/xman.xml doc/build/man/xman.html man_src := $(wildcard src/*.1) $(wildcard src/*.7) man_tgt := $(subst src/,doc/build/man/,$(addsuffix .html,$(man_src))) doc/build/man/index.html : $(doc-dir) # Integrate in Webdoc doc/build/man/xman.xml : $(man_tgt) $(doc-dir) @echo "Generating MAN XML webdooc document $@" @echo "" > "$@" @for fullName in $(man_src) ; \ do \ fileName=$${fullName#src/} ; \ stem=$${fileName%.*} ; \ ( \ echo "" ; \ echo "" ; \ echo "" \ ) >> "$@" ; \ done echo "" >> "$@" # Index page doc/build/man/xman.html : $(man_tgt) $(doc-dir) @echo "Generating MAN HTML index page $@" @echo "
    " > "$@" @for fullName in $(man_src) ; \ do \ fileName=$${fullName#src/} ; \ stem=$${fileName%.*} ; \ echo "
  • $${stem}
    • " >> "$@" ; \ done echo "
" >> "$@" # Convert the various man pages doc/build/man/%.html : src/% $(doc-dir) @$(print-command MAN2HTML, $@) @( \ echo '' ; \ echo "" ; \ $(GROFF) -mandoc -Thtml < "$<" | \ $(TIDY) -asxhtml 2>/dev/null | \ sed -e '1,// d' -e '/<\/body>/,$$ d' ; \ echo "" \ ) > "$@" # -------------------------------------------------------------------- # FIG and SVG figures and images # -------------------------------------------------------------------- doc_fig_src := $(wildcard docsrc/figures/*.fig) doc_svg_src := $(wildcard docsrc/figures/*.svg) doc_fig_tgt += $(subst docsrc/,doc/,$(doc_fig_src:.fig=.png)) $(subst docsrc/,doc/,$(doc_svg_src:.svg=.png)) .PRECIOUS: doc/build/figures/%.pdf .PRECIOUS: doc/build/figures/%.tex doc/figures/%.png : doc/build/figures/%.pdf $(call C,CONVERT) -density 300 "$<" -resample $(screen_dpi) -trim "$@" # Inkscape doc/build/figures/%-raw.pdf doc/build/figures/%-raw.tex: docsrc/figures/%.svg $(call C,INKSCAPE) --export-pdf=doc/build/figures/$(*)-raw.pdf --export-latex "$<" @$(MV) doc/build/figures/$(*)-raw.pdf_tex doc/build/figures/$(*)-raw.tex @$(SED) -e 's/$(*)-raw/doc\/build\/figures\/$(*)-raw/g' -i.bak 'doc/build/figures/$(*)-raw.tex' # Fig doc/build/figures/%-raw.tex : docsrc/figures/%.fig $(doc-dir) $(call C,FIG2DEV) -L pdftex_t -p doc/build/figures/$*-raw.pdf "$<" "$@" doc/build/figures/%-raw.pdf : docsrc/figures/%.fig $(doc-dir) $(call C,FIG2DEV) -L pdftex "$<" "$@" doc/build/figures/%.pdf doc/build/figures/%.aux doc/build/figures/%.log : \ doc/build/figures/%.tex doc/build/figures/%-raw.tex doc/build/figures/%-raw.pdf $(doc-dir) $(call C,PDFLATEX) -shell-escape -interaction=batchmode -output-directory="$(dir $@)" "$<" 2>/dev/null doc/build/figures/%.tex : $(doc-dir) @$(print-command GEN, $@) @/bin/echo '\documentclass[landscape]{article}' >$@ @/bin/echo '\usepackage[paper=a2paper,margin=0pt]{geometry}' >>$@ @/bin/echo '\usepackage{graphicx,color}' >>$@ @/bin/echo '\begin{document}' >>$@ @/bin/echo '\pagestyle{empty}' >>$@ @/bin/echo '\input{doc/build/figures/$(*)-raw.tex}' >>$@ @/bin/echo '\end{document}' >>$@ # -------------------------------------------------------------------- # Doxygen # -------------------------------------------------------------------- doc-api: doc/api/index.html doc/api/index.html: docsrc/doxygen.conf docsrc/vlfeat.bib VERSION \ $(dll_src) $(dll_hdr) $(doc_fig_tgt) toolbox/mexutils.h \ doc/build/doxygen_header.html doc/build/doxygen_footer.html ln -sf docsrc/vlfeat.bib vlfeat.bib $(DOXYGEN) $< 2>&1 | sed -e 's/Warning:/warning: /g' rm vlfeat.bib cp -fv docsrc/doxygen.css doc/api/doxygen.css # -------------------------------------------------------------------- # Webdoc # -------------------------------------------------------------------- webdoc_src = $(wildcard docsrc/*.xml) $(wildcard docsrc/*.html) doc: doc/index.html doc/vlfeat.css doc/pygmentize.css $(doc_fig_tgt) # prebuild to generate doxygen header and footer doc/build/doxygen_header.html doc/build/doxygen_footer.html: $(webdoc_src) $(doc-dir) VERSION=$(VER) $(PYTHON) docsrc/webdoc.py \ --outdir=doc/build/ \ --verbose \ docsrc/vlfeat-website-preproc.xml cat doc/build/api/index.html | \ sed -n '//q;p' > doc/build/doxygen_header.html echo '
' >> doc/build/doxygen_header.html cat doc/build/api/index.html | \ sed -n '//,$$p' > doc/build/doxygen_footer.html doc/vlfeat.css : docsrc/vlfeat.css cp -fv "$<" "$@" doc/pygmentize.css : docsrc/pygmentize.css cp -fv "$<" "$@" # build: this is the last step integrating all documentation doc/index.html: $(webdoc_src) $(doc-dir) \ doc/api/index.html \ doc/build/matlab/mdoc.html \ doc/build/man/xman.xml \ doc/build/man/xman.html \ doc/build/matlab/demo.xml \ docsrc/webdoc.py \ $(html_src) cp doc/api/index.html doc/api/index.html.bak VERSION=$(VER) $(PYTHON) docsrc/webdoc.py \ --outdir=doc \ --verbose \ --doxytag=doc/doxygen.tag \ --doxydir=api \ docsrc/vlfeat-website.xml mv doc/api/index.html.bak doc/api/index.html rsync -r docsrc/images doc/ # -------------------------------------------------------------------- # Maintenance and others # -------------------------------------------------------------------- .PHONY: doc-clean, doc-archclean, doc-distclean no_dep_targets := doc-clean doc-archclean doc-distclean VERSION: vl/generic.h echo "$(VER)" > VERSION doc-clean: rm -rf doc/build doc-archclean: doc-distclean: rm -f docsrc/*.pyc rm -rf doc # -------------------------------------------------------------------- # Debug Makefile # -------------------------------------------------------------------- .PHONY: doc-info doc-info : $(call echo-title,Documentation) $(call dump-var,man_src) $(call dump-var,fig_src) $(call dump-var,demo_src) $(call dump-var,pdf_tgt) $(call dump-var,eps_tgt) $(call dump-var,png_tgt) $(call dump-var,jpg_tgt) $(call dump-var,man_tgt) $(call dump-var,DOXYGEN) @echo # -------------------------------------------------------------------- # Xcode Support # -------------------------------------------------------------------- .PHONY: dox- dox- : dox .PHONY: dox-clean dox-clean: # Local variables: # mode: Makefile # End: vlfeat/make/nmake_helper.mak0000644000175000017500000000423412237004745015015 0ustar dimadima# -------------------------------------------------------------------- # Detect Visual Studio version # -------------------------------------------------------------------- !IF "$(MSVSVER)" == "" !IF "$(_NMAKE_VER)" == "" VL_MSVC = 4.0 VL_MSVS = 40 !ERROR *** Failed to determine version of Visual C++ !ELSEIF "$(_NMAKE_VER)" == "162" VL_MSVC = 5.0 VL_MSVS = 50 !ERROR *** Detected Visual C++ 5.0 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "6.00.8168.0" VL_MSVC = 6.0 VL_MSVS = 60 VL_MSC = 1200 !ERROR *** Detected Visual C++ 6.0 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "7.00.9466" VL_MSVC = 7.0 VL_MSVS = 70 VL_MSC = 1300 !ERROR *** Detected Visual C++ 7.0 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "7.10.3077" VL_MSVC = 7.1 VL_MSVS = 71 VL_MSC = 1310 !ERROR *** Detected Visual C++ 7.1 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "8.00.50727.42" VL_MSVC = 8.0 VL_MSVS = 80 VL_MSC = 1400 !ERROR *** Detected Visual C++ 8.0 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "8.00.50727.762" VL_MSVC = 8.0 VL_MSVS = 80 VL_MSC = 1400 !ERROR *** Detected Visual C++ 8.0 - NOT SUPPORTED !ELSEIF "$(_NMAKE_VER)" == "9.00.21022.08" VL_MSVC = 9.0 VL_MSVS = 90 VL_MSC = 1500 !ELSEIF "$(_NMAKE_VER)" == "9.00.30729.01" VL_MSVC = 9.0 VL_MSVS = 90 VL_MSC = 1500 !ELSEIF "$(_NMAKE_VER)" == "10.00.30128.01" VL_MSVC = 10.0 VL_MSVS = 100 VL_MSC = 1600 !ELSEIF "$(_NMAKE_VER)" == "10.00.30319.01" VL_MSVC = 10.0 VL_MSVS = 100 VL_MSC = 1600 !ELSEIF "$(_NMAKE_VER)" == "11.00.40825.2" VL_MSVC = 11.0 VL_MSVS = 110 VL_MSC = 1700 !ELSEIF "$(_NMAKE_VER)" == "11.00.51106.1" VL_MSVC = 11.0 VL_MSVS = 110 VL_MSC = 1700 !ELSEIF "$(_NMAKE_VER)" == "11.00.50727.1" VL_MSVC = 11.0 VL_MSVS = 110 VL_MSC = 1700 !ELSEIF "$(_NMAKE_VER)" == "11.00.60315.1" VL_MSVC = 11.0 VL_MSVS = 110 VL_MSC = 1700 !ELSEIF "$(_NMAKE_VER)" == "11.00.60430.2" VL_MSVC = 11.0 VL_MSVS = 110 VL_MSC = 1700 !ELSE VL_MSVC = VL_MSVS = VL_MSC = !ENDIF MSVSVER=$(VL_MSVS) !ENDIF !IF "$(MSVSVER)" == "" !MESSAGE *** Cannot determine Visual C++ version !ERROR *** Aborting make job !ELSE !MESSAGE *** Using Microsoft NMAKE version $(_NMAKE_VER) !MESSAGE *** Using Microsoft Visual C++ version $(MSVSVER) !MESSAGE *** !ENDIF vlfeat/make/octave.mak0000644000175000017500000000761512237042135013645 0ustar dimadima# file: octave.mak # description: Build MEX files for GNU Octave # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). # Octave support is experimental. Currently, the MEX files compile # successfully but a number of bugs and subtle MATLAB # imcompatibilities make VLFeat not fully functional. # # Octave support is enabled if $(MKOCTFILE) is executable. Therefore # setting MKOCTFILE to the empty string disables Octave support. OCTAVE ?= octave MKOCTFILE ?= OCTAVE_ENABLE ?= $(strip $(shell type -P $(MKOCTFILE) 2>&1 >/dev/null && \ $(MKOCTFILE) -p OCTAVE_LIBS 2>&1 | \ grep octave)) # if expand to empty string, set to empty string for use with ifdef ifeq ($(OCTAVE_ENABLE),) OCTAVE_ENABLE= endif ifdef OCTAVE_ENABLE all: octave-mex-all clean: octave-mex-clean archclean: octave-mex-archclean distclean: octave-mex-distclean endif info: octave-mex-info OCTAVE_MEX_SUFFIX := mex OCTAVE_MEX_BINDIR := toolbox/mex/octave/$(MEX_SUFFIX) OCTAVE_MEX_FLAGS = -L$(BINDIR) -lvl OCTAVE_MEX_CFLAGS = -I$(VLDIR)/toolbox OCTAVE_MEX_CFLAGS += $(if $(DEBUG), -g, -O) OCTAVE_MEX_CFLAGS += $(if $(PROFILE), -O -g,) OCTAVE_MEX_CFLAGS += $(STD_CFLAGS) OCTAVE_MEX_LDFLAGS = # Mac OS X on Intel 32 bit processor ifeq ($(ARCH),maci) endif # Mac OS X on Intel 64 bit processor ifeq ($(ARCH),maci64) endif # Linux on 32 bit processor ifeq ($(ARCH),glnx86) OCTAVE_MEX_FLAGS += -Wl,--rpath,\\\$$ORIGIN/ endif # Linux on 64 bit processorm ifeq ($(ARCH),glnxa64) OCTAVE_MEX_FLAGS += -Wl,--rpath,\\\$$ORIGIN/ endif # -------------------------------------------------------------------- # Build # -------------------------------------------------------------------- .PHONY: octave-all, octave-mex-all, octave-mex-dir, octave-info .PHONY: octave-clean octave-archclean octave-distclean no_dep_targets += octave-info no_dep_targets += octave-clean octave-archclean octave-distclean octave_mex_src := $(shell find $(VLDIR)/toolbox -name "*.c") octave_mex_tgt := $(addprefix $(OCTAVE_MEX_BINDIR)/,\ $(notdir $(mex_src:.c=.$(OCTAVE_MEX_SUFFIX)) ) ) octave_mex_dep := $(octave_mex_tgt:.$(OCTAVE_MEX_SUFFIX)=.d) octave_mex_dll := $(OCTAVE_MEX_BINDIR)/lib$(DLL_NAME).$(DLL_SUFFIX) ifdef OCTAVE_ENABLE arch_bins += $(octave_mex_tgt) $(octave_mex_dll) comm_bins += deps += $(octave_mex_dep) endif vpath vl_%.c $(shell find $(VLDIR)/toolbox -type d) octave-mex-all: $(octave_mex_tgt) matlab-noprefix # generate octave-mex-dir target $(eval $(call gendir, octave-mex, $(OCTAVE_MEX_BINDIR))) $(OCTAVE_MEX_BINDIR)/%.d : %.c $(octave-mex-dir) $(call C,MKOCTFILE) $(OCTAVE_MEX_FLAGS) -M "$(<)" @mv "$(<:.c=.d)" $(OCTAVE_MEX_BINDIR) $(octave_mex_dll) : $(dll_tgt) cp -v "$(<)" "$(@)" $(OCTAVE_MEX_BINDIR)/%.$(OCTAVE_MEX_SUFFIX) %.o : %.c $(octave-mex-dir) $(octave_mex_dll) CFLAGS="$(OCTAVE_MEX_CFLAGS)" \ CXXFLAGS="$(OCTAVE_MEX_CXXFLAGS)" \ LDFLAGS="$(OCTAVE_MEX_LDFLAGS)" \ $(MKOCTFILE) \ --mex $(OCTAVE_MEX_FLAGS) \ "$(<)" --output "$(@)" -v @rm -f $(<:.c=.o) octave-mex-info: $(call echo-title,Octave support) $(if $(OCTAVE_ENABLE),\ @echo "OCTAVE support enabled (MKOCTFILE found)",\ @echo "OCTAVE support disabled (MKOCTFILE not found)") $(call dump-var,octave_mex_src) $(call dump-var,octave_mex_tgt) $(call dump-var,octave_mex_dep) $(call echo-var,OCTAVE) $(call echo-var,MKOCTFILE) $(call echo-var,OCTAVE_MEX_BINDIR) $(call echo-var,OCTAVE_MEX_FLAGS) $(call echo-var,OCTAVE_MEX_CFLAGS) $(call echo-var,OCTAVE_MEX_LDLAGS) @echo octave-mex-clean: rm -f $(octave_mex_dep) octave-mex-archclean: octave-clean rm -f $(octave_mex_tgt) octave-mex-distclean: octave-archclean rm -rf toolbox/mex/octave # Emacs: # Local variables: # mode: Makefile # End: vlfeat/make/dist.mak0000644000175000017500000002025712237042135013324 0ustar dimadima# file: dist.mak # description: Build VLFeat DLL # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). clean: dist-bin-clean archclean: dist-bin-archclean distclean: dist-bin-distclean dist-src-distclean info: dist-bin-info dist-src-info # -------------------------------------------------------------------- # Configuration # -------------------------------------------------------------------- GIT ?= git RSYNC ?= rsync VER ?= $(shell cat vl/generic.h | sed -n \ 's/.*VL_VERSION_STRING.*\"\([0-9.]*\)\".*/\1/p') NAME := vlfeat DIST := $(NAME)-$(VER) BINDIST := $(DIST)-bin HOST := vlfeat-admin:vlfeat.org/sandbox # -------------------------------------------------------------------- # Build source package # -------------------------------------------------------------------- .PHONY: dist-src, dist-src-clean, dist-src-info no_dep_targets += dist-src, dist-src-clean, dist-src-info dist-src: COPYFILE_DISABLE=1 \ COPY_EXTENDED_ATTRIBUTES_DISABLE=1 \ $(GIT) archive --prefix=$(NAME)-$(VER)/ v$(VER) | gzip > $(DIST).tar.gz dist-src-distclean: rm -f $(DIST).tar.gz dist-src-info: $(call echo-title,Source distribution) $(call echo-var,DIST) @echo # -------------------------------------------------------------------- # Build binary package # -------------------------------------------------------------------- # dist-bin-release: Rebuild binaries with optimizations and no debug # symbols. # # dist-bin-commit: Creates a new vXX.XX.XX-ARCH branch with the # binaries and pushes it to the remote called bin. # # dist-bin-commit-common: Creates a new vXX.XX.XX-common branch with # the architecture-independent parts of VLFeat. # # dist-bin-merge: Creates a new vXX.XX.XX-bin branch by merging the # architecture specific binary branches and adding additional # files. It then pushes the result to the remote called bin. # # dist-bin: Packs the commit remotes/bin/vXX.XX.XX-bin. # # dist-src: Packs the commit vXX.XX.XX. .PHONY: dist-bin, dist-bin-release, dist-bin-commit, dist-bin-info .PHONY: dist-bin-clean, dist-bin-archclean, dist-bin-distclean no_dep_targets += dist-bin dist-bin-release dist-bin-commit dist-bin-info no_dep_targets += dist-bin-clean dist-bin-archclean dist-bin-distclean dist-bin-release: tmp-dir=$(TMPDIR)/tmp-$(NAME)-$(VER)-$(ARCH) dist-bin-release: @echo Cloning VLFeat ; test -e "$(tmp-dir)" || $(GIT) clone --no-checkout . "$(tmp-dir)" ; \ $(GIT) --git-dir="$(tmp-dir)/.git" config remote.bin.url $$($(GIT) config --get remote.bin.url) ; \ $(GIT) --git-dir="$(tmp-dir)/.git" config remote.origin.url $$($(GIT) config --get remote.origin.url) ; @echo Checking out v$(VER) ; cd "$(tmp-dir)" ; $(GIT) fetch origin --tags v$(VER) ; cd "$(tmp-dir)" ; $(GIT) fetch origin v$(VER) ; cd "$(tmp-dir)" ; $(GIT) checkout v$(VER) ; echo Rebuilding binaries for release ; make -C "$(tmp-dir)" ARCH=$(ARCH) all dist-bin-commit: tmp-dir=$(TMPDIR)/tmp-$(NAME)-$(VER)-$(ARCH) dist-bin-commit: branch=v$(VER)-$(ARCH) dist-bin-commit: dist-bin-release @echo Setting $(branch) to v$(VER) ; cd "$(tmp-dir)" ; $(GIT) branch -f $(branch) v$(VER) ; cd "$(tmp-dir)" ; $(GIT) checkout $(branch) ; @echo Adding binaries to $(branch) ; cd "$(tmp-dir)" ; $(GIT) add -f $(arch_bins) ; cd "$(tmp-dir)" ; \ if test -z "$$($(GIT) diff --cached)" ; \ then \ echo No changes to commit ; \ exit 1 ; \ else \ echo Commiting changes ; \ $(GIT) commit -m "$(ARCH) binaries for version $(VER)" ; \ echo Pushing $(branch) to the server ; \ $(GIT) push -v --force bin $(branch):refs/heads/$(branch) ; \ fi dist-bin-commit-common: tmp-dir=$(TMPDIR)/tmp-$(NAME)-$(VER)-$(ARCH) dist-bin-commit-common: branch=v$(VER)-common dist-bin-commit-common: dist-bin-release @echo Building doc make -C "$(tmp-dir)" ARCH=$(ARCH) doc-deep @echo Setting up $(branch) to v$(VER) ; cd "$(tmp-dir)" ; $(GIT) branch -f $(branch) v$(VER) cd "$(tmp-dir)" ; $(GIT) checkout $(branch) @echo Adding products to $(branch) cd "$(tmp-dir)" ; $(GIT) add -f $(m_lnk) cd "$(tmp-dir)" ; $(GIT) add -f $$(find doc \ -name '*.html' -or -name '*.jpg' -or -name '*.png' -or -name '*.css') cd "$(tmp-dir)" ; \ if test -z "$$($(GIT) diff --cached)" ; \ then \ echo No changes to commit ; \ exit 1 ; \ else \ echo Commiting changes ; \ $(GIT) commit -m "common products for $(VER)" ; \ echo Pushing $(branch) to the server ; \ $(GIT) push -v --force bin $(branch):refs/heads/$(branch); \ fi dist-bin-merge: tmp-dir=$(TMPDIR)/tmp-$(NAME)-$(VER)-merge dist-bin-merge: branch=v$(VER)-bin dist-bin-merge: @echo Cleaning up merge directory rm -rf "$(tmp-dir)" @echo Cloning VLFeat $(GIT) clone --no-checkout . "$(tmp-dir)" ; $(GIT) --git-dir=$(tmp-dir)/.git config remote.bin.url $$($(GIT) config --get remote.bin.url) ; $(GIT) --git-dir=$(tmp-dir)/.git config remote.origin.url $$($(GIT) config --get remote.origin.url) ; echo Creating or resetting and checking out branch $(branch) to v$(VER); cd "$(tmp-dir)" ; $(GIT) fetch origin --tags v$(VER); cd "$(tmp-dir)" ; $(GIT) fetch origin v$(VER) ; cd "$(tmp-dir)" ; $(GIT) checkout v$(VER) ; cd "$(tmp-dir)" ; $(GIT) branch -f $(branch) v$(VER) ; cd "$(tmp-dir)" ; $(GIT) checkout $(branch) ; cd "$(tmp-dir)" ; \ MERGE_BRANCHES=; \ FETCH_BRANCHES=; \ for ALT_ARCH in common maci maci64 glnx86 glnxa64 win32 win64 ; \ do \ MERGE_BRANCH=v$(VER)-$$ALT_ARCH ; \ MERGE_BRANCHES="$$MERGE_BRANCHES bin/$$MERGE_BRANCH" ; \ FETCH_BRANCHES="$$FETCH_BRANCHES $$MERGE_BRANCH:remotes/bin/$$MERGE_BRANCH" ; \ done ; \ echo Fetching binaries ; \ echo $(GIT) fetch -f bin $$FETCH_BRANCHES ; \ $(GIT) fetch -f bin $$FETCH_BRANCHES ; \ echo merging $$MERGE_BRANCHES ; \ $(GIT) merge -m "Merged binaries $$MERGE_BRANCHES" $$MERGE_BRANCHES ; echo Pushing to server the merged binaries ; \ cd "$(tmp-dir)" ; $(GIT) push -v --force bin $(branch):refs/heads/$(branch) ; dist-bin: branch=v$(VER)-bin dist-bin: echo Fetching binaries ; \ $(GIT) fetch -f bin $(branch):remotes/bin/$(branch) ; \ echo Creating archive ; \ COPYFILE_DISABLE=1 \ COPY_EXTENDED_ATTRIBUTES_DISABLE=1 \ $(GIT) archive --prefix=$(NAME)-$(VER)/ bin/v$(VER)-bin | gzip > $(BINDIST).tar.gz dist-bin-clean: dist-bin-archclean: rm -rf $(TMPDIR)/tmp-$(NAME)-$(VER)-$(ARCH) dist-bin-distclean: rm -rf $(TMPDIR)/tmp-$(NAME)-$(VER)-* rm -f $(BINDIST).tar.gz dist-bin-info: $(call echo-title,Binary distribution) $(call dump-var,arch_bins) $(call dump-var,comm_bins) $(call echo-var,NAME) $(call echo-var,BINDIST) @echo # -------------------------------------------------------------------- # Post packages on the web # -------------------------------------------------------------------- .PHONY: post, post-doc post: $(RSYNC) \ -aP $(DIST).tar.gz $(BINDIST).tar.gz \ $(HOST)/download post-doc: tar xzvf $(BINDIST).tar.gz -C $(TMPDIR)/ $(NAME)-$(VER)/doc/ $(RSYNC) \ --recursive \ --perms \ --group=lab \ --chmod=Dg+s,g+w,o-w \ --exclude=*.eps \ --exclude=download \ --exclude=cvpr10wiki \ --exclude=benchmarks \ --exclude=man-src \ --exclude=mdoc.build \ --exclude=.htaccess \ --exclude=favicon.ico \ --delete \ --progress \ $(TMPDIR)/$(NAME)-$(VER)/doc/ $(HOST) # Local variables: # mode: Makefile # End: vlfeat/make/bin.mak0000644000175000017500000000520212237042135013122 0ustar dimadima# file: bin.mak # description: Build VLFeat command line utilities # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). all: bin-all clean: bin-clean archclean: bin-archclean distclean: bin-distclean info: bin-info # -------------------------------------------------------------------- # Configuration # -------------------------------------------------------------------- BIN_CFLAGS = $(STD_CFLAGS) -I$(VLDIR) BIN_LDFLAGS = $(STD_LDFLAGS) -L$(BINDIR) -lvl # Mac OS X Intel 32 ifeq ($(ARCH),maci) endif # Mac OS X Intel 64 ifeq ($(ARCH),maci64) endif # Linux-32 ifeq ($(ARCH),glnx86) BIN_LDFLAGS += -Wl,--rpath,\$$ORIGIN/ endif # Linux-64 ifeq ($(ARCH),glnxa64) BIN_LDFLAGS += -Wl,--rpath,\$$ORIGIN/ endif # -------------------------------------------------------------------- # Build # -------------------------------------------------------------------- # On Mac OS X the library install_name is prefixed with @loader_path/. # At run time this causes the loader to search for a local copy of the # library for any binary which is linked against it. The install_name # can be modified later by install_name_tool. bin_src := $(wildcard $(VLDIR)/src/*.c) bin_tgt := $(addprefix $(BINDIR)/, $(patsubst %.c,%,$(notdir $(bin_src)))) bin_dep := $(addsuffix .d, $(bin_tgt)) deps += $(bin_dep) arch_bins += $(bin_tgt) comm_bins += .PHONY: bin-all, bin-info .PHONY: bin-clean, bin-archclean, bin-distclean no_dep_targets += bin-dir bin-clean bin-archclean bin-distclean no_dep_targets += bin-info bin-all: $(dll-dir) $(dll_tgt) $(bin_tgt) # BIN_LDFLAGS includes the libraries to link to and must be # specified after the object "$<" that uses them. If not, stricter # linkers (e.g. --as-needed option with the GNU toolchain) # will break as they will not include the dependencies. See # also http://wiki.debian.org/ToolChain/DSOLinking $(BINDIR)/% : $(VLDIR)/src/%.c $(call C,CC) $(BIN_CFLAGS) "$<" $(BIN_LDFLAGS) -o "$@" $(BINDIR)/%.d : $(VLDIR)/src/%.c $(dll-dir) $(call C,CC) $(BIN_CFLAGS) -M -MT \ '$(BINDIR)/$* $(BINDIR)/$*.d' \ "$<" -MF "$@" bin-clean: rm -f $(bin_dep) bin-archclean: bin-clean bin-distclean: bin-info: $(call echo-title,Command line utilities) $(call dump-var,bin_src) $(call dump-var,bin_tgt) $(call dump-var,bin_dep) $(call echo-var,BIN_CFLAGS) $(call echo-var,BIN_LDFLAGS) @echo # Local variables: # mode: Makefile # End: vlfeat/docsrc/0000755000175000017500000000000012237042512012220 5ustar dimadimavlfeat/docsrc/aib.html0000644000175000017500000000370212237004736013651 0ustar dimadima

The Agglomerative Information Bottleneck (AIB) algorithm greedily compresses discrete data by iteratively merging the two elements which cause the mutual information between the data and the class labels to decreases as little as possible.

Here we test AIB on the problem of finding a discriminatively optimal quantization of a mixture of Gaussians. The data in this case is 2 dimensional:

Random data generated from a Gaussian mixture with three components (class labels are indicated by color).

We quantize this data on a fixed lattice (a 20x20 grid shown in the figures below), and construct histograms for each class.

f1 = quantize(X1,D,K) ; f2 = quantize(X2,D,K) ; f3 = quantize(X3,D,K) ; Pcx(1,:) = vl_binsum(Pcx(1,:), ones(size(f1)), f1) ; Pcx(2,:) = vl_binsum(Pcx(2,:), ones(size(f2)), f2) ; Pcx(3,:) = vl_binsum(Pcx(3,:), ones(size(f3)), f3) ;

Next we apply AIB:

[parents, cost] = vl_aib(Pcx) ;

This provides us with a list of parents of each column in Pcx, forming a tree of merges. We can now "cut" this tree to obtain any number of clusters.

Three "cuts" of the merge tree, showing 10, 3, and 2 clusters. The gray squares are nodes of the tree which did not have any data points which were quantized to them.

Notice that the resulting clusters do not have to be contiguous in the original space.

 vlfeat/docsrc/doc.html0000644000175000017500000000233512237004745013664 0ustar dimadima

The VLFeat reference documentation has three parts:

MATLAB functions

The reference documentation of VLFeat MATLAB commands (this is an on-line version of the documentation built in the command themsevles).

C API reference

This documentation includes descriptions of all the algorithms and hence it is useful even if you do not plan to use the C library directly.

Man pages

The documentation of the command line utilities bundled with VLFeat (this is an on-line version of the Unix man found in the src/ subdirectory.

In addition to the documentation, there are also tutorials which introduce many of the algorithms contained in the library.

 vlfeat/docsrc/gmm.html0000644000175000017500000001273012237004745013677 0ustar dimadima %tableofcontents;

This tutorial shows how to estiamte Gaussian mixture model using the VlFeat implementation of the Expectation Maximization (EM) algorithm.

A GMM is a collection of $K$ Gaussian distribution. Each distribution is called a mode of the GMM and represents a cluster of data points. In computer vision applications, GMM are often used to model dictionaries of visual words. One important application is the computation of Fisher vectors encodings.

Learning a GMM with expectation maximization

Consider a dataset containing 1000 randomly sampled 2D points:

numPoints = 1000 ; dimension = 2 ; data = rand(dimension,N) ;

The goal is to fit a GMM to this data. This can be obtained by running the vl_gmm function, implementing the EM algorithm.

numClusters = 30 ; [means, covariances, priors] = vl_gmm(data, numClusters) ;

Here means, covariances and priors are respectively the means $\mu_k$, diagonal covariance matrices $\Sigma_k$, and prior probabilities $\pi_k$ of the numClusters Gaussian modes.

These modes can be visualized on the 2D plane by plotting ellipses corresponding to the equation: \[ \{ \bx: (\bx-\mu_k)^\top \Sigma_k^{-1} (\bx-\mu_k) = 1 \} \] for each of the modes. To this end, we can use the vl_plotframe:

figure ; hold on ; plot(data(1,:),data(2,:),'r.') ; for i=1:numClusters vl_plotframe([means(:,i)' sigmas(1,i) 0 sigmas(2,i)]); end

This results in the figure:

GMM fittting 2D random points.

Diagonal covariance restriction

Note that the ellipses in the previous example are axis alligned. This is a restriction of the vl_gmm implementation that imposes covariance matrices to be diagonal.

This is suitable for most computer vision applications, where estimating a full covariance matrix would be prohebitive due to the relative high dimensionality of the data. For example, when clustering SIFT features, the data has dimension 128, and each full covariance matrix would contain more than 8k parameters.

For this reason, it is sometimes desirable to globally decorrelated the data before learning a GMM mode. This can be obtained by pre-multiplying the data by the inverse of a square root of its covariance.

Initializing a GMM model before running EM

The EM algorithm is a local optimization method, and hence particularly sensitive to the initialization of the model. The simplest way to initiate the GMM is to pick numClusters data points at random as mode means, initialize the individual covariances as the covariance of the data, and assign equa prior probabilities to the modes. This is the default initialization method used by vl_gmm.

Alternatively, a user can specifiy manually the initial paramters of the GMM model by using the custom initalization method. To do so, set the 'Initialization' option to 'Custom' and also the options 'InitMeans', 'InitCovariances' and 'IniPriors' to the desired values.

A common approach to obtain an initial value for these parameters is to run KMeans first, as demonstrated in the following code snippet:

numClusters = 30; numData = 1000; dimension = 2; data = rand(dimension,numData); % Run KMeans to pre-cluster the data [initMeans, assignments] = vl_kmeans(data, numClusters, ... 'Algorithm','Lloyd', ... 'MaxNumIterations',5); initCovariances = zeros(dimension,numClusters); initPriors = zeros(1,numClusters); % Find the initial means, covariances and priors for i=1:numClusters data_k = data(:,assignments==i); initPriors(i) = size(data_k,2) / numClusters; if size(data_k,1) == 0 || size(data_k,2) == 0 initCovariances(:,i) = diag(cov(data')); else initCovariances(:,i) = diag(cov(data_k')); end end % Run EM starting from the given parameters [means,covariances,priors,ll,posteriors] = vl_gmm(data, numClusters, ... 'initialization','custom', ... 'InitMeans',initMeans, ... 'InitCovariances',initCovariances, ... 'InitPriors',initPriors);

The demo scripts vl_demo_gmm_2d and vl_demo_gmm_3d also produce cute colorized figures such as these:

The figure shows how the estimated gaussian mixture looks like with and without the kmeans initialization.
 vlfeat/docsrc/webdoc.py0000644000175000017500000012210412237004745014043 0ustar dimadima#!/usr/bin/python # file: webdoc.py # author: Andrea Vedaldien # description: Implementation of webdoc. # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file).s import cProfile import types import xml.sax import xml.sax.saxutils import re import os import sys import random import copy import htmlentitydefs from xml.sax.handler import ContentHandler from xml.sax import parse from urlparse import urlparse from urlparse import urlunparse from optparse import OptionParser from doxytag import Doxytag # this is used for syntax highlighting try: import pygments import pygments.lexers import pygments.formatters has_pygments = True except ImportError: has_pygments = False DOCTYPE_XHTML_TRANSITIONAL = \ '' # Create a dictonary that maps unicode characters to HTML entities mapUnicodeToHtmlEntity = { } for k, v in htmlentitydefs.name2codepoint.items(): c = unichr(v) if c == u'&' or c == u'<' or c == u'>': continue mapUnicodeToHtmlEntity [c] = "&%s;" % k # This indexes the document nodes by ID nodeIndex = { } nodeUniqueCount = 0 doxygenIndex = None doxygenDir = '' def getUniqueNodeID(id = None): """ getUniqueNodeID() generates an unique ID for a document node. getUniqueNodeID(id) generates an unique ID adding a suffix to id. """ global nodeUniqueCount if id is None: id = "id" uniqueId = id while uniqueId in nodeIndex: nodeUniqueCount += 1 uniqueId = "%s-%d" % (id, nodeUniqueCount) return uniqueId def dumpIndex(): """ Dump the node index, for debugging purposes. """ for x in nodeIndex.itervalues(): print x def ensureDir(dirName): """ Create the directory DIRNAME if it does not exsits. """ if os.path.isdir(dirName): pass elif os.path.isfile(dirName): raise OSError("cannot create the direcory '%s'" "because there exists already " "a file with that name" % newdir) else: head, tail = os.path.split(dirName) if head and not os.path.isdir(head): ensureDir(head) if tail: os.mkdir(dirName) def calcRelURL(toURL, fromURL): """ Calculates a relative URL. """ fromURL = urlparse(fromURL) toURL = urlparse(toURL) if not fromURL.scheme == toURL.scheme: return urlunparse(toURL) if not fromURL.netloc == toURL.netloc: return urlunparse(toURL) fromPath = fromURL.path.split("/") toPath = toURL.path.split("/") for j in xrange(len(fromPath) - 1): fromPath[j] += u"/" for j in xrange(len(toPath) - 1): toPath[j] += u"/" # abs path: ['/', 'dir1/', ..., 'dirN/', 'file'] # rel path: ['dir1/', ..., 'dirN/', 'file'] # path with no file: ['dir1/', ..., 'dirN/', ''] # find common path (but do not count file name) i = 0 while True: if i >= len(fromPath) - 1: break if i >= len(toPath) - 1: break if not fromPath[i] == toPath[i]: break i = i + 1 # a/b/c/d.html --> ../../../d.html # a/b//c/d.html --> ../../../d.html for j in xrange(len(fromPath) - 1): if len(fromPath[j]) > 1: fromPath[j] = u"../" else: fromPath[j] = u"" fromPath = fromPath[i:-1] toPath = toPath[i:] relPath = u"".join(fromPath) + "".join(toPath) return urlunparse(("", "", relPath, "", "", toURL.fragment)) def walkNodes(rootNode, nodeType = None, nodeBarrier = None): if nodeBarrier and rootNode.isA(nodeBarrier): return for n in rootNode.getChildren(): for m in walkNodes(n, nodeType): yield m if not nodeType or rootNode.isA(nodeType): yield rootNode def walkAncestors(leafNode, nodeType = None): if not nodeType or leafNode.isA(nodeType): yield leafNode p = leafNode.getParent() if p: for m in walkAncestors(p, nodeType): yield m # -------------------------------------------------------------------- class DocLocation: # -------------------------------------------------------------------- """ A location consisting of a URL (file), a row number, and a column number. """ def __init__(self, URL, row, column): self.URL = URL self.row = row self.column = column def __str__(self): return "%s:%d:%d" % (self.URL, self.row, self.column) # -------------------------------------------------------------------- class DocError(BaseException): # -------------------------------------------------------------------- """ An error consisting of a stack of locations and a message. """ def __init__(self, message): BaseException.__init__(self,message) self.locations = [] def __str__(self): str = "" if len(self.locations) > 0: for i in xrange(len(self.locations)-1,0,-1): str += "included from %s:\n" % self.locations[i] return str + "%s:%s" % (self.locations[0], BaseException.__str__(self)) else: return self.message def appendLocation(self, location): self.locations.append(location) return self # -------------------------------------------------------------------- class makeGuard(object): # -------------------------------------------------------------------- """ Decorates the method of a DocNode object so that, on raising a DocError exception, the location of the node is appended to it. """ def __init__(self, func): self.func = func def __call__(self, obj, *args, **keys): try: self.func(obj, *args, **keys) except DocError, e: if len(e.locations) == 0: e.appendLocation(obj.getLocation()) raise e except: raise def __get__(self, obj, type=None): return types.MethodType(self, obj, type) # -------------------------------------------------------------------- class DocBareNode: # -------------------------------------------------------------------- """ A node of the document tree without parent, children, or any other attribute. It is used to implement common leaf nodes such as text chunks. """ def __init__(self): pass def isA(self, classInfo): """ Returns TRUE if the node is of class CLASSINFO. """ return isinstance(self, classInfo) def getChildren(self): """ Returs an empty list """ return [] def setParent(self, parent): pass def getPublishDirName(self): pass def getPublishFileName(self): pass def getPublishURL(self): pass def visit(self, generator): pass def publish(self, generator, pageNode = None): pass def publishIndex(self, gen, pageNode, openNodeStack): return False # -------------------------------------------------------------------- class DocNode(DocBareNode): # -------------------------------------------------------------------- """ A node of the document with a parent, childern, attributes, and additional meta-information such as the location of the XML element that caused this node to be generated. """ def __init__(self, attrs, URL, locator): self.parent = None self.children = [] self.attrs = attrs self.sourceURL = None self.sourceRow = None self.sourceColumn = None if attrs.has_key('id'): self.id = attrs['id'] else: self.id = getUniqueNodeID() self.sourceURL = URL if locator: self.sourceRow = locator.getLineNumber() self.sourceColumn = locator.getColumnNumber() nodeIndex[self.id] = self def __str__(self): return "%s:%s" % (self.getLocation(), self.getID()) def dump(self): """ Recusively dump the tree of nodes, for debugging purposes. """ depth = self.getDepth() print " " * depth, self for x in self.children: x.dump() def getID(self): """ Return the node ID. """ return self.id def getParent(self): """ Return the node parent. """ return self.parent def getChildren(self): """ Return the list of node children. """ return self.children def getAttributes(self): """ Return the dictionary of node attributes. """ return self.attrs def getDepth(self): """ Return the depth of the node in the tree. """ if self.parent: return self.parent.getDepth() + 1 else: return 0 def setParent(self, parent): """ Set the parent of the node. """ self.parent = parent def adopt(self, orfan): """ Adds ORFAN to the node children and make the node the parent of ORFAN. ORFAN can also be a sequence of orfans. """ self.children.append(orfan) orfan.setParent(self) def findAncestors(self, nodeType = None): """ Return the node ancestors of type NODETYPE. If NODETYPE is None, returns all ancestors. """ if nodeType is None: nodeType = DocNode if self.parent: if self.parent.isA(nodeType): found = [self.parent] else: found = [] found = found + self.parent.findAncestors(nodeType) return found return [] def findChildren(self, nodeType = None): """ Returns the node chldren of type NODTYPE. If NODETYPE is None, returns all children. """ if nodeType is None: nodeType = DocNode return [x for x in self.children if x.isA(nodeType)] def getLocation(self): """ Get the location (file, row number, and column number) where this node was instanitated """ location = DocLocation(self.sourceURL, self.sourceRow, self.sourceColumn) if self.parent: parentLocation = self.parent.getLocation() if location.URL is None: location.URL = parentLocation.URL if location.row is None: location.URL = parentLocation.row if location.column is None: location.URL = parentLocation.column return location def getPublishDirName(self): """ Returns the publish dir name of the parent. """ if self.parent: return self.parent.getPublishDirName() return None def getPublishFileName(self): """ Returns NONE. """ return None def getPublishURL(self): """ Returns NONE. """ return None def visit(self, generator): """ Recursively calls VISIT() on its children. """ for c in self.getChildren(): c.visit(generator) return None def publish(self, generator, pageNode = None): """ Recursively calls PUBLISH() on its children. """ for c in self.getChildren(): c.publish(generator, pageNode) return None publish = makeGuard(publish) def publishIndex(self, gen, pageNode, openNodeStack): """ Recursively calls PUBLISHINDEX() on its children. """ hasIndexedChildren = False for c in self.getChildren(): hasIndexedChildren = c.publishIndex(gen, pageNode, openNodeStack) \ or hasIndexedChildren return hasIndexedChildren def publishTableOfContents(self, gen, pageNode): """ Create a TOC by lookin at H1, H2, ... tags in a DocPage." """ gen.putString("
\n") gen.putString("

Table of Contents

") previousLevel = 0 for q in pageNode.getChildren(): for x in walkNodes(q, DocHtmlElement, DocPage): if x.tag not in ['h1', 'h2', 'h3', 'h4', 'h5']: continue level = int(x.tag[1]) title = "".join([y.text for y in walkNodes(x, DocHtmlText)]) while previousLevel < level: gen.putString("
    ") previousLevel += 1 while previousLevel > level: gen.putString("
\n") previousLevel -= 1 gen.putString('
  • ' '%s' '
    • \n' % (level, x.id, title)) while previousLevel > 0: gen.putString("\n") previousLevel -= 1 gen.putString("
    \n") def expandAttr(self, value, pageNode): """ Expand an attribute by substituting any directive with its value. """ xvalue = "" next = 0 for m in re.finditer("%[-\w._#:]+;", value): if next < m.start(): xvalue += value[next : m.start()] next = m.end() directive = value[m.start()+1 : m.end()-1] mo = re.match('pathto:(.*)', directive) if mo: toNodeID = mo.group(1) toNodeURL = None if nodeIndex.has_key(toNodeID): toNodeURL = nodeIndex[toNodeID].getPublishURL() if toNodeURL is None: print "%s:warning: could not cross-reference '%s'" % (self.getLocation(), toNodeID) toNodeURL = toNodeID fromPageURL = pageNode.getPublishURL() xvalue += calcRelURL(toNodeURL, fromPageURL) continue mo = re.match('env:(.*)', directive) if mo: envName = mo.group(1) if envName in os.environ: xvalue += os.environ[envName] else: print "%s:warning: the environment variable '%s' not defined" % (self.getLocation(), envName) continue mo = re.match('dox:(.*)', directive) if mo: if doxygenIndex is None: print "%s:warning: no Doxygen tag file loaded, skipping this directive." % self.getLocation() continue if not mo.group(1) in doxygenIndex.index: print "%s:warning: the ID %s was not found in the Doxygen tag file." % (self.getLocation(), mo.group(2)) continue toNodeURL = nodeIndex['root'].getPublishURL() + '/' + doxygenDir + '/' + doxygenIndex.index[mo.group(1)] fromPageURL = pageNode.getPublishURL() xvalue += calcRelURL(toNodeURL, fromPageURL) continue raise DocError("unknown directive '%s' found while expanding an attribute" % directive) if next < len(value): xvalue += value[next:] #print "EXPAND: ", value, " -> ", xvalue return xvalue # -------------------------------------------------------------------- class DocInclude(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) if not attrs.has_key("src"): raise DocError("include missing 'src' attribute") self.filePath = attrs["src"] def __str__(self): return DocNode.__str__(self) + ":" \ % xml.sax.saxutils.quoteattr(self.filePath) # -------------------------------------------------------------------- class DocDir(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) if not attrs.has_key("name"): raise DocError("dir tag missing 'name' attribute") self.dirName = attrs["name"] def __str__(self): return DocNode.__str__(self) + ":" \ % xml.sax.saxutils.quoteattr(self.dirName) def getPublishDirName(self): return self.parent.getPublishDirName() + self.dirName + os.sep def visit(self, generator): generator.changeDir(self.dirName) DocNode.visit(self, generator) generator.parentDir() # -------------------------------------------------------------------- class DocGroup(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) def __str__(self): return DocNode.__str__(self) + ":" # -------------------------------------------------------------------- class DocCDATAText(DocBareNode): # -------------------------------------------------------------------- def __init__(self, text): DocBareNode.__init__(self) self.text = text def __str__(self): return DocNode.__str__(self) + ":CDATA text:" + self.text def publish(self, gen, pageNode = None): gen.putString(self.text) # -------------------------------------------------------------------- class DocCDATA(DocNode): # -------------------------------------------------------------------- def __init__(self): DocNode.__init__(self, {}, None, None) def __str__(self): return DocNode.__str__(self) + ":CDATA" def publish(self, gen, pageNode = None): gen.putString("") publish = makeGuard(publish) # -------------------------------------------------------------------- class DocHtmlText(DocBareNode): # -------------------------------------------------------------------- def __init__(self, text): DocBareNode.__init__(self) self.text = text def __str__(self): return DocNode.__str__(self) + ":text:'" + \ self.text.encode('utf-8').encode('string_escape') + "'" def publish(self, gen, pageNode = None): # find occurences of %directive; in the text node and do the # appropriate substitutions next = 0 for m in re.finditer("%(\w+)(:.*)?;", self.text): if next < m.start(): gen.putXMLString(self.text[next : m.start()]) next = m.end() directive = self.text[m.start()+1 : m.end()-1] directive = m.group(1) if directive == "content": pageNode.publish(gen, pageNode) elif directive == "pagestyle": for q in pageNode.getChildren(): for s in walkNodes(q, DocPageStyle, DocPage): s.expand(gen, pageNode) elif directive == "pagescript": for q in pageNode.getChildren(): for s in walkNodes(q, DocPageScript, DocPage): s.expand(gen, pageNode) elif directive == "pagetitle": gen.putString(pageNode.title) elif directive == "path": ancPages = [x for x in walkAncestors(pageNode, DocPage)] ancPages.reverse() gen.putString(" - ".join([x.title for x in ancPages])) elif directive == "navigation": gen.putString("
      \n") # get the branch of DocPage nodes from the site root to this page openNodeStack = [x for x in walkAncestors(pageNode, DocPage)] # find the root site node and publish the contents siteNode = walkAncestors(pageNode, DocSite).next() siteNode.publishIndex(gen, pageNode, openNodeStack) gen.putString("
    \n") elif directive == "tableofcontents": pageNode.publishTableOfContents(gen, pageNode) elif directive == "env": envName = m.group(2)[1:] if envName in os.environ: gen.putString(os.environ[envName]) else: print "warning: environment variable '%s' not defined" % envName else: print "warning: ignoring unknown directive '%s'" % label if next < len(self.text): gen.putXMLString(self.text[next:]) # -------------------------------------------------------------------- class DocCodeText(DocBareNode): # -------------------------------------------------------------------- def __init__(self, text): DocBareNode.__init__(self) self.text = text def __str__(self): return DocNode.__str__(self) + ":text:'" + \ self.text.encode('utf-8').encode('string_escape') + "'" # -------------------------------------------------------------------- class DocCode(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL = None, locator = None): DocNode.__init__(self, attrs, URL, locator) self.type = "plain" if attrs.has_key("type"): self.type = attrs["type"] def __str__(self): str = " type = " + self.type return DocNode.__str__(self) + ":" + str def publish(self, gen, pageNode = None): code = "" for n in self.getChildren(): if n.isA(DocCodeText): code = code + n.text if has_pygments and not self.type == "plain": try: lexer = pygments.lexers.get_lexer_by_name(self.type) gen.putString(pygments.highlight(code, lexer, pygments.formatters.HtmlFormatter())) except pygments.util.ClassNotFound: print "warning: could not find a syntax highlighter for '%s'" % self.type gen.putString("
    " + code + "
    ") else: gen.putString("
    " + code + "
    ") DocNode.publish(self, gen, pageNode) publish = makeGuard(publish) # -------------------------------------------------------------------- class DocHtmlElement(DocNode): # -------------------------------------------------------------------- def __init__(self, tag, attrs, URL = None, locator = None): DocNode.__init__(self, attrs, URL, locator) self.tag = tag def __str__(self): str = "" return DocNode.__str__(self) + ":" + str def getPublishURL(self): anc = self.findAncestors(DocPage) if len(anc) == 0: return None return anc[0].getPublishURL() + "#" + self.id def publish(self, gen, pageNode = None): gen.putString("<") gen.putString(self.tag) # make sure headings have and id (for ToCs) if self.tag in ['h1', 'h2', 'h3', 'h4', 'h5'] and \ not "id" in self.attrs: self.attrs["id"] = self.id ; for name, value in self.attrs.items(): gen.putString(" ") gen.putString(name) gen.putString("=") gen.putXMLAttr(self.expandAttr(value, pageNode)) if self.tag == 'br': # workaround for browser that do not like

    gen.putString("/>") elif self.tag == 'code': # expand tags such as vl_function as links gen.putString("/>") text = "".join([y.text for y in walkNodes(self, DocHtmlText)]) ok = nodeIndex.has_key(text) if ok: gen.putString("") DocNode.publish(self, gen, pageNode) if ok: gen.putString("") gen.putString("") else: gen.putString(">") DocNode.publish(self, gen, pageNode) gen.putString("") publish = makeGuard(publish) # -------------------------------------------------------------------- class DocTemplate(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) # -------------------------------------------------------------------- class DocPageStyle(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) def publish(self, gen, pageNode = None): return None def expand(self, gen, pageNode = None): sa = self.getAttributes() if sa.has_key("href"): gen.putString("\n") else: gen.putString("\n") expand = makeGuard(expand) # -------------------------------------------------------------------- class DocPageScript(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) def publish(self, gen, pageNode = None): return None def expand(self, gen, pageNode = None): sa = self.getAttributes() gen.putString("\n") expand = makeGuard(expand) # -------------------------------------------------------------------- class DocPage(DocNode): # -------------------------------------------------------------------- counter = 0 def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) DocPage.counter = 1 + DocPage.counter self.templateID = "template.default" self.name = "page%d" % DocPage.counter self.title = "untitled" self.hide = False for k, v in self.attrs.items(): if k == 'src': self.title = v elif k == 'name': self.name = v elif k == 'id': pass elif k == 'title': self.title = v elif k == 'hide': self.hide = (v.lower() == 'yes') else: raise DocError( "web:page cannot have '%s' attribute" % k) def __str__(self): return DocNode.__str__(self) + ":" \ % (xml.sax.saxutils.escape(self.name), xml.sax.saxutils.escape(self.title)) def getPublishFileName(self): return self.name + ".html" def getPublishURL(self): siteNode = self.findAncestors(DocSite)[0] return siteNode.getPublishURL() + \ self.getPublishDirName() + \ self.getPublishFileName() def visit(self, generator): generator.open(self.getPublishFileName()) templateNode = nodeIndex[self.templateID] templateNode.publish(generator, self) generator.close() DocNode.visit(self, generator) def publish(self, generator, pageNode = None): if pageNode is self: # this is the page being published, so go on print "publishing " + self.__str__() DocNode.publish(self, generator, pageNode) # otherwise this page has been encountered recursively # during publishing return None def publishIndex(self, gen, pageNode, openNodeStack): if self.hide: return gen.putString("
  • ") gen.putXMLString(self.title) gen.putString("\n") pos = gen.tell() gen.putString("
      \n") hasIndexedChildren = False if len(openNodeStack) > 0 and self == openNodeStack[-1]: openNodeStack.pop() hasIndexedChildren = DocNode.publishIndex(self, gen, pageNode, openNodeStack) if hasIndexedChildren: gen.putString("
    ") else: gen.seek(pos) gen.putString("
    • \n") return True # -------------------------------------------------------------------- class DocSite(DocNode): # -------------------------------------------------------------------- def __init__(self, attrs, URL, locator): DocNode.__init__(self, attrs, URL, locator) self.siteURL = "http://www.foo.org/" self.outDir = "html" def __str__(self): return DocNode.__str__(self) + ":" def getPublishURL(self): return self.siteURL def getPublishDirName(self): return "" def getOutDir(self): return self.outDir def setOutDir(self, outDir): self.outDir = outDir def publish(self): generator = Generator(self.outDir) self.visit(generator) publish = makeGuard(publish) # -------------------------------------------------------------------- class Generator: # -------------------------------------------------------------------- def __init__(self, rootDir): ensureDir(rootDir) self.fileStack = [] self.dirStack = [rootDir] ensureDir(rootDir) #print "CD ", rootDir def open(self, filePath): filePath = os.path.join(self.dirStack[-1], filePath) fid = open(filePath, "w") self.fileStack.append(fid) fid.write(DOCTYPE_XHTML_TRANSITIONAL) #print "OPEN ", filePath def putString(self, str): fid = self.fileStack[-1] try: encoded = str.encode('utf-8') fid.write(encoded) except (UnicodeEncodeError, IOError), e: print str raise DocError("writing text:" + e.__str__()) except: raise def putXMLString(self, str): fid = self.fileStack[-1] xstr = xml.sax.saxutils.escape(str, mapUnicodeToHtmlEntity) try: fid.write(xstr.encode('utf-8')) except (UnicodeEncodeError, IOError), e: raise DocError("writing XML-escaped string:" + e.__str__()) except: raise def putXMLAttr(self, str): fid = self.fileStack[-1] xstr = xml.sax.saxutils.quoteattr(str) try: fid.write(xstr.encode('utf-8')) except (UnicodeEncodeError, IOError), e: raise DocError("writing XML-escaped attribute:" + e.__str__()) except: raise def close(self): self.fileStack.pop().close() #print "CLOSE" def changeDir(self, dirName): currentDir = self.dirStack[-1] newDir = os.path.join(currentDir, dirName) ensureDir(newDir) self.dirStack.append(newDir) #print "CD ", newDir def parentDir(self): self.dirStack.pop() #print "CD .." def tell(self): fid = self.fileStack[-1] return fid.tell() def seek(self, pos): fid = self.fileStack[-1] fid.seek(pos) # -------------------------------------------------------------------- class DocHandler(ContentHandler): # -------------------------------------------------------------------- def __init__(self): ContentHandler.__init__(self) self.rootNode = None self.stack = [] self.locatorStack = [] self.filePathStack = [] self.verbosity = 1 self.inDTD = False def resolveEntity(self, publicid, systemid): """ Resolve XML entities by mapping to a local copy of the (X)HTML DTDs. """ return open(os.path.join( os.path.dirname(__file__), 'dtd/xhtml1', systemid[systemid.rfind('/')+1:]), "rb") def lookupFile(self, filePath): if os.path.exists(filePath): return filePath if filePath[0] == '/': return None for path in self.filePathStack: dir = os.path.dirname(path) qualFilePath = os.path.join(dir, filePath) if os.path.exists(qualFilePath): return qualFilePath return None def makeError(self, message): e = DocError(message) for i in xrange(len(self.filePathStack)-1,-1,-1): URL = self.filePathStack[i] locator = self.locatorStack[i] e.appendLocation(DocLocation(URL, locator.getLineNumber(), locator.getColumnNumber())) return e def startElement(self, name, attrs): """ SAX interface: starting of XML element. The function creates a new document node, i.e. a specialized class of DocNode for the type of XML element encountered. It then appends it as the head of the parsing stack for further processing." """ # convert attrs to a dictionary (implicitly copies as required by the doc) attrs_ = {} for k, v in attrs.items(): attrs_[k] = v attrs = attrs_ URL = self.getCurrentFileName() locator = self.getCurrentLocator() # The element is not parsed recusrively; instead # it simply switches to parsing the specified file. if name == "include": if not attrs.has_key("src"): raise self.makeError(" lacks the 'src' attribute") filePath = attrs["src"] qualFilePath = self.lookupFile(filePath) if qualFilePath is None: raise self.makeError("the file '%s' could not be found while expanding " % filePath) if self.verbosity > 0: print "parsing '%s'" % qualFilePath if attrs.has_key("type"): includeType = attrs["type"] else: includeType = "webdoc" if includeType == "webdoc": self.load(qualFilePath) elif includeType == "text": self.characters(open(qualFilePath, 'r').read()) else: raise makeError("'%s' is not a valid type" % includeType) return if len(self.stack) == 0: parent = None else: parent = self.stack[-1] node = None if name == "site": node = DocSite(attrs, URL, locator) elif name == "page": node = DocPage(attrs, URL, locator) elif name == "dir": node = DocDir(attrs, URL, locator) elif name == "template": node = DocTemplate(attrs, URL, locator) elif name == "pagestyle": node = DocPageStyle(attrs, URL, locator) elif name == "pagescript": node = DocPageScript(attrs, URL, locator) elif name == "group": node = DocGroup(attrs, URL, locator) elif name == "precode": node = DocCode(attrs, URL, locator) else: node = DocHtmlElement(name, attrs, URL, locator) if parent: parent.adopt(node) self.stack.append(node) def endElement(self, name): """ SAX interface: closing of XML element. """ if name == "include": return node = self.stack.pop() if len(self.stack) == 0: self.rootNode = node def load(self, qualFilePath): self.filePathStack.append(qualFilePath) parser = xml.sax.make_parser() parser.setContentHandler(self) parser.setEntityResolver(self) parser.setProperty(xml.sax.handler.property_lexical_handler, self) try: parser.parse(qualFilePath) except xml.sax.SAXParseException, e: raise self.makeError("XML parsing error: %s" % e.getMessage()) def setDocumentLocator(self, locator): """SAX interface: This is called when a new file is parsed to set the locator object.""" self.locatorStack.append(locator) def getCurrentLocator(self): if len(self.locatorStack) > 0: return self.locatorStack[-1] else: return None def characters(self, content): """ SAX interface: characters. """ parent = self.stack[-1] if parent.isA(DocCDATA): node = DocCDATAText(content) elif parent.isA(DocCode): node = DocCodeText(content) else: node = DocHtmlText(content) parent.adopt(node) def ignorableWhitespace(self, ws): self.characters(ws) def getCurrentFileName(self): return self.filePathStack[-1] def endDocument(self): self.locatorStack.pop() self.filePathStack.pop() def startCDATA(self): node = DocCDATA() self.stack[-1].adopt(node) self.stack.append(node) def endCDATA(self): node = self.stack.pop() if len(self.stack) == 0: self.rootNode = node def comment(self, body): if self.inDTD: return node = DocCDATAText("") self.stack[-1].adopt(node) def startEntity(self, name): pass def endEntity(self, name): pass def startDTD(self, name, public_id, system_id): self.inDTD = True def endDTD(self): self.inDTD = False # -------------------------------------------------------------------- def start(filePath, opts): # -------------------------------------------------------------------- global doxygenIndex global doxygenDir if not has_pygments and opts.verb: print "Warning: Pygments module not found: syntax coloring disabled." handler = DocHandler() try: handler.load(filePath) except DocError, e: print e sys.exit(-1) # configure handler.rootNode.setOutDir(opts.outdir) if opts.doxytag: if opts.verb: print "Loading doxygen tag file", opts.doxytag try: doxygenIndex = Doxytag(opts.doxytag) doxygenDir = opts.doxydir except Exception, e: print "Error parsing Doxygen tag file ", opts.doxytag print e sys.exit(-1) #print "== Index Content ==" # dumpIndex() #print #print "== Node Tree ==" #handler.rootNode.dump() if opts.verb: print "== All pages ==" for x in walkNodes(handler.rootNode, DocPage): print x if opts.verb: print "== Publish ==" try: handler.rootNode.publish() except DocError, e: print e sys.exit(-1) sys.exit(0) # -------------------------------------------------------------------- if __name__ == '__main__': # -------------------------------------------------------------------- usage = """webdoc [OPTIONS...] --outdir Set output directory --verbose Be verbose --doxytag Doxygen tag file --doxydir Doxygen documentation location """ parser = OptionParser(usage=usage) parser.add_option( "-v", "--verbose", dest = "verb", default = False, action = "store_true", help = "print debug informations") parser.add_option( "-o", "--outdir", dest = "outdir", default = "html", action = "store", help = "write output to this directory") parser.add_option( "", "--doxytag", dest = "doxytag", default = None, action = "store", help = "use this doxygen tag file") parser.add_option( "", "--doxydir", dest = "doxydir", default = ".", action = "store", help = "find doxygen documentation here") parser.add_option( "", "--profile", dest = "profile", default = False, action = "store_true", help = "run the profiler") (opts, args) = parser.parse_args() if opts.profile: cProfile.run('start(args[0], opts)') else: start(args[0], opts) vlfeat/docsrc/about.html0000644000175000017500000001544412237004745014236 0ustar dimadima img.photo { float: left ; margin: 0px 13px 0px 0px ; width: 100px ; } %tableofcontents;

    VLFeat is a cross-platform open source collection of vision algorithms with a special focus on visual features (for instance SIFT and MSER) and clustering (k-means, hierarchical k-means, agglomerative information bottleneck). It bundles a MATLAB toolbox, a clean and portable C library and a number of command line utilities. Thus it is possible to use the same algorithm both from MATLAB, the command line, and your own programs.

    Many parties make the development of VLFeat possible:

    Sponsors

    PASCAL2 credits In 2012 the development of VLFeat is supported by the PASCAL Harvest programme. Several people have been working in Oxford to add new functionalities to the library. Moreover, leading researchers in computer vision were consulted as advisors of the project. Some of these advancements have been presented at a tutorial at the European Conference in Computer Vision (ECCV) 2012. See the PASCAL Harvest Roadmap for further details.

    Yandex credits

    Team

    VLFeat is developed by a team of computer vision researchers, including master and PhD students, postgraduates, and senior researchers, as listed below. VLFeat was created by Andrea Vedaldi and Brian Fulkerson in 2007, based on previously published software by the same authors.

    Andrea Vedaldi picture Andrea Vedaldi joined the faculty at the University of Oxford in 2012. Since 2008 he was junior research fellow in the Oxford Visual Geometry Group. He received the Ph.D. and Master's degrees in Computer Science from the University of California - Los Angeles, in 2008 and 2005, and the Bachelor's degree in Information Engineering from the University of Padova, Italy, in 2003. He is the recipient of the UCLA outstanding Master's and Ph.D. awards.

    Brian Fulkerson picture Brian Fulkerson received his B.S. in Computer Engineering from the University of California - San Diego in 2004, and his M.S. and Ph.D. in Computer Science from the University of California - Los Angeles Vision Lab in 2006 and 2010.

    Karel Lenc picture Karel Lenc Karel Lenc received his B.S. degree in Cybernetics and Measurements from the Czech Technical University in 2010. He is currently pursuing M.S. degree in Computer Vision and Computer Engineering at the CTU, Center for Machine Perception. He visited the Department of Engineering Mathematics, University of Bristol in 2011 and the Oxford Visual Geometry Group in 2012.

    Daniele Perrone picture Daniele Perrone received his B.S. degree in Computer Engineering from Università della Calabria in 2007, and his M.S. degree in Artificial Intelligence from "Sapienza" Università di Roma in 2009. He started his Ph.D. in June 2010 at Heriot-Watt University, Edinburgh, UK. From June 2012 he joined the Computer Vision Group at the Universität Bern, Switzerland.

    Michal Perdoch picture Michal Perdoch received his Bachelor's degree in Software Engineering from the Slovak University of Technology in Bratislava in 2001, Master's degree in Computer Science and Ph.D. degree in Artificial Intelligence and Biocybernetics from the Czech Technical University in Prague, in 2004 and 2011. He is currently a postdoctoral researcher at the CTU Center for Machine Perception.

    Milan Sulc picture Milan Sulc received his Bachelor's degree in Cybernetics and Robotics from the Czech Technical University in 2012. He is currently pursuing Master degrees in Computer Vision and Artificial Intelligence at the CTU and Entrepreneurship and Commercial Engineering in Industry at CTU. He is a student intern at the CTU Center for Machine Perception.

    Hana Sarbortova picture Hana Sarbortova received her Bachelor of Engineering degree in Digital Signal and Image Processing from the University of Central Lancashire in 2012. She is currently studying Computer Vision and Digital Image at the Czech Technical University in Prague. She is a student intern at the CTU Center for Machine Perception.

    Advisors

    The development of VLFeat is supported by a number of computer vision groups and researchers:

    • Prof. Andrew Zisserman, Oxford VGG Lab.
    • Prof. Jiri Matas, Czech Technical University in Prague.
    • Prof. Tinne Tuytelaars, KU Leuven.
    • Dr. Cordelia Schmid, LEAR, Grenoble.
    • Dr. Krystian Mikolajczyk, Surrey.
    • Prof. Stefano Soatto, UCLA Vision Lab.

    Users and community

    The authors would like to thank the many colleagues that have contributed to VLFeat by testing and providing helpful suggestions and comments.

     vlfeat/docsrc/index.html0000644000175000017500000002573212237035754014240 0ustar dimadima #content p.widePar { padding-right: 1em ; } #changes dd { margin-bottom: 1em ; } .award { margin: 1em 0em ; width: 10em ; font-size: 0.9em ; text-align:center ; float: right ; border: 1px solid #DDD ; background-color: #f6f6f6 ; } #changes .date { font-weight: normal ; font-style: italic ; width: 10em ; }
    ACM OpenSource Award 2010

    The VLFeat open source library implements popular computer vision algorithms including HOG, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is %env:VERSION;.

    Download

    Documentation

    Tutorials

    Example applications

    Citing

    @misc{vedaldi08vlfeat,
 Author = {A. Vedaldi and B. Fulkerson},
 Title = {{VLFeat}: An Open and Portable Library
          of Computer Vision Algorithms},
 Year  = {2008},
 Howpublished = {\url{http://www.vlfeat.org/}}

    Acknowledgments

    PASCAL2 credits Yandex credits UCLA Vision Lab Oxford VGG.

    News

    &nsbp;
    22/06/2013 VLFeat 0.9.17 released
    Rewritten SVM implementation, adding support for SGD and SDCA optimisers and various loss functions (hinge, squared hinge, logistic, etc.) and improving the interface. Added infrastructure to support multi-core computations using OpenMP. Added OpenMP support to KD-trees and KMeans. Added new Gaussian Mixture Models, VLAD encoding, and Fisher Vector encodings (also with OpenMP support). Added LIOP feature descriptors. Added new object category recognition example code, supporting several standard benchmarks off-the-shelf. This is the third point update supported by the PASCAL Harvest programme. [Details]
    01/10/2012 VLBenchmarks 1.0-beta released.
    This new project provides simple to use benchmarking code for feature detectors and descriptors. Its development was supported by the PASCAL Harvest programme. [Details]
    01/10/2012 VLFeat 0.9.16 released
    Added VL_COVDET() (covariant feature detector). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme. [Details]
    11/9/2012 VLFeat 0.9.15 released
    Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly). A big thank to our sponsor! [Details].
    10/1/2012 PASCAL2 Harvest funding
    In the upcoming months many new functionalities will be added to VLFeat thanks to the PASCAL Harvest! See here for details.
    vlfeat/docsrc/hikm.html0000644000175000017500000000732512237004745014053 0ustar dimadima %tableofcontents;

    VLFeat offers a hierarchical version of integer k-means, which recursively applies vl_ikmeans to compute finer and finer partitions. For more details see Hierarchical Integer k-means API reference and the Integer k-means tutorial.

    Usage

    First, we generate some random data to cluster in [0,255]^2:

    data = uint8(rand(2,10000) * 255) ; datat = uint8(rand(2,100000)* 255) ;

    To cluster this data, we simply use vl_hikmeans:

    K = 3 ; nleaves = 100 ; [tree,A] = vl_hikmeans(data,K,nleaves) ;

    Here nleaves is the desired number of leaf clusters. The algorithm terminates when there are at least nleaves nodes, creating a tree with depth = floor(log(K nleaves))

    To assign labels to the new data, we use vl_hikmeanspush:

    AT = vl_hikmeanspush(tree,datat) ;
    Hierarchical integer K-means. Left: A depiction of the recursive clusters. Each node is a cluster center. The root note is not depicted (its center would be the mean of the dataset). Right: Clusters are represented as different colors (here are more than 100 clusters, but only three colors are used).

    Tree structure

    The output tree is a MATLAB structure representing the tree of clusters:

    > tree tree = K: 3 depth: 5 centers: [2x3 int32] sub: [1x3 struct]

    The field centers is the matrix of the cluster centers at the root node. If the depth of the tree is larger than 1, then the field sub is a structure array with one entry for each cluster. Each element is in turn a tree:

    > tree.sub ans = 1x3 struct array with fields: centers sub

    with a field centers for its clusters and a field sub for its children. When there are no children, this field is equal to the empty matrix

    > tree.sub(1).sub(1).sub(1).sub(1) ans = centers: [2x3 int32] sub: []

    Elkan

    VLFeat supports two different implementations of k-means. While they produce identical output, the Elkan method is sometimes faster. The method parameters controls which method is used. Consider the case when K=10 and our data is now 128 dimensional (e.g. SIFT descriptors):

    K=10; nleaves = 1000; data = uint8(rand(128,10000) * 255); tic; [tree,A] = vl_hikmeans(data,K,nleaves,'method', 'lloyd') ; % default t_lloyd = toc tic; [tree,A] = vl_hikmeans(data,K,nleaves,'method', 'elkan') ; t_elkan = toc t_lloyd = 8.0743 t_elkan = 3.0427     vlfeat/docsrc/vlfeat-website-main-content.xml0000644000175000017500000000677612237042456020304 0ustar dimadima vlfeat/docsrc/install-c.html0000644000175000017500000000225212237004745015003 0ustar dimadima

    These instructions explain how to use VLFeat in your C programs.

    Download and unpack the latest VLFeat binary distribution in a directory of your choice (e.g. ~/src/vlfeat). The following tutorials provide step-by-step instructions for two typical use cases:

    In general, let VLFEATROOT denote the path to the directory where you unpacked VLFeat. To compile and link your C program against VLFeat, include in your C source code the appropriate files from the VLFEATROOT/vl hierarchy and link against the library vl located in the appropriate subdirectory vlfeat/bin/ARCH, where ARCH depends on your architecture.

    VLFeat is a C library. To use it from a C++ program, do not forget to enclose your #include statements in a extern "C" block.

     vlfeat/docsrc/utils.html0000644000175000017500000000405712237004745014262 0ustar dimadima

    This is a list of other notable MATLAB functions included in VLFeat

    • vl_imsmooth smoothes an image by a Gaussian kernel (simple but very useful as it is generally much faster than MATLAB's general purpose smoothing functions).
    • vl_plotframe plots a variety of feature frame types (such as oriented ellipses).
    • vl_binsum performs binned summations, useful as a building block for the fast computation of histograms. vl_whistc computes weighed histograms.
    • vl_imarray and vl_imarraysc arrange and visualize multiple images in a grid
    • vl_imsc scales the image range;
    • vl_tightsubplot is similar to built-in subplot, but produces narrower margins.
    • vl_cf makes a copy of a figure.
    • vl_rodr and vl_irodr compute the Rodrigues' formula and its inverse
    • vl_override overrides members of a structure with members of another structure.
    • vl_imwbackward warps an image by the inverse mapping method (generally much faster than MATLAB general purpose warping functions).
    • vl_waffine computes the affine warp of a set of points. vl_tps, vl_tpsu, vl_wtps compute the thin-plate spline warp of a set of points. vl_witps computes the inverse thin plate warp of a set of point (by numerically inverting the transformation). They may be used in combination with vl_imwbackward.
    • vl_xyz2lab, vl_xyz2luv, vl_xyz2rgb, vl_rgb2xyz convert color spaces.
    • vl_rcos, vl_gaussian, vl_dgaussian, vl_ddgaussian compute some useful special functions.
     vlfeat/docsrc/notfound.html0000644000175000017500000000055612237004745014756 0ustar dimadima

    The page you attempted to view no longer exists, never existed, or is not currently available.

    Try searching for what you need:



     vlfeat/docsrc/slic.html0000644000175000017500000000270212237004745014047 0ustar dimadima

    SLIC is superpixel extraction (segmentation) method based on a local version of k-means. For a detailed description of the algorithm, see the SLIC API reference.

    This demo shows how to use SLIC to extract superpixels from this image:

    The image to be segmented

    In the simplest form, SLIC can be called as:

    % im contains the input RGB image as a SINGLE array regionSize = 10 ; regularizer = 10 ; segments = vl_slic(im, regionSize, regularizer) ;

    By making varting regionSize and regularizer one obtains the segmentations:

    SLIC segmentations regionSize spaning the values 10,30, and regularizer the values 0.01, 0.1, 1.

    SLIC is often intended to be applied on top of LAB rather than RGB images. To do this, simpyl convert the image to LAB format before calling vl_slic.

    % IM contains the image in RGB format as before imlab = vl_xyz2lab(vl_rgb2xyz(im)) ; segments = vl_slic(imlab, 10, 0.1) ;     vlfeat/docsrc/imdisttf.html0000644000175000017500000000624112237004745014742 0ustar dimadima

    The distance transform of an image image is defined as

      dt(u,v) = min  image(u',v') + alpha (u'-u-u0)^2 + beta (v'-v'-v0)^2
            u'v'

    The most common use of the image distance transform is to propagate the response of a feature detector to nearby image locations. This is used, for example, in the implementation of certain deformable part models or the computation of the Chamfer distance. In this tutorial, the image distance transform is used to compute the distance of each image pixel to the nearest element in an edge map, obtained from the Canny's edge detector. The code of this tutorial is located in the VLFeat folder in toolbox/demo/vl_demo_imdisttf.m.

    VLFeat implements the fast distance transform algorithm of Felzenszwalb and Huttelnocher [1], which has a linear time complexity in the number of image pixels.

    Consider the edge map extracted by the MATLAB built-in Canny edge detector on one of VLFeat test images:

    im = vl_impattern('roofs1') ; im = im(1:100,1:100,:) ; imSize = [size(im,1) size(im,2)] ; edges = zeros(imSize) + inf; edges(edge(rgb2gray(im), 'canny')) = 0 ;
    Left: A detail of the source image. Right: Extracted Canny edges. Figure generated by vl_demo_imdisttf.

    The edge map is preprocessed to assign value -inf to the pixels that do not contain an edge element and o to the pixels that do. In this way, the distance transform of the image has for each pixel the distance to the nearest edge element, provided that one chooses alpha=beta=1 and v0=u0=0 in the definition. Since these are the default values for VLFeat implementations, the result an be computed by

    [distanceTransform, neighbors] = vl_imdisttf(single(edges)) ;

    The matrix neighbors contains for each pixel (u,v) the index of the pixel (u',v') where the maximum is attained in the definition of the distance transform. This allows to associate to know for each pixel which is the nearest edge element, not just its distance, as exemplified by the following figure:

    The distance sqrt(distanceTransform) to the closest edge element (left) and arrows connecting pixels to their closest edge element (right). Figure generated by vl_demo_imdisttf.

    References

    • [1] P. F. Felzenszwalb and D. P. Huttenlocher. Distance transforms of sampled functions. Technical report, Cornell University, 2004.

     vlfeat/docsrc/tutorials.html0000644000175000017500000000641412237042456015150 0ustar dimadima %tableofcontents;

    This section features a number of tutorials illustrating some of the algorithms implemented in VLFeat, roughly divided into visual features such as SIFT and Fisher vectors and statistical methods, such as K-means, GMMs, KDTrees, and SVMs.

    Visual features

    Statistical methods

    Obsolete tutorials

     vlfeat/docsrc/mser.html0000644000175000017500000002106612237042456014070 0ustar dimadima %tableofcontents;

    Maximally Stable Extremal Regions (MSER) is a feature detector; Like the SIFT detector, the MSER algorithm extracts from an image I a number of co-variant regions, called MSERs. An MSER is a stable connected component of some level sets of the image I. Optionally, elliptical frames are attached to the MSERs by fitting ellipses to the regions. For a more in-depth explanation of the MSER detector, see our API reference for MSER

    Extracting MSERs

    Each MSERs can be identified uniquely by (at least) one of its pixels x, as the connected component of the level set at level I(x) which contains x. Such a pixel is called seed of the region.

    To demonstrate the usage of the MATLAB command vl_mser we open MATLAB and load a test image

    pfx = fullfile(vl_root,'data','spots.jpg') ; I = imread(pfx) ; image(I) ;
    A test image.

    We then convert the image to a format that is suitable for the vl_mser command.

    I = uint8(rgb2gray(I)) ;

    We compute the region seeds and the elliptical frames by

    [r,f] = vl_mser(I,'MinDiversity',0.7,... 'MaxVariation',0.2,... 'Delta',10) ;

    We plot the region frames by

    f = vl_ertr(f) ; vl_plotframe(f) ;

    vl_ertr transposes the elliptical frame and is required here because the vl_mser code assumes that the row index is the first index, but the normal image convention assumes that this is the x (column) index.

    Plotting the MSERs themselves is a bit more involved as they have arbitrary shape. To this end, we exploit two functions: vl_erfill, which, given an image and a region seed, returns a list of the pixels belonging to that region, and the MATLAB built-in contour, which draws the contour lines of a function. We start by

    M = zeros(size(I)) ; for x=r' s = vl_erfill(I,x) ; M(s) = M(s) + 1; end

    which computes a matrix M whose value are equal to the number of overlapping extremal regions. Next, we use M and contour to display the region boundaries:

    figure(2) ; clf ; imagesc(I) ; hold on ; axis equal off; colormap gray ; [c,h]=contour(M,(0:max(M(:)))+.5) ; set(h,'color','y','linewidth',3) ;
    Extracted MSERs (left) and fitted ellipses (right).

    MSER parameters

    In the original formulation, MSERs are controlled by a single parameter Δ, which controls how the stability is calculated. Its effect is shown in the figure below.

    Effect of Δ. We start with a synthetic image which has an intensity profile as shown. The bumps have heights equal to 32, 64, 96, 128 and 160. As we increase Δ, fewer and fewer regions are detected until finally at Δ=160 there is no region R which is stable at R(+Δ).

    The stability of an extremal region R is the inverse of the relative area variation of the region R when the intensity level is increased by Δ. Formally, the variation is defined as:

    |R(+Δ) - R| ----------- |R|

    where |R| denotes the area of the extremal region R, R(+Δ) is the extremal region +Δ levels up which contains R and |R(+Δ) - R| is the area difference of the two regions.

    A stable region has a small variation. The algorithm finds regions which are "maximally stable", meaning that they have a lower variation than the regions one level below or above. Note that due to the discrete nature of the image, the region below / above may be coincident with the actual region, in which case the region is still deemed maximal.

    However, even if an extremal region is maximally stable, it might be rejected if:

    • it is too big (see the parameter MaxArea);
    • it is too small (see the parameter MinArea);
    • it is too unstable (see the parameter MaxVariation);
    • it is too similar to its parent MSER (see the parameter MinDiversity).

    By default, MSERs are extracted for both dark-on-bright regions and bright-on-dark regions. To control this, parmeters BrightOnDark and DarkOnBright which take values 0 or 1 to enable or disable the regions. For example:

    [r,f] = vl_mser(I,'MinDiversity',0.7,... 'MaxVariation',0.2,... 'Delta',10,... 'BrightOnDark',1,'DarkOnBright',0) ;

    computes the regions in green in the figure below.

    Extracted MSERs (left) and fitted ellipses (right) for both bright-on-dark (green) and dark-on-bright (yellow).

    Conventions

    As mentioned in the introduction, vl_mser uses matrix indices as image coordinates. Compared to the usual MATLAB convention for images, this means that the x and y axis are swapped (this has been done to make the convention consistent with images with three or more dimensions). Thus the frames computed by the program may need to be "transposed" as in:

    [r,f] = vl_mser(I) ; f = vl_ertr(f) ;

    On the other hand, the region seeds r are already in row major format, which is the standard MATLAB format for pixel indices.

    Instead of transposing the frames, one can start by transposing the image. In this case, the frames f have the standard image convention, but the region seeds are in column-major format and may need to be "transposed" as in:

    [r,f] = vl_mser(I') ; [i,j] = sub2ind(size(I'),r) ; r = ind2sub(size(I),j,i) ;

    The command line utility mser uses the normal image convention (because images are rasterized in column-major order). Therefore the image frames are in the standard format, and the region seeds are in column major format.

    In order to convert from the command line utility convention to the MATLAB convention one needs also to recall that MATLAB coordinates starts from (1,1), but the command line utility uses the more common convention (0,0). For instance, let the files image.frame and image.seed contain the feature frames and seeds in ASCII format as generated by the command line utility. Then

    r_ = load('image.seed')' + 1 ; f_ = load('image.frame')' ; f_(1:2,:) = f_(1:2,:) + 1 ; [r,f] = vl_mser(I') ; % notice the transpose

    produces identical (up to numerical noise) region seeds r and r_ and frames f and f_.

     vlfeat/docsrc/kmeans.html0000644000175000017500000001223512237004745014375 0ustar dimadima %tableofcontents;

    This tutorial shows how to use the K-means algorithm using the VlFeat implementation of Llloyd's algorithm as well as other faster variants.

    Running K-means

    KMeans is a clustering algorithm. Its purpose is to partition a set of vectors into $K$ groups that cluster around common mean vector. This can also be thought as approximating the input each of the input vector with one of the means, so the clustering process finds, in principle, the best dictionary or codebook to vector quantize the data.

    Consider a dataset containing 1000 randomly sampled 2D points:

    numData = 5000 ; dimension = 2 ; data = rand(dimension,numData) ;

    The function vl_kmeans can be used to cluster the data into, say, 30 groups:

    numClusters = 30 ; [centers, assignments] = vl_kmeans(data, numClusters);

    By default, this uses the the Lloyd algorithm, a method that alternates between optimizing the cluster centers and the data-to-center assignments. Once this process terminates, the matrix centers contains the cluster centers and the vector assignments the (hard) assignments of the input data to the clusters. The cluster centers are also called means because it can be shown that, when the clustering is optimal, the centers are the means of the corresponding data points. The cluster centers and assignments can be visualized as follows:

    KMeans clustering of 5000 randomly sampled data points. The black dots are the cluster centers.

    Given a new data point x, this can be mapped to one of the clusters by looking for the closest center:

    x = rand(dimension, 1) ; [~, k] = min(vl_alldist(x, centers)) ;

    For larger datastes, this process may be significantly accelerated by using KDTrees or other approximate nearest neighbor procedures.

    Choosing an initialization method

    K-means uses local optimization algorithms and is therefore sensitive to initalization. By default, vl_kmeans initializes the cluster centers by picks $K$ data points at random. Other initalization strategies can be selected as well. kmeans++ is a popular method that greedily pick $K$ data points that are maximally different, and can be use as follows:

    [centers, assignments] = vl_kmeans(data, numClusters, 'Initialization', 'plusplus') ;

    Choosing an optimization algorithm

    In addition to the original KMeans algorithm proposed by Lloyd, vl_kmeans supports two additional algorithms: Elkan's variant, an exact algorithm using an acceleration technique based the triangular inequality, and ANN, an approximated algorithm using approximate nearest neighbours.

    These optimization methods can be enabled by setting the 'Algorithm' parameter to 'Lloyd', 'Elkan' or 'ANN' respectively. When using the 'ANN' algorithm, the user can also specify the parameters 'MaxNumComparisons' and 'NumTrees' to configure the KD-tree used used as ANN. In particular, 'MaxNumComparisons' controls the trade-off between approximation quality and speed.

    vl_demo_kmeans_ann_speed compares the speed of the three algorithms. Because of the random initialization, each of the KMeans calls converges to a different local minimum in a different amount of iterations. In order to measure more accurately the speed of each pass, a relatively small number of 'MaxNumIterations' option) is selected. Note that the speedup factor are in general much more dramatic when truly large datasets are considered.

    Comparisons of Elkan, Lloyd and ANN for different values of MaxNumComparison, expressed as a fraction of the number of clusters. The figure reports the duration, final energy value, and speedup factor, using both the serial and parallel versions of the code. The figure was generated using vl_demo_kmeans_ann_speed.
     vlfeat/docsrc/svm.html0000644000175000017500000001265212237004745013727 0ustar dimadima %tableofcontents;

    VLFeat includes fast SVM solvers, SGC [1] and (S)DCA [2], both implemented in vl_svmtrain. The function also implements features, like Homogeneous kernel map expansion and SVM online statistics. (S)DCA can also be used with different loss functions.

    Support vector machine

    A simple example on how to use vl_svmtrain is presented below. Let's first load and plot the training data:

    % Load training data X and their labels y vl_setup demo % to load the demo data load('vl_demo_svm_data.mat'); Xp = X(:,y==1); Xn = X(:,y==-1); figure plot(Xn(1,:),Xn(2,:),'*r') hold on plot(Xp(1,:),Xp(2,:),'*b') axis equal ;

    Now we have a plot of the tutorial training data:

    Training Data.

    Now we will set the learning parameters:

    lambda = 0.01 ; % Regularization parameter maxIter = 1000 ; % Maximum number of iterations

    Learning a linear classifier can be easily done with the following 1 line of code:

    [w b info] = vl_svmtrain(X, y, lambda, 'MaxNumIterations', maxIter)

    Now we can plot the output model over the training data.

    % Visualisation eq = [num2str(w(1)) '*x+' num2str(w(2)) '*y+' num2str(b)]; line = ezplot(eq, [-0.9 0.9 -0.9 0.9]); set(line, 'Color', [0 0.8 0],'linewidth', 2);

    The result is plotted in the following figure.

    Learned model.

    The output info is a struct containing some statistic on the learned SVM:

    info = solver: 'sdca' lambda: 0.0100 biasMultiplier: 1 bias: 0.0657 objective: 0.2105 regularizer: 0.0726 loss: 0.1379 dualObjective: 0.2016 dualLoss: 0.2742 dualityGap: 0.0088 iteration: 525 epoch: 3 elapsedTime: 0.0300

    It is also possible to use under some assumptions [3] a homogeneous kernel map expanded online inside the solver. This can be done with the following commands:

    % create a structure with kernel map parameters hom.kernel = 'KChi2'; hom.order = 2; % create the dataset structure dataset = vl_svmdataset(X, 'homkermap', hom); % learn the SVM with online kernel map expansion using the dataset structure [w b info] = vl_svmtrain(dataset, y, lambda, 'MaxNumIterations', maxIter)

    The above code creates a training set without applying any homogeneous kernel map to the data. When the solver is called it will expand each data point with a Chi Squared kernel of period 2.

    Diagnostics

    VLFeat allows to get statistics during the training process. It is sufficient to pass a function handle to the solver. The function will be then called every DiagnosticFrequency time.

    (S)DCA diagnostics also provides the duality gap value (the difference between primal and dual energy), which is the upper bound of the primal task sub-optimality.

    % Diagnostic function function diagnostics(svm) energy = [energy [svm.objective ; svm.dualObjective ; svm.dualityGap ] ] ; end % Training the SVM energy = [] ; [w b info] = vl_svmtrain(X, y, lambda,... 'MaxNumIterations',maxIter,... 'DiagnosticFunction',@diagnostics,... 'DiagnosticFrequency',1)

    The objective values for the past iterations are kept in the matrix energy. Now we can plot the objective values from the learning process.

    figure hold on plot(energy(1,:),'--b') ; plot(energy(2,:),'-.g') ; plot(energy(3,:),'r') ; legend('Primal objective','Dual objective','Duality gap') xlabel('Diagnostics iteration') ylabel('Energy')
    SVM objective values plot.

    References

    • [1] Y. Singer and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In Proc. ICML, 2007.
    • [2] S. Shalev-Schwartz and T. Zhang. Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization. 2013.
    • [3] A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In PAMI, 2011.
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HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any # documentation blocks found inside the body of a function. # If set to NO (the default) these blocks will be appended to the # function's detailed documentation block. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation # that is typed after a \internal command is included. If the tag is set # to NO (the default) then the documentation will be excluded. # Set it to YES to include the internal documentation. INTERNAL_DOCS = YES # If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate # file names in lower-case letters. If set to YES upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen # will show members with their full class and namespace scopes in the # documentation. If set to YES the scope will be hidden. HIDE_SCOPE_NAMES = NO # If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen # will put a list of the files that are included by a file in the documentation # of that file. SHOW_INCLUDE_FILES = YES # If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen # will list include files with double quotes in the documentation # rather than with sharp brackets. FORCE_LOCAL_INCLUDES = NO # If the INLINE_INFO tag is set to YES (the default) then a tag [inline] # is inserted in the documentation for inline members. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen # will sort the (detailed) documentation of file and class members # alphabetically by member name. If set to NO the members will appear in # declaration order. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the # brief documentation of file, namespace and class members alphabetically # by member name. If set to NO (the default) the members will appear in # declaration order. SORT_BRIEF_DOCS = NO # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen # will sort the (brief and detailed) documentation of class members so that # constructors and destructors are listed first. If set to NO (the default) # the constructors will appear in the respective orders defined by # SORT_MEMBER_DOCS and SORT_BRIEF_DOCS. # This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO # and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO. SORT_MEMBERS_CTORS_1ST = NO # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the # hierarchy of group names into alphabetical order. If set to NO (the default) # the group names will appear in their defined order. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be # sorted by fully-qualified names, including namespaces. If set to # NO (the default), the class list will be sorted only by class name, # not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the # alphabetical list. SORT_BY_SCOPE_NAME = NO # If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to # do proper type resolution of all parameters of a function it will reject a # match between the prototype and the implementation of a member function even # if there is only one candidate or it is obvious which candidate to choose # by doing a simple string match. By disabling STRICT_PROTO_MATCHING doxygen # will still accept a match between prototype and implementation in such cases. STRICT_PROTO_MATCHING = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or # disable (NO) the todo list. This list is created by putting \todo # commands in the documentation. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or # disable (NO) the test list. This list is created by putting \test # commands in the documentation. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or # disable (NO) the bug list. This list is created by putting \bug # commands in the documentation. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or # disable (NO) the deprecated list. This list is created by putting # \deprecated commands in the documentation. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional # documentation sections, marked by \if sectionname ... \endif. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines # the initial value of a variable or macro consists of for it to appear in # the documentation. If the initializer consists of more lines than specified # here it will be hidden. Use a value of 0 to hide initializers completely. # The appearance of the initializer of individual variables and macros in the # documentation can be controlled using \showinitializer or \hideinitializer # command in the documentation regardless of this setting. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated # at the bottom of the documentation of classes and structs. If set to YES the # list will mention the files that were used to generate the documentation. SHOW_USED_FILES = YES # Set the SHOW_FILES tag to NO to disable the generation of the Files page. # This will remove the Files entry from the Quick Index and from the # Folder Tree View (if specified). The default is YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the # Namespaces page. # This will remove the Namespaces entry from the Quick Index # and from the Folder Tree View (if specified). The default is YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command , where is the value of # the FILE_VERSION_FILTER tag, and is the name of an input file # provided by doxygen. Whatever the program writes to standard output # is used as the file version. See the manual for examples. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed # by doxygen. The layout file controls the global structure of the generated # output files in an output format independent way. To create the layout file # that represents doxygen's defaults, run doxygen with the -l option. # You can optionally specify a file name after the option, if omitted # DoxygenLayout.xml will be used as the name of the layout file. LAYOUT_FILE = # The CITE_BIB_FILES tag can be used to specify one or more bib files # containing the references data. This must be a list of .bib files. The # .bib extension is automatically appended if omitted. Using this command # requires the bibtex tool to be installed. See also # http://en.wikipedia.org/wiki/BibTeX for more info. For LaTeX the style # of the bibliography can be controlled using LATEX_BIB_STYLE. To use this # feature you need bibtex and perl available in the search path. CITE_BIB_FILES = vlfeat.bib #--------------------------------------------------------------------------- # configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated # by doxygen. Possible values are YES and NO. If left blank NO is used. QUIET = NO # The WARNINGS tag can be used to turn on/off the warning messages that are # generated by doxygen. Possible values are YES and NO. If left blank # NO is used. WARNINGS = YES # If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings # for undocumented members. If EXTRACT_ALL is set to YES then this flag will # automatically be disabled. WARN_IF_UNDOCUMENTED = YES # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some # parameters in a documented function, or documenting parameters that # don't exist or using markup commands wrongly. WARN_IF_DOC_ERROR = YES # The WARN_NO_PARAMDOC option can be enabled to get warnings for # functions that are documented, but have no documentation for their parameters # or return value. If set to NO (the default) doxygen will only warn about # wrong or incomplete parameter documentation, but not about the absence of # documentation. WARN_NO_PARAMDOC = NO # The WARN_FORMAT tag determines the format of the warning messages that # doxygen can produce. The string should contain the $file, $line, and $text # tags, which will be replaced by the file and line number from which the # warning originated and the warning text. Optionally the format may contain # $version, which will be replaced by the version of the file (if it could # be obtained via FILE_VERSION_FILTER) WARN_FORMAT = "$file:$line: $text" # The WARN_LOGFILE tag can be used to specify a file to which warning # and error messages should be written. If left blank the output is written # to stderr. WARN_LOGFILE = #--------------------------------------------------------------------------- # configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag can be used to specify the files and/or directories that contain # documented source files. You may enter file names like "myfile.cpp" or # directories like "/usr/src/myproject". Separate the files or directories # with spaces. INPUT = vl \ toolbox/mexutils.h \ src/generic-driver.h # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is # also the default input encoding. Doxygen uses libiconv (or the iconv built # into libc) for the transcoding. See http://www.gnu.org/software/libiconv for # the list of possible encodings. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank the following patterns are tested: # *.c *.cc *.cxx *.cpp *.c++ *.d *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh # *.hxx *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.dox *.py # *.f90 *.f *.for *.vhd *.vhdl FILE_PATTERNS = *.h \ *.c \ *.tc # The RECURSIVE tag can be used to turn specify whether or not subdirectories # should be searched for input files as well. Possible values are YES and NO. # If left blank NO is used. RECURSIVE = NO # The EXCLUDE tag can be used to specify files and/or directories that should be # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. # Note that relative paths are relative to the directory from which doxygen is # run. EXCLUDE = # The EXCLUDE_SYMLINKS tag can be used to select whether or not files or # directories that are symbolic links (a Unix file system feature) are excluded # from the input. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. Note that the wildcards are matched # against the file with absolute path, so to exclude all test directories # for example use the pattern */test/* EXCLUDE_PATTERNS = test_* # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or # directories that contain example code fragments that are included (see # the \include command). EXAMPLE_PATH = vl/doc # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank all files are included. EXAMPLE_PATTERNS = # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude # commands irrespective of the value of the RECURSIVE tag. # Possible values are YES and NO. If left blank NO is used. EXAMPLE_RECURSIVE = NO # The IMAGE_PATH tag can be used to specify one or more files or # directories that contain image that are included in the documentation (see # the \image command). IMAGE_PATH = doc/figures # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command , where # is the value of the INPUT_FILTER tag, and is the name of an # input file. Doxygen will then use the output that the filter program writes # to standard output. # If FILTER_PATTERNS is specified, this tag will be # ignored. # Transformations: # \[ --> @f[ # \] --> @f] # $ --> @f$ # \cite{xyz} --> @cite xyz INPUT_FILTER = "\ sed -e 's/\/\*\* -----------* *$/\/** /g' \ -e \"s/__VLFEAT_VERSION__/$(cat VERSION)/g\" \ -e \"s/@cite{\([a-zA-Z0-9-]*\)}/@cite \1 /g\" \ -e 's/\([^f]\)\$/\1@f$/g' \ -e 's/^\$/@f$/g' \ -e 's/\\\[/@f[/g' \ -e 's/\\\]/@f]/g' \ " # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. # Doxygen will compare the file name with each pattern and apply the # filter if there is a match. # The filters are a list of the form: # pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further # info on how filters are used. If FILTER_PATTERNS is empty or if # non of the patterns match the file name, INPUT_FILTER is applied. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will be used to filter the input files when producing source # files to browse (i.e. when SOURCE_BROWSER is set to YES). FILTER_SOURCE_FILES = NO # The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file # pattern. A pattern will override the setting for FILTER_PATTERN (if any) # and it is also possible to disable source filtering for a specific pattern # using *.ext= (so without naming a filter). This option only has effect when # FILTER_SOURCE_FILES is enabled. FILTER_SOURCE_PATTERNS = #--------------------------------------------------------------------------- # configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will # be generated. Documented entities will be cross-referenced with these sources. # Note: To get rid of all source code in the generated output, make sure also # VERBATIM_HEADERS is set to NO. SOURCE_BROWSER = NO # Setting the INLINE_SOURCES tag to YES will include the body # of functions and classes directly in the documentation. INLINE_SOURCES = NO # Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct # doxygen to hide any special comment blocks from generated source code # fragments. Normal C, C++ and Fortran comments will always remain visible. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES # then for each documented function all documented # functions referencing it will be listed. REFERENCED_BY_RELATION = NO # If the REFERENCES_RELATION tag is set to YES # then for each documented function all documented entities # called/used by that function will be listed. REFERENCES_RELATION = NO # If the REFERENCES_LINK_SOURCE tag is set to YES (the default) # and SOURCE_BROWSER tag is set to YES, then the hyperlinks from # functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will # link to the source code. # Otherwise they will link to the documentation. REFERENCES_LINK_SOURCE = NO # If the USE_HTAGS tag is set to YES then the references to source code # will point to the HTML generated by the htags(1) tool instead of doxygen # built-in source browser. The htags tool is part of GNU's global source # tagging system (see http://www.gnu.org/software/global/global.html). You # will need version 4.8.6 or higher. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen # will generate a verbatim copy of the header file for each class for # which an include is specified. Set to NO to disable this. VERBATIM_HEADERS = NO #--------------------------------------------------------------------------- # configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index # of all compounds will be generated. Enable this if the project # contains a lot of classes, structs, unions or interfaces. ALPHABETICAL_INDEX = YES # If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then # the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns # in which this list will be split (can be a number in the range [1..20]) COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all # classes will be put under the same header in the alphabetical index. # The IGNORE_PREFIX tag can be used to specify one or more prefixes that # should be ignored while generating the index headers. IGNORE_PREFIX = #--------------------------------------------------------------------------- # configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES (the default) Doxygen will # generate HTML output. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `html' will be used as the default path. HTML_OUTPUT = api # The HTML_FILE_EXTENSION tag can be used to specify the file extension for # each generated HTML page (for example: .htm,.php,.asp). If it is left blank # doxygen will generate files with .html extension. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a personal HTML header for # each generated HTML page. If it is left blank doxygen will generate a # standard header. Note that when using a custom header you are responsible # for the proper inclusion of any scripts and style sheets that doxygen # needs, which is dependent on the configuration options used. # It is advised to generate a default header using "doxygen -w html # header.html footer.html stylesheet.css YourConfigFile" and then modify # that header. Note that the header is subject to change so you typically # have to redo this when upgrading to a newer version of doxygen or when # changing the value of configuration settings such as GENERATE_TREEVIEW! HTML_HEADER = doc/build/doxygen_header.html # The HTML_FOOTER tag can be used to specify a personal HTML footer for # each generated HTML page. If it is left blank doxygen will generate a # standard footer. HTML_FOOTER = doc/build/doxygen_footer.html # The HTML_STYLESHEET tag can be used to specify a user-defined cascading # style sheet that is used by each HTML page. It can be used to # fine-tune the look of the HTML output. If the tag is left blank doxygen # will generate a default style sheet. Note that doxygen will try to copy # the style sheet file to the HTML output directory, so don't put your own # style sheet in the HTML output directory as well, or it will be erased! HTML_STYLESHEET = # The HTML_EXTRA_FILES tag can be used to specify one or more extra images or # other source files which should be copied to the HTML output directory. Note # that these files will be copied to the base HTML output directory. Use the # $relpath$ marker in the HTML_HEADER and/or HTML_FOOTER files to load these # files. In the HTML_STYLESHEET file, use the file name only. Also note that # the files will be copied as-is; there are no commands or markers available. HTML_EXTRA_FILES = # The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. # Doxygen will adjust the colors in the style sheet and background images # according to this color. Hue is specified as an angle on a colorwheel, # see http://en.wikipedia.org/wiki/Hue for more information. # For instance the value 0 represents red, 60 is yellow, 120 is green, # 180 is cyan, 240 is blue, 300 purple, and 360 is red again. # The allowed range is 0 to 359. HTML_COLORSTYLE_HUE = 220 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of # the colors in the HTML output. For a value of 0 the output will use # grayscales only. A value of 255 will produce the most vivid colors. HTML_COLORSTYLE_SAT = 100 # The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to # the luminance component of the colors in the HTML output. Values below # 100 gradually make the output lighter, whereas values above 100 make # the output darker. The value divided by 100 is the actual gamma applied, # so 80 represents a gamma of 0.8, The value 220 represents a gamma of 2.2, # and 100 does not change the gamma. HTML_COLORSTYLE_GAMMA = 80 # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML # page will contain the date and time when the page was generated. Setting # this to NO can help when comparing the output of multiple runs. HTML_TIMESTAMP = YES # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. HTML_DYNAMIC_SECTIONS = NO # With HTML_INDEX_NUM_ENTRIES one can control the preferred number of # entries shown in the various tree structured indices initially; the user # can expand and collapse entries dynamically later on. Doxygen will expand # the tree to such a level that at most the specified number of entries are # visible (unless a fully collapsed tree already exceeds this amount). # So setting the number of entries 1 will produce a full collapsed tree by # default. 0 is a special value representing an infinite number of entries # and will result in a full expanded tree by default. HTML_INDEX_NUM_ENTRIES = 100 # If the GENERATE_DOCSET tag is set to YES, additional index files # will be generated that can be used as input for Apple's Xcode 3 # integrated development environment, introduced with OSX 10.5 (Leopard). # To create a documentation set, doxygen will generate a Makefile in the # HTML output directory. Running make will produce the docset in that # directory and running "make install" will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find # it at startup. # See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html # for more information. GENERATE_DOCSET = NO # When GENERATE_DOCSET tag is set to YES, this tag determines the name of the # feed. A documentation feed provides an umbrella under which multiple # documentation sets from a single provider (such as a company or product suite) # can be grouped. DOCSET_FEEDNAME = "Doxygen generated docs" # When GENERATE_DOCSET tag is set to YES, this tag specifies a string that # should uniquely identify the documentation set bundle. This should be a # reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen # will append .docset to the name. DOCSET_BUNDLE_ID = org.doxygen.Project # When GENERATE_PUBLISHER_ID tag specifies a string that should uniquely identify # the documentation publisher. This should be a reverse domain-name style # string, e.g. com.mycompany.MyDocSet.documentation. DOCSET_PUBLISHER_ID = org.doxygen.Publisher # The GENERATE_PUBLISHER_NAME tag identifies the documentation publisher. DOCSET_PUBLISHER_NAME = Publisher # If the GENERATE_HTMLHELP tag is set to YES, additional index files # will be generated that can be used as input for tools like the # Microsoft HTML help workshop to generate a compiled HTML help file (.chm) # of the generated HTML documentation. GENERATE_HTMLHELP = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can # be used to specify the file name of the resulting .chm file. You # can add a path in front of the file if the result should not be # written to the html output directory. CHM_FILE = # If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can # be used to specify the location (absolute path including file name) of # the HTML help compiler (hhc.exe). If non-empty doxygen will try to run # the HTML help compiler on the generated index.hhp. HHC_LOCATION = # If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag # controls if a separate .chi index file is generated (YES) or that # it should be included in the master .chm file (NO). GENERATE_CHI = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING # is used to encode HtmlHelp index (hhk), content (hhc) and project file # content. CHM_INDEX_ENCODING = # If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag # controls whether a binary table of contents is generated (YES) or a # normal table of contents (NO) in the .chm file. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members # to the contents of the HTML help documentation and to the tree view. TOC_EXPAND = NO # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and # QHP_VIRTUAL_FOLDER are set, an additional index file will be generated # that can be used as input for Qt's qhelpgenerator to generate a # Qt Compressed Help (.qch) of the generated HTML documentation. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can # be used to specify the file name of the resulting .qch file. # The path specified is relative to the HTML output folder. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#namespace QHP_NAMESPACE = org.doxygen.Project # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#virtual-folders QHP_VIRTUAL_FOLDER = doc # If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to # add. For more information please see # http://doc.trolltech.com/qthelpproject.html#custom-filters QHP_CUST_FILTER_NAME = # The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the # custom filter to add. For more information please see # # Qt Help Project / Custom Filters. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this # project's # filter section matches. # # Qt Help Project / Filter Attributes. QHP_SECT_FILTER_ATTRS = # If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can # be used to specify the location of Qt's qhelpgenerator. # If non-empty doxygen will try to run qhelpgenerator on the generated # .qhp file. QHG_LOCATION = # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files # will be generated, which together with the HTML files, form an Eclipse help # plugin. To install this plugin and make it available under the help contents # menu in Eclipse, the contents of the directory containing the HTML and XML # files needs to be copied into the plugins directory of eclipse. The name of # the directory within the plugins directory should be the same as # the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before # the help appears. GENERATE_ECLIPSEHELP = NO # A unique identifier for the eclipse help plugin. When installing the plugin # the directory name containing the HTML and XML files should also have # this name. ECLIPSE_DOC_ID = org.doxygen.Project # The DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) # at top of each HTML page. The value NO (the default) enables the index and # the value YES disables it. Since the tabs have the same information as the # navigation tree you can set this option to NO if you already set # GENERATE_TREEVIEW to YES. DISABLE_INDEX = NO # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. # If the tag value is set to YES, a side panel will be generated # containing a tree-like index structure (just like the one that # is generated for HTML Help). For this to work a browser that supports # JavaScript, DHTML, CSS and frames is required (i.e. any modern browser). # Windows users are probably better off using the HTML help feature. # Since the tree basically has the same information as the tab index you # could consider to set DISABLE_INDEX to NO when enabling this option. GENERATE_TREEVIEW = NONE # The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values # (range [0,1..20]) that doxygen will group on one line in the generated HTML # documentation. Note that a value of 0 will completely suppress the enum # values from appearing in the overview section. ENUM_VALUES_PER_LINE = 4 # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be # used to set the initial width (in pixels) of the frame in which the tree # is shown. TREEVIEW_WIDTH = 250 # When the EXT_LINKS_IN_WINDOW option is set to YES doxygen will open # links to external symbols imported via tag files in a separate window. EXT_LINKS_IN_WINDOW = NO # Use this tag to change the font size of Latex formulas included # as images in the HTML documentation. The default is 10. Note that # when you change the font size after a successful doxygen run you need # to manually remove any form_*.png images from the HTML output directory # to force them to be regenerated. FORMULA_FONTSIZE = 13 # Use the FORMULA_TRANSPARENT tag to determine whether or not the images # generated for formulas are transparent PNGs. Transparent PNGs are # not supported properly for IE 6.0, but are supported on all modern browsers. # Note that when changing this option you need to delete any form_*.png files # in the HTML output before the changes have effect. FORMULA_TRANSPARENT = YES # Enable the USE_MATHJAX option to render LaTeX formulas using MathJax # (see http://www.mathjax.org) which uses client side Javascript for the # rendering instead of using prerendered bitmaps. Use this if you do not # have LaTeX installed or if you want to formulas look prettier in the HTML # output. When enabled you may also need to install MathJax separately and # configure the path to it using the MATHJAX_RELPATH option. USE_MATHJAX = YES # When MathJax is enabled you need to specify the location relative to the # HTML output directory using the MATHJAX_RELPATH option. The destination # directory should contain the MathJax.js script. For instance, if the mathjax # directory is located at the same level as the HTML output directory, then # MATHJAX_RELPATH should be ../mathjax. The default value points to # the MathJax Content Delivery Network so you can quickly see the result without # installing MathJax. # However, it is strongly recommended to install a local # copy of MathJax from http://www.mathjax.org before deployment. MATHJAX_RELPATH = http://www.mathjax.org/mathjax # The MATHJAX_EXTENSIONS tag can be used to specify one or MathJax extension # names that should be enabled during MathJax rendering. MATHJAX_EXTENSIONS = # When the SEARCHENGINE tag is enabled doxygen will generate a search box # for the HTML output. The underlying search engine uses javascript # and DHTML and should work on any modern browser. Note that when using # HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets # (GENERATE_DOCSET) there is already a search function so this one should # typically be disabled. For large projects the javascript based search engine # can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution. SEARCHENGINE = NO # When the SERVER_BASED_SEARCH tag is enabled the search engine will be # implemented using a PHP enabled web server instead of at the web client # using Javascript. Doxygen will generate the search PHP script and index # file to put on the web server. The advantage of the server # based approach is that it scales better to large projects and allows # full text search. The disadvantages are that it is more difficult to setup # and does not have live searching capabilities. SERVER_BASED_SEARCH = NO #--------------------------------------------------------------------------- # configuration options related to the LaTeX output #--------------------------------------------------------------------------- # If the GENERATE_LATEX tag is set to YES (the default) Doxygen will # generate Latex output. GENERATE_LATEX = NO # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `latex' will be used as the default path. LATEX_OUTPUT = latex # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be # invoked. If left blank `latex' will be used as the default command name. # Note that when enabling USE_PDFLATEX this option is only used for # generating bitmaps for formulas in the HTML output, but not in the # Makefile that is written to the output directory. LATEX_CMD_NAME = latex # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to # generate index for LaTeX. If left blank `makeindex' will be used as the # default command name. MAKEINDEX_CMD_NAME = makeindex # If the COMPACT_LATEX tag is set to YES Doxygen generates more compact # LaTeX documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_LATEX = YES # The PAPER_TYPE tag can be used to set the paper type that is used # by the printer. Possible values are: a4, letter, legal and # executive. If left blank a4wide will be used. PAPER_TYPE = letter # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX # packages that should be included in the LaTeX output. EXTRA_PACKAGES = amsmath \ amsfonts \ amssymb \ sfmath # The LATEX_HEADER tag can be used to specify a personal LaTeX header for # the generated latex document. The header should contain everything until # the first chapter. If it is left blank doxygen will generate a # standard header. Notice: only use this tag if you know what you are doing! LATEX_HEADER = # The LATEX_FOOTER tag can be used to specify a personal LaTeX footer for # the generated latex document. The footer should contain everything after # the last chapter. If it is left blank doxygen will generate a # standard footer. Notice: only use this tag if you know what you are doing! LATEX_FOOTER = # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated # is prepared for conversion to pdf (using ps2pdf). The pdf file will # contain links (just like the HTML output) instead of page references # This makes the output suitable for online browsing using a pdf viewer. PDF_HYPERLINKS = YES # If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of # plain latex in the generated Makefile. Set this option to YES to get a # higher quality PDF documentation. USE_PDFLATEX = YES # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. # command to the generated LaTeX files. This will instruct LaTeX to keep # running if errors occur, instead of asking the user for help. # This option is also used when generating formulas in HTML. LATEX_BATCHMODE = YES # If LATEX_HIDE_INDICES is set to YES then doxygen will not # include the index chapters (such as File Index, Compound Index, etc.) # in the output. LATEX_HIDE_INDICES = NO # If LATEX_SOURCE_CODE is set to YES then doxygen will include # source code with syntax highlighting in the LaTeX output. # Note that which sources are shown also depends on other settings # such as SOURCE_BROWSER. LATEX_SOURCE_CODE = NO # The LATEX_BIB_STYLE tag can be used to specify the style to use for the # bibliography, e.g. plainnat, or ieeetr. The default style is "plain". See # http://en.wikipedia.org/wiki/BibTeX for more info. LATEX_BIB_STYLE = plain #--------------------------------------------------------------------------- # configuration options related to the RTF output #--------------------------------------------------------------------------- # If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output # The RTF output is optimized for Word 97 and may not look very pretty with # other RTF readers or editors. GENERATE_RTF = NO # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `rtf' will be used as the default path. RTF_OUTPUT = rtf # If the COMPACT_RTF tag is set to YES Doxygen generates more compact # RTF documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_RTF = NO # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated # will contain hyperlink fields. The RTF file will # contain links (just like the HTML output) instead of page references. # This makes the output suitable for online browsing using WORD or other # programs which support those fields. # Note: wordpad (write) and others do not support links. RTF_HYPERLINKS = NO # Load style sheet definitions from file. Syntax is similar to doxygen's # config file, i.e. a series of assignments. You only have to provide # replacements, missing definitions are set to their default value. RTF_STYLESHEET_FILE = # Set optional variables used in the generation of an rtf document. # Syntax is similar to doxygen's config file. RTF_EXTENSIONS_FILE = #--------------------------------------------------------------------------- # configuration options related to the man page output #--------------------------------------------------------------------------- # If the GENERATE_MAN tag is set to YES (the default) Doxygen will # generate man pages GENERATE_MAN = NO # The MAN_OUTPUT tag is used to specify where the man pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `man' will be used as the default path. MAN_OUTPUT = man # The MAN_EXTENSION tag determines the extension that is added to # the generated man pages (default is the subroutine's section .3) MAN_EXTENSION = .3 # If the MAN_LINKS tag is set to YES and Doxygen generates man output, # then it will generate one additional man file for each entity # documented in the real man page(s). These additional files # only source the real man page, but without them the man command # would be unable to find the correct page. The default is NO. MAN_LINKS = NO #--------------------------------------------------------------------------- # configuration options related to the XML output #--------------------------------------------------------------------------- # If the GENERATE_XML tag is set to YES Doxygen will # generate an XML file that captures the structure of # the code including all documentation. GENERATE_XML = NO # The XML_OUTPUT tag is used to specify where the XML pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `xml' will be used as the default path. XML_OUTPUT = xml # The XML_SCHEMA tag can be used to specify an XML schema, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_SCHEMA = # The XML_DTD tag can be used to specify an XML DTD, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_DTD = # If the XML_PROGRAMLISTING tag is set to YES Doxygen will # dump the program listings (including syntax highlighting # and cross-referencing information) to the XML output. Note that # enabling this will significantly increase the size of the XML output. XML_PROGRAMLISTING = NO #--------------------------------------------------------------------------- # configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will # generate an AutoGen Definitions (see autogen.sf.net) file # that captures the structure of the code including all # documentation. Note that this feature is still experimental # and incomplete at the moment. GENERATE_AUTOGEN_DEF = NO #--------------------------------------------------------------------------- # configuration options related to the Perl module output #--------------------------------------------------------------------------- # If the GENERATE_PERLMOD tag is set to YES Doxygen will # generate a Perl module file that captures the structure of # the code including all documentation. Note that this # feature is still experimental and incomplete at the # moment. GENERATE_PERLMOD = NO # If the PERLMOD_LATEX tag is set to YES Doxygen will generate # the necessary Makefile rules, Perl scripts and LaTeX code to be able # to generate PDF and DVI output from the Perl module output. PERLMOD_LATEX = NO # If the PERLMOD_PRETTY tag is set to YES the Perl module output will be # nicely formatted so it can be parsed by a human reader. # This is useful # if you want to understand what is going on. # On the other hand, if this # tag is set to NO the size of the Perl module output will be much smaller # and Perl will parse it just the same. PERLMOD_PRETTY = YES # The names of the make variables in the generated doxyrules.make file # are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. # This is useful so different doxyrules.make files included by the same # Makefile don't overwrite each other's variables. PERLMOD_MAKEVAR_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the preprocessor #--------------------------------------------------------------------------- # If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will # evaluate all C-preprocessor directives found in the sources and include # files. ENABLE_PREPROCESSING = YES # If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro # names in the source code. If set to NO (the default) only conditional # compilation will be performed. Macro expansion can be done in a controlled # way by setting EXPAND_ONLY_PREDEF to YES. MACRO_EXPANSION = YES # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES # then the macro expansion is limited to the macros specified with the # PREDEFINED and EXPAND_AS_DEFINED tags. EXPAND_ONLY_PREDEF = YES # If the SEARCH_INCLUDES tag is set to YES (the default) the includes files # pointed to by INCLUDE_PATH will be searched when a #include is found. SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by # the preprocessor. INCLUDE_PATH = vl # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard # patterns (like *.h and *.hpp) to filter out the header-files in the # directories. If left blank, the patterns specified with FILE_PATTERNS will # be used. INCLUDE_FILE_PATTERNS = # The PREDEFINED tag can be used to specify one or more macro names that # are defined before the preprocessor is started (similar to the -D option of # gcc). The argument of the tag is a list of macros of the form: name # or name=definition (no spaces). If the definition and the = are # omitted =1 is assumed. To prevent a macro definition from being # undefined via #undef or recursively expanded use the := operator # instead of the = operator. PREDEFINED = __DOXYGEN__=1 \ VL_INLINE=inline \ VL_EXPORT= \ VL_UNUSED= # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then # this tag can be used to specify a list of macro names that should be expanded. # The macro definition that is found in the sources will be used. # Use the PREDEFINED tag if you want to use a different macro definition that # overrules the definition found in the source code. EXPAND_AS_DEFINED = # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then # doxygen's preprocessor will remove all references to function-like macros # that are alone on a line, have an all uppercase name, and do not end with a # semicolon, because these will confuse the parser if not removed. SKIP_FUNCTION_MACROS = YES #--------------------------------------------------------------------------- # Configuration::additions related to external references #--------------------------------------------------------------------------- # The TAGFILES option can be used to specify one or more tagfiles. For each # tag file the location of the external documentation should be added. The # format of a tag file without this location is as follows: # # TAGFILES = file1 file2 ... # Adding location for the tag files is done as follows: # # TAGFILES = file1=loc1 "file2 = loc2" ... # where "loc1" and "loc2" can be relative or absolute paths # or URLs. Note that each tag file must have a unique name (where the name does # NOT include the path). If a tag file is not located in the directory in which # doxygen is run, you must also specify the path to the tagfile here. TAGFILES = # When a file name is specified after GENERATE_TAGFILE, doxygen will create # a tag file that is based on the input files it reads. GENERATE_TAGFILE = doc/doxygen.tag # If the ALLEXTERNALS tag is set to YES all external classes will be listed # in the class index. If set to NO only the inherited external classes # will be listed. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed # in the modules index. If set to NO, only the current project's groups will # be listed. EXTERNAL_GROUPS = YES # The PERL_PATH should be the absolute path and name of the perl script # interpreter (i.e. the result of `which perl'). PERL_PATH = /usr/bin/perl #--------------------------------------------------------------------------- # Configuration options related to the dot tool #--------------------------------------------------------------------------- # If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will # generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base # or super classes. Setting the tag to NO turns the diagrams off. Note that # this option also works with HAVE_DOT disabled, but it is recommended to # install and use dot, since it yields more powerful graphs. CLASS_DIAGRAMS = NO # You can define message sequence charts within doxygen comments using the \msc # command. Doxygen will then run the mscgen tool (see # http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the # documentation. The MSCGEN_PATH tag allows you to specify the directory where # the mscgen tool resides. If left empty the tool is assumed to be found in the # default search path. MSCGEN_PATH = # If set to YES, the inheritance and collaboration graphs will hide # inheritance and usage relations if the target is undocumented # or is not a class. HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz, a graph visualization # toolkit from AT&T and Lucent Bell Labs. The other options in this section # have no effect if this option is set to NO (the default) HAVE_DOT = NO # The DOT_NUM_THREADS specifies the number of dot invocations doxygen is # allowed to run in parallel. When set to 0 (the default) doxygen will # base this on the number of processors available in the system. You can set it # explicitly to a value larger than 0 to get control over the balance # between CPU load and processing speed. 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Setting this tag to YES will force the # CLASS_DIAGRAMS tag to NO. CLASS_GRAPH = NO # If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect implementation dependencies (inheritance, containment, and # class references variables) of the class with other documented classes. COLLABORATION_GRAPH = YES # If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen # will generate a graph for groups, showing the direct groups dependencies GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling # Language. UML_LOOK = NO # If the UML_LOOK tag is enabled, the fields and methods are shown inside # the class node. If there are many fields or methods and many nodes the # graph may become too big to be useful. 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INCLUDED_BY_GRAPH = YES # If the CALL_GRAPH and HAVE_DOT options are set to YES then # doxygen will generate a call dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable call graphs # for selected functions only using the \callgraph command. CALL_GRAPH = NO # If the CALLER_GRAPH and HAVE_DOT tags are set to YES then # doxygen will generate a caller dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable caller # graphs for selected functions only using the \callergraph command. CALLER_GRAPH = NO # If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen # will generate a graphical hierarchy of all classes instead of a textual one. GRAPHICAL_HIERARCHY = YES # If the DIRECTORY_GRAPH and HAVE_DOT tags are set to YES # then doxygen will show the dependencies a directory has on other directories # in a graphical way. The dependency relations are determined by the #include # relations between the files in the directories. DIRECTORY_GRAPH = YES # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. Possible values are svg, png, jpg, or gif. # If left blank png will be used. If you choose svg you need to set # HTML_FILE_EXTENSION to xhtml in order to make the SVG files # visible in IE 9+ (other browsers do not have this requirement). DOT_IMAGE_FORMAT = png # If DOT_IMAGE_FORMAT is set to svg, then this option can be set to YES to # enable generation of interactive SVG images that allow zooming and panning. # Note that this requires a modern browser other than Internet Explorer. # Tested and working are Firefox, Chrome, Safari, and Opera. For IE 9+ you # need to set HTML_FILE_EXTENSION to xhtml in order to make the SVG files # visible. Older versions of IE do not have SVG support. INTERACTIVE_SVG = NO # The tag DOT_PATH can be used to specify the path where the dot tool can be # found. If left blank, it is assumed the dot tool can be found in the path. DOT_PATH = # The DOTFILE_DIRS tag can be used to specify one or more directories that # contain dot files that are included in the documentation (see the # \dotfile command). DOTFILE_DIRS = # The MSCFILE_DIRS tag can be used to specify one or more directories that # contain msc files that are included in the documentation (see the # \mscfile command). MSCFILE_DIRS = # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of # nodes that will be shown in the graph. If the number of nodes in a graph # becomes larger than this value, doxygen will truncate the graph, which is # visualized by representing a node as a red box. Note that doxygen if the # number of direct children of the root node in a graph is already larger than # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note # that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. DOT_GRAPH_MAX_NODES = 50 # The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the # graphs generated by dot. A depth value of 3 means that only nodes reachable # from the root by following a path via at most 3 edges will be shown. Nodes # that lay further from the root node will be omitted. Note that setting this # option to 1 or 2 may greatly reduce the computation time needed for large # code bases. Also note that the size of a graph can be further restricted by # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. MAX_DOT_GRAPH_DEPTH = 0 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent # background. This is disabled by default, because dot on Windows does not # seem to support this out of the box. Warning: Depending on the platform used, # enabling this option may lead to badly anti-aliased labels on the edges of # a graph (i.e. they become hard to read). DOT_TRANSPARENT = YES # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) # support this, this feature is disabled by default. DOT_MULTI_TARGETS = NO # If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will # generate a legend page explaining the meaning of the various boxes and # arrows in the dot generated graphs. GENERATE_LEGEND = YES # If the DOT_CLEANUP tag is set to YES (the default) Doxygen will # remove the intermediate dot files that are used to generate # the various graphs. DOT_CLEANUP = YES vlfeat/docsrc/quickshift.html0000644000175000017500000000735212237004745015275 0ustar dimadima %tableofcontents;

    Quick shift is a mode seeking algorithm (like mean shift) which instead of iteratively shifting each point towards a local mean instead forms a tree of links to the nearest neighbor which increases the density. For a more in-depth description of the algorithm, see our API reference for quick shift

    Using quick shift to find superpixels

    This demo shows quick shift in a simple superpixelization problem where we seek to segment this image:

    The image we wish to segment

    As a feature vector, we choose the LAB colorspace representation of the image augmented with the x,y location of the pixel. vl_quickseg is a convenient wrapper function which takes care of the transformation of the image to LAB and performs segmentation, making our job as easy as:

    ratio = 0.5; kernelsize = 2; Iseg = vl_quickseg(I, ratio, kernelsize, maxdist);

    where ratio is the tradeoff between color importance and spatial importance (larger values give more importance to color), kernelsize is the size of the kernel used to estimate the density, and maxdist is the maximum distance between points in the feature space that may be linked if the density is increased.

    The effect of maxdist on the superpixelization. As we increase maxdist, superpixels become larger and larger since we can link less similar points. Top: maxdist=10. Bottom: maxdist=20.

    Multiple segmentations

    Quick shift arranges all of the data points into a tree where parents in the tree are the nearest neighbors in the feature space which increase the estimate of the density. By imposing a limit on the distance between nearest neighbors (maxdist), we decrease the amount of computation required to search for the nearest neighbors. However, we also break our tree into a forest, because local modes of the density will now have no neighbor which is close enough in the feature space to form a link.

    In the previous section, we created a superpixel segmentation by taking each of the trees in this forest as a distinct cluster. However, since maxdist simply prevents new links from forming, the segmentation formed by every dist < maxdist is contained in the result. vl_quickvis lets us visualize this by running quick shift once and forming multiple segmentations by cutting links in the tree which are smaller and smaller.

    maxdist = 50; ndists = 10; Iedge = vl_quickvis(I, ratio, kernelsize, maxdist, ndists) imagesc(Iedge); axis equal off tight; colormap gray;
    A visualization of multiple maxdist thresholds on a single image. Here, boundaries are colored by the largest maxdist where the boundary is preserved.
     vlfeat/docsrc/hog.html0000644000175000017500000001314712237004745013677 0ustar dimadima %tableofcontents;

    The HOG features are widely use for object detection. HOG decomposes an image into small squared cells, computes an histogram of oriented gradients in each cell, normalizes the result using a block-wise pattern, and return a descriptor for each cell.

    Stacking the cells into a squared image region can be used as an image window descriptor for object detection, for example by means of an SVM.

    This tutorial shows how to use the VLFeat function vl_hog to compute HOG features of various kind and manipulate them.

    Basic HOG computation

    We start by considering an example input image:

    An example image.

    HOG is computed by calling the vl_hog function:

    cellSize = 8 ; hog = vl_hog(im, cellSize, 'verbose') ;

    The same function can also be used to generate a pictorial rendition of the features, although this unavoidably destroys some of the information contained in the feature itself. To this end, use the render command:

    imhog = vl_hog('render', hog, 'verbose') ; clf ; imagesc(imhog) ; colormap gray ;

    This should produce the following image:

    Standard HOG features with a cell size of eight pixels.

    HOG is an array of cells, with the third dimension spanning feature components:

    > size(hog) ans = 16 16 31

    In this case the feature has 31 dimensions. HOG exists in many variants. VLFeat supports two: the UoCTTI variant (used by default) and the original Dalal-Triggs variant (with 2×2 square HOG blocks for normalization). The main difference is that the UoCTTI variant computes bot directed and undirected gradients as well as a four dimensional texture-energy feature, but projects the result down to 31 dimensions. Dalal-Triggs works instead with undirected gradients only and does not do any compression, for a total of 36 dimension. The Dalal-Triggs variant can be computed as

    % Dalal-Triggs variant cellSize = 8 ; hog = vl_hog(im, cellSize, 'verbose', 'variant', 'dalaltriggs') ; imhog = vl_hog('render', hog, 'verbose', 'variant', 'dalaltriggs') ;

    The result is visually very similar:

    Dalal-Triggs variant. Differences with the standard version are difficult to appreciated in the rendition.

    Flipping HOG from left to right

    Often it is necessary to flip HOG features from left to right (for example in order to model an axis symmetric object). This can be obtained analytically from the feature itself by permuting the histogram dimensions appropriately. The permutation is obtained as follows:

    % Get permutation to flip a HOG cell from left to right perm = vl_hog('permutation') ;

    Then these two examples produce identical results (provided that the image contains an exact number of cells:

    imHog = vl_hog('render', hog) ; imHogFromFlippedImage = vl_hog('render', hogFromFlippedImage) ; imFlippedHog = vl_hog('render', flippedHog) ;

    This is shown in the figure:

    Flipping HOG features from left to right either by flipping the input image or the features directly.

    Other HOG parameters

    vl_hog supports other parameters as well. For example, one can specify the number of orientations in the histograms by the numOrientations option:

    % Specify the number of orientations hog = vl_hog(im, cellSize, 'verbose', 'numOrientations', o) ; imhog = vl_hog('render', hog, 'verbose', 'numOrientations', o) ;

    Changing the number of orientations changes the features quite significantly:

    HOG features for numOrientations equal to 3, 4, 5, 9, and 21 repsectively.

    Another useful option is BilinearOrientations switching on the bilinear orientation assignment of the gradient (this is not used in certain implementation like UoCTTI).

    % Specify the number of orientations hog = vl_hog(im,cellSize,'numOrientations', 4) ; imhog = vl_hog('render', hog, 'numOrientations', 4) ;

    resulting in

    From left to right: input image, hard orientation assigments for numOrientations equals to four, and soft orientation assigments.
     vlfeat/docsrc/vlfeat-website.xml0000644000175000017500000000251212237004745015671 0ustar dimadima

    These man pages describe VLFeat command line utilities.

         vlfeat/docsrc/compiling.html0000644000175000017500000001057012237004745015100 0ustar dimadima %tableofcontents;

    These instructions explain how to compile VLFeat on your own. This can be useful to customize or contribute to VLFeat, but it is otherwise not necessary (use the binary package instead).

    Compiling on Unix

    Compiling for UNIX-like platforms (e.g. Mac OS X, Linux) uses the standard GNU toolchain. It is usually sufficient to change to VLFeat root directory, denoted VLFEATROOT in the following, and type make:

    $ cd VLFEATROOT $ make

    The make script attempts to automatically detect the host architecture and configure itself accordingly. If the architecture is not detected correctly, it can be specified manually. For instance

    $ make ARCH=glnx86

    compiles for GNU/Linux 32-bit. make help can be used to obtain a list of other useful options. You can also use make info to obtain a list of the configuration parameters used by the Makefile, which might allow you do debug any potential issue.

    MATLAB support. In order for MATLAB support to be compiled, the MATLAB mex script must be in the current path. If it is not, its location must be passed to make as follows. First, determine MATLAB's root directory by running a MATLAB session and issuing the matlabroot command. Let MATLABROOT denote the returned path (e.g. /Applications/MATLAB_R2009b.app/). The mex script is usually located in MALTABROOT/bin/mex. Then run the compilation with the command

    $ make MEX=MATLABROOT/bin/mex

    VLFeat must be compiled for the architecture used by MATLAB (run MATLAB's computer command to obtain this information). On Mac OS X it is often necessary to turn on 64-bit support explicitly by setting ARCH=maci64 as both the 32 and 64 bit versions are plausible targets on 64-bit machines.

    Mac OS X 10.6 (Snow Leopard), 64-bit, and MATLAB 2009b. MATLAB 2009b for Snow Leopard has added 64 bit mex support and a corresponding extension mexmaci64. VLFeat now supports compiling for this platform, but the architecture must be specified manually when compiling, either like:

    $ make ARCH=maci64

    or

    $ make ARCH=maci

    Unfortunately, MATLAB 2009b's mex script has a bug that must be manually fixed in order for this procedure to work correctly. It is recommend to make backup of the mex script before attempting this. Th fix the bug, edit the MATLABROOT/bin/mex script by cutting the line:

    get_arch # Call $MATLAB/bin/util/arch.sh

    and pasting it after the processing of options:

    ... shift done #PASTE get_arch call here get_arch # Call $MATLAB/bin/util/arch.sh if [ $v7_compat -eq 1 ] ; then fc_flags="$fc_flags -DMX_COMPAT_32" ...

    Compiling on Windows

    For Windows, the library bundles an NMake makefile (Makefile.mak). In order to use it, you must edit Makefile.mak to adjust the values of a number of configuration variables to match your setup. Once you have done that, start the Visual Studio Command Prompt and type

    $ nmake /f Makefile.mak # for the Windows 32 target $ nmake /f Makefile.mak ARCH=win64 # for the Windows 64 target

    For Windows platform, it is also possible to compile just the MATLAB MEX files from within MATLAB (using the vl_compile command). This is meant to help less experienced users that may need to recompile the mex file due to binary incompatibilities with older MATLAB versions.

    Windows troubleshooting

    syntax error: '=' unexpected:

    Use nmake /f Makefile.mak. Without /f, nmake will default to the wrong makefile.

    'long' followed by 'long' is illegal:

    This error is usually caused by attempting to compile outside of the Visual Studio Command Prompt.

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    The latest version of VLFeat is %env:VERSION;. To use VLFeat, simply download and unpack the latest binary package and add the appropriate paths to your environment (see below for details).

    Downloads

    Install
    &nsbp;

    Repository access

    VLFeat is under active development. You can browse our Git repository or download it by

    git clone git://github.com/vlfeat/vlfeat.git

    (This will require Git to be installed). The top of the master branch corresponds to the most recent version of VLFeat, but it could be unstable.

    We welcome contributions to both the documentation and the source code of VLFeat. You can create patches against our Git repository and send them to us for inclusion in the next version. There are two ways to contribute. For minor changes, simply clone our public repository, as explained above. Once you have made and committed your changes locally, you can submit them to the project via e-mail using a command like git format-patch:

    git format-patch origin

    For major additions, we prefer to handle collaboration through github. Follow their tutorial to fork our project and submit your modifications using a pull request.

     vlfeat/docsrc/doxytag.py0000644000175000017500000000403712237004745014263 0ustar dimadima#!/bin/python # file: Doxytag.py # brief: Parse doxygen.tag file # author: Andrea Vedaldi """ DOXYTAG parses doxygen.tag files. """ import xml.dom.minidom import sys import os import re __mpname__ = 'Doxytag' __version__ = '0.1' __date__ = '2013-07-09' __description__ = 'Doxygen tags parser' __long_description__ = __doc__ __license__ = 'BSD' __author__ = 'Andrea Vedaldi' # -------------------------------------------------------------------- class Doxytag: # -------------------------------------------------------------------- """ d = Doxytag(FILENAME) """ def __init__ (self, filename): self.index = {} dom = xml.dom.minidom.parse(filename) self.xBuildIndex(dom) def getText(self, nodelist): rc = [] for node in nodelist: if node.nodeType == node.TEXT_NODE: rc.append(node.data) return ''.join(rc) def xBuildIndex(self, node): if node.nodeType == node.ELEMENT_NODE: if node.tagName == "docanchor": anchor=self.getText(node.childNodes) filename=node.getAttribute("file") if os.path.splitext(filename)[1] != '.html': filename += '.html' self.index[anchor] = filename + "#" + anchor # do filenames filename = node.getElementsByTagName("filename") if filename.length > 0: name = node.getElementsByTagName("name") if name.length > 0: s1 = self.getText(name[0].childNodes) s2 = self.getText(filename[0].childNodes) if os.path.splitext(s2)[1] != '.html': s2 = s2 + '.html' self.index[s1] = s2 for x in node.childNodes: self.xBuildIndex(x) def dump(self): for k in self.index: print "%40s -> %40s" % (k, self.index[k]) if __name__ == '__main__': d = Doxytag("doc/doxygen.tag") d.dump() vlfeat/docsrc/plots-rank.html0000644000175000017500000002175612237004745015221 0ustar dimadima

    This tutorial illustrates the use of the functions vl_roc, vl_det, and vl_pr to generate ROC, DET, and precision-recall curves.

    VLFeat includes support for plotting starndard information retrieval curves such as the Receiver Operating Characteristic (ROC) and the Precision-Recall (PR) curves.

    Consider a set of samples with labels labels and score scores. scores is typically the output of a classifier, with higher scores corresponding to positive labels. Ideally, sorting the data by decreasing scores should leave all the positive samples first and the negative samples last. In practice, a classifier is not perfect and the ranking is not ideal. The tools discussed in this tutorial allow to evaluate and visualize the quality of the ranking.

    For the sake of the illustration generate some data randomly as follows:

    numPos = 20 ; numNeg = 100 ; labels = [ones(1, numPos) -ones(1,numNeg)] ; scores = randn(size(labels)) + labels ;

    In this case, there have five times more negative samples than positive ones. The scores are correlated to the labels as expected, but do not allow for a perfect separation of the two classes.

    ROC and DET curves

    To visualize the quality of the ranking, one can plot the ROC curve by using the vl_roc function:

    vl_roc(labels, scores) ;

    This produces the figure

    An example ROC curve.

    The ROC curve is the parametric curve given by the true positve rate (TPR) against the true negative rate (TNR). These two quantities can be obtained from vl_roc as follows:

    [tpr, tnr] = vl_roc(labels, scores) ;

    The TPR value tpr(k) is the percentage of positive samples that have rank smaller or equal than k (where ranks are assigned by decreasing scores). tnr(k) is instead the percentage of negative samples that have rank larger than k. Therefore, if one classifies the samples with rank smaller or equal than k to be positive and the rest to be negative, tpr(k) and tnr(k) are repsectively the probability that a positive/negative sample is classified correctly.

    Moving from rank k to rank k+1, if the sample of rank k+1 is positive then tpr increases; otherwise tnr decreases. An ideal classifier has all the positive samples first, and the corresponding ROC curve is one that describes two sides of the unit square.

    The Area Under the Curve (AUC) is an indicator of the overall quality of a ROC curve. For example, the ROC of the ideal classifier has AUC equal to 1. Another indicator is the Equal Error Rate (EER), the point on the ROC curve that corresponds to have an equal probability of miss-classifying a positive or negative sample. This point is obtained by intersecting the ROC curve with a diagonal of the unit square. Both AUC and EER can be computed by vl_roc:

    [tpr, tnr, info] = vl_roc(labels, scores) ; disp(info.auc) ; disp(info.eer) ;

    vl_roc has a couple of useful functionalities:

    • Any sample with label equal to zero is effecitvely ignored in the evaluation.
    • Samples with scores equal to -inf are assumed to be never retrieved by the classifier. For these, the TNR is conventionally set to be equal to zero.
    • Additional negative and positive samples with -inf score can be added to the evaluation by means of the numNegatives and numPositives options. For example, vl_roc(labels,scores,'numNegatives',1e4) sets the number of negative samples to 10,000. This can be useful when evaluating large retrieval systems, for which one may want to record in labels and scores only the top ranked results from a classifier.
    • Different variants of the ROC plot can be produced. For example vl_roc(labels,scores,'plot','tptn') swaps the two axis, plotting the TNR against the TPR. Since the TPR is also the recall (i.e., the percentage of positive samples retrieved up to a certain rank), this makes the plot more directly comparable to a precision-recall plot.
      Variants of the ROC plot.

    A limitation of the ROC curves in evaluating a typical retrieval system is that they put equal emphasis on false positive and false negative errors. In a tipical retrieval application, however, the vast majority of the samples are negative, so the false negative rate is typically very small for any operating point of interest. Therefore the emphasis is usually on the very first portion of the rank, where the few positive samples should concentrate. This can be emphasized by using either precision-recall plot or a variant of the ROC curves called Detection Error Tradeoff (DET) curves.

    A DET curve plots the FNR (also called false alarm rate) against teh FPR (also called miss rate) in logarithmic coordiantes. It can be generated by vl_det function call:

    An example DET curve.

    Precision-recall curves

    Both ROC and DET curves normalize out the relative proportions of positive and negative samples. By contrast, a Precision-Recall (PR) curve reflects this directly. One can plot the PR curve by using the vl_pr function:

    vl_pr(labels, scores) ;

    This produces the figure

    An example precision-recall curve.

    The PR curve is the parametric curve given by precision and recall. These two quantities can be obtained from vl_roc as follows:

    [recall, precision] = vl_roc(labels, scores) ;

    The precision value precision(k) is the proportion of samples with rank smaller or equal than k-1 that are positive(where ranks are assigned by decreasing scores). recall(k) is instead the percentage of positive samples that have rank smaller or equal than k-1. For example, if the first two samples are one positive and one negative, precision(3) is 1/2. If there are in total 5 positive samples, then recall(3) is 1/5.

    Moving from rank k to rank k+1, if the sample of rank k+1 is positive then both precision and recall increase; otherwise precision decreases and recall stays constant. This gives the PR curve a characteristic saw-shape. For aan ideal classifier that ranks all the positive samples first the PR curve is one that describes two sides of the unit square.

    Similar to the ROC curves, the Area Under the Curve (AUC) can be used to summarize the quality of a ranking in term of precision and recall. This can be obtained as info.auc by

    [rc, pr, info] = vl_pr(labels, scores) ; disp(info.auc) ; disp(info.ap) ; disp(info.ap_interp_11) ;

    The AUC is obtained by trapezoidal interpolation of the precision. An alternative and usually almost equivalent metric is the Average Precision (AP), returned as info.ap. This is the average of the precision obtained every time a new positive sample is recalled. It is the same as the AUC if precision is interpolated by constant segments and is the definition used by TREC most often. Finally, the 11 points interpolated average precision, returned as info.ap_interp_11. This is an older TREC definition and is obtained by taking the average of eleven precision values, obtained as the maximum precision for recalls largerer than 0.0, 0.1, ..., 1.0. This particular metric was used, for example, in the PASCAL VOC challenge until the 2008 edition.

     vlfeat/docsrc/apps.html0000644000175000017500000001173312237042456014065 0ustar dimadima #content table.checker { border-collapse: collapse ; margin-left: auto ; margin-right: auto ; } #content table.checker td { background-color: #f6f6f6 ; border: 1px solid #DDD ; padding: 0.5em ; }

    This page lists a number of sample VLFeat applications. Their code can be found in the VLROOT/apps/ subdirectory.

    Basic recognition

    Caltech-101 Collage

    This sample application uses VLFeat to train an test an image classifier on the Caltech-101 data. The classifier achieves 65% average accuracy by using a single feature and 15 training images per class. It uses:

    • PHOW features (dense multi-scale SIFT descriptors)
    • Elkan k-means for fast visual word dictionary construction
    • Spatial histograms as image descriptors
    • A homogeneous kernel map to transform a Chi2 support vector machine (SVM) into a linear one
    • SVM classifiers

    The program is fully contained in a single MATLAB M-file, and can also be simply adapted to use your own data (change conf.calDir).

    Advanced encodings for recognition

    This example application extends the Caltech-101 demo above in many ways: it supports multiple encoding methods, including BoVW, VLAD, and Fisher Vectors, tweaked image featuers, and multiple benchmark datasets. The code is located int apps/recognition. Start from the main file.

    The following tables report results on a few standard benchmark datasets (PASCAL VOC 2007 classification challenge, Caltech 101 30 training images, MIT Scene 67, and Flickr Material Dataset) for a number of different encodings:

    methodVOC07Caltech 101Scene 67FMD
    FV59.12% mAP73.02% Acc58.25% Acc59.60% Acc
    FV + aug.60.25% mAP75.61% Acc57.57% Acc60.80% Acc
    FV + s.p.62.23% mAP77.63% Acc61.83% Acc60.80% Acc
    VLAD + aug.54.66% mAP78.68% Acc53.29% Acc49.40% Acc
    BOVW + aug.49.87% mAP75.98% Acc50.22% Acc46.00% Acc

    The baseline feature is SIFT (vl_dsift) computed at seven scales with a factor $\sqrt{2}$ between successive scales, bins 8 pixel wide, and comptued with a step of 4 pixels. All experiments but the Caltech-101 ones start by doubling the resolution of the input image. The details of the encodings are as follows:

    • Bag-of-visual-words uses 4096 vector quantized visual words histogram square rooting, followed by $L^2$ normalization (Hellinger's kernel).
    • VLAD uses 256 vector quantized visual words, signed square-rooting, component wise $L^2$ normalization, and global $L^2$ normalization (see vl_vlad).
    • Fisher vectors uses a 256 visual words GMM and the improved formulation (signed square-rooting followed by $L^2$ normlization, see vl_fisher).
    • Learning uses a linear SVM (see vl_svmtrain). The parameter $C$ is set to 10 for all dataset except PASCAL VOC, for which it is set to 1.
    • Experiments labelled with “aug.” encode spatial information by appending the feature coordinates to the descriptor; the ones labelled with “s.p.” use a spatial pyramid with 1x1 and 3x1 subdivisions.
     vlfeat/docsrc/install-octave.html0000644000175000017500000000305412237004745016043 0ustar dimadima

    This page shows how to compile VLFeat MEX files for usage in GNU Octave (tested on version 3.6.4).

    Make sure that the image toolbox is installed in Octave. This can be obtained, for example, by using the pkg builtin Octave command:

    >> pkg install image

    Unpack VLFeat and use the Makefile to compile the MEX files. To instruct the Makefile to target Octave as well, specify the path to the mkoctfile command. For example, if mkoctfile is in your current path and VLFeat is unpacked in VLFEATROOT, the following should work:

    > cd VLFEATROOT > MKOCTFILE=mkoctfile make

    After the MEX files are successfully compiled (look for them into toolbox/mex/octave/), you can start using VLFeat in Octave in the same way as MATLAB. Do not forget to use the vl_setup command to initalize the paths:

    octave:1> addpath VLFEATROOT/toolbox octave:2> vl_setup octave:3> vl_version verbose VLFeat version 0.9.17 Static config: X64, little_endian, GNU C 40201 LP64, POSIX_threads, SSE2, OpenMP 4 CPU(s): GenuineIntel MMX SSE SSE2 SSE3 SSE41 SSE42 OpenMP: max threads: 4 (library: 4) Debug: yes SIMD enabled: yes     vlfeat/docsrc/covdet.html0000644000175000017500000003323212237042456014404 0ustar dimadima %tableofcontents;

    This tutorial introduces the vl_covdet VLFeat command implementing a number of co-variant feature detectors and corresponding descriptors. This family of detectors include SIFT as well as multi-scale conern (Harris-Laplace), and blob (Hessian-Laplace and Hessian-Hessian) detectors.

    Extracting frames and descriptors

    The first example shows how to use vl_covdet to compute and visualise co-variant features. Fist, let us load an example image and visualize it:

    im = vl_impattern('roofs1') ; figure(1) ; clf ; image(im) ; axis image off ;
    An example input image.

    The image must be converted to gray=scale and single precision. Then vl_covdet can be called in order to extract features (by default this uses the DoG cornerness measure, similarly to SIFT).

    imgs = im2single(rgb2gray(im)) ; frames = vl_covdet(imgs, 'verbose') ;

    The verbose option is not necessary, but it produces some useful information:

    vl_covdet: doubling image: yes vl_covdet: detector: DoG vl_covdet: peak threshold: 0.01, edge threshold: 10 vl_covdet: detected 3518 features vl_covdet: kept 3413 inside the boundary margin (2)

    The vl_plotframe command can then be used to plot these features

    hold on ; vl_plotframe(frames) ;

    which results in the image

    The default features detected by vl_covdet use the DoG cornerness measure (like SIFT).

    In addition to the DoG detector, vl_covdet supports a number of other ones:

    • The Difference of Gaussian operator (also known as trace of the Hessian operator or Laplacian operator) uses the local extrema trace of the multiscale Laplacian operator to detect features in scale and space (as in SIFT).
    • The Hessian operator uses the local extrema of the mutli-scale determinant of Hessian operator.
    • The Hessian Laplace detector uses the extrema of the multiscale determinant of Hessian operator for localisation in space, and the extrema of the multiscale Laplacian operator for localisation in scale.
    • Harris Laplace uses the multiscale Harris cornerness measure instead of the determinant of the Hessian for localization in space, and is otherwise identical to the previous detector..
    • Hessian Multiscale detects features spatially at multiple scales by using the multiscale determinant of Hessian operator, but does not attempt to estimate their scale.
    • Harris Multiscale is like the previous one, but uses the multiscale Harris measure instead.

    For example, to use the Hessian-Laplace operator instead of DoG, use the code:

    frames = vl_covdet(imgs, 'method', 'HarrisLaplace') ;

    The following figure shows example of the output of these detectors:

    Different detectors can produce a fairly different set of features.

    Understanding feature frames

    To understand the rest of the tutorial, it is important to understand the geometric meaning of a feature frame. Features computed by vl_covdet are oriented ellipses and are defined by a translation $T$ and linear map $A$ (a $2\times 2$) which can be extracted as follows:

    T = frame(1:2) ; A = reshape(frame(3:6),2,2)) ;

    The map $(A,T)$ moves pixels from the feature frame (also called normalised patch domain) to the image frame. The feature is represented as a circle of unit radius centered at the origin in the feature reference frame, and this is transformed into an image ellipse by $(A,T)$.

    In term of extent, the normalised patch domain is a square box centered at the origin, whereas the image domain uses the standard MATLAB convention and starts at (1,1). The Y axis points downward and the X axis to the right. These notions are important in the computation of normalised patches and descriptors (see later).

    Affine adaptation

    Affine adaptation is the process of estimating the &ldqo;affine shape&rdqo; of an image region in order to construct an affinely co-variant feature frame. This is useful in order to compensate for deformations of the image like slant, arising for example for small perspective distortion.

    To switch on affine adaptation, use the EstimateAffineShape option:

    frames = vl_covdet(imgs, 'EstimateAffineShape', true) ;

    which detects the following features:

    Affinely adapted features.

    Feature orientation

    The detection methods discussed so far are rotationally invariant. This means that they detect the same circular or elliptical regions regardless of an image rotation, but they do not allow to fix and normalise rotation in the feature frame. Instead, features are estimated to be upright by default (formally, this means that the affine transformation $(A,T)$ maps the vertical axis $(0,1)$ to itself).

    Estimating and removing the effect of rotation from a feature frame is needed in order to compute rotationally invariant descriptors. This can be obtained by specifying the EstimateOrientation option:

    frames = vl_covdet(imgs, 'EstimateOrientation', true, 'verbose') ;

    which results in the following features being detected:

    Features with orientation detection.

    The method used is the same as the one proposed by D. Lowe: the orientation is given by the dominant gradient direction. Intuitively, this means that, in the normalized frame, brighter stuff should appear on the right, or that there should be a left-to-right dark-to-bright pattern.

    In practice, this method may result in an ambiguous detection of the orientations; in this case, up to four different orientations may be assigned to the same frame, resulting in a multiplication of them.

    Computing descriptors

    vl_covdet can also compute descriptors. Three are supported so far: SIFT, LIOP and raw patches (from which any other descriptor can be computed). To use this functionality simply add an output argument:

    [frames, descrs] = vl_covdet(imgs) ;

    This will compute SIFT descriptors for all the features. Each column of descrs is a 128-dimensional descriptor vector in single precision. Alternatively, to compute patches use:

    [frames, descrs] = vl_covdet(imgs, 'descriptor', 'liop') ;

    Using default settings, each column will be a 144-dimensional descriptor vector in single precision. If you wish to change the settings, use arguments described in LIOP tutorial

    [frames, descrs] = vl_covdet(imgs, 'descriptor', 'patch') ;

    In this case each column of descrs is a stacked patch. To visualize the first 100 patches, one can use for example:

    w = sqrt(size(patches,1)) ; vl_imarraysc(reshape(patches(:,1:10*10), w,w,[])) ;
    Patches extracted with the standard detectors (left) and adding affine adaptation (right).

    There are several parameters affecting the patches associated to features. First, PatchRelativeExtent can be used to control how large a patch is relative to the feature scale. The extent is half of the side of the patch domain, a square in the frame reference frame. Since most detectors latch on image structures (e.g. blobs) that, in the normalised frame reference, have a size comparable to a circle of radius one, setting PatchRelativeExtent to 6 makes the patch about six times largerer than the size of the corner structure. This is approximately the default extent of SIFT feature descriptors.

    A second important parameter is PatchRelativeSigma which expresses the amount of smoothing applied to the image in the normalised patch frame. By default this is set to 1.0, but can be reduced to get &ldqo;sharper&rdqo; patches. Of course, the amount of smoothing is bounded below by the resolution of the input image: a smoothing of, say, less than half a pixel cannot be recovered due to the limited sampling rate of the latter. Moreover, the patch must be sampled finely enough to avoid aliasing (see next).

    The last parameter is PatchResolution. If this is equal to $w$, then the patch has a side of $2w+1$ pixels. (hence the sampling step in the normalised frame is given by PatchRelativeExtent/PatchResolution). Extracting higher resolution patches may be needed for larger extent and smaller smoothing. A good setting for this parameter may be PatchRelativeExtent/PatchRelativeSigma.

    Custom frames

    Finally, it is possible to use vl_covdet to compute descriptors on custom feature frames, or to apply affine adaptation and/or orientation estimation to these.

    For example

    delta = 30 ; xr = delta:delta:size(im,2)-delta+1 ; yr = delta:delta:size(im,1)-delta+1 ; [x,y] = meshgrid(xr,yr) ; frames = [x(:)'; y(:)'] ; frames(end+1,:) = delta/2 ; [frames, patches] = vl_covdet(imgs, ... 'frames', frames, ... 'estimateAffineShape', true, ... 'estimateOrientation', true) ;

    computes affinely adapted and oriented features on a grid:

    Custom frame (on a grid) after affine adaptation.

    Getting the scale spaces

    vl_covdet can return additional information about the features, including the scale spaces and scores for each detected feature. To do so use the syntax:

    [frames, descrs, info] = vl_covdet(imgs) ;

    This will return a structure info

    info = gss: [1x1 struct] css: [1x1 struct] peakScores: [1x351 single] edgeScores: [1x351 single] orientationScore: [1x351 single] laplacianScaleScore: [1x351 single]
    \[ test \]

    The last four fields are the peak, edge, orientation, and Laplacian scale scores of the detected features. The first two were discussed before, and the last two are the scores associated to a specific orientation during orientation assignment and to a specific scale during Laplacian scale estimation.

    The first two fields are the Gaussian scale space and the cornerness measure scale space, which can be plotted by means of vl_plotss. The following is the of the Gaussian scale space for our example image:

    Gaussian scale space.

    The following is an example of the corresponding cornerness measure:

    Cornerness scale space (Difference of Gaussians).
    vlfeat/docsrc/roadmap.html0000644000175000017500000000466712237004745014554 0ustar dimadima

    PASCAL2 credits In 2012 the development of VLFeat is supported by the PASCAL Harvest programme. Several people have been working in Oxford to add new functionalities to the library. Moreover, leading researchers in computer vision were consulted as advisors of the project. These contributions will be made public in the next several weeks as the code is tuned and finalised.

    A particularly significant contribution is the creation of a new sub-project, VLBenchmakrs, for the evaluation of feature detectors and descriptors. VLBenchmarks is meant to provide a future-proof benchmarking suite. The first release includes reimplementations of standard feature benchmarks to replace ageing legacy code and a brand new benchmark. In the future, it will be used to deliver to the community new, modern benchmarks in a consistent and simple to use pacakge.

    Version 0.9.17 (released)

    • Large scale Approximate Nearest Neighbours (ANN) K-means.
    • Gaussian Mixture Models (GMMs).
    • Fisher Kernel encoding.
    • VLAD encoding.
    • New SVM algorithms: SGD and SDCA with support for multiple loss functions, data weighting, and other useful features.
    • LIOP feature descriptor.
    • Multicore computations through OpenMP.
    • New extensive image recognition examples running on several standard computer vision benchmark datasets.
    • Improved documentation.

    Version 0.9.16 (released)

    • VLBenchmarks: detector repeatability, descriptor matching score, and image retrieval benchmarks.
    • Affine covariant feature detectors. The aim is to create a future proof replacement of the legacy implementation.

    Version 0.9.15 (released)

    • Histogram of Oriented Gradients (HOG) features.
    • New Stochastic Gradient Descent (SGD) Support Vector Machine (SVM) implementation. Layed down a good infrastructure to add more learning algorithms in the future.
    • Several utility functions (hashed summation, integral images, plotting functions).
    • Significant cleanup of the codebase, outstanding bugs squashed.
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%HTMLsymbol; %HTMLspecial; vlfeat/docsrc/dtd/xhtml1/xhtml-lat1.ent0000644000175000017500000002677712237004736016744 0ustar dimadima vlfeat/docsrc/dtd/xhtml1/xhtml-symbol.ent0000644000175000017500000003303012237004736017364 0ustar dimadima vlfeat/docsrc/dtd/xhtml1/xhtml.soc0000644000175000017500000000111712237004736016060 0ustar dimadimaOVERRIDE YES -- Oasis entity catalog for Extensible HTML 1.0 -- PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "xhtml1-strict.dtd" PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "xhtml1-transitional.dtd" PUBLIC "-//W3C//DTD XHTML 1.0 Frameset//EN" "xhtml1-frameset.dtd" -- ISO latin 1 entity set for Extensible HTML (XML 1.0 format) -- PUBLIC "-//W3C//ENTITIES Latin 1 for XHTML//EN" "xhtml-lat1.ent" PUBLIC "-//W3C//ENTITIES Symbols for XHTML//EN" "xhtml-symbol.ent" PUBLIC "-//W3C//ENTITIES Special for XHTML//EN" "xhtml-special.ent" SGMLDECL "xhtml1.dcl" vlfeat/docsrc/dtd/xhtml1/xhtml1.dcl0000644000175000017500000001631412237004736016124 0ustar dimadima" PIC "?>" SHORTREF NONE NAMES SGMLREF QUANTITY NONE -- Quantities are not restricted in XML -- ENTITIES "amp" 38 "lt" 60 "gt" 62 "quot" 34 "apos" 39 FEATURES MINIMIZE DATATAG NO OMITTAG NO RANK NO SHORTTAG STARTTAG EMPTY NO UNCLOSED NO NETENABL IMMEDNET ENDTAG EMPTY NO UNCLOSED NO ATTRIB DEFAULT YES OMITNAME NO VALUE NO EMPTYNRM YES IMPLYDEF ATTLIST NO -- VALID: was YES -- DOCTYPE NO ELEMENT NO -- VALID: was YES -- ENTITY NO NOTATION NO -- VALID: was YES -- LINK SIMPLE NO IMPLICIT NO EXPLICIT NO OTHER CONCUR NO SUBDOC NO FORMAL NO URN NO KEEPRSRE YES VALIDITY TYPE -- VALID: was NOASSERT -- ENTITIES REF ANY INTEGRAL YES APPINFO NONE SEEALSO "ISO 8879//NOTATION Extensible Markup Language (XML) 1.0//EN" > vlfeat/docsrc/dtd/xhtml1/xhtml1-transitional.dtd0000644000175000017500000007656012237004736020653 0ustar dimadima %HTMLlat1; %HTMLsymbol; %HTMLspecial; vlfeat/docsrc/dtd/xhtml1/xhtml1-strict.dtd0000644000175000017500000006160112237004736017442 0ustar dimadima %HTMLlat1; %HTMLsymbol; %HTMLspecial; vlfeat/docsrc/dtd/xhtml1/xhtml-special.ent0000644000175000017500000001004312237004736017476 0ustar dimadima vlfeat/docsrc/liop.html0000644000175000017500000000336112237004745014062 0ustar dimadima %tableofcontents;

    This tutorial shows how to extract Local Intensity Order Pattern (LIOP) deascriptors using VLFeat. LIOP is implemented in the vl_liop as well as in vl_covdet.

    LIOP descriptor computation

    A LIOP descriptor can be computed from a square, gray scale image with and odd side length patch. This is usually not a restriction since local patches are obtained by cropping and warpinig an input image. The descriptor is computed using the vl_liop function:

    descr = vl_covdet(patch) ;

    You can use the verbose option verbose if you wish to see the parametr and descriptor details.

    descr = vl_covdet(patch,'Verbose') ;

    patch can be a 3D array, with one image per 2D layer. In this case, descr is a matrix with as many columns as images. LIOP is also integrated into vl_covdet.

    vl_liop allows full customization of the LIOP descriptor parameters. These are discussed here. A parameter that is commonly tweaked is the intensity threshold to downweight unstalbe intensity order patterns in the descriptor computation. For example

    descr = vl_covdet(patch,'IntensityThreshold', 0.1) ;

    set this threshold to 0.1.

     vlfeat/docsrc/mdoc.py0000644000175000017500000003750312237004745013532 0ustar dimadima#!/usr/bin/python # file: mdoc.py # author: Brian Fulkerson and Andrea Vedaldi # description: MDoc main # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). import sys, os, re, shutil import subprocess, signal from wikidoc import wikidoc from formatter import Formatter from optparse import OptionParser excludeRegexList = [] format = 'html' verb = 0 sitexml = "" usage = """usage: %prog [options] Takes all .m files in basedir and its subdirectories and converts them to html documentation, placing the results in docdir.""" parser = OptionParser(usage=usage) parser.add_option( "-f", "--format", dest = "format", default = "html", action = "store", help = "specify the output format (html, wiki, web)", metavar = "STRING") parser.add_option( "-x", "--exclude", dest = "excludeList", action = "append", type = "string", help = "exclude files matching the specified regexp") parser.add_option( "-v", "--verbose", dest = "verb", default = False, action = "store_true", help = "print debug information") parser.add_option( "-t", "--helptoc", dest = "helptoc", default = False, action = "store_true", help = "create helptoc.xml") parser.add_option( "", "--helptoc-toolbox-name", dest = "helptoc_toolbox_name", default = "Example", action = "store", type = "string", help = "helptoc.xml: Toolbox Name") # -------------------------------------------------------------------- def runcmd(cmd): # -------------------------------------------------------------------- """ runcmd(CMD) runs the command CMD. The function waits for the command to complete and correctly react to Ctrl-C by stopping the process and raising an exception. """ try: p = subprocess.Popen(cmd, shell=True) sts = os.waitpid(p.pid, 0) except (KeyboardInterrupt, SystemExit): os.kill(p.pid, signal.SIGKILL) raise # -------------------------------------------------------------------- class MFile: # -------------------------------------------------------------------- """ MFile('sub/file.m') represents a MATLAB M-File. """ def __init__(self, basedir, dirname, name): funcname = os.path.splitext(name)[0] self.funcname = funcname #.upper() self.path = os.path.join(basedir, dirname, name) self.mdocname = funcname.replace(os.path.sep, '_') self.webname = funcname.replace(os.path.sep, '.') self.htmlname = self.mdocname + '.html' self.wikiname = 'MDoc_' + (os.path.join(dirname, funcname) .upper().replace(os.path.sep, '_')) self.prev = None self.next = None self.node = None def getId (self, format='html'): if format == 'html': return self.htmlname elif format == 'web': return self.webname elif format == 'wiki': return self.wikiname def getRef (self, format='html'): if format == 'html': return self.htmlname elif format == 'web': return '%pathto:' + self.webname + ';' elif format == 'wiki': return self.wikiname def __cmp__(self, other): return cmp(self.webname, other.webname) def __str__(self): str = "MFile: %s\n" % (self.funcname) str += " path : %s\n" % (self.path) str += " mdocname: %s\n" % (self.mdocname) str += " htmlname: %s\n" % (self.htmlname) str += " wikiname: %s\n" % (self.wikiname) return str # -------------------------------------------------------------------- class Node: # -------------------------------------------------------------------- """ A Node N represents a node in the toolbox hierechy. A node is a directory in the toolbox hierarchy and contains both M-files and other sub-directories. """ def __init__(self, dirname): self.dirname = dirname self.children = [] self.mfiles = [] def addChildNode(self, node): "Add a child node (toolbox subdirectory) to this node" self.children.append(node) def addMFile(self, mfile): "Add a MATLAB M-File to this node" self.mfiles.append(mfile) mfile.node = self def toIndexPage(self, format='html', depth=1): "Converts the node hierarchy rooted here into an index." page = "" if format == 'html' or format == 'web': if len(self.mfiles) > 0: page += "%s" % (self.dirname.upper()) page += "
      \n" for m in self.mfiles: page += "
    • " page += "%s" % (m.getRef(format), m.funcname) page += " %s" % (m.brief) page += "
      • " page += "
    \n" elif format == 'wiki': if len(self.mfiles) > 0: if depth > 1: page += "=== %s ===\n" % (self.dirname.upper()) for m in self.mfiles: page += "* [[%s|%s]]" % (m.getRef(format), m.funcname) page += " %s\n" % (m.brief) elif format == 'helptoc': for m in self.mfiles: page += "%s\n" % (m.getRef('html'), m.funcname) else: assert False for n in self.children: page += n.toIndexPage(format, depth+1) return page def toIndexXML(self): xml = "" for m in self.mfiles: dirname = m.node.dirname.upper() if len(dirname) > 0: xml += \ "" \ "
    " \ "
    \n" % (m.getId('web'), m.funcname, dirname, m.funcname, m.htmlname) else: xml += \ "" \ "
    " \ "
    \n" % (m.getId('web'), m.funcname, m.funcname, m.htmlname) for n in self.children: xml += n.toIndexXML() ; return xml def __str__(self): s = "Node: %s\n" % self.dirname for m in self.mfiles: s += m.__str__() for n in self.children: s += n.__str__() return s # -------------------------------------------------------------------- def depth_first(node): # -------------------------------------------------------------------- """ depth_first(NODE) is a generator that implements a depth first visit of the node hierarchy rooted at NODE. """ yield node for n in node.children: for m in depth_first(n): yield m return # -------------------------------------------------------------------- def extract(path): # -------------------------------------------------------------------- """ (BODY, FUNC, BRIEF) = extract(PATH) extracts the comment BODY, the function name FUNC and the brief description BRIEF from the MATLAB M-file located at PATH. """ body = [] func = "" brief = "" seenfunction = False seenpercent = False for l in open(path): # Remove whitespace and newline line = l.strip().lstrip() if line.startswith('%'): seenpercent = True if line.startswith('function'): seenfunction = True continue if not line.startswith('%'): if (seenfunction and seenpercent) or not seenfunction: break else: continue # remove leading `%' character line = line[1:] # body.append('%s\n' % line) # Extract header from body if len(body) > 0: head = body[0] body = body[1:] match = re.match(r"^\s*(\w+)\s*(\S.*)\n$", head) func = match.group(1) brief = match.group(2) return (body, func, brief) # -------------------------------------------------------------------- def xscan(baseDir, subDir=''): # -------------------------------------------------------------------- """ NODE = xscan(BASEDIR) recusrively scans the directory BASEDIR and construct the toolbox hierarchy rooted at NODE. """ node = Node(subDir) dir = os.listdir(os.path.join(baseDir, subDir)) fileNames = [f for f in dir if os.path.isfile( os.path.join(baseDir, subDir, f))] subSubDirs = [s for s in dir if os.path.isdir ( os.path.join(baseDir, subDir, s))] fileNames.sort() # Scan M-FileNames for fileName in fileNames: # only m-files if not os.path.splitext(fileName)[1] == '.m': continue # skip if in the exclude list exclude = False for rx in excludeRegexList: fileRelPath = os.path.join(subDir, fileName) mo = rx.match(fileRelPath) if mo and (mo.end() - mo.start() == len(fileRelPath)): if verb: print "mdoc: excluding ''%s''." % fileRelPath exclude = True if exclude: continue node.addMFile(MFile(baseDir, subDir, fileName)) # Scan sub-directories for s in subSubDirs: node.addChildNode(xscan(basedir, os.path.join(subDir, s))) return node # -------------------------------------------------------------------- def breadCrumb(m): # -------------------------------------------------------------------- breadcrumb = "
      " if format == 'web': breadcrumb += "
    • Index
      • " else: breadcrumb += "
    • Index
      • " if m.prev: breadcrumb += "
    • Prev
      • " % m.prev.getRef(format) if m.next: breadcrumb += "
    • Next
      • " % m.next.getRef(format) breadcrumb += "
    " #breadcrumb += "%s" % m.node.dirname.upper() return breadcrumb # -------------------------------------------------------------------- if __name__ == '__main__': # -------------------------------------------------------------------- # # Parse comand line options # (options, args) = parser.parse_args() if options.verb: verb = 1 format = options.format helptoc = options.helptoc print options.excludeList for ex in options.excludeList: rx = re.compile(ex) excludeRegexList.append(rx) if len(args) != 2: parser.print_help() sys.exit(2) basedir = args[0] docdir = args[1] if not basedir.endswith('/'): basedir = basedir + "/" if not basedir.endswith('/'): docdir = docdir + "/" if verb: print "mdoc: search path: %s" % basedir print "mdoc: output path: %s" % docdir print "mdoc: output format: %s" % format # # Search for mfiles # toolbox = xscan(basedir) # # Extract dictionaries of links and M-Files # linkdict = {} mfiles = {} prev = None next = None for n in depth_first(toolbox): for m in n.mfiles: if prev: prev.next = m m.prev = prev prev = m func = m.funcname.upper() mfiles[func] = m linkdict[func] = m.getRef(format) if verb: print "mdoc: num mfiles: %d" % (len(mfiles)) # Create output directory if not os.access(docdir, os.F_OK): os.makedirs(docdir) # ---------------------------------------------------------------- # Extract comment block and run formatter # ---------------------------------------------------------------- for (func, m) in mfiles.items(): if format == 'wiki': outname = m.wikiname elif format == 'html': outname = m.htmlname elif format == 'web': outname = m.htmlname if verb: print "mdoc: generating %s from %s" % (outname, m.path) # extract comment block from file (lines, func, brief) = extract(m.path) m.brief = brief # Run formatter content = "" if len(lines) > 0: if format == 'wiki' : formatter = Formatter(lines, linkdict, 'wiki') else: formatter = Formatter(lines, linkdict, 'a') content = formatter.toDOM().toxml("UTF-8") content = content[content.find('?>')+2:] # add decorations if not format == 'wiki': content = breadCrumb(m) + content if format == 'web': content = "\n" + content + "\n" # save the result to an html file if format == 'wiki': f = open(os.path.join(docdir, m.wikiname), 'w') else: f = open(os.path.join(docdir, m.htmlname), 'w') f.write(content) f.close() # ---------------------------------------------------------------- # Make index page # ---------------------------------------------------------------- page = "" if format == 'html': pagename = 'index.html' page += toolbox.toIndexPage('html') elif format == 'web': pagename = 'mdoc.html' page += '\n' + toolbox.toIndexPage('web') + '\n' elif format =='wiki' : pagename = 'MDoc' page = "== Documentation ==\n" page += toolbox.toIndexPage('wiki') f = open(os.path.join(docdir, pagename), 'w') f.write(page) f.close() if format == 'web': f = open(os.path.join(docdir, "mdoc.xml"), 'w') f.write(""+toolbox.toIndexXML()+"\n") f.close() # ---------------------------------------------------------------- # Make helptoc.xml # ---------------------------------------------------------------- if helptoc: page = """ %s Functions """ % (options.helptoc_toolbox_name, pagename) page += toolbox.toIndexPage('helptoc') page += """ """ f = open(os.path.join(docdir, "helptoc.xml"), 'w') f.write(page) f.close() # ---------------------------------------------------------------- # Checkin files to wiki # ---------------------------------------------------------------- def towiki(docdir, pagename): pagenamewiki = pagename + '.wiki' runcmd("cd %s ; mvs update %s" % (docdir, pagenamewiki)) if verb: print "mdoc: converting", pagename, "to", pagenamewiki wikidoc(os.path.join(docdir, pagenamewiki), os.path.join(docdir, pagename)) runcmd("cd %s ; mvs commit -M -m 'Documentation update' %s" % (docdir, pagenamewiki)) if format == 'wiki' : try: towiki(docdir, pagename) except (KeyboardInterrupt, SystemExit): sys.exit(1) for (func, m) in mfiles.items(): try: towiki(docdir, m.wikiname) except (KeyboardInterrupt, SystemExit): sys.exit(1) vlfeat/docsrc/install-shell.html0000644000175000017500000000523112237004745015670 0ustar dimadima #content table.checker { border-collapse: collapse ; margin-left: auto ; margin-right: auto ; } #content table.checker td { background-color: #f6f6f6 ; border: none ; border-top: 1px solid #DDD ; border-bottom: 1px solid #DDD ; padding: 0.5em ; }

    These instructions explain how to use VLFeat from the command line (shell).

    Download and unpack the latest VLFeat binary distribution in a directory of your choice (e.g. ~/src/vlfeat). Let VLFEATROOT denote this directory.

    The command line tools are located in VLFEATROOT/bin/ARCH. Here ARCH denotes the subdirectory relative to your architecture (e.g. mac for Mac OS X PPC, maci for Mac OS X Intel, glnx86 for Linux, and so on). For the sake of illustration, the following table gives the path to the SIFT feature extraction program for the varius architectures:

    Platform ARCH Path to command
    Windows 32 win32 VLFEATROOT\bin\w32\sift.exe
    Windows 64 win64 VLFEATROOT\bin\w64\sift.exe
    Mac Intel 32 maci VLFEATROOT/bin/maci/sift
    Mac Intel 64 maci64 VLFEATROOT/bin/maci64/sift
    Linux 32 glnx86 VLFEATROOT/bin/glnx86/sift
    Linux 64 glnxa64 VLFEATROOT/bin/glnxa64/sift

    All commands have a corresponding man page found in VLFEATROOT/src. For UNIX based systems, the man pages can be viewed with the man utility. For instance

    > man VLFEATROOT/src/sift.1

    It might be convenient to add VLFeat to the system search paths. In Linux and Mac OS X this involves modifying the PATH and MANPATH environment variables. The exact details may vary, but it should be enough to add the following to your ~/.bash_profile:

    export PATH=VLFEATROOT/bin/ARCH:$PATH export MANPATH=VLFEATROOT/src:$MANPATH

    Alternatively, you can copy the executables and man pages to appropriate system-wide directories.

     vlfeat/docsrc/formatter.py0000644000175000017500000004503212237004745014607 0ustar dimadima#!/usr/bin/python # file: formatter.py # author: Andrea Vedaldi # description: Utility to format MATLAB comments. # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). """ MDOC fromats the help block of a MATLAB M-file based on a simple set of rules. Pharagraphs, verbatim sections, lists and other structures are automatically instantiated by looking at blank lines, indentation and a few decoration symbols. The documentation starts at a conventional indentation level N (by default 2). A block of non-epmty lines prefixed by N characters is considered a paragraph. For instance | Bla bla bla | bla bla bla. | | Bla bla. generates two pharagraps. If there are more than N white spaces, then the block is taken verbatim instead (and rendered in 
     HTML
tags). For instance

 |  Bla bla bla
 |   Code Code Code
 |
 |   Code Code Code

generates one paragraph followed by one verbatim section.
"""

import xml.dom.minidom
import sys
import os
import re

__mpname__           = 'MDocFormatter'
__version__          = '0.1'
__date__             = '2008-01-01'
__description__      = 'MDoc formatting module'
__long_description__ = __doc__
__license__          = 'BSD'
__author__           = 'Andrea Vedaldi'

# terminal
class Terminal:
    def isa(self, classinfo):
        return isinstance(self, classinfo)

# empty terminal
class E (Terminal):
    pass

# blank line
class B (Terminal):
    content = ""

# non-blank line
class L (Terminal):
    indent  = 0

# regular line
class PL (L):
    pass

# line with bullet
class BL (L):
    bullet = None
    inner_indent = 0

# line with description
class DL (L):
    pass

# --------------------------------------------------------------------
def lex(line):
# --------------------------------------------------------------------
    """
    Parse the string LINE to a terminal symbol. Each line corresponds
    to exactly one terminal type. Terminal types are the leaf of a
    hierarchy of types.
    """

    # a blank line
    match = re.match(r"\s*\n?$", line) ;
    if match: return B()

    # a line of the type '  content::inner_content'
    match = re.match(r"(\s*)(.*)::(.*)\n?$", line)
    if match:
        x = DL()
        x.indent        = len(match.group(1))
        x.content       = match.group(2)
        x.inner_content = match.group(3)
        return x

    # a line of the type '  - inner_contet'
    match = re.match(r"(\s*)([-\*#]\s*)(\S.*)\n?$", line)
    if match:
        x = BL()
        x.indent        = len(match.group(1))
        x.inner_content = match.group(3)
        x.bullet        = match.group(2)
        x.inner_indent  = x.indent + len(x.bullet)
        x.content       = x.bullet + x.inner_content
        return x

    # a line of the type  '   content'
    match = re.match(r"(\s*)(\S.*)\n?$", line)
    if match:
        x = PL()
        x.indent  = len(match.group(1))
        x.content = match.group(2)
        return x

# --------------------------------------------------------------------
class Lexer(object):
# --------------------------------------------------------------------
    """
    l = Lexer(LINES) parses the array of strings LINES. Lexer has a
    head pointing to the current line. The head can be controlled by
    the following methods:

    l.next() advances the head and fetches the next terminal.
    l.back() moves back the head.
    l.getpos() returns the head position.
    l.seek(POS) sets the head position to POS.
    """
    def __init__(self, lines):
        self.tokens = []
        self.pos    = -1
        for line in lines:
            self.tokens.append(lex(line))

    def next(self):
        self.pos = self.pos + 1
        if self.pos >= len(self.tokens):
            return E()
        else:
            return self.tokens [self.pos]

    def seek(self, pos):
        self.pos = pos

    def back(self):
        if self.pos >=0: self.pos -= 1

    def rewrite(self, str):
        self.tokens [self.pos] = str ;

    def getpos(self):
        return self.pos

    def __str__(self):
        str = ""
        for i,t in enumerate(self.tokens):
             str += "%5d) %s %s\n" % (i, t.__class__.__name__,t.content)
        return str

# --------------------------------------------------------------------
class Formatter:
# --------------------------------------------------------------------
    """
    f = Formatter(LINES) parses the array of strings LINES.

    f = Formatter(LINES, FUNCS) takes the dictionary of functions
    FUNCS. Function names must be uppercase. The dictionary entries
    are used to cross link functions in the generated documentation.

    Formatter(LINES, FUNCS, LINKTYPE) produces links of the specified
    type.  Use 'a' for HTML anchors and 'wiki' for MediaWiki style
    links.

    f.toDOM() process the data to construct an XML (HTML) representation
    of them.
    """
    def __init__ (self, lines, funcs={}, linktype='a'):
        self.indentinit = 0
        lineone = lines[0]
        while lineone.startswith(' '):
            lineone = lineone[1:]
            self.indentinit += 1

        self.tokens = Lexer(lines)
        self.xmldoc = xml.dom.minidom.Document()
        self.funcs = funcs
        self.linktype = linktype
        #print self.tokens

    def toTextNode(self,s):
        return self.xmldoc.createTextNode(unicode(s, 'iso-8859-1'))

    def addAttr(self, tag, attr, val):
        x = self.xmldoc.createAttribute(attr)
        x.nodeValue = val
        tag.setAttributeNode(x)

    def addText(self, tag, s):
        txt = self.toTextNode(s)
        tag.appendChild(txt)

    def addFancyText(self, tag, s):
        "Adds text while transforming function references to links."
        xs = []
        last = -1
        iter = re.finditer(r'(?:'
                           r'(?P[A-Z][A-Z0-9_]*)'
                           r'\([^\)]*\)'
                           r')|(?:'
                           r''
                           r'(?P[^<]*)'
                           r''
                           r')',s)

                           # r'(?P[a-zA-Z0-9_]*)'
                           # r')', s)



                           # r')', s)

        for i in iter:
            func_name = i.group("function")
            page_name = i.group("page")

            if func_name and self.funcs.has_key(func_name.upper()):
                # retrieve function HTML location
                func_href = self.funcs[func_name.upper()]

                # add text so far
                xs.append(self.toTextNode(s[last+1:i.start()]))

                if self.linktype == 'a':
                    # add link to function
                    atag = self.xmldoc.createElement(u"a")
                    self.addText(atag, i.group('function'))
                    atag.setAttribute(u"href", u"%s" % (func_href))
                    xs.append(atag)
                elif self.linktype == 'wiki':
                    linktxt = "[[%s|%s]]" % (func_href, i.group('function'))
                    xs.append(self.toTextNode(linktxt))

                # set head
                last = i.start()+len(i.group(1))-1

            elif page_name:
                #print "page %s:" % page_name, i.group("text")
                page_href = "%%dox:%s;" % page_name

                # add text so far
                xs.append(self.toTextNode(s[last+1:i.start()]))

                if self.linktype == 'a':
                    # add link to function
                    atag = self.xmldoc.createElement(u"a")
                    self.addText(atag, i.group('text'))
                    atag.setAttribute(u"href", u"%s" % (page_href))
                    xs.append(atag)
                elif self.linktype == 'wiki':
                    linktxt = "[[%s|%s]]" % (func_href, i.group('function'))
                    xs.append(self.toTextNode(linktxt))

                # set head
                last = i.end()-1

        xs.append(self.toTextNode(s[last+1:]))
        for x in xs:
            tag.appendChild(x)

    # ................................................................
    # E, B, L, PL, BL, DL, ...
    def parse_Terminal(self, T):
        "If the next terminal on the stream is of type T, the terminal"
        "is extracted and returned. Otherwise the function returns None"
        pos = self.tokens.getpos()
        t = self.tokens.next()
        if t.isa(T):
            return t
        self.tokens.seek(pos)
        return None

    # ................................................................
    # DIV(N) -> (B | P(N) | BL(N) | DL(N) | V(N))+
    def parse_DIV(self, indent):
        "Parse a DIV(N) symbol. A DIV(N) a sequence of blank"
        "lines (B or other blocks at indentation level N, such as"
        "pharagraphs P(N), bullet lists BL(N), description lists DN(N)"
        pos = self.tokens.getpos()
        xs = []
        while True:
            x = self.parse_Terminal(B)
            if x: continue

            x = self.parse_P(indent)
            if x:
                xs.append(x)
                continue

            x = self.parse_V(indent)
            if x:
                xs.append(x)
                continue

            x = self.parse_UL(indent)
            if x:
                xs.append(x)
                continue

            x = self.parse_DL(indent)
            if x:
                xs.append(x)
                continue

            break
        if len(xs) == 0: return None
        return xs

    # ................................................................
    # P(N) -> PL(N) L(N)*
    def parse_P(self, indent):
        content = "\n"
        good = False
        pos = self.tokens.getpos()

        # Introduced by PL
        x = self.parse_Terminal(PL)
        if x:
            if x.indent == indent:
                content += x.content + "\n"
                good = True
            else:
                self.tokens.back()
        if not good:
            return None

        # Continued by zero or more L
        while True:
            x = self.parse_Terminal(L)
            if x:
                if x.indent == indent:
                    content += x.content + "\n"
                    good = True
                    continue
                else:
                    self.tokens.back()
            break

        ptag = self.xmldoc.createElement("p")
        self.addFancyText(ptag, content)
        return ptag

    # ................................................................
    # V(N) -> L(M)+, M > N
    def parse_V(self, indent):
        content = "\n"
        good = False
        pos = self.tokens.getpos()
        while True:
            x = self.parse_Terminal(L)
            if x:
                if x.indent > indent:
                    content += " "*(x.indent - indent) + x.content + "\n"
                    good = True
                    continue
                else:
                    self.tokens.back()
            x = self.parse_Terminal(B)
            if x:
                content += "\n"
                continue
            break
        if good:
            ptag = self.xmldoc.createElement("pre")
            # remove potential blank line at the end
            if content[-2:] == "\n\n":
                content= content[:-1]
            self.addText(ptag, content)
            return ptag
        self.tokens.seek(pos)
        return None

    # ................................................................
    # UL(N) -> ULI(N)+
    def parse_UL(self, indent):
        xs = []
        while True:
            x = self.parse_ULI(indent)
            if x:
                xs.append(x)
                continue
            break
        if len(xs) == 0: return None
        ultag = self.xmldoc.createElement("ul")
        for x in xs:
            ultag.appendChild(x)
        return ultag

    # ................................................................
    # ULI(N) -> UL(N,M) L(M)* DIV(M), M > N
    def parse_ULI(self, indent):
        content = "\n"
        good = False
        pos = self.tokens.getpos()

        # Introduced by UL
        x = self.parse_Terminal(BL)
        if x:
            if x.indent == indent:
                content += x.inner_content + "\n"
                indent   = x.inner_indent
                good = True
            else:
                self.tokens.back()
        if not good:
            return None

        # Continued by zero or more L
        while True:
            x = self.parse_Terminal(L)
            if x:
                if x.indent == indent:
                    content += x.content + "\n"
                    good = True
                    continue
                else:
                    self.tokens.back()
            break
        litag = self.xmldoc.createElement(u"li")
        ptag  = self.xmldoc.createElement(u"p")
        self.addFancyText(ptag, content)
        litag.appendChild(ptag)

        # Continued by DIV
        xs = self.parse_DIV(indent)
        if xs:
            for x in xs:
                litag.appendChild(x)

        return litag


    # ................................................................
    # DL(N) -> DI(N)+
    def parse_DL(self, indent):
        xs = []
        while True:
            x = self.parse_DI(indent)
            if x:
                xs += x
                continue
            break
        if len(xs) == 0: return None
        dltag = self.xmldoc.createElement(u"dl")
        for x in xs:
            dltag.appendChild(x)
        return dltag

    # ................................................................
    # DI(N) -> DL(N) DIV(M)?, M > N
    def parse_DI(self, indent):
        content = "\n"
        good   = False
        pos    = self.tokens.getpos()
        xs     = []

        # Introduced by DL
        x = self.parse_Terminal(DL)
        if x:
            if x.indent == indent:
                content += x.content + "\n"
                good = True
            else:
                self.tokens.back()
        if not good:
            return None

        if False:
            # adds text after :: as part of the description dd
            dttag = self.xmldoc.createElement(u"dt")
            dttxt = self.toTextNode(content)
            dttag.appendChild(dttxt)
            xs.append(dttag)

            # Inject inner_content
            c = x.inner_content.strip()
            if len(c) > 0:
                tk = PL()
                tk.content = x.inner_content
                t = self.tokens.next()
                self.tokens.back()
                if t.isa(L) and t.indent > indent:
                    tk.indent = t.indent
                else:
                    tk.indent = indent+1 ;
                    self.tokens.rewrite(tk)
                    self.tokens.back()
        else:
            # adds text after :: as part of the description term dt
            dttag = self.xmldoc.createElement(u"dt")
            dttxt = self.toTextNode(content)
            dttag.appendChild(dttxt)
            c = x.inner_content.strip()
            if len(c) > 0:
                deftag = self.xmldoc.createElement(u"span")
                self.addAttr(deftag, "class", "defaults")
                self.addText(deftag, c)
                dttag.appendChild(deftag)
            xs.append(dttag)

        # Continued by DIV
        t = self.tokens.next()
        self.tokens.back()
        if t.isa(L) and t.indent > indent:
            xs_ = self.parse_DIV(t.indent)
            if len(xs_) > 0:
                ddtag = self.xmldoc.createElement(u"dd")
                for x in xs_:
                    ddtag.appendChild(x)
                xs.append(ddtag)

        return xs

    # ................................................................
    def toDOM(self):
        # write 
        xmf = self.xmldoc.createElement("div")
        xmf.setAttribute(u"class", u"documentation")

        self.xmldoc.appendChild(xmf)

        # parse documentation
        xs = self.parse_DIV(self.indentinit)
        for x in xs: xmf.appendChild(x)

        return self.xmldoc


if __name__ == '__main__':
    text=""" Lorem Ipsum is simply dummy text of the printing and typesetting
 industry. Lorem Ipsum has been the industry's standard dummy text
 ever since the 1500s, when an unknown printer took a galley of type
 and scrambled it to make a type specimen book. It has survived not
 only five centuries, but also the leap into electronic typesetting,
 remaining essentially unchanged. It was popularised in the 1960s with
 the release of Letraset sheets containing Lorem Ipsum passages, and
 more recently with desktop publishing software like Aldus PageMaker
 including versions of Lorem Ipsum.

 Also Fisher vectors.

 These are links BL(), BL(A,B) and BLA(A,A) (as long as the dictionary
 cites them).

 Mimamama
   verbatim1
   verbatim2
   verbatim3

   verbatim4
   verbatim5
 Lorem Ipsum is simply dummy text of the printing and typesetting
 industry. Lorem Ipsum has been the industry's standard dummy text
 ever since the 1500s, when an unknown printer took a galley of type
 and scrambled it to make a type specimen book. It has survived not
 only five centuries, but also the leap into electronic typesetting,
 remaining essentially unchanged. It was popularised in the 1960s with
 the release of Letraset sheets containing Lorem Ipsum passages, and
 more recently with desktop publishing software like Aldus PageMaker
 including versions of Lorem Ipsum.

 - outer1 /
   outer1 line 2 /
   outer1 line 3 /

   outer1 new paragarph

   - inner1
   - inner2
   - inner3
     continued on next line
       continued with verbatim

       more verbatim after blank
   - inner4
 - outer again
 - outer
 bla

 - list2
 - list4
 - BL()
 - BL(A,B)

 Test descrition::
     Lorem Ipsum is simply dummy text of the printing
     and typesetting industry. Lorem Ipsum has been the industry's
     standard dummy text ever since the 1500s, when an unknown printer
     took a galley of type and scrambled it to make a type specimen
     book. It has survived not only five centuries, but also the leap
     into electronic typesetting, remaining essentially unchanged. It
     was popularised in the 1960s with the release of Letraset sheets
     containing Lorem Ipsum passages, and more recently with desktop
     publishing software like Aldus PageMaker including versions of
     Lorem Ipsum.

 Ancora::
     Bli bli bli
     Blu blu blu

     - list
     - lust
     - last

     Bli bla

  Verbatimmo
"""
    lines = text.splitlines()
    formatter = Formatter(lines, {'BL':'http://www.google.com'}, 'a')
    print formatter.toDOM().toxml("UTF-8")
vlfeat/docsrc/kdtree.html0000644000175000017500000001362512237004745014401 0ustar  dimadima


%tableofcontents;


    VLFeat implements the randomized kd-tree forest from
FLANN.
This enables fast medium and large scale nearest neighbor queries
among high dimensional data points (such as those produced by SIFT).

    



    Introduction
    



    A kd-tree is a data structure used to quickly solve
nearest-neighbor queries. Consider a set of 2D points uniformly
distributed in the unit square:
    


  X = rand(2, 100) ;



    A kd-tree is generated by using the vl_kdtreebuild function:

    


  kdtree = vl_kdtreebuild(X) ;



    The returned kdtree indexes the set of
points X. Given a query point Q, the
function vl_kdtreequery returns its nearest neighbor
in X:
    


  Q = rand(2, 1) ;
  [index, distance] = vl_kdtreequery(kdforest, X, Q) ;



    Here index stores the index of the column
of X that is closest to the point Q.
distance is the squared euclidean distance between X(index),Q.
    


    A kd-tree is a hierarchal structure built by partitioning the data
recursively along the dimension of maximum variance. At
each iteration the variance of each column is computed and the data is
split into two parts on the column with maximum variance. The
splitting threshold can be selected to be the mean or the median (use
the ThresholdMethod option of
vl_kdtreebuild).
    


    
 
 
 
    
  
   kd-tree partitions of a uniform set of data points, using
   the mean (left image) and the median (right image) thresholding
   options of vl_kdtreebuild. On the bottom right corner
   a query point is marked along with the ten closest neighbors as
   found by vl_kdtreequery. Figure generated
   by vl_demo_kdtree.
  
 
    

    



    Querying
    



    vl_kdtreequery uses a best-bin first search
heuristic. This is a branch-and-bound technique that maintains an
estimate of the smallest distance from the query point to any of the
data points down all of the open paths.
    


    vl_kdtreequery supports two important operations:
  approximate nearest-neighbor search and k-nearest
  neighbor search. The latter can be used to return the
  k nearest neighbors to a given query point Q.
  For instance:

    


[index, distance] = vl_kdtreequery(kdtree, X, Q, 'NumNeighbors', 10) ;



    returns the closest 10 neighbors to Q
in X and their distances, stored along the columns of
index and distance.
    


    The MaxComparisons option is used to run an ANN query.
The parameter specifies how many paths in the best-bin-first search of
the kd-tree can be checked before giving up and returning the closest
point encountered so far. For instance:
    


[index, distance] = vl_kdtreequery(kdtree, X, Q, 'NumNeighbors', 10, 'MaxComparisons', 15) ;



    does not compare any point in Q with more than 15
points in X.
    


    
 
 
 
 
 
    
  
    Finding the 10 approximated nearest neighbors for increasing
    values of the MaxComparisons parameter. Note that at
    most MaxComparisons neighbors can be returned (if more
    are requested, they are ignored). Figure generated
    by vl_demo_kdtree_ann.
  
 
    

    



    Randomized kd-tree forests
    



    VLFeat supports constructing randomized forests of
kd-trees to improve the effectiveness of the representation in high
dimensions. The parameter NumTrees of
vl_kdtreebuild specifies how many trees to use in
constructing the forest. Each tree is constructed independently.
Instead of always splitting on the maximally variant dimension, each
tree chooses randomly among the top five most variant dimensions at
each level. When querying, vl_kdtreequery runs
best-bin-first across all the trees in parallel. For instance
    


  kdtree = vl_kdtreebuild(X, 'NumTrees', 4) ;
  [index, distance] = vl_kdtreequery(kdtree, X, Q) ;



    constructs four trees and queries them.
    


    
 
 
 
 
 
    
  
    The parameter NumTrees
    tells vl_kdtreebuild to construct a number of
    randomized kd-trees. Figure generated
    by vl_demo_kdtree_forest.
  
 
    

    


    
vlfeat/docsrc/vlfeat-website-template.xml0000644000175000017500000000672012237004745017507 0ustar  dimadima

 
  
  
  

  
  VLFeat - %path;

  
  
  
  
  %pagestyle;

  
  %pagescript;

  
  
  

  
  
 

 
 
  
  
    
    
    
      %path;
    
    
  
    
  
    
    
    
       
      
    
        %content;
      
    
      
     
    
    
    
  
     
   
 
 

vlfeat/docsrc/using-vsexpress.html0000644000175000017500000000531212237004745016302 0ustar  dimadima


  
    These instructions show how to setup a basic VLFeat
  project with Visual C++ Express 2008 on Windows XP (32 bit).
  Instructions for other versions of Visual Studio should be
  similar.
    

  
    First, we create a new project called vlfeat-client.
  Open Visual C++ Express 2008 and select File > New > Project
  and choose General > Empty Project. Give your project a
  name and location (here vlfeat-client) and click OK.
    

 New Project
 
    
 Empty Project

  
    Next, we have to modify the project properties to include the
  VLFeat library and include directories. Right click on the
  project and select properties.
    

 Properties

 
    We want these settings to apply to both Debug and Release builds, so
 switch to all configurations.
    

 All Configurations

 
    Add an additional include path which points to the root of the
 VLFeat folder:
    

 Additional Includes

  
    Add an additional library include path pointing to the bin/w32
  folder in the distribution, and add vl.dll as a
  dependency:
    

 Additional LIBDIR

 Additional Dependencies

 
    This will allow us to compile, but we will not be able to run,
 getting  because vl.dll will not be found:
    

 vl.dll was not found

 
    To remedy this, we add a post-build step which copies vl.dll to
 the debug or release folder.  Since this only needs to be done once
 for each project, we could instead copy the file manually.
    

 Post-build step

 
    Now that we have created our project, add a new .cpp file (right
 click on Source Files and choose Add > New Item) with
 this code:
    


extern "C" {
#include <vl/generic.h>
}

int main (int argc, const char * argv[]) {
  VL_PRINT ("Hello world!\n") ;
  return 0;
}


 
    Build and run the project (Ctrl+F5). It should run correctly, and
 you should see this:
    

 Visual C++ OK

    
vlfeat/docsrc/using-xcode.html0000644000175000017500000000725012237004745015345 0ustar  dimadima


 
    These instructions show how to setup a basic VLFeat project
  with Apple Xcode.  For the sake of simplicty, we create a command
  line tool written in C. However, these steps apply with minor
  modifications to other project types and to the C++ lanuage.
    

 
    First, let us create a new project
  called vlfeat-client. Open Xcode and select File
  > New Project > Command Line Utility > Standard Tool
  and click Choose. Give a name to your project (in our
  case vlfeat-client), and click Save.
    

 
    
 Xcode new project
 
    

 
    Now we need to add VLFeat to the C compiler include search
  path. To do this, select the vlfeat-client target and
  open the information panel (the blue button,
  or Command-i). Then select the Build panel, search for
  the field Header Search Paths, and add
  VLFeat root path (in our case this is
  just ~/src/vlfeat).
    

 Xcode info

 
    Next, we add the libvl.dylib library file to the
  project resources so that Xcode links against it. To do this, drag
  and drop the libvl.dylib file (in our example
  ~/src/vlfeat/bin/maci/libvl.dylib) to the left panel and click
  Add.
    

 Xcode dylib

 
    Next, edit the main.c source file and type the following code:
    


#include <vl/generic.h>

int main (int argc, const char * argv[]) {
  VL_PRINT ("Hello world!") ;
  return 0;
}


 Xcode edit

 
    If you try to build the project, it should compile without errors
  (if you are using C++, do not forget to wrap the include
  statements in a extern "C" {} block). However, if you try
  to run the program, it will fail, complaining that it cannot find the
  library image.
    

 Xcode error

 
    The reason is that libvl.dylib is compiled with the
  library install_name equal
  to @loader_path/libvl.dylib. This causes the run-time
  loader to look for the library in the same directory of the
  executable. There are two ways around this problem: The first is to
  install the library in a standard location
  (e.g. /usr/local/lib) and use the otool
  command to change the
  library install_name. The other is to simply copy
  the libvl.dylib file in the executable directory. Here we
  demonstrate the second technique.
    

 
    To copy libvl.dylib in the executable directory, we
  add a Copy Files build phase to the project. Right-click
  the vlfeat-client target in the project panel and select
  Add > New Build Phase > New Copy Files Build
   Phase. Select Destination: Executables. Then drag-and-drop
  the libvl.dylib item from the panel to the Copy
   Files build phase.
    

 Xcode copy
 Xcode copy

 
    Now rebuild the project, and run it. It should run correctly,
  and if you open the debugger console you should see this:
    

 Xcode ok

    
vlfeat/docsrc/images/0000755000175000017500000000000012237035722013472 5ustar  dimadimavlfeat/docsrc/images/vl_blue.png0000644000175000017500000000402612237004736015633 0ustar  dimadima‰PNG


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    These instructions explain how to setup VLFeat in MATLAB (at least
2009B) using the binary distribution (it is also possible
to compile the library and toolbox
from source, including running on earlier MATLAB versions by disabling
some features such as OpenMP support).
    


    One-time setup
    


    Download and unpack the latest
VLFeat binary distribution in a directory of your choice
(e.g. ~/src/vlfeat). Let VLFEATROOT denote
this directory. VLFeat must be added to MATLAB search path by running
the vl_setup command found in
the VLFEATROOT/toolbox directory. From MATLAB prompt
enter
    


>> run('VLFEATROOT/toolbox/vl_setup')
VLFeat 0.9.17 ready.



    To check that VLFeat is sucessfully installed, try to run
the vl_version command:
    


>> vl_version verbose
VLFeat version 0.9.17
    Static config: X64, little_endian, GNU C 40201 LP64, POSIX_threads, SSE2, OpenMP
    4 CPU(s): GenuineIntel MMX SSE SSE2 SSE3 SSE41 SSE42
    OpenMP: max threads: 4 (library: 4)
    Debug: yes
    SIMD enabled: yes



    Permanent setup
    


    To permanently add VLFeat to your MATLAB environment, add this line
to your
startup.m
file:
    


run('VLFEATROOT/toolbox/vl_setup')



    When you restart MATLAB, you should see the VLFeat greeting
message.
    


    Getting started
    


    All commands embed interface documentation that can be viewed with
the
builtin help
command (e.g. help vl_sift).
    


    VLFeat bundles a large number of demos. To use them, add the demo
path with vl_setup demo. For example, a sift demo
vl_demo_sift_basic can be run using the following:
    


>> vl_setup demo

>> vl_demo_sift_basic



    To see a list of demos TAB-complete vl_demo at MATLAB
prompt, after running vl_setup demo.
    

    
vlfeat/docsrc/vlfeat-website-preproc.xml0000644000175000017500000000177212237004745017350 0ustar  dimadima


 

 
 

 
 
  

  
   
   
  

  
    
      
      
      
      
      
    
        
      
    
        
      

  
    
    
  
     

    
vlfeat/docsrc/license.html0000644000175000017500000000037312237004736014541 0ustar  dimadima



    VLFeat is distributed under the BSD license:
    

    
    
vlfeat/docsrc/using-gcc.html0000644000175000017500000000175312237004745015001 0ustar  dimadima



    These instructions show how to setup a basic C++ project which uses the
VLFeat library and the g++ compiler. Our project will consist
of the following simple file, which we will refer to
as main.cpp:
    


extern "C" {
  #include <vl/generic.h>
}

int main (int argc, const char * argv[]) {
  VL_PRINT ("Hello world!") ;
  return 0;
}


 
    Notice that the extern "C" is required for C++ projects.
 Compiling the project is straightforward. From the command prompt type:
    


$ g++ main.cpp -o vlfeat-test -IVLROOT -LVLROOT/bin/a64/ -lvl


 
    In this example, replace VLROOT with the path to your VLFeat
 installation and replace a64 with your architecture
 (a64 for 64 bit linux, glx for 32 bit linux).
    

    
vlfeat/docsrc/figures/0000755000175000017500000000000012237035722013671 5ustar  dimadimavlfeat/docsrc/figures/mser-tree.fig0000644000175000017500000000464012237004736016270 0ustar  dimadima#FIG 3.2  Produced by xfig version 3.2.5-alpha5
Landscape
Center
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Letter  
100.00
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1200 2
2 2 2 0 0 15 50 -1 20 1.500 0 0 -1 0 0 5
	 4125 2475 5625 2475 5625 2550 4125 2550 4125 2475
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	 3750 2100 6150 2100 6150 2175 3750 2175 3750 2100
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	1 1 2.00 90.00 135.00
	 2175 3300 7200 3300
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	 2100 2475 2700 2475 2700 2550 2100 2550 2100 2475
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	 2100 2100 2925 2100 2925 2175 2100 2175 2100 2100
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	 6750 2475 7050 2475 7050 2550 6750 2550 6750 2475
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	 4350 2850 5400 2850 5400 2925 4350 2925 4350 2850
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	 2100 2850 2550 2850 2550 2925 2100 2925 2100 2850
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	 6975 2850 7050 2850 7050 2925 6975 2925 6975 2850
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	 2100 1725 7050 1725 7050 1800 2100 1800 2100 1725
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	1 1 1.00 60.00 120.00
	 4875 2475 4875 2175
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	1 1 1.00 60.00 120.00
	 4875 2100 4875 1800
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	1 1 1.00 60.00 120.00
	 2325 2850 2325 2550
2 1 0 1 0 0 45 -1 20 3.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 2325 2475 2325 2175
2 1 0 1 0 0 45 -1 20 3.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 2325 2100 4875 1800
2 1 0 1 0 0 45 -1 20 3.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 7050 2850 7050 2550
2 1 0 1 0 0 45 -1 20 3.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 7050 2475 7050 2175
2 1 0 1 0 0 45 -1 20 3.000 0 0 -1 1 0 2
	1 1 1.00 60.00 120.00
	 7050 2100 4950 1800
3 2 0 4 0 7 43 -1 -1 0.000 0 0 0 14
	 2475 3000 2700 2475 3000 2100 3375 1950 3750 2100 4125 2475
	 4425 2925 4800 3150 5250 3000 5625 2475 6075 2100 6450 2100
	 6825 2625 7050 3000
	 0.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000 -1.000
	 -1.000 -1.000 -1.000 -1.000 -1.000 0.000
4 0 0 47 -1 0 12 0.0000 2 195 4920 2250 3600 Extremal regions are arranged in a tree of netsed regions.\001
4 0 0 45 -1 0 12 0.0000 2 210 540 7125 3075 $I(x)$\001
4 0 0 45 -1 0 12 0.0000 2 180 315 7200 3375 $x$\001
vlfeat/docsrc/figures/cell-bins.fig0000644000175000017500000001054512237004736016236 0ustar  dimadima#FIG 3.2  Produced by xfig version 3.2.5b
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150.00
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1200 2
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1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 4500 102150 60 60 4500 102150 4560 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 4800 102150 60 60 4800 102150 4860 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 5100 102150 60 60 5100 102150 5160 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 5400 102150 60 60 5400 102150 5460 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 5700 102150 60 60 5700 102150 5760 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 6000 102150 60 60 6000 102150 6060 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 6300 102150 60 60 6300 102150 6360 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 6600 102150 60 60 6600 102150 6660 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 6900 102150 60 60 6900 102150 6960 102150
1 3 0 1 0 0 50 -1 20 0.000 1 0.0000 7200 102150 60 60 7200 102150 7260 102150
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2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
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2 1 1 1 0 7 50 -1 -1 4.000 0 0 -1 0 0 2
	 4200 102150 4200 101250
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2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 4050 102150 4050 102000
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	 4350 102150 4350 102000
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	 4650 102150 4650 102000
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	 4950 102150 4950 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 5250 102150 5250 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 5550 102150 5550 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 5850 102150 5850 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6150 102150 6150 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6450 102150 6450 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6750 102150 6750 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 7050 102150 7050 102000
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 7350 102150 7350 102000
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6000 102150 5100 101250
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 5100 101250 4200 102150
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 4
	 3750 102600 3750 102750 4650 102750 4650 102600
2 1 2 1 0 7 50 -1 -1 2.000 0 0 -1 0 1 2
	2 1 1.00 60.00 60.00
	 3750 101925 3750 101100
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 4
	 4650 102600 4650 102750 5550 102750 5550 102600
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 4200 103125 4200 103275
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	 5100 103125 5100 103275
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	 3750 103200 6450 103200
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	 6000 101250 5100 102150
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6900 102150 6000 101250
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	 5550 102600 5550 102750 6450 102750 6450 102600
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 4200 101250 3750 101700
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 4
	 6450 102600 6450 102750 7350 102750 7350 102600
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 6900 101250 6000 102150
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 7650 102150 7650 102000
2 1 2 1 0 7 50 -1 -1 2.000 0 0 -1 0 0 4
	 7350 102600 7350 102750 8250 102750 8250 102600
2 1 0 2 1 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 7650 102000 6900 101250
2 1 2 1 0 7 50 -1 -1 2.000 0 0 -1 0 1 2
	2 1 1.00 60.00 60.00
	 7650 101925 7650 101100
2 1 0 1 0 7 50 -1 -1 0.000 0 0 -1 0 0 2
	 3750 102150 7650 102150
4 1 0 50 -1 0 10 0.0000 2 135 225 3750 102450 $0$\001
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4 1 0 50 -1 0 10 0.0000 2 135 225 4650 102450 $0$\001
4 1 0 50 -1 0 10 0.0000 2 135 540 4200 102675 $\\Delta$\001
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4 1 0 50 -1 0 10 0.0000 2 135 225 5100 103425 $1$\001
4 1 0 50 -1 0 10 0.0000 2 135 495 6000 102975 cell $2$\001
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4 1 0 50 -1 0 10 0.0000 2 135 495 6900 102975 cell $3$\001
4 1 0 50 -1 0 10 0.0000 2 135 1305 7650 101025 right image boundary\001
vlfeat/docsrc/figures/mser-er.fig0000644000175000017500000000664712237004736015750 0ustar  dimadima#FIG 3.2  Produced by xfig version 3.2.5-alpha5
Landscape
Center
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Letter  
100.00
Single
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1200 2
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	 12225 1800 12225 3000
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4 0 0 45 -1 0 12 0.0000 2 180 315 13500 3375 $x$\001
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4 0 0 45 -1 0 12 0.0000 2 210 540 7125 3075 $I(x)$\001
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vlfeat/docsrc/encode.html0000644000175000017500000001047112237042456014355 0ustar  dimadima


%tableofcontents;


    This short tutorial shows how to
compute Fisher vector and
VLAD encodings with VLFeat MATLAB
interface.
    


    These encoding serve a similar purposes: summarising in a vectorial
statistic a number of local feature descriptors
(e.g. SIFT). Similarly to bag of visual
words, they compare local descriptor to a dictionary, obtained with
vectonr quantization (KMeans) in the case of VLAD
and Gaussina Mixture Models for Fisher
Vectors. However, rather than storing visual word occurences only,
these representation store the difference between dictonary elements
and pooled local features.
    



    Fisher encoding
    



    The Fisher encoding uses GMM to construct a visual word
dicionary. To exemplify constructing a GMM, we consider a number of 2
dimensional data points (see also the GMM
tutorial). In practice, these would be a collection of SIFT or
other local image features:
    


numFeatures = 5000 ;
dimension = 2 ;
data = rand(dimension,numFeatures) ;

numClusters = 30 ;
[means, covariances, priors] = vl_gmm(data, numClusters);



    Next we create another random set of vectors, which should be
encoded using the Fisher vector representation and the GMM just
obtained.
    


numDataToBeEncoded = 1000;
dataToBeEncoded = rand(dimension,numDataToBeEncoded);



    The Fisher vector encoding enc of these vectors is
obtained by calling the vl_fisher function using the
output of the vl_gmm function:
    


encoding = vl_fisher(datatoBeEncoded, means, covariances, priors);



    The encoding vector is the Fisher vector
representation of the data dataToBeEncoded.
    


    Note that Fihser vectors support
several normalization options
that can affect substantially the performance of the
representation.
    



    VLAD encoding
    



    The Vector
of Linearly Agregated Descriptors is similar to
Fisher vectors but (i) it does not store second-order information
about the features and (ii) it typically use KMeans instead of GMMs to
generate the feature vocabulary (although the latter is also an
option).
    


    Consider the same 2D data matrix data used in the
previous section to train the Fisher vector representation. To compute
VLAD, we first need to obtain a visual word dictionary. This time, we
use K-means:
    


numClusters = 30 ;
centers = vl_kmeans(dataLearn, numClusters);



    Now consider the data dataToBeEncoded and use
the vl_vlad function to compute the encoding. Differently
from vl_fhiser, vl_vlad requires the
data-to-cluster assignments to be passed in. This allows
using a fast vector quantization technique (e.g. kd-tree) as well as
switching from soft to hard assignment.
    


    In this example, we use a kd-tree for quantization:
    


kdtree = vl_kdtreebuild(centers) ;
nn = vl_kdtreequery(kdtree, centers, dataEncode) ;



    Now we have in the nn the indeces of the nearest
center to each vector in the matrix dataToBeEncoded. The
next step is to create an assignment matrix:
    


assignments = zeros(numClusters,numDataToBeEncoded);
assignments(sub2ind(size(assignments), nn, 1:length(nn))) = 1;



    It is now possible to encode the data using
the vl_vlad function:
    


enc = vl_vlad(dataToBeEncoded,centers,assignments);



    Note that, similarly to Fisher vectors, VLAD supports
several normalization options
that can affect substantially the performance of the
representation.
    

    




vlfeat/docsrc/vlfeat.bib0000644000175000017500000001376112237004745014175 0ustar  dimadima%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/


%% Created for Andrea Vedaldi at 2013-07-12 10:15:57 +0100 


%% Saved with string encoding Unicode (UTF-8) 


@string{bmvc = {Proc. {BMVC}}}

@string{cvpr = {Proc. {CVPR}}}

@string{eccv = {Proc. {ECCV}}}

@string{iccv = {Proc. {ICCV}}}

@string{icip = {Proc. {ICIP}}}

@string{icml = {Proc. {ICML}}}

@string{icpr = {Proc. {ICPR}}}

@string{ijcv = {{IJCV}}}

@string{inria = {{INRIA}}}

@string{mit = {{MIT}}}

@string{nips = {Proc. {NIPS}}}

@string{pami = {{PAMI}}}

@string{siggraph = {Proc. {SIGGRAPH}}}


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@inproceedings{perronnin06fisher,
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@inproceedings{vedaldi08quick,
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	Title = {Maximum likelihood from incomplete data via the EM algorithm},
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vlfeat/docsrc/wikidoc.py0000644000175000017500000000245512237004736014237 0ustar  dimadima#!/usr/bin/python

import sys,os

def usage():
    print "Usage:", sys.argv[0], " "
    print ""
    print "Replaces wiki documentation with documentation from html files"

def wikidoc(pagename, htmlfile):
    f = open(htmlfile)
    doclines = f.readlines()
    f.close()

    starttag = ""
    endtag   = ""
    doclines.insert(0, starttag + '\n')
    doclines.append('\n' + endtag + '\n')

    wikilines = []
    if os.path.exists(pagename):
        f = open(pagename)
        wikilines = f.readlines()
        f.close()

    startline = -1
    endline = -1
    for i in range(len(wikilines)):
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        if wikilines[i].strip() == endtag   and endline == -1:   endline = i

    if startline == -1 or endline == -1 or startline >= endline:
        print "Wikidoc warning: Didn't find tags for", pagename
        startline = 0
        endline = 0

    wikilines = wikilines[:startline] + doclines + wikilines[endline+1:]
    f = open(pagename, 'w')
    f.writelines(wikilines)
    f.close()

if __name__ == '__main__':
    if len(sys.argv) != 3:
        usage()
        sys.exit(2)

    print "wikidoc: using", sys.argv[1], "for page", sys.argv[2]
    wikidoc(sys.argv[1], sys.argv[2])
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vlfeat/docsrc/ikm.html0000644000175000017500000000624112237004745013677 0ustar  dimadima


%tableofcontents;


    VLFeat includes a basic implementation of k-means clustering
and hierarchical k-means clustering. They are designed to be
lightweight in order to work on large datasets. In particular, they
assume that the data are vectors of unsigned chars (one byte). While
this is limiting for some application, it works well for clustering
image descriptors, where very high precision is usually unnecessary.
For more details, see the
Integer k-means API
  reference.
    



    Usage
    



    Integer k-means (IKM) is run by the command
vl_ikmeans. In order to demonstrate the usage of this
command, we sample 1000 random points in the [0,255]^2
integer square and use vl_ikmeans to get k=3
clusters:
    


K = 3 ;
data = uint8(rand(2,1000) * 255) ;
[C,A] = vl_ikmeans(data,K) ;



    The program returns both the cluster centers C and the
data-to-cluster assignments A. By means of the cluster
centers
C we can project more data on the same clusters
    


datat = uint8(rand(2,10000) * 255) ;
AT = vl_ikmeanspush(datat,C) ;



    In order to visualize the results, we associate to each cluster a
color and we plot the points:
    


cl = get(gca,'ColorOrder') ;
ncl = size(cl,1) ;
for k=1:K
  sel  = find(A  == k) ;
  selt = find(AT == k) ;
  plot(data(1,sel),  data(2,sel),  '.',...
       'Color',cl(mod(k,ncl)+1,:)) ;
  plot(datat(1,selt),datat(2,selt),'+',...
       'Color',cl(mod(k,ncl)+1,:)) ;
end



    


    

Integer k-means. We show clusters of 2-D points obtained by
integer k-means.  There are k=3 clusters represented with
different colors. The clusters have been estimated from 1000 points
(displayed as dots). Then 10000 different points have been projected on
the same clusters (displayed as crosses). The three big markers
represent the cluster centers.


    

    



    Elkan
    



    VLFeat supports two different implementations of k-means. While
they produce identical output, the Elkan method requires fewer
distance computations.  The method parameters controls
which method is used. Consider the case when K=100 and our
data is now 128 dimensional (e.g. SIFT descriptors):
    


K=100;
data = uint8(rand(128,10000) * 255);
tic;
[C,A] = vl_ikmeans(data,K,'method', 'lloyd') ; % default
t_lloyd = toc
tic;
[C,A] = vl_ikmeans(data,K,'method', 'elkan') ;
t_elkan = toc

t_lloyd =

   10.2884

t_elkan =

    5.1405



    
vlfeat/Makefile.mak0000644000175000017500000004212612237004745013165 0ustar  dimadima# file: Makefile.mak
# descrption: Microsoft NMake makefile
# authors: Andrea Vedaldi, Brian Fulkerson, Mircea Cimpoi

# Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
# All rights reserved.
#
# This file is part of the VLFeat library and is made available under
# the terms of the BSD license (see the COPYING file).

# --------------------------------------------------------------------
#                                                        Customization
# --------------------------------------------------------------------
# To modify this script to run on your platform it is usually
# sufficient to modify the following variables:
#
# ARCH: Either win32 or win64 [win64]
# DEBUG: Set to yes to ativate debugging [no]
# MATLABROOT: Path to MATLAB
# MSVSVER: Visual Studio version (e.g. 80, 90, 100) [90 for VS 9.0]
# MSVCROOT: Visual C++ location [$(VCInstallDir)].
# WINSDKROOT: Windows SDK location [$(WindowsSdkDir)]
#
# Note that some of these variables depend on the architecture
# (either win32 or win64).

VER = 0.9.17
ARCH = win64
DEBUG = no
BRANCH = v$(VER)-$(ARCH)
MSVSVER =
MSVCROOT = $(VCINSTALLDIR)
WINSDKROOT = $(WINDOWSSDKDIR)
GIT = git

!if "$(MSVCROOT)" == ""
MSVCROOT = C:\Program Files\Microsoft Visual Studio 10.0\VC
!endif

!if "$(WINSDKROOT)" == ""
WINSDKROOT = C:\Program Files\Microsoft SDKs\Windows\v7.0A
!endif

!include make/nmake_helper.mak

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 32-bit
!if "$(ARCH)" == "win32"
!message === COMPILING FOR 32-BIT

MATLABROOT = C:\Program Files (x86)\MATLAB\R2009b
MEX = "$(MATLABROOT)\bin\mex.bat"
MEXOPT = "$(MATLABROOT)\bin\win32\mexopts\msvc$(MSVSVER)opts.bat"
MEXEXT = mexw32
MEX_FLAGS =

CC = "$(MSVCROOT)\bin\cl.exe"
LINK = "$(MSVCROOT)\bin\link.exe"
MSVCR_PATH = $(MSVCROOT)\redist\x86\Microsoft.VC$(MSVSVER).CRT

LFLAGS = /MACHINE:X86 \
         /LIBPATH:"$(MSVCROOT)\lib" \
         /LIBPATH:"$(WINSDKROOT)\lib"

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 64-bit
!elseif "$(ARCH)" == "win64"
!message === COMPILING FOR 64-BIT

MATLABROOT = C:\Program Files\MATLAB\R2009b
MEX = "$(MATLABROOT)\bin\mex.bat"
MEXOPT = "$(MATLABROOT)\bin\win64\mexopts\msvc$(MSVSVER)opts.bat"
MEXEXT = mexw64
MEX_FLAGS = -largeArrayDims

CC = "$(MSVCROOT)\bin\amd64\cl.exe"
LINK = "$(MSVCROOT)\bin\amd64\link.exe"
!if $(MSVSVER) >= 100
MSVCR_PATH = $(MSVCROOT)\redist\x64\Microsoft.VC$(MSVSVER).CRT
!else
MSVCR_PATH = $(MSVCROOT)\redist\amd64\Microsoft.VC$(MSVSVER).CRT
!endif

LFLAGS = /MACHINE:X64 \
         /LIBPATH:"$(MSVCROOT)\lib\amd64" \
         /LIBPATH:"$(WINSDKROOT)\lib\x64"
!else
!error ARCH = $(ARCH) is an unknown architecture.
!endif

# --------------------------------------------------------------------
#                                                                Flags
# --------------------------------------------------------------------
# Debug info is embedded in .obj and .lib files (CodeView /Z7 option)
# but in .pdb files for .exe and .dll (since the linker does not
# produce CodeView output anymore).
#
# CFLAGS
#   /nologo            : CL does not display splash
#   _CRT_NO_DEPRECATE  : Do not deprecate `insecure' fscanf, snprintf, ...
#   __LITTLE_ENDIAN__  : Signal little endian architecture
#   /I.                : Add VLROOT to include search path
#   /MD                : Multi-thread run-time library dynamically linked
#   /TC                : Source code is C (not C++)
#   /W3                : Usa all warnings
#   /Zp8               : Align structures to 8 bytes
#   /Ox                : Turn on optimizations
#   /D"DEBUG"          : [DEBUG] Turn on debugging in VLFeat
#   /Z7                : [DEBUG] Embedded CodeView debug info in .obj
#   /D"NDEBUG"         : [NO DEBUG] Switches off asserts
#
# LFLAGS
#   /NOLOGO            : LINK does not display splash
#   /INCREMENTAL:NO    : No incremental linking
#   /MANIFEST          : See DLL HELL below
#   /DEBUG             : [DEBUG] Generate debug info (.pdb files)
#
# MEX_FLAGS
#   -I                 : Include VLFeat
#   -L                 : Add a library search path
#   -l                 : Link a dll
#
# ======================= ABOUT THE DLL HELL =========================
#
# This makefile compiles VLFeat to make use of the side-by-side
# deployment model, redestribtin the appropraite Visual C runtime
# library with the library and executables. In Visual Studio < 10.0
# this meant including a manifest file, while in version >= 10.0 this
# requirement has been relaxed.
#
# References:
#   http://www.codeguru.com/forum/showthread.php?t=408061
#   http://mariusbancila.ro/blog/2010/03/24/visual-studio-2010-changes-for-vc-part-5
#   http://social.msdn.microsoft.com/Forums/is/vcgeneral/thread/ca9177b2-2d02-42d8-8892-c6a25e6cfadb
#

bindir = bin\$(ARCH)
mexdir = toolbox\mex\$(MEXEXT)
objdir = $(bindir)\objs

CFLAGS = /nologo /TC /MD \
         /D"_CRT_SECURE_NO_DEPRECATE" \
         /D"__LITTLE_ENDIAN__" \
         /D"VL_DISABLE_AVX" \
         /I. \
         /W1 /Zp8 /openmp

LFLAGS = $(LFLAGS) /NOLOGO \
         /INCREMENTAL:NO \
         /MANIFEST

!if "$(DEBUG)" != "no"
!message === DEBUGGING ON
CFLAGS = $(CFLAGS) /Z7 /D"DEBUG"
LFLAGS = $(LFLAGS) /DEBUG
MEX_FLAGS = $(MEX_FLAGS) -g
!else
!message === DEBUGGING OFF
CFLAGS = $(CFLAGS) /D"NDEBUG" /Ox
!endif

DLL_CFLAGS = /D"VL_BUILD_DLL"
EXE_LFLAGS = $(LFLAGS) /LIBPATH:"$(bindir)" vl.lib
MEX_FLAGS = $(MEX_FLAGS) -f $(MEXOPT) -I. -Itoolbox -L"$(bindir)" -lvl

libsrc = \
  vl\aib.c \
  vl\array.c \
  vl\covdet.c \
  vl\dsift.c \
  vl\fisher.c \
  vl\generic.c \
  vl\getopt_long.c \
  vl\gmm.c \
  vl\hikmeans.c \
  vl\hog.c \
  vl\homkermap.c \
  vl\host.c \
  vl\ikmeans.c \
  vl\imopv.c \
  vl\imopv_sse2.c \
  vl\kdtree.c \
  vl\kmeans.c \
  vl\lbp.c \
  vl\liop.c \
  vl\mathop.c \
  vl\mathop_avx.c \
  vl\mathop_sse2.c \
  vl\mser.c \
  vl\pgm.c \
  vl\quickshift.c \
  vl\random.c \
  vl\rodrigues.c \
  vl\scalespace.c \
  vl\sift.c \
  vl\slic.c \
  vl\stringop.c \
  vl\svm.c \
  vl\svmdataset.c \
  vl\vlad.c

cmdsrc = \
  src\aib.c \
  src\mser.c \
  src\sift.c \
  src\test_gauss_elimination.c \
  src\test_getopt_long.c \
  src\test_gmm.c \
  src\test_heap-def.c \
  src\test_host.c \
  src\test_imopv.c \
  src\test_kmeans.c \
  src\test_liop.c \
  src\test_mathop.c \
  src\test_mathop_abs.c \
  src\test_nan.c \
  src\test_qsort-def.c \
  src\test_rand.c \
  src\test_stringop.c \
  src\test_svd2.c \
  src\test_threads.c \
  src\test_vec_comp.c

cmdsrc = \
  src\aib.c \
  src\mser.c \
  src\sift.c \
  src\test_gauss_elimination.c \
  src\test_getopt_long.c \
  src\test_gmm.c \
  src\test_heap-def.c \
  src\test_host.c \
  src\test_imopv.c \
  src\test_kmeans.c \
  src\test_liop.c \
  src\test_mathop.c \
  src\test_mathop_abs.c \
  src\test_nan.c \
  src\test_qsort-def.c \
  src\test_rand.c \
  src\test_stringop.c \
  src\test_svd2.c \
  src\test_threads.c \
  src\test_vec_comp.c

mexsrc = \
  toolbox\aib\vl_aib.c \
  toolbox\aib\vl_aibhist.c \
  toolbox\fisher\vl_fisher.c \
  toolbox\geometry\vl_irodr.c \
  toolbox\geometry\vl_rodr.c \
  toolbox\gmm\vl_gmm.c \
  toolbox\imop\vl_imdisttf.c \
  toolbox\imop\vl_imintegral.c \
  toolbox\imop\vl_imsmooth.c \
  toolbox\imop\vl_imwbackwardmx.c \
  toolbox\imop\vl_tpsumx.c \
  toolbox\kmeans\vl_hikmeans.c \
  toolbox\kmeans\vl_hikmeanspush.c \
  toolbox\kmeans\vl_ikmeans.c \
  toolbox\kmeans\vl_ikmeanspush.c \
  toolbox\kmeans\vl_kmeans.c \
  toolbox\misc\vl_alldist.c \
  toolbox\misc\vl_alldist2.c \
  toolbox\misc\vl_binsearch.c \
  toolbox\misc\vl_binsum.c \
  toolbox\misc\vl_cummax.c \
  toolbox\misc\vl_getpid.c \
  toolbox\misc\vl_hog.c \
  toolbox\misc\vl_homkermap.c \
  toolbox\misc\vl_ihashfind.c \
  toolbox\misc\vl_ihashsum.c \
  toolbox\misc\vl_inthist.c \
  toolbox\misc\vl_kdtreebuild.c \
  toolbox\misc\vl_kdtreequery.c \
  toolbox\misc\vl_lbp.c \
  toolbox\misc\vl_localmax.c \
  toolbox\misc\vl_sampleinthist.c \
  toolbox\misc\vl_simdctrl.c \
  toolbox\misc\vl_svmtrain.c \
  toolbox\misc\vl_threads.c \
  toolbox\misc\vl_twister.c \
  toolbox\misc\vl_version.c \
  toolbox\mser\vl_erfill.c \
  toolbox\mser\vl_mser.c \
  toolbox\quickshift\vl_quickshift.c \
  toolbox\sift\vl_covdet.c \
  toolbox\sift\vl_dsift.c \
  toolbox\sift\vl_liop.c \
  toolbox\sift\vl_sift.c \
  toolbox\sift\vl_siftdescriptor.c \
  toolbox\sift\vl_ubcmatch.c \
  toolbox\slic\vl_slic.c \
  toolbox\vlad\vl_vlad.c

!if "$(ARCH)" == "win32"
libobj = $(libsrc:vl\=bin\win32\objs\)
cmdexe = $(cmdsrc:src\=bin\win32\)
mexdll = $(mexsrc:.c=.mexw32)
mexdll = $(mexdll:toolbox\fisher=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\sift=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\mser=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\imop=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\geometry=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\gmm=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\kmeans=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\misc=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\aib=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\quickshift=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\slic=toolbox\mex\mexw32)
mexdll = $(mexdll:toolbox\vlad=toolbox\mex\mexw32)
mexpdb = $(mexdll:.dll=.pdb)

!elseif "$(ARCH)" == "win64"
libobj = $(libsrc:vl\=bin\win64\objs\)
cmdexe = $(cmdsrc:src\=bin\win64\)
mexdll = $(mexsrc:.c=.mexw64)
mexdll = $(mexdll:toolbox\fisher=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\sift=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\mser=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\imop=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\geometry=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\gmm=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\kmeans=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\misc=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\aib=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\quickshift=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\slic=toolbox\mex\mexw64)
mexdll = $(mexdll:toolbox\vlad=toolbox\mex\mexw64)
mexpdb = $(mexdll:.mexw64=.pdb)
!endif

libobj = $(libobj:.c=.obj)
cmdexe = $(cmdexe:.c=.exe)
cmdpdb = $(cmdexe:.exe=.pdb)

# Visual Studio redistributable files
MSVCR = Microsoft.VC$(MSVSVER).CRT
!if $(MSVSVER) <= 90
# VS <= 2008 needs a manifest too
bincrt = $(bindir)\msvcr$(MSVSVER).dll $(bindir)\$(MSVCR).manifest
mexcrt = $(mexdir)\msvcr$(MSVSVER).dll $(mexdir)\$(MSVCR).manifest
!else
bincrt = $(bindir)\msvcr$(MSVSVER).dll
mexcrt = $(mexdir)\msvcr$(MSVSVER).dll
!endif

!ifdef MATLABROOT
all: $(bindir) $(objdir) $(mexdir) \
     $(bincrt) $(bindir)\vl.lib $(bindir)\vl.dll \
     $(cmdexe) \
     $(mexcrt) $(mexdir)\vl.dll $(mexdll)
!else
all: $(bindir) $(objdir) \
     $(bincrt) $(bindir)\vl.lib $(bindir)\vl.dll \
     $(cmdexe)
!endif

BUILD_MEX=@echo .... CC [MEX] $(@) && \
	$(MEX) $(MEX_FLAGS) "$(<)" -output $(@)

# --------------------------------------------------------------------
#                                                    Maintenance rules
# --------------------------------------------------------------------

clean:
	del /f /Q $(libobj)
	del /f /Q $(objdir)
	del /f /Q $(cmdpdb)
	del /f /Q $(mexpdb)

archclean:
	if exist bin\$(ARCH) rmdir /S /Q bin\$(ARCH)
	if exist $(mexdir) rmdir /S /Q $(mexdir)

distclean:
	if exist bin rmdir /S /Q bin
	if exist toolbox\mex rmdir /S /Q toolbox\mex

info:
	@echo $(mexx)
	@echo ** bindir      = $(bindir)
	@echo ** mexdir      = $(mexdir)
	@echo ** objdir      = $(objdir)
	@echo ** libsrc      = $(libsrc)
	@echo ** libobj      = $(libobj)
	@echo ** cmdsrc      = $(cmdsrc)
	@echo ** cmdexe      = $(cmdexe)
	@echo ** mexsrc      = $(mexsrc)
	@echo ** mexdll      = $(mexdll)
	@echo ** CC          = $(CC)
	@echo ** CFLAGS      = $(CFLAGS)
	@echo ** DLL_CFLAGS  = $(DLL_CFLAGS)
	@echo ** MEX_FLAGS   = $(MEX_FLAGS)
	@echo ** BUILD_MEX   = "$(BUILD_MEX)"
	@echo ** MATLABROOT  = $(MATLABROOT)
	@echo ** MEX         = $(MEX)
	@echo ** MEXEXT      = $(MEXEXT)
	@echo ** MEXOPT      = $(MEXOPT)
	@echo ** MSVSVER     = $(MSVSVER)
	@echo ** MSVCROOT    = $(MSVCROOT)
	@echo ** MSVCR       = $(MSVCR)
	@echo ** MSVCR_PATH  = $(MSVCR_PATH)
	@echo ** bincrt      = $(bincrt)
	@echo ** mexcrt      = $(mexcrt)
	@echo ** WINSDKROOT  = $(WINSDKROOT)
	@echo ** DEBUG       = $(DEBUG)

# --------------------------------------------------------------------
#                                                          Build rules
# --------------------------------------------------------------------

# create directory if missing
$(bindir) :
	mkdir $(bindir)

$(objdir) :
	mkdir $(objdir)

$(mexdir) :
	mkdir $(mexdir)

# --------------------------------------------------------------------
#                                      Rules to compile the VLFeat DLL
# --------------------------------------------------------------------

# special sources with SSE2 support
$(objdir)\mathop_sse2.obj : vl\mathop_sse2.c
	@echo .... CC [+SSE2] $(@)
	@$(CC) $(CFLAGS) $(DLL_CFLAGS) /arch:SSE2 /D"__SSE2__" /c /Fo"$(@)" "vl\$(@B).c"

$(objdir)\imopv_sse2.obj : vl\imopv_sse2.c
	@echo .... CC [+SSE2] $(@)
	@$(CC) $(CFLAGS) $(DLL_CFLAGS) /arch:SSE2 /D"__SSE2__" /c /Fo"$(@)" "vl\$(@B).c"

# vl\*.c -> $objdir\*.obj
{vl}.c{$(objdir)}.obj:
	@echo .... CC $(@)
	@$(CC) $(CFLAGS) $(DLL_CFLAGS) /c /Fo"$(@)" "$(<)"

# Link VLFeat DLL
$(bindir)\vl.dll : $(libobj)
	@echo .. LINK [DLL] $(@R).dll
	$(LINK) /DLL  $(LFLAGS) $(**) /OUT:"$(@)"
	@mt /nologo /outputresource:"$(@);#2" /manifest "$(@R).dll.manifest"
	@-del "$(@R).dll.manifest"

# *.obj -> *.lib
$(bindir)\vl.lib : $(libobj)
	@echo ... LIB $(@R).lib
	@lib $(**) /OUT:"$(@)" /NOLOGO

# redistributable: msvcr__.dll => bin/win{32,64}/msvcr__.dll
$(bindir)\$(MSVCR).manifest : "$(MSVCR_PATH)\$(MSVCR).manifest"
        copy $(**) "$(@)"

$(bindir)\msvcr$(MSVSVER).dll: "$(MSVCR_PATH)\msvcr$(MSVSVER).dll"
        copy $(**) "$(@)"

# --------------------------------------------------------------------
#                                Rules to compile the VLFeat EXE files
# --------------------------------------------------------------------

# src\*.c -> $bindir\*.exe
{src}.c{$(bindir)}.exe:
	@echo .... CC [EXE] $(@)
	@$(CC) $(CFLAGS) /Fe"$(@)" /Fo"$(@R).obj" "$(<)" /link $(EXE_LFLAGS)
	@MT /nologo /outputresource:"$(@);#1" /manifest "$(@).manifest"
	@-del "$(@).manifest"
	@-del "$(@R).obj"

# --------------------------------------------------------------------
#                                Rules to compile the VLFeat MEX files
# --------------------------------------------------------------------

startmatlab:
	"$(MATLABROOT)/bin/matlab.exe" -nodesktop

# toolbox\*.c -> toolbox\*.dll
{toolbox\sift}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\mser}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\imop}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\gmm}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\geometry}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\kmeans}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\aib}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\quickshift}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\misc}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\slic}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\vlad}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

{toolbox\fisher}.c{$(mexdir)}.$(MEXEXT):
	$(BUILD_MEX)

# vl.dll => mexw{32,64}\vl.dll
$(mexdir)\vl.dll : $(bindir)\vl.dll
	copy "$(**)" "$(@)"

# Ideally, the DLL should be linked to Intel compatibility library libiomp5md.dll that
# ships with MATLAB. However, there does not seem to be a clean way to do so without
# the .lib file. This is suboptimal as it casues two OMP libraries to be used (vcomp and iomp5).
# Possible work arounds that did not work yet: generate the .lib file from the .dll file,
# redirect somehow vcomp to iomp5.

#$(LINK) /LIBPATH:"$(MATLABROOT)\extern\lib\win64\microsoft" /DLL $(LFLAGS) $(**) libmwblas.lib /nodefaultlib:vcomp /OUT:"$(@)"
#$(mexdir)\vl.dll : $(libobj)
#	@echo .. LINK [DLL] $(@R).dll
#  $(LINK) /DLL $(LFLAGS) $(**) /OUT:"$(@)"
#	@-del "$(@R).dll.manifest"

# redistributable: msvcr__.dll => bin/win{32,64}/msvcr__.dll
$(mexdir)\$(MSVCR).manifest : "$(MSVCR_PATH)\$(MSVCR).manifest"
        copy $(**) "$(@)"

$(mexdir)\msvcr$(MSVSVER).dll: "$(MSVCR_PATH)\msvcr$(MSVSVER).dll"
        copy $(**) "$(@)"

# --------------------------------------------------------------------
#                                       Rules to post the binary files
# --------------------------------------------------------------------

bin-release:
	echo Fetching remote tags && \
	$(GIT) fetch --tags && \
	echo Checking out v$(VER) && \
	$(GIT) checkout v$(VER)
	echo Rebuilding binaries for release
	if exist "bin\$(ARCH)" del /f /Q "bin\$(ARCH)"
	if exist "bin\mex\$(ARCH)" del /f /Q "toolbox\mex$(ARCH)"
	nmake /f Makefile.mak ARCH=$(ARCH)

bin-commit: bin-release
	@echo Fetching remote tags && \
	$(GIT) fetch --tags
	@echo Crearing/resetting and checking out branch $(BRANCH) to v$(VER) && \
	$(GIT) branch -f $(BRANCH) v$(VER) && \
	$(GIT) checkout $(BRANCH)
	@echo Adding binaries && \
	$(GIT) add -f $(bincrt) && \
	$(GIT) add -f "$(bindir)\vl.lib" && \
	$(GIT) add -f "$(bindir)\vl.dll" && \
	$(GIT) add -f $(cmdexe) && \
	@echo Adding MEX files && \
	$(GIT) add -f $(mexcrt) && \
	$(GIT) add -f "$(mexdir)\vl.dll" && \
	$(GIT) add -f $(mexdll) && \
	@echo Commiting changes && \
	$(GIT) commit -m "$(ARCH) binaries for version $(VER)"
	@echo Commiting and pushing to server the binaries && \
	$(GIT) push -v --force bin $(BRANCH):refs/heads/$(BRANCH) && \
	$(GIT) checkout v$(VER) && \
	$(GIT) branch -D $(BRANCH)
vlfeat/COPYING0000644000175000017500000000252512237004745012010 0ustar  dimadimaCopyright (C) 2007-11, Andrea Vedaldi and Brian Fulkerson
Copyright (C) 2012-13, The VLFeat Team
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
1. Redistributions of source code must retain the above copyright
   notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
   notice, this list of conditions and the following disclaimer in the
   documentation and/or other materials provided with the
   distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
vlfeat/.gitattributes0000644000175000017500000000007312237004736013644 0ustar  dimadima*.manifest -crlf -diff -merge
*.vcproj binary
*.sln binary
vlfeat/README0000644000175000017500000001330712237004745011635 0ustar  dimadima                   VLFeat (Vision Library Features)
                            Version 0.9.17

ABOUT

  The VLFeat open source library implements popular computer vision
  algorithms including SIFT, MSER, k-means, hierarchical k-means,
  agglomerative information bottleneck, and quick shift. It is written
  in C for efficiency and compatibility, with interfaces in MATLAB for
  ease of use, and detailed documentation throughout. It supports
  Windows, Mac OS X, and Linux.

  VLFeat is distributed under the BSD license (see the COPYING file).

  The documentation is available online at
  http://www.vlfeat.org/index.html. A copy of the same is shipped with
  the library in doc/index.html. See also:

  * Installing VLFeat permanently in MATLAB: http://www.vlfeat.org/install-matlab.html
  * Using the command line utilities: http://www.vlfeat.org/install-shell.html
  * Linking to your C program: http://www.vlfeat.org/install-c.html
  * Compiling from source: http://www.vlfeat.org/compiling.html

QUICK START WITH MATLAB

  To start using VLFeat as a MATLAB toolbox, download the latest
  VLFeat binary package from http://www.vlfeat.org/download/. Note
  that the pre-compiled binaries require MATLAB 2009B and
  later. Unpack it, for example by using WinZIP (Windows), by double
  clicking on the archive (Mac), or by using the command line (Linux
  and Mac):

  > tar xzf vlfeat-X.Y.Z-bin.tar.gz

  Here X.Y.Z denotes the latest version. Start MATLAB and run the
  VLFeat setup command:

  > run VLFEATROOT/toolbox/vl_setup

  Here VLFEATROOT is the path to the VLFeat directory created by
  unpacking the archive. All VLFeat demos can now be run in a row by
  the command:

  > vl_demo

OCTAVE SUPPORT

  The toolbox should be laregly compatible with GNU Octave, an open
  source MATLAB equivalent. However, the binary distribution does not
  ship with pre-built GNU Octave MEX files. To compile them use

  > cd 
  > MKOCTFILE= make

CHANGES
  0.9.17     Rewritten SVM implementation, adding support for SGD and
             SDCA optimisers and various loss functions (hinge,
             squared hinge, logistic, etc.) and improving the
             interface. Added infrastructure to support multi-core
             computations using OpenMP (MATLAB 2009B or later
             required). Added OpenMP support to KD-trees and
             KMeans. Added new Gaussian Mixture Models, VLAD encoding,
             and Fisher Vector encodings (also with OpenMP
             support). Added LIOP feature descriptors. Added new
             object category recognition example code, supporting
             several standard benchmarks off-the-shelf.

  0.9.16     Added VL_COVDET(). This function implements the following
             detectors: DoG, Hessian, Harris Laplace, Hessian Laplace,
             Multiscale Hessian, Multiscale Harris. It also implements
             affine adaptation, estiamtion of feature orientation,
             computation of descriptors on the affine patches
             (including raw patches), and sourcing of custom feature
             frame.

  0.9.15     Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and
             a vastly improved SVM implementation. Added IHASHSUM (hashed
             counting). Improved INTHIST (integral histogram). Added
             VL_CUMMAX(). Improved the implementation of VL_ROC() and
             VL_PR(). Added VL_DET() (Detection Error Trade-off (DET)
             curves). Improved the verbosity control to AIB. Added
             support for Xcode 4.3, improved support for past and
             future Xcode versions. Completed the migration of the old
             test code in toolbox/test, moving the functionality to
             the new unit tests toolbox/xtest.

  0.9.14     Added SLIC superpixels. Added VL_ALPHANUM(). Improved
             Windows binary package and added support for Visual
             Studio 2010. Improved the documentation layout and added
             a proper bibliography. Bugfixes and other minor
             improvements. Moved from the GPL to the less restrictive
             BSD license.

  0.9.13     Fixes Windows binary package.

  0.9.12     Fixes vl_compile and the architecture string on Linux 32 bit.

  0.9.11     Fixes a compatibility problem on older Mac OS X versions.
             A few bugfixes are included too.

  0.9.10     Improves the homogeneous kernel map. Plenty of small tweaks
             and improvements. Make maci64 the default architecture on
             the Mac.

  0.9.9      Added: sift matching example. Extended Caltech-101
             classification example to use kd-trees.

  0.9.8      Added: image distance transform, PEGASOS, floating point
             K-means, homogeneous kernel maps, a Caltech-101
             classification example. Improved documentation.

  0.9.7      Changed the Mac OS X binary distribution to require
             a less recent version of Mac OS X (10.5).

  0.9.6      Changed the GNU/Linux binary distribution to require
             a less recent version of the C library.

  0.9.5      Added kd-tree and new SSE-accelerated vector/histogram
             comparison code.  Improved dense SIFT (dsift) implementation.
             Added Snow Leopard and MATLAB R2009b support.

  0.9.4      Added quick shift. Renamed dhog to dsift and improved
             implementation and documentation. Improved tutorials.
             Added 64 bit Windows binaries. Many other small changes.

  0.9.3      Namespace change (everything begins with a vl_ prefix
             now). Many other changes to provide compilation support
             on Windows with MATLAB 7.

  beta-3     Completions to the ikmeans code.

  beta-2     Many completions.

  beta-1     Initial public release.
vlfeat/Makefile0000644000175000017500000002724412237042135012415 0ustar  dimadima# file: Makefile
# description: Build everything
# author: Andrea Vedaldi

# Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
# All rights reserved.
#
# This file is part of the VLFeat library and is made available under
# the terms of the BSD license (see the COPYING file).

# VLFEAT BUILDING INSTRUCTIONS
#
# This makefile builds VLFeat on modern UNIX boxes with the GNU
# toolchain. Mac OS X and Linux are explicitly supported, and support
# for similar architectures can be easily added.
#
# Usually, compiling VLFeat reduces to typing
#
# > cd PATH_TO_VLFEAT_SOURCE_TREE
# > make
#
# The makefile attempts to automatically determine the host
# architecture. If this fails, or if the architecture is ambiguous,
# the architecture can be set by specifying the ARCH variable. For
# instance:
#
# > make ARCH=maci64
#
# builds VLFeat for Mac OS X Intel 64 bit.
#
# !! Unforunately MATLAB mex script (at least up to version 2009B) has
# !! a bug that prevents selecting an architecture different from the
# !! default one for compilation. For instance, compiling maci64 may
# !! not work as the default architecture is maci if both 32 and 64
# !! bit MATLAB are installed. This bug is easy to fix, but requires
# !! patching the mex script. See www.vlfeat.org for detailed
# !! instructions.
#
# Other useful variables are listed below (their default value is in
# square bracked).
#
#   ARCH [undefined] - Active architecture. The supported
#       architectures are maci, maci64, glnx86, or glnxa64 (these are
#       the same architecture identifers used by MATLAB:
#       http://www.mathworks.com/help/techdoc/ref/computer.html). If
#       undefined, the makefile attempts to automatically detect the
#       architecture.
#
#   DEBUG [undefined] - If defined, turns on debugging symbols and
#       turns off optimizations
#
#   PROFILE [undefined] - If defined, turns on debugging symbols but
#       does NOT turn off optimizations.
#
#   VERB [undefined] - If defined, display in full the command
#       executed and their output.
#
#   MEX [mex]- Path to MATLAB MEX compiler. If undefined, MATLAB supprot
#       is disabled.
#
#   MKOCTFILE [undefined] - Path to Octave MKOCTFILE compiler. If undefined,
#       Octave support is disabled.
#
# To completely remove all build products use
#
# > make distclean
#
# Other useful targets include:
#
#   clean - Removes intermediate build products for the active architecture.
#   archclean - Removes all build products for the active architecture.
#   distclean - Removes all build products.
#   info - Display a list of the variables defined by the Makefile.
#   help - Print this message.
#
# VLFeat is compsed of different parts (DLL, command line utilities,
# MATLAB interface, Octave interface) so the makefile is divided in
# components, located in make/*.mak. Please check out the
# corresponding files in order to adjust parameters.

# Copyright (C) 2007-13 Andrea Vedaldi and Brian Fulkerson.
# All rights reserved.
#
# This file is part of the VLFeat library and is made available under
# the terms of the BSD license (see the COPYING file).

SHELL = /bin/bash

.PHONY : all
all:

# Select which features to disable
# DISABLE_SSE2=yes
# DISABLE_AVX=yes
# DISABLE_THREADS=yes
# DISABLE_OPENMP=yes

# --------------------------------------------------------------------
#                                                       Error Messages
# --------------------------------------------------------------------

err_no_arch  =
err_no_arch +=$(shell echo "** Unknown host architecture '$(UNAME)'. This identifier"   1>&2)
err_no_arch +=$(shell echo "** was obtained by running 'uname -sm'. Edit the Makefile " 1>&2)
err_no_arch +=$(shell echo "** to add the appropriate configuration."                   1>&2)
err_no_arch +=config

err_internal  =$(shell echo Internal error)
err_internal +=internal

err_spaces  = $(shell echo "** VLFeat root dir VLDIR='$(VLDIR)' contains spaces."  1>&2)
err_spaces += $(shell echo "** This is not supported due to GNU Make limitations." 1>&2)
err_spaces +=spaces

# --------------------------------------------------------------------
#                                             Auto-detect architecture
# --------------------------------------------------------------------

Darwin_PPC_ARCH := mac
Darwin_Power_Macintosh_ARCH := mac
Darwin_i386_ARCH := maci64
Darwin_x86_64_ARCH := maci64
Linux_i386_ARCH := glnx86
Linux_i686_ARCH := glnx86
Linux_unknown_ARC := glnx86
Linux_x86_64_ARCH := glnxa64

UNAME := $(shell uname -sm)
ARCH ?= $($(shell echo "$(UNAME)" | tr \  _)_ARCH)

# sanity check
ifeq ($(ARCH),)
die:=$(error $(err_no_arch))
endif

ifneq ($(VLDIR),$(shell echo "$(VLDIR)" | sed 's/ //g'))
die:=$(error $(err_spaces))
endif

# --------------------------------------------------------------------
#                                                        Configuration
# --------------------------------------------------------------------

VLDIR ?= .
LIBTOOL ?= libtool

STD_CLFAGS = $(CFLAGS)
STD_CFLAGS += -std=c99
STD_CFLAGS += -Wall -Wextra

STD_CFLAGS += -Wno-unused-function -Wno-long-long -Wno-variadic-macros
STD_CFLAGS += $(if $(DISABLE_THREADS), -DVL_DISABLE_THREADS)
STD_CFLAGS += $(if $(DISABLE_SSE2), -DVL_DISABLE_SSE2)
STD_CFLAGS += $(if $(DISABLE_OPENMP), -DVL_DISABLE_OPENMP)
STD_CFLAGS += $(if $(DEBUG), -DDEBUG -O0 -g, -DNDEBUG -O3)
STD_CFLAGS += $(if $(DISABLE_AVX), -DVL_DISABLE_AVX)
STD_CFLAGS += $(if $(PROFILE), -g)

STD_LDFLAGS = $(LDFLAGS)

# Architecture specific ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# Mac OS X Intel 32
ifeq ($(ARCH),maci)
#SDKROOT ?= $(shell xcode-select -print-path)/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.7.sdk
SDKROOT ?= $(shell xcodebuild -version -sdk macosx | sed -n '/^Path\:/p' | sed 's/^Path: //')
MACOSX_DEPLOYMENT_TARGET ?= 10.4
STD_CFLAGS += -m32 -isysroot $(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET)
STD_LDFLAGS += -Wl,-syslibroot,$(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET)
DISABLE_AVX ?= yes
CC = gcc
endif

# Mac OS X Intel 64
ifeq ($(ARCH),maci64)
#SDKROOT ?= $(shell xcode-select -print-path)/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.7.sdk
SDKROOT ?= $(shell xcodebuild -version -sdk macosx | sed -n '/^Path\:/p' | sed 's/^Path: //')
MACOSX_DEPLOYMENT_TARGET ?= 10.4
STD_CFLAGS += -m64 -isysroot $(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET)
STD_LDFLAGS += -Wl,-syslibroot,$(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET)
DISABLE_AVX ?= yes # GCC 4.2 need for OpenMP does not support -mavx
CC = gcc
endif

# Linux-32
ifeq ($(ARCH),glnx86)
# Target compatibility with GLIBC 2.3.4
# 1) _GNU_SOURCE avoids using isoc99_fscanf, limiting binray portability to recent GLIBC.
# 2) -fno-stack-protector avoids using a feature requiring GLBIC 2.4
STD_CFLAGS += -m32 -D_GNU_SOURCE -fno-stack-protector
STD_LDFLAGS += -m32 -Wl,--rpath,\$$ORIGIN/ -Wl,--as-needed -lpthread -lm
CC ?= gcc
endif

# Linux-64
ifeq ($(ARCH),glnxa64)
STD_CFLAGS += -D_GNU_SOURCE -fno-stack-protector
STD_LDFLAGS += -Wl,--rpath,\$$ORIGIN/ -Wl,--as-needed -lpthread -lm
CC ?= gcc
endif

# --------------------------------------------------------------------
#                                                            Functions
# --------------------------------------------------------------------

# $(call if-like,FILTER,WHY,WHAT)
define if-like
$(if $(filter $(1),$(2)),$(3))
endef

# $(call dump-var,VAR) pretty-prints the content of a variable VAR on
# multiple columns
ifdef VERB
define dump-var
@echo "$(1) ="
@echo "$($(1))" | sed 's/\([^ ][^ ]* [^ ][^ ]*\) */\1#/g' | \
tr '#' '\n' | column -t | sed 's/\(.*\)/  \1/g'
endef
else
define dump-var
@printf "%15s = %s\n" "$(1)" \
"$$(echo '$($(1))' | sed -e 's/[^ ][^ ]* /\.\.\./3' -e 's/\.\.\..*$$/\.\.\./g')"
endef
endif

# $(call echo-var,VAR) pretty-prints the content of a variable VAR on
# one line
define echo-var
@printf "%15s = %s\n" "$(1)" "$($(1))"
endef

# $(call echo-title,TITLE) pretty-prints TITLE as a title
define echo-title
@printf "** %s\n" "$(1)"
endef

# $(call print-command, CMD, TGT) prints a message
define print-command
@printf "%15s %s\n" "$(strip $(1))" "$(strip $(2))"
endef

# $(call C, CMD) runs $(CMD) silently
define C
@$(call print-command, $(1), "$(@)")
@quiet ()                                                            \
{                                                                    \
    local cmd out err ;					             \
    cmd="$($(1))";                                                   \
    out=$$($${cmd} "$${@}" 2>&1) ;                                   \
    err=$${?} ;                                                      \
    if test $${err} -gt 0 ; then                                     \
        echo "******* Offending Command:";                           \
        printf "%s" "$${cmd}" ;                                      \
	for i in "$${@}" ; do printf " '%s'" "$$i" ; done ;          \
	echo ;                                                       \
        echo "******* Error Code: $${err}";                          \
	echo "******* Command Output:";                              \
        echo "$${out}";                                              \
    fi;                                                              \
    echo "$${out}" | grep [Ww]arning ;                               \
    return $${err};                                                  \
} ; quiet
endef

# If verbose print everything
ifdef VERB
C = $($(1))
endif

# rule to create a directory
.PRECIOUS: %/.dirstamp
%/.dirstamp :
	@printf "%15s %s\n" MK "$(dir $@)"
	@mkdir -p $(dir $@)
	@echo "Directory generated by make." > $@

# $(call gendir, TARGET, DIR1 DIR2 ...) creates a target TARGET-dir that
# triggers the creation of the directories DIR1, DIR2
define gendir
$(1)-dir=$(foreach x,$(2),$(x)/.dirstamp)
endef

# --------------------------------------------------------------------
#                                                                Build
# --------------------------------------------------------------------

# Each Makefile submodule appends appropriate dependencies to the all,
# clean, archclean, distclean, and info targets. In addition, it
# appends to the deps and bins variables the list of .d files (to be
# inclued by make as auto-dependencies) and the list of files to be
# added to the binary distribution.

include make/dll.mak
include make/bin.mak
include make/matlab.mak
include make/octave.mak
include make/doc.mak
include make/dist.mak

.PHONY: clean, archclean, distclean, info, help
no_dep_targets += clean archclean distclean info help

clean:
	rm -f  `find . -name '*~'`
	rm -f  `find . -name '.DS_Store'`
	rm -f  `find . -name '.gdb_history'`
	rm -f  `find . -name '._*'`
	rm -rf ./results

archclean: clean

distclean:

info:
	$(call echo-title,General settings)
	$(call dump-var,deps)
	$(call echo-var,PROFILE)
	$(call echo-var,DEBUG)
	$(call echo-var,VER)
	$(call echo-var,ARCH)
	$(call echo-var,CC)
	$(call echo-var,STD_CFLAGS)
	$(call echo-var,STD_LDFLAGS)
	$(call echo-var,DISABLE_SSE2)
	$(call echo-var,DISABLE_AVX)
	$(call echo-var,DISABLE_THREADS)
	$(call echo-var,DISABLE_OPENMP)
	@printf "\nThere are %s lines of code.\n" \
	`cat $(m_src) $(mex_src) $(dll_src) $(dll_hdr) $(bin_src) | wc -l`

# Holw help works: cat this file,
# skip the first block until an empty line is found (twice)
# print the first block until an empty line,
# remove the `# ' prefix from each remaining line

help:
	@cat Makefile | \
	sed -n '1,/^$$/!p' | \
	sed -n '1,/^$$/!p' | \
	sed -n '1,/^$$/p' | \
	sed 's/^# \{0,1\}\(.*\)$$/\1/'

# --------------------------------------------------------------------
#                                                 Include dependencies
# --------------------------------------------------------------------

.PRECIOUS: $(deps)

ifeq ($(filter $(no_dep_targets), $(MAKECMDGOALS)),)
-include $(deps)
endif
vlfeat/vl/0000755000175000017500000000000012237035722011371 5ustar  dimadimavlfeat/vl/svm.c0000644000175000017500000020622112237004745012346 0ustar  dimadima/** @file svm.c
 ** @brief Support Vector Machines (SVM) - Implementation
 ** @author Milan Sulc
 ** @author Daniele Perrone
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 Milan Sulc.
Copyright (C) 2012 Daniele Perrone.
Copyright (C) 2011-13 Andrea Vedaldi.

All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file svm.h
 ** @see @ref svm.
 **/

/**

@page svm Support Vector Machines (SVM)
@author Milan Sulc
@author Daniele Perrone
@author Andrea Vedaldi
@tableofcontents


*Support Vector Machines* (SVMs) are one of the most popular types of
discriminate classifiers. VLFeat implements two solvers, SGD and SDCA,
capable of learning linear SVMs on a large scale. These linear solvers
can be combined with explicit feature maps to learn non-linear models
as well. The solver supports a few variants of the standard
SVM formulation, including using loss functions other than the hinge
loss.

@ref svm-starting demonstrates how to use VLFeat to learn an SVM.
Information on SVMs and the corresponding optimization algorithms as
implemented by VLFeat are given in:

- @subpage svm-fundamentals - Linear SVMs and their learning.
- @subpage svm-advanced - Loss functions, dual objective, and other details.
- @subpage svm-sgd - The SGD algorithm.
- @subpage svm-sdca - The SDCA algorithm.


@section svm-starting Getting started


This section demonstrates how to learn an SVM by using VLFeat. SVM
learning is implemented by the ::VlSvm object type. Let's
start by a complete example:

@code
#include 
#include 

int main()
{
  vl_size const numData = 4 ;
  vl_size const dimension = 2 ;
  double x [dimension * numData] = {
    0.0, -0.5,
    0.6, -0.3,
    0.0,  0.5
    0.6,  0.0} ;
  double y [numData] = {1, 1, -1, 1} ;
  double lambda = 0.01;
  double * const model ;
  double bias ;

  VlSvm * svm = vl_svm_new(VlSvmSolverSgd,
                           x, dimension, numData,
                           y,
                           lambda) ;
  vl_svm_train(svm) ;

  model = vl_svm_get_model(svm) ;
  bias = vl_svm_get_bias(svm) ;

  printf("model w = [ %f , %f ] , bias b = %f \n",
         model[0],
         model[1],
         bias);

  vl_svm_delete(svm) ;
  return 0;
}
@endcode

This code learns a binary linear SVM using the SGD algorithm on
four two-dimensional points using 0.01 as regularization parameter.

::VlSvmSolverSdca can be specified in place of ::VlSvmSolverSdca
in orer to use the SDCA algorithm instead.

Convergence and other diagnostic information can be obtained after
training by using the ::vl_svm_get_statistics function. Algorithms
regularly check for convergence (usally after each pass over the data).
The ::vl_svm_set_diagnostic_function can be used to specify a callback
to be invoked when diagnostic is run. This can be used, for example,
to dump information on the screen as the algorithm progresses.

Convergence is reached after a maximum number of iterations
(::vl_svm_set_max_num_iterations) or after a given criterion falls
below a threshold (::vl_svm_set_epsilon). The meaning of these
may depend on the specific algorithm (see @ref svm for further details).

::VlSvm is a quite powerful object. Algorithms only need to perform
inner product and accumulation operation on the data (see @ref svm-advanced).
This is used to abstract from the data type and support almost anything
by speciying just two functions (::vl_svm_set_data_functions).

A simple interface to this advanced functionality is provided by the
::VlSvmDataset object. This supports natively @c float and @c double
data types, as well as applying on the fly the homogeneous kernel map
(@ref homkermap). This is exemplified in @ref svmdataset-starting.

*/

/**

@page svm-fundamentals SVM fundamentals
@tableofcontents


This page introduces the SVM formulation used in VLFeat. See @ref svm
for more information on VLFeat SVM support.

Let $ \bx \in \real^d $ be a vector representing, for example, an
image, an audio track, or a fragment of text. Our goal is to design a
*classifier*, i.e. a function that associates to each vector $\bx$ a
positive or negative label based on a desired criterion, for example
the fact that the image contains or not a cat, that the audio track
contains or not English speech, or that the text is or not a
scientific paper.

The vector $\bx$ is classified by looking at the sign of a *linear
scoring function* $\langle \bx, \bw \rangle$. The goal of learning is
to estimate the parameter $\bw \in \real^d$ in such a way that the
score is positive if the vector $\bx$ belongs to the positive class
and negative otherwise. In fact, in the standard SVM formulation the
the goal is to have a score of *at least 1* in the first case, and of
*at most -1* in the second one, imposing a *margin*.

The parameter $\bw$ is estimated or *learned* by fitting the scoring
function to a training set of $n$ example pairs $(\bx_i,y_i),
i=1,\dots,n$. Here $y_i \in \{-1,1\}$ are the *ground truth labels* of
the corresponding example vectors. The fit quality is measured by a
*loss function* which, in standard SVMs, is the *hinge loss*:

\[
\ell_i(\langle \bw,\bx\rangle) = \max\{0, 1 - y_i \langle \bw,\bx\rangle\}.
\]

Note that the hinge loss is zero only if the score $\langle
\bw,\bx\rangle$ is at least 1 or at most -1, depending on the label
$y_i$.

Fitting the training data is usually insufficient. In order for the
scoring function *generalize to future data* as well, it is usually
preferable to trade off the fitting accuracy with the *regularity* of
the learned scoring function $\langle \bx, \bw \rangle$. Regularity in
the standard formulation is measured by the norm of the parameter
vector $\|\bw\|^2$ (see @ref svm-advanced). Averaging the loss on all
training samples and adding to it the regularizer weighed by a
parameter $\lambda$ yields the *regularized loss objective*

@f{equation}{
\boxed{\displaystyle
E(\bw) =  \frac{\lambda}{2} \left\| \bw \right\|^2
+ \frac{1}{n} \sum_{i=1}^n \max\{0, 1 - y_i \langle \bw,\bx\rangle\}.
\label{e:svm-primal-hinge}
}
@f}

Note that this objective function is *convex*, so that there exists a
single global optimum.

The scoring function $\langle \bx, \bw \rangle$ considered so far has
been linear and unbiased. @ref svm-bias discusses how a bias term can
be added to the SVM and @ref svm-feature-maps shows how non-linear
SVMs can be reduced to the linear case by computing suitable feature
maps.

@ref svm-learning shows how VLFeat can be used to learn an SVM by
minimizing $E(\bw)$.


@section svm-learning Learning


Learning an SVM amounts to finding the minimizer $\bw^*$ of the cost
function $E(\bw)$. While there are dozens of methods that can be used
to do so, VLFeat implements two large scale methods, designed to work
with linear SVMs (see @ref svm-feature-maps to go beyond linear):

- @ref svm-sgd
- @ref svm-sdca

Using these solvers is exemplified in @ref svm-starting.


@section svm-bias Adding a bias


It is common to add to the SVM scoring function a *bias term* $b$, and
to consider the score $\langle \bx,\bw \rangle + b$. In practice the
bias term can be crucial to fit the training data optimally, as there
is no reason why the inner products $\langle \bx,\bw \rangle$ should
be naturally centered at zero.

Some SVM learning algorithms can estimate both $\bw$ and $b$
directly. However, other algorithms such as SGD and SDCA cannot. In
this case, a simple workaround is to add a constant component $B > 0$
(we call this constant the *bias multiplier*) to the data,
i.e. consider the extended data vectors:
\[
\bar \bx = \begin{bmatrix} \bx \\ B \end{bmatrix},
\quad
\bar \bw = \begin{bmatrix} \bw \\ w_b \end{bmatrix}.
\]
In this manner the scoring function incorporates implicitly a bias $b = B w_b$:
\[
\langle \bar\bx, \bar\bw \rangle =
\langle \bx, \bw \rangle + B w_b.
\]

The disadvantage of this reduction is that the term $w_b^2$ becomes
part of the SVM regularizer, which shrinks the bias $b$ towards
zero. This effect can be alleviated by making $B$ sufficiently large,
because in this case $\|\bw\|^2 \gg w_b^2$ and the shrinking effect is
negligible.

Unfortunately, making $B$ too large makes the problem numerically
unbalanced, so a reasonable trade-off between shrinkage and stability
is generally sought. Typically, a good trade-off is obtained by
normalizing the data to have unitary Euclidean norm and then choosing
$B \in [1, 10]$.

Specific implementations of SGD and SDCA may provide explicit support
to learn the bias in this manner, but it is important to understand
the implications on speed and accuracy of the learning if this is
done.


@section svm-feature-maps Non-linear SVMs and feature maps


So far only linear scoring function $\langle \bx,\bw \rangle$ have
been considered. Implicitly, however, this assumes that the objects to
be classified (e.g. images) have been encoded as vectors $\bx$ in a
way that makes linear classification possible. This encoding step can
be made explicit by introducing the *feature map* $\Phi(\bx) \in
\real^d$. Including the feature map yields a scoring function
*non-linear* in $\bx$:
\[
\bx\in\mathcal{X} \quad\longrightarrow\quad \langle \Phi(\bx), \bw \rangle.
\]
The nature of the input space $\mathcal{X}$ can be arbitrary and might
not have a vector space structure at all.

The representation or encoding captures a notion of *similarity*
between objects: if two vectors $\Phi(\bx_1)$ and $\Phi(\bx_2)$ are
similar, then their scores will also be similar. Note that choosing a
feature map amounts to incorporating this information in the model
*prior* to learning.

The relation of feature maps to similarity functions is formalized by
the notion of a *kernel*, a positive definite function $K(\bx,\bx')$
measuring the similarity of a pair of objects. A feature map defines a
kernel by

\[
K(\bx,\bx') = \langle \Phi(\bx),\Phi(\bx') \rangle.
\]

Viceversa, any kernel function can be represented by a feature map in
this manner, establishing an equivalence.

So far, all solvers in VLFeat assume that the feature map $\Psi(\bx)$
can be explicitly computed. Although classically kernels were
introduced to generalize solvers to non-linear SVMs for which a
feature map *cannot* be computed (e.g. for a Gaussian kernel the
feature map is infinite dimensional), in practice using explicit
feature representations allow to use much faster solvers, so it makes
sense to *reverse* this process.
*/

/**

@page svm-advanced Advanced SVM topics
@tableofcontents


This page discusses advanced SVM topics. For an introduction to SVMs,
please refer to @ref svm and @ref svm-fundamentals.


@section svm-loss-functions Loss functions


The SVM formulation given in @ref svm-fundamentals uses the
hinge loss, which is only one of a variety of loss functions that
are often used for SVMs. More in general, one
can consider the objective

@f{equation}{
E(\bw) =  \frac{\lambda}{2} \left\| \bw \right\|^2 + \frac{1}{n} \sum_{i=1}^n \ell_i(\langle \bw,\bx\rangle).
\label{e:svm-primal}
@f}

where the loss $\ell_i(z)$ is a convex function of the scalar variable
$z$. Losses differ by: (i) their purpose (some are suitable for
classification, other for regression), (ii) their smoothness (which
usually affects how quickly the SVM objective function can be
minimized), and (iii) their statistical interpretation (for example
the logistic loss can be used to learn logistic models).

Concrete examples are the:


    

    
Name
Loss $\ell_i(z)$
Description

    

    
Hinge
$\max\{0, 1-y_i z\}$
The standard SVM loss function.

    

    
Square hinge
$\max\{0, 1-y_i z\}^2$
The standard SVM loss function, but squared. This version is
smoother and may yield numerically easier problems.

    

    
Square or l2
$(y_i - z)^2$
This loss yields the ridge regression model (l2 regularised least
square).

    

    
Linear or l1
$|y_i - z|$
Another loss suitable for regression, usually more robust but
harder to optimize than the squared one.

    

    
Insensitive l1
$\max\{0, |y_i - z| - \epsilon\}$.
This is a variant of the previous loss, proposed in the original
Support Vector Regression formulation. Differently from the previous
two losses, the insensitivity may yield to a sparse selection of
support vectors.

    

    
Logistic
$\log(1 + e^{-y_i z})$
This corresponds to regularized logisitc regression. The loss can
be seen as a negative log-likelihood: $\ell_i(z) = -\log P[y_i | z] =
- \log \sigma(y_iz/2)$, where $\sigma(z) = e^z/(1 + e^z)$ is the
sigmoid function, mapping a score $z$ to a probability. The $1/2$
factor in the sigmoid is due to the fact that labels are in $\{-1,1\}$
rather than $\{0,1\}$ as more common for the standard sigmoid
model.

    

    


@section svm-data-abstraction Data abstraction: working with compressed data


VLFeat learning algorithms (SGD and SDCA) access the data by means of
only two operations:

- *inner product*: computing the inner product between the model and
a data vector, i.e. $\langle \bw, \bx \rangle$.
- *accumulation*: summing a data vector to the model, i.e. $\bw
\leftarrow \bw + \beta \bx$.

VLFeat learning algorithms are *parameterized* in these two
operations. As a consequence, the data can be stored in any format
suitable to the user (e.g. dense matrices, sparse matrices,
block-sparse matrices, disk caches, and so on) provided that these two
operations can be implemented efficiently. Differently from the data,
however, the model vector $\bw$ is represented simply as a dense array
of doubles. This choice is adequate in almost any case.

A particularly useful aspect of this design choice is that the
training data can be store in *compressed format* (for example by
using product quantization (PQ)). Furthermore, higher-dimensional
encodings such as the homogeneous kernel map (@ref homkermap) and the
intersection kernel map can be *computed on the fly*. Such techniques
are very important when dealing with GBs of data.


@section svm-dual-problem Dual problem


In optimization, the *dual objective* $D(\balpha)$ of the SVM
objective $E(\bw)$ is of great interest. To obtain the dual objective,
one starts by approximating each loss term from below by a family of planes:
\[
\ell_i(z) = \sup_{u} (u z - \ell_i^*(u) ),
\qquad
\ell_i^*(u) = \sup_{z} (z u - \ell_i(z) )
\]
where $\ell_i^*(u)$ is the *dual conjugate* of the loss and gives the
intercept of each approximating plane as a function of the slope. When
the loss function is convex, the approximation is in fact exact. Examples
include:


    

    
Name
Loss $\ell_i(z)$
Conjugate loss $\ell_i^*(u)$

    

    
Hinge
$\max\{0, 1-y_i z\}$
\[
\ell_i^*(u) =
\begin{cases}
y_i u, & -1 \leq y_i u \leq 0, \\
+\infty, & \text{otherwise}
\end{cases}
\]

    

    
Square hinge
$\max\{0, 1-y_i z\}^2$
\[\ell_i^*(u) =
\begin{cases}
y_i u + \frac{u^2}{4}, & y_i u \leq 0, \\
+\infty, & \text{otherwise} \\
\end{cases}\]

    

    
Linear or l1
$|y_i - z|$
\[\ell_i^*(u) =
\begin{cases}
y_i u, & -1 \leq y_i u \leq 1, \\
+\infty, & \text{otherwise} \\
\end{cases}\]

    

    
Square or l2
$(y_i - z)^2$
\[\ell_i^*(u)=y_iu + \frac{u^2}{4}\]

    

    
Insensitive l1
$\max\{0, |y_i - z| - \epsilon\}$.


    

    
Logistic
$\log(1 + e^{-y_i z})$
\[\ell_i^*(u) =
 \begin{cases}
 (1+u) \log(1+u) - u \log(-u), & -1 \leq y_i u \leq 0, \\
 +\infty, & \text{otherwise} \\
 \end{cases}\]


    

    

Since each plane $- z \alpha_i - \ell^*_i(-\alpha_i) \leq \ell_i(z)$
bounds the loss from below, by substituting in $E(\bw)$ one can write
a lower bound for the SVM objective
\[
F(\bw,\balpha) = \frac{\lambda}{2} \|\bw\|^2 -
\frac{1}{n}\sum_{i=1}^n (\bw^\top \bx_i\alpha_i + \ell_i^*(-\alpha_i))
\leq E(\bw).
\]
for each setting of the *dual variables* $\alpha_i$. The dual
objective function $D(\balpha)$ is obtained by minimizing the lower
bound $F(\bw,\balpha)$ w.r.t. to $\bw$:
\[
D(\balpha) = \inf_{\bw} F(\bw,\balpha) \leq E(\bw).
\]
The minimizer and the dual objective are now easy to find:
\[
\boxed{\displaystyle
\bw(\balpha) =
\frac{1}{\lambda n}
\sum_{i=1}^n \bx_i \alpha_i = \frac{1}{\lambda n} X\balpha,
\quad
D(\balpha) = - \frac{1}{2\lambda n^2} \balpha^\top X^\top X \balpha +
\frac{1}{n} \sum_{i=1}^n - \ell_i^*(-\alpha_i)
}
\]
where $X = [\bx_1, \dots, \bx_n]$ is the data matrix. Since the dual
is uniformly smaller than the primal, one has the *duality gap* bound:
\[
D(\balpha) \leq P(\bw^*) \leq P(\bw(\balpha))
\]
This bound can be used to evaluate how far off $\bw(\balpha)$ is from
the primal minimizer $\bw^*$. In fact, due to convexity, this bound
can be shown to be zero when $\balpha^*$ is the dual maximizer (strong
duality):
\[
D(\balpha^*) = P(\bw^*) = P(\bw(\balpha^*)),
\quad \bw^* = \bw(\balpha^*).
\]


@section svm-C Parametrization in C


Often a slightly different form of the SVM objective is considered,
where a parameter $C$ is used to scale the loss instead of the regularizer:

\[
E_C(\bw) = \frac{1}{2} \|\bw\|^2 + C \sum_{i=1}^n \ell_i(\langle \bx_i, \bw\rangle)
\]

This and the objective function $E(\bw)$ in $\lambda$ are equivalent
(proportional) if

\[
\lambda = \frac{1}{nC},
\qquad C = \frac{1}{n\lambda}.
\] up to an overall scaling factor to the problem.

**/

/**


@page svm-sdca Stochastic Dual Coordinate Ascent
@tableofcontents


This page describes the *Stochastic Dual Coordinate Ascent* (SDCA)
linear SVM solver. Please see @ref svm for an overview of VLFeat SVM
support.

SDCA maximizes the dual SVM objective (see @ref svm-dual-problem
for a derivation of this expression):

\[
D(\balpha) = - \frac{1}{2\lambda n^2} \balpha^\top X^\top X \balpha +
\frac{1}{n} \sum_{i=1}^n - \ell_i^*(-\alpha_i)
\]

where $X$ is the data matrix. Recall that the primal parameter
corresponding to a given setting of the dual variables is:

\[
\bw(\balpha) = \frac{1}{\lambda n} \sum_{i=1}^n \bx_i \alpha_i = \frac{1}{\lambda n} X\balpha
\]

In its most basic form, the *SDCA algorithm* can be summarized as follows:

- Let $\balpha_0 = 0$.
- Until the duality gap $P(\bw(\balpha_t)) -  D(\balpha_t) < \epsilon$
  - Pick a dual variable $q$ uniformly at random in $1, \dots, n$.
  - Maximize the dual with respect to this variable: $\Delta\alpha_q = \max_{\Delta\alpha_q} D(\balpha_t + \Delta\alpha_q \be_q )$
  - Update $\balpha_{t+1} = \balpha_{t} + \be_q \Delta\alpha_q$.

In VLFeat, we partially use the nomenclature from @cite{shwartz13a-dual} and @cite{hsieh08a-dual}.


@section svm-sdca-dual-max Dual coordinate maximization


The updated dual objective can be expanded as:
\[
D(\balpha_t + \be_q \Delta\alpha_q) =
\text{const.}
- \frac{1}{2\lambda n^2} \bx_q^\top \bx_q (\Delta\alpha_q)^2
- \frac{1}{n} \bx_q^\top \frac{X\alpha_t}{\lambda n} \Delta\alpha_q
- \frac{1}{n} \ell^*_q(- \alpha_q - \Delta\alpha_q)
\]
This can also be written as
@f{align*}
D(\balpha_t + \be_q \Delta\alpha_q) &\propto
- \frac{A}{2} (\Delta\alpha_q)^2
- B \Delta\alpha_q
- \ell^*_q(- \alpha_q - \Delta\alpha_q),
\\
A &= \frac{1}{\lambda n} \bx_q^\top \bx_q = \frac{1}{\lambda n} \| \bx_q \|^2,
\\
B &= \bx_q^\top \frac{X\balpha_t}{\lambda n} = \bx_q^\top \bw_t.
@f}
Maximizing this quantity in the scalar variable $\Delta\balpha$ is usually
not difficult. It is convenient to store and incrementally
update the model $\bw_t$ after the optimal step $\Delta\balpha$ has been
determined:
\[
\bw_t = \frac{X \balpha_t}{\lambda n},
\quad \bw_{t+1} = \bw_t + \frac{1}{\lambda n }\bx_q \be_q \Delta\alpha_q.
\]

For example, consider the hinge loss as given in @ref svm-advanced :
\[
\ell_q^*(u) =
\begin{cases}
y_q u, & -1 \leq y_q u \leq 0, \\
+\infty, & \text{otherwise}.
\end{cases}
\]
The maximizer $\Delta\alpha_q$ of the update objective must be in the
range where the conjugate loss is not infinite. Ignoring such bounds,
the update can be obtained by setting the derivative of the objective
to zero, obtaining
\[
\tilde {\Delta \alpha_q}= \frac{y_q - B}{A}.
\]
Note that $B$ is simply current score associated by the SVM to
the sample $\bx_q$. Incorporating the constraint $-1 \leq - y_q
(\alpha_q + \Delta \alpha_q) \leq 0$,
i.e. $0 \leq y_q (\alpha_q + \Delta \alpha_q) \leq 1$, one obtains the update
\[
\Delta\alpha_q =  y_q \max\{0, \min\{1, \tilde {\Delta\alpha_q } + y_q \alpha_q\}\} - \alpha_q.
\]


@section svm-sdca-details Implementation details


Rather than visiting points completely at random, VLFeat SDCA follows
the best practice of visiting all the points at every epoch (pass
through the data), changing the order of the visit randomly by picking
every time a new random permutation.
**/

/**

@page svm-sgd Stochastic Gradient Descent
@tableofcontents


This page describes the *Stochastic Gradient Descent* (SGD) linear SVM
solver. SGD minimizes directly the primal SVM objective (see @ref svm):

\[
E(\bw) = \frac{\lambda}{2} \left\| \bw \right\|^2 + \frac{1}{n} \sum_{i=1}^n
\ell_i(\langle \bw,\bx\rangle)
\]

Firts, rewrite the objective as the average

\[
E(\bw) = \frac{1}{n} \sum_{i=1}^n E_i(\bw),
\quad
E_i(\bw) = \frac{\lambda}{2}  \left\| \bw \right\|^2 + \ell_i(\langle \bw,\bx\rangle).
\]

Then SGD performs gradient steps by considering at each iteration
one term $E_i(\bw)$ selected at random from this average.
In its most basic form, the algorithm is:

- Start with $\bw_0 = 0$.
- For $t=1,2,\dots T$:
  - Sample one index $i$ in $1,\dots,n$ uniformly at random.
  - Compute a subgradient $\bg_t$ of $E_i(\bw)$ at $\bw_t$.
  - Compute the learning rate $\eta_t$.
  - Update $\bw_{t+1} = \bw_t - \eta_t \bg_t$.

Provided that the learning rate $\eta_t$ is chosen correctly, this
simple algorithm is guaranteed to converge to the minimizer $\bw^*$ of
$E$.


@section svm-sgd-convergence Convergence and speed


The goal of the SGD algorithm is to bring the *primal suboptimality*
below a threshold $\epsilon_P$:
\[
E(\bw_t) - E(\bw^*) \leq \epsilon_P.
\]

If the learning rate $\eta_t$ is selected appropriately, SGD can be
shown to converge properly. For example,
@cite{shalev-shwartz07pegasos} show that, since $E(\bw)$ is
$\lambda$-strongly convex, then using the learning rate
\[
\boxed{\eta_t = \frac{1}{\lambda t}}
\]
guarantees that the algorithm reaches primal-suboptimality $\epsilon_P$ in
\[
\tilde O\left( \frac{1}{\lambda \epsilon_P} \right).
\]
iterations. This particular SGD variant is sometimes known as PEGASOS
@cite{shalev-shwartz07pegasos} and is the version implemented in
VLFeat.

The *convergence speed* is not sufficient to tell the *learning speed*,
i.e. how quickly an algorithm can learn an SVM that performs optimally
on the test set. The following two observations
can be used to link convergence speed to learning speed:

- The regularizer strength is often heuristically selected to be
  inversely proportional to the number of training samples: $\lambda =
  \lambda_0 /n$. This reflects the fact that with more training data
  the prior should count less.
- The primal suboptimality $\epsilon_P$ should be about the same as
  the estimation error of the SVM primal. This estimation error is due
  to the finite training set size and can be shown to be of the order
  of $1/\lambda n = 1 / \lambda_0$.

Under these two assumptions, PEGASOS can learn a linear SVM in time
$\tilde O(n)$, which is *linear in the number of training
examples*. This fares much better with $O(n^2)$ or worse of non-linear
SVM solvers.


@section svm-sgd-bias The bias term


Adding a bias $b$ to the SVM scoring function $\langle \bw, \bx
\rangle +b$ is done, as explained in @ref svm-bias, by appending a
constant feature $B$ (the *bias multiplier*) to the data vectors $\bx$
and a corresponding weight element $w_b$ to the weight vector $\bw$,
so that $b = B w_b$ As noted, the bias multiplier should be
relatively large in order to avoid shrinking the bias towards zero,
but small to make the optimization stable. In particular, setting $B$
to zero learns an unbiased SVM (::vl_svm_set_bias_multiplier).

To counter instability caused by a large bias multiplier, the learning
rate of the bias is slowed down by multiplying the overall learning
rate $\eta_t$ by a bias-specific rate coefficient
(::vl_svm_set_bias_learning_rate).

As a rule of thumb, if the data vectors $\bx$ are $l^2$ normalized (as
they typically should for optimal performance), then a reasonable bias
multiplier is in the range 1 to 10 and a reasonable bias learning rate
is somewhere in the range of the inverse of that (in this manner the
two parts of the extended feature vector $(\bx, B)$ are balanced).


@section svm-sgd-starting-iteration Adjusting the learning rate


Initially, the learning rate $\eta_t = 1/\lambda t$ is usually too
fast: as usually $\lambda \ll 1$, $\eta_1 \gg 1$. But this is clearly
excessive (for example, without a loss term, the best learning rate at
the first iteration is simply $\eta_1=1$, as this nails the optimum in
one step). Thus, the learning rate formula is modified to be $\eta_t =
1 / \lambda (t + t_0)$, where $t_0 \approx 0.5/\lambda$, which is
equivalent to start $t_0$ iterations later. In this manner $\eta_1
\approx 1/2$.


@subsection svm-sgd-warm-start Warm start


Starting from a given model $\bw$ is easy in SGD as the optimization
runs in the primal. However, the starting iteration index $t$ should
also be advanced for a warm start, as otherwise the initial setting of
$\bw$ is rapidly forgot (::vl_svm_set_model, ::vl_svm_set_bias,
::vl_svm_set_iteration_number).


@section svm-sgd-details Implementation details


@par "Random sampling of points"

Rather than visiting points completely at random, VLFeat SDCA follows
the best practice of visiting all the points at every epoch (pass
through the data), changing the order of the visit randomly by picking
every time a new random permutation.

@par "Factored representation"

At each iteration, the SGD algorithm updates the vector $\bw$
(including the additional bias component $w_b$) as $\bw_{t+1}
\leftarrow \bw_t - \eta_t \bw_t - \eta \bg_t$, where $\eta_t$ is the
learning rate. If the subgradient of the loss function $\bg_t$ is zero
at a given iteration, this amounts to simply shrink $\bw$ towards the
origin by multiplying it by the factor $1 - \eta$. Thus such an
iteration can be accelerated significantly by representing internally
$\bw_t = f_t \bu_t$, where $f_t$ is a scaling factor. Then, the update
becomes
\[
   f_{t+1} \bu_{t+1}
   = f_{t} \bu_{t} - \eta_t f_{t} \bu_{t} - \eta_t \bg_t
   = (1-\eta_t) f_{t} \bu_{t} - \eta \bg_t.
\]
Setting $f_{t+1} = (1-\eta_t) f_{t}$, this gives the update equation
for $\bu_t$
\[
\bu_{t+1} = \bu_{t} - \frac{\eta_t}{f_{t+1}} \bg_t.
\]
but this step can be skipped whenever $\bg_t$ is equal to zero.

When the bias component has a different learning rate, this scheme
must be adjusted slightly by adding a separated factor for the bias,
but it is otherwise identical.


**/

/*


@section svm-pegasos PEGASOS



@subsection svm-pegasos-algorithm Algorithm


PEGASOS @cite{shalev-shwartz07pegasos} is a stochastic subgradient
optimizer. At the t-th iteration the algorithm:

- Samples uniformly at random as subset @f$ A_t @f$ of k of
training pairs @f$(x,y)@f$ from the m pairs provided for
training (this subset is called mini batch).
- Computes a subgradient @f$ \nabla_t @f$ of the function @f$ E_t(w) =
\frac{1}{2}\|w\|^2 + \frac{1}{k} \sum_{(x,y) \in A_t} \ell(w;(x,y))
@f$ (this is the SVM objective function restricted to the
minibatch).
- Compute an intermediate weight vector @f$ w_{t+1/2} @f$ by doing a
step @f$ w_{t+1/2} = w_t - \alpha_t \nabla_t @f$ with learning rate
@f$ \alpha_t = 1/(\eta t) @f$ along the subgradient. Note that the
learning rate is inversely proportional to the iteration number.
- Back projects the weight vector @f$ w_{t+1/2} @f$ on the
hypersphere of radius @f$ \sqrt{\lambda} @f$ to obtain the next
model estimate @f$ w_{t+1} @f$:
@f[
w_t = \min\{1, \sqrt{\lambda}/\|w\|\} w_{t+1/2}.
@f]
The hypersphere is guaranteed to contain the optimal weight vector
@f$ w^* @f$.

VLFeat implementation fixes to one the size of the mini batches @f$ k
@f$.



@subsection svm-pegasos-permutation Permutation


VLFeat PEGASOS can use a user-defined permutation to decide the order
in which data points are visited (instead of using random
sampling). By specifying a permutation the algorithm is guaranteed to
visit each data point exactly once in each loop. The permutation needs
not to be bijective. This can be used to visit certain data samples
more or less often than others, implicitly reweighting their relative
importance in the SVM objective function. This can be used to balance
the data.


@subsection svm-pegasos-kernels Non-linear kernels


PEGASOS can be extended to non-linear kernels, but the algorithm is
not particularly efficient in this setting [1]. When possible, it may
be preferable to work with explicit feature maps.

Let @f$ k(x,y) @f$ be a positive definite kernel. A feature
map is a function @f$ \Psi(x) @f$ such that @f$ k(x,y) = \langle
\Psi(x), \Psi(y) \rangle @f$. Using this representation the non-linear
SVM learning objective function writes:

@f[
\min_{w} \frac{\lambda}{2} \|w\|^2 + \frac{1}{m} \sum_{i=1}^n
\ell(w; (\Psi(x)_i,y_i)).
@f]

Thus the only difference with the linear case is that the feature @f$
\Psi(x) @f$ is used in place of the data @f$ x @f$.

@f$ \Psi(x) @f$ can be learned off-line, for instance by using the
incomplete Cholesky decomposition @f$ V^\top V @f$ of the Gram matrix
@f$ K = [k(x_i,x_j)] @f$ (in this case @f$ \Psi(x_i) @f$ is the
i-th columns of V). Alternatively, for additive
kernels (e.g. intersection, Chi2) the explicit feature map computed by
@ref homkermap.h can be used.

For additive kernels it is also possible to perform the feature
expansion online inside the solver, setting the specific feature map
via ::vl_svmdataset_set_map. This is particular useful to keep the
size of the training data small, when the number of the samples is big
or the memory is limited.
*/

#include "svm.h"
#include "mathop.h"
#include 

struct VlSvm_ {
  VlSvmSolverType solver ;      /**< SVM solver type. */

  vl_size dimension ;           /**< Model dimension. */
  double * model ;              /**< Model ($\bw$ vector). */
  double bias ;                 /**< Bias. */
  double biasMultiplier ;       /**< Bias feature multiplier. */

  /* valid during a run */
  double lambda ;               /**< Regularizer multiplier. */
  void const * data ;
  vl_size numData ;
  double const * labels ;       /**< Data labels. */
  double const * weights ;      /**< Data weights. */

  VlSvmDataset * ownDataset ;   /**< Optional owned dataset. */

  VlSvmDiagnosticFunction diagnosticFn ;
  void * diagnosticFnData ;
  vl_size diagnosticFrequency ; /**< Frequency of diagnostic. */

  VlSvmLossFunction lossFn ;
  VlSvmLossFunction conjugateLossFn ;
  VlSvmLossFunction lossDerivativeFn ;
  VlSvmDcaUpdateFunction dcaUpdateFn ;
  VlSvmInnerProductFunction innerProductFn ;
  VlSvmAccumulateFunction accumulateFn ;

  vl_size iteration ;           /**< Current iterations number. */
  vl_size maxNumIterations ;    /**< Maximum number of iterations. */
  double epsilon ;              /**< Stopping threshold. */

  /* Book keeping */
  VlSvmStatistics statistics ;  /**< Statistcs. */
  double * scores ;

  /* SGD specific */
  double  biasLearningRate ;    /**< Bias learning rate. */

  /* SDCA specific */
  double * alpha ;              /**< Dual variables. */
} ;

/* ---------------------------------------------------------------- */

/** @brief Create a new object with plain data.
 ** @param type type of SMV solver.
 ** @param data a pointer to a matrix of data.
 ** @param dimension dimension of the SVM model.
 ** @param numData number of training samples.
 ** @param labels training labels.
 ** @param lambda regularizer parameter.
 ** @return the new object.
 **
 ** @a data has one column per sample, in @c double format.
 ** More advanced inputs can be used with ::vl_svm_new_with_dataset
 ** and ::vl_svm_new_with_abstract_data.
 **
 ** @sa ::vl_svm_delete
 **/

VlSvm *
vl_svm_new (VlSvmSolverType type,
            double const * data,
            vl_size dimension,
            vl_size numData,
            double const * labels,
            double lambda)
{
  VlSvmDataset * dataset = vl_svmdataset_new(VL_TYPE_DOUBLE, (void*)data, dimension, numData) ;
  VlSvm * self = vl_svm_new_with_dataset (type, dataset, labels, lambda) ;
  self->ownDataset = dataset ;
  return self ;
}

/** @brief Create a new object with a dataset.
 ** @param solver type of SMV solver.
 ** @param dataset SVM dataset object
 ** @param labels training samples labels.
 ** @param lambda regularizer parameter.
 ** @return the new object.
 ** @sa ::vl_svm_delete
 **/

VlSvm *
vl_svm_new_with_dataset (VlSvmSolverType solver,
                         VlSvmDataset * dataset,
                         double const * labels,
                         double lambda)
{
  VlSvm * self = vl_svm_new_with_abstract_data (solver,
                                             dataset,
                                             vl_svmdataset_get_dimension(dataset),
                                             vl_svmdataset_get_num_data(dataset),
                                             labels,
                                             lambda) ;
  vl_svm_set_data_functions (self,
                             vl_svmdataset_get_inner_product_function(dataset),
                             vl_svmdataset_get_accumulate_function(dataset)) ;
  return self ;
}

/** @brief Create a new object with abstract data.
 ** @param solver type of SMV solver.
 ** @param data pointer to the data.
 ** @param dimension dimension of the SVM model.
 ** @param numData num training samples.
 ** @param labels training samples labels.
 ** @param lambda regularizer parameter.
 ** @return the new object.
 **
 ** After calling this function, ::vl_svm_set_data_functions *must*
 ** be used to setup suitable callbacks for the inner product
 ** and accumulation operations (@see svm-data-abstraction).
 **
 ** @sa ::vl_svm_delete
 **/

VlSvm *
vl_svm_new_with_abstract_data (VlSvmSolverType solver,
                               void * data,
                               vl_size dimension,
                               vl_size numData,
                               double const * labels,
                               double lambda)
{
  VlSvm * self = vl_calloc(1,sizeof(VlSvm)) ;

  assert(dimension >= 1) ;
  assert(numData >= 1) ;
  assert(labels) ;

  self->solver = solver ;

  self->dimension = dimension ;
  self->model = 0 ;
  self->bias = 0 ;
  self->biasMultiplier = 1.0 ;

  self->lambda = lambda ;
  self->data = data ;
  self->numData = numData ;
  self->labels = labels ;

  self->diagnosticFrequency = numData ;
  self->diagnosticFn = 0 ;
  self->diagnosticFnData = 0 ;

  self->lossFn = vl_svm_hinge_loss ;
  self->conjugateLossFn = vl_svm_hinge_conjugate_loss ;
  self->lossDerivativeFn = vl_svm_hinge_loss_derivative ;
  self->dcaUpdateFn = vl_svm_hinge_dca_update ;

  self->innerProductFn = 0 ;
  self->accumulateFn = 0 ;

  self->iteration = 0 ;
  self->maxNumIterations = VL_MAX((double)numData, vl_ceil_f(10.0 / lambda)) ;
  self->epsilon = 1e-2 ;

  /* SGD */
  self->biasLearningRate = 0.01 ;

  /* SDCA */
  self->alpha = 0 ;

  /* allocations */
  self->model = vl_calloc(dimension, sizeof(double)) ;
  if (self->model == NULL) goto err_alloc ;

  if (self->solver == VlSvmSolverSdca) {
    self->alpha = vl_calloc(self->numData, sizeof(double)) ;
    if (self->alpha == NULL) goto err_alloc ;
  }

  self->scores = vl_calloc(numData, sizeof(double)) ;
  if (self->scores == NULL) goto err_alloc ;

  return self ;

err_alloc:
  if (self->scores) {
    vl_free (self->scores) ;
    self->scores = 0 ;
  }
  if (self->model) {
    vl_free (self->model) ;
    self->model = 0 ;
  }
  if (self->alpha) {
    vl_free (self->alpha) ;
    self->alpha = 0 ;
  }
  return 0 ;
}

/** @brief Delete object.
 ** @param self object.
 ** @sa ::vl_svm_new
 **/

void
vl_svm_delete (VlSvm * self)
{
  if (self->model) {
    vl_free (self->model) ;
    self->model = 0 ;
  }
  if (self->alpha) {
    vl_free (self->alpha) ;
    self->alpha = 0 ;
  }
  if (self->ownDataset) {
    vl_svmdataset_delete(self->ownDataset) ;
    self->ownDataset = 0 ;
  }
  vl_free (self) ;
}

/* ---------------------------------------------------------------- */
/*                                              Setters and getters */
/* ---------------------------------------------------------------- */

/** @brief Set the convergence threshold
 ** @param self object
 ** @param epsilon threshold (non-negative).
 **/

void vl_svm_set_epsilon (VlSvm *self, double epsilon)
{
  assert(self) ;
  assert(epsilon >= 0) ;
  self->epsilon = epsilon ;
}

/** @brief Get the convergence threshold
 ** @param self object
 ** @return epsilon threshold.
 **/

double vl_svm_get_epsilon (VlSvm const *self)
{
  assert(self) ;
  return self->epsilon ;
}

/** @brief Set the bias learning rate
 ** @param self object
 ** @param rate bias learning rate (positive).
 **
 ** This parameter applies only to the SGD solver.
 **/

void vl_svm_set_bias_learning_rate (VlSvm *self, double rate)
{
  assert(self) ;
  assert(rate > 0) ;
  self->biasLearningRate = rate ;
}

/** @brief Get the bias leraning rate.
 ** @param self object
 ** @return bias learning rate.
 **/

double vl_svm_get_bias_learning_rate (VlSvm const *self)
{
  assert(self) ;
  return self->biasLearningRate ;
}

/** @brief Set the bias multiplier.
 ** @param self object
 ** @param b bias multiplier.
 **
 ** The *bias multiplier* is the value of the constant feature
 ** appended to the data vectors to implement the bias (@ref svm-bias).
 **/

void vl_svm_set_bias_multiplier (VlSvm * self, double b)
{
  assert(self) ;
  assert(b >= 0) ;
  self->biasMultiplier = b ;
}

/** @brief Get the bias multiplier.
 ** @param self object.
 ** @return bias multiplier.
 **/

double vl_svm_get_bias_multiplier (VlSvm const * self)
{
  assert(self) ;
  return self->biasMultiplier ;
}

/** @brief Set the current iteratio number.
 ** @param self object.
 ** @param n iteration number.
 **
 ** If called before training,
 ** this can be used with SGD for a warm start, as the net
 ** effect is to slow down the learning rate.
 **/

void vl_svm_set_iteration_number (VlSvm *self, vl_uindex n)
{
  assert(self) ;
  self->iteration = n ;
}

/** @brief Get the current iteration number.
 ** @param self object.
 ** @return current iteration number.
 **/

vl_size vl_svm_get_iteration_number (VlSvm const *self)
{
  assert(self) ;
  return self->iteration ;
}

/** @brief Set the maximum number of iterations.
 ** @param self object.
 ** @param n maximum number of iterations.
 **/

void vl_svm_set_max_num_iterations (VlSvm *self, vl_size n)
{
  assert(self) ;
  self->maxNumIterations = n ;
}

/** @brief Get the maximum number of iterations.
 ** @param self object.
 ** @return maximum number of iterations.
 **/

vl_size vl_svm_get_max_num_iterations (VlSvm const *self)
{
  assert(self) ;
  return self->maxNumIterations ;
}

/** @brief Set the diagnostic frequency.
 ** @param self object.
 ** @param f diagnostic frequency (@c >= 1).
 **
 ** A diagnostic round (to test for convergence and to printout
 ** information) is performed every @a f iterations.
 **/

void vl_svm_set_diagnostic_frequency (VlSvm *self, vl_size f)
{
  assert(self) ;
  assert(f > 0) ;
  self->diagnosticFrequency = f ;
}

/** @brief Get the diagnostic frequency.
 ** @param self object.
 ** @return diagnostic frequency.
 **/

vl_size vl_svm_get_diagnostic_frequency (VlSvm const *self)
{
  assert(self) ;
  return self->diagnosticFrequency ;
}

/** @brief Get the SVM solver type.
 ** @param self object.
 ** @return SVM solver type.
 **/

VlSvmSolverType vl_svm_get_solver (VlSvm const * self)
{
  assert(self) ;
  return self->solver ;
}

/** @brief Set the regularizer parameter lambda.
 ** @param self object.
 ** @param lambda regularizer parameter.
 **
 ** Note that @a lambda is usually set when calling a
 ** constructor for ::VlSvm as certain parameters, such
 ** as the maximum number of iterations, are tuned accordingly.
 ** This tuning is not performed when @a lambda is changed
 ** using this function.
 **/

void vl_svm_set_lambda (VlSvm * self, double lambda)
{
  assert(self) ;
  assert(lambda >= 0) ;
  self->lambda = lambda ;
}

/** @brief Get the regularizer parameter lambda.
 ** @param self object.
 ** @return diagnostic frequency.
 **/

double vl_svm_get_lambda (VlSvm const * self)
{
  assert(self) ;
  return self->lambda ;
}

/** @brief Set the data weights.
 ** @param self object.
 ** @param weights data weights.
 **
 ** @a weights must be an array of non-negative weights.
 ** The loss of each data point is multiplied by the corresponding
 ** weight.
 **
 ** Set @a weights to @c NULL to weight the data uniformly by 1 (default).
 **
 ** Note that the @a weights array is *not* copied and must be valid
 ** througout the object lifetime (unless it is replaced).
 **/

void vl_svm_set_weights (VlSvm * self, double const *weights)
{
  assert(self) ;
  self->weights = weights ;
}

/** @brief Get the data weights.
 ** @param self object.
 ** @return data weights.
 **/

double const *vl_svm_get_weights (VlSvm const * self)
{
  assert(self) ;
  return self->weights ;
}

/* ---------------------------------------------------------------- */
/*                                                         Get data */
/* ---------------------------------------------------------------- */

/** @brief Get the model dimenison.
 ** @param self object.
 ** @return model dimension.
 **
 ** This is the dimensionality of the weight vector $\bw$.
 **/

vl_size vl_svm_get_dimension (VlSvm *self)
{
  assert(self) ;
  return self->dimension ;
}

/** @brief Get the number of data samples.
 ** @param self object.
 ** @return model number of data samples
 **
 ** This is the dimensionality of the weight vector $\bw$.
 **/

vl_size vl_svm_get_num_data (VlSvm *self)
{
  assert(self) ;
  return self->numData ;
}

/** @brief Get the SVM model.
 ** @param self object.
 ** @return model.
 **
 ** This is the weight vector $\bw$.
 **/

double const * vl_svm_get_model (VlSvm const *self)
{
  assert(self) ;
  return self->model ;
}

/** @brief Set the SVM model.
 ** @param self object.
 ** @param model model.
 **
 ** The function *copies* the content of the vector @a model to the
 ** internal model buffer. This operation can be used for warm start
 ** with the SGD algorithm, but has undefined effect with the SDCA algorithm.
 **/

void vl_svm_set_model (VlSvm *self, double const *model)
{
  assert(self) ;
  assert(model) ;
  memcpy(self->model, model, sizeof(double) * vl_svm_get_dimension(self)) ;
}

/** @brief Set the SVM bias.
 ** @param self object.
 ** @param b bias.
 **
 ** The function set the internal representation of the SVM bias to
 ** be equal to @a b (the bias multiplier
 ** is applied). The same remark
 ** that applies to ::vl_svm_set_model applies here too.
 **/

void vl_svm_set_bias (VlSvm *self, double b)
{
  assert(self);
  if (self->biasMultiplier) {
    self->bias = b / self->biasMultiplier ;
  }
}

/** @brief Get the value of the bias.
 ** @param self object.
 ** @return bias $b$.
 **
 ** The value of the bias returned already include the effect of
 ** bias mutliplier.
 **/

double vl_svm_get_bias (VlSvm const *self)
{
  assert(self) ;
  return self->bias * self->biasMultiplier ;
}

/** @brief Get the solver statistics.
 ** @param self object.
 ** @return statistics.
 **/

VlSvmStatistics const * vl_svm_get_statistics (VlSvm const *self)
{
  assert(self) ;
  return &self->statistics ;
}

/** @brief Get the scores of the data points.
 ** @param self object.
 ** @return vector of scores.
 **
 ** After training or during the diagnostic callback,
 ** this function can be used to retrieve the scores
 ** of the points, i.e. $\langle \bx_i, \bw \rangle + b$.
 **/

double const * vl_svm_get_scores (VlSvm const *self)
{
  return self->scores ;
}

/* ---------------------------------------------------------------- */
/*                                                        Callbacks */
/* ---------------------------------------------------------------- */

/** @typedef VlSvmDiagnosticFunction
 ** @brief SVM diagnostic function pointer.
 ** @param svm is an instance of ::VlSvm .
 **/

/** @typedef VlSvmAccumulateFunction
 ** @brief Pointer to a function that adds to @a model the data point at
 ** position @a element multiplied by the constant @a multiplier.
 **/

/** @typedef VlSvmInnerProductFunction
 ** @brief Pointer to a function that defines the inner product
 ** between the data point at position @a element and the SVM model
 **/

/** @brief Set the diagnostic function callback
 ** @param self object.
 ** @param f diagnostic function pointer.
 ** @param data pointer to data used by the diagnostic function.
 **/

void
vl_svm_set_diagnostic_function (VlSvm *self, VlSvmDiagnosticFunction f, void *data) {
  self->diagnosticFn = f ;
  self->diagnosticFnData = data ;
}

/** @brief Set the data functions.
 ** @param self object.
 ** @param inner inner product function.
 ** @param acc accumulate function.
 **
 ** See @ref svm-data-abstraction.
 **/

void vl_svm_set_data_functions (VlSvm *self, VlSvmInnerProductFunction inner, VlSvmAccumulateFunction acc)
{
  assert(self) ;
  assert(inner) ;
  assert(acc) ;
  self->innerProductFn = inner ;
  self->accumulateFn = acc ;
}

/** @brief Set the loss function callback.
 ** @param self object.
 ** @param f loss function callback.
 **
 ** Note that setting up a loss requires specifying more than just one
 ** callback. See @ref svm-loss-functions for details.
 **/

void vl_svm_set_loss_function (VlSvm *self, VlSvmLossFunction f)
{
  assert(self) ;
  self->lossFn = f ;
}

/** @brief Set the loss derivative function callback.
 ** @copydetails vl_svm_set_loss_function.
 **/

void vl_svm_set_loss_derivative_function (VlSvm *self, VlSvmLossFunction f)
{
  assert(self) ;
  self->lossDerivativeFn = f ;
}

/** @brief Set the conjugate loss function callback.
 ** @copydetails vl_svm_set_loss_function.
 **/

void vl_svm_set_conjugate_loss_function (VlSvm *self, VlSvmLossFunction f)
{
  assert(self) ;
  self->conjugateLossFn = f ;
}

/** @brief Set the DCA update function callback.
 ** @copydetails vl_svm_set_loss_function.
 **/

void vl_svm_set_dca_update_function (VlSvm *self, VlSvmDcaUpdateFunction f)
{
  assert(self) ;
  self->dcaUpdateFn = f ;
}

/** @brief Set the loss function to one of the default types.
 ** @param self object.
 ** @param loss type of loss function.
 ** @sa @ref svm-loss-functions.
 **/

void
vl_svm_set_loss (VlSvm *self, VlSvmLossType loss)
{
#define SETLOSS(x,y) \
case VlSvmLoss ## x: \
  vl_svm_set_loss_function(self, vl_svm_ ## y ## _loss) ; \
  vl_svm_set_loss_derivative_function(self, vl_svm_ ## y ## _loss_derivative) ; \
  vl_svm_set_conjugate_loss_function(self, vl_svm_ ## y ## _conjugate_loss) ; \
  vl_svm_set_dca_update_function(self, vl_svm_ ## y ## _dca_update) ; \
  break;

  switch (loss) {
      SETLOSS(Hinge, hinge) ;
      SETLOSS(Hinge2, hinge2) ;
      SETLOSS(L1, l1) ;
      SETLOSS(L2, l2) ;
      SETLOSS(Logistic, logistic) ;
    default:
      assert(0) ;
  }
}

/* ---------------------------------------------------------------- */
/*                                               Pre-defined losses */
/* ---------------------------------------------------------------- */

/** @typedef VlSvmLossFunction
 ** @brief SVM loss function pointer.
 ** @param inner inner product between sample and model $\bw^\top \bx$.
 ** @param label sample label $y$.
 ** @return value of the loss.
 **
 ** The interface is the same for a loss function, its derivative,
 ** or the conjugate loss.
 **
 ** @sa @ref svm-fundamentals
 **/

/** @typedef VlSvmDcaUpdateFunction
 ** @brief SVM SDCA update function pointer.
 ** @param alpha current value of the dual variable.
 ** @param inner inner product $\bw^\top \bx$ of the sample with the SVM model.
 ** @param norm2 normalization factor $\|\bx\|^2/\lambda n$.
 ** @param label label $y$ of the sample.
 ** @return incremental update $\Delta\alpha$ of the dual variable.
 **
 ** @sa @ref svm-sdca
 **/

/** @brief SVM hinge loss
 ** @copydetails VlSvmLossFunction */
double
vl_svm_hinge_loss (double inner, double label)
{
  return VL_MAX(1 - label * inner, 0.0);
}

/** @brief SVM hinge loss derivative
 ** @copydetails VlSvmLossFunction */
double
vl_svm_hinge_loss_derivative (double inner, double label)
{
  if (label * inner < 1.0) {
    return - label ;
  } else {
    return 0.0 ;
  }
}

/** @brief SVM hinge loss conjugate
 ** @param u dual variable.
 ** @param label label value.
 ** @return conjugate loss.
 **/
double
vl_svm_hinge_conjugate_loss (double u, double label) {
  double z = label * u ;
  if (-1 <= z && z <= 0) {
    return label * u ;
  } else {
    return VL_INFINITY_D ;
  }
}

/** @brief SVM hinge loss DCA update
 ** @copydetails VlSvmDcaUpdateFunction */
double
vl_svm_hinge_dca_update (double alpha, double inner, double norm2, double label) {
  double palpha = (label - inner) / norm2 + alpha ;
  return label * VL_MAX(0, VL_MIN(1, label * palpha)) - alpha ;
}

/** @brief SVM square hinge loss
 ** @copydetails VlSvmLossFunction */
double
vl_svm_hinge2_loss (double inner,double label)
{
  double z = VL_MAX(1 - label * inner, 0.0) ;
  return z*z ;
}

/** @brief SVM square hinge loss derivative
 ** @copydetails VlSvmLossFunction */
double
vl_svm_hinge2_loss_derivative (double inner, double label)
{
  if (label * inner < 1.0) {
    return 2 * (inner - label) ;
  } else {
    return 0 ;
  }
}

/** @brief SVM square hinge loss conjugate
 ** @copydetails vl_svm_hinge_conjugate_loss */
double
vl_svm_hinge2_conjugate_loss (double u, double label) {
  if (label * u <= 0) {
    return (label + u/4) * u ;
  } else {
    return VL_INFINITY_D ;
  }
}

/** @brief SVM square hinge loss DCA update
 ** @copydetails VlSvmDcaUpdateFunction */
double
vl_svm_hinge2_dca_update (double alpha, double inner, double norm2, double label) {
  double palpha = (label - inner - 0.5*alpha) / (norm2 + 0.5) + alpha ;
  return label * VL_MAX(0, label * palpha) - alpha ;
}

/** @brief SVM l1 loss
 ** @copydetails VlSvmLossFunction */
double
vl_svm_l1_loss (double inner,double label)
{
  return vl_abs_d(label - inner) ;
}

/** @brief SVM l1 loss derivative
 ** @copydetails VlSvmLossFunction */
double
vl_svm_l1_loss_derivative (double inner, double label)
{
  if (label > inner) {
    return - 1.0 ;
  } else {
    return + 1.0 ;
  }
}

/** @brief SVM l1 loss conjugate
 ** @copydetails vl_svm_hinge_conjugate_loss */
double
vl_svm_l1_conjugate_loss (double u, double label) {
  if (vl_abs_d(u) <= 1) {
    return label*u ;
  } else {
    return VL_INFINITY_D ;
  }
}

/** @brief SVM l1 loss DCA update
 ** @copydetails VlSvmDcaUpdateFunction */
double
vl_svm_l1_dca_update (double alpha, double inner, double norm2, double label) {
  if (vl_abs_d(alpha) <= 1) {
    double palpha = (label - inner) / norm2 + alpha ;
    return VL_MAX(-1.0, VL_MIN(1.0, palpha)) - alpha ;
  } else {
    return VL_INFINITY_D ;
  }
}

/** @brief SVM l2 loss
 ** @copydetails VlSvmLossFunction */
double
vl_svm_l2_loss (double inner,double label)
{
  double z = label - inner ;
  return z*z ;
}

/** @brief SVM l2 loss derivative
 ** @copydetails VlSvmLossFunction */
double
vl_svm_l2_loss_derivative (double inner, double label)
{
  return - 2 * (label - inner) ;
}

/** @brief SVM l2 loss conjugate
 ** @copydetails vl_svm_hinge_conjugate_loss */
double
vl_svm_l2_conjugate_loss (double u, double label) {
  return (label + u/4) * u ;
}

/** @brief SVM l2 loss DCA update
 ** @copydetails VlSvmDcaUpdateFunction */
double
vl_svm_l2_dca_update (double alpha, double inner, double norm2, double label) {
  return (label - inner - 0.5*alpha) / (norm2 + 0.5) ;
}

/** @brief SVM l2 loss
 ** @copydetails VlSvmLossFunction */
double
vl_svm_logistic_loss (double inner,double label)
{
  double z = label * inner ;
  if (z >= 0) {
    return log(1.0 + exp(-z)) ;
  } else {
    return -z + log(exp(z) + 1.0) ;
  }
}

/** @brief SVM l2 loss derivative
 ** @copydetails VlSvmLossFunction */
double
vl_svm_logistic_loss_derivative (double inner, double label)
{
  double z = label * inner ;
  double t = 1 / (1 + exp(-z)) ; /* this is stable for z << 0 too */
  return label * (t - 1) ; /*  = -label exp(-z) / (1 + exp(-z)) */
}

VL_INLINE double xlogx(double x)
{
  if (x <= 1e-10) return 0 ;
  return x*log(x) ;
}

/** @brief SVM l2 loss conjugate
 ** @copydetails vl_svm_hinge_conjugate_loss */
double
vl_svm_logistic_conjugate_loss (double u, double label) {
  double z = label * u ;
  if (-1 <= z && z <= 0) {
    return xlogx(-z) + xlogx(1+z) ;
  } else {
    return VL_INFINITY_D ;
  }
}

/** @brief SVM l2 loss DCA update
 ** @copydetails VlSvmDcaUpdateFunction */
double
vl_svm_logistic_dca_update (double alpha, double inner, double norm2, double label) {
  /*
   The goal is to solve the problem

   min_delta A/2 delta^2 + B delta + l*(-alpha - delta|y),  -1 <= - y (alpha+delta) <= 0

   where A = norm2, B = inner, and y = label. To simplify the notation, we set

     f(beta) = beta * log(beta) + (1 - beta) * log(1 - beta)

   where beta = y(alpha + delta) such that

     l*(-alpha - delta |y) = f(beta).

   Hence 0 <= beta <= 1, delta = + y beta - alpha. Substituting

     min_beta A/2 beta^2 + y (B - A alpha) beta + f(beta) + const

   The Newton step is then given by

     beta = beta - (A beta + y(B - A alpha) + df) / (A + ddf).

   However, the function is singluar for beta=0 and beta=1 (infinite
   first and second order derivatives). Since the function is monotonic
   (second derivarive always strictly greater than zero) and smooth,
   we canuse bisection to find the zero crossing of the first derivative.
   Once one is sufficiently close to the optimum, a one or two Newton
   steps are sufficien to land on it with excellent accuracy.
   */

  double  df, ddf, der, dder ;
  vl_index t ;

  /* bisection */
  double beta1 = 0 ;
  double beta2 = 1 ;
  double beta = 0.5 ;

  for (t = 0 ; t < 5 ; ++t) {
    df = log(beta) - log(1-beta) ;
    der = norm2 * beta + label * (inner - norm2*alpha) + df ;
    if (der >= 0) {
      beta2 = beta ;
    } else {
      beta1 = beta ;
    }
    beta = 0.5 * (beta1 + beta2) ;
  }

#if 1
  /* a final Newton step, but not too close to the singularities */
  for (t = 0 ; (t < 2) & (beta > VL_EPSILON_D) & (beta < 1-VL_EPSILON_D) ; ++t) {
    df = log(beta) - log(1-beta) ;
    ddf = 1 / (beta * (1-beta)) ;
    der = norm2 * beta + label * (inner - norm2*alpha) + df ;
    dder = norm2 + ddf ;
    beta -= der / dder ;
    beta = VL_MAX(0, VL_MIN(1, beta)) ;
  }
#endif

  return label * beta - alpha ;
}

/* ---------------------------------------------------------------- */

/** @internal @brief Update SVM statistics
 ** @param self object.
 **/

void _vl_svm_update_statistics (VlSvm *self)
{
  vl_size i, k ;
  double inner, p ;

  memset(&self->statistics, 0, sizeof(VlSvmStatistics)) ;

  self->statistics.regularizer = self->bias * self->bias ;
  for (i = 0; i < self->dimension; i++) {
    self->statistics.regularizer += self->model[i] * self->model[i] ;
  }
  self->statistics.regularizer *= self->lambda * 0.5 ;

  for (k = 0; k < self->numData ; k++) {
    p = (self->weights) ? self->weights[k] : 1.0 ;
    if (p <= 0) continue ;
    inner = self->innerProductFn(self->data, k, self->model) ;
    inner += self->bias * self->biasMultiplier ;
    self->scores[k] = inner ;
    self->statistics.loss += p * self->lossFn(inner, self->labels[k]) ;
    if (self->solver == VlSvmSolverSdca) {

      self->statistics.dualLoss -= p * self->conjugateLossFn(- self->alpha[k] / p, self->labels[k]) ;
    }
  }

  self->statistics.loss /= self->numData ;
  self->statistics.objective = self->statistics.regularizer + self->statistics.loss ;

  if (self->solver == VlSvmSolverSdca) {
    self->statistics.dualLoss /= self->numData ;
    self->statistics.dualObjective = - self->statistics.regularizer + self->statistics.dualLoss ;
    self->statistics.dualityGap = self->statistics.objective - self->statistics.dualObjective ;
  }
}

/* ---------------------------------------------------------------- */
/*                                       Evaluate rather than solve */
/* ---------------------------------------------------------------- */

void _vl_svm_evaluate (VlSvm *self)
{
  double startTime = vl_get_cpu_time () ;

  _vl_svm_update_statistics (self) ;

  self->statistics.elapsedTime = vl_get_cpu_time() - startTime ;
  self->statistics.iteration = 0 ;
  self->statistics.epoch = 0 ;
  self->statistics.status = VlSvmStatusConverged ;

  if (self->diagnosticFn) {
    self->diagnosticFn(self, self->diagnosticFnData) ;
  }
}

/* ---------------------------------------------------------------- */
/*                         Stochastic Dual Coordinate Ascent Solver */
/* ---------------------------------------------------------------- */

void _vl_svm_sdca_train (VlSvm *self)
{
  double * norm2 ;
  vl_index * permutation ;
  vl_uindex i, t  ;
  double inner, delta, multiplier, p ;

  double startTime = vl_get_cpu_time () ;
  VlRand * rand = vl_get_rand() ;

  norm2 = (double*) vl_calloc(self->numData, sizeof(double));
  permutation = vl_calloc(self->numData, sizeof(vl_index)) ;

  {
    double * buffer = vl_calloc(self->dimension, sizeof(double)) ;
    for (i = 0 ; i < (unsigned)self->numData; i++) {
      double n2 ;
      permutation [i] = i ;
      memset(buffer, 0, self->dimension * sizeof(double)) ;
      self->accumulateFn (self->data, i, buffer, 1) ;
      n2 = self->innerProductFn (self->data, i, buffer) ;
      n2 += self->biasMultiplier * self->biasMultiplier ;
      norm2[i] = n2 / (self->lambda * self->numData) ;
    }
    vl_free(buffer) ;
  }

  for (t = 0 ; 1 ; ++t) {

    if (t % self->numData == 0) {
      /* once a new epoch is reached (all data have been visited),
       change permutation */
      vl_rand_permute_indexes(rand, permutation, self->numData) ;
    }

    /* pick a sample and compute update */
    i = permutation[t % self->numData] ;
    p = (self->weights) ? self->weights[i] : 1.0 ;
    if (p > 0) {
      inner = self->innerProductFn(self->data, i, self->model) ;
      inner += self->bias * self->biasMultiplier ;
      delta = p * self->dcaUpdateFn(self->alpha[i] / p, inner, p * norm2[i], self->labels[i]) ;
    } else {
      delta = 0 ;
    }

    /* apply update */
    if (delta != 0) {
      self->alpha[i] += delta ;
      multiplier = delta / (self->numData * self->lambda) ;
      self->accumulateFn(self->data,i,self->model,multiplier) ;
      self->bias += self->biasMultiplier * multiplier;
    }

    /* call diagnostic occasionally */
    if ((t + 1) % self->diagnosticFrequency == 0 || t + 1 == self->maxNumIterations) {
      _vl_svm_update_statistics (self) ;
      self->statistics.elapsedTime = vl_get_cpu_time() - startTime ;
      self->statistics.iteration = t ;
      self->statistics.epoch = t / self->numData ;

      self->statistics.status = VlSvmStatusTraining ;
      if (self->statistics.dualityGap < self->epsilon) {
        self->statistics.status = VlSvmStatusConverged ;
      }
      else if (t + 1 == self->maxNumIterations) {
        self->statistics.status = VlSvmStatusMaxNumIterationsReached ;
      }

      if (self->diagnosticFn) {
        self->diagnosticFn(self, self->diagnosticFnData) ;
      }

      if (self->statistics.status != VlSvmStatusTraining) {
        break ;
      }
    }
  } /* next iteration */

  vl_free (norm2) ;
  vl_free (permutation) ;
}

/* ---------------------------------------------------------------- */
/*                               Stochastic Gradient Descent Solver */
/* ---------------------------------------------------------------- */

void _vl_svm_sgd_train (VlSvm *self)
{
  vl_index * permutation ;
  double * scores ;
  double * previousScores ;
  vl_uindex i, t, k ;
  double inner, gradient, rate, biasRate, p ;
  double factor = 1.0 ;
  double biasFactor = 1.0 ; /* to allow slower bias learning rate */
  vl_index t0 = VL_MAX(2, vl_ceil_d(1.0 / self->lambda)) ;
  //t0=2 ;

  double startTime = vl_get_cpu_time () ;
  VlRand * rand = vl_get_rand() ;

  permutation = vl_calloc(self->numData, sizeof(vl_index)) ;
  scores = vl_calloc(self->numData * 2, sizeof(double)) ;
  previousScores = scores + self->numData ;

  for (i = 0 ; i < (unsigned)self->numData; i++) {
    permutation [i] = i ;
    previousScores [i] = - VL_INFINITY_D ;
  }

  /*
   We store the w vector as the product fw (factor * model).
   We also use a different factor for the bias: biasFactor * biasMultiplier
   to enable a slower learning rate for the bias.

   Given this representation, it is easy to carry the two key operations:

   * Inner product:  = f 

   * Model update: fp wp = fw - rate * lambda * w - rate * g
                         = f(1 - rate * lambda) w - rate * g

     Thus the update equations are:

                   fp = f(1 - rate * lambda), and
                   wp = w + rate / fp * g ;

   * Realization of the scaling factor. Before the statistics function
     is called, or training finishes, the factor (and biasFactor)
     are explicitly applied to the model and the bias.
  */

  for (t = 0 ; 1 ; ++t) {

    if (t % self->numData == 0) {
      /* once a new epoch is reached (all data have been visited),
       change permutation */
      vl_rand_permute_indexes(rand, permutation, self->numData) ;
    }

    /* pick a sample and compute update */
    i = permutation[t % self->numData] ;
    p = (self->weights) ? self->weights[i] : 1.0 ;
    p = VL_MAX(0.0, p) ; /* we assume non-negative weights, so this is just for robustness */
    inner = factor * self->innerProductFn(self->data, i, self->model) ;
    inner += biasFactor * (self->biasMultiplier * self->bias) ;
    gradient = p * self->lossDerivativeFn(inner, self->labels[i]) ;
    previousScores[i] = scores[i] ;
    scores[i] = inner ;

    /* apply update */
    rate = 1.0 /  (self->lambda * (t + t0)) ;
    biasRate = rate * self->biasLearningRate ;
    factor *= (1.0 - self->lambda * rate) ;
    biasFactor *= (1.0 - self->lambda * biasRate) ;

    /* debug: realize the scaling factor all the times */
    /*
    for (k = 0 ; k < self->dimension ; ++k) self->model[k] *= factor ;
    self->bias *= biasFactor;
    factor = 1.0 ;
    biasFactor = 1.0 ;
    */

    if (gradient != 0) {
      self->accumulateFn(self->data, i, self->model, - gradient * rate / factor) ;
      self->bias += self->biasMultiplier * (- gradient * biasRate / biasFactor) ;
    }

    /* call diagnostic occasionally */
    if ((t + 1) % self->diagnosticFrequency == 0 || t + 1 == self->maxNumIterations) {

      /* realize factor before computing statistics or completing training */
      for (k = 0 ; k < self->dimension ; ++k) self->model[k] *= factor ;
      self->bias *= biasFactor;
      factor = 1.0 ;
      biasFactor = 1.0 ;

      _vl_svm_update_statistics (self) ;

      for (k = 0 ; k < self->numData ; ++k) {
        double delta = scores[k] - previousScores[k] ;
        self->statistics.scoresVariation += delta * delta ;
      }
      self->statistics.scoresVariation = sqrt(self->statistics.scoresVariation) / self->numData ;

      self->statistics.elapsedTime = vl_get_cpu_time() - startTime ;
      self->statistics.iteration = t ;
      self->statistics.epoch = t / self->numData ;

      self->statistics.status = VlSvmStatusTraining ;
      if (self->statistics.scoresVariation < self->epsilon) {
        self->statistics.status = VlSvmStatusConverged ;
      }
      else if (t + 1 == self->maxNumIterations) {
        self->statistics.status = VlSvmStatusMaxNumIterationsReached ;
      }

      if (self->diagnosticFn) {
        self->diagnosticFn(self, self->diagnosticFnData) ;
      }

      if (self->statistics.status != VlSvmStatusTraining) {
        break ;
      }
    }
  } /* next iteration */

  vl_free (scores) ;
  vl_free (permutation) ;
}

/* ---------------------------------------------------------------- */
/*                                                       Dispatcher */
/* ---------------------------------------------------------------- */

/** @brief Run the SVM solver
 ** @param self object.
 **
 ** The data on which the SVM operates is passed upon the cration of
 ** the ::VlSvm object. This function runs a solver to learn a
 ** corresponding model. See @ref svm-starting.
 **/

void vl_svm_train (VlSvm * self)
{
  assert (self) ;
  switch (self->solver) {
    case VlSvmSolverSdca:
      _vl_svm_sdca_train(self) ;
      break ;
    case VlSvmSolverSgd:
      _vl_svm_sgd_train(self) ;
      break ;
    case VlSvmSolverNone:
      _vl_svm_evaluate(self) ;
      break ;
    default:
      assert(0) ;
  }
}
vlfeat/vl/qsort-def.h0000644000175000017500000001200412237004745013444 0ustar  dimadima/** @file   qsort-def.h
 ** @brief  QSort preprocessor metaprogram
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file qsort-def.h

@section qsort-def-overview Overview

@ref qsort-def.h is a metaprogram to define specialized instances
of the quick-sort algorithm.

@section qsort-def-usage Usage

@ref qsort-def.h is used to define a specialization of the
::VL_QSORT_sort function that operates
on a given type of array. For instance the code

@code
#define VL_QSORT_type float
#define VL_QSORT_prefix my_qsort
#include 
@endcode

defines a function @c my_qsort_sort that operates on an array of floats.

@todo large array compatibility.
**/

#include "host.h"
#include 

#ifndef VL_QSORT_prefix
#error "VL_QSORT_prefix must be defined"
#endif

#ifndef VL_QSORT_array
#ifndef VL_QSORT_type
#error "VL_QSORT_type must be defined if VL_QSORT_array is not"
#endif
#define VL_QSORT_array VL_QSORT_type*
#define VL_QSORT_array_const VL_QSORT_type const*
#endif

#ifdef __DOXYGEN__
#define VL_QSORT_prefix  QSortPrefix       /**< Prefix of the qsort functions */
#define VL_QSORT_type    QSortType         /**< Data type of the qsort elements */
#define VL_QSORT_array   QSortType*        /**< Data type of the qsort container */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_QSORT_cmp) || defined(__DOXYGEN__)
#define VL_QSORT_cmp VL_XCAT(VL_QSORT_prefix, _cmp)

/** @brief Compare two array elements
 ** @param array qsort array.
 ** @param indexA index of the first element @c A to compare.
 ** @param indexB index of the second element @c B to comapre.
 ** @return a negative number if @c AB.
 **/

VL_INLINE VL_QSORT_type
VL_QSORT_cmp
(VL_QSORT_array_const array,
 vl_uindex indexA,
 vl_uindex indexB)
{
  return array[indexA] - array[indexB] ;
}

/* VL_QSORT_cmp */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_QSORT_swap) || defined(__DOXYGEN__)
#define VL_QSORT_swap VL_XCAT(VL_QSORT_prefix, _swap)

/** @brief Swap two array elements
 ** @param array qsort array.
 ** @param indexA index of the first element to swap.
 ** @param indexB index of the second element to swap.
 **
 ** The function swaps the two elements @a a and @ b. The function
 ** uses a temporary element of type ::VL_QSORT_type
 ** and the copy operator @c =.
 **/

VL_INLINE void
VL_QSORT_swap
(VL_QSORT_array array,
 vl_uindex indexA,
 vl_uindex indexB)
{
  VL_QSORT_type t = array [indexA] ;
  array [indexA] = array [indexB] ;
  array [indexB] = t ;
}

/* VL_QSORT_swap */
#endif

/* ---------------------------------------------------------------- */
#if ! defined(VL_QSORT_sort_recursive) || defined(__DOXYGEN__)
#define VL_QSORT_sort_recursive VL_XCAT(VL_QSORT_prefix, _sort_recursive)

/** @brief Sort portion of an array using quicksort
 ** @param array (in/out) pointer to the array.
 ** @param begin first element of the array portion.
 ** @param end last element of the array portion.
 **
 ** The function sorts the array using quick-sort. Note that
 ** @c begin must be not larger than @c end.
 **/

VL_INLINE void
VL_QSORT_sort_recursive
(VL_QSORT_array array, vl_uindex begin, vl_uindex end)
{
  vl_uindex pivot = (end + begin) / 2 ;
  vl_uindex lowPart, i ;

  assert (begin <= end) ;

  /* swap pivot with last */
  VL_QSORT_swap (array, pivot, end) ;
  pivot = end ;

  /*
   Now scan from left to right, moving all element smaller
   or equal than the pivot to the low part
   array[0], array[1], ..., array[lowPart - 1].
   */
  lowPart = begin ;
  for (i = begin; i < end ; ++i) { /* one less */
    if (VL_QSORT_cmp (array, i, pivot) <= 0) {
      /* array[i] must be moved into the low part */
      VL_QSORT_swap (array, lowPart, i) ;
      lowPart ++ ;
    }
  }

  /* the pivot should also go into the low part */
  VL_QSORT_swap (array, lowPart, pivot) ;
  pivot = lowPart ;

  /* do recursion */
  if (pivot > begin) {
    /* note that pivot-1 stays non-negative */
    VL_QSORT_sort_recursive (array, begin, pivot - 1) ;
  }
  if (pivot < end) {
    VL_QSORT_sort_recursive (array, pivot + 1, end) ;
  }
}

/* VL_QSORT_sort_recursive */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_QSORT_sort) || defined(__DOXYGEN__)
#define VL_QSORT_sort VL_XCAT(VL_QSORT_prefix, _sort)

/** @brief Sort array using quicksort
 ** @param array (in/out) pointer to the array.
 ** @param size size of the array.
 **
 ** The function sorts the array using quick-sort.
 **/

VL_INLINE void
VL_QSORT_sort
(VL_QSORT_array array, vl_size size)
{
  assert (size >= 1) ;
  VL_QSORT_sort_recursive (array, 0, size - 1) ;
}

/* VL_QSORT_qsort */
#endif

#undef VL_QSORT_prefix
#undef VL_QSORT_swap
#undef VL_QSORT_sort
#undef VL_QSORT_sort_recursive
#undef VL_QSORT_type
#undef VL_QSORT_array
#undef VL_QSORT_cmp

vlfeat/vl/mathop_sse2.c0000644000175000017500000002761412237004745013774 0ustar  dimadima/** @file mathop_sse2.c
 ** @brief mathop for SSE2 - Definition
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/* ---------------------------------------------------------------- */
#ifndef VL_MATHOP_SSE2_INSTANTIATING

#include "mathop_sse2.h"

#undef FLT
#define FLT VL_TYPE_DOUBLE
#define VL_MATHOP_SSE2_INSTANTIATING
#include "mathop_sse2.c"

#undef FLT
#define FLT VL_TYPE_FLOAT
#define VL_MATHOP_SSE2_INSTANTIATING
#include "mathop_sse2.c"

/* ---------------------------------------------------------------- */
/* VL_MATHOP_SSE2_INSTANTIATING */
#else
#ifndef VL_DISABLE_SSE2

#include 
#include "mathop.h"
#include "generic.h"
#include "float.th"

VL_INLINE T
VL_XCAT(_vl_vhsum_sse2_, SFX)(VTYPE x)
{
  T acc ;
#if (VSIZE == 4)
  {
    VTYPE sum ;
    VTYPE shuffle ;
    /* shuffle = [1 0 3 2] */
    /* sum     = [3+1 2+0 1+3 0+2] */
    /* shuffle = [2+0 3+1 0+2 1+3] */
    /* vacc    = [3+1+2+0 3+1+2+0 1+3+0+2 0+2+1+3] */
    shuffle = VSHU (x, x, _MM_SHUFFLE(1, 0, 3, 2)) ;
    sum     = VADD (x, shuffle) ;
    shuffle = VSHU (sum, sum, _MM_SHUFFLE(2, 3, 0, 1)) ;
    x       = VADD (sum, shuffle) ;
  }
#else
  {
    VTYPE shuffle ;
    /* acc     = [1   0  ] */
    /* shuffle = [0   1  ] */
    /* sum     = [1+0 0+1] */
    shuffle = VSHU (x, x, _MM_SHUFFLE2(0, 1)) ;
    x       = VADD (x, shuffle) ;
  }
#endif
  VST1(&acc, x);
  return acc ;
}



VL_EXPORT T
VL_XCAT(_vl_dot_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE d = VMUL(a, b) ;
      vacc = VADD(vacc, d) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE d = VMUL(a, b) ;
      vacc = VADD(vacc, d) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    acc += a * b ;
  }

  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_l2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      vacc = VADD(vacc, delta2) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      vacc = VADD(vacc, delta2) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    acc += delta * delta ;
  }

  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_mahalanobis_sq_sse2_, SFX)
(vl_size dimension, T const * X, T const * MU, T const * S)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(MU) & VALIGNED(S);

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)MU ;
      VTYPE c = *(VTYPE*)S ;

      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      VTYPE delta2div = VMUL(delta2,c);

      vacc = VADD(vacc, delta2div) ;

      X  += VSIZE ;
      MU += VSIZE ;
      S  += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {

      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(MU) ;
      VTYPE c = VLDU(S) ;

      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      VTYPE delta2div = VMUL(delta2,c);

      vacc = VADD(vacc, delta2div) ;

      X  += VSIZE ;
      MU += VSIZE ;
      S  += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *MU++ ;
    T c = *S++ ;
    T delta = a - b ;
    acc += (delta * delta) * c;
  }

  return acc ;
}



VL_EXPORT T
VL_XCAT(_vl_distance_l1_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X + dimension - VSIZE ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  VTYPE vminus = VL_XCAT(_mm_set1_p, VSFX) ((T) -0.0) ; /* sign bit */
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE delta = VSUB(a, b) ;
      vacc = VADD(vacc, VANDN(vminus, delta)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE delta = VSUB(a, b) ;
      vacc = VADD(vacc, VANDN(vminus, delta)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    acc += VL_MAX(delta, - delta) ;
  }

  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_chi2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X + dimension - VSIZE ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE delta = VSUB(a, b) ;
      VTYPE denom = VADD(a, b) ;
      VTYPE numer = VMUL(delta, delta) ;
      VTYPE ratio = VDIV(numer, denom) ;
      ratio = VAND(ratio, VNEQ(denom, VSTZ())) ;
      vacc = VADD(vacc, ratio) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE delta = VSUB(a, b) ;
      VTYPE denom = VADD(a, b) ;
      VTYPE numer = VMUL(delta, delta) ;
      VTYPE ratio = VDIV(numer, denom) ;
      ratio = VAND(ratio, VNEQ(denom, VSTZ())) ;
      vacc = VADD(vacc, ratio) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    T denom = a + b ;
    T numer = delta * delta ;
    if (denom) {
      T ratio = numer / denom ;
      acc += ratio ;
    }
  }
  return acc ;
}


VL_EXPORT T
VL_XCAT(_vl_kernel_l2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      vacc = VADD(vacc, VMUL(a,b)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      vacc = VADD(vacc, VMUL(a,b)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    acc += a * b ;
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_l1_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  VTYPE vminus = VL_XCAT(_mm_set1_p, VSFX) ((T) -0.0) ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE a_ = VANDN(vminus, a) ;
      VTYPE b_ = VANDN(vminus, b) ;
      VTYPE sum = VADD(a_,b_) ;
      VTYPE diff = VSUB(a, b) ;
      VTYPE diff_ = VANDN(vminus, diff) ;
      vacc = VADD(vacc, VSUB(sum, diff_)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE a_ = VANDN(vminus, a) ;
      VTYPE b_ = VANDN(vminus, b) ;
      VTYPE sum = VADD(a_,b_) ;
      VTYPE diff = VSUB(a, b) ;
      VTYPE diff_ = VANDN(vminus, diff) ;
      vacc = VADD(vacc, VSUB(sum, diff_)) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T a_ = VL_XCAT(vl_abs_, SFX) (a) ;
    T b_ = VL_XCAT(vl_abs_, SFX) (b) ;
    acc += a_ + b_ - VL_XCAT(vl_abs_, SFX) (a - b) ;
  }

  return acc / ((T)2) ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_chi2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X + dimension - VSIZE ;
  T acc ;
  VTYPE vacc = VSTZ() ;
  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE denom = VADD(a, b) ;
      VTYPE numer = VMUL(a,b) ;
      VTYPE ratio = VDIV(numer, denom) ;
      ratio = VAND(ratio, VNEQ(denom, VSTZ())) ;
      vacc = VADD(vacc, ratio) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE denom = VADD(a, b) ;
      VTYPE numer = VMUL(a,b) ;
      VTYPE ratio = VDIV(numer, denom) ;
      ratio = VAND(ratio, VNEQ(denom, VSTZ())) ;
      vacc = VADD(vacc, ratio) ;
      X += VSIZE ;
      Y += VSIZE ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_sse2_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T denom = a + b ;
    if (denom) {
      T ratio = a * b / denom ;
      acc += ratio ;
    }
  }
  return ((T)2) * acc ;
}
//
VL_EXPORT void
VL_XCAT(_vl_weighted_sigma_sse2_, SFX)
(vl_size dimension, T * S, T const * X, T const * Y, T const W)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;

  vl_bool dataAligned = VALIGNED(X) & VALIGNED(Y) & VALIGNED(S);

  VTYPE w = VLD1 (&W) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE b = *(VTYPE*)Y ;
      VTYPE s = *(VTYPE*)S ;

      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      VTYPE delta2w = VMUL(delta2, w) ;
      VTYPE sigmaStore = VADD(s,delta2w);

      *(VTYPE *)S = sigmaStore;

      X += VSIZE ;
      Y += VSIZE ;
      S += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a = VLDU(X) ;
      VTYPE b = VLDU(Y) ;
      VTYPE s = VLDU(S) ;

      VTYPE delta = VSUB(a, b) ;
      VTYPE delta2 = VMUL(delta, delta) ;
      VTYPE delta2w = VMUL(delta2, w) ;
      VTYPE sigmaStore = VADD(s,delta2w);

      VST2U(S,sigmaStore);

      X += VSIZE ;
      Y += VSIZE ;
      S += VSIZE ;
    }
  }


  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    *S += ((delta * delta)*W) ;
    S++;
  }
}

VL_EXPORT void
VL_XCAT(_vl_weighted_mean_sse2_, SFX)
(vl_size dimension, T * MU, T const * X, T const W)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZE + 1 ;

  vl_bool dataAligned = VALIGNED(X) & VALIGNED(MU);
  VTYPE w = VLD1 (&W) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPE a = *(VTYPE*)X ;
      VTYPE mu = *(VTYPE*)MU ;

      VTYPE aw = VMUL(a, w) ;
      VTYPE meanStore = VADD(aw, mu);

      *(VTYPE *)MU = meanStore;

      X += VSIZE ;
      MU += VSIZE ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPE a  = VLDU(X) ;
      VTYPE mu = VLDU(MU) ;

      VTYPE aw = VMUL(a, w) ;
      VTYPE meanStore = VADD(aw, mu);

      VST2U(MU,meanStore);

      X += VSIZE ;
      MU += VSIZE ;
    }
  }

  while (X < X_end) {
    T a = *X++ ;
    *MU += a * W ;
    MU++;
  }
}

/* VL_DISABLE_SSE2 */
#endif
#undef VL_MATHOP_SSE2_INSTANTIATING
#endif
vlfeat/vl/hog.c0000644000175000017500000011166512237004745012325 0ustar  dimadima/** @file hog.c
 ** @brief Histogram of Oriented Gradients (HOG) - Definition
 ** @author Andrea Vedaldi
 **/

/*
 Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
 All rights reserved.

 This file is part of the VLFeat library and is made available under
 the terms of the BSD license (see the COPYING file).
*/

#include "hog.h"
#include "mathop.h"
#include 

/**


@page hog Histogram of Oriented Gradients (HOG) features
@author Andrea Vedaldi


@ref hog.h implements the Histogram of Oriented Gradients (HOG) features
in the variants of Dalal Triggs @cite{dalal05histograms} and of UOCTTI
@cite{felzenszwalb09object}. Applications include object detection
and deformable object detection.

- @ref hog-overview
- @ref hog-tech


@section hog-overview Overview


HOG is a standard image feature used, among others, in object detection
and deformable object detection. It decomposes the image into square cells
of a given size (typically eight pixels), compute a histogram of oriented
gradient in each cell (similar to @ref sift), and then renormalizes
the cells by looking into adjacent blocks.

VLFeat implements two HOG variants: the original one of Dalal-Triggs
@cite{dalal05histograms} and the one proposed in Felzenszwalb et al.
@cite{felzenszwalb09object}.

In order to use HOG, start by creating a new HOG object, set the desired
parameters, pass a (color or grayscale) image, and read off the results.

@code
VlHog * hog = vl_hog_new(VlHogVariantDalalTriggs, numOrientations, VL_FALSE) ;
vl_hog_put_image(hog, image, height, width, numChannels, cellSize) ;
hogWidth = vl_hog_get_width(hog) ;
hogHeight = vl_hog_get_height(hog) ;
hogDimenison = vl_hog_get_dimension(hog) ;
hogArray = vl_malloc(hogWidth*hogHeight*hogDimension*sizeof(float)) ;
vl_hog_extract(hog, hogArray) ;
vl_hog_delete(hog) ;
@endcode

HOG is a feature array of the dimension returned by ::vl_hog_get_width,
::vl_hog_get_height, with each feature (histogram) having
dimension ::vl_hog_get_dimension. The array is stored in row major order,
with the slowest varying dimension beying the dimension indexing the histogram
elements.

The number of entreis in the histogram as well as their meaning depends
on the HOG variant and is detailed later. However, it is usually
unnecessary to know such details. @ref hog.h provides support for
creating an inconic representation of a HOG feature array:

@code
glyphSize = vl_hog_get_glyph_size(hog) ;
imageHeight = glyphSize * hogArrayHeight ;
imageWidth = glyphSize * hogArrayWidth ;
image = vl_malloc(sizeof(float)*imageWidth*imageHeight) ;
vl_hog_render(hog, image, hogArray) ;
@endcode

It is often convenient to mirror HOG features from left to right. This
can be obtained by mirroring an array of HOG cells, but the content
of each cell must also be rearranged. This can be done by
the permutation obtaiend by ::vl_hog_get_permutation.

Furthermore, @ref hog.h suppots computing HOG features not from
images but from vector fields ::vl_


@section hog-tech Technical details


HOG divdes the input image into square cells of size @c cellSize,
fitting as many cells as possible, filling the image domain from
the upper-left corner down to the right one. For each row and column,
the last cell is at least half contained in the image.
More precisely, the number of cells obtained in this manner is:

@code
hogWidth = (width + cellSize/2) / cellSize ;
hogHeight = (height + cellSize/2) / cellSize ;
@endcode

Then the image gradient @f$ \nabla \ell(x,y) @f$
is computed by using central difference (for colour image
the channel with the largest gradient at that pixel is used).
The gradient @f$ \nabla \ell(x,y) @f$ is assigned to one of @c 2*numOrientations orientation in the
range @f$ [0,2\pi) @f$ (see @ref hog-conventions for details).
Contributions are then accumulated by using bilinear interpolation
to four neigbhour cells, as in @ref sift.
This results in an histogram  @f$h_d@f$ of dimension
2*numOrientations, called of @e directed orientations
since it accounts for the direction as well as the orientation
of the gradient. A second histogram @f$h_u@f$ of undirected orientations
of half the size is obtained by folding @f$ h_d @f$ into two.

Let a block of cell be a @f$ 2\times 2 @f$ sub-array of cells.
Let the norm of a block be the @f$ l^2 @f$ norm of the stacking of the
respective unoriented histogram. Given a HOG cell, four normalisation
factors are then obtained as the inverse of the norm of the four
blocks that contain the cell.

For the Dalal-Triggs variant, each histogram @f$ h_d @f$ is copied
four times, normalised using the four different normalisation factors,
the four vectors are stacked, saturated at 0.2, and finally stored as the descriptor
of the cell. This results in a @c numOrientations * 4 dimensional
cell descriptor. Blocks are visited from left to right and top to bottom
when forming the final descriptor.

For the UOCCTI descriptor, the same is done for both the undirected
as well as the directed orientation histograms. This would yield
a dimension of @c 4*(2+1)*numOrientations elements, but the resulting
vector is projected down to @c (2+1)*numOrientations elements
by averaging corresponding histogram dimensions. This was shown to
be an algebraic approximation of PCA for descriptors computed on natural
images.

In addition, for the UOCTTI variant the l1 norm of each of the
four l2 normalised undirected histograms is computed and stored
as additional four dimensions, for a total of
@c 4+3*numOrientations dimensions.


@subsection hog-conventions Conventions


The orientation of a gradient is expressed as the angle it forms with the
horizontal axis of the image. Angles are measured clock-wise (as the vertical
image axis points downards), and the null angle corresponds to
an horizontal vector pointing right. The quantized directed
orientations are @f$ \mathrm{k} \pi / \mathrm{numOrientations} @f$, where
@c k is an index that varies in the ingeger
range @f$ \{0, \dots, 2\mathrm{numOrientations} - 1\} @f$.

Note that the orientations capture the orientation of the gradeint;
image edges would be oriented at 90 degrees from these.

**/

/* ---------------------------------------------------------------- */
/** @brief Create a new HOG object
 ** @param variant HOG descriptor variant.
 ** @param numOrientations number of distinguished orientations.
 ** @param transposed wether images are transposed (column major).
 ** @return the new HOG object.
 **
 ** The function creates a new HOG object to extract descriptors of
 ** the prescribed @c variant. The angular resolution is set by
 ** @a numOrientations, which specifies the number of undirected
 ** orientations. The object can work with column major images
 ** by setting @a transposed to true.
 **/

VlHog *
vl_hog_new (VlHogVariant variant, vl_size numOrientations, vl_bool transposed)
{
  vl_index o, k ;
  VlHog * self = vl_calloc(1, sizeof(VlHog)) ;

  assert(numOrientations >= 1) ;

  self->variant = variant ;
  self->numOrientations = numOrientations ;
  self->glyphSize = 21 ;
  self->transposed = transposed ;
  self->useBilinearOrientationAssigment = VL_FALSE ;
  self->orientationX = vl_malloc(sizeof(float) * self->numOrientations) ;
  self->orientationY = vl_malloc(sizeof(float) * self->numOrientations) ;

  /*
   Create a vector along the center of each orientation bin. These
   are used to map gradients to bins. If the image is transposed,
   then this can be adjusted here by swapping X and Y in these
   vectors.
   */
  for(o = 0 ; o < (signed)self->numOrientations ; ++o) {
    double angle = o * VL_PI / self->numOrientations ;
    if (!self->transposed) {
      self->orientationX[o] = (float) cos(angle) ;
      self->orientationY[o] = (float) sin(angle) ;
    } else {
      self->orientationX[o] = (float) sin(angle) ;
      self->orientationY[o] = (float) cos(angle) ;
    }
  }

  /*
   If the number of orientation is equal to 9, one gets:

   Uoccti:: 18 directed orientations + 9 undirected orientations + 4 texture
   DalalTriggs:: 9 undirected orientations x 4 blocks.
   */
  switch (self->variant) {
    case VlHogVariantUoctti:
      self->dimension = 3*self->numOrientations + 4 ;
      break ;

    case VlHogVariantDalalTriggs:
      self->dimension = 4*self->numOrientations ;
      break ;

    default:
      assert(0) ;
  }

  /*
   A permutation specifies how to permute elements in a HOG
   descriptor to flip it horizontally. Since the first orientation
   of index 0 points to the right, this must be swapped with orientation
   self->numOrientation that points to the left (for the directed case,
   and to itself for the undirected one).
   */

  self->permutation = vl_malloc(self->dimension * sizeof(vl_index)) ;
  switch (self->variant) {
    case VlHogVariantUoctti:
      for(o = 0 ; o < (signed)self->numOrientations ; ++o) {
        vl_index op = self->numOrientations - o ;
        self->permutation[o] = op ;
        self->permutation[o + self->numOrientations] = (op + self->numOrientations) % (2*self->numOrientations) ;
        self->permutation[o + 2*self->numOrientations] = (op % self->numOrientations) + 2*self->numOrientations ;
      }
      for (k = 0 ; k < 4 ; ++k) {
        /* The texture features correspond to four displaced block around
         a cell. These permute with a lr flip as for DalalTriggs. */
        vl_index blockx = k % 2 ;
        vl_index blocky = k / 2 ;
        vl_index q = (1 - blockx) + blocky * 2 ;
        self->permutation[k + self->numOrientations * 3] = q + self->numOrientations * 3 ;
      }
      break ;

    case VlHogVariantDalalTriggs:
      for(k = 0 ; k < 4 ; ++k) {
        /* Find the corresponding block. Blocks are listed in order 1,2,3,4,...
           from left to right and top to bottom */
        vl_index blockx = k % 2 ;
        vl_index blocky = k / 2 ;
        vl_index q = (1 - blockx) + blocky * 2 ;
        for(o = 0 ; o < (signed)self->numOrientations ; ++o) {
          vl_index op = self->numOrientations - o ;
          self->permutation[o + k*self->numOrientations] = (op % self->numOrientations) + q*self->numOrientations ;
        }
      }
      break ;

    default:
      assert(0) ;
  }

  /*
   Create glyphs for representing the HOG features/ filters. The glyphs
   are simple bars, oriented orthogonally to the gradients to represent
   image edges. If the object is configured to work on transposed image,
   the glyphs images are also stored in column-major.
   */
  self->glyphs = vl_calloc(self->glyphSize * self->glyphSize * self->numOrientations, sizeof(float)) ;
#define atglyph(x,y,k) self->glyphs[(x) + self->glyphSize * (y) + self->glyphSize * self->glyphSize * (k)]
  for (o = 0 ; o < (signed)self->numOrientations ; ++o) {
    double angle = fmod(o * VL_PI / self->numOrientations + VL_PI/2, VL_PI) ;
    double x2 = self->glyphSize * cos(angle) / 2 ;
    double y2 = self->glyphSize * sin(angle) / 2 ;

    if (angle <= VL_PI / 4 || angle >= VL_PI * 3 / 4) {
      /* along horizontal direction */
      double slope = y2 / x2 ;
      double offset = (1 - slope) * (self->glyphSize - 1) / 2 ;
      vl_index skip = (1 - fabs(cos(angle))) / 2 * self->glyphSize ;
      vl_index i, j ;
      for (i = skip ; i < (signed)self->glyphSize - skip ; ++i) {
        j = vl_round_d(slope * i + offset) ;
        if (! self->transposed) {
          atglyph(i,j,o) = 1 ;
        } else {
          atglyph(j,i,o) = 1 ;
        }
      }
    } else {
      /* along vertical direction */
      double slope = x2 / y2 ;
      double offset = (1 - slope) * (self->glyphSize - 1) / 2 ;
      vl_index skip = (1 - sin(angle)) / 2 * self->glyphSize ;
      vl_index i, j ;
      for (j = skip ; j < (signed)self->glyphSize - skip; ++j) {
        i = vl_round_d(slope * j + offset) ;
        if (! self->transposed) {
          atglyph(i,j,o) = 1 ;
        } else {
          atglyph(j,i,o) = 1 ;
        }
      }
    }
  }
  return self ;
}

/* ---------------------------------------------------------------- */
/** @brief Delete a HOG object
 ** @param self HOG object to delete.
 **/

void
vl_hog_delete (VlHog * self)
{
  if (self->orientationX) {
    vl_free(self->orientationX) ;
    self->orientationX = NULL ;
  }

  if (self->orientationY) {
    vl_free(self->orientationY) ;
    self->orientationY = NULL ;
  }

  if (self->glyphs) {
    vl_free(self->glyphs) ;
    self->glyphs = NULL ;
  }

  if (self->permutation) {
    vl_free(self->permutation) ;
    self->permutation = NULL ;
  }

  if (self->hog) {
    vl_free(self->hog) ;
    self->hog = NULL ;
  }

  if (self->hogNorm) {
    vl_free(self->hogNorm) ;
    self->hogNorm = NULL ;
  }

  vl_free(self) ;
}


/* ---------------------------------------------------------------- */
/** @brief Get HOG glyph size
 ** @param self HOG object.
 ** @return size (height and width) of a glyph.
 **/

vl_size
vl_hog_get_glyph_size (VlHog const * self)
{
  return self->glyphSize ;
}

/* ---------------------------------------------------------------- */
/** @brief Get HOG left-right flip permutation
 ** @param self HOG object.
 ** @return left-right permutation.
 **
 ** The function returns a pointer to an array @c permutation of ::vl_hog_get_dimension
 ** elements. Given a HOG descriptor (for a cell) @c hog, which is also
 ** a vector of ::vl_hog_get_dimension elements, the
 ** descriptor obtained for the same image flipped horizotnally is
 ** given by flippedHog[i] = hog[permutation[i]].
 **/

vl_index const *
vl_hog_get_permutation (VlHog const * self)
{
  return self->permutation ;
}

/* ---------------------------------------------------------------- */
/** @brief Turn bilinear interpolation of assignments on or off
 ** @param self HOG object.
 ** @param x @c true if orientations should be assigned with bilinear interpolation.
 **/

void
vl_hog_set_use_bilinear_orientation_assignments (VlHog * self, vl_bool x) {
  self->useBilinearOrientationAssigment = x ;
}

/** @brief Tell whether assignments use bilinear interpolation or not
 ** @param self HOG object.
 ** @return @c true if orientations are be assigned with bilinear interpolation.
 **/

vl_bool
vl_hog_get_use_bilinear_orientation_assignments (VlHog const * self) {
  return self->useBilinearOrientationAssigment ;
}

/* ---------------------------------------------------------------- */
/** @brief Render a HOG descriptor to a glyph image
 ** @param self HOG object.
 ** @param image glyph image (output).
 ** @param descriptor HOG descriptor.
 ** @param width HOG descriptor width.
 ** @param height HOG descriptor height.
 **
 ** The function renders the HOG descriptor or filter
 ** @a descriptor as an image (for visualization) and stores the result in
 ** the buffer @a image. This buffer
 ** must be an array of dimensions @c width*glyphSize
 ** by @c height*glyphSize elements, where @c glyphSize is
 ** obtained from ::vl_hog_get_glyph_size and is the size in pixels
 ** of the image element used to represent the descriptor of one
 ** HOG cell.
 **/

void
vl_hog_render (VlHog const * self,
               float * image,
               float const * descriptor,
               vl_size width,
               vl_size height)
{
  vl_index x, y, k, cx, cy ;
  vl_size hogStride = width * height ;

  assert(self) ;
  assert(image) ;
  assert(descriptor) ;
  assert(width > 0) ;
  assert(height > 0) ;

  for (y = 0 ; y < (signed)height ; ++y) {
    for (x = 0 ; x < (signed)width ; ++x) {
      float minWeight = 0 ;
      float maxWeight = 0 ;

      for (k = 0 ; k < (signed)self->numOrientations ; ++k) {
        float weight ;
        float const * glyph = self->glyphs + k * (self->glyphSize*self->glyphSize) ;
        float * glyphImage = image + self->glyphSize * x + y * width * (self->glyphSize*self->glyphSize) ;

        switch (self->variant) {
          case VlHogVariantUoctti:
            weight =
            descriptor[k * hogStride] +
            descriptor[(k + self->numOrientations) * hogStride] +
            descriptor[(k + 2 * self->numOrientations) * hogStride] ;
            break ;
          case VlHogVariantDalalTriggs:
            weight =
            descriptor[k * hogStride] +
            descriptor[(k + self->numOrientations) * hogStride] +
            descriptor[(k + 2 * self->numOrientations) * hogStride] +
            descriptor[(k + 3 * self->numOrientations) * hogStride] ;
            break ;
          default:
            abort() ;
        }
        maxWeight = VL_MAX(weight, maxWeight) ;
        minWeight = VL_MIN(weight, minWeight);

        for (cy = 0 ; cy < (signed)self->glyphSize ; ++cy) {
          for (cx = 0 ; cx < (signed)self->glyphSize ; ++cx) {
            *glyphImage++ += weight * (*glyph++) ;
          }
          glyphImage += (width - 1) * self->glyphSize  ;
        }
      } /* next orientation */

      {
        float * glyphImage = image + self->glyphSize * x + y * width * (self->glyphSize*self->glyphSize) ;
        for (cy = 0 ; cy < (signed)self->glyphSize ; ++cy) {
          for (cx = 0 ; cx < (signed)self->glyphSize ; ++cx) {
            float value = *glyphImage ;
            *glyphImage++ = VL_MAX(minWeight, VL_MIN(maxWeight, value)) ;
          }
          glyphImage += (width - 1) * self->glyphSize  ;
        }
      }

      ++ descriptor ;
    } /* next column of cells (x) */
  } /* next row of cells (y) */
}

/* ---------------------------------------------------------------- */
/** @brief Get the dimension of the HOG features
 ** @param self HOG object.
 ** @return imension of a HOG cell descriptors.
 **/

vl_size
vl_hog_get_dimension (VlHog const * self)
{
  return self->dimension ;
}

/** @brief Get the width of the HOG cell array
 ** @param self HOG object.
 ** @return number of HOG cells in the horizontal direction.
 **/

vl_size
vl_hog_get_width (VlHog * self)
{
  return self->hogWidth ;
}

/** @brief Get the height of the HOG cell array
 ** @param self HOG object.
 ** @return number of HOG cells in the vertical direction.
 **/

vl_size
vl_hog_get_height (VlHog * self)
{
  return self->hogHeight ;
}

/* ---------------------------------------------------------------- */
/** @internal @brief Prepare internal buffers
 ** @param self HOG object.
 ** @param width image width.
 ** @param height image height.
 ** @param cellSize size of a HOG cell.
 **/

static void
vl_hog_prepare_buffers (VlHog * self, vl_size width, vl_size height, vl_size cellSize)
{
  vl_size hogWidth = (width + cellSize/2) / cellSize ;
  vl_size hogHeight = (height + cellSize/2) / cellSize ;

  assert(width > 3) ;
  assert(height > 3) ;
  assert(hogWidth > 0) ;
  assert(hogHeight > 0) ;

  if (self->hog &&
      self->hogWidth == hogWidth &&
      self->hogHeight == hogHeight) {
    /* a suitable buffer is already allocated */
    memset(self->hog, 0, sizeof(float) * hogWidth * hogHeight * self->numOrientations * 2) ;
    memset(self->hogNorm, 0, sizeof(float) * hogWidth * hogHeight) ;
    return ;
  }

  if (self->hog) {
    vl_free(self->hog) ;
    self->hog = NULL ;
  }

  if (self->hogNorm) {
    vl_free(self->hogNorm) ;
    self->hogNorm = NULL ;
  }

  self->hog = vl_calloc(hogWidth * hogHeight * self->numOrientations * 2, sizeof(float)) ;
  self->hogNorm = vl_calloc(hogWidth * hogHeight, sizeof(float)) ;
  self->hogWidth = hogWidth ;
  self->hogHeight = hogHeight ;
}

/* ---------------------------------------------------------------- */
/** @brief Process features starting from an image
 ** @param self HOG object.
 ** @param image image to process.
 ** @param width image width.
 ** @param height image height.
 ** @param numChannels number of image channles.
 ** @param cellSize size of a HOG cell.
 **
 ** The buffer @c hog must be a three-dimensional array.
 ** The first two dimensions are @c (width + cellSize/2)/cellSize and
 ** @c (height + cellSize/2)/cellSize, where divisions are integer.
 ** This is approximately @c width/cellSize and @c height/cellSize,
 ** adjusted so that the last cell is at least half contained in the
 ** image.
 **
 ** The image @c width and @c height must be not smaller than three
 ** pixels and not smaller than @c cellSize.
 **/

void
vl_hog_put_image (VlHog * self,
                  float const * image,
                  vl_size width, vl_size height, vl_size numChannels,
                  vl_size cellSize)
{
  vl_size hogStride ;
  vl_size channelStride = width * height ;
  vl_index x, y ;
  vl_uindex k ;

  assert(self) ;
  assert(image) ;

  /* clear features */
  vl_hog_prepare_buffers(self, width, height, cellSize) ;
  hogStride = self->hogWidth * self->hogHeight ;

#define at(x,y,k) (self->hog[(x) + (y) * self->hogWidth + (k) * hogStride])

  /* compute gradients and map the to HOG cells by bilinear interpolation */
  for (y = 1 ; y < (signed)height - 1 ; ++y) {
    for (x = 1 ; x < (signed)width - 1 ; ++x) {
      float gradx = 0 ;
      float grady = 0 ;
      float grad ;
      float orientationWeights [2] = {0,0} ;
      vl_index orientationBins [2] = {-1,-1} ;
      vl_index orientation = 0 ;
      float hx, hy, wx1, wx2, wy1, wy2 ;
      vl_index binx, biny, o ;

      /*
       Compute the gradient at (x,y). The image channel with
       the maximum gradient at each location is selected.
       */
      {
        float const * iter = image + y * width + x ;
        float grad2 = 0 ;
        for (k = 0 ; k < numChannels ; ++k) {
          float gradx_ = *(iter + 1) - *(iter - 1) ;
          float grady_ = *(iter + width)  - *(iter - width) ;
          float grad2_ = gradx_ * gradx_ + grady_ * grady_ ;
          if (grad2_ > grad2) {
            gradx = gradx_ ;
            grady = grady_ ;
            grad2 = grad2_ ;
          }
          iter += channelStride ;
        }
        grad = sqrtf(grad2) ;
        gradx /= VL_MAX(grad, 1e-10) ;
        grady /= VL_MAX(grad, 1e-10) ;
      }

      /*
       Map the gradient to the closest and second closets orientation bins.
       There are numOrientations orientation in the interval [0,pi).
       The next numOriantations are the symmetric ones, for a total
       of 2*numOrientation directed orientations.
       */
      for (k = 0 ; k < self->numOrientations ; ++k) {
        float orientationScore_ = gradx * self->orientationX[k] +  grady * self->orientationY[k] ;
        vl_index orientationBin_ = k ;
        if (orientationScore_ < 0) {
          orientationScore_ = - orientationScore_ ;
          orientationBin_ += self->numOrientations ;
        }
        if (orientationScore_ > orientationWeights[0]) {
          orientationBins[1] = orientationBins[0] ;
          orientationWeights[1] = orientationWeights[0] ;
          orientationBins[0] = orientationBin_ ; ;
          orientationWeights[0] = orientationScore_ ;
        } else if (orientationScore_ > orientationWeights[1]) {
          orientationBins[1] = orientationBin_ ;
          orientationWeights[1] = orientationScore_ ;
        }
      }

      if (self->useBilinearOrientationAssigment) {
        /* min(1.0,...) guards against small overflows causing NaNs */
        float angle0 = acosf(VL_MIN(orientationWeights[0],1.0)) ;
        orientationWeights[1] = angle0 / (VL_PI / self->numOrientations) ;
        orientationWeights[0] = 1 - orientationWeights[1] ;
      } else {
        orientationWeights[0] = 1 ;
        orientationBins[1] = -1 ;
      }

      for (o = 0 ; o < 2 ; ++o) {
        /*
         Accumulate the gradient. hx is the distance of the
         pixel x to the cell center at its left, in units of cellSize.
         With this parametrixation, a pixel on the cell center
         has hx = 0, which gradually increases to 1 moving to the next
         center.
         */

        orientation = orientationBins[o] ;
        if (orientation < 0) continue ;

        /*  (x - (w-1)/2) / w = (x + 0.5)/w - 0.5 */
        hx = (x + 0.5) / cellSize - 0.5 ;
        hy = (y + 0.5) / cellSize - 0.5 ;
        binx = vl_floor_f(hx) ;
        biny = vl_floor_f(hy) ;
        wx2 = hx - binx ;
        wy2 = hy - biny ;
        wx1 = 1.0 - wx2 ;
        wy1 = 1.0 - wy2 ;

        wx1 *= orientationWeights[o] ;
        wx2 *= orientationWeights[o] ;
        wy1 *= orientationWeights[o] ;
        wy2 *= orientationWeights[o] ;

        /*VL_PRINTF("%d %d - %d %d %f %f - %f %f %f %f - %d \n ",x,y,binx,biny,hx,hy,wx1,wx2,wy1,wy2,o);*/

        if (binx >= 0 && biny >=0) {
          at(binx,biny,orientation) += grad * wx1 * wy1 ;
        }
        if (binx < (signed)self->hogWidth - 1 && biny >=0) {
          at(binx+1,biny,orientation) += grad * wx2 * wy1 ;
        }
        if (binx < (signed)self->hogWidth - 1 && biny < (signed)self->hogHeight - 1) {
          at(binx+1,biny+1,orientation) += grad * wx2 * wy2 ;
        }
        if (binx >= 0 && biny < (signed)self->hogHeight - 1) {
          at(binx,biny+1,orientation) += grad * wx1 * wy2 ;
        }
      } /* next o */
    } /* next x */
  } /* next y */
}

/* ---------------------------------------------------------------- */
/** @brief Process features starting from a field in polar notation
 ** @param self HOG object.
 ** @param modulus image gradient modulus.
 ** @param angle image gradient angle.
 ** @param directed wrap the gradient angles at 2pi (directed) or pi (undirected).
 ** @param width image width.
 ** @param height image height.
 ** @param cellSize size of a HOG cell.
 **
 ** The function behaves like ::vl_hog_put_image, but foregoes the internal
 ** computation of the gradient field, allowing the user to specify
 ** their own. Angles are measure clockwise, the y axis pointing downwards,
 ** starting from the x axis (pointing to the right).
 **/

void vl_hog_put_polar_field (VlHog * self,
                             float const * modulus,
                             float const * angle,
                             vl_bool directed,
                             vl_size width, vl_size height,
                             vl_size cellSize)
{
  vl_size hogStride ;
  vl_index x, y, o ;
  vl_index period = self->numOrientations * (directed ? 2 : 1) ;
  double angleStep = VL_PI / self->numOrientations ;

  assert(self) ;
  assert(modulus) ;
  assert(angle) ;

  /* clear features */
  vl_hog_prepare_buffers(self, width, height, cellSize) ;
  hogStride = self->hogWidth * self->hogHeight ;

#define at(x,y,k) (self->hog[(x) + (y) * self->hogWidth + (k) * hogStride])
#define atNorm(x,y) (self->hogNorm[(x) + (y) * self->hogWidth])

  /* fill HOG cells from gradient field */
  for (y = 0 ; y < (signed)height ; ++y) {
    for (x = 0 ; x < (signed)width ; ++x) {
      float ho, hx, hy, wo1, wo2, wx1, wx2, wy1, wy2 ;
      vl_index bino, binx, biny ;
      float orientationWeights [2] = {0,0} ;
      vl_index orientationBins [2] = {-1,-1} ;
      vl_index orientation = 0 ;
      float thisAngle = *angle++ ;
      float thisModulus = *modulus++ ;

      if (thisModulus <= 0.0f) continue ;

      /*  (x - (w-1)/2) / w = (x + 0.5)/w - 0.5 */

      ho = (float)thisAngle / angleStep ;
      bino = vl_floor_f(ho) ;
      wo2 = ho - bino ;
      wo1 = 1.0f - wo2 ;

      while (bino < 0) { bino += self->numOrientations * 2 ; }

      if (self->useBilinearOrientationAssigment) {
        orientationBins[0] = bino % period ;
        orientationBins[1] = (bino + 1) % period ;
        orientationWeights[0] = wo1 ;
        orientationWeights[1] = wo2 ;
      } else {
        orientationBins[0] = (bino + ((wo1 > wo2) ? 0 : 1)) % period ;
        orientationWeights[0] = 1 ;
        orientationBins[1] = -1 ;
      }

      for (o = 0 ; o < 2 ; ++o) {
        /*
         Accumulate the gradient. hx is the distance of the
         pixel x to the cell center at its left, in units of cellSize.
         With this parametrixation, a pixel on the cell center
         has hx = 0, which gradually increases to 1 moving to the next
         center.
         */

        orientation = orientationBins[o] ;
        if (orientation < 0) continue ;

        hx = (x + 0.5) / cellSize - 0.5 ;
        hy = (y + 0.5) / cellSize - 0.5 ;
        binx = vl_floor_f(hx) ;
        biny = vl_floor_f(hy) ;
        wx2 = hx - binx ;
        wy2 = hy - biny ;
        wx1 = 1.0 - wx2 ;
        wy1 = 1.0 - wy2 ;

        wx1 *= orientationWeights[o] ;
        wx2 *= orientationWeights[o] ;
        wy1 *= orientationWeights[o] ;
        wy2 *= orientationWeights[o] ;

        /*VL_PRINTF("%d %d - %d %d %f %f - %f %f %f %f - %d \n ",x,y,binx,biny,hx,hy,wx1,wx2,wy1,wy2,o);*/

        if (binx >= 0 && biny >=0) {
          at(binx,biny,orientation) += thisModulus * wx1 * wy1 ;
        }
        if (binx < (signed)self->hogWidth - 1 && biny >=0) {
          at(binx+1,biny,orientation) += thisModulus * wx2 * wy1 ;
        }
        if (binx < (signed)self->hogWidth - 1 && biny < (signed)self->hogHeight - 1) {
          at(binx+1,biny+1,orientation) += thisModulus * wx2 * wy2 ;
        }
        if (binx >= 0 && biny < (signed)self->hogHeight - 1) {
          at(binx,biny+1,orientation) += thisModulus * wx1 * wy2 ;
        }
      } /* next o */
    } /* next x */
  } /* next y */
}

/* ---------------------------------------------------------------- */
/** @brief Extract HOG features
 ** @param self HOG object.
 ** @param features HOG features (output).
 **
 ** This method is called after ::vl_hog_put_image or ::vl_hog_put_polar_field
 ** in order to retrieve the computed HOG features. The buffer @c features must have the dimensions returned by
 ** ::vl_hog_get_width, ::vl_hog_get_height, and ::vl_hog_get_dimension.
 **/

void
vl_hog_extract (VlHog * self, float * features)
{
  vl_index x, y ;
  vl_uindex k ;
  vl_size hogStride = self->hogWidth * self->hogHeight ;

  assert(features) ;

#define at(x,y,k) (self->hog[(x) + (y) * self->hogWidth + (k) * hogStride])
#define atNorm(x,y) (self->hogNorm[(x) + (y) * self->hogWidth])

  /*
   Computes the squared L2 norm of each HOG cell. This is the norm of the
   undirected orientation histogram, counting only numOrientations. This
   histogram is obtained by folding the 2*numOrientations directed
   orientations that are computed.
   */
  {
    float const * iter = self->hog ;
    for (k = 0 ; k < self->numOrientations ; ++k) {
      float * niter = self->hogNorm ;
      float * niterEnd = self->hogNorm + self->hogWidth * self->hogHeight ;
      vl_size stride = self->hogWidth*self->hogHeight*self->numOrientations ;
      while (niter != niterEnd) {
        float h1 = *iter ;
        float h2 = *(iter + stride) ;
        float h = h1 + h2 ;
        *niter += h * h ;
        niter++ ;
        iter++ ;
      }
    }
  }

  /*
   HOG block-normalisation. For each cell, there are four 2x2 blocks
   covering it. For example, the cell number 5 in the following scheme
   is covered by the four blocks 1245, 2356, 4578, 5689.

                           +---+---+---+
                           | 1 | 2 | 3 |
                           +---+---+---+
                           | 4 | 5 | 6 |
                           +---+---+---+
                           | 7 | 8 | 9 |
                           +---+---+---+

   In the Dalal-Triggs implementation, one forms all possible 2x2 blocks
   of cells, computes a descriptor vector for each by stacking the corresponding
   2x2 HOG cells, and L2 normalizes (and truncates) the result.

   Thus each HOG cell appears in four blocks. These are then decomposed
   again to produce descriptors for each cell. Each descriptor is simply
   the stacking of the portion of each block descriptor that arised
   from that cell. This process result in a descriptor
   of each cell which contains four copies of the original HOG,
   with four different normalization factors.

   @remark By stacking together the cell descriptors for a large retangular block
   of cells, one effectively stacks together the block descriptors of
   an equal number of blocks (except for the boundaries, for which
   blocks are only partially included). Since blocks are L2 normalized
   (up to truncation), this implies that the L2 norm of the resulting
   vector is approximately equal to the area of the region.

   */
  {
    float const * iter = self->hog ;
    for (y = 0 ; y < (signed)self->hogHeight ; ++y) {
      for (x = 0 ; x < (signed)self->hogWidth ; ++x) {

        /* norm of upper-left, upper-right, ... blocks */
        vl_index xm = VL_MAX(x - 1, 0) ;
        vl_index xp = VL_MIN(x + 1, (signed)self->hogWidth - 1) ;
        vl_index ym = VL_MAX(y - 1, 0) ;
        vl_index yp = VL_MIN(y + 1, (signed)self->hogHeight - 1) ;

        double norm1 = atNorm(xm,ym) ;
        double norm2 = atNorm(x,ym) ;
        double norm3 = atNorm(xp,ym) ;
        double norm4 = atNorm(xm,y) ;
        double norm5 = atNorm(x,y) ;
        double norm6 = atNorm(xp,y) ;
        double norm7 = atNorm(xm,yp) ;
        double norm8 = atNorm(x,yp) ;
        double norm9 = atNorm(xp,yp) ;

        double factor1, factor2, factor3, factor4 ;

		double t1 = 0 ;
		double t2 = 0 ;
        double t3 = 0 ;
        double t4 = 0 ;

		float * oiter = features + x + self->hogWidth * y ;

        /* each factor is the inverse of the l2 norm of one of the 2x2 blocks surrounding
           cell x,y */
#if 0
        if (self->transposed) {
          /* if the image is transposed, y and x are swapped */
          factor1 = 1.0 / VL_MAX(sqrt(norm1 + norm2 + norm4 + norm5), 1e-10) ;
          factor3 = 1.0 / VL_MAX(sqrt(norm2 + norm3 + norm5 + norm6), 1e-10) ;
          factor2 = 1.0 / VL_MAX(sqrt(norm4 + norm5 + norm7 + norm8), 1e-10) ;
          factor4 = 1.0 / VL_MAX(sqrt(norm5 + norm6 + norm8 + norm9), 1e-10) ;
        } else {
          factor1 = 1.0 / VL_MAX(sqrt(norm1 + norm2 + norm4 + norm5), 1e-10) ;
          factor2 = 1.0 / VL_MAX(sqrt(norm2 + norm3 + norm5 + norm6), 1e-10) ;
          factor3 = 1.0 / VL_MAX(sqrt(norm4 + norm5 + norm7 + norm8), 1e-10) ;
          factor4 = 1.0 / VL_MAX(sqrt(norm5 + norm6 + norm8 + norm9), 1e-10) ;
        }
#else
        /* as implemented in UOCTTI code */
        if (self->transposed) {
          /* if the image is transposed, y and x are swapped */
          factor1 = 1.0 / sqrt(norm1 + norm2 + norm4 + norm5 + 1e-4) ;
          factor3 = 1.0 / sqrt(norm2 + norm3 + norm5 + norm6 + 1e-4) ;
          factor2 = 1.0 / sqrt(norm4 + norm5 + norm7 + norm8 + 1e-4) ;
          factor4 = 1.0 / sqrt(norm5 + norm6 + norm8 + norm9 + 1e-4) ;
        } else {
          factor1 = 1.0 / sqrt(norm1 + norm2 + norm4 + norm5 + 1e-4) ;
          factor2 = 1.0 / sqrt(norm2 + norm3 + norm5 + norm6 + 1e-4) ;
          factor3 = 1.0 / sqrt(norm4 + norm5 + norm7 + norm8 + 1e-4) ;
          factor4 = 1.0 / sqrt(norm5 + norm6 + norm8 + norm9 + 1e-4) ;
        }
#endif

        for (k = 0 ; k < self->numOrientations ; ++k) {
          double ha = iter[hogStride * k] ;
          double hb = iter[hogStride * (k + self->numOrientations)] ;
          double hc ;

          double ha1 = factor1 * ha ;
          double ha2 = factor2 * ha ;
          double ha3 = factor3 * ha ;
          double ha4 = factor4 * ha ;

          double hb1 = factor1 * hb ;
          double hb2 = factor2 * hb ;
          double hb3 = factor3 * hb ;
          double hb4 = factor4 * hb ;

          double hc1 = ha1 + hb1 ;
          double hc2 = ha2 + hb2 ;
          double hc3 = ha3 + hb3 ;
          double hc4 = ha4 + hb4 ;

          ha1 = VL_MIN(0.2, ha1) ;
          ha2 = VL_MIN(0.2, ha2) ;
          ha3 = VL_MIN(0.2, ha3) ;
          ha4 = VL_MIN(0.2, ha4) ;

          hb1 = VL_MIN(0.2, hb1) ;
          hb2 = VL_MIN(0.2, hb2) ;
          hb3 = VL_MIN(0.2, hb3) ;
          hb4 = VL_MIN(0.2, hb4) ;

          hc1 = VL_MIN(0.2, hc1) ;
          hc2 = VL_MIN(0.2, hc2) ;
          hc3 = VL_MIN(0.2, hc3) ;
          hc4 = VL_MIN(0.2, hc4) ;

          t1 += hc1 ;
          t2 += hc2 ;
          t3 += hc3 ;
          t4 += hc4 ;

          switch (self->variant) {
            case VlHogVariantUoctti :
              ha = 0.5 * (ha1 + ha2 + ha3 + ha4) ;
              hb = 0.5 * (hb1 + hb2 + hb3 + hb4) ;
              hc = 0.5 * (hc1 + hc2 + hc3 + hc4) ;
              *oiter = ha ;
              *(oiter + hogStride * self->numOrientations) = hb ;
              *(oiter + 2 * hogStride * self->numOrientations) = hc ;
              break ;

            case VlHogVariantDalalTriggs :
              *oiter = hc1 ;
              *(oiter + hogStride * self->numOrientations) = hc2 ;
              *(oiter + 2 * hogStride * self->numOrientations) = hc3 ;
              *(oiter + 3 * hogStride * self->numOrientations) = hc4 ;
              break ;
          }
          oiter += hogStride ;

        } /* next orientation */

        switch (self->variant) {
          case VlHogVariantUoctti :
            oiter += 2 * hogStride * self->numOrientations ;
            *oiter = (1.0f/sqrtf(18.0f)) * t1 ; oiter += hogStride ;
            *oiter = (1.0f/sqrtf(18.0f)) * t2 ; oiter += hogStride ;
            *oiter = (1.0f/sqrtf(18.0f)) * t3 ; oiter += hogStride ;
            *oiter = (1.0f/sqrtf(18.0f)) * t4 ; oiter += hogStride ;
            break ;

          case VlHogVariantDalalTriggs :
            break ;
        }
        ++iter ;
      } /* next x */
    } /* next y */
  } /* block normalization */
}

vlfeat/vl/random.c0000644000175000017500000001756012237004745013027 0ustar  dimadima/** @file random.c
 ** @brief Random number generator - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page random Random number generator
@author Andrea Vedaldi
@tableofcontents


The module @ref random.h implements random number generation in
VLFeat.  The generator is based on the popular Mersenne Twister
algorithm @cite{matsumoto98mersenne} (which is the same as MATLAB
random generator from MATLAB version 7.4 onwards).


@section random-starting Getting started


In VLFeat, a random number generator is implemented by an object of
type ::VlRand. The simplest way to obtain such an object is to get the
default random generator by

@code
VlRand * rand = vl_get_rand() ;
vl_int32 signedRandomInteger = vl_rand_int31(rand) ;
@code

Note that there is one such generator per thread (see
::vl_get_rand). If more control is desired, a new ::VlRand object can
be easily created.  The object is lightweight, designed to be
allocated on the stack:

@code
VlRand rand ;
vl_rand_init (&rand) ;
@endcode

The generator can be seeded by ::vl_rand_seed and ::vl_rand_seed_by_array.
For instance:

@code
vl_rand_seed (&rand, clock()) ;
@endcode

The generator can be used to obtain random quantities of
various types:

- ::vl_rand_int31, ::vl_rand_uint32 for 32-bit random integers;
- ::vl_rand_real1 for a double in [0,1];
- ::vl_rand_real2 for a double in [0,1);
- ::vl_rand_real3 for a double in (0,1);
- ::vl_rand_res53 for a double in [0,1) with high resolution.

There is no need to explicitly destroy a ::VlRand instance.

**/

#include "random.h"

/*
A C-program for MT19937, with initialization improved 2002/1/26.
Coded by Takuji Nishimura and Makoto Matsumoto.

Before using, initialize the state by using init_genrand(seed)
or init_by_array(init_key, keySize).

Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. The names of its contributors may not be used to endorse or promote
products derived from this software without specific prior written
permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Any feedback is very welcome.
http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
*/

#include 
#include 

/* Period parameters */
#define N 624
#define M 397
#define MATRIX_A VL_UINT32_C(0x9908b0df)   /* constant vector a */
#define UPPER_MASK VL_UINT32_C(0x80000000) /* most asignificant w-r bits */
#define LOWER_MASK VL_UINT32_C(0x7fffffff) /* least significant r bits */

/* initializes mt[N] with a seed */

/** @brief Initialise random number generator
 ** @param self number generator.
 **/

void
vl_rand_init (VlRand * self)
{
  memset (self->mt, 0, sizeof(self->mt[0]) * N) ;
  self->mti = N + 1 ;
}

/** @brief Seed the state of the random number generator
 ** @param self random number generator.
 ** @param s seed.
 **/

void
vl_rand_seed (VlRand * self, vl_uint32 s)
{
#define mti self->mti
#define mt self->mt
  mt[0]= s & VL_UINT32_C(0xffffffff);
  for (mti=1; mti> 30)) + mti);
    /* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
    /* In the previous versions, MSBs of the seed affect   */
    /* only MSBs of the array mt[].                        */
    /* 2002/01/09 modified by Makoto Matsumoto             */
    mt[mti] &= VL_UINT32_C(0xffffffff);
    /* for >32 bit machines */
  }
#undef mti
#undef mt
}

/** @brief Seed the state of the random number generator by an array
 ** @param self     random number generator.
 ** @param key      array of numbers.
 ** @param keySize  length of the array.
 **/

void
vl_rand_seed_by_array (VlRand * self, vl_uint32 const key [], vl_size keySize)
{
#define mti self->mti
#define mt self->mt
  int i, j, k;
  vl_rand_seed (self, VL_UINT32_C(19650218));
  i=1; j=0;
  k = (N > keySize ? N : (int)keySize);
  for (; k; k--) {
    mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * VL_UINT32_C(1664525)))
      + key[j] + j; /* non linear */
    mt[i] &= VL_UINT32_C(0xffffffff); /* for WORDSIZE > 32 machines */
    i++; j++;
    if (i>=N) { mt[0] = mt[N-1]; i=1; }
    if (j>=(signed)keySize) j=0;
  }
  for (k=N-1; k; k--) {
    mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * VL_UINT32_C(1566083941)))
      - i; /* non linear */
    mt[i] &= VL_UINT32_C(0xffffffff) ; /* for WORDSIZE > 32 machines */
    i++;
    if (i>=N) { mt[0] = mt[N-1]; i=1; }
  }

  mt[0] = VL_UINT32_C(0x80000000); /* MSB is 1; assuring non-zero initial array */
#undef mti
#undef mt
}

/** @brief Randomly permute and array of indexes.
 ** @param self random number generator.
 ** @param array array of indexes.
 ** @param size number of element in the array.
 **
 ** The function uses *Algorithm P*, also known as *Knuth shuffle*.
 **/

void
vl_rand_permute_indexes (VlRand *self, vl_index *array, vl_size size)
{
  vl_index i, j, tmp;
  for (i = size - 1 ; i > 0; i--) {
    /* Pick a random index j in the range 0, i + 1 and swap it with i */
    j = (vl_int) vl_rand_uindex (self, i + 1) ;
    tmp = array[i] ; array[i] = array[j] ; array[j] = tmp ;
  }
}


/** @brief Generate a random UINT32
 ** @param self random number generator.
 ** @return a random number in [0, 0xffffffff].
 **/

vl_uint32
vl_rand_uint32 (VlRand * self)
{
  vl_uint32 y;
  static vl_uint32 mag01[2]={VL_UINT32_C(0x0), MATRIX_A};
  /* mag01[x] = x * MATRIX_A  for x=0,1 */

#define mti self->mti
#define mt self->mt

  if (mti >= N) { /* generate N words at one time */
    int kk;

    if (mti == N+1)   /* if init_genrand() has not been called, */
      vl_rand_seed (self, VL_UINT32_C(5489)); /* a default initial seed is used */

    for (kk=0;kk> 1) ^ mag01[y & VL_UINT32_C(0x1)];
    }
    for (;kk> 1) ^ mag01[y & VL_UINT32_C(0x1)];
    }
    y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK);
    mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & VL_UINT32_C(0x1)];

    mti = 0;
  }

  y = mt[mti++];

  /* Tempering */
  y ^= (y >> 11);
  y ^= (y << 7) & VL_UINT32_C(0x9d2c5680);
  y ^= (y << 15) & VL_UINT32_C(0xefc60000);
  y ^= (y >> 18);

  return (vl_uint32)y;

#undef mti
#undef mt
}
vlfeat/vl/ikmeans_init.tc0000644000175000017500000000722012237004736014375 0ustar  dimadima/** @file ikmeans_init.tc
 ** @brief Integer K-Means - Initialization - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#include "random.h"

/* pairs are used to generate random permutations of data */
typedef struct
{
  vl_int32 w;
  vl_int32 j;
} pair_t;

static int cmp_pair (void const *a, void const *b)
{
  pair_t *pa = (pair_t *) a;
  pair_t *pb = (pair_t *) b;
  vl_int32 d = pa->w - pb->w ;
  if (d) return d ;
  /* break ties (qsort not stable) */
  return pa->j - pb->j;
}

VL_INLINE vl_uint32 calc_dist2 (vl_int32 const* A, vl_uint8 const* B, int M)
{
  vl_uint32 acc = 0;
  int i = 0 ;
  for (i = 0 ; i < M ; ++i) {
    vl_int32 dist = A [i] - B [i] ;
    acc += dist * dist ;
  }
  return acc ;
}

/** @internal
 ** @brief Helper function to allocate memory for an IKM quantizer
 ** @param f quantizer.
 ** @param M data dimensionality.
 ** @param K number of clusters.
 **/

static void alloc (VlIKMFilt *f, int M, int K)
{
  if (f-> centers) vl_free (f-> centers) ;
  f-> K = K ;
  f-> M = M ;
  f-> centers = vl_malloc (sizeof(vl_ikm_acc) * M * K) ;
}


/** @brief Helper function to initialize the quantizer
 ** @param f IKM quantizer.
 **/

static
void vl_ikm_init_helper (VlIKMFilt *f)
{
  switch (f-> method) {
  case VL_IKM_LLOYD:  vl_ikm_init_lloyd (f) ; break ;
  case VL_IKM_ELKAN:  vl_ikm_init_elkan (f) ; break ;
  }
}

/** @brief Initialize quantizer with centers
 ** @param f       IKM quantizer.
 ** @param centers centers.
 ** @param M       data dimensionality.
 ** @param K       number of clusters.
 **/

VL_EXPORT
void
vl_ikm_init (VlIKMFilt* f, vl_ikm_acc const * centers, int M, int K)
{
  alloc (f, M, K) ;

  memcpy (f-> centers, centers, sizeof(vl_ikm_acc) * M * K) ;

  vl_ikm_init_helper (f) ;
}

/** @brief Initialize quantizer with random centers
 ** @param f IKM quantizer.
 ** @param M data dimensionality.
 ** @param K number of clusters.
 **/

VL_EXPORT
void
vl_ikm_init_rand
(VlIKMFilt* f, int M, int K)
{
  int k, i ;
  VlRand * rand = vl_get_rand() ;

  alloc (f, M, K) ;

  for (k = 0 ; k < K ; ++ k) {
    for (i = 0 ; i < M ; ++ i) {
      f-> centers [k * M + i] = (vl_ikm_acc) (vl_rand_uint32 (rand)) ;
    }
  }

  vl_ikm_init_helper (f) ;
}

/** @brief Initialize with centers from random data
 ** @param f IKM quantizer.
 ** @param data data.
 ** @param M data dimensionality.
 ** @param N number of data.
 ** @param K number of clusters.
 **/

VL_EXPORT
void
vl_ikm_init_rand_data
(VlIKMFilt* f, vl_uint8 const* data, int M, int N, int K)
{
  int i, j, k ;
  VlRand * rand = vl_get_rand () ;
  pair_t  *pairs = (pair_t *) vl_malloc (sizeof(pair_t) * N);

  alloc (f, M, K) ;

  /* permute the data randomly */
  for (j = 0 ; j < N ; ++j) {
    pairs[j].j = j ;
    pairs[j].w = ((vl_int32) vl_rand_uint32 (rand)) >> 2 ;
  }

  qsort (pairs, N, sizeof(pair_t), cmp_pair);

  /* initialize centers from random data points */
  for (j = 0, k = 0 ; k < K ; ++ k) {

    /* search for the next candidate which is not a dup */
    for ( ; j < N - 1 ; ++j) {
      int prevk = 0 ;
      for (prevk = 0 ; prevk < k ; ++ prevk) {
        vl_uint32 dist = calc_dist2
          (f-> centers + prevk * M,
           data + (vl_uint64)pairs[j].j * M, M) ;
        if (dist == 0)  break ;
      }
      if (prevk == k) break ;
    }

    for (i = 0 ; i < M ; ++ i) {
      f-> centers [k * M + i] = data [(vl_uint64)pairs[j].j * M + i] ;
    }

    if (j < N - 1) ++ j ;
  }

  vl_free (pairs) ;
  vl_ikm_init_helper (f) ;
}

/*
 * Local Variables: *
 * mode: C *
 * End: *
 */
vlfeat/vl/imopv.h0000644000175000017500000001367312237004736012707 0ustar  dimadima/** @file imopv.h
 ** @brief Vectorized image operations
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_IMOPV_H
#define VL_IMPOV_H

#include "generic.h"

/** @name Image convolution flags
 ** @{ */
#define VL_PAD_BY_ZERO       (0x0 << 0) /**< @brief Pad with zeroes. */
#define VL_PAD_BY_CONTINUITY (0x1 << 0) /**< @brief Pad by continuity. */
#define VL_PAD_MASK          (0x3)      /**< @brief Padding field selector. */
#define VL_TRANSPOSE         (0x1 << 2) /**< @brief Transpose result. */
/** @} */

/** @name Image convolution
 ** @{ */
VL_EXPORT
void vl_imconvcol_vf (float* dst, vl_size dst_stride,
                      float const* src,
                      vl_size src_width, vl_size src_height, vl_size src_stride,
                      float const* filt, vl_index filt_begin, vl_index filt_end,
                      int step, unsigned int flags) ;

VL_EXPORT
void vl_imconvcol_vd (double* dst, vl_size dst_stride,
                      double const* src,
                      vl_size src_width, vl_size src_height, vl_size src_stride,
                      double const* filt, vl_index filt_begin, vl_index filt_end,
                      int step, unsigned int flags) ;

VL_EXPORT
void vl_imconvcoltri_f (float * dest, vl_size destStride,
                        float const * image,
                        vl_size imageWidth, vl_size imageHeight, vl_size imageStride,
                        vl_size filterSize,
                        vl_size step, int unsigned flags) ;

VL_EXPORT
void vl_imconvcoltri_d (double * dest, vl_size destStride,
                        double const * image,
                        vl_size imageWidth, vl_size imageHeight, vl_size imageStride,
                        vl_size filterSize,
                        vl_size step, int unsigned flags) ;
/** @} */

/** @name Integral image
 ** @{ */
VL_EXPORT
void vl_imintegral_f (float * integral,  vl_size integralStride,
                      float const * image,
                      vl_size imageWidth, vl_size imageHeight, vl_size imageStride) ;

VL_EXPORT
void vl_imintegral_d (double * integral,  vl_size integralStride,
                      double const * image,
                      vl_size imageWidth, vl_size imageHeight, vl_size imageStride) ;

VL_EXPORT
void vl_imintegral_i32 (vl_int32 * integral,  vl_size integralStride,
                        vl_int32 const * image,
                        vl_size imageWidth, vl_size imageHeight, vl_size imageStride) ;

VL_EXPORT
void vl_imintegral_ui32 (vl_uint32 * integral,  vl_size integralStride,
                         vl_uint32 const * image,
                         vl_size imageWidth, vl_size imageHeight, vl_size imageStride) ;
/** @} */

/** @name Distance transform */
/** @{ */

VL_EXPORT void
vl_image_distance_transform_d (double const * image,
                               vl_size numColumns,
                               vl_size numRows,
                               vl_size columnStride,
                               vl_size rowStride,
                               double * distanceTransform,
                               vl_uindex * indexes,
                               double coeff,
                               double offset) ;

VL_EXPORT void
vl_image_distance_transform_f (float const * image,
                               vl_size numColumns,
                               vl_size numRows,
                               vl_size columnStride,
                               vl_size rowStride,
                               float * distanceTransform,
                               vl_uindex * indexes,
                               float coeff,
                               float offset) ;

/** @} */

/* ---------------------------------------------------------------- */
/** @name Image smoothing */
/** @{ */

VL_EXPORT void
vl_imsmooth_f (float *smoothed, vl_size smoothedStride,
               float const *image, vl_size width, vl_size height, vl_size stride,
               double sigmax, double sigmay) ;

VL_EXPORT void
vl_imsmooth_d (double *smoothed, vl_size smoothedStride,
               double const *image, vl_size width, vl_size height, vl_size stride,
               double sigmax, double sigmay) ;

/** @} */

/* ---------------------------------------------------------------- */
/** @name Image gradients */
/** @{ */
VL_EXPORT void
vl_imgradient_polar_f (float* amplitudeGradient, float* angleGradient,
                       vl_size gradWidthStride, vl_size gradHeightStride,
                       float const* image,
                       vl_size imageWidth, vl_size imageHeight,
                       vl_size imageStride);

VL_EXPORT void
vl_imgradient_polar_d (double* amplitudeGradient, double* angleGradient,
                       vl_size gradWidthStride, vl_size gradHeightStride,
                       double const* image,
                       vl_size imageWidth, vl_size imageHeight,
                       vl_size imageStride);

VL_EXPORT void
vl_imgradient_f (float* xGradient, float* yGradient,
                 vl_size gradWidthStride, vl_size gradHeightStride,
                 float const *image,
                 vl_size imageWidth, vl_size imageHeight, vl_size imageStride);

VL_EXPORT void
vl_imgradient_d(double* xGradient, double* yGradient,
                vl_size gradWidthStride, vl_size gradHeightStride,
                double const *image,
                vl_size imageWidth, vl_size imageHeight, vl_size imageStride);

VL_EXPORT void
vl_imgradient_polar_f_callback(float const *sourceImage,
                               int sourceImageWidth, int sourceImageHeight,
                               float *dstImage,
                               int dstWidth, int dstHeight,
                               int octave, int level,
                               void *params);

/** @} */

/* VL_IMOPV_H */
#endif
vlfeat/vl/rodrigues.c0000644000175000017500000002151512237004745013545 0ustar  dimadima/** @file rodrigues.c
 ** @brief Rodrigues formulas - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-13 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#include "generic.h"
#include "mathop.h"
#include "rodrigues.h"

#include 

/** @brief Rodrigues' formula
 ** @param R_pt  3x3 matrix - array of 9 double (in) .
 ** @param dR_pt 9x3 matrix - array of 27 double (in).
 ** @param om_pt 3 vector - array of 3 dobule (out).
 **/

void
vl_rodrigues(double* R_pt, double* dR_pt, const double* om_pt)
{
  /*
    Let

       th = |om|,  r=w/th,
       sth=sin(th),  cth=cos(th),
       ^om = hat(om)

    Then the rodrigues formula is an expansion of the exponential
    function:

     rodrigues(om) = exp ^om = I + ^r sth + ^r^2 (1 - cth).

    The derivative can be computed by elementary means and
    results:

    d(vec rodrigues(om))    sth  d ^r    1 - cth  d (^r)^2
    -------------------- =  ---- ----- + -------  -------- +
          d om^T             th  d r^T     th      d r^T

                          sth                     1 - cth
          + vec^r (cth - -----) + vec^r^2 (sth - 2-------)r^T
                          th                         th
  */

#define OM(i)   om_pt[(i)]
#define R(i,j)  R_pt[(i)+3*(j)]
#define DR(i,j) dR_pt[(i)+9*(j)]
#undef small

  const double small = 1e-6 ;

  double th = sqrt( OM(0)*OM(0) +
                    OM(1)*OM(1) +
                    OM(2)*OM(2) ) ;

  if( th < small ) {
    R(0,0) = 1.0 ; R(0,1) = 0.0 ; R(0,2) = 0.0 ;
    R(1,0) = 0.0 ; R(1,1) = 1.0 ; R(1,2) = 0.0 ;
    R(2,0) = 0.0 ; R(2,1) = 0.0 ; R(2,2) = 1.0 ;

    if(dR_pt) {
      DR(0,0) = 0  ; DR(0,1) = 0   ; DR(0,2) = 0 ;
      DR(1,0) = 0  ; DR(1,1) = 0   ; DR(1,2) = 1 ;
      DR(2,0) = 0  ; DR(2,1) = -1  ; DR(2,2) = 0 ;

      DR(3,0) = 0  ; DR(3,1) = 0   ; DR(3,2) = -1 ;
      DR(4,0) = 0  ; DR(4,1) = 0   ; DR(4,2) = 0 ;
      DR(5,0) = 1  ; DR(5,1) = 0   ; DR(5,2) = 0 ;

      DR(6,0) = 0  ; DR(6,1) = 1   ; DR(6,2) = 0 ;
      DR(7,0) = -1 ; DR(7,1) = 0   ; DR(7,2) = 0 ;
      DR(8,0) = 0  ; DR(8,1) = 0   ; DR(8,2) = 0 ;
    }
    return ;
  }

  {
    double x = OM(0) / th ;
    double y = OM(1) / th ;
    double z = OM(2) / th ;

    double xx = x*x ;
    double xy = x*y ;
    double xz = x*z ;
    double yy = y*y ;
    double yz = y*z ;
    double zz = z*z ;

    const double yx = xy ;
    const double zx = xz ;
    const double zy = yz ;

    double sth  = sin(th) ;
    double cth  = cos(th) ;
    double mcth = 1.0 - cth ;

    R(0,0) = 1          - mcth * (yy+zz) ;
    R(1,0) =     sth*z  + mcth * xy ;
    R(2,0) =   - sth*y  + mcth * xz ;

    R(0,1) =   - sth*z  + mcth * yx ;
    R(1,1) = 1          - mcth * (zz+xx) ;
    R(2,1) =     sth*x  + mcth * yz ;

    R(0,2) =     sth*y  + mcth * xz ;
    R(1,2) =   - sth*x  + mcth * yz ;
    R(2,2) = 1          - mcth * (xx+yy) ;

    if(dR_pt) {
      double a =  sth / th ;
      double b = mcth / th ;
      double c = cth - a ;
      double d = sth - 2*b ;

      DR(0,0) =                         - d * (yy+zz) * x ;
      DR(1,0) =        b*y   + c * zx   + d * xy      * x ;
      DR(2,0) =        b*z   - c * yx   + d * xz      * x ;

      DR(3,0) =        b*y   - c * zx   + d * xy      * x ;
      DR(4,0) =     -2*b*x              - d * (zz+xx) * x ;
      DR(5,0) =  a           + c * xx   + d * yz      * x ;

      DR(6,0) =        b*z   + c * yx   + d * zx      * x ;
      DR(7,0) = -a           - c * xx   + d * zy      * x ;
      DR(8,0) =     -2*b*x              - d * (yy+xx) * x ;

      DR(0,1) =     -2*b*y              - d * (yy+zz) * y ;
      DR(1,1) =        b*x   + c * zy   + d * xy      * y ;
      DR(2,1) = -a           - c * yy   + d * xz      * y ;

      DR(3,1) =        b*x   - c * zy   + d * xy      * y ;
      DR(4,1) =                         - d * (zz+xx) * y ;
      DR(5,1) =        b*z   + c * xy   + d * yz      * y ;

      DR(6,1) = a            + c * yy   + d * zx      * y ;
      DR(7,1) =        b*z   - c * xy   + d * zy      * y ;
      DR(8,1) =     -2*b*y              - d * (yy+xx) * y ;

      DR(0,2) =     -2*b*z              - d * (yy+zz) * z ;
      DR(1,2) =  a           + c * zz   + d * xy      * z ;
      DR(2,2) =        b*x   - c * yz   + d * xz      * z ;

      DR(3,2) =  -a          - c * zz   + d * xy      * z ;
      DR(4,2) =     -2*b*z              - d * (zz+xx) * z ;
      DR(5,2) =        b*y   + c * xz   + d * yz      * z ;

      DR(6,2) =        b*x   + c * yz   + d * zx      * z ;
      DR(7,2) =        b*y   - c * xz   + d * zy      * z ;
      DR(8,2) =                         - d * (yy+xx) * z ;
    }
  }

#undef OM
#undef R
#undef DR

}

/** @brief Inverse Rodrigues formula
 ** @param om_pt  3    vector - array of 3   dobule (out).
 ** @param dom_pt 3x9  matrix - array of 3x9 dobule (out).
 ** @param R_pt   3x3  matrix - array of 9   double (in).
 **
 ** This function computes the Rodrigues formula of the argument @a
 ** om_pt. The result is stored int the matrix @a R_pt. If @a dR_pt is
 ** non null, then the derivative of the Rodrigues formula is computed
 ** and stored into the matrix @a dR_pt.
 **/

VL_EXPORT
void vl_irodrigues(double* om_pt, double* dom_pt, const double* R_pt)
{
  /*
                    tr R - 1          1    [ R32 - R23 ]
      th = cos^{-1} --------,  r =  ------ [ R13 - R31 ], w = th r.
                        2           2 sth  [ R12 - R21 ]

      sth = sin(th)

       dw    th*cth-sth      dw     th   [di3 dj2 - di2 dj3]
      ---- = ---------- r,  ---- = ----- [di1 dj3 - di3 dj1].
      dRii     2 sth^2      dRij   2 sth [di1 dj2 - di2 dj1]

      trace(A) < -1 only for small num. errors.
  */

#define OM(i)    om_pt[(i)]
#define DOM(i,j) dom_pt[(i)+3*(j)]
#define R(i,j)   R_pt[(i)+3*(j)]
#define W(i,j)   W_pt[(i)+3*(j)]

  const double small = 1e-6 ;

  double th = acos
    (0.5*(VL_MAX(R(0,0)+R(1,1)+R(2,2),-1.0) - 1.0)) ;

  double sth = sin(th) ;
  double cth = cos(th) ;

  if(fabs(sth) < small && cth < 0) {
    /*
      we have this singularity when the rotation  is about pi (or -pi)
      we use the fact that in this case

      hat( sqrt(1-cth) * r )^2 = W = (0.5*(R+R') - eye(3))

      which gives

      (1-cth) rx^2 = 0.5 * (W(1,1)-W(2,2)-W(3,3))
      (1-cth) ry^2 = 0.5 * (W(2,2)-W(3,3)-W(1,1))
      (1-cth) rz^2 = 0.5 * (W(3,3)-W(1,1)-W(2,2))
    */

    double W_pt [9], x, y, z ;
    W_pt[0] = 0.5*( R(0,0) + R(0,0) ) - 1.0 ;
    W_pt[1] = 0.5*( R(1,0) + R(0,1) ) ;
    W_pt[2] = 0.5*( R(2,0) + R(0,2) );

    W_pt[3] = 0.5*( R(0,1) + R(1,0) );
    W_pt[4] = 0.5*( R(1,1) + R(1,1) ) - 1.0;
    W_pt[5] = 0.5*( R(2,1) + R(1,2) );

    W_pt[6] =  0.5*( R(0,2) + R(2,0) ) ;
    W_pt[7] =  0.5*( R(1,2) + R(2,1) ) ;
    W_pt[8] =  0.5*( R(2,2) + R(2,2) ) - 1.0 ;

    /* these are only absolute values */
    x = sqrt( 0.5 * (W(0,0)-W(1,1)-W(2,2)) ) ;
    y = sqrt( 0.5 * (W(1,1)-W(2,2)-W(0,0)) ) ;
    z = sqrt( 0.5 * (W(2,2)-W(0,0)-W(1,1)) ) ;

    /* set the biggest component to + and use the element of the
    ** matrix W to determine the sign of the other components
    ** then the solution is either (x,y,z) or its opposite */
    if( x >= y && x >= z ) {
      y = (W(1,0) >=0) ? y : -y ;
      z = (W(2,0) >=0) ? z : -z ;
    } else if( y >= x && y >= z ) {
      z = (W(2,1) >=0) ? z : -z ;
      x = (W(1,0) >=0) ? x : -x ;
    } else {
      x = (W(2,0) >=0) ? x : -x ;
      y = (W(2,1) >=0) ? y : -y ;
    }

    /* we are left to chose between (x,y,z) and (-x,-y,-z)
    ** unfortunately we cannot (as the rotation is too close to pi) and
    ** we just keep what we have. */
    {
      double scale = th / sqrt( 1 - cth ) ;
      OM(0) = scale * x ;
      OM(1) = scale * y ;
      OM(2) = scale * z ;

      if( dom_pt ) {
        int k ;
        for(k=0; k<3*9; ++k)
          dom_pt [k] = VL_NAN_D ;
      }
      return ;
    }

  } else {
    double a = (fabs(sth) < small) ? 1 : th/sin(th) ;
    double b ;
    OM(0) = 0.5*a*(R(2,1) - R(1,2)) ;
    OM(1) = 0.5*a*(R(0,2) - R(2,0)) ;
    OM(2) = 0.5*a*(R(1,0) - R(0,1)) ;

    if( dom_pt ) {
      if( fabs(sth) < small ) {
        a = 0.5 ;
        b = 0 ;
      } else {
        a = th/(2*sth) ;
        b = (th*cth - sth)/(2*sth*sth)/th ;
      }

      DOM(0,0) = b*OM(0) ;
      DOM(1,0) = b*OM(1) ;
      DOM(2,0) = b*OM(2) ;

      DOM(0,1) = 0 ;
      DOM(1,1) = 0 ;
      DOM(2,1) = a ;

      DOM(0,2) = 0 ;
      DOM(1,2) = -a ;
      DOM(2,2) = 0 ;

      DOM(0,3) = 0 ;
      DOM(1,3) = 0 ;
      DOM(2,3) = -a ;

      DOM(0,4) = b*OM(0) ;
      DOM(1,4) = b*OM(1) ;
      DOM(2,4) = b*OM(2) ;

      DOM(0,5) = a ;
      DOM(1,5) = 0 ;
      DOM(2,5) = 0 ;

      DOM(0,6) = 0 ;
      DOM(1,6) = a ;
      DOM(2,6) = 0 ;

      DOM(0,7) = -a ;
      DOM(1,7) = 0 ;
      DOM(2,7) = 0 ;

      DOM(0,8) = b*OM(0) ;
      DOM(1,8) = b*OM(1) ;
      DOM(2,8) = b*OM(2) ;
    }
  }

#undef OM
#undef DOM
#undef R
#undef W
}
vlfeat/vl/liop.c0000644000175000017500000005061712237004745012512 0ustar  dimadima/** @file liop.c
 ** @brief Local Intensity Order Pattern (LIOP) descriptor - Definition
 ** @author Hana Sarbortova
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 Hana Sarbortova and Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page liop Local Intensity Order Pattern (LIOP) descriptor
@author Hana Sarbortova
@author Andrea Vedaldi


@ref liop.h implements *Local Intensity Order Pattern descriptor*
(LIOP) of @cite{wang11local}. LIOP is a local image descriptor,
similarly to the @ref sift "SIFT descriptor".

@ref liop-starting demonstrates how to use the C API to compute the
LIOP descriptor of a patch. For further details refer to:

- @subpage liop-fundamentals - LIOP definition and parameters.


@section liop-starting Getting started with LIOP


The following code fragment demonstrates how tow to use @ref liop.h in
a C program in order to compute the LIOP descriptor of an image patch.

@code
#include 

// Create a new object instance (these numbers corresponds to parameter
// values proposed by authors of the paper, except for 41)
vl_size sideLength = 41 ;
VlLiopDesc * liop = vl_liopdesc_new_basic (sideLength);

// allocate the descriptor array
vl_size dimension = vl_liopdesc_get_dimension(liop) ;
float * desc = vl_malloc(sizeof(float) * dimension) ;

// compute descriptor from a patch (an array of length sideLegnth *
// sideLength)
vl_liopdesc_process(liop, desc, patch) ;

// delete the object
vl_liopdesc_delete(liop) ;
@endcode

The image patch must be of odd side length and in single
precision. There are several parameters affecting the LIOP
descriptor. An example is the @ref liop-weighing "threshold" used to
discard low-contrast oder pattern in the computation of the
statistics. This is changed by using ::vl_liopdesc_set_threshold.

**/


/**

@page liop-fundamentals LIOP fundamentals
@tableofcontents


The *Local Invariant Order Pattern* (LIOP) descriptor
@cite{wang11local} is a local image descriptor based on the concept of
*local order pattern*. An order pattern is simply the order obtained
by sorting selected image samples by increasing intensity. Consider in
particular a pixel $\bx$ and $n$ neighbors
$\bx_1,\bx_2,\dots,\bx_n$. The local order pattern at $\bx$ is the
permutation $\sigma$ that sorts the neighbours by increasing intensity
$I(\bx_{\sigma(1)}) \leq I(\bx_{\sigma(2)}) \leq \dots \leq
I(\bx_{\sigma(2)})$.

An advantage of order patterns is that they are invariant to monotonic
changes of the image intensity. However, an order pattern describes
only a small portion of a patch and is not very distinctive. LIOP
assembles local order patterns computed at all image locations to
obtain a descriptor that at the same time distinctive and invariant to
monotonic intensity changes as well as image rotations.

In order to make order patterns rotation invariant, the neighborhood
of samples around $\bx$ is taken in a rotation-covariant manner. In
particular, the points $\bx_1,\dots,\bx_n$ are sampled anticlockwise
on a circle of radius $r$ around $\bx$, as shown in the following
figure:

@image html liop.png "LIOP descriptor layout: square input patch (shaded area), circular measurement region (white area), local neighborhood of a point (blue)."

Since the sample points do not necessarily have integer coordinates,
$I(\bx_i)$ is computed using bilinear interpolation.


@section liop-spatial-binning Intensity rank spatial binning


Once local order patterns are computed for all pixels $\bx$ in the
image, they can be pooled into a histogram to form an image
descriptor. Pooling discards spatial information resulting in a
warp-invariant statistics. In practice, there are two restriction on
which pixels can be used for this purpose:

- A margin of $r$ pixels from the image boundary must be maintained so
  that neighborhoods fall within the image boundaries.
- Rotation invariance requires the pooling regions to be rotation
  co-variant.  A way to do so is to make the shape of the pooling
  region rotation invariant.

For this reason, the histogram pooling region is restricted to the
circular region shown with a light color in the figure above.

In order to increase distinctiveness of the descriptor, LIOP pools
multiple histograms from a number of regions $R_1,\dots,R_m$ (spatial
pooling). These regions are selected in an illumination-invariant and
rotation-covariant manner by looking at level sets:
\[
R_t = \{\bx :\tau_{t} \leq I(\bx) < \tau_{t+1} \}.
\]
In order to be invariant to monotonic changes of the intensity, the
thresholds $\tau_t$ are selected so that all regions contain the same
number of pixels. This can be done efficiently by sorting pixels by
increasing intensity and then partitioning the resulting list into $m$
equal parts (when $m$ does not divide the number of pixels exactly,
the remaining pixels are incorporated into the last partition).


@section liop-weighing Weighted pooling


In order to compute a histogram of order pattern occurrences, one
needs to map permutations to histogram bins. This is obtained by
sorting permutation in lexycogrpahical order. For example, for $n=4$
neighbors one has the following $n!=24$ permutations:

Permutation   | Lexycographical rank
--------------|----------------------
1 2 3 4       | 1
1 2 4 3       | 2
1 3 2 4       | 3
1 3 4 2       | 4
...           | ...
4 3 1 2       | 23
4 3 2 1       | 24

In the following, $q(\bx) \in [1, n!]$ will denote the index of the
local order pattern $\sigma$ centered at pixel $\bx$.

The local order patterns $q(\bx)$ in a region $R_t$ are then pooled to
form a histogram of size $!n$. In this process, patterns are weighted
based on their stability. The latter is assumed to be proportional to
the number of pairs of pixels in the neighborhood that have a
sufficiently large intensity difference:

@f[
w(\bx) = \sum_{i=1}^n \sum_{j=1}^n [ |I(\bx_{i}) - I(\bx_{j})| >  \Theta) ]
@f]

where $[\cdot]$ is the indicator function.

In VLFeat LIOP implementation, the threshold $\Theta$ is either set as
an absolute value, or as a faction of the difference between the
maximum and minimum intensity in the image (restricted to the pixels
in the light area in the figure above).

Overall, LIOP consists of $m$ histograms of size $n!$ obtained as

\[
  h_{qt} = \sum_{\bx : q(\bx) = q \ \wedge\  \bx \in R_t} w(\bx).
\]



@section liop-normalization Normalization


After computing the weighted counts $h_{qt}$, the LIOP descriptor is
obtained by stacking the values $\{h_{qt}\}$ into a vector
$\mathbf{h}$ and then normalising it:

\[
  \Phi = \frac{\mathbf{h}}{\|\mathbf{h}\|_2}
\]

The dimensionality is therefore $m n!$, where $m$ is the @c
numSpatialBins number of spatial bins and $n$ is the @c numNeighbours
number of neighbours (see ::vl_liopdesc_new). By default, this
descriptor is stored in @c single format. It can be stored as a
sequence of bytes by premultiplying the values by the constant 255 and
then rounding:

\[
 \operatorname{round}\left[ 255\, \times \Phi\right].
\]

*/

#include "liop.h"
#include "mathop.h"
#include "imopv.h"
#include 

#define DEFAULT_INTENSITY_THRESHOLD -(5.0/255)
#define DEFAULT_RADIUS 6.0
#define DEFAULT_NUM_SPATIAL_BINS 6
#define DEFAULT_NUM_NEIGHBOURS 4

/* ---------------------------------------------------------------- */
/*                                                 Helper functions */
/* ---------------------------------------------------------------- */

static
vl_int factorial(vl_int num)
{
  vl_int result = 1;
  while(num > 1){
    result = num*result;
    num--;
  }
  return result ;
}

/** @internal @brief Compute permutation index.
 ** @param permutation array containing all values from 0 to (size - 1) (input/output).
 ** @param size size of the permutation array.
 ** @return permutation index.
 **
 ** Compute the position of @a permutation in the lexycographcial
 ** sorting of permutations of the given @a size.
 **
 ** For example, in the lexicographical ordering, permutations of four elements
 ** are listed as [1 2 3 4], [1 2 4 3], [1 3 2 4], [1 3 4 2], [1 4 2 3],
 ** [1 4 3 2], [2 1 3 4], ..., [4 3 2 1].
 **
 ** The index can be computed as follows. First pick the first digit
 ** perm[1]. This is either 1,2,...,n. For each
 ** choice of the first digits, there are (n-1)! other permutations, separated
 ** therefore by (n-1)! elements in lexicographical order.
 **
 ** Process then the second digit perm[2]. This can be though as finding
 ** the lexycotraphical index of perm[2], ..., perm[n], a permutation of
 ** n-1 elements. This can be explicitly obtained by taking out 1 from
 ** all elements perm[i] > perm[1]. */

VL_INLINE vl_index get_permutation_index(vl_uindex *permutation, vl_size size){
  vl_index index = 0 ;
  vl_index i ;
  vl_index j ;

  for (i = 0 ; i < (signed)size ; ++i) {
    index = index * ((signed)size - i) + permutation[i] ;
    for (j = i + 1 ; j < (signed)size ; ++j) {
      if (permutation[j] > permutation[i]) { permutation[j] -- ; }
    }
  }
  return index ;
}

/* instantiate two quick sort algorithms */
VL_INLINE float patch_cmp (VlLiopDesc * liop, vl_index i, vl_index j)
{
  vl_index ii = liop->patchPermutation[i] ;
  vl_index jj = liop->patchPermutation[j] ;
  return liop->patchIntensities[ii] - liop->patchIntensities[jj] ;
}

VL_INLINE void patch_swap (VlLiopDesc * liop, vl_index i, vl_index j)
{
  vl_index tmp = liop->patchPermutation[i] ;
  liop->patchPermutation[i] = liop->patchPermutation[j] ;
  liop->patchPermutation[j] = tmp ;
}

#define VL_QSORT_prefix patch
#define VL_QSORT_array VlLiopDesc*
#define VL_QSORT_cmp patch_cmp
#define VL_QSORT_swap patch_swap
#include "qsort-def.h"

VL_INLINE float neigh_cmp (VlLiopDesc * liop, vl_index i, vl_index j)
{
  vl_index ii = liop->neighPermutation[i] ;
  vl_index jj = liop->neighPermutation[j] ;
  return liop->neighIntensities[ii] - liop->neighIntensities[jj] ;
}

VL_INLINE void neigh_swap (VlLiopDesc * liop, vl_index i, vl_index j)
{
  vl_index tmp = liop->neighPermutation[i] ;
  liop->neighPermutation[i] = liop->neighPermutation[j] ;
  liop->neighPermutation[j] = tmp ;
}

#define VL_QSORT_prefix neigh
#define VL_QSORT_array VlLiopDesc*
#define VL_QSORT_cmp neigh_cmp
#define VL_QSORT_swap neigh_swap
#include "qsort-def.h"

/* ---------------------------------------------------------------- */
/*                                            Construct and destroy */
/* ---------------------------------------------------------------- */

/** @brief Create a new LIOP object instance.
 ** @param numNeighbours number of neighbours.
 ** @param numSpatialBins number of bins.
 ** @param radius radius of the cirucal sample neighbourhoods.
 ** @param sideLength width of the input image patch (the patch is square).
 ** @return new object instance.
 **
 ** The value of @a radius should be at least less than half the @a
 ** sideLength of the patch.
 **/

VlLiopDesc *
vl_liopdesc_new (vl_int numNeighbours, vl_int numSpatialBins,
                 float radius, vl_size sideLength)
{
  vl_index i, t ;
  VlLiopDesc * self = vl_calloc(sizeof(VlLiopDesc), 1);

  assert(radius <= sideLength/2) ;

  self->numNeighbours = numNeighbours ;
  self->numSpatialBins = numSpatialBins ;
  self->neighRadius = radius ;
  self->intensityThreshold = DEFAULT_INTENSITY_THRESHOLD ;

  self->dimension = factorial(numNeighbours) * numSpatialBins ;

  /*
   Precompute a list of pixels within a circular patch inside
   the square image. Leave a suitable marging for sampling around
   these pixels.
   */

  self->patchSize = 0 ;
  self->patchPixels = vl_malloc(sizeof(vl_uindex)*sideLength*sideLength) ;
  self->patchSideLength = sideLength ;

  {
    vl_index x, y ;
    vl_index center = (sideLength - 1) / 2 ;
    double t = center - radius + 0.6 ;
    vl_index t2 = (vl_index) (t * t) ;
    for (y = 0 ; y < (signed)sideLength ; ++y) {
      for (x = 0 ; x < (signed)sideLength ; ++x) {
        vl_index dx = x - center ;
        vl_index dy = y - center ;
        if (x == 0 && y == 0) continue ;
        if (dx*dx + dy*dy <= t2) {
          self->patchPixels[self->patchSize++] = x + y * sideLength ;
        }
      }
    }
  }

  self->patchIntensities = vl_malloc(sizeof(vl_uindex)*self->patchSize) ;
  self->patchPermutation = vl_malloc(sizeof(vl_uindex)*self->patchSize) ;

  /*
   Precompute the samples in the circular neighbourhood of each
   measurement point.
   */

  self->neighPermutation = vl_malloc(sizeof(vl_uindex) * self->numNeighbours) ;
  self->neighIntensities = vl_malloc(sizeof(float) * self->numNeighbours) ;
  self->neighSamplesX = vl_calloc(sizeof(double), self->numNeighbours * self->patchSize) ;
  self->neighSamplesY = vl_calloc(sizeof(double), self->numNeighbours * self->patchSize) ;

  for (i = 0 ; i < (signed)self->patchSize ; ++i) {
    vl_index pixel ;
    double x, y ;
    double dangle = 2*VL_PI / (double)self->numNeighbours ;
    double angle0 ;
    vl_index center = (sideLength - 1) / 2 ;

    pixel = self->patchPixels[i] ;
    x = (pixel % (signed)self->patchSideLength) - center ;
    y = (pixel / (signed)self->patchSideLength) - center ;

    angle0 = atan2(y,x) ;

    for (t = 0 ; t < (signed)self->numNeighbours ; ++t) {
      double x1 = x + radius * cos(angle0 + dangle * t) + center ;
      double y1 = y + radius * sin(angle0 + dangle * t) + center ;
      self->neighSamplesX[t + (signed)self->numNeighbours * i] = x1 ;
      self->neighSamplesY[t + (signed)self->numNeighbours * i] = y1 ;
    }
  }
  return self ;
}

/** @brief Create a new object with default parameters
 ** @param patchSideLength size of the patches to be processed.
 ** @return new object.
 **
 ** @see ::vl_liopdesc_new. */

VlLiopDesc * vl_liopdesc_new_basic (vl_size sideLength)
{
  return vl_liopdesc_new(DEFAULT_NUM_NEIGHBOURS,
                         DEFAULT_NUM_SPATIAL_BINS,
                         DEFAULT_RADIUS,
                         sideLength) ;
}

/** @brief Delete object instance.
 ** @param self object instance. */

void
vl_liopdesc_delete (VlLiopDesc * self)
{
  vl_free (self->patchPixels) ;
  vl_free (self->patchIntensities) ;
  vl_free (self->patchPermutation) ;
  vl_free (self->neighSamplesX) ;
  vl_free (self->neighSamplesY) ;
  vl_free (self) ;
}

/* ---------------------------------------------------------------- */
/*                                          Compute LIOP descriptor */
/* ---------------------------------------------------------------- */

/** @brief Compute liop descriptor for a patch
 ** @param self object instance
 ** @param desc descriptor to be computed (output).
 ** @param patch patch to process
 **
 ** Use ::vl_liopdesc_get_dimension to get the size of the descriptor
 ** @desc. */

void
vl_liopdesc_process (VlLiopDesc * self, float * desc, float const * patch)
{
  vl_index i,t ;
  vl_index offset,numPermutations ;
  vl_index spatialBinArea, spatialBinEnd, spatialBinIndex ;
  float threshold ;

  memset(desc, 0, self->dimension) ;

  /*
   * Sort pixels in the patch by increasing intensity.
   */

  for (i = 0 ; i < (signed)self->patchSize ; ++i) {
    vl_index pixel = self->patchPixels[i] ;
    self->patchIntensities[i] = patch[pixel] ;
    self->patchPermutation[i] = i ;
  }
  patch_sort(self, self->patchSize) ;

  /*
   * Tune the threshold if needed
   */

  if (self->intensityThreshold < 0) {
    i = self->patchPermutation[0] ;
    t = self->patchPermutation[self->patchSize-1] ;
    threshold = - self->intensityThreshold
    * (self->patchIntensities[t] - self->patchIntensities[i]);
  } else {
    threshold = self->intensityThreshold ;
  }

  /*
   * Process pixels in order of increasing intenisity, dividing them into
   * spatial bins on the fly.
   */

  numPermutations = factorial(self->numNeighbours) ;
  spatialBinArea = self->patchSize / self->numSpatialBins ;
  spatialBinEnd = spatialBinArea ;
  spatialBinIndex = 0 ;
  offset = 0 ;

  for (i = 0 ; i < (signed)self->patchSize ; ++i) {
    vl_index permIndex ;
    double *sx, *sy ;

    /* advance to the next spatial bin if needed */
    if (i >= (signed)spatialBinEnd && spatialBinIndex < (signed)self->numSpatialBins - 1) {
      spatialBinEnd += spatialBinArea ;
      spatialBinIndex ++ ;
      offset += numPermutations ;
    }

    /* get intensities of neighbours of the current patch element and sor them */
    sx = self->neighSamplesX + self->numNeighbours * self->patchPermutation[i] ;
    sy = self->neighSamplesY + self->numNeighbours * self->patchPermutation[i] ;
    for (t = 0 ; t < self->numNeighbours ; ++t) {
      double x = *sx++ ;
      double y = *sy++ ;

      /* bilinear interpolation */
      vl_index ix = vl_floor_d(x) ;
      vl_index iy = vl_floor_d(y) ;

      double wx = x - ix ;
      double wy = y - iy ;

      double a = 0, b = 0, c = 0, d = 0 ;

      int L = (int) self->patchSideLength ;

      if (ix >= 0 && iy >= 0) { a = patch[ix   + iy * L] ; }
      if (ix <  L && iy >= 0) { b = patch[ix+1 + iy * L] ; }
      if (ix >= 0 && iy <  L) { c = patch[ix   + (iy+1) * L] ; }
      if (ix <  L && iy <  L) { d = patch[ix+1 + (iy+1) * L] ; }

      self->neighPermutation[t] = t;
      self->neighIntensities[t] = (1 - wy) * (a + (b - a) * wx) + wy * (c + (d - c) * wx) ;
    }
    neigh_sort (self, self->numNeighbours) ;

    /* get permutation index */
    permIndex = get_permutation_index(self->neighPermutation, self->numNeighbours);

    /*
     * Compute weight according to difference in intensity values and
     * accumulate.
     */
    {
      int k, t ;
      float weight = 0 ;
      for(k = 0; k < self->numNeighbours ; ++k) {
        for(t = k + 1; t < self->numNeighbours; ++t){
          double a = self->neighIntensities[k] ;
          double b = self->neighIntensities[t] ;
          weight += (a > b + threshold || b > a + threshold) ;
        }
      }
      desc[permIndex + offset] += weight ;
    }
  }

  /* normalization */
  {
    float norm = 0;
    for(i = 0; i < (signed)self->dimension; i++) {
      norm += desc[i]*desc[i];
    }
    norm = VL_MAX(sqrt(norm), 1e-12) ;
    for(i = 0; i < (signed)self->dimension; i++){
      desc[i] /= norm ;
    }
  }
}


/* ---------------------------------------------------------------- */
/*                                              Getters and setters */
/* ---------------------------------------------------------------- */

/** @brief Get the dimension of a LIOP descriptor.
 ** @return dimension. */

vl_size
vl_liopdesc_get_dimension (VlLiopDesc const * self)
{
  return self->dimension ;
}


/** @brief Get the number of neighbours.
 ** @param self object.
 ** @return number of neighbours.
 **/

vl_size
vl_liopdesc_get_num_neighbours (VlLiopDesc const * self)
{
  assert(self) ;
  return self->numNeighbours ;
}

/** @brief Get the intensity threshold
 ** @param self object.
 ** @return intensity threshold.
 ** @see liop-weighing
 **/

float
vl_liopdesc_get_intensity_threshold (VlLiopDesc const * self)
{
  assert(self) ;
  return self->intensityThreshold ;
}

/** @brief Set the intensity threshold
 ** @param self object.
 ** @param x intensity threshold.
 **
 ** If non-negative, the threshold as is is used when comparing
 ** intensities. If negative, the absolute value of the specified
 ** number is multipled by the maximum intensity difference inside a
 ** patch to obtain the threshold.
 **
 ** @see liop-weighing
 **/

void
vl_liopdesc_set_intensity_threshold (VlLiopDesc * self, float x)
{
  assert(self) ;
  self->intensityThreshold = x ;
}

/** @brief Get the neighbourhood radius.
 ** @param self object.
 ** @return neighbourhood radius.
 **/

double
vl_liopdesc_get_neighbourhood_radius (VlLiopDesc const * self)
{
  assert(self) ;
  return self->neighRadius ;
}

/** @brief Get the number of spatial bins.
 ** @param self object.
 ** @return number of spatial bins.
 **/

vl_size
vl_liopdesc_get_num_spatial_bins (VlLiopDesc const * self)
{
  assert(self) ;
  return self->numSpatialBins ;
}
vlfeat/vl/imopv.c0000644000175000017500000010462212237004745012675 0ustar  dimadima/** @file imopv.c
 ** @brief Vectorized image operations - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file imopv.h
 **
 ** This module provides the following image operations:
 **
 ** - Separable convolution. The function ::vl_imconvcol_vf()
 **   can be used to compute separable convolutions.
 **
 ** - Convolution by a triangular kernel. The function
 **   vl_imconvcoltri_vf() is an optimized convolution routine for
 **   triangular kernels.
 **
 ** - Distance transform. ::vl_image_distance_transform_f() is
 **   a linear algorithm to compute the distance transform of an
 **   image.
 **
 ** @remark  Some operations are optimized to exploit possible SIMD
 ** instructions. This requires image data to be properly aligned (typically
 ** to 16 bytes). Similalry, the image stride (the number of bytes to skip to move
 ** to the next image row), must be aligned.
  **/

#ifndef VL_IMOPV_INSTANTIATING

#include "imopv.h"
#include "imopv_sse2.h"
#include "mathop.h"

#define FLT VL_TYPE_FLOAT
#define VL_IMOPV_INSTANTIATING
#include "imopv.c"

#define FLT VL_TYPE_DOUBLE
#define VL_IMOPV_INSTANTIATING
#include "imopv.c"

#define FLT VL_TYPE_UINT32
#define VL_IMOPV_INSTANTIATING
#include "imopv.c"

#define FLT VL_TYPE_INT32
#define VL_IMOPV_INSTANTIATING
#include "imopv.c"

/* VL_IMOPV_INSTANTIATING */
#endif

#if defined(VL_IMOPV_INSTANTIATING) || defined(__DOXYGEN__)

#include "float.th"

/* ---------------------------------------------------------------- */
/*                                                Image Convolution */
/* ---------------------------------------------------------------- */

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

/** @fn vl_imconvcol_vd(double*,vl_size,double const*,vl_size,vl_size,vl_size,double const*,vl_index,vl_index,int,unsigned int)
 ** @brief Convolve image along columns
 **
 ** @param dst destination image.
 ** @param dst_stride width of the destination image including padding.
 ** @param src source image.
 ** @param src_width width of the source image.
 ** @param src_height height of the source image.
 ** @param src_stride width of the source image including padding.
 ** @param filt filter kernel.
 ** @param filt_begin coordinate of the first filter element.
 ** @param filt_end coordinate of the last filter element.
 ** @param step sub-sampling step.
 ** @param flags operation modes.
 **
 ** The function convolves the column of the image @a src by the
 ** filter @a filt and saves the result to the image @a dst. The size
 ** of @a dst must be equal to the size of @a src.  Formally, this
 ** results in the calculation
 **
 ** @f[
 ** \mathrm{dst} [x,y] = \sum_{p=y-\mathrm{filt\_end}}^{y-\mathrm{filt\_begin}}
 ** \mathrm{src}[x,y] \mathrm{filt}[y - p - \mathrm{filt\_begin}]
 ** @f]
 **
 ** The function subsamples the image along the columns according to
 ** the parameter @a step. Setting @a step to 1 (one) computes the
 ** elements @f$\mathrm{dst}[x,y]@f$ for all pairs (x,0), (x,1), (x,2)
 ** and so on. Setting @a step two 2 (two) computes only (x,0), (x,2)
 ** and so on (in this case the height of the destination image is
 ** floor(src_height/step)+1).
 **
 ** Calling twice the function can be used to compute 2-D separable
 ** convolutions.  Use the flag ::VL_TRANSPOSE to transpose the result
 ** (in this case @a dst has transposed dimension as well).
 **
 ** The function allows the support of the filter to be any range.
 ** Usually the support is @a filt_end = -@a filt_begin.
 **
 ** The convolution operation may pick up values outside the image
 ** boundary. To cope with this edge cases, the function either pads
 ** the image by zero (::VL_PAD_BY_ZERO) or with the values at the
 ** boundary (::VL_PAD_BY_CONTINUITY).
 **/

/** @fn vl_imconvcol_vf(float*,vl_size,float const*,vl_size,vl_size,vl_size,float const*,vl_index,vl_index,int,unsigned int)
 ** @see ::vl_imconvcol_vd
 **/

VL_EXPORT void
VL_XCAT(vl_imconvcol_v, SFX)
(T* dst, vl_size dst_stride,
 T const* src,
 vl_size src_width, vl_size src_height, vl_size src_stride,
 T const* filt, vl_index filt_begin, vl_index filt_end,
 int step, unsigned int flags)
{
  vl_index x = 0 ;
  vl_index y ;
  vl_index dheight = (src_height - 1) / step + 1 ;
  vl_bool transp = flags & VL_TRANSPOSE ;
  vl_bool zeropad = (flags & VL_PAD_MASK) == VL_PAD_BY_ZERO ;

  /* dispatch to accelerated version */
#ifndef VL_DISABLE_SSE2
  if (vl_cpu_has_sse2() && vl_get_simd_enabled()) {
    VL_XCAT3(_vl_imconvcol_v,SFX,_sse2)
    (dst,dst_stride,
     src,src_width,src_height,src_stride,
     filt,filt_begin,filt_end,
     step,flags) ;
    return ;
  }
#endif

  /* let filt point to the last sample of the filter */
  filt += filt_end - filt_begin ;

  while (x < (signed)src_width) {
    /* Calculate dest[x,y] = sum_p image[x,p] filt[y - p]
     * where supp(filt) = [filt_begin, filt_end] = [fb,fe].
     *
     * CHUNK_A: y - fe <= p < 0
     *          completes VL_MAX(fe - y, 0) samples
     * CHUNK_B: VL_MAX(y - fe, 0) <= p < VL_MIN(y - fb, height - 1)
     *          completes fe - VL_MAX(fb, height - y) + 1 samples
     * CHUNK_C: completes all samples
     */
    T const *filti ;
    vl_index stop ;

    for (y = 0 ; y < (signed)src_height ; y += step) {
      T acc = 0 ;
      T v = 0, c ;
      T const* srci ;

      filti = filt ;
      stop = filt_end - y ;
      srci = src + x - stop * src_stride ;

      if (stop > 0) {
        if (zeropad) {
          v = 0 ;
        } else {
          v = *(src + x) ;
        }
        while (filti > filt - stop) {
          c = *filti-- ;
          acc += v * c ;
          srci += src_stride ;
        }
      }

      stop = filt_end - VL_MAX(filt_begin, y - (signed)src_height + 1) + 1 ;
      while (filti > filt - stop) {
        v = *srci ;
        c = *filti-- ;
        acc += v * c ;
        srci += src_stride ;
      }

      if (zeropad) v = 0 ;

      stop = filt_end - filt_begin + 1 ;
      while (filti > filt - stop) {
        c = *filti-- ;
        acc += v * c ;
      }

      if (transp) {
        *dst = acc ; dst += 1 ;
      } else {
        *dst = acc ; dst += dst_stride ;
      }
    } /* next y */
    if (transp) {
      dst += 1 * dst_stride - dheight * 1 ;
    } else {
      dst += 1 * 1 - dheight * dst_stride ;
    }
    x += 1 ;
  } /* next x */
}

/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif

/* ---------------------------------------------------------------- */
/*                                         Image distance transform */
/* ---------------------------------------------------------------- */

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

/** @fn ::vl_image_distance_transform_d(double const*,vl_size,vl_size,vl_size,vl_size,double*,vl_uindex*,double,double)
 ** @brief Compute the distance transform of an image
 ** @param image image.
 ** @param numColumns number of columns of the image.
 ** @param numRows number of rows of the image.
 ** @param columnStride offset from one column to the next.
 ** @param rowStride offset from one row to the next.
 ** @param distanceTransform distance transform (out).
 ** @param indexes nearest neighbor indexes (in/out).
 ** @param coeff quadratic cost coefficient (non-negative).
 ** @param offset quadratic cost offset.
 **
 ** The function computes the distance transform along the first
 ** dimension of the image @a image. Let @f$ I(u,v) @f$ be @a image.
 ** Its distance transfrom @f$ D(u,v) @f$ is given by:
 **
 ** @f[
 **   u^*(u,v) = \min_{u'} I(u',v) + \mathtt{coeff} (u' - u - \mathtt{offset})^2,
 **   \quad D(u,v) = I(u^*(u,v),v).
 ** @f]
 **
 ** Notice that @a coeff must be non negative.
 **
 ** The function fills in the buffer @a distanceTransform with @f$ D
 ** @f$.  This buffer must have the same size as @a image.
 **
 ** If @a indexes is not @c NULL, it must be a matrix of the same size
 ** o the image. The function interprets the value of this matrix as
 ** indexes of the pixels, i.e @f$ \mathtt{indexes}(u,v) @f$ is the
 ** index of pixel @f$ (u,v) @f$. On output, the matrix @a indexes
 ** contains @f$ \mathtt{indexes}(u^*(u,v),v) @f$. This information
 ** can be used to determine for each pixel @f$ (u,v) @f$ its
 ** “nearest neighbor&rdquo.
 **
 ** Notice that by swapping @a numRows and @a numColumns and @a
 ** columnStride and @a rowStride, the function can be made to operate
 ** along the other image dimension. Specifically, to compute the
 ** distance transform along columns and rows, call the functinon
 ** twice:
 ***
 ** @code
 **   for (i = 0 ; i < numColumns * numRows ; ++i) indexes[i] = i ;
 **   vl_image_distance_transform_d(image,numColumns,numRows,1,numColumns,
 **                                 distanceTransform,indexes,u_coeff,u_offset) ;
 **   vl_image_distance_transform_d(distanceTransform,numRows,numColumns,numColumns,1,
 **                                 distanceTransform,indexes,u_coeff,u_offset) ;
 ** @endcode
 **
 ** @par Algorithm
 **
 ** The function implements the algorithm described in:
 ** P. F. Felzenszwalb and D. P. Huttenlocher, Distance Transforms
 ** of Sampled Functions, Technical Report, Cornell University,
 ** 2004.
 **
 ** Since the algorithm operates along one dimension per time,
 ** consider the 1D version of the problem for simplicity:
 **
 ** @f[
 **  d(y) = \min_{x} g(y;x), \quad g(y;x) = f(x) + \alpha (y - x - \beta)^2,
 **  \quad x,y \in \{0,1,\dots,N-1\}.
 ** @f]
 **
 ** Hence the distance transform @f$ d(y) @f$ is the lower envelope of
 ** the family of parabolas @f$ g(y;x) @f$ indexed by @f$ x
 ** @f$. Notice that all parabolas have the same curvature and that
 ** their centers are located at @f$ x + \beta, @f$ @f$ x=0,\dots,N-1
 ** @f$. The algorithm considers one parabola per time, from left to
 ** right, and finds the interval for which the parabola belongs to
 ** the lower envelope (if any).
 **
 ** Initially, only the leftmost parabola @f$ g(y;0) @f$ has been
 ** considered, and its validity interval is @f$(-\infty, \infty) @f$.
 ** Then the second parabola @f$ g(y;1) @f$ is considered. As long as
 ** @f$ \alpha > 0 @f$, the two parabolas @f$ g(y;0),\ g(y;1) @f$
 ** intersect at a unique point @f$ \bar y @f$. Then the first
 ** parabola belongs to the envelope in the interval @f$ (-\infty,
 ** \bar y] @f$ and the second one in the interval @f$ (\bar y,
 ** +\infty] @f$. When the third parabola @f$ g(y;2) @f$ is
 ** considered, the intersection point @f$ \hat y @f$ with the
 ** previously added parabola @f$ g(y;1) @f$ is found. Now two cases
 ** may arise:
 **
 ** - @f$ \hat y > \bar y @f$, in which case all three parabolas
 **   belong to the envelope in the intervals @f$ (-\infty,\bar y],
 **   (\bar y, \hat y], (\hat y, +\infty] @f$.
 **
 ** - @f$ \hat y \leq \bar y @f$, in which case the second parabola
 **   @f$ g(y;1) @f$ has no point beloning to the envelope, and it is
 **   removed.  One then remains with the two parabolas @f$ g(y;0),\
 **   g(y;2) @f$ and the algorithm is re-iterated.
 **
 ** The algorithm proceeds in this fashion. Every time a new parabola
 ** is considered, its intersection point with the previously added
 ** parabola on the left is computed, and that parabola is potentially
 ** removed.  The cost of an iteration is 1 plus the number of deleted
 ** parabolas. Since there are @f$ N @f$ iterations and at most @f$ N
 ** @f$ parabolas to delete overall, the complexity is linear,
 ** i.e. @f$ O(N) @f$.
 **/

/** @fn ::vl_image_distance_transform_f(float const*,vl_size,vl_size,vl_size,vl_size,float*,vl_uindex*,float,float)
 ** @see ::vl_image_distance_transform_d
 **/

VL_EXPORT void
VL_XCAT(vl_image_distance_transform_,SFX)
(T const * image,
 vl_size numColumns,
 vl_size numRows,
 vl_size columnStride,
 vl_size rowStride,
 T * distanceTransform,
 vl_uindex * indexes,
 T coeff,
 T offset)
{
  /* Each image pixel corresponds to a parabola. The algorithm scans
   such parabolas from left to right, keeping track of which
   parabolas belong to the lower envelope and in which interval. There are
   NUM active parabolas, FROM stores the beginning of the interval
   for which a certain parabola is part of the envoelope, and WHICH store
   the index of the parabola (that is, the pixel x from which the parabola
   originated).
   */
  vl_uindex x, y ;
  T * from = vl_malloc (sizeof(T) * (numColumns + 1)) ;
  T * base = vl_malloc (sizeof(T) * numColumns) ;
  vl_uindex * baseIndexes = vl_malloc (sizeof(vl_uindex) * numColumns) ;
  vl_uindex * which = vl_malloc (sizeof(vl_uindex) * numColumns) ;
  vl_uindex num = 0 ;

  for (y = 0 ; y < numRows ; ++y) {
    num = 0 ;
    for (x = 0 ; x < numColumns ; ++x) {
      T r = image[x  * columnStride + y * rowStride] ;
      T x2 = x * x ;
#if (FLT == VL_TYPE_FLOAT)
      T from_ = - VL_INFINITY_F ;
#else
      T from_ = - VL_INFINITY_D ;
#endif

      /*
       Add next parabola (there are NUM so far). The algorithm finds
       intersection INTERS with the previously added parabola. If
       the intersection is on the right of the "starting point" of
       this parabola, then the previous parabola is kept, and the
       new one is added to its right. Otherwise the new parabola
       "eats" the old one, which gets deleted and the check is
       repeated with the parabola added before the deleted one.
       */

      while (num >= 1) {
        vl_uindex x_ = which[num - 1] ;
        T x2_ = x_ * x_ ;
        T r_ = image[x_ * columnStride + y * rowStride] ;
        T inters ;
        if (r == r_) {
          /* handles the case r = r_ = \pm inf */
          inters = (x + x_) / 2.0 + offset ;
        }
#if (FLT == VL_TYPE_FLOAT)
        else if (coeff > VL_EPSILON_F)
#else
        else if (coeff > VL_EPSILON_D)
#endif
        {
          inters = ((r - r_) + coeff * (x2 - x2_)) / (x - x_) / (2*coeff) + offset ;
        } else {
          /* If coeff is very small, the parabolas are flat (= lines).
           In this case the previous parabola should be deleted if the current
           pixel has lower score */
#if (FLT == VL_TYPE_FLOAT)
          inters = (r < r_) ? - VL_INFINITY_F : VL_INFINITY_F ;
#else
          inters = (r < r_) ? - VL_INFINITY_D : VL_INFINITY_D ;
#endif
        }
        if (inters <= from [num - 1]) {
          /* delete a previous parabola */
          -- num ;
        } else {
          /* accept intersection */
          from_ = inters ;
          break ;
        }
      }

      /* add a new parabola */
      which[num] = x ;
      from[num] = from_ ;
      base[num] = r ;
      if (indexes) baseIndexes[num] = indexes[x  * columnStride + y * rowStride] ;
      num ++ ;
    } /* next column */

#if (FLT == VL_TYPE_FLOAT)
    from[num] = VL_INFINITY_F ;
#else
    from[num] = VL_INFINITY_D ;
#endif

    /* fill in */
    num = 0 ;
    for (x = 0 ; x < numColumns ; ++x) {
      double delta ;
      while (x >= from[num + 1]) ++ num ;
      delta = (double) x - (double) which[num] - offset ;
      distanceTransform[x  * columnStride + y * rowStride]
      = base[num] + coeff * delta * delta ;
      if (indexes) {
        indexes[x  * columnStride + y * rowStride]
        = baseIndexes[num] ;
      }
    }
  } /* next row */

  vl_free (from) ;
  vl_free (which) ;
  vl_free (base) ;
  vl_free (baseIndexes) ;
}

/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif

/* ---------------------------------------------------------------- */
/*                         Image convolution by a triangular kernel */
/* ---------------------------------------------------------------- */

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

/** @fn vl_imconvcoltri_d(double*,vl_size,double const*,vl_size,vl_size,vl_size,vl_size,vl_size,int unsigned)
 ** @brief Convolve an image along the columns with a triangular kernel
 ** @param dest destination image.
 ** @param destStride destination image stride.
 ** @param image image to convolve.
 ** @param imageWidth width of the image.
 ** @param imageHeight height of the image.
 ** @param imageStride width of the image including padding.
 ** @param filterSize size of the triangular filter.
 ** @param step sub-sampling step.
 ** @param flags operation modes.
 **
 ** The function convolves the columns of the image @a image with the
 ** triangular kernel
 **
 ** @f[
 **   k(t) = \frac{1}{\Delta^2} \max\{ \Delta -  |t|, 0 \},
 **   \quad t \in \mathbb{Z}
 ** @f]
 **
 ** The paramter @f$ \Delta @f$, equal to the function argument @a
 ** filterSize, controls the width of the kernel. Notice that the
 ** support of @f$ k(x) @f$ as a continuous function of @f$ x @f$ is
 ** the open interval @f$ (-\Delta,\Delta) @f$, which has length @f$
 ** 2\Delta @f$.  However, @f$ k(x) @f$ restricted to the ingeter
 ** domain @f$ x \in \mathcal{Z} @f$ has support @f$ \{ -\Delta + 1,
 ** \Delta +2, \dots, \Delta-1 \} @f$, which counts @f$ 2 \Delta - 1
 ** @f$ elements only. In particular, the discrete kernel is symmetric
 ** about the origin for all values of @f$ \Delta @f$.
 **
 ** The normalization factor @f$ 1 / \Delta^2 @f$ guaratnees that the
 ** filter is normalized to one, i.e.:
 **
 ** @f[
 **   \sum_{t=-\infty}^{+\infty} k(t) = 1
 ** @f]
 **
 ** @par Algorithm
 **
 ** The function exploits the fact that convolution by a triangular
 ** kernel can be expressed as the repeated convolution by a
 ** rectangular kernel, and that the latter can be performed in time
 ** indepenedent on the fiter width by using an integral-image type
 ** trick. Overall, the algorithm complexity is independent on the
 ** parameter @a filterSize and linear in the nubmer of image pixels.
 **
 ** @see ::vl_imconvcol_d for details on the meaning of the other parameters.
 **/

/** @fn vl_imconvcoltri_f(float*,vl_size,float const*,vl_size,vl_size,vl_size,vl_size,vl_size,int unsigned)
 ** @brief Convolve an image along the columns with a triangular kernel
 ** @see ::vl_imconvcoltri_d()
 **/

VL_EXPORT void
VL_XCAT(vl_imconvcoltri_, SFX)
(T * dest, vl_size destStride,
 T const * image,
 vl_size imageWidth, vl_size imageHeight, vl_size imageStride,
 vl_size filterSize,
 vl_size step, unsigned int flags)
{
  vl_index x, y, dheight ;
  vl_bool transp = flags & VL_TRANSPOSE ;
  vl_bool zeropad = (flags & VL_PAD_MASK) == VL_PAD_BY_ZERO ;
  T scale = (T) (1.0 / ((double)filterSize * (double)filterSize)) ;
  T * buffer = vl_malloc (sizeof(T) * (imageHeight + filterSize)) ;
  buffer += filterSize ;

  if (imageHeight == 0) {
    return  ;
  }

  x = 0 ;
  dheight = (imageHeight - 1) / step + 1 ;

  while (x < (signed)imageWidth) {
    T const * imagei ;
    imagei = image + x + imageStride * (imageHeight - 1) ;

    /* We decompose the convolution by a triangluar signal as the convolution
     * by two rectangular signals. The rectangular convolutions are computed
     * quickly by computing the integral signals. Each rectangular convolution
     * introduces a delay, which is compensated by convolving each in opposite
     * directions.
     */

    /* integrate backward the column */
    buffer[imageHeight - 1] = *imagei ;
    for (y = (signed)imageHeight - 2 ; y >=  0 ; --y) {
      imagei -= imageStride ;
      buffer[y] = buffer[y + 1] + *imagei ;
    }
    if (zeropad) {
      for ( ; y >= - (signed)filterSize ; --y) {
        buffer[y] = buffer[y + 1] ;
      }
    } else {
      for ( ; y >= - (signed)filterSize ; --y) {
        buffer[y] = buffer[y + 1] + *imagei ;
      }
    }

    /* compute the filter forward */
    for (y = - (signed)filterSize ;
         y < (signed)imageHeight - (signed)filterSize ; ++y) {
      buffer[y] = buffer[y] - buffer[y + filterSize] ;
    }
    if (! zeropad) {
      for (y = (signed)imageHeight - (signed)filterSize ;
           y < (signed)imageHeight ;
           ++y) {
        buffer[y] = buffer[y] - buffer[imageHeight - 1]  *
        ((signed)imageHeight - (signed)filterSize - y) ;
      }
    }

    /* integrate forward the column */
    for (y = - (signed)filterSize + 1 ;
         y < (signed)imageHeight ; ++y) {
      buffer[y] += buffer[y - 1] ;
    }

    /* compute the filter backward */
    {
      vl_size stride = transp ? 1 : destStride ;
      dest += dheight * stride ;
      for (y = step * (dheight - 1) ; y >= 0 ; y -= step) {
        dest -= stride ;
        *dest = scale * (buffer[y] - buffer[y - (signed)filterSize]) ;
      }
      dest += transp ? destStride : 1 ;
    }
    x += 1 ;
  } /* next x */
  vl_free (buffer - filterSize) ;
}

/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif

/* ---------------------------------------------------------------- */
/*                                               Gaussian Smoothing */
/* ---------------------------------------------------------------- */

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

/** @fn vl_imsmooth_d(double*,vl_size,double const*,vl_size,vl_size,vl_size,double,double)
 ** @brief Smooth an image with a Gaussian filter
 ** @param smoothed
 ** @param smoothedStride
 ** @param image
 ** @param width
 ** @param height
 ** @param stride
 ** @param sigmax
 ** @param sigmay
 **/

/** @fn vl_imsmooth_f(float*,vl_size,float const*,vl_size,vl_size,vl_size,double,double)
 ** @brief Smooth an image with a Gaussian filter
 ** @see ::vl_imsmooth_d
 **/

static T*
VL_XCAT(_vl_new_gaussian_fitler_,SFX)(vl_size *size, double sigma)
{
  T* filter ;
  T mass = (T)1.0 ;
  vl_index i ;
  vl_size width = vl_ceil_d(sigma * 3.0) ;
  *size = 2 * width + 1 ;

  assert(size) ;

  filter = vl_malloc((*size) * sizeof(T)) ;
  filter[width] = 1.0 ;
  for (i = 1 ; i <= (signed)width ; ++i) {
    double x = (double)i / sigma ;
    double g = exp(-0.5 * x * x) ;
    mass += g + g ;
    filter[width-i] = g ;
    filter[width+i] = g ;
  }
  for (i = 0 ; i < (signed)(*size) ; ++i) {filter[i] /= mass ;}
  return filter ;
}

VL_EXPORT void
VL_XCAT(vl_imsmooth_, SFX)
(T * smoothed, vl_size smoothedStride,
 T const *image, vl_size width, vl_size height, vl_size stride,
 double sigmax, double sigmay)
{
  T *filterx, *filtery, *buffer ;
  vl_size sizex, sizey ;

  filterx = VL_XCAT(_vl_new_gaussian_fitler_,SFX)(&sizex,sigmax) ;
  if (sigmax == sigmay) {
    filtery = filterx ;
    sizey = sizex ;
  } else {
    filtery = VL_XCAT(_vl_new_gaussian_fitler_,SFX)(&sizey,sigmay) ;
  }
  buffer = vl_malloc(width*height*sizeof(T)) ;

  VL_XCAT(vl_imconvcol_v,SFX) (buffer, height,
                               image, width, height, stride,
                               filtery,
                               -((signed)sizey-1)/2, ((signed)sizey-1)/2,
                               1, VL_PAD_BY_CONTINUITY | VL_TRANSPOSE) ;

  VL_XCAT(vl_imconvcol_v,SFX) (smoothed, smoothedStride,
                               buffer, height, width, height,
                               filterx,
                               -((signed)sizex-1)/2, ((signed)sizex-1)/2,
                               1, VL_PAD_BY_CONTINUITY | VL_TRANSPOSE) ;

  vl_free(buffer) ;
  vl_free(filterx) ;
  if (sigmax != sigmay) {
    vl_free(filtery) ;
  }
}

/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif

/* ---------------------------------------------------------------- */
/*                                                   Image Gradient */
/* ---------------------------------------------------------------- */

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

/** @fn vl_imgradient_d(double*,double*,vl_size,vl_size,double*,vl_size,vl_size,vl_size)
 ** @brief Compute image gradient
 ** @param xGradient Pointer to amplitude gradient plane
 ** @param yGradient Pointer to angle gradient plane
 ** @param gradWidthStride Width of the gradient plane including padding
 ** @param gradHeightStride Height of the gradient plane including padding
 ** @param image Pointer to the source image
 ** @param imageWidth Source image width
 ** @param imageHeight Source image height
 ** @param imageStride Width of the image including padding.
 **
 ** This functions computes the amplitudes and angles of input image gradient.
 **
 ** Gradient is computed simple by gradient kernel \f$ (-1 ~ 1) \f$,
 ** \f$ (-1 ~ 1)^T \f$ for border pixels and with sobel filter kernel
 ** \f$ (-0.5 ~ 0 ~ 0.5) \f$, \f$ (-0.5 ~ 0 ~ 0.5)^T \f$ otherwise on the input
 ** image @a image yielding x-gradient \f$ dx \f$, stored in @a xGradient and
 ** y-gradient \f$ dy \f$, stored in @a yGradient, respectively.
 **
 ** This function also allows to process only part of the input image
 ** defining the @a imageStride as original image width and @a width as
 ** width of the sub-image.
 **
 ** Also it allows to easily align the output data by definition
 ** of the @a gradWidthStride and @a gradHeightStride .
 **/

/** @fn vl_imgradient_f(float*,float*,vl_size,vl_size,float*,vl_size,vl_size,vl_size)
 ** @brief Compute image gradient
 ** @see ::vl_imgradient_d
 **/

VL_EXPORT void
VL_XCAT(vl_imgradient_, SFX)
(T * xGradient, T * yGradient,
 vl_size gradWidthStride, vl_size gradHeightStride,
 T const * image,
 vl_size imageWidth, vl_size imageHeight,
 vl_size imageStride)
{
  /* Shortcuts */
  vl_index const xo = 1 ;
  vl_index const yo = imageStride ;
  vl_size const w = imageWidth;
  vl_size const h = imageHeight;

  T const *src, *end ;
  T *pgrad_x, *pgrad_y;
  vl_size y;

  src  = image ;
  pgrad_x = xGradient ;
  pgrad_y = yGradient ;

  /* first pixel of the first row */
  *pgrad_x = src[+xo] - src[0] ;
  pgrad_x += gradWidthStride;
  *pgrad_y = src[+yo] - src[0] ;
  pgrad_y += gradWidthStride;
  src++;

  /* middle pixels of the  first row */
  end = (src - 1) + w - 1 ;
  while (src < end) {
    *pgrad_x = 0.5 * (src[+xo] - src[-xo]) ;
    pgrad_x += gradWidthStride;
    *pgrad_y =        src[+yo] - src[0] ;
    pgrad_y += gradWidthStride;
    src++;
  }

  /* last pixel of the first row */
  *pgrad_x = src[0]   - src[-xo] ;
  pgrad_x += gradWidthStride;
  *pgrad_y = src[+yo] - src[0] ;
  pgrad_y += gradWidthStride;
  src++;

  xGradient += gradHeightStride;
  pgrad_x = xGradient;
  yGradient += gradHeightStride;
  pgrad_y = yGradient;
  image += yo;
  src = image;

  for (y = 1 ; y < h -1 ; ++y) {

    /* first pixel of the middle rows */
    *pgrad_x =        src[+xo] - src[0] ;
    pgrad_x += gradWidthStride;
    *pgrad_y = 0.5 * (src[+yo] - src[-yo]) ;
    pgrad_y += gradWidthStride;
    src++;

    /* middle pixels of the middle rows */
    end = (src - 1) + w - 1 ;
    while (src < end) {
      *pgrad_x = 0.5 * (src[+xo] - src[-xo]) ;
      pgrad_x += gradWidthStride;
      *pgrad_y = 0.5 * (src[+yo] - src[-yo]) ;
      pgrad_y += gradWidthStride;
      src++;
    }

    /* last pixel of the middle row */
    *pgrad_x =        src[0]   - src[-xo] ;
    pgrad_x += gradWidthStride;
    *pgrad_y = 0.5 * (src[+yo] - src[-yo]) ;
    pgrad_y += gradWidthStride;
    src++;

    xGradient += gradHeightStride;
    pgrad_x = xGradient;
    yGradient += gradHeightStride;
    pgrad_y = yGradient;
    image += yo;
    src = image;
  }

  /* first pixel of the last row */
  *pgrad_x = src[+xo] - src[0] ;
  pgrad_x += gradWidthStride;
  *pgrad_y = src[  0] - src[-yo] ;
  pgrad_y += gradWidthStride;
  src++;

  /* middle pixels of the last row */
  end = (src - 1) + w - 1 ;
  while (src < end) {
    *pgrad_x = 0.5 * (src[+xo] - src[-xo]) ;
    pgrad_x += gradWidthStride;
    *pgrad_y =        src[0]   - src[-yo] ;
    pgrad_y += gradWidthStride;
    src++;
  }

  /* last pixel of the last row */
  *pgrad_x = src[0]   - src[-xo] ;
  *pgrad_y = src[0]   - src[-yo] ;
}
/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif


/** @fn vl_imgradient_polar_d(double*,double*,vl_size,vl_size,double const*,vl_size,vl_size,vl_size)
 ** @brief Compute gradient mangitudes and directions of an image.
 ** @param amplitudeGradient Pointer to amplitude gradient plane
 ** @param angleGradient Pointer to angle gradient plane
 ** @param gradWidthStride Width of the gradient plane including padding
 ** @param gradHeightStride Height of the gradient plane including padding
 ** @param image Pointer to the source image
 ** @param imageWidth Source image width
 ** @param imageHeight Source image height
 ** @param imageStride Width of the source image including padding.
 **
 ** This functions computes the amplitudes and angles of input image gradient.
 **
 ** Gradient is computed simple by gradient kernel \f$ (-1 ~ 1) \f$,
 ** \f$ (-1 ~ 1)^T \f$ for border pixels and with sobel filter kernel
 ** \f$ (-0.5 ~ 0 ~ 0.5) \f$, \f$ (-0.5 ~ 0 ~ 0.5)^T \f$ otherwise on
 ** the input image @a image yielding x-gradient \f$ dx \f$, stored in
 ** @a xGradient and y-gradient \f$ dy \f$, stored in @a yGradient,
 ** respectively.
 **
 ** The amplitude of the gradient, stored in plane @a
 ** amplitudeGradient, is then calculated as \f$ \sqrt(dx^2+dy^2) \f$
 ** and the angle of the gradient, stored in @a angleGradient is \f$
 ** atan(\frac{dy}{dx}) \f$ normalised into interval 0 and @f$ 2\pi
 ** @f$.
 **
 ** This function also allows to process only part of the input image
 ** defining the @a imageStride as original image width and @a width
 ** as width of the sub-image.
 **
 ** Also it allows to easily align the output data by definition
 ** of the @a gradWidthStride and @a gradHeightStride .
 **/

/** @fn vl_imgradient_polar_f(float*,float*,vl_size,vl_size,float const*,vl_size,vl_size,vl_size)
 ** @see ::vl_imgradient_polar_d
 **/

#if (FLT == VL_TYPE_FLOAT || FLT == VL_TYPE_DOUBLE)

VL_EXPORT void
VL_XCAT(vl_imgradient_polar_, SFX)
(T * gradientModulus, T * gradientAngle,
 vl_size gradientHorizontalStride, vl_size gradHeightStride,
 T const* image,
 vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
{
  /* Shortcuts */
  vl_index const xo = 1 ;
  vl_index const yo = imageStride ;
  vl_size const w = imageWidth;
  vl_size const h = imageHeight;

  T const *src, *end;
  T *pgrad_angl, *pgrad_ampl;
  T gx, gy ;
  vl_size y;

#define SAVE_BACK                                                    \
*pgrad_ampl = vl_fast_sqrt_f (gx*gx + gy*gy) ;                       \
pgrad_ampl += gradientHorizontalStride ;                             \
*pgrad_angl = vl_mod_2pi_f   (vl_fast_atan2_f (gy, gx) + 2*VL_PI) ;  \
pgrad_angl += gradientHorizontalStride ;                             \
++src ;                                                              \

  src  = image ;
  pgrad_angl = gradientAngle ;
  pgrad_ampl = gradientModulus ;

  /* first pixel of the first row */
  gx = src[+xo] - src[0] ;
  gy = src[+yo] - src[0] ;
  SAVE_BACK ;

  /* middle pixels of the  first row */
  end = (src - 1) + w - 1 ;
  while (src < end) {
    gx = 0.5 * (src[+xo] - src[-xo]) ;
    gy =        src[+yo] - src[0] ;
    SAVE_BACK ;
  }

  /* last pixel of the first row */
  gx = src[0]   - src[-xo] ;
  gy = src[+yo] - src[0] ;
  SAVE_BACK ;

  gradientModulus += gradHeightStride;
  pgrad_ampl = gradientModulus;
  gradientAngle += gradHeightStride;
  pgrad_angl = gradientAngle;
  image += imageStride;
  src = image;

  for (y = 1 ; y < h -1 ; ++y) {

    /* first pixel of the middle rows */
    gx =        src[+xo] - src[0] ;
    gy = 0.5 * (src[+yo] - src[-yo]) ;
    SAVE_BACK ;

    /* middle pixels of the middle rows */
    end = (src - 1) + w - 1 ;
    while (src < end) {
      gx = 0.5 * (src[+xo] - src[-xo]) ;
      gy = 0.5 * (src[+yo] - src[-yo]) ;
      SAVE_BACK ;
    }

    /* last pixel of the middle row */
    gx =        src[0]   - src[-xo] ;
    gy = 0.5 * (src[+yo] - src[-yo]) ;
    SAVE_BACK ;

    gradientModulus += gradHeightStride;
    pgrad_ampl = gradientModulus;
    gradientAngle += gradHeightStride;
    pgrad_angl = gradientAngle;
    image += imageStride;
    src = image;
  }

  /* first pixel of the last row */
  gx = src[+xo] - src[0] ;
  gy = src[  0] - src[-yo] ;
  SAVE_BACK ;

  /* middle pixels of the last row */
  end = (src - 1) + w - 1 ;
  while (src < end) {
    gx = 0.5 * (src[+xo] - src[-xo]) ;
    gy =        src[0]   - src[-yo] ;
    SAVE_BACK ;
  }

  /* last pixel of the last row */
  gx = src[0]   - src[-xo] ;
  gy = src[0]   - src[-yo] ;
  SAVE_BACK ;

}
/* VL_TYPE_FLOAT, VL_TYPE_DOUBLE */
#endif

/* ---------------------------------------------------------------- */
/*                                                   Integral Image */
/* ---------------------------------------------------------------- */

/** @fn vl_imintegral_d(double*,vl_size,double const*,vl_size,vl_size,vl_size)
 ** @brief Compute integral image
 **
 ** @param integral integral image.
 ** @param integralStride integral image stride.
 ** @param image source image.
 ** @param imageWidth source image width.
 ** @param imageHeight source image height.
 ** @param imageStride source image stride.
 **
 ** Let @f$ I(x,y), (x,y) \in [0, W-1] \times [0, H-1] @f$. The
 ** function computes the integral image @f$ J(x,y) @f$ of @f$ I(x,g)
 ** @f$:
 **
 ** @f[
 **   J(x,y) = \sum_{x'=0}^{x} \sum_{y'=0}^{y} I(x',y')
 ** @f]
 **
 ** The integral image @f$ J(x,y) @f$ can be used to compute quickly
 ** the integral of of @f$ I(x,y) @f$ in a rectangular region @f$ R =
 ** [x',x'']\times[y',y''] @f$:
 **
 ** @f[
 **  \sum_{(x,y)\in[x',x'']\times[y',y'']} I(x,y) =
 **  (J(x'',y'') - J(x'-1, y'')) - (J(x'',y'-1) - J(x'-1,y'-1)).
 ** @f]
 **
 ** Note that the order of operations is important when the integral image
 ** has an unsigned data type (e.g. ::vl_uint32). The formula
 ** is easily derived as follows:
 **
 ** @f{eqnarray*}
 **   \sum_{(x,y)\in R} I(x,y)
 **   &=& \sum_{x=x'}^{x''} \sum_{y=y'}^{y''} I(x,y)\\
 **   &=& \sum_{x=0}^{x''}  \sum_{y=y'}^{y''} I(x,y)
 **     - \sum_{x=0}^{x'-1} \sum_{y=y'}^{y''} I(x,y)\\
 **   &=& \sum_{x=0}^{x''}  \sum_{y=0}^{y''}  I(x,y)
 **     - \sum_{x=0}^{x''}  \sum_{y=0}^{y'-1} I(x,y)
 **     - \sum_{x=0}^{x'-1} \sum_{y=0}^{y''}  I(x,y)
 **     + \sum_{x=0}^{x'-1} \sum_{y=0}^{y'-1} I(x,y)\\
 **   &=& J(x'',y'') - J(x'-1,y'') - J(x'',y'-1) + J(x'-1,y'-1).
 ** @f}
 **/

/** @fn vl_imintegral_f(float*,vl_size,float const*,vl_size,vl_size,vl_size)
 ** @brief Compute integral image
 ** @see ::vl_imintegral_d.
 **/

/** @fn vl_imintegral_ui32(vl_uint32*,vl_size,vl_uint32 const*,vl_size,vl_size,vl_size)
 ** @brief Compute integral image
 ** @see ::vl_imintegral_d.
 **/

/** @fn vl_imintegral_i32(vl_int32*,vl_size,vl_int32 const*,vl_size,vl_size,vl_size)
 ** @brief Compute integral image
 ** @see ::vl_imintegral_d.
 **/

VL_EXPORT void
VL_XCAT(vl_imintegral_, SFX)
(T * integral, vl_size integralStride,
 T const * image,
 vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
{
  vl_uindex x, y ;
  T temp  = 0 ;

  if (imageHeight > 0) {
    for (x = 0 ; x < imageWidth ; ++ x) {
      temp += *image++ ;
      *integral++ = temp ;
    }
  }

  for (y = 1 ; y < imageHeight ; ++ y) {
    T * integralPrev ;
    integral += integralStride - imageWidth ;
    image += imageStride - imageWidth ;
    integralPrev = integral - integralStride ;

    temp = 0 ;
    for (x = 0 ; x < imageWidth ; ++ x) {
      temp += *image++ ;
      *integral++ = *integralPrev++ + temp ;
    }
  }
}

/* endif VL_IMOPV_INSTANTIATING */
#undef FLT
#undef VL_IMOPV_INSTANTIATING
#endif
vlfeat/vl/getopt_long.c0000644000175000017500000002173412237004736014066 0ustar  dimadima/** @file getopt_long.c
 ** @brief getopt_long - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**
@file   getopt_long.h
@brief  getopt_long
@author Andrea Vedaldi

This is a drop-in replacament of GNU getopt_long meant to be used
on platforms that do not support such functionality.
**/

#include 
#include 
#include 

#include "generic.h"
#include "getopt_long.h"

int    opterr = 1 ;
int    optind = 1 ;
int    optopt ;
char * optarg ;
int    optreset ;

#define BADCH	'?'
#define BADARG	':'
#define EEND    -1
#define EMSG	""

/** @brief Parse long options (BSD style)
 ** @param argc number of arguments.
 ** @param argv pointer to the vector of arguments.
 ** @param optstring list of abbreviated options
 ** @param longopts list of long options.
 ** @param longindex index of current option in @a longopts.
 ** @return the code of the next option.
 **
 ** This function extract long and short options from the argument
 ** list @a argv of @a argc entries.
 **
 ** A short options sequence is introduced by a single dash character
 ** @c -. Each short option is described by a single character in the
 ** string @a optstring, possibly followed by a @c : character to
 ** denote a (mandatory) argument of the short option. A short option
 ** with an argument cannot appear in the middle of a short option
 ** sequence, but only at the end.
 **
 ** A long option is introduced by a double dash @c --. Each long
 ** option is described by an instance of the ::option structure in
 ** the @a longopts table (the last entry must be filled with zeroes
 ** to denote the end).
 **
 ** Illegal options and missing arguments cause the function to skip
 ** the option and return '?'. If ::opterr is @c true (default), the
 ** function prints an error message to @a stderr. Finally, if @a
 ** optstring has a leading @c :, then error messages are suppressed
 ** and a missing argument causes @a : to be returned.
 **
 ** @remark The function is currently not thread safe.
 **/

VL_EXPORT int
getopt_long(int argc, char *const argv[],
            const char *optstring,
            const struct option * longopts,
            int *longindex)
{
  static char *place = EMSG;	/* option letter processing */
  static int   optbegin  = 0 ;
  static int   optend    = 0 ;
  char        *oli;		/* option letter list index */
  int          has_colon = 0 ;
  int          ret_val   = 0 ;

  /*
     A semicolon at the beginning of optstring has a special meaning.
     If we find one, we annote and remove it.
  */
  has_colon = optstring && optstring[0] == ':' ;
  if (has_colon) ++ optstring ;

  /*
   Here we are either processing a short option sequence or
   we start processing a new option. This is indicated by optreset.
  */

  if (optreset || *place == '\0') {

    /* ---------------------------------------------------------------
     *                                 Look for next short/long option
     * ------------------------------------------------------------ */
    optreset = 0 ;

    /* no more arguments ? */
    if (optind >= argc) {
      place = EMSG ;
      return -1 ;
    }

    /* next argument that may hold an option */
    optbegin = optind ;

    /* ---------------------------------------------------------------
     *                                     Look for an option to parse
     * ------------------------------------------------------------ */

  parse_option_at_optbegin :

    /* place points to the candidate option */
    place = argv [optbegin] ;

    /* an option is introduced by '-' */
    if (place [0] != '-') {
      /* this argument is not an option: try next argument */
      ++ optbegin ;
      if (optbegin >= argc) {
        /* no more arguments to look for options */
        place = EMSG ;
        return -1 ;
      }
      goto parse_option_at_optbegin ;
    }

    /* consume leading `-' */
    ++ place ;

    /* assume the option is composed of one argument only */
    optend = optbegin + 1 ;

    /* assume no argument */
    optarg = 0 ;

    /* ---------------------------------------------------------------
     *                                                     option `--'
     * ------------------------------------------------------------ */

    /* this special option (void long option) ends the option processing */
    if (place[0]        &&
        place[0] == '-' &&
        place[1] == '\0') {

      optind  = optend ;
      place   = EMSG ;
      ret_val = -1 ;
      goto done_option ;
    }

    /* ---------------------------------------------------------------
     *                                                     long option
     * ------------------------------------------------------------ */

    if (place[0]        &&
        place[0] == '-' &&
        place[1] ) {

      size_t namelen ;
      int i ;

      /* consume second `-' */
      ++ place ;

      /* count characters before `=' */
      namelen = strcspn(place, "=") ;

      /* scan longopts for this option */
      for (i = 0 ; longopts[i].name != NULL ; ++ i) {

        if (strlen  (       longopts[i].name) == namelen &&
            strncmp (place, longopts[i].name, namelen) == 0 ) {

          /* save back long option index */
          if (longindex) *longindex = i ;

          /* process long option argument */
          if (longopts[i].has_arg == required_argument ||
              longopts[i].has_arg == optional_argument) {

            /* --option=value style */
            if (place[namelen] == '=') {
              optarg = place + namelen + 1 ;
            }

            /* --option value style (only required_argument) */
            else if (longopts[i].has_arg == required_argument) {
              /* missing argument ? */
              if (optbegin >= argc - 1) {
                if (! has_colon && opterr)
                  fprintf(stderr,
                          "%s: option requires an argument -- %s\n",
                          argv[0], place);
                place   = EMSG ;
                ret_val = has_colon ? BADARG : BADCH ;
                goto done_option ;
              }
              optarg = argv [optend] ;
              ++ optend ;
            }
          }

          /* determine return value */
          if (longopts[i].flag == NULL) {
            ret_val = longopts[i].val ;
          }
          else {
            *longopts[i].flag = longopts[i].val;
            ret_val = 0 ;
          }

          /* mark sequence closed */
          place = EMSG ;
          goto done_option ;
        } /* if match */

      } /* scan longoptions */

      /* no matching option found */
      if (! has_colon && opterr)
        fprintf(stderr,
                "%s: illegal option -- %s\n", argv[0], place) ;
      place   = EMSG ;
      ret_val = BADCH ;
      goto done_option ;
    }
  } /* end new option */

  /* -----------------------------------------------------------------
   *                                      Finish short option sequence
   * -------------------------------------------------------------- */
  optopt = (int) *place++ ;

  /* search charcater in option list */
  oli = strchr(optstring, optopt);

  /* short option not found */
  if (!oli) {

    if (! has_colon && opterr)
      fprintf(stderr,
              "%s: illegal option -- %c\n",
              argv[0], optopt);

    if (*place) {
      /* more short options in the list */
      return BADCH ;
    }

    else {
      /* error occured as last option in the list */
      place   = EMSG ;
      ret_val = BADCH ;
      goto done_option ;
    }
  } /* end short option not found */

  if (oli[1] != ':') {
    /* short option with no argument */

    if (*place) {
      /* more short options in the list */
      return optopt ;
    }
    else {
      /* last option in the list */
      place   = EMSG ;
      ret_val = optopt ;
      goto done_option ;
    }

  } else {
    /* short option with argument */

    /* -ovalue style */
    if (*place) {
      optarg  = place ;
      place   = EMSG ;
      ret_val = optopt ;
      goto done_option ;
    }
    /* -o value style: missing argument */
    else if (optbegin >= argc - 1) {
      if (! has_colon && opterr)
        fprintf(stderr,
                "%s: option requires an argument -- %c\n",
                argv[0], optopt);
      place   = EMSG ;
      ret_val = has_colon ? BADARG : BADCH ;
      goto done_option ;
    }

    /* -o value style: process argument */
    optarg = argv [optend] ;
    ++ optend ;
    place   = EMSG ;
    ret_val = optopt ;
    goto done_option ;
  } /* short with argument */

 done_option :
  {
    int pos = optend - optbegin ;  /* n of circular shifts */
    int c   = pos ;

    while (c --) {
      int i ;
      char *tmp = argv [optend - 1] ;
      for (i = optend - 1 ; i > optind ; -- i) {
        ((char**)argv) [i] = argv [i-1] ;
      }
      ((char**)argv) [optind] = tmp ;
    }
    optind += pos ;
  }

  return ret_val ;
}
vlfeat/vl/vlad.c0000644000175000017500000002425112237004745012470 0ustar  dimadima/** @file vlad.c
 ** @brief VLAD - Declaration
 ** @author David Novotny
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 David Novotny and Andera Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page vlad Vector of Locally Aggregated Descriptors (VLAD) encoding
@author David Novotny
@author Andrea Vedaldi


@ref vlad.h implements the *Vector of Linearly Aggregated Descriptors*
(VLAD) image representation @cite{jegou10aggregating}
@cite{arandjelovic13all-about}.

@ref vlad-starting demonstreates how to use the C API to compute the
VLAD representation of an image. For further details on the VLAD image
representation refer to:

- @subpage vlad-fundamentals - VLAD definition and computation.


@section vlad-starting Getting started with VLAD


The VLAD encoding of a set of features is obtained by using the
function ::vl_vlad_encode.  The function can be applied to both @c
float or @c double data types.

::vl_vlad_encode requires a visual dictionary, for example obtained by
using @ref kmeans. Furthermore, the assignments of features to
dictionary elements must be pre-computed, for example by using @ref
kdtree.

In the following example code, the vocabulary is first created using
the KMeans clustering, then the points, that are to be encoded are
assigned to its corresponding nearest vocabulary words, after that the
original vlad encoding routine without any normalization option takes place.
At the end of the process the encoding is stored in the @c enc variable.

@code
vl_uint32 * indexes;
float * assignments;
float * enc
int i;

// create a KMeans object and run clustering to get vocabulary words (centers)
kmeans = vl_kmeans_new (VLDistanceL2, VL_TYPE_FLOAT) ;
vl_kmeans_cluster (kmeans,
                   data,
                   dimension,
                   numData,
                   numCenters) ;

// find nearest cliuster centers for the data that should be encoded
indexes = vl_malloc(sizeof(vl_uint32) * numDataToEncode);
vl_kmeans_quantize(kmeans,indexes,dataToEncode,numDataToEncode);

// convert indexes array to assignments array,
// which can be processed by vl_vlad_encode
assignments = vl_malloc(sizeof(float) * numDataToEncode * numCenters);
memset(assignments, 0, sizeof(float) * numDataToEncode * numCenters);
for(i = 0; i < numDataToEncode; i++) {
  assignments[i + numDataToEncode * indexes[i]] = 1.;
}

// allocate space for vlad encoding
enc = vl_malloc(sizeof(TYPE) * dimension * numCenters);

// do the encoding job
vl_vlad_encode (enc, VL_F_TYPE,
                vl_kmeans_get_centers(kmeans), dimension, numCenters,
                data, numData,
                assignments,
                0) ;
@endcode

Various @ref vlad-normalization normalizations can be applied to the
VLAD vectors. These are controlled by the parameter @a flag of
::vl_vlad_encode.


@page vlad-fundamentals VLAD fundamentals
@tableofcontents


This page describes the *Vector of Locally Aggregated Descriptors*
(VLAD) image encoding of @cite{jegou10aggregating}. See @ref vlad for
an overview of the C API.

VLAD is a *feature encoding and pooling* method, similar to @ref
fisher "Fisher vectors". VLAD encodes a set of local feature
descriptors $I=(\bx_1,\dots,\bx_n)$ extracted from an image using a
dictionary built using a clustering method such as @ref gmm or @ref
kmeans. Let $q_{ik}$ be the strength of the association of data vector
$\bx_i$ to cluster $\mu_k$, such that $q_{ik} \geq 0$ and
$\sum_{k=1}^K q_{ik} = 1$. The association may be either soft
(e.g. obtained as the posterior probabilities of the GMM clusters) or
hard (e.g. obtained by vector quantization with K-means).

$\mu_k$ are the cluster *means*, vectors of the same dimension as the
data $\bx_i$. VLAD encodes feature $\bx$ by considering the *residuals*
\[
 \bv_k = \sum_{i=1}^{N} q_{ik} (\bx_{i} - \mu_k).
\]
The residulas are stacked together to obtain the vector
\[
\hat\Phi(I) =
\begin{bmatrix}
\vdots \\
\bv_k \\
\vdots
\end{bmatrix}
\]

Before the VLAD encoding is used it is usually normalized, as
explained @ref vlad-normalization next.


@section vlad-normalization VLAD normalization


VLFeat VLAD implementation supports a number of different
normalization strategies. These are optionally applied in this order:

- **Component-wise mass normalization.** Each vector $\bv_k$ is
  divided by the total mass of features associated to it $\sum_{i=1}^N
  q_{ik}$.

- **Square-rooting.** The function $\sign(z)\sqrt{|z|}$ is applied to
  all scalar components of the VLAD descriptor.

- **Component-wise $l^2$ normalization.** The vectors $\bv_k$ are
  divided by their norm $\|\bv_k\|_2$.

- **Global $l^2$ normalization.** The VLAD descriptor $\hat\Phi(I)$ is
  divided by its norm $\|\hat\Phi(I)\|_2$.
*/

#include "vlad.h"
#include "mathop.h"
#include 
#include 
#include 

#if defined(_OPENMP)
#include 
#endif

/* ================================================================ */
#ifdef VL_VLAD_INSTANTIATING

static void
VL_XCAT(_vl_vlad_encode_, SFX)
(TYPE * enc,
 TYPE const * means, vl_size dimension, vl_size numClusters,
 TYPE const * data, vl_size numData,
 TYPE const * assignments,
 int flags)
{
  vl_uindex dim ;
  vl_index i_cl, i_d ;

  memset(enc, 0, sizeof(TYPE) * dimension * numClusters) ;

#if defined(_OPENMP)
#pragma omp parallel for default(shared) private(i_cl,i_d,dim) num_threads(vl_get_max_threads())
#endif
  for (i_cl = 0; i_cl < (signed)numClusters; i_cl++) {
    double clusterMass = 0 ;
    for (i_d = 0; i_d < (signed)numData; i_d++) {
      if (assignments[i_d*numClusters + i_cl] > 0) {
        double q = assignments[i_d*numClusters+i_cl] ;
        clusterMass +=  q ;
        for(dim = 0; dim < dimension; dim++) {
          enc [i_cl * dimension + dim] += q * data [i_d  * dimension + dim] ;
        }
      }
    }

    if (clusterMass > 0) {
      if (flags & VL_VLAD_FLAG_NORMALIZE_MASS) {
        for(dim = 0; dim < dimension; dim++) {
          enc[i_cl*dimension + dim] /= clusterMass ;
          enc[i_cl*dimension + dim] -= means[i_cl*dimension+dim];
        }
      } else {
        for(dim = 0; dim < dimension; dim++) {
          enc[i_cl*dimension + dim] -= clusterMass * means[i_cl*dimension+dim];
        }
      }
    }

    if (flags & VL_VLAD_FLAG_SQUARE_ROOT) {
      for(dim = 0; dim < dimension; dim++) {
        TYPE z = enc[i_cl*dimension + dim] ;
        if (z >= 0) {
          enc[i_cl*dimension + dim] = VL_XCAT(vl_sqrt_, SFX)(z) ;
        } else {
          enc[i_cl*dimension + dim] = - VL_XCAT(vl_sqrt_, SFX)(- z) ;
        }
      }
    }

    if (flags & VL_VLAD_FLAG_NORMALIZE_COMPONENTS) {
      TYPE n = 0 ;
      dim = 0 ;
      for(dim = 0; dim < dimension; dim++) {
        TYPE z = enc[i_cl*dimension + dim] ;
        n += z * z ;
      }
      n = VL_XCAT(vl_sqrt_, SFX)(n) ;
      n = VL_MAX(n, 1e-12) ;
      for(dim = 0; dim < dimension; dim++) {
        enc[i_cl*dimension + dim] /= n ;
      }
    }
  }

  if (! (flags & VL_VLAD_FLAG_UNNORMALIZED)) {
    TYPE n = 0 ;
    for(dim = 0 ; dim < dimension * numClusters ; dim++) {
      TYPE z = enc [dim] ;
      n += z * z ;
    }
    n = VL_XCAT(vl_sqrt_, SFX)(n) ;
    n = VL_MAX(n, 1e-12) ;
    for(dim = 0 ; dim < dimension * numClusters ; dim++) {
      enc[dim] /= n ;
    }
  }
}

/* VL_FISHER_INSTANTIATING */
#else

#ifndef __DOXYGEN__
#define FLT VL_TYPE_FLOAT
#define TYPE float
#define SFX f
#define VL_VLAD_INSTANTIATING
#include "vlad.c"

#define FLT VL_TYPE_DOUBLE
#define TYPE double
#define SFX d
#define VL_VLAD_INSTANTIATING
#include "vlad.c"
#endif

/* VL_VLAD_INSTANTIATING */
#endif

/* ================================================================ */
#ifndef VL_VLAD_INSTANTIATING

/** @brief VLAD encoding of a set of vectors.
 ** @param enc output VLAD encoding (out).
 ** @param dataType the type of the input data (::VL_TYPE_DOUBLE or ::VL_TYPE_FLOAT).
 ** @param numData number of data vectors to encode.
 ** @param means cluster means.
 ** @param numClusters number of clusters.
 ** @param data the data vectors to encode.
 ** @param dimension dimensionality of the data.
 ** @param assignments data to cluster soft assignments.
 ** @param flags options.
 **
 ** @a enc is the VLAD vector of size @a numClusters by
 ** @a dimension. @a means is a matrix with @a numClusters columns and
 ** @a dimension rows. @a data is the matrix of vectors to be encoded,
 ** with @a dimension rows and @a numData columns. @a assignments is a
 ** matrix with @a numClusters rows and @a numData columns.
 ** All the matrices should be stored in a row major order.
 **
 ** @a flag allows controlling further options:
 ** ::VL_VLAD_FLAG_NORMALIZE_COMPONENTS, ::VL_VLAD_FLAG_SQUARE_ROOT,
 ** ::VL_VLAD_FLAG_UNNORMALIZED, and ::VL_VLAD_FLAG_NORMALIZE_MASS.
 **
 ** @sa @ref vlad
 **/

void
vl_vlad_encode (void * enc, vl_type dataType,
                void const * means, vl_size dimension, vl_size numClusters,
                void const * data, vl_size numData,
                void const * assignments,
                int flags)
{
  switch(dataType) {
    case VL_TYPE_FLOAT:
      _vl_vlad_encode_f ((float *) enc,
                         (float const *) means, dimension, numClusters,
                         (float const *) data,  numData,
                         (float const *) assignments, flags) ;
      break;
    case VL_TYPE_DOUBLE:
      _vl_vlad_encode_d ((double *) enc,
                         (double const *) means, dimension, numClusters,
                         (double const *) data, numData,
                         (double const *) assignments, flags) ;
      break;
    default:
      abort();
  }
}

/* ! VL_VLAD_INSTANTIATING */
#endif

#undef SFX
#undef TYPE
#undef FLT
#undef VL_VLAD_INSTANTIATING
vlfeat/vl/kmeans.h0000644000175000017500000002664512237004745013036 0ustar  dimadima/** @file kmeans.h
 ** @brief K-means (@ref kmeans)
 ** @author Andrea Vedaldi
 ** @author David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi and David Novotny.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_KMEANS_H
#define VL_KMEANS_H

#include "generic.h"
#include "random.h"
#include "mathop.h"
#include "kdtree.h"

/* ---------------------------------------------------------------- */

/** @brief K-means algorithms */

typedef enum _VlKMeansAlgorithm {
  VlKMeansLloyd,       /**< Lloyd algorithm */
  VlKMeansElkan,       /**< Elkan algorithm */
  VlKMeansANN          /**< Approximate nearest neighbors */
} VlKMeansAlgorithm ;

/** @brief K-means initialization algorithms */

typedef enum _VlKMeansInitialization {
  VlKMeansRandomSelection,  /**< Randomized selection */
  VlKMeansPlusPlus          /**< Plus plus raondomized selection */
} VlKMeansInitialization ;

/** ------------------------------------------------------------------
 ** @brief K-means quantizer
 **/

typedef struct _VlKMeans
{

  vl_type dataType ;                      /**< Data type. */
  vl_size dimension ;                     /**< Data dimensionality. */
  vl_size numCenters ;                    /**< Number of centers. */
  vl_size numTrees ;                      /**< Number of trees in forest when using ANN-kmeans. */
  vl_size maxNumComparisons ;             /**< Maximum number of comparisons when using ANN-kmeans. */

  VlKMeansInitialization initialization ; /**< Initalization algorithm. */
  VlKMeansAlgorithm algorithm ;           /**< Clustring algorithm. */
  VlVectorComparisonType distance ;       /**< Distance. */
  vl_size maxNumIterations ;              /**< Maximum number of refinement iterations. */
  vl_size numRepetitions ;                /**< Number of clustering repetitions. */
  int verbosity ;                         /**< Verbosity level. */

  void * centers ;                        /**< Centers */
  void * centerDistances ;                /**< Centers inter-distances. */

  double energy ;                         /**< Current solution energy. */
  VlFloatVectorComparisonFunction floatVectorComparisonFn ;
  VlDoubleVectorComparisonFunction doubleVectorComparisonFn ;
} VlKMeans ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlKMeans * vl_kmeans_new (vl_type dataType, VlVectorComparisonType distance) ;
VL_EXPORT VlKMeans * vl_kmeans_new_copy (VlKMeans const * kmeans) ;
VL_EXPORT void vl_kmeans_delete (VlKMeans * self) ;
/** @} */

/** @name Basic data processing
 ** @{
 **/
VL_EXPORT void vl_kmeans_reset (VlKMeans * self) ;

VL_EXPORT double vl_kmeans_cluster (VlKMeans * self,
                                    void const * data,
                                    vl_size dimension,
                                    vl_size numData,
                                    vl_size numCenters) ;

VL_EXPORT void vl_kmeans_quantize (VlKMeans * self,
                                   vl_uint32 * assignments,
                                   void * distances,
                                   void const * data,
                                   vl_size numData) ;

VL_EXPORT void vl_kmeans_quantize_ANN (VlKMeans * self,
                                   vl_uint32 * assignments,
                                   void * distances,
                                   void const * data,
                                   vl_size numData,
                                   vl_size iteration );
/** @} */

/** @name Advanced data processing
 ** @{
 **/
VL_EXPORT void vl_kmeans_set_centers (VlKMeans * self,
                                      void const * centers,
                                      vl_size dimension,
                                      vl_size numCenters) ;

VL_EXPORT void vl_kmeans_init_centers_with_rand_data
                  (VlKMeans * self,
                   void const * data,
                   vl_size dimensions,
                   vl_size numData,
                   vl_size numCenters) ;

VL_EXPORT void vl_kmeans_init_centers_plus_plus
                  (VlKMeans * self,
                   void const * data,
                   vl_size dimensions,
                   vl_size numData,
                   vl_size numCenters) ;

VL_EXPORT double vl_kmeans_refine_centers (VlKMeans * self,
                                           void const * data,
                                           vl_size numData) ;

/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_INLINE vl_type vl_kmeans_get_data_type (VlKMeans const * self) ;
VL_INLINE VlVectorComparisonType vl_kmeans_get_distance (VlKMeans const * self) ;

VL_INLINE VlKMeansAlgorithm vl_kmeans_get_algorithm (VlKMeans const * self) ;
VL_INLINE VlKMeansInitialization vl_kmeans_get_initialization (VlKMeans const * self) ;
VL_INLINE vl_size vl_kmeans_get_num_repetitions (VlKMeans const * self) ;

VL_INLINE vl_size vl_kmeans_get_dimension (VlKMeans const * self) ;
VL_INLINE vl_size vl_kmeans_get_num_centers (VlKMeans const * self) ;

VL_INLINE int vl_kmeans_get_verbosity (VlKMeans const * self) ;
VL_INLINE vl_size vl_kmeans_get_max_num_iterations (VlKMeans const * self) ;
VL_INLINE vl_size vl_kmeans_get_max_num_comparisons (VlKMeans const * self) ;
VL_INLINE vl_size vl_kmeans_get_num_trees (VlKMeans const * self) ;
VL_INLINE double vl_kmeans_get_energy (VlKMeans const * self) ;
VL_INLINE void const * vl_kmeans_get_centers (VlKMeans const * self) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_INLINE void vl_kmeans_set_algorithm (VlKMeans * self, VlKMeansAlgorithm algorithm) ;
VL_INLINE void vl_kmeans_set_initialization (VlKMeans * self, VlKMeansInitialization initialization) ;
VL_INLINE void vl_kmeans_set_num_repetitions (VlKMeans * self, vl_size numRepetitions) ;
VL_INLINE void vl_kmeans_set_max_num_iterations (VlKMeans * self, vl_size maxNumIterations) ;
VL_INLINE void vl_kmeans_set_verbosity (VlKMeans * self, int verbosity) ;
VL_INLINE void vl_kmeans_set_max_num_comparisons (VlKMeans * self, vl_size maxNumComparisons) ;
VL_INLINE void vl_kmeans_set_num_trees (VlKMeans * self, vl_size numTrees) ;
/** @} */

/** ------------------------------------------------------------------
 ** @brief Get data type
 ** @param self KMeans object instance.
 ** @return data type.
 **/

VL_INLINE vl_type
vl_kmeans_get_data_type (VlKMeans const * self)
{
  return self->dataType ;
}

/** @brief Get data dimension
 ** @param self KMeans object instance.
 ** @return data dimension.
 **/

VL_INLINE vl_size
vl_kmeans_get_dimension (VlKMeans const * self)
{
  return self->dimension ;
}

/** @brief Get data type
 ** @param self KMeans object instance.
 ** @return data type.
 **/

VL_INLINE VlVectorComparisonType
vl_kmeans_get_distance (VlKMeans const * self)
{
  return self->distance ;
}

/** @brief Get the number of centers (K)
 ** @param self KMeans object instance.
 ** @return number of centers.
 **/

VL_INLINE vl_size
vl_kmeans_get_num_centers (VlKMeans const * self)
{
  return self->numCenters ;
}

/** @brief Get the number energy of the current fit
 ** @param self KMeans object instance.
 ** @return energy.
 **/

VL_INLINE double
vl_kmeans_get_energy (VlKMeans const * self)
{
  return self->energy ;
}

/** ------------------------------------------------------------------
 ** @brief Get verbosity level
 ** @param self KMeans object instance.
 ** @return verbosity level.
 **/

VL_INLINE int
vl_kmeans_get_verbosity (VlKMeans const * self)
{
  return self->verbosity ;
}

/** @brief Set verbosity level
 ** @param self KMeans object instance.
 ** @param verbosity verbosity level.
 **/

VL_INLINE void
vl_kmeans_set_verbosity (VlKMeans * self, int verbosity)
{
  self->verbosity = verbosity ;
}

/** ------------------------------------------------------------------
 ** @brief Get centers
 ** @param self KMeans object instance.
 ** @return cluster centers.
 **/

VL_INLINE void const *
vl_kmeans_get_centers (VlKMeans const * self)
{
  return self->centers ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of iterations
 ** @param self KMeans object instance.
 ** @return maximum number of iterations.
 **/

VL_INLINE vl_size
vl_kmeans_get_max_num_iterations (VlKMeans const * self)
{
  return self->maxNumIterations ;
}

/** @brief Set maximum number of iterations
 ** @param self KMeans filter.
 ** @param maxNumIterations maximum number of iterations.
 **/

VL_INLINE void
vl_kmeans_set_max_num_iterations (VlKMeans * self, vl_size maxNumIterations)
{
  self->maxNumIterations = maxNumIterations ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of repetitions.
 ** @param self KMeans object instance.
 ** @return current number of repretitions for quantization.
 **/

VL_INLINE vl_size
vl_kmeans_get_num_repetitions (VlKMeans const * self)
{
  return self->numRepetitions ;
}

/** @brief Set maximum number of repetitions
 ** @param self KMeans object instance.
 ** @param numRepetitions maximum number of repetitions.
 ** The number of repetitions cannot be smaller than 1.
 **/

VL_INLINE void
vl_kmeans_set_num_repetitions (VlKMeans * self,
                               vl_size numRepetitions)
{
  assert (numRepetitions >= 1) ;
  self->numRepetitions = numRepetitions ;
}

/** ------------------------------------------------------------------
 ** @brief Get K-means algorithm
 ** @param self KMeans object.
 ** @return algorithm.
 **/

VL_INLINE VlKMeansAlgorithm
vl_kmeans_get_algorithm (VlKMeans const * self)
{
  return self->algorithm ;
}

/** @brief Set K-means algorithm
 ** @param self KMeans object.
 ** @param algorithm K-means algorithm.
 **/

VL_INLINE void
vl_kmeans_set_algorithm (VlKMeans * self, VlKMeansAlgorithm algorithm)
{
  self->algorithm = algorithm ;
}

/** ------------------------------------------------------------------
 ** @brief Get K-means initialization algorithm
 ** @param self KMeans object.
 ** @return algorithm.
 **/

VL_INLINE VlKMeansInitialization
vl_kmeans_get_initialization (VlKMeans const * self)
{
  return self->initialization ;
}

/** @brief Set K-means initialization algorithm
 ** @param self KMeans object.
 ** @param initialization initialization.
 **/

VL_INLINE void
vl_kmeans_set_initialization (VlKMeans * self,
                              VlKMeansInitialization initialization)
{
  self->initialization = initialization ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of comparisons in ANN-KD-Tree.
 ** @param self KMeans object instance.
 ** @return maximum number of comparisons.
 **/

VL_INLINE vl_size
vl_kmeans_get_max_num_comparisons (VlKMeans const * self)
{
  return self->maxNumComparisons ;
}

/** @brief Set maximum number of comparisons in ANN-KD-Tree.
 ** @param self KMeans filter.
 ** @param maxNumComparisons maximum number of comparisons.
 **/

VL_INLINE void
vl_kmeans_set_max_num_comparisons (VlKMeans * self,
                              vl_size maxNumComparisons)
{
    self->maxNumComparisons = maxNumComparisons;
}

/** ------------------------------------------------------------------
 ** @brief Get number of trees in ANN-KD-Tree.
 ** @param self KMeans object instance.
 ** @return number of trees.
 **/

VL_INLINE void
vl_kmeans_set_num_trees (VlKMeans * self, vl_size numTrees)
{
    self->numTrees = numTrees;
}

VL_INLINE vl_size
vl_kmeans_get_num_trees (VlKMeans const * self)
{
    return self->numTrees;
}


/* VL_IKMEANS_H */
#endif
vlfeat/vl/aib.h0000644000175000017500000000674112237004736012306 0ustar  dimadima/** @file aib.h
 ** @brief AIB (@ref aib)
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_AIB_H
#define VL_AIB_H

#include "generic.h"
#include "mathop.h"

/** ------------------------------------------------------------------
 ** @internal
 ** @brief AIB algorithm data
 **
 ** The implementation is quite straightforward, but the way feature
 ** values are handled in order to support efficient joins,
 ** deletions and re-arrangement needs to be explained. This is
 ** achieved by adding a layer of indirection:
 ** - Call each feature value (either original or obtained by a join
 **   operation) a node. Nodes are identified by numbers.
 ** - Call each element of the various arrays (such as VlAIB::Px)
 **    an entry.
 ** - Entries are dynamically associated to nodes as specified by
 **   VlAIB::nodes. For example, @c Px[i] refers to the node @c
 **   nodes[i].
 **/

typedef struct _VlAIB
{
  vl_uint   *nodes ;    /**< Entires to nodes */
  vl_uint    nentries ; /**< Total number of entries (= # active nodes) */
  double    *beta ;     /**< Minimum distance to an entry  */
  vl_uint   *bidx ;     /**< Closest entry */


  vl_uint   *which ;    /**< List of entries to update */
  vl_uint    nwhich ;   /**< Number of entries to update */

  double    *Pcx;       /**< Joint probability table */
  double    *Px;        /**< Marginal. */
  double    *Pc;        /**< Marginal. */
  vl_uint    nvalues;   /**< Number of feature values */
  vl_uint    nlabels;   /**< Number of labels */

  vl_uint   *parents;   /**< Array of parents */
  double    *costs;     /**< Cost of each merge */

  vl_uint verbosity ; /** Verbosity level */
} VlAIB;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT
VlAIB * vl_aib_new(double * Pcx, vl_uint nvalues, vl_uint nlabels);

VL_EXPORT
void vl_aib_delete (VlAIB * aib);
/** @} */

/** @name Process data
 ** @{
 **/
VL_EXPORT
void vl_aib_process(VlAIB * aib);
/** @} */

/** @name Retrieve results
 ** @{
 **/
VL_INLINE vl_uint * vl_aib_get_parents(VlAIB const * aib);
VL_INLINE double  * vl_aib_get_costs(VlAIB const * aib);
/** @} */


/* -------------------------------------------------------------------
 *                                     Inline functions implementation
 * ---------------------------------------------------------------- */

/** ------------------------------------------------------------------
 ** @brief Get resulting list of parents
 ** @param aib AIB filter.
 ** @return An array of parents
 **/
VL_INLINE
vl_uint * vl_aib_get_parents(VlAIB const * aib)
{
  return aib->parents;
}

/** ------------------------------------------------------------------
 ** @brief Get a list of merge costs
 ** @param aib AIB filter.
 ** @return An array of costs
 **/
VL_INLINE
double * vl_aib_get_costs(VlAIB const * aib)
{
  return aib->costs;
}

/* ----------------------------------------------------------------- */
/** @brief Set the verbosity
 ** @param self AIB object.
 ** @param verbosity a non-negative integer.
 **/
VL_INLINE void
vl_aib_set_verbosity (VlAIB * self, int verbosity)
{
  self->verbosity = verbosity ;
}

/** @brief Get the verbosity
 ** @param self AIB object.
 ** @return the verbosity level.
 **/
VL_INLINE int
vl_aib_get_verbosity (VlAIB const * self)
{
  return self->verbosity ;
}

/* VL_AIB_H */
#endif
vlfeat/vl/fisher.h0000644000175000017500000000275712237004745013036 0ustar  dimadima/** @file fisher.h
 ** @brief Fisher encoding (@ref fisher)
 ** @author David Novotny
 ** @author Andrea Vedaldi
 ** @see @ref fisher
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_FISHER_H
#define VL_FISHER_H

#include "generic.h"

/** @name Fisher vector options
 ** @{ */
#define VL_FISHER_FLAG_SQUARE_ROOT          (0x1 << 0)
#define VL_FISHER_FLAG_NORMALIZED           (0x1 << 1)
#define VL_FISHER_FLAG_IMPROVED             (VL_FISHER_FLAG_NORMALIZED|VL_FISHER_FLAG_SQUARE_ROOT)
#define VL_FISHER_FLAG_FAST                 (0x1 << 2)

/** @def VL_FISHER_FLAG_SQUARE_ROOT
 ** @brief Use signed squared-root.
 **/

/** @def VL_FISHER_FLAG_NORMALIZED
 ** @brief Gobally normalize the Fisher vector in L2 norm.
 **/

/** @def VL_FISHER_FLAG_IMPROVED
 ** @brief Improved Fisher vector.
 ** This is the same as @c VL_FISHER_FLAG_SQUARE_ROOT|VL_FISHER_FLAG_NORMALIZED.
 **/

/** @def VL_FISHER_FLAG_FAST
 ** @brief Fast but more approximate calculations
 ** Clamps to zero data to cluster assignments that are very small.
 ** Typically this makes the computation much faster.
 **/

/** @} */

VL_EXPORT void vl_fisher_encode
(void * enc, vl_type dataType,
 void const * means, vl_size dimension, vl_size numClusters,
 void const * covariances,
 void const * priors,
 void const * data, vl_size numData,
 int flags) ;

/* VL_FISHER_H */
#endif
vlfeat/vl/mathop.h0000644000175000017500000004611712237004745013044 0ustar  dimadima/** @file mathop.h
 ** @brief Math operations
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_MATHOP_H
#define VL_MATHOP_H

#include "generic.h"
#include 
#include 

/** @brief Euler constant*/
#define VL_E 2.718281828459045

/** @brief Logarithm of 2 (math constant)*/
#define VL_LOG_OF_2 0.693147180559945

/** @brief Pi (math constant) */
#define VL_PI 3.141592653589793

/** @brief IEEE single precision epsilon (math constant)
 **
 ** 1.0F + VL_EPSILON_F is the smallest representable
 ** single precision number greater than @c 1.0F. Numerically,
 ** ::VL_EPSILON_F is equal to @f$ 2^{-23} @f$.
 **
 **/
#define VL_EPSILON_F 1.19209290E-07F

/** @brief IEEE double precision epsilon (math constant)
 **
 ** 1.0 + VL_EPSILON_D is the smallest representable
 ** double precision number greater than @c 1.0. Numerically,
 ** ::VL_EPSILON_D is equal to @f$ 2^{-52} @f$.
 **/
#define VL_EPSILON_D 2.220446049250313e-16

/*
   For the code below: An ANSI C compiler takes the two expressions,
   LONG_VAR and CHAR_VAR, and implicitly casts them to the type of the
   first member of the union. Refer to K&R Second Edition Page 148,
   last paragraph.
*/

/** @internal @brief IEEE single precision quiet NaN constant */
static union { vl_uint32 raw ; float value ; }
  const vl_nan_f =
    { 0x7FC00000UL } ;

/** @internal @brief IEEE single precision infinity constant */
static union { vl_uint32 raw ; float value ; }
  const vl_infinity_f =
    { 0x7F800000UL } ;

/** @internal @brief IEEE double precision quiet NaN constant */
static union { vl_uint64 raw ; double value ; }
  const vl_nan_d =
#ifdef VL_COMPILER_MSC
    { 0x7FF8000000000000ui64 } ;
#else
    { 0x7FF8000000000000ULL } ;
#endif

/** @internal @brief IEEE double precision infinity constant */
static union { vl_uint64 raw ; double value ; }
  const vl_infinity_d =
#ifdef VL_COMPILER_MSC
    { 0x7FF0000000000000ui64 } ;
#else
    { 0x7FF0000000000000ULL } ;
#endif

/** @brief IEEE single precision NaN (not signaling) */
#define VL_NAN_F (vl_nan_f.value)

/** @brief IEEE single precision positive infinity (not signaling) */
#define VL_INFINITY_F (vl_infinity_f.value)

/** @brief IEEE double precision NaN (not signaling) */
#define VL_NAN_D (vl_nan_d.value)

/** @brief IEEE double precision positive infinity (not signaling) */
#define VL_INFINITY_D (vl_infinity_d.value)

/* ---------------------------------------------------------------- */

/** @brief Fast mod(x, 2 * VL_PI)
 **
 ** @param x input value.
 ** @return mod(x, 2 * VL_PI)
 **
 ** The function is optimized for small absolute values of @a x.
 **
 ** The result is guaranteed to be not smaller than 0. However, due to
 ** finite numerical precision and rounding errors, the result can be
 ** equal to 2 * VL_PI (for instance, if @c x is a very small negative
 ** number).
 **/

VL_INLINE float
vl_mod_2pi_f (float x)
{
  while (x > (float)(2 * VL_PI)) x -= (float) (2 * VL_PI) ;
  while (x < 0.0F) x += (float) (2 * VL_PI);
  return x ;
}

/** @brief Fast mod(x, 2 * VL_PI)
 ** @see vl_mod_2pi_f
 **/

VL_INLINE double
vl_mod_2pi_d (double x)
{
  while (x > 2.0 * VL_PI) x -= 2 * VL_PI ;
  while (x < 0.0) x += 2 * VL_PI ;
  return x ;
}

/** @brief Floor and convert to integer
 ** @param x argument.
 ** @return Similar to @c (int) floor(x)
 **/

VL_INLINE long int
vl_floor_f (float x)
{
  long int xi = (long int) x ;
  if (x >= 0 || (float) xi == x) return xi ;
  else return xi - 1 ;
}

/** @brief Floor and convert to integer
 ** @see vl_floor_f
 **/

VL_INLINE long int
vl_floor_d (double x)
{
  long int xi = (long int) x ;
  if (x >= 0 || (double) xi == x) return xi ;
  else return xi - 1 ;
}

/** @brief Ceil and convert to integer
 ** @param x argument.
 ** @return @c lceilf(x)
 **/

VL_INLINE long int
vl_ceil_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return (long int) __builtin_ceilf(x) ;
#else
  return (long int) ceilf(x) ;
#endif
}

/** @brief Ceil and convert to integer
 ** @see vl_ceil_f
 **/

VL_INLINE long int
vl_ceil_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_ceil(x) ;
#else
  return (long int) ceil(x) ;
#endif
}

/** @brief Round
 ** @param x argument.
 ** @return @c lroundf(x)
 ** This function is either the same or similar to C99 @c lroundf().
 **/

VL_INLINE long int
vl_round_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_lroundf(x) ;
#elif VL_COMPILER_MSC
  if (x >= 0.0F) {
    return vl_floor_f(x + 0.5F) ;
  } else {
    return vl_ceil_f(x - 0.5F) ;
  }
#else
  return lroundf(x) ;
#endif
}

/** @brief Round
 ** @param x argument.
 ** @return @c lround(x)
 ** This function is either the same or similar to C99 @c lround().
 **/

VL_INLINE long int
vl_round_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_lround(x) ;
#elif VL_COMPILER_MSC
  if (x >= 0.0) {
    return vl_floor_d(x + 0.5) ;
  } else {
    return vl_ceil_d(x - 0.5) ;
  }
#else
  return lround(x) ;
#endif
}

/** @brief Fast @c abs(x)
 ** @param x argument.
 ** @return @c abs(x)
 **/

VL_INLINE float
vl_abs_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_fabsf (x) ;
#else
  return fabsf(x) ;
#endif
}

/** @brief Fast @c abs(x)
 ** @sa vl_abs_f
 **/

VL_INLINE double
vl_abs_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_fabs (x) ;
#else
  return fabs(x) ;
#endif
}

/** @brief Base-2 logaritghm
 ** @param x argument.
 ** @return @c log(x).
 **/

VL_INLINE double
vl_log2_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_log2(x) ;
#elif VL_COMPILER_MSC
  return log(x) / 0.693147180559945 ;
#else
  return log2(x) ;
#endif
}

/** @copydoc vl_log2_d */
VL_INLINE float
vl_log2_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_log2f (x) ;
#elif VL_COMPILER_MSC
  return logf(x) / 0.6931472F ;
#else
  return log2(x) ;
#endif
}

/** @brief Square root.
 ** @param x argument.
 ** @return @c sqrt(x).
 **/

VL_INLINE double
vl_sqrt_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_sqrt(x) ;
#else
  return sqrt(x) ;
#endif
}

/** @copydoc vl_sqrt_d */
VL_INLINE float
vl_sqrt_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_sqrtf(x) ;
#else
  return sqrtf(x) ;
#endif
}

/** @brief Check whether a floating point value is NaN
 ** @param x argument.
 ** @return true if @a x is NaN.
 **/
VL_INLINE vl_bool
vl_is_nan_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_isnan (x) ;
#elif VL_COMPILER_MSC
  return _isnan(x) ;
#else
  return isnan(x) ;
#endif
}

/** @copydoc vl_is_nan_f */
VL_INLINE vl_bool
vl_is_nan_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_isnan (x) ;
#elif VL_COMPILER_MSC
  return _isnan(x) ;
#else
  return isnan(x) ;
#endif
}

/** @brief Check whether a floating point value is infinity
 ** @param x argument.
 ** @return true if @a x is infinity.
 **/
VL_INLINE vl_bool
vl_is_inf_f (float x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_isinf (x) ;
#elif VL_COMPILER_MSC
  return ! _finite(x) ;
#else
  return isinf(x) ;
#endif
}

/** @copydoc vl_is_inf_f */
VL_INLINE vl_bool
vl_is_inf_d (double x)
{
#ifdef VL_COMPILER_GNUC
  return __builtin_isinf (x) ;
#elif VL_COMPILER_MSC
  return ! _finite(x) ;
#else
  return isinf(x) ;
#endif
}

/** ------------------------------------------------------------------
 ** @brief Fast @c atan2 approximation
 ** @param y argument.
 ** @param x argument.
 **
 ** The function computes a relatively rough but fast approximation of
 ** @c atan2(y,x).
 **
 ** @par Algorithm
 **
 ** The algorithm approximates the function @f$ f(r)=atan((1-r)/(1+r))
 ** @f$, @f$ r \in [-1,1] @f$ with a third order polynomial @f$
 ** f(r)=c_0 + c_1 r + c_2 r^2 + c_3 r^3 @f$.  To fit the polynomial
 ** we impose the constraints
 **
 ** @f{eqnarray*}
 ** f(+1) &=& c_0 + c_1 + c_2 + c_3  = atan(0)       = 0,\\
 ** f(-1) &=& c_0 - c_1 + c_2 - c_3  = atan(\infty)  = \pi/2,\\
 ** f(0)  &=& c_0                    = atan(1)       = \pi/4.
 ** @f}
 **
 ** The last degree of freedom is fixed by minimizing the @f$
 ** l^{\infty} @f$ error, which yields
 **
 ** @f[
 ** c_0=\pi/4, \quad
 ** c_1=-0.9675, \quad
 ** c_2=0, \quad
 ** c_3=0.1821,
 ** @f]
 **
 ** with maximum error of 0.0061 radians at 0.35 degrees.
 **
 ** @return Approximation of @c atan2(y,x).
 **/

VL_INLINE float
vl_fast_atan2_f (float y, float x)
{
  float angle, r ;
  float const c3 = 0.1821F ;
  float const c1 = 0.9675F ;
  float abs_y    = vl_abs_f (y) + VL_EPSILON_F ;

  if (x >= 0) {
    r = (x - abs_y) / (x + abs_y) ;
    angle = (float) (VL_PI / 4) ;
  } else {
    r = (x + abs_y) / (abs_y - x) ;
    angle = (float) (3 * VL_PI / 4) ;
  }
  angle += (c3*r*r - c1) * r ;
  return (y < 0) ? - angle : angle ;
}

/** @brief Fast @c atan2 approximation
 ** @sa vl_fast_atan2_f
 **/

VL_INLINE double
vl_fast_atan2_d (double y, double x)
{
  double angle, r ;
  double const c3 = 0.1821 ;
  double const c1 = 0.9675 ;
  double abs_y = vl_abs_d (y) + VL_EPSILON_D ;

  if (x >= 0) {
    r = (x - abs_y) / (x + abs_y) ;
    angle = VL_PI / 4 ;
  } else {
    r = (x + abs_y) / (abs_y - x) ;
    angle = 3 * VL_PI / 4 ;
  }
  angle += (c3*r*r - c1) * r ;
  return (y < 0) ? - angle : angle ;
}

/** ------------------------------------------------------------------
 ** @brief Fast @c resqrt approximation
 ** @param x argument.
 ** @return approximation of @c resqrt(x).
 **
 ** The function quickly computes an approximation of @f$ x^{-1/2}
 ** @f$.
 **
 ** @par Algorithm
 **
 ** The goal is to compute @f$ y = x^{-1/2} @f$, which we do by
 ** finding the solution of @f$ 0 = f(y) = y^{-2} - x @f$ by two Newton
 ** steps. Each Newton iteration is given by
 **
 ** @f[
 **   y \leftarrow
 **   y - \frac{f(y)}{\frac{df(y)}{dy}} =
 **   y + \frac{1}{2} (y-xy^3) =
 **   \frac{y}{2} \left( 3 - xy^2 \right)
 ** @f]
 **
 ** which yields a simple polynomial update rule.
 **
 ** The clever bit (attributed to either J. Carmack or G. Tarolli) is
 ** the way an initial guess @f$ y \approx x^{-1/2} @f$ is chosen.
 **
 ** @see Inverse Sqare Root.
 **
 **/

VL_INLINE float
vl_fast_resqrt_f (float x)
{
  /* 32-bit version */
  union {
    float x ;
    vl_int32  i ;
  } u ;

  float xhalf = (float) 0.5 * x ;

  /* convert floating point value in RAW integer */
  u.x = x ;

  /* gives initial guess y0 */
  u.i = 0x5f3759df - (u.i >> 1);
  /*u.i = 0xdf59375f - (u.i>>1);*/

  /* two Newton steps */
  u.x = u.x * ( (float) 1.5  - xhalf*u.x*u.x) ;
  u.x = u.x * ( (float) 1.5  - xhalf*u.x*u.x) ;
  return u.x ;
}

/** @brief Fast @c resqrt approximation
 ** @sa vl_fast_resqrt_d
 **/

VL_INLINE double
vl_fast_resqrt_d (double x)
{
  /* 64-bit version */
  union {
    double x ;
    vl_int64  i ;
  } u ;

  double xhalf = (double) 0.5 * x ;

  /* convert floating point value in RAW integer */
  u.x = x ;

  /* gives initial guess y0 */
#ifdef VL_COMPILER_MSC
  u.i = 0x5fe6ec85e7de30dai64 - (u.i >> 1) ;
#else
  u.i = 0x5fe6ec85e7de30daLL - (u.i >> 1) ;
#endif

  /* two Newton steps */
  u.x = u.x * ( (double) 1.5  - xhalf*u.x*u.x) ;
  u.x = u.x * ( (double) 1.5  - xhalf*u.x*u.x) ;
  return u.x ;
}

/** ------------------------------------------------------------------
 ** @brief Fast @c sqrt approximation
 ** @param x argument.
 ** @return approximation of @c sqrt(x).
 **
 ** The function computes a cheap approximation of @c sqrt(x).
 **
 ** @par Floating-point algorithm
 **
 ** For the floating point cases, the function uses ::vl_fast_resqrt_f
 ** (or ::vl_fast_resqrt_d) to compute x *
 ** vl_fast_resqrt_f(x).
 **
 ** @par Integer algorithm
 **
 ** We seek for the largest integer @e y such that @f$ y^2 \leq x @f$.
 ** Write @f$ y = w + b_k 2^k + z @f$ where the binary expansion of the
 ** various variable is
 **
 ** @f[
 **  x = \sum_{i=0}^{n-1} 2^i a_i, \qquad
 **  w = \sum_{i=k+1}^{m-1} 2^i b_i, \qquad
 **  z = \sum_{i=0}^{k-1} 2^i b_i.
 ** @f]
 **
 ** Assume @e w known. Expanding the square and using the fact that
 ** @f$ b_k^2=b_k @f$, we obtain the following constraint for @f$ b_k
 ** @f$ and @e z:
 **
 ** @f[
 **    x - w^2 \geq 2^k ( 2 w + 2^k ) b_k + z (z + 2wz + 2^{k+1}z b_k)
 ** @f]
 **
 ** A necessary condition for @f$ b_k = 1 @f$ is that this equation is
 ** satisfied for @f$ z = 0 @f$ (as the second term is always
 ** non-negative). In fact, this condition is also sufficient, since
 ** we are looking for the @e largest solution @e y.
 **
 ** This yields the following iterative algorithm. First, note that if
 ** @e x is stored in @e n bits, where @e n is even, then the integer
 ** square root does not require more than @f$ m = n / 2 @f$ bit to be
 ** stored.  Thus initially, @f$ w = 0 @f$ and @f$ k = m - 1 = n/2 - 1
 ** @f$. Then, at each iteration the equation is tested, determining
 ** @f$ b_{m-1}, b_{m-2}, ... @f$ in this order.
 **/

VL_INLINE float
vl_fast_sqrt_f (float x)
{
  return (x < 1e-8) ? 0 : x * vl_fast_resqrt_f (x) ;
}

/** @brief Fast @c sqrt approximation
 ** @sa vl_fast_sqrt_f
 **/

VL_INLINE double
vl_fast_sqrt_d (float x)
{
  return (x < 1e-8) ? 0 : x * vl_fast_resqrt_d (x) ;
}

/** @brief Fast @c sqrt approximation
 ** @sa vl_fast_sqrt_f
 **/

VL_INLINE vl_uint32 vl_fast_sqrt_ui32 (vl_uint32 x) ;

/** @brief Fast @c sqrt approximation
 ** @sa vl_fast_sqrt_f
 **/

VL_INLINE vl_uint16 vl_fast_sqrt_ui16 (vl_uint16 x) ;

/** @brief Fast @c sqrt approximation
 ** @sa vl_fast_sqrt_f
 **/

VL_INLINE vl_uint8  vl_fast_sqrt_ui8  (vl_uint8  x) ;

#define VL_FAST_SQRT_UI(T,SFX)                                       \
  VL_INLINE T                                                        \
  vl_fast_sqrt_ ## SFX (T x)                                         \
{                                                                    \
    T y = 0 ; /* w / 2^k */                                          \
    T tmp = 0 ;                                                      \
    int twok ;                                                       \
                                                                     \
    for (twok = 8 * sizeof(T) - 2 ;                                  \
         twok >= 0 ; twok -= 2) {                                    \
      y <<= 1 ; /* y = 2 * y */                                      \
      tmp = (2*y + 1) << twok ;                                      \
      if (x >= tmp) {                                                \
        x -= tmp ;                                                   \
        y += 1 ;                                                     \
      }                                                              \
    }                                                                \
    return y ;                                                       \
  }

VL_FAST_SQRT_UI(vl_uint32,ui32)
VL_FAST_SQRT_UI(vl_uint16,ui16)
VL_FAST_SQRT_UI(vl_uint8,ui8)

/* ---------------------------------------------------------------- */
/*                                Vector distances and similarities */
/* ---------------------------------------------------------------- */

/** @typedef VlFloatVectorComparisonFunction
 ** @brief Pointer to a function to compare vectors of floats
 **/
typedef float (*VlFloatVectorComparisonFunction)(vl_size dimension, float const * X, float const * Y) ;

/** @typedef VlDoubleVectorComparisonFunction
 ** @brief Pointer to a function to compare vectors of doubles
 **/
typedef double (*VlDoubleVectorComparisonFunction)(vl_size dimension, double const * X, double const * Y) ;

/** @typedef VlFloatVector3ComparisonFunction
 ** @brief Pointer to a function to compare 3 vectors of doubles
 **/
typedef float (*VlFloatVector3ComparisonFunction)(vl_size dimension, float const * X, float const * Y, float const * Z) ;

/** @typedef VlDoubleVector3ComparisonFunction
 ** @brief Pointer to a function to compare 3 vectors of doubles
 **/
typedef double (*VlDoubleVector3ComparisonFunction)(vl_size dimension, double const * X, double const * Y, double const * Z) ;

/** @brief Vector comparison types */
enum _VlVectorComparisonType {
  VlDistanceL1,        /**< l1 distance (squared intersection metric) */
  VlDistanceL2,        /**< squared l2 distance */
  VlDistanceChi2,      /**< squared Chi2 distance */
  VlDistanceHellinger, /**< squared Hellinger's distance */
  VlDistanceJS,        /**< squared Jensen-Shannon distance */
  VlDistanceMahalanobis,     /**< squared mahalanobis distance */
  VlKernelL1,          /**< intersection kernel */
  VlKernelL2,          /**< l2 kernel */
  VlKernelChi2,        /**< Chi2 kernel */
  VlKernelHellinger,   /**< Hellinger's kernel */
  VlKernelJS           /**< Jensen-Shannon kernel */
} ;

/** @brief Vector comparison types */
typedef enum _VlVectorComparisonType VlVectorComparisonType ;

/** @brief Get the symbolic name of a vector comparison type
 ** @param type vector comparison type.
 ** @return data symbolic name.
 **/

VL_INLINE char const *
vl_get_vector_comparison_type_name (int type)
{
  switch (type) {
    case VlDistanceL1   : return "l1" ;
    case VlDistanceL2   : return "l2" ;
    case VlDistanceChi2 : return "chi2" ;
    case VlDistanceMahalanobis  : return "mahalanobis" ;
    case VlKernelL1     : return "kl1" ;
    case VlKernelL2     : return "kl2" ;
    case VlKernelChi2   : return "kchi2" ;
    default: return NULL ;
  }
}

VL_EXPORT VlFloatVectorComparisonFunction
vl_get_vector_comparison_function_f (VlVectorComparisonType type) ;

VL_EXPORT VlDoubleVectorComparisonFunction
vl_get_vector_comparison_function_d (VlVectorComparisonType type) ;

VL_EXPORT VlFloatVector3ComparisonFunction
vl_get_vector_3_comparison_function_f (VlVectorComparisonType type) ;

VL_EXPORT VlDoubleVector3ComparisonFunction
vl_get_vector_3_comparison_function_d (VlVectorComparisonType type) ;


VL_EXPORT void
vl_eval_vector_comparison_on_all_pairs_f (float * result, vl_size dimension,
                                          float const * X, vl_size numDataX,
                                          float const * Y, vl_size numDataY,
                                          VlFloatVectorComparisonFunction function) ;

VL_EXPORT void
vl_eval_vector_comparison_on_all_pairs_d (double * result, vl_size dimension,
                                          double const * X, vl_size numDataX,
                                          double const * Y, vl_size numDataY,
                                          VlDoubleVectorComparisonFunction function) ;

/* ---------------------------------------------------------------- */
/*                                               Numerical analysis */
/* ---------------------------------------------------------------- */

VL_EXPORT void
vl_svd2 (double* S, double *U, double *V, double const *M) ;

VL_EXPORT void
vl_lapack_dlasv2 (double *smin,
                  double *smax,
                  double *sv,
                  double *cv,
                  double *su,
                  double *cu,
                  double f,
                  double g,
                  double h) ;


VL_EXPORT int
vl_solve_linear_system_3 (double * x, double const * A, double const *b) ;

VL_EXPORT int
vl_solve_linear_system_2 (double * x, double const * A, double const *b) ;

VL_EXPORT int
vl_gaussian_elimination (double * A, vl_size numRows, vl_size numColumns) ;

/* VL_MATHOP_H */
#endif
vlfeat/vl/ikmeans_lloyd.tc0000644000175000017500000000650712237004736014564 0ustar  dimadima/** @file ikmeans_lloyd.tc
 ** @brief Integer K-Means - LLoyd Algorithm - Definition
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @internal
 ** @brief Helper function to initialize a filter for Lloyd algorithm
 **
 ** @param f filter.
 **/

static void
vl_ikm_init_lloyd (VlIKMFilt * f VL_UNUSED)
{ }

/** @internal
 ** @brief LLoyd algorithm
 ** @param f     IKM quantizer.
 ** @param data  Training data.
 ** @param N     Number of traning data.
 **/

static int
vl_ikm_train_lloyd (VlIKMFilt* f, vl_uint8 const* data, int N)
{
  int iter, i, j, k,
    K = f-> K,
    M = f-> M,
    err =  0 ;
  vl_ikm_acc *centers = f->centers ;
  vl_uint    *asgn    = vl_malloc (sizeof(vl_uint) * N) ;
  vl_uint    *counts  = vl_malloc (sizeof(vl_uint) * N) ;

  for (iter = 0 ; 1 ; ++ iter) {
    vl_bool done = 1 ;

    /* ---------------------------------------------------------------
     *                                               Calc. assignments
     * ------------------------------------------------------------ */

    for (j = 0 ; j < N ; ++j) {
      vl_ikm_acc best_dist = 0 ;
      vl_uint    best = (vl_uint)-1 ;

      for (k = 0; k < K; ++k) {
        vl_ikm_acc dist = 0;

        /* compute distance with this center */
        for (i = 0; i < M; ++i) {
          vl_ikm_acc delta =
            data [j * M + i] - centers [k * M + i] ;
          dist += delta * delta ;
        }

        /* compare with current best */
        if (best == (vl_uint) -1 || dist < best_dist) {
          best = k ;
          best_dist = dist ;
        }
      }
      if (asgn [j] != best) {
        asgn [j] = best ;
        done = 0 ;
      }
    }

    /* stopping condition */
    if (done || iter == f->max_niters) break ;

    /* -----------------------------------------------------------------
     *                                                     Calc. centers
     * -------------------------------------------------------------- */

    /* re-compute centers */
    memset (centers, 0, sizeof (vl_int32) * M * K);
    memset (counts,  0, sizeof (vl_int32) * K);
    for (j = 0; j < N; ++j) {
      int this_center = asgn [j] ;
      ++ counts [this_center] ;
      for (i = 0; i < M; ++i)
        centers [this_center * M + i] += data[j * M + i];
    }

    for (k = 0; k < K; ++k) {
      vl_int32 n = counts [k];
      if (n > 0xffffff) {
        err = 1 ;
      }
      if (n > 0) {
        for (i = 0; i < M; ++i)
          centers [k * M + i] /= n;
      } else {
        /*
           If no data are assigned to the center, it is not changed
           with respect to the previous iteration, so we do not do anything.
        */
      }
    }
  }

  vl_free (counts) ;
  vl_free (asgn) ;
  return err ;
}


/** @internal
 ** @brief LLoyd algorithm
 ** @param f     IKM quantizer.
 ** @param asgn  Assignments (out).
 ** @param data  Data to quantize.
 ** @param N     Number of data.
 **/

static void
vl_ikm_push_lloyd (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
{
  int j ;
  for(j=0 ; j < N ; ++j) {
    asgn[j] = vl_ikm_push_one (f->centers, data + j * f->M, f->M, f->K);
  }
}

/*
 * Local Variables: *
 * mode: C *
 * End: *
 */
vlfeat/vl/getopt_long.h0000644000175000017500000000270712237004736014072 0ustar  dimadima/** @file getopt_long.h
 ** @brief getopt_long
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_GETOPT_LONG_H
#define VL_GETOPT_LONG_H

#include "generic.h"

VL_EXPORT int    opterr ;   /**< code of the last error occured while parsing an option */
VL_EXPORT int    optind ;   /**< index of the next option to process in @c argv */
VL_EXPORT int    optopt ;   /**< current option */
VL_EXPORT char * optarg ;   /**< argument of the current option */
VL_EXPORT int    optreset ; /**< reset flag */

/** @brief ::getopt_long option */
struct option
{
  const char *name ;  /**< option long name */
  int	      has_arg ; /**< flag indicating whether the option has no, required or optional argument */
  int	     *flag ;    /**< pointer to a variable to set (if @c NULL, the value is returned instead) */
  int	      val ;     /**< value to set or to return */
} ;

#define no_argument       0 /**< ::option with no argument */
#define required_argument 1 /**< ::option with required argument */
#define optional_argument 2 /**< ::option with optional argument */

VL_EXPORT int getopt_long(int argc, char * const argv[],
                          const char * optstring,
                          const struct option * longopts, int * longindex);

/* VL_GETOPT_LONG_H */
#endif
vlfeat/vl/lbp.h0000644000175000017500000000240712237004736012323 0ustar  dimadima/** @file lbp.h
 ** @brief Local Binary Patterns
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_LBP_H
#define VL_LBP_H

#include "generic.h"

/** @brief Type of quantization for LBP features */
typedef enum _VlLbpMappingType
{
  VlLbpUniform     /**< Uniform patterns */
} VlLbpMappingType ;

/** @brief Local Binary Pattern extractor */
typedef struct VlLbp_
{
  vl_size dimension ;
  vl_uint8 mapping [256] ;
  vl_bool transposed ;
} VlLbp ;

VL_EXPORT VlLbp * vl_lbp_new(VlLbpMappingType type, vl_bool transposed) ;
VL_EXPORT void vl_lbp_delete(VlLbp * self) ;
VL_EXPORT void vl_lbp_process (VlLbp * self,
                               float * features,
                               float * image, vl_size width, vl_size height,
                               vl_size cellSize) ;


/** @brief Get the dimension of the LBP histograms
 ** @return dimension of the LBP histograms.
 ** The dimension depends on the type of quantization used.
 ** @see ::vl_lbp_new().
 **/

VL_INLINE vl_size vl_lbp_get_dimension(VlLbp * self)
{
  return self->dimension ;
}

/* VL_LBP_H */
#endif
vlfeat/vl/mathop_sse2.h0000644000175000017500000000365412237004745013777 0ustar  dimadima/** @file mathop_sse2.h
 ** @brief mathop for sse2
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/* ---------------------------------------------------------------- */
#ifndef VL_MATHOP_SSE2_H_INSTANTIATING

#ifndef VL_MATHOP_SSE2_H
#define VL_MATHOP_SSE2_H

#undef FLT
#define FLT VL_TYPE_DOUBLE
#define VL_MATHOP_SSE2_H_INSTANTIATING
#include "mathop_sse2.h"

#undef FLT
#define FLT VL_TYPE_FLOAT
#define VL_MATHOP_SSE2_H_INSTANTIATING
#include "mathop_sse2.h"

/* VL_MATHOP_SSE2_H */
#endif

/* ---------------------------------------------------------------- */
/* VL_MATHOP_SSE2_H_INSTANTIATING */
#else

#ifndef VL_DISABLE_SSE2

#include "generic.h"
#include "float.th"

VL_EXPORT T
VL_XCAT(_vl_dot_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_distance_l2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_distance_l1_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_distance_chi2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_kernel_l2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_kernel_l1_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_kernel_chi2_sse2_, SFX)
(vl_size dimension, T const * X, T const * Y) ;

VL_EXPORT T
VL_XCAT(_vl_distance_mahalanobis_sq_sse2_, SFX)
(vl_size dimension, T const * X, T const * MU, T const * S);

VL_EXPORT void
VL_XCAT(_vl_weighted_sigma_sse2_, SFX)
(vl_size dimension, T * S, T const * X, T const * Y, T const W);

VL_EXPORT void
VL_XCAT(_vl_weighted_mean_sse2_, SFX)
(vl_size dimension, T * MU, T const * X, T const W);

/* ! VL_DISABLE_SSE2 */
#endif
#undef VL_MATHOP_SSE2_INSTANTIATING
#endif
vlfeat/vl/covdet.h0000644000175000017500000002033512237004736013032 0ustar  dimadima/** @file covdet.h
 ** @brief Covariant feature detectors (@ref covdet)
 ** @author Karel Lenc
 ** @author Andrea Vedaldi
 ** @author Michal Perdoch
 **/

/*
 Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
 All rights reserved.

 This file is part of the VLFeat library and is made available under
 the terms of the BSD license (see the COPYING file).
 */

#ifndef VL_COVDET_H
#define VL_COVDET_H

#include "generic.h"
#include "stringop.h"
#include "scalespace.h"

#include 

/* ---------------------------------------------------------------- */
/*                                                   Feature Frames */
/* ---------------------------------------------------------------- */

/** @name Feature frames
 ** @{ */

/** @brief Types of feature frames */
typedef enum _VlFrameType {
  VL_FRAMETYPE_DISC = 1,         /**< A disc. */
  VL_FRAMETYPE_ORIENTED_DISC,    /**< An oriented disc. */
  VL_FRAMETYPE_ELLIPSE,          /**< An ellipse. */
  VL_FRAMETYPE_ORIENTED_ELLIPSE, /**< An oriented ellipse. */
  VL_FRAMETYPE_NUM
} VlFrameType ;

/** @brief Names of the frame types */
VL_EXPORT const char* vlFrameNames [VL_FRAMETYPE_NUM] ;

/** @brief Mapping between string values and VlFrameType values */
VL_EXPORT VlEnumerator vlFrameTypes [VL_FRAMETYPE_NUM] ;

/** @brief Disc feature frame */
typedef struct _VlFrameDisc
{
  float x ;     /**< center x-coordinate */
  float y ;     /**< center y-coordinate */
  float sigma ; /**< radius or scale */
} VlFrameDisc ;

/** @brief Oriented disc feature frame
 ** An upright frame has @c angle equal to zero.
 **/
typedef struct _VlFrameOrientedDisc {
  float x ;     /**< center x-coordinate */
  float y ;     /**< center y-coordinate */
  float sigma ; /**< radius or scale */
  float angle ; /**< rotation angle (rad) */
} VlFrameOrientedDisc ;

/** @brief Ellipse feature frame */
typedef struct _VlFrameEllipse {
  float x ;     /**< center x-coordinate */
  float y ;     /**< center y-coordinate */
  float e11 ;   /**< */
  float e12 ;
  float e22 ;
} VlFrameEllipse ;

/** @brief Oriented ellipse feature frame
 ** The affine transformation transforms the ellipse shape into
 ** a circular region. */
typedef struct _VlFrameOrientedEllipse {
  float x ;     /**< center x-coordinate */
  float y ;     /**< center y-coordinate */
  float a11 ;   /**< */
  float a12 ;
  float a21 ;
  float a22 ;
} VlFrameOrientedEllipse;

/** @brief Get the size of a frame structure
 ** @param frameType identifier of the type of frame.
 ** @return size of the corresponding frame structure in bytes.
 **/
VL_INLINE vl_size
vl_get_frame_size (VlFrameType frameType) {
  switch (frameType) {
    case VL_FRAMETYPE_DISC: return sizeof(VlFrameDisc);
    case VL_FRAMETYPE_ORIENTED_DISC: return sizeof(VlFrameOrientedDisc);
    case VL_FRAMETYPE_ELLIPSE: return sizeof(VlFrameEllipse);
    case VL_FRAMETYPE_ORIENTED_ELLIPSE: return sizeof(VlFrameOrientedEllipse);
    default:
      assert(0);
      break;
  }
  return 0;
}

/** @brief Get the size of a frame structure
 ** @param frameType identifier of the type of frame.
 ** @return size of the corresponding frame structure in bytes.
 **/
VL_INLINE VlFrameType
vl_get_frame_type (vl_bool affineAdaptation, vl_bool orientation)
{
  if (affineAdaptation) {
    if (orientation) {
      return VL_FRAMETYPE_ORIENTED_ELLIPSE;
    } else {
      return VL_FRAMETYPE_ELLIPSE;
    }
  } else {
    if (orientation) {
      return VL_FRAMETYPE_ORIENTED_DISC;
    } else {
      return VL_FRAMETYPE_DISC;
    }
  }
}

/* ---------------------------------------------------------------- */
/*                                       Covariant Feature Detector */
/* ---------------------------------------------------------------- */

/** @brief A detected feature */
typedef struct _VlCovDetFeature
{
  VlFrameOrientedEllipse frame ; /**< feature frame. */
  float peakScore ; /**< peak score. */
  float edgeScore ; /**< edge score. */
  float orientationScore ; /**< orientation score. */
  float laplacianScaleScore ; /**< Laplacian scale score. */
} VlCovDetFeature ;

typedef struct _VlCovDetFeatureOrientation
{
  double angle ;
  double score ;
} VlCovDetFeatureOrientation ;

typedef struct _VlCovDetFeatureLaplacianScale
{
  double scale ;
  double score ;
} VlCovDetFeatureLaplacianScale ;

/** @brief Covariant feature detection method */
typedef enum _VlCovDetMethod
{
  VL_COVDET_METHOD_DOG = 1,
  VL_COVDET_METHOD_HESSIAN,
  VL_COVDET_METHOD_HESSIAN_LAPLACE,
  VL_COVDET_METHOD_HARRIS_LAPLACE,
  VL_COVDET_METHOD_MULTISCALE_HESSIAN,
  VL_COVDET_METHOD_MULTISCALE_HARRIS,
  VL_COVDET_METHOD_NUM
} VlCovDetMethod;

/** @brief Mapping between strings and ::VlCovDetMethod values */
VL_EXPORT VlEnumerator vlCovdetMethods [VL_COVDET_METHOD_NUM] ;

typedef struct _VlCovDet VlCovDet ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlCovDet * vl_covdet_new (VlCovDetMethod method) ;
VL_EXPORT void vl_covdet_delete (VlCovDet * self) ;
VL_EXPORT void vl_covdet_reset (VlCovDet * self) ;
/** @} */

/** @name Process data
 ** @{
 **/
VL_EXPORT int vl_covdet_put_image (VlCovDet * self,
                                    float const * image,
                                    vl_size width, vl_size height) ;

VL_EXPORT void vl_covdet_detect (VlCovDet * self) ;
VL_EXPORT int vl_covdet_append_feature (VlCovDet * self, VlCovDetFeature const * feature) ;
VL_EXPORT void vl_covdet_extract_orientations (VlCovDet * self) ;
VL_EXPORT void vl_covdet_extract_laplacian_scales (VlCovDet * self) ;
VL_EXPORT void vl_covdet_extract_affine_shape (VlCovDet * self) ;

VL_EXPORT VlCovDetFeatureOrientation *
vl_covdet_extract_orientations_for_frame (VlCovDet * self,
                                          vl_size *numOrientations,
                                          VlFrameOrientedEllipse frame) ;

VL_EXPORT VlCovDetFeatureLaplacianScale *
vl_covdet_extract_laplacian_scales_for_frame (VlCovDet * self,
                                              vl_size * numScales,
                                              VlFrameOrientedEllipse frame) ;
VL_EXPORT int
vl_covdet_extract_affine_shape_for_frame (VlCovDet * self,
                                          VlFrameOrientedEllipse * adapted,
                                          VlFrameOrientedEllipse frame) ;

VL_EXPORT vl_bool
vl_covdet_extract_patch_for_frame (VlCovDet * self, float * patch,
                                   vl_size resolution,
                                   double extent,
                                   double sigma,
                                   VlFrameOrientedEllipse frame) ;

VL_EXPORT void
vl_covdet_drop_features_outside (VlCovDet * self, double margin) ;

/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_EXPORT vl_size vl_covdet_get_num_features (VlCovDet const * self) ;
VL_EXPORT void * vl_covdet_get_features (VlCovDet * self) ;
VL_EXPORT vl_index vl_covdet_get_first_octave (VlCovDet const * self) ;
VL_EXPORT vl_size vl_covdet_get_octave_resolution (VlCovDet const * self) ;
VL_EXPORT double vl_covdet_get_peak_threshold (VlCovDet const * self) ;
VL_EXPORT double vl_covdet_get_edge_threshold (VlCovDet const * self) ;
VL_EXPORT vl_bool vl_covdet_get_transposed (VlCovDet const * self) ;
VL_EXPORT VlScaleSpace *  vl_covdet_get_gss (VlCovDet const * self) ;
VL_EXPORT VlScaleSpace *  vl_covdet_get_css (VlCovDet const * self) ;
VL_EXPORT vl_bool vl_covdet_get_aa_accurate_smoothing (VlCovDet const * self) ;
VL_EXPORT vl_size const * vl_covdet_get_laplacian_scales_statistics (VlCovDet const * self, vl_size * numScales) ;
VL_EXPORT double vl_covdet_get_non_extrema_suppression_threshold (VlCovDet const * self) ;
VL_EXPORT vl_size vl_covdet_get_num_non_extrema_suppressed (VlCovDet const * self) ;

/** @} */

/** @name Set parameters
 ** @{
 **/
VL_EXPORT void vl_covdet_set_first_octave (VlCovDet * self, vl_index o) ;
VL_EXPORT void vl_covdet_set_octave_resolution (VlCovDet * self, vl_size r) ;
VL_EXPORT void vl_covdet_set_peak_threshold (VlCovDet * self, double peakThreshold) ;
VL_EXPORT void vl_covdet_set_edge_threshold (VlCovDet * self, double edgeThreshold) ;
VL_EXPORT void vl_covdet_set_transposed (VlCovDet * self, vl_bool t) ;
VL_EXPORT void vl_covdet_set_aa_accurate_smoothing (VlCovDet * self, vl_bool x) ;
VL_EXPORT void vl_covdet_set_non_extrema_suppression_threshold (VlCovDet * self, double x) ;
/** @} */

/* VL_COVDET_H */
#endif
vlfeat/vl/ikmeans.c0000644000175000017500000001452712237004736013176 0ustar  dimadima/** @file ikmeans.c
 ** @brief Integer K-Means clustering - Definition
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file ikmeans.h
 **
 ** Integer K-means (IKM) is an implementation of K-means clustering
 ** (or Vector Quantization, VQ) for integer data. This is
 ** particularly useful for clustering large collections of visual
 ** descriptors.
 **
 ** Use the function ::vl_ikm_new() to create a IKM
 ** quantizer. Initialize the IKM quantizer with @c K clusters by
 ** ::vl_ikm_init() or similar function. Use ::vl_ikm_train() to train
 ** the quantizer. Use ::vl_ikm_push() or ::vl_ikm_push_one() to
 ** quantize new data.
 **
 ** Given data @f$x_1,\dots,x_N\in R^d@f$ and a number of clusters
 ** @f$K@f$, the goal is to find assignments @f$a_i\in\{1,\dots,K\},@f$
 ** and centers @f$c_1,\dots,c_K\in R^d@f$ so that the expected
 ** distortion
 **
 ** @f[
 **   E(\{a_{i}, c_j\}) = \frac{1}{N} \sum_{i=1}^N d(x_i, c_{a_i})
 ** @f]
 **
 ** is minimized. Here @f$d(x_i, c_{a_i})@f$ is the
 ** distortion, i.e. the cost we pay for representing @f$ x_i
 ** @f$ by @f$ c_{a_i} @f$. IKM uses the squared distortion
 ** @f$d(x,y)=\|x-y\|^2_2@f$.
 **
 ** @section ikmeans-algo Algorithms
 **
 ** @subsection ikmeans-alg-init Initialization
 **
 ** Most K-means algorithms are iterative and needs an initialization
 ** in the form of an initial choice of the centers
 ** @f$c_1,\dots,c_K@f$. We include the following options:
 **
 ** - User specified centers (::vl_ikm_init);
 ** - Random centers (::vl_ikm_init_rand);
 ** - Centers from @c K randomly selected data points (::vl_ikm_init_rand_data).
 **
 ** @subsection ikmeans-alg-lloyd Lloyd
 **
 ** The Lloyd (also known as Lloyd-Max and LBG) algorithm iteratively:
 **
 ** - Fixes the centers, optimizing the assignments (minimizing by
 **   exhaustive search the association of each data point to the
 **   centers);
 ** - Fixes the assignments and optimizes the centers (by descending
 **   the distortion error function). For the squared distortion, this
 **   step is in closed form.
 **
 ** This algorithm is not particularly efficient because all data
 ** points need to be compared to all centers, for a complexity
 ** @f$O(dNKT)@f$, where T is the total number of iterations.
 **
 ** @subsection ikmeans-alg-elkan Elkan
 **
 ** The Elkan algorithm is an optimized variant of Lloyd. By making
 ** use of the triangle inequality, many comparisons of data points
 ** and centers are avoided, especially at later iterations.
 ** Usually 4-5 times less comparisons than Lloyd are preformed,
 ** providing a dramatic speedup in the execution time.
 **
 **/

#include "ikmeans.h"

#include 
#include 
#include  /* memset */
#include "assert.h"

static void    vl_ikm_init_lloyd      (VlIKMFilt*) ;
static void    vl_ikm_init_elkan      (VlIKMFilt*) ;
static int     vl_ikm_train_lloyd     (VlIKMFilt*, vl_uint8 const*, int) ;
static int     vl_ikm_train_elkan     (VlIKMFilt*, vl_uint8 const*, int) ;
static void    vl_ikm_push_lloyd      (VlIKMFilt*, vl_uint*, vl_uint8 const*, int) ;
static void    vl_ikm_push_elkan      (VlIKMFilt*, vl_uint*, vl_uint8 const*, int) ;

/** @brief Create a new IKM quantizer
 **
 ** @param method Clustering algorithm.
 **
 ** The function allocates initializes a new IKM quantizer to
 ** operate based algorithm @a method.
 **
 ** @a method has values in the enumerations ::VlIKMAlgorithms.
 **
 ** @return new IKM quantizer.
 **/

VlIKMFilt *
vl_ikm_new (int method)
{
  VlIKMFilt *f =  vl_malloc (sizeof(VlIKMFilt)) ;
  f -> centers = 0 ;
  f -> inter_dist = 0 ;

  f -> M = 0 ;
  f -> K = 0 ;
  f -> method     = method ;
  f -> max_niters = 200 ;
  f -> verb       = 0 ;

  return f ;
}

/** @brief Delete IKM quantizer
 **
 ** @param f IKM quantizer.
 **/

void vl_ikm_delete (VlIKMFilt* f)
{
  if (f) {
    if (f-> centers)    vl_free (f-> centers) ;
    if (f-> inter_dist) vl_free (f-> inter_dist) ;
    vl_free (f) ;
  }
}

/** @brief Train clusters
 **
 ** @param f     IKM quantizer.
 ** @param data  data.
 ** @param N     number of data (@a N @c >= 1).
 **
 ** @return -1 if an overflow may have occurred.
 **/

VL_EXPORT
int vl_ikm_train (VlIKMFilt *f, vl_uint8 const *data, int N)
{
  int err ;

  if (f-> verb) {
    VL_PRINTF ("ikm: training with %d data\n",  N) ;
    VL_PRINTF ("ikm: %d clusters\n",  f -> K) ;
  }

  switch (f -> method) {
  case VL_IKM_LLOYD : err = vl_ikm_train_lloyd (f, data, N) ; break ;
  case VL_IKM_ELKAN : err = vl_ikm_train_elkan (f, data, N) ; break ;
  default :
    abort() ;
  }
  return err ;
}

/** @brief Project data to clusters
 **
 ** @param f     IKM quantizer.
 ** @param asgn  Assignments (out).
 ** @param data  data.
 ** @param N     number of data (@a N @c >= 1).
 **
 ** The function projects the data @a data on the integer K-means
 ** clusters specified by the IKM quantizer @a f. Notice that the
 ** quantizer must be initialized.
 **/

VL_EXPORT
void
vl_ikm_push (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N) {
  switch (f -> method) {
  case VL_IKM_LLOYD : vl_ikm_push_lloyd (f, asgn, data, N) ; break ;
  case VL_IKM_ELKAN : vl_ikm_push_elkan (f, asgn, data, N) ; break ;
  default :
    abort() ;
  }
}

/** @brief Project one datum to clusters
 **
 ** @param centers centers.
 ** @param data    datum to project.
 ** @param K       number of centers.
 ** @param M       dimensionality of the datum.
 **
 ** The function projects the specified datum @a data on the clusters
 ** specified by the centers @a centers.
 **
 ** @return the cluster index.
 **/

VL_EXPORT
vl_uint
vl_ikm_push_one (vl_ikm_acc const *centers,
		 vl_uint8 const *data,
		 int M, int K)
{
  int i,k ;

  /* assign data to centers */
  vl_int32 best_dist = 0 ;
  vl_uint  best      = (vl_uint)-1 ;

  for(k = 0 ; k < K ; ++k) {
    vl_int32 dist = 0 ;

    /* compute distance with this center */
    for(i = 0 ; i < M ; ++i) {
      vl_int32 delta = data[i] - centers[k*M + i] ;
      dist += delta * delta ;
    }

    /* compare with current best */
    if (best == (vl_uint)-1 || dist < best_dist) {
      best = k  ;
      best_dist = dist ;
    }
  }
  return best;
}

#include "ikmeans_init.tc"
#include "ikmeans_lloyd.tc"
#include "ikmeans_elkan.tc"
vlfeat/vl/mser.h0000644000175000017500000003222512237004745012515 0ustar  dimadima/** @file mser.h
 ** @brief MSER (@ref mser)
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_MSER
#define VL_MSER

#include "generic.h"

/** @brief MSER image data type
 **
 ** This is the data type of the image pixels. It has to be an
 ** integer.
 **/
typedef vl_uint8 vl_mser_pix ;

/** @brief Maximum value
 **
 ** Maximum value of the integer type ::vl_mser_pix.
 **/
#define VL_MSER_PIX_MAXVAL 256

/** @brief MSER Filter
 **
 ** The MSER filter computes the Maximally Stable Extremal Regions of
 ** an image.
 **
 ** @sa @ref mser
 **/
typedef struct _VlMserFilt VlMserFilt ;

/** @brief MSER filter statistics */
typedef struct _VlMserStats VlMserStats ;

/** @brief MSER filter statistics definition */
struct _VlMserStats
{
  int num_extremal ;      /**< number of extremal regions                                */
  int num_unstable ;      /**< number of unstable extremal regions                       */
  int num_abs_unstable ;  /**< number of regions that failed the absolute stability test */
  int num_too_big ;       /**< number of regions that failed the maximum size test       */
  int num_too_small ;     /**< number of regions that failed the minimum size test       */
  int num_duplicates ;    /**< number of regions that failed the duplicate test          */
} ;

/** @name Construction and Destruction
 ** @{
 **/
VL_EXPORT VlMserFilt*      vl_mser_new     (int ndims, int const* dims) ;
VL_EXPORT void             vl_mser_delete  (VlMserFilt *f) ;
/** @} */

/** @name Processing
 ** @{
 **/
VL_EXPORT void             vl_mser_process (VlMserFilt *f,
                                            vl_mser_pix const *im) ;
VL_EXPORT void             vl_mser_ell_fit (VlMserFilt *f) ;
/** @} */

/** @name Retrieving data
 ** @{
 **/
VL_INLINE vl_uint          vl_mser_get_regions_num  (VlMserFilt const *f) ;
VL_INLINE vl_uint const*   vl_mser_get_regions      (VlMserFilt const *f) ;
VL_INLINE float const*     vl_mser_get_ell          (VlMserFilt const *f) ;
VL_INLINE vl_uint          vl_mser_get_ell_num      (VlMserFilt const *f) ;
VL_INLINE vl_uint          vl_mser_get_ell_dof      (VlMserFilt const *f) ;
VL_INLINE VlMserStats const*  vl_mser_get_stats     (VlMserFilt const *f) ;
/** @} */

/** @name Retrieving parameters
 ** @{
 **/
VL_INLINE vl_mser_pix  vl_mser_get_delta          (VlMserFilt const *f) ;
VL_INLINE double       vl_mser_get_min_area       (VlMserFilt const *f) ;
VL_INLINE double       vl_mser_get_max_area       (VlMserFilt const *f) ;
VL_INLINE double       vl_mser_get_max_variation  (VlMserFilt const *f) ;
VL_INLINE double       vl_mser_get_min_diversity  (VlMserFilt const *f) ;
/** @} */

/** @name Setting parameters
 ** @{
 **/
VL_INLINE void  vl_mser_set_delta           (VlMserFilt *f, vl_mser_pix x) ;
VL_INLINE void  vl_mser_set_min_area        (VlMserFilt *f, double      x) ;
VL_INLINE void  vl_mser_set_max_area        (VlMserFilt *f, double      x) ;
VL_INLINE void  vl_mser_set_max_variation   (VlMserFilt *f, double      x) ;
VL_INLINE void  vl_mser_set_min_diversity   (VlMserFilt *f, double      x) ;
/** @} */

/* ====================================================================
 *                                                   INLINE DEFINITIONS
 * ================================================================== */

/** @internal @brief MSER accumulator data type
 **
 ** This is a large integer type. It should be large enough to contain
 ** a number equal to the area (volume) of the image by the image
 ** width by the image height (for instance, if the image is a square
 ** of side 256, the maximum value is 256 x 256 x 256).
 **/
typedef float vl_mser_acc ;

/** @internal @brief Basic region flag: null region */
#ifdef VL_COMPILER_MSC
#define VL_MSER_VOID_NODE ((1ui64<<32) - 1)
#else
#define VL_MSER_VOID_NODE ((1ULL<<32) - 1)
#endif

/* ----------------------------------------------------------------- */
/** @internal @brief MSER: basic region (declaration)
 **
 ** Extremal regions and maximally stable extremal regions are
 ** instances of image regions.
 **
 ** There is an image region for each pixel of the image. Each region
 ** is represented by an instance of this structure.  Regions are
 ** stored into an array in pixel order.
 **
 ** Regions are arranged into a forest. VlMserReg::parent points to
 ** the parent node, or to the node itself if the node is a root.
 ** VlMserReg::parent is the index of the node in the node array
 ** (which therefore is also the index of the corresponding
 ** pixel). VlMserReg::height is the distance of the fartest leaf. If
 ** the node itself is a leaf, then VlMserReg::height is zero.
 **
 ** VlMserReg::area is the area of the image region corresponding to
 ** this node.
 **
 ** VlMserReg::region is the extremal region identifier. Not all
 ** regions are extremal regions however; if the region is NOT
 ** extremal, this field is set to ....
 **/
struct _VlMserReg
{
  vl_uint parent ;   /**< points to the parent region.            */
  vl_uint shortcut ; /**< points to a region closer to a root.    */
  vl_uint height ;   /**< region height in the forest.            */
  vl_uint area ;     /**< area of the region.                     */
} ;

/** @internal @brief MSER: basic region */
typedef struct _VlMserReg VlMserReg ;

/* ----------------------------------------------------------------- */
/** @internal @brief MSER: extremal region (declaration)
 **
 ** Extremal regions (ER) are extracted from the region forest. Each
 ** region is represented by an instance of this structure. The
 ** structures are stored into an array, in arbitrary order.
 **
 ** ER are arranged into a tree. @a parent points to the parent ER, or
 ** to itself if the ER is the root.
 **
 ** An instance of the structure represents the extremal region of the
 ** level set of intensity VlMserExtrReg::value and containing the
 ** pixel VlMserExtReg::index.
 **
 ** VlMserExtrReg::area is the area of the extremal region and
 ** VlMserExtrReg::area_top is the area of the extremal region
 ** containing this region in the level set of intensity
 ** VlMserExtrReg::area + @c delta.
 **
 ** VlMserExtrReg::variation is the relative area variation @c
 ** (area_top-area)/area.
 **
 ** VlMserExtrReg::max_stable is a flag signaling whether this extremal
 ** region is also maximally stable.
 **/
struct _VlMserExtrReg
{
  int          parent ;     /**< index of the parent region                   */
  int          index ;      /**< index of pivot pixel                         */
  vl_mser_pix  value ;      /**< value of pivot pixel                         */
  vl_uint      shortcut ;   /**< shortcut used when building a tree           */
  vl_uint      area ;       /**< area of the region                           */
  float    variation ;  /**< rel. area variation                          */
  vl_uint      max_stable ; /**< max stable number (=0 if not maxstable)      */
} ;

/** @internal @brief MSER: extremal region */
typedef struct _VlMserExtrReg VlMserExtrReg ;

/* ----------------------------------------------------------------- */
/** @internal @brief MSER filter
 ** @see @ref mser
 **/
struct _VlMserFilt
{

  /** @name Image data and meta data @internal */
  /*@{*/
  int                ndims ;   /**< number of dimensions                    */
  int               *dims ;    /**< dimensions                              */
  int                nel ;     /**< number of image elements (pixels)       */
  int               *subs ;    /**< N-dimensional subscript                 */
  int               *dsubs ;   /**< another subscript                       */
  int               *strides ; /**< strides to move in image data           */
  /*@}*/

  vl_uint           *perm ;    /**< pixel ordering                          */
  vl_uint           *joins ;   /**< sequence of join ops                    */
  int                njoins ;  /**< number of join ops                      */

  /** @name Regions */
  /*@{*/
  VlMserReg         *r ;       /**< basic regions                           */
  VlMserExtrReg     *er ;      /**< extremal tree                           */
  vl_uint           *mer ;     /**< maximally stable extremal regions       */
  int                ner ;     /**< number of extremal regions              */
  int                nmer ;    /**< number of maximally stable extr. reg.   */
  int                rer ;     /**< size of er buffer                       */
  int                rmer ;    /**< size of mer buffer                      */
  /*@}*/

  /** @name Ellipsoids fitting */
  /*@{*/
  float         *acc ;     /**< moment accumulator.                    */
  float         *ell ;     /**< ellipsoids list.                       */
  int                rell ;    /**< size of ell buffer                     */
  int                nell ;    /**< number of ellipsoids extracted         */
  int                dof ;     /**< number of dof of ellipsoids.           */

  /*@}*/

  /** @name Configuration */
  /*@{*/
  vl_bool   verbose ;          /**< be verbose                             */
  int       delta ;            /**< delta filter parameter                  */
  double    max_area ;         /**< badness test parameter                 */
  double    min_area ;         /**< badness test parameter                 */
  double    max_variation ;    /**< badness test parameter                 */
  double    min_diversity ;    /**< minimum diversity                      */
  /*@}*/

  VlMserStats stats ;          /** run statistic                           */
} ;

/* ----------------------------------------------------------------- */
/** @brief Get delta
 ** @param f MSER filter.
 ** @return value of @c delta.
 **/
VL_INLINE vl_mser_pix
vl_mser_get_delta (VlMserFilt const *f)
{
  return f-> delta ;
}

/** @brief Set delta
 ** @param f MSER filter.
 ** @param x value of @c delta.
 **/
VL_INLINE void
vl_mser_set_delta (VlMserFilt *f, vl_mser_pix x)
{
  f-> delta = x ;
}

/* ----------------------------------------------------------------- */
/** @brief Get minimum diversity
 ** @param  f MSER filter.
 ** @return value of @c minimum diversity.
 **/
VL_INLINE double
vl_mser_get_min_diversity (VlMserFilt const *f)
{
  return f-> min_diversity ;
}

/** @brief Set minimum diversity
 ** @param f MSER filter.
 ** @param x value of @c minimum diversity.
 **/
VL_INLINE void
vl_mser_set_min_diversity (VlMserFilt *f, double x)
{
  f-> min_diversity = x ;
}

/* ----------------------------------------------------------------- */
/** @brief Get statistics
 ** @param f MSER filter.
 ** @return statistics.
 **/
VL_INLINE VlMserStats const*
vl_mser_get_stats (VlMserFilt const *f)
{
  return & f-> stats ;
}

/* ----------------------------------------------------------------- */
/** @brief Get maximum region area
 ** @param f MSER filter.
 ** @return maximum region area.
 **/
VL_INLINE double
vl_mser_get_max_area (VlMserFilt const *f)
{
  return f-> max_area ;
}

/** @brief Set maximum region area
 ** @param f MSER filter.
 ** @param x maximum region area.
 **/
VL_INLINE void
vl_mser_set_max_area (VlMserFilt *f, double x)
{
  f-> max_area = x ;
}

/* ----------------------------------------------------------------- */
/** @brief Get minimum region area
 ** @param f MSER filter.
 ** @return minimum region area.
 **/
VL_INLINE double
vl_mser_get_min_area (VlMserFilt const *f)
{
  return f-> min_area ;
}

/** @brief Set minimum region area
 ** @param f MSER filter.
 ** @param x minimum region area.
 **/
VL_INLINE void
vl_mser_set_min_area (VlMserFilt *f, double x)
{
  f-> min_area = x ;
}

/* ----------------------------------------------------------------- */
/** @brief Get maximum region variation
 ** @param f MSER filter.
 ** @return maximum region variation.
 **/
VL_INLINE double
vl_mser_get_max_variation (VlMserFilt const *f)
{
  return f-> max_variation ;
}

/** @brief Set maximum region variation
 ** @param f MSER filter.
 ** @param x maximum region variation.
 **/
VL_INLINE void
vl_mser_set_max_variation (VlMserFilt *f, double x)
{
  f-> max_variation = x ;
}

/* ----------------------------------------------------------------- */
/** @brief Get maximally stable extremal regions
 ** @param f MSER filter.
 ** @return array of MSER pivots.
 **/
VL_INLINE vl_uint const *
vl_mser_get_regions (VlMserFilt const* f)
{
  return f-> mer ;
}

/** @brief Get number of maximally stable extremal regions
 ** @param f MSER filter.
 ** @return number of MSERs.
 **/
VL_INLINE vl_uint
vl_mser_get_regions_num (VlMserFilt const* f)
{
  return f-> nmer ;
}

/* ----------------------------------------------------------------- */
/** @brief Get ellipsoids
 ** @param f MSER filter.
 ** @return ellipsoids.
 **/
VL_INLINE float const *
vl_mser_get_ell (VlMserFilt const* f)
{
  return f-> ell ;
}

/** @brief Get number of degrees of freedom of ellipsoids
 ** @param f MSER filter.
 ** @return number of degrees of freedom.
 **/
VL_INLINE vl_uint
vl_mser_get_ell_dof (VlMserFilt const* f)
{
  return f-> dof ;
}

/** @brief Get number of ellipsoids
 ** @param f MSER filter.
 ** @return number of ellipsoids
 **/
VL_INLINE vl_uint
vl_mser_get_ell_num (VlMserFilt const* f)
{
  return f-> nell ;
}

/* VL_MSER */
#endif
vlfeat/vl/mathop_avx.c0000644000175000017500000001424212237004745013707 0ustar  dimadima/** @file mathop_avx.c
 ** @brief mathop for AVX - Definition
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/* ---------------------------------------------------------------- */
#if ! defined(VL_MATHOP_AVX_INSTANTIATING)

#include "mathop_avx.h"

#undef FLT
#define FLT VL_TYPE_DOUBLE
#define VL_MATHOP_AVX_INSTANTIATING
#include "mathop_avx.c"

#undef FLT
#define FLT VL_TYPE_FLOAT
#define VL_MATHOP_AVX_INSTANTIATING
#include "mathop_avx.c"

/* ---------------------------------------------------------------- */
/* VL_MATHOP_AVX_INSTANTIATING */
#else
#ifndef VL_DISABLE_AVX

#include 
#include "generic.h"
#include "mathop.h"
#include "float.th"

VL_INLINE T
VL_XCAT(_vl_vhsum_avx_, SFX)(VTYPEavx x)
{
  T acc ;
#if (VSIZEavx == 8)
  {
    //VTYPEavx hsum = _mm256_hadd_ps(x, x);
    //hsum = _mm256_add_ps(hsum, _mm256_permute2f128_ps(hsum, hsum, 0x1));
    //_mm_store_ss(&acc, _mm_hadd_ps( _mm256_castps256_ps128(hsum), _mm256_castps256_ps128(hsum) ) );
    VTYPEavx hsum = VHADD2avx(x, x);
    hsum = VADDavx(hsum, VPERMavx(hsum, hsum, 0x1));
    VST1(&acc, VHADDavx( VCSTavx(hsum), VCSTavx(hsum) ) );
  }
#else
  {
    //VTYPEavx hsum = _mm256_add_pd(x, _mm256_permute2f128_pd(x, x, 0x1));
    VTYPEavx hsum = VADDavx(x, VPERMavx(x, x, 0x1));

    //_mm_store_sd(&acc, _mm_hadd_pd( _mm256_castpd256_pd128(hsum), _mm256_castpd256_pd128(hsum) ) );
    VST1(&acc, VHADDavx( VCSTavx(hsum), VCSTavx(hsum) ) );
  }
#endif
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_l2_avx_, SFX)
(vl_size dimension, T const * X, T const * Y)
{

  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZEavx + 1 ;
  T acc ;
  VTYPEavx vacc = VSTZavx() ;
  vl_bool dataAligned = VALIGNEDavx(X) & VALIGNEDavx(Y) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPEavx a = *(VTYPEavx*)X ;
      VTYPEavx b = *(VTYPEavx*)Y ;
      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      vacc = VADDavx(vacc, delta2) ;
      X += VSIZEavx ;
      Y += VSIZEavx ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPEavx a = VLDUavx(X) ;
      VTYPEavx b = VLDUavx(Y) ;
      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      vacc = VADDavx(vacc, delta2) ;
      X += VSIZEavx ;
      Y += VSIZEavx ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_avx_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    acc += delta * delta ;
  }

  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_mahalanobis_sq_avx_, SFX)
(vl_size dimension, T const * X, T const * MU, T const * S)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZEavx + 1 ;
  T acc ;
  VTYPEavx vacc = VSTZavx() ;
  vl_bool dataAligned = VALIGNEDavx(X) & VALIGNEDavx(MU) & VALIGNEDavx(S);

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPEavx a = *(VTYPEavx*)X ;
      VTYPEavx b = *(VTYPEavx*)MU ;
      VTYPEavx c = *(VTYPEavx*)S ;

      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      VTYPEavx delta2div = VMULavx(delta2,c);

      vacc = VADDavx(vacc, delta2div) ;

      X  += VSIZEavx ;
      MU += VSIZEavx ;
      S  += VSIZEavx ;
    }
  } else {
    while (X < X_vec_end) {

      VTYPEavx a = VLDUavx(X) ;
      VTYPEavx b = VLDUavx(MU) ;
      VTYPEavx c = VLDUavx(S) ;

      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      VTYPEavx delta2div = VMULavx(delta2,c);

      vacc = VADDavx(vacc, delta2div) ;

      X  += VSIZEavx ;
      MU += VSIZEavx ;
      S  += VSIZEavx ;
    }
  }

  acc = VL_XCAT(_vl_vhsum_avx_, SFX)(vacc) ;

  while (X < X_end) {
    T a = *X++ ;
    T b = *MU++ ;
    T c = *S++ ;
    T delta = a - b ;
    acc += (delta * delta) * c;
  }

  return acc ;
}

VL_EXPORT void
VL_XCAT(_vl_weighted_mean_avx_, SFX)
(vl_size dimension, T * MU, T const * X, T const  W)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZEavx + 1 ;

  vl_bool dataAligned = VALIGNEDavx(X) & VALIGNEDavx(MU);
  VTYPEavx w = VLD1avx (&W) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPEavx a = *(VTYPEavx*)X ;
      VTYPEavx mu = *(VTYPEavx*)MU ;

      VTYPEavx aw = VMULavx(a, w) ;
      VTYPEavx meanStore = VADDavx(aw, mu);

      *(VTYPEavx *)MU = meanStore;

      X += VSIZEavx ;
      MU += VSIZEavx ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPEavx a  = VLDUavx(X) ;
      VTYPEavx mu = VLDUavx(MU) ;

      VTYPEavx aw = VMULavx(a, w) ;
      VTYPEavx meanStore = VADDavx(aw, mu);

      VST2Uavx(MU,meanStore);

      X += VSIZEavx ;
      MU += VSIZEavx ;
    }
  }

  while (X < X_end) {
    T a = *X++ ;
    *MU += a * W ;
    MU++;
  }
}

VL_EXPORT void
VL_XCAT(_vl_weighted_sigma_avx_, SFX)
(vl_size dimension, T * S, T const * X, T const * Y, T const W)
{
  T const * X_end = X + dimension ;
  T const * X_vec_end = X_end - VSIZEavx + 1 ;

  vl_bool dataAligned = VALIGNEDavx(X) & VALIGNEDavx(Y) & VALIGNEDavx(S);

  VTYPEavx w = VLD1avx (&W) ;

  if (dataAligned) {
    while (X < X_vec_end) {
      VTYPEavx a = *(VTYPEavx*)X ;
      VTYPEavx b = *(VTYPEavx*)Y ;
      VTYPEavx s = *(VTYPEavx*)S ;

      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      VTYPEavx delta2w = VMULavx(delta2, w) ;
      VTYPEavx sigmaStore = VADDavx(s,delta2w);

      *(VTYPEavx *)S = sigmaStore;

      X += VSIZEavx ;
      Y += VSIZEavx ;
      S += VSIZEavx ;
    }
  } else {
    while (X < X_vec_end) {
      VTYPEavx a = VLDUavx(X) ;
      VTYPEavx b = VLDUavx(Y) ;
      VTYPEavx s = VLDUavx(S) ;

      VTYPEavx delta = VSUBavx(a, b) ;
      VTYPEavx delta2 = VMULavx(delta, delta) ;
      VTYPEavx delta2w = VMULavx(delta2, w) ;
      VTYPEavx sigmaStore = VADDavx(s,delta2w);

      VST2Uavx(S,sigmaStore);

      X += VSIZEavx ;
      Y += VSIZEavx ;
      S += VSIZEavx ;
    }
  }

  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    *S += ((delta * delta)*W) ;
    S++;
  }
}

/* VL_DISABLE_AVX */
#endif
#undef VL_MATHOP_AVX_INSTANTIATING
#endif
vlfeat/vl/gmm.h0000644000175000017500000001051012237004745012320 0ustar  dimadima/** @file gmm.h
 ** @brief GMM (@ref gmm)
 ** @author David Novotny
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 David Novotny and Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_GMM_H
#define VL_GMM_H

#include "kmeans.h"

/** @brief GMM initialization algorithms */
typedef enum _VlGMMInitialization
{
  VlGMMKMeans, /**< Initialize GMM from KMeans clustering. */
  VlGMMRand,   /**< Initialize GMM parameters by selecting points at random. */
  VlGMMCustom  /**< User specifies the initial GMM parameters. */
} VlGMMInitialization ;


#ifndef __DOXYGEN__
struct _VlGMM ;
typedef struct _VlGMM VlGMM ;
#else
/** @brief GMM quantizer */
typedef OPAQUE VlGMM ;
#endif

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlGMM * vl_gmm_new (vl_type dataType, vl_size dimension, vl_size numComponents) ;
VL_EXPORT VlGMM * vl_gmm_new_copy (VlGMM const * gmm) ;
VL_EXPORT void vl_gmm_delete (VlGMM * self) ;
VL_EXPORT void vl_gmm_reset (VlGMM * self);
/** @} */

/** @name Basic data processing
 ** @{
 **/
VL_EXPORT double
vl_gmm_cluster
(VlGMM * self,
 void const * data,
 vl_size numData);
/** @} */

/** @name Fine grained data processing
 ** @{ */

VL_EXPORT void
vl_gmm_init_with_rand_data
(VlGMM * self,
 void const * data,
 vl_size numData) ;

VL_EXPORT void
vl_gmm_init_with_kmeans
(VlGMM * self,
 void const * data,
 vl_size numData,
 VlKMeans * kmeansInit);

VL_EXPORT double
vl_gmm_em
(VlGMM * self,
 void const * data,
 vl_size numData);
/** @} */

VL_EXPORT void
vl_gmm_set_means
(VlGMM * self,
 void const * means);

VL_EXPORT void
vl_gmm_set_covariances
(VlGMM * self,
 void const * covariances);

VL_EXPORT void
vl_gmm_set_priors
(VlGMM * self,
 void const * priors);

VL_EXPORT double
vl_get_gmm_data_posteriors_f(float * posteriors,
                             vl_size numClusters,
                             vl_size numData,
                             float const * priors,
                             float const * means,
                             vl_size dimension,
                             float const * covariances,
                             float const * data) ;

VL_EXPORT double
vl_get_gmm_data_posteriors_d(double * posteriors,
                             vl_size numClusters,
                             vl_size numData,
                             double const * priors,
                             double const * means,
                             vl_size dimension,
                             double const * covariances,
                             double const * data) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_EXPORT void vl_gmm_set_num_repetitions (VlGMM * self, vl_size numRepetitions) ;
VL_EXPORT void vl_gmm_set_max_num_iterations (VlGMM * self, vl_size maxNumIterations) ;
VL_EXPORT void vl_gmm_set_verbosity (VlGMM * self, int verbosity) ;
VL_EXPORT void vl_gmm_set_initialization (VlGMM * self, VlGMMInitialization init);
VL_EXPORT void vl_gmm_set_kmeans_init_object (VlGMM * self, VlKMeans * kmeans);
VL_EXPORT void vl_gmm_set_covariance_lower_bounds (VlGMM * self, double const * bounds);
VL_EXPORT void vl_gmm_set_covariance_lower_bound (VlGMM * self, double bound) ;
/** @} */

/** @name Get parameters
 ** @{
 **/
VL_EXPORT void const * vl_gmm_get_means (VlGMM const * self);
VL_EXPORT void const * vl_gmm_get_covariances (VlGMM const * self);
VL_EXPORT void const * vl_gmm_get_priors (VlGMM const * self);
VL_EXPORT void const * vl_gmm_get_posteriors (VlGMM const * self);
VL_EXPORT vl_type vl_gmm_get_data_type (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_dimension (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_num_repetitions (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_num_data (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_num_clusters (VlGMM const * self);
VL_EXPORT double vl_gmm_get_loglikelyhood (VlGMM const * self);
VL_EXPORT int vl_gmm_get_verbosity (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_max_num_iterations (VlGMM const * self);
VL_EXPORT vl_size vl_gmm_get_num_repetitions (VlGMM const * self);
VL_EXPORT VlGMMInitialization vl_gmm_get_initialization (VlGMM const * self);
VL_EXPORT VlKMeans * vl_gmm_get_kmeans_init_object (VlGMM const * self);
VL_EXPORT double const * vl_gmm_get_covariance_lower_bounds (VlGMM const * self);
/** @} */

/* VL_GMM_H */
#endif
vlfeat/vl/pgm.h0000644000175000017500000000446412237004745012336 0ustar  dimadima/** @file pgm.h
 ** @brief Portable graymap format (PGM) parser
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_PGM_H
#define VL_PGM_H

#include "generic.h"
#include "mathop.h"
#include 

/** @name PGM parser error codes
 ** @{ */
#define VL_ERR_PGM_INV_HEAD  101 /**< Invalid PGM header section. */
#define VL_ERR_PGM_INV_META  102 /**< Invalid PGM meta section. */
#define VL_ERR_PGM_INV_DATA  103 /**< Invalid PGM data section.*/
#define VL_ERR_PGM_IO        104 /**< Generic I/O error. */
/** @} */

/** @brief PGM image meta data
 **
 ** A PGM image is a 2-D array of pixels of width #width and height
 ** #height. Each pixel is an integer one or two bytes wide, depending
 ** whether #max_value is smaller than 256.
 **/

typedef struct _VlPgmImage
{
  vl_size width ;      /**< image width.                     */
  vl_size height ;     /**< image height.                    */
  vl_size max_value ;  /**< pixel maximum value (<= 2^16-1). */
  vl_bool is_raw ;     /**< is RAW format?                   */
} VlPgmImage ;

/** @name Core operations
 ** @{ */
VL_EXPORT int vl_pgm_extract_head (FILE *f, VlPgmImage *im) ;
VL_EXPORT int vl_pgm_extract_data (FILE *f, VlPgmImage const *im, void *data) ;
VL_EXPORT int vl_pgm_insert (FILE *f,
                             VlPgmImage const *im,
                             void const*data ) ;
VL_EXPORT vl_size vl_pgm_get_npixels (VlPgmImage const *im) ;
VL_EXPORT vl_size vl_pgm_get_bpp (VlPgmImage const *im) ;
/** @} */

/** @name Helper functions
 ** @{ */
VL_EXPORT int vl_pgm_write (char const *name,
                            vl_uint8 const *data,
                            int width, int height) ;
VL_EXPORT int vl_pgm_write_f (char const *name,
                              float const *data,
                              int width, int height) ;
VL_EXPORT int vl_pgm_read_new (char const *name,
                               VlPgmImage *im,
                               vl_uint8 **data) ;
VL_EXPORT int vl_pgm_read_new_f (char const *name,
                                 VlPgmImage *im,
                                 float **data) ;

/** @} */

/* VL_PGM_H */
#endif
vlfeat/vl/imopv_sse2.h0000644000175000017500000000335012237004736013632 0ustar  dimadima/** @file imopv_sse2.h
 ** @brief Vectorized image operations - SSE2
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_IMOPV_SSE2_H
#define VL_IMOPV_SSE2_H

#include "generic.h"

#ifndef VL_DISABLE_SSE2

VL_EXPORT
void _vl_imconvcol_vf_sse2 (float* dst, vl_size dst_stride,
                            float const* src,
                            vl_size src_width, vl_size src_height, vl_size src_stride,
                            float const* filt, vl_index filt_begin, vl_index filt_end,
                            int step, unsigned int flags) ;

VL_EXPORT
void _vl_imconvcol_vd_sse2 (double* dst, vl_size dst_stride,
                            double const* src,
                            vl_size src_width, vl_size src_height, vl_size src_stride,
                            double const* filt, vl_index filt_begin, vl_index filt_end,
                            int step, unsigned int flags) ;

/*
VL_EXPORT
void _vl_imconvcoltri_vf_sse2 (float* dst, int dst_stride,
                               float const* src,
                               int src_width, int src_height, int src_stride,
                               int filt_size,
                               int step, unsigned int flags) ;

VL_EXPORT
void _vl_imconvcoltri_vd_sse2 (double* dst, int dst_stride,
                               double const* src,
                               int src_width, int src_height, int src_stride,
                               int filt_size,
                               int step, unsigned int flags) ;
*/

#endif

/* VL_IMOPV_SSE2_H */
#endif
vlfeat/vl/kdtree.h0000644000175000017500000001314412237004745013024 0ustar  dimadima/** @file kdtree.h
 ** @brief KD-tree (@ref kdtree)
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_KDTREE_H
#define VL_KDTREE_H

#include "generic.h"
#include "mathop.h"

#define VL_KDTREE_SPLIT_HEAP_SIZE 5
#define VL_KDTREE_VARIANCE_EST_NUM_SAMPLES 1024

typedef struct _VlKDTreeNode VlKDTreeNode ;
typedef struct _VlKDTreeSplitDimension VlKDTreeSplitDimension ;
typedef struct _VlKDTreeDataIndexEntry VlKDTreeDataIndexEntry ;
typedef struct _VlKDForestSearchState VlKDForestSearchState ;

struct _VlKDTreeNode
{
  vl_uindex parent ;
  vl_index lowerChild ;
  vl_index upperChild ;
  unsigned int splitDimension ;
  double splitThreshold ;
  double lowerBound ;
  double upperBound ;
} ;

struct _VlKDTreeSplitDimension
{
  unsigned int dimension ;
  double mean ;
  double variance ;
} ;

struct _VlKDTreeDataIndexEntry
{
  vl_index index ;
  double value ;
} ;

/** @brief Thresholding method */
typedef enum _VlKDTreeThresholdingMethod
{
  VL_KDTREE_MEDIAN,
  VL_KDTREE_MEAN
} VlKDTreeThresholdingMethod ;

/** @brief Neighbor of a query point */
typedef struct _VlKDForestNeighbor {
  double distance ;   /**< distance to the query point */
  vl_uindex index ;   /**< index of the neighbor in the KDTree data */
} VlKDForestNeighbor ;

typedef struct _VlKDTree
{
  VlKDTreeNode * nodes ;
  vl_size numUsedNodes ;
  vl_size numAllocatedNodes ;
  VlKDTreeDataIndexEntry * dataIndex ;
  unsigned int depth ;
} VlKDTree ;

struct _VlKDForestSearchState
{
  VlKDTree * tree ;
  vl_uindex nodeIndex ;
  double distanceLowerBound ;
} ;

struct _VlKDForestSearcher;

/** @brief KDForest object */
typedef struct _VlKDForest
{
  vl_size dimension ;

  /* random number generator */
  VlRand * rand ;

  /* indexed data */
  vl_type dataType ;
  void const * data ;
  vl_size numData ;
  VlVectorComparisonType distance;
  void (*distanceFunction)(void) ;

  /* tree structure */
  VlKDTree ** trees ;
  vl_size numTrees ;

  /* build */
  VlKDTreeThresholdingMethod thresholdingMethod ;
  VlKDTreeSplitDimension splitHeapArray [VL_KDTREE_SPLIT_HEAP_SIZE] ;
  vl_size splitHeapNumNodes ;
  vl_size splitHeapSize ;
  vl_size maxNumNodes;

  /* query */
  vl_size searchMaxNumComparisons ;
  vl_size numSearchers;
  struct _VlKDForestSearcher * headSearcher ;  /* head of the double linked list with searchers */

} VlKDForest ;

/** @brief KDForest searcher object */
struct _VlKDForestSearcher
{
  /* maintain a linked list of searchers for later disposal*/
  struct _VlKDForestSearcher * next;
  struct _VlKDForestSearcher * previous;

  vl_uindex * searchIdBook ;
  VlKDForestSearchState * searchHeapArray ;
  VlKDForest * forest;

  vl_size searchNumComparisons;
  vl_size searchNumRecursions ;
  vl_size searchNumSimplifications ;

  vl_size searchHeapNumNodes ;
  vl_uindex searchId ;
} ;

typedef struct _VlKDForestSearcher VlKDForestSearcher ;

/** @name Creating, copying and disposing
 ** @{ */
VL_EXPORT VlKDForest * vl_kdforest_new (vl_type dataType,
                                        vl_size dimension, vl_size numTrees, VlVectorComparisonType normType) ;
VL_EXPORT VlKDForestSearcher * vl_kdforest_new_searcher (VlKDForest * kdforest);
VL_EXPORT void vl_kdforest_delete (VlKDForest * self) ;
VL_EXPORT void vl_kdforestsearcher_delete (VlKDForestSearcher * searcher) ;
/** @} */

/** @name Building and querying
 ** @{ */
VL_EXPORT void vl_kdforest_build (VlKDForest * self,
                                  vl_size numData,
                                  void const * data) ;

VL_EXPORT vl_size vl_kdforest_query (VlKDForest * self,
                                     VlKDForestNeighbor * neighbors,
                                     vl_size numNeighbors,
                                     void const * query) ;

VL_EXPORT vl_size vl_kdforest_query_with_array (VlKDForest * self,
                                                vl_uint32 * index,
                                                vl_size numNeighbors,
                                                vl_size numQueries,
                                                void * distance,
                                                void const * queries) ;

VL_EXPORT vl_size vl_kdforestsearcher_query (VlKDForestSearcher * self,
                                             VlKDForestNeighbor * neighbors,
                                             vl_size numNeighbors,
                                             void const * query) ;
/** @} */

/** @name Retrieving and setting parameters
 ** @{ */
VL_EXPORT vl_size vl_kdforest_get_depth_of_tree (VlKDForest const * self, vl_uindex treeIndex) ;
VL_EXPORT vl_size vl_kdforest_get_num_nodes_of_tree (VlKDForest const * self, vl_uindex treeIndex) ;
VL_EXPORT vl_size vl_kdforest_get_num_trees (VlKDForest const * self) ;
VL_EXPORT vl_size vl_kdforest_get_data_dimension (VlKDForest const * self) ;
VL_EXPORT vl_type vl_kdforest_get_data_type (VlKDForest const * self) ;
VL_EXPORT void vl_kdforest_set_max_num_comparisons (VlKDForest * self, vl_size n) ;
VL_EXPORT vl_size vl_kdforest_get_max_num_comparisons (VlKDForest * self) ;
VL_EXPORT void vl_kdforest_set_thresholding_method (VlKDForest * self, VlKDTreeThresholdingMethod method) ;
VL_EXPORT VlKDTreeThresholdingMethod vl_kdforest_get_thresholding_method (VlKDForest const * self) ;
VL_EXPORT VlKDForest * vl_kdforest_searcher_get_forest (VlKDForestSearcher const * self) ;
VL_EXPORT VlKDForestSearcher * vl_kdforest_get_searcher (VlKDForest const * self, vl_uindex pos) ;
/** @} */


/* VL_KDTREE_H */
#endif
vlfeat/vl/scalespace.c0000644000175000017500000006045512237004745013653 0ustar  dimadima/** @file scalespace.c
 ** @brief Scale Space - Definition
 ** @author Karel Lenc
 ** @author Andrea Vedaldi
 ** @author Michal Perdoch
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page scalespace Gaussian Scale Space (GSS)
@author Karel Lenc
@author Andrea Vedaldi
@author Michal Perdoch


@ref scalespace.h implements a Gaussian scale space, a data structure
representing an image at multiple resolutions
@cite{witkin83scale-space} @cite{koenderink84the-structure}
@cite{lindeberg94scale-space}. Scale spaces are used, for example, to
detect scale invariant features @cite{lindeberg98principles} such as
SIFT, Hessian-Affine, Harris-Affine, Harris-Laplace, etc.

@tableofcontents


@section scalespace-overview Overview


A *scale space* is representation of an image at multiple resolution
levels. An image is a function $\ell(x,y)$ of two coordinates $x$,
$y$; the scale space $\ell(x,y,\sigma)$ adds a third coordinate
$\sigma$ indexing the *scale*. @ref scalespace.h implements in
particular the Gaussian scale space, where the image
$\ell(x,y,\sigma)$ is obtained by smoothing $\ell(x,y)$ by a Gaussian
kernel of isotropic standard deviation $\sigma$.

In practice, the spatial coordiantes $(x,y)$ and the scale coordinate
$\sigma$ are sampled. The density of the spatial sampling is adjusted
as a function of the scale: intuitively, coarser resolution images can
be sampled more coarsely. Thus, the scale space has the structure of a
*pyramid*: a collection of images of progressively coarser resolution
and smaller size (in pixels).

The pyramid is organised in a number of *octaves*, indexed by a
parameter `o`, and further *sublevels* for each octave, indexed by a
parameter `s`. These are related to the scale $\sigma$ by

\[
  \sigma(s,o) = \sigma_o 2^{\displaystyle o + \frac{s}{\mathtt{octaveResolution}}}
\]

where `octaveResolution` is the resolution of the octave subsampling
$\sigma_0$ is the *base smoothing*.

Given an input image `image`, the following example uses the
::VlScaleSpace object to compute the image `level`, which is the
version of `image` at scales `(o,s)`:

@code
float* level ;
VlScaleSpace ss = vl_scalespace_new(imageWidth, imageHeight) ;
vl_scalespace_put_image(ss, image) ;
level = vl_scalespace_get_level(ss, o, s) ;
@endcode

Note that `level` has not necessarily the same dimensions or sampling
density of `image`. The geometry of `level` can be obtained as

@code
VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(ss, o) ;
ogeom.width \\ width of level (in number of pixels)
ogeom.height \\ height of level (in number of pixels)
ogeom.step \\ spatial sampling step
@endcode

This means that $\ell(x,y,\sigma) = \mathtt{level}[i,j]$, where
\[
 x = \mathtt{ogeom.step} \times  i,
 \quad
 0 \leq i < \mathtt{ogeom.width},
\]

and similarly for $y$. The other impotant information in order to use
scale space is to know the range of the paramerters `o` and `s`. This
can be obtained as

@code
VlScaleSpaceGeometry geom = vl_scalespace_get_geomerty(ss) ;
@endcode

So for example `o` varies in the range from `geom.firstOctave` to
`geom.lastOctave` and `s` in the range `geom.octaveFirstSubdivision`
to `geom.octaveLastSubdivision`.


@subsection scalespace-finer Finer control


::VlScaleSpace offers more control on how the pyramid is constructed.
Two usage cases are the following:

- In feature detection it is often convenient to sample a few redudant
  sublevels per octave, effectively representing the same scale with
  two different spatial samplings. This simplifies detecting extrema
  in scales at boundary scale levels.

- Often there is a convenience in oversampling (at more than the
  Nyquist frequency) the finer scales. While oversampling does not add
  information to the image, it may simplify significantly the
  implementation of filers such as derivatives.

All this is possible by using a more advanced interface to
::VlScaleSpace.  For example, in oerder to start sampling at octave -1
and have two redundant subdivisions per octave, one can use:

@code
VlScaleSpaceGeometry geom = vl_scalespace_get_default_geometry(imageWidth, imageHeight) ;
geom.firstOctave = -1 ;
geom.octaveFirstSubdivision = -1 ;
geom.octaveLastSubdivision = geom.octaveResolution ;
VlScaleSpacae ss = vl_scalespace_new_with_geometry (geom) ;
@endcode


@section scalespace-algorithm Algorithm and limitations


Formally, the *Gaussian scale space* of an image $\ell(x,y)$ is
defined as

\[
   \ell(x,y,\sigma) =
   [g_{\sigma} * \ell](x,y,\sigma)
\]

where $g_\sigma$ denotes a 2D Gaussian kernel of isotropic standard
deviation $\sigma$:

\[
  g_{\sigma}(x,y) = \frac{1}{2\pi\sigma^2}
  \exp\left(
  - \frac{x^2 + y^2}{2\sigma^2}
  \right).
\]

An important detail is that the algorithm is not fed with the dieal
infinite resolution image $\ell(x,y)$, but with a pre-smoothed and
sampled version measured by the CCD. This is modelled by assuming that
the input is in fact the sampled image $\ell(x,y,\sigma_n)$, where
$\sigma_n$ is a *nominal smoothing*, usually taken to be 0.5 (half a
pixel standard deviation). This also means that $\sigma = \sigma_n =
0.5$ is the *finest scale* that can actually be computed.

Given $\ell(x,y,\sigma_n)$, any of a vast number digitial filtering
techniques can be used to compute the succesive scales. Presently,
::VlScaleSpace uses a basic FIR implementation of the Gaussian
filters.

The FIR implementation is obtained by sampling the Gaussian function
and re-normalizing it to have unit norm. As a rule of thumb, such a
filter works sufficiently well for, say, feature detection if the
standard deviation $\sigma$ is at least 1.6 times the spatial sampling
step. The same filter works better still the input image is
oversampled.

The limitations on the FIR filters have impliciations on the pyramid
itself, as the latter is constructed by *incremental smoothing*: each
successive level is obtained from the previous one by adding the
needed amount of smoothing. In this manner, the size of the FIR
filters remains small; however, if the scale sublevels are too packed,
the incremental smoothing is so small that the FIR implementation may
break.
*/

#include "scalespace.h"
#include "mathop.h"

#include 
#include 
#include 
#include 
#include 

/** @file scalespace.h
 ** @struct VlScaleSpace
 ** @brief Scale space class
 **
 ** This is an opaque class used to compute the scale space of an
 ** image.
 **/

struct _VlScaleSpace
{
  VlScaleSpaceGeometry geom ; /**< Geometry of the scale space */
  float **octaves ; /**< Data */
} ;

/* ---------------------------------------------------------------- */
/** @brief Get the default geometry for a given image size.
 ** @param width image width.
 ** @param height image height.
 ** @return the default scale space geometry.
 **
 ** Both @a width and @a height must be at least one pixel wide.
 **/

VlScaleSpaceGeometry
vl_scalespace_get_default_geometry (vl_size width, vl_size height)
{
  VlScaleSpaceGeometry geom ;
  assert(width >= 1) ;
  assert(height >= 1) ;
  geom.width = width ;
  geom.height = height ;
  geom.firstOctave = 0 ;
  geom.lastOctave = VL_MAX(floor(vl_log2_d(VL_MIN(width, height))) - 3, 0) ;
  geom.octaveResolution= 3 ;
  geom.octaveFirstSubdivision = 0 ;
  geom.octaveLastSubdivision = geom.octaveResolution - 1 ;
  geom.baseScale = 1.6 * pow(2.0, 1.0 / geom.octaveResolution) ;
  geom.nominalScale = 0.5 ;
  return geom ;
}

#define is_valid_geometry(geom) (\
geom.firstOctave <= geom.lastOctave && \
geom.octaveResolution >= 1 && \
geom.octaveFirstSubdivision <= geom.octaveLastSubdivision && \
geom.baseScale >= 0.0 && \
geom.nominalScale >= 0.0)

/** @brief Check scale space geometries for equality
 ** @param a first geometry.
 ** @param b second geometry.
 ** @return true if equal.
 **/

vl_bool
vl_scalespacegeometry_is_equal (VlScaleSpaceGeometry a,
                                VlScaleSpaceGeometry b)
{
  return
  a.width == b.width &&
  a.height == b.height &&
  a.firstOctave == b.firstOctave &&
  a.lastOctave == b.lastOctave &&
  a.octaveResolution == b.octaveResolution &&
  a.octaveFirstSubdivision == b.octaveLastSubdivision &&
  a.baseScale == b.baseScale &&
  a.nominalScale == b.nominalScale ;
}

/** @brief Get the geometry of the scale space.
 ** @param self object.
 ** @return the scale space geometry.
 **/

VlScaleSpaceGeometry
vl_scalespace_get_geometry (VlScaleSpace const * self)
{
  return self->geom ;
}

/** @brief Get the geometry of an octave of the scalespace.
 ** @param self object.
 ** @param o octave index.
 ** @return the geometry of octave @a o.
 **/

VlScaleSpaceOctaveGeometry
vl_scalespace_get_octave_geometry (VlScaleSpace const * self, vl_index o)
{
  VlScaleSpaceOctaveGeometry ogeom ;
  ogeom.width = VL_SHIFT_LEFT(self->geom.width, -o) ;
  ogeom.height = VL_SHIFT_LEFT(self->geom.height, -o) ;
  ogeom.step = pow(2.0, o) ;
  return ogeom ;
}

/** @brief Get the data of a scale space level
 ** @param self object.
 ** @param o octave index.
 ** @param s level index.
 ** @return pointer to the data for octave @a o, level @a s.
 **
 ** The octave index @a o must be in the range @c firstOctave
 ** to @c lastOctave and the scale index @a s must be in the
 ** range @c octaveFirstSubdivision to @c octaveLastSubdivision.
 **/

float *
vl_scalespace_get_level (VlScaleSpace *self, vl_index o, vl_index s)
{
  VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self,o) ;
  float * octave ;
  assert(self) ;
  assert(o >= self->geom.firstOctave) ;
  assert(o <= self->geom.lastOctave) ;
  assert(s >= self->geom.octaveFirstSubdivision) ;
  assert(s <= self->geom.octaveLastSubdivision) ;

  octave = self->octaves[o - self->geom.firstOctave] ;
  return octave + ogeom.width * ogeom.height * (s - self->geom.octaveFirstSubdivision) ;
}


/** @brief Get the data of a scale space level (const)
 ** @param self object.
 ** @param o octave index.
 ** @param s level index.
 ** @return pointer to the data for octave @a o, level @a s.
 **
 ** This function is the same as ::vl_scalespce_get_level but reutrns
 ** a @c const pointer to the data.
 **/

float const *
vl_scalespace_get_level_const (VlScaleSpace const * self, vl_index o, vl_index s)
{
  return vl_scalespace_get_level((VlScaleSpace*)self, o, s) ;
}


/** ------------------------------------------------------------------
 ** @brief Get the scale of a given octave and sublevel
 ** @param self object.
 ** @param o octave index.
 ** @param s sublevel index.
 **
 ** The function returns the scale $\sigma(o,s)$ as a function of the
 ** octave index @a o and sublevel @a s.
 **/

double
vl_scalespace_get_level_sigma (VlScaleSpace const *self, vl_index o, vl_index s)
{
  return self->geom.baseScale * pow(2.0, o + (double) s / self->geom.octaveResolution) ;
}

/** ------------------------------------------------------------------
 ** @internal @brief Upsample the rows and take the transpose
 ** @param destination output image.
 ** @param source input image.
 ** @param width input image width.
 ** @param height input image height.
 **
 ** The output image has dimensions @a height by 2 @a width (so the
 ** destination buffer must be at least as big as two times the
 ** input buffer).
 **
 ** Upsampling is performed by linear interpolation.
 **/

static void
copy_and_upsample
(float *destination,
 float const *source, vl_size width, vl_size height)
{
  vl_index x, y, ox, oy ;
  float v00, v10, v01, v11 ;

  assert(destination) ;
  assert(source) ;

  for(y = 0 ; y < (signed)height ; ++y) {
    oy = (y < ((signed)height - 1)) * width ;
    v10 = source[0] ;
    v11 = source[oy] ;
    for(x = 0 ; x < (signed)width ; ++x) {
      ox = x < ((signed)width - 1) ;
      v00 = v10 ;
      v01 = v11 ;
      v10 = source[ox] ;
      v11 = source[ox + oy] ;
      destination[0] = v00 ;
      destination[1] = 0.5f * (v00 + v10) ;
      destination[2*width] = 0.5f * (v00 + v01) ;
      destination[2*width+1] = 0.25f * (v00 + v01 + v10 + v11) ;
      destination += 2 ;
      source ++;
    }
    destination += 2*width ;
  }
}

/** ------------------------------------------------------------------
 ** @internal @brief Downsample
 ** @param destination output imgae buffer.
 ** @param source input image buffer.
 ** @param width input image width.
 ** @param height input image height.
 ** @param numOctaves octaves (non negative).
 **
 ** The function downsamples the image @a d times, reducing it to @c
 ** 1/2^d of its original size. The parameters @a width and @a height
 ** are the size of the input image. The destination image @a dst is
 ** assumed to be floor(width/2^d) pixels wide and
 ** floor(height/2^d) pixels high.
 **/

static void
copy_and_downsample
(float *destination,
 float const *source,
 vl_size width, vl_size height, vl_size numOctaves)
{
  vl_index x, y ;
  vl_size step = 1 << numOctaves ; /* step = 2^numOctaves */

  assert(destination) ;
  assert(source) ;

  if (numOctaves == 0) {
    memcpy(destination, source, sizeof(float) * width * height) ;
  } else {
    for(y = 0 ; y < (signed)height ; y += step) {
      float const *p = source + y * width ;
      for(x = 0 ; x < (signed)width - ((signed)step - 1) ; x += step) {
        *destination++ = *p ;
        p += step ;
      }
    }
  }
}

/* ---------------------------------------------------------------- */
/** @brief Create a new scale space object
 ** @param width image width.
 ** @param height image height.
 ** @return new scale space object.
 **
 ** This function is the same as ::vl_scalespace_new_with_geometry()
 ** but it uses ::vl_scalespace_get_default_geometry to initialise
 ** the geometry of the scale space from the image size.
 **
 ** @sa ::vl_scalespace_new_with_geometry(), ::vl_scalespace_delete().
 **/

VlScaleSpace *
vl_scalespace_new (vl_size width, vl_size height)
{
  VlScaleSpaceGeometry geom ;
  geom = vl_scalespace_get_default_geometry(width, height) ;
  return vl_scalespace_new_with_geometry(geom) ;
}

/** ------------------------------------------------------------------
 ** @brief Create a new scale space with the specified geometry
 ** @param geom scale space geomerty.
 ** @return new scale space object.
 **
 ** If the geometry is not valid (see ::VlScaleSpaceGeometry), the
 ** result is unpredictable.
 **
 ** The function returns `NULL` if it was not possible to allocate the
 ** object because of an out-of-memory condition.
 **
 ** @sa ::VlScaleSpaceGeometry, ::vl_scalespace_delete().
 **/

VlScaleSpace *
vl_scalespace_new_with_geometry (VlScaleSpaceGeometry geom)
{

  vl_index o ;
  vl_size numSublevels = geom.octaveLastSubdivision - geom.octaveFirstSubdivision + 1 ;
  vl_size numOctaves = geom.lastOctave - geom.firstOctave + 1 ;
  VlScaleSpace *self ;

  assert(is_valid_geometry(geom)) ;
  numOctaves = geom.lastOctave - geom.firstOctave + 1 ;
  numSublevels = geom.octaveLastSubdivision - geom.octaveFirstSubdivision + 1 ;

  self = vl_calloc(1, sizeof(VlScaleSpace)) ;
  if (self == NULL) goto err_alloc_self ;
  self->geom = geom ;
  self->octaves = vl_calloc(numOctaves, sizeof(float*)) ;
  if (self->octaves == NULL) goto err_alloc_octave_list ;
  for (o = self->geom.firstOctave ; o <= self->geom.lastOctave ; ++o) {
    VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self,o) ;
    vl_size octaveSize = ogeom.width * ogeom.height * numSublevels ;
    self->octaves[o - self->geom.firstOctave] = vl_malloc(octaveSize * sizeof(float)) ;
    if (self->octaves[o - self->geom.firstOctave] == NULL) goto err_alloc_octaves;
  }
  return self ;

err_alloc_octaves:
  for (o = self->geom.firstOctave ; o <= self->geom.lastOctave ; ++o) {
    if (self->octaves[o - self->geom.firstOctave]) {
      vl_free(self->octaves[o - self->geom.firstOctave]) ;
    }
  }
err_alloc_octave_list:
  vl_free(self) ;
err_alloc_self:
  return NULL ;
}

/* ---------------------------------------------------------------- */
/** @brief Create a new copy of the object
 ** @param self object to copy from.
 **
 ** The function returns `NULL` if the copy cannot be made due to an
 ** out-of-memory condition.
 **/

VlScaleSpace *
vl_scalespace_new_copy (VlScaleSpace* self)
{
  vl_index o  ;
  VlScaleSpace * copy = vl_scalespace_new_shallow_copy(self) ;
  if (copy == NULL) return NULL ;

  for (o = self->geom.firstOctave ; o <= self->geom.lastOctave ; ++o) {
    VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self,o) ;
    vl_size numSubevels = self->geom.octaveLastSubdivision - self->geom.octaveFirstSubdivision + 1;
    memcpy(copy->octaves[o - self->geom.firstOctave],
           self->octaves[o - self->geom.firstOctave],
           ogeom.width * ogeom.height * numSubevels * sizeof(float)) ;
  }
  return copy ;
}

/* ---------------------------------------------------------------- */
/** @brief Create a new shallow copy of the object
 ** @param self object to copy from.
 **
 ** The function works like ::vl_scalespace_new_copy() but only allocates
 ** the scale space, without actually copying the data.
 **/

VlScaleSpace *
vl_scalespace_new_shallow_copy (VlScaleSpace* self)
{
  return vl_scalespace_new_with_geometry (self->geom) ;
}

/* ---------------------------------------------------------------- */
/** @brief Delete object
 ** @param self object to delete.
 ** @sa ::vl_scalespace_new()
 **/

void
vl_scalespace_delete (VlScaleSpace * self)
{
  if (self) {
    if (self->octaves) {
      vl_index o ;
      for (o = self->geom.firstOctave ; o <= self->geom.lastOctave ; ++o) {
        if (self->octaves[o - self->geom.firstOctave]) {
          vl_free(self->octaves[o - self->geom.firstOctave]) ;
        }
      }
      vl_free(self->octaves) ;
    }
    vl_free(self) ;
  }
}

/** ------------------------------------------------------------------
 ** @internal @brief Fill octave starting from the first level
 ** @param self object instance.
 ** @param o octave to process.
 **
 ** The function takes the first sublevel of octave @a o (the one at
 ** sublevel `octaveFirstLevel` and iteratively
 ** smoothes it to obtain the other octave levels.
 **/

void
_vl_scalespace_fill_octave (VlScaleSpace *self, vl_index o)
{
  vl_index s ;
  VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self, o) ;

  for(s = self->geom.octaveFirstSubdivision + 1 ;
      s <= self->geom.octaveLastSubdivision ; ++s) {
    double sigma = vl_scalespace_get_level_sigma(self, o, s) ;
    double previousSigma = vl_scalespace_get_level_sigma(self, o, s - 1) ;
    double deltaSigma = sqrtf(sigma*sigma - previousSigma*previousSigma) ;

    float* level = vl_scalespace_get_level (self, o, s) ;
    float* previous = vl_scalespace_get_level (self, o, s-1) ;
    vl_imsmooth_f (level, ogeom.width,
                   previous, ogeom.width, ogeom.height, ogeom.width,
                   deltaSigma / ogeom.step, deltaSigma / ogeom.step) ;
  }
}

/** ------------------------------------------------------------------
 ** @internal @brief Initialize the first level of an octave from an image
 ** @param self ::VlScaleSpace object instance.
 ** @param image image data.
 ** @param o octave to start.
 **
 ** The function initializes the first level of octave @a o from
 ** image @a image. The dimensions of the image are the ones set
 ** during the creation of the ::VlScaleSpace object instance.
 **/

static void
_vl_scalespace_start_octave_from_image (VlScaleSpace *self,
                                        float const *image,
                                        vl_index o)
{
  float *level ;
  double sigma, imageSigma ;
  vl_index op ;

  assert(self) ;
  assert(image) ;
  assert(o >= self->geom.firstOctave) ;
  assert(o <= self->geom.lastOctave) ;

  /*
   * Copy the image to self->geom.octaveFirstSubdivision of octave o, upscaling or
   * downscaling as needed.
   */

  level = vl_scalespace_get_level(self, VL_MAX(0, o), self->geom.octaveFirstSubdivision) ;
  copy_and_downsample(level, image, self->geom.width, self->geom.height, VL_MAX(0, o)) ;

  for (op = -1 ; op >= o ; --op) {
    VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self, op + 1) ;
    float *succLevel = vl_scalespace_get_level(self, op + 1, self->geom.octaveFirstSubdivision) ;
    level = vl_scalespace_get_level(self, op, self->geom.octaveFirstSubdivision) ;
    copy_and_upsample(level, succLevel, ogeom.width, ogeom.height) ;
  }

  /*
   * Adjust the smoothing of the first level just initialised, accounting
   * for the fact that the input image is assumed to be a nominal scale
   * level self->sigman.
   */

  sigma = vl_scalespace_get_level_sigma(self, o, self->geom.octaveFirstSubdivision) ;
  imageSigma = self->geom.nominalScale ;

  if (sigma > imageSigma) {
    VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self, o) ;
    double deltaSigma = sqrt (sigma*sigma - imageSigma*imageSigma) ;
    level = vl_scalespace_get_level (self, o, self->geom.octaveFirstSubdivision) ;
    vl_imsmooth_f (level, ogeom.width,
                   level, ogeom.width, ogeom.height, ogeom.width,
                   deltaSigma / ogeom.step, deltaSigma / ogeom.step) ;
  }
}

/** ------------------------------------------------------------------
 ** @internal @brief Initialize the first level of an octave from the previous octave
 ** @param ::VlScaleSpace objet instance.
 ** @param o octave to initialize.
 **
 ** The function initializes the first level of octave @a o from the
 ** content of octave o - 1.
 **/

static void
_vl_scalespace_start_octave_from_previous_octave (VlScaleSpace *self, vl_index o)
{
  double sigma, prevSigma ;
  float *level, *prevLevel ;
  vl_index prevLevelIndex ;
  VlScaleSpaceOctaveGeometry ogeom ;

  assert(self) ;
  assert(o > self->geom.firstOctave) ; /* must not be the first octave */
  assert(o <= self->geom.lastOctave) ;

  /*
   * From the previous octave pick the level which is closer to
   * self->geom.octaveFirstSubdivision in this octave.
   * The is self->geom.octaveFirstSubdivision + self->numLevels since there are
   * self->numLevels levels in an octave, provided that
   * this value does not exceed self->geom.octaveLastSubdivision.
   */

  prevLevelIndex = VL_MIN(self->geom.octaveFirstSubdivision
                          + (signed)self->geom.octaveResolution,
                          self->geom.octaveLastSubdivision) ;
  prevLevel = vl_scalespace_get_level (self, o - 1, prevLevelIndex) ;
  level = vl_scalespace_get_level (self, o, self->geom.octaveFirstSubdivision) ;
  ogeom = vl_scalespace_get_octave_geometry(self, o - 1) ;

  copy_and_downsample (level, prevLevel, ogeom.width, ogeom.height, 1) ;

  /*
   * Add remaining smoothing, if any.
   */

  sigma = vl_scalespace_get_level_sigma(self, o, self->geom.octaveFirstSubdivision) ;
  prevSigma = vl_scalespace_get_level_sigma(self, o - 1, prevLevelIndex) ;


  if (sigma > prevSigma) {
    VlScaleSpaceOctaveGeometry ogeom = vl_scalespace_get_octave_geometry(self, o) ;
    double deltaSigma = sqrt (sigma*sigma - prevSigma*prevSigma) ;
    level = vl_scalespace_get_level (self, o, self->geom.octaveFirstSubdivision) ;

    /* todo: this may fail due to an out-of-memory condition */
    vl_imsmooth_f (level, ogeom.width,
                   level, ogeom.width, ogeom.height, ogeom.width,
                   deltaSigma / ogeom.step, deltaSigma / ogeom.step) ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Initialise Scale space with new image
 ** @param self ::VlScaleSpace object instance.
 ** @param image image to process.
 **
 ** Compute the data of all the defined octaves and scales of the scale
 ** space @a self.
 **/

void
vl_scalespace_put_image (VlScaleSpace *self, float const *image)
{
  vl_index o ;
  _vl_scalespace_start_octave_from_image(self, image, self->geom.firstOctave) ;
  _vl_scalespace_fill_octave(self, self->geom.firstOctave) ;
  for (o = self->geom.firstOctave + 1 ; o <= self->geom.lastOctave ; ++o) {
    _vl_scalespace_start_octave_from_previous_octave(self, o) ;
    _vl_scalespace_fill_octave(self, o) ;
  }
}
vlfeat/vl/stringop.h0000644000175000017500000000427312237004736013416 0ustar  dimadima/** @file stringop.h
 ** @brief String operations
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_STRINGOP_H
#define VL_STRINGOP_H

#include "generic.h"

/* file protocols */
enum {
  VL_PROT_UNKNOWN = -1, /**< unknown protocol */
  VL_PROT_NONE    =  0, /**< no protocol      */
  VL_PROT_ASCII,        /**< ASCII protocol   */
  VL_PROT_BINARY        /**< Binary protocol  */
} ;


VL_EXPORT vl_size vl_string_copy (char *destination, vl_size destinationSize, char const *source) ;
VL_EXPORT vl_size vl_string_copy_sub (char *destination, vl_size destinationSize,
                                      char const *beginning, char const *end) ;
VL_EXPORT char *vl_string_parse_protocol (char const *string, int *protocol) ;
VL_EXPORT char const *vl_string_protocol_name (int prot) ;
VL_EXPORT vl_size vl_string_basename (char *destination, vl_size destinationSize,
                                      char const *source, vl_size maxNumStrippedExtension) ;
VL_EXPORT vl_size vl_string_replace_wildcard (char * destination, vl_size destinationSize,
                                              char const *src, char wildcardChar, char escapeChar,
                                              char const *replacement) ;
VL_EXPORT char *vl_string_find_char_rev (char const *beginning, char const *end, char c) ;
VL_EXPORT vl_size vl_string_length (char const *string) ;
VL_EXPORT int vl_string_casei_cmp (const char *string1, const char *string2) ;

/** @name String enumerations
 ** @{ */

/** @brief Member of an enumeration */
typedef struct _VlEnumerator
{
  char const *name ; /**< enumeration member name. */
  vl_index value ;   /**< enumeration member value. */
} VlEnumerator ;

VL_EXPORT VlEnumerator *vl_enumeration_get (VlEnumerator const *enumeration, char const *name) ;
VL_EXPORT VlEnumerator *vl_enumeration_get_casei (VlEnumerator const *enumeration, char const *name) ;
VL_EXPORT VlEnumerator *vl_enumeration_get_by_value (VlEnumerator const *enumeration, vl_index value) ;
/** @} */

/* VL_STRINGOP_H */
#endif
vlfeat/vl/slic.h0000644000175000017500000000127212237004736012477 0ustar  dimadima/** @file slic.h
 ** @brief SLIC superpixels (@ref slic)
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_SLIC_H
#define VL_SLIC_H

#include "generic.h"

VL_EXPORT void
vl_slic_segment (vl_uint32 * segmentation,
                 float const * image,
                 vl_size width,
                 vl_size height,
                 vl_size numChannels,
                 vl_size regionSize,
                 float regularization,
                 vl_size minRegionSize) ;

/* VL_SLIC_H */
#endif
vlfeat/vl/stringop.c0000644000175000017500000003240712237004736013411 0ustar  dimadima/** @file stringop.c
 ** @brief String operations - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**
@file stringop.h
@brief String operations
@author Andrea Vedaldi

@ref stringop.h implements basic string operations. All functions that
write to strings use range checking, which makes them safer than some
standard POSIX equivalent (see @ref vl-stringop-err).

- @ref vl-stringop-enumeration
- @ref vl-stringop-file-protocol
- @ref vl-stringop-err

@section vl-stringop-enumeration Enumerations

@ref stringop.h defines a simple enumeration data type. This is given
by an array of enumeration members, represented by
instances of the ::VlEnumerator strucutre, each storing a
name-value pair. The enumeration must end by a member whose
name is set to @c NULL.

Use ::vl_enumeration_get and ::vl_enumeration_get_casei
to retrieve an enumeration member by name.

@section vl-stringop-file-protocols File protocols

@ref stringop.h defines a few file "protocols" and helps parsing them
from URL-like formatted strings. The supported protocols are:


    
File protocols

    ProtocolCodeURL prefix
    

    ASCII::VL_PROT_ASCIIascii://
    

    BINARY::VL_PROT_BINARYbinary://
    

    

@section vl-stringop-err Detecting overflow

@ref stringop.h functions that write a string to a character buffer take
both the buffer and its size @c n as input. If @c n is not large
enough, the output may be truncated but it is always a null terminated
string (provided that @c n >= 1). Such functions also return the
length of the string that would have been written @c r (which does not
include the terminating null character) had the buffer been large
enough.  Hence an overflow can be detected by testing if @c r
>= @c n, @c r can be used to re-allocate a buffer large enough to
contain the result, and the operation can be repeated.
**/

#include "stringop.h"

#include 
#include 

/** ------------------------------------------------------------------
 ** @brief Extract the protocol prefix from a string
 ** @param string string.
 ** @param protocol protocol code (output).
 ** @return pointer to the first character after the protocol prefix.
 **
 ** The function extracts the prefix of the string @a string
 ** terminated by the first occurrence of the @c :// substring (if
 ** any). It then matches the suffix terminated by @c :// to the
 ** supported @ref vl-stringop-file-protocols protocols. If @c protocol is not
 ** @c NULL, the corresponding protocol code is written to @a protocol
 **
 ** The function writes to @a protocol the value ::VL_PROT_NONE if no
 ** suffix is detected and ::VL_PROT_UNKNOWN if there is a suffix but
 ** it cannot be matched to any of the supported protocols.
 **/

VL_EXPORT char *
vl_string_parse_protocol (char const *string, int *protocol)
{
  char const * cpt ;
  int dummy ;

  /* handle the case prot = 0 */
  if (protocol == 0)
    protocol = &dummy ;

  /* look for :// */
  cpt = strstr(string, "://") ;

  if (cpt == 0) {
    *protocol = VL_PROT_NONE ;
    cpt = string ;
  }
  else {
    if (strncmp(string, "ascii", cpt - string) == 0) {
      *protocol = VL_PROT_ASCII ;
    }
    else if (strncmp(string, "bin",   cpt - string) == 0) {
      *protocol = VL_PROT_BINARY ;
    }
    else {
      *protocol = VL_PROT_UNKNOWN ;
    }
    cpt += 3 ;
  }
  return (char*) cpt ;
}

/** ------------------------------------------------------------------
 ** @brief Get protocol name
 ** @param protocol protocol code.
 ** @return pointer protocol name string.
 **
 ** The function returns a pointer to a string containing the name of
 ** the protocol @a protocol (see the @a vl-file-protocols protocols
 ** list).  If the protocol is unknown the function returns the empty
 ** string.
 **/

VL_EXPORT char const *
vl_string_protocol_name (int protocol)
{
  switch (protocol) {
  case VL_PROT_ASCII:
    return "ascii" ;
  case VL_PROT_BINARY:
    return "bin" ;
  case VL_PROT_NONE :
    return "" ;
  default:
    return 0 ;
  }
}


/** ------------------------------------------------------------------
 ** @brief Extract base of file name
 ** @param destination destination buffer.
 ** @param destinationSize size of destination buffer.
 ** @param source input string.
 ** @param maxNumStrippedExtensions maximum number of extensions to strip.
 ** @return length of the destination string.
 **
 ** The function removes the leading path and up to @c
 ** maxNumStrippedExtensions trailing extensions from the string @a
 ** source and writes the result to the buffer @a destination.
 **
 ** The leading path is the longest suffix that ends with either the
 ** @c \ or @c / characters. An extension is a string starting with
 ** the . character not containing it. For instance, the string @c
 ** file.png contains the extension .png and the string @c
 ** file.tar.gz contains two extensions (.tar and @c .gz).
 **
 ** @sa @ref vl-stringop-err.
 **/

VL_EXPORT vl_size
vl_string_basename (char * destination,
                    vl_size destinationSize,
                    char const * source,
                    vl_size maxNumStrippedExtensions)
{
  char c ;
  vl_uindex k = 0, beg, end ;

  /* find beginning */
  beg = 0 ;
  for (k = 0 ; (c = source[k]) ; ++ k) {
    if (c == '\\' || c == '/') beg = k + 1 ;
  }

  /* find ending */
  end = strlen (source) ;
  for (k = end ; k > beg ; --k) {
    if (source[k - 1] == '.' && maxNumStrippedExtensions > 0) {
      -- maxNumStrippedExtensions ;
      end = k - 1 ;
    }
  }

  return vl_string_copy_sub (destination, destinationSize,
                             source + beg, source + end) ;
}

/** ------------------------------------------------------------------
 ** @brief Replace wildcard characters by a string
 ** @param destination output buffer.
 ** @param destinationSize size of the output buffer.
 ** @param source input string.
 ** @param wildcardChar wildcard character.
 ** @param escapeChar escape character.
 ** @param replacement replacement string.
 **
 ** The function replaces the occurrence of the specified wildcard
 ** character @a wildcardChar by the string @a replacement. The result
 ** is written to the buffer @a destination of size @a
 ** destinationSize.
 **
 ** Wildcard characters may be escaped by preceding them by the @a esc
 ** character. More in general, anything following an occurrence of @a
 ** esc character is copied verbatim. To disable the escape characters
 ** simply set @a esc to 0.
 **
 ** @return length of the result.
 ** @sa @ref vl-stringop-err.
 **/

VL_EXPORT vl_size
vl_string_replace_wildcard (char * destination,
                            vl_size destinationSize,
                            char const * source,
                            char wildcardChar,
                            char escapeChar,
                            char const * replacement)
{
  char c ;
  vl_uindex k = 0 ;
  vl_bool escape = 0 ;

  while ((c = *source++)) {

    /* enter escape mode ? */
    if (! escape && c == escapeChar) {
      escape = 1 ;
      continue ;
    }

    /* wildcard or regular? */
    if (! escape && c == wildcardChar) {
      char const * repl = replacement ;
      while ((c = *repl++)) {
        if (destination && k + 1 < destinationSize) {
          destination[k] = c ;
        }
        ++ k ;
      }
    }
    /* regular character */
    else {
      if (destination && k + 1 < destinationSize) {
        destination[k] = c ;
      }
      ++ k ;
    }
    escape = 0 ;
  }

  /* add trailing 0 */
  if (destinationSize > 0) {
    destination[VL_MIN(k, destinationSize - 1)] = 0 ;
  }
  return  k ;
}

/** ------------------------------------------------------------------
 ** @brief Copy string
 ** @param destination output buffer.
 ** @param destinationSize size of the output buffer.
 ** @param source string to copy.
 ** @return length of the source string.
 **
 ** The function copies the string @a source to the buffer @a
 ** destination of size @a destinationSize.
 **
 ** @sa @ref vl-stringop-err.
 **/

VL_EXPORT vl_size
vl_string_copy (char * destination, vl_size destinationSize,
                char const * source)
{
  char c ;
  vl_uindex k = 0 ;

  while ((c = *source++)) {
    if (destination && k + 1 < destinationSize) {
      destination[k] = c ;
    }
    ++ k ;
  }

  /* finalize */
  if (destinationSize > 0) {
    destination[VL_MIN(k, destinationSize - 1)] = 0 ;
  }
  return  k ;
}

/** ------------------------------------------------------------------
 ** @brief Copy substring
 ** @param destination output buffer.
 ** @param destinationSize  size of output buffer.
 ** @param beginning start of the substring.
 ** @param end end of the substring.
 ** @return length of the destination string.
 **
 ** The function copies the substring from at @a beginning to @a end
 ** (not included) to the buffer @a destination of size @a
 ** destinationSize. If, however, the null character is found before
 ** @a end, the substring terminates there.
 **
 ** @sa @ref vl-stringop-err.
 **/

VL_EXPORT vl_size
vl_string_copy_sub (char * destination,
                    vl_size destinationSize,
                    char const * beginning,
                    char const * end)
{
  char c ;
  vl_uindex k = 0 ;

  while (beginning < end && (c = *beginning++)) {
    if (destination && k + 1 < destinationSize) {
      destination[k] = c ;
    }
    ++ k ;
  }

  /* finalize */
  if (destinationSize > 0) {
    destination[VL_MIN(k, destinationSize - 1)] = 0 ;
  }
  return  k ;
}

/** ------------------------------------------------------------------
 ** @brief Search character in reversed order
 ** @param beginning pointer to the substring beginning.
 ** @param end pointer to the substring end.
 ** @param c character to search for.
 ** @return pointer to last occurrence of @a c, or 0 if none.
 **
 ** The function searches for the last occurrence of the character @a c
 ** in the substring from @a beg to @a end (the latter not being included).
 **/

VL_EXPORT char *
vl_string_find_char_rev (char const *beginning, char const* end, char c)
{
  while (end -- != beginning) {
    if (*end == c) {
      return (char*) end ;
    }
  }
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @brief Calculate string length
 ** @param string string.
 ** @return string length.
 **/

VL_EXPORT vl_size
vl_string_length (char const *string)
{
  vl_uindex i ;
  for (i = 0 ; string[i] ; ++i) ;
  return i ;
}

/** ------------------------------------------------------------------
 ** @brief Compare strings case-insensitive
 ** @param string1 fisrt string.
 ** @param string2 second string.
 ** @return an integer =,<,> 0 if @c string1 =,<,> @c string2
 **/

VL_EXPORT int
vl_string_casei_cmp (const char * string1, const char * string2)
{
  while (tolower((char unsigned)*string1) ==
         tolower((char unsigned)*string2))
  {
    if (*string1 == 0) {
      return 0 ;
    }
    string1 ++ ;
    string2 ++ ;
  }
  return
    (int)tolower((char unsigned)*string1) -
    (int)tolower((char unsigned)*string2) ;
}

/* -------------------------------------------------------------------
 *                                                       VlEnumeration
 * ---------------------------------------------------------------- */

/** @brief Get a member of an enumeration by name
 ** @param enumeration array of ::VlEnumerator objects.
 ** @param name the name of the desired member.
 ** @return enumerator matching @a name.
 **
 ** If @a name is not found in the enumeration, then the value
 ** @c NULL is returned.
 **
 ** @sa vl-stringop-enumeration
 **/

VL_EXPORT VlEnumerator *
vl_enumeration_get (VlEnumerator const *enumeration, char const *name)
{
  assert(enumeration) ;
  while (enumeration->name) {
    if (strcmp(name, enumeration->name) == 0) return (VlEnumerator*)enumeration ;
    enumeration ++ ;
  }
  return NULL ;
}

/** @brief Get a member of an enumeration by name (case insensitive)
 ** @param enumeration array of ::VlEnumerator objects.
 ** @param name the name of the desired member.
 ** @return enumerator matching @a name.
 **
 ** If @a name is not found in the enumeration, then the value
 ** @c NULL is returned. @a string is matched case insensitive.
 **
 **  @sa vl-stringop-enumeration
 **/

VL_EXPORT VlEnumerator *
vl_enumeration_get_casei (VlEnumerator const *enumeration, char const *name)
{
  assert(enumeration) ;
  while (enumeration->name) {
    if (vl_string_casei_cmp(name, enumeration->name) == 0) return (VlEnumerator*)enumeration ;
    enumeration ++ ;
  }
  return NULL ;
}

/** @brief Get a member of an enumeration by value
 ** @param enumeration array of ::VlEnumerator objects.
 ** @param value value of the desired member.
 ** @return enumerator matching @a value.
 **
 ** If @a value is not found in the enumeration, then the value
 ** @c NULL is returned.
 **
 ** @sa vl-stringop-enumeration
 **/

VL_EXPORT VlEnumerator *
vl_enumeration_get_by_value (VlEnumerator const *enumeration, vl_index value)
{
  assert(enumeration) ;
  while (enumeration->name) {
    if (enumeration->value == value) return (VlEnumerator*)enumeration ;
    enumeration ++ ;
  }
  return NULL ;
}

vlfeat/vl/svm.h0000644000175000017500000001751112237004745012355 0ustar  dimadima/** @file svm.h
 ** @brief Support Vector Machines (@ref svm)
 ** @author Milan Sulc
 ** @author Daniele Perrone
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 Milan Sulc.
Copyright (C) 2012 Daniele Perrone.
Copyright (C) 2011-13 Andrea Vedaldi.

All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_SVM_H
#define VL_SVM_H

#include "generic.h"
#include "svmdataset.h"

/** @typedef VlSvm
 ** @brief SVM solver.
 ** This object implements VLFeat SVM solvers (see @ref svm.h).
 **/

#ifndef __DOXYGEN__
struct VlSvm_ ;
typedef struct VlSvm_ VlSvm ;
#else
typedef OPAQUE VlSvm ;
#endif

/** @brief Type of SVM solver */
typedef enum
{
  VlSvmSolverNone = 0, /**< No solver (used to evaluate an SVM). */
  VlSvmSolverSgd = 1,  /**< SGD algorithm (@ref svm-sgd). */
  VlSvmSolverSdca      /**< SDCA algorithm (@ref svm-sdca). */
} VlSvmSolverType ;

/** @brief Type of SVM loss
 **
 ** Default SVM loss types. The loss can be set by using ::vl_svm_set_loss.
 ** Note that custom losses can be used too by using ::vl_svm_set_loss_function,
 ** ::vl_svm_set_loss_derivative_function, etc.
 **
 ** @sa svm-loss-functions
 **/
typedef enum
{
  VlSvmLossHinge = 0,   /**< Standard hinge loss. */
  VlSvmLossHinge2 = 1,  /**< Hinge loss squared. */
  VlSvmLossL1,          /**< L1 loss. */
  VlSvmLossL2,          /**< L2 loss. */
  VlSvmLossLogistic     /**< Logistic loss. */
} VlSvmLossType ;

/** @brief Solver status */
typedef enum
{
  VlSvmStatusTraining = 1, /**< Optimization in progress. */
  VlSvmStatusConverged, /**< Optimization finished because the convergence criterion was met. */
  VlSvmStatusMaxNumIterationsReached /**< Optimization finished without convergence. */
} VlSvmSolverStatus ;

/** @brief SVM statistics
 ** This structure contains statistics characterising the state of
 ** the SVM solver, such as the current value of the objective function.
 **
 ** Not all fields are used by all solvers.
 **/
typedef struct VlSvmStatistics_ {
  VlSvmSolverStatus status ;    /**< Solver status. */
  vl_size iteration ;           /**< Solver iteration. */
  vl_size epoch ;               /**< Solver epoch (iteration / num samples). */
  double objective ;            /**< Objective function value. */
  double regularizer ;          /**< Regularizer value. */
  double loss ;                 /**< Loss value. */
  double dualObjective ;        /**< Dual objective value. */
  double dualLoss ;             /**< Dual loss value. */
  double dualityGap ;           /**< Duality gap = objective - dualObjective. */
  double scoresVariation ;      /**< Variance of the score updates. */
  double elapsedTime ;          /**< Time elapsed from the start of training. */
} VlSvmStatistics ;

/** @name Create and destroy
 ** @{ */
VL_EXPORT VlSvm * vl_svm_new (VlSvmSolverType type,
                              double const * data,
                              vl_size dimension,
                              vl_size numData,
                              double const * labels,
                              double lambda) ;

VL_EXPORT VlSvm * vl_svm_new_with_dataset (VlSvmSolverType type,
                                           VlSvmDataset * dataset,
                                           double const * labels,
                                           double lambda) ;

VL_EXPORT VlSvm * vl_svm_new_with_abstract_data (VlSvmSolverType type,
                                              void * data,
                                              vl_size dimension,
                                              vl_size numData,
                                              double const * labels,
                                              double lambda) ;

VL_EXPORT void vl_svm_delete (VlSvm * self) ;
/** @} */

/** @name Retrieve parameters and data
 ** @{ */
VL_EXPORT VlSvmStatistics const * vl_svm_get_statistics (VlSvm const *self) ;
VL_EXPORT double const * vl_svm_get_model (VlSvm const *self) ;
VL_EXPORT double vl_svm_get_bias (VlSvm const *self) ;
VL_EXPORT vl_size vl_svm_get_dimension (VlSvm *self) ;
VL_EXPORT vl_size vl_svm_get_num_data (VlSvm *self) ;
VL_EXPORT double vl_svm_get_epsilon (VlSvm const *self) ;
VL_EXPORT double vl_svm_get_bias_learning_rate (VlSvm const *self) ;
VL_EXPORT vl_size vl_svm_get_max_num_iterations (VlSvm const *self) ;
VL_EXPORT vl_size vl_svm_get_diagnostic_frequency (VlSvm const *self) ;
VL_EXPORT VlSvmSolverType vl_svm_get_solver (VlSvm const *self) ;
VL_EXPORT double vl_svm_get_bias_multiplier (VlSvm const *self) ;
VL_EXPORT double vl_svm_get_lambda (VlSvm const *self) ;
VL_EXPORT vl_size vl_svm_get_iteration_number (VlSvm const *self) ;
VL_EXPORT double const * vl_svm_get_scores (VlSvm const *self) ;
VL_EXPORT double const * vl_svm_get_weights (VlSvm const *self) ;
/** @} */

/** @name Set parameters
 ** @{ */
VL_EXPORT void vl_svm_set_epsilon (VlSvm *self, double epsilon) ;
VL_EXPORT void vl_svm_set_bias_learning_rate (VlSvm *self, double rate) ;
VL_EXPORT void vl_svm_set_max_num_iterations (VlSvm *self, vl_size maxNumIterations) ;
VL_EXPORT void vl_svm_set_diagnostic_frequency (VlSvm *self, vl_size f) ;
VL_EXPORT void vl_svm_set_bias_multiplier (VlSvm *self, double b) ;
VL_EXPORT void vl_svm_set_model (VlSvm *self, double const *model) ;
VL_EXPORT void vl_svm_set_bias (VlSvm *self, double b) ;
VL_EXPORT void vl_svm_set_iteration_number (VlSvm *self, vl_uindex n) ;
VL_EXPORT void vl_svm_set_weights (VlSvm *self, double const *weights) ;

VL_EXPORT void vl_svm_set_diagnostic_function (VlSvm *self, VlSvmDiagnosticFunction f, void *data) ;
VL_EXPORT void vl_svm_set_loss_function (VlSvm *self, VlSvmLossFunction f) ;
VL_EXPORT void vl_svm_set_loss_derivative_function (VlSvm *self, VlSvmLossFunction f) ;
VL_EXPORT void vl_svm_set_conjugate_loss_function (VlSvm *self, VlSvmLossFunction f) ;
VL_EXPORT void vl_svm_set_dca_update_function (VlSvm *self, VlSvmDcaUpdateFunction f) ;
VL_EXPORT void vl_svm_set_data_functions (VlSvm *self, VlSvmInnerProductFunction inner, VlSvmAccumulateFunction acc) ;
VL_EXPORT void vl_svm_set_loss (VlSvm *self, VlSvmLossType loss) ;
/** @} */

/** @name Process data
 ** @{ */
VL_EXPORT void vl_svm_train (VlSvm * self) ;
/** @} */

/** @name Loss functions
 ** @sa @ref svm-advanced
 ** @{ */

/* hinge */
VL_EXPORT double vl_svm_hinge_loss (double label, double inner) ;
VL_EXPORT double vl_svm_hinge_loss_derivative (double label, double inner) ;
VL_EXPORT double vl_svm_hinge_conjugate_loss (double label, double u) ;
VL_EXPORT double vl_svm_hinge_dca_update (double alpha, double inner, double norm2, double label) ;

/* square hinge */
VL_EXPORT double vl_svm_hinge2_loss (double label, double inner) ;
VL_EXPORT double vl_svm_hinge2_loss_derivative (double label, double inner) ;
VL_EXPORT double vl_svm_hinge2_conjugate_loss (double label, double u) ;
VL_EXPORT double vl_svm_hinge2_dca_update (double alpha, double inner, double norm2, double label) ;

/* l1 */
VL_EXPORT double vl_svm_l1_loss (double label, double inner) ;
VL_EXPORT double vl_svm_l1_loss_derivative (double label, double inner) ;
VL_EXPORT double vl_svm_l1_conjugate_loss (double label, double u) ;
VL_EXPORT double vl_svm_l1_dca_update (double alpha, double inner, double norm2, double label) ;

/* l2 */
VL_EXPORT double vl_svm_l2_loss (double label, double inner) ;
VL_EXPORT double vl_svm_l2_loss_derivative (double label, double inner) ;
VL_EXPORT double vl_svm_l2_conjugate_loss (double label, double u) ;
VL_EXPORT double vl_svm_l2_dca_update (double alpha, double inner, double norm2, double label) ;

/* logistic */
VL_EXPORT double vl_svm_logistic_loss (double label, double inner) ;
VL_EXPORT double vl_svm_logistic_loss_derivative (double label, double inner) ;
VL_EXPORT double vl_svm_logistic_conjugate_loss (double label, double u) ;
VL_EXPORT double vl_svm_logistic_dca_update (double alpha, double inner, double norm2, double label) ;
/** } */

/* VL_SVM_H */
#endif
vlfeat/vl/homkermap.h0000644000175000017500000000504012237004745013525 0ustar  dimadima/** @file homkermap.h
 ** @brief Homogeneous kernel map (@ref homkermap)
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_HOMKERMAP_H
#define VL_HOMKERMAP_H

#include "generic.h"

#include 

/** @brief Type of kernel */
typedef enum {
  VlHomogeneousKernelIntersection = 0, /**< intersection kernel */
  VlHomogeneousKernelChi2, /**< Chi2 kernel */
  VlHomogeneousKernelJS /**< Jensen-Shannon kernel */
} VlHomogeneousKernelType ;

/** @brief Type of spectral windowing function  */
typedef enum {
  VlHomogeneousKernelMapWindowUniform = 0, /**< uniform window */
  VlHomogeneousKernelMapWindowRectangular = 1, /**< rectangular window */
} VlHomogeneousKernelMapWindowType ;

#ifndef __DOXYGEN__
struct _VlHomogeneousKernelMap ;
typedef struct _VlHomogeneousKernelMap VlHomogeneousKernelMap ;
#else
/** @brief Homogeneous kernel map object */
typedef OPAQUE VlHomogeneousKernelMap ;
#endif

/** @name Create and destroy
 ** @{ */
VL_EXPORT VlHomogeneousKernelMap *
vl_homogeneouskernelmap_new (VlHomogeneousKernelType kernelType,
                             double gamma,
                             vl_size order,
                             double period,
                             VlHomogeneousKernelMapWindowType windowType) ;
VL_EXPORT void
vl_homogeneouskernelmap_delete (VlHomogeneousKernelMap * self) ;
/** @} */

/** @name Process data
 ** @{ */
VL_EXPORT void
vl_homogeneouskernelmap_evaluate_d (VlHomogeneousKernelMap const * self,
                                    double * destination,
                                    vl_size stride,
                                    double x) ;

VL_EXPORT void
vl_homogeneouskernelmap_evaluate_f (VlHomogeneousKernelMap const * self,
                                    float * destination,
                                    vl_size stride,
                                    double x) ;
/** @} */


/** @name Retrieve data and parameters
 ** @{ */
VL_EXPORT vl_size
vl_homogeneouskernelmap_get_order (VlHomogeneousKernelMap const * self) ;

VL_EXPORT vl_size
vl_homogeneouskernelmap_get_dimension (VlHomogeneousKernelMap const * self) ;

VL_EXPORT VlHomogeneousKernelType
vl_homogeneouskernelmap_get_kernel_type (VlHomogeneousKernelMap const * self) ;

VL_EXPORT VlHomogeneousKernelMapWindowType
vl_homogeneouskernelmap_get_window_type (VlHomogeneousKernelMap const * self) ;
/** @} */

/* VL_HOMKERMAP_H */
#endif
vlfeat/vl/slic.c0000644000175000017500000003342012237004745012472 0ustar  dimadima/** @file slic.c
 ** @brief SLIC superpixels - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page slic Simple Linear Iterative Clustering (SLIC)
@author Andrea Vedaldi


@ref slic.h implements the Simple Linear Iterative Clustering (SLIC)
algorithm, an image segmentation method described in @cite{achanta10slic}.

- @ref slic-overview
- @ref slic-usage
- @ref slic-tech


@section slic-overview Overview


SLIC @cite{achanta10slic} is a simple and efficient method to decompose
an image in visually homogeneous regions. It is based on a spatially
localized version of k-means clustering. Similar to mean shift or
quick shift (@ref quickshift.h), each pixel is associated to a feature
vector

@f[
\Psi(x,y) =
\left[
\begin{array}{c}
\lambda x \\
\lambda y \\
I(x,y)
\end{array}
\right]
@f]

and then k-means clustering is run on those. As discussed below, the
coefficient @f$ \lambda @f$ balances the spatial and appearance
components of the feature vectors, imposing a degree of spatial
regularization to the extracted regions.

SLIC takes two parameters: the nominal size of the regions
(superpixels) @c regionSize and the strength of the spatial
regularization @c regularizer. The image is first divided into a grid
with step @c regionSize. The center of each grid tile is then used to
initialize a corresponding k-means (up to a small shift to avoid
image edges). Finally, the k-means centers and clusters are refined by
using the Lloyd algorithm, yielding segmenting the image. As a
further restriction and simplification, during the k-means iterations
each pixel can be assigned to only the 2 x 2 centers
corresponding to grid tiles adjacent to the pixel.

The parameter @c regularizer sets the trade-off between clustering
appearance and spatial regularization. This is obtained by setting

@f[
 \lambda = \frac{\mathtt{regularizer}}{\mathtt{regionSize}}
@f]

in the definition of the feature @f$ \psi(x,y) @f$.

After the k-means step, SLIC optionally
removes any segment whose area is smaller than a threshld @c minRegionSize
by merging them into larger ones.


@section slic-usage Usage from the C library


To compute the SLIC superpixels of an image use the function
::vl_slic_segment.


@section slic-tech Technical details


SLIC starts by dividing the image domain into a regular grid with @f$
M \times N @f$ tiles, where

@f[
   M = \lceil \frac{\mathtt{imageWidth}}{\mathtt{regionSize}} \rceil,
   \quad
   N = \lceil \frac{\mathtt{imageHeight}}{\mathtt{regionSize}} \rceil.
@f]

A region (superpixel or k-means cluster) is initialized from each grid
center

@f[
  x_i = \operatorname{round} i \frac{\mathtt{imageWidth}}{\mathtt{regionSize}}
  \quad
  y_j = \operatorname{round} j \frac{\mathtt{imageWidth}}{\mathtt{regionSize}}.
@f]

In order to avoid placing these centers on top of image
discontinuities, the centers are then moved in a 3 x 3
neighbourohood to minimize the edge strength

@f[
   \operatorname{edge}(x,y) =
   \| I(x+1,y) - I(x-1,y) \|_2^2 +
   \| I(x,y+1) - I(x,y-1) \|_2^2.
@f]

Then the regions are obtained by running k-means clustering, started
from the centers

@f[
  C = \{ \Psi(x_i,y_j), i=0,1,\dots,M-1\ j=0,1,\dots,N-1 \}
@f]

thus obtained. K-means uses the standard LLoyd algorithm alternating
assigning pixels to the clostest centers a re-estiamting the centers
as the average of the corresponding feature vectors of the pixel
assigned to them. The only difference compared to standard k-means is
that each pixel can be assigned only to the center originated from the
neighbour tiles. This guarantees that there are exactly four
pixel-to-center comparisons at each round of minimization, which
threfore cost @f$ O(n) @f$, where @f$ n @f$ is the number of
superpixels.

After k-means has converged, SLIC eliminates any connected region whose
area is less than @c minRegionSize pixels. This is done by greedily
merging regions to neighbour ones: the pixels @f$ p @f$ are scanned in
lexicographical order and the corresponding connected components
are visited. If a region has already been visited, it is skipped; if not,
its area is computed and if this is less than  @c minRegionSize its label
is changed to the one of a neighbour
region at @f$ p @f$ that has already been vistied (there is always one
except for the very first pixel).

*/

#include "slic.h"
#include "mathop.h"
#include 
#include 

/** @brief SLIC superpixel segmentation
 ** @param segmentation segmentation.
 ** @param image image to segment.
 ** @param width image width.
 ** @param height image height.
 ** @param numChannels number of image channels (depth).
 ** @param regionSize nominal size of the regions.
 ** @param regularization trade-off between appearance and spatial terms.
 ** @param minRegionSize minimum size of a segment.
 **
 ** The function computes the SLIC superpixels of the specified image @a image.
 ** @a image is a pointer to an @c width by @c height by @c by numChannles array of @c float.
 ** @a segmentation is a pointer to a @c width by @c height array of @c vl_uint32.
 ** @a segmentation contain the labels of each image pixels, from 0 to
 ** the number of regions minus one.
 **
 ** @sa @ref slic-overview, @ref slic-tech
 **/

void
vl_slic_segment (vl_uint32 * segmentation,
                 float const * image,
                 vl_size width,
                 vl_size height,
                 vl_size numChannels,
                 vl_size regionSize,
                 float regularization,
                 vl_size minRegionSize)
{
  vl_index i, x, y, u, v, k, region ;
  vl_uindex iter ;
  vl_size const numRegionsX = (vl_size) ceil((double) width / regionSize) ;
  vl_size const numRegionsY = (vl_size) ceil((double) height / regionSize) ;
  vl_size const numRegions = numRegionsX * numRegionsY ;
  vl_size const numPixels = width * height ;
  float * centers ;
  float * edgeMap ;
  float previousEnergy = VL_INFINITY_F ;
  float startingEnergy ;
  vl_uint32 * masses ;
  vl_size const maxNumIterations = 100 ;

  assert(segmentation) ;
  assert(image) ;
  assert(width >= 1) ;
  assert(height >= 1) ;
  assert(numChannels >= 1) ;
  assert(regionSize >= 1) ;
  assert(regularization >= 0) ;

#define atimage(x,y,k) image[(x)+(y)*width+(k)*width*height]
#define atEdgeMap(x,y) edgeMap[(x)+(y)*width]

  edgeMap = vl_calloc(numPixels, sizeof(float)) ;
  masses = vl_malloc(sizeof(vl_uint32) * numPixels) ;
  centers = vl_malloc(sizeof(float) * (2 + numChannels) * numRegions) ;

  /* compute edge map (gradient strength) */
  for (k = 0 ; k < (signed)numChannels ; ++k) {
    for (y = 1 ; y < (signed)height-1 ; ++y) {
      for (x = 1 ; x < (signed)width-1 ; ++x) {
        float a = atimage(x-1,y,k) ;
        float b = atimage(x+1,y,k) ;
        float c = atimage(x,y+1,k) ;
        float d = atimage(x,y-1,k) ;
        atEdgeMap(x,y) += (a - b)  * (a - b) + (c - d) * (c - d) ;
      }
    }
  }

  /* initialize K-means centers */
  i = 0 ;
  for (v = 0 ; v < (signed)numRegionsY ; ++v) {
    for (u = 0 ; u < (signed)numRegionsX ; ++u) {
      vl_index xp ;
      vl_index yp ;
      vl_index centerx = 0 ;
      vl_index centery = 0 ;
      float minEdgeValue = VL_INFINITY_F ;

      x = (vl_index) vl_round_d(regionSize * (u + 0.5)) ;
      y = (vl_index) vl_round_d(regionSize * (v + 0.5)) ;

      x = VL_MAX(VL_MIN(x, (signed)width-1),0) ;
      y = VL_MAX(VL_MIN(y, (signed)height-1),0) ;

      /* search in a 3x3 neighbourhood the smallest edge response */
      for (yp = VL_MAX(0, y-1) ; yp <= VL_MIN((signed)height-1, y+1) ; ++ yp) {
        for (xp = VL_MAX(0, x-1) ; xp <= VL_MIN((signed)width-1, x+1) ; ++ xp) {
          float thisEdgeValue = atEdgeMap(xp,yp) ;
          if (thisEdgeValue < minEdgeValue) {
            minEdgeValue = thisEdgeValue ;
            centerx = xp ;
            centery = yp ;
          }
        }
      }

      /* initialize the new center at this location */
      centers[i++] = (float) centerx ;
      centers[i++] = (float) centery ;
      for (k  = 0 ; k < (signed)numChannels ; ++k) {
        centers[i++] = atimage(centerx,centery,k) ;
      }
    }
  }

  /* run k-means iterations */
  for (iter = 0 ; iter < maxNumIterations ; ++iter) {
    float factor = regularization / (regionSize * regionSize) ;
    float energy = 0 ;

    /* assign pixels to centers */
    for (y = 0 ; y < (signed)height ; ++y) {
      for (x = 0 ; x < (signed)width ; ++x) {
        vl_index u = floor((double)x / regionSize - 0.5) ;
        vl_index v = floor((double)y / regionSize - 0.5) ;
        vl_index up, vp ;
        float minDistance = VL_INFINITY_F ;

        for (vp = VL_MAX(0, v) ; vp <= VL_MIN((signed)numRegionsY-1, v+1) ; ++vp) {
          for (up = VL_MAX(0, u) ; up <= VL_MIN((signed)numRegionsX-1, u+1) ; ++up) {
            vl_index region = up  + vp * numRegionsX ;
            float centerx = centers[(2 + numChannels) * region + 0]  ;
            float centery = centers[(2 + numChannels) * region + 1] ;
            float spatial = (x - centerx) * (x - centerx) + (y - centery) * (y - centery) ;
            float appearance = 0 ;
            float distance ;
            for (k = 0 ; k < (signed)numChannels ; ++k) {
              float centerz = centers[(2 + numChannels) * region + k + 2]  ;
              float z = atimage(x,y,k) ;
              appearance += (z - centerz) * (z - centerz) ;
            }
            distance = appearance + factor * spatial ;
            if (minDistance > distance) {
              minDistance = distance ;
              segmentation[x + y * width] = (vl_uint32)region ;
            }
          }
        }
        energy += minDistance ;
      }
    }

    /*
     VL_PRINTF("vl:slic: iter %d: energy: %g\n", iter, energy) ;
    */

    /* check energy termination conditions */
    if (iter == 0) {
      startingEnergy = energy ;
    } else {
      if ((previousEnergy - energy) < 1e-5 * (startingEnergy - energy)) {
        break ;
      }
    }
    previousEnergy = energy ;

    /* recompute centers */
    memset(masses, 0, sizeof(vl_uint32) * width * height) ;
    memset(centers, 0, sizeof(float) * (2 + numChannels) * numRegions) ;

    for (y = 0 ; y < (signed)height ; ++y) {
      for (x = 0 ; x < (signed)width ; ++x) {
        vl_index pixel = x + y * width ;
        vl_index region = segmentation[pixel] ;
        masses[region] ++ ;
        centers[region * (2 + numChannels) + 0] += x ;
        centers[region * (2 + numChannels) + 1] += y ;
        for (k = 0 ; k < (signed)numChannels ; ++k) {
          centers[region * (2 + numChannels) + k + 2] += atimage(x,y,k) ;
        }
      }
    }

    for (region = 0 ; region < (signed)numRegions ; ++region) {
      float mass = VL_MAX(masses[region], 1e-8) ;
      for (i = (2 + numChannels) * region ;
           i < (signed)(2 + numChannels) * (region + 1) ;
           ++i) {
        centers[i] /= mass ;
      }
    }
  }

  vl_free(masses) ;
  vl_free(centers) ;

  /* elimiate small regions */
  {
    vl_uint32 * cleaned = vl_calloc(numPixels, sizeof(vl_uint32)) ;
    vl_uindex * segment = vl_malloc(sizeof(vl_uindex) * numPixels) ;
    vl_size segmentSize ;
    vl_uint32 label ;
    vl_uint32 cleanedLabel ;
    vl_size numExpanded ;
    vl_index const dx [] = {+1, -1,  0,  0} ;
    vl_index const dy [] = { 0,  0, +1, -1} ;
    vl_index direction ;
    vl_index pixel ;

    for (pixel = 0 ; pixel < (signed)numPixels ; ++pixel) {
      if (cleaned[pixel]) continue ;
      label = segmentation[pixel] ;
      numExpanded = 0 ;
      segmentSize = 0 ;
      segment[segmentSize++] = pixel ;

      /*
       find cleanedLabel as the label of an already cleaned
       region neihbour of this pixel
       */
      cleanedLabel = label + 1 ;
      cleaned[pixel] = label + 1 ;
      x = pixel % width ;
      y = pixel / width ;
      for (direction = 0 ; direction < 4 ; ++direction) {
        vl_index xp = x + dx[direction] ;
        vl_index yp = y + dy[direction] ;
        vl_index neighbor = xp + yp * width ;
        if (0 <= xp && xp < (signed)width &&
            0 <= yp && yp < (signed)height &&
            cleaned[neighbor]) {
          cleanedLabel = cleaned[neighbor] ;
        }
      }

      /* expand the segment */
      while (numExpanded < segmentSize) {
        vl_index open = segment[numExpanded++] ;
        x = open % width ;
        y = open / width ;
        for (direction = 0 ; direction < 4 ; ++direction) {
          vl_index xp = x + dx[direction] ;
          vl_index yp = y + dy[direction] ;
          vl_index neighbor = xp + yp * width ;
          if (0 <= xp && xp < (signed)width &&
              0 <= yp && yp < (signed)height &&
              cleaned[neighbor] == 0 &&
              segmentation[neighbor] == label) {
            cleaned[neighbor] = label + 1 ;
            segment[segmentSize++] = neighbor ;
          }
        }
      }

      /* change label to cleanedLabel if the semgent is too small */
      if (segmentSize < minRegionSize) {
        while (segmentSize > 0) {
          cleaned[segment[--segmentSize]] = cleanedLabel ;
        }
      }
    }
    /* restore base 0 indexing of the regions */
    for (pixel = 0 ; pixel < (signed)numPixels ; ++pixel) cleaned[pixel] -- ;

    memcpy(segmentation, cleaned, numPixels * sizeof(vl_uint32)) ;
    vl_free(cleaned) ;
    vl_free(segment) ;
  }
}
vlfeat/vl/host.c0000644000175000017500000003707112237004745012523 0ustar  dimadima/** @file host.c
 ** @brief Host - Definition
 ** @author Andrea Vedaldi
 ** @see @ref portability
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page portability Portability features
@author Andrea Vedaldi
@tableofcontents


Platform dependent details are isolated in the @ref host.h.  This
module provides functionalities to identify the host operating system,
C compiler, and CPU architecture. It also provides a few features to
abstract from such details.

@see http://predef.sourceforge.net/index.php
@see http://en.wikipedia.org/wiki/64-bit_computing

@section host-os Host operating system

The module defines a symbol to identify the host operating system:
::VL_OS_WIN for Windows, ::VL_OS_LINUX for Linux, ::VL_OS_MACOSX for
Mac OS X, and so on.

@section host-compiler Host compiler

The module defines a symbol to identify the host compiler:
::VL_COMPILER_MSC for Microsoft Visual C++, ::VL_COMPILER_GNUC for
GNU C, and so on. The (integer) value of such symbols corresponds the
version of the compiler.

The module defines a symbol to identify the data model of the
compiler: ::VL_COMPILER_ILP32, ::VL_COMPILER_LP64, or
::VL_COMPILER_LLP64 (see Sect. @ref host-compiler-data-model).  For
convenience, it also defines a number of atomic types of prescribed
width (::vl_int8, ::vl_int16, ::vl_int32, etc.).

@remark While some of such functionalities are provided by the
standard header @c stdint.h, the latter is not supported by all
platforms.

@subsection host-compiler-data-model Data models

The C language defines a number of atomic data types (such as @c char,
@c short, @c int and so on). The number of bits (width) used to
represent each data type depends on the compiler data model. The
different models are *ILP32* (@c int, @c long, and pointer 32 bit),
*LP64* (@c int 32 bit, @c long and pointer 64 bit), *ILP64* (@c int,
@c long, and pointer 64 bit), and *LLP64* (@c int, @c long 32 bit and
pointer 64 -- and `long long` -- 64 bit). Note in particular that
`long long` is 64 bit in all models of interest. The following table
summarizes them:


    Compiler data models. 

    
Data model
short
int
long
long long
void*
Compiler

    

    
ILP32
16
32
32
64
32
Most 32 bit architectures.

    

    
LP64
16
32
64
64
64
UNIX-64 (Linux, Mac OS X)

    

    
ILP64
16
64
64
64
64
Alpha, Cray

    

    
SLIP64
64
64
64
64
64


    

    
LLP64
16
32
32
64
64
Windows-64

    

    

Macros such as ::VL_UINT32_C can be used to generate integer literal
with the correct suffix for a type of a given width.

@subsection host-compiler-other Other compiler-specific features

The module provides the macro ::VL_EXPORT to declare symbols exported
from the library and the macro ::VL_INLINE to declare inline
functions.  Such features are not part of the C89 standard, and
change depending on the compiler.

@par "Example:"
The following header file declares a function @c f that
should be visible from outside the library.
@code
#include 
VL_EXPORT void f () ;
VL_EXPORT int i ;
@endcode
Notice that the macro ::VL_EXPORT needs not to be included again
when the function is defined.

@par "Example:"
The following header file declares an inline function @c f:
@code
#include 
VL_INLINE int f() ;

VL_INLINE int f() { return 1 ; }
@endcode

Here the first instruction defines the function @c f, where the
second declares it. Notice that since this is an inline function, its
definition must be found in the header file rather than in an
implementation file.  Notice also that definition and declaration can
be merged.

These macros translate according to the following tables:


    
Macros for exporting library symbols

    
Platform
Macro name
Value when building the library
Value when importing the library

    

    
Unix/GCC
::VL_EXPORT
empty (assumes -visibility=hidden GCC option)
__attribute__((visibility ("default")))

    

    
Win/Visual C++
::VL_EXPORT
@c __declspec(dllexport)
@c __declspec(dllimport)

    

    


    
Macros for declaring inline functions

    
Platform
Macro name
Value

    

    
Unix/GCC
::VL_INLINE
static inline

    

    
Win/Visual C++
::VL_INLINE
static __inline

    

    

@section host-arch Host CPU architecture

The module defines a symbol to identify the host CPU architecture:
::VL_ARCH_IX86 for Intel x86, ::VL_ARCH_IA64 for Intel 64, and so on.

@subsection host-arch-endianness Endianness

The module defines a symbol to identify the host CPU endianness:
::VL_ARCH_BIG_ENDIAN for big endian and ::VL_ARCH_LITTLE_ENDIAN for
little endian. The functions ::vl_swap_host_big_endianness_8(),
::vl_swap_host_big_endianness_4(), ::vl_swap_host_big_endianness_2()
to change the endianness of data (from/to host and network order).

Recall that endianness concerns the way multi-byte data
types (such as 16, 32 and 64 bits integers) are stored into the
addressable memory.  All CPUs uses a contiguous address range to
store atomic data types (e.g. a 16-bit integer could be assigned to
the addresses 0x10001 and 0x10002), but the order may
differ.

- The convention is big endian, or in network
  order, if the most significant byte of the multi-byte data
  types is assigned to the smaller memory address. This is the
  convention used for instance by the PPC architecture.

- The convention is little endian if the least significant
  byte is assigned to the smaller memory address. This is the
  convention used for instance by the x86 architecture.

@remark The names “big endian” and “little
endian” are a little confusing. “Big endian” means
“big endian first”, i.e.  the address of the most
significant byte comes first. Similarly, “little endian”
means “little endian first”, in the sense that the
address of the least significant byte comes first.

Endianness is a concern when data is either exchanged with processors
that use different conventions, transmitted over a network, or stored
to a file. For the latter two cases, one usually saves data in big
endian (network) order regardless of the host CPU.

@section host-threads Multi-threading

The file defines #VL_THREADS_WIN if multi-threading support is
enabled and the host supports Windows threads and #VL_THREADS_POSIX if
it supports POSIX threads.
**/

/** @def VL_OS_LINUX
 ** @brief Defined if the host operating system is Linux.
 **/

/** @def VL_OS_MACOSX
 ** @brief Defined if the host operating system is Mac OS X.
 **/

/** @def VL_OS_WIN
 ** @brief Defined if the host operating system is Windows (32 or 64)
 **/

/** @def VL_OS_WIN64
 ** @brief Defined if the host operating system is Windows-64.
 **/

/** @def VL_COMPILER_GNUC
 ** @brief Defined if the host compiler is GNU C.
 **
 ** This macro is defined if the compiler is GNUC.
 ** Its value is calculated as
 ** @code
 ** 10000 * MAJOR + 100 * MINOR + PATCHLEVEL
 ** @endcode
 ** @see @ref host-compiler
 **/

/** @def VL_COMPILER_MSC
 ** @brief Defined if the host compiler is Microsoft Visual C++.
 ** @see @ref host-compiler
 **/

/** @def VL_COMPILER_LCC
 ** @brief Defined if the host compiler is LCC.
 ** @deprecated The LCC is not supported anymore.
 ** @see @ref host-compiler
 **/

/** @def VL_COMPILER_LLP64
 ** @brief Defined if the host compiler data model is LLP64.
 ** @see @ref host-compiler-data-model
 **/

/** @def VL_COMPILER_LP64
 ** @brief Defined if the host compiler data model is LP64.
 ** @see @ref host-compiler-data-model
 **/

/** @def VL_COMPILER_ILP32
 ** @brief Defined if the host compiler data model is ILP32.
 ** @see @ref host-compiler-data-model
 **/

/** @def VL_INT8_C(x)
 ** @brief Create an integer constant of the specified width and sign
 ** @param x integer constant.
 ** @return @a x with the correct suffix for the given sign and size.
 ** The suffix used depends on the @ref host-compiler-data-model.
 ** @par "Example:"
 ** The macro VL_INT64_C(1234) is expanded as @c 123L in
 ** a LP64 system and as @c 123LL in a LLP64 system.
 **/

/** @def VL_INT16_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_INT32_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_INT64_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_UINT8_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_UINT16_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_UINT32_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_UINT64_C(x)
 ** @copydoc VL_INT8_C */

/** @def VL_ARCH_IX86
 ** @brief Defined if the host CPU is of the Intel x86 family.
 ** @see @ref host-arch
 **/

/** @def VL_ARCH_IA64
 ** @brief Defined if the host CPU is of the Intel Architecture-64 family.
 ** @see @ref host-arch
 **/

/** @def VL_ARCH_LITTLE_ENDIAN
 ** @brief Defined if the host CPU is little endian
 ** @see @ref host-arch-endianness
 **/

/** @def VL_ARCH_BIG_ENDIAN
 ** @brief Defined if the host CPU is big endian
 ** @see @ref host-arch-endianness
 **/

/** @def VL_INLINE
 ** @brief Adds appropriate inline function qualifier
 ** @see @ref host-compiler-other
 **/

/** @def VL_EXPORT
 ** @brief Declares a DLL exported symbol
 ** @see @ref host-compiler-other
 **/

/** @def VL_DISABLE_SSE2
 ** @brief Defined if SSE2 support if disabled
 **
 ** Define this symbol during compliation of the library and linking
 ** to another project to disable VLFeat SSE2 support.
 **/

/** @def VL_DISABLE_THREADS
 ** @brief Defined if multi-threading support is disabled
 **
 ** Define this symbol during compilation of the library and linking
 ** to another project to disable VLFeat multi-threading support.
 **/

/** @def VL_DISABLE_OPENMP
 ** @brief Defined if OpenMP support is disabled
 **
 ** Define this symbol during compilation of the library and linking
 ** to another project to disable VLFeat OpenMP support.
 **/

/** @def VL_THREADS_WIN
 ** @brief Defined if the host uses Windows threads.
 ** @see @ref host-threads
 **/

/** @def VL_THREADS_POSIX
 ** @brief Defiend if the host uses POISX threads.
 ** @see @ref host-threads
 **/

/** --------------------------------------------------------------- */

#include "host.h"
#include "generic.h"
#include 

#if defined(VL_ARCH_IX86) || defined(VL_ARCH_IA64) || defined(VL_ARCH_X64)
#define HAS_CPUID
#else
#undef HAS_CPUID
#endif

#if defined(HAS_CPUID) & defined(VL_COMPILER_MSC)
#include 
VL_INLINE void
_vl_cpuid (vl_int32* info, int function)
{
  __cpuid(info, function) ;
}
#endif

#if defined(HAS_CPUID) & defined(VL_COMPILER_GNUC)
VL_INLINE void
_vl_cpuid (vl_int32* info, int function)
{
#if defined(VL_ARCH_IX86) && (defined(__PIC__) || defined(__pic__))
  /* This version is compatible with -fPIC on x386 targets. This special
   * case is required becaus
   * on such platform -fPIC alocates ebx as global offset table pointer.
   * Note that =r below will be mapped to a register different from ebx,
   * so the code is sound. */
  __asm__ __volatile__
  ("pushl %%ebx      \n" /* save %ebx */
   "cpuid            \n"
   "movl %%ebx, %1   \n" /* save what cpuid just put in %ebx */
   "popl %%ebx       \n" /* restore the old %ebx */
   : "=a"(info[0]), "=r"(info[1]), "=c"(info[2]), "=d"(info[3])
   : "a"(function)
   : "cc") ; /* clobbered (cc=condition codes) */
#else /* no -fPIC or -fPIC with a 64-bit target */
  __asm__ __volatile__
  ("cpuid"
   : "=a"(info[0]), "=b"(info[1]), "=c"(info[2]), "=d"(info[3])
   : "a"(function)
   : "cc") ;
#endif
}

#endif

void
_vl_x86cpu_info_init (VlX86CpuInfo *self)
{
  vl_int32 info [4] ;
  int max_func = 0 ;
  _vl_cpuid(info, 0) ;
  max_func = info[0] ;
  self->vendor.words[0] = info[1] ;
  self->vendor.words[1] = info[3] ;
  self->vendor.words[2] = info[2] ;

  if (max_func >= 1) {
    _vl_cpuid(info, 1) ;
    self->hasMMX   = info[3] & (1 << 23) ;
    self->hasSSE   = info[3] & (1 << 25) ;
    self->hasSSE2  = info[3] & (1 << 26) ;
    self->hasSSE3  = info[2] & (1 <<  0) ;
    self->hasSSE41 = info[2] & (1 << 19) ;
    self->hasSSE42 = info[2] & (1 << 20) ;
    self->hasAVX   = info[2] & (1 << 28) ;
  }
}

char *
_vl_x86cpu_info_to_string_copy (VlX86CpuInfo const *self)
{
  char * string = 0 ;
  int length = 0 ;
  while (string == 0) {
    if (length > 0) {
      string = vl_malloc(sizeof(char) * length) ;
      if (string == NULL) break ;
    }
    length = snprintf(string, length, "%s%s%s%s%s%s%s%s",
                      self->vendor.string,
                      self->hasMMX   ? " MMX" : "",
                      self->hasSSE   ? " SSE" : "",
                      self->hasSSE2  ? " SSE2" : "",
                      self->hasSSE3  ? " SSE3" : "",
                      self->hasSSE41 ? " SSE41" : "",
                      self->hasSSE42 ? " SSE42" : "",
                      self->hasAVX   ? " AVX" : "") ;
    length += 1 ;
  }
  return string ;
}

/** ------------------------------------------------------------------
 ** @brief Human readable static library configuration
 ** @return a new string with the static configuration.
 **
 ** The string includes information about the compiler, the host, and
 ** other static configuration parameters. The string must be released
 ** by ::vl_free.
 **/

VL_EXPORT char *
vl_static_configuration_to_string_copy ()
{
  char const * hostString =
#ifdef VL_ARCH_X64
  "X64"
#endif
#ifdef VL_ARCH_IA64
  "IA64"
#endif
#ifdef VL_ARCH_IX86
  "IX86"
#endif
#ifdef VL_ARCH_PPC
  "PPC"
#endif
  ", "
#ifdef VL_ARCH_BIG_ENDIAN
  "big_endian"
#endif
#ifdef VL_ARCH_LITTLE_ENDIAN
  "little_endian"
#endif
  ;

  char compilerString [1024] ;

  char const * libraryString =
#ifndef VL_DISABLE_THREADS
#ifdef VL_THREADS_WIN
  "Windows_threads"
#elif VL_THREADS_POSIX
  "POSIX_threads"
#endif
#else
  "No_threads"
#endif
#ifndef VL_DISABLE_SSE2
  ", SSE2"
#endif
#if defined(_OPENMP)
  ", OpenMP"
#endif
  ;

snprintf(compilerString, 1024,
#ifdef VL_COMPILER_MSC
  "Microsoft Visual C++ %d"
#define v VL_COMPILER_MSC
#endif
#ifdef VL_COMPILER_GNUC
  "GNU C %d"
#define v VL_COMPILER_GNUC
#endif
  " "
#ifdef VL_COMPILER_LP64
  "LP64"
#endif
#ifdef VL_COMPILER_LLP64
  "LP64"
#endif
#ifdef VL_COMPILER_ILP32
  "ILP32"
#endif
           , v) ;

  {
    char * string = 0 ;
    int length = 0 ;
    while (string == 0) {
      if (length > 0) {
        string = vl_malloc(sizeof(char) * length) ;
        if (string == NULL) break ;
      }
      length = snprintf(string, length, "%s, %s, %s",
                        hostString,
                        compilerString,
                        libraryString) ;
      length += 1 ;
    }
    return string ;
  }
}
vlfeat/vl/generic.c0000644000175000017500000014762612237004745013172 0ustar  dimadima/** @file generic.c
 ** @brief Generic - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@mainpage Vision Lab Features Library (VLFeat)
@version __VLFEAT_VERSION__
@author The VLFeat Team
@par Copyright © 2007-11 Andrea Vedaldi and Brian Fulkerson
@par Copyright © 2012-13 The VLFeat Authors


The VLFeat C library implements common computer
vision algorithms, with a special focus on visual features, as used
in state-of-the-art object recognition and image
matching applications.

VLFeat strives to be clutter-free, simple, portable, and well documented.


@section main-contents Contents


- **Visual feature detectors and descriptors**
  - @subpage sift
  - @subpage dsift
  - @subpage mser
  - @subpage covdet
  - @subpage scalespace
  - @subpage hog
  - @subpage fisher
  - @subpage vlad
  - @subpage liop

- **Clustering and indexing**
  - @subpage kmeans
  - @subpage ikmeans.h  "Integer K-means (IKM)"
  - @subpage hikmeans.h "Hierarchical Integer K-means (HIKM)"
  - @subpage gmm
  - @subpage aib
  - @subpage kdtree

- **Segmentation**
  - @subpage slic
  - @subpage quickshift

- **Statistical methods**
  - @subpage aib
  - @subpage homkermap
  - @subpage svm

- **Utilities**
  - @subpage random
  - @subpage mathop.h    "Math operations"
  - @subpage stringop.h  "String operations"
  - @subpage imopv.h     "Image operations"
  - @subpage pgm.h       "PGM image format"
  - @subpage heap-def.h  "Generic heap object (priority queue)"
  - @subpage rodrigues.h "Rodrigues formula"
  - @subpage mexutils.h  "MATLAB MEX helper functions"
  - @subpage getopt_long.h "Drop-in @c getopt_long replacement"

- **General support functionalities**
  - @subpage generic
  - @subpage portability
  - @ref resources
  - @subpage objects
  - @ref threads
  - @subpage matlab
  - @subpage metaprogram

- @subpage dev
- @subpage glossary
**/

/**

@page resources Memory and resource handling
@author Andrea Vedaldi


Some VLFeat functions return pointers to memory blocks or
objects. Only ::vl_malloc, ::vl_calloc, ::vl_realloc and functions
whose name contains either the keywords @c new or @c copy transfer the
ownership of the memory block or object to the caller. The caller must
dispose explicitly of all the resources it owns (by calling ::vl_free
for a memory block, or the appropriate deletion function for an
object).

The memory allocation functions can be customized by
::vl_set_alloc_func (which sets the implementations of ::vl_malloc,
::vl_realloc, ::vl_calloc and ::vl_free). Remapping the memory
allocation functions can be done only if there are no currently
allocated VLFeat memory blocks or objects -- thus typically at the
very beginning of a program. The memory allocation functions are a
global property, shared by all threads.

VLFeat uses three rules that simplify handling exceptions when used in
combination which certain environment such as MATLAB.

- The library allocates local memory only through the reprogrammable
  ::vl_malloc, ::vl_calloc, and ::vl_realloc functions.

- The only resource referenced by VLFeat objects is memory (for
  instance, it is illegal for an object to reference an open file).
  Other resources such as files or threads may be allocated within a
  VLFeat function call, but they are all released before the function
  ends, or their ownership is directly transferred to the caller.

- The global library state is an exception. It cannot reference any
  local object created by the caller and uses the standard C memory
  allocation functions.

In this way, the VLFeat local state can be reset at any time simply by
disposing of all the memory allocated by the library so far. The
latter can be done easily by mapping the memory allocation functions
to implementations that track the memory blocks allocated, and then
disposing of all such blocks. Since the global state does not
reference any local object nor uses the remapped memory functions, it
is unaffected by such an operation; conversely, since no VLFeat object
references anything but memory, this guarantees that all allocated
resources are properly disposed (avoiding leaking resource). This is
used extensively in the design of MATLAB MEX files (see @ref
matlab).
**/

/**

@page objects Objects
@author Andrea Vedaldi
@tableofcontents


Many VLFeat algorithms are available in the form of *objects*. The C
language, used by VLFeat, does not support objects explicitly. Here an
object is intended a C structure along with a number of functions (the
object member functions or methods) operating on it. Ideally, the
object data structure is kept opaque to the user, for example by
defining it in the @c .c implementation files which are not accessible
to the library user.

Object names are capitalized and start with the Vl prefix
(for example @c VlExampleObject). Object methods are lowercase and
start with the vl__ suffix
(e.g. @c vl_example_object_new).


@section objects-lifecycle Object lifecycle


Conceptually, an object undergoes four phases during its lifecylce:
allocation, initialization, finalization, and deallocation:

- **Allocation.** The memory to hold the object structure is allocated.
  This is usually done by calling a memory allocation function such as
  ::vl_calloc to reserve an object of the required size @c
  sizeof(VlExampleObject). Alternatively, the object can simply by
  allocated on the stack by declaring a local variable of type
  VlExampleObject.
- **Initialization.** The object is initialized by assigning a value to
  its data members and potentially allocating a number of resources,
  including other objects or memory buffers. Initialization is
  done by methods containing the @c init keyword, e.g.  @c
  vl_example_object_init. Several such methods may be provided.
- **Finalization.** Initialization is undone by finalization, whose main
  purpose is to release any resource allocated and still owned by the
  object. Finalization is done by the @c vl_example_object_finalize
  method.
- **Deallocation.** The memory holding the object structure is
  disposed of, for example by calling ::vl_free or automatically when
  the corresponding local variable is popped from the stack.

In practice, most VlFeat object are supposed to be created on the
heap. To this end, allocation/initialization and
finalization/deallocation are combined into two operations:

- **Creating a new object.** This allocates a new object on the heap
  and initializes it, combining allocation and initialization in a
  single operation. It is done by methods containing the @c new keyword,
  e.g. @c vl_example_object_new.
- **Deleting an object.** This disposes of an object created by a @c
  new method, combining finalization and deallocation, for example
  @c vl_example_object_delete.


@section objects-getters-setters Getters and setters


Most objects contain a number of methods to get (getters) and set
(setters) properties. These should contain the @c get and @c set
keywords in their name, for example

@code
double x = vl_example_object_get_property () ;
vl_example_object_set_property(x) ;
@endcode
**/

/**

@page matlab MATLAB integration
@author Andrea Vedaldi


The VLFeat C library is designed to integrate seamlessly with MATLAB.
Binary compatibility is simplified by the use of the C language
(rather than C++). In addition, the library design follows certain
restrictions that make it compatible with the MATLAB MEX interface.

The main issue in calling a library function from a MATLAB MEX
function is that MATLAB can abort the execution of the MEX function
at any point, either due to an error, or directly upon a user request
(Ctrl-C) (empirically, however, a MEX function seems to be
incorruptible only during the invocation of certain functions of the
MEX API such as @c mexErrMsgTxt).

When a MEX function is interrupted, resources (memory blocks or
objects) whose ownership was transferred from VLFeat to the MEX
function may be leaked. Notice that interrupting a MEX function would
similarly leak any memory block allocated within the MEX function. To
solve this issue, MATLAB provides his own memory manager (@c
mxMalloc, @c mxRealloc, ...). When a MEX file is interrupted or ends,
all memory blocks allocated by using one of such functions are
released, preventing leakage.

In order to integrate VLFeat with this model in the most seamless
way, VLFeat memory allocation functions (::vl_malloc, ::vl_realloc,
::vl_calloc) are mapped to the corresponding MEX memory allocation
functions. Such functions automatically dispose of all the memory
allocated by a MEX function when the function ends (even because of
an exception). Because of the restrictions of the library design
illustrated in @ref resources, this operation is safe and
correctly dispose of VLFeat local state. As a consequence, it is
possible to call @c mexErrMsgTxt at any point in the MEX function
without worring about leaking resources.

This however comes at the price of some limitations. Beyond the
restrictions illustred in @ref resources, here we note that no
VLFeat local resoruce (memory blocks or objects) can persist across
MEX file invocations. This implies that any result produced by a
VLFeat MEX function must be converted back to a MATLAB object such as
a vector or a structure. In particular, there is no direct way of
creating an object within a MEX file, returning it to MATLAB, and
passing it again to another MEX file.
**/

/**

@page metaprogram Preprocessor metaprogramming
@author Andrea Vedaldi


Part of VLFeat code uses a simple form of perprocessor metaprogramming.
This technique is used, similarly to C++ templates, to instantiate
multiple version of a given algorithm for different data types
(e.g. @c float and @c double).

In most cases preprocessor metaprogramming is invisible to the library
user, as it is used only internally.
**/

/**

@page glossary Glossary


 - Column-major. A M x N  matrix A is
 stacked with column-major order as the sequence \f$(A_{11}, A_{21},
 \dots, A_{12}, \dots)\f$. More in general, when stacking a multi
 dimensional array this indicates that the first index is the one
 varying most quickly, with the other followed in the natural order.
 - Opaque structure. A structure is opaque if the user is not supposed
 to access its member directly, but through appropriate interface functions.
 Opaque structures are commonly used to define objects.
 - Row-major. A M x N  matrix A is
 stacked with row-major order as the sequence \f$(A_{11}, A_{12},
 \dots, A_{21}, \dots)\f$. More in general, when stacking a multi
 dimensional array this indicates that the last index is the one
 varying most quickly, with the other followed in reverse order.
 - Feature frame. A feature frame is the geometrical
 description of a visual features. For instance, the frame of
 a @ref sift.h "SIFT feature" is oriented disk and the frame of
 @ref mser.h "MSER feature" is either a compact and connected set or
 a disk.
 - Feature descriptor. A feature descriptor is a quantity
 (usually a vector) which describes compactly the appearance of an
 image region (usually corresponding to a feature frame).
**/

/**


@page dev Developing the library
@tableofcontents


This page contains information useful to the developer of VLFeat.


@section dev-copy Copyright


A short copyright notice is added at the beginning of each file. For
example:


    Copyright (C) 2013 Milan Sulc
Copyright (C) 2012 Daniele Perrone.
Copyright (C) 2011-13 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).

    

The copyright of each file is assigned to the authors of the file.
Every author making a substantial contribution to a file should
note its copyright by adding a line to the copyright list with the year
of the modification. Year ranges are acceptable. Lines are never
deleted, only appended, or potentially modified to list
more years.


@section dev-style Coding style



      


    • Look at existing code before you start. The general rule
is: try to match the style of the existing code as much as
possible.
      • 


    • No white spaces at the end of lines. White spaces introduce
invisible changes in the code that are however picked up by control
version systems such as Git.
      • 


    • Descriptive variable names. Most variable names start with
a lower case letter and are capitalized, e.g., @c numElements. Only
the following abbreviations are considered acceptable: @c num. The @c
dimension of a vector is the number of elements it contains (for other
objects that could be a @c size, a @c length, or a @c
numElements). For multi-dimensional arrays, @c dimensions could
indicate the array with each of the @c numDimensions dimensions.
      • 


    • Short variable names. For indexes in short for loops it is
fine to use short index names such as @c i, @c j, and @c k. For example:

      for (i = 0 ; i < numEntries ; ++i) values[i] ++ ;

      
is considered acceptable.
      • 


    • Function arguments. VLFeat functions that operate on an
object (member functions) should be passed the object address as first
argument; this argument should be called @c self. For example:

         void vl_object_do_something(VlObject *self) ;

      
Multi-dimensional arrays should be specified first by their address,
and then by their dimensions. For example

        void vl_use_array (float * array, vl_size numColumns, vl_size numRows) ; // good
  void vl_use_array (vl_size numColumns, vl_size numRows, float * array) ; // bad

      
Arguments that are used as outputs should be specified first (closer to
the left-hand side of an expression). For example

       void vl_sum_numbers (float * output, float input1, float input2) ; // good
 void vl_sum_numbers (float input1, float input2, float * output) ; // bad

      
These rules can be combined. For example

       void vl_object_sum_to_array (VlObject * self, float * outArray,
        vl_size numColumns, vl_size numRows, float * inArray) ; // good

      
Note that in this case no dimension for @c inArray is specified as it
is assumed that @c numColumns and @c numRows are the dimensions of
both arrays.

      • 

    


@subsection dev-style-matlab MATLAB coding style



      

    • Help messages. Each @c .m file should include a standard
help comment block (accessible from MATLAB @c help() command).
The first line of the block has a space, the name of the function,
4 spaces, and a brief command description. The body of the help
message is indented with 4 spaces. For example
@code
% VL_FUNCTION    An example function
%    VL_FUNCTION() does nothing.
@endcode
The content HELP message itself should follow MATLAB default style.
For example, rather than giving a list of formal input and output
arguments as often done, one simply shows how to use the function, explaining
along the way the different ways the function can be called and
the format of the parameters.

      • 

    


@section dev-doc Documenting the code


The VLFeat C library code contains its own in documentation Doxygen format. The
documentation consists in generic pages, such as the @ref index
"index" and the page you are reading, and documentations for each
library module, usually corresponding to a certain header file.

- **Inline comments.** Inline Doxygen comments are discouraged except
  in the documentation of data members of structures. They start with
  a capital letter and end with a period. For example:
  @code
  struct VlExampleStructure {
    int aMember ; /\*\*< A useful data member.
  }
  @endcode

- **Brief comments.** Brief Doxygen comments starts by a capital
  and end with a period. The documentation of all functions start
  with a brief comment.


@subsection devl-doc-modules Documenting the library modules


A library module groups a number of data types and functions that
implement a certain functionaltiy of VLFeat. The documentation of a
library module is generally organized as follows:

1. A page introducing the module and including a getting started
   section (3.g. @ref svm-starting) containing a short tutorial to
   quickly familiarize the user with the module (e.g. @ref svm).
2. One or more pages of detailed technical background discussing the
   algorithms implemented. These sections are used not just as part of
   the C API, but also as documentation for other APIs such as MATLAB
   (e.g. @ref svm-fundamentals).
3. One or more pages with the structure and function documentation
   (e.g. @ref svm.h).

More in detail, consider a module called Example Module. Then one would
typically have:


      

    • A header or declaration file @c example-module.h. Such a file has an
heading of the type:

@verbinclude example-module-doc.h

This comment block contains a file directive, causing the file to be
included in the documentation, a brief directive, specifying a short
description of what the file is, and a list of authors. A
(non-Doxygen) comment block with a short the copyright notice follows.
The brief directive should include a @@ref directive to point
to the main documentation page describing the module, if there is one.

      • 


    •  An implementation or definition file @c example-module.c. This file
has an heading of the type:

@verbinclude example-module-doc.c

This is similar to the declearation file, except for the content of the
brief comment.

      • 

    


@subsection devl-doc-functions Documenting functions



@subsection devl-doc-structures Documenting structures



@subsection devl-doc-structures Documenting objects


As seen in @ref objects, VLFeat treats certain structures with
an object-like semantics. Usually, a module defines exactly one such
objects. In this case, the object member functions should be grouped
(by using Doxygen grouping functionality) as

- **Construct and destroy** for the @c vl_object_new, @c
    vl_object_delete and similar member functions.
- **Set parameters** for setter functions.
- **Retrieve parameters and data** for getter functions.
- **Process data** for functions processing data.


@subsection devl-doc-bib Bibliographic references


Since version 0.9.14, the VLFeat C library documentation makes use of
a proper bibliographic reference in BibTeX format (see the file @c
docsrc/vlfeat.bib). Doxygen uses this file when it sees instances of
the @@cite{xyz} command.  Here @c xyz is a BibTeX
key. For example, @c vlfeat.bib file contains the entry:


    @@inproceedings{martin97the-det-curve,
	Author = {A. Martin and G. Doddington and T. Kamm and M. Ordowski and M. Przybocki},
	Booktitle = {Proc. Conf. on Speech Communication and Technology},
	Title = {The {DET} curve in assessment of detection task performance},
	Year = {1997}}

    

For example, the Doxygen directive
@@cite{martin97the-det-curve} generates the output
@cite{martin97the-det-curve}, which is a link to the corresponding
entry in the bibliography.

**/

/**

@file generic.h


@page generic General support functionalities
@author Andrea Vedaldi


VLFeat contains several support functionalities addressing the C
preprocessors, using multiple threads (including parallel computations),
handling errors, allocating memory, etc. These are described in
the following pages:

- @subpage resources
- @subpage threads
- @subpage misc
**/

/**


@page misc Preprocssor, library state, etc.
@author Andrea Vedaldi
@tableofcontents



@section misc-preproc C preprocessor helpers


VLFeat provides a few C preprocessor macros of general
utility. These include stringification (::VL_STRINGIFY,
::VL_XSTRINGIFY) and concatenation (::VL_CAT, ::VL_XCAT) of symbols.


@section misc-state VLFeat state and configuration parameters


VLFeat has some global configuration parameters that can
changed. Changing the configuration is thread unsave
(@ref threads). Use ::vl_set_simd_enabled to toggle the use of
a SIMD unit (Intel SSE code), ::vl_set_alloc_func to change
the memory allocation functions, and ::vl_set_printf_func
to change the logging function.


@section misc-error Error handling


Some VLFeat functions signal errors in a way similar to the
standard C library. In case of error, a VLFeat function
may return an error code directly,
or an invalid result (for instance a negative file descriptor or a null
pointer). Then ::vl_get_last_error and ::vl_get_last_error_message can be used
to retrieve further details about the error (these functions should be
used right after an error has occurred, before any other VLFeat call).


@section misc-memory Memory allocation


VLFeat uses the ::vl_malloc, ::vl_realloc, ::vl_calloc and ::vl_free
functions to allocate memory. Normally these functions are mapped to
the underlying standard C library implementations. However
::vl_set_alloc_func can be used to map them to other
implementations.  For instance, in MATALB MEX files these functions
are mapped to the MATLAB equivalent which has a garbage collection
mechanism to cope with interruptions during execution.


@section misc-logging Logging


VLFeat uses the macros ::VL_PRINT and ::VL_PRINTF to print progress
or debug informations. These functions are normally mapped to the @c
printf function of the underlying standard C library. However
::vl_set_printf_func can be used to map it to a different
implementation. For instance, in MATLAB MEX files this function is
mapped to @c mexPrintf. Setting the function to @c NULL disables
logging.


@section misc-time Measuring time


VLFeat provides ::vl_tic and ::vl_toc as an easy way of measuring
elapsed time.

**/

/**

@page threads Threading
@tableofcontents


VLFeat supports for threaded computations can be used to take advantage
of multi-core architectures. Threading support includes:

- Supporting using VLFeat functions and objects from multiple threads
  simultaneously. This is discussed in @ref threads-multiple.
- Using multiple cores to accelerate computations. This is
  discussed in @ref threads-parallel.


@section threads-multiple Using VLFeat from multiple threads


VLFeat can be used from multiple threads simultaneously if proper
rules are followed.

- A VLFeat object instance is accessed only from one thread at any
  given time. Functions operating on objects (member functions)
  are conditionally thread safe: the same function may be called
  simultaneously from multiple threads provided that it operates on
  different, independent objects. However, modifying the same object
  from multiple threads (using the same or different member functions)
  is possible only from one thread at any given time, and should
  therefore be synchronized. Certain VLFeat objects may contain
  features specific to simplify multi-threaded operations
  (e.g. ::VlKDForest).
- Thread-safe global functions are used. These include
  thread-specific operations such as retrieving the last error by
  ::vl_get_last_error and obtaining the thread-specific random number
  generator instance by ::vl_get_rand. In these cases, the functions
  operate on thread-specific data that VLFeat creates and
  maintains. Note in particular that each thread has an independent
  default random number generator (as returned by
  ::vl_get_rand). VLFeat objects that involve using random numbers
  will typically use the random number generator of the thread
  currently accessing the object (although an object-specific
  generator can be often be specified instead).
- Any other global function is considered non-thread safe and is
  accessed exclusively by one thread at a time. A small number of
  operations are non-reentrant and affect all threads
  simultaneously. These are restricted to changing certain global
  configuration parameters, such as the memory allocation functions by
  ::vl_set_alloc_func. These operations are not thread safe
  and are preferably executed before multiple threads start to operate
  with the library.


@section threads-parallel Parallel computations


VLFeat uses OpenMP to implement parallel computations. VLFeat avoids
changing OpenMP global state, such as the desired number of
computational threads, as this may affect the rest of the application
(e.g. MATLAB) in undesriable ways. Instead, it duplicates OpenMP
controls when appropriate (this is similar to the method used by other
libraries such as Intel MKL).

The maximum number of threads available to the application can be
obtained by ::vl_get_thread_limit. This limit is controlled by the
OpenMP library (the function is a wrapper around @c
omp_get_thread_limit), which in turn may determined that based on the
number of computational cores or the value of the @c OMP_THREAD_LIMIT
variable when the program is launched.

The desired number of computational threads is set by
::vl_set_num_threads() and retrieved by ::vl_get_max_threads().  This
number is a target value as well as an upper bound to the number of
threads used by VLFeat. @c vl_set_num_threads(1) disables the use of
multiple threads and @c vl_set_num_threads(0) uses OpenMP value
(retrieved by calling @c omp_get_max_threads()). The actual number
used in a specific computation is decided by OpenMP based on the
number of threads available, accounting for example for nested
parallelism when appropriate.

@sa http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_win/GUID-C2295BC8-DD22-466B-94C9-5FAA79D4F56D.htm
 http://software.intel.com/sites/products/documentation/doclib/mkl_sa/11/mkl_userguide_win/index.htm#GUID-DEEF0363-2B34-4BAB-87FA-A75DBE842040.htm
 http://software.intel.com/sites/products/documentation/hpc/mkl/lin/MKL_UG_managing_performance/Using_Additional_Threading_Control.htm

**/

#include "generic.h"

#include 
#include 
#include 
#include 
#include 

#if defined(VL_OS_WIN)
#include 
#endif

#if ! defined(VL_DISABLE_THREADS) && defined(VL_THREADS_POSIX)
#include 
#endif

#if defined(VL_OS_MACOSX) || defined(VL_OS_LINUX)
#include 
#endif

#if defined(_OPENMP)
#include 
#endif

/* ---------------------------------------------------------------- */
/*                                         Global and thread states */
/* ---------------------------------------------------------------- */

/* Thread state */
typedef struct _VlThreadState
{
  /* errors */
  int lastError ;
  char lastErrorMessage [VL_ERR_MSG_LEN] ;

  /* random number generator */
  VlRand rand ;

  /* time */
#if defined(VL_OS_WIN)
  LARGE_INTEGER ticFreq ;
  LARGE_INTEGER ticMark ;
#else
  clock_t ticMark ;
#endif
} VlThreadState ;

/* Gobal state */
typedef struct _VlState
{
  /* The thread state uses either a mutex (POSIX)
    or a critical section (Win) */
#if defined(VL_DISABLE_THREADS)
  VlThreadState * threadState ;
#else
#if defined(VL_THREADS_POSIX)
  pthread_key_t threadKey ;
  pthread_mutex_t mutex ;
  pthread_t mutexOwner ;
  pthread_cond_t mutexCondition ;
  size_t mutexCount ;
#elif defined(VL_THREADS_WIN)
  DWORD tlsIndex ;
  CRITICAL_SECTION mutex ;
#endif
#endif /* VL_DISABLE_THREADS */

  /* Configurable functions */
  int   (*printf_func)  (char const * format, ...) ;
  void *(*malloc_func)  (size_t) ;
  void *(*realloc_func) (void*,size_t) ;
  void *(*calloc_func)  (size_t, size_t) ;
  void  (*free_func)    (void*) ;

#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  VlX86CpuInfo cpuInfo ;
#endif
  vl_size numCPUs ;
  vl_bool simdEnabled ;
  vl_size numThreads ;
} VlState ;

/* Global state instance */
VlState _vl_state ;

/* ----------------------------------------------------------------- */
VL_INLINE VlState * vl_get_state () ;
VL_INLINE VlThreadState * vl_get_thread_specific_state () ;
static void vl_lock_state (void) ;
static void vl_unlock_state (void) ;
static VlThreadState * vl_thread_specific_state_new (void) ;
static void vl_thread_specific_state_delete (VlThreadState * self) ;

/** @brief Get VLFeat version string
 ** @return the library version string.
 **/

char const *
vl_get_version_string ()
{
  return VL_VERSION_STRING ;
}

/** @brief Get VLFeat configuration string.
 ** @return a new configuration string.
 **
 ** The function returns a new string containing a human readable
 ** description of the library configuration.
 **/

char *
vl_configuration_to_string_copy ()
{
  char * string = 0 ;
  int length = 0 ;
  char * staticString = vl_static_configuration_to_string_copy() ;
  char * cpuString =
#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  _vl_x86cpu_info_to_string_copy(&vl_get_state()->cpuInfo) ;
#else
  "Generic CPU" ;
#endif
#if defined(DEBUG)
  int const debug = 1 ;
#else
  int const debug = 0 ;
#endif

  while (string == 0) {
    if (length > 0) {
      string = vl_malloc(sizeof(char) * length) ;
      if (string == NULL) break ;
    }
    length = snprintf(string, length,
                      "VLFeat version %s\n"
                      "    Static config: %s\n"
                      "    %" VL_FMT_SIZE " CPU(s): %s\n"
#if defined(_OPENMP)
                      "    OpenMP: max threads: %d (library: %" VL_FMT_SIZE ")\n"
#endif
                      "    Debug: %s\n",
                      vl_get_version_string (),
                      staticString,
                      vl_get_num_cpus(), cpuString,
#if defined(_OPENMP)
                      omp_get_max_threads(), vl_get_max_threads(),
#endif
                      VL_YESNO(debug)) ;
    length += 1 ;
  }

  if (staticString) vl_free(staticString) ;
  if (cpuString) vl_free(cpuString) ;
  return string ;
}

/** @internal @brief A printf that does not do anything */
static int
do_nothing_printf (char const* format VL_UNUSED, ...)
{
  return 0 ;
}

/** @internal@brief Lock VLFeat state
 **
 ** The function locks VLFeat global state mutex.
 **
 ** The mutex is recursive: locking multiple times from the same thread
 ** is a valid operations, but requires an equivalent number
 ** of calls to ::vl_unlock_state.
 **
 ** @sa ::vl_unlock_state
 **/

static void
vl_lock_state (void)
{
#if ! defined(VL_DISABLE_THREADS)
#if   defined(VL_THREADS_POSIX)
  VlState * state = vl_get_state () ;
  pthread_t thisThread = pthread_self () ;
  pthread_mutex_lock (&state->mutex) ;
  if (state->mutexCount >= 1 &&
      pthread_equal (state->mutexOwner, thisThread)) {
    state->mutexCount ++ ;
  } else {
    while (state->mutexCount >= 1) {
      pthread_cond_wait (&state->mutexCondition, &state->mutex) ;
    }
    state->mutexOwner = thisThread ;
    state->mutexCount = 1 ;
  }
  pthread_mutex_unlock (&state->mutex) ;
#elif defined(VL_THREADS_WIN)
  EnterCriticalSection (&vl_get_state()->mutex) ;
#endif
#endif
}

/** @internal@brief Unlock VLFeat state
 **
 ** The function unlocks VLFeat global state mutex.
 **
 ** @sa ::vl_lock_state
 **/

static void
vl_unlock_state (void)
{
#if ! defined(VL_DISABLE_THREADS)
#if   defined(VL_THREADS_POSIX)
  VlState * state = vl_get_state () ;
  pthread_mutex_lock (&state->mutex) ;
  -- state->mutexCount ;
  if (state->mutexCount == 0) {
    pthread_cond_signal (&state->mutexCondition) ;
  }
  pthread_mutex_unlock (&state->mutex) ;
#elif defined(VL_THREADS_WIN)
  LeaveCriticalSection (&vl_get_state()->mutex) ;
#endif
#endif
}

/** @internal@brief Return VLFeat global state
 **
 ** The function returns a pointer to VLFeat global state.
 **
 ** @return pointer to the global state structure.
 **/

VL_INLINE VlState *
vl_get_state (void)
{
  return &_vl_state ;
}

/** @internal@brief Get VLFeat thread state
 ** @return pointer to the thread state structure.
 **
 ** The function returns a pointer to VLFeat thread state.
 **/

VL_INLINE VlThreadState *
vl_get_thread_specific_state (void)
{
#ifdef VL_DISABLE_THREADS
  return vl_get_state()->threadState ;
#else
  VlState * state ;
  VlThreadState * threadState ;

  vl_lock_state() ;
  state = vl_get_state() ;

#if defined(VL_THREADS_POSIX)
  threadState = (VlThreadState *) pthread_getspecific(state->threadKey) ;
#elif defined(VL_THREADS_WIN)
  threadState = (VlThreadState *) TlsGetValue(state->tlsIndex) ;
#endif

  if (! threadState) {
    threadState = vl_thread_specific_state_new () ;
  }

#if defined(VL_THREADS_POSIX)
  pthread_setspecific(state->threadKey, threadState) ;
#elif defined(VL_THREADS_WIN)
  TlsSetValue(state->tlsIndex, threadState) ;
#endif

  vl_unlock_state() ;
  return threadState ;
#endif
}

/* ---------------------------------------------------------------- */
/** @brief Get the number of CPU cores of the host
 ** @return number of CPU cores.
 **/

vl_size
vl_get_num_cpus (void)
{
  return vl_get_state()->numCPUs ;
}

/** @fn ::vl_set_simd_enabled(vl_bool)
 ** @brief Toggle usage of SIMD instructions
 ** @param x @c true if SIMD instructions are used.
 **
 ** Notice that SIMD instructions are used only if the CPU model
 ** supports them. Note alsoc that data alignment may restrict the use
 ** of such instructions.
 **
 ** @see ::vl_cpu_has_sse2(), ::vl_cpu_has_sse3(), etc.
 **/

void
vl_set_simd_enabled (vl_bool x)
{
  vl_get_state()->simdEnabled = x ;
}

/** @brief Are SIMD instructons enabled?
 ** @return @c true if SIMD instructions are enabled.
 **/

vl_bool
vl_get_simd_enabled (void)
{
  return vl_get_state()->simdEnabled ;
}

/** @brief Check for AVX instruction set
 ** @return @c true if AVX is present.
 **/

vl_bool
vl_cpu_has_avx (void)
{
#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  return vl_get_state()->cpuInfo.hasAVX ;
#else
  return VL_FALSE ;
#endif
}

/** @brief Check for SSE3 instruction set
 ** @return @c true if SSE3 is present.
 **/

vl_bool
vl_cpu_has_sse3 (void)
{
#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  return vl_get_state()->cpuInfo.hasSSE3 ;
#else
  return VL_FALSE ;
#endif
}

/** @brief Check for SSE2 instruction set
 ** @return @c true if SSE2 is present.
 **/

vl_bool
vl_cpu_has_sse2 (void)
{
#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  return vl_get_state()->cpuInfo.hasSSE2 ;
#else
  return VL_FALSE ;
#endif
}

/* ---------------------------------------------------------------- */

#if 0
/** @brief Get the number of computational threads available.
 ** @return number of threads.
 ** @sa @ref threads-parallel
 **/

vl_size
vl_get_thread_limit (void)
{
#if defined(_OPENMP)
  return omp_get_thread_limit() ;
#else
  return 1 ;
#endif
}
#endif

/** @brief Get the maximum number of computational threads.
 ** @return number of threads.
 ** @sa vl_set_num_threads(), @ref threads-parallel
 **/

vl_size
vl_get_max_threads (void)
{
#if defined(_OPENMP)
  return vl_get_state()->numThreads ;
#else
  return 1 ;
#endif
}

/** @brief Set the number of threads targeted for parallel computations.
 ** @param numThreads number of threads to use.
 ** @sa vl_get_max_threads(), @ref threads-parallel
 **/

void
vl_set_num_threads (
#if defined(_OPENMP)
                    vl_size numThreads)
#else
                    vl_size numThreads VL_UNUSED)
#endif
{
#if defined(_OPENMP)
  if (numThreads == 0) {
    numThreads = omp_get_max_threads() ;
  }
  vl_get_state()->numThreads = numThreads ;
#else
  return ;
#endif
}

/* ---------------------------------------------------------------- */
/** @brief Set last VLFeat error
 ** @param error error code.
 ** @param errorMessage error message format string.
 ** @param ... format string arguments.
 ** @return error code.
 **
 ** The function sets the code and optionally the error message
 ** of the last encountered error. @a errorMessage is the message
 ** format. It uses the @c printf convention and is followed by
 ** the format arguments. The maximum lenght of the error message is
 ** given by ::VL_ERR_MSG_LEN (longer messages are truncated).
 **
 ** Passing @c NULL as @a errorMessage
 ** sets the error message to the empty string.
 **/

int
vl_set_last_error (int error, char const * errorMessage, ...)
{
  VlThreadState * state = vl_get_thread_specific_state() ;
  va_list args;
  va_start(args, errorMessage) ;
  if (errorMessage) {
#ifdef VL_COMPILER_LCC
    vsprintf(state->lastErrorMessage, errorMessage, args) ;
#else
    vsnprintf(state->lastErrorMessage,
              sizeof(state->lastErrorMessage)/sizeof(char),
              errorMessage, args) ;
#endif
  } else {
    state->lastErrorMessage[0] = 0 ;
  }
  state->lastError = error ;
  va_end(args) ;
  return error ;
}

/** @brief Get the code of the last error
 ** @return error code.
 ** @sa ::vl_get_last_error_message.
 **/

int
vl_get_last_error (void) {
  return vl_get_thread_specific_state()->lastError ;
}

/** @brief Get the last error message
 ** @return pointer to the error message.
 ** @sa ::vl_get_last_error.
 **/

char const *
vl_get_last_error_message (void)
{
  return vl_get_thread_specific_state()->lastErrorMessage ;
}

/* ---------------------------------------------------------------- */
/** @brief Set memory allocation functions
 ** @param malloc_func  pointer to @c malloc.
 ** @param realloc_func pointer to @c realloc.
 ** @param calloc_func  pointer to @c calloc.
 ** @param free_func    pointer to @c free.
 **/

void
vl_set_alloc_func (void *(*malloc_func)  (size_t),
                   void *(*realloc_func) (void*, size_t),
                   void *(*calloc_func)  (size_t, size_t),
                   void  (*free_func)    (void*))
{
  VlState * state ;
  vl_lock_state () ;
  state = vl_get_state() ;
  state->malloc_func  = malloc_func ;
  state->realloc_func = realloc_func ;
  state->calloc_func  = calloc_func ;
  state->free_func    = free_func ;
  vl_unlock_state () ;
}

/** @brief Allocate a memory block
 ** @param n size in bytes of the new block.
 ** @return pointer to the allocated block.
 **
 ** This function allocates a memory block of the specified size.
 ** The synopsis is the same as the POSIX @c malloc function.
 **/

void *
vl_malloc (size_t n)
{
  return (vl_get_state()->malloc_func)(n) ;
  //return (memalign)(32,n) ;
}


/** @brief Reallocate a memory block
 ** @param ptr pointer to a memory block previously allocated.
 ** @param n size in bytes of the new block.
 ** @return pointer to the new block.
 **
 ** This function reallocates a memory block to change its size.
 ** The synopsis is the same as the POSIX @c realloc function.
 **/

void *
vl_realloc (void* ptr, size_t n)
{
  return (vl_get_state()->realloc_func)(ptr, n) ;
}

/** @brief Free and clear a memory block
 ** @param n number of items to allocate.
 ** @param size size in bytes of an item.
 ** @return pointer to the new block.
 **
 ** This function allocates and clears a memory block.
 ** The synopsis is the same as the POSIX @c calloc function.
 **/

void *
vl_calloc (size_t n, size_t size)
{
  return (vl_get_state()->calloc_func)(n, size) ;
}

/** @brief Free a memory block
 ** @param ptr pointer to the memory block.
 **
 ** This function frees a memory block allocated by ::vl_malloc,
 ** ::vl_calloc, or ::vl_realloc. The synopsis is the same as the POSIX
 ** @c malloc function.
 **/

void
vl_free (void *ptr)
{
  (vl_get_state()->free_func)(ptr) ;
}

/* ---------------------------------------------------------------- */

/** @brief Set the printf function
 ** @param printf_func pointer to a @c printf implementation.
 ** Set @c print_func to NULL to disable printf.
 **/

void
vl_set_printf_func (printf_func_t printf_func)
{
  vl_get_state()->printf_func = printf_func ? printf_func : do_nothing_printf ;
}

/** @brief Get the printf function
 ** @return printf_func pointer to the @c printf implementation.
 ** @sa ::vl_set_printf_func.
 **/

printf_func_t
vl_get_printf_func (void) {
  return vl_get_state()->printf_func ;
}

/* ---------------------------------------------------------------- */
/** @brief Get processor time
 ** @return processor time in seconds.
 ** @sa ::vl_tic, ::vl_toc
 **/

double
vl_get_cpu_time ()
{
  #ifdef VL_OS_WIN
  VlThreadState * threadState = vl_get_thread_specific_state() ;
  LARGE_INTEGER mark ;
  QueryPerformanceCounter (&mark) ;
  return (double)mark.QuadPart / (double)threadState->ticFreq.QuadPart ;
#else
  return (double)clock() / (double)CLOCKS_PER_SEC ;
#endif
}

/** @brief Reset processor time reference
 ** The function resets VLFeat TIC/TOC time reference. There is one
 ** such reference per thread.
 ** @sa ::vl_get_cpu_time, ::vl_toc.
 **/

void
vl_tic (void)
{
  VlThreadState * threadState = vl_get_thread_specific_state() ;
#ifdef VL_OS_WIN
  QueryPerformanceCounter (&threadState->ticMark) ;
#else
  threadState->ticMark = clock() ;
#endif
}

/** @brief Get elapsed time since tic
 ** @return elapsed time in seconds.
 **
 ** The function
 ** returns the processor time elapsed since ::vl_tic was called last.
 **
 ** @remark In multi-threaded applications, there is an independent
 ** timer for each execution thread.
 **
 ** @remark On UNIX, this function uses the @c clock() system call.
 ** On Windows, it uses the @c QueryPerformanceCounter() system call,
 ** which is more accurate than @c clock() on this platform.
 **/

double
vl_toc (void)
{
  VlThreadState * threadState = vl_get_thread_specific_state() ;
#ifdef VL_OS_WIN
  LARGE_INTEGER tocMark ;
  QueryPerformanceCounter(&tocMark) ;
  return (double) (tocMark.QuadPart - threadState->ticMark.QuadPart) /
    threadState->ticFreq.QuadPart ;
#else
  return (double) (clock() - threadState->ticMark) / CLOCKS_PER_SEC ;
#endif
}

/* ---------------------------------------------------------------- */
/** @brief Get the default random number generator.
 ** @return random number generator.
 **
 ** The function returns a pointer to the default
 ** random number genrator.
 ** There is one such generator per thread.
 **/

VL_EXPORT VlRand *
vl_get_rand (void)
{
  return &vl_get_thread_specific_state()->rand ;
}

/* ---------------------------------------------------------------- */
/*                    Library construction and destruction routines */
/*  --------------------------------------------------------------- */

/** @internal@brief Construct a new thread state object
 ** @return new state structure.
 **/

static VlThreadState *
vl_thread_specific_state_new (void)
{
  VlThreadState * self ;
#if defined(DEBUG)
  printf("VLFeat DEBUG: thread constructor begins.\n") ;
#endif
  self = malloc(sizeof(VlThreadState)) ;
  self->lastError = 0 ;
  self->lastErrorMessage[0] = 0 ;
#if defined(VL_OS_WIN)
  QueryPerformanceFrequency (&self->ticFreq) ;
  self->ticMark.QuadPart = 0 ;
#else
  self->ticMark = 0 ;
#endif
  vl_rand_init (&self->rand) ;

  return self ;
}

/** @internal@brief Delete a thread state structure
 ** @param self thread state object.
 **/

static void
vl_thread_specific_state_delete (VlThreadState * self)
{
#if defined(DEBUG)
  printf("VLFeat DEBUG: thread destructor begins.\n") ;
#endif
  free (self) ;
}
/* ---------------------------------------------------------------- */
/*                                        DLL entry and exit points */
/* ---------------------------------------------------------------- */
/* A constructor and a destructor must be called to initalize or dispose of VLFeat
 * state when the DLL is loaded or unloaded. This is obtained
 * in different ways depending on the operating system.
 */

#if (defined(VL_OS_LINUX) || defined(VL_OS_MACOSX)) && defined(VL_COMPILER_GNUC)
static void vl_constructor () __attribute__ ((constructor)) ;
static void vl_destructor () __attribute__ ((destructor))  ;
#endif

#if defined(VL_OS_WIN)
static void vl_constructor () ;
static void vl_destructor () ;

BOOL WINAPI DllMain(
    HINSTANCE hinstDLL,  // handle to DLL module
    DWORD fdwReason,     // reason for calling function
    LPVOID lpReserved )  // reserved
{
  VlState * state ;
  VlThreadState * threadState ;
  switch (fdwReason) {
    case DLL_PROCESS_ATTACH:
      /* Initialize once for each new process */
      vl_constructor () ;
      break ;

    case DLL_THREAD_ATTACH:
      /* Do thread-specific initialization */
      break ;

    case DLL_THREAD_DETACH:
      /* Do thread-specific cleanup */
#if ! defined(VL_DISABLE_THREADS) && defined(VL_THREADS_WIN)
      state = vl_get_state() ;
      threadState = (VlThreadState*) TlsGetValue(state->tlsIndex) ;
      if (threadState) {
        vl_thread_specific_state_delete (threadState) ;
      }
#endif
      break;

    case DLL_PROCESS_DETACH:
      /* Perform any necessary cleanup */
      vl_destructor () ;
      break;
    }
    return TRUE ; /* Successful DLL_PROCESS_ATTACH */
}
#endif /* VL_OS_WIN */

/* ---------------------------------------------------------------- */
/*                               Library constructor and destructor */
/* ---------------------------------------------------------------- */

/** @internal @brief Initialize VLFeat state */
static void
vl_constructor (void)
{
  VlState * state ;
#if defined(DEBUG)
  printf("VLFeat DEBUG: constructor begins.\n") ;
#endif

  state = vl_get_state() ;

#if ! defined(VL_DISABLE_THREADS)
#if defined(DEBUG)
  printf("VLFeat DEBUG: constructing thread specific state.\n") ;
#endif
#if defined(VL_THREADS_POSIX)
  {
    typedef void (*destructorType)(void * );
    pthread_key_create (&state->threadKey,
                        (destructorType)
                          vl_thread_specific_state_delete) ;
    pthread_mutex_init (&state->mutex, NULL) ;
    pthread_cond_init (&state->mutexCondition, NULL) ;
  }
#elif defined(VL_THREADS_WIN)
  InitializeCriticalSection (&state->mutex) ;
  state->tlsIndex = TlsAlloc () ;
#endif
#else

/* threading support disabled */
#if defined(DEBUG)
  printf("VLFeat DEBUG: constructing the generic thread state instance (threading support disabled).\n") ;
#endif
  vl_get_state()->threadState = vl_thread_specific_state_new() ;
#endif

  state->malloc_func  = malloc ;
  state->realloc_func = realloc ;
  state->calloc_func  = calloc ;
  state->free_func    = free ;
  state->printf_func  = printf ;

  /* on x86 platforms read the CPUID register */
#if defined(VL_ARCH_IX86) || defined(VL_ARCH_X64) || defined(VL_ARCH_IA64)
  _vl_x86cpu_info_init (&state->cpuInfo) ;
#endif

  /* get the number of CPUs */
#if defined(VL_OS_WIN)
  {
    SYSTEM_INFO info;
    GetSystemInfo (&info) ;
    state->numCPUs = info.dwNumberOfProcessors ;
  }
#elif defined(VL_OS_MACOSX) || defined(VL_OS_LINUX)
  state->numCPUs = sysconf(_SC_NPROCESSORS_ONLN) ;
#else
  state->numCPUs = 1 ;
#endif
  state->simdEnabled = VL_TRUE ;

  /* get the number of (OpenMP) threads used by the library */
#if defined(_OPENMP)
  state->numThreads = omp_get_max_threads() ;
#else
  state->numThreads = 1 ;
#endif

#if defined(DEBUG)
  printf("VLFeat DEBUG: constructor ends.\n") ;
#endif
}

/** @internal @brief Destruct VLFeat */
static void
vl_destructor ()
{
  VlState * state ;
#if defined(DEBUG)
  printf("VLFeat DEBUG: destructor begins.\n") ;
#endif

  state = vl_get_state() ;

#if ! defined(VL_DISABLE_THREADS)
#if defined(DEBUG)
  printf("VLFeat DEBUG: destroying a thread specific state instance.\n") ;
#endif
#if   defined(VL_THREADS_POSIX)
  {
    /* Delete the thread state of this thread as the
       destructor is not called by pthread_key_delete or after
       the key is deleted. When the library
       is unloaded, this thread should also be the last one
       using the library, so this is fine.
     */
    VlThreadState * threadState =
       pthread_getspecific(state->threadKey) ;
    if (threadState) {
      vl_thread_specific_state_delete (threadState) ;
      pthread_setspecific(state->threadKey, NULL) ;
    }
  }
  pthread_cond_destroy (&state->mutexCondition) ;
  pthread_mutex_destroy (&state->mutex) ;
  pthread_key_delete (state->threadKey) ;
#elif defined(VL_THREADS_WIN)
 {
    /* Delete the thread state of this thread as the
       destructor is not called by pthread_key_delete or after
       the key is deleted. When the library
       is unloaded, this thread should also be the last one
       using the library, so this is fine.
     */
    VlThreadState * threadState =
       TlsGetValue(state->tlsIndex) ;
    if (threadState) {
      vl_thread_specific_state_delete (threadState) ;
      TlsSetValue(state->tlsIndex, NULL) ;
    }
  }
  TlsFree (state->tlsIndex) ;
  DeleteCriticalSection (&state->mutex) ;
#endif
#else
#if defined(DEBUG)
  printf("VLFeat DEBUG: destroying the generic thread state instance (threading support disabled).\n") ;
#endif
  vl_thread_specific_state_delete(vl_get_state()->threadState) ;
#endif

#if defined(DEBUG)
  printf("VLFeat DEBUG: destructor ends.\n") ;
#endif
}
vlfeat/vl/host.h0000644000175000017500000004117212237004745012525 0ustar  dimadima/** @file host.h
 ** @brief Host
 ** @author Andrea Vedaldi
 ** @sa @ref portability
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_HOST_H
#define VL_HOST_H

/** ------------------------------------------------------------------
 ** @name Configuration options
 ** @{ */

#if defined(__DOXYGEN__)
#define VL_DISABLE_THREADS
#define VL_DISABLE_SSE2
#define VL_DISABLE_OPENMP
#endif

/** @} */

/** ------------------------------------------------------------------
 ** @name Defining functions
 ** @{ */

#if defined(__DOXYGEN__)
#define VL_EXPORT
#define VL_INLINE
#endif

/** @} */

/** ------------------------------------------------------------------
 ** @name C preprocessor helper macros
 ** @{ */

/** @brief Convert the argument to a string
 ** @param x value to be stringified.
 **
 ** This macro stringifies the argument @a x by means of the
 ** # prerpocessor operator.
 **
 ** The standard C preprocessor does not prescan arguments which are
 ** stringified, so
 **
 ** @code
 ** #define A B
 ** char const * str = VL_STRINGIFY(A) ;
 ** @endcode
 **
 ** initializes str with a pointer to the string
 ** "A", which mihgt be unexpected. To fix this issue,
 ** you can use ::VL_XSTRINGIFY.
 **
 ** @sa ::VL_XSTRINGIFY
 **/

#define VL_STRINGIFY(x) # x

/** @brief Expand and then convert the argument to a string
 ** @param x value to be macro-expanded and converted.
 **
 ** This macro macro-expands the argument @a x and stringifies the
 ** result of the expansion. For instance
 **
 ** @code
 ** #define A B
 ** char const * str = VL_STRINGIFY(A) ;
 ** @endcode
 **
 ** initializes str with a pointer to the string
 ** "B".
 **
 ** @sa ::VL_STRINGIFY
 **/

#define VL_XSTRINGIFY(x) VL_STRINGIFY(x)

/** @brief Concatenate two arguments into a lexical unit
 ** @param x first argument to be concatenated.
 ** @param y second argument to be concatenated.
 **
 ** This macro concatenates its arguments into a single lexical unit
 ** by means of the ## preprocessor operator. Notice that
 ** arguments concatenated by ## are not pre-expanded by
 ** the C preprocessor. To macro-expand the arguments and then
 ** concatenate them,use ::VL_XCAT.
 **
 ** @see ::VL_XCAT
 **/

#define VL_CAT(x,y) x ## y

/** @brief Expand and then concatenate two arguments into a lexical unit
 ** @param x first argument to be concatenated.
 ** @param y second argument to be concatenated.
 **
 ** This macro is the same as ::VL_CAT, except that the arguments are
 ** macro expanded before being concatenated.
 **
 ** @see ::VL_CAT
 **/

#define VL_XCAT(x,y) VL_CAT(x,y)

/** @brief Expand and then concatenate three arguments into a lexical unit
 ** @param x first argument to be concatenated.
 ** @param y second argument to be concatenated.
 ** @param z third argument to be concatenated.
 **
 ** This macro is the same as ::VL_XCAT, except that it has three arguments.
 **
 ** @see ::VL_XCAT
 **/

#define VL_XCAT3(x,y,z) VL_XCAT(VL_XCAT(x,y),z)

/** @brief Expand and then concatenate four arguments into a lexical unit
 ** @param x first argument to be concatenated.
 ** @param y second argument to be concatenated.
 ** @param z third argument to be concatenated.
 ** @param u fourth argument to be concatenated.
 **
 ** This macro is the same as ::VL_XCAT, except that it has four arguments.
 **
 ** @see ::VL_XCAT
 **/

#define VL_XCAT4(x,y,z,u) VL_XCAT(VL_XCAT3(x,y,z),u)

/** @brief Expand and then concatenate five arguments into a lexical unit
 ** @param x first argument to be concatenated.
 ** @param y second argument to be concatenated.
 ** @param z third argument to be concatenated.
 ** @param u fourth argument to be concatenated.
 ** @param v fifth argument to be concatenated.
 **
 ** This macro is the same as ::VL_XCAT, except that it has five arguments.
 **
 ** @see ::VL_XCAT
 **/

#define VL_XCAT5(x,y,z,u,v) VL_XCAT(VL_XCAT4(x,y,z,u),v)

/** @brief Convert a boolean to "yes" or "no" strings
 ** @param x boolean to convert.
 **
 ** A pointer to either the string "yes" (if @a x is true)
 ** or the string "no".
 **
 ** @par Example
 ** @code
 ** VL_PRINTF("Is x true? %s.", VL_YESNO(x))
 ** @endcode
 **/

#define VL_YESNO(x) ((x)?"yes":"no")

/** @} */

/*
 The following macros identify the host OS, architecture and compiler.
 They are derived from http://predef.sourceforge.net/
 */

/** @name Identifying the host operating system
 ** @{ */
#if defined(linux)     || \
    defined(__linux)   || \
    defined(__linux__) || \
    defined(__DOXYGEN__)
#define VL_OS_LINUX 1
#endif

#if (defined(__APPLE__) & defined(__MACH__)) || \
     defined(__DOXYGEN__)
#define VL_OS_MACOSX 1
#endif

#if defined(__WIN32__) || \
    defined(_WIN32)    || \
    defined(__DOXYGEN__)
#define VL_OS_WIN 1
#endif

#if defined(_WIN64) || \
    defined(__DOXYGEN__)
#define VL_OS_WIN64 1
#endif
/** @} */

/** @name Identifying the host threading library
 ** @{ */
#if defined(VL_OS_MACOSX) || defined(VL_OS_LINUX) || \
defined(__DOXYGEN__)
#define VL_THREADS_POSIX 1
#endif

#if defined(VL_OS_WIN) || defined(VL_OS_WIN64) || \
defined(__DOXYGEN__)
#define VL_THREADS_WIN 1
#endif
/** @} */

/** @name Identifying the host compiler
 ** @{ */
#if defined(__GNUC__) || defined(__DOXYGEN__)
# if defined(__GNUC_PATCHLEVEL__)
#  define VL_COMPILER_GNUC (__GNUC__ * 10000 \
+ __GNUC_MINOR__ * 100 \
+ __GNUC_PATCHLEVEL__)
# else
#  define VL_COMPILER_GNUC (__GNUC__ * 10000 \
+ __GNUC_MINOR__ * 100)
# endif
#endif

#if defined(_MSC_VER) || defined(__DOXYGEN__)
#define VL_COMPILER_MSC _MSC_VER
#endif

#if defined(__LCC__) || defined(__DOXYGEN__)
#warning "LCC support is experimental!"
#define VL_COMPILER_LCC 1
#endif

/** @} */

/** @name Identifying the host CPU architecture
 ** @{ */
#if defined(i386)     || \
    defined(__i386__) || \
    defined(__DOXYGEN__)
#define VL_ARCH_IX86 300
#elif defined(__i486__)
#define VL_ARCH_IX86 400
#elif defined(__i586__)
#define VL_ARCH_IX86 500
#elif defined(__i686__)
#define VL_ARCH_IX86 600
#elif defined(_M_IX86)
#define VL_ARCH_IX86 _M_IX86
#endif

#if defined(_M_X64)    || \
    defined(__amd64__) || \
    defined(__amd64)   || \
    defined(__x86_64)  || \
    defined(__x86_64)
#define VL_ARCH_X64
#endif

#if defined(__ia64__)   || \
    defined(_IA64)      || \
    defined(__IA64)     || \
    defined(__ia64)     || \
    defined(_M_IA64)    || \
    defined(__DOXYGEN__)
#define VL_ARCH_IA64
#endif
/** @} */

/** @name Identifying the host data model
 ** @{ */
#if defined(__LLP64__) || \
    defined(__LLP64)   || \
    defined(__LLP64)   || \
    (defined(VL_COMPILER_MSC) & defined(VL_OS_WIN64)) || \
    (defined(VL_COMPILER_LCC) & defined(VL_OS_WIN64)) || \
    defined(__DOXYGEN__)
#define VL_COMPILER_LLP64
#endif

#if defined(__LP64__) || \
    defined(__LP64)   || \
    defined(__LP64)   || \
    (defined(VL_OS_MACOSX) & defined(VL_ARCH_IA64)) || \
    defined(__DOXYGEN__)
#define VL_COMPILER_LP64
#endif

#if (!defined(VL_COMPILER_LLP64) & !defined(VL_COMPILER_LP64)) || \
    defined(__DOXYGEN__)
#define VL_COMPILER_ILP32
#endif
/** @} */

/** @name Identifying the host endianness
 ** @{ */
#if defined(__LITTLE_ENDIAN__) || \
    defined(VL_ARCH_IX86)      || \
    defined(VL_ARCH_IA64)      || \
    defined(VL_ARCH_X64)       || \
    defined(__DOXYGEN__)
#define VL_ARCH_LITTLE_ENDIAN
#endif

#if  defined(__DOXYGEN__) || \
    !defined(VL_ARCH_LITTLE_ENDIAN)
#define VL_ARCH_BIG_ENDIAN
#endif
/** @} */

#if defined(VL_COMPILER_MSC) & ! defined(__DOXYGEN__)
#  define VL_UNUSED
#  define VL_INLINE static __inline
#  define snprintf _snprintf
#  define isnan _isnan
#  ifdef VL_BUILD_DLL
#    ifdef __cplusplus
#      define VL_EXPORT extern "C" __declspec(dllexport)
#    else
#      define VL_EXPORT extern __declspec(dllexport)
#    endif
#  else
#    ifdef __cplusplus
#      define VL_EXPORT extern "C" __declspec(dllimport)
#    else
#      define VL_EXPORT extern __declspec(dllimport)
#    endif
#  endif
#endif

#if defined(VL_COMPILER_LCC) & ! defined(__DOXYGEN__)
#  define VL_UNUSED
#  define VL_INLINE static __inline
#  define snprintf _snprintf
#  define isnan _isnan
VL_INLINE float fabsf(float x) { return (float) fabs((double) x) ; }
#  ifdef VL_BUILD_DLL
#    define VL_EXPORT extern __declspec(dllexport)
#  else
#    define VL_EXPORT extern
#  endif
#endif

#if defined(VL_COMPILER_GNUC) & ! defined(__DOXYGEN__)
#  define VL_UNUSED __attribute__((unused))
#  define VL_INLINE static __inline__
#  ifdef VL_BUILD_DLL
#    ifdef __cplusplus
#      define VL_EXPORT __attribute__((visibility ("default"))) extern "C"
#    else
#      define VL_EXPORT __attribute__((visibility ("default"))) extern
#    endif
#  else
#    ifdef __cplusplus
#      define VL_EXPORT extern "C"
#    else
#      define VL_EXPORT extern
#    endif
#  endif
#endif

VL_EXPORT char * vl_static_configuration_to_string_copy () ;

/** ------------------------------------------------------------------
 ** @name Atomic data types
 ** @{
 **/

#define VL_TRUE 1   /**< @brief @c true (1) constant */
#define VL_FALSE 0  /**< @brief @c false (0) constant */

#if defined(VL_COMPILER_LP64) || defined(VL_COMPILER_LLP64)
typedef long long           vl_int64 ;   /**< @brief Signed 64-bit integer. */
typedef int                 vl_int32 ;   /**< @brief Signed 32-bit integer. */
typedef short               vl_int16 ;   /**< @brief Signed 16-bit integer. */
typedef char                vl_int8  ;   /**< @brief Signed  8-bit integer. */

typedef long long unsigned  vl_uint64 ;  /**< @brief Unsigned 64-bit integer. */
typedef int       unsigned  vl_uint32 ;  /**< @brief Unsigned 32-bit integer. */
typedef short     unsigned  vl_uint16 ;  /**< @brief Unsigned 16-bit integer. */
typedef char      unsigned  vl_uint8 ;   /**< @brief Unsigned  8-bit integer. */

typedef int                 vl_int ;     /**< @brief Same as @c int. */
typedef unsigned int        vl_uint ;    /**< @brief Same as unsigned int. */

typedef int                 vl_bool ;    /**< @brief Boolean. */
typedef vl_int64            vl_intptr ;  /**< @brief Integer holding a pointer. */
typedef vl_uint64           vl_uintptr ; /**< @brief Unsigned integer holding a pointer. */
typedef vl_uint64           vl_size ;    /**< @brief Unsigned integer holding the size of a memory block. */
typedef vl_int64            vl_index ;   /**< @brief Signed version of ::vl_size and ::vl_uindex */
typedef vl_uint64           vl_uindex ;  /**< @brief Same as ::vl_size */
#endif

#if defined(VL_COMPILER_ILP32)

#ifdef VL_COMPILER_MSC
typedef __int64             vl_int64 ;
#else
typedef long long           vl_int64 ;
#endif

typedef int                 vl_int32 ;
typedef short               vl_int16 ;
typedef char                vl_int8  ;

#ifdef VL_COMPILER_MSC
typedef __int64   unsigned  vl_uint64 ;
#else
typedef long long unsigned  vl_uint64 ;
#endif
typedef int       unsigned  vl_uint32 ;
typedef short     unsigned  vl_uint16 ;
typedef char      unsigned  vl_uint8 ;

typedef int                 vl_int ;
typedef unsigned int        vl_uint ;

typedef int                 vl_bool ;
typedef vl_int32            vl_intptr ;
typedef vl_uint32           vl_uintptr ;
typedef vl_uint32           vl_size ;
typedef vl_int32            vl_index ;
typedef vl_uint32           vl_uindex ;
#endif
/** @} */

/** @name Creating integer constants
 ** @{ */
#if defined(VL_COMPILER_LP64) || defined(__DOXYGEN__)
#define VL_INT8_C(x) x
#define VL_INT16_C(x) x
#define VL_INT32_C(x) x
#define VL_INT64_C(x) x ## L

#define VL_UINT8_C(x) x
#define VL_UINT16_C(x) x
#define VL_UINT32_C(x) x ## U
#define VL_UINT64_C(x) x ## UL
#endif

#if (defined(VL_COMPILER_LLP64) || defined(VL_COMPILER_ILP32)) \
    & !defined(__DOXYGEN__)
#define VL_INT8_C(x) x
#define VL_INT16_C(x) x
#define VL_INT32_C(x) x
#define VL_INT64_C(x) x ## LL

#define VL_UINT8_C(x) x
#define VL_UINT16_C(x) x
#define VL_UINT32_C(x) x ## U
#define VL_UINT64_C(x) x ## ULL
#endif
/** @} */

/** ------------------------------------------------------------------
 ** @name Printing the atomic data types
 ** @{ */

/* Lengths only: */

/** @def VL_FL_INT64
 ** @brief @c printf length flag for ::vl_int64 and ::vl_uint64.
 **/

/** @def VL_FL_INT32
 ** @brief @c printf length flag for ::vl_int32 and ::vl_uint32.
 **/

/** @def VL_FL_INT16
 ** @brief @c printf length flag for ::vl_int16 and ::vl_uint16.
 **/

/** @def VL_FL_INT8
 ** @brief @c printf length flag for ::vl_int8 and ::vl_uint8.
 **/

/** @def VL_FL_INDEX
 ** @brief @c printf length flag for ::vl_index and ::vl_uindex
 **/

#ifdef VL_COMPILER_MSC
#define VL_FL_INT64  "I64"
#else
#define VL_FL_INT64  "ll"
#endif
#define VL_FL_INT32  ""
#define VL_FL_INT16  "h"
#define VL_FL_INT8   "hh"

#if defined(VL_COMPILER_LP64) || defined(VL_COMPILER_LLP64)
#define VL_FL_INDEX VL_FL_INT64
#endif

#if defined(VL_COMPILER_ILP32)
#define VL_FL_INDEX VL_FL_INT32
#endif

/* Formats (but not conversions!): */

/** @def VL_FMT_SIZE
 ** @brief @c printf flag for ::vl_size
 **/

/** @def VL_FMT_INDEX
 ** @brief @c printf flag for ::vl_index
 **/

/** @def VL_FMT_UINDEX
 ** @brief @c printf flag for ::vl_uindex
 **/

/** @def VL_FMT_INTPTR
 ** @brief @c printf flag for ::vl_intptr
 **/

/** @def VL_FMT_UINTPTR
 ** @brief @c printf flag for ::vl_uintptr
 **/

#define VL_FMT_INDEX   VL_FL_INDEX "d"
#define VL_FMT_INTPTR  VL_FMT_INDEX
#define VL_FMT_UINDEX  VL_FL_INDEX "u"
#define VL_FMT_SIZE    VL_FMT_UINDEX
#define VL_FMT_UINTPTR VL_FMT_UINDEX

/** @} */

/** ------------------------------------------------------------------
 ** @name Atomic data types limits
 ** @{ */

/** @brief Largest integer (math constant) */
#define VL_BIG_INT  0x7FFFFFFFL

/** @brief Smallest integer (math constant) */
#define VL_SMALL_INT  (- VL_BIG_INT - 1)

/** @brief Largest unsigned integer (math constant) */
#define VL_BIG_UINT 0xFFFFFFFFUL

/** @} */

/** ------------------------------------------------------------------
 ** @name Endianness detection and conversion
 ** @{
 **/
VL_INLINE void vl_swap_host_big_endianness_8 (void *dst, void* src) ;
VL_INLINE void vl_swap_host_big_endianness_4 (void *dst, void* src) ;
VL_INLINE void vl_swap_host_big_endianness_2 (void *dst, void* src) ;
/** @} */

/** ------------------------------------------------------------------
 ** @name Obtaining host info at run time
 ** @{ */

typedef struct _VlX86CpuInfo
{
  union {
    char string [0x20] ;
    vl_uint32 words [0x20 / 4] ;
  } vendor ;
  vl_bool hasAVX ;
  vl_bool hasSSE42 ;
  vl_bool hasSSE41 ;
  vl_bool hasSSE3 ;
  vl_bool hasSSE2 ;
  vl_bool hasSSE ;
  vl_bool hasMMX ;
} VlX86CpuInfo ;

void _vl_x86cpu_info_init (VlX86CpuInfo *self) ;
char * _vl_x86cpu_info_to_string_copy (VlX86CpuInfo const *self) ;

/** @} */

/** ------------------------------------------------------------------
 ** @brief Host <-> big endian transformation for 8-bytes value
 **
 ** @param dst destination 8-byte buffer.
 ** @param src source 8-byte bufffer.
 ** @see @ref host-arch-endianness.
 **/

VL_INLINE void
vl_swap_host_big_endianness_8 (void *dst, void* src)
{
  char *dst_ = (char*) dst ;
  char *src_ = (char*) src ;
#if defined(VL_ARCH_BIG_ENDIAN)
    dst_ [0] = src_ [0] ;
    dst_ [1] = src_ [1] ;
    dst_ [2] = src_ [2] ;
    dst_ [3] = src_ [3] ;
    dst_ [4] = src_ [4] ;
    dst_ [5] = src_ [5] ;
    dst_ [6] = src_ [6] ;
    dst_ [7] = src_ [7] ;
#else
    dst_ [0] = src_ [7] ;
    dst_ [1] = src_ [6] ;
    dst_ [2] = src_ [5] ;
    dst_ [3] = src_ [4] ;
    dst_ [4] = src_ [3] ;
    dst_ [5] = src_ [2] ;
    dst_ [6] = src_ [1] ;
    dst_ [7] = src_ [0] ;
#endif
}

/** ------------------------------------------------------------------
 ** @brief Host <-> big endian transformation for 4-bytes value
 **
 ** @param dst destination 4-byte buffer.
 ** @param src source 4-byte bufffer.
 ** @sa @ref host-arch-endianness.
 **/

VL_INLINE void
vl_swap_host_big_endianness_4 (void *dst, void* src)
{
  char *dst_ = (char*) dst ;
  char *src_ = (char*) src ;
#if defined(VL_ARCH_BIG_ENDIAN)
    dst_ [0] = src_ [0] ;
    dst_ [1] = src_ [1] ;
    dst_ [2] = src_ [2] ;
    dst_ [3] = src_ [3] ;
#else
    dst_ [0] = src_ [3] ;
    dst_ [1] = src_ [2] ;
    dst_ [2] = src_ [1] ;
    dst_ [3] = src_ [0] ;
#endif
}

/** ------------------------------------------------------------------
 ** @brief Host <-> big endian transformation for 2-bytes value
 **
 ** @param dst destination 2-byte buffer.
 ** @param src source 2-byte bufffer.
 ** @see @ref host-arch-endianness.
 **/

VL_INLINE void
vl_swap_host_big_endianness_2 (void *dst, void* src)
{
  char *dst_ = (char*) dst ;
  char *src_ = (char*) src ;
#if defined(VL_ARCH_BIG_ENDIAN)
    dst_ [0] = src_ [0] ;
    dst_ [1] = src_ [1] ;
#else
    dst_ [0] = src_ [1] ;
    dst_ [1] = src_ [0] ;
#endif
}

/* VL_HOST_H */
#endif
vlfeat/vl/covdet.c0000644000175000017500000022332712237004745013033 0ustar  dimadima/** @file covdet.c
 ** @brief Covariant feature detectors - Definition
 ** @author Karel Lenc
 ** @author Andrea Vedaldi
 ** @author Michal Perdoch
 **/

/*
 Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
 All rights reserved.

 This file is part of the VLFeat library and is made available under
 the terms of the BSD license (see the COPYING file).
 */

/**

@page covdet Covariant feature detectors
@auhtor Karel Lenc
@author Andrea Vedaldi
@author Michal Perdoch


@ref covdet.h implements a number of covariant feature detectors, based
on three cornerness measures (determinant of the Hessian, trace of the Hessian
(aka Difference of Gaussians, and Harris). It supprots affine adaptation,
orientation estimation, as well as Laplacian scale detection.

**/

#include "covdet.h"
#include 

/** @brief Reallocate buffer
 ** @param buffer
 ** @param bufferSize
 ** @param targetSize
 ** @return error code
 **/

static int
_vl_resize_buffer (void ** buffer, vl_size * bufferSize, vl_size targetSize) {
  void * newBuffer ;
  if (*buffer == NULL) {
    *buffer = vl_malloc(targetSize) ;
    if (*buffer) {
      *bufferSize = targetSize ;
      return VL_ERR_OK ;
    } else {
      *bufferSize = 0 ;
      return VL_ERR_ALLOC ;
    }
  }
  newBuffer = vl_realloc(*buffer, targetSize) ;
  if (newBuffer) {
    *buffer = newBuffer ;
    *bufferSize = targetSize ;
    return VL_ERR_OK ;
  } else {
    return VL_ERR_ALLOC ;
  }
}

/** @brief Enlarge buffer
 ** @param buffer
 ** @param bufferSize
 ** @param targetSize
 ** @return error code
 **/

static int
_vl_enlarge_buffer (void ** buffer, vl_size * bufferSize, vl_size targetSize) {
  if (*bufferSize >= targetSize) return VL_ERR_OK ;
  return _vl_resize_buffer(buffer,bufferSize,targetSize) ;
}

/* ---------------------------------------------------------------- */
/*                                            Finding local extrema */
/* ---------------------------------------------------------------- */

/* Todo: make this generally available in the library */

typedef struct _VlCovDetExtremum2
{
  vl_index xi ;
  vl_index yi ;
  float x ;
  float y ;
  float peakScore ;
  float edgeScore ;
} VlCovDetExtremum2 ;

typedef struct _VlCovDetExtremum3
{
  vl_index xi ;
  vl_index yi ;
  vl_index zi ;
  float x ;
  float y ;
  float z ;
  float peakScore ;
  float edgeScore ;
} VlCovDetExtremum3 ;

VL_EXPORT vl_size
vl_find_local_extrema_3 (vl_index ** extrema, vl_size * bufferSize,
                         float const * map,
                         vl_size width, vl_size height, vl_size depth,
                         double threshold) ;

VL_EXPORT vl_size
vl_find_local_extrema_2 (vl_index ** extrema, vl_size * bufferSize,
                         float const * map,
                         vl_size width, vl_size height,
                         double threshold) ;

VL_EXPORT vl_bool
vl_refine_local_extreum_3 (VlCovDetExtremum3 * refined,
                           float const * map,
                           vl_size width, vl_size height, vl_size depth,
                           vl_index x, vl_index y, vl_index z) ;

VL_EXPORT vl_bool
vl_refine_local_extreum_2 (VlCovDetExtremum2 * refined,
                           float const * map,
                           vl_size width, vl_size height,
                           vl_index x, vl_index y) ;

/** @brief Find extrema in 3D function
 ** @param extrema
 ** @param bufferSize
 ** @param map a 3D array representing the map.
 ** @param width of the map.
 ** @param height of the map.
 ** @param deepth of the map.
 ** @param threshold minumum extremum value.
 ** @return number of extrema found.
 **/

vl_size
vl_find_local_extrema_3 (vl_index ** extrema, vl_size * bufferSize,
                         float const * map,
                         vl_size width, vl_size height, vl_size depth,
                         double threshold)
{
  vl_index x, y, z ;
  vl_size const xo = 1 ;
  vl_size const yo = width ;
  vl_size const zo = width * height ;
  float const *pt = map + xo + yo + zo ;

  vl_size numExtrema = 0 ;
  vl_size requiredSize = 0 ;

#define CHECK_NEIGHBORS_3(v,CMP,SGN)     (\
v CMP ## = SGN threshold &&               \
v CMP *(pt + xo) &&                       \
v CMP *(pt - xo) &&                       \
v CMP *(pt + zo) &&                       \
v CMP *(pt - zo) &&                       \
v CMP *(pt + yo) &&                       \
v CMP *(pt - yo) &&                       \
\
v CMP *(pt + yo + xo) &&                  \
v CMP *(pt + yo - xo) &&                  \
v CMP *(pt - yo + xo) &&                  \
v CMP *(pt - yo - xo) &&                  \
\
v CMP *(pt + xo      + zo) &&             \
v CMP *(pt - xo      + zo) &&             \
v CMP *(pt + yo      + zo) &&             \
v CMP *(pt - yo      + zo) &&             \
v CMP *(pt + yo + xo + zo) &&             \
v CMP *(pt + yo - xo + zo) &&             \
v CMP *(pt - yo + xo + zo) &&             \
v CMP *(pt - yo - xo + zo) &&             \
\
v CMP *(pt + xo      - zo) &&             \
v CMP *(pt - xo      - zo) &&             \
v CMP *(pt + yo      - zo) &&             \
v CMP *(pt - yo      - zo) &&             \
v CMP *(pt + yo + xo - zo) &&             \
v CMP *(pt + yo - xo - zo) &&             \
v CMP *(pt - yo + xo - zo) &&             \
v CMP *(pt - yo - xo - zo) )

  for (z = 1 ; z < (signed)depth - 1 ; ++z) {
    for (y = 1 ; y < (signed)height - 1 ; ++y) {
      for (x = 1 ; x < (signed)width - 1 ; ++x) {
        float value = *pt ;
        if (CHECK_NEIGHBORS_3(value,>,+) || CHECK_NEIGHBORS_3(value,<,-)) {
          numExtrema ++ ;
          requiredSize += sizeof(vl_index) * 3 ;
          if (*bufferSize < requiredSize) {
            int err = _vl_resize_buffer((void**)extrema, bufferSize,
                                        requiredSize + 2000 * 3 * sizeof(vl_index)) ;
            if (err != VL_ERR_OK) abort() ;
          }
          (*extrema) [3 * (numExtrema - 1) + 0] = x ;
          (*extrema) [3 * (numExtrema - 1) + 1] = y ;
          (*extrema) [3 * (numExtrema - 1) + 2] = z ;
        }
        pt += xo ;
      }
      pt += 2*xo ;
    }
    pt += 2*yo ;
  }
  return numExtrema ;
}

/** @brief Find extrema in 3D function
 ** @param extrema
 ** @param bufferSize
 ** @param map a 3D array representing the map.
 ** @param width of the map.
 ** @param height of the map.
 ** @param deepth of the map.
 ** @param threshold minumum extremum value.
 ** @return number of extrema found.
 **/

vl_size
vl_find_local_extrema_2 (vl_index ** extrema, vl_size * bufferSize,
                         float const* map,
                         vl_size width, vl_size height,
                         double threshold)
{
  vl_index x, y ;
  vl_size const xo = 1 ;
  vl_size const yo = width ;
  float const *pt = map + xo + yo ;

  vl_size numExtrema = 0 ;
  vl_size requiredSize = 0 ;
#define CHECK_NEIGHBORS_2(v,CMP,SGN)     (\
v CMP ## = SGN threshold &&               \
v CMP *(pt + xo) &&                       \
v CMP *(pt - xo) &&                       \
v CMP *(pt + yo) &&                       \
v CMP *(pt - yo) &&                       \
\
v CMP *(pt + yo + xo) &&                  \
v CMP *(pt + yo - xo) &&                  \
v CMP *(pt - yo + xo) &&                  \
v CMP *(pt - yo - xo) )

  for (y = 1 ; y < (signed)height - 1 ; ++y) {
    for (x = 1 ; x < (signed)width - 1 ; ++x) {
      float value = *pt ;
      if (CHECK_NEIGHBORS_2(value,>,+) || CHECK_NEIGHBORS_2(value,<,-)) {
        numExtrema ++ ;
        requiredSize += sizeof(vl_index) * 2 ;
        if (*bufferSize < requiredSize) {
          int err = _vl_resize_buffer((void**)extrema, bufferSize,
                                      requiredSize + 2000 * 2 * sizeof(vl_index)) ;
          if (err != VL_ERR_OK) abort() ;
        }
        (*extrema) [2 * (numExtrema - 1) + 0] = x ;
        (*extrema) [2 * (numExtrema - 1) + 1] = y ;
      }
      pt += xo ;
    }
    pt += 2*xo ;
  }
  return numExtrema ;
}

/** @brief Refine extrema in 3D function
 ** @param refined refined extrema.
 ** @param map a 3D array representing the map.
 ** @param width of the map.
 ** @param height of the map.
 ** @param deepth of the map.
 ** @param x
 ** @param y
 ** @param z
 ** @return extrema refinement was stable.
 **/

VL_EXPORT vl_bool
vl_refine_local_extreum_3 (VlCovDetExtremum3 * refined,
                           float const * map,
                           vl_size width, vl_size height, vl_size depth,
                           vl_index x, vl_index y, vl_index z)
{
  vl_size const xo = 1 ;
  vl_size const yo = width ;
  vl_size const zo = width * height ;

  double Dx=0,Dy=0,Dz=0,Dxx=0,Dyy=0,Dzz=0,Dxy=0,Dxz=0,Dyz=0 ;
  double A [3*3], b [3] ;

#define at(dx,dy,dz) (*(pt + (dx)*xo + (dy)*yo + (dz)*zo))
#define Aat(i,j) (A[(i)+(j)*3])

  float const * pt ;
  vl_index dx = 0 ;
  vl_index dy = 0 ;
  /*vl_index dz = 0 ;*/
  vl_index iter ;
  int err ;

  assert (map) ;
  assert (1 <= x && x <= (signed)width - 2) ;
  assert (1 <= y && y <= (signed)height - 2) ;
  assert (1 <= z && z <= (signed)depth - 2) ;

  for (iter = 0 ; iter < 5 ; ++iter) {
    x += dx ;
    y += dy ;
    pt = map + x*xo + y*yo + z*zo ;

    /* compute the gradient */
    Dx = 0.5 * (at(+1,0,0) - at(-1,0,0)) ;
    Dy = 0.5 * (at(0,+1,0) - at(0,-1,0));
    Dz = 0.5 * (at(0,0,+1) - at(0,0,-1)) ;

    /* compute the Hessian */
    Dxx = (at(+1,0,0) + at(-1,0,0) - 2.0 * at(0,0,0)) ;
    Dyy = (at(0,+1,0) + at(0,-1,0) - 2.0 * at(0,0,0)) ;
    Dzz = (at(0,0,+1) + at(0,0,-1) - 2.0 * at(0,0,0)) ;

    Dxy = 0.25 * (at(+1,+1,0) + at(-1,-1,0) - at(-1,+1,0) - at(+1,-1,0)) ;
    Dxz = 0.25 * (at(+1,0,+1) + at(-1,0,-1) - at(-1,0,+1) - at(+1,0,-1)) ;
    Dyz = 0.25 * (at(0,+1,+1) + at(0,-1,-1) - at(0,-1,+1) - at(0,+1,-1)) ;

    /* solve linear system */
    Aat(0,0) = Dxx ;
    Aat(1,1) = Dyy ;
    Aat(2,2) = Dzz ;
    Aat(0,1) = Aat(1,0) = Dxy ;
    Aat(0,2) = Aat(2,0) = Dxz ;
    Aat(1,2) = Aat(2,1) = Dyz ;

    b[0] = - Dx ;
    b[1] = - Dy ;
    b[2] = - Dz ;

    err = vl_solve_linear_system_3(b, A, b) ;

    if (err != VL_ERR_OK) {
      b[0] = 0 ;
      b[1] = 0 ;
      b[2] = 0 ;
      break ;
    }

    /* Keep going if there is sufficient translation */

    dx = (b[0] > 0.6 && x < (signed)width - 2 ?  1 : 0)
    + (b[0] < -0.6 && x > 1 ? -1 : 0) ;

    dy = (b[1] > 0.6 && y < (signed)height - 2 ?  1 : 0)
    + (b[1] < -0.6 && y > 1 ? -1 : 0) ;

    if (dx == 0 && dy == 0) break ;
  }

  /* check threshold and other conditions */
  {
    double peakScore = at(0,0,0)
    + 0.5 * (Dx * b[0] + Dy * b[1] + Dz * b[2]) ;
    double alpha = (Dxx+Dyy)*(Dxx+Dyy) / (Dxx*Dyy - Dxy*Dxy) ;
    double edgeScore ;

    if (alpha < 0) {
      /* not an extremum */
      edgeScore = VL_INFINITY_D ;
    } else {
      edgeScore = (0.5*alpha - 1) + sqrt(VL_MAX(0.25*alpha - 1,0)*alpha) ;
    }

    refined->xi = x ;
    refined->yi = y ;
    refined->zi = z ;
    refined->x = x + b[0] ;
    refined->y = y + b[1] ;
    refined->z = z + b[2] ;
    refined->peakScore = peakScore ;
    refined->edgeScore = edgeScore ;

    return
    err == VL_ERR_OK &&
    vl_abs_d(b[0]) < 1.5 &&
    vl_abs_d(b[1]) < 1.5 &&
    vl_abs_d(b[2]) < 1.5 &&
    0 <= refined->x && refined->x <= (signed)width - 1 &&
    0 <= refined->y && refined->y <= (signed)height - 1 &&
    0 <= refined->z && refined->z <= (signed)depth - 1 ;
  }
#undef Aat
#undef at
}

/** @brief Refine extrema in 3D function
 ** @param refined refined extrema.
 ** @param map a 3D array representing the map.
 ** @param width of the map.
 ** @param height of the map.
 ** @param deepth of the map.
 ** @param x
 ** @param y
 ** @param z
 ** @return extrema refinement was stable.
 **/

VL_EXPORT vl_bool
vl_refine_local_extreum_2 (VlCovDetExtremum2 * refined,
                           float const * map,
                           vl_size width, vl_size height,
                           vl_index x, vl_index y)
{
  vl_size const xo = 1 ;
  vl_size const yo = width ;

  double Dx=0,Dy=0,Dxx=0,Dyy=0,Dxy=0;
  double A [2*2], b [2] ;

#define at(dx,dy) (*(pt + (dx)*xo + (dy)*yo ))
#define Aat(i,j) (A[(i)+(j)*2])

  float const * pt ;
  vl_index dx = 0 ;
  vl_index dy = 0 ;
  vl_index iter ;
  int err ;

  assert (map) ;
  assert (1 <= x && x <= (signed)width - 2) ;
  assert (1 <= y && y <= (signed)height - 2) ;

  for (iter = 0 ; iter < 5 ; ++iter) {
    x += dx ;
    y += dy ;
    pt = map + x*xo + y*yo  ;

    /* compute the gradient */
    Dx = 0.5 * (at(+1,0) - at(-1,0)) ;
    Dy = 0.5 * (at(0,+1) - at(0,-1));

    /* compute the Hessian */
    Dxx = (at(+1,0) + at(-1,0) - 2.0 * at(0,0)) ;
    Dyy = (at(0,+1) + at(0,-1) - 2.0 * at(0,0)) ;
    Dxy = 0.25 * (at(+1,+1) + at(-1,-1) - at(-1,+1) - at(+1,-1)) ;

    /* solve linear system */
    Aat(0,0) = Dxx ;
    Aat(1,1) = Dyy ;
    Aat(0,1) = Aat(1,0) = Dxy ;

    b[0] = - Dx ;
    b[1] = - Dy ;

    err = vl_solve_linear_system_2(b, A, b) ;

    if (err != VL_ERR_OK) {
      b[0] = 0 ;
      b[1] = 0 ;
      break ;
    }

    /* Keep going if there is sufficient translation */

    dx = (b[0] > 0.6 && x < (signed)width - 2 ?  1 : 0)
    + (b[0] < -0.6 && x > 1 ? -1 : 0) ;

    dy = (b[1] > 0.6 && y < (signed)height - 2 ?  1 : 0)
    + (b[1] < -0.6 && y > 1 ? -1 : 0) ;

    if (dx == 0 && dy == 0) break ;
  }

  /* check threshold and other conditions */
  {
    double peakScore = at(0,0) + 0.5 * (Dx * b[0] + Dy * b[1]) ;
    double alpha = (Dxx+Dyy)*(Dxx+Dyy) / (Dxx*Dyy - Dxy*Dxy) ;
    double edgeScore ;

    if (alpha < 0) {
      /* not an extremum */
      edgeScore = VL_INFINITY_D ;
    } else {
      edgeScore = (0.5*alpha - 1) + sqrt(VL_MAX(0.25*alpha - 1,0)*alpha) ;
    }

    refined->xi = x ;
    refined->yi = y ;
    refined->x = x + b[0] ;
    refined->y = y + b[1] ;
    refined->peakScore = peakScore ;
    refined->edgeScore = edgeScore ;

    return
    err == VL_ERR_OK &&
    vl_abs_d(b[0]) < 1.5 &&
    vl_abs_d(b[1]) < 1.5 &&
    0 <= refined->x && refined->x <= (signed)width - 1 &&
    0 <= refined->y && refined->y <= (signed)height - 1 ;
  }
#undef Aat
#undef at
}

/* ---------------------------------------------------------------- */
/*                                                Covarant detector */
/* ---------------------------------------------------------------- */

#define VL_COVDET_MAX_NUM_ORIENTATIONS 4
#define VL_COVDET_MAX_NUM_LAPLACIAN_SCALES 4
#define VL_COVDET_AA_PATCH_RESOLUTION 20
#define VL_COVDET_AA_MAX_NUM_ITERATIONS 15
#define VL_COVDET_OR_NUM_ORIENTATION_HISTOGAM_BINS 36
#define VL_COVDET_AA_RELATIVE_INTEGRATION_SIGMA 3
#define VL_COVDET_AA_RELATIVE_DERIVATIVE_SIGMA 1
#define VL_COVDET_AA_MAX_ANISOTROPY 5
#define VL_COVDET_AA_CONVERGENCE_THRESHOLD 1.001
#define VL_COVDET_AA_ACCURATE_SMOOTHING VL_FALSE
#define VL_COVDET_AA_PATCH_EXTENT (3*VL_COVDET_AA_RELATIVE_INTEGRATION_SIGMA)
#define VL_COVDET_OR_ADDITIONAL_PEAKS_RELATIVE_SIZE 0.8
#define VL_COVDET_LAP_NUM_LEVELS 10
#define VL_COVDET_LAP_PATCH_RESOLUTION 16
#define VL_COVDET_DOG_DEF_PEAK_THRESHOLD 0.01
#define VL_COVDET_DOG_DEF_EDGE_THRESHOLD 10.0
#define VL_COVDET_HARRIS_DEF_PEAK_THRESHOLD 0.000002
#define VL_COVDET_HARRIS_DEF_EDGE_THRESHOLD 10.0
#define VL_COVDET_HESSIAN_DEF_PEAK_THRESHOLD 0.003
#define VL_COVDET_HESSIAN_DEF_EDGE_THRESHOLD 10.0

/** @brief Covariant feature detector */
struct _VlCovDet
{
  VlScaleSpace *gss ;        /**< Gaussian scale space. */
  VlScaleSpace *css ;        /**< Cornerness scale space. */
  VlCovDetMethod method ;    /**< feature extraction method. */
  double peakThreshold ;     /**< peak threshold. */
  double edgeThreshold ;     /**< edge threshold. */
  vl_size octaveResolution ; /**< resolution of each octave. */
  vl_index firstOctave ;     /**< index of the first octave. */

  double nonExtremaSuppression ;
  vl_size numNonExtremaSuppressed ;

  VlCovDetFeature *features ;
  vl_size numFeatures ;
  vl_size numFeatureBufferSize ;

  float * patch ;
  vl_size patchBufferSize ;

  vl_bool transposed ;
  VlCovDetFeatureOrientation orientations [VL_COVDET_MAX_NUM_ORIENTATIONS] ;
  VlCovDetFeatureLaplacianScale scales [VL_COVDET_MAX_NUM_LAPLACIAN_SCALES] ;

  vl_bool aaAccurateSmoothing ;
  float aaPatch [(2*VL_COVDET_AA_PATCH_RESOLUTION+1)*(2*VL_COVDET_AA_PATCH_RESOLUTION+1)] ;
  float aaPatchX [(2*VL_COVDET_AA_PATCH_RESOLUTION+1)*(2*VL_COVDET_AA_PATCH_RESOLUTION+1)] ;
  float aaPatchY [(2*VL_COVDET_AA_PATCH_RESOLUTION+1)*(2*VL_COVDET_AA_PATCH_RESOLUTION+1)] ;
  float aaMask [(2*VL_COVDET_AA_PATCH_RESOLUTION+1)*(2*VL_COVDET_AA_PATCH_RESOLUTION+1)] ;

  float lapPatch [(2*VL_COVDET_LAP_PATCH_RESOLUTION+1)*(2*VL_COVDET_LAP_PATCH_RESOLUTION+1)] ;
  float laplacians [(2*VL_COVDET_LAP_PATCH_RESOLUTION+1)*(2*VL_COVDET_LAP_PATCH_RESOLUTION+1)*VL_COVDET_LAP_NUM_LEVELS] ;
  vl_size numFeaturesWithNumScales [VL_COVDET_MAX_NUM_LAPLACIAN_SCALES + 1] ;
}  ;

VlEnumerator vlCovdetMethods [VL_COVDET_METHOD_NUM] = {
  {"DoG" ,              (vl_index)VL_COVDET_METHOD_DOG               },
  {"Hessian",           (vl_index)VL_COVDET_METHOD_HESSIAN           },
  {"HessianLaplace",    (vl_index)VL_COVDET_METHOD_HESSIAN_LAPLACE   },
  {"HarrisLaplace",     (vl_index)VL_COVDET_METHOD_HARRIS_LAPLACE    },
  {"MultiscaleHessian", (vl_index)VL_COVDET_METHOD_MULTISCALE_HESSIAN},
  {"MultiscaleHarris",  (vl_index)VL_COVDET_METHOD_MULTISCALE_HARRIS },
  {0,                   0                                            }
} ;

/** @brief Create a new object instance
 ** @param method method for covariant feature detection.
 ** @return new covariant detector.
 **/

VlCovDet *
vl_covdet_new (VlCovDetMethod method)
{
  VlCovDet * self = vl_calloc(sizeof(VlCovDet),1) ;
  self->method = method ;
  self->octaveResolution = 3 ;
  self->firstOctave = -1 ;
  switch (self->method) {
    case VL_COVDET_METHOD_DOG :
      self->peakThreshold = VL_COVDET_DOG_DEF_PEAK_THRESHOLD ;
      self->edgeThreshold = VL_COVDET_DOG_DEF_EDGE_THRESHOLD ;
      break ;
    case VL_COVDET_METHOD_HARRIS_LAPLACE:
    case VL_COVDET_METHOD_MULTISCALE_HARRIS:
      self->peakThreshold = VL_COVDET_HARRIS_DEF_PEAK_THRESHOLD ;
      self->edgeThreshold = VL_COVDET_HARRIS_DEF_EDGE_THRESHOLD ;
      break ;
    case VL_COVDET_METHOD_HESSIAN :
    case VL_COVDET_METHOD_HESSIAN_LAPLACE:
    case VL_COVDET_METHOD_MULTISCALE_HESSIAN:
      self->peakThreshold = VL_COVDET_HESSIAN_DEF_PEAK_THRESHOLD ;
      self->edgeThreshold = VL_COVDET_HESSIAN_DEF_EDGE_THRESHOLD ;
      break;
    default:
      assert(0) ;
  }

  self->nonExtremaSuppression = 0.5 ;
  self->features = NULL ;
  self->numFeatures = 0 ;
  self->numFeatureBufferSize = 0 ;
  self->patch = NULL ;
  self->patchBufferSize = 0 ;
  self->transposed = VL_FALSE ;
  self->aaAccurateSmoothing = VL_COVDET_AA_ACCURATE_SMOOTHING ;

  {
    vl_index const w = VL_COVDET_AA_PATCH_RESOLUTION ;
    vl_index i,j ;
    double step = (2.0 * VL_COVDET_AA_PATCH_EXTENT) / (2*w+1) ;
    double sigma = VL_COVDET_AA_RELATIVE_INTEGRATION_SIGMA ;
    for (j = -w ; j <= w ; ++j) {
      for (i = -w ; i <= w ; ++i) {
        double dx = i*step/sigma ;
        double dy = j*step/sigma ;
        self->aaMask[(i+w) + (2*w+1)*(j+w)] = exp(-0.5*(dx*dx+dy*dy)) ;
      }
    }
  }

  {
    /*
     Covers one octave of Laplacian filters, from sigma=1 to sigma=2.
     The spatial sampling step is 0.5.
     */
    vl_index s ;
    for (s = 0 ; s < VL_COVDET_LAP_NUM_LEVELS ; ++s) {
      double sigmaLap = pow(2.0, -0.5 +
                            (double)s / (VL_COVDET_LAP_NUM_LEVELS - 1)) ;
      double const sigmaImage = 1.0 / sqrt(2.0) ;
      double const step = 0.5 * sigmaImage ;
      double const sigmaDelta = sqrt(sigmaLap*sigmaLap - sigmaImage*sigmaImage) ;
      vl_size const w = VL_COVDET_LAP_PATCH_RESOLUTION ;
      vl_size const num = 2 * w + 1  ;
      float * pt = self->laplacians + s * (num * num) ;

      memset(pt, 0, num * num * sizeof(float)) ;

#define at(x,y) pt[(x+w)+(y+w)*(2*w+1)]
      at(0,0) = - 4.0 ;
      at(-1,0) = 1.0 ;
      at(+1,0) = 1.0 ;
      at(0,1) = 1.0 ;
      at(0,-1) = 1.0 ;
#undef at

      vl_imsmooth_f(pt, num,
                    pt, num, num, num,
                    sigmaDelta / step, sigmaDelta / step) ;

#if 0
      {
        char name [200] ;
        snprintf(name, 200, "/tmp/%f-lap.pgm", sigmaDelta) ;
        vl_pgm_write_f(name, pt, num, num) ;
      }
#endif

    }
  }
  return self ;
}


/** @brief Reset object
 ** @param self object.
 **
 ** This function removes any buffered features and frees other
 ** internal buffers.
 **/

void
vl_covdet_reset (VlCovDet * self)
{
  if (self->features) {
    vl_free(self->features) ;
    self->features = NULL ;
  }
  if (self->css) {
    vl_scalespace_delete(self->css) ;
    self->css = NULL ;
  }
  if (self->gss) {
    vl_scalespace_delete(self->gss) ;
    self->gss = NULL ;
  }
}

/** @brief Delete object instance
 ** @param self object.
 **/

void
vl_covdet_delete (VlCovDet * self)
{
  vl_covdet_reset(self) ;
  if (self->patch) vl_free (self->patch) ;
  vl_free(self) ;
}

/** @brief Append a feature to the internal buffer.
 ** @param self object.
 ** @param feature a pointer to the feature to append.
 ** @return status.
 **
 ** The feature is copied. The function may fail with @c status
 ** equal to ::VL_ERR_ALLOC if there is insufficient memory.
 **/

int
vl_covdet_append_feature (VlCovDet * self, VlCovDetFeature const * feature)
{
  vl_size requiredSize ;
  assert(self) ;
  assert(feature) ;
  self->numFeatures ++ ;
  requiredSize = self->numFeatures * sizeof(VlCovDetFeature) ;
  if (requiredSize > self->numFeatureBufferSize) {
    int err = _vl_resize_buffer((void**)&self->features, &self->numFeatureBufferSize,
                                (self->numFeatures + 1000) * sizeof(VlCovDetFeature)) ;
    if (err) {
      self->numFeatures -- ;
      return err ;
    }
  }
  self->features[self->numFeatures - 1] = *feature ;
  return VL_ERR_OK ;
}

/* ---------------------------------------------------------------- */
/*                                              Process a new image */
/* ---------------------------------------------------------------- */

/** @brief Detect features in an image
 ** @param self object.
 ** @param image image to process.
 ** @param width image width.
 ** @param height image height.
 ** @return status.
 **
 ** @a width and @a height must be at least one pixel. The function
 ** fails by returing ::VL_ERR_ALLOC if the memory is insufficient.
 **/

int
vl_covdet_put_image (VlCovDet * self,
                     float const * image,
                     vl_size width, vl_size height)
{
  vl_size const minOctaveSize = 16 ;
  vl_index lastOctave ;
  vl_index octaveFirstSubdivision ;
  vl_index octaveLastSubdivision ;
  VlScaleSpaceGeometry geom = vl_scalespace_get_default_geometry(width,height) ;

  assert (self) ;
  assert (image) ;
  assert (width >= 1) ;
  assert (height >= 1) ;

  /* (minOctaveSize - 1) 2^lastOctave <= min(width,height) - 1 */
  lastOctave = vl_floor_d(vl_log2_d(VL_MIN((double)width-1,(double)height-1) / (minOctaveSize - 1))) ;

  if (self->method == VL_COVDET_METHOD_DOG) {
    octaveFirstSubdivision = -2 ;
    octaveLastSubdivision = self->octaveResolution ;
  } else if (self->method == VL_COVDET_METHOD_HESSIAN) {
    octaveFirstSubdivision = -1 ;
    octaveLastSubdivision = self->octaveResolution ;
  } else {
    octaveFirstSubdivision = 0 ;
    octaveLastSubdivision = self->octaveResolution - 1 ;
  }

  geom.width = width ;
  geom.height = height ;
  geom.firstOctave = self->firstOctave ;
  geom.lastOctave = lastOctave ;
  geom.octaveResolution = self->octaveResolution ;
  geom.octaveFirstSubdivision = octaveFirstSubdivision ;
  geom.octaveLastSubdivision = octaveLastSubdivision ;

  if (self->gss == NULL ||
      ! vl_scalespacegeometry_is_equal (geom,
                                        vl_scalespace_get_geometry(self->gss)))
  {
    if (self->gss) vl_scalespace_delete(self->gss) ;
    self->gss = vl_scalespace_new_with_geometry(geom) ;
    if (self->gss == NULL) return VL_ERR_ALLOC ;
  }
  vl_scalespace_put_image(self->gss, image) ;
  return VL_ERR_OK ;
}

/* ---------------------------------------------------------------- */
/*                                              Cornerness measures */
/* ---------------------------------------------------------------- */

/** @brief Scaled derminant of the Hessian filter
 ** @param hessian output image.
 ** @param image input image.
 ** @param width image width.
 ** @param height image height.
 ** @param step image sampling step (pixel size).
 ** @param sigma Gaussian smoothing of the input image.
 **/

static void
_vl_det_hessian_response (float * hessian,
                          float const * image,
                          vl_size width, vl_size height,
                          double step, double sigma)
{
  float factor = (float) pow(sigma/step, 4.0) ;
  vl_index const xo = 1 ; /* x-stride */
  vl_index const yo = width;  /* y-stride */
  vl_size r, c;

  float p11, p12, p13, p21, p22, p23, p31, p32, p33;

  /* setup input pointer to be centered at 0,1 */
  float const *in = image + yo ;

  /* setup output pointer to be centered at 1,1 */
  float *out = hessian + xo + yo;

  /* move 3x3 window and convolve */
  for (r = 1; r < height - 1; ++r)
  {
    /* fill in shift registers at the beginning of the row */
    p11 = in[-yo]; p12 = in[xo - yo];
    p21 = in[  0]; p22 = in[xo     ];
    p31 = in[+yo]; p32 = in[xo + yo];
    /* setup input pointer to (2,1) of the 3x3 square */
    in += 2;
    for (c = 1; c < width - 1; ++c)
    {
      float Lxx, Lyy, Lxy;
      /* fetch remaining values (last column) */
      p13 = in[-yo]; p23 = *in; p33 = in[+yo];

      /* Compute 3x3 Hessian values from pixel differences. */
      Lxx = (-p21 + 2*p22 - p23);
      Lyy = (-p12 + 2*p22 - p32);
      Lxy = ((p11 - p31 - p13 + p33)/4.0f);

      /* normalize and write out */
      *out = (Lxx * Lyy - Lxy * Lxy) * factor ;

      /* move window */
      p11=p12; p12=p13;
      p21=p22; p22=p23;
      p31=p32; p32=p33;

      /* move input/output pointers */
      in++; out++;
    }
    out += 2;
  }

  /* Copy the computed values to borders */
  in = hessian + yo + xo ;
  out = hessian + xo ;

  /* Top row without corners */
  memcpy(out, in, (width - 2)*sizeof(float));
  out--;
  in -= yo;

  /* Left border columns without last row */
  for (r = 0; r < height - 1; r++){
    *out = *in;
    *(out + yo - 1) = *(in + yo - 3);
    in += yo;
    out += yo;
  }

  /* Bottom corners */
  in -= yo;
  *out = *in;
  *(out + yo - 1) = *(in + yo - 3);

  /* Bottom row without corners */
  out++;
  memcpy(out, in, (width - 2)*sizeof(float));
}

/** @brief Scale-normalised Harris response
 ** @param harris output image.
 ** @param image input image.
 ** @param width image width.
 ** @param height image height.
 ** @param step image sampling step (pixel size).
 ** @param sigma Gaussian smoothing of the input image.
 ** @param sigmaI integration scale.
 ** @param alpha factor in the definition of the Harris score.
 **/

static void
_vl_harris_response (float * harris,
                     float const * image,
                     vl_size width, vl_size height,
                     double step, double sigma,
                     double sigmaI, double alpha)
{
  float factor = (float) pow(sigma/step, 4.0) ;
  vl_index k ;

  float * LxLx ;
  float * LyLy ;
  float * LxLy ;

  LxLx = vl_malloc(sizeof(float) * width * height) ;
  LyLy = vl_malloc(sizeof(float) * width * height) ;
  LxLy = vl_malloc(sizeof(float) * width * height) ;

  vl_imgradient_f (LxLx, LyLy, 1, width, image, width, height, width) ;

  for (k = 0 ; k < (signed)(width * height) ; ++k) {
    float dx = LxLx[k] ;
    float dy = LyLy[k] ;
    LxLx[k] = dx*dx ;
    LyLy[k] = dy*dy ;
    LxLy[k] = dx*dy ;
  }

  vl_imsmooth_f(LxLx, width, LxLx, width, height, width,
                sigmaI / step, sigmaI / step) ;

  vl_imsmooth_f(LyLy, width, LyLy, width, height, width,
                sigmaI / step, sigmaI / step) ;

  vl_imsmooth_f(LxLy, width, LxLy, width, height, width,
                sigmaI / step, sigmaI / step) ;

  for (k = 0 ; k < (signed)(width * height) ; ++k) {
    float a = LxLx[k] ;
    float b = LyLy[k] ;
    float c = LxLy[k] ;

    float determinant = a * b - c * c ;
    float trace = a + b ;

    harris[k] = factor * (determinant - alpha * (trace * trace)) ;
  }

  vl_free(LxLy) ;
  vl_free(LyLy) ;
  vl_free(LxLx) ;
}

/** @brief Difference of Gaussian
 ** @param dog output image.
 ** @param level1 input image at the smaller Gaussian scale.
 ** @param level2 input image at the larger Gaussian scale.
 ** @param width image width.
 ** @param height image height.
 **/

static void
_vl_dog_response (float * dog,
                  float const * level1,
                  float const * level2,
                  vl_size width, vl_size height)
{
  vl_index k ;
  for (k = 0 ; k < (signed)(width*height) ; ++k) {
    dog[k] = level2[k] - level1[k] ;
  }
}

/* ---------------------------------------------------------------- */
/*                                                  Detect features */
/* ---------------------------------------------------------------- */

/** @brief Detect scale-space features
 ** @param method
 ** @return new covariant detector.
 **/

void
vl_covdet_detect (VlCovDet * self)
{
  VlScaleSpaceGeometry geom = vl_scalespace_get_geometry(self->gss) ;
  VlScaleSpaceGeometry cgeom ;
  float * levelxx = NULL ;
  float * levelyy = NULL ;
  float * levelxy = NULL ;
  vl_index o, s ;

  assert (self) ;
  assert (self->gss) ;

  /* clear previous detections if any */
  self->numFeatures = 0 ;

  /* prepare buffers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
  cgeom = geom ;
  if (self->method == VL_COVDET_METHOD_DOG) {
    cgeom.octaveFirstSubdivision = -1 ;
  }
  if (!self->css ||
      !vl_scalespacegeometry_is_equal(cgeom,
                                      vl_scalespace_get_geometry(self->css)))
  {
    if (self->css) vl_scalespace_delete(self->css) ;
    self->css = vl_scalespace_new_with_geometry(cgeom) ;
  }
  if (self->method == VL_COVDET_METHOD_HARRIS_LAPLACE ||
      self->method == VL_COVDET_METHOD_MULTISCALE_HARRIS) {
    VlScaleSpaceOctaveGeometry oct = vl_scalespace_get_octave_geometry(self->gss, geom.firstOctave) ;
    levelxx = vl_malloc(oct.width * oct.height * sizeof(float)) ;
    levelyy = vl_malloc(oct.width * oct.height * sizeof(float)) ;
    levelxy = vl_malloc(oct.width * oct.height * sizeof(float)) ;
  }

  /* compute cornerness ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
  for (o = cgeom.firstOctave ; o <= cgeom.lastOctave ; ++o) {
    VlScaleSpaceOctaveGeometry oct = vl_scalespace_get_octave_geometry(self->css, o) ;

    for (s = cgeom.octaveFirstSubdivision ; s <= cgeom.octaveLastSubdivision ; ++s) {
      float * level = vl_scalespace_get_level(self->gss, o, s) ;
      float * clevel = vl_scalespace_get_level(self->css, o, s) ;
      double sigma = vl_scalespace_get_level_sigma(self->css, o, s) ;
      switch (self->method) {
        case VL_COVDET_METHOD_DOG:
          _vl_dog_response(clevel, level,
                           vl_scalespace_get_level(self->gss, o, s - 1),
                           oct.width, oct.height) ;
          break ;

        case VL_COVDET_METHOD_HARRIS_LAPLACE:
        case VL_COVDET_METHOD_MULTISCALE_HARRIS:
          _vl_harris_response(clevel,
                              level, oct.width, oct.height, oct.step,
                              sigma, 1.4 * sigma, 0.05) ;
          break ;

        case VL_COVDET_METHOD_HESSIAN:
        case VL_COVDET_METHOD_HESSIAN_LAPLACE:
        case VL_COVDET_METHOD_MULTISCALE_HESSIAN:
          _vl_det_hessian_response(clevel, level, oct.width, oct.height, oct.step, sigma) ;
          break ;

        default:
          assert(0) ;
      }
    }
  }

  /* find and refine local maxima ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
  {
    vl_index * extrema = NULL ;
    vl_size extremaBufferSize = 0 ;
    vl_size numExtrema ;
    vl_size index ;
    for (o = cgeom.firstOctave ; o <= cgeom.lastOctave ; ++o) {
      VlScaleSpaceOctaveGeometry octgeom = vl_scalespace_get_octave_geometry(self->css, o) ;
      double step = octgeom.step ;
      vl_size width = octgeom.width ;
      vl_size height = octgeom.height ;
      vl_size depth = cgeom.octaveLastSubdivision - cgeom.octaveFirstSubdivision + 1 ;

      switch (self->method) {
        case VL_COVDET_METHOD_DOG:
        case VL_COVDET_METHOD_HESSIAN:
        {
          /* scale-space extrema */
          float const * octave =
          vl_scalespace_get_level(self->css, o, cgeom.octaveFirstSubdivision) ;
          numExtrema = vl_find_local_extrema_3(&extrema, &extremaBufferSize,
                                               octave, width, height, depth,
                                               0.8 * self->peakThreshold);
          for (index = 0 ; index < numExtrema ; ++index) {
            VlCovDetExtremum3 refined ;
            VlCovDetFeature feature ;
            vl_bool ok ;
            memset(&feature, 0, sizeof(feature)) ;
            ok = vl_refine_local_extreum_3(&refined,
                                           octave, width, height, depth,
                                           extrema[3*index+0],
                                           extrema[3*index+1],
                                           extrema[3*index+2]) ;
            ok &= fabs(refined.peakScore) > self->peakThreshold ;
            ok &= refined.edgeScore < self->edgeThreshold ;
            if (ok) {
              double sigma = cgeom.baseScale *
              pow(2.0, o + (refined.z - cgeom.octaveFirstSubdivision)
                  / cgeom.octaveResolution) ;
              feature.frame.x = refined.x * step ;
              feature.frame.y = refined.y * step ;
              feature.frame.a11 = sigma ;
              feature.frame.a12 = 0.0 ;
              feature.frame.a21 = 0.0 ;
              feature.frame.a22 = sigma ;
              feature.peakScore = refined.peakScore ;
              feature.edgeScore = refined.edgeScore ;
              vl_covdet_append_feature(self, &feature) ;
            }
          }
          break ;
        }

        default:
        {
          for (s = cgeom.octaveFirstSubdivision ; s < cgeom.octaveLastSubdivision ; ++s) {
            /* space extrema */
            float const * level = vl_scalespace_get_level(self->css,o,s) ;
            numExtrema = vl_find_local_extrema_2(&extrema, &extremaBufferSize,
                                                 level,
                                                 width, height,
                                                 0.8 * self->peakThreshold);
            for (index = 0 ; index < numExtrema ; ++index) {
              VlCovDetExtremum2 refined ;
              VlCovDetFeature feature ;
              vl_bool ok ;
              memset(&feature, 0, sizeof(feature)) ;
              ok = vl_refine_local_extreum_2(&refined,
                                             level, width, height,
                                             extrema[2*index+0],
                                             extrema[2*index+1]);
              ok &= fabs(refined.peakScore) > self->peakThreshold ;
              ok &= refined.edgeScore < self->edgeThreshold ;
              if (ok) {
                double sigma = cgeom.baseScale *
                pow(2.0, o + (double)s / cgeom.octaveResolution) ;
                feature.frame.x = refined.x * step ;
                feature.frame.y = refined.y * step ;
                feature.frame.a11 = sigma ;
                feature.frame.a12 = 0.0 ;
                feature.frame.a21 = 0.0 ;
                feature.frame.a22 = sigma ;
                feature.peakScore = refined.peakScore ;
                feature.edgeScore = refined.edgeScore ;
                vl_covdet_append_feature(self, &feature) ;
              }
            }
          }
          break ;
        }
      }
    } /* next octave */

    if (extrema) { vl_free(extrema) ; extrema = 0 ; }
  }

  /* Laplacian scale selection for certain methods */
  switch (self->method) {
    case VL_COVDET_METHOD_HARRIS_LAPLACE :
    case VL_COVDET_METHOD_HESSIAN_LAPLACE :
      vl_covdet_extract_laplacian_scales (self) ;
      break ;
    default:
      break ;
  }

  if (self->nonExtremaSuppression) {
    vl_index i, j ;
    double tol = self->nonExtremaSuppression ;
    self->numNonExtremaSuppressed = 0 ;
    for (i = 0 ; i < (signed)self->numFeatures ; ++i) {
      double x = self->features[i].frame.x ;
      double y = self->features[i].frame.y ;
      double sigma = self->features[i].frame.a11 ;
      double score = self->features[i].peakScore ;

      for (j = 0 ; j < (signed)self->numFeatures ; ++j) {
        double dx_ = self->features[j].frame.x - x ;
        double dy_ = self->features[j].frame.y - y ;
        double sigma_ = self->features[j].frame.a11 ;
        double score_ = self->features[j].peakScore ;
        if (score_ == 0) continue ;
        if (sigma < (1+tol) * sigma_ &&
            sigma_ < (1+tol) * sigma &&
            vl_abs_d(dx_) < tol * sigma &&
            vl_abs_d(dy_) < tol * sigma &&
            vl_abs_d(score) > vl_abs_d(score_)) {
          self->features[j].peakScore = 0 ;
          self->numNonExtremaSuppressed ++ ;
        }
      }
    }
    j = 0 ;
    for (i = 0 ; i < (signed)self->numFeatures ; ++i) {
      VlCovDetFeature feature = self->features[i] ;
      if (self->features[i].peakScore != 0) {
        self->features[j++] = feature ;
      }
    }
    self->numFeatures = j ;
  }

  if (levelxx) vl_free(levelxx) ;
  if (levelyy) vl_free(levelyy) ;
  if (levelxy) vl_free(levelxy) ;
}

/* ---------------------------------------------------------------- */
/*                                                  Extract patches */
/* ---------------------------------------------------------------- */

/** @brief Helper for extracting patches
 ** @param self object.
 ** @param sigma1 actual patch smoothing along the first axis (out).
 ** @param sigma2 actual patch smoothing along the second axis (out).
 ** @param patch buffer.
 ** @param resolution patch resolution.
 ** @param extent patch extent.
 ** @param sigma desired smoothing in the patch frame.
 ** @param A linear transfomration from patch to image.
 ** @param T translation from patch to image.
 ** @param d1 first singular value @a A.
 ** @param d2 second singular value of @a A.
 **/

vl_bool
vl_covdet_extract_patch_helper (VlCovDet * self,
                                double * sigma1,
                                double * sigma2,
                                float * patch,
                                vl_size resolution,
                                double extent,
                                double sigma,
                                double (A_) [4],
                                double (T_) [2],
                                double d1, double d2)
{
  vl_index o, s ;
  double factor ;
  double sigma_ ;
  float const * level ;
  vl_size width, height ;
  double step ;

  double A [4] = {A_[0], A_[1], A_[2], A_[3]} ;
  double T [2] = {T_[0], T_[1]} ;

  VlScaleSpaceGeometry geom = vl_scalespace_get_geometry(self->gss) ;
  VlScaleSpaceOctaveGeometry oct ;

  /* Starting from a pre-smoothed image at scale sigma_
     because of the mapping A the resulting smoothing in
     the warped patch is S, where

        sigma_^2 I = A S A',

        S = sigma_^2 inv(A) inv(A)' = sigma_^2 V D^-2 V',

        A = U D V'.

     Thus we rotate A by V to obtain an axis-aligned smoothing:

        A = U*D,

        S = sigma_^2 D^-2.

     Then we search the scale-space for the best sigma_ such
     that the target smoothing is approximated from below:

        max sigma_(o,s) :    simga_(o,s) factor <= sigma,
        factor = max{abs(D11), abs(D22)}.
   */


  /*
   Determine the best level (o,s) such that sigma_(o,s) factor <= sigma.
   This can be obtained by scanning octaves from smalles to largest
   and stopping when no level in the octave satisfies the relation.

   Given the range of octave availables, do the best you can.
   */

  factor = 1.0 / VL_MIN(d1, d2) ;

  for (o = geom.firstOctave + 1 ; o <= geom.lastOctave ; ++o) {
    s = vl_floor_d(vl_log2_d(sigma / (factor * geom.baseScale)) - o) ;
    s = VL_MAX(s, geom.octaveFirstSubdivision) ;
    s = VL_MIN(s, geom.octaveLastSubdivision) ;
    sigma_ = geom.baseScale * pow(2.0, o + (double)s / geom.octaveResolution) ;
    /*VL_PRINTF(".. %d D=%g %g; sigma_=%g factor*sigma_=%g\n", o, d1, d2, sigma_, factor* sigma_) ;*/
    if (factor * sigma_ > sigma) {
      o -- ;
      break ;
    }
  }
  o = VL_MIN(o, geom.lastOctave) ;
  s = vl_floor_d(vl_log2_d(sigma / (factor * geom.baseScale)) - o) ;
  s = VL_MAX(s, geom.octaveFirstSubdivision) ;
  s = VL_MIN(s, geom.octaveLastSubdivision) ;
  sigma_ = geom.baseScale * pow(2.0, o + (double)s / geom.octaveResolution) ;
  if (sigma1) *sigma1 = sigma_ / d1 ;
  if (sigma2) *sigma2 = sigma_ / d2 ;

  /*VL_PRINTF("%d %d %g %g %g %g\n", o, s, factor, sigma_, factor * sigma_, sigma) ;*/

  /*
   Now the scale space level to be used for this warping has been
   determined.

   If the patch is partially or completely out of the image boundary,
   create a padded copy of the required region first.
   */

  level = vl_scalespace_get_level(self->gss, o, s) ;
  oct = vl_scalespace_get_octave_geometry(self->gss, o) ;
  width = oct.width ;
  height = oct.height ;
  step = oct.step ;

  A[0] /= step ;
  A[1] /= step ;
  A[2] /= step ;
  A[3] /= step ;
  T[0] /= step ;
  T[1] /= step ;

  {
    /*
     Warp the patch domain [x0hat,y0hat,x1hat,y1hat] to the image domain/
     Obtain a box [x0,y0,x1,y1] enclosing that wrapped box, and then
     an integer vertexes version [x0i, y0i, x1i, y1i], making room
     for one pixel at the boundaty to simplify bilinear interpolation
     later on.
     */
    vl_index x0i, y0i, x1i, y1i ;
    double x0 = +VL_INFINITY_D ;
    double x1 = -VL_INFINITY_D ;
    double y0 = +VL_INFINITY_D ;
    double y1 = -VL_INFINITY_D ;
    double boxx [4] = {extent, extent, -extent, -extent} ;
    double boxy [4] = {-extent, extent, extent, -extent} ;
    int i ;
    for (i = 0 ; i < 4 ; ++i) {
      double x = A[0] * boxx[i] + A[2] * boxy[i] + T[0] ;
      double y = A[1] * boxx[i] + A[3] * boxy[i] + T[1] ;
      x0 = VL_MIN(x0, x) ;
      x1 = VL_MAX(x1, x) ;
      y0 = VL_MIN(y0, y) ;
      y1 = VL_MAX(y1, y) ;
    }

    /* Leave one pixel border for bilinear interpolation. */
    x0i = floor(x0) - 1 ;
    y0i = floor(y0) - 1 ;
    x1i = ceil(x1) + 1 ;
    y1i = ceil(y1) + 1 ;

    /*
     If the box [x0i,y0i,x1i,y1i] is not fully contained in the
     image domain, then create a copy of this region by padding
     the image. The image is extended by continuity.
     */

    if (x0i < 0 || x1i > (signed)width-1 ||
        y0i < 0 || y1i > (signed)height-1) {
      vl_index xi, yi ;

      /* compute the amount of l,r,t,b padding needed to complete the patch */
      vl_index padx0 = VL_MAX(0, - x0i) ;
      vl_index pady0 = VL_MAX(0, - y0i) ;
      vl_index padx1 = VL_MAX(0, x1i - ((signed)width - 1)) ;
      vl_index pady1 = VL_MAX(0, y1i - ((signed)height - 1)) ;

      /* make enough room for the patch */
      vl_index patchWidth = x1i - x0i + 1 ;
      vl_index patchHeight = y1i - y0i + 1 ;
      vl_size patchBufferSize = patchWidth * patchHeight * sizeof(float) ;
      if (patchBufferSize > self->patchBufferSize) {
        int err = _vl_resize_buffer((void**)&self->patch, &self->patchBufferSize, patchBufferSize) ;
        if (err) return vl_set_last_error(VL_ERR_ALLOC, NULL) ;
      }

      if (pady0 < patchHeight - pady1) {
        /* start by filling the central horizontal band */
        for (yi = y0i + pady0 ; yi < y0i + patchHeight - pady1 ; ++ yi) {
          float *dst = self->patch + (yi - y0i) * patchWidth ;
          float const *src = level + yi * width + VL_MIN(VL_MAX(0, x0i),(signed)width-1) ;
          for (xi = x0i ; xi < x0i + padx0 ; ++xi) *dst++ = *src ;
          for ( ; xi < x0i + patchWidth - padx1 - 2 ; ++xi) *dst++ = *src++ ;
          for ( ; xi < x0i + patchWidth ; ++xi) *dst++ = *src ;
        }
        /* now extend the central band up and down */
        for (yi = 0 ; yi < pady0 ; ++yi) {
          memcpy(self->patch + yi * patchWidth,
                 self->patch + pady0 * patchWidth,
                 patchWidth * sizeof(float)) ;
        }
        for (yi = patchHeight - pady1 ; yi < patchHeight ; ++yi) {
          memcpy(self->patch + yi * patchWidth,
                 self->patch + (patchHeight - pady1 - 1) * patchWidth,
                 patchWidth * sizeof(float)) ;
        }
      } else {
        /* should be handled better! */
        memset(self->patch, 0, self->patchBufferSize) ;
      }
#if 0
      {
        char name [200] ;
        snprintf(name, 200, "/tmp/%20.0f-ext.pgm", 1e10*vl_get_cpu_time()) ;
        vl_pgm_write_f(name, patch, patchWidth, patchWidth) ;
      }
#endif

      level = self->patch ;
      width = patchWidth ;
      height = patchHeight ;
      T[0] -= x0i ;
      T[1] -= y0i ;
    }
  }

  /*
   Resample by using bilinear interpolation.
   */
  {
    float * pt = patch ;
    double yhat = -extent ;
    vl_index xxi ;
    vl_index yyi ;
    double stephat = extent / resolution ;

    for (yyi = 0 ; yyi < 2 * (signed)resolution + 1 ; ++yyi) {
      double xhat = -extent ;
      double rx = A[2] * yhat + T[0] ;
      double ry = A[3] * yhat + T[1] ;
      for (xxi = 0 ; xxi < 2 * (signed)resolution + 1 ; ++xxi) {
        double x = A[0] * xhat + rx ;
        double y = A[1] * xhat + ry ;
        vl_index xi = vl_floor_d(x) ;
        vl_index yi = vl_floor_d(y) ;
        double i00 = level[yi * width + xi] ;
        double i10 = level[yi * width + xi + 1] ;
        double i01 = level[(yi + 1) * width + xi] ;
        double i11 = level[(yi + 1) * width + xi + 1] ;
        double wx = x - xi ;
        double wy = y - yi ;

        assert(xi >= 0 && xi <= (signed)width - 1) ;
        assert(yi >= 0 && yi <= (signed)height - 1) ;

        *pt++ =
        (1.0 - wy) * ((1.0 - wx) * i00 + wx * i10) +
        wy * ((1.0 - wx) * i01 + wx * i11) ;

        xhat += stephat ;
      }
      yhat += stephat ;
    }
  }
#if 0
    {
      char name [200] ;
      snprintf(name, 200, "/tmp/%20.0f.pgm", 1e10*vl_get_cpu_time()) ;
      vl_pgm_write_f(name, patch, 2*resolution+1, 2*resolution+1) ;
    }
#endif
  return VL_ERR_OK ;
}

/** @brief Helper for extracting patches
 ** @param self object.
 ** @param patch buffer.
 ** @param resolution patch resolution.
 ** @param extent patch extent.
 ** @param sigma desired smoothing in the patch frame.
 ** @param frame feature frame.
 **
 ** The function considers a patch of extent [-extent,extent]
 ** on each side, with a side counting 2*resolution+1 pixels.
 ** In attempts to extract from the scale space a patch
 ** baed on the affine warping specified by @a frame in such a way
 ** that the resulting smoothing of the image is @a sigma (in the
 ** patch frame).
 **
 ** The transformation is specified by the matrices @c A and @c T
 ** embedded in the feature @a frame. Note that this transformation maps
 ** pixels from the patch frame to the image frame.
 **/

vl_bool
vl_covdet_extract_patch_for_frame (VlCovDet * self,
                                   float * patch,
                                   vl_size resolution,
                                   double extent,
                                   double sigma,
                                   VlFrameOrientedEllipse frame)
{
  double A[2*2] = {frame.a11, frame.a21, frame.a12, frame.a22} ;
  double T[2] = {frame.x, frame.y} ;
  double D[4], U[4], V[4] ;

  vl_svd2(D, U, V, A) ;

  return vl_covdet_extract_patch_helper
  (self, NULL, NULL, patch, resolution, extent, sigma, A, T, D[0], D[3]) ;
}

/* ---------------------------------------------------------------- */
/*                                                     Affine shape */
/* ---------------------------------------------------------------- */

/** @brief Extract the affine shape for a feature frame
 ** @param self object.
 ** @param adapted the shape-adapted frame.
 ** @param frame the input frame.
 ** @return ::VL_ERR_OK if affine adaptation is successful.
 **
 ** This function may fail if adaptation is unsuccessful or if
 ** memory is insufficient.
 **/

int
vl_covdet_extract_affine_shape_for_frame (VlCovDet * self,
                                          VlFrameOrientedEllipse * adapted,
                                          VlFrameOrientedEllipse frame)
{
  vl_index iter = 0 ;

  double A [2*2] = {frame.a11, frame.a21, frame.a12, frame.a22} ;
  double T [2] = {frame.x, frame.y} ;
  double U [2*2] ;
  double V [2*2] ;
  double D [2*2] ;
  double M [2*2] ;
  double P [2*2] ;
  double P_ [2*2] ;
  double Q [2*2] ;
  double sigma1, sigma2 ;
  double sigmaD = VL_COVDET_AA_RELATIVE_DERIVATIVE_SIGMA ;
  double factor ;
  double anisotropy ;
  double referenceScale ;
  vl_size const resolution = VL_COVDET_AA_PATCH_RESOLUTION ;
  vl_size const side = 2*VL_COVDET_AA_PATCH_RESOLUTION + 1 ;
  double const extent = VL_COVDET_AA_PATCH_EXTENT ;


  *adapted = frame ;

  while (1) {
    double lxx = 0, lxy = 0, lyy = 0 ;
    vl_index k ;
    int err ;

    /* A = U D V' */
    vl_svd2(D, U, V, A) ;
    anisotropy = VL_MAX(D[0]/D[3], D[3]/D[0]) ;

    /* VL_PRINTF("anisot: %g\n", anisotropy); */

    if (anisotropy > VL_COVDET_AA_MAX_ANISOTROPY) {
      /* diverged, give up with current solution */
      break ;
    }

    /* make sure that the smallest singluar value stays fixed
       after the first iteration */
    if (iter == 0) {
      referenceScale = VL_MIN(D[0], D[3]) ;
      factor = 1.0 ;
    } else {
      factor = referenceScale / VL_MIN(D[0],D[3]) ;
    }

    D[0] *= factor ;
    D[3] *= factor ;

    A[0] = U[0] * D[0] ;
    A[1] = U[1] * D[0] ;
    A[2] = U[2] * D[3] ;
    A[3] = U[3] * D[3] ;

    adapted->a11 = A[0] ;
    adapted->a21 = A[1] ;
    adapted->a12 = A[2] ;
    adapted->a22 = A[3] ;

    if (++iter >= VL_COVDET_AA_MAX_NUM_ITERATIONS) break ;

    err = vl_covdet_extract_patch_helper(self,
                                         &sigma1, &sigma2,
                                         self->aaPatch,
                                         resolution,
                                         extent,
                                         sigmaD,
                                         A, T, D[0], D[3]) ;
    if (err) return err ;

    if (self->aaAccurateSmoothing ) {
      double deltaSigma1 = sqrt(VL_MAX(sigmaD*sigmaD - sigma1*sigma1,0)) ;
      double deltaSigma2 = sqrt(VL_MAX(sigmaD*sigmaD - sigma2*sigma2,0)) ;
      double stephat = extent / resolution ;
      vl_imsmooth_f(self->aaPatch, side,
                    self->aaPatch, side, side, side,
                    deltaSigma1 / stephat, deltaSigma2 / stephat) ;
    }

    /* compute second moment matrix */
    vl_imgradient_f (self->aaPatchX, self->aaPatchY, 1, side,
                     self->aaPatch, side, side, side) ;

    for (k = 0 ; k < (signed)(side*side) ; ++k) {
      double lx = self->aaPatchX[k] ;
      double ly = self->aaPatchY[k] ;
      lxx += lx * lx * self->aaMask[k] ;
      lyy += ly * ly * self->aaMask[k] ;
      lxy += lx * ly * self->aaMask[k] ;
    }
    M[0] = lxx ;
    M[1] = lxy ;
    M[2] = lxy ;
    M[3] = lyy ;

    if (lxx == 0 || lyy == 0) {
      *adapted = frame ;
      break ;
    }

    /* decompose M = P * Q * P' */
    vl_svd2 (Q, P, P_, M) ;

    /*
     Setting A <- A * dA results in M to change approximatively as

     M --> dA'  M dA = dA' P Q P dA

     To make this proportional to the identity, we set

     dA ~= P Q^1/2

     we also make it so the smallest singular value of A is unchanged.
     */

    if (Q[3]/Q[0] < VL_COVDET_AA_CONVERGENCE_THRESHOLD &&
        Q[0]/Q[3] < VL_COVDET_AA_CONVERGENCE_THRESHOLD) {
      break ;
    }

    {
      double Ap [4] ;
      double q0 = sqrt(Q[0]) ;
      double q1 = sqrt(Q[3]) ;
      Ap[0] = (A[0] * P[0] + A[2] * P[1]) / q0 ;
      Ap[1] = (A[1] * P[0] + A[3] * P[1]) / q0 ;
      Ap[2] = (A[0] * P[2] + A[2] * P[3]) / q1 ;
      Ap[3] = (A[1] * P[2] + A[3] * P[3]) / q1 ;
      memcpy(A,Ap,4*sizeof(double)) ;
    }

  } /* next iteration */

  /*
   Make upright.

   Shape adaptation does not estimate rotation. This is fixed by default
   so that a selected axis is not rotated at all (usually this is the
   vertical axis for upright features). To do so, the frame is rotated
   as follows.
   */
  {
    double A [2*2] = {adapted->a11, adapted->a21, adapted->a12, adapted->a22} ;
    double ref [2] ;
    double ref_ [2] ;
    double angle ;
    double angle_ ;
    double dangle ;
    double r1, r2 ;

    if (self->transposed) {
      /* up is the x axis */
      ref[0] = 1 ;
      ref[1] = 0 ;
    } else {
      /* up is the y axis */
      ref[0] = 0 ;
      ref[1] = 1 ;
    }

    vl_solve_linear_system_2 (ref_, A, ref) ;
    angle = atan2(ref[1], ref[0]) ;
    angle_ = atan2(ref_[1], ref_[0]) ;
    dangle = angle_ - angle ;
    r1 = cos(dangle) ;
    r2 = sin(dangle) ;
    adapted->a11 = + A[0] * r1 + A[2] * r2 ;
    adapted->a21 = + A[1] * r1 + A[3] * r2 ;
    adapted->a12 = - A[0] * r2 + A[2] * r1 ;
    adapted->a22 = - A[1] * r2 + A[3] * r1 ;
  }

  return VL_ERR_OK ;
}

/** @brief Extract the affine shape for the stored features
 ** @param self object.
 **
 ** This function may discard features for which no affine
 ** shape can reliably be detected.
 **/

void
vl_covdet_extract_affine_shape (VlCovDet * self)
{
  vl_index i, j = 0 ;
  vl_size numFeatures = vl_covdet_get_num_features(self) ;
  VlCovDetFeature * feature = vl_covdet_get_features(self);
  for (i = 0 ; i < (signed)numFeatures ; ++i) {
    int status ;
    VlFrameOrientedEllipse adapted ;
    status = vl_covdet_extract_affine_shape_for_frame(self, &adapted, feature[i].frame) ;
    if (status == VL_ERR_OK) {
      feature[j] = feature[i] ;
      feature[j].frame = adapted ;
      ++ j ;
    }
  }
  self->numFeatures = j ;
}

/* ---------------------------------------------------------------- */
/*                                                      Orientation */
/* ---------------------------------------------------------------- */

static int
_vl_covdet_compare_orientations_descending (void const * a_,
                                            void const * b_)
{
  VlCovDetFeatureOrientation const * a = a_ ;
  VlCovDetFeatureOrientation const * b = b_ ;
  if (a->score > b->score) return -1 ;
  if (a->score < b->score) return +1 ;
  return 0 ;
}

/** @brief Extract the orientation(s) for a feature frame
 ** @param self object.
 ** @param numOrientations the number of detected orientations.
 ** @return an array of detected orientations with their scores.
 **
 ** The returned array is a matrix of size @f$ 2 \times n @f$
 ** where n is the number of detected orientations.
 **
 ** The function returns @c NULL if memory is insufficient.
 **/

VlCovDetFeatureOrientation *
vl_covdet_extract_orientations_for_frame (VlCovDet * self,
                                          vl_size * numOrientations,
                                          VlFrameOrientedEllipse frame)
{
  int err ;
  vl_index k, i ;
  vl_index iter ;

  double extent = VL_COVDET_AA_PATCH_EXTENT ;
  vl_size resolution = VL_COVDET_AA_PATCH_RESOLUTION ;
  vl_size side = 2 * resolution + 1  ;

  vl_size const numBins = VL_COVDET_OR_NUM_ORIENTATION_HISTOGAM_BINS ;
  double hist [VL_COVDET_OR_NUM_ORIENTATION_HISTOGAM_BINS] ;
  double const binExtent = 2 * VL_PI / VL_COVDET_OR_NUM_ORIENTATION_HISTOGAM_BINS ;
  double const peakRelativeSize = VL_COVDET_OR_ADDITIONAL_PEAKS_RELATIVE_SIZE ;
  double maxPeakValue ;

  double A [2*2] = {frame.a11, frame.a21, frame.a12, frame.a22} ;
  double T [2] = {frame.x, frame.y} ;
  double U [2*2] ;
  double V [2*2] ;
  double D [2*2] ;
  double sigma1, sigma2 ;
  double sigmaD = 1.0 ;
  double theta0 ;

  assert(self);
  assert(numOrientations) ;

  /*
   The goal is to estimate a rotation R(theta) such that the patch given
   by the transformation A R(theta) has the strongest average
   gradient pointing right (or down for transposed conventions).

   To compensate for tha anisotropic smoothing due to warping,
   A is decomposed as A = U D V' and the patch is warped by
   U D only, meaning that the matrix R_(theta) will be estimated instead,
   where:

      A R(theta) = U D V' R(theta) = U D R_(theta)

   such that R(theta) = V R(theta). That is an extra rotation of
   theta0 = atan2(V(2,1),V(1,1)).
   */

  /* axis aligned anisotropic smoothing for easier compensation */
  vl_svd2(D, U, V, A) ;

  A[0] = U[0] * D[0] ;
  A[1] = U[1] * D[0] ;
  A[2] = U[2] * D[3] ;
  A[3] = U[3] * D[3] ;

  theta0 = atan2(V[1],V[0]) ;

  err = vl_covdet_extract_patch_helper(self,
                                       &sigma1, &sigma2,
                                       self->aaPatch,
                                       resolution,
                                       extent,
                                       sigmaD,
                                       A, T, D[0], D[3]) ;

  if (err) {
    *numOrientations = 0 ;
    return NULL ;
  }

  if (1) {
    double deltaSigma1 = sqrt(VL_MAX(sigmaD*sigmaD - sigma1*sigma1,0)) ;
    double deltaSigma2 = sqrt(VL_MAX(sigmaD*sigmaD - sigma2*sigma2,0)) ;
    double stephat = extent / resolution ;
    vl_imsmooth_f(self->aaPatch, side,
                  self->aaPatch, side, side, side,
                  deltaSigma1 / stephat, deltaSigma2 / stephat) ;
  }

  /* histogram of oriented gradients */
  vl_imgradient_polar_f (self->aaPatchX, self->aaPatchY, 1, side,
                         self->aaPatch, side, side, side) ;

  memset (hist, 0, sizeof(double) * numBins) ;

  for (k = 0 ; k < (signed)(side*side) ; ++k) {
    double modulus = self->aaPatchX[k] ;
    double angle = self->aaPatchY[k] ;
    double weight = self->aaMask[k] ;

    double x = angle / binExtent ;
    vl_index bin = vl_floor_d(x) ;
    double w2 = x - bin ;
    double w1 = 1.0 - w2 ;

    hist[(bin + numBins) % numBins] += w1 * (modulus * weight) ;
    hist[(bin + numBins + 1) % numBins] += w2 * (modulus * weight) ;
  }

  /* smooth histogram */
  for (iter = 0; iter < 6; iter ++) {
    double prev = hist [numBins - 1] ;
    double first = hist [0] ;
    vl_index i ;
    for (i = 0; i < (signed)numBins - 1; ++i) {
      double curr = (prev + hist[i] + hist[(i + 1) % numBins]) / 3.0 ;
      prev = hist[i] ;
      hist[i] = curr ;
    }
    hist[i] = (prev + hist[i] + first) / 3.0 ;
  }

  /* find the histogram maximum */
  maxPeakValue = 0 ;
  for (i = 0 ; i < (signed)numBins ; ++i) {
    maxPeakValue = VL_MAX (maxPeakValue, hist[i]) ;
  }

  /* find peaks within 80% from max */
  *numOrientations = 0 ;
  for(i = 0 ; i < (signed)numBins ; ++i) {
    double h0 = hist [i] ;
    double hm = hist [(i - 1 + numBins) % numBins] ;
    double hp = hist [(i + 1 + numBins) % numBins] ;

    /* is this a peak? */
    if (h0 > peakRelativeSize * maxPeakValue && h0 > hm && h0 > hp) {
      /* quadratic interpolation */
      double di = - 0.5 * (hp - hm) / (hp + hm - 2 * h0) ;
      double th = binExtent * (i + di) + theta0 ;
      if (self->transposed) {
        /* the axis to the right is y, measure orientations from this */
        th = th - VL_PI/2 ;
      }
      self->orientations[*numOrientations].angle = th ;
      self->orientations[*numOrientations].score = h0 ;
      *numOrientations += 1 ;
      //VL_PRINTF("%d %g\n", *numOrientations, th) ;

      if (*numOrientations >= VL_COVDET_MAX_NUM_ORIENTATIONS) break ;
    }
  }

  /* sort the oritentations by decreasing scores */
  qsort(self->orientations,
        *numOrientations,
        sizeof(VlCovDetFeatureOrientation),
        _vl_covdet_compare_orientations_descending) ;

  return self->orientations ;
}

/** @brief Extract the orientation(s) for the stored features.
 ** @param self object.
 **
 ** Note that, since more than one orientation can be detected
 ** for each feature, this function may create copies of them,
 ** one for each orientation.
 **/

void
vl_covdet_extract_orientations (VlCovDet * self)
{
  vl_index i, j  ;
  vl_size numFeatures = vl_covdet_get_num_features(self) ;
  for (i = 0 ; i < (signed)numFeatures ; ++i) {
    vl_size numOrientations ;
    VlCovDetFeature feature = self->features[i] ;
    VlCovDetFeatureOrientation* orientations =
    vl_covdet_extract_orientations_for_frame(self, &numOrientations, feature.frame) ;

    for (j = 0 ; j < (signed)numOrientations ; ++j) {
      double A [2*2] = {
        feature.frame.a11,
        feature.frame.a21,
        feature.frame.a12,
        feature.frame.a22} ;
      double r1 = cos(orientations[j].angle) ;
      double r2 = sin(orientations[j].angle) ;
      VlCovDetFeature * oriented ;

      if (j == 0) {
        oriented = & self->features[i] ;
      } else {
        vl_covdet_append_feature(self, &feature) ;
        oriented = & self->features[self->numFeatures -1] ;
      }

      oriented->orientationScore = orientations[j].score ;
      oriented->frame.a11 = + A[0] * r1 + A[2] * r2 ;
      oriented->frame.a21 = + A[1] * r1 + A[3] * r2 ;
      oriented->frame.a12 = - A[0] * r2 + A[2] * r1 ;
      oriented->frame.a22 = - A[1] * r2 + A[3] * r1 ;
    }
  }
}

/* ---------------------------------------------------------------- */
/*                                                 Laplacian scales */
/* ---------------------------------------------------------------- */

/** @brief Extract the Laplacian scale(s) for a feature frame.
 ** @param self object.
 ** @param numScales the number of detected scales.
 ** @return an array of detected scales.
 **
 ** The function returns @c NULL if memory is insufficient.
 **/

VlCovDetFeatureLaplacianScale *
vl_covdet_extract_laplacian_scales_for_frame (VlCovDet * self,
                                              vl_size * numScales,
                                              VlFrameOrientedEllipse frame)
{
  /*
   We try to explore one octave, with the nominal detection scale 1.0
   (in the patch reference frame) in the middle. Thus the goal is to sample
   the response of the tr-Laplacian operator at logarithmically
   spaced scales in 1/sqrt(2), sqrt(2).

   To this end, the patch is warped with a smoothing of at most
   sigmaImage = 1 / sqrt(2) (beginning of the scale), sampled at
   roughly twice the Nyquist frequency (so step = 1 / (2*sqrt(2))).
   This maes it possible to approximate the Laplacian operator at
   that scale by simple finite differences.

   */
  int err ;
  double const sigmaImage = 1.0 / sqrt(2.0) ;
  double const step = 0.5 * sigmaImage ;
  double actualSigmaImage ;
  vl_size const resolution = VL_COVDET_LAP_PATCH_RESOLUTION ;
  vl_size const num = 2 * resolution + 1 ;
  double extent = step * resolution ;
  double scores [VL_COVDET_LAP_NUM_LEVELS] ;
  double factor = 1.0 ;
  float const * pt ;
  vl_index k ;

  double A[2*2] = {frame.a11, frame.a21, frame.a12, frame.a22} ;
  double T[2] = {frame.x, frame.y} ;
  double D[4], U[4], V[4] ;
  double sigma1, sigma2 ;

  assert(self) ;
  assert(numScales) ;

  *numScales = 0 ;

  vl_svd2(D, U, V, A) ;

  err = vl_covdet_extract_patch_helper
  (self, &sigma1, &sigma2, self->lapPatch, resolution, extent, sigmaImage, A, T, D[0], D[3]) ;
  if (err) return NULL ;

  /* the actual smoothing after warping is never the target one */
  if (sigma1 == sigma2) {
    actualSigmaImage = sigma1 ;
  } else {
    /* here we could compensate */
    actualSigmaImage = sqrt(sigma1*sigma2) ;
  }

  /* now multiply by the bank of Laplacians */
  pt = self->laplacians ;
  for (k = 0 ; k < VL_COVDET_LAP_NUM_LEVELS ; ++k) {
    vl_index q ;
    double score = 0 ;
    double sigmaLap = pow(2.0, -0.5 + (double)k / (VL_COVDET_LAP_NUM_LEVELS - 1)) ;
    /* note that the sqrt argument cannot be negative since by construction
     sigmaLap >= sigmaImage */
    sigmaLap = sqrt(sigmaLap*sigmaLap
                    - sigmaImage*sigmaImage
                    + actualSigmaImage*actualSigmaImage) ;

    for (q = 0 ; q < (signed)(num * num) ; ++q) {
      score += (*pt++) * self->lapPatch[q] ;
    }
    scores[k] = score * sigmaLap * sigmaLap ;
  }

  /* find and interpolate maxima */
  for (k = 1 ; k < VL_COVDET_LAP_NUM_LEVELS - 1 ; ++k) {
    double a = scores[k-1] ;
    double b = scores[k] ;
    double c = scores[k+1] ;
    double t = VL_COVDET_DOG_DEF_PEAK_THRESHOLD ;

    if ((((b > a) && (b > c)) || ((b < a) && (b < c))) && (vl_abs_d(b) >= t)) {
      double dk = - 0.5 * (c - a) / (c + a - 2 * b) ;
      double s = k + dk ;
      double sigmaLap = pow(2.0, -0.5 + s / (VL_COVDET_LAP_NUM_LEVELS - 1)) ;
      double scale ;
      sigmaLap = sqrt(sigmaLap*sigmaLap
                      - sigmaImage*sigmaImage
                      + actualSigmaImage*actualSigmaImage) ;
      scale = sigmaLap / 1.0 ;
      /*
       VL_PRINTF("** k:%d, s:%f, sigmaLapFilter:%f, sigmaLap%f, scale:%f (%f %f %f)\n",
       k,s,sigmaLapFilter,sigmaLap,scale,a,b,c) ;
       */
      if (*numScales < VL_COVDET_MAX_NUM_LAPLACIAN_SCALES) {
        self->scales[*numScales].scale = scale * factor ;
        self->scales[*numScales].score = b + 0.5 * (c - a) * dk ;
        *numScales += 1 ;
      }
    }
  }
  return self->scales ;
}

/** @brief Extract the Laplacian scales for the stored features
 ** @param self object.
 **
 ** Note that, since more than one orientation can be detected
 ** for each feature, this function may create copies of them,
 ** one for each orientation.
 **/
void
vl_covdet_extract_laplacian_scales (VlCovDet * self)
{
  vl_index i, j  ;
  vl_bool dropFeaturesWithoutScale = VL_TRUE ;
  vl_size numFeatures = vl_covdet_get_num_features(self) ;
  memset(self->numFeaturesWithNumScales, 0,
         sizeof(self->numFeaturesWithNumScales)) ;

  for (i = 0 ; i < (signed)numFeatures ; ++i) {
    vl_size numScales ;
    VlCovDetFeature feature = self->features[i] ;
    VlCovDetFeatureLaplacianScale const * scales =
    vl_covdet_extract_laplacian_scales_for_frame(self, &numScales, feature.frame) ;

    self->numFeaturesWithNumScales[numScales] ++ ;

    if (numScales == 0 && dropFeaturesWithoutScale) {
      self->features[i].peakScore = 0 ;
    }

    for (j = 0 ; j < (signed)numScales ; ++j) {
      VlCovDetFeature * scaled ;

      if (j == 0) {
        scaled = & self->features[i] ;
      } else {
        vl_covdet_append_feature(self, &feature) ;
        scaled = & self->features[self->numFeatures -1] ;
      }

      scaled->laplacianScaleScore = scales[j].score ;
      scaled->frame.a11 *= scales[j].scale ;
      scaled->frame.a21 *= scales[j].scale ;
      scaled->frame.a12 *= scales[j].scale ;
      scaled->frame.a22 *= scales[j].scale ;
    }
  }
  if (dropFeaturesWithoutScale) {
    j = 0 ;
    for (i = 0 ; i < (signed)self->numFeatures ; ++i) {
      VlCovDetFeature feature = self->features[i] ;
      if (feature.peakScore) {
        self->features[j++] = feature ;
      }
    }
    self->numFeatures = j ;
  }

}

/* ---------------------------------------------------------------- */
/*                       Checking that features are inside an image */
/* ---------------------------------------------------------------- */

vl_bool
_vl_covdet_check_frame_inside (VlCovDet * self, VlFrameOrientedEllipse frame, double margin)
{
  double extent = margin ;
  double A [2*2] = {frame.a11, frame.a21, frame.a12, frame.a22} ;
  double T[2] = {frame.x, frame.y} ;
  double x0 = +VL_INFINITY_D ;
  double x1 = -VL_INFINITY_D ;
  double y0 = +VL_INFINITY_D ;
  double y1 = -VL_INFINITY_D ;
  double boxx [4] = {extent, extent, -extent, -extent} ;
  double boxy [4] = {-extent, extent, extent, -extent} ;
  VlScaleSpaceGeometry geom = vl_scalespace_get_geometry(self->gss) ;
  int i ;
  for (i = 0 ; i < 4 ; ++i) {
    double x = A[0] * boxx[i] + A[2] * boxy[i] + T[0] ;
    double y = A[1] * boxx[i] + A[3] * boxy[i] + T[1] ;
    x0 = VL_MIN(x0, x) ;
    x1 = VL_MAX(x1, x) ;
    y0 = VL_MIN(y0, y) ;
    y1 = VL_MAX(y1, y) ;
  }

  return
  0 <= x0 && x1 <= geom.width-1 &&
  0 <= y0 && y1 <= geom.height-1 ;
}

/** @brief Drop features (partially) outside the image
 ** @param self object.
 ** @param margin geometric marging.
 **
 ** The feature extent is defined by @c maring. A bounding box
 ** in the normalised feature frame containin a circle of radius
 ** @a maring is created and mapped to the image by
 ** the feature frame transformation. Then the feature
 ** is dropped if the bounding box is not contained in the image.
 **
 ** For example, setting @c margin to zero drops a feature only
 ** if its center is not contained.
 **
 ** Typically a valua of @c margin equal to 1 or 2 is used.
 **/

void
vl_covdet_drop_features_outside (VlCovDet * self, double margin)
{
  vl_index i, j = 0 ;
  vl_size numFeatures = vl_covdet_get_num_features(self) ;
  for (i = 0 ; i < (signed)numFeatures ; ++i) {
    vl_bool inside =
    _vl_covdet_check_frame_inside (self, self->features[i].frame, margin) ;
    if (inside) {
      self->features[j] = self->features[i] ;
      ++j ;
    }
  }
  self->numFeatures = j ;
}

/* ---------------------------------------------------------------- */
/*                                              Setters and getters */
/* ---------------------------------------------------------------- */

/* ---------------------------------------------------------------- */
/** @brief Get wether images are passed in transposed
 ** @param self ::VlCovDet object.
 ** @return whether images are transposed.
 **/
vl_bool
vl_covdet_get_transposed (VlCovDet const  * self)
{
  return self->transposed ;
}

/** @brief Set the index of the first octave
 ** @param self ::VlCovDet object.
 ** @param t whether images are transposed.
 **/
void
vl_covdet_set_transposed (VlCovDet * self, vl_bool t)
{
  self->transposed = t ;
}

/* ---------------------------------------------------------------- */
/** @brief Get the edge threshold
 ** @param self ::VlCovDet object.
 ** @return the edge threshold.
 **/
double
vl_covdet_get_edge_threshold (VlCovDet const * self)
{
  return self->edgeThreshold ;
}

/** @brief Set the edge threshold
 ** @param self ::VlCovDet object.
 ** @param the edge threshold.
 **
 ** The edge threshold must be non-negative.
 **/
void
vl_covdet_set_edge_threshold (VlCovDet * self, double edgeThreshold)
{
  assert(edgeThreshold >= 0) ;
  self->edgeThreshold = edgeThreshold ;
}

/* ---------------------------------------------------------------- */
/** @brief Get the peak threshold
 ** @param self ::VlCovDet object.
 ** @return the peak threshold.
 **/
double
vl_covdet_get_peak_threshold (VlCovDet const * self)
{
  return self->peakThreshold ;
}

/** @brief Set the peak threshold
 ** @param self ::VlCovDet object.
 ** @param the peak threshold.
 **
 ** The peak threshold must be non-negative.
 **/
void
vl_covdet_set_peak_threshold (VlCovDet * self, double peakThreshold)
{
  assert(peakThreshold >= 0) ;
  self->peakThreshold = peakThreshold ;
}

/* ---------------------------------------------------------------- */
/** @brief Get the index of the first octave
 ** @param self ::VlCovDet object.
 ** @return index of the first octave.
 **/
vl_index
vl_covdet_get_first_octave (VlCovDet const * self)
{
  return self->firstOctave ;
}

/** @brief Set the index of the first octave
 ** @param self ::VlCovDet object.
 ** @param index of the first octave.
 **
 ** Calling this function resets the detector.
 **/
void
vl_covdet_set_first_octave (VlCovDet * self, vl_index o)
{
  self->firstOctave = o ;
  vl_covdet_reset(self) ;
}

/* ---------------------------------------------------------------- */
/** @brief Get the octave resolution.
 ** @param self ::VlCovDet object.
 ** @return octave resolution.
 **/

vl_size
vl_covdet_get_octave_resolution (VlCovDet const * self)
{
  return self->octaveResolution ;
}

/** @brief Set the octave resolutuon.
 ** @param self ::VlCovDet object.
 ** @param octave resoltuion.
 **
 ** Calling this function resets the detector.
 **/

void
vl_covdet_set_octave_resolution (VlCovDet * self, vl_size r)
{
  self->octaveResolution = r ;
  vl_covdet_reset(self) ;
}

/* ---------------------------------------------------------------- */
/** @brief Get whether affine adaptation uses accurate smoothing.
 ** @param self object.
 ** @return @c true if accurate smoothing is used.
 **/

vl_bool
vl_covdet_get_aa_accurate_smoothing (VlCovDet const * self)
{
  return self->aaAccurateSmoothing ;
}

/** @brief Set whether affine adaptation uses accurate smoothing.
 ** @param self object.
 ** @param x whether accurate smoothing should be usd.
 **/

void
vl_covdet_set_aa_accurate_smoothing (VlCovDet * self, vl_bool x)
{
  self->aaAccurateSmoothing = x ;
}

/* ---------------------------------------------------------------- */
/** @brief Get the non-extrema suppression threshold
 ** @param self object.
 ** @return threshold.
 **/

double
vl_covdet_get_non_extrema_suppression_threshold (VlCovDet const * self)
{
  return self->nonExtremaSuppression ;
}

/** @brief Set the non-extrema suppression threshod
 ** @param self object.
 ** @param x threshold.
 **/

void
vl_covdet_set_non_extrema_suppression_threshold (VlCovDet * self, double x)
{
  self->nonExtremaSuppression = x ;
}

/** @brief Get the number of non-extrema suppressed
 ** @param self object.
 ** @return number.
 **/

vl_size
vl_covdet_get_num_non_extrema_suppressed (VlCovDet const * self)
{
  return self->numNonExtremaSuppressed ;
}


/* ---------------------------------------------------------------- */
/** @brief Get number of stored frames
 ** @return number of frames stored in the detector.
 **/
vl_size
vl_covdet_get_num_features (VlCovDet const * self)
{
  return self->numFeatures ;
}

/** @brief Get the stored frames
 ** @return frames stored in the detector.
 **/
void *
vl_covdet_get_features (VlCovDet * self)
{
  return self->features ;
}

/** @brief Get the Gaussian scale space
 ** @return Gaussian scale space.
 **
 ** A Gaussian scale space exists only after calling ::vl_covdet_put_image.
 ** Otherwise the function returns @c NULL.
 **/

VlScaleSpace *
vl_covdet_get_gss (VlCovDet const * self)
{
  return self->gss ;
}

/** @brief Get the cornerness measure scale space
 ** @return cornerness measure scale space.
 **
 ** A cornerness measure scale space exists only after calling
 ** ::vl_covdet_detect. Otherwise the function returns @c NULL.
 **/

VlScaleSpace *
vl_covdet_get_css (VlCovDet const * self)
{
  return self->css ;
}

/** @brief Get the number of features found with a certain number of scales
 ** @param sellf object.
 ** @param numScales length of the histogram (out).
 ** @return histogram.
 **
 ** Calling this function makes sense only after running a detector
 ** that uses the Laplacian as a secondary measure for scale
 ** detection
 **/

vl_size const *
vl_covdet_get_laplacian_scales_statistics (VlCovDet const * self,
                                           vl_size * numScales)
{
  *numScales = VL_COVDET_MAX_NUM_LAPLACIAN_SCALES ;
  return self->numFeaturesWithNumScales ;
}
vlfeat/vl/lbp.c0000644000175000017500000002130612237004736012315 0ustar  dimadima/** @file lbp.c
 ** @brief Local Binary Patterns - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file lbp.h

::VlLbp implements the local binary pattern features.

An LBP feature [1] is a l2-normalized local histogram of quantized
local binary patterns (LBP).

@section lbp Local Binary Patterns

A LBP is a string of bit obtained by binarizing a local neighbourhood
of pixels with respect to the brightness of the central pixel.
::VlLbp implements only the case of 3x3 pixel neighbourhoods (this
setting perform best in applications).  Thus the LBP at location @f$
(x,y) @f$ is a string of eight bits. Each bit is equal to one if the
corresponding pixel is brighter than the central one.  Pixels are
scanned starting from the one to the right in anti-clockwise sense.
For example the first bit is one if, and only if, @f$ I(x+1,y) >
I(x,y), and the second bit if, and only if, @f$ I(x+1,y-1) > I(x,y).

@subsection lbp-quantization Quantized LBP

For a 3x3 neighborhood, an LBP is a string of eight bits and so there
are 256 possible LBPs. These are usually too many for a reliable
statistics (histogram) to be computed. Therefore the 256 patterns are
further quantized into a smaller number of patterns according to one
of the following rules:

- Uniform (::VlLbpUniform) There is one quantized pattern for
  each LBP that has exactly a transitions from 0 to 1 and one from 1
  to 0 when scanned in anti-clockwise order, plus one quantized
  pattern comprising the two uniform LBPs, and one quantized pattern
  comprising all the other LBPs. This yields a total of 58 quantized
  patterns.

The number of quantized LBPs, which depends on the quantization type,
can be obtained by ::vl_lbp_get_dimension.

@subsection lbp-histograms Histograms of LBPs

The quantized LBP patterns are further grouped into local
histograms. The image is divided into a number of cells of a
prescribed size (@c cellSize).  Then the quantized LBPs are aggregated
into histogram by using bilinear interpolation along the two spatial
dimensions (similar to HOG and SIFT).

@subsection lbp-usage Usage

First, one creates a ::VlLbp object instance by specifying the type of
quantization (this initializes some internal tables to speedup the
computation). Then, one obtains all the quantized LBPs histograms by
calling ::vl_lbp_process. This function expects as input a buffer
large enough to contain the computed features. If the image has size
@c width x @c height, there are exactly @c floor(width/cellSize) x @c
floor(height/cellSize) cells, each of which has a histogram of LBPs of
size @c dimension (as returned by ::vl_lbp_get_dimension). Thus the
required buffer has size @c floor(width/cellSize) x @c
floor(height/cellSize) x @c dimension.

::VlLbp supports computing transposed LPBs as well. A transposed LBP
is the LBP obtained by transposing the underlying image patch
(regarded as a matrix). This can be useful to compute the features
when the image is column major rather than row major.

@section lbp-references References

[1] T. Ojala, M. Pietikainen, and M. Maenpaa. "Multiresolution
    gray-scale and rotation invariant texture classification with
    local binary patterns". PAMI, 2010.

**/

#include "lbp.h"
#include "mathop.h"

/* ---------------------------------------------------------------- */
/*                                           Initialization helpers */
/* ---------------------------------------------------------------- */

static void
_vl_lbp_init_uniform(VlLbp * self)
{
  int i, j ;

  /* one bin for constant patterns, 8*7 for 2-uniform, one for rest */
  self->dimension = 58 ;

  /* all but selected patterns map to 57 */
  for (i = 0 ; i < 256 ; ++i) {
    self->mapping[i] = 57 ;
  }

  /* uniform patterns map to 56 */
  self->mapping[0x00] = 56 ;
  self->mapping[0xff] = 56 ;

  /* now uniform pattenrs, in order */
  /* locations: 0:E, 1:SE, 2:S, ..., 7:NE */
  for (i = 0 ; i < 8 ; ++i) { /* string[i-1]=0, string[i]=1 */
    for (j = 1 ; j <= 7 ; ++j) { /* length of sequence of ones */
      /* string starting with j ones */
      int unsigned string = (1 << j) - 1 ;
      /* put i zeroes in front */
      string <<= i ;
      /* wrap around 8 bit boundaries */
      string = (string | (string >> 8)) & 0xff ;

      /* optionally transpose the pattern */
      if (self->transposed) {
        int unsigned original = string;
        int k ;
        /* flip the string left-right */
        string = 0 ;
        for (k = 0 ; k < 8 ; ++k) {
          string <<= 1 ;
          string |= original & 0x1  ;
          original >>= 1 ;
        }
        /* rotate 90 degrees */
        string <<= 3 ;
        string = (string | (string >> 8)) & 0xff ;
      }

      self->mapping[string] = i * 7 + (j-1) ;
    }
  }
}

/* ---------------------------------------------------------------- */

/** @brief Create a new LBP object
 ** @param type type of LBP features.
 ** @param transposed if @c true, then transpose each LBP pattern.
 ** @return new VlLbp object instance.
 **/

VlLbp *
vl_lbp_new(VlLbpMappingType type, vl_bool transposed)
{
  VlLbp * self = vl_malloc(sizeof(VlLbp)) ;
  if (self == NULL) {
    vl_set_last_error(VL_ERR_ALLOC, NULL) ;
    return NULL ;
  }
  self->transposed = transposed ;
  switch (type) {
    case VlLbpUniform: _vl_lbp_init_uniform(self) ; break ;
    default: exit(1) ;
  }
  return self ;
}

/** @brief Delete VlLbp object
 ** @param self object to delete.
 **/

void
vl_lbp_delete(VlLbp * self) {
  vl_free(self) ;
}


/* ---------------------------------------------------------------- */

/** @brief Extract LBP features
 ** @param self LBP object.
 ** @param features buffer to write the features to.
 ** @param image image.
 ** @param width image width.
 ** @param height image height.
 ** @param cellSize size of the LBP cells.
 **
 ** @a features is a  @c numColumns x @c numRows x @c dimension where
 ** @c dimension is the dimension of a LBP feature obtained from ::vl_lbp_get_dimension,
 ** @c numColumns is equal to @c floor(width / cellSize), and similarly
 ** for @c numRows.
 **/

VL_EXPORT void
vl_lbp_process (VlLbp * self,
                float * features,
                float * image, vl_size width, vl_size height,
                vl_size cellSize)
{
  vl_size cwidth = width / cellSize;
  vl_size cheight = height / cellSize ;
  vl_size cstride = cwidth * cheight ;
  vl_size cdimension = vl_lbp_get_dimension(self) ;
  vl_index x,y,cx,cy,k,bin ;

#define at(u,v) (*(image + width * (v) + (u)))
#define to(u,v,w) (*(features + cstride * (w) + cwidth * (v) + (u)))

  /* accumulate pixel-level measurements into cells */
  for (y = 1 ; y < (signed)height - 1 ; ++y) {
    float wy1 = (y + 0.5f) / (float)cellSize - 0.5f ;
    int cy1 = (int) vl_floor_f(wy1) ;
    int cy2 = cy1 + 1 ;
    float wy2 = wy1 - (float)cy1 ;
    wy1 = 1.0f - wy2 ;
    if (cy1 >= (signed)cheight) continue ;

    for (x = 1 ; x < (signed)width - 1; ++x) {
      float wx1 = (x + 0.5f) / (float)cellSize - 0.5f ;
      int cx1 = (int) vl_floor_f(wx1) ;
      int cx2 = cx1 + 1 ;
      float wx2 = wx1 - (float)cx1 ;
      wx1 = 1.0f - wx2 ;
      if (cx1 >= (signed)cwidth) continue ;

      {
        int unsigned bitString = 0 ;
        float center = at(x,y) ;
        if(at(x+1,y+0) > center) bitString |= 0x1 << 0; /*  E */
        if(at(x+1,y+1) > center) bitString |= 0x1 << 1; /* SE */
        if(at(x+0,y+1) > center) bitString |= 0x1 << 2; /* S  */
        if(at(x-1,y+1) > center) bitString |= 0x1 << 3; /* SW */
        if(at(x-1,y+0) > center) bitString |= 0x1 << 4; /*  W */
        if(at(x-1,y-1) > center) bitString |= 0x1 << 5; /* NW */
        if(at(x+0,y-1) > center) bitString |= 0x1 << 6; /* N  */
        if(at(x+1,y-1) > center) bitString |= 0x1 << 7; /* NE */
        bin = self->mapping[bitString] ;
      }

      if ((cx1 >= 0) & (cy1 >=0)) {
        to(cx1,cy1,bin) += wx1 * wy1;
      }
      if ((cx2 < (signed)cwidth)  & (cy1 >=0)) {
        to(cx2,cy1,bin) += wx2 * wy1 ;
      }
      if ((cx1 >= 0) & (cy2 < (signed)cheight)) {
        to(cx1,cy2,bin) += wx1 * wy2 ;
      }
      if ((cx2 < (signed)cwidth) & (cy2 < (signed)cheight)) {
        to(cx2,cy2,bin) += wx2 * wy2 ;
      }
    } /* x */
  } /* y */

  /* normalize cells */
  for (cy = 0 ; cy < (signed)cheight ; ++cy) {
    for (cx = 0 ; cx < (signed)cwidth ; ++ cx) {
      float norm = 0 ;
      for (k = 0 ; k < (signed)cdimension ; ++k) {
        norm += features[k * cstride] ;
      }
      norm = sqrtf(norm) + 1e-10f; ;
      for (k = 0 ; k < (signed)cdimension ; ++k) {
        features[k * cstride] = sqrtf(features[k * cstride]) / norm  ;
      }
      features += 1 ;
    }
  } /* next cell to normalize */
}
vlfeat/vl/quickshift.c0000644000175000017500000003436312237004736013721 0ustar  dimadima/** @file quickshift.c
 ** @brief Quick shift - Definition
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page quickshift Quick shift image segmentation
@author Brian Fulkerson
@author Andrea Vedaldi


@ref quickshift.h implements an image segmentation algorithm based on
the quick shift clustering algorithm @cite{vedaldi08quick}.

- @ref quickshift-intro
- @ref quickshift-usage
- @ref quickshift-tech


@section quickshift-intro Overview


Quick shift @cite{vedaldi08quick} is a fast mode seeking algorithm,
similar to mean shift. The algorithm segments an RGB image (or any
image with more than one channel) by identifying clusters of pixels in
the joint spatial and color dimensions. Segments are local
(superpixels) and can be used as a basis for further processing.

Given an image, the algorithm calculates a forest of pixels whose
branches are labeled with a distance value
(::vl_quickshift_get_parents, ::vl_quickshift_get_dists). This
specifies a hierarchical segmentation of the image, with segments
corresponding to subtrees. Useful superpixels can be identified by
cutting the branches whose distance label is above a given threshold
(the threshold can be either fixed by hand, or determined by cross
validation).

Parameter influencing the algorithm are:

- Kernel size. The pixel density and its modes are estimated by
using a Parzen window estimator with a Gaussian kernel of the
specified size (::vl_quickshift_set_kernel_size). The larger the size,
the larger the neighborhoods of pixels considered.
- Maximum distance. This (::vl_quickshift_set_max_dist) is the
maximum distance between two pixels that the algorithm considers when
building the forest. In principle, it can be infinity (so that a tree
is returned), but in practice it is much faster to consider only
relatively small distances (the maximum distance can be set to a small
multiple of the kernel size).


@section quickshift-usage Usage


- Create a new quick shift object (::vl_quickshift_new). The object
  can be reused for multiple images of the same size.
- Configure quick shift by setting the kernel size
  (::vl_quickshift_set_kernel_size) and the maximum gap
  (::vl_quickshift_set_max_dist). The latter is in principle not
  necessary, but useful to speedup processing.
- Process an image (::vl_quickshift_process).
- Retrieve the parents (::vl_quickshift_get_parents) and the distances
  (::vl_quickshift_get_dists). These can be used to segment
  the image in superpixels.
- Delete the quick shift object (::vl_quickshift_delete).


@section quickshift-tech Technical details


For each pixel (x,y), quick shift regards @f$ (x,y,I(x,y))
@f$ as a sample from a d + 2 dimensional vector space. It
then calculates the Parzen density estimate (with a Gaussian kernel of
standard deviation @f$ \sigma @f$)

@f[
E(x,y) = P(x,y,I(x,y)) = \sum_{x'y'}
\frac{1}{(2\pi\sigma)^{d+2}}
\exp
\left(
-\frac{1}{2\sigma^2}
\left[
\begin{array}{c}
x - x' \\
y - y' \\
I(x,y) - I(x',y') \\
\end{array}
\right]
\right)
@f]

Then quick shift construct a tree connecting each image pixel to its
nearest neighbor which has greater density value. Formally, write @f$
(x',y') >_P (x,y) @f$ if, and only if,

@f[
  P(x',y',I(x',y')) > P(x,y,I(x,y))}.
@f]

Each pixel (x, y) is connected to the closest higher density
pixel parent(x, y) that achieves the minimum distance in

@f[
 \mathrm{dist}(x,y) =
 \mathrm{min}_{(x',y') > P(x,y)}
\left(
(x - x')^2 +
(y - y')^2 +
\| I(x,y) - I(x',y') \|_2^2
\right).
@f]

**/

#include "quickshift.h"
#include "mathop.h"
#include 
#include 
#include 

/** -----------------------------------------------------------------
 ** @internal
 ** @brief Computes the accumulated channel L2 distance between
 **        i,j + the distance between i,j
 **
 ** @param I    input image buffer
 ** @param N1   size of the first dimension of the image
 ** @param N2   size of the second dimension of the image
 ** @param K    number of channels
 ** @param i1   first dimension index of the first pixel to compare
 ** @param i2   second dimension of the first pixel
 ** @param j1   index of the second pixel to compare
 ** @param j2   second dimension of the second pixel
 **
 ** Takes the L2 distance between the values in I at pixel i and j,
 ** accumulating along K channels and adding in the distance
 ** between i,j in the image.
 **
 ** @return the distance as described above
 **/

VL_INLINE
vl_qs_type
vl_quickshift_distance(vl_qs_type const * I,
         int N1, int N2, int K,
         int i1, int i2,
         int j1, int j2)
{
  vl_qs_type dist = 0 ;
  int d1 = j1 - i1 ;
  int d2 = j2 - i2 ;
  int k ;
  dist += d1*d1 + d2*d2 ;
  /* For k = 0...K-1, d+= L2 distance between I(i1,i2,k) and
   * I(j1,j2,k) */
  for (k = 0 ; k < K ; ++k) {
    vl_qs_type d =
      I [i1 + N1 * i2 + (N1*N2) * k] -
      I [j1 + N1 * j2 + (N1*N2) * k] ;
    dist += d*d ;
  }
  return dist ;
}

/** -----------------------------------------------------------------
 ** @internal
 ** @brief Computes the accumulated channel inner product between i,j + the
 **        distance between i,j
 **
 ** @param I    input image buffer
 ** @param N1   size of the first dimension of the image
 ** @param N2   size of the second dimension of the image
 ** @param K    number of channels
 ** @param i1   first dimension index of the first pixel to compare
 ** @param i2   second dimension of the first pixel
 ** @param j1   index of the second pixel to compare
 ** @param j2   second dimension of the second pixel
 **
 ** Takes the channel-wise inner product between the values in I at
 ** pixel i and j, accumulating along K channels and adding in the
 ** inner product between i,j in the image.
 **
 ** @return the inner product as described above
 **/

VL_INLINE
vl_qs_type
vl_quickshift_inner(vl_qs_type const * I,
      int N1, int N2, int K,
      int i1, int i2,
      int j1, int j2)
{
  vl_qs_type ker = 0 ;
  int k ;
  ker += i1*j1 + i2*j2 ;
  for (k = 0 ; k < K ; ++k) {
    ker +=
      I [i1 + N1 * i2 + (N1*N2) * k] *
      I [j1 + N1 * j2 + (N1*N2) * k] ;
  }
  return ker ;
}

/** -----------------------------------------------------------------
 ** @brief Create a quick shift object
 ** @param image the image.
 ** @param height the height (number of rows) of the image.
 ** @param width the width (number of columns) of the image.
 ** @param channels the number of channels of the image.
 ** @return new quick shift object.
 **
 ** The @c image is an array of ::vl_qs_type values with three
 ** dimensions (respectively @c widht, @c height, and @c
 ** channels). Typically, a color (e.g, RGB) image has three
 ** channels. The linear index of a pixel is computed with:
 ** @c channels * @c width* @c height + @c row + @c height * @c col.
 **/

VL_EXPORT
VlQS *
vl_quickshift_new(vl_qs_type const * image, int height, int width,
                       int channels)
{
  VlQS * q = vl_malloc(sizeof(VlQS));

  q->image    = (vl_qs_type *)image;
  q->height   = height;
  q->width    = width;
  q->channels = channels;

  q->medoid   = VL_FALSE;
  q->tau      = VL_MAX(height,width)/50;
  q->sigma    = VL_MAX(2, q->tau/3);

  q->dists    = vl_calloc(height*width, sizeof(vl_qs_type));
  q->parents  = vl_calloc(height*width, sizeof(int));
  q->density  = vl_calloc(height*width, sizeof(vl_qs_type)) ;

  return q;
}

/** -----------------------------------------------------------------
 ** @brief Create a quick shift objet
 ** @param q quick shift object.
 **/

VL_EXPORT
void vl_quickshift_process(VlQS * q)
{
  vl_qs_type const *I = q->image;
  int        *parents = q->parents;
  vl_qs_type *E = q->density;
  vl_qs_type *dists = q->dists;
  vl_qs_type *M = 0, *n = 0 ;
  vl_qs_type sigma = q->sigma ;
  vl_qs_type tau = q->tau;
  vl_qs_type tau2 = tau*tau;

  int K = q->channels, d;
  int N1 = q->height, N2 = q->width;
  int i1,i2, j1,j2, R, tR;

  d = 2 + K ; /* Total dimensions include spatial component (x,y) */

  if (q->medoid) { /* n and M are only used in mediod shift */
    M = (vl_qs_type *) vl_calloc(N1*N2*d, sizeof(vl_qs_type)) ;
    n = (vl_qs_type *) vl_calloc(N1*N2,   sizeof(vl_qs_type)) ;
  }

  R = (int) ceil (3 * sigma) ;
  tR = (int) ceil (tau) ;

  /* -----------------------------------------------------------------
   *                                                                 n
   * -------------------------------------------------------------- */

  /* If we are doing medoid shift, initialize n to the inner product of the
   * image with itself
   */
  if (n) {
    for (i2 = 0 ; i2 < N2 ; ++ i2) {
      for (i1 = 0 ; i1 < N1 ; ++ i1) {
        n [i1 + N1 * i2] = vl_quickshift_inner(I,N1,N2,K,
                                               i1,i2,
                                               i1,i2) ;
      }
    }
  }

  /* -----------------------------------------------------------------
   *                                                 E = - [oN'*F]', M
   * -------------------------------------------------------------- */

  /*
     D_ij = d(x_i,x_j)
     E_ij = exp(- .5 * D_ij / sigma^2) ;
     F_ij = - E_ij
     E_i  = sum_j E_ij
     M_di = sum_j X_j F_ij

     E is the parzen window estimate of the density
     0 = dissimilar to everything, windowsize = identical
  */

  for (i2 = 0 ; i2 < N2 ; ++ i2) {
    for (i1 = 0 ; i1 < N1 ; ++ i1) {

      int j1min = VL_MAX(i1 - R, 0   ) ;
      int j1max = VL_MIN(i1 + R, N1-1) ;
      int j2min = VL_MAX(i2 - R, 0   ) ;
      int j2max = VL_MIN(i2 + R, N2-1) ;

      /* For each pixel in the window compute the distance between it and the
       * source pixel */
      for (j2 = j2min ; j2 <= j2max ; ++ j2) {
        for (j1 = j1min ; j1 <= j1max ; ++ j1) {
          vl_qs_type Dij = vl_quickshift_distance(I,N1,N2,K, i1,i2, j1,j2) ;
          /* Make distance a similarity */
          vl_qs_type Fij = - exp(- Dij / (2*sigma*sigma)) ;

          /* E is E_i above */
          E [i1 + N1 * i2] -= Fij ;

          if (M) {
            /* Accumulate votes for the median */
            int k ;
            M [i1 + N1*i2 + (N1*N2) * 0] += j1 * Fij ;
            M [i1 + N1*i2 + (N1*N2) * 1] += j2 * Fij ;
            for (k = 0 ; k < K ; ++k) {
              M [i1 + N1*i2 + (N1*N2) * (k+2)] +=
                I [j1 + N1*j2 + (N1*N2) * k] * Fij ;
            }
          }

        } /* j1 */
      } /* j2 */

    }  /* i1 */
  } /* i2 */

  /* -----------------------------------------------------------------
   *                                               Find best neighbors
   * -------------------------------------------------------------- */

  if (q->medoid) {

    /*
       Qij = - nj Ei - 2 sum_k Gjk Mik
       n is I.^2
    */

    /* medoid shift */
    for (i2 = 0 ; i2 < N2 ; ++i2) {
      for (i1 = 0 ; i1 < N1 ; ++i1) {

        vl_qs_type sc_best = 0  ;
        /* j1/j2 best are the best indicies for each i */
        vl_qs_type j1_best = i1 ;
        vl_qs_type j2_best = i2 ;

        int j1min = VL_MAX(i1 - R, 0   ) ;
        int j1max = VL_MIN(i1 + R, N1-1) ;
        int j2min = VL_MAX(i2 - R, 0   ) ;
        int j2max = VL_MIN(i2 + R, N2-1) ;

        for (j2 = j2min ; j2 <= j2max ; ++ j2) {
          for (j1 = j1min ; j1 <= j1max ; ++ j1) {

            vl_qs_type Qij = - n [j1 + j2 * N1] * E [i1 + i2 * N1] ;
            int k ;

            Qij -= 2 * j1 * M [i1 + i2 * N1 + (N1*N2) * 0] ;
            Qij -= 2 * j2 * M [i1 + i2 * N1 + (N1*N2) * 1] ;
            for (k = 0 ; k < K ; ++k) {
              Qij -= 2 *
                I [j1 + j2 * N1 + (N1*N2) * k] *
                M [i1 + i2 * N1 + (N1*N2) * (k + 2)] ;
            }

            if (Qij > sc_best) {
              sc_best = Qij ;
              j1_best = j1 ;
              j2_best = j2 ;
            }
          }
        }

        /* parents_i is the linear index of j which is the best pair
         * dists_i is the score of the best match
         */
        parents [i1 + N1 * i2] = j1_best + N1 * j2_best ;
        dists[i1 + N1 * i2] = sc_best ;
      }
    }

  } else {

    /* Quickshift assigns each i to the closest j which has an increase in the
     * density (E). If there is no j s.t. Ej > Ei, then dists_i == inf (a root
     * node in one of the trees of merges).
     */
    for (i2 = 0 ; i2 < N2 ; ++i2) {
      for (i1 = 0 ; i1 < N1 ; ++i1) {

        vl_qs_type E0 = E [i1 + N1 * i2] ;
        vl_qs_type d_best = VL_QS_INF ;
        vl_qs_type j1_best = i1   ;
        vl_qs_type j2_best = i2   ;

        int j1min = VL_MAX(i1 - tR, 0   ) ;
        int j1max = VL_MIN(i1 + tR, N1-1) ;
        int j2min = VL_MAX(i2 - tR, 0   ) ;
        int j2max = VL_MIN(i2 + tR, N2-1) ;

        for (j2 = j2min ; j2 <= j2max ; ++ j2) {
          for (j1 = j1min ; j1 <= j1max ; ++ j1) {
            if (E [j1 + N1 * j2] > E0) {
              vl_qs_type Dij = vl_quickshift_distance(I,N1,N2,K, i1,i2, j1,j2) ;
              if (Dij <= tau2 && Dij < d_best) {
                d_best = Dij ;
                j1_best = j1 ;
                j2_best = j2 ;
              }
            }
          }
        }

        /* parents is the index of the best pair */
        /* dists_i is the minimal distance, inf implies no Ej > Ei within
         * distance tau from the point */
        parents [i1 + N1 * i2] = j1_best + N1 * j2_best ;
        dists[i1 + N1 * i2] = sqrt(d_best) ;
      }
    }
  }

  if (M) vl_free(M) ;
  if (n) vl_free(n) ;
}

/** -----------------------------------------------------------------
 ** @brief Delete quick shift object
 ** @param q quick shift object.
 **/

void vl_quickshift_delete(VlQS * q)
{
  if (q) {
    if (q->parents) vl_free(q->parents);
    if (q->dists)   vl_free(q->dists);
    if (q->density) vl_free(q->density);

    vl_free(q);
  }
}
vlfeat/vl/heap-def.h0000644000175000017500000003251512237004736013222 0ustar  dimadima/** @file   heap-def.h
 ** @brief  Heap preprocessor metaprogram
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file heap-def.h

 A heap organizes an array of objects in a priority queue. This module
 is a template metaprogram that defines heap operations on array of
 generic objects, or even generic object containers.

 - @ref heap-def-overview "Overview"
   - @ref heap-def-overview-general "General usage"
 - @ref heap-def-tech "Technical details"

 
 @section heap-def-overview Overview
 

 To use @ref heap-def.h one must specify at least a prefix and the data
 type for the heap elements:

 @code
 #define VL_HEAP_prefix  my_heap
 #define VL_HEAP_type    float
 #include 
 @endcode

 This code fragment defines a number of functions prefixed by
 ::VL_HEAP_prefix, such as @c my_heap_push (::VL_HEAP_push) and @c
 my_heap_pop (::VL_HEAP_pop), that implement the heap operations.
 These functions operate on an array that has type ::VL_HEAP_array.
 By default, this is defined to be:

 @code
 #define VL_HEAP_array VL_HEAP_type*
 #define VL_HEAP_array_const VL_HEAP_type const*
 @endcode

 The array itself is accessed uniquely by means of two functions:

 - ::VL_HEAP_cmp, that compares two array elements. The default
   implementation assumes that ::VL_HEAP_type is numeric.
 - ::VL_HEAP_swap, that swaps two array elements. The default
   implementation assumes that ::VL_HEAP_type can be copied by the @c
   = operator.

 The heap state is a integer @c numElements (of type ::vl_size) counting
 the number of elements of the array that are currently part of the heap
 and the content of the first @c numElements elements of the array. The
 portion of the array that constitutes the heap satisfies a certain
 invariant property (heap property, @ref heap-def-tech). From a user
 viewpoint, the most important consequence is that the first element
 of the array (the one of index 0) is also the smallest (according to
 ::VL_HEAP_cmp).

 Elements are added to the heap by ::VL_HEAP_push and removed from the
 heap by ::VL_HEAP_pop.  A push operation adds to the heap the array
 element immediately after the last element already in the heap
 (i.e. the element of index @c numElements) and increases the number of
 heap elements @c numElements. Elements in the heap are swapped as required in
 order to maintain the heap consistency.  Similarly, a pop operation
 removes the first (smaller) element from the heap and decreases the
 number of heap elements @c numElements.

 The values of nodes currently in the heap can be updated by
 ::VL_HEAP_update. Notice however that using this function requires
 knowing the index of the element that needs to be updated up to the
 swapping operations that the heap performs to maintain
 consistency. Typically, this requires redefining ::VL_HEAP_swap to
 keep track of such changes (@ref heap-def-overview-general).

 
 @subsection heap-def-overview-general General usage
 

 The heap container may be mapped to any type by reimplementing
 ::VL_HEAP_cmp and ::VL_HEAP_swap explicitly. For instance
 the following code redefines ::VL_HEAP_cmp to deal with the case
 in which the heap is an array of structures:

 @code
 typedef struct _S { int x ; } S ;
 int s_cmp (S const * v, vl_uindex a, vl_uindex b) {
   return v[a].x - v[b].x ;
 }
 #define VL_HEAP_prefix  s_heap
 #define VL_HEAP_type    S
 #define VL_HEAP_cmp     s_cmp
 #include 
 @endcode

 In the following example, the heap itself is an arbitrary structure:

 @code
 typedef struct _H { int* array ; } H ;
 int h_cmp (H const * h, vl_uindex a, vl_uindex b) {
   return h->array[a] - h->array[b] ;
 }
 int h_swap (H * h, vl_uindex a, vl_uindex b) {
   int t = h->array[a] ;
   h->array[a] = h->array[b] ;
   h->array[b] = t ;
 }
 #define VL_HEAP_prefix  h_heap
 #define VL_HEAP_swap    h_swap
 #define VL_HEAP_cmp     h_cmp
 #include 
 @endcode

 
 @section heap-def-tech Technical details
 

 The heap is organised as a binary tree with the property (heap
 property) that any node is not larger than any of its
 children. In particular, the root is the smallest node.

 @ref heap-def.h uses the standard binary tree representation as a linear
 array. Tree nodes are mapped to array elements as follows:
 array[0] corresponds to the root, array[1]
 and array[2] to the root left and right children and so
 on.  In this way, the tree structure is fully specified by the total
 number of nodes N.

 Assuming that the heap has N nodes (from
 array[0] to array[N-1]), adding the node
 array[N] to the heap is done by a push down
 operation: if the node array[N] is smaller than its
 parent (violating the heap property) it is pushed down by swapping it
 with the parent, and so on recursively.

 Removing the smallest element array[0] with an heap of
 N nodes is done by swapping array[0] with
 array[N-1]. If then array[0] is larger than
 any of its children, it is swapped with the smallest of the two and
 so on recursively (push up operation).

 Restoring the heap property after an element array[i]
 has been modified can be done by a push up or push down operation on
 that node.

 **/

#include "host.h"
#include 

#ifndef VL_HEAP_prefix
#error "VL_HEAP_prefix must be defined"
#endif

#ifndef VL_HEAP_array
#ifndef VL_HEAP_type
#error "VL_HEAP_type must be defined if VL_HEAP_array is not"
#endif
#define VL_HEAP_array       VL_HEAP_type*
#define VL_HEAP_array_const VL_HEAP_type const*
#endif

#ifndef VL_HEAP_array_const
#define VL_HEAP_array_const VL_HEAP_array
#endif

#ifdef __DOXYGEN__
#define VL_HEAP_prefix  HeapObject       /**< Prefix of the heap functions */
#define VL_HEAP_type    HeapType         /**< Data type of the heap elements */
#define VL_HEAP_array   HeapType*        /**< Data type of the heap container */
#define VL_HEAP_array   HeapType const*  /**< Const data type of the heap container */
#endif

/* ---------------------------------------------------------------- */

#ifndef VL_HEAP_DEF_H
#define VL_HEAP_DEF_H

/** @internal @brief Get index of parent node
 ** @param index a node index.
 ** @return index of the parent node.
 **/

VL_INLINE vl_uindex
vl_heap_parent (vl_uindex index)
{
  if (index == 0) return 0 ;
  return (index - 1) / 2 ;
}

/** @internal @brief Get index of left child
 ** @param index a node index.
 ** @return index of the left child.
 **/

VL_INLINE vl_uindex
vl_heap_left_child (vl_uindex index)
{
  return 2 * index + 1 ;
}

/** @internal @brief Get index of right child
 ** @param index a node index.
 ** @return index of the right child.
 **/

VL_INLINE vl_uindex
vl_heap_right_child (vl_uindex index)
{
  return vl_heap_left_child (index) + 1 ;
}

/* VL_HEAP_DEF_H */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_cmp) || defined(__DOXYGEN__)
#define VL_HEAP_cmp VL_XCAT(VL_HEAP_prefix, _cmp)

/** @brief Compare two heap elements
 ** @param array heap array.
 ** @param indexA index of the first element @c A to compare.
 ** @param indexB index of the second element @c B to comapre.
 ** @return a negative number if @c AB.
 **/

VL_INLINE VL_HEAP_type
VL_HEAP_cmp
(VL_HEAP_array_const array,
 vl_uindex indexA,
 vl_uindex indexB)
{
  return array[indexA] - array[indexB] ;
}

/* VL_HEAP_cmp */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_swap) || defined(__DOXYGEN__)
#define VL_HEAP_swap VL_XCAT(VL_HEAP_prefix, _swap)

/** @brief Swap two heap elements
 ** @param array array of nodes.
 ** @param array heap array.
 ** @param indexA index of the first node to swap.
 ** @param indexB index of the second node to swap.
 **
 ** The function swaps the two heap elements @a a and @ b. The function
 ** uses a temporary element and the copy operator, which must be
 ** well defined for the heap elements.
 **/

VL_INLINE void
VL_HEAP_swap
(VL_HEAP_array array,
 vl_uindex indexA,
 vl_uindex indexB)
{
  VL_HEAP_type t = array [indexA] ;
  array [indexA] = array [indexB] ;
  array [indexB] = t ;
}

/* VL_HEAP_swap */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_up) || defined(__DOXYGEN__)
#define VL_HEAP_up VL_XCAT(VL_HEAP_prefix, _up)

/** @brief Heap up operation
 ** @param array pointer to the heap array.
 ** @param heapSize size of the heap.
 ** @param index index of the node to push up.
 **/

VL_INLINE void
VL_HEAP_up
(VL_HEAP_array array, vl_size heapSize, vl_uindex index)
{
  vl_uindex leftIndex  = vl_heap_left_child (index) ;
  vl_uindex rightIndex = vl_heap_right_child (index) ;

  /* no childer: stop */
  if (leftIndex >= heapSize) return ;

  /* only left childer: easy */
  if (rightIndex >= heapSize) {
    if (VL_HEAP_cmp (array, index, leftIndex) > 0) {
      VL_HEAP_swap (array, index, leftIndex) ;
    }
    return ;
  }

  /* both childern */
  {
    if (VL_HEAP_cmp (array, leftIndex, rightIndex) < 0) {
      /* swap with left */
      if (VL_HEAP_cmp (array, index, leftIndex) > 0) {
        VL_HEAP_swap (array, index, leftIndex) ;
        VL_HEAP_up (array, heapSize, leftIndex) ;
      }
    } else {
      /* swap with right */
      if (VL_HEAP_cmp (array, index, rightIndex) > 0) {
        VL_HEAP_swap (array, index, rightIndex) ;
        VL_HEAP_up (array, heapSize, rightIndex) ;
      }
    }
  }
}

/* VL_HEAP_up */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_down) || defined(__DOXYGEN__)
#define VL_HEAP_down VL_XCAT(VL_HEAP_prefix, _down)

/** @brief Heap down operation
 ** @param array pointer to the heap node array.
 ** @param index index of the node to push up.
 **/

VL_INLINE void
VL_HEAP_down
(VL_HEAP_array array, vl_uindex index)
{
  vl_uindex parentIndex ;

  if (index == 0) return  ;

  parentIndex = vl_heap_parent (index) ;

  if (VL_HEAP_cmp (array, index, parentIndex) < 0) {
    VL_HEAP_swap (array, index, parentIndex) ;
    VL_HEAP_down (array, parentIndex) ;
  }
}

/* VL_HEAP_down */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_push) || defined(__DOXYGEN__)
#define VL_HEAP_push VL_XCAT(VL_HEAP_prefix, _push)

/** @brief Heap push operation
 ** @param array pointer to the heap array.
 ** @param heapSize (in/out) size of the heap.
 **
 ** The function adds to the heap the element of index @c heapSize
 ** and increments @c heapSize.
 **/

VL_INLINE void
VL_HEAP_push
(VL_HEAP_array array, vl_size *heapSize)
{
  VL_HEAP_down (array, *heapSize) ;
  *heapSize += 1 ;
}

/* VL_HEAP_push */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_pop) || defined(__DOXYGEN__)
#define VL_HEAP_pop VL_XCAT(VL_HEAP_prefix, _pop)

/** @brief Heap pop operation
 ** @param array pointer to the heap array.
 ** @param heapSize (in/out) size of the heap.
 ** @return index of the popped element.
 **
 ** The function extracts from the heap the element of index 0
 ** (the smallest element) and decreases @c heapSize.
 **
 ** The element extracted is moved as the first element after
 ** the heap end (thus it has index @c heapSize). For convenience,
 ** this index is returned by the function.
 **
 ** Popping from an empty heap is undefined.
 **/

VL_INLINE vl_uindex
VL_HEAP_pop
(VL_HEAP_array array, vl_size *heapSize)
{
  assert (*heapSize) ;

  *heapSize -= 1 ;

  VL_HEAP_swap (array, 0, *heapSize) ;

  if (*heapSize > 1) {
    VL_HEAP_up (array, *heapSize, 0) ;
  }

  return *heapSize ;
}

/* VL_HEAP_pop */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_HEAP_update) || defined(__DOXYGEN__)
#define VL_HEAP_update VL_XCAT(VL_HEAP_prefix, _update)

/** @brief Heap update operation
 ** @param array pointer to the heap array.
 ** @param heapSize size of the heap.
 ** @param index index of the node to update.
 **
 ** The function updates the heap to account for a change to the
 ** element of index @c index in the heap.
 **
 ** Notice that using this
 ** function requires knowing the index of the heap index of
 ** element that was changed. Since the heap swaps elements in the
 ** array, this is in general different from the index that that
 ** element had originally.
 **/

VL_INLINE void
VL_HEAP_update
(VL_HEAP_array array,
 vl_size heapSize,
 vl_uindex index)
{
  VL_HEAP_up (array, heapSize, index) ;
  VL_HEAP_down (array, index) ;
}

/* VL_HEAP_update */
#endif

/* ---------------------------------------------------------------- */

#undef VL_HEAP_cmp
#undef VL_HEAP_swap
#undef VL_HEAP_up
#undef VL_HEAP_down
#undef VL_HEAP_push
#undef VL_HEAP_pop
#undef VL_HEAP_update
#undef VL_HEAP_prefix
#undef VL_HEAP_type
#undef VL_HEAP_array
#undef VL_HEAP_array_const
vlfeat/vl/mathop_avx.h0000644000175000017500000000253712237004745013720 0ustar  dimadima/** @file mathop_avx.h
 ** @brief mathop for avx
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/* ---------------------------------------------------------------- */
#ifndef VL_MATHOP_AVX_H_INSTANTIATING

#ifndef VL_MATHOP_AVX_H
#define VL_MATHOP_AVX_H

#undef FLT
#define FLT VL_TYPE_DOUBLE
#define VL_MATHOP_AVX_H_INSTANTIATING
#include "mathop_avx.h"

#undef FLT
#define FLT VL_TYPE_FLOAT
#define VL_MATHOP_AVX_H_INSTANTIATING
#include "mathop_avx.h"

/* VL_MATHOP_AVX_H */
#endif

/* ---------------------------------------------------------------- */
/* VL_MATHOP_AVX_H_INSTANTIATING */
#else

#ifndef VL_DISABLE_AVX
#include "generic.h"
#include "float.th"

VL_EXPORT T
VL_XCAT(_vl_distance_mahalanobis_sq_avx_, SFX)
(vl_size dimension, T const * X, T const * MU, T const * S);

VL_EXPORT T
VL_XCAT(_vl_distance_l2_avx_, SFX)
(vl_size dimension, T const * X, T const * Y);

VL_EXPORT void
VL_XCAT(_vl_weighted_sigma_avx_, SFX)
(vl_size dimension, T * S, T const * X, T const * Y, T const W);

VL_EXPORT void
VL_XCAT(_vl_weighted_mean_avx_, SFX)
(vl_size dimension, T * MU, T const * X, T const W);

/* ! VL_DISABLE_AVX */
#endif

#undef VL_MATHOP_AVX_H_INSTANTIATING
#endif
vlfeat/vl/array.c0000644000175000017500000001305112237004736012654 0ustar  dimadima/** @file array.h
 ** @brief Array
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#include "array.h"
#include 

/** @brief Get number of elements in array
 ** @param self array.
 ** @return number of elements.
 **/

VL_EXPORT vl_size
vl_array_get_num_elements (VlArray const * self)
{
  vl_size numElements = 1 ;
  vl_uindex k ;
  if (self->numDimensions == 0) {
    return 0 ;
  }
  for (k = 0 ; k < self->numDimensions ; ++k) {
    numElements *= self->dimensions[k] ;
  }
  return numElements ;
}

/* ---------------------------------------------------------------- */
/*                                                  init &  dealloc */
/* ---------------------------------------------------------------- */

/** @brief New numeric array
 ** @param self array to initialize.
 ** @param type data type.
 ** @param numDimensions number of dimensions.
 ** @param dimensions dimensions.
 **
 ** The function initializes the specified array and allocates
 ** the necessary memory for storage.
 **/

VL_EXPORT VlArray *
vl_array_init (VlArray* self, vl_type type,
               vl_size numDimensions, vl_size const * dimensions)
{
  assert (numDimensions <= VL_ARRAY_MAX_NUM_DIMENSIONS) ;
  self->type = type ;
  self->numDimensions = numDimensions ;
  memcpy(self->dimensions, dimensions, sizeof(vl_size) * numDimensions) ;
  self->data = vl_malloc(vl_get_type_size(type) * vl_array_get_num_elements (self)) ;
  self->isEnvelope = VL_FALSE ;
  self->isSparse = VL_FALSE ;
  return self ;
}

/** @brief New numeric array envelope
 ** @param self array to initialize.
 ** @param data data to envelople.
 ** @param type data type.
 ** @param numDimensions number of dimensions.
 ** @param dimensions dimensions.
 **
 ** The function initializes the specified array wrapping the
 ** specified buffer.
 **/

VL_EXPORT VlArray *
vl_array_init_envelope (VlArray * self, void * data, vl_type type,
                        vl_size numDimensions, vl_size const * dimensions)
{
  assert (numDimensions <= VL_ARRAY_MAX_NUM_DIMENSIONS) ;
  self->type = type ;
  self->numDimensions = numDimensions ;
  memcpy(self->dimensions, dimensions, sizeof(vl_size) * numDimensions) ;
  self->data = data ;
  self->isEnvelope = VL_TRUE ;
  self->isSparse = VL_FALSE ;
  return self ;
}

/** @brief New numeric array with matrix shape
 ** @param self array to initialize.
 ** @param type type.
 ** @param numRows number of rows.
 ** @param numColumns number of columns.
 **/

VL_EXPORT VlArray *
vl_array_init_matrix (VlArray * self, vl_type type, vl_size numRows, vl_size numColumns)
{
  vl_size dimensions [2] = {numRows, numColumns} ;
  return vl_array_init (self, type, 2, dimensions) ;
}

/** @brief New numeric array envelpe with matrix shape
 ** @param self array to initialize.
 ** @param data data to envelope.
 ** @param type type.
 ** @param numRows number of rows.
 ** @param numColumns number of columns.
 **/

VL_EXPORT VlArray *
vl_array_init_matrix_envelope (VlArray * self, void * data,
                                vl_type type, vl_size numRows, vl_size numColumns)
{
  vl_size dimensions [2] = {numRows, numColumns} ;
  return vl_array_init_envelope (self, data, type, 2, dimensions) ;
}

/** @brief Delete array
 ** @param self array.
 **/

VL_EXPORT void
vl_array_dealloc (VlArray * self)
{
  if (! self->isEnvelope) {
    if (self->data) {
      vl_free(self->data) ;
      self->data = NULL ;
    }
  }
}

/* ---------------------------------------------------------------- */
/*                                                    new &  delete */
/* ---------------------------------------------------------------- */


/** @brief New numeric array
 ** @param type data type.
 ** @param numDimensions number of dimensions.
 ** @param dimensions dimensions.
 **
 ** The function creates a new VLArray instance and allocates
 ** the necessary memory for storage.
 **/

VL_EXPORT VlArray *
vl_array_new (vl_type type, vl_size numDimensions, vl_size const * dimensions)
{
  VlArray * self = vl_malloc(sizeof(VlArray)) ;
  return vl_array_init(self, type, numDimensions, dimensions) ;
}

/** @brief New numeric array with matrix shape
 ** @param type type.
 ** @param numRows number of rows.
 ** @param numColumns number of columns.
 **/

VL_EXPORT VlArray *
vl_array_new_matrix (vl_type type, vl_size numRows, vl_size numColumns)
{
  vl_size dimensions [2] = {numRows, numColumns} ;
  return vl_array_new (type, 2, dimensions) ;
}

/** @brief New numeric array envelope
 ** @param data data to envelople.
 ** @param type data type.
 ** @param numDimensions number of dimensions.
 ** @param dimensions dimensions.
 **/

VL_EXPORT VlArray *
vl_array_new_envelope (void * data, vl_type type,
                       vl_size numDimensions, vl_size const * dimensions)
{
  VlArray * self = vl_malloc(sizeof(VlArray)) ;
  return vl_array_init_envelope(self, data, type, numDimensions, dimensions) ;
}

/** @brief New numeric array envelpe with matrix shape
 ** @param data data to envelope.
 ** @param type type.
 ** @param numRows number of rows.
 ** @param numColumns number of columns.
 **/

VL_EXPORT VlArray *
vl_array_new_matrix_envelope (void * data, vl_type type, vl_size numRows, vl_size numColumns)
{
  vl_size dimensions [2] = {numRows, numColumns} ;
  return vl_array_new_envelope (data, type, 2, dimensions) ;
}

/** @brief Delete array
 ** @param self array.
 **/

VL_EXPORT void
vl_array_delete (VlArray * self)
{
  vl_array_dealloc(self) ;
  vl_free(self) ;
}
vlfeat/vl/random.h0000644000175000017500000000777012237004745013036 0ustar  dimadima/** @file random.h
 ** @brief Random number generator (@ref random)
 ** @author Andrea Vedaldi
 ** @see @ref random
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_RANDOM_H
#define VL_RANDOM_H

#include "host.h"

/** @brief Random numbber generator state */
typedef struct _VlRand {
  vl_uint32 mt [624] ;
  vl_uint32 mti ;
} VlRand ;

/** @name Setting and reading the state
 **
 ** @{ */
VL_EXPORT void vl_rand_init (VlRand * self) ;
VL_EXPORT void vl_rand_seed (VlRand * self, vl_uint32 s) ;
VL_EXPORT void vl_rand_seed_by_array (VlRand * self,
                                      vl_uint32 const key [],
                                      vl_size keySize) ;
/** @} */

/** @name Generate random numbers
 **
 ** @{ */
VL_INLINE vl_uint64 vl_rand_uint64 (VlRand * self) ;
VL_INLINE vl_int64  vl_rand_int63  (VlRand * self) ;
VL_EXPORT vl_uint32 vl_rand_uint32 (VlRand * self) ;
VL_INLINE vl_int32  vl_rand_int31  (VlRand * self) ;
VL_INLINE double    vl_rand_real1  (VlRand * self) ;
VL_INLINE double    vl_rand_real2  (VlRand * self) ;
VL_INLINE double    vl_rand_real3  (VlRand * self) ;
VL_INLINE double    vl_rand_res53  (VlRand * self) ;
VL_INLINE vl_uindex vl_rand_uindex (VlRand * self, vl_uindex range) ;
/** @} */

VL_EXPORT void vl_rand_permute_indexes (VlRand * self, vl_index* array, vl_size size) ;

/* ---------------------------------------------------------------- */

/** @brief Generate a random index in a given range
 ** @param self random number generator.
 ** @param range range.
 ** @return an index sampled uniformly at random in the interval [0, @c range - 1]
 **
 ** @remark Currently, this function uses a simple algorithm that
 ** may yield slightly biased samples if @c range is not a power of
 ** two.
 **/

VL_INLINE vl_uindex
vl_rand_uindex (VlRand * self, vl_uindex range)
{
  if (range <= 0xffffffff) {
    /* 32-bit version */
    return (vl_rand_uint32 (self) % (vl_uint32)range) ;
  } else {
    /* 64-bit version */
    return (vl_rand_uint64 (self) % range) ;
  }
}

/** @brief Generate a random UINT64
 ** @param self random number generator.
 ** @return a random number in [0, 0xffffffffffffffff].
 **/

VL_INLINE vl_uint64
vl_rand_uint64 (VlRand * self)
{
  vl_uint64 a = vl_rand_uint32 (self) ;
  vl_uint64 b = vl_rand_uint32 (self) ;
  return (a << 32) | b ;
}

/** @brief Generate a random INT63
 ** @param self random number generator.
 ** @return a random number in [0, 0x7fffffffffffffff].
 **/

VL_INLINE vl_int64
vl_rand_int63 (VlRand * self)
{
  return (vl_int64)(vl_rand_uint64 (self) >> 1) ;
}

/** @brief Generate a random INT31
 ** @param self random number generator.
 ** @return a random number in [0, 0x7fffffff].
 **/

VL_INLINE vl_int32
vl_rand_int31 (VlRand * self)
{
  return (vl_int32)(vl_rand_uint32 (self) >> 1) ;
}

/** @brief Generate a random number in [0,1]
 ** @param self random number generator.
 ** @return a random number.
 **/

VL_INLINE double
vl_rand_real1 (VlRand * self)
{
  return vl_rand_uint32(self)*(1.0/4294967295.0);
  /* divided by 2^32-1 */
}

/** @brief Generate a random number in [0,1)
 ** @param self random number generator.
 ** @return a random number.
 **/

VL_INLINE double
vl_rand_real2 (VlRand * self)
{
  return vl_rand_uint32(self)*(1.0/4294967296.0);
  /* divided by 2^32 */
}

/** @brief Generate a random number in (0,1)
 ** @param self random number generator.
 ** @return a random number.
 **/

VL_INLINE double
vl_rand_real3 (VlRand * self)
{
  return (((double)vl_rand_uint32(self)) + 0.5)*(1.0/4294967296.0);
  /* divided by 2^32 */
}

/** @brief Generate a random number in [0,1) with 53-bit resolution
 ** @param self random number generator.
 ** @return a random number.
 **/

VL_INLINE double
vl_rand_res53 (VlRand * self)
{
  vl_uint32
  a = vl_rand_uint32(self) >> 5,
  b = vl_rand_uint32(self) >> 6 ;
  return (a * 67108864.0 + b) * (1.0 / 9007199254740992.0) ;
}

/* VL_RANDOM_H */
#endif
vlfeat/vl/gmm.c0000644000175000017500000014756512237004745012340 0ustar  dimadima/** @file gmm.c
 ** @brief Gaussian Mixture Models - Implementation
 ** @author David Novotny
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2013 David Novotny and Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page gmm Gaussian Mixture Models (GMM)
@author David Novotny
@author Andrea Vedaldi
@tableofcontents


@ref gmm.h is an implementation of *Gaussian Mixture Models* (GMMs).
The main functionality provided by this module is learning GMMs from
data by maximum likelihood. Model optimization uses the Expectation
Maximization (EM) algorithm @cite{dempster77maximum}. The
implementation supports @c float or @c double data types, is
parallelized, and is tuned to work reliably and effectively on
datasets of visual features. Stability is obtained in part by
regularizing and restricting the parameters of the GMM.

@ref gmm-starting demonstreates how to use the C API to compute the FV
representation of an image. For further details refer to:

- @subpage gmm-fundamentals


@section gmm-starting Getting started


In order to use @ref gmm.h to learn a GMM from training data, create a
new ::VlGMM object instance, set the parameters as desired, and run
the training code. The following example learns @c numClusters
Gaussian components from @c numData vectors of dimension @c dimension
and storage class @c float using at most 100 EM iterations:

@code
float * means ;
float * covariances ;
float * priors ;
float * posteriors ;

double loglikelihood ;

// create a new instance of a GMM object for float data
gmm = vl_gmm_new (VL_TYPE_FLOAT, dimension, numClusters) ;

// set the maximum number of EM iterations to 100
vl_gmm_set_max_num_iterations (gmm, 100) ;

// set the initialization to random selection
vl_gmm_set_initialization (gmm,VlGMMRand);

// cluster the data, i.e. learn the GMM
vl_gmm_cluster (gmm, data, numData);

// get the means, covariances, and priors of the GMM
means = vl_gmm_get_means(gmm);
covariances = vl_gmm_get_covariances(gmm);
priors = vl_gmm_get_priors(gmm);

// get loglikelihood of the estimated GMM
loglikelihood = vl_gmm_get_loglikelihood(gmm) ;

// get the soft assignments of the data points to each cluster
posteriors = vl_gmm_get_posteriors(gmm) ;
@endcode

@note ::VlGMM assumes that the covariance matrices of the GMM are
diagonal. This reduces significantly the number of parameters to learn
and is usually an acceptable compromise in vision applications. If the
data is significantly correlated, it can be beneficial to de-correlate
it by PCA rotation or projection in pre-processing.

::vl_gmm_get_loglikelihood is used to get the final loglikelihood of
the estimated mixture, ::vl_gmm_get_means and ::vl_gmm_get_covariances
to obtain the means and the diagonals of the covariance matrices of
the estimated Gaussian modes, and ::vl_gmm_get_posteriors to get the
posterior probabilities that a given point is associated to each of
the modes (soft assignments).

The learning algorithm, which uses EM, finds a local optimum of the
objective function. Therefore the initialization is crucial in
obtaining a good model, measured in term of the final
loglikelihood. ::VlGMM supports a few methods (use
::vl_gmm_set_initialization to choose one) as follows:

Method                | ::VlGMMInitialization enumeration       | Description
----------------------|-----------------------------------------|-----------------------------------------------
Random initialization | ::VlGMMRand                             | Random initialization of the mixture parameters
KMeans                | ::VlGMMKMeans                           | Initialization of the mixture parameters using ::VlKMeans
Custom                | ::VlGMMCustom                           | User specified initialization

Note that in the case of ::VlGMMKMeans initialization, an object of
type ::VlKMeans object must be created and passed to the ::VlGMM
instance (see @ref kmeans to see how to correctly set up this object).

When a user wants to use the ::VlGMMCustom method, the initial means,
covariances and priors have to be specified using the
::vl_gmm_set_means, ::vl_gmm_set_covariances and ::vl_gmm_set_priors
methods.
**/

/**

@page gmm-fundamentals GMM fundamentals
@tableofcontents


A *Gaussian Mixture Model* (GMM) is a mixture of $K$ multivariate
Gaussian distributions. In order to sample from a GMM, one samples
first the component index $k \in \{1,\dots,K\}$ with *prior
probability* $\pi_k$, and then samples the vector $\bx \in
\mathbb{R}^d$ from the $k$-th Gaussian distribution
$p(\bx|\mu_k,\Sigma_k)$. Here $\mu_k$ and $\Sigma_k$ are respectively
the *mean* and *covariance* of the distribution. The GMM is completely
specified by the parameters $\Theta=\{\pi_k,\mu_k,\Sigma_k; k =
1,\dots,K\}$

The density $p(\bx|\Theta)$ induced on the training data is obtained
by marginalizing the component selector $k$, obtaining
\[
p(\bx|\Theta)
= \sum_{k=1}^{K} \pi_k p( \bx_i |\mu_k,\Sigma_k),
\qquad
p( \bx |\mu_k,\Sigma_k)
=
\frac{1}{\sqrt{(2\pi)^d\det\Sigma_k}}
\exp\left[
-\frac{1}{2} (\bx-\mu_k)^\top\Sigma_k^{-1}(\bx-\mu_k)
\right].
\]
Learning a GMM to fit a dataset $X=(\bx_1, \dots, \bx_n)$ is usually
done by maximizing the log-likelihood of the data:
@f[
 \ell(\Theta;X)
 = E_{\bx\sim\hat p} [ \log p(\bx|\Theta) ]
 = \frac{1}{n}\sum_{i=1}^{n} \log \sum_{k=1}^{K} \pi_k p(\bx_i|\mu_k, \Sigma_k)
@f]
where $\hat p$ is the empirical distribution of the data. An algorithm
to solve this problem is introduced next.


@section gmm-em Learning a GMM by expectation maximization


The direct maximization of the log-likelihood function of a GMM is
difficult due to the fact that the assignments of points to Gaussian
mode is not observable and, as such, must be treated as a latent
variable.

Usually, GMMs are learned by using the *Expectation Maximization* (EM)
algorithm @cite{dempster77maximum}. Consider in general the problem of
estimating to the maximum likelihood a distribution $p(x|\Theta) =
\int p(x,h|\Theta)\,dh$, where $x$ is a measurement, $h$ is a *latent
variable*, and $\Theta$ are the model parameters. By introducing an
auxiliary distribution $q(h|x)$ on the latent variable, one can use
Jensen inequality to obtain the following lower bound on the
log-likelihood:

@f{align*}
\ell(\Theta;X) =
E_{x\sim\hat p} \log p(x|\Theta)
&= E_{x\sim\hat p} \log \int p(x,h|\Theta) \,dh \\
&= E_{x\sim\hat p} \log \int \frac{p(x,h|\Theta)}{q(h|x)} q(h|x)\,dh \\
&\geq E_{x\sim\hat p} \int q(h) \log \frac{p(x,h|\Theta)}{q(h|x)}\,dh \\
&= E_{(x,q) \sim q(h|x) \hat p(x)} \log p(x,h|\Theta) -
   E_{(x,q) \sim q(h|x) \hat p(x)} \log q(h|x)
@f}

The first term of the last expression is the log-likelihood of the
model where both the $x$ and $h$ are observed and joinlty distributed
as $q(x|h)\hat p(x)$; the second term is the a average entropy of the
latent variable, which does not depend on $\Theta$. This lower bound
is maximized and becomes tight by setting $q(h|x) = p(h|x,\Theta)$ to
be the posterior distribution on the latent variable $h$ (given the
current estimate of the parameters $\Theta$). In fact:

\[
E_{x \sim \hat p} \log p(x|\Theta)
=
E_{(x,h) \sim p(h|x,\Theta) \hat p(x)}\left[ \log \frac{p(x,h|\Theta)}{p(h|x,\Theta)} \right]
=
E_{(x,h) \sim p(h|x,\Theta) \hat p(x)} [ \log p(x|\Theta) ]
=
\ell(\Theta;X).
\]

EM alternates between updating the latent variable auxiliary
distribution $q(h|x) = p(h|x,\Theta_t)$ (*expectation step*) given the
current estimate of the parameters $\Theta_t$, and then updating the
model parameters $\Theta_{t+1}$ by maximizing the log-likelihood lower
bound derived (*maximization step*). The simplification is that in the
maximization step both $x$ and $h$ are now ``observed'' quantities.
This procedure converges to a local optimum of the model
log-likelihood.

@subsection gmm-expectation-step Expectation step

In the case of a GMM, the latent variables are the point-to-cluster
assignments $k_i, i=1,\dots,n$, one for each of $n$ data points. The
auxiliary distribution $q(k_i|\bx_i) = q_{ik}$ is a matrix with $n
\times K$ entries. Each row $q_{i,:}$ can be thought of as a vector of
soft assignments of the data points $\bx_i$ to each of the Gaussian
modes. Setting $q_{ik} = p(k_i | \bx_i, \Theta)$ yields

\[
 q_{ik} =
\frac
{\pi_k p(\bx_i|\mu_k,\Sigma_k)}
{\sum_{l=1}^K \pi_l p(\bx_i|\mu_l,\Sigma_l)}
\]

where the Gaussian density $p(\bx_i|\mu_k,\Sigma_k)$ was given above.

One important point to keep in mind when these probabilities are
computed is the fact that the Gaussian densities may attain very low
values and underflow in a vanilla implementation. Furthermore, VLFeat
GMM implementation restricts the covariance matrices to be
diagonal. In this case, the computation of the determinant of
$\Sigma_k$ reduces to computing the trace of the matrix and the
inversion of $\Sigma_k$ could be obtained by inverting the elements on
the diagonal of the covariance matrix.

@subsection gmm-maximization-step  Maximization step

The M step estimates the parameters of the Gaussian mixture components
and the prior probabilities $\pi_k$ given the auxiliary distribution
on the point-to-cluster assignments computed in the E step. Since all
the variables are now ``observed'', the estimate is quite simple. For
example, the mean $\mu_k$ of a Gaussian mode is obtained as the mean
of the data points assigned to it (accounting for the strength of the
soft assignments). The other quantities are obtained in a similar
manner, yielding to:

@f{align*}
 \mu_k &= { { \sum_{i=1}^n q_{ik} \bx_{i} } \over { \sum_{i=1}^n q_{ik} } },
\\
 \Sigma_k &= { { \sum_{i=1}^n { q_{ik} (\bx_{i} - \mu_{k}) {(\bx_{i} - \mu_{k})}^T } } \over { \sum_{i=1}^n q_{ik} } },
\\
 \pi_k &= { \sum_{i=1}^n { q_{ik} } \over { \sum_{i=1}^n \sum_{l=1}^K q_{il} } }.
@f}


@section gmm-fundamentals-init Initialization algorithms


The EM algorithm is a local optimization method. As such, the quality
of the solution strongly depends on the quality of the initial values
of the parameters (i.e.  of the locations and shapes of the Gaussian
modes).

@ref gmm.h supports the following cluster initialization algorithms:

- Random data points. (::vl_gmm_init_with_random_data) This method
  sets the means of the modes by sampling at random a corresponding
  number of data points, sets the covariance matrices of all the modes
  are to the covariance of the entire dataset, and sets the prior
  probabilities of the Gaussian modes to be uniform. This
  initialization method is the fastest, simplest, as well as the one
  most likely to end in a bad local minimum.

- KMeans initialization (::vl_gmm_init_with_kmeans) This
  method uses KMeans to pre-cluster the points. It then sets the means
  and covariances of the Gaussian distributions the sample means and
  covariances of each KMeans cluster. It also sets the prior
  probabilities to be proportional to the mass of each cluster. In
  order to use this initialization method, a user can specify an
  instance of ::VlKMeans by using the function
  ::vl_gmm_set_kmeans_init_object, or let ::VlGMM create one
  automatically.

Alternatively, one can manually specify a starting point
(::vl_gmm_set_priors, ::vl_gmm_set_means, ::vl_gmm_set_covariances).
**/

#include "gmm.h"
#include 
#include 
#include 

#ifdef _OPENMP
#include 
#endif

#ifndef VL_DISABLE_SSE2
#include "mathop_sse2.h"
#endif

#ifndef VL_DISABLE_AVX
#include "mathop_avx.h"
#endif

/* ---------------------------------------------------------------- */
#ifndef VL_GMM_INSTANTIATING
/* ---------------------------------------------------------------- */

#define VL_GMM_MIN_VARIANCE 1e-6
#define VL_GMM_MIN_POSTERIOR 1e-2
#define VL_GMM_MIN_PRIOR 1e-6

struct _VlGMM
{
  vl_type dataType ;                  /**< Data type. */
  vl_size dimension ;                 /**< Data dimensionality. */
  vl_size numClusters ;               /**< Number of clusters  */
  vl_size numData ;                   /**< Number of last time clustered data points.  */
  vl_size maxNumIterations ;          /**< Maximum number of refinement iterations. */
  vl_size numRepetitions   ;          /**< Number of clustering repetitions. */
  int     verbosity ;                 /**< Verbosity level. */
  void *  means;                      /**< Means of Gaussian modes. */
  void *  covariances;                /**< Diagonals of covariance matrices of Gaussian modes. */
  void *  priors;                     /**< Weights of Gaussian modes. */
  void *  posteriors;                 /**< Probabilities of correspondences of points to clusters. */
  double * sigmaLowBound ;            /**< Lower bound on the diagonal covariance values. */
  VlGMMInitialization initialization; /**< Initialization option */
  VlKMeans * kmeansInit;              /**< Kmeans object for initialization of gaussians */
  double LL ;                         /**< Current solution loglikelihood */
  vl_bool kmeansInitIsOwner; /**< Indicates whether a user provided the kmeans initialization object */
} ;

/* ---------------------------------------------------------------- */
/*                                                       Life-cycle */
/* ---------------------------------------------------------------- */

static void
_vl_gmm_prepare_for_data (VlGMM* self, vl_size numData)
{
  if (self->numData < numData) {
    vl_free(self->posteriors) ;
    self->posteriors = vl_malloc(vl_get_type_size(self->dataType) * numData * self->numClusters) ;
  }
  self->numData = numData ;
}

/** @brief Create a new GMM object
 ** @param dataType type of data (::VL_TYPE_FLOAT or ::VL_TYPE_DOUBLE)
 ** @return new GMM object instance.
 **/

VlGMM *
vl_gmm_new (vl_type dataType, vl_size dimension, vl_size numComponents)
{
  vl_index i ;
  vl_size size = vl_get_type_size(dataType) ;
  VlGMM * self = vl_calloc(1, sizeof(VlGMM)) ;
  self->dataType = dataType;
  self->numClusters = numComponents ;
  self->numData = 0;
  self->dimension = dimension ;
  self->initialization = VlGMMRand;
  self->verbosity = 0 ;
  self->maxNumIterations = 50;
  self->numRepetitions = 1;
  self->sigmaLowBound =  NULL ;
  self->priors = NULL ;
  self->covariances = NULL ;
  self->means = NULL ;
  self->posteriors = NULL ;
  self->kmeansInit = NULL ;
  self->kmeansInitIsOwner = VL_FALSE;

  self->priors = vl_calloc (numComponents, size) ;
  self->means = vl_calloc (numComponents * dimension, size) ;
  self->covariances = vl_calloc (numComponents * dimension, size) ;
  self->sigmaLowBound = vl_calloc (dimension, sizeof(double)) ;

  for (i = 0 ; i < (unsigned)self->dimension ; ++i)  { self->sigmaLowBound[i] = 1e-4 ; }
  return self ;
}

/** @brief Reset state
 ** @param self object.
 **
 ** The function reset the state of the GMM object. It deletes
 ** any stored posterior and other internal state variables.
 **/

void
vl_gmm_reset (VlGMM * self)
{
  if (self->posteriors) {
    vl_free(self->posteriors) ;
    self->posteriors = NULL ;
    self->numData = 0 ;
  }
  if (self->kmeansInit && self->kmeansInitIsOwner) {
    vl_kmeans_delete(self->kmeansInit) ;
    self->kmeansInit = NULL ;
    self->kmeansInitIsOwner = VL_FALSE ;
  }
}

/** @brief Deletes a GMM object
 ** @param self GMM object instance.
 **
 ** The function deletes the GMM object instance created
 ** by ::vl_gmm_new.
 **/

void
vl_gmm_delete (VlGMM * self)
{
  if(self->means) vl_free(self->means);
  if(self->covariances) vl_free(self->covariances);
  if(self->priors) vl_free(self->priors);
  if(self->posteriors) vl_free(self->posteriors);
  if(self->kmeansInit && self->kmeansInitIsOwner) {
    vl_kmeans_delete(self->kmeansInit);
  }
  vl_free(self);
}

/* ---------------------------------------------------------------- */
/*                                              Getters and setters */
/* ---------------------------------------------------------------- */

/** @brief Get data type
 ** @param self object
 ** @return data type.
 **/

vl_type
vl_gmm_get_data_type (VlGMM const * self)
{
  return self->dataType ;
}

/** @brief Get the number of clusters
 ** @param self object
 ** @return number of clusters.
 **/

vl_size
vl_gmm_get_num_clusters (VlGMM const * self)
{
  return self->numClusters ;
}

/** @brief Get the number of data points
 ** @param self object
 ** @return number of data points.
 **/

vl_size
vl_gmm_get_num_data (VlGMM const * self)
{
  return self->numData ;
}

/** @brief Get the log likelihood of the current mixture
 ** @param self object
 ** @return loglikelihood.
 **/

double
vl_gmm_get_loglikelihood (VlGMM const * self)
{
  return self->LL ;
}

/** @brief Get verbosity level
 ** @param self object
 ** @return verbosity level.
 **/

int
vl_gmm_get_verbosity (VlGMM const * self)
{
  return self->verbosity ;
}

/** @brief Set verbosity level
 ** @param self object
 ** @param verbosity verbosity level.
 **/

void
vl_gmm_set_verbosity (VlGMM * self, int verbosity)
{
  self->verbosity = verbosity ;
}

/** @brief Get means
 ** @param self object
 ** @return cluster means.
 **/

void const *
vl_gmm_get_means (VlGMM const * self)
{
  return self->means ;
}

/** @brief Get covariances
 ** @param self object
 ** @return diagonals of cluster covariance matrices.
 **/

void const *
vl_gmm_get_covariances (VlGMM const * self)
{
  return self->covariances ;
}

/** @brief Get priors
 ** @param self object
 ** @return priors of cluster gaussians.
 **/

void const *
vl_gmm_get_priors (VlGMM const * self)
{
  return self->priors ;
}

/** @brief Get posteriors
 ** @param self object
 ** @return posterior probabilities of cluster memberships.
 **/

void const *
vl_gmm_get_posteriors (VlGMM const * self)
{
  return self->posteriors ;
}

/** @brief Get maximum number of iterations
 ** @param self object
 ** @return maximum number of iterations.
 **/

vl_size
vl_gmm_get_max_num_iterations (VlGMM const * self)
{
  return self->maxNumIterations ;
}

/** @brief Set maximum number of iterations
 ** @param self VlGMM filter.
 ** @param maxNumIterations maximum number of iterations.
 **/

void
vl_gmm_set_max_num_iterations (VlGMM * self, vl_size maxNumIterations)
{
  self->maxNumIterations = maxNumIterations ;
}

/** @brief Get maximum number of repetitions.
 ** @param self object
 ** @return current number of repretitions for quantization.
 **/

vl_size
vl_gmm_get_num_repetitions (VlGMM const * self)
{
  return self->numRepetitions ;
}

/** @brief Set maximum number of repetitions
 ** @param self object
 ** @param numRepetitions maximum number of repetitions.
 ** The number of repetitions cannot be smaller than 1.
 **/

void
vl_gmm_set_num_repetitions (VlGMM * self, vl_size numRepetitions)
{
  assert (numRepetitions >= 1) ;
  self->numRepetitions = numRepetitions ;
}

/** @brief Get data dimension
 ** @param self object
 ** @return data dimension.
 **/

vl_size
vl_gmm_get_dimension (VlGMM const * self)
{
  return self->dimension ;
}

/** @brief Get initialization algorithm
 ** @param self object
 ** @return initialization algorithm.
 **/

VlGMMInitialization
vl_gmm_get_initialization (VlGMM const * self)
{
  return self->initialization ;
}

/** @brief Set initialization algorithm.
 ** @param self object
 ** @param init initialization algorithm.
 **/
void
vl_gmm_set_initialization (VlGMM * self, VlGMMInitialization init)
{
  self->initialization = init;
}

/** @brief Get KMeans initialization object.
 ** @param self object
 ** @return kmeans initialization object.
 **/
VlKMeans * vl_gmm_get_kmeans_init_object (VlGMM const * self)
{
  return self->kmeansInit;
}

/** @brief Set KMeans initialization object.
 ** @param self object
 ** @param kmeans initialization KMeans object.
 **/
void vl_gmm_set_kmeans_init_object (VlGMM * self, VlKMeans * kmeans)
{
  if (self->kmeansInit && self->kmeansInitIsOwner) {
    vl_kmeans_delete(self->kmeansInit) ;
  }
  self->kmeansInit = kmeans;
  self->kmeansInitIsOwner = VL_FALSE;
}

/** @brief Get the lower bound on the diagonal covariance values.
 ** @param self object
 ** @return lower bound on covariances.
 **/
double const * vl_gmm_get_covariance_lower_bounds (VlGMM const * self)
{
  return self->sigmaLowBound;
}

/** @brief Set the lower bounds on diagonal covariance values.
 ** @param self object.
 ** @param bounds bounds.
 **
 ** There is one lower bound per dimension. Use ::vl:gmm_set_covariance_lower_bound
 ** to set all of them to a given scalar.
 **/
void vl_gmm_set_covariance_lower_bounds (VlGMM * self, double const * bounds)
{
  memcpy(self->sigmaLowBound, bounds, sizeof(double) * self->dimension) ;
}

/** @brief Set the lower bounds on diagonal covariance values.
 ** @param self object.
 ** @param bound bound.
 **
 ** While there is one lower bound per dimension, this function sets
 ** all of them to the specified scalar. Use ::vl:gmm_set_covariance_lower_bounds
 ** to set them individually.
 **/
void vl_gmm_set_covariance_lower_bound (VlGMM * self, double bound)
{
  int i ;
  for (i = 0 ; i < (signed)self->dimension ; ++i) {
    self->sigmaLowBound[i] = bound ;
  }
}

/* ---------------------------------------------------------------- */
/* Instantiate shuffle algorithm */

#define VL_SHUFFLE_type vl_uindex
#define VL_SHUFFLE_prefix _vl_gmm
#include "shuffle-def.h"

/* #ifdef VL_GMM_INSTANTITATING */
#endif

/* ---------------------------------------------------------------- */
#ifdef VL_GMM_INSTANTIATING
/* ---------------------------------------------------------------- */

/* ---------------------------------------------------------------- */
/*                                            Posterior assignments */
/* ---------------------------------------------------------------- */

/** @fn vl_get_gmm_data_posterior_f(float*,vl_size,vl_size,float const*,float const*,vl_size,float const*,float const*)
 ** @brief Get Gaussian modes posterior probabilities
 ** @param posteriors posterior probabilities (output)/
 ** @param numClusters number of modes in the GMM model.
 ** @param numData number of data elements.
 ** @param priors prior mode probabilities of the GMM model.
 ** @param means means of the GMM model.
 ** @param dimension data dimension.
 ** @param covariances diagonal covariances of the GMM model.
 ** @param data data.
 ** @return data log-likelihood.
 **
 ** This is a helper function that does not require a ::VlGMM object
 ** instance to operate.
 **/

double
VL_XCAT(vl_get_gmm_data_posteriors_, SFX)
(TYPE * posteriors,
 vl_size numClusters,
 vl_size numData,
 TYPE const * priors,
 TYPE const * means,
 vl_size dimension,
 TYPE const * covariances,
 TYPE const * data)
{
  vl_index i_d, i_cl;
  vl_size dim;
  double LL = 0;

  TYPE halfDimLog2Pi = (dimension / 2.0) * log(2.0*VL_PI);
  TYPE * logCovariances ;
  TYPE * logWeights ;
  TYPE * invCovariances ;

#if (FLT == VL_TYPE_FLOAT)
  VlFloatVector3ComparisonFunction distFn = vl_get_vector_3_comparison_function_f(VlDistanceMahalanobis) ;
#else
  VlDoubleVector3ComparisonFunction distFn = vl_get_vector_3_comparison_function_d(VlDistanceMahalanobis) ;
#endif

  logCovariances = vl_malloc(sizeof(TYPE) * numClusters) ;
  invCovariances = vl_malloc(sizeof(TYPE) * numClusters * dimension) ;
  logWeights = vl_malloc(numClusters * sizeof(TYPE)) ;

#if defined(_OPENMP)
#pragma omp parallel for private(i_cl,dim) num_threads(vl_get_max_threads())
#endif
  for (i_cl = 0 ; i_cl < (signed)numClusters ; ++ i_cl) {
    TYPE logSigma = 0 ;
    if (priors[i_cl] < VL_GMM_MIN_PRIOR) {
      logWeights[i_cl] = - (TYPE) VL_INFINITY_D ;
    } else {
      logWeights[i_cl] = log(priors[i_cl]);
    }
    for(dim = 0 ; dim < dimension ; ++ dim) {
      logSigma += log(covariances[i_cl*dimension + dim]);
      invCovariances [i_cl*dimension + dim] = (TYPE) 1.0 / covariances[i_cl*dimension + dim];
    }
    logCovariances[i_cl] = logSigma;
  } /* end of parallel region */

#if defined(_OPENMP)
#pragma omp parallel for private(i_cl,i_d) reduction(+:LL) \
num_threads(vl_get_max_threads())
#endif
  for (i_d = 0 ; i_d < (signed)numData ; ++ i_d) {
    TYPE clusterPosteriorsSum = 0;
    TYPE maxPosterior = (TYPE)(-VL_INFINITY_D) ;

    for (i_cl = 0 ; i_cl < (signed)numClusters ; ++ i_cl) {
      TYPE p =
      logWeights[i_cl]
      - halfDimLog2Pi
      - 0.5 * logCovariances[i_cl]
      - 0.5 * distFn (dimension,
                      data + i_d * dimension,
                      means + i_cl * dimension,
                      invCovariances + i_cl * dimension) ;
      posteriors[i_cl + i_d * numClusters] = p ;
      if (p > maxPosterior) { maxPosterior = p ; }
    }

    for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
      TYPE p = posteriors[i_cl + i_d * numClusters] ;
      p =  exp(p - maxPosterior) ;
      posteriors[i_cl + i_d * numClusters] = p ;
      clusterPosteriorsSum += p ;
    }

    LL +=  log(clusterPosteriorsSum) + (double) maxPosterior ;

    for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
      posteriors[i_cl + i_d * numClusters] /= clusterPosteriorsSum ;
    }
  } /* end of parallel region */

  vl_free(logCovariances);
  vl_free(logWeights);
  vl_free(invCovariances);

  return LL;
}

/* ---------------------------------------------------------------- */
/*                                 Restarts zero-weighted Gaussians */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_gmm_maximization_, SFX)
(VlGMM * self,
 TYPE * posteriors,
 TYPE * priors,
 TYPE * covariances,
 TYPE * means,
 TYPE const * data,
 vl_size numData) ;

static vl_size
VL_XCAT(_vl_gmm_restart_empty_modes_, SFX) (VlGMM * self, TYPE const * data)
{
  vl_size dimension = self->dimension;
  vl_size numClusters = self->numClusters;
  vl_index i_cl, j_cl, i_d, d;
  vl_size zeroWNum = 0;
  TYPE * priors = (TYPE*)self->priors ;
  TYPE * means = (TYPE*)self->means ;
  TYPE * covariances = (TYPE*)self->covariances ;
  TYPE * posteriors = (TYPE*)self->posteriors ;

  //VlRand * rand = vl_get_rand() ;

  TYPE * mass = vl_calloc(sizeof(TYPE), self->numClusters) ;

  if (numClusters <= 1) { return 0 ; }

  /* compute statistics */
  {
    vl_uindex i, k ;
    vl_size numNullAssignments = 0 ;
    for (i = 0 ; i < self->numData ; ++i) {
      for (k = 0 ; k < self->numClusters ; ++k) {
        TYPE p = ((TYPE*)self->posteriors)[k + i * self->numClusters] ;
        mass[k] += p ;
        if (p < VL_GMM_MIN_POSTERIOR) {
          numNullAssignments ++ ;
        }
      }
    }
    if (self->verbosity) {
      VL_PRINTF("gmm: sparsity of data posterior: %.1f%%\n", (double)numNullAssignments / (self->numData * self->numClusters) * 100) ;
    }
  }

#if 0
  /* search for cluster with negligible weight and reassign them to fat clusters */
  for (i_cl = 0 ; i_cl < numClusters ; ++i_cl) {
    if (priors[i_cl] < 0.00001/numClusters) {
      double mass = priors[0]  ;
      vl_index best = 0 ;

      for (j_cl = 1 ; j_cl < numClusters ; ++j_cl) {
        if (priors[j_cl] > mass) { mass = priors[j_cl] ; best = j_cl ; }
      }

      if (j_cl == i_cl) {
        /* this should never happen */
        continue ;
      }

      j_cl = best ;
      zeroWNum ++ ;

      VL_PRINTF("gmm: restarting mode %d by splitting mode %d (with prior %f)\n", i_cl,j_cl,mass) ;

      priors[i_cl] = mass/2 ;
      priors[j_cl] = mass/2 ;
      for (d = 0 ; d < dimension ; ++d) {
        TYPE sigma2 =  covariances[j_cl*dimension + d] ;
        TYPE sigma = VL_XCAT(vl_sqrt_,SFX)(sigma2) ;
        means[i_cl*dimension + d] = means[j_cl*dimension + d] + 0.001 * (vl_rand_real1(rand) - 0.5) * sigma ;
        covariances[i_cl*dimension + d] = sigma2 ;
      }
    }
  }
#endif

  /* search for cluster with negligible weight and reassign them to fat clusters */
  for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
    double size = - VL_INFINITY_D ;
    vl_index best = -1 ;

    if (mass[i_cl] >= VL_GMM_MIN_POSTERIOR *
        VL_MAX(1.0, (double) self->numData / self->numClusters))
    {
      continue ;
    }

    if (self->verbosity) {
      VL_PRINTF("gmm: mode %d is nearly empty (mass %f)\n", i_cl, mass[i_cl]) ;
    }

    /*
     Search for the cluster that (approximately)
     maximally contribute to make the log-likelihood
     small.
     */

    for (j_cl = 0 ; j_cl < (signed)numClusters ; ++j_cl) {
      double size_ ;
      if (priors[j_cl] < VL_GMM_MIN_PRIOR) { continue ; }
      size_ = - 0.5 * (1.0 + log(2*VL_PI)) ;
      for(d = 0 ; d < (signed)dimension ; d++) {
        double sigma2 = covariances[j_cl * dimension + d] ;
        size_ -= 0.5 * log(sigma2) ;
      }
      size_ *= priors[j_cl] ;

      if (self->verbosity > 2) {
        VL_PRINTF("gmm: mode %d: prior %f, mass %f, score %f\n",
                  j_cl, priors[j_cl], mass[j_cl], size_) ;
      }

      if (size_ > size) {
        size = size_ ;
        best = j_cl ;
      }
    }

    j_cl = best ;

    if (j_cl == i_cl || j_cl < 0) {
      if (self->verbosity) {
        VL_PRINTF("gmm: mode %d is empty, "
                  "but no other mode to split could be found\n", i_cl) ;
      }
      continue ;
    }

    if (self->verbosity) {
      VL_PRINTF("gmm: reinitializing empty mode %d with mode %d (prior %f, mass %f, score %f)\n",
                i_cl, j_cl, priors[j_cl], mass[j_cl], size) ;
    }

    /*
     Search for the dimension with maximum variance.
     */

    size = - VL_INFINITY_D ;
    best = - 1 ;

    for(d = 0; d < (signed)dimension; d++) {
      double sigma2 = covariances[j_cl * dimension + d] ;
      if (sigma2 > size) {
        size = sigma2 ;
        best = d ;
      }
    }

    /*
     Reassign points j_cl (mode to split) to i_cl (empty mode).
     */
    {
      TYPE mu = means[best + j_cl * self->dimension] ;
      for(i_d = 0 ; i_d < (signed)self->numData ; ++ i_d) {
        TYPE p = posteriors[j_cl + self->numClusters * i_d] ;
        TYPE q = posteriors[i_cl + self->numClusters * i_d] ; /* ~= 0 */
        if (data[best + i_d * self->dimension] < mu) {
          /* assign this point to i_cl */
          posteriors[i_cl + self->numClusters * i_d] += p ;
          posteriors[j_cl + self->numClusters * i_d] = 0 ;
        } else {
          /* assign this point to j_cl */
          posteriors[i_cl + self->numClusters * i_d] = 0 ;
          posteriors[j_cl + self->numClusters * i_d] += q ;
        }
      }
    }

    /*
     Re-estimate.
     */
    VL_XCAT(_vl_gmm_maximization_, SFX)
    (self,posteriors,priors,covariances,means,data,self->numData) ;
  }

  return zeroWNum;
}

/* ---------------------------------------------------------------- */
/*                                                          Helpers */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_gmm_apply_bounds_, SFX)(VlGMM * self)
{
  vl_uindex dim ;
  vl_uindex k ;
  vl_size numAdjusted = 0 ;
  TYPE * cov = (TYPE*)self->covariances ;
  double const * lbs = self->sigmaLowBound ;

  for (k = 0 ; k < self->numClusters ; ++k) {
    vl_bool adjusted = VL_FALSE ;
    for (dim = 0 ; dim < self->dimension ; ++dim) {
      if (cov[k * self->dimension + dim] < lbs[dim] ) {
        cov[k * self->dimension + dim] = lbs[dim] ;
        adjusted = VL_TRUE ;
      }
    }
    if (adjusted) { numAdjusted ++ ; }
  }

  if (numAdjusted > 0 && self->verbosity > 0) {
    VL_PRINT("gmm: detected %d of %d modes with at least one dimension "
             "with covariance too small (set to lower bound)\n",
             numAdjusted, self->numClusters) ;
  }
}

/* ---------------------------------------------------------------- */
/*                                           EM - Maximization step */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_gmm_maximization_, SFX)
(VlGMM * self,
 TYPE * posteriors,
 TYPE * priors,
 TYPE * covariances,
 TYPE * means,
 TYPE const * data,
 vl_size numData)
{
  vl_size numClusters = self->numClusters;
  vl_index i_d, i_cl;
  vl_size dim ;
  TYPE * oldMeans ;
  double time = 0 ;

  if (self->verbosity > 1) {
    VL_PRINTF("gmm: em: entering maximization step\n") ;
    time = vl_get_cpu_time() ;
  }

  oldMeans = vl_malloc(sizeof(TYPE) * self->dimension * numClusters) ;
  memcpy(oldMeans, means, sizeof(TYPE) * self->dimension * numClusters) ;

  memset(priors, 0, sizeof(TYPE) * numClusters) ;
  memset(means, 0, sizeof(TYPE) * self->dimension * numClusters) ;
  memset(covariances, 0, sizeof(TYPE) * self->dimension * numClusters) ;

#if defined(_OPENMP)
#pragma omp parallel default(shared) private(i_d, i_cl, dim) \
                     num_threads(vl_get_max_threads())
#endif
  {
    TYPE * clusterPosteriorSum_, * means_, * covariances_ ;

#if defined(_OPENMP)
#pragma omp critical
#endif
    {
      clusterPosteriorSum_ = vl_calloc(sizeof(TYPE), numClusters) ;
      means_ = vl_calloc(sizeof(TYPE), self->dimension * numClusters) ;
      covariances_ = vl_calloc(sizeof(TYPE), self->dimension * numClusters) ;
    }

    /*
      Accumulate weighted sums and sum of square differences. Once normalized,
      these become the means and covariances of each Gaussian mode.

      The squared differences will be taken w.r.t. the old means however. In this manner,
      one avoids doing two passes across the data. Eventually, these are corrected to account
      for the new means properly. In principle, one could set the old means to zero, but
      this may cause numerical instabilities (by accumulating large squares).
    */

#if defined(_OPENMP)
#pragma omp for
#endif
    for (i_d = 0 ; i_d < (signed)numData ; ++i_d) {
      for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
        TYPE p = posteriors[i_cl + i_d * self->numClusters] ;
        vl_bool calculated = VL_FALSE ;

        /* skip very small associations for speed */
        if (p < VL_GMM_MIN_POSTERIOR / numClusters) { continue ; }

        clusterPosteriorSum_ [i_cl] += p ;

        #ifndef VL_DISABLE_AVX
        if (vl_get_simd_enabled() && vl_cpu_has_avx()) {
          VL_XCAT(_vl_weighted_mean_sse2_, SFX)
          (self->dimension,
           means_+ i_cl * self->dimension,
           data + i_d * self->dimension,
           p) ;

          VL_XCAT(_vl_weighted_sigma_sse2_, SFX)
          (self->dimension,
           covariances_ + i_cl * self->dimension,
           data + i_d * self->dimension,
           oldMeans + i_cl * self->dimension,
           p) ;

          calculated = VL_TRUE;
        }
        #endif
        #ifndef VL_DISABLE_SSE2
        if (vl_get_simd_enabled() && vl_cpu_has_sse2() && !calculated) {
          VL_XCAT(_vl_weighted_mean_sse2_, SFX)
          (self->dimension,
           means_+ i_cl * self->dimension,
           data + i_d * self->dimension,
           p) ;

           VL_XCAT(_vl_weighted_sigma_sse2_, SFX)
          (self->dimension,
           covariances_ + i_cl * self->dimension,
           data + i_d * self->dimension,
           oldMeans + i_cl * self->dimension,
           p) ;

          calculated = VL_TRUE;
        }
        #endif
        if(!calculated) {
          for (dim = 0 ; dim < self->dimension ; ++dim) {
            TYPE x = data[i_d * self->dimension + dim] ;
            TYPE mu = oldMeans[i_cl * self->dimension + dim] ;
            TYPE diff = x - mu ;
            means_ [i_cl * self->dimension + dim] += p * x ;
            covariances_ [i_cl * self->dimension + dim] += p * (diff*diff) ;
          }
        }
      }
    }

    /* accumulate */
#if defined(_OPENMP)
#pragma omp critical
#endif
    {
      for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
        priors [i_cl] += clusterPosteriorSum_ [i_cl];
        for (dim = 0 ; dim < self->dimension ; ++dim) {
          means [i_cl * self->dimension + dim] += means_ [i_cl * self->dimension + dim] ;
          covariances [i_cl * self->dimension + dim] += covariances_ [i_cl * self->dimension + dim] ;
        }
      }
      vl_free(means_);
      vl_free(covariances_);
      vl_free(clusterPosteriorSum_);
    }
  } /* parallel section */

  /* at this stage priors[] contains the total mass of each cluster */
  for (i_cl = 0 ; i_cl < (signed)numClusters ; ++ i_cl) {
    TYPE mass = priors[i_cl] ;
    /* do not update modes that do not recieve mass */
    if (mass >= 1e-6 / numClusters) {
      for (dim = 0 ; dim < self->dimension ; ++dim) {
        means[i_cl * self->dimension + dim] /= mass ;
        covariances[i_cl * self->dimension + dim] /= mass ;
      }
    }
  }

  /* apply old to new means correction */
  for (i_cl = 0 ; i_cl < (signed)numClusters ; ++ i_cl) {
    TYPE mass = priors[i_cl] ;
    if (mass >= 1e-6 / numClusters) {
      for (dim = 0 ; dim < self->dimension ; ++dim) {
        TYPE mu = means[i_cl * self->dimension + dim] ;
        TYPE oldMu = oldMeans[i_cl * self->dimension + dim] ;
        TYPE diff = mu - oldMu ;
        covariances[i_cl * self->dimension + dim] -= diff * diff ;
      }
    }
  }

  VL_XCAT(_vl_gmm_apply_bounds_,SFX)(self) ;

  {
    TYPE sum = 0;
    for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
      sum += priors[i_cl] ;
    }
    sum = VL_MAX(sum, 1e-12) ;
    for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
      priors[i_cl] /= sum ;
    }
  }

  if (self->verbosity > 1) {
    VL_PRINTF("gmm: em: maximization step completed in %.2f s\n",
              vl_get_cpu_time() - time) ;
  }

  vl_free(oldMeans);
}

/* ---------------------------------------------------------------- */
/*                                                    EM iterations */
/* ---------------------------------------------------------------- */


static double
VL_XCAT(_vl_gmm_em_, SFX)
(VlGMM * self,
 TYPE const * data,
 vl_size numData)
{
  vl_size iteration, restarted ;
  double previousLL = (TYPE)(-VL_INFINITY_D) ;
  double LL = (TYPE)(-VL_INFINITY_D) ;
  double time = 0 ;

  _vl_gmm_prepare_for_data (self, numData) ;

  VL_XCAT(_vl_gmm_apply_bounds_,SFX)(self) ;

  for (iteration = 0 ; 1 ; ++ iteration) {
    double eps ;

    /*
     Expectation: assign data to Gaussian modes
     and compute log-likelihood.
     */

    if (self->verbosity > 1) {
      VL_PRINTF("gmm: em: entering expectation step\n") ;
      time = vl_get_cpu_time() ;
    }

    LL = VL_XCAT(vl_get_gmm_data_posteriors_,SFX)
    (self->posteriors,
     self->numClusters,
     numData,
     self->priors,
     self->means,
     self->dimension,
     self->covariances,
     data) ;

    if (self->verbosity > 1) {
      VL_PRINTF("gmm: em: expectation step completed in %.2f s\n",
                vl_get_cpu_time() - time) ;
    }

    /*
     Check the termination conditions.
     */
    if (self->verbosity) {
      VL_PRINTF("gmm: em: iteration %d: loglikelihood = %f (variation = %f)\n",
                iteration, LL, LL - previousLL) ;
    }
    if (iteration >= self->maxNumIterations) {
      if (self->verbosity) {
        VL_PRINTF("gmm: em: terminating because "
                  "the maximum number of iterations "
                  "(%d) has been reached.\n", self->maxNumIterations) ;
      }
      break ;
    }

    eps = vl_abs_d ((LL - previousLL) / (LL));
    if ((iteration > 0) && (eps < 0.00001)) {
      if (self->verbosity) {
        VL_PRINTF("gmm: em: terminating because the algorithm "
                  "fully converged (log-likelihood variation = %f).\n", eps) ;
      }
      break ;
    }
    previousLL = LL ;

    /*
     Restart empty modes.
     */
    if (iteration > 1) {
      restarted = VL_XCAT(_vl_gmm_restart_empty_modes_, SFX)
        (self, data);
      if ((restarted > 0) & (self->verbosity > 0)) {
        VL_PRINTF("gmm: em: %d Gaussian modes restarted because "
                  "they had become empty.\n", restarted);
      }
    }

    /*
      Maximization: reestimate the GMM parameters.
    */
    VL_XCAT(_vl_gmm_maximization_, SFX)
      (self,self->posteriors,self->priors,self->covariances,self->means,data,numData) ;
  }
  return LL;
}


/* ---------------------------------------------------------------- */
/*                                Kmeans initialization of mixtures */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_gmm_init_with_kmeans_, SFX)
(VlGMM * self,
 TYPE const * data,
 vl_size numData,
 VlKMeans * kmeansInit)
{
  vl_size i_d ;
  vl_uint32 * assignments = vl_malloc(sizeof(vl_uint32) * numData);

  _vl_gmm_prepare_for_data (self, numData) ;

  memset(self->means,0,sizeof(TYPE) * self->numClusters * self->dimension) ;
  memset(self->priors,0,sizeof(TYPE) * self->numClusters) ;
  memset(self->covariances,0,sizeof(TYPE) * self->numClusters * self->dimension) ;
  memset(self->posteriors,0,sizeof(TYPE) * self->numClusters * numData) ;

  /* setup speified KMeans initialization object if any */
  if (kmeansInit) { vl_gmm_set_kmeans_init_object (self, kmeansInit) ; }

  /* if a KMeans initalization object is still unavailable, create one */
  if(self->kmeansInit == NULL) {
    vl_size ncomparisons = VL_MAX(numData / 4, 10) ;
    vl_size niter = 5 ;
    vl_size ntrees = 1 ;
    vl_size nrepetitions = 1 ;
    VlKMeansAlgorithm algorithm = VlKMeansANN ;
    VlKMeansInitialization initialization = VlKMeansRandomSelection ;

    VlKMeans * kmeansInitDefault = vl_kmeans_new(self->dataType,VlDistanceL2) ;
    vl_kmeans_set_initialization(kmeansInitDefault, initialization);
    vl_kmeans_set_max_num_iterations (kmeansInitDefault, niter) ;
    vl_kmeans_set_max_num_comparisons (kmeansInitDefault, ncomparisons) ;
    vl_kmeans_set_num_trees (kmeansInitDefault, ntrees);
    vl_kmeans_set_algorithm (kmeansInitDefault, algorithm);
    vl_kmeans_set_num_repetitions(kmeansInitDefault, nrepetitions);
    vl_kmeans_set_verbosity (kmeansInitDefault, self->verbosity);

    self->kmeansInit = kmeansInitDefault;
    self->kmeansInitIsOwner = VL_TRUE ;
  }

  /* Use k-means to assign data to clusters */
  vl_kmeans_cluster (self->kmeansInit, data, self->dimension, numData, self->numClusters);
  vl_kmeans_quantize (self->kmeansInit, assignments, NULL, data, numData) ;

  /* Transform the k-means assignments in posteriors and estimates the mode parameters */
  for(i_d = 0; i_d < numData; i_d++) {
    ((TYPE*)self->posteriors)[assignments[i_d] + i_d * self->numClusters] = (TYPE) 1.0 ;
  }

  /* Update cluster parameters */
  VL_XCAT(_vl_gmm_maximization_, SFX)
    (self,self->posteriors,self->priors,self->covariances,self->means,data,numData);
  vl_free(assignments) ;
}

/* ---------------------------------------------------------------- */
/*                                Random initialization of mixtures */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_gmm_compute_init_sigma_, SFX)
(VlGMM * self,
 TYPE const * data,
 TYPE * initSigma,
 vl_size dimension,
 vl_size numData)
{
  vl_size dim;
  vl_uindex i;

  TYPE * dataMean ;

  memset(initSigma,0,sizeof(TYPE)*dimension) ;
  if (numData <= 1) return ;

  dataMean = vl_malloc(sizeof(TYPE)*dimension);
  memset(dataMean,0,sizeof(TYPE)*dimension) ;

  /* find mean of the whole dataset */
  for(dim = 0 ; dim < dimension ; dim++) {
    for(i = 0 ; i < numData ; i++) {
      dataMean[dim] += data[i*dimension + dim];
    }
    dataMean[dim] /= numData;
  }

  /* compute variance of the whole dataset */
  for(dim = 0; dim < dimension; dim++) {
    for(i = 0; i < numData; i++) {
      TYPE diff = (data[i*self->dimension + dim] - dataMean[dim]) ;
      initSigma[dim] += diff*diff ;
    }
    initSigma[dim] /= numData - 1 ;
  }

  vl_free(dataMean) ;
}

static void
VL_XCAT(_vl_gmm_init_with_rand_data_, SFX)
(VlGMM * self,
 TYPE const * data,
 vl_size numData)
{
  vl_uindex i, k, dim ;
  VlKMeans * kmeans ;

  _vl_gmm_prepare_for_data(self, numData) ;

  /* initilaize priors of gaussians so they are equal and sum to one */
  for (i = 0 ; i < self->numClusters ; ++i) { ((TYPE*)self->priors)[i] = (TYPE) (1.0 / self->numClusters) ; }

  /* initialize diagonals of covariance matrices to data covariance */
  VL_XCAT(_vl_gmm_compute_init_sigma_, SFX) (self, data, self->covariances, self->dimension, numData);
  for (k = 1 ; k < self->numClusters ; ++ k) {
    for(dim = 0; dim < self->dimension; dim++) {
      *((TYPE*)self->covariances + k * self->dimension + dim) =
      *((TYPE*)self->covariances + dim) ;
    }
  }

  /* use kmeans++ initialization to pick points at random */
  kmeans = vl_kmeans_new(self->dataType,VlDistanceL2) ;
  vl_kmeans_init_centers_plus_plus(kmeans, data, self->dimension, numData, self->numClusters) ;
  memcpy(self->means, vl_kmeans_get_centers(kmeans), sizeof(TYPE) * self->dimension * self->numClusters) ;
  vl_kmeans_delete(kmeans) ;
}

/* ---------------------------------------------------------------- */
#else /* VL_GMM_INSTANTIATING */
/* ---------------------------------------------------------------- */

#ifndef __DOXYGEN__
#define FLT VL_TYPE_FLOAT
#define TYPE float
#define SFX f
#define VL_GMM_INSTANTIATING
#include "gmm.c"

#define FLT VL_TYPE_DOUBLE
#define TYPE double
#define SFX d
#define VL_GMM_INSTANTIATING
#include "gmm.c"
#endif

/* VL_GMM_INSTANTIATING */
#endif

/* ---------------------------------------------------------------- */
#ifndef VL_GMM_INSTANTIATING
/* ---------------------------------------------------------------- */

/** @brief Create a new GMM object by copy
 ** @param self object.
 ** @return new copy.
 **
 ** Most parameters, including the cluster priors, means, and
 ** covariances are copied. Data posteriors (available after
 ** initalization or EM) are not; nor is the KMeans object used for
 ** initialization, if any.
 **/

VlGMM *
vl_gmm_new_copy (VlGMM const * self)
{
  vl_size size = vl_get_type_size(self->dataType) ;
  VlGMM * gmm = vl_gmm_new(self->dataType, self->dimension, self->numClusters);
  gmm->initialization = self->initialization;
  gmm->maxNumIterations = self->maxNumIterations;
  gmm->numRepetitions = self->numRepetitions;
  gmm->verbosity = self->verbosity;
  gmm->LL = self->LL;

  memcpy(gmm->means, self->means, size*self->numClusters*self->dimension);
  memcpy(gmm->covariances, self->covariances, size*self->numClusters*self->dimension);
  memcpy(gmm->priors, self->priors, size*self->numClusters);
  return gmm ;
}

/** @brief Initialize mixture before EM takes place using random initialization
 ** @param self GMM object instance.
 ** @param data data points which should be clustered.
 ** @param dimension dimensionality of the data points.
 ** @param numData number of data points.
 ** @param numClusters number of gaussians which should be estimated.
 **/

void
vl_gmm_init_with_rand_data
(VlGMM * self,
 void const * data,
 vl_size numData)
{
  vl_gmm_reset (self) ;
  switch (self->dataType) {
    case VL_TYPE_FLOAT : _vl_gmm_init_with_rand_data_f (self, (float const *)data, numData) ; break ;
    case VL_TYPE_DOUBLE : _vl_gmm_init_with_rand_data_d (self, (double const *)data, numData) ; break ;
    default:
      abort() ;
  }
}

/** @brief Initializes the GMM using KMeans
 ** @param self GMM object instance.
 ** @param data data points which should be clustered.
 ** @param dimension dimensionality of the data points.
 ** @param numData number of data points.
 ** @param numClusters number of gaussians which should be estimated.
 ** @param kmeansInit KMeans object to use.
 **/

void
vl_gmm_init_with_kmeans
(VlGMM * self,
 void const * data,
 vl_size numData,
 VlKMeans * kmeansInit)
{
  vl_gmm_reset (self) ;
  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_gmm_init_with_kmeans_f
      (self, (float const *)data, numData, kmeansInit) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_gmm_init_with_kmeans_d
      (self, (double const *)data, numData, kmeansInit) ;
      break ;
    default:
      abort() ;
  }
}

#if 0
#include
#endif

/** @brief Run GMM clustering - includes initialization and EM
 ** @param self GMM object instance.
 ** @param data data points which should be clustered.
 ** @param dimension dimensionality of the data points.
 ** @param numData number of data points.
 ** @param numClusters number of gaussians which should be estimated.
 **/

double vl_gmm_cluster (VlGMM * self,
                       void const * data,
                       vl_size numData)
{
  void * bestPriors = NULL ;
  void * bestMeans = NULL;
  void * bestCovariances = NULL;
  void * bestPosteriors = NULL;
  vl_size size = vl_get_type_size(self->dataType) ;
  double bestLL = -VL_INFINITY_D;
  vl_uindex repetition;

  assert(self->numRepetitions >=1) ;

  bestPriors = vl_malloc(size * self->numClusters) ;
  bestMeans = vl_malloc(size * self->dimension * self->numClusters) ;
  bestCovariances = vl_malloc(size * self->dimension * self->numClusters) ;
  bestPosteriors = vl_malloc(size * self->numClusters * numData) ;

#if 0
  feenableexcept(FE_DIVBYZERO | FE_INVALID | FE_OVERFLOW);
#endif

  for (repetition = 0 ; repetition < self->numRepetitions ; ++ repetition) {
    double LL ;
    double timeRef ;

    if (self->verbosity) {
      VL_PRINTF("gmm: clustering: starting repetition %d of %d\n", repetition + 1, self->numRepetitions) ;
    }

    /* initialize a new mixture model */
    timeRef = vl_get_cpu_time() ;
    switch (self->initialization) {
      case VlGMMKMeans : vl_gmm_init_with_kmeans (self, data, numData, NULL) ; break ;
      case VlGMMRand : vl_gmm_init_with_rand_data (self, data, numData) ; break ;
      case VlGMMCustom : break ;
      default: abort() ;
    }
    if (self->verbosity) {
      VL_PRINTF("gmm: model initialized in %.2f s\n",
                vl_get_cpu_time() - timeRef) ;
    }

    /* fit the model to data by running EM */
    timeRef = vl_get_cpu_time () ;
    LL = vl_gmm_em (self, data, numData) ;
    if (self->verbosity) {
      VL_PRINTF("gmm: optimization terminated in %.2f s with loglikelihood %f\n",
                vl_get_cpu_time() - timeRef, LL) ;
    }

    if (LL > bestLL || repetition == 0) {
      void * temp ;

      temp = bestPriors ;
      bestPriors = self->priors ;
      self->priors = temp ;

      temp = bestMeans ;
      bestMeans = self->means ;
      self->means = temp ;

      temp = bestCovariances ;
      bestCovariances = self->covariances ;
      self->covariances = temp ;

      temp = bestPosteriors ;
      bestPosteriors = self->posteriors ;
      self->posteriors = temp ;

      bestLL = LL;
    }
  }

  vl_free (self->priors) ;
  vl_free (self->means) ;
  vl_free (self->covariances) ;
  vl_free (self->posteriors) ;

  self->priors = bestPriors ;
  self->means = bestMeans ;
  self->covariances = bestCovariances ;
  self->posteriors = bestPosteriors ;
  self->LL = bestLL;

  if (self->verbosity) {
    VL_PRINTF("gmm: all repetitions terminated with final loglikelihood %f\n", self->LL) ;
  }

  return bestLL ;
}

/** @brief Invoke the EM algorithm.
 ** @param self GMM object instance.
 ** @param data data points which should be clustered.
 ** @param numData number of data points.
 **/

double vl_gmm_em (VlGMM * self, void const * data, vl_size numData)
{
  switch (self->dataType) {
    case VL_TYPE_FLOAT:
      return _vl_gmm_em_f (self, (float const *)data, numData) ; break ;
    case VL_TYPE_DOUBLE:
      return _vl_gmm_em_d (self, (double const *)data, numData) ; break ;
    default:
      abort() ;
  }
  return 0 ;
}

/** @brief Explicitly set the initial means for EM.
 ** @param self GMM object instance.
 ** @param means initial values of means.
 **/

void
vl_gmm_set_means (VlGMM * self, void const * means)
{
  memcpy(self->means,means,
         self->dimension * self->numClusters * vl_get_type_size(self->dataType));
}

/** @brief Explicitly set the initial sigma diagonals for EM.
 ** @param self GMM object instance.
 ** @param covariances initial values of covariance matrix diagonals.
 **/

void vl_gmm_set_covariances (VlGMM * self, void const * covariances)
{
  memcpy(self->covariances,covariances,
         self->dimension * self->numClusters * vl_get_type_size(self->dataType));
}

/** @brief Explicitly set the initial priors of the gaussians.
 ** @param self GMM object instance.
 ** @param priors initial values of the gaussian priors.
 **/

void vl_gmm_set_priors (VlGMM * self, void const * priors)
{
  memcpy(self->priors,priors,
         self->numClusters * vl_get_type_size(self->dataType));
}

/* VL_GMM_INSTANTIATING */
#endif

#undef SFX
#undef TYPE
#undef FLT
#undef VL_GMM_INSTANTIATING
vlfeat/vl/fisher.c0000644000175000017500000004113312237004745013020 0ustar  dimadima/** @file fisher.c
 ** @brief Fisher - Declaration
 ** @author David Novotny
 **/

/*
 Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
 All rights reserved.

 This file is part of the VLFeat library and is made available under
 the terms of the BSD license (see the COPYING file).
 */

/**

@page fisher Fisher Vector encoding (FV)
@author David Novotny
@author Andrea Vedaldi


@ref fisher.h implements the Fisher Vectors (FV) image representation
@cite{perronnin06fisher} @cite{perronnin10improving}. A FV is a
statistics capturing the distribution of a set of vectors, usually a
set of local image descriptors.

@ref fisher-starting demonstrates how to use the C API to compute the
FV representation of an image. For further details refer to:

- @subpage fisher-fundamentals - Fisher Vector definition.
- @subpage fisher-derivation - Deriving the Fisher Vectors as a Fisher Kernel.
- @subpage fisher-kernel - The Fisher Kernel in general.


@section fisher-starting Getting started


The Fisher Vector encoding of a set of features is obtained by using
the function ::vl_fisher_encode. Note that the function requires a
@ref gmm "Gaussian Mixture Model" (GMM) of the encoded feature
distribution. In the following code, the result of the coding process
is stored in the @c enc array and the improved fisher vector
normalization is used.

@code
float * means ;
float * covariances ;
float * priors ;
float * posteriors ;
float * enc;

// create a GMM object and cluster input data to get means, covariances
// and priors of the estimated mixture
gmm = vl_gmm_new (VL_TYPE_FLOAT) ;
vl_gmm_cluster (gmm, data, dimension, numData, numClusters);

// allocate space for the encoding
enc = vl_malloc(sizeof(float) * 2 * dimension * numClusters);

// run fisher encoding
vl_fisher_encode
    (enc, VL_F_TYPE,
     vl_gmm_get_means(gmm), dimension, numClusters,
     vl_gmm_get_covariances(gmm),
     vl_gmm_get_priors(gmm),
     dataToEncode, numDataToEncode,
     VL_FISHER_FLAG_IMPROVED
     ) ;
@endcode

The performance of the standard Fisher Vector can be significantly
improved @cite{perronnin10improving} by using appropriate @ref
fisher-normalization normalizations. These are controlled by the @c
flag parameter of ::vl_fisher_encode.


@page fisher-fundamentals Fisher vector fundamentals
@tableofcontents


This page describes the *Fisher Vector* (FV) of
@cite{perronnin06fisher} @cite{perronnin10improving}. See @ref fisher
for an overview of the C API and @ref fisher-kernel for its relation
to the more general notion of Fisher kernel.

The FV is an image representation obtained by pooling local image
features. It is frequently used as a global image descriptor in visual
classification.

While the FV can be @ref fisher-kernel "derived" as a special,
approximate, and improved case of the general Fisher Kernel framework,
it is easy to describe directly. Let $I = (\bx_1,\dots,\bx_N)$ be a
set of $D$ dimensional feature vectors (e.g. SIFT descriptors)
extracted from an image. Let
$\Theta=(\mu_k,\Sigma_k,\pi_k:k=1,\dots,K)$ be the parameters of a
@ref gmm "Gaussian Mixture Model" fitting the distribution of
descriptors. The GMM associates each vector $\bx_i$ to a mode $k$ in
the mixture with a strength given by the posterior probability:

\[
  q_{ik} =
  \frac
  {(\bx_i - \mu_k)^T \Sigma_k^{-1} (\bx_i - \mu_k)}
  {\sum_{t=1}^K (\bx_i - \mu_t)^T \Sigma_t^{-1} (\bx_i - \mu_t)}.
\]

For each mode $k$, consider the mean and covariance deviation vectors

@f{align*}
u_{jk} &=
{1 \over {N \sqrt{\pi_k}}}
\sum_{i=1}^{N}
q_{ik} \frac{x_{ji} - \mu_{ik}}{\sigma_i},
\\
v_{jk} &=
{1 \over {N \sqrt{2 \pi_k}}}
\sum_{i=1}^{N}
q_{ik} \left[ \left(\frac{x_{ji} - \mu_{ik}}{\sigma_j}\right)^2 - 1 \right].
@f}

where $j=1,2,\dots,D$ spans the vector dimensions. The FV of image $I$
is the stacking of the vectors $\bu_k$ and then of the vectors
$\bv_k$ for each of the $K$ modes in the Gaussian mixtures:

\[
 \Phi(I) = \begin{bmatrix} \vdots \\ \bu_k \\ \vdots \\ \bv_k \\ \vdots \end{bmatrix}.
\]


@section fisher-normalization Normalization and improved Fisher vectors


The *improved* Fisher Vector @cite{perronnin10improving} (IFV) improves the
classification performance of the representation by using to ideas:

1. *Non-linear additive kernel.* The Hellinger's kernel (or
   Bhattacharya coefficient) can be used instead of the linear one at
   no cost by signed squared rooting. This is obtained by applying the
   function $|z| \sign z$ to each dimension of the vector $\Phi(I)$.
   Other @ref homkermap "additive kernels" can also be used at an
   increased space or time cost.
2. *Normalization.* Before using the representation in a linear model
   (e.g. a @ref svm "support vector machine"), the vector $\Phi(I)$ is
   further normalized by the $l^2$ norm (note that the standard Fisher
   vector is normalized by the number of encoded feature vectors).

After square-rooting and normalization, the IFV is often used in a
linear classifier such as an @ref svm "SVM".


@page fisher-derivation Fisher vector derivation


The FV of @cite{perronnin06fisher} is a special case of the @ref
fisher-kernel "Fisher kernel" construction. It is designed to encode
local image features in a format that is suitable for learning and
comparison with simple metrics such as the Euclidean. In this
construction, an image is modeled as a collection of $D$-dimensional
feature vectors $I=(\bx_1,\dots,\bx_n)$ generated by a GMM with $K$
components $\Theta=(\mu_k,\Sigma_k,\pi_k:k=1,\dots,K)$. The covariance
matrices are assumed to be diagonal, i.e. $\Sigma_k = \diag
\bsigma_k^2$, $\bsigma_k \in \real^D_+$.

The generative model of *one* feature vector $\bx$ is given by the GMM
density function:

\[
 p(\bx|\Theta) =
\sum_{k=1}^K \pi_k p(\bx|\Theta_k),
\quad
p(\bx|\Theta_k)
=
\frac{1}{(2\pi)^\frac{D}{2} (\det \Sigma_k)^{\frac{1}{2}}}
\exp
\left[
-\frac{1}{2}
(\bx - \mu_k)^\top \Sigma_k^{-1} (\bx - \mu_k)
\right]
\]

where $\Theta_k = (\mu_k,\Sigma_k)$. The Fisher Vector requires
computing the derivative of the log-likelihood function with respect
to the various model parameters. Consider in particular the parameters
$\Theta_k$ of a mode. Due to the exponent in the Gaussian density
function, the derivative can be written as

\[
\nabla_{\Theta_k} p(\bx|\Theta_k) =
p(\bx|\Theta_k)
g(\bx|\Theta_k)
\]

for a simple vector function $g$. The derivative of the log-likelihood
function is then

\[
\nabla_{\Theta_k} \log p(\bx|\Theta)
=
\frac{\pi_k p(\bx|\Theta_k)}{\sum_{t=1}^K \pi_k p(\bx|\Theta_k)}
g(\bx|\Theta_k)
=
q_k(\bx) g(\bx|\Theta_k)
\]

where $q_k(\bx)$ is the soft-assignment of the point $\bx$ to the mode
$k$. We make the approximation that $q_k(\bx)\approx 1$ if $\bx$ is
sampled from mode $k$ and $\approx 0$ otherwise
@cite{perronnin06fisher}. Hence one gets:

\[
E_{\bx \sim p(\bx|\Theta)}
[
\nabla_{\Theta_k} \log p(\bx|\Theta)
\nabla_{\Theta_t} \log p(\bx|\Theta)^\top
]
\approx
\begin{cases}
\pi_k E_{\bx \sim p(\bx|\Theta_k)} [ g(\bx|\Theta_k) g(\bx|\Theta_k)^\top], & t = k, \\
0, & t\not=k.
\end{cases}
\]

Thus under this approximation there is no correlation between the
parameters of the various Gaussian modes.

The function $g$ can be further broken down as the stacking of the
derivative w.r.t. the mean and the diagonal covariance.

\[
g(\bx|\Theta_k)
=
\begin{bmatrix}
g(\bx|\mu_k) \\
g(\bx|\bsigma_k)
\end{bmatrix},
\quad
[g(\bx|\mu_k)]_j
=
\frac{x_j - \mu_{jk}}{\sigma_{jk}^2},
\quad
[g(\bx|\bsigma_k^2)]_j
=
\frac{1}{2\sigma_{jk}^2}
\left(
\left(\frac{x_j - \mu_{jk}}{\sigma_{jk}}\right)^2
-
1
\right)
\]

Thus the covariance of the model (Fisher information) is diagonal and
the diagonal entries are given by

\[
 H_{\mu_{jk}} = \pi_k E[g(\bx|\mu_{jk})g(\bx|\mu_{jk})]
 = \frac{\pi_k}{\sigma_{jk}^2},
 \quad
 H_{\sigma_{jk}^2} = \frac{\pi_k}{2 \sigma_{jk}^4}.
\]

where in the calculation it was used the fact that the fourth moment
of the standard Gaussian distribution is 3. Multiplying the inverse
square root of the matrix $H$ by the derivative of the log-likelihood
function results in the Fisher vector encoding of one image feature
$\bx$:

\[
 \Phi_{\mu_{jk}}(\bx) = H_{\mu_{jk}}^{-\frac{1}{2}} q_k(\bx) g(\bx|\mu_{jk})
= q_k(\bx) \frac{x_j - \mu_{jk}}{\sqrt{\pi_k}\sigma_{jk}},
\qquad
 \Phi_{\sigma^2_{jk}}(\bx) =
\frac{q_k(\bx)}{\sqrt{2 \pi_k}}
\left(
\left(\frac{x_j - \mu_{jk}}{\sigma_{jk}}\right)^2
-
1
\right)
\]

Assuming that features are sampled i.i.d. from the GMM results in the
formulas given in @ref fisher-fundamentals (note the normalization
factor). Note that:

* The Fisher components relative to the prior probabilities $\pi_k$
  have been ignored. This is because they have little effect on the
  representation @cite{perronnin10improving}.

* Technically, the derivation of the Fisher Vector for multiple image
  features requires the number of features to be the same in both
  images. Ultimately, however, the representation can be computed by
  using any number of features.


@page fisher-kernel Fisher kernel


This page discusses the Fisher Kernels (FK) of
@cite{jaakkola98exploiting} and shows how the FV of
@cite{perronnin06fisher} can be derived from it as a special case. The
FK induces a similarity measures between data points $\bx$ and $\bx'$
from a parametric generative model $p(\bx|\Theta)$ of the data. The
parameter $\Theta$ of the model is selected to fit the a-priori
distribution of the data, and is usually the Maximum Likelihood (MLE)
estimate obtained from a set of training examples. Once the generative
model is learned, each particular datum $\bx$ is represented by
looking at how it affects the MLE parameter estimate. This effect is
measured by computing the gradient of the log-likelihood term
corresponding to $\bx$:

\[
  \hat\Phi(\bx) = \nabla_\Theta \log p(\bx|\Theta)
\]

The vectors $\hat\Phi(\bx)$ should be appropriately scaled before they
can be meaningfully compared. This is obtained by *whitening* the data
by multiplying the vectors by the inverse of the square root of their
*covariance matrix*. The covariance matrix can be obtained from the
generative model $p(\bx|\Theta)$ itself. Since $\Theta$ is the ML
parameter and $\hat\Phi(\bx)$ is the gradient of the log-likelihood
function, its expected value $E[\hat\Phi(\bx)]$ is zero. Thus, since
the vectors are already centered, their covariance matrix is simply:

\[
H = E_{\bx \sim p(\bx|\Theta)} [\hat\Phi(\bx) \hat\Phi(\bx)^\top]
\]

Note that $H$ is also the *Fisher information matrix* of the
model. The final FV encoding $\Phi(\bx)$ is given by the whitened
gradient of the log-likelihood function, i.e.:

\[
 \Phi(\bx) = H^{-\frac{1}{2}}  \nabla_\Theta \log p(\bx|\Theta).
\]

Taking the inner product of two such vectors yields the *Fisher
kernel*:

\[
 K(\bx,\bx')
= \langle \Phi(\bx),\Phi(\bx') \rangle
=  \nabla_\Theta \log p(\bx|\Theta)^\top H^{-1} \nabla_\Theta \log p(\bx'|\Theta).
\]

**/

#include "fisher.h"
#include "gmm.h"
#include "mathop.h"

#include 
#include 
#include 

#ifndef VL_FISHER_INSTANTIATING

#endif

#ifdef VL_FISHER_INSTANTIATING

static void
VL_XCAT(_vl_fisher_encode_, SFX)
(TYPE * enc,
 TYPE const * means, vl_size dimension, vl_size numClusters,
 TYPE const * covariances,
 TYPE const * priors,
 TYPE const * data, vl_size numData,
 int flags)
{
  vl_size dim;
  vl_index i_cl, i_d;
  TYPE * posteriors ;
  TYPE * sqrtInvSigma;

  posteriors = vl_malloc(sizeof(TYPE) * numClusters * numData);
  sqrtInvSigma = vl_malloc(sizeof(TYPE) * dimension * numClusters);

  memset(enc, 0, sizeof(TYPE) * 2 * dimension * numClusters) ;

  for (i_cl = 0 ; i_cl < (signed)numClusters ; ++i_cl) {
    for(dim = 0; dim < dimension; dim++) {
      sqrtInvSigma[i_cl*dimension + dim] = sqrt(1.0 / covariances[i_cl*dimension + dim]);
    }
  }

  VL_XCAT(vl_get_gmm_data_posteriors_, SFX)(posteriors, numClusters, numData,
                                            priors,
                                            means, dimension,
                                            covariances,
                                            data) ;

#if defined(_OPENMP)
#pragma omp parallel for default(shared) private(i_cl, i_d, dim) num_threads(vl_get_max_threads())
#endif
  for(i_cl = 0; i_cl < (signed)numClusters; ++ i_cl) {
    TYPE uprefix;
    TYPE vprefix;

    TYPE * uk = enc + i_cl*dimension ;
    TYPE * vk = enc + i_cl*dimension + numClusters * dimension ;

    if (priors[i_cl] < 1e-6) { continue ; }

    for(i_d = 0; i_d < (signed)numData; i_d++) {
      TYPE p = posteriors[i_cl + i_d * numClusters] ;
      if (p == 0) continue ;
      for(dim = 0; dim < dimension; dim++) {
        TYPE diff = data[i_d*dimension + dim] - means[i_cl*dimension + dim] ;
        diff *= sqrtInvSigma[i_cl*dimension + dim] ;
        *(uk + dim) += p * diff ;
        *(vk + dim) += p * (diff * diff - 1);
      }
    }

    uprefix = 1/(numData*sqrt(priors[i_cl]));
    vprefix = 1/(numData*sqrt(2*priors[i_cl]));

    for(dim = 0; dim < dimension; dim++) {
      *(uk + dim) = *(uk + dim) * uprefix;
      *(vk + dim) = *(vk + dim) * vprefix;
    }
  }

  vl_free(posteriors);
  vl_free(sqrtInvSigma) ;

  if (flags & VL_FISHER_FLAG_SQUARE_ROOT) {
    for(dim = 0; dim < 2 * dimension * numClusters ; dim++) {
      TYPE z = enc [dim] ;
      if (z >= 0) {
        enc[dim] = VL_XCAT(vl_sqrt_, SFX)(z) ;
      } else {
        enc[dim] = - VL_XCAT(vl_sqrt_, SFX)(- z) ;
      }
    }
  }

  if (flags & VL_FISHER_FLAG_NORMALIZED) {
    TYPE n = 0 ;
    for(dim = 0 ; dim < 2 * dimension * numClusters ; dim++) {
      TYPE z = enc [dim] ;
      n += z * z ;
    }
    n = VL_XCAT(vl_sqrt_, SFX)(n) ;
    n = VL_MAX(n, 1e-12) ;
    for(dim = 0 ; dim < 2 * dimension * numClusters ; dim++) {
      enc[dim] /= n ;
    }
  }
}

/* VL_FISHER_INSTANTIATING */
#else

#ifndef __DOXYGEN__
#define FLT VL_TYPE_FLOAT
#define TYPE float
#define SFX f
#define VL_FISHER_INSTANTIATING
#include "fisher.c"

#define FLT VL_TYPE_DOUBLE
#define TYPE double
#define SFX d
#define VL_FISHER_INSTANTIATING
#include "fisher.c"
#endif

#endif

/* ================================================================ */
#ifndef VL_FISHER_INSTANTIATING

/** @brief Fisher vector encoding of a set of vectors.
 ** @param dataType the type of the input data (::VL_TYPE_DOUBLE or ::VL_TYPE_FLOAT).
 ** @param enc Fisher vector (output).
 ** @param means Gaussian mixture means.
 ** @param dimension dimension of the data.
 ** @param numClusters number of Gaussians mixture components.
 ** @param covariances Gaussian mixture diagonal covariances.
 ** @param priors Gaussian mixture prior probabilities.
 ** @param data vectors to encode.
 ** @param numData number of vectors to encode.
 ** @param flags options.
 **
 ** @a means and @a covariances have @a dimension rows and @a numCluster columns.
 ** @a priors is a vector of size @a numCluster. @a data has @a dimension
 ** rows and @a numData columns. @a enc is a vecotr of size equal
 ** to twice the product of @a dimension and @a numClusters.
 ** All these vectors and matrices have the same class, as specified
 ** by @a dataType.
 **
 ** @a flag can be used to control several options:
 ** ::VL_FISHER_FLAG_SQUARE_ROOT, ::VL_FISHER_FLAG_NORMALIZED,
 ** ::VL_FISHER_FLAG_IMPROVED.
 **
 ** @sa @ref fisher
 **/

VL_EXPORT void
vl_fisher_encode
(void * enc, vl_type dataType,
 void const * means, vl_size dimension, vl_size numClusters,
 void const * covariances,
 void const * priors,
 void const * data,  vl_size numData,
 int flags
)
{
  switch(dataType) {
    case VL_TYPE_FLOAT:
      _vl_fisher_encode_f
      ((float *) enc,
       (float const *) means, dimension, numClusters,
       (float const *) covariances,
       (float const *) priors,
       (float const *) data, numData,
       flags);
      break;
    case VL_TYPE_DOUBLE:
      _vl_fisher_encode_d
      ((double *) enc,
       (double const *) means, dimension, numClusters,
       (double const *) covariances,
       (double const *) priors,
       (double const *) data, numData,
       flags);
      break;
    default:
      abort();
  }
}

#endif

#undef SFX
#undef TYPE
#undef FLT
#undef VL_FISHER_INSTANTIATING
vlfeat/vl/homkermap.c0000644000175000017500000004607112237004745013531 0ustar  dimadima/** @file homkermap.c
 ** @brief Homogeneous kernel map - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file homkermap.h


@page homkermap Homogeneous kernel map
@author Andrea Vedaldi
@tableofcontents


@ref homkermap.h implements the homogeneous kernel maps introduced in
@cite{vedaldi10efficient},@cite{vedaldi12efficient}.  Such maps are
efficient linear representations of popular kernels such as the
intersection, $\chi^2$, and Jensen-Shannon ones.


@section homkermap-starting Getting started


The homogeneous kernel map is implemented as an object of type
::VlHomogeneousKernelMap. To use thois object, first create an
instance by using ::vl_homogeneouskernelmap_new, then use
::vl_homogeneouskernelmap_evaluate_d or
::vl_homogeneouskernelmap_evaluate_f (depdening on whether the data is
@c double or @c float) to compute the feature map $ \Psi(x)
$. When done, dispose of the object by calling
::vl_homogeneouskernelmap_delete.

@code
double gamma = 1.0 ;
int order = 1 ;
double period = -1 ; // use default
double psi [3] ;
vl_size psiStride = 1 ;
double x = 0.5 ;
VlHomogeneousKernelMap * hom = vl_homogeneouskernelmap_new(
  VlHomogeneousKernelChi2, gamma, order, period,
  VlHomogeneousKernelMapWindowRectangular) ;
vl_homogeneouskernelmap_evaluate_d(hom, psi, psiStride, x) ;
vl_homogeneouskernelmap_delete(x) ;
@endcode

The constructor ::vl_homogeneouskernelmap_new takes the kernel type @c
kernel (see ::VlHomogeneousKernelType), the homogeneity order @c gamma
(use one for the standard $1$-homogeneous kernels), the approximation
order @c order (usually order one is enough), the period @a period
(use a negative value to use the default period), and a window type @c
window (use ::VlHomogeneousKernelMapWindowRectangular if unsure). The
approximation order trades off the quality and dimensionality of the
approximation. The resulting feature map $ \Psi(x) $, computed by
::vl_homogeneouskernelmap_evaluate_d or
::vl_homogeneouskernelmap_evaluate_f , is 2*order+1
dimensional.

The code pre-computes the map $ \Psi(x) $ for efficient
evaluation. The table spans values of $ x $ in the range
$[2^{-20}, 2^{8}) $. In particular, values smaller than $
2^{-20} $ are treated as zeroes (which results in a null feature).


@section homkermap-fundamentals Fundamentals


The homogeneous kernel map is a finite dimensional linear
approximation of homogeneous kernels, including the intersection,
$\chi^2$, and Jensen-Shannon kernels. These kernels are frequently
used in computer vision applications because they are particular
suited to data in the format of histograms, which includes many common
visual descriptors.

Let $x,y \in \mathbb{R}_+$ be non-negative scalars and let $k(x,y) \in
\mathbb{R}$ be an homogeneous kernel such as the $\chi^2$ and or the
intersection ones:

@f[
  k_{\mathrm{inters}}(x,y) = \min\{x, y\},
  \quad
  k_{\chi^2}(x,y) = 2 \frac{(x - y)^2}{x+y}.
@f]

For vectorial data $ \mathbf{x},\mathbf{y} \in \mathbb{R}_+^d $, the
homogeneous kernels is defined as an additive combination of
scalar kernels $K(\mathbf{x},\mathbf{y}) = \sum_{i=1}^d k(x_i,y_i)$.

The homogeneous kernel map of order $n$ is a vector function
$\Psi(x) \in \mathbb{R}^{2n+1}$ such that, for any choice of $x, y \in
\mathbb{R}_+$, the following approximation holds:

@f[
  k(x,y) \approx \langle \Psi(x), \Psi(y) \rangle.
@f]

Given the feature map for the scalar case, the corresponding feature
map $\Psi(\mathbf{x})$ for the vectorial case is obtained by stacking
$[\Psi(x_1), \dots, \Psi(x_n)]$.  Note that the stacked feature
$\Psi(\mathbf{x})$ has dimension $d(2n+1)$.

Using linear analysis tools (e.g. a linear support vector machine)
on top of dataset that has been encoded by the homogeneous kernel map
is therefore approximately equivalent to using a method based
on the corresponding non-linear kernel.


@subsection homkermap-overview-negative Extension to the negative reals


Any positive (semi-)definite kernel $k(x,y)$ defined on the
non-negative reals $x,y \in \mathbb{R}_+$ can be extended to the
entire real line by using the definition:

@f[
k_\pm(x,y) = \operatorname{sign}(x) \operatorname{sign}(y) k(|x|,|y|).
@f]

The homogeneous kernel map implements this extension by defining
$\Psi_\pm(x) = \operatorname{sign}(x) \Psi(|x|)$. Note that other
extensions are possible, such as

@f[
k_\pm(x,y) = H(xy) \operatorname{sign}(y) k(|x|,|y|)
@f]

where $H$ is the Heaviside function, but may result in higher
dimensional feature maps.


@subsection homkermap-overview-homogeneity Homogeneity degree


Any (1-)homogeneous kernel $k_1(x,y)$ can be extended to a so called
$\gamma$-homgeneous kernel $k_\gamma(x,y)$ by the definition

@f[
  k_\gamma(x,y) = (xy)^{\frac{\gamma}{2}} \frac{k_1(x,y)}{\sqrt{xy}}
@f]

Smaller values of $\gamma$ enhance the kernel non-linearity and are
sometimes beneficial in applications (see
@cite{vedaldi10efficient},@cite{vedaldi12efficient} for details).


@subsection homkermap-overview-window Windowing and period


This section discusses aspects of the homogeneous kernel map which are
more technical and may be skipped. The homogeneous kernel map
approximation is based on periodizing the kernel; given the kernel
signature

@f[
    \mathcal{K}(\lambda) = k(e^{\frac{\lambda}{2}}, e^{-\frac{\lambda}{2}})
@f]

the homogeneous kernel map is a feature map for the windowed and
periodized kernel whose signature is given by

@f[
   \hat{\mathcal{K}}(\lambda)
   =
   \sum_{i=-\infty}^{+\infty} \mathcal{K}(\lambda + k \Lambda) W(\lambda + k \Lambda)
@f]

where $W(\lambda)$ is a windowing function and $\Lambda$ is the
period. This implementation of the homogeneous kernel map supports the
use of a uniform window ($ W(\lambda) = 1 $) or of a
rectangular window ($ W(\lambda) =
\operatorname{rect}(\lambda/\Lambda) $). Note that $ \lambda =
\log(y/x) $ is equal to the logarithmic ratio of the arguments of the
kernel. Empirically, the rectangular window seems to have a slight
edge in applications.


@section homkermap-details Implementation details


This implementation uses the expressions given in
@cite{vedaldi10efficient},@cite{vedaldi11efficient} to compute in
closed form the maps $\Psi(x)$ for the supported kernel types. For
efficiency reasons, it precomputes $\Psi(x)$ for a large range of
values of the argument when the homogeneous kernel map object is
created.

The internal table stores $\Psi(x) \in \mathbb{R}^{2n+1}$ by sampling
$x\geq 0$. This uses the internal decomposition of IEEE floating point
representations (@c float and @c double) in mantissa and exponent:

      x = mantissa * (2**exponent),
  minExponent <= exponent <= maxExponent,
  1 <= matnissa < 2.

    
Each octave is further sampled in @c numSubdivisions sublevels.

When the map $\Psi(x)$ is evaluated, @c x is decomposed again into
exponent and mantissa to index the table. The output is obtained by
bilinear interpolation from the appropriate table entries.

**/

/* ---------------------------------------------------------------- */
#ifndef VL_HOMKERMAP_INSTANTIATING
/* ---------------------------------------------------------------- */

#include "homkermap.h"
#include "mathop.h"
#include 

struct _VlHomogeneousKernelMap
{
  VlHomogeneousKernelType kernelType ;
  double gamma ;
  VlHomogeneousKernelMapWindowType windowType ;
  vl_size order ;
  double period ;
  vl_size numSubdivisions ;
  double subdivision  ;
  vl_index minExponent ;
  vl_index maxExponent ;
  double * table ;
} ;

/** @internal @brief Sample the kernel specturm
 ** @param self homogeneous kernel map.
 ** @param omega sampling frequency.
 ** @return the spectrum sampled at @a omega.
 **/

VL_INLINE double
vl_homogeneouskernelmap_get_spectrum (VlHomogeneousKernelMap const * self, double omega)
{
  assert (self) ;
  switch (self->kernelType) {
    case VlHomogeneousKernelIntersection:
      return (2.0 / VL_PI) / (1 + 4 * omega*omega) ;
    case VlHomogeneousKernelChi2:
      return 2.0 / (exp(VL_PI * omega) + exp(-VL_PI * omega)) ;
    case VlHomogeneousKernelJS:
      return (2.0 / log(4.0)) *
      2.0 / (exp(VL_PI * omega) + exp(-VL_PI * omega)) /
      (1 + 4 * omega*omega) ;
    default:
      abort() ;
  }
}

/* helper */
VL_INLINE double sinc(double x)
{
  if (x == 0.0) return 1.0 ;
  return sin(x) / x ;
}

/** @internal @brief Sample the smoothed kernel spectrum
 ** @param self homogeneous kernel map.
 ** @param omega sampling frequency.
 ** @return the spectrum sampled at @a omega after smoothing.
 **/

VL_INLINE double
vl_homogeneouskernelmap_get_smooth_spectrum (VlHomogeneousKernelMap const * self, double omega)
{
  double kappa_hat = 0 ;
  double omegap ;
  double epsilon = 1e-2 ;
  double const omegaRange = 2.0 / (self->period * epsilon) ;
  double const domega = 2 * omegaRange / (2 * 1024.0 + 1) ;
  assert (self) ;
  switch (self->windowType) {
    case VlHomogeneousKernelMapWindowUniform:
      kappa_hat = vl_homogeneouskernelmap_get_spectrum(self, omega) ;
      break ;
    case VlHomogeneousKernelMapWindowRectangular:
      for (omegap = - omegaRange ; omegap <= omegaRange ; omegap += domega) {
        double win = sinc((self->period/2.0) * omegap) ;
        win *= (self->period/(2.0*VL_PI)) ;
        kappa_hat += win * vl_homogeneouskernelmap_get_spectrum(self, omegap + omega) ;
      }
      kappa_hat *= domega ;
      /* project on the postivie orthant (see PAMI) */
      kappa_hat = VL_MAX(kappa_hat, 0.0) ;
      break ;
    default:
      abort() ;
  }
  return kappa_hat ;
}

/* ---------------------------------------------------------------- */
/*                                     Constructors and destructors */
/* ---------------------------------------------------------------- */

/** @brief Create a new homgeneous kernel map
 ** @param kernelType type of homogeneous kernel.
 ** @param gamma kernel homogeneity degree.
 ** @param order approximation order.
 ** @param period kernel period.
 ** @param windowType type of window used to truncate the kernel.
 ** @return the new homogeneous kernel map.
 **
 ** The function intializes a new homogeneous kernel map for the
 ** specified kernel type, homogeneity degree, approximation order,
 ** period, and truncation window. See @ref homkermap-fundamentals for
 ** details.
 **
 ** The homogeneity degree @c gamma must be positive (the standard
 ** kernels are obtained by setting @c gamma to 1). When unsure, set
 ** @c windowType to ::VlHomogeneousKernelMapWindowRectangular. The @c
 ** period should be non-negative; specifying a negative or null value
 ** causes the function to switch to a default value.
 **
 ** The function returns @c NULL if there is not enough free memory.
 **/

VlHomogeneousKernelMap *
vl_homogeneouskernelmap_new (VlHomogeneousKernelType kernelType,
                             double gamma,
                             vl_size order,
                             double period,
                             VlHomogeneousKernelMapWindowType windowType)
{
  int tableWidth, tableHeight ;
  VlHomogeneousKernelMap * self = vl_malloc(sizeof(VlHomogeneousKernelMap)) ;
  if (! self) return NULL ;

  assert(gamma > 0) ;

  assert(kernelType == VlHomogeneousKernelIntersection ||
         kernelType == VlHomogeneousKernelChi2 ||
         kernelType == VlHomogeneousKernelJS) ;

  assert(windowType == VlHomogeneousKernelMapWindowUniform ||
         windowType == VlHomogeneousKernelMapWindowRectangular) ;

  if (period < 0) {
    switch (windowType) {
    case VlHomogeneousKernelMapWindowUniform:
      switch (kernelType) {
      case VlHomogeneousKernelChi2:         period = 5.86 * sqrt(order + 0)  + 3.65 ; break ;
      case VlHomogeneousKernelJS:           period = 6.64 * sqrt(order + 0)  + 7.24 ; break ;
      case VlHomogeneousKernelIntersection: period = 2.38 * log(order + 0.8) + 5.6 ; break ;
      }
      break ;
    case VlHomogeneousKernelMapWindowRectangular:
      switch (kernelType) {
      case VlHomogeneousKernelChi2:         period = 8.80 * sqrt(order + 4.44) - 12.6 ; break ;
      case VlHomogeneousKernelJS:           period = 9.63 * sqrt(order + 1.00) - 2.93;  break ;
      case VlHomogeneousKernelIntersection: period = 2.00 * log(order + 0.99)  + 3.52 ; break ;
      }
      break ;
    }
    period = VL_MAX(period, 1.0) ;
  }

  self->kernelType = kernelType ;
  self->windowType = windowType ;
  self->gamma = gamma ;
  self->order = order ;
  self->period = period ;
  self->numSubdivisions = 8 + 8*order ;
  self->subdivision = 1.0 / self->numSubdivisions ;
  self->minExponent = -20 ;
  self->maxExponent = 8 ;

  tableHeight = (int) (2*self->order + 1) ;
  tableWidth = (int) (self->numSubdivisions * (self->maxExponent - self->minExponent + 1)) ;
  self->table = vl_malloc (sizeof(double) *
                           (tableHeight * tableWidth + 2*(1+self->order))) ;
  if (! self->table) {
    vl_free(self) ;
    return NULL ;
  }

  {
    vl_index exponent ;
    vl_uindex i, j ;
    double * tablep = self->table ;
    double * kappa = self->table + tableHeight * tableWidth ;
    double * freq = kappa + (1+self->order) ;
    double L = 2.0 * VL_PI / self->period ;

    /* precompute the sampled periodicized spectrum */
    j = 0 ;
    i = 0 ;
    while (i <= self->order) {
      freq[i] = j ;
      kappa[i] = vl_homogeneouskernelmap_get_smooth_spectrum(self, j * L) ;
      ++ j ;
      if (kappa[i] > 0 || j >= 3*i) ++ i ;
    }

    /* fill table */
    for (exponent  = self->minExponent ;
         exponent <= self->maxExponent ; ++ exponent) {

      double x, Lxgamma, Llogx, xgamma ;
      double sqrt2kappaLxgamma ;
      double mantissa = 1.0 ;

      for (i = 0 ; i < self->numSubdivisions ;
           ++i, mantissa += self->subdivision) {
        x = ldexp(mantissa, (int)exponent) ;
        xgamma = pow(x, self->gamma) ;
        Lxgamma = L * xgamma ;
        Llogx = L * log(x) ;

        *tablep++ = sqrt(Lxgamma * kappa[0]) ;
        for (j = 1 ; j <= self->order ; ++j) {
          sqrt2kappaLxgamma = sqrt(2.0 * Lxgamma * kappa[j]) ;
          *tablep++ = sqrt2kappaLxgamma * cos(freq[j] * Llogx) ;
          *tablep++ = sqrt2kappaLxgamma * sin(freq[j] * Llogx) ;
        }
      } /* next mantissa */
    } /* next exponent */
  }
  return self ;
}

/** @brief Delete an object instance.
 ** @param self object.
 ** The function deletes the specified map object.
 **/

void
vl_homogeneouskernelmap_delete (VlHomogeneousKernelMap * self)
{
  vl_free(self->table) ;
  self->table = NULL ;
  vl_free(self) ;
}

/* ---------------------------------------------------------------- */
/*                                     Retrieve data and parameters */
/* ---------------------------------------------------------------- */

/** @brief Get the map order.
 ** @param self object.
 ** @return the map order.
 **/

vl_size
vl_homogeneouskernelmap_get_order (VlHomogeneousKernelMap const * self)
{
  assert(self) ;
  return self->order ;
}

/** @brief Get the map dimension.
 ** @param self object.
 ** @return the map dimension (2 @c order  +1).
 **/

vl_size
vl_homogeneouskernelmap_get_dimension (VlHomogeneousKernelMap const * self)
{
  assert(self) ;
  return 2 * self->order + 1 ;
}

/** @brief Get the kernel type.
 ** @param self object.
 ** @return kernel type.
 **/

VlHomogeneousKernelType
vl_homogeneouskernelmap_get_kernel_type (VlHomogeneousKernelMap const * self)
{
  assert(self) ;
  return self->kernelType ;
}

/** @brief Get the window type.
 ** @param self object.
 ** @return window type.
 **/

VlHomogeneousKernelMapWindowType
vl_homogeneouskernelmap_get_window_type (VlHomogeneousKernelMap const * self)
{
  assert(self) ;
  return self->windowType ;
}

/* ---------------------------------------------------------------- */
/*                                                     Process data */
/* ---------------------------------------------------------------- */

/** @fn ::vl_homogeneouskernelmap_evaluate_d(VlHomogeneousKernelMap const*,double*,vl_size,double)
 ** @brief Evaluate map
 ** @param self map object.
 ** @param destination output buffer.
 ** @param stride stride of the output buffer.
 ** @param x value to expand.
 **
 ** The function evaluates the feature map on @a x and stores the
 ** resulting 2*order+1 dimensional vector to
 ** @a destination[0], @a destination[stride], @a destination[2*stride], ....
 **/

/** @fn ::vl_homogeneouskernelmap_evaluate_f(VlHomogeneousKernelMap const*,float*,vl_size,double)
 ** @copydetails ::vl_homogeneouskernelmap_evaluate_d(VlHomogeneousKernelMap const*,double*,vl_size,double)
 **/

#define FLT VL_TYPE_FLOAT
#define VL_HOMKERMAP_INSTANTIATING
#include "homkermap.c"

#define FLT VL_TYPE_DOUBLE
#define VL_HOMKERMAP_INSTANTIATING
#include "homkermap.c"

/* VL_HOMKERMAP_INSTANTIATING */
#endif

/* ---------------------------------------------------------------- */
#ifdef VL_HOMKERMAP_INSTANTIATING
/* ---------------------------------------------------------------- */

#include "float.th"

void
VL_XCAT(vl_homogeneouskernelmap_evaluate_,SFX)
(VlHomogeneousKernelMap const * self,
 T * destination,
 vl_size stride,
 double x)
{
  /* break value into exponent and mantissa */
  int exponent ;
  int unsigned j ;
  double mantissa = frexp(x, &exponent) ;
  double sign = (mantissa >= 0.0) ? +1.0 : -1.0 ;
  mantissa *= 2*sign ;
  exponent -- ;

  if (mantissa == 0 ||
      exponent <= self->minExponent ||
      exponent >= self->maxExponent) {
    for (j = 0 ; j < 2*self->order+1 ; ++j) {
      *destination = (T) 0.0 ;
      destination += stride ;
    }
    return  ;
  }
  {
    vl_size featureDimension = 2*self->order + 1 ;
    double const * v1 = self->table +
    (exponent - self->minExponent) * self->numSubdivisions * featureDimension ;
    double const * v2 ;
    double f1, f2 ;

    mantissa -= 1.0 ;
    while (mantissa >= self->subdivision) {
      mantissa -= self->subdivision ;
      v1 += featureDimension ;
    }
    v2 = v1 + featureDimension ;
    for (j = 0 ; j < featureDimension ; ++j) {
      f1 = *v1++ ;
      f2 = *v2++ ;
      *destination = (T) sign * ((f2 - f1) * (self->numSubdivisions * mantissa) + f1) ;
      destination += stride ;
    }
  }
}

#undef FLT
#undef VL_HOMKERMAP_INSTANTIATING
/* VL_HOMKERMAP_INSTANTIATING */
#endif
vlfeat/vl/mathop.c0000644000175000017500000006070012237004745013031 0ustar  dimadima/** @file mathop.c
 ** @brief Math operations - Definition
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file mathop.h

 @section mathop-usage-vector-comparison Comparing vectors

 @ref mathop.h includes a number of functions to quickly compute
 distances or similarity of pairs of vector. Applications include
 clustering and evaluation of SVM-like classifiers.

 Use ::vl_get_vector_comparison_function_f or
 ::vl_get_vector_comparison_function_d obtain an approprite function
 to comprare vectors of floats or doubles, respectively.  Such
 functions are usually optimized (for instance, on X86 platforms they
 use the SSE vector extension) and are several times faster than a
 naive implementation.  ::vl_eval_vector_comparison_on_all_pairs_f and
 ::vl_eval_vector_comparison_on_all_pairs_d can be used to evaluate
 the comparison function on all pairs of one or two sequences of
 vectors.

 Let @f$ \mathbf{x} = (x_1,\dots,x_d) @f$ and @f$ \mathbf{y} =
 (y_1,\dots,y_d) @f$ be two vectors.  The following comparison
 functions are supported:

 
    
 
    
 @f$ l^1 @f$
 ::VlDistanceL1
 @f$ \sum_{i=1}^d |x_i - y_i| @f$
 l1 distance (squared intersection metric)
 
    
 
    
 @f$ l^2 @f$
 ::VlDistanceL2
 @f$\sum_{i=1}^d (x_i - y_i)^2@f$
 Squared Euclidean disance
 
    
 
    
 @f$ \chi^2 @f$
 ::VlDistanceChi2
 @f$\sum_{i=1}^d \frac{(x_i - y_i)^2}{x_i + y_i}@f$
 Squared chi-square distance
 
    
 
    
 -
 ::VlDistanceHellinger
 @f$\sum_{i=1}^d (\sqrt{x_i} - \sqrt{y_i})^2@f$
 Squared Hellinger's distance
 
    
 
    
 -
 ::VlDistanceJS
 @f$
 \sum_{i=1}^d
 \left(
   x_i \log\frac{2x_i}{x_i+y_i}
 + y_i \log\frac{2y_i}{x_i+y_i}
 \right)
 @f$
 
 Squared Jensen-Shannon distance
 
    
 
    
 @f$ l^1 @f$
 ::VlKernelL1
 @f$ \sum_{i=1}^d \min\{ x_i, y_i \} @f$
 intersection kernel
 
    
 
    
 @f$ l^2 @f$
 ::VlKernelL2
 @f$\sum_{i=1}^d x_iy_i @f$
 linear kernel
 
    
 
    
 @f$ \chi^2 @f$
 ::VlKernelChi2
 @f$\sum_{i=1}^d 2 \frac{x_iy_i}{x_i + y_i}@f$
 chi-square kernel
 
    
 
    
 -
 ::VlKernelHellinger
 @f$\sum_{i=1}^d 2 \sqrt{x_i y_i}@f$
 Hellinger's kernel (Bhattacharya coefficient)
 
    
 
    
 -
 ::VlKernelJS
 @f$
 \sum_{i=1}^d
 \left(
   \frac{x_i}{2} \log_2\frac{x_i+y_i}{x_i}
 + \frac{y_i}{2} \log_2\frac{x_i+y_i}{y_i}
 \right)
 @f$
 
 Jensen-Shannon kernel
 
    
 
    

 @remark The definitions have been choosen so that corresponding kernels and
 distances are related by the equation:
 @f[
  d^2(\mathbf{x},\mathbf{y})
  =
  k(\mathbf{x},\mathbf{x})
  +k(\mathbf{y},\mathbf{y})
  -k(\mathbf{x},\mathbf{y})
  -k(\mathbf{y},\mathbf{x})
 @f]
 This means that each of these distances can be interpreted as a
 squared distance or metric in the corresponding reproducing kernel
 Hilbert space. Notice in particular that the @f$ l^1 @f$ or Manhattan
 distance is also a squared distance in this sense.

 **/

/** @fn vl_get_vector_comparison_function_f(VlVectorComparisonType)
 **
 ** @brief Get vector comparison function from comparison type
 ** @param type vector comparison type.
 ** @return comparison function.
 **/

/** @fn vl_get_vector_comparison_function_d(VlVectorComparisonType)
 ** @brief Get vector comparison function from comparison type
 ** @sa vl_get_vector_comparison_function_f
 **/

/** @fn vl_eval_vector_comparison_on_all_pairs_f(float*,vl_size,
 **     float const*,vl_size,float const*,vl_size,VlFloatVectorComparisonFunction)
 **
 ** @brief Evaluate vector comparison function on all vector pairs
 ** @param result comparison matrix (output).
 ** @param dimension number of vector components (rows of @a X and @a Y).
 ** @param X data matrix X.
 ** @param Y data matrix Y.
 ** @param numDataX number of vectors in @a X (columns of @a X)
 ** @param numDataY number of vectros in @a Y (columns of @a Y)
 ** @param function vector comparison function.
 **
 ** The function evaluates @a function on all pairs of columns
 ** from matrices @a X and @a Y, filling a @a numDataX by @a numDataY
 ** matrix.
 **
 ** If @a Y is a null pointer the function compares all columns from
 ** @a X with themselves.
 **/

/** @fn vl_eval_vector_comparison_on_all_pairs_d(double*,vl_size,
 **     double const*,vl_size,double const*,vl_size,VlDoubleVectorComparisonFunction)
 ** @brief Evaluate vector comparison function on all vector pairs
 ** @sa vl_eval_vector_comparison_on_all_pairs_f
 **/

/* ---------------------------------------------------------------- */
#ifndef VL_MATHOP_INSTANTIATING

#include "mathop.h"
#include "mathop_sse2.h"
 #include "mathop_avx.h"
#include 

#undef FLT
#define FLT VL_TYPE_FLOAT
#define VL_MATHOP_INSTANTIATING
#include "mathop.c"

#undef FLT
#define FLT VL_TYPE_DOUBLE
#define VL_MATHOP_INSTANTIATING
#include "mathop.c"
#endif

/* ---------------------------------------------------------------- */
#ifdef VL_MATHOP_INSTANTIATING
#include "float.th"

#undef COMPARISONFUNCTION_TYPE
#undef COMPARISONFUNCTION3_TYPE
#if (FLT == VL_TYPE_FLOAT)
#  define COMPARISONFUNCTION_TYPE VlFloatVectorComparisonFunction
#  define COMPARISONFUNCTION3_TYPE VlFloatVector3ComparisonFunction
#else
#  define COMPARISONFUNCTION_TYPE VlDoubleVectorComparisonFunction
#  define COMPARISONFUNCTION3_TYPE VlDoubleVector3ComparisonFunction
#endif

/* ---------------------------------------------------------------- */

VL_EXPORT T
VL_XCAT(_vl_distance_l2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T d = *X++ - *Y++ ;
    acc += d * d ;
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_l1_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T d = *X++ - *Y++ ;
    acc += VL_MAX(d, -d) ;
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_chi2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T delta = a - b ;
    T denom = (a + b) ;
    T numer = delta * delta ;
    if (denom) {
      T ratio = numer / denom ;
      acc += ratio ;
    }
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_hellinger_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
#if (FLT == VL_TYPE_FLOAT)
    acc += a + b - 2.0 * sqrtf (a*b) ;
#else
    acc += a + b - 2.0 * sqrt (a*b) ;
#endif
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_js_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T x = *X++ ;
    T y = *Y++ ;
    if (x) acc += x - x * VL_XCAT(vl_log2_,SFX)(1 + y/x) ;
    if (y) acc += y - y * VL_XCAT(vl_log2_,SFX)(1 + x/y) ;
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_l2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    acc += a * b ;
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_l1_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T a_ = VL_XCAT(vl_abs_, SFX) (a) ;
    T b_ = VL_XCAT(vl_abs_, SFX) (b) ;
    acc += a_ + b_ - VL_XCAT(vl_abs_, SFX) (a - b) ;
  }
  return acc / ((T)2) ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_chi2_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
    T denom = (a + b) ;
    if (denom) {
      T numer = 2 * a * b ;
      T ratio = numer / denom ;
      acc += ratio ;
    }
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_hellinger_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T a = *X++ ;
    T b = *Y++ ;
#if (FLT == VL_TYPE_FLOAT)
    acc += sqrtf (a*b) ;
#else
    acc += sqrt (a*b) ;
#endif
  }
  return acc ;
}

VL_EXPORT T
VL_XCAT(_vl_kernel_js_, SFX)
(vl_size dimension, T const * X, T const * Y)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T x = *X++ ;
    T y = *Y++ ;
    if (x) acc += x * VL_XCAT(vl_log2_,SFX)(1 + y/x) ;
    if (y) acc += y * VL_XCAT(vl_log2_,SFX)(1 + x/y) ;
  }
  return (T)0.5 * acc ;
}

VL_EXPORT T
VL_XCAT(_vl_distance_mahalanobis_sq_, SFX)
(vl_size dimension, T const * X, T const * MU, T const * S)
{
  T const * X_end = X + dimension ;
  T acc = 0.0 ;
  while (X < X_end) {
    T d = *X++ - *MU++ ;
    acc += d * d / (*S++) ;
  }
  return acc ;
}

/* ---------------------------------------------------------------- */

VL_EXPORT COMPARISONFUNCTION_TYPE
VL_XCAT(vl_get_vector_comparison_function_, SFX)(VlVectorComparisonType type)
{
  COMPARISONFUNCTION_TYPE function = 0 ;
  switch (type) {
    case VlDistanceL2        : function = VL_XCAT(_vl_distance_l2_,             SFX) ; break ;
    case VlDistanceL1        : function = VL_XCAT(_vl_distance_l1_,             SFX) ; break ;
    case VlDistanceChi2      : function = VL_XCAT(_vl_distance_chi2_,           SFX) ; break ;
    case VlDistanceHellinger : function = VL_XCAT(_vl_distance_hellinger_,      SFX) ; break ;
    case VlDistanceJS        : function = VL_XCAT(_vl_distance_js_,             SFX) ; break ;
    case VlKernelL2          : function = VL_XCAT(_vl_kernel_l2_,               SFX) ; break ;
    case VlKernelL1          : function = VL_XCAT(_vl_kernel_l1_,               SFX) ; break ;
    case VlKernelChi2        : function = VL_XCAT(_vl_kernel_chi2_,             SFX) ; break ;
    case VlKernelHellinger   : function = VL_XCAT(_vl_kernel_hellinger_,        SFX) ; break ;
    case VlKernelJS          : function = VL_XCAT(_vl_kernel_js_,               SFX) ; break ;
    default: abort() ;
  }

#ifndef VL_DISABLE_SSE2
  /* if a SSE2 implementation is available, use it */
  if (vl_cpu_has_sse2() && vl_get_simd_enabled()) {
    switch (type) {
      case VlDistanceL2    : function = VL_XCAT(_vl_distance_l2_sse2_,             SFX) ; break ;
      case VlDistanceL1    : function = VL_XCAT(_vl_distance_l1_sse2_,             SFX) ; break ;
      case VlDistanceChi2  : function = VL_XCAT(_vl_distance_chi2_sse2_,           SFX) ; break ;
      case VlKernelL2      : function = VL_XCAT(_vl_kernel_l2_sse2_,               SFX) ; break ;
      case VlKernelL1      : function = VL_XCAT(_vl_kernel_l1_sse2_,               SFX) ; break ;
      case VlKernelChi2    : function = VL_XCAT(_vl_kernel_chi2_sse2_,             SFX) ; break ;
      default: break ;
    }
  }
#endif

#ifndef VL_DISABLE_AVX
  /* if an AVX implementation is available, use it */
  if (vl_cpu_has_avx() && vl_get_simd_enabled()) {
    switch (type) {
      case VlDistanceL2    : function = VL_XCAT(_vl_distance_l2_avx_,             SFX) ; break ;
      default: break ;
    }
  }
#endif

  return function ;
}

/* ---------------------------------------------------------------- */

VL_EXPORT COMPARISONFUNCTION3_TYPE
VL_XCAT(vl_get_vector_3_comparison_function_, SFX)(VlVectorComparisonType type)
{
  COMPARISONFUNCTION3_TYPE function = 0 ;
  switch (type) {
    case VlDistanceMahalanobis : function = VL_XCAT(_vl_distance_mahalanobis_sq_, SFX) ; break ;
    default: abort() ;
  }

#ifndef VL_DISABLE_SSE2
  /* if a SSE2 implementation is available, use it */
  if (vl_cpu_has_sse2() && vl_get_simd_enabled()) {
    switch (type) {
      case VlDistanceMahalanobis : function = VL_XCAT(_vl_distance_mahalanobis_sq_sse2_, SFX) ; break ;
      default: break ;
    }
  }
#endif

#ifndef VL_DISABLE_AVX
  /* if an AVX implementation is available, use it */
  if (vl_cpu_has_avx() && vl_get_simd_enabled()) {
    switch (type) {
      case VlDistanceMahalanobis : function = VL_XCAT(_vl_distance_mahalanobis_sq_avx_, SFX) ; break ;
      default: break ;
    }
  }
#endif

  return function ;
}

/* ---------------------------------------------------------------- */

VL_EXPORT void
VL_XCAT(vl_eval_vector_comparison_on_all_pairs_, SFX)
(T * result, vl_size dimension,
 T const * X, vl_size numDataX,
 T const * Y, vl_size numDataY,
 COMPARISONFUNCTION_TYPE function)
{
  vl_uindex xi ;
  vl_uindex yi ;

  if (dimension == 0) return ;
  if (numDataX == 0) return ;
  assert (X) ;

  if (Y) {
    if (numDataY == 0) return ;
    for (yi = 0 ; yi < numDataY ; ++ yi) {
      for (xi = 0 ; xi < numDataX ; ++ xi) {
        *result++ = (*function)(dimension, X, Y) ;
        X += dimension ;
      }
      X -= dimension * numDataX ;
      Y += dimension ;
    }
  } else {
    T * resultTransp = result ;
    Y = X ;
    for (yi = 0 ; yi < numDataX ; ++ yi) {
      for (xi = 0 ; xi <= yi ; ++ xi) {
        T z = (*function)(dimension, X, Y) ;
        X += dimension ;
        *result       = z ;
        *resultTransp = z ;
        result        += 1 ;
        resultTransp  += numDataX ;
      }
      X -= dimension * (yi + 1) ;
      Y += dimension ;
      result       += numDataX - (yi + 1) ;
      resultTransp += 1        - (yi + 1) * numDataX ;
    }
  }
}

/* VL_MATHOP_INSTANTIATING */
#endif


/* ---------------------------------------------------------------- */
/*                                               Numerical analysis */
/* ---------------------------------------------------------------- */

#ifndef VL_MATHOP_INSTANTIATING

/** @brief SVD of a 2x2 real matrix
 ** @param S 2x2 real diagonal matrix of the singular values (out).
 ** @param U first 2x2 real orthonormal matrix (out).
 ** @param V second 2x2 real orthonormal matrix (out).
 ** @param M 2x2 matrix.
 **
 ** The function comptues the SVD decomposition of the 2x2
 ** real matrix @f$ M @f$:
 ** @f[
 **    M = U \operatorname S V^\top
 ** @f]
 ** where @f$ U @f$ and @f$ V @f$ are real orthonormal matrices
 ** and @f$ S @f$ is the diagonal matrix of the singular values
 ** in decreasing order.
 **
 ** @par Algorithm
 **
 ** The fist step is to find rotation matrices @f$ U_1 @f$ and
 ** @f$ V_1 @f$ such taht
 ** @f[
 **     M = U_1 R V_1^\top, \quad
 **     U_1 = \begin{barray} c_{u1} & - s_{u1} \\ s_{u1} & c_{u1} \end{barray}, \quad
 **     V_1 = \begin{barray} c_{v1} & - s_{v1} \\ s_{v1} & c_{v1} \end{barray}, \quad
 **     R = \begin{barray} f & g \\ 0 & h \end{barray}.
 ** @f]
 ** Gives a 2x2 triangular matrix. The second step is to call
 ** ::vl_lapack_dlasv2 on the matrix @f$ R @f$ obtaining
 ** @f[
 **   M = U_1 (U_2 S V_2^\top) V_2.
 ** @f]
 **/

void
vl_svd2 (double* S, double *U, double *V, double const *M)
{
  double m11 = M[0] ;
  double m21 = M[1] ;
  double m12 = M[2] ;
  double m22 = M[3] ;
  double cu1 = m11 ;
  double su1 = m21 ;
  double norm = sqrt(cu1*cu1 + su1*su1) ;
  double cu2, su2, cv2, sv2 ;
  double f, g, h ;
  double smin, smax ;
  cu1 /= norm ;
  su1 /= norm ;

  f = cu1 * m11 + su1 * m21 ;
  g = cu1 * m12 + su1 * m22 ;
  h = - su1 * m12 + cu1 * m22 ;

  vl_lapack_dlasv2 (&smin, &smax,
                    &sv2, &cv2,
                    &su2, &cu2,
                    f, g, h) ;

  assert(S) ;
  S[0] = smax ;
  S[1] = 0 ;
  S[2] = 0 ;
  S[3] = smin ;

  if (U) {
    U[0] = cu2*cu1 - su2*su1 ;
    U[1] = su2*cu1 + cu2*su1 ;
    U[2] = - cu2*su1 - su2*cu1 ;
    U[3] = - su2*su1 + cu2*cu1 ;
  }
  if (V) {
    V[0] = cv2 ;
    V[1] = sv2 ;
    V[2] = - sv2 ;
    V[3] = cv2 ;
  }
}

/** @brief SVD of a 2x2 upper triangular matrix (LAPACK @c dlasv2 equivalent)
 ** @param smin smallest (in modulus) singular value (out).
 ** @param smax largest (in modulus) singuarl value (out).
 ** @param sv second component of the right singular vector of @c smax (out).
 ** @param cv first component of the right singular vector of @c smax (out).
 ** @param su second component of the left singular vector of @c smax (out).
 ** @param cu first component of the left singular vector of @c smax (out).
 ** @param f first entry of the upper triangular matrix.
 ** @param g second entry of the upper triangular matrix.
 ** @param h third entry of the upper triangular matrix.
 **
 ** @f[
 **  \begin{bmatrix} f & g \\ 0 & h \end{bmatrix}
 **  =
 **  \begin{bmatrix} cv & - sv \\ sv & cv \end{bmatrix}
 **  \begon{bmatrix} smax & 0 \\ 0 & smin \end{bmatrix}
 **  \begin{bmatrix} cv & - sv \\ sv & cv \end{bmatrix}
 ** @f]
 **
 ** Z.Bai and J.Demmel,
 ** "Computing the Generalized Singular Value Decomposition",
 ** SIAM J. Sci. Comput., Vol. 14, No. 6, pp. 1464-1486, November 1993
 **/

#define isign(i) ((i)<0 ? (-1) : (+1))  /* integer sign function */
#define sign(x) ((x)<0.0 ? (-1) : (+1)) /* double sign function */

void
vl_lapack_dlasv2 (double *smin,
                  double *smax,
                  double *sv,
                  double *cv,
                  double *su,
                  double *cu,
                  double f,
                  double g,
                  double h)
{
  double svt, cvt, sut, cut; /* temporary sv, cv, su, and cu */
  double ft = f, gt = g, ht = h; /* temporary f, g, h */
  double fa = fabs(f), ga = fabs(g), ha = fabs(h); /* |f|, |g|, and |h| */
  int pmax = 1 ; /* pointer to max abs entry */
  int swap = 0 ; /* is swapped */
  int glarge = 0 ; /* is g very large */
  int tsign ; /* tmp sign */
  double fmh ; /* |f| -|h| */
  double d ; /* (|f| -|h|)/|f| */
  double dd ; /* d*d */
  double q ; /* g/f */
  double qq ; /* q*q */
  double s ; /* (|f| + |h|)/|f| */
  double ss ; /* s*s */
  double spq ; /* sqrt(ss + qq) */
  double dpq ; /* sqrt(dd + qq) */
  double a ; /* (spq + dpq)/2 */
  double tmp ; /* temporaries */
  double tt;

  /* make fa >= ha */
  if (fa < ha) {
    pmax = 3 ;
    tmp =ft ; ft = ht ; ht = tmp ; /* swap ft and ht */
    tmp =fa ; fa = ha ; ha = tmp ; /* swap fa and ha */
    swap = 1 ;
  }

  if (ga == 0.0) { /* diagonal */
    *smin = ha ;
    *smax = fa ;
    /* identity matrix */
    cut = 1.0 ; sut = 0.0 ;
    cvt = 1.0 ; svt = 0.0 ;
  }
  else { /* not diagonal */
    if (ga > fa) { /* g is the largest entry */
      pmax = 2 ;
      if ((fa / ga) < VL_EPSILON_D) { /* g is very large */
        glarge = 1 ;
        *smax = ga ; /* 1 ulp */
        if (ha > 1.0) {
          *smin = fa / (ga / ha) ; /* 2 ulps */
        } else {
          *smin = (fa / ga) * ha ; /* 2 ulps */
        }
        cut = 1.0 ; sut = ht / gt ;
        cvt = 1.0 ; svt = ft / gt ;
      }
    }

    if (glarge == 0) { /* normal case */
      fmh = fa - ha ; /* 1ulp */
      if (fmh == fa) {  /* cope with infinite f or h */
        d = 1.0 ;
      } else {
        d = fmh / fa ; /* note 0<=d<=1.0, 2 ulps */
      }
      q = gt / ft ; /* note |q|<1/EPS, 1 ulp */
      s = 2.0 - d ; /* note s>=1.0, 3 ulps */
      dd = d*d ;
      qq = q*q ;
      ss = s*s ;
      spq = sqrt(ss + qq) ; /* note 1<=spq<=1+1/EPS, 5 ulps */
      if (d == 0.0) {
        dpq = fabs(q) ; /* 0 ulp */
      } else {
        dpq = sqrt(dd + qq) ; /* note 0<=dpq<=1+1/EPS, 3.5 ulps */
      }
      a = 0.5 * (spq + dpq) ; /* note 1<=a<=1 + |q|, 6 ulps */
      *smin = ha / a; /* 7 ulps */
      *smax = fa * a; /* 7 ulps */
      if (qq==0.0) { /* qq underflow */
        if (d==0.0) {
          tmp = sign(ft)*2*sign(gt); /* 0ulp */
        }
        else {
          tmp = gt/(sign(ft)*fmh) + q/s; /* 6 ulps */
        }
      } else {
        tmp = (q/(spq + s) + q/(dpq + d))*(1.0 + a);  /* 17 ulps */
      }
      /* if qq */
      tt = sqrt(tmp*tmp + 4.0) ; /* 18.5 ulps */
      cvt = 2.0 / tt ; /* 19.5 ulps */
      svt = tmp / tt ; /* 36.5 ulps */
      cut = (cvt + svt*q) / a ; /* 46.5 ulps */
      sut = (ht / ft) * svt / a ; /* 45.5 ulps */
    } /* if g not large */
  } /* if ga */
  if (swap == 1) {
    *cu = svt ; *su = cvt ;
    *cv = sut ; *sv = cut ;
  } else {
    *cu = cut ; *su = sut ;
    *cv = cvt ; *sv = svt ;
  }
  /* correct the signs of smax and smin */
  if (pmax==1) { tsign = sign(*cv) * sign(*cu) * sign(f) ; }
  if (pmax==2) { tsign = sign(*sv) * sign(*cu) * sign(g) ; }
  if (pmax==3) { tsign = sign(*sv) * sign(*su) * sign(h) ; }
  *smax = isign(tsign) * (*smax);
  *smin = isign(tsign * sign(f) * sign(h)) * (*smin) ;
}


/** @brief Solve a 3x3 linear system
 ** @param x result.
 ** @param A system matrix.
 ** @param b coefficients.
 **
 ** The function computes a solution to @f$ Ax =b @f$ for a 3x3
 ** matrix.
 **/

VL_EXPORT int
vl_solve_linear_system_3 (double * x, double const * A, double const *b)
{
  int err ;
  double M[3*4] ;
  M[0] = A[0] ;
  M[1] = A[1] ;
  M[2] = A[2] ;
  M[3] = A[3] ;
  M[4] = A[4] ;
  M[5] = A[5] ;
  M[6] = A[6] ;
  M[7] = A[7] ;
  M[8] = A[8] ;
  M[9] = b[0] ;
  M[10] = b[1] ;
  M[11] = b[2] ;
  err = vl_gaussian_elimination(M,3,4) ;
  x[0] = M[9] ;
  x[1] = M[10] ;
  x[2] = M[11] ;
  return err ;
}

/** @brief Solve a 2x2 linear system
 ** @param x result.
 ** @param A system matrix.
 ** @param b coefficients.
 **
 ** The function computes a solution to @f$ Ax =b @f$ for a 2x2
 ** matrix.
 **/

VL_EXPORT int
vl_solve_linear_system_2 (double * x, double const * A, double const *b)
{
  int err ;
  double M[2*3] ;
  M[0] = A[0] ;
  M[1] = A[1] ;
  M[2] = A[2] ;
  M[3] = A[3] ;
  M[4] = b[0];
  M[5] = b[1] ;
  err = vl_gaussian_elimination(M,2,3) ;
  x[0] = M[4] ;
  x[1] = M[5] ;
  return err ;
}

/** @brief Gaussian elimination
 ** @param M matrix.
 ** @param numRows number of rows of @c M.
 ** @param numColumns number of columns of @c M.
 **
 ** The function runs Gaussian elimination with pivoting
 ** on the matrix @a M in place.
 ** @c numRows must be not larger than @c numColumns.
 **
 ** Let @f$ M = [A, b] @f$ to obtain the solution to the linear
 ** system @f$ Ax=b @f$ (as the last column of @c M after
 ** elimination).
 **
 ** Let @f$ M = [A, I] @f$ to compute the inverse of @c A in
 ** a similar manner.
 **/

VL_EXPORT vl_bool
vl_gaussian_elimination (double * A, vl_size numRows, vl_size numColumns)
{
  vl_index i, j, ii, jj ;
  assert(A) ;
  assert(numRows <= numColumns) ;

#define Aat(i,j) A[(i) + (j)*numRows]

  /* Gauss elimination */
  for(j = 0 ; j < (signed)numRows ; ++j) {
    double maxa = 0 ;
    double maxabsa = 0 ;
    vl_index maxi = -1 ;
    double tmp ;

#if 0
    {
      vl_index iii, jjj ;
      for (iii = 0 ; iii < 2 ; ++iii) {
        for (jjj = 0 ; jjj < 3 ; ++jjj) {
          VL_PRINTF("%5.2g ", Aat(iii,jjj)) ;

        }
        VL_PRINTF("\n") ;
      }
      VL_PRINTF("\n") ;
    }
#endif

    /* look for the maximally stable pivot */
    for (i = j ; i < (signed)numRows ; ++i) {
      double a = Aat(i,j) ;
      double absa = vl_abs_d (a) ;
      if (absa > maxabsa) {
        maxa = a ;
        maxabsa = absa ;
        maxi = i ;
      }
    }
    i = maxi ;

    /* if singular give up */
    if (maxabsa < 1e-10) return VL_ERR_OVERFLOW ;

    /* swap j-th row with i-th row and normalize j-th row */
    for(jj = j ; jj < (signed)numColumns ; ++jj) {
      tmp = Aat(i,jj) ; Aat(i,jj) = Aat(j,jj) ; Aat(j,jj) = tmp ;
      Aat(j,jj) /= maxa ;
    }

#if 0
    {
      vl_index iii, jjj ;
      VL_PRINTF("after swap %d %d\n", j, i);
      for (iii = 0 ; iii < 2 ; ++iii) {
        for (jjj = 0 ; jjj < 3 ; ++jjj) {
          VL_PRINTF("%5.2g ", Aat(iii,jjj)) ;

        }
        VL_PRINTF("\n") ;
      }
      VL_PRINTF("\n") ;
    }
#endif

    /* elimination */
    for (ii = j+1 ; ii < (signed)numRows ; ++ii) {
      double x = Aat(ii,j) ;
      for (jj = j ; jj < (signed)numColumns ; ++jj) {
        Aat(ii,jj) -= x * Aat(j,jj) ;
      }
    }

#if 0
    {
      VL_PRINTF("after elimination\n");

      vl_index iii, jjj ;
      for (iii = 0 ; iii < 2 ; ++iii) {
        for (jjj = 0 ; jjj < 3 ; ++jjj) {
          VL_PRINTF("%5.2g ", Aat(iii,jjj)) ;

        }
        VL_PRINTF("\n") ;
      }
      VL_PRINTF("\n") ;
    }
#endif

  }

  /* backward substitution */
  for (i = numRows - 1 ; i > 0 ; --i) {
    /* substitute in all rows above */
    for (ii = i - 1 ; ii >= 0 ; --ii) {
      double x = Aat(ii,i) ;
      /* j = numRows */
      for (j = numRows ; j < (signed)numColumns ; ++j) {
        Aat(ii,j) -= x * Aat(i,j) ;
      }
    }
  }

#if 0
  {
    VL_PRINTF("after substitution\n");

    vl_index iii, jjj ;
    for (iii = 0 ; iii < 2 ; ++iii) {
      for (jjj = 0 ; jjj < 3 ; ++jjj) {
        VL_PRINTF("%5.2g ", Aat(iii,jjj)) ;

      }
      VL_PRINTF("\n") ;
    }
    VL_PRINTF("\n") ;
  }
#endif


  return VL_ERR_OK ;
}


/* VL_MATHOP_INSTANTIATING */
#endif

#undef VL_MATHOP_INSTANTIATING
vlfeat/vl/float.th0000644000175000017500000000534012237004745013036 0ustar  dimadima/** @file float.th
 ** @brief Float - Template
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#include "generic.h"

#undef  T
#undef  SFX
#undef  VSIZE
#undef  VSFX
#undef  VTYPE

#undef  VSIZEavx
#undef  VSFXavx
#undef  VTYPEavx

#if (FLT == VL_TYPE_FLOAT)
#  define T      float
#  define SFX    f
#elif (FLT == VL_TYPE_DOUBLE)
#  define T      double
#  define SFX    d
#elif (FLT == VL_TYPE_UINT32)
#  define T      vl_uint32
#  define SFX    ui32
#elif (FLT == VL_TYPE_INT32)
#  define T      vl_int32
#  define SFX    i32
#endif

#ifdef __AVX__

#if (FLT == VL_TYPE_FLOAT)
#  define VSIZEavx  8
#  define VSFXavx   s
#  define VTYPEavx  __m256
#elif (FLT == VL_TYPE_DOUBLE)
#  define VSIZEavx  4
#  define VSFXavx   d
#  define VTYPEavx  __m256d
#endif

#define VALIGNEDavx(x) (! (((vl_uintptr)(x)) & 0x1F))

#define VMULavx  VL_XCAT(_mm256_mul_p,     VSFX)
#define VDIVavx  VL_XCAT(_mm256_div_p,     VSFX)
#define VADDavx  VL_XCAT(_mm256_add_p,     VSFX)
#define VHADDavx  VL_XCAT(_mm_hadd_p,     VSFX)
#define VHADD2avx  VL_XCAT(_mm256_hadd_p,     VSFX)
#define VSUBavx  VL_XCAT(_mm256_sub_p,     VSFX)
#define VSTZavx  VL_XCAT(_mm256_setzero_p, VSFX)
#define VLD1avx  VL_XCAT(_mm256_broadcast_s,   VSFX)
#define VLDUavx  VL_XCAT(_mm256_loadu_p,   VSFX)
#define VST1avx  VL_XCAT(_mm256_store_s,   VSFX)
#define VST2avx  VL_XCAT(_mm256_store_p,   VSFX)
#define VST2Uavx VL_XCAT(_mm256_storeu_p,  VSFX)
#define VPERMavx VL_XCAT(_mm256_permute2f128_p,  VSFX)
//#define VCSTavx VL_XCAT( _mm256_castps256_ps128,  VSFX)
#define VCSTavx  VL_XCAT5(_mm256_castp,VSFX,256_p,VSFX,128)
/* __AVX__ */
#endif

#ifdef  __SSE2__

#if (FLT == VL_TYPE_FLOAT)
#  define VSIZE  4
#  define VSFX   s
#  define VTYPE  __m128
#elif (FLT == VL_TYPE_DOUBLE)
#  define VSIZE  2
#  define VSFX   d
#  define VTYPE  __m128d
#endif

#define VALIGNED(x) (! (((vl_uintptr)(x)) & 0xF))

#define VMAX  VL_XCAT(_mm_max_p,     VSFX)
#define VMUL  VL_XCAT(_mm_mul_p,     VSFX)
#define VDIV  VL_XCAT(_mm_div_p,     VSFX)
#define VADD  VL_XCAT(_mm_add_p,     VSFX)
#define VSUB  VL_XCAT(_mm_sub_p,     VSFX)
#define VSTZ  VL_XCAT(_mm_setzero_p, VSFX)
#define VLD1  VL_XCAT(_mm_load1_p,   VSFX)
#define VLDU  VL_XCAT(_mm_loadu_p,   VSFX)
#define VST1  VL_XCAT(_mm_store_s,   VSFX)
#define VSET1 VL_XCAT(_mm_set_s,     VSFX)
#define VSHU  VL_XCAT(_mm_shuffle_p, VSFX)
#define VNEQ  VL_XCAT(_mm_cmpneq_p,  VSFX)
#define VAND  VL_XCAT(_mm_and_p,     VSFX)
#define VANDN VL_XCAT(_mm_andnot_p,  VSFX)
#define VST2  VL_XCAT(_mm_store_p,   VSFX)
#define VST2U VL_XCAT(_mm_storeu_p,  VSFX)

/* __SSE2__ */
#endif

vlfeat/vl/svmdataset.c0000644000175000017500000002755412237004745013726 0ustar  dimadima/** @file svmdataset.c
 ** @brief SVM Dataset - Definition
 ** @author Daniele Perrone
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2012 Daniele Perrone.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**
@file svmdataset.h
@tableofcontents
@author Daniele Perrone
@author Andrea Vedaldi

The SVM solver object ::VlSvm, supporting SVM learning in VLFeat,
uses an abstraction mechanism to work on arbitrary data types.
This module provides an helper object, ::VlSvmDataset,
that simplify taking advantage of this functionality, supporting for example
different data types and the computation of feature maps out of the box.


@section svmdataset-starting Getting started


As discussed in @ref svm-advanced, most linear SVM solvers,
such as the ones implemented in VLFeat in @ref svm, require only two
operations to be defined on the data:

- *Inner product* between a data point $\bx$ and the model vector $\bw$.
  This is implemented by a function of type ::VlSvmInnerProductFunction.
- *Accumulation* of a dataobint $\bx$ to the model vector $\bw$:
  $\bw \leftarrow \bw + \alpha \bx$. This is implemented
  by a function of the type ::VlSvmAccumulateFunction .

The SVM solver needs to know nothing about the data once these two
operations are defined. These functions can do any number of things,
such as supporting different formats for the data (dense or sparse,
float or double), computing feature maps, or expanding compressed
representations such as Product Quantization.

VLFeat provides the helper object ::VlSvmDataset to support some
of these functionalities out of the box (it is important to remark
that its use with the SVM solver ::VlSvm is entirely optional).

Presently, ::VlSvmDataset supports:

- @c float and @c double dense arrays.
- The on-the-fly application of the homogeneous kernel map to implement
  additive non-linear kernels (see @ref homkermap).

For example, to learn a linear SVM on SINGLE data:

@code
int main()
{
  vl_size const numData = 4 ;
  vl_size const dimension = 2 ;
  single x [dimension * numData] = {
    0.0, -0.5,
    0.6, -0.3,
    0.0,  0.5,
    0.6,  0.0} ;
  double y [numData] = {1, 1, -1, 1} ;
  double lambda = 0.01;
  double * const model ;
  double bias ;

  VlSvmDataset * dataset = vl_svmdataset_new (VL_TYPE_SINGLE, x, dimension, numData) ;
  VlSvm * svm = vl_svm_new_with_dataset (VlSvmSolverSgd, dataset, y, lambda) ;

  vl_svm_train(svm) ;

  model = vl_svm_get_model(svm) ;
  bias = vl_svm_get_bias(svm) ;

  printf("model w = [ %f , %f ] , bias b = %f \n",
         model[0],
         model[1],
         bias);

  vl_svm_delete(svm) ;
  vl_svmdataset_delete(dataset) ;
  return 0;
}
@endcode

**/

/* ---------------------------------------------------------------- */
#ifndef VL_SVMDATASET_INSTANTIATING
/* ---------------------------------------------------------------- */

#include "svmdataset.h"
#include 
#include 

struct VlSvmDataset_ {
  vl_type dataType ;                /**< Data type. */
  void * data ;                     /**< Pointer to data. */
  vl_size numData ;                 /**< Number of wrapped data. */
  vl_size dimension ;               /**< Data point dimension. */
  VlHomogeneousKernelMap * hom ;    /**< Homogeneous kernel map (optional). */
  void * homBuffer ;                /**< Homogeneous kernel map buffer. */
  vl_size homDimension ;            /**< Homogeneous kernel map dimension. */
} ;

/* templetized parts of the implementation */
#define FLT VL_TYPE_FLOAT
#define VL_SVMDATASET_INSTANTIATING
#include "svmdataset.c"

#define FLT VL_TYPE_DOUBLE
#define VL_SVMDATASET_INSTANTIATING
#include "svmdataset.c"

/** @brief Create a new object wrapping a dataset.
 ** @param dataType of data (@c float and @c double supported).
 ** @param data pointer to the data.
 ** @param dimension the dimension of a data vector.
 ** @param numData number of wrapped data vectors.
 ** @return new object.
 **
 ** The function allocates and returns a new SVM dataset object
 ** wrapping the data pointed by @a data. Note that no copy is made
 ** of data, so the caller should keep the data allocated as the object exists.
 **
 ** @sa ::vl_svmdataset_delete
 **/

VlSvmDataset*
vl_svmdataset_new (vl_type dataType, void *data, vl_size dimension, vl_size numData)
{
  VlSvmDataset * self ;
  assert(dataType == VL_TYPE_DOUBLE || dataType == VL_TYPE_FLOAT) ;
  assert(data) ;

  self = vl_calloc(1, sizeof(VlSvmDataset)) ;
  if (self == NULL) return NULL ;

  self->dataType = dataType ;
  self->data = data ;
  self->dimension = dimension ;
  self->numData = numData ;
  self->hom = NULL ;
  self->homBuffer = NULL ;
  return self ;
}

/** @brief Delete the object.
 ** @param self object to delete.
 **
 ** The function frees the resources allocated by
 ** ::vl_svmdataset_new(). Notice that the wrapped data will *not*
 ** be freed as it is not owned by the object.
 **/

void vl_svmdataset_delete (VlSvmDataset *self)
{
  if (self->homBuffer) {
    vl_free(self->homBuffer) ;
    self->homBuffer = 0 ;
  }
  vl_free (self) ;
}

/** @brief Get the wrapped data.
 ** @param self object.
 ** @return a pointer to the wrapped data.
 **/

void*
vl_svmdataset_get_data (VlSvmDataset const *self)
{
  return self->data ;
}

/** @brief Get the number of wrapped data elements.
 ** @param self object.
 ** @return number of wrapped data elements.
 **/

vl_size
vl_svmdataset_get_num_data (VlSvmDataset const *self)
{
  return self->numData ;
}

/** @brief Get the dimension of the wrapped data.
 ** @param self object.
 ** @return dimension of the wrapped data.
 **/

vl_size
vl_svmdataset_get_dimension (VlSvmDataset const *self)
{
  if (self->hom) {
    return self->dimension * vl_homogeneouskernelmap_get_dimension(self->hom) ;
  }
  return self->dimension ;
}

/** @brief Get the homogeneous kernel map object.
 ** @param self object.
 ** @return homogenoeus kernel map object (or @c NULL if any).
 **/

VlHomogeneousKernelMap *
vl_svmdataset_get_homogeneous_kernel_map (VlSvmDataset const *self)
{
  assert(self) ;
  return self->hom ;
}

/** @brief Set the homogeneous kernel map object.
 ** @param self object.
 ** @param hom homogeneous kernel map object to use.
 **
 ** After changing the kernel map, the inner product and accumulator
 ** function should be queried again (::vl_svmdataset_get_inner_product_function
 ** adn ::vl_svmdataset_get_accumulate_function).
 **
 ** Set this to @c NULL to avoid using a kernel map.
 **
 ** Note that this does *not* transfer the ownership of the object
 ** to the function. Furthermore, ::VlSvmDataset holds to the
 ** object until it is destroyed or the object is replaced or removed
 ** by calling this function again.
 **/

void
vl_svmdataset_set_homogeneous_kernel_map (VlSvmDataset * self,
                                          VlHomogeneousKernelMap * hom)
{
  assert(self) ;
  self->hom = hom ;
  self->homDimension = 0 ;
  if (self->homBuffer) {
    vl_free (self->homBuffer) ;
    self->homBuffer = 0 ;
  }
  if (self->hom) {
    self->homDimension = vl_homogeneouskernelmap_get_dimension(self->hom) ;
    self->homBuffer = vl_calloc(self->homDimension, vl_get_type_size(self->dataType)) ;
  }
}

/** @brief Get the accumulate function
 ** @param self object.
 ** @return a pointer to the accumulate function to use with this data.
 **/

VlSvmAccumulateFunction
vl_svmdataset_get_accumulate_function(VlSvmDataset const *self)
{
  if (self->hom == NULL) {
    switch (self->dataType) {
      case VL_TYPE_FLOAT:
        return (VlSvmAccumulateFunction) vl_svmdataset_accumulate_f ;
        break ;
      case VL_TYPE_DOUBLE:
        return (VlSvmAccumulateFunction) vl_svmdataset_accumulate_d ;
        break ;
    }
  } else {
    switch (self->dataType) {
      case VL_TYPE_FLOAT:
        return (VlSvmAccumulateFunction) vl_svmdataset_accumulate_hom_f ;
        break ;
      case VL_TYPE_DOUBLE:
        return (VlSvmAccumulateFunction) vl_svmdataset_accumulate_hom_d ;
        break ;
    }
  }
  assert(0) ;
  return NULL ;
}

/** @brief Get the inner product function.
 ** @param self object.
 ** @return a pointer to the inner product function to use with this data.
 **/

VlSvmInnerProductFunction
vl_svmdataset_get_inner_product_function (VlSvmDataset const *self)
{
  if (self->hom == NULL) {
    switch (self->dataType) {
      case VL_TYPE_FLOAT:
        return (VlSvmInnerProductFunction) _vl_svmdataset_inner_product_f ;
        break ;
      case VL_TYPE_DOUBLE:
        return (VlSvmInnerProductFunction) _vl_svmdataset_inner_product_d ;
        break ;
      default:
        assert(0) ;
    }
  } else {
    switch (self->dataType) {
      case VL_TYPE_FLOAT:
        return (VlSvmInnerProductFunction) _vl_svmdataset_inner_product_hom_f ;
        break ;
      case VL_TYPE_DOUBLE:
        return (VlSvmInnerProductFunction) _vl_svmdataset_inner_product_hom_d ;
        break ;
      default:
        assert(0) ;
    }
  }

  return NULL;
}

/* VL_SVMDATASET_INSTANTIATING */
#endif

/* ---------------------------------------------------------------- */
#ifdef VL_SVMDATASET_INSTANTIATING
/* ---------------------------------------------------------------- */

#include "float.th"

double
VL_XCAT(_vl_svmdataset_inner_product_,SFX) (VlSvmDataset const *self,
                                            vl_uindex element,
                                            double const *model)
{
  double product = 0 ;
  T* data = ((T*)self->data) + self->dimension * element ;
  T* end = data + self->dimension ;
  while (data != end) {
    product += (*data++) * (*model++) ;
  }
  return product ;
}

void
VL_XCAT(vl_svmdataset_accumulate_,SFX)(VlSvmDataset const *self,
                                       vl_uindex element,
                                       double *model,
                                       const double multiplier)
{
  T* data = ((T*)self->data) + self->dimension * element ;
  T* end = data + self->dimension ;
  while (data != end) {
    *model += (*data++) * multiplier ;
    model++ ;
  }
}

double
VL_XCAT(_vl_svmdataset_inner_product_hom_,SFX) (VlSvmDataset const *self,
                                                vl_uindex element,
                                                double const *model)
{
  double product = 0 ;
  T* data = ((T*)self->data) + self->dimension * element ;
  T* end = data + self->dimension ;
  T* bufEnd = ((T*)self->homBuffer)+ self->homDimension ;
  while (data != end) {
    /* TODO: zeros in data could be optimized by skipping over them */
    T* buf = self->homBuffer ;
    VL_XCAT(vl_homogeneouskernelmap_evaluate_,SFX)(self->hom,
                                                   self->homBuffer,
                                                   1,
                                                   (*data++)) ;
    while (buf != bufEnd) {
      product += (*buf++) * (*model++) ;
    }
  }
  return product ;
}

void
VL_XCAT(vl_svmdataset_accumulate_hom_,SFX)(VlSvmDataset const *self,
                                           vl_uindex element,
                                           double *model,
                                           const double multiplier)
{
  T* data = ((T*)self->data) + self->dimension * element ;
  T* end = data + self->dimension ;
  T* bufEnd = ((T*)self->homBuffer)+ self->homDimension ;
  while (data != end) {
    /* TODO: zeros in data could be optimized by skipping over them */
    T* buf = self->homBuffer ;
    VL_XCAT(vl_homogeneouskernelmap_evaluate_,SFX)(self->hom,
                                                   self->homBuffer,
                                                   1,
                                                   (*data++)) ;
    while (buf != bufEnd) {
      *model += (*buf++) * multiplier ;
      model++ ;
    }
  }
}

#undef FLT
#undef VL_SVMDATASET_INSTANTIATING

/* VL_SVMDATASET_INSTANTIATING */
#endif
vlfeat/vl/hikmeans.h0000644000175000017500000001031612237004736013343 0ustar  dimadima/** @file hikmeans.h
 ** @brief Hierarchical Integer K-Means Clustering
 ** @author Brian Fulkerson
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_HIKMEANS_H
#define VL_HIKMEANS_H

#include "generic.h"
#include "ikmeans.h"

struct _VLHIKMTree ;
struct _VLHIKMNode ;

/** @brief HIKM tree node
 **
 ** The number of children @a K is not bigger than the @a K parameter
 ** of the HIKM tree.
 **/
typedef struct _VlHIKMNode
{
  VlIKMFilt *filter ;             /**< IKM filter for this node*/
  struct _VlHIKMNode **children ; /**< Node children (if any) */
} VlHIKMNode ;

/** @brief HIKM tree */
typedef struct _VlHIKMTree {
  int M ;               /**< IKM: data dimensionality */
  int K ;               /**< IKM: K */
  int max_niters ;      /**< IKM: maximum # of iterations */
  int method ;          /**< IKM: method */
  int verb ;            /**< Verbosity level */

  int depth ;           /**< Depth of the tree */
  VlHIKMNode * root;    /**< Tree root node */
} VlHIKMTree ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlHIKMTree  *vl_hikm_new    (int method) ;
VL_EXPORT void         vl_hikm_delete (VlHIKMTree *f) ;
/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_INLINE int vl_hikm_get_ndims      (VlHIKMTree const *f) ;
VL_INLINE int vl_hikm_get_K          (VlHIKMTree const *f) ;
VL_INLINE int vl_hikm_get_depth      (VlHIKMTree const *f) ;
VL_INLINE int vl_hikm_get_verbosity  (VlHIKMTree const *f) ;
VL_INLINE int vl_hikm_get_max_niters (VlHIKMTree const *f) ;
VL_INLINE VlHIKMNode const * vl_hikm_get_root (VlHIKMTree const *f) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_INLINE void vl_hikm_set_verbosity  (VlHIKMTree *f, int verb) ;
VL_INLINE void vl_hikm_set_max_niters (VlHIKMTree *f, int max_niters) ;
/** @} */

/** @name Process data
 ** @{
 **/
VL_EXPORT void vl_hikm_init  (VlHIKMTree *f, int M, int K, int depth) ;
VL_EXPORT void vl_hikm_train (VlHIKMTree *f, vl_uint8 const *data, int N) ;
VL_EXPORT void vl_hikm_push  (VlHIKMTree *f, vl_uint *asgn, vl_uint8 const *data, int N) ;
/** @} */

/** ------------------------------------------------------------------
 ** @brief Get data dimensionality
 ** @param f HIKM tree.
 ** @return data dimensionality.
 **/

VL_INLINE int
vl_hikm_get_ndims (VlHIKMTree const* f)
{
  return f-> M ;
}

/** ------------------------------------------------------------------
 ** @brief Get K
 ** @param f HIKM tree.
 ** @return K.
 **/

VL_INLINE int
vl_hikm_get_K (VlHIKMTree const* f)
{
  return f-> K ;
}

/** ------------------------------------------------------------------
 ** @brief Get depth
 ** @param f HIKM tree.
 ** @return depth.
 **/

VL_INLINE int
vl_hikm_get_depth (VlHIKMTree const* f)
{
  return f-> depth ;
}


/** ------------------------------------------------------------------
 ** @brief Get verbosity level
 ** @param f HIKM tree.
 ** @return verbosity level.
 **/

VL_INLINE int
vl_hikm_get_verbosity (VlHIKMTree const* f)
{
  return f-> verb ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of iterations
 ** @param f HIKM tree.
 ** @return maximum number of iterations.
 **/

VL_INLINE int
vl_hikm_get_max_niters (VlHIKMTree const* f)
{
  return f-> max_niters ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of iterations
 ** @param f HIKM tree.
 ** @return maximum number of iterations.
 **/

VL_INLINE VlHIKMNode const *
vl_hikm_get_root (VlHIKMTree const* f)
{
  return f-> root ;
}

/** ------------------------------------------------------------------
 ** @brief Set verbosity level
 ** @param f    HIKM tree.
 ** @param verb verbosity level.
 **/

VL_INLINE void
vl_hikm_set_verbosity (VlHIKMTree *f, int verb)
{
  f-> verb = verb ;
}

/** ------------------------------------------------------------------
 ** @brief Set maximum number of iterations
 ** @param f          HIKM tree.
 ** @param max_niters maximum number of iterations.
 **/

VL_INLINE void
vl_hikm_set_max_niters (VlHIKMTree *f, int max_niters)
{
  f-> max_niters = max_niters ;
}

/* VL_HIKMEANS_H */
#endif
vlfeat/vl/kdtree.c0000644000175000017500000010422412237004745013017 0ustar  dimadima/** @file kdtree.c
 ** @brief KD-tree - Definition
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**


@page kdtree KD-trees and forests
@author Andrea Vedaldi, David Novotny


@ref kdtree.h implements a KD-tree object, a data structure that can
efficiently index moderately dimensional vector spaces. Both
best-bin-first @cite{beis97shape} and randomized KD-tree forests are
implemented
@cite{silpa-anan08optimised},@cite{muja09fast}. Applications include
fast matching of feature descriptors.

- @ref kdtree-overview
- @ref kdtree-tech


@section kdtree-overview Overview


To create a ::VlKDForest object use ::vl_kdforest_new specifying the
dimensionality of the data and the number of trees in the forest.
With one tree only, the algorithm is analogous to @cite{beis97shape}
(best-bin KDTree). Multiple trees correspond to the randomized KDTree
forest as in @cite{silpa-anan08optimised},@cite{muja09fast}.

To let the KD-tree index some data use ::vl_kdforest_build. Note that
for efficiency KD-tree does not copy the data but retains a pointer to
it. Therefore the data must exist (and not change) until the KD-tree
is deleted. To delete the KD-tree object, use ::vl_kdforest_delete.

To find the N nearest neighbors to a query point first instantiate
a ::VlKDForestSearcher and then start search using a ::vl_kdforest_query
with the searcher object as an argument. To set a maximum number of
comparisons per query and calculate approximate nearest neighbors use
::vl_kdforest_set_max_num_comparisons.


@section kdtree-tech Technical details


::VlKDForest implements the best-bin-first kd-tree of @cite{beis97shape}.

Construction. Given a set of points @f$ x_1,\dots,x_n \in
\mathbb{R}^d @f$, the algorithm recursively partitions the @e d
dimensional Euclidean space @f$ \mathbb{R}^d @f$ into (hyper-)
rectangles.

Partitions are organized into a binary tree with the root
corresponding to the whole space @f$ \mathbb{R}^d @f$. The algorithm
refines each partition by dividing it into two halves by thresholding
along a given dimension. Both the splitting dimension and the
threshold are determined as a statistic of the data points contained
in the partition. The splitting dimension is the one which has largest
sample variance and the splitting threshold is either the sample mean
or the median. Leaves are atomic partitions and they contain a list of
zero or more data points (typically one).

Querying. Querying amounts to finding the N data points closer
to a given query point @f$ x_q \in \mathbb{R}^d @f$. This is done by
branch-and-bound. A search state is an active partition (initially the
root) and it is weighed by the lower bound on the distance of any
point in the partition and the query point. Such a lower bound is
trivial to compute because partitions are hyper-rectangles.

Querying usage. As said before a user has to create an instance
::VlKDForestSearcher using ::vl_kdforest_new_searcher in order to be able
to make queries. When a user wants to delete a KD-Tree all the searchers
bound to the given KD-Forest are erased automatically. If a user wants to
delete some of the searchers before the KD-Tree erase, he could do it
using the vl_kdforest_delete_searcher method.
**/

#include "kdtree.h"
#include "generic.h"
#include "random.h"
#include "mathop.h"
#include 

#if defined(_OPENMP)
#include 
#endif

#define VL_HEAP_prefix     vl_kdforest_search_heap
#define VL_HEAP_type       VlKDForestSearchState
#define VL_HEAP_cmp(v,x,y) (v[x].distanceLowerBound - v[y].distanceLowerBound)
#include "heap-def.h"

#define VL_HEAP_prefix     vl_kdtree_split_heap
#define VL_HEAP_type       VlKDTreeSplitDimension
#define VL_HEAP_cmp(v,x,y) (v[x].variance - v[y].variance)
#include "heap-def.h"

#define VL_HEAP_prefix     vl_kdforest_neighbor_heap
#define VL_HEAP_type       VlKDForestNeighbor
#define VL_HEAP_cmp(v,x,y) (v[y].distance - v[x].distance)
#include "heap-def.h"

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Allocate a new node from the tree pool
 **/

static vl_uindex
vl_kdtree_node_new (VlKDTree * tree, vl_uindex parentIndex)
{
  VlKDTreeNode * node = NULL ;
  vl_uindex nodeIndex = tree->numUsedNodes ;
  tree -> numUsedNodes += 1 ;

  assert (tree->numUsedNodes <= tree->numAllocatedNodes) ;

  node = tree->nodes + nodeIndex ;
  node -> parent = parentIndex ;
  node -> lowerChild = 0 ;
  node -> upperChild = 0 ;
  node -> splitDimension = 0 ;
  node -> splitThreshold = 0 ;
  return nodeIndex ;
}

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Compare KDTree index entries for sorting
 **/

VL_INLINE int
vl_kdtree_compare_index_entries (void const * a,
                                 void const * b)
{
  double delta =
    ((VlKDTreeDataIndexEntry const*)a) -> value -
    ((VlKDTreeDataIndexEntry const*)b) -> value ;
  if (delta < 0) return -1 ;
  if (delta > 0) return +1 ;
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Build KDTree recursively
 ** @param forest forest to which the tree belongs.
 ** @param tree tree being built.
 ** @param nodeIndex node to process.
 ** @param dataBegin begin of data for this node.
 ** @param dataEnd end of data for this node.
 ** @param depth depth of this node.
 **/

static void
vl_kdtree_build_recursively
(VlKDForest * forest,
 VlKDTree * tree, vl_uindex nodeIndex,
 vl_uindex dataBegin, vl_uindex dataEnd,
 unsigned int depth)
{
  vl_uindex d, i, medianIndex, splitIndex ;
  VlKDTreeNode * node = tree->nodes + nodeIndex ;
  VlKDTreeSplitDimension * splitDimension ;

  /* base case: there is only one data point */
  if (dataEnd - dataBegin <= 1) {
    if (tree->depth < depth) tree->depth = depth ;
    node->lowerChild = - dataBegin - 1;
    node->upperChild = - dataEnd - 1 ;
    return ;
  }

  /* compute the dimension with largest variance > 0 */
  forest->splitHeapNumNodes = 0 ;
  for (d = 0 ; d < forest->dimension ; ++ d) {
    double mean = 0 ; /* unnormalized */
    double secondMoment = 0 ;
    double variance = 0 ;
    vl_size numSamples = VL_KDTREE_VARIANCE_EST_NUM_SAMPLES;
    vl_bool useAllData = VL_FALSE;

    if(dataEnd - dataBegin <= VL_KDTREE_VARIANCE_EST_NUM_SAMPLES) {
      useAllData = VL_TRUE;
      numSamples = dataEnd - dataBegin;
    }

    for (i = 0; i < numSamples ; ++ i) {
      vl_uint32 sampleIndex;
      vl_index di;
      double datum ;

      if(useAllData == VL_TRUE) {
        sampleIndex = (vl_uint32)i;
      } else {
        sampleIndex = (vl_rand_uint32(forest->rand) % VL_KDTREE_VARIANCE_EST_NUM_SAMPLES);
      }
      sampleIndex += dataBegin;

      di = tree->dataIndex[sampleIndex].index ;

      switch(forest->dataType) {
        case VL_TYPE_FLOAT: datum = ((float const*)forest->data)
          [di * forest->dimension + d] ;
          break ;
        case VL_TYPE_DOUBLE: datum = ((double const*)forest->data)
          [di * forest->dimension + d] ;
          break ;
        default:
          abort() ;
      }
      mean += datum ;
      secondMoment += datum * datum ;
    }

    mean /= numSamples ;
    secondMoment /= numSamples ;
    variance = secondMoment - mean * mean ;

    if (variance <= 0) continue ;

    /* keep splitHeapSize most varying dimensions */
    if (forest->splitHeapNumNodes < forest->splitHeapSize) {
      VlKDTreeSplitDimension * splitDimension
        = forest->splitHeapArray + forest->splitHeapNumNodes ;
      splitDimension->dimension = (unsigned int)d ;
      splitDimension->mean = mean ;
      splitDimension->variance = variance ;
      vl_kdtree_split_heap_push (forest->splitHeapArray, &forest->splitHeapNumNodes) ;
    } else {
      VlKDTreeSplitDimension * splitDimension = forest->splitHeapArray + 0 ;
      if (splitDimension->variance < variance) {
        splitDimension->dimension = (unsigned int)d ;
        splitDimension->mean = mean ;
        splitDimension->variance = variance ;
        vl_kdtree_split_heap_update (forest->splitHeapArray, forest->splitHeapNumNodes, 0) ;
      }
    }
  }

  /* additional base case: the maximum variance is equal to 0 (overlapping points) */
  if (forest->splitHeapNumNodes == 0) {
    node->lowerChild = - dataBegin - 1 ;
    node->upperChild = - dataEnd - 1 ;
    return ;
  }

  /* toss a dice to decide the splitting dimension (variance > 0) */
  splitDimension = forest->splitHeapArray
  + (vl_rand_uint32(forest->rand) % VL_MIN(forest->splitHeapSize, forest->splitHeapNumNodes)) ;

  node->splitDimension = splitDimension->dimension ;

  /* sort data along largest variance dimension */
  for (i = dataBegin ; i < dataEnd ; ++ i) {
    vl_index di = tree->dataIndex[i].index ;
    double datum ;
    switch (forest->dataType) {
      case VL_TYPE_FLOAT: datum = ((float const*)forest->data)
        [di * forest->dimension + splitDimension->dimension] ;
        break ;
      case VL_TYPE_DOUBLE: datum = ((double const*)forest->data)
        [di * forest->dimension + splitDimension->dimension] ;
        break ;
      default:
        abort() ;
    }
    tree->dataIndex [i] .value = datum ;
  }
  qsort (tree->dataIndex + dataBegin,
         dataEnd - dataBegin,
         sizeof (VlKDTreeDataIndexEntry),
         vl_kdtree_compare_index_entries) ;

  /* determine split threshold */
  switch (forest->thresholdingMethod) {
    case VL_KDTREE_MEAN :
      node->splitThreshold = splitDimension->mean ;
      for (splitIndex = dataBegin ;
           splitIndex < dataEnd && tree->dataIndex[splitIndex].value <= node->splitThreshold ;
           ++ splitIndex) ;
      splitIndex -= 1 ;
      /* If the mean does not provide a proper partition, fall back to
       * median. This usually happens if all points have the same
       * value and the zero variance test fails for numerical accuracy
       * reasons. In this case, also due to numerical accuracy, the
       * mean value can be smaller, equal, or larger than all
       * points. */
      if (dataBegin <= splitIndex && splitIndex + 1 < dataEnd) break ;

    case VL_KDTREE_MEDIAN :
      medianIndex = (dataBegin + dataEnd - 1) / 2 ;
      splitIndex = medianIndex ;
      node -> splitThreshold = tree->dataIndex[medianIndex].value ;
      break ;

    default:
      abort() ;
  }

  /* divide subparts */
  node->lowerChild = vl_kdtree_node_new (tree, nodeIndex) ;
  vl_kdtree_build_recursively (forest, tree, node->lowerChild, dataBegin, splitIndex + 1, depth + 1) ;

  node->upperChild = vl_kdtree_node_new (tree, nodeIndex) ;
  vl_kdtree_build_recursively (forest, tree, node->upperChild, splitIndex + 1, dataEnd, depth + 1) ;
}

/** ------------------------------------------------------------------
 ** @brief Create new KDForest object
 ** @param dataType type of data (::VL_TYPE_FLOAT or ::VL_TYPE_DOUBLE)
 ** @param dimension data dimensionality.
 ** @param numTrees number of trees in the forest.
 ** @param distance type of distance norm (::VlDistanceL1 or ::VlDistanceL2).
 ** @return new KDForest.
 **/

VlKDForest *
vl_kdforest_new (vl_type dataType,
                 vl_size dimension, vl_size numTrees, VlVectorComparisonType distance)
{
  VlKDForest * self = vl_calloc (sizeof(VlKDForest), 1) ;

  assert(dataType == VL_TYPE_FLOAT || dataType == VL_TYPE_DOUBLE) ;
  assert(dimension >= 1) ;
  assert(numTrees >= 1) ;

  self -> rand = vl_get_rand () ;
  self -> dataType = dataType ;
  self -> numData = 0 ;
  self -> data = 0 ;
  self -> dimension = dimension ;
  self -> numTrees = numTrees ;
  self -> trees = 0 ;
  self -> thresholdingMethod = VL_KDTREE_MEDIAN ;
  self -> splitHeapSize = VL_MIN(numTrees, VL_KDTREE_SPLIT_HEAP_SIZE) ;
  self -> splitHeapNumNodes = 0 ;
  self -> distance = distance;
  self -> maxNumNodes = 0 ;
  self -> numSearchers = 0 ;
  self -> headSearcher = 0 ;

  switch (self->dataType) {
    case VL_TYPE_FLOAT:
      self -> distanceFunction = (void(*)(void))
      vl_get_vector_comparison_function_f (distance) ;
      break;
    case VL_TYPE_DOUBLE :
      self -> distanceFunction = (void(*)(void))
      vl_get_vector_comparison_function_d (distance) ;
      break ;
    default :
      abort() ;
  }

  return self ;
}

/** ------------------------------------------------------------------
 ** @brief Create a KDForest searcher object, used for processing queries
 ** @param kdforest a forest to which the queries should be pointing.
 ** @return KDForest searcher object.
 **
 ** A searcher is an object attached to the forest which must be created
 ** before running the queries. Each query has to be invoked with the
 ** searcher as its argument.
 **
 ** When using a multi-threaded approach a user should at first instantiate
 ** a correct number of searchers - each used in one thread.
 ** Then in each thread a query to the given searcher could be run.
 **
 **/

VlKDForestSearcher *
vl_kdforest_new_searcher (VlKDForest * kdforest)
{
  VlKDForestSearcher * self = vl_calloc(sizeof(VlKDForestSearcher), 1);
  if(kdforest->numSearchers == 0) {
    kdforest->headSearcher = self;
    self->previous = NULL;
    self->next = NULL;
  } else {
    VlKDForestSearcher * lastSearcher = kdforest->headSearcher;
    while (1) {
      if(lastSearcher->next) {
        lastSearcher = lastSearcher->next;
      } else {
        lastSearcher->next = self;
        self->previous = lastSearcher;
        self->next = NULL;
        break;
      }
    }
  }

  kdforest->numSearchers++;

  self->forest = kdforest;
  self->searchHeapArray = vl_malloc (sizeof(VlKDForestSearchState) * kdforest->maxNumNodes) ;
  self->searchIdBook = vl_calloc (sizeof(vl_uindex), kdforest->numData) ;
  return self ;
}

/** ------------------------------------------------------------------
 ** @brief Delete object
 ** @param self object.
 **/

void
vl_kdforestsearcher_delete (VlKDForestSearcher * self)
{
  if (self->previous && self->next) {
    self->previous->next = self->next;
    self->next->previous = self->previous;
  } else if (self->previous && !self->next) {
    self->previous->next = NULL;
  } else if (!self->previous && self->next) {
    self->next->previous = NULL;
    self->forest->headSearcher = self->next;
  } else {
    self->forest->headSearcher = NULL;
  }
  self->forest->numSearchers -- ;
  vl_free(self->searchHeapArray) ;
  vl_free(self->searchIdBook) ;
  vl_free(self) ;
}

VlKDForestSearcher *
vl_kdforest_get_searcher (VlKDForest const * self, vl_uindex pos)
{
  VlKDForestSearcher * lastSearcher = self->headSearcher ;
  vl_uindex i ;

  for(i = 0; (i < pos) & (lastSearcher != NULL) ; ++i) {
    lastSearcher = lastSearcher->next ;
  }
  return lastSearcher ;
}

/** ------------------------------------------------------------------
 ** @brief Delete KDForest object
 ** @param self KDForest object to delete
 ** @sa ::vl_kdforest_new
 **/

void
vl_kdforest_delete (VlKDForest * self)
{
  vl_uindex ti ;
  VlKDForestSearcher * searcher ;

  while ((searcher = vl_kdforest_get_searcher(self, 0))) {
    vl_kdforestsearcher_delete(searcher) ;
  }

  if (self->trees) {
    for (ti = 0 ; ti < self->numTrees ; ++ ti) {
      if (self->trees[ti]) {
        if (self->trees[ti]->nodes) vl_free (self->trees[ti]->nodes) ;
        if (self->trees[ti]->dataIndex) vl_free (self->trees[ti]->dataIndex) ;
        vl_free (self->trees[ti]) ;
      }
    }
    vl_free (self->trees) ;
  }
  vl_free (self) ;
}

/** ------------------------------------------------------------------
 ** @internal @brief Compute tree bounds recursively
 ** @param tree KDTree object instance.
 ** @param nodeIndex node index to start from.
 ** @param searchBounds 2 x numDimension array of bounds.
 **/

static void
vl_kdtree_calc_bounds_recursively (VlKDTree * tree,
                                   vl_uindex nodeIndex, double * searchBounds)
{
  VlKDTreeNode * node = tree->nodes + nodeIndex ;
  vl_uindex i = node->splitDimension ;
  double t = node->splitThreshold ;

  node->lowerBound = searchBounds [2 * i + 0] ;
  node->upperBound = searchBounds [2 * i + 1] ;

  //VL_PRINT("%f %f\n",node->lowerBound,node->upperBound);

  if (node->lowerChild > 0) {
    searchBounds [2 * i + 1] = t ;
    vl_kdtree_calc_bounds_recursively (tree, node->lowerChild, searchBounds) ;
    searchBounds [2 * i + 1] = node->upperBound ;
  }
  if (node->upperChild > 0) {
    searchBounds [2 * i + 0] = t ;
    vl_kdtree_calc_bounds_recursively (tree, node->upperChild, searchBounds) ;
    searchBounds [2 * i + 0] = node->lowerBound ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Build KDTree from data
 ** @param self KDTree object
 ** @param numData number of data points.
 ** @param data pointer to the data.
 **
 ** The function builds the KDTree by processing the data @a data. For
 ** efficiency, KDTree does not copy the data, but retains a pointer to it.
 ** Therefore the data must survive (and not change) until the KDTree
 ** is deleted.
 **/

void
vl_kdforest_build (VlKDForest * self, vl_size numData, void const * data)
{
  vl_uindex di, ti ;
  vl_size maxNumNodes ;
  double * searchBounds;

  /* need to check: if alredy built, clean first */
  self->data = data ;
  self->numData = numData ;
  self->trees = vl_malloc (sizeof(VlKDTree*) * self->numTrees) ;
  maxNumNodes = 0 ;

  for (ti = 0 ; ti < self->numTrees ; ++ ti) {
    self->trees[ti] = vl_malloc (sizeof(VlKDTree)) ;
    self->trees[ti]->dataIndex = vl_malloc (sizeof(VlKDTreeDataIndexEntry) * self->numData) ;
    for (di = 0 ; di < self->numData ; ++ di) {
      self->trees[ti]->dataIndex[di].index = di ;
    }
    self->trees[ti]->numUsedNodes = 0 ;
    /* num. nodes of a complete binary tree with numData leaves */
    self->trees[ti]->numAllocatedNodes = 2 * self->numData - 1 ;
    self->trees[ti]->nodes = vl_malloc (sizeof(VlKDTreeNode) * self->trees[ti]->numAllocatedNodes) ;
    self->trees[ti]->depth = 0 ;
    vl_kdtree_build_recursively (self, self->trees[ti],
                                 vl_kdtree_node_new(self->trees[ti], 0), 0,
                                 self->numData, 0) ;
    maxNumNodes += self->trees[ti]->numUsedNodes ;
  }

  searchBounds = vl_malloc(sizeof(double) * 2 * self->dimension);

  for (ti = 0 ; ti < self->numTrees ; ++ ti) {
    double * iter = searchBounds  ;
    double * end = iter + 2 * self->dimension ;
    while (iter < end) {
      *iter++ = - VL_INFINITY_F ;
      *iter++ = + VL_INFINITY_F ;
    }

    vl_kdtree_calc_bounds_recursively (self->trees[ti], 0, searchBounds) ;
  }

  vl_free(searchBounds);
  self -> maxNumNodes = maxNumNodes;
}


/** ------------------------------------------------------------------
 ** @internal @brief
 **/

vl_uindex
vl_kdforest_query_recursively (VlKDForestSearcher * searcher,
                               VlKDTree * tree,
                               vl_uindex nodeIndex,
                               VlKDForestNeighbor * neighbors,
                               vl_size numNeighbors,
                               vl_size * numAddedNeighbors,
                               double dist,
                               void const * query)
{

  VlKDTreeNode const * node = tree->nodes + nodeIndex ;
  vl_uindex i = node->splitDimension ;
  vl_index nextChild, saveChild ;
  double delta, saveDist ;
  double x ;
  double x1 = node->lowerBound ;
  double x2 = node->splitThreshold ;
  double x3 = node->upperBound ;
  VlKDForestSearchState * searchState ;

  searcher->searchNumRecursions ++ ;

  switch (searcher->forest->dataType) {
    case VL_TYPE_FLOAT :
      x = ((float const*) query)[i] ;
      break ;
    case VL_TYPE_DOUBLE :
      x = ((double const*) query)[i] ;
      break ;
    default :
      abort() ;
  }

  /* base case: this is a leaf node */
  if (node->lowerChild < 0) {

    vl_index begin = - node->lowerChild - 1 ;
    vl_index end   = - node->upperChild - 1 ;
    vl_index iter ;

    for (iter = begin ;
         iter < end &&
         (searcher->forest->searchMaxNumComparisons == 0 ||
          searcher->searchNumComparisons < searcher->forest->searchMaxNumComparisons) ;
         ++ iter) {

      vl_index di = tree->dataIndex [iter].index ;

      /* multiple KDTrees share the database points and we must avoid
       * adding the same point twice */
      if (searcher->searchIdBook[di] == searcher->searchId) continue ;
      searcher->searchIdBook[di] = searcher->searchId ;

      /* compare the query to this point */
      switch (searcher->forest->dataType) {
        case VL_TYPE_FLOAT:
          dist = ((VlFloatVectorComparisonFunction)searcher->forest->distanceFunction)
                 (searcher->forest->dimension,
                  ((float const *)query),
                  ((float const*)searcher->forest->data) + di * searcher->forest->dimension) ;
          break ;
        case VL_TYPE_DOUBLE:
          dist = ((VlDoubleVectorComparisonFunction)searcher->forest->distanceFunction)
                 (searcher->forest->dimension,
                  ((double const *)query),
                  ((double const*)searcher->forest->data) + di * searcher->forest->dimension) ;
          break ;
        default:
          abort() ;
      }
      searcher->searchNumComparisons += 1 ;

      /* see if it should be added to the result set */
      if (*numAddedNeighbors < numNeighbors) {
        VlKDForestNeighbor * newNeighbor = neighbors + *numAddedNeighbors ;
        newNeighbor->index = di ;
        newNeighbor->distance = dist ;
        vl_kdforest_neighbor_heap_push (neighbors, numAddedNeighbors) ;
      } else {
        VlKDForestNeighbor * largestNeighbor = neighbors + 0 ;
        if (largestNeighbor->distance > dist) {
          largestNeighbor->index = di ;
          largestNeighbor->distance = dist ;
          vl_kdforest_neighbor_heap_update (neighbors, *numAddedNeighbors, 0) ;
        }
      }
    } /* next data point */


    return nodeIndex ;
  }

#if 0
  assert (x1 <= x2 && x2 <= x3) ;
  assert (node->lowerChild >= 0) ;
  assert (node->upperChild >= 0) ;
#endif

  /*
   *   x1  x2 x3
   * x (---|---]
   *   (--x|---]
   *   (---|x--]
   *   (---|---] x
   */

  delta = x - x2 ;
  saveDist = dist + delta*delta ;

  if (x <= x2) {
    nextChild = node->lowerChild ;
    saveChild = node->upperChild ;
    if (x <= x1) {
      delta = x - x1 ;
      saveDist -= delta*delta ;
    }
  } else {
    nextChild = node->upperChild ;
    saveChild = node->lowerChild ;
    if (x > x3) {
      delta = x - x3 ;
      saveDist -= delta*delta ;
    }
  }

  if (*numAddedNeighbors < numNeighbors || neighbors[0].distance > saveDist) {
    searchState = searcher->searchHeapArray + searcher->searchHeapNumNodes ;
    searchState->tree = tree ;
    searchState->nodeIndex = saveChild ;
    searchState->distanceLowerBound = saveDist ;
    vl_kdforest_search_heap_push (searcher->searchHeapArray ,
                                  &searcher->searchHeapNumNodes) ;
  }

  return vl_kdforest_query_recursively (searcher,
                                        tree,
                                        nextChild,
                                        neighbors,
                                        numNeighbors,
                                        numAddedNeighbors,
                                        dist,
                                        query) ;
}

/** ------------------------------------------------------------------
 ** @brief Query the forest
 ** @param selft object.
 ** @param neighbors list of nearest neighbors found (output).
 ** @param numNeighbors number of nearest neighbors to find.
 ** @param query query point.
 ** @return number of tree leaves visited.
 **
 ** A neighbor is represented by an instance of the structure
 ** ::VlKDForestNeighbor. Each entry contains the index of the
 ** neighbor (this is an index into the KDTree data) and its distance
 ** to the query point. Neighbors are sorted by increasing distance.
 **/

vl_size
vl_kdforest_query (VlKDForest * self,
                   VlKDForestNeighbor * neighbors,
                   vl_size numNeighbors,
                   void const * query)
{
  VlKDForestSearcher * searcher = vl_kdforest_get_searcher(self, 0) ;
  if (searcher == NULL) {
    searcher = vl_kdforest_new_searcher(self) ;
  }
  return vl_kdforestsearcher_query(searcher,
                                   neighbors,
                                   numNeighbors,
                                   query) ;
}

/** ------------------------------------------------------------------
 ** @brief Query the forest
 ** @param self object.
 ** @param neighbors list of nearest neighbors found (output).
 ** @param numNeighbors number of nearest neighbors to find.
 ** @param query query point.
 ** @return number of tree leaves visited.
 **
 ** A neighbor is represented by an instance of the structure
 ** ::VlKDForestNeighbor. Each entry contains the index of the
 ** neighbor (this is an index into the KDTree data) and its distance
 ** to the query point. Neighbors are sorted by increasing distance.
 **/

vl_size
vl_kdforestsearcher_query (VlKDForestSearcher * self,
                           VlKDForestNeighbor * neighbors,
                           vl_size numNeighbors,
                           void const * query)
{

  vl_uindex i, ti ;
  vl_bool exactSearch = self->forest->searchMaxNumComparisons == 0 ;

  VlKDForestSearchState * searchState  ;
  vl_size numAddedNeighbors = 0 ;

  assert (neighbors) ;
  assert (numNeighbors > 0) ;
  assert (query) ;

  /* this number is used to differentiate a query from the next */
  self -> searchId += 1 ;
  self -> searchNumRecursions = 0 ;

  self->searchNumComparisons = 0 ;
  self->searchNumSimplifications = 0 ;

  /* put the root node into the search heap */
  self->searchHeapNumNodes = 0 ;
  for (ti = 0 ; ti < self->forest->numTrees ; ++ ti) {
    searchState = self->searchHeapArray + self->searchHeapNumNodes ;
    searchState -> tree = self->forest->trees[ti] ;
    searchState -> nodeIndex = 0 ;
    searchState -> distanceLowerBound = 0 ;

    vl_kdforest_search_heap_push (self->searchHeapArray, &self->searchHeapNumNodes) ;
  }

  /* branch and bound */
  while (exactSearch || self->searchNumComparisons < self->forest->searchMaxNumComparisons)
  {
    /* pop the next optimal search node */
    VlKDForestSearchState * searchState ;

    /* break if search space completed */
    if (self->searchHeapNumNodes == 0) {
      break ;
    }
    searchState = self->searchHeapArray +
                  vl_kdforest_search_heap_pop (self->searchHeapArray, &self->searchHeapNumNodes) ;
    /* break if no better solution may exist */
    if (numAddedNeighbors == numNeighbors &&
        neighbors[0].distance < searchState->distanceLowerBound) {
      self->searchNumSimplifications ++ ;
      break ;
    }
    vl_kdforest_query_recursively (self,
                                   searchState->tree,
                                   searchState->nodeIndex,
                                   neighbors,
                                   numNeighbors,
                                   &numAddedNeighbors,
                                   searchState->distanceLowerBound,
                                   query) ;
  }

  /* sort neighbors by increasing distance */
  for (i = numAddedNeighbors ; i < numNeighbors ; ++ i) {
    neighbors[i].index = -1 ;
    neighbors[i].distance = VL_NAN_F ;
  }

  while (numAddedNeighbors) {
    vl_kdforest_neighbor_heap_pop (neighbors, &numAddedNeighbors) ;
  }

  return self->searchNumComparisons ;
}

/** ------------------------------------------------------------------
 ** @brief Run multiple queries
 ** @param self object.
 ** @param indexes assignments of points.
 ** @param distances distances of query points.
 ** @param numQueries number of query points.
 ** @param numNeighbors number of nearest neighbors to be found for each data point
 **
 ** @a indexes and @a distances are @a numNeighbors by @a numQueries
 ** matrices containing the indexes and distances of the nearest neighbours
 ** for each of the @a numQueries queries @a queries.
 **
 ** This function is similar to ::vl_kdforest_query. The main
 ** difference is that the function can use multiple cores to query
 ** large amounts of data.
 **
 ** @sa ::vl_kdforest_query.
 **/

vl_size
vl_kdforest_query_with_array (VlKDForest * self,
                              vl_uint32 * indexes,
                              vl_size numNeighbors,
                              vl_size numQueries,
                              void * distances,
                              void const * queries)
{
  vl_size numComparisons = 0;
  vl_type dataType = vl_kdforest_get_data_type(self) ;
  vl_size dimension = vl_kdforest_get_data_dimension(self) ;

#ifdef _OPENMP
#pragma omp parallel default(shared) num_threads(vl_get_max_threads())
#endif
  {
    vl_index qi ;
    vl_size thisNumComparisons = 0 ;
    VlKDForestSearcher * searcher ;
    VlKDForestNeighbor * neighbors ;

#ifdef _OPENMP
#pragma omp critical
#endif
    {
      searcher = vl_kdforest_new_searcher(self) ;
      neighbors = vl_calloc (sizeof(VlKDForestNeighbor), numNeighbors) ;
    }

#ifdef _OPENMP
#pragma omp for
#endif
    for(qi = 0 ; qi < (signed)numQueries; ++ qi) {
      switch (dataType) {
        case VL_TYPE_FLOAT: {
          vl_size ni;
          thisNumComparisons += vl_kdforestsearcher_query (searcher, neighbors, numNeighbors,
                                                           (float const *) (queries) + qi * dimension) ;
          for (ni = 0 ; ni < numNeighbors ; ++ni) {
            indexes [qi*numNeighbors + ni] = (vl_uint32) neighbors[ni].index ;
            if (distances){
              *((float*)distances + qi*numNeighbors + ni) = neighbors[ni].distance ;
            }
          }
          break ;
        }
        case VL_TYPE_DOUBLE: {
          vl_size ni;
          thisNumComparisons += vl_kdforestsearcher_query (searcher, neighbors, numNeighbors,
                                                           (double const *) (queries) + qi * dimension) ;
          for (ni = 0 ; ni < numNeighbors ; ++ni) {
            indexes [qi*numNeighbors + ni] = (vl_uint32) neighbors[ni].index ;
            if (distances){
              *((double*)distances + qi*numNeighbors + ni) = neighbors[ni].distance ;
            }
          }
          break ;
        }
        default:
          abort() ;
      }
    }

#ifdef _OPENMP
#pragma omp critical
#endif
    {
      numComparisons += thisNumComparisons ;
      vl_kdforestsearcher_delete (searcher) ;
      vl_free (neighbors) ;
    }
  }
  return numComparisons ;
}

/** ------------------------------------------------------------------
 ** @brief Get the number of nodes of a given tree
 ** @param self KDForest object.
 ** @param treeIndex index of the tree.
 ** @return number of trees.
 **/

vl_size
vl_kdforest_get_num_nodes_of_tree (VlKDForest const * self, vl_uindex treeIndex)
{
  assert (treeIndex < self->numTrees) ;
  return self->trees[treeIndex]->numUsedNodes ;
}

/** ------------------------------------------------------------------
 ** @brief Get the detph of a given tree
 ** @param self KDForest object.
 ** @param treeIndex index of the tree.
 ** @return number of trees.
 **/

vl_size
vl_kdforest_get_depth_of_tree (VlKDForest const * self, vl_uindex treeIndex)
{
  assert (treeIndex < self->numTrees) ;
  return self->trees[treeIndex]->depth ;
}

/** ------------------------------------------------------------------
 ** @brief Get the number of trees in the forest
 **
 ** @param self KDForest object.
 ** @return number of trees.
 **/

vl_size
vl_kdforest_get_num_trees (VlKDForest const * self)
{
  return self->numTrees ;
}

/** ------------------------------------------------------------------
 ** @brief Set the maximum number of comparisons for a search
 **
 ** @param self KDForest object.
 ** @param n maximum number of leaves.
 **
 ** This function sets the maximum number of comparisons for a
 ** nearest neighbor search. Setting it to 0 means unbounded comparisons.
 **
 ** @sa ::vl_kdforest_query, ::vl_kdforest_get_max_num_comparisons.
 **/

void
vl_kdforest_set_max_num_comparisons (VlKDForest * self, vl_size n)
{
  self->searchMaxNumComparisons = n ;
}

/** ------------------------------------------------------------------
 ** @brief Get the maximum number of comparisons for a search
 **
 ** @param self KDForest object.
 ** @return maximum number of leaves.
 **
 ** @sa ::vl_kdforest_set_max_num_comparisons.
 **/

vl_size
vl_kdforest_get_max_num_comparisons (VlKDForest * self)
{
  return self->searchMaxNumComparisons ;
}

/** ------------------------------------------------------------------
 ** @brief Set the thresholding method
 ** @param self KDForest object.
 ** @param method one of ::VlKDTreeThresholdingMethod.
 **
 ** @sa ::vl_kdforest_get_thresholding_method
 **/

 void
vl_kdforest_set_thresholding_method (VlKDForest * self, VlKDTreeThresholdingMethod method)
{
  assert(method == VL_KDTREE_MEDIAN || method == VL_KDTREE_MEAN) ;
  self->thresholdingMethod = method ;
}

/** ------------------------------------------------------------------
 ** @brief Get the thresholding method
 **
 ** @param self KDForest object.
 ** @return thresholding method.
 **
 ** @sa ::vl_kdforest_set_thresholding_method
 **/

 VlKDTreeThresholdingMethod
vl_kdforest_get_thresholding_method (VlKDForest const * self)
{
  return self->thresholdingMethod ;
}

/** ------------------------------------------------------------------
 ** @brief Get the dimension of the data
 ** @param self KDForest object.
 ** @return dimension of the data.
 **/

 vl_size
vl_kdforest_get_data_dimension (VlKDForest const * self)
{
  return self->dimension ;
}

/** ------------------------------------------------------------------
 ** @brief Get the data type
 ** @param self KDForest object.
 ** @return data type (one of ::VL_TYPE_FLOAT, ::VL_TYPE_DOUBLE).
 **/

vl_type
vl_kdforest_get_data_type (VlKDForest const * self)
{
  return self->dataType ;
}

/** ------------------------------------------------------------------
 ** @brief Get the forest linked to the searcher
 ** @param self object.
 ** @return correspoinding KD-Forest.
 **/

VlKDForest *
vl_kdforestsearcher_get_forest (VlKDForestSearcher const * self)
{
  return self->forest;
}
vlfeat/vl/hikmeans.c0000644000175000017500000001661312237004736013344 0ustar  dimadima/** @file hikmeans.c
 ** @brief Hierarchical Integer K-Means Clustering - Declaration
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file hikmeans.h
 ** @brief Hierarchical integer K-Means clustering
 **
 ** Hierarchical integer K-Means clustering (HIKM) is a simple
 ** hierarchical version of integer K-Means (@ref ikmeans.h
 ** "IKM"). The algorithm recursively applies integer K-means to create
 ** more refined partitions of the data.
 **
 ** Create a tree with ::vl_hikm_new() and delete it with
 ** ::vl_hikm_delete(). Use ::vl_hikm_train() to build the tree
 ** from training data and ::vl_hikm_push() to project new data down
 ** a HIKM tree.
 **
 ** @section hikm-tree HIKM tree
 **
 ** The HIKM tree is represented by a ::VlHIKMTree structure, which
 ** contains a tree composed of ::VlHIKMNode. Each node is an
 ** integer K-means filter which partitions the data into @c K
 ** clusters.
 **/

#include 
#include 
#include 
#include 

#include "hikmeans.h"

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Copy a subset of the data to a buffer
 **
 ** @param data  Data
 ** @param ids   Data labels
 ** @param N     Number of indices
 ** @param M     Data dimensionality
 ** @param id    Label of data to copy
 ** @param N2    Number of data copied (out)
 **
 ** @return a new buffer with a copy of the selected data.
 **/

vl_uint8*
vl_hikm_copy_subset (vl_uint8 const * data,
                     vl_uint * ids,
                     int N, int M,
                     vl_uint id, int *N2)
{
  int i ;
  int count = 0;

  /* count how many data points with this label there are */
  for (i = 0 ; i < N ; i++)
    if (ids[i] == id)
      count ++ ;
  *N2 = count ;

  /* copy each datum to the buffer */
  {
    vl_uint8 * new_data = vl_malloc (sizeof(vl_uint8) * M * count);
    count = 0;
    for (i = 0 ; i < N ; i ++)
      if (ids[i] == id)
        {
          memcpy(new_data + count * M,
                 data     + i     * M,
                 sizeof(vl_uint8) * M);
          count ++ ;
        }
    *N2 = count ;
    return new_data ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Compute HIKM clustering.
 **
 ** @param tree   HIKM tree to initialize.
 ** @param data   Data to cluster.
 ** @param N      Number of data points.
 ** @param K      Number of clusters for this node.
 ** @param height Tree height.
 **
 ** @remark height cannot be smaller than 1.
 **
 ** @return a new HIKM node representing a sub-clustering.
 **/

static VlHIKMNode *
xmeans (VlHIKMTree *tree,
        vl_uint8 const *data,
        int N, int K, int height)
{
  VlHIKMNode *node = vl_malloc (sizeof(VlHIKMNode)) ;
  vl_uint     *ids = vl_malloc (sizeof(vl_uint) * N) ;

  node-> filter   = vl_ikm_new (tree -> method) ;
  node-> children = (height == 1) ? 0 : vl_malloc (sizeof(VlHIKMNode*) * K) ;

  vl_ikm_set_max_niters (node->filter, tree->max_niters) ;
  vl_ikm_set_verbosity  (node->filter, tree->verb - 1  ) ;
  vl_ikm_init_rand_data (node->filter, data, tree->M, N, K) ;
  vl_ikm_train          (node->filter, data, N) ;
  vl_ikm_push           (node->filter, ids, data, N) ;

  /* recurse for each child */
  if (height > 1) {
    int k ;
    for (k = 0 ; k < K ; k ++) {
      int partition_N ;
      int partition_K ;
      vl_uint8 *partition ;

      partition = vl_hikm_copy_subset
        (data, ids, N, tree->M, k, &partition_N) ;

      partition_K = VL_MIN (K, partition_N) ;

      node->children [k] = xmeans
        (tree, partition, partition_N, partition_K, height - 1) ;

      vl_free (partition) ;

      if (tree->verb > tree->depth - height) {
        VL_PRINTF("hikmeans: branch at depth %d: %6.1f %% completed\n",
                  tree->depth - height,
                  (double) (k+1) / K * 100) ;
      }
    }
  }

  vl_free (ids) ;
  return node ;
}

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Delete node
 **
 ** @param node to delete.
 **
 ** The function deletes recursively @a node and all its descendent.
 **/

static void
xdelete (VlHIKMNode *node)
{
  if(node) {
    int k ;
    if (node->children) {
      for(k = 0 ; k < vl_ikm_get_K (node->filter) ; ++k)
        xdelete (node->children[k]) ;
      vl_free (node->children) ;
    }
    if (node->filter)
      vl_ikm_delete (node->filter) ;
    vl_free(node);
  }
}

/** ------------------------------------------------------------------
 ** @brief New HIKM tree
 ** @param method clustering method.
 ** @return new HIKM tree.
 **/

VL_EXPORT
VlHIKMTree *
vl_hikm_new (int method)
{
  VlHIKMTree *f = vl_malloc (sizeof(VlHIKMTree)) ;
  f -> M          = 0 ;
  f -> K          = 0 ;
  f -> max_niters = 200 ;
  f -> method     = method ;
  f -> verb       = 0 ;
  f -> depth      = 0 ;
  f -> root       = 0 ;
  return f ;
}

/** ------------------------------------------------------------------
 ** @brief Delete HIKM tree
 ** @param f HIKM tree.
 **/

VL_EXPORT
void
vl_hikm_delete (VlHIKMTree *f)
{
  if (f) {
    xdelete (f -> root) ;
    vl_free (f) ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Initialize HIKM tree
 **
 ** @param f     HIKM tree.
 ** @param M     Data dimensionality.
 ** @param K     Number of clusters per node.
 ** @param depth Tree depth.
 **
 ** @remark @a depth cannot be smaller than 1.
 **
 ** @return a new HIKM tree representing the clustering.
 **/

VL_EXPORT
void
vl_hikm_init (VlHIKMTree *f, int M, int K, int depth)
{
  assert(depth > 0) ;
  assert(M     > 0) ;
  assert(K     > 0) ;

  xdelete (f -> root) ;
  f -> root = 0;

  f -> M = M ;
  f -> K = K ;
  f -> depth = depth ;
}

/** ------------------------------------------------------------------
 ** @brief Train HIKM tree
 ** @param f       HIKM tree.
 ** @param data    Data to cluster.
 ** @param N       Number of data.
 **/

VL_EXPORT
void
vl_hikm_train (VlHIKMTree *f, vl_uint8 const *data, int N)
{
  f -> root  = xmeans (f, data, N, VL_MIN(f->K, N), f->depth) ;
}

/** ------------------------------------------------------------------
 ** @brief Project data down HIKM tree
 **
 ** @param f    HIKM tree.
 ** @param asgn Path down the tree (out).
 ** @param data Data to project.
 ** @param N    Number of data.
 **
 ** The function writes to @a asgn the path of the data @a data
 ** down the HIKM tree @a f. The parameter @a asgn must point to
 ** an array of @c M by @c N elements, where @c M is the depth of
 ** the HIKM tree and @c N is the number of data point to process.
 **/

VL_EXPORT
void
vl_hikm_push (VlHIKMTree *f, vl_uint *asgn, vl_uint8 const *data, int N)
{
  int i, d,
    M = vl_hikm_get_ndims (f),
    depth = vl_hikm_get_depth (f) ;

  /* for each datum */
  for(i = 0 ; i < N ; i++) {
    VlHIKMNode *node = f->root ;
    d = 0 ;
    while (node) {
      /*
      vl_uint best =
        vl_ikm_push_one (vl_ikm_get_centers (node->filter),
                         data + i * M,
                         M,
                         vl_ikm_get_K (node->filter)) ;
      */

      vl_uint best ;
      vl_ikm_push (node->filter, &best, data + i * M, 1) ;

      asgn [i*depth + d] = best ;
      ++ d ;

      if (!node->children) break ;
      node = node->children [best] ;
    }
  }
}
vlfeat/vl/imopv_sse2.c0000644000175000017500000001703412237004745013631 0ustar  dimadima/** @file imopv_sse2.c
 ** @brief Vectorized image operations - SSE2 - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#if ! defined(VL_DISABLE_SSE2) & ! defined(__SSE2__)
#error "Compiling with SSE2 enabled, but no __SSE2__ defined"
#endif

#if ! defined(VL_DISABLE_SSE2)

#ifndef VL_IMOPV_SSE2_INSTANTIATING

#include 

#include "imopv.h"
#include "imopv_sse2.h"

#define FLT VL_TYPE_FLOAT
#define VL_IMOPV_SSE2_INSTANTIATING
#include "imopv_sse2.c"

#define FLT VL_TYPE_DOUBLE
#define VL_IMOPV_SSE2_INSTANTIATING
#include "imopv_sse2.c"

/* ---------------------------------------------------------------- */
/* VL_IMOPV_SSE2_INSTANTIATING */
#else

#include "float.th"

/* ---------------------------------------------------------------- */
void
VL_XCAT3(_vl_imconvcol_v, SFX, _sse2)
(T* dst, vl_size dst_stride,
 T const* src,
 vl_size src_width, vl_size src_height, vl_size src_stride,
 T const* filt, vl_index filt_begin, vl_index filt_end,
 int step, unsigned int flags)
{
  vl_index x = 0 ;
  vl_index y ;
  vl_index dheight = (src_height - 1) / step + 1 ;
  vl_bool use_simd  = VALIGNED(src_stride) ;
  vl_bool transp    = flags & VL_TRANSPOSE ;
  vl_bool zeropad   = (flags & VL_PAD_MASK) == VL_PAD_BY_ZERO ;
  double totcol = 0 ;
  double simdcol = 0 ;

  /* let filt point to the last sample of the filter */
  filt += filt_end - filt_begin ;

  while (x < (signed)src_width) {
    /* Calculate dest[x,y] = sum_p image[x,p] filt[y - p]
     * where supp(filt) = [filt_begin, filt_end] = [fb,fe].
     *
     * CHUNK_A: y - fe <= p < 0
     *          completes VL_MAX(fe - y, 0) samples
     * CHUNK_B: VL_MAX(y - fe, 0) <= p < VL_MIN(y - fb, height - 1)
     *          completes fe - VL_MAX(fb, height - y) + 1 samples
     * CHUNK_C: completes all samples
     */

    T const *filti ;
    vl_index stop ;

    if ((x + VSIZE < (signed)src_width) &
        VALIGNED(src + x) & use_simd)
    {
      /* ----------------------------------------------  Vectorized */
      for (y = 0 ; y < (signed)src_height ; y += step)  {
        union {VTYPE v ; T x [VSIZE] ; } acc ;
        VTYPE v, c ;
        T const *srci ;
        acc.v = VSTZ () ;
        v = VSTZ() ;

        filti = filt ;
        stop = filt_end - y ;
        srci = src + x - stop * src_stride ;

        if (stop > 0) {
          if (zeropad) {
            v = VSTZ () ;
          } else {
            v = * (VTYPE*) (src + x) ;
          }
          while (filti > filt - stop) {
            c = VLD1 (filti--) ;
            acc.v = VADD (acc.v,  VMUL (v, c)) ;
            srci += src_stride ;
          }
        }

        stop = filt_end - VL_MAX(filt_begin, y - (signed)src_height + 1) + 1 ;
        while (filti > filt - stop) {
          v = * (VTYPE*) srci ;
          c = VLD1 (filti--) ;
          acc.v = VADD (acc.v, VMUL (v, c)) ;
          srci += src_stride ;
        }

        if (zeropad) v = VSTZ () ;

        stop = filt_end - filt_begin + 1;
        while (filti > filt - stop) {
          c = VLD1 (filti--) ;
          acc.v = VADD (acc.v, VMUL (v, c)) ;
        }

        if (transp) {
          *dst = acc.x[0] ; dst += dst_stride ;
          *dst = acc.x[1] ; dst += dst_stride ;
#if(VSIZE == 4)
          *dst = acc.x[2] ; dst += dst_stride ;
          *dst = acc.x[3] ; dst += dst_stride ;
#endif
          dst += 1 * 1 - VSIZE * dst_stride ;
        } else {
          *dst = acc.x[0] ; dst += 1 ;
          *dst = acc.x[1] ; dst += 1 ;
#if(VSIZE == 4)
          *dst = acc.x[2] ; dst += 1 ;
          *dst = acc.x[3] ; dst += 1 ;
#endif
          dst += 1 * dst_stride - VSIZE * 1 ;
        }
      } /* next y */
      if (transp) {
        dst += VSIZE * dst_stride - dheight * 1 ;
      } else {
        dst += VSIZE * 1 - dheight * dst_stride ;
      }
      x       += VSIZE ;
      simdcol += VSIZE ;
      totcol  += VSIZE ;
    } else {
      /* -------------------------------------------------  Vanilla */
      for (y = 0 ; y < (signed)src_height ; y += step) {
        T acc = 0 ;
        T v = 0, c ;
        T const* srci ;

        filti = filt ;
        stop = filt_end - y ;
        srci = src + x - stop * src_stride ;

        if (stop > 0) {
          if (zeropad) {
            v = 0 ;
          } else {
            v = *(src + x) ;
          }
          while (filti > filt - stop) {
            c = *filti-- ;
            acc += v * c ;
            srci += src_stride ;
          }
        }

        stop = filt_end - VL_MAX(filt_begin, y - (signed)src_height + 1) + 1 ;
        while (filti > filt - (signed)stop) {
          v = *srci ;
          c = *filti-- ;
          acc += v * c ;
          srci += src_stride ;
        }

        if (zeropad) v = 0 ;

        stop = filt_end - filt_begin + 1 ;
        while (filti > filt - stop) {
          c = *filti-- ;
          acc += v * c ;
        }

        if (transp) {
          *dst = acc ; dst += 1 ;
        } else {
          *dst = acc ; dst += dst_stride ;
        }
      } /* next y */
      if (transp) {
        dst += 1 * dst_stride - dheight * 1 ;
      } else {
        dst += 1 * 1 - dheight * dst_stride ;
      }
      x      += 1 ;
      totcol += 1 ;
    } /* next x */
  }
}

/* ---------------------------------------------------------------- */
#if 0
void
VL_XCAT(_vl_imconvcoltri_v, SFX, sse2)
(T* dst, int dst_stride,
 T const* src,
 int src_width, int src_height, int src_stride,
 int filt_size,
 int step, unsigned int flags)
{
  int x = 0 ;
  int y ;
  int dheight = (src_height - 1) / step + 1 ;
  vl_bool use_simd  = ((src_stride & ALIGNSTRIDE) == 0) &&
  (! (flags & VL_NO_SIMD)) ;
  vl_bool transp = flags & VL_TRANSPOSE ;
  vl_bool zeropad = (flags & VL_PAD_MASK) == VL_PAD_BY_ZERO ;

  T * buff = vl_malloc(sizeof(T) * (src_height + filt_size)) ;
#define fa (1.0 / (double) (filt_size + 1))
  T scale = fa*fa*fa*fa ;
  buff += filt_size ;

  while (x < src_width) {
    T const *srci ;

    use_simd = 0 ;
    if ((x + VSIZE < src_width) &
        (((vl_ptrint)(src + x) & ALIGNPTR) == 0) &
        use_simd)
    {

    } else {
      int stridex = transp ? dst_stride : 1 ;
      int stridey = transp ? 1 : dst_stride ;
      srci = src + x + src_stride * (src_height - 1) ;

      /* integrate backward the column */
      buff [src_height - 1] = *srci ;
      for (y = src_height-2 ; y >=  0 ; --y) {
        srci -= src_stride ;
        buff [y] = buff [y+1] + *srci ;
      }
      if (zeropad) {
        for ( ; y >= - filt_size ; --y) {
          buff [y] = buff [y+1] ;
        }
      } else {
        for ( ; y >= - filt_size ; --y) {
          buff [y] = buff[y+1] + *srci ;
        }
      }

      /* compute the filter forward */
      for (y = - filt_size ; y < src_height - filt_size ; ++y) {
        buff [y] = buff [y] - buff [y + filt_size] ;
      }
      if (! zeropad) {
        for (y = src_height - filt_size ; y < src_height ; ++y) {
          buff [y] = buff [y] - buff [src_height-1]  *
          (src_height - filt_size - y) ;
        }
      }

      /* integrate forward the column */
      for (y = - filt_size + 1 ; y < src_height ; ++y) {
        buff [y] += buff [y - 1] ;
      }

      /* compute the filter backward */
      for (y = src_height - 1 ; y >= 0 ; --y) {
        dst [x*stridex + y*stridey]
        = scale * (buff [y] - buff [y - filt_size]) ;
      }
    } /* next y */
    x += 1 ;
  }
  vl_free (buff - filt_size) ;
}
#endif

#undef FLT
#undef VL_IMOPV_SSE2_INSTANTIATING
#endif

/* ! VL_DISABLE_SSE2 */
#endif
vlfeat/vl/hog.h0000644000175000017500000000533012237004736012321 0ustar  dimadima/** @file hog.h
 ** @brief Histogram of Oriented Gradients (@ref hog)
 ** @author Andrea Vedaldi
 **/

/*
 Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
 All rights reserved.

 This file is part of the VLFeat library and is made available under
 the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_HOG_H
#define VL_HOG_H

#include "generic.h"

enum VlHogVariant_ { VlHogVariantDalalTriggs, VlHogVariantUoctti } ;

typedef enum VlHogVariant_ VlHogVariant ;

struct VlHog_
{
  VlHogVariant variant ;
  vl_size dimension ;
  vl_size numOrientations ;
  vl_bool transposed ;
  vl_bool useBilinearOrientationAssigment ;

  /* left-right flip permutation */
  vl_index * permutation ;

  /* glyphs */
  float * glyphs ;
  vl_size glyphSize ;

  /* helper vectors */
  float * orientationX ;
  float * orientationY ;

  /* buffers */
  float * hog ;
  float * hogNorm ;
  vl_size hogWidth ;
  vl_size hogHeight ;
} ;

typedef struct VlHog_ VlHog ;

VL_EXPORT VlHog * vl_hog_new (VlHogVariant variant, vl_size numOrientations, vl_bool transposed) ;
VL_EXPORT void vl_hog_delete (VlHog * self) ;
VL_EXPORT void vl_hog_process (VlHog * self,
                               float * features,
                               float const * image,
                               vl_size width, vl_size height, vl_size numChannels,
                               vl_size cellSize) ;

VL_EXPORT void vl_hog_put_image (VlHog * self,
                                 float const * image,
                                 vl_size width, vl_size height, vl_size numChannels,
                                 vl_size cellSize) ;

VL_EXPORT void vl_hog_put_polar_field (VlHog * self,
                                       float const * modulus,
                                       float const * angle,
                                       vl_bool directed,
                                       vl_size width, vl_size height, vl_size cellSize) ;

VL_EXPORT void vl_hog_extract (VlHog * self, float * features) ;
VL_EXPORT vl_size vl_hog_get_height (VlHog * self) ;
VL_EXPORT vl_size vl_hog_get_width (VlHog * self) ;


VL_EXPORT void vl_hog_render (VlHog const * self,
                              float * image,
                              float const * features,
                              vl_size width,
                              vl_size height) ;

VL_EXPORT vl_size vl_hog_get_dimension (VlHog const * self) ;
VL_EXPORT vl_index const * vl_hog_get_permutation (VlHog const * self) ;
VL_EXPORT vl_size vl_hog_get_glyph_size (VlHog const * self) ;

VL_EXPORT vl_bool vl_hog_get_use_bilinear_orientation_assignments (VlHog const * self) ;
VL_EXPORT void vl_hog_set_use_bilinear_orientation_assignments (VlHog * self, vl_bool x) ;

/* VL_HOG_H */
#endif
vlfeat/vl/array.h0000644000175000017500000000513612237004736012666 0ustar  dimadima/** @file array.h
 ** @brief Array - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_ARRAY_H
#define VL_ARRAY_H

#include "generic.h"

/** @brief Maximum number of array dimensions */
#define VL_ARRAY_MAX_NUM_DIMENSIONS 16

/** @brief Numeric array */
typedef struct _VlArray
{
  vl_type type ;
  vl_bool isEnvelope ;
  vl_bool isSparse ;
  vl_size numDimensions ;
  vl_size dimensions [VL_ARRAY_MAX_NUM_DIMENSIONS] ;
  void * data ;
  void * rowPointers ;
  void * columnPointers ;
} VlArray ;


/** @name Get data and parameters
 ** @{ */

/** @brief Get number of dimensions
 ** @param self array.
 ** @return number of dimensions.
 **/

VL_INLINE vl_size
vl_array_get_num_dimensions (VlArray const * self)
{
  return self->numDimensions ;
}

/** @brief Get dimensions
 ** @param self array.
 ** @return dimensions.
 **/

VL_INLINE vl_size const *
vl_array_get_dimensions (VlArray const * self)
{
  return self->dimensions ;
}

/** @brief Get data
 ** @param self array.
 ** @return data.
 **/

VL_INLINE void *
vl_array_get_data (VlArray const * self)
{
  return self->data;
}

/** @brief Get type
 ** @param self array.
 ** @return type.
 **/

VL_INLINE vl_type
vl_array_get_data_type (VlArray const * self)
{
  return self->type ;
}

VL_EXPORT vl_size vl_array_get_num_elements (VlArray const * self) ;

/** @{ */

/** @name Constructing and destroying
 ** @{ */

VL_EXPORT VlArray * vl_array_init (VlArray * self, vl_type type, vl_size numDimension, vl_size const * dimensions) ;
VL_EXPORT VlArray * vl_array_init_envelope (VlArray *self, void * data, vl_type type, vl_size numDimension, vl_size const * dimensions) ;
VL_EXPORT VlArray * vl_array_init_matrix (VlArray * self, vl_type type, vl_size numRows, vl_size numColumns) ;
VL_EXPORT VlArray * vl_array_init_matrix_envelope (VlArray * self, void * data, vl_type type, vl_size numRows, vl_size numColumns) ;

VL_EXPORT VlArray * vl_array_new (vl_type type, vl_size numDimension, vl_size const * dimensions) ;
VL_EXPORT VlArray * vl_array_new_envelope (void * data, vl_type type, vl_size numDimension, vl_size const * dimensions) ;
VL_EXPORT VlArray * vl_array_new_matrix (vl_type type, vl_size numRows, vl_size numColumns) ;
VL_EXPORT VlArray * vl_array_new_matrix_envelope (void * data, vl_type type, vl_size numRows, vl_size numColumns) ;

VL_EXPORT void vl_array_dealloc (VlArray * self) ;
VL_EXPORT void vl_array_delete (VlArray * self) ;
/** @} */

/* VL_ARRAY_H */
#endif
vlfeat/vl/quickshift.h0000644000175000017500000001255712237004736013727 0ustar  dimadima/** @file quickshift.h
 ** @brief Quick shift (@ref quickshift)
 ** @author Andrea Vedaldi
 ** @author Brian Fulkerson
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_QUICKSHIFT_H
#define VL_QUICKSHIFT_H

#include "generic.h"
#include "mathop.h"

/** @brief quick shift datatype */
typedef double vl_qs_type ;

/** @brief quick shift infinity constant */
#define VL_QS_INF VL_INFINITY_D /* Change to _F for float math */

/** ------------------------------------------------------------------
 ** @brief quick shift results
 **
 ** This implements quick shift mode seeking.
 **/

typedef struct _VlQS
{
  vl_qs_type *image ;   /**< height x width x channels feature image */
  int height;           /**< height of the image */
  int width;            /**< width of the image */
  int channels;         /**< number of channels in the image */

  vl_bool medoid;
  vl_qs_type sigma;
  vl_qs_type tau;

  int *parents ;
  vl_qs_type *dists ;
  vl_qs_type *density ;
} VlQS ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT
VlQS*  vl_quickshift_new (vl_qs_type const * im, int height, int width,
                          int channels);

VL_EXPORT
void   vl_quickshift_delete (VlQS *q) ;
/** @} */

/** @name Process data
 ** @{
 **/

VL_EXPORT
void   vl_quickshift_process (VlQS *q) ;

/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_INLINE vl_qs_type    vl_quickshift_get_max_dist      (VlQS const *q) ;
VL_INLINE vl_qs_type    vl_quickshift_get_kernel_size    (VlQS const *q) ;
VL_INLINE vl_bool       vl_quickshift_get_medoid   (VlQS const *q) ;

VL_INLINE int *        vl_quickshift_get_parents  (VlQS const *q) ;
VL_INLINE vl_qs_type * vl_quickshift_get_dists    (VlQS const *q) ;
VL_INLINE vl_qs_type * vl_quickshift_get_density  (VlQS const *q) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_INLINE void vl_quickshift_set_max_dist    (VlQS *f, vl_qs_type tau) ;
VL_INLINE void vl_quickshift_set_kernel_size  (VlQS *f, vl_qs_type sigma) ;
VL_INLINE void vl_quickshift_set_medoid (VlQS *f, vl_bool medoid) ;
/** @} */

/* -------------------------------------------------------------------
 *                                     Inline functions implementation
 * ---------------------------------------------------------------- */

/** ------------------------------------------------------------------
 ** @brief Get tau.
 ** @param q quick shift object.
 ** @return the maximum distance in the feature space between nodes in the
 **         quick shift tree.
 **/

VL_INLINE vl_qs_type
vl_quickshift_get_max_dist (VlQS const *q)
{
  return q->tau ;
}

/** ------------------------------------------------------------------
 ** @brief Get sigma.
 ** @param q quick shift object.
 ** @return the standard deviation of the kernel used in the Parzen density
 **         estimate.
 **/

VL_INLINE vl_qs_type
vl_quickshift_get_kernel_size (VlQS const *q)
{
  return q->sigma ;
}

/** ------------------------------------------------------------------
 ** @brief Get medoid.
 ** @param q quick Shift object.
 ** @return @c true if medoid shift is used instead of quick shift.
 **/

VL_INLINE vl_bool
vl_quickshift_get_medoid (VlQS const *q)
{
  return q->medoid ;
}

/** ------------------------------------------------------------------
 ** @brief Get parents.
 ** @param q quick shift object.
 ** @return a @c height x @c width matrix where each element contains the
 **         linear index of its parent node. The node is a root if its
 **         value is its own linear index.
 **/

VL_INLINE int *
vl_quickshift_get_parents (VlQS const *q)
{
  return q->parents ;
}

/** ------------------------------------------------------------------
 ** @brief Get dists.
 ** @param q quick shift object.
 ** @return for each pixel, the distance in feature space to the pixel
 **         that is its parent in the quick shift tree. The distance is
 **         set to 'inf' if the pixel is a root node.
 **/

VL_INLINE vl_qs_type *
vl_quickshift_get_dists (VlQS const *q)
{
  return q->dists ;
}

/** ------------------------------------------------------------------
 ** @brief Get density.
 ** @param q quick shift object.
 ** @return the estimate of the density at each pixel.
 **/

VL_INLINE vl_qs_type *
vl_quickshift_get_density (VlQS const *q)
{
  return q->density ;
}

/** ------------------------------------------------------------------
 ** @brief Set sigma
 ** @param q quick shift object.
 ** @param sigma standard deviation of the kernel used in the Parzen density
 **        estimate.
 **/

VL_INLINE void
vl_quickshift_set_kernel_size (VlQS *q, vl_qs_type sigma)
{
  q -> sigma = sigma ;
}

/** ------------------------------------------------------------------
 ** @brief Set max distance
 ** @param q quick shift object.
 ** @param tau the maximum distance in the feature space between nodes in the
 **            quick shift tree.
 **/

VL_INLINE void
vl_quickshift_set_max_dist (VlQS *q, vl_qs_type tau)
{
  q -> tau = tau ;
}

/** ------------------------------------------------------------------
 ** @brief Set medoid
 ** @param q quick shift object.
 ** @param medoid @c true to use kernelized medoid shift, @c false (default) uses
 **        quick shift.
 **/

VL_INLINE void
vl_quickshift_set_medoid (VlQS *q, vl_bool medoid)
{
  q -> medoid = medoid ;
}


#endif
vlfeat/vl/aib.c0000644000175000017500000005171712237004736012304 0ustar  dimadima/** @file aib.c
 ** @brief AIB - Definition
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**


@page aib Agglomerative Information Bottleneck (AIB)
@author Brian Fulkerson
@author Andrea Vedaldi


@ref aib.h implemens the Agglomerative Information Bottleneck (AIB)
algorithm as first described in @cite{slonim99agglomerative}.

AIB takes a discrete valued feature @f$x@f$ and a label @f$c@f$ and
gradually compresses @f$x@f$ by iteratively merging values which
minimize the loss in mutual information @f$I(x,c)@f$.

While the algorithm is equivalent to the one described in
@cite{slonim99agglomerative}, it has some speedups that enable
handling much larger datasets. Let N be the number of feature
values and C the number of labels. The algorithm of
@cite{slonim99agglomerative} is @f$O(N^2)@f$ in space and @f$O(C
N^3)@f$ in time. This algorithm is @f$O(N)@f$ space and @f$O(C N^2)@f$
time in common cases (@f$O(C N^3)@f$ in the worst case).


@section aib-overview Overview


Given a discrete feature @f$x \in \mathcal{X} = \{x_1,\dots,x_N\}@f$
and a category label @f$c = 1,\dots,C@f$ with joint probability
@f$p(x,c)@f$, AIB computes a compressed feature @f$[x]_{ij}@f$ by
merging two values @f$x_i@f$ and @f$x_j@f$.  Among all the pairs
@f$ij@f$, AIB chooses the one that yields the smallest loss in the
mutual information

@f[
   D_{ij} = I(x,c) - I([x]_{ij},c) =
   \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)}   +
   \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)}   -
   \sum_c (p(x_i)+p(x_j)) \log \frac {p(x_i,c)+p(x_i,c)}{(p(x_i)+p(x_j))p(c)}
@f]

AIB iterates this procedure until the desired level of
compression is achieved.

@section aib-algorithm Algorithm details

Computing @f$D_{ij}@f$ requires @f$O(C)@f$ operations. For example, in
standard AIB we need to calculate

@f[
   D_{ij} = I(x,c) - I([x]_{ij},c) =
   \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)}   +
   \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)}   -
   \sum_c (p(x_i)+p(x_j)) \log \frac {p(x_i,c)+p(x_i,c)}{(p(x_i)+p(x_j))p(c)}
@f]

Thus in a basic implementation of AIB, finding the optimal pair
@f$ij@f$ of feature values requires @f$O(CN^2)@f$ operations in
total. In order to join all the @f$N@f$ values, we repeat this
procedure @f$O(N)@f$ times, yielding @f$O(N^3 C)@f$ time and
@f$O(1)@f$ space complexity (this does not account for the space need
to store the input).

The complexity can be improved by reusing computations. For instance,
we can store the matrix @f$D = [ D_{ij} ]@f$ (which requires
@f$O(N^2)@f$ space). Then, after joining @f$ij@f$, all of the matrix
D except the rows and columns (the matrix is symmetric) of
indexes i and j is unchanged. These two rows and
columns are deleted and a new row and column, whose computation
requires @f$O(NC)@f$ operations, are added for the merged value
@f$x_{ij}@f$.  Finding the minimal element of the matrix still
requires @f$O(N^2)@f$ operations, so the complexity of this algorithm
is @f$O(N^2C + N^3)@f$ time and @f$O(N^2)@f$ space.

We can obtain a much better expected complexity as follows. First,
instead of storing the whole matrix D, we store the smallest
element (index and value) of each row as @f$(q_i, D_i)@f$ (notice that
this is also the best element of each column since D is
symmetric). This requires @f$O(N)@f$ space and finding the minimal
element of the matrix requires @f$O(N)@f$ operations.  After joining
@f$ij@f$, we have to efficiently update this representation. This is
done as follows:

- The entries @f$(q_i,D_i)@f$ and @f$(q_j,D_j)@f$ are deleted.
- A new entry @f$(q_{ij},D_{ij})@f$ for the joint value @f$x_{ij}@f$
  is added. This requires @f$O(CN)@f$ operations.
- We test which other entries @f$(q_{k},D_{k})@f$ need to
  be updated. Recall that @f$(q_{k},D_{k})@f$ means that, before the
  merge, the value
  closest to @f$x_k@f$ was @f$x_{q_k}@f$ at a distance @f$D_k@f$. Then
  - If @f$q_k \not = i@f$, @f$q_k \not = j@f$ and @f$D_{k,ij} \geq D_k@f$, then
    @f$q_k@f$ is still the closest element and we do not do anything.
  - If @f$q_k \not = i@f$, @f$q_k \not = j@f$ and @f$D_{k,ij} <
    D_k@f$, then the closest element is @f$ij@f$ and we update the
    entry in constant time.
  - If @f$q_k = i@f$ or @f$q_k = j@f$, then we need to re-compute
    the closest element in @f$O(CN)@f$ operations.

This algorithm requires only @f$O(N)@f$ space and @f$O(\gamma(N) C
N^2)@f$ time, where @f$\gamma(N)@f$ is the expected number of times we
fall in the last case. In common cases one has @f$\gamma(N) \approx
\mathrm{const.}@f$, so the time saving is significant.

**/

#include "aib.h"
#include 
#include 
#include 
#include 

/* The maximum value which beta may take */
#define BETA_MAX DBL_MAX

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Normalizes an array of probabilities to sum to 1
 **
 ** @param P        The array of probabilities
 ** @param nelem    The number of elements in the array
 **
 ** @return Modifies P to contain values which sum to 1
 **/

void vl_aib_normalize_P (double * P, vl_uint nelem)
{
    vl_uint i;
    double sum = 0;
    for(i=0; ibeta to find the minimum value and fills @a minbeta and
 ** @a besti and @a bestj with this information.
 **/

void vl_aib_min_beta
(VlAIB * aib, vl_uint * besti, vl_uint * bestj, double * minbeta)
{
    vl_uint i;
    *minbeta = aib->beta[0];
    *besti   = 0;
    *bestj   = aib->bidx[0];

    for(i=0; inentries; i++)
    {
        if(aib->beta[i] < *minbeta)
        {
            *minbeta = aib->beta[i];
            *besti = i;
            *bestj = aib->bidx[i];
        }
    }
}

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Merges two nodes i,j in the internal datastructure
 **
 ** @param aib  A pointer to the internal data structure
 ** @param i    The index of one member of the pair to merge
 ** @param j    The index of the other member of the pair to merge
 ** @param new  The index of the new node which corresponds to the union of
 **             (@a i, @a j).
 **
 ** Nodes are merged by replacing the entry @a i with the union of @c
 ** ij, moving the node stored in last position (called @c lastnode)
 ** back to jth position and the entry at the end.
 **
 ** After the nodes have been merged, it updates which nodes should be
 ** considered on the next iteration based on which beta values could
 ** potentially change. The merged node will always be part of this
 ** list.
 **/

void
vl_aib_merge_nodes (VlAIB * aib, vl_uint i, vl_uint j, vl_uint new)
{
  vl_uint last_entry = aib->nentries - 1 ;
  vl_uint c, n ;

  /* clear the list of nodes to update */
  aib->nwhich = 0;

  /* make sure that i is smaller than j */
  if(i > j) { vl_uint tmp = j; j = i; i = tmp; }

  /* -----------------------------------------------------------------
   *                    Merge entries i and j, storing the result in i
   * -------------------------------------------------------------- */

  aib-> Px   [i] += aib->Px[j] ;
  aib-> beta [i]  = BETA_MAX ;
  aib-> nodes[i]  = new ;

  for (c = 0; c < aib->nlabels; c++)
    aib-> Pcx [i*aib->nlabels + c] += aib-> Pcx [j*aib->nlabels + c] ;

  /* -----------------------------------------------------------------
   *                                              Move last entry to j
   * -------------------------------------------------------------- */

  aib-> Px    [j]  = aib-> Px    [last_entry];
  aib-> beta  [j]  = aib-> beta  [last_entry];
  aib-> bidx  [j]  = aib-> bidx  [last_entry];
  aib-> nodes [j]  = aib-> nodes [last_entry];

  for (c = 0 ;  c < aib->nlabels ; c++)
    aib-> Pcx[j*aib->nlabels + c] = aib-> Pcx [last_entry*aib->nlabels + c] ;

  /* delete last entry */
  aib-> nentries -- ;

  /* -----------------------------------------------------------------
   *                                        Scan for entries to update
   * -------------------------------------------------------------- */

  /*
   * After mergin entries i and j, we need to update all other entries
   * that had one of these two as closest match. We also need to
   * update the renewend entry i. This is added by the loop below
   * since bidx [i] = j exactly because i was merged.
   *
   * Additionaly, since we moved the last entry back to the entry j,
   * we need to adjust the valeus of bidx to reflect this.
   */

  for (n = 0 ; n < aib->nentries; n++) {
    if(aib->bidx[n] == i || aib->bidx[n] == j) {
        aib->bidx  [n] = 0;
        aib->beta  [n] = BETA_MAX;
        aib->which [aib->nwhich++] = n ;
      }
    else if(aib->bidx[n] == last_entry) {
      aib->bidx[n] = j ;
    }
  }
}

/** ------------------------------------------------------------------
 ** @internal
 ** @brief Updates @c aib->beta and @c aib->bidx according to @c aib->which
 **
 ** @param aib AIB data structure.
 **
 ** The function calculates @c beta[i] and @c bidx[i] for the nodes @c
 ** i listed in @c aib->which.  @c beta[i] is the minimal variation of mutual
 ** information (or other score) caused by merging entry @c i with another entry
 ** and @c bidx[i] is the index of this best matching entry.
 **
 ** Notice that for each entry @c i that we need to update, a full
 ** scan of all the other entries must be performed.
 **/

void
vl_aib_update_beta (VlAIB * aib)
{

#define PLOGP(x) ((x)*log((x)))

  vl_uint i;
  double * Px  = aib->Px;
  double * Pcx = aib->Pcx;
  double * tmp = vl_malloc(sizeof(double)*aib->nentries);
  vl_uint a, b, c ;

  /*
   * T1 = I(x,c) - I([x]_ij) = A + B - C
   *
   * A  = \sum_c p(xa,c)           \log ( p(xa,c)          /  p(xa)       )
   * B  = \sum_c p(xb,c)           \log ( p(xb,c)          /  p(xb)       )
   * C  = \sum_c (p(xa,c)+p(xb,c)) \log ((p(xa,c)+p(xb,c)) / (p(xa)+p(xb)))
   *
   * C  = C1 + C2
   * C1 = \sum_c (p(xa,c)+p(xb,c)) \log (p(xa,c)+p(xb,c))
   * C2 = - (p(xa)+p(xb) \log (p(xa)+p(xb))
   */

  /* precalculate A and B */
  for (a = 0; a < aib->nentries; a++) {
    tmp[a] = 0;
    for (c = 0; c < aib->nlabels; c++) {
        double Pac = Pcx [a*aib->nlabels + c] ;
        if(Pac != 0) tmp[a] += Pac * log (Pac / Px[a]) ;
    }
  }

  /* for each entry listed in which */
  for (i = 0 ; i < aib->nwhich; i++) {
    a = aib->which[i];

    /* for each other entry */
    for(b = 0 ; b < aib->nentries ; b++) {
      double T1 = 0 ;

      if (a == b || Px [a] == 0 || Px [b] == 0) continue ;


      T1 = PLOGP ((Px[a] + Px[b])) ;                  /* - C2 */
      T1 += tmp[a] + tmp[b] ;                         /* + A + B */

      for (c = 0 ; c < aib->nlabels; ++ c) {
        double Pac = Pcx [a*aib->nlabels + c] ;
        double Pbc = Pcx [b*aib->nlabels + c] ;
        if (Pac == 0 && Pbc == 0) continue;
        T1 += - PLOGP ((Pac + Pbc)) ;                 /* - C1 */
      }

      /*
       * Now we have beta(a,b). We check wether this is the best beta
       * for entries a and b.
       */
      {
        double beta = T1 ;

        if (beta < aib->beta[a])
          {
            aib->beta[a] = beta;
            aib->bidx[a] = b;
          }
        if (beta < aib->beta[b])
          {
            aib->beta[b] = beta;
            aib->bidx[b] = a;
          }
      }
    }
  }
  vl_free(tmp);
}

/** ------------------------------------------------------------------
 ** @internal @brief Calculates the current information and entropy
 **
 ** @param aib      A pointer to the internal data structure
 ** @param I        The current mutual information (out).
 ** @param H        The current entropy (out).
 **
 ** Calculates the current mutual information and entropy of Pcx and sets
 ** @a I and @a H to these new values.
 **/
void vl_aib_calculate_information(VlAIB * aib, double * I, double * H)
{
    vl_uint r, c;
    *H = 0;
    *I = 0;

    /*
     * H(x)   = - sum_x p(x)    \ log p(x)
     * I(x,c) =   sum_xc p(x,c) \ log (p(x,c) / p(x)p(c))
     */

    /* for each entry */
    for(r = 0 ; r< aib->nentries ; r++) {

      if (aib->Px[r] == 0) continue ;
      *H += -log(aib->Px[r]) * aib->Px[r] ;

      for(c=0; cnlabels; c++) {
        if (aib->Pcx[r*aib->nlabels+c] == 0) continue;
        *I += aib->Pcx[r*aib->nlabels+c] *
          log (aib->Pcx[r*aib->nlabels+c] / (aib->Px[r]*aib->Pc[c])) ;
      }
    }
}

/** ------------------------------------------------------------------
 ** @brief Allocates and initializes the internal data structure
 **
 ** @param Pcx      A pointer to a 2D array of probabilities
 ** @param nvalues    The number of rows in the array
 ** @param nlabels    The number of columns in the array
 **
 ** Creates a new @a VlAIB struct containing pointers to all the data that
 ** will be used during the AIB process.
 **
 ** Allocates memory for the following:
 ** - Px (nvalues*sizeof(double))
 ** - Pc (nlabels*sizeof(double))
 ** - nodelist (nvalues*sizeof(vl_uint))
 ** - which (nvalues*sizeof(vl_uint))
 ** - beta (nvalues*sizeof(double))
 ** - bidx (nvalues*sizeof(vl_uint))
 ** - parents ((2*nvalues-1)*sizeof(vl_uint))
 ** - costs (nvalues*sizeof(double))
 **
 ** Since it simply copies to pointer to Pcx, the total additional memory
 ** requirement is:
 **
 ** (3*nvalues+nlabels)*sizeof(double) + 4*nvalues*sizeof(vl_uint)
 **
 ** @returns An allocated and initialized @a VlAIB pointer
 **/
VlAIB * vl_aib_new(double * Pcx, vl_uint nvalues, vl_uint nlabels)
{
    VlAIB * aib = vl_malloc(sizeof(VlAIB));
    vl_uint i ;

    aib->verbosity = 0 ;
    aib->Pcx   = Pcx ;
    aib->nvalues = nvalues ;
    aib->nlabels = nlabels ;

    vl_aib_normalize_P (aib->Pcx, aib->nvalues * aib->nlabels) ;

    aib->Px = vl_aib_new_Px (aib->Pcx, aib->nvalues, aib->nlabels) ;
    aib->Pc = vl_aib_new_Pc (aib->Pcx, aib->nvalues, aib->nlabels) ;

    aib->nentries = aib->nvalues ;
    aib->nodes    = vl_aib_new_nodelist(aib->nentries) ;
    aib->beta     = vl_malloc(sizeof(double) * aib->nentries) ;
    aib->bidx     = vl_malloc(sizeof(vl_uint)   * aib->nentries) ;

    for(i = 0 ; i < aib->nentries ; i++)
      aib->beta [i] = BETA_MAX ;

    /* Initially we must consider all nodes */
    aib->nwhich = aib->nvalues;
    aib->which  = vl_aib_new_nodelist (aib->nwhich) ;

    aib->parents = vl_malloc(sizeof(vl_uint)*(aib->nvalues*2-1));
    /* Initially, all parents point to a nonexistent node */
    for (i = 0 ; i < 2 * aib->nvalues - 1 ; i++)
      aib->parents [i] = 2 * aib->nvalues ;

    /* Allocate cost output vector */
    aib->costs = vl_malloc (sizeof(double) * (aib->nvalues - 1 + 1)) ;


    return aib ;
}

/** ------------------------------------------------------------------
 ** @brief Deletes AIB data structure
 ** @param aib data structure to delete.
 **/

void
vl_aib_delete (VlAIB * aib)
{
  if (aib) {
    if (aib-> nodes)   vl_free (aib-> nodes);
    if (aib-> beta)    vl_free (aib-> beta);
    if (aib-> bidx)    vl_free (aib-> bidx);
    if (aib-> which)   vl_free (aib-> which);
    if (aib-> Px)      vl_free (aib-> Px);
    if (aib-> Pc)      vl_free (aib-> Pc);
    if (aib-> parents) vl_free (aib-> parents);
    if (aib-> costs)   vl_free (aib-> costs);

    vl_free (aib) ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Runs AIB on Pcx
 **
 ** @param aib     AIB object to process
 **
 ** The function runs Agglomerative Information Bottleneck (AIB) on
 ** the joint probability table @a aib->Pcx which has labels along the
 ** columns and feature values along the rows. AIB iteratively merges
 ** the two values of the feature @c x that causes the smallest
 ** decrease in mutual information between the random variables @c x
 ** and @c c.
 **
 ** Merge operations are arranged in a binary tree. The nodes of the
 ** tree correspond to the original feature values and any other value
 ** obtained as a result of a merge operation. The nodes are indexed
 ** in breadth-first order, starting from the leaves. The first index
 ** is zero. In this way, the leaves correspond directly to the
 ** original feature values.  In total there are @c 2*nvalues-1 nodes.
 **
 ** The results may be accessed through vl_aib_get_parents which
 ** returns an array with one element per tree node. Each
 ** element is the index the parent node. The root parent is equal to
 ** zero. The array has @c 2*nvalues-1 elements.
 **
 ** Feature values with null probability are ignored by the algorithm
 ** and their nodes have parents indexing a non-existent tree node (a
 ** value bigger than @c 2*nvalues-1).
 **
 ** Then the function will also compute the information level after each
 ** merge. vl_get_costs will return a vector with the information level
 ** after each merge. @a
 ** cost has @c nvalues entries: The first is the value of the cost
 ** functional before any merge, and the others are the cost after the
 ** @c nvalues-1 merges.
 **
 **/

VL_EXPORT
void vl_aib_process(VlAIB *aib)
{
    vl_uint i, besti, bestj, newnode, nodei, nodej;
    double I, H;
    double minbeta;

    /* Calculate initial value of cost function */
    vl_aib_calculate_information (aib, &I, &H) ;
    aib->costs[0] = I;

    /* Initially which = all */

    /* For each merge */
    for(i = 0 ; i < aib->nvalues - 1 ; i++) {

      /* update entries in aib-> which */
      vl_aib_update_beta(aib);

      /* find best pair of nodes to merge */
      vl_aib_min_beta (aib, &besti, &bestj, &minbeta);

      if(minbeta == BETA_MAX)
        /* only null-probability entries remain */
        break;

      /* Add the parent pointers for the new node */
      newnode = aib->nvalues + i ;
      nodei = aib->nodes[besti];
      nodej = aib->nodes[bestj];

      aib->parents [nodei] = newnode ;
      aib->parents [nodej] = newnode ;
      aib->parents [newnode] = 0 ;

      /* Merge the nodes which produced the minimum beta */
      vl_aib_merge_nodes (aib, besti, bestj, newnode) ;
      vl_aib_calculate_information (aib, &I, &H) ;

      aib->costs[i+1] = I;

      if (aib->verbosity > 0) {
        VL_PRINTF ("aib: (%5d,%5d)=%5d dE: %10.3g I: %6.4g H: %6.4g updt: %5d\n",
                   nodei,
                   nodej,
                   newnode,
                   minbeta,
                   I,
                   H,
                   aib->nwhich) ;
      }
    }

    /* fill ignored entries with NaNs */
    for(; i < aib->nvalues - 1 ; i++)
        aib->costs[i+1] = VL_NAN_D ;
}
vlfeat/vl/ikmeans.h0000644000175000017500000001060012237004736013167 0ustar  dimadima/** @file ikmeans.h
 ** @brief Integer K-Means clustering
 ** @author Brian Fulkerson
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_IKMEANS_H
#define VL_IKMEANS_H

#include "generic.h"
#include "random.h"

typedef vl_int32 vl_ikm_acc ;  /**< IKM accumulator data type */

/** ------------------------------------------------------------------
 ** @brief IKM algorithms
 **/

enum VlIKMAlgorithms {
  VL_IKM_LLOYD,       /**< Lloyd algorithm */
  VL_IKM_ELKAN        /**< Elkan algorithm */
} ;

/** ------------------------------------------------------------------
 ** @brief IKM quantizer
 **/

typedef struct _VlIKMFilt
{
  int M ;                  /**< data dimensionality */
  int K ;                  /**< number of centers   */

  int method ;             /**< Learning method */
  int max_niters ;         /**< Lloyd: maximum number of iterations */
  int verb ;               /**< verbosity level */

  vl_ikm_acc *centers ;    /**< centers */
  vl_ikm_acc *inter_dist ; /**< centers inter-distances */
} VlIKMFilt ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlIKMFilt *vl_ikm_new    (int method) ;
VL_EXPORT void       vl_ikm_delete (VlIKMFilt *f) ;
/** @} */

/** @name Process data
 ** @{
 **/
VL_EXPORT void vl_ikm_init           (VlIKMFilt *f, vl_ikm_acc const *centers, int M, int K) ;
VL_EXPORT void vl_ikm_init_rand      (VlIKMFilt *f, int M, int K) ;
VL_EXPORT void vl_ikm_init_rand_data (VlIKMFilt *f, vl_uint8 const *data, int M, int N, int K) ;
VL_EXPORT int  vl_ikm_train          (VlIKMFilt *f, vl_uint8 const *data, int N) ;
VL_EXPORT void vl_ikm_push           (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N) ;

VL_EXPORT
vl_uint  vl_ikm_push_one   (vl_ikm_acc const *centers,
                            vl_uint8 const *data,
                            int M, int K) ;
/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_INLINE int vl_ikm_get_ndims      (VlIKMFilt const *f) ;
VL_INLINE int vl_ikm_get_K          (VlIKMFilt const *f) ;
VL_INLINE int vl_ikm_get_verbosity  (VlIKMFilt const *f) ;
VL_INLINE int vl_ikm_get_max_niters (VlIKMFilt const *f) ;
VL_INLINE vl_ikm_acc const * vl_ikm_get_centers (VlIKMFilt const *f) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_INLINE void vl_ikm_set_verbosity  (VlIKMFilt *f, int verb) ;
VL_INLINE void vl_ikm_set_max_niters (VlIKMFilt *f, int max_niters) ;
/** @} */

/** ------------------------------------------------------------------
 ** @brief Get data dimensionality
 ** @param f IKM filter.
 ** @return data dimensionality.
 **/

VL_INLINE int
vl_ikm_get_ndims (VlIKMFilt const* f)
{
  return f-> M ;
}

/** ------------------------------------------------------------------
 ** @brief Get the number of centers K
 ** @param f IKM filter.
 ** @return number of centers K.
 **/

VL_INLINE int
vl_ikm_get_K (VlIKMFilt const* f)
{
  return f-> K ;
}

/** ------------------------------------------------------------------
 ** @brief Get verbosity level
 ** @param f IKM filter.
 ** @return verbosity level.
 **/

VL_INLINE int
vl_ikm_get_verbosity (VlIKMFilt const* f)
{
  return f-> verb ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of iterations
 ** @param f IKM filter.
 ** @return maximum number of iterations.
 **/

VL_INLINE int
vl_ikm_get_max_niters (VlIKMFilt const* f)
{
  return f-> max_niters ;
}

/** ------------------------------------------------------------------
 ** @brief Get maximum number of iterations
 ** @param f IKM filter.
 ** @return maximum number of iterations.
 **/

VL_INLINE vl_ikm_acc const *
vl_ikm_get_centers (VlIKMFilt const* f)
{
  return f-> centers ;
}

/** ------------------------------------------------------------------
 ** @brief Set verbosity level
 ** @param f    IKM filter.
 ** @param verb verbosity level.
 **/

VL_INLINE void
vl_ikm_set_verbosity (VlIKMFilt *f, int verb)
{
  f-> verb = VL_MAX(0,verb) ;
}

/** ------------------------------------------------------------------
 ** @brief Set maximum number of iterations
 ** @param f          IKM filter.
 ** @param max_niters maximum number of iterations.
 **/

VL_INLINE void
vl_ikm_set_max_niters (VlIKMFilt *f, int max_niters)
{
  f-> max_niters = max_niters ;
}

/* VL_IKMEANS_H */
#endif
vlfeat/vl/liop.h0000644000175000017500000000435012237004745012510 0ustar  dimadima/** @file liop.h
 ** @brief Local Intensity Order Pattern (LIOP) descriptor (@ref liop)
 ** @author Hana Sarbortova
 ** @author Andrea Vedaldi
 ** @see @ref liop
 **/

/*
Copyright (C) 2013 Hana Sarbortova and Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_LIOP_H
#define VL_LIOP_H

#include "generic.h"

/** @brief LIOP descriptor extractor object */
typedef struct _VlLiopDesc
{
  vl_int numNeighbours; /**< Number of neighbours. */
  vl_int numSpatialBins; /**< Number of bins. */
  float intensityThreshold; /**< Weight threshold. */
  vl_size dimension; /**< LIOP descriptor size. */

  /* Pixels in the circular patch */
  vl_size patchSideLength ;
  vl_size patchSize ; /* only circular neighbourhood */
  vl_uindex * patchPixels ;
  float * patchIntensities ;
  vl_uindex * patchPermutation ;

  /* Neighbourhoods of each pixel (samples in a circle) */
  float neighRadius; /**< Point to neighbour radius (distance). */

  float * neighIntensities ;
  vl_uindex * neighPermutation ;
  double * neighSamplesX ;
  double * neighSamplesY ;

} VlLiopDesc ;

/** @name Construct and destroy
 ** @{ */
VL_EXPORT
VlLiopDesc * vl_liopdesc_new (vl_int numNeighbours,
                              vl_int numSpatialBins,
                              float radius,
                              vl_size sideLength) ;

VL_EXPORT
VlLiopDesc * vl_liopdesc_new_basic (vl_size sideLength) ;

VL_EXPORT
void vl_liopdesc_delete (VlLiopDesc * self) ;
/** @} */

/**  @name Get data and parameters
 **  @{ */
VL_EXPORT vl_size vl_liopdesc_get_dimension (VlLiopDesc const * self) ;
VL_EXPORT vl_size vl_liopdesc_get_num_neighbours (VlLiopDesc const * self) ;
VL_EXPORT float vl_liopdesc_get_intensity_threshold (VlLiopDesc const * self) ;
VL_EXPORT vl_size vl_liopdesc_get_num_spatial_bins (VlLiopDesc const * self) ;
VL_EXPORT double vl_liopdesc_get_neighbourhood_radius (VlLiopDesc const * self) ;
VL_EXPORT void vl_liopdesc_set_intensity_threshold (VlLiopDesc * self, float x) ;
/** @} */

/**  @name Compute LIOP descriptor
 **  @{ */
VL_EXPORT
void vl_liopdesc_process (VlLiopDesc * liop, float * desc, float const * patch) ;
/** @} */

/* VL_LIOP_H */
#endif
vlfeat/vl/pgm.c0000644000175000017500000003203512237004745012324 0ustar  dimadima/** @file pgm.c
 ** @brief Portable graymap format (PGM) parser - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file pgm.h

This module implements basic input and ouptut of images in PGM
format.

Extracting an image encoded in PGM format from an imput
file stream involves the following steps:

- use ::vl_pgm_extract_head to extract the image meta data
  (size and bit depth);
- allocate a buffer to store the image data;
- use ::vl_pgm_extract_data to extract the image data to the allocated
  buffer.

Writing an image in PGM format to an ouptut file stream
can be done by using ::vl_pgm_insert.

To quickly read/write a PGM image from/to a given file, use
::vl_pgm_read_new() and ::vl_pgm_write(). To to the same from a
buffer in floating point format use ::vl_pgm_read_new_f() and
::vl_pgm_write_f().

**/

#include "pgm.h"

#include 
#include 
#include 

/** ------------------------------------------------------------------
 ** @internal @brief Remove all characters to the next new-line.
 ** @param f file to strip.
 ** @return number of characters removed.
 **/

static int
remove_line(FILE* f)
{
  int count = 0 ;
  int c ;

  while (1) {
    c = fgetc(f) ;
    ++ count ;

    switch(c) {
    case '\n' :
      goto quit_remove_line ;

    case EOF :
      -- count ;
      goto quit_remove_line ;
    }
  }
 quit_remove_line :
  return count ;
}

/** ------------------------------------------------------------------
 ** @internal @brief Remove white-spaces and comments.
 ** @param f file to strip.
 ** @return number of characters removed.
 **/

static int
remove_blanks(FILE* f)
{
  int count = 0 ;
  int c ;

  while (1) {
    c = fgetc(f) ;

    switch(c) {

    case '\t' : case '\n' :
    case '\r' : case ' '  :
      ++ count ;
      break ;

    case '#' :
      count += 1 + remove_line(f) ;
      break ;

    case EOF :
      goto quit_remove_blanks ;

    default:
      ungetc(c, f) ;
      goto quit_remove_blanks ;
    }
  }
 quit_remove_blanks:
  return count ;
}

/** ------------------------------------------------------------------
 ** @brief Get PGM image number of pixels.
 ** @param im PGM image descriptor.
 ** @return number of pixels of the image.
 **
 ** The functions returns the number of pixels of the PGM image @a im.
 **
 ** To calculate the image data size in bytes, this value must be
 ** multiplied by the number of byte per pixels (see
 ** ::vl_pgm_get_bpp()).
 **/

VL_EXPORT vl_size
vl_pgm_get_npixels (VlPgmImage const *im)
{
  return im->width * im->height ;
}

/** ------------------------------------------------------------------
 ** @brief Get PGM image bytes per pixel.
 ** @param im PGM image descriptor.
 ** @return number of bytes per pixel.
 **
 ** The function returns the number of bytes for each pixel of the
 ** PGM image @a im.
 **/

VL_EXPORT vl_size
vl_pgm_get_bpp (VlPgmImage const *im)
{
  return (im->max_value >= 256) + 1 ;
}

/** ------------------------------------------------------------------
 ** @brief Extract PGM header from stream.
 ** @param f  input file.
 ** @param im image structure to fill.
 ** @return error code.
 **
 ** The function extracts from the file @a f the meta-data section of
 ** an image encoded in PGM format. The function fills the structure
 ** ::VlPgmImage accordingly.
 **
 ** The error may be either ::VL_ERR_PGM_INV_HEAD or ::VL_ERR_PGM_INV_META
 ** depending whether the error occurred in decoding the header or
 ** meta section of the PGM file.
 **/

VL_EXPORT int
vl_pgm_extract_head (FILE* f, VlPgmImage *im)
{
  char magic [2] ;
  int c ;
  int is_raw ;
  int width ;
  int height ;
  int max_value ;
  size_t sz ;
  vl_bool good ;

  /* -----------------------------------------------------------------
   *                                                check magic number
   * -------------------------------------------------------------- */
  sz = fread(magic, 1, 2, f) ;

  if (sz < 2) {
    return vl_set_last_error(VL_ERR_PGM_INV_HEAD, "Invalid PGM header") ;
  }

  good = magic [0] == 'P' ;

  switch (magic [1]) {
  case '2' : /* ASCII format */
    is_raw = 0 ;
    break ;

  case '5' : /* RAW format */
    is_raw = 1 ;
    break ;

  default :
    good = 0 ;
    break ;
  }

  if( ! good ) {
    return vl_set_last_error(VL_ERR_PGM_INV_HEAD, "Invalid PGM header") ;
  }

  /* -----------------------------------------------------------------
   *                                    parse width, height, max_value
   * -------------------------------------------------------------- */
  good = 1 ;

  c = remove_blanks(f) ;
  good &= c > 0 ;

  c = fscanf(f, "%d", &width) ;
  good &= c == 1 ;

  c = remove_blanks(f) ;
  good &= c > 0 ;

  c = fscanf(f, "%d", &height) ;
  good &= c == 1 ;

  c = remove_blanks(f) ;
  good &= c > 0 ;

  c = fscanf(f, "%d", &max_value) ;
  good &= c == 1 ;

  /* must end with a single blank */
  c = fgetc(f) ;
  good &=
    c == '\n' ||
    c == '\t' ||
    c == ' '  ||
    c == '\r' ;

  if(! good) {
    return vl_set_last_error(VL_ERR_PGM_INV_META, "Invalid PGM meta information");
  }

  if(! max_value >= 65536) {
    return vl_set_last_error(VL_ERR_PGM_INV_META, "Invalid PGM meta information");
  }

  /* exit */
  im-> width     = width ;
  im-> height    = height ;
  im-> max_value = max_value ;
  im-> is_raw    = is_raw ;
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @brief Extract PGM data from stream.
 ** @param f input file.
 ** @param im PGM image descriptor.
 ** @param data data buffer to fill.
 ** @return error code.
 **
 ** The function extracts from the file @a f the data section of an
 ** image encoded in PGM format. The function fills the buffer @a data
 ** according. The buffer @a data should be ::vl_pgm_get_npixels() by
 ** ::vl_pgm_get_bpp() bytes large.
 **/

VL_EXPORT
int
vl_pgm_extract_data (FILE* f, VlPgmImage const *im, void *data)
{
  vl_size bpp = vl_pgm_get_bpp(im) ;
  vl_size data_size = vl_pgm_get_npixels(im) ;
  vl_bool good = 1 ;
  size_t c ;

  /* -----------------------------------------------------------------
   *                                                         read data
   * -------------------------------------------------------------- */

  /*
     In RAW mode we read directly an array of bytes or shorts.  In
     the latter case, however, we must take care of the
     endianess. PGM files are sorted in big-endian format. If our
     architecture is little endian, we must do a conversion.
  */
  if (im->is_raw) {

    c = fread( data,
               bpp,
               data_size,
               f ) ;
    good = (c == data_size) ;

    /* adjust endianess */
#if defined(VL_ARCH_LITTLE_ENDIAN)
    if (bpp == 2) {
      vl_uindex i ;
      vl_uint8 *pt = (vl_uint8*) data ;
      for(i = 0 ; i < 2 * data_size ; i += 2) {
        vl_uint8 tmp = pt [i] ;
        pt [i]   = pt [i+1] ;
        pt [i+1] = tmp ;
      }
    }
#endif
  }
  /*
     In ASCII mode we read a sequence of decimal numbers separated
     by whitespaces.
  */
  else {
    vl_uindex i ;
    int unsigned v ;
    for(good = 1, i = 0 ;
        i < data_size && good ;
        ++i) {
      c = fscanf(f, " %ud", &v) ;
      if (bpp == 1) {
        * ((vl_uint8* )  data + i) = (vl_uint8)  v ;
      } else {
        * ((vl_uint16*)  data + i) = (vl_uint16) v ;
      }
      good &= c == 1 ;
    }
  }

  if(! good ) {
    return vl_set_last_error(VL_ERR_PGM_INV_DATA, "Invalid PGM data") ;
  }
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @brief Insert a PGM image into a stream.
 ** @param f output file.
 ** @param im   PGM image meta-data.
 ** @param data image data.
 ** @return error code.
 **/

VL_EXPORT
int
vl_pgm_insert(FILE* f, VlPgmImage const *im, void const *data)
{
  vl_size bpp = vl_pgm_get_bpp (im) ;
  vl_size data_size = vl_pgm_get_npixels (im) ;
  size_t c ;

  /* write preamble */
  fprintf(f,
          "P5\n%d\n%d\n%d\n",
          (signed)im->width,
          (signed)im->height,
          (signed)im->max_value) ;

  /* take care of endianness */
#if defined(VL_ARCH_LITTLE_ENDIAN)
  if (bpp == 2) {
    vl_uindex i ;
    vl_uint8* temp = vl_malloc (2 * data_size) ;
    memcpy(temp, data, 2 * data_size) ;
    for(i = 0 ; i < 2 * data_size ; i += 2) {
      vl_uint8 tmp = temp [i] ;
      temp [i]   = temp [i+1] ;
      temp [i+1] = tmp ;
    }
    c = fwrite(temp, 2, data_size, f) ;
    vl_free (temp) ;
  }
  else {
#endif
    c = fwrite(data, bpp, data_size, f) ;
#if defined(VL_ARCH_LITTLE_ENDIAN)
  }
#endif

  if(c != data_size) {
    return vl_set_last_error(VL_ERR_PGM_IO, "Error writing PGM data") ;
  }
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @brief Read a PGM file.
 ** @param name file name.
 ** @param im a pointer to the PGM image structure to fill.
 ** @param data a pointer to the pointer to the allocated buffer.
 ** @return error code.
 **
 ** The function reads a PGM image from file @a name and initializes
 ** the structure @a im and the buffer @a data accordingly.
 **
 ** The ownership of the buffer @a data is transfered to the caller.
 ** @a data should be freed by means of ::vl_free().
 **
 ** @bug Only PGM files with 1 BPP are supported.
 **/

VL_EXPORT
int vl_pgm_read_new (char const *name, VlPgmImage *im, vl_uint8** data)
{
  int err = 0 ;
  FILE *f = fopen (name, "rb") ;

  if (! f) {
    return vl_set_last_error(VL_ERR_PGM_IO, "Error opening PGM file `%s' for reading", name) ;
  }

  err = vl_pgm_extract_head(f, im) ;
  if (err) {
    fclose (f) ;
    return err ;
  }

  if (vl_pgm_get_bpp(im) > 1) {
    return vl_set_last_error(VL_ERR_BAD_ARG, "PGM with BPP > 1 not supported") ;
  }

  *data = vl_malloc (vl_pgm_get_npixels(im) * sizeof(vl_uint8)) ;
  err = vl_pgm_extract_data(f, im, *data) ;

  if (err) {
    vl_free (data) ;
    fclose (f) ;
  }

  fclose (f) ;
  return err ;
}

/** ------------------------------------------------------------------
 ** @brief Read floats from a PGM file.
 ** @param name file name.
 ** @param im a pointer to the PGM image structure to fill.
 ** @param data a pointer to the pointer to the allocated buffer.
 ** @return error code.
 **
 ** The function reads a PGM image from file @a name and initializes
 ** the structure @a im and the buffer @a data accordingly. The buffer
 ** @a data is an array of floats in the range [0, 1].
 **
 ** The ownership of the buffer @a data is transfered to the caller.
 ** @a data should be freed by means of ::vl_free().
 **
 ** @bug Only PGM files with 1 BPP are supported.
 **/

VL_EXPORT
int vl_pgm_read_new_f (char const *name,  VlPgmImage *im, float** data)
{
  int err = 0 ;
  size_t npixels ;
  vl_uint8 *idata ;

  err = vl_pgm_read_new (name, im, &idata) ;
  if (err) {
    return err ;
  }

  npixels = vl_pgm_get_npixels(im) ;
  *data = vl_malloc (sizeof(float) * npixels) ;
  {
    size_t k ;
    float scale = 1.0f / (float)im->max_value ;
    for (k = 0 ; k < npixels ; ++ k) (*data)[k] = scale * idata[k] ;
  }

  vl_free (idata) ;
  return 0 ;
}

/** ------------------------------------------------------------------
 ** @brief Write bytes to a PGM file.
 ** @param name file name.
 ** @param data data to write.
 ** @param width width of the image.
 ** @param height height of the image.
 ** @return error code.
 **
 ** The function dumps the image @a data to the PGM file of the specified
 ** name. This is an helper function simplifying the usage of
 ** vl_pgm_insert().
 **/

VL_EXPORT
int vl_pgm_write (char const *name, vl_uint8 const* data, int width, int height)
{
  int err = 0 ;
  VlPgmImage pgm ;
  FILE *f = fopen (name, "wb") ;

  if (! f) {
    return vl_set_last_error(VL_ERR_PGM_IO,
             "Error opening PGM file '%s' for writing", name) ;
  }

  pgm.width = width ;
  pgm.height = height ;
  pgm.is_raw = 1 ;
  pgm.max_value = 255 ;

  err = vl_pgm_insert (f, &pgm, data) ;
  fclose (f) ;

  return err ;
}

/** -------------------------------------------------------------------
 ** @brief Write floats to PGM file
 ** @param name file name.
 ** @param data data to write.
 ** @param width width of the image.
 ** @param height height of the image.
 ** @return error code.
 **
 ** The function dumps the image @a data to the PGM file of the
 ** specified name. The data is re-scaled to fit in the range 0-255.
 ** This is an helper function simplifying the usage of
 ** vl_pgm_insert().
 **/

VL_EXPORT
int vl_pgm_write_f (char const *name, float const* data, int width, int height)
{
  int err = 0 ;
  int k ;
  float min = + VL_INFINITY_F ;
  float max = - VL_INFINITY_F ;
  float scale ;

  vl_uint8 * buffer = vl_malloc (sizeof(float) * width * height) ;

  for (k = 0 ; k < width * height ; ++k) {
    min = VL_MIN(min, data [k]) ;
    max = VL_MAX(max, data [k]) ;
  }

  scale = 255 / (max - min + VL_EPSILON_F) ;

  for (k = 0 ; k < width * height ; ++k) {
    buffer [k] = (vl_uint8) ((data [k] - min) * scale) ;
  }

  err = vl_pgm_write (name, buffer, width, height) ;

  vl_free (buffer) ;
  return err ;
}
vlfeat/vl/svmdataset.h0000644000175000017500000000530012237004745013714 0ustar  dimadima/** @file svmdataset.h
 ** @brief SVM Dataset
 ** @author Daniele Perrone
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2012 Daniele Perrone.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_SVMDATASET_H
#define VL_SVMDATASET_H

#include "generic.h"
#include "homkermap.h"

struct VlSvm_ ;

/** @typedef VlSvmDataset
 ** @brief SVM dataset object
 **
 ** This objects contain a training set to be used in combination with
 ** the SVM solver object ::VlSvm. Its main purpose is to implement
 ** the two basic operations inner product (::VlSvmInnerProductFunction)
 ** and accumulation (::VlSvmAccumulateFunction).
 **
 ** See @ref svm and @ref svm-advanced for further information.
 **/

#ifndef __DOXYGEN__
struct VlSvmDataset_ ;
typedef struct VlSvmDataset_ VlSvmDataset ;
#else
typedef OPAQUE VlSvmDataset ;
#endif

/** @name SVM callbacks
 ** @{ */
typedef void (*VlSvmDiagnosticFunction) (struct VlSvm_ *svm, void *data) ;
typedef double (*VlSvmLossFunction) (double inner, double label) ;
typedef double (*VlSvmDcaUpdateFunction) (double alpha, double inner, double norm2, double label) ;
typedef double (*VlSvmInnerProductFunction)(const void *data, vl_uindex element, double *model) ;
typedef void (*VlSvmAccumulateFunction) (const void *data, vl_uindex element, double *model, double multiplier) ;
/* typedef double (*VlSvmSquareNormFunction) (const void *data, vl_uindex element) ; */
/** @} */

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlSvmDataset* vl_svmdataset_new (vl_type dataType, void *data, vl_size dimension, vl_size numData) ;
VL_EXPORT void vl_svmdataset_delete (VlSvmDataset * dataset) ;
/** @} */

/** @name Set parameters
 ** @{
 **/
VL_EXPORT void vl_svmdataset_set_homogeneous_kernel_map (VlSvmDataset * self,
                                                         VlHomogeneousKernelMap * hom) ;
/** @} */

/** @name Get data and parameters
 ** @{
 **/
VL_EXPORT void* vl_svmdataset_get_data (VlSvmDataset const *self) ;
VL_EXPORT vl_size vl_svmdataset_get_num_data (VlSvmDataset const *self) ;
VL_EXPORT vl_size vl_svmdataset_get_dimension (VlSvmDataset const *self) ;
VL_EXPORT void* vl_svmdataset_get_map (VlSvmDataset const *self) ;
VL_EXPORT vl_size vl_svmdataset_get_mapDim (VlSvmDataset const *self) ;
VL_EXPORT VlSvmAccumulateFunction vl_svmdataset_get_accumulate_function (VlSvmDataset const *self) ;
VL_EXPORT VlSvmInnerProductFunction vl_svmdataset_get_inner_product_function (VlSvmDataset const * self) ;
VL_EXPORT VlHomogeneousKernelMap * vl_svmdataset_get_homogeneous_kernel_map (VlSvmDataset const * self) ;
/** @} */

/* VL_SVMDATASET_H */
#endif


vlfeat/vl/generic.h0000644000175000017500000001475712237004745013175 0ustar  dimadima/** @file generic.h
 ** @brief Generic (@ref generic)
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_GENERIC_H
#define VL_GENERIC_H

#include "host.h"
#include "random.h"

#include 
#include 
#include 
#include 

/** @brief Library version string */
#define VL_VERSION_STRING "0.9.17"

/** @brief Maximum length (in characters) of an error message */
#define VL_ERR_MSG_LEN 1024

/** @name Type identifiers for atomic data types
 ** @{ */

#define VL_TYPE_FLOAT   1     /**< @c float type */
#define VL_TYPE_DOUBLE  2     /**< @c double type */
#define VL_TYPE_INT8    3     /**< @c ::vl_int8 type */
#define VL_TYPE_UINT8   4     /**< @c ::vl_uint8 type */
#define VL_TYPE_INT16   5     /**< @c ::vl_int16 type */
#define VL_TYPE_UINT16  6     /**< @c ::vl_uint16 type */
#define VL_TYPE_INT32   7     /**< @c ::vl_int32 type */
#define VL_TYPE_UINT32  8     /**< @c ::vl_uint32 type */
#define VL_TYPE_INT64   9     /**< @c ::vl_int64 type */
#define VL_TYPE_UINT64  10    /**< @c ::vl_uint64 type */

typedef vl_uint32 vl_type ;

/** @brief Get the name of a data type.
 ** @param type data type.
 ** @return data name of the data type.
 **
 ** @c type is one of ::VL_TYPE_FLOAT, ::VL_TYPE_DOUBLE,
 ** ::VL_TYPE_INT8, ::VL_TYPE_INT16, ::VL_TYPE_INT32, ::VL_TYPE_INT64,
 ** ::VL_TYPE_UINT8, ::VL_TYPE_UINT16, ::VL_TYPE_UINT32, ::VL_TYPE_UINT64.
 **/

VL_INLINE char const *
vl_get_type_name (vl_type type)
{
  switch (type) {
    case VL_TYPE_FLOAT   : return "float"  ;
    case VL_TYPE_DOUBLE  : return "double" ;
    case VL_TYPE_INT8    : return "int8"   ;
    case VL_TYPE_INT16   : return "int16"  ;
    case VL_TYPE_INT32   : return "int32"  ;
    case VL_TYPE_INT64   : return "int64"  ;
    case VL_TYPE_UINT8   : return "int8"   ;
    case VL_TYPE_UINT16  : return "int16"  ;
    case VL_TYPE_UINT32  : return "int32"  ;
    case VL_TYPE_UINT64  : return "int64"  ;
    default: return NULL ;
  }
}

/** @brief Get data type size.
 ** @param type data type.
 ** @return size (in byte)
 **
 ** @c type is one of ::VL_TYPE_FLOAT, ::VL_TYPE_DOUBLE,
 ** ::VL_TYPE_INT8, ::VL_TYPE_INT16, ::VL_TYPE_INT32, ::VL_TYPE_INT64,
 ** ::VL_TYPE_UINT8, ::VL_TYPE_UINT16, ::VL_TYPE_UINT32, ::VL_TYPE_UINT64.
 **/

VL_INLINE vl_size
vl_get_type_size (vl_type type)
{
  vl_size dataSize = 0 ;
  switch (type) {
    case VL_TYPE_DOUBLE : dataSize = sizeof(double) ; break ;
    case VL_TYPE_FLOAT  : dataSize = sizeof(float) ; break ;
    case VL_TYPE_INT64  : case VL_TYPE_UINT64 : dataSize = sizeof(vl_int64) ; break ;
    case VL_TYPE_INT32  : case VL_TYPE_UINT32 : dataSize = sizeof(vl_int32) ; break ;
    case VL_TYPE_INT16  : case VL_TYPE_UINT16 : dataSize = sizeof(vl_int16) ; break ;
    case VL_TYPE_INT8   : case VL_TYPE_UINT8  : dataSize = sizeof(vl_int8)  ; break ;
    default:
      abort() ;
  }
  return dataSize ;
}
/** @} */

VL_EXPORT char const * vl_get_version_string (void) ;
VL_EXPORT char * vl_configuration_to_string_copy (void) ;
VL_EXPORT void vl_set_simd_enabled (vl_bool x) ;
VL_EXPORT vl_bool vl_get_simd_enabled (void) ;
VL_EXPORT vl_bool vl_cpu_has_avx (void) ;
VL_EXPORT vl_bool vl_cpu_has_sse3 (void) ;
VL_EXPORT vl_bool vl_cpu_has_sse2 (void) ;
VL_EXPORT vl_size vl_get_num_cpus (void) ;
VL_EXPORT vl_size vl_get_max_threads (void) ;
VL_EXPORT void vl_set_num_threads (vl_size n) ;
VL_EXPORT VlRand * vl_get_rand (void) ;

#if 0
VL_EXPORT vl_size vl_get_thread_limit (void) ;
#endif

/** ------------------------------------------------------------------
 ** @name Error handling
 ** @{ */
#define VL_ERR_OK       0  /**< No error */
#define VL_ERR_OVERFLOW 1  /**< Buffer overflow error */
#define VL_ERR_ALLOC    2  /**< Resource allocation error */
#define VL_ERR_BAD_ARG  3  /**< Bad argument or illegal data error */
#define VL_ERR_IO       4  /**< Input/output error */
#define VL_ERR_EOF      5  /**< End-of-file or end-of-sequence error */
#define VL_ERR_NO_MORE  5  /**< End-of-sequence @deprecated */

VL_EXPORT int vl_get_last_error (void) ;
VL_EXPORT char const *  vl_get_last_error_message (void) ;
VL_EXPORT int vl_set_last_error (int error, char const * errorMessage, ...) ;
/** @} */

/** ------------------------------------------------------------------
 ** @name Memory allocation
 ** @{ */
VL_EXPORT void
vl_set_alloc_func (void *(*malloc_func)  (size_t),
                   void *(*realloc_func) (void*,size_t),
                   void *(*calloc_func)  (size_t, size_t),
                   void  (*free_func)    (void*)) ;
VL_EXPORT void *vl_malloc (size_t n) ;
VL_EXPORT void *vl_realloc (void *ptr, size_t n) ;
VL_EXPORT void *vl_calloc (size_t n, size_t size) ;
VL_EXPORT void *vl_memalign (size_t n, size_t size) ;
VL_EXPORT void  vl_free (void* ptr) ;
/** @} */

/** ------------------------------------------------------------------
 ** @name Logging
 ** @{ */
/** @brief Customizable printf function pointer type */
typedef int(*printf_func_t) (char const *format, ...) ;
VL_EXPORT void vl_set_printf_func (printf_func_t printf_func) ;
VL_EXPORT printf_func_t vl_get_printf_func (void) ;

/** @def VL_PRINTF
 ** @brief Call user-customizable @c printf function
 **
 ** The function calls the user customizable @c printf.
 **/

/** @def VL_PRINT
 ** @brief Same as ::VL_PRINTF (legacy code)
 **/

#define VL_PRINTF (*vl_get_printf_func())
#define VL_PRINT (*vl_get_printf_func())
/** @} */

/** ------------------------------------------------------------------
 ** @name Common operations
 ** @{ */

/** @brief Compute the minimum between two values
 ** @param x value
 ** @param y value
 ** @return the minimum of @a x and @a y.
 **/
#define VL_MIN(x,y) (((x)<(y))?(x):(y))

/** @brief Compute the maximum between two values
 ** @param x value.
 ** @param y value.
 ** @return the maximum of @a x and @a y.
 **/
#define VL_MAX(x,y) (((x)>(y))?(x):(y))

/** @brief Signed left shift operation
 ** @param x value.
 ** @param n number of shift positions.
 ** @return @c x << n .
 ** The macro is equivalent to the builtin @c << operator, but it
 ** supports negative shifts too.
 **/
#define VL_SHIFT_LEFT(x,n) (((n)>=0)?((x)<<(n)):((x)>>-(n)))
/* @} */

/** ------------------------------------------------------------------
 ** @name Measuring time
 ** @{
 **/
VL_EXPORT void vl_tic (void) ;
VL_EXPORT double vl_toc (void) ;
VL_EXPORT double vl_get_cpu_time (void) ;
/** @} */

/* VL_GENERIC_H */
#endif
vlfeat/vl/rodrigues.h0000644000175000017500000000141212237004745013544 0ustar  dimadima/** @file rodrigues.h
 ** @brief Rodrigues formulas
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-13 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file rodrigues.h

@section rodrigues Rodrigues formulas

- Use ::vl_rodrigues to compute the Rodrigues formula and its derivative.
- Use ::vl_irodrigues to compute the inverse Rodrigues formula and
  its derivative.

**/

#ifndef VL_RODRIGUES
#define VL_RODRIGUES

#include "generic.h"

VL_EXPORT void vl_rodrigues  (double* R_pt,  double* dR_pt, const double* om_pt) ;
VL_EXPORT void vl_irodrigues (double* om_pt, double* dom_pt, const double* R_pt) ;

/* VL_RODRIGUES */
#endif
vlfeat/vl/shuffle-def.h0000644000175000017500000000472612237004736013744 0ustar  dimadima/** @file   shuffle-def.h
 ** @brief  Shuffle preprocessor metaprogram
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @file shuffle-def.h

 @todo large array compatibility.
 **/

#include "host.h"
#include "random.h"
#include 

#ifndef VL_SHUFFLE_prefix
#error "VL_SHUFFLE_prefix must be defined"
#endif

#ifndef VL_SHUFFLE_array
#ifndef VL_SHUFFLE_type
#error "VL_SHUFFLE_type must be defined if VL_SHUFFLE_array is not"
#endif
#define VL_SHUFFLE_array VL_SHUFFLE_type*
#endif

#ifdef __DOXYGEN__
#define VL_SHUFFLE_prefix  ShufflePrefix       /**< Prefix of the shuffle functions */
#define VL_SHUFFLE_type    ShuffleType         /**< Data type of the shuffle elements */
#define VL_SHUFFLE_array   ShuffleType*        /**< Data type of the shuffle container */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_SHUFFLE_swap) || defined(__DOXYGEN__)
#define VL_SHUFFLE_swap VL_XCAT(VL_SHUFFLE_prefix, _swap)

/** @brief Swap two array elements
 ** @param array shuffle array.
 ** @param indexA index of the first element to swap.
 ** @param indexB index of the second element to swap.
 **
 ** The function swaps the two elements @a a and @ b. The function
 ** uses a temporary element of type ::VL_SHUFFLE_type
 ** and the copy operator @c =.
 **/

VL_INLINE void
VL_SHUFFLE_swap
(VL_SHUFFLE_array array,
 vl_uindex indexA,
 vl_uindex indexB)
{
  VL_SHUFFLE_type t = array [indexA] ;
  array [indexA] = array [indexB] ;
  array [indexB] = t ;
}

/* VL_SHUFFLE_swap */
#endif

/* ---------------------------------------------------------------- */

#if ! defined(VL_SHUFFLE_shuffle) || defined(__DOXYGEN__)
#define VL_SHUFFLE_shuffle VL_XCAT(VL_SHUFFLE_prefix, _shuffle)

/** @brief Shuffle
 ** @param array (in/out) pointer to the array.
 ** @param size size of the array.
 ** @param rand random number generator to use.
 **
 ** The function randomly permutes the array.
 **/

VL_INLINE void
VL_SHUFFLE_shuffle
(VL_SHUFFLE_array array, vl_size size, VlRand * rand)
{
  vl_uindex n = size ;
  while (n > 1) {
    vl_uindex k = vl_rand_uindex (rand, n) ;
    n -- ;
    VL_SHUFFLE_swap (array, n, k) ;
  }
}

/* VL_SHUFFLE_shuffle */
#endif

#undef VL_SHUFFLE_prefix
#undef VL_SHUFFLE_swap
#undef VL_SHUFFLE_shuffle
#undef VL_SHUFFLE_type
#undef VL_SHUFFLE_array
vlfeat/vl/mser.c0000644000175000017500000007205212237004745012512 0ustar  dimadima/** @file mser.c
 ** @brief MSER - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-13 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page mser Maximally Stable Extremal Regions (MSER)
@author Andrea Vedaldi


*Maximally Stable Extremal Regions* (MSER) @cite{matas03robust} is a
standard local feature detector. MSER extracts as features the
connected components of the level sets of the (intensity) image. Among
all such regions, the ones that are locally maximally stable are
selected. MSERs are affine co-variant, as well as largely co-variant
to generic diffeomorphic transformations.

@ref mser.h implements the MSER feature detector. This version is
capable of working on images of arbitrary dimensions (e.g. volumes).

@tableofcontents


@section mser-overview Maximally Stable Extremal Regions Overview


Running the MSER filter usually involves the following steps:

- Initialize the MSER filter by ::vl_mser_new(). The
  filter can be reused for images of the same size.
- Compute the MSERs by ::vl_mser_process().
- Optionally fit ellipsoids to the MSERs by  ::vl_mser_ell_fit().
- Retrieve the results by ::vl_mser_get_regions() (and optionally ::vl_mser_get_ell()).
- Optionally retrieve filter statistics by ::vl_mser_get_stats().
- Delete the MSER filter by ::vl_mser_delete().


@section mser-definition MSER definition


An extremal region @f$R_l@f$ of an image is a connected component
of the level set @f$S_l = \{ x : I(x) \leq l \}@f$.

@image html mser-er.png

For each intensity @f$l@f$, one has multiple disjoint extremal
regions in the level set @f$S_l@f$. Let @f$l@f$ span a finite
number of values @f$\mathcal{L}=\{0,\dots,M-1\}@f$ (a sampling of
the image range).  One obtains a family of regions @f$R_l@f$; by
connecting two regions @f$R_l@f$and @f$R_{l+1}@f$ if, and only if,
@f$R_l\subset R_{l+1}@f$, regions form a tree:

@image html mser-tree.png

The maximally stable extremal regions are extremal
regions which satisfy a stability criterion. Here we use a
criterion which is similar but not identical to the original
paper. This definition is somewhat simpler both to understand and
code (it also runs faster).

Let @f$B(R_l)=(R_l,R_{l+1},\dots,R_{l+\Delta})@f$ be the branch of
the tree rooted at @f$R_l@f$.  We associate to the branch the
(in)stability score

@f[
  v(R_l) = \frac{|R_{l+\Delta} - R_l|}{|R_l|}.
@f]

The score is low if the regions along the branch have similar area
(and thus similar shape). We aim to select maximally stable
branches; then a maximally stable region is just a representative
region selected from a maximally stable branch (for simplicity we
select @f$R_l@f$, but one could choose for example
@f$R_{l+\Delta/2}@f$).

Roughly speaking, a branch is maximally stable if it is a local
minimum of the (in)stability score. More accurately, we start by
assuming that all branches are maximally stable. Then we consider
each branch @f$B(R_{l})@f$ and its parent branch
@f$B(R_{l+1}):R_{l+1}\supset R_l@f$ (notice that, due to the
discrete nature of the calculations, they might be geometrically
identical) and we mark as unstable the less stable one, i.e.:

  - if @f$v(R_l)v(R_{l+1})@f$, mark @f$R_{l}@f$ as unstable;
  - otherwise, do nothing.

This criterion selects among nearby regions the ones that are more
stable. We optionally refine the selection by running (starting
from the bigger and going to the smaller regions) the following
tests:

- @f$a_- \leq |R_{l}|/|R_{\infty}| \leq a_+@f$: exclude MSERs too
  small or too big (@f$|R_{\infty}|@f$ is the area of the image).

- @f$v(R_{l}) < v_+@f$: exclude MSERs too unstable.

- For any MSER @f$R_l@f$, find the parent MSER @f$R_{l'}@f$ and check
  if
  @f$|R_{l'} - R_l|/|R_l'| < d_+@f$: remove duplicated MSERs.

 
    
 
    
  parameter
  alt. name
  standard value
  set by
 
    
 
    
   @f$\Delta@f$
   @c delta
   5
   ::vl_mser_set_delta()
 
    
 
    
   @f$a_+@f$
   @c max_area
   0.75
   ::vl_mser_set_max_area()
 
    
 
    
   @f$a_-@f$
   @c min_area
   3.0/@f$|R_\infty|@f$
   ::vl_mser_set_min_area()
 
    
 
    
   @f$v_+@f$
   @c max_var
   0.25
   ::vl_mser_set_max_variation()
 
    
 
    
   @f$d_+@f$
   @c min_diversity
   0.2
   ::vl_mser_set_min_diversity()
 
    

    


@section mser-vol Volumetric images


The code supports images of arbitrary dimension. For instance, it
is possible to find the MSER regions of volumetric images or time
sequences. See ::vl_mser_new() for further details


@section mser-ell Ellipsoids


Usually extremal regions are returned as a set of ellipsoids
fitted to the actual regions (which have arbitrary shape). The fit
is done by calculating the mean and variance of the pixels
composing the region:
@f[
\mu_l = \frac{1}{|R_l|}\sum_{x\in R_l}x,
\qquad
\Sigma_l = \frac{1}{|R_l|}\sum_{x\in R_l} (x-\mu_l)^\top(x-\mu_l)
@f]
Ellipsoids are fitted by ::vl_mser_ell_fit().  Notice that for a
n dimensional image, the mean has n components
and the variance has n(n+1)/2 independent components. The
total number of components is obtained by ::vl_mser_get_ell_dof()
and the total number of fitted ellipsoids by
::vl_mser_get_ell_num(). A matrix with an ellipsoid per column is
returned by ::vl_mser_get_ell(). The column is the stacking of the
mean and of the independent components of the variance, in the
order (1,1),(1,2),..,(1,n), (2,2),(2,3).... In the
calculations, the pixel coordinate @f$x=(x_1,...,x_n)@f$ use the
standard index order and ranges.


@section mser-algo Algorithm


The algorithm is quite efficient. While some details may be
tricky, the overall idea is easy to grasp.

- Pixels are sorted by increasing intensity.
- Pixels are added to a forest by increasing intensity. The forest has the
  following properties:
  - All the descendent of a certain pixels are subset of an extremal region.
  - All the extremal regions are the descendants of some pixels.
- Extremal regions are extracted from the region tree and the extremal regions tree is
  calculated.
- Stable regions are marked.
- Duplicates and other bad regions are removed.

@remark The extremal region tree which is calculated is a subset
of the actual extremal region tree. In particular, it does not
contain redundant entries extremal regions that coincide as
sets. So, for example, in the calculated extremal region tree, the
parent @f$R_q@f$ of an extremal region @f$R_{l}@f$ may or may
not correspond to @f$R_{l+1}@f$, depending whether
@f$q\leq l+1@f$ or not. These subtleties are important when
calculating the stability tests.

**/

#include "mser.h"
#include
#include
#include

/** -------------------------------------------------------------------
 ** @brief Advance N-dimensional subscript
 **
 ** The function increments by one the subscript @a subs indexing an
 ** array the @a ndims dimensions @a dims.
 **
 ** @param ndims number of dimensions.
 ** @param dims dimensions.
 ** @param subs subscript to advance.
 **/

VL_INLINE void
adv(int ndims, int const *dims, int *subs)
{
  int d = 0 ;
  while(d < ndims) {
    if( ++subs[d]  < dims[d] ) return ;
    subs[d++] = 0 ;
  }
}

/** -------------------------------------------------------------------
 ** @brief Climb the region forest to reach aa root
 **
 ** The function climbs the regions forest @a r starting from the node
 ** @a idx to the corresponding root.
 **
 ** To speed-up the operation, the function uses the
 ** VlMserReg::shortcut field to quickly jump to the root. After the
 ** root is reached, all the used shortcut are updated.
 **
 ** @param r regions' forest.
 ** @param idx stating node.
 ** @return index of the reached root.
 **/

VL_INLINE vl_uint
climb (VlMserReg* r, vl_uint idx)
{

  vl_uint prev_idx = idx ;
  vl_uint next_idx ;
  vl_uint root_idx ;

  /* move towards root to find it */
  while (1) {

    /* next jump to the root */
    next_idx = r [idx] .shortcut ;

    /* recycle shortcut to remember how we came here */
    r [idx] .shortcut = prev_idx ;

    /* stop if the root is found */
    if( next_idx == idx ) break ;

    /* next guy */
    prev_idx = idx ;
    idx      = next_idx ;
  }

  root_idx = idx ;

  /* move backward to update shortcuts */
  while (1) {

    /* get previously visited one */
    prev_idx = r [idx] .shortcut ;

    /* update shortcut to point to the new root */
    r [idx] .shortcut = root_idx ;

    /* stop if the first visited node is reached */
    if( prev_idx == idx ) break ;

    /* next guy */
    idx = prev_idx ;
  }

  return root_idx ;
}

/** -------------------------------------------------------------------
 ** @brief Create a new MSER filter
 **
 ** Initializes a new MSER filter for images of the specified
 ** dimensions. Images are @a ndims -dimensional arrays of dimensions
 ** @a dims.
 **
 ** @param ndims number of dimensions.
 ** @param dims  dimensions.
 **/
VL_EXPORT
VlMserFilt*
vl_mser_new (int ndims, int const* dims)
{
  VlMserFilt* f ;
  int *strides, k ;

  f = vl_calloc (sizeof(VlMserFilt), 1) ;

  f-> ndims   = ndims ;
  f-> dims    = vl_malloc (sizeof(int) * ndims) ;
  f-> subs    = vl_malloc (sizeof(int) * ndims) ;
  f-> dsubs   = vl_malloc (sizeof(int) * ndims) ;
  f-> strides = vl_malloc (sizeof(int) * ndims) ;

  /* shortcuts */
  strides = f-> strides ;

  /* copy dims to f->dims */
  for(k = 0 ; k < ndims ; ++k) {
    f-> dims [k] = dims [k] ;
  }

  /* compute strides to move into the N-dimensional image array */
  strides [0] = 1 ;
  for(k = 1 ; k < ndims ; ++k) {
    strides [k] = strides [k-1] * dims [k-1] ;
  }

  /* total number of pixels */
  f-> nel = strides [ndims-1] * dims [ndims-1] ;

  /* dof of ellipsoids */
  f-> dof = ndims * (ndims + 1) / 2 + ndims ;

  /* more buffers */
  f-> perm   = vl_malloc (sizeof(vl_uint)   * f-> nel) ;
  f-> joins  = vl_malloc (sizeof(vl_uint)   * f-> nel) ;
  f-> r      = vl_malloc (sizeof(VlMserReg) * f-> nel) ;

  f-> er     = 0 ;
  f-> rer    = 0 ;
  f-> mer    = 0 ;
  f-> rmer   = 0 ;
  f-> ell    = 0 ;
  f-> rell   = 0 ;

  /* other parameters */
  f-> delta         = 5 ;
  f-> max_area      = 0.75 ;
  f-> min_area      = 3.0 / f-> nel ;
  f-> max_variation = 0.25 ;
  f-> min_diversity = 0.2 ;

  return f ;
}

/** -------------------------------------------------------------------
 ** @brief Delete MSER filter
 **
 ** The function releases the MSER filter @a f and all its resources.
 **
 ** @param f MSER filter to be deleted.
 **/
VL_EXPORT
void
vl_mser_delete (VlMserFilt* f)
{
  if(f) {
    if(f-> acc   )  vl_free( f-> acc    ) ;
    if(f-> ell   )  vl_free( f-> ell    ) ;

    if(f-> er    )  vl_free( f-> er     ) ;
    if(f-> r     )  vl_free( f-> r      ) ;
    if(f-> joins )  vl_free( f-> joins  ) ;
    if(f-> perm  )  vl_free( f-> perm   ) ;

    if(f-> strides) vl_free( f-> strides) ;
    if(f-> dsubs  ) vl_free( f-> dsubs  ) ;
    if(f-> subs   ) vl_free( f-> subs   ) ;
    if(f-> dims   ) vl_free( f-> dims   ) ;

    if(f-> mer    ) vl_free( f-> mer    ) ;
    vl_free (f) ;
  }
}


/** -------------------------------------------------------------------
 ** @brief Process image
 **
 ** The functions calculates the Maximally Stable Extremal Regions
 ** (MSERs) of image @a im using the MSER filter @a f.
 **
 ** The filter @a f must have been initialized to be compatible with
 ** the dimensions of @a im.
 **
 ** @param f MSER filter.
 ** @param im image data.
 **/
VL_EXPORT
void
vl_mser_process (VlMserFilt* f, vl_mser_pix const* im)
{
  /* shortcuts */
  vl_uint        nel     = f-> nel  ;
  vl_uint       *perm    = f-> perm ;
  vl_uint       *joins   = f-> joins ;
  int            ndims   = f-> ndims ;
  int           *dims    = f-> dims ;
  int           *subs    = f-> subs ;
  int           *dsubs   = f-> dsubs ;
  int           *strides = f-> strides ;
  VlMserReg     *r       = f-> r ;
  VlMserExtrReg *er      = f-> er ;
  vl_uint       *mer     = f-> mer ;
  int            delta   = f-> delta ;

  int njoins = 0 ;
  int ner    = 0 ;
  int nmer   = 0 ;
  int nbig   = 0 ;
  int nsmall = 0 ;
  int nbad   = 0 ;
  int ndup   = 0 ;

  int i, j, k ;

  /* delete any previosuly computed ellipsoid */
  f-> nell = 0 ;

  /* -----------------------------------------------------------------
   *                                          Sort pixels by intensity
   * -------------------------------------------------------------- */

  {
    vl_uint buckets [ VL_MSER_PIX_MAXVAL ] ;

    /* clear buckets */
    memset (buckets, 0, sizeof(vl_uint) * VL_MSER_PIX_MAXVAL ) ;

    /* compute bucket size (how many pixels for each intensity
       value) */
    for(i = 0 ; i < (int) nel ; ++i) {
      vl_mser_pix v = im [i] ;
      ++ buckets [v] ;
    }

    /* cumulatively add bucket sizes */
    for(i = 1 ; i < VL_MSER_PIX_MAXVAL ; ++i) {
      buckets [i] += buckets [i-1] ;
    }

    /* empty buckets computing pixel ordering */
    for(i = nel ; i >= 1 ; ) {
      vl_mser_pix v = im [ --i ] ;
      vl_uint j = -- buckets [v] ;
      perm [j] = i ;
    }
  }

  /* initialize the forest with all void nodes */
  for(i = 0 ; i < (int) nel ; ++i) {
    r [i] .parent = VL_MSER_VOID_NODE ;
  }

  /* -----------------------------------------------------------------
   *                        Compute regions and count extremal regions
   * -------------------------------------------------------------- */
  /*
     In the following:

     idx    : index of the current pixel
     val    : intensity of the current pixel
     r_idx  : index of the root of the current pixel
     n_idx  : index of the neighbors of the current pixel
     nr_idx : index of the root of the neighbor of the current pixel

  */

  /* process each pixel by increasing intensity */
  for(i = 0 ; i < (int) nel ; ++i) {

    /* pop next node xi */
    vl_uint     idx = perm [i] ;
    vl_mser_pix val = im [idx] ;
    vl_uint     r_idx ;

    /* add the pixel to the forest as a root for now */
    r [idx] .parent   = idx ;
    r [idx] .shortcut = idx ;
    r [idx] .area     = 1 ;
    r [idx] .height   = 1 ;

    r_idx = idx ;

    /* convert the index IDX into the subscript SUBS; also initialize
       DSUBS to (-1,-1,...,-1) */
    {
      vl_uint temp = idx ;
      for(k = ndims - 1 ; k >= 0 ; --k) {
        dsubs [k] = -1 ;
        subs  [k] = temp / strides [k] ;
        temp      = temp % strides [k] ;
      }
    }

    /* examine the neighbors of the current pixel */
    while (1) {
      vl_uint n_idx = 0 ;
      vl_bool good = 1 ;

      /*
         Compute the neighbor subscript as NSUBS+SUB, the
         corresponding neighbor index NINDEX and check that the
         neighbor is within the image domain.
      */
      for(k = 0 ; k < ndims && good ; ++k) {
        int temp  = dsubs [k] + subs [k] ;
        good     &= (0 <= temp) && (temp < dims [k]) ;
        n_idx    += temp * strides [k] ;
      }

      /*
         The neighbor should be processed if the following conditions
         are met:

         1. The neighbor is within image boundaries.

         2. The neighbor is indeed different from the current node
            (the opposite happens when DSUB=(0,0,...,0)).

         3. The neighbor is already in the forest, meaning that it has
            already been processed.
      */
      if (good &&
          n_idx != idx &&
          r [n_idx] .parent != VL_MSER_VOID_NODE ) {

        vl_mser_pix nr_val = 0 ;
        vl_uint     nr_idx = 0 ;
        int         hgt   = r [ r_idx] .height ;
        int         n_hgt = r [nr_idx] .height ;

        /*
          Now we join the two subtrees rooted at

           R_IDX = ROOT(  IDX)
          NR_IDX = ROOT(N_IDX).

          Note that R_IDX = ROOT(IDX) might change as we process more
          neighbors, so we need keep updating it.
        */

         r_idx = climb(r,   idx) ;
        nr_idx = climb(r, n_idx) ;

        /*
          At this point we have three possibilities:

          (A) ROOT(IDX) == ROOT(NR_IDX). In this case the two trees
              have already been joined and we do not do anything.

          (B) I(ROOT(IDX)) == I(ROOT(NR_IDX)). In this case the pixel
              IDX is extending an extremal region with the same
              intensity value. Since ROOT(NR_IDX) will NOT be an
              extremal region of the full image, ROOT(IDX) can be
              safely added as children of ROOT(NR_IDX) if this
              reduces the height according to the union rank
              heuristic.

          (C) I(ROOT(IDX)) > I(ROOT(NR_IDX)). In this case the pixel
              IDX is starting a new extremal region. Thus ROOT(NR_IDX)
              WILL be an extremal region of the final image and the
              only possibility is to add ROOT(NR_IDX) as children of
              ROOT(IDX), which becomes parent.
        */

        if( r_idx != nr_idx ) { /* skip if (A) */

          nr_val = im [nr_idx] ;

          if( nr_val == val && hgt < n_hgt ) {

            /* ROOT(IDX) becomes the child */
            r [r_idx]  .parent   = nr_idx ;
            r [r_idx]  .shortcut = nr_idx ;
            r [nr_idx] .area    += r [r_idx] .area ;
            r [nr_idx] .height   = VL_MAX(n_hgt, hgt+1) ;

            joins [njoins++] = r_idx ;

          } else {

            /* cases ROOT(IDX) becomes the parent */
            r [nr_idx] .parent   = r_idx ;
            r [nr_idx] .shortcut = r_idx ;
            r [r_idx]  .area    += r [nr_idx] .area ;
            r [r_idx]  .height   = VL_MAX(hgt, n_hgt + 1) ;

            joins [njoins++] = nr_idx ;

            /* count if extremal */
            if (nr_val != val) ++ ner ;

          } /* check b vs c */
        } /* check a vs b or c */
      } /* neighbor done */

      /* move to next neighbor */
      k = 0 ;
      while(++ dsubs [k] > 1) {
        dsubs [k++] = -1 ;
        if(k == ndims) goto done_all_neighbors ;
      }
    } /* next neighbor */
  done_all_neighbors : ;
  } /* next pixel */

  /* the last root is extremal too */
  ++ ner ;

  /* save back */
  f-> njoins = njoins ;

  f-> stats. num_extremal = ner ;

  /* -----------------------------------------------------------------
   *                                          Extract extremal regions
   * -------------------------------------------------------------- */

  /*
     Extremal regions are extracted and stored into the array ER.  The
     structure R is also updated so that .SHORTCUT indexes the
     corresponding extremal region if any (otherwise it is set to
     VOID).
  */

  /* make room */
  if (f-> rer < ner) {
    if (er) vl_free (er) ;
    f->er  = er = vl_malloc (sizeof(VlMserExtrReg) * ner) ;
    f->rer = ner ;
  } ;

  /* save back */
  f-> nmer = ner ;

  /* count again */
  ner = 0 ;

  /* scan all regions Xi */
  for(i = 0 ; i < (int) nel ; ++i) {

    /* pop next node xi */
    vl_uint     idx = perm [i] ;

    vl_mser_pix val   = im [idx] ;
    vl_uint     p_idx = r  [idx] .parent ;
    vl_mser_pix p_val = im [p_idx] ;

    /* is extremal ? */
    vl_bool is_extr = (p_val > val) || idx == p_idx ;

    if( is_extr ) {

      /* if so, add it */
      er [ner] .index      = idx ;
      er [ner] .parent     = ner ;
      er [ner] .value      = im [idx] ;
      er [ner] .area       = r  [idx] .area ;

      /* link this region to this extremal region */
      r [idx] .shortcut = ner ;

      /* increase count */
      ++ ner ;
    } else {
      /* link this region to void */
      r [idx] .shortcut =   VL_MSER_VOID_NODE ;
    }
  }

  /* -----------------------------------------------------------------
   *                                   Link extremal regions in a tree
   * -------------------------------------------------------------- */

  for(i = 0 ; i < ner ; ++i) {

    vl_uint idx = er [i] .index ;

    do {
      idx = r[idx] .parent ;
    } while (r[idx] .shortcut == VL_MSER_VOID_NODE) ;

    er[i] .parent   = r[idx] .shortcut ;
    er[i] .shortcut = i ;
  }

  /* -----------------------------------------------------------------
   *                            Compute variability of +DELTA branches
   * -------------------------------------------------------------- */
  /* For each extremal region Xi of value VAL we look for the biggest
   * parent that has value not greater than VAL+DELTA. This is dubbed
   * `top parent'. */

  for(i = 0 ; i < ner ; ++i) {

    /* Xj is the current region the region and Xj are the parents */
    int     top_val = er [i] .value + delta ;
    int     top     = er [i] .shortcut ;

    /* examine all parents */
    while (1) {
      int next     = er [top]  .parent ;
      int next_val = er [next] .value ;

      /* Break if:
       * - there is no node above the top or
       * - the next node is above the top value.
       */
      if (next == top || next_val > top_val) break ;

      /* so next could be the top */
      top = next ;
    }

    /* calculate branch variation */
    {
      int area     = er [i  ] .area ;
      int area_top = er [top] .area ;
      er [i] .variation  = (float) (area_top - area) / area ;
      er [i] .max_stable = 1 ;
    }

    /* Optimization: since extremal regions are processed by
     * increasing intensity, all next extremal regions being processed
     * have value at least equal to the one of Xi. If any of them has
     * parent the parent of Xi (this comprises the parent itself), we
     * can safely skip most intermediate node along the branch and
     * skip directly to the top to start our search. */
    {
      int parent = er [i] .parent ;
      int curr   = er [parent] .shortcut ;
      er [parent] .shortcut =  VL_MAX (top, curr) ;
    }
  }

  /* -----------------------------------------------------------------
   *                                  Select maximally stable branches
   * -------------------------------------------------------------- */

  nmer = ner ;
  for(i = 0 ; i < ner ; ++i) {
    vl_uint    parent = er [i     ] .parent ;
    vl_mser_pix   val = er [i     ] .value ;
    float     var = er [i     ] .variation ;
    vl_mser_pix p_val = er [parent] .value ;
    float   p_var = er [parent] .variation ;
    vl_uint     loser ;

    /*
       Notice that R_parent = R_{l+1} only if p_val = val + 1. If not,
       this and the parent region coincide and there is nothing to do.
    */
    if(p_val > val + 1) continue ;

    /* decide which one to keep and put that in loser */
    if(var < p_var) loser = parent ; else loser = i ;

    /* make loser NON maximally stable */
    if(er [loser] .max_stable) {
      -- nmer ;
      er [loser] .max_stable = 0 ;
    }
  }

  f-> stats. num_unstable = ner - nmer ;

  /* -----------------------------------------------------------------
   *                                                 Further filtering
   * -------------------------------------------------------------- */
  /* It is critical for correct duplicate detection to remove regions
   * from the bottom (smallest one first).                          */
  {
    float max_area = (float) f-> max_area * nel ;
    float min_area = (float) f-> min_area * nel ;
    float max_var  = (float) f-> max_variation ;
    float min_div  = (float) f-> min_diversity ;

    /* scan all extremal regions (intensity value order) */
    for(i = ner-1 ; i >= 0L  ; --i) {

      /* process only maximally stable extremal regions */
      if (! er [i] .max_stable) continue ;

      if (er [i] .variation >= max_var ) { ++ nbad ;   goto remove ; }
      if (er [i] .area      >  max_area) { ++ nbig ;   goto remove ; }
      if (er [i] .area      <  min_area) { ++ nsmall ; goto remove ; }

      /*
       * Remove duplicates
       */
      if (min_div < 1.0) {
        vl_uint   parent = er [i] .parent ;
        int       area, p_area ;
        float div ;

        /* check all but the root mser */
        if((int) parent != i) {

          /* search for the maximally stable parent region */
          while(! er [parent] .max_stable) {
            vl_uint next = er [parent] .parent ;
            if(next == parent) break ;
            parent = next ;
          }

          /* Compare with the parent region; if the current and parent
           * regions are too similar, keep only the parent. */
          area    = er [i]      .area ;
          p_area  = er [parent] .area ;
          div     = (float) (p_area - area) / (float) p_area ;

          if (div < min_div) { ++ ndup ; goto remove ; }
        } /* remove dups end */

      }
      continue ;
    remove :
      er [i] .max_stable = 0 ;
      -- nmer ;
    } /* check next region */

    f-> stats .num_abs_unstable = nbad ;
    f-> stats .num_too_big      = nbig ;
    f-> stats .num_too_small    = nsmall ;
    f-> stats .num_duplicates   = ndup ;
  }
  /* -----------------------------------------------------------------
   *                                                   Save the result
   * -------------------------------------------------------------- */

  /* make room */
  if (f-> rmer < nmer) {
    if (mer) vl_free (mer) ;
    f->mer  = mer = vl_malloc( sizeof(vl_uint) * nmer) ;
    f->rmer = nmer ;
  }

  /* save back */
  f-> nmer = nmer ;

  j = 0 ;
  for (i = 0 ; i < ner ; ++i) {
    if (er [i] .max_stable) mer [j++] = er [i] .index ;
  }
}

/** -------------------------------------------------------------------
 ** @brief Fit ellipsoids
 **
 ** @param f MSER filter.
 **
 ** @sa @ref mser-ell
 **/

VL_EXPORT
void
vl_mser_ell_fit (VlMserFilt* f)
{
  /* shortcuts */
  int                nel = f-> nel ;
  int                dof = f-> dof ;
  int              *dims = f-> dims ;
  int              ndims = f-> ndims ;
  int              *subs = f-> subs ;
  int             njoins = f-> njoins ;
  vl_uint         *joins = f-> joins ;
  VlMserReg           *r = f-> r ;
  vl_uint           *mer = f-> mer ;
  int               nmer = f-> nmer ;
  vl_mser_acc       *acc = f-> acc ;
  vl_mser_acc       *ell = f-> ell ;

  int d, index, i, j ;

  /* already fit ? */
  if (f->nell == f->nmer) return ;

  /* make room */
  if (f->rell < f->nmer) {
    if (f->ell) vl_free (f->ell) ;
    f->ell  = vl_malloc (sizeof(float) * f->nmer * f->dof) ;
    f->rell = f-> nmer ;
  }

  if (f->acc == 0) {
    f->acc = vl_malloc (sizeof(float) * f->nel) ;
  }

  acc = f-> acc ;
  ell = f-> ell ;

  /* -----------------------------------------------------------------
   *                                                 Integrate moments
   * -------------------------------------------------------------- */

  /* for each dof */
  for(d = 0 ; d < f->dof ; ++d) {

    /* start from the upper-left pixel (0,0,...,0) */
    memset (subs, 0, sizeof(int) * ndims) ;

    /* step 1: fill acc pretending that each region has only one pixel */
    if(d < ndims) {
      /* 1-order ................................................... */

      for(index = 0 ; index < nel ; ++ index) {
        acc [index] = subs [d] ;
        adv(ndims, dims, subs) ;
      }
    }
    else {
      /* 2-order ................................................... */

      /* map the dof d to a second order moment E[x_i x_j] */
      i = d - ndims ;
      j = 0 ;
      while(i > j) {
        i -= j + 1 ;
        j ++ ;
      }
      /* initialize acc with  x_i * x_j */
      for(index = 0 ; index < nel ; ++ index){
        acc [index] = subs [i] * subs [j] ;
        adv(ndims, dims, subs) ;
      }
    }

    /* step 2: integrate */
    for(i = 0 ; i < njoins ; ++i) {
      vl_uint index  = joins [i] ;
      vl_uint parent = r [index] .parent ;
      acc [parent] += acc [index] ;
    }

    /* step 3: save back to ellpises */
    for(i = 0 ; i < nmer ; ++i) {
      vl_uint idx = mer [i] ;
      ell [d + dof*i] = acc [idx] ;
    }

  }  /* next dof */

  /* -----------------------------------------------------------------
   *                                           Compute central moments
   * -------------------------------------------------------------- */

  for(index = 0 ; index < nmer ; ++index) {
    float  *pt  = ell + index * dof ;
    vl_uint    idx  = mer [index] ;
    float  area = r [idx] .area ;

    for(d = 0 ; d < dof ; ++d) {

      pt [d] /= area ;

      if(d >= ndims) {
        /* remove squared mean from moment to get variance */
        i = d - ndims ;
        j = 0 ;
        while(i > j) {
          i -= j + 1 ;
          j ++ ;
        }
        pt [d] -= pt [i] * pt [j] ;
      }

    }
  }

  /* save back */
  f-> nell = nmer ;
}
vlfeat/vl/ikmeans_elkan.tc0000644000175000017500000002363512237004736014534 0ustar  dimadima/** @file ikmeans_elkan.tc
 ** @brief Integer K-Means - Elkan Algorithm - Definition
 ** @author Andrea Vedaldi
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/** @internal
 ** Square root of an integer value
 **
 ** @author Jim Ulery
 **/

static unsigned long
isqrt(unsigned long val) {
  unsigned long temp, g=0, b = 0x8000, bshft = 15;
  do {
    if (val >= (temp = (((g << 1) + b)<>= 1);
  return g;
}


/** @internal
 **
 ** Update inter cluster distance table.
 **/

static void
vl_ikm_elkan_update_inter_dist (VlIKMFilt *f)
{
  int
    i, k, kp,
    K = f-> K,
    M = f-> M ;
  vl_ikm_acc dist, delta ;

  /* inter cluster distances */
  for(k = 0 ; k < K ; ++ k) {
    for(kp = 0 ; kp < K ; ++ kp) {
      dist = 0 ;
      if (k != kp) {
        for(i = 0 ; i < M ; ++i) {
          delta  = f->centers [kp*M + i] - f->centers [k*M + i] ;
          dist  += delta * delta ;
        }
      }
      f->inter_dist [k*K + kp] = f->inter_dist [kp*K + k] = dist >> 2 ;
    }
  }
}

/** @internal
 ** @brief Helper function to initialize filter for Triangle algorithm
 ** @param f filter.
 **/

static void
vl_ikm_init_elkan (VlIKMFilt *f)
{
  if (f-> inter_dist) {
    vl_free (f->  inter_dist) ;
  }
  f-> inter_dist = vl_malloc (sizeof(vl_ikm_acc) * f->K*f->K) ;
  vl_ikm_elkan_update_inter_dist (f) ;
}


/** @internal
 ** @brief Elkan algorithm
 ** @param f     IKM quantizer.
 ** @param data  Data to quantize.
 ** @param N     Number of data elements.
 **/

static int
vl_ikm_train_elkan (VlIKMFilt* f, vl_uint8 const* data, int N)
{
  /* REMARK !! All distances are squared !! */
  int i,pass,c,cp,x,cx ;
  int dist_calc = 0 ;
  int
    K = f-> K,
    M = f-> M ;

  vl_ikm_acc  dist ;
  vl_ikm_acc *m_pt = vl_malloc(sizeof(vl_ikm_acc)* M*K) ; /* new centers (temp)    */
  vl_ikm_acc *u_pt = vl_malloc(sizeof(vl_ikm_acc)*   N) ; /* upper bound (may str) */
  char       *r_pt = vl_malloc(sizeof(char)      * 1*N) ; /* flag: u is strict     */
  vl_ikm_acc *s_pt = vl_malloc(sizeof(vl_ikm_acc)*   K) ; /* min cluster dist.     */
  vl_ikm_acc *l_pt = vl_malloc(sizeof(vl_ikm_acc)* N*K) ; /* lower bound           */
  vl_ikm_acc *d_pt = f-> inter_dist ;                     /* half inter clst dist  */
  vl_uint    *asgn = vl_malloc (sizeof(vl_uint) * N) ;
  vl_uint    *counts=vl_malloc (sizeof(vl_uint) * N) ;

  int done = 0 ;

  /* do passes */
  vl_ikm_elkan_update_inter_dist (f) ;

  /* init */
  memset(l_pt, 0, sizeof(vl_ikm_acc) * N*K ) ;
  memset(u_pt, 0, sizeof(vl_ikm_acc) * N   ) ;
  memset(r_pt, 0, sizeof(char)  * N   ) ;
  for(x = 0 ; x < N ; ++x) {
    vl_ikm_acc best_dist ;

    /* do first cluster `by hand' */
    dist_calc ++ ;
    for(dist = 0, i = 0 ; i < M ; ++i) {
      vl_ikm_acc delta = data[x*M + i] - f->centers[i] ;
      dist += delta*delta ;
    }
    cx        = 0 ;
    best_dist = dist ;
    l_pt[x]   = dist ;

    /* do other clusters */
    for(c = 1 ; c < K ; ++c) {
      if(d_pt[K*cx+c] < best_dist) {
        /* might need to be updated */

        dist_calc++ ;
        for(dist=0, i = 0 ; i < M ; ++i) {
          vl_ikm_acc delta = data[x*M + i] - f->centers[c*M + i] ;
          dist += delta*delta ;
        }

        /* lower bound */
        l_pt[N*c + x] = dist ;

        if(dist < best_dist) {
          best_dist = dist ;
          cx        = c ;
        }
      }
    }

    asgn[x] = cx ;
    u_pt[x] = best_dist ;
  }

  /* --------------------------------------------------------------------
   *                                                               Passes
   * ------------------------------------------------------------------ */

  for (pass = 0 ; 1 ; ++ pass) {

    /* ------------------------------------------------------------------
     *                                                 Re-calculate means
     * ---------------------------------------------------------------- */
    memset(m_pt,    0, sizeof(vl_ikm_acc) * M * K) ;
    memset(counts,  0, sizeof(vl_ikm_acc)     * K) ;

    /* accumulate */
    for(x = 0 ; x < N ; ++x) {
      int cx = asgn[x] ;
      ++ counts[ cx ] ;
      for(i = 0 ; i < M ; ++i) {
        m_pt[cx*M + i] += data[x*M + i] ;
      }
    }

    /* normalize */
    for(c = 0 ; c < K ; ++c) {
      vl_ikm_acc n = counts[c] ;
      if(n > 0) {
        for(i = 0 ; i < M ; ++i) {
          m_pt[c*M + i] /= n ;
        }
      } else {
        for(i = 0 ; i < M ; ++i) {
          /*m_pt[c*M + i] = data[pairs_pt[c].j*M + i] ;*/
        }
      }
    }

    /* ------------------------------------------------------------------
     *                                                      Update bounds
     * --------------------------------------------------------------- */
    for(c = 0 ; c < K ; ++c) {

      /* distance d(m(c),c) and update c */
      dist_calc++ ;
      for(dist = 0, i = 0 ; i < M ; ++i) {
        vl_ikm_acc delta = m_pt[c*M + i] - f->centers[c*M + i] ;
        f->centers[c*M + i] = m_pt[c*M +i] ;
        dist += delta*delta ;
      }
      for(x = 0 ; x < N ; ++x) {
        vl_ikm_acc lxc = l_pt[c*N + x] ;
        int cx  = (int) asgn[x] ;

        /* lower bound */
        if(dist < lxc) {
          lxc = (vl_ikm_acc) (lxc + dist - 2*(isqrt(lxc)+1)*(isqrt(dist)+1)) ;
        } else {
          lxc = 0 ;
        }
        l_pt[c*N + x]  = lxc ;

        /* upper bound */
        if(c == cx) {
          vl_ikm_acc ux = u_pt[x] ;
          u_pt[x] = (vl_ikm_acc) (ux + dist + 2 * (isqrt(ux)+1)*(isqrt(dist)+1)) ;
          r_pt[x] = 1 ;
        }
      }
    }

    /* inter cluster distances */
    for(c = 0 ; c < K ; ++c) {
      for(cp = 0 ; cp < K ; ++cp) {
        dist = 0 ;
        if( c != cp ) {
          dist_calc++;
          for(i = 0 ; i < M ; ++i) {
            vl_ikm_acc delta = f->centers[ cp*M + i ] - f->centers[ c*M + i ] ;
            dist += delta*delta ;
          }
        }
        d_pt[c*K+cp] = d_pt[cp*K+c] = dist>>2 ;
      }
    }

    /* closest cluster distance */
    for(c = 0  ; c < K ; ++c) {
      vl_ikm_acc best_dist = VL_BIG_INT ;
      for(cp = 0 ; cp < K ; ++cp) {
        dist = d_pt[c*K+cp] ;
        if(c != cp && dist < best_dist) best_dist = dist ;
      }
      s_pt[c] = best_dist >> 2 ;
    }


    /* ------------------------------------------------------------------
     * Assign data to centers
     * ---------------------------------------------------------------- */
    done = 1 ;
    for(x=0 ; x < N ; ++x) {
      int cx = (int) asgn[x] ;
      vl_ikm_acc ux = u_pt[x] ;

      /* ux is an upper bound of the distance of x to its
         current center cx. s_pt[cx] is half of the minum distance
         between the cluster cx and any other cluster center.  If
         ux <= s_pt[cx] then x remains attached to cx. */

      if(ux <= s_pt[cx])  continue ;

      for(c = 0 ; c < K ; ++c) {
        vl_ikm_acc dist = 0 ;
        /* so x might need to be re-associated from cx to c. We can
           exclude c if

           1 - cx = c (trivial) or
           2 - u(x) <= l(x,c)    as this implies d(x,cx) <= d(x,c) or
           3 - u(x) <= d(cx,c)/2 as this implies d(x,cx) <= d(x,c).
        */
        if(c  == cx               ||
           ux <= l_pt[N*c +  x]   ||
           ux <= d_pt[K*c + cx] )
          continue ;

        /* we need to make a true comparison */

        /* if u_pt[x] is stale (i.e. not strictly equal to
           d(x,cx)), then re-calcualte it. */
        if( r_pt[x] ) {
          dist_calc++;
          for(dist = 0, i = 0 ; i < M ; ++i) {
            vl_ikm_acc delta = data[ x*M + i ] - f->centers[ cx*M + i ] ;
            dist += delta*delta ;
          }
          ux = u_pt[x] = dist ;
          r_pt[x] = 0 ;

          /* now that u_pt[x] is updated, we check the conditions
             again */
          if(
             ux <= l_pt[N*c +  x]  ||
             ux <= d_pt[K*c + cx]  )
            continue ;
        }

        /* no way... we need to compute the distance d(x,c) */
        dist_calc++ ;
        for(dist = 0, i = 0 ; i < M ; ++i) {
          vl_ikm_acc delta = data[ x*M + i ] - f->centers[ c*M + i ] ;
          dist += delta*delta ;
        }

        l_pt[N*c + x] =  dist ;

        if( dist < ux ) {
          ux = u_pt[x] = dist ;
          /* r_pt[x] already 0 */
          asgn[x] = c ;
          done = 0 ;
        }
      }
    } /* next data point */

      /* stopping condition */
    if(done || pass == f->max_niters) {
      break ;
    }
  }

  vl_free (counts) ;
  vl_free (asgn) ;
  vl_free (l_pt) ;
  vl_free (s_pt) ;
  vl_free (r_pt) ;
  vl_free (u_pt) ;
  vl_free (m_pt) ;

  if (f-> verb) {
    VL_PRINTF ("ikm: Elkan algorithm: total iterations: %d\n", pass) ;
    VL_PRINTF ("ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f)\n",
               dist_calc, (float)N*K*(pass+2) / dist_calc -1) ;
  }
  return 0 ;
}

/** @internal
 ** @brief Elkan algorithm
 ** @param f    IKM quantizer.
 ** @param asgn Assignment of data to centers (out).
 ** @param data Data to quantize.
 ** @param N    Number of data elements.
 **/

static void
vl_ikm_push_elkan (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
{
  vl_uint i,c,cx,x,
    dist_calc = 0,
    K = f-> K,
    M = f-> M ;
  vl_ikm_acc dist, best_dist ;
  vl_ikm_acc *d_pt = f-> inter_dist ;

  /* assign data to centers */
  for(x=0 ; x < (vl_uint)N ; ++x) {
    best_dist = VL_BIG_INT ;
    cx = 0 ;

    for(c = 0 ; c < K ; ++c) {
      if(d_pt[K*cx+c] < best_dist) {
        /* might need to be updated */
        dist_calc ++ ;
        for(dist=0, i = 0 ; i < M ; ++i) {
          vl_ikm_acc delta = data[x*M + i] - f->centers[c*M + i] ;
          dist += delta*delta ;
        }

        /* u_pt is strict at the beginning */
        if(dist < best_dist) {
          best_dist = dist ;
          cx        = c ;
        }
      }
    }
    asgn [x] = cx ;
  }
}

/*
 * Local Variables: *
 * mode: C *
 * End: *
 */
vlfeat/vl/vlad.h0000644000175000017500000000240412237004745012471 0ustar  dimadima/** @file vlad.h
 ** @brief VLAD encoding (@ref vlad)
 ** @author David Novotny
 ** @author Andrea Vedaldi
 ** @see @ref vlad
 **/

/*
Copyright (C) 2013 David Novotny and Andera Vedaldi.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_VLAD_H
#define VL_VLAD_H

#include "generic.h"

/** @name VLAD options
 ** @{ */
#define VL_VLAD_FLAG_NORMALIZE_COMPONENTS (0x1 << 0)
#define VL_VLAD_FLAG_SQUARE_ROOT          (0x1 << 1)
#define VL_VLAD_FLAG_UNNORMALIZED         (0x1 << 2)
#define VL_VLAD_FLAG_NORMALIZE_MASS       (0x1 << 3)

/** @def VL_VLAD_FLAG_NORMALIZE_COMPONENTS
 ** @brief Normalize each VLAD component individually.
 **/

/** @def VL_VLAD_FLAG_SQUARE_ROOT
 ** @brief Use signed squared-root.
 **/

/** @def VL_VLAD_FLAG_UNNORMALIZED
 ** @brief Do not globally normalize the VLAD descriptor.
 **/

/** @def VL_VLAD_FLAG_NORMALIZE_MASS
 ** @brief Normalize each component by the number of features assigned to it.
 **/
/** @} */

VL_EXPORT void vl_vlad_encode
  (void * enc, vl_type dataType,
   void const * means, vl_size dimension, vl_size numClusters,
   void const * data, vl_size numData,
   void const * assignments,
   int flags) ;

/* VL_VLAD_H */
#endif
vlfeat/vl/kmeans.c0000644000175000017500000021351012237004745013016 0ustar  dimadima/** @file kmeans.c
 ** @brief K-means - Declaration
 ** @author Andrea Vedaldi, David Novotny
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
Copyright (C) 2013 Andrea Vedaldi and David Novotny.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

/**

@page kmeans K-means clustering
@author Andrea Vedaldi
@author David Novotny
@tableofcontents


@ref kmeans.h implements a number of algorithm for **K-means
quantization**: Lloyd @cite{lloyd82least}, an accelerated version by
Elkan @cite{elkan03using}, and a large scale algorithm based on
Approximate Nearest Neighbors (ANN). All algorithms support @c float
or @c double data and can use the $l^1$ or the $l^2$ distance for
clustering. Furthermore, all algorithms can take advantage of multiple
CPU cores.

Please see @subpage kmeans-fundamentals for a technical description of
K-means and of the algorithms implemented here.


@section kmeans-starting Getting started


The goal of K-means is to partition a dataset into $K$
“compact” clusters. The following example demonstrates
using @ref kmeans.h in the C programming language to partition @c
numData @c float vectors into compute @c numCenters clusters using
Lloyd's algorithm:

@code
#include 
double energy ;
double * centers ;

// Use float data and the L2 distance for clustering
KMeans * kmeans = vl_kmeans_new (VLDistanceL2, VL_TYPE_FLOAT) ;

// Use Lloyd algorithm
vl_kmeans_set_algorithm (kmeans, VlKMeansLloyd) ;

// Initialize the cluster centers by randomly sampling the data
vl_kmeans_init_centers_with_rand_data (kmeans, data, dimension, numData, numCenters) ;

// Run at most 100 iterations of cluster refinement using Lloyd algorithm
vl_kmeans_set_max_num_iterations (kmeans, 100) ;
vl_kmeans_refine_centers (kmeans, data, numData) ;

// Obtain the energy of the solution
energy = vl_kmeans_get_energy(kmeans) ;

// Obtain the cluster centers
centers = vl_kmeans_get_centers(kmeans) ;
@endcode

Once the centers have been obtained, new data points can be assigned
to clusters by using the ::vl_kmeans_quantize function:

@code
vl_uint32 * assignments = vl_malloc(sizeof(vl_uint32) * numData) ;
float * distances = vl_malloc(sizeof(float) * numData) ;
vl_kmeans_quantize(kmeans, assignments, distances, data, numData) ;
@endcode

Alternatively, one can directly assign new pointers to the closest
centers, without bothering with a ::VlKMeans object.

There are several considerations that may impact the performance of
KMeans. First, since K-means is usually based local optimization
algorithm, the **initialization method** is important. The following
initialization methods are supported:

Method         | Function                                | Description
---------------|-----------------------------------------|-----------------------------------------------
Random samples | ::vl_kmeans_init_centers_with_rand_data | Random data points
K-means++      | ::vl_kmeans_init_centers_plus_plus      | Random selection biased towards diversity
Custom         | ::vl_kmeans_set_centers                 | Choose centers (useful to run quantization only)

See @ref kmeans-init for further details. The initialization methods
use a randomized selection of the data points; the random number
generator init is controlled by ::vl_rand_init.

The second important choice is the **optimization algorithm**. The
following optimization algorithms are supported:

Algorithm   | Symbol           | See               | Description
------------|------------------|-------------------|-----------------------------------------------
Lloyd       | ::VlKMeansLloyd  | @ref kmeans-lloyd | Alternate EM-style optimization
Elkan       | ::VlKMeansElkan  | @ref kmeans-elkan | A speedup using triangular inequalities
ANN         | ::VlKMeansANN    | @ref kmeans-ann   | A speedup using approximated nearest neighbors

See the relative sections for further details. These algorithm are
iterative, and stop when either a **maximum number of iterations**
(::vl_kmeans_set_max_num_iterations) is reached, or when the energy
changes sufficiently slowly in one iteration (::vl_kmeans).


All the three algorithms support multithreaded computations. The number
of threads used is usually controlled globally by ::vl_set_num_threads.
**/

/**

@page kmeans-fundamentals K-means fundamentals
@tableofcontents


Given $n$ points $\bx_1,\dots,\bx_n \in \real^d$, the goal of K-means
is find $K$ `centers` $\bc_1,\dots,\bc_m \in \real^d$ and
`assignments` $q_1,\dots,q_n \in \{1,\dots,K\}$ of the points to the
centers such that the sum of distances

\[
 E(\bc_1,\dots,\bc_k,q_1,\dots,q_n)
 = \sum_{i=1}^n \|\bx_i - \bc_{q_i} \|_p^p
\]

is minimized. $K$-means is obtained for the case $p=2$ ($l^2$ norm),
because in this case the optimal centers are the means of the input
vectors assigned to them. Here the generalization $p=1$ ($l^1$ norm)
will also be considered.

Up to normalization, the K-means objective $E$ is also the average
reconstruction error if the original points are approximated with the
cluster centers. Thus K-means is used not only to group the input
points into cluster, but also to `quantize` their values.

K-means is widely used in computer vision, for example in the
construction of vocabularies of visual features (visual words). In
these applications the number $n$ of points to cluster and/or the
number $K$ of clusters is often large. Unfortunately, minimizing the
objective $E$ is in general a difficult combinatorial problem, so
locally optimal or approximated solutions are sought instead.

The basic K-means algorithm alternate between re-estimating the
centers and the assignments (@ref kmeans-lloyd). Combined with a good
initialization strategy (@ref kmeans-init) and, potentially, by
re-running the optimization from a number of randomized starting
states, this algorithm may attain satisfactory solutions in practice.

However, despite its simplicity, Lloyd's algorithm is often too slow.
A good replacement is Elkan's algorithm (@ref kmeans-elkan), which
uses the triangular inequality to cut down significantly the cost of
Lloyd's algorithm. Since this algorithm is otherwise equivalent, it
should often be preferred.

For very large problems (millions of point to clusters and hundreds,
thousands, or more clusters to find), even Elkan's algorithm is not
sufficiently fast. In these cases, one can resort to a variant of
Lloyd's algorithm that uses an approximated nearest neighbors routine
(@ref kmeans-ann).


@section kmeans-init Initialization methods


All the $K$-means algorithms considered here find locally optimal
solutions; as such the way they are initialized is important. @ref
kmeans.h supports the following initialization algorithms:

@par Random data samples

The simplest initialization method is to sample $K$ points at random
from the input data and use them as initial values for the cluster
centers.

@par K-means++

@cite{arthur07k-means} proposes a randomized initialization of the
centers which improves upon random selection. The first center $\bc_1$
is selected at random from the data points $\bx_1, \dots, \bx_n $ and
the distance from this center to all points $\|\bx_i - \bc_1\|_p^p$ is
computed. Then the second center $\bc_2$ is selected at random from
the data points with probability proportional to the distance. The
procedure is repeated to obtain the other centers by using the minimum
distance to the centers collected so far.


@section kmeans-lloyd Lloyd's algorithm


The most common K-means method is Lloyd's algorithm
@cite{lloyd82least}. This algorithm is based on the observation that,
while jointly optimizing clusters and assignment is difficult,
optimizing one given the other is easy. Lloyd's algorithm alternates
the steps:

1. **Quantization.** Each point $\bx_i$ is reassigned to the center
   $\bc_{q_j}$ closer to it. This requires finding for each point the
   closest among $K$ other points, which is potentially slow.
2. **Center estimation.** Each center $\bc_q$ is updated to minimize
   its average distances to the points assigned to it. It is easy to
   show that the best center is the mean or median of the points,
   respectively if the $l^2$ or $l^1$ norm is considered.

A naive implementation of the assignment step requires $O(dnK)$
operations, where $d$ is the dimensionality of the data, $n$ the
number of data points, and $K$ the number of centers. Updating the
centers is much cheaper: $O(dn)$ operations suffice to compute the $K$
means and a slightly higher cost is required for the medians. Clearly,
the bottleneck is the assignment computation, and this is what the
other K-means algorithm try to improve.

During the iterations, it can happen that a cluster becomes empty. In
this case, K-means automatically **“restarts” the
cluster** center by selecting a training point at random.


@section kmeans-elkan Elkan's algorithm


Elkan's algorithm @cite{elkan03using} is a variation of Lloyd
alternate optimization algorithm (@ref kmeans-lloyd) that uses the
triangular inequality to avoid many distance calculations when
assigning points to clusters. While much faster than Lloyd, Elkan's
method uses storage proportional to the umber of clusters by data
points, which makes it unpractical for a very large number of
clusters.

The idea of this algorithm is that, if a center update does not move
them much, then most of the point-to-center computations can be
avoided when the point-to-center assignments are recomputed. To detect
which distances need evaluation, the triangular inequality is used to
lower and upper bound distances after a center update.

Elkan algorithms uses two key observations. First, one has

\[
\|\bx_i - \bc_{q_i}\|_p \leq \|\bc - \bc_{q_i}\|_p / 2
\quad\Rightarrow\quad
\|\bx_i - \bc_{q_i}\|_p \leq \|\bx_i - \bc\|_p.
\]

Thus if the distance between $\bx_i$ and its current center
$\bc_{q_i}$ is less than half the distance of the center $\bc_{q_i}$
to another center $\bc$, then $\bc$ can be skipped when the new
assignment for $\bx_i$ is searched. Checking this requires keeping
track of all the inter-center distances, but centers are typically a
small fraction of the training data, so overall this can be a
significant saving. In particular, if this condition is satisfied for
all the centers $\bc \not= \bc_{q_i}$, the point $\bx_i$ can be
skipped completely. Furthermore, the condition can be tested also
based on an upper bound $UB_i$ of $\|\bx_i - \bc_{q_i}\|_p$.

Second, if a center $\bc$ is updated to $\hat{\bc}$, then the new
distance from $\bx$ to $\hat{\bc}$ is bounded from below and above by

\[
\|\bx - \bc\|_p - \|bc - \hat\bc\|_p
\leq
\|\bx - \hat{\bc}\|_p
\leq
\|\bx - \hat{\bc}\|_p + \|\bc + \hat{\bc}\|_p.
\]

This allows to maintain an upper bound on the distance of $\bx_i$ to
its current center $\bc_{q_i}$ and a lower bound to any other center
$\bc$:

@f{align*}
  UB_i      & \leftarrow UB_i + \|\bc_{q_i} - \hat{\bc}_{q_i} \|_p \\
  LB_i(\bc) & \leftarrow LB_i(\bc) - \|\bc -\hat \bc\|_p.
@f}

Thus the K-means algorithm becomes:

1.  **Initialization.** Compute $LB_i(\bc) = \|\bx_i -\hat \bc\|_p$ for
    all points and centers.  Find the current assignments $q_i$ and
    bounds $UB_i$ by finding the closest centers to each point: $UB_i =
    \min_{\bc} LB_i(\bc)$.
2.  **Center estimation.**
    1. Recompute all the centers based on the new means; call the updated
       version $\hat{\bc}$.
    2. Update all the bounds based on the distance $\|\bc - \hat\bc\|_p$
       as explained above.
    3. Set $\bc \leftarrow \hat\bc$ for all the centers and go to the next
       iteration.
3.  **Quantization.**
    1. Skip any point $\bx_i$ such that $UB_i \leq \frac{1}{2} \|\bc_{q_i} - \bc\|_p$
       for all centers $\bc \not= \bc_{q_i}$.
    2. For each remaining point $\bx_i$ and center $\bc \not= \bc_{q_i}$:
       1. Skip $\bc$ if
          \[
           UB_i \leq \frac{1}{2} \| \bc_{q_i} - \bc \|
           \quad\text{or}\quad
           UB_i \leq LB_i(\bc).
           \]
          The first condition reflects the first observation above; the
          second uses the bounds to decide if $\bc$ can be closer than the
          current center $\bc_{q_i}$ to the point $\bx_i$. If the center
          cannot be skipped, continue as follows.
       3. Skip $\bc$ if the condition above is satisfied after making the
          upper bound tight:
          \[
          UB_i = LB_i(\bc_{q_i}) = \| \bx_i - \bc_{q_i} \|_p.
          \]
          Note that the latter calculation can be done only once for $\bx_i$.
          If the center cannot be skipped still, continue as follows.
       4. Tighten the lower bound too:
          \[
          LB_i(\bc) = \| \bx_i - \bc \|_p.
          \]
          At this point both $UB_i$ and $LB_i(\bc)$ are tight. If $LB_i <
          UB_i$, then the point $\bx_i$ should be reassigned to
          $\bc$. Update $q_i$ to the index of center $\bc$ and reset $UB_i
          = LB_i(\bc)$.


@section kmeans-ann ANN algorithm


The *Approximate Nearest Neighbor* (ANN) K-means algorithm
@cite{beis97shape} @cite{silpa-anan08optimised} @cite{muja09fast} is a
variant of Lloyd's algorithm (@ref kmeans-lloyd) uses a best-bin-first
randomized KD-tree algorithm to approximately (and quickly) find the
closest cluster center to each point. The KD-tree implementation is
based on @ref kdtree.

The algorithm can be summarized as follows:

1. **Quantization.** Each point $\bx_i$ is reassigned to the center
   $\bc_{q_j}$ closer to it. This starts by indexing the $K$ centers
   by a KD-tree and then using the latter to quickly find the closest
   center for every training point. The search is approximated to
   further improve speed. This opens up the possibility that a data
   point may receive an assignment that is *worse* than the current
   one. This is avoided by checking that the new assignment estimated
   by using ANN is an improvement; otherwise the old assignment is
   kept.
2. **Center estimation.** Each center $\bc_q$ is updated to minimize
   its average distances to the points assigned to it. It is easy to
   show that the best center is the mean or median of the points,
   respectively if the $l^2$ or $l^1$ norm is considered.

The key is to trade-off carefully the speedup obtained by using the
ANN algorithm and the loss in accuracy when retrieving neighbors.  Due
to the curse of dimensionality, KD-trees become less effective for
higher dimensional data, so that the search cost, which in the best
case is logarithmic with this data structure, may become effectively
linear. This is somehow mitigated by the fact that new a new KD-tree
is computed at each iteration, reducing the likelihood that points may
get stuck with sub-optimal assignments.

Experiments with the quantization of 128-dimensional SIFT features
show that the ANN algorithm may use one quarter of the comparisons of
Elkan's while retaining a similar solution accuracy.

*/

#include "kmeans.h"
#include "generic.h"
#include "mathop.h"
#include 

#ifdef _OPENMP
#include 
#endif

/* ================================================================ */
#ifndef VL_KMEANS_INSTANTIATING


/** ------------------------------------------------------------------
 ** @brief Reset state
 **
 ** The function reset the state of the KMeans object. It deletes
 ** any stored centers, releasing the corresponding memory. This
 ** cancels the effect of seeding or setting the centers, but
 ** does not change the other configuration parameters.
 **/

VL_EXPORT void
vl_kmeans_reset (VlKMeans * self)
{
  self->numCenters = 0 ;
  self->dimension = 0 ;

  if (self->centers) vl_free(self->centers) ;
  if (self->centerDistances) vl_free(self->centerDistances) ;

  self->centers = NULL ;
  self->centerDistances = NULL ;
}

/** ------------------------------------------------------------------
 ** @brief Create a new KMeans object
 ** @param dataType type of data (::VL_TYPE_FLOAT or ::VL_TYPE_DOUBLE)
 ** @param distance distance.
 ** @return new KMeans object instance.
**/

VL_EXPORT VlKMeans *
vl_kmeans_new (vl_type dataType,
               VlVectorComparisonType distance)
{
  VlKMeans * self = vl_calloc(1, sizeof(VlKMeans)) ;

  self->algorithm = VlKMeansLloyd ;
  self->distance = distance ;
  self->dataType = dataType ;
  self->verbosity = 0 ;
  self->maxNumIterations = 100 ;
  self->numRepetitions = 1 ;
  self->centers = NULL ;
  self->centerDistances = NULL ;
  self->numTrees = 3;
  self->maxNumComparisons = 100;

  vl_kmeans_reset (self) ;
  return self ;
}

/** ------------------------------------------------------------------
 ** @brief Create a new KMeans object by copy
 ** @param kmeans KMeans object to copy.
 ** @return new copy.
 **/

VL_EXPORT VlKMeans *
vl_kmeans_new_copy (VlKMeans const * kmeans)
{
  VlKMeans * self = vl_malloc(sizeof(VlKMeans)) ;

  self->algorithm = kmeans->algorithm ;
  self->distance = kmeans->distance ;
  self->dataType = kmeans->dataType ;

  self->verbosity = kmeans->verbosity ;
  self->maxNumIterations = kmeans->maxNumIterations ;
  self->numRepetitions = kmeans->numRepetitions ;

  self->dimension = kmeans->dimension ;
  self->numCenters = kmeans->numCenters ;
  self->centers = NULL ;
  self->centerDistances = NULL ;

  self->numTrees = kmeans->numTrees;
  self->maxNumComparisons = kmeans->maxNumComparisons;

  if (kmeans->centers) {
    vl_size dataSize = vl_get_type_size(self->dataType) * self->dimension * self->numCenters ;
    self->centers = vl_malloc(dataSize) ;
    memcpy (self->centers, kmeans->centers, dataSize) ;
  }

  if (kmeans->centerDistances) {
    vl_size dataSize = vl_get_type_size(self->dataType) * self->numCenters * self->numCenters ;
    self->centerDistances = vl_malloc(dataSize) ;
    memcpy (self->centerDistances, kmeans->centerDistances, dataSize) ;
  }

  return self ;
}

/** ------------------------------------------------------------------
 ** @brief Deletes a KMeans object
 ** @param self KMeans object instance.
 **
 ** The function deletes the KMeans object instance created
 ** by ::vl_kmeans_new.
 **/

VL_EXPORT void
vl_kmeans_delete (VlKMeans * self)
{
  vl_kmeans_reset (self) ;
  vl_free (self) ;
}

/* an helper structure */
typedef struct _VlKMeansSortWrapper {
  vl_uint32 * permutation ;
  void const * data ;
  vl_size stride ;
} VlKMeansSortWrapper ;


/* ---------------------------------------------------------------- */
/* Instantiate shuffle algorithm */

#define VL_SHUFFLE_type vl_uindex
#define VL_SHUFFLE_prefix _vl_kmeans
#include "shuffle-def.h"

/* #ifdef VL_KMEANS_INSTANTITATING */
#endif

/* ================================================================ */
#ifdef VL_KMEANS_INSTANTIATING

/* ---------------------------------------------------------------- */
/*                                                      Set centers */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_kmeans_set_centers_, SFX)
(VlKMeans * self,
 TYPE const * centers,
 vl_size dimension,
 vl_size numCenters)
{
  self->dimension = dimension ;
  self->numCenters = numCenters ;
  self->centers = vl_malloc (sizeof(TYPE) * dimension * numCenters) ;
  memcpy ((TYPE*)self->centers, centers,
          sizeof(TYPE) * dimension * numCenters) ;
}

/* ---------------------------------------------------------------- */
/*                                                   Random seeding */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_kmeans_init_centers_with_rand_data_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size dimension,
 vl_size numData,
 vl_size numCenters)
{
  vl_uindex i, j, k ;
  VlRand * rand = vl_get_rand () ;

  self->dimension = dimension ;
  self->numCenters = numCenters ;
  self->centers = vl_malloc (sizeof(TYPE) * dimension * numCenters) ;

  {
    vl_uindex * perm = vl_malloc (sizeof(vl_uindex) * numData) ;
#if (FLT == VL_TYPE_FLOAT)
    VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
    VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif
    TYPE * distances = vl_malloc (sizeof(TYPE) * numCenters) ;

    /* get a random permutation of the data point */
    for (i = 0 ; i < numData ; ++i) perm[i] = i ;
    _vl_kmeans_shuffle (perm, numData, rand) ;

    for (k = 0, i = 0 ; k < numCenters ; ++ i) {

      /* compare the next data point to all centers collected so far
       to detect duplicates (if there are enough left)
       */
      if (numCenters - k < numData - i) {
        vl_bool duplicateDetected = VL_FALSE ;
        VL_XCAT(vl_eval_vector_comparison_on_all_pairs_, SFX)(distances,
            dimension,
            data + dimension * perm[i], 1,
            (TYPE*)self->centers, k,
            distFn) ;
        for (j = 0 ; j < k ; ++j) {
          duplicateDetected |= (distances[j] == 0) ;
        }
        if (duplicateDetected) continue ;
      }

      /* ok, it is not a duplicate so we can accept it! */
      memcpy ((TYPE*)self->centers + dimension * k,
              data + dimension * perm[i],
              sizeof(TYPE) * dimension) ;
      k ++ ;
    }
    vl_free(distances) ;
    vl_free(perm) ;
  }
}

/* ---------------------------------------------------------------- */
/*                                                 kmeans++ seeding */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_kmeans_init_centers_plus_plus_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size dimension,
 vl_size numData,
 vl_size numCenters)
{
  vl_uindex x, c ;
  VlRand * rand = vl_get_rand () ;
  TYPE * distances = vl_malloc (sizeof(TYPE) * numData) ;
  TYPE * minDistances = vl_malloc (sizeof(TYPE) * numData) ;
#if (FLT == VL_TYPE_FLOAT)
  VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
  VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif

  self->dimension = dimension ;
  self->numCenters = numCenters ;
  self->centers = vl_malloc (sizeof(TYPE) * dimension * numCenters) ;

  for (x = 0 ; x < numData ; ++x) {
    minDistances[x] = (TYPE) VL_INFINITY_D ;
  }

  /* select the first point at random */
  x = vl_rand_uindex (rand, numData) ;
  c = 0 ;
  while (1) {
    TYPE energy = 0 ;
    TYPE acc = 0 ;
    TYPE thresh = (TYPE) vl_rand_real1 (rand) ;

    memcpy ((TYPE*)self->centers + c * dimension,
            data + x * dimension,
            sizeof(TYPE) * dimension) ;

    c ++ ;
    if (c == numCenters) break ;

    VL_XCAT(vl_eval_vector_comparison_on_all_pairs_, SFX)
    (distances,
     dimension,
     (TYPE*)self->centers + (c - 1) * dimension, 1,
     data, numData,
     distFn) ;

    for (x = 0 ; x < numData ; ++x) {
      minDistances[x] = VL_MIN(minDistances[x], distances[x]) ;
      energy += minDistances[x] ;
    }

    for (x = 0 ; x < numData - 1 ; ++x) {
      acc += minDistances[x] ;
      if (acc >= thresh * energy) break ;
    }
  }

  vl_free(distances) ;
  vl_free(minDistances) ;
}

/* ---------------------------------------------------------------- */
/*                                                     Quantization */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_kmeans_quantize_, SFX)
(VlKMeans * self,
 vl_uint32 * assignments,
 TYPE * distances,
 TYPE const * data,
 vl_size numData)
{
  vl_index i ;

#ifdef _OPENMP
  vl_size numThreads = vl_get_max_threads() ;
#endif

#if (FLT == VL_TYPE_FLOAT)
  VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
  VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif

#ifdef _OPENMP
#pragma omp parallel default(none) \
            shared(self, distances, assignments, numData, distFn, data) \
            num_threads(numThreads)
#endif
  {
    /* vl_malloc cannot be used here if mapped to MATLAB malloc */
    TYPE * distanceToCenters = malloc(sizeof(TYPE) * self->numCenters) ;

#ifdef _OPENMP
#pragma omp for
#endif
    for (i = 0 ; i < (signed)numData ; ++i) {
      vl_uindex k ;
      TYPE bestDistance = (TYPE) VL_INFINITY_D ;
      VL_XCAT(vl_eval_vector_comparison_on_all_pairs_, SFX)(distanceToCenters,
                                                            self->dimension,
                                                            data + self->dimension * i, 1,
                                                            (TYPE*)self->centers, self->numCenters,
                                                            distFn) ;
      for (k = 0 ; k < self->numCenters ; ++k) {
        if (distanceToCenters[k] < bestDistance) {
          bestDistance = distanceToCenters[k] ;
          assignments[i] = (vl_uint32)k ;
        }
      }
      if (distances) distances[i] = bestDistance ;
    }

    free(distanceToCenters) ;
  }
}

/* ---------------------------------------------------------------- */
/*                                                 ANN quantization */
/* ---------------------------------------------------------------- */

static void
VL_XCAT(_vl_kmeans_quantize_ann_, SFX)
(VlKMeans * self,
 vl_uint32 * assignments,
 TYPE * distances,
 TYPE const * data,
 vl_size numData,
 vl_bool update)
{
#if (FLT == VL_TYPE_FLOAT)
  VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
  VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif

  VlKDForest * forest = vl_kdforest_new(self->dataType,self->dimension,self->numTrees, self->distance) ;
  vl_kdforest_set_max_num_comparisons(forest,self->maxNumComparisons);
  vl_kdforest_set_thresholding_method(forest,VL_KDTREE_MEDIAN);
  vl_kdforest_build(forest,self->numCenters,self->centers);

#ifdef _OPENMP
#pragma omp parallel default(none) \
  num_threads(vl_get_max_threads()) \
  shared(self, forest, update, assignments, distances, data, numData, distFn)
#endif
  {
    VlKDForestNeighbor neighbor ;
    VlKDForestSearcher * searcher ;
    vl_index x;

#ifdef _OPENMP
#pragma omp critical
#endif
    searcher = vl_kdforest_new_searcher (forest) ;

#ifdef _OPENMP
#pragma omp for
#endif
    for(x = 0 ; x < (signed)numData ; ++x) {
      vl_kdforestsearcher_query (searcher, &neighbor, 1, (TYPE const *) (data + x*self->dimension));

      if (distances) {
        if(!update) {
          distances[x] = (TYPE) neighbor.distance;
          assignments[x] = (vl_uint32) neighbor.index ;
        } else {
          TYPE prevDist = (TYPE) distFn(self->dimension,
                                        data + self->dimension * x,
                                        (TYPE*)self->centers + self->dimension *assignments[x]);
          if (prevDist > (TYPE) neighbor.distance) {
            distances[x] = (TYPE) neighbor.distance ;
            assignments[x] = (vl_uint32) neighbor.index ;
          } else {
            distances[x] = prevDist ;
          }
        }
      } else {
        assignments[x] = (vl_uint32) neighbor.index ;
      }
    } /* end for */
  } /* end of parallel region */

  vl_kdforest_delete(forest);
}

/* ---------------------------------------------------------------- */
/*                                                 Helper functions */
/* ---------------------------------------------------------------- */

/* The sorting routine is used to find increasing permutation of each
 * data dimension. This is used to quickly find the median for l1
 * distance clustering. */

VL_INLINE TYPE
VL_XCAT3(_vl_kmeans_, SFX, _qsort_cmp)
(VlKMeansSortWrapper * array, vl_uindex indexA, vl_uindex indexB)
{
  return
    ((TYPE*)array->data) [array->permutation[indexA] * array->stride]
    -
    ((TYPE*)array->data) [array->permutation[indexB] * array->stride] ;
}

VL_INLINE void
VL_XCAT3(_vl_kmeans_, SFX, _qsort_swap)
(VlKMeansSortWrapper * array, vl_uindex indexA, vl_uindex indexB)
{
  vl_uint32 tmp = array->permutation[indexA] ;
  array->permutation[indexA] = array->permutation[indexB] ;
  array->permutation[indexB] = tmp ;
}

#define VL_QSORT_prefix  VL_XCAT3(_vl_kmeans_, SFX, _qsort)
#define VL_QSORT_array   VlKMeansSortWrapper*
#define VL_QSORT_cmp     VL_XCAT3(_vl_kmeans_, SFX, _qsort_cmp)
#define VL_QSORT_swap    VL_XCAT3(_vl_kmeans_, SFX, _qsort_swap)
#include "qsort-def.h"

static void
VL_XCAT(_vl_kmeans_sort_data_helper_, SFX)
(VlKMeans * self, vl_uint32 * permutations, TYPE const * data, vl_size numData)
{
  vl_uindex d, x ;

  for (d = 0 ; d < self->dimension ; ++d) {
    VlKMeansSortWrapper array ;
    array.permutation = permutations + d * numData ;
    array.data = data + d ;
    array.stride = self->dimension ;
    for (x = 0 ; x < numData ; ++x) {
      array.permutation[x] = (vl_uint32)x ;
    }
    VL_XCAT3(_vl_kmeans_, SFX, _qsort_sort)(&array, numData) ;
  }
}

/* ---------------------------------------------------------------- */
/*                                                 Lloyd refinement */
/* ---------------------------------------------------------------- */

static double
VL_XCAT(_vl_kmeans_refine_centers_lloyd_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size numData)
{
  vl_size c, d, x, iteration ;
  double previousEnergy = VL_INFINITY_D ;
  double energy ;
  TYPE * distances = vl_malloc (sizeof(TYPE) * numData) ;

  vl_uint32 * assignments = vl_malloc (sizeof(vl_uint32) * numData) ;
  vl_size * clusterMasses = vl_malloc (sizeof(vl_size) * numData) ;
  vl_uint32 * permutations = NULL ;
  vl_size * numSeenSoFar = NULL ;
  VlRand * rand = vl_get_rand () ;
  vl_size totNumRestartedCenters = 0 ;
  vl_size numRestartedCenters = 0 ;

  if (self->distance == VlDistanceL1) {
    permutations = vl_malloc(sizeof(vl_uint32) * numData * self->dimension) ;
    numSeenSoFar = vl_malloc(sizeof(vl_size) * self->numCenters) ;
    VL_XCAT(_vl_kmeans_sort_data_helper_, SFX)(self, permutations, data, numData) ;
  }

  for (energy = VL_INFINITY_D,
       iteration = 0;
       1 ;
       ++ iteration) {

    /* assign data to cluters */
    VL_XCAT(_vl_kmeans_quantize_, SFX)(self, assignments, distances, data, numData) ;

    /* compute energy */
    energy = 0 ;
    for (x = 0 ; x < numData ; ++x) energy += distances[x] ;
    if (self->verbosity) {
      VL_PRINTF("kmeans: Lloyd iter %d: energy = %g\n", iteration,
                energy) ;
    }

    /* check termination conditions */
    if (iteration >= self->maxNumIterations) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: Lloyd terminating because maximum number of iterations reached\n") ;
      }
      break ;
    }
    if (energy == previousEnergy) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: Lloyd terminating because the algorithm fully converged\n") ;
      }
      break ;
    }

    /* begin next iteration */
    previousEnergy = energy ;

    /* update clusters */
    memset(clusterMasses, 0, sizeof(vl_size) * numData) ;
    for (x = 0 ; x < numData ; ++x) {
      clusterMasses[assignments[x]] ++ ;
    }

    numRestartedCenters = 0 ;
    switch (self->distance) {
      case VlDistanceL2:
        memset(self->centers, 0, sizeof(TYPE) * self->dimension * self->numCenters) ;
        for (x = 0 ; x < numData ; ++x) {
          TYPE * cpt = (TYPE*)self->centers + assignments[x] * self->dimension ;
          TYPE const * xpt = data + x * self->dimension ;
          for (d = 0 ; d < self->dimension ; ++d) {
            cpt[d] += xpt[d] ;
          }
        }
        for (c = 0 ; c < self->numCenters ; ++c) {
          TYPE * cpt = (TYPE*)self->centers + c * self->dimension ;
          if (clusterMasses[c] > 0) {
            TYPE mass = clusterMasses[c] ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] /= mass ;
            }
          } else {
            vl_uindex x = vl_rand_uindex(rand, numData) ;
            numRestartedCenters ++ ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] = data[x * self->dimension + d] ;
            }
          }
        }
        break ;
      case VlDistanceL1:
        for (d = 0 ; d < self->dimension ; ++d) {
          vl_uint32 * perm = permutations + d * numData ;
          memset(numSeenSoFar, 0, sizeof(vl_size) * self->numCenters) ;
          for (x = 0; x < numData ; ++x) {
            c = assignments[perm[x]] ;
            if (2 * numSeenSoFar[c] < clusterMasses[c]) {
              ((TYPE*)self->centers) [d + c * self->dimension] =
                data [d + perm[x] * self->dimension] ;
            }
            numSeenSoFar[c] ++ ;
          }
          /* restart the centers as required  */
          for (c = 0 ; c < self->numCenters ; ++c) {
            if (clusterMasses[c] == 0) {
              TYPE * cpt = (TYPE*)self->centers + c * self->dimension ;
              vl_uindex x = vl_rand_uindex(rand, numData) ;
              numRestartedCenters ++ ;
              for (d = 0 ; d < self->dimension ; ++d) {
                cpt[d] = data[x * self->dimension + d] ;
              }
            }
          }
        }
        break ;
      default:
        abort();
    } /* done compute centers */

    totNumRestartedCenters += numRestartedCenters ;
    if (self->verbosity && numRestartedCenters) {
      VL_PRINTF("kmeans: Lloyd iter %d: restarted %d centers\n", iteration,
                numRestartedCenters) ;
    }
  } /* next Lloyd iteration */

  if (permutations) {
    vl_free(permutations) ;
  }
  if (numSeenSoFar) {
    vl_free(numSeenSoFar) ;
  }
  vl_free(distances) ;
  vl_free(assignments) ;
  vl_free(clusterMasses) ;
  return energy ;
}

static double
VL_XCAT(_vl_kmeans_update_center_distances_, SFX)
(VlKMeans * self)
{
#if (FLT == VL_TYPE_FLOAT)
  VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
  VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif

  if (! self->centerDistances) {
    self->centerDistances = vl_malloc (sizeof(TYPE) *
                                       self->numCenters *
                                       self->numCenters) ;
  }
  VL_XCAT(vl_eval_vector_comparison_on_all_pairs_, SFX)(self->centerDistances,
      self->dimension,
      self->centers, self->numCenters,
      NULL, 0,
      distFn) ;
  return self->numCenters * (self->numCenters - 1) / 2 ;
}

static double
VL_XCAT(_vl_kmeans_refine_centers_ann_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size numData)
{
  vl_size c, d, x, iteration ;
  double previousEnergy = VL_INFINITY_D ;
  double energy ;

  vl_uint32 * permutations = NULL ;
  vl_size * numSeenSoFar = NULL ;
  VlRand * rand = vl_get_rand () ;
  vl_size totNumRestartedCenters = 0 ;
  vl_size numRestartedCenters = 0 ;

  vl_uint32 * assignments = vl_malloc (sizeof(vl_uint32) * numData) ;
  vl_size * clusterMasses = vl_malloc (sizeof(vl_size) * numData) ;
  TYPE * distances = vl_malloc (sizeof(TYPE) * numData) ;

  if (self->distance == VlDistanceL1) {
    permutations = vl_malloc(sizeof(vl_uint32) * numData * self->dimension) ;
    numSeenSoFar = vl_malloc(sizeof(vl_size) * self->numCenters) ;
    VL_XCAT(_vl_kmeans_sort_data_helper_, SFX)(self, permutations, data, numData) ;
  }

  for (energy = VL_INFINITY_D,
       iteration = 0;
       1 ;
       ++ iteration) {

	double eps;

    /* assign data to cluters */
    VL_XCAT(_vl_kmeans_quantize_ann_, SFX)(self, assignments, distances, data, numData, iteration > 0) ;

    /* compute energy */
    energy = 0 ;
    for (x = 0 ; x < numData ; ++x) energy += distances[x] ;
    if (self->verbosity) {
      VL_PRINTF("kmeans: ANN iter %d: energy = %g\n", iteration,
                energy) ;
    }

    /* check termination conditions */
    if (iteration >= self->maxNumIterations) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: ANN terminating because the maximum number of iterations has been reached\n") ;
      }
      break ;
    }

    eps = (previousEnergy - energy)/previousEnergy;
    if (energy == previousEnergy || eps < 0.00001) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: ANN terminating because the algorithm fully converged\n") ;
      }
      break ;
    }

    /* begin next iteration */
    previousEnergy = energy ;

    /* update clusters */
    memset(clusterMasses, 0, sizeof(vl_size) * numData) ;
    for (x = 0 ; x < numData ; ++x) {
      clusterMasses[assignments[x]] ++ ;
    }

    numRestartedCenters = 0 ;
    switch (self->distance) {
      case VlDistanceL2:
        memset(self->centers, 0, sizeof(TYPE) * self->dimension * self->numCenters) ;
        for (x = 0 ; x < numData ; ++x) {
          TYPE * cpt = (TYPE*)self->centers + assignments[x] * self->dimension ;
          TYPE const * xpt = data + x * self->dimension ;
          for (d = 0 ; d < self->dimension ; ++d) {
            cpt[d] += xpt[d] ;
          }
        }
        for (c = 0 ; c < self->numCenters ; ++c) {
          TYPE * cpt = (TYPE*)self->centers + c * self->dimension ;
          if (clusterMasses[c] > 0) {
            TYPE mass = clusterMasses[c] ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] /= mass ;
            }
          } else {
            vl_uindex x = vl_rand_uindex(rand, numData) ;
            numRestartedCenters ++ ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] = data[x * self->dimension + d] ;
            }
          }
        }
        break ;
      case VlDistanceL1:
        for (d = 0 ; d < self->dimension ; ++d) {
          vl_uint32 * perm = permutations + d * numData ;
          memset(numSeenSoFar, 0, sizeof(vl_size) * self->numCenters) ;
          for (x = 0; x < numData ; ++x) {
            c = assignments[perm[x]] ;
            if (2 * numSeenSoFar[c] < clusterMasses[c]) {
              ((TYPE*)self->centers) [d + c * self->dimension] =
                data [d + perm[x] * self->dimension] ;
            }
            numSeenSoFar[c] ++ ;
          }
          /* restart the centers as required  */
          for (c = 0 ; c < self->numCenters ; ++c) {
            if (clusterMasses[c] == 0) {
              TYPE * cpt = (TYPE*)self->centers + c * self->dimension ;
              vl_uindex x = vl_rand_uindex(rand, numData) ;
              numRestartedCenters ++ ;
              for (d = 0 ; d < self->dimension ; ++d) {
                cpt[d] = data[x * self->dimension + d] ;
              }
            }
          }
        }
        break ;
      default:
        VL_PRINT("bad distance set: %d\n",self->distance);
        abort();
    } /* done compute centers */

    totNumRestartedCenters += numRestartedCenters ;
    if (self->verbosity && numRestartedCenters) {
      VL_PRINTF("kmeans: ANN iter %d: restarted %d centers\n", iteration,
                numRestartedCenters) ;
    }
  }

  if (permutations) {
    vl_free(permutations) ;
  }
  if (numSeenSoFar) {
    vl_free(numSeenSoFar) ;
  }

  vl_free(distances) ;
  vl_free(assignments) ;
  vl_free(clusterMasses) ;
  return energy ;
}

/* ---------------------------------------------------------------- */
/*                                                 Elkan refinement */
/* ---------------------------------------------------------------- */

static double
VL_XCAT(_vl_kmeans_refine_centers_elkan_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size numData)
{
  vl_size d, iteration ;
  vl_index x ;
  vl_uint32 c, j ;
  vl_bool allDone ;
  TYPE * distances = vl_malloc (sizeof(TYPE) * numData) ;
  vl_uint32 * assignments = vl_malloc (sizeof(vl_uint32) * numData) ;
  vl_size * clusterMasses = vl_malloc (sizeof(vl_size) * numData) ;
  VlRand * rand = vl_get_rand () ;

#if (FLT == VL_TYPE_FLOAT)
  VlFloatVectorComparisonFunction distFn = vl_get_vector_comparison_function_f(self->distance) ;
#else
  VlDoubleVectorComparisonFunction distFn = vl_get_vector_comparison_function_d(self->distance) ;
#endif

  TYPE * nextCenterDistances = vl_malloc (sizeof(TYPE) * self->numCenters) ;
  TYPE * pointToClosestCenterUB = vl_malloc (sizeof(TYPE) * numData) ;
  vl_bool * pointToClosestCenterUBIsStrict = vl_malloc (sizeof(vl_bool) * numData) ;
  TYPE * pointToCenterLB = vl_malloc (sizeof(TYPE) * numData * self->numCenters) ;
  TYPE * newCenters = vl_malloc(sizeof(TYPE) * self->dimension * self->numCenters) ;
  TYPE * centerToNewCenterDistances = vl_malloc (sizeof(TYPE) * self->numCenters) ;

  vl_uint32 * permutations = NULL ;
  vl_size * numSeenSoFar = NULL ;

  double energy ;

  vl_size totDistanceComputationsToInit = 0 ;
  vl_size totDistanceComputationsToRefreshUB = 0 ;
  vl_size totDistanceComputationsToRefreshLB = 0 ;
  vl_size totDistanceComputationsToRefreshCenterDistances = 0 ;
  vl_size totDistanceComputationsToNewCenters = 0 ;
  vl_size totDistanceComputationsToFinalize = 0 ;
  vl_size totNumRestartedCenters = 0 ;

  if (self->distance == VlDistanceL1) {
    permutations = vl_malloc(sizeof(vl_uint32) * numData * self->dimension) ;
    numSeenSoFar = vl_malloc(sizeof(vl_size) * self->numCenters) ;
    VL_XCAT(_vl_kmeans_sort_data_helper_, SFX)(self, permutations, data, numData) ;
  }

  /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
  /*                          Initialization                        */
  /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */

  /* An iteration is: get_new_centers + reassign + get_energy.
   This counts as iteration 0, where get_new_centers is assumed
   to be performed before calling the train function by
   the initialization function */

  /* update distances between centers */
  totDistanceComputationsToInit +=
  VL_XCAT(_vl_kmeans_update_center_distances_, SFX)(self) ;

  /* assigmen points to the initial centers and initialize bounds */
  memset(pointToCenterLB, 0, sizeof(TYPE) * self->numCenters *  numData) ;
  for (x = 0 ; x < (signed)numData ; ++x) {
    TYPE distance ;

    /* do the first center */
    assignments[x] = 0 ;
    distance = distFn(self->dimension,
                      data + x * self->dimension,
                      (TYPE*)self->centers + 0) ;
    pointToClosestCenterUB[x] = distance ;
    pointToClosestCenterUBIsStrict[x] = VL_TRUE ;
    pointToCenterLB[0 + x * self->numCenters] = distance ;
    totDistanceComputationsToInit += 1 ;

    /* do other centers */
    for (c = 1 ; c < self->numCenters ; ++c) {

      /* Can skip if the center assigned so far is twice as close
       as its distance to the center under consideration */

      if (((self->distance == VlDistanceL1) ? 2.0 : 4.0) *
          pointToClosestCenterUB[x] <=
          ((TYPE*)self->centerDistances)
          [c + assignments[x] * self->numCenters]) {
        continue ;
      }

      distance = distFn(self->dimension,
                        data + x * self->dimension,
                        (TYPE*)self->centers + c * self->dimension) ;
      pointToCenterLB[c + x * self->numCenters] = distance ;
      totDistanceComputationsToInit += 1 ;
      if (distance < pointToClosestCenterUB[x]) {
        pointToClosestCenterUB[x] = distance ;
        assignments[x] = c ;
      }
    }
  }

  /* compute UB on energy */
  energy = 0 ;
  for (x = 0 ; x < (signed)numData ; ++x) {
    energy += pointToClosestCenterUB[x] ;
  }

  if (self->verbosity) {
    VL_PRINTF("kmeans: Elkan iter 0: energy = %g, dist. calc. = %d\n",
              energy, totDistanceComputationsToInit) ;
  }

  /* #define SANITY*/
#ifdef SANITY
  {
    int xx ;
    int cc ;
    TYPE tol = 1e-5 ;
    VL_PRINTF("inconsistencies after initial assignments:\n");
    for (xx = 0 ; xx < numData ; ++xx) {
      for (cc = 0 ; cc < self->numCenters ; ++cc) {
        TYPE a = pointToCenterLB[cc + xx * self->numCenters] ;
        TYPE b = distFn(self->dimension,
                        data + self->dimension * xx,
                        (TYPE*)self->centers + self->dimension * cc) ;
        if (cc == assignments[xx]) {
          TYPE z = pointToClosestCenterUB[xx] ;
          if (z+tolb+tol) VL_PRINTF("LB %d %d = %f  > %f\n",
                               cc, xx, a, b) ;
      }
    }
  }
#endif

  /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
  /*                          Iterations                            */
  /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */

  for (iteration = 1 ; 1; ++iteration) {

    vl_size numDistanceComputationsToRefreshUB = 0 ;
    vl_size numDistanceComputationsToRefreshLB = 0 ;
    vl_size numDistanceComputationsToRefreshCenterDistances = 0 ;
    vl_size numDistanceComputationsToNewCenters = 0 ;
    vl_size numRestartedCenters = 0 ;

    /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
    /*                         Compute new centers                  */
    /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */

    memset(clusterMasses, 0, sizeof(vl_size) * numData) ;
    for (x = 0 ; x < (signed)numData ; ++x) {
      clusterMasses[assignments[x]] ++ ;
    }

    switch (self->distance) {
      case VlDistanceL2:
        memset(newCenters, 0, sizeof(TYPE) * self->dimension * self->numCenters) ;
        for (x = 0 ; x < (signed)numData ; ++x) {
          TYPE * cpt = newCenters + assignments[x] * self->dimension ;
          TYPE const * xpt = data + x * self->dimension ;
          for (d = 0 ; d < self->dimension ; ++d) {
            cpt[d] += xpt[d] ;
          }
        }
        for (c = 0 ; c < self->numCenters ; ++c) {
          TYPE * cpt = newCenters + c * self->dimension ;
          if (clusterMasses[c] > 0) {
            TYPE mass = clusterMasses[c] ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] /= mass ;
            }
          } else {
            /* restart the center */
            vl_uindex x = vl_rand_uindex(rand, numData) ;
            numRestartedCenters ++ ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] = data[x * self->dimension + d] ;
            }
          }
        }
        break ;
      case VlDistanceL1:
        for (d = 0 ; d < self->dimension ; ++d) {
          vl_uint32 * perm = permutations + d * numData ;
          memset(numSeenSoFar, 0, sizeof(vl_size) * self->numCenters) ;
          for (x = 0; x < (signed)numData ; ++x) {
            c = assignments[perm[x]] ;
            if (2 * numSeenSoFar[c] < clusterMasses[c]) {
              newCenters [d + c * self->dimension] =
              data [d + perm[x] * self->dimension] ;
            }
            numSeenSoFar[c] ++ ;
          }
        }
        /* restart the centers as required  */
        for (c = 0 ; c < self->numCenters ; ++c) {
          if (clusterMasses[c] == 0) {
            TYPE * cpt = newCenters + c * self->dimension ;
            vl_uindex x = vl_rand_uindex(rand, numData) ;
            numRestartedCenters ++ ;
            for (d = 0 ; d < self->dimension ; ++d) {
              cpt[d] = data[x * self->dimension + d] ;
            }
          }
        }
        break ;
      default:
        abort();
    } /* done compute centers */

    /* compute the distance from the old centers to the new centers */
    for (c = 0 ; c < self->numCenters ; ++c) {
      TYPE distance = distFn(self->dimension,
                             newCenters + c * self->dimension,
                             (TYPE*)self->centers + c * self->dimension) ;
      centerToNewCenterDistances[c] = distance ;
      numDistanceComputationsToNewCenters += 1 ;
    }

    /* make the new centers current */
    {
      TYPE * tmp = self->centers ;
      self->centers = newCenters ;
      newCenters = tmp ;
    }

    /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */
    /*                Reassign points to a centers                  */
    /* ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */

    /*
     Update distances between centers.
     */
    numDistanceComputationsToRefreshCenterDistances
    += VL_XCAT(_vl_kmeans_update_center_distances_, SFX)(self) ;

    for (c = 0 ; c < self->numCenters ; ++c) {
      nextCenterDistances[c] = (TYPE) VL_INFINITY_D ;
      for (j = 0 ; j < self->numCenters ; ++j) {
        if (j == c) continue ;
        nextCenterDistances[c] = VL_MIN(nextCenterDistances[c],
                                        ((TYPE*)self->centerDistances)
                                        [j + c * self->numCenters]) ;
      }
    }

    /*
     Update upper bounds on point-to-closest-center distances
     based on the center variation.
     */
    for (x = 0 ; x < (signed)numData ; ++x) {
      TYPE a = pointToClosestCenterUB[x] ;
      TYPE b = centerToNewCenterDistances[assignments[x]] ;
      if (self->distance == VlDistanceL1) {
        pointToClosestCenterUB[x] = a + b ;
      } else {
#if (FLT == VL_TYPE_FLOAT)
        TYPE sqrtab =  sqrtf (a * b) ;
#else
        TYPE sqrtab =  sqrt (a * b) ;
#endif
        pointToClosestCenterUB[x] = a + b + 2.0 * sqrtab ;
      }
      pointToClosestCenterUBIsStrict[x] = VL_FALSE ;
    }

    /*
     Update lower bounds on point-to-center distances
     based on the center variation.
     */

#if defined(_OPENMP)
#pragma omp parallel for default(shared) private(x,c) num_threads(vl_get_max_threads())
#endif
    for (x = 0 ; x < (signed)numData ; ++x) {
      for (c = 0 ; c < self->numCenters ; ++c) {
        TYPE a = pointToCenterLB[c + x * self->numCenters] ;
        TYPE b = centerToNewCenterDistances[c] ;
        if (a < b) {
          pointToCenterLB[c + x * self->numCenters] = 0 ;
        } else {
          if (self->distance == VlDistanceL1) {
            pointToCenterLB[c + x * self->numCenters]  = a - b ;
          } else {
#if (FLT == VL_TYPE_FLOAT)
            TYPE sqrtab =  sqrtf (a * b) ;
#else
            TYPE sqrtab =  sqrt (a * b) ;
#endif
            pointToCenterLB[c + x * self->numCenters]  = a + b - 2.0 * sqrtab ;
          }
        }
      }
    }

#ifdef SANITY
    {
      int xx ;
      int cc ;
      TYPE tol = 1e-5 ;
      VL_PRINTF("inconsistencies before assignments:\n");
      for (xx = 0 ; xx < numData ; ++xx) {
        for (cc = 0 ; cc < self->numCenters ; ++cc) {
          TYPE a = pointToCenterLB[cc + xx * self->numCenters] ;
          TYPE b = distFn(self->dimension,
                          data + self->dimension * xx,
                          (TYPE*)self->centers + self->dimension * cc) ;
          if (cc == assignments[xx]) {
            TYPE z = pointToClosestCenterUB[xx] ;
            if (z+tolb+tol) VL_PRINTF("LB %d %d = %f  > %f (assign = %d)\n",
                                 cc, xx, a, b, assignments[xx]) ;
        }
      }
    }
#endif

    /*
     Scan the data and do the reassignments. Use the bounds to
     skip as many point-to-center distance calculations as possible.
     */
    allDone = VL_TRUE ;

#if defined(_OPENMP)
#pragma omp parallel for \
            default(none) \
            shared(self,numData, \
              pointToClosestCenterUB,pointToCenterLB, \
              nextCenterDistances,pointToClosestCenterUBIsStrict, \
              assignments,data,distFn,allDone) \
            private(c,x) \
            reduction(+:numDistanceComputationsToRefreshUB,numDistanceComputationsToRefreshLB) \
            num_threads(vl_get_max_threads())
#endif
    for (x = 0 ; x < (signed)numData ; ++ x) {
      /*
       A point x sticks with its current center assignmets[x]
       the UB to d(x, c[assigmnets[x]]) is not larger than half
       the distance of c[assigments[x]] to any other center c.
       */
      if (((self->distance == VlDistanceL1) ? 2.0 : 4.0) *
          pointToClosestCenterUB[x] <= nextCenterDistances[assignments[x]]) {
        continue ;
      }

      for (c = 0 ; c < self->numCenters ; ++c) {
        vl_uint32 cx = assignments[x] ;
        TYPE distance ;

        /* The point is not reassigned to a given center c
         if either:

         0 - c is already the assigned center
         1 - The UB of d(x, c[assignments[x]]) is smaller than half
         the distance of c[assigments[x]] to c, OR
         2 - The UB of d(x, c[assignmets[x]]) is smaller than the
         LB of the distance of x to c.
         */
        if (cx == c) {
          continue ;
        }
        if (((self->distance == VlDistanceL1) ? 2.0 : 4.0) *
            pointToClosestCenterUB[x] <= ((TYPE*)self->centerDistances)
            [c + cx * self->numCenters]) {
          continue ;
        }
        if (pointToClosestCenterUB[x] <= pointToCenterLB
            [c + x * self->numCenters]) {
          continue ;
        }

        /* If the UB is loose, try recomputing it and test again */
        if (! pointToClosestCenterUBIsStrict[x]) {
          distance = distFn(self->dimension,
                            data + self->dimension * x,
                            (TYPE*)self->centers + self->dimension * cx) ;
          pointToClosestCenterUB[x] = distance ;
          pointToClosestCenterUBIsStrict[x] = VL_TRUE ;
          pointToCenterLB[cx + x * self->numCenters] = distance ;
          numDistanceComputationsToRefreshUB += 1 ;

          if (((self->distance == VlDistanceL1) ? 2.0 : 4.0) *
              pointToClosestCenterUB[x] <= ((TYPE*)self->centerDistances)
              [c + cx * self->numCenters]) {
            continue ;
          }
          if (pointToClosestCenterUB[x] <= pointToCenterLB
              [c + x * self->numCenters]) {
            continue ;
          }
        }

        /*
         Now the UB is strict (equal to d(x, assignments[x])), but
         we still could not exclude that x should be reassigned to
         c. We therefore compute the distance, update the LB,
         and check if a reassigmnet must be made
         */
        distance = distFn(self->dimension,
                          data + x * self->dimension,
                          (TYPE*)self->centers + c *  self->dimension) ;
        numDistanceComputationsToRefreshLB += 1 ;
        pointToCenterLB[c + x * self->numCenters] = distance ;

        if (distance < pointToClosestCenterUB[x]) {
          assignments[x] = c ;
          pointToClosestCenterUB[x] = distance ;
          allDone = VL_FALSE ;
          /* the UB strict flag is already set here */
        }

      } /* assign center */
    } /* next data point */


    totDistanceComputationsToRefreshUB
    += numDistanceComputationsToRefreshUB ;

    totDistanceComputationsToRefreshLB
    += numDistanceComputationsToRefreshLB ;

    totDistanceComputationsToRefreshCenterDistances
    += numDistanceComputationsToRefreshCenterDistances ;

    totDistanceComputationsToNewCenters
    += numDistanceComputationsToNewCenters ;

    totNumRestartedCenters
    += numRestartedCenters ;

#ifdef SANITY
    {
      int xx ;
      int cc ;
      TYPE tol = 1e-5 ;
      VL_PRINTF("inconsistencies after assignments:\n");
      for (xx = 0 ; xx < numData ; ++xx) {
        for (cc = 0 ; cc < self->numCenters ; ++cc) {
          TYPE a = pointToCenterLB[cc + xx * self->numCenters] ;
          TYPE b = distFn(self->dimension,
                          data + self->dimension * xx,
                          (TYPE*)self->centers + self->dimension * cc) ;
          if (cc == assignments[xx]) {
            TYPE z = pointToClosestCenterUB[xx] ;
            if (z+tolb+tol) VL_PRINTF("LB %d %d = %f  > %f (assign = %d)\n",
                                 cc, xx, a, b, assignments[xx]) ;
        }
      }
    }
#endif

    /* compute UB on energy */
    energy = 0 ;
    for (x = 0 ; x < (signed)numData ; ++x) {
      energy += pointToClosestCenterUB[x] ;
    }

    if (self->verbosity) {
      vl_size numDistanceComputations =
      numDistanceComputationsToRefreshUB +
      numDistanceComputationsToRefreshLB +
      numDistanceComputationsToRefreshCenterDistances +
      numDistanceComputationsToNewCenters ;
      VL_PRINTF("kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d\n",
                iteration,
                energy,
                numDistanceComputations) ;
      if (numRestartedCenters) {
        VL_PRINTF("kmeans: Elkan iter %d: restarted %d centers\n",
                  iteration,
                  energy,
                  numRestartedCenters) ;
      }
      if (self->verbosity > 1) {
        VL_PRINTF("kmeans: Elkan iter %d: total dist. calc. per type: "
                  "UB: %.1f%% (%d), LB: %.1f%% (%d), "
                  "intra_center: %.1f%% (%d), "
                  "new_center: %.1f%% (%d)\n",
                  iteration,
                  100.0 * numDistanceComputationsToRefreshUB / numDistanceComputations,
                  numDistanceComputationsToRefreshUB,
                  100.0 *numDistanceComputationsToRefreshLB / numDistanceComputations,
                  numDistanceComputationsToRefreshLB,
                  100.0 * numDistanceComputationsToRefreshCenterDistances / numDistanceComputations,
                  numDistanceComputationsToRefreshCenterDistances,
                  100.0 * numDistanceComputationsToNewCenters / numDistanceComputations,
                  numDistanceComputationsToNewCenters) ;
      }
    }

    /* check termination conditions */
    if (iteration >= self->maxNumIterations) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: Elkan terminating because maximum number of iterations reached\n") ;
      }
      break ;
    }
    if (allDone) {
      if (self->verbosity) {
        VL_PRINTF("kmeans: Elkan terminating because the algorithm fully converged\n") ;
      }
      break ;
    }

  } /* next Elkan iteration */

  /* compute true energy */
  energy = 0 ;
  for (x = 0 ; x < (signed)numData ; ++ x) {
    vl_uindex cx = assignments [x] ;
    energy += distFn(self->dimension,
                     data + self->dimension * x,
                     (TYPE*)self->centers + self->dimension * cx) ;
    totDistanceComputationsToFinalize += 1 ;
  }

  {
    vl_size totDistanceComputations =
    totDistanceComputationsToInit +
    totDistanceComputationsToRefreshUB +
    totDistanceComputationsToRefreshLB +
    totDistanceComputationsToRefreshCenterDistances +
    totDistanceComputationsToNewCenters +
    totDistanceComputationsToFinalize ;

    double saving = (double)totDistanceComputations
    / (iteration * self->numCenters * numData) ;

    if (self->verbosity) {
      VL_PRINTF("kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd)\n",
                totDistanceComputations, saving * 100.0) ;
      if (totNumRestartedCenters) {
        VL_PRINTF("kmeans: Elkan: there have been %d restarts\n",
                  totNumRestartedCenters) ;
      }
    }

    if (self->verbosity > 1) {
      VL_PRINTF("kmeans: Elkan: total dist. calc. per type: "
                "init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), "
                "intra_center: %.1f%% (%d), "
                "new_center: %.1f%% (%d), "
                "finalize: %.1f%% (%d)\n",
                100.0 * totDistanceComputationsToInit / totDistanceComputations,
                totDistanceComputationsToInit,
                100.0 * totDistanceComputationsToRefreshUB / totDistanceComputations,
                totDistanceComputationsToRefreshUB,
                100.0 *totDistanceComputationsToRefreshLB / totDistanceComputations,
                totDistanceComputationsToRefreshLB,
                100.0 * totDistanceComputationsToRefreshCenterDistances / totDistanceComputations,
                totDistanceComputationsToRefreshCenterDistances,
                100.0 * totDistanceComputationsToNewCenters / totDistanceComputations,
                totDistanceComputationsToNewCenters,
                100.0 * totDistanceComputationsToFinalize / totDistanceComputations,
                totDistanceComputationsToFinalize) ;
    }
  }

  if (permutations) {
    vl_free(permutations) ;
  }
  if (numSeenSoFar) {
    vl_free(numSeenSoFar) ;
  }

  vl_free(distances) ;
  vl_free(assignments) ;
  vl_free(clusterMasses) ;

  vl_free(nextCenterDistances) ;
  vl_free(pointToClosestCenterUB) ;
  vl_free(pointToClosestCenterUBIsStrict) ;
  vl_free(pointToCenterLB) ;
  vl_free(newCenters) ;
  vl_free(centerToNewCenterDistances) ;

  return energy ;
}

/* ---------------------------------------------------------------- */
static double
VL_XCAT(_vl_kmeans_refine_centers_, SFX)
(VlKMeans * self,
 TYPE const * data,
 vl_size numData)
{
  switch (self->algorithm) {
    case VlKMeansLloyd:
      return
        VL_XCAT(_vl_kmeans_refine_centers_lloyd_, SFX)(self, data, numData) ;
      break ;
    case VlKMeansElkan:
      return
        VL_XCAT(_vl_kmeans_refine_centers_elkan_, SFX)(self, data, numData) ;
      break ;
    case VlKMeansANN:
      return
        VL_XCAT(_vl_kmeans_refine_centers_ann_, SFX)(self, data, numData) ;
      break ;
    default:
      abort() ;
  }
}

/* VL_KMEANS_INSTANTIATING */
#else

#ifndef __DOXYGEN__
#define FLT VL_TYPE_FLOAT
#define TYPE float
#define SFX f
#define VL_KMEANS_INSTANTIATING
#include "kmeans.c"

#define FLT VL_TYPE_DOUBLE
#define TYPE double
#define SFX d
#define VL_KMEANS_INSTANTIATING
#include "kmeans.c"
#endif

/* VL_KMEANS_INSTANTIATING */
#endif

/* ================================================================ */
#ifndef VL_KMEANS_INSTANTIATING

/** ------------------------------------------------------------------
 ** @brief Set centers
 ** @param self KMeans object.
 ** @param centers centers to copy.
 ** @param dimension data dimension.
 ** @param numCenters number of centers.
 **/

VL_EXPORT void
vl_kmeans_set_centers
(VlKMeans * self,
 void const * centers,
 vl_size dimension,
 vl_size numCenters)
{
  vl_kmeans_reset (self) ;

  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_kmeans_set_centers_f
      (self, (float const *)centers, dimension, numCenters) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_kmeans_set_centers_d
      (self, (double const *)centers, dimension, numCenters) ;
      break ;
    default:
      abort() ;
  }
}

/** ------------------------------------------------------------------
 ** @brief init centers by randomly sampling data
 ** @param self KMeans object.
 ** @param data data to sample from.
 ** @param dimension data dimension.
 ** @param numData nmber of data points.
 ** @param numCenters number of centers.
 **
 ** The function inits the KMeans centers by randomly sampling
 ** the data @a data.
 **/

VL_EXPORT void
vl_kmeans_init_centers_with_rand_data
(VlKMeans * self,
 void const * data,
 vl_size dimension,
 vl_size numData,
 vl_size numCenters)
{
  vl_kmeans_reset (self) ;

  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_kmeans_init_centers_with_rand_data_f
      (self, (float const *)data, dimension, numData, numCenters) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_kmeans_init_centers_with_rand_data_d
      (self, (double const *)data, dimension, numData, numCenters) ;
      break ;
    default:
      abort() ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Seed centers by the KMeans++ algorithm
 ** @param self KMeans object.
 ** @param data data to sample from.
 ** @param dimension data dimension.
 ** @param numData nmber of data points.
 ** @param numCenters number of centers.
 **/

VL_EXPORT void
vl_kmeans_init_centers_plus_plus
(VlKMeans * self,
 void const * data,
 vl_size dimension,
 vl_size numData,
 vl_size numCenters)
{
  vl_kmeans_reset (self) ;

  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_kmeans_init_centers_plus_plus_f
      (self, (float const *)data, dimension, numData, numCenters) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_kmeans_init_centers_plus_plus_d
      (self, (double const *)data, dimension, numData, numCenters) ;
      break ;
    default:
      abort() ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Quantize data
 ** @param self KMeans object.
 ** @param assignments data to closest center assignments (output).
 ** @param distances data to closest center distance (output).
 ** @param data data to quantize.
 ** @param numData number of data points to quantize.
 **/

VL_EXPORT void
vl_kmeans_quantize
(VlKMeans * self,
 vl_uint32 * assignments,
 void * distances,
 void const * data,
 vl_size numData)
{
  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_kmeans_quantize_f
      (self, assignments, distances, (float const *)data, numData) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_kmeans_quantize_d
      (self, assignments, distances, (double const *)data, numData) ;
      break ;
    default:
      abort() ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Quantize data using approximate nearest neighbours (ANN).
 ** @param self KMeans object.
 ** @param assignments data to centers assignments (output).
 ** @param distances data to closes center distance (output)
 ** @param data data to quantize.
 ** @param numData number of data points.
 ** @param update choose wether to update current assignments.
 **
 ** The function uses an ANN procedure to compute the approximate
 ** nearest neighbours of the input data point.
 **
 ** Setting @a update to ::VL_TRUE will cause the algorithm
 ** to *update existing assignments*. This means that each
 ** element of @a assignments and @a distances is updated ony if the
 ** ANN procedure can find a better assignment of the existing one.
 **/

VL_EXPORT void
vl_kmeans_quantize_ann
(VlKMeans * self,
 vl_uint32 * assignments,
 void * distances,
 void const * data,
 vl_size numData,
 vl_bool update)
{
  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      _vl_kmeans_quantize_ann_f
      (self, assignments, distances, (float const *)data, numData, update) ;
      break ;
    case VL_TYPE_DOUBLE :
      _vl_kmeans_quantize_ann_d
      (self, assignments, distances, (double const *)data, numData, update) ;
      break ;
    default:
      abort() ;
  }
}

/** ------------------------------------------------------------------
 ** @brief Refine center locations.
 ** @param self KMeans object.
 ** @param data data to quantize.
 ** @param numData number of data points.
 ** @return K-means energy at the end of optimization.
 **
 ** The function calls the underlying K-means quantization algorithm
 ** (@ref VlKMeansAlgorithm) to quantize the specified data @a data.
 ** The function assumes that the cluster centers have already
 ** been assigned by using one of the seeding functions, or by
 ** setting them.
 **/

VL_EXPORT double
vl_kmeans_refine_centers
(VlKMeans * self,
 void const * data,
 vl_size numData)
{
  assert (self->centers) ;

  switch (self->dataType) {
    case VL_TYPE_FLOAT :
      return
        _vl_kmeans_refine_centers_f
        (self, (float const *)data, numData) ;
    case VL_TYPE_DOUBLE :
      return
        _vl_kmeans_refine_centers_d
        (self, (double const *)data, numData) ;
    default:
      abort() ;
  }
}


/** ------------------------------------------------------------------
 ** @brief Cluster data.
 ** @param self KMeans object.
 ** @param data data to quantize.
 ** @param dimension data dimension.
 ** @param numData number of data points.
 ** @param numCenters number of clusters.
 ** @return K-means energy at the end of optimization.
 **
 ** The function initializes the centers by using the initialization
 ** algorithm set by ::vl_kmeans_set_initialization and refines them
 ** by the quantization algorithm set by ::vl_kmeans_set_algorithm.
 ** The process is repeated one or more times (see
 ** ::vl_kmeans_set_num_repetitions) and the resutl with smaller
 ** energy is retained.
 **/

VL_EXPORT double
vl_kmeans_cluster (VlKMeans * self,
                   void const * data,
                   vl_size dimension,
                   vl_size numData,
                   vl_size numCenters)
{
  vl_uindex repetition ;
  double bestEnergy = VL_INFINITY_D ;
  void * bestCenters = NULL ;

  for (repetition = 0 ; repetition < self->numRepetitions ; ++ repetition) {
    double energy ;
    double timeRef ;

    if (self->verbosity) {
      VL_PRINTF("kmeans: repetition %d of %d\n", repetition + 1, self->numRepetitions) ;
    }

    timeRef = vl_get_cpu_time() ;
    switch (self->initialization) {
      case VlKMeansRandomSelection :
        vl_kmeans_init_centers_with_rand_data (self,
                                               data, dimension, numData,
                                               numCenters) ;
        break ;
      case VlKMeansPlusPlus :
        vl_kmeans_init_centers_plus_plus (self,
                                          data, dimension, numData,
                                          numCenters) ;
        break ;
      default:
        abort() ;
    }

    if (self->verbosity) {
      VL_PRINTF("kmeans: K-means initialized in %.2f s\n",
                vl_get_cpu_time() - timeRef) ;
    }

    timeRef = vl_get_cpu_time () ;
    energy = vl_kmeans_refine_centers (self, data, numData) ;
    if (self->verbosity) {
      VL_PRINTF("kmeans: K-means terminated in %.2f s with energy %g\n",
                vl_get_cpu_time() - timeRef, energy) ;
    }

    /* copy centers to output if current solution is optimal */
    /* check repetition == 0 as well in case energy = NaN, which */
    /* can happen if the data contain NaNs */
    if (energy < bestEnergy || repetition == 0) {
      void * temp ;
      bestEnergy = energy ;

      if (bestCenters == NULL) {
        bestCenters = vl_malloc(vl_get_type_size(self->dataType) *
                                self->dimension *
                                self->numCenters) ;
      }

      /* swap buffers */
      temp = bestCenters ;
      bestCenters = self->centers ;
      self->centers = temp ;
    } /* better energy */
  } /* next repetition */

  vl_free (self->centers) ;
  self->centers = bestCenters ;
  return bestEnergy ;
}

/* VL_KMEANS_INSTANTIATING */
#endif

#undef SFX
#undef TYPE
#undef FLT
#undef VL_KMEANS_INSTANTIATING
vlfeat/vl/scalespace.h0000644000175000017500000000675212237004745013660 0ustar  dimadima/** @file scalespace.h
 ** @brief Scale Space (@ref scalespace)
 ** @author Andrea Vedaldi
 ** @author Karel Lenc
 ** @author Michal Perdoch
 **/

/*
Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
All rights reserved.

This file is part of the VLFeat library and is made available under
the terms of the BSD license (see the COPYING file).
*/

#ifndef VL_SCALESPACE_H
#define VL_SCALESPACE_H

#include "generic.h"
#include "imopv.h"
#include "mathop.h"

/* ---------------------------------------------------------------- */
/*                                             VlScaleSpaceGeometry */
/* ---------------------------------------------------------------- */

/** @brief Geometry of a scale space
 **
 ** There are a few restrictions on the valid geometrties.
 */
typedef struct _VlScaleSpaceGeometry
{
  vl_size width ; /**< Image width */
  vl_size height ; /**< Image height */
  vl_index firstOctave ; /**< Index of the fisrt octave */
  vl_index lastOctave ; /**< Index of the last octave */
  vl_size octaveResolution ; /**< Number of octave subdivisions */
  vl_index octaveFirstSubdivision ; /**< Index of the first octave subdivsion */
  vl_index octaveLastSubdivision ; /**< Index of the last octave subdivision */
  double baseScale ; /**< Base smoothing (smoothing of octave 0, level 0) */
  double nominalScale ; /**< Nominal smoothing of the original image */
} VlScaleSpaceGeometry ;

VL_EXPORT
vl_bool vl_scalespacegeometry_is_equal (VlScaleSpaceGeometry a,
                                        VlScaleSpaceGeometry b) ;

/* ---------------------------------------------------------------- */
/*                                       VlScaleSpaceOctaveGeometry */
/* ---------------------------------------------------------------- */

/** @brief Geometry of one octave of a scale space */
typedef struct _VlScaleSpaceOctaveGeometry
{
  vl_size width ; /**< Width (number of pixels) */
  vl_size height ; /**< Height (number of pixels) */
  double step ; /**< Sampling step (size of a pixel) */
} VlScaleSpaceOctaveGeometry ;

/* ---------------------------------------------------------------- */
/*                                                     VlScaleSpace */
/* ---------------------------------------------------------------- */

typedef struct _VlScaleSpace VlScaleSpace ;

/** @name Create and destroy
 ** @{
 **/
VL_EXPORT VlScaleSpaceGeometry vl_scalespace_get_default_geometry(vl_size width, vl_size height) ;
VL_EXPORT VlScaleSpace * vl_scalespace_new (vl_size width, vl_size height) ;
VL_EXPORT VlScaleSpace * vl_scalespace_new_with_geometry (VlScaleSpaceGeometry geom) ;
VL_EXPORT VlScaleSpace * vl_scalespace_new_copy (VlScaleSpace* src);
VL_EXPORT VlScaleSpace * vl_scalespace_new_shallow_copy (VlScaleSpace* src);
VL_EXPORT void vl_scalespace_delete (VlScaleSpace *self) ;
/** @} */

/** @name Process data
 ** @{
 **/
VL_EXPORT void
vl_scalespace_put_image (VlScaleSpace *self, float const* image);
/** @} */

/** @name Retrieve data and parameters
 ** @{
 **/
VL_EXPORT VlScaleSpaceGeometry vl_scalespace_get_geometry (VlScaleSpace const * self) ;
VL_EXPORT VlScaleSpaceOctaveGeometry vl_scalespace_get_octave_geometry (VlScaleSpace const * self, vl_index o) ;
VL_EXPORT float *
vl_scalespace_get_level (VlScaleSpace * self, vl_index o, vl_index s) ;
VL_EXPORT float const *
vl_scalespace_get_level_const (VlScaleSpace const * self, vl_index o, vl_index s) ;
VL_EXPORT double
vl_scalespace_get_level_sigma (VlScaleSpace const *self, vl_index o, vl_index s) ;
/** @} */

/* VL_SCALESPACE_H */
#endif