pax_global_header00006660000000000000000000000064127751757720014536gustar00rootroot0000000000000052 comment=cfb2af1cf263647467bad345c60145eb83fb8209 facedetect-0.1/000077500000000000000000000000001277517577200134655ustar00rootroot00000000000000facedetect-0.1/COPYING.txt000066400000000000000000000431031277517577200153370ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. 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(Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. 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If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. 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It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. facedetect-0.1/README.rst000066400000000000000000000164231277517577200151620ustar00rootroot00000000000000facedetect: a simple face detector for batch processing ======================================================= `facedetect` is a simple face detector for batch processing. It answers the basic question: "Is there a face in this image?" and gives back either an exit code or the coordinates of each detected face in the standard output. The aim is to provide a basic command-line interface that's consistent and easy to use with software such as ImageMagick_, while progressively improving the detection algorithm over time. `facedetect` is used in software such as fgallery_ to improve the thumbnail cutting region, so that faces are always centered. Basic Usage ----------- By default `facedetect` outputs the rectangles of all the detected faces:: ./facedetect path/to/image.jpg 289 139 56 56 295 283 55 55 The output values are the X Y coordinates (from the top-left corner), followed by width and height. For debugging, you can examine the face positions directly overlaid on the source image using the ``-o`` flag:: ./facedetect -o test.jpg path/to/image.jpg To simply check if an image contains a face, use the ``-q`` switch and check the exit status:: ./facedetect -q path/to/image.jpg echo $? An exit status of 0 indicates the presence of at least one face. An exit status of 2 means that no face could be detected (1 is reserved for failures). The ``--center`` flag also exists for scripting convenience, and simply outputs the X Y coordinates of face centers:: ./facedetect --center path/to/image.jpg 317 167 322 310 The ``--biggest`` flag only outputs the biggest face in the image, while ``--best`` will attempt to select the face in focus and/or in the center of the frame. .. figure:: doc/biggest-best.jpg :align: center Comparison between ``--best`` (top) and ``--biggest`` (bottom). The chosen face is highlighted in yellow. Unless DOF or motion blur is used effectively by the photographer to separate the subject, ``--biggest`` would in most cases select the same face as ``--best``, while being significantly faster to compute. Examples -------- Sorting images with and without faces ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following example sorts pictures into two different "landscape" and "people" directories using the exit code:: for file in path/to/pictures/*.jpg; do name=$(basename "$file") if facedetect -q "$file"; then mv "$file" "path/to/people/$name" else mv "$file" "path/to/landscape/$name" fi done Blurring faces within an image ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following example uses the coordinates from `facedetect` to pixelate the faces in all the source images using `mogrify` (from ImageMagick_):: for file in path/to/pictures/*.jpg; do name=$(basename "$file") out="path/to/blurred/$name" cp "$file" "$out" facedetect "$file" | while read x y w h; do mogrify -gravity NorthWest -region "${w}x${h}+${x}+${y}" \ -scale '10%' -scale '1000%' "$out" done done Here ``mogrify`` is called for each output line of `facedetect` (which is sub-optimal), modifying the file in-place. Extracting all faces to separate images ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The following example uses ``convert`` from ImageMagick_ to extract each face in each source image ``img.jpg`` to a separated image ``img_N.jpg``:: for file in path/to/pictures/*.jpg; do name=$(basename "$file") i=0 facedetect "$file" | while read x y w h; do convert "$file" -crop ${w}x${h}+${x}+${y} "path/to/faces/${name%.*}_${i}.${name##*.}" i=$(($i+1)) done done Searching for a face -------------------- `facedetect` has some naïve support to search for a specific face as supplied with the ``-s`` file argument. The file provided must be an image containing preferably a *single* face. `facedetect` will then compare all faces against it, and output only the matches which are above the requested similarity threshold (30% by default). When face search is used with ``-q`` (query), and exit status of 0 is only emitted if there is at least one face matching the requested template. The similarity threshold can be controlled with ``--search-threshold``, which is a value between -100 and 100, with greater values resulting in greater similarity. The current matching algorithm is based on simple MSSIM which is far from perfect (see `Development status and ideas`_). Dependencies ------------ The following software is currently required for `facedetect`: - Python - Python OpenCV (``python-opencv``) - OpenCV data files (``opencv-data`` if available, or ``libopencv-dev``) On Debian/Ubuntu, you can install all the required dependencies with:: sudo apt-get install python python-opencv libopencv-dev and then install `facedetect` with:: sudo cp facedetect /usr/local/bin Development status and ideas ---------------------------- Currently `facedetect` is not much beyond a simple wrapper over the Haar Cascade classifier of OpenCV and the ``frontalface_alt2`` profile, which provided the best results in terms of accuracy/detection rate for the general, real life photos at my disposal. In terms of speed, the LBP classifier was faster. But while the general theory states that it should also be more accurate, the ``lbp_frontalface`` profile didn't provide comparable results, suggesting that additional training is necessary. If some training dataset is found though, creating an LBP profile would probably be a better solution especially for the processing speed. ``haar_profileface`` had too many false positives in my tests to be usable. Using it in combination with ``haar_eye`` (and other face parts) though, to reduce the false positive rates and/or rank the regions, might be a very good solution instead. Both LBP and Haar don't play too well with rotated faces. This is particularly evident with "artistic" portraits shot at an angle. Pre-rotating the image using the information from a Hough transform might boost the detection rate in many cases, and should be relatively straightforward to implement. Face matching has the interface that user's expect ("find me *this* face"), but doesn't work as it should. Faces are currently compared using pairwise MSSIM, which is a far cry from proper face segmentation. MSSIM will only find faces that have comparable orientation, expression and lighting conditions. HAAR features do not provide the positioning accuracy required to perform even the simplest face segmentation operations, such as inter-eye distance. Interestingly, computing a score using 1:1 SIFT feature matches performed even worse than plain MSSIM (not enough granularity in most scenarios). Building a GUI on top of facedetect to train SVM models (which can then be fed back to ``-s``) seems a better way to go given the far greater accuracy, but somehow deviates from the original intention of unsupervised search. Authors and Copyright --------------------- `facedetect` can be found at https://www.thregr.org/~wavexx/software/facedetect/ | `facedetect` is distributed under GPLv2+ (see COPYING) WITHOUT ANY WARRANTY. | Copyright(c) 2013-2016 by wave++ "Yuri D'Elia" . facedetect's GIT repository is publicly accessible at:: git://src.thregr.org/facedetect or at https://github.com/wavexx/facedetect .. _ImageMagick: http://www.imagemagick.org .. _fgallery: https://www.thregr.org/~wavexx/software/fgallery/ facedetect-0.1/doc/000077500000000000000000000000001277517577200142325ustar00rootroot00000000000000facedetect-0.1/doc/biggest-best.jpg000066400000000000000000003211741277517577200173230ustar00rootroot00000000000000JFIFHHC     C   a"R9xq."RHPf^ۜYm1+mWӆn-F93f7i$Ā6L-=nE.M9WO=6IB2PδgjSuNǨW;WEaFP \<&ф3 jGppppppppr y*i\J8gG5ihCMR3.0 hhSpipn!&m<}ŦkMّE̍`v dX@ qEF Ex6zZ=A}ə[ (N Btmr퉲fRͶ(E_V\X}̼qdYDQȚJw~c6i}"GAc&٤tX D9 $ݦ,~J( jp@IHL؈Wg*E|" 7Zze|PH}\dhS_˹ B"سe_"=EY8pn4=f}*rK?1{QHpe[3|# DDzy2\2)77A]eu#ţmr l$NQS۵m̟#Ѹ'd6r(xBV[r4[8MB3#"` y} j4ͩ(}j,Q2"[#&b-I4l#eH"1}-벝&9a`?*.$% LΖ\U8Y; b%\ӡfhho~y=*鳡P1:[Yt.P2DbK@ F@su%⻱q򆙖suj4#ӌa!MrfDfJFBU*mǫb "EZ)eQN5\+,tuZ83@N[vQ-/ f&S9%UyEjkizMD3lX)@+d ;NϚÈaP6NyJ~& 16qmDmtRq&ʝt͵g r?_6vx]wˊo-o? 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facedetect: a simple face detector for batch processing # Copyright(c) 2013-2016 by wave++ "Yuri D'Elia" # Distributed under GPLv2+ (see COPYING) WITHOUT ANY WARRANTY. from __future__ import print_function, division import argparse import numpy as np import cv2 import math import sys import os # CV compatibility stubs if 'IMREAD_GRAYSCALE' not in dir(cv2): # <2.4 cv2.IMREAD_GRAYSCALE = 0 if 'cv' in dir(cv2): # <3.0 cv2.CASCADE_DO_CANNY_PRUNING = cv2.cv.CV_HAAR_DO_CANNY_PRUNING cv2.CASCADE_FIND_BIGGEST_OBJECT = cv2.cv.CV_HAAR_FIND_BIGGEST_OBJECT cv2.FONT_HERSHEY_SIMPLEX = cv2.cv.InitFont(cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 0.5, 0.5, 0, 1, cv2.cv.CV_AA) cv2.LINE_AA = cv2.cv.CV_AA def getTextSize(buf, font, scale, thickness): return cv2.cv.GetTextSize(buf, font) def putText(im, line, pos, font, scale, color, thickness, lineType): return cv2.cv.PutText(cv2.cv.fromarray(im), line, pos, font, color) cv2.getTextSize = getTextSize cv2.putText = putText # Profiles DATA_DIR = '/usr/share/opencv/' CASCADES = {} PROFILES = { 'HAAR_FRONTALFACE_ALT2': 'haarcascades/haarcascade_frontalface_alt2.xml', } # Face normalization NORM_SIZE = 100 NORM_MARGIN = 10 # Support functions def error(msg): sys.stderr.write("{}: error: {}\n".format(os.path.basename(sys.argv[0]), msg)) def fatal(msg): error(msg) sys.exit(1) def load_cascades(data_dir): for k, v in PROFILES.iteritems(): v = os.path.join(data_dir, v) try: if not os.path.exists(v): raise cv2.error('no such file') CASCADES[k] = cv2.CascadeClassifier(v) except cv2.error: fatal("cannot load {} from {}".format(k, v)) def crop_rect(im, rect, shave=0): return im[rect[1]+shave:rect[1]+rect[3]-shave, rect[0]+shave:rect[0]+rect[2]-shave] def shave_margin(im, margin): return im[margin:-margin, margin:-margin] def norm_rect(im, rect, equalize=True, same_aspect=False): roi = crop_rect(im, rect) if equalize: roi = cv2.equalizeHist(roi) side = NORM_SIZE + NORM_MARGIN if same_aspect: scale = side / max(rect[2], rect[3]) dsize = (int(rect[2] * scale), int(rect[3] * scale)) else: dsize = (side, side) roi = cv2.resize(roi, dsize, interpolation=cv2.INTER_CUBIC) return shave_margin(roi, NORM_MARGIN) def rank(im, rects): scores = [] best = None for i in range(len(rects)): rect = rects[i] roi_n = norm_rect(im, rect, equalize=False, same_aspect=True) roi_l = cv2.Laplacian(roi_n, cv2.CV_8U) e = np.sum(roi_l) / (roi_n.shape[0] * roi_n.shape[1]) dx = im.shape[1] / 2 - rect[0] + rect[2] / 2 dy = im.shape[0] / 2 - rect[1] + rect[3] / 2 d = math.sqrt(dx ** 2 + dy ** 2) / (max(im.shape) / 2) s = (rect[2] + rect[3]) / 2 scores.append({'s': s, 'e': e, 'd': d}) sMax = max([x['s'] for x in scores]) eMax = max([x['e'] for x in scores]) for i in range(len(scores)): s = scores[i] sN = s['sN'] = s['s'] / sMax eN = s['eN'] = s['e'] / eMax f = s['f'] = eN * 0.7 + (1 - s['d']) * 0.1 + sN * 0.2 ranks = range(len(scores)) ranks = sorted(ranks, reverse=True, key=lambda x: scores[x]['f']) for i in range(len(scores)): scores[ranks[i]]['RANK'] = i return scores, ranks[0] def mssim_norm(X, Y, K1=0.01, K2=0.03, win_size=11, sigma=1.5): C1 = K1 ** 2 C2 = K2 ** 2 cov_norm = win_size ** 2 ux = cv2.GaussianBlur(X, (win_size, win_size), sigma) uy = cv2.GaussianBlur(Y, (win_size, win_size), sigma) uxx = cv2.GaussianBlur(X * X, (win_size, win_size), sigma) uyy = cv2.GaussianBlur(Y * Y, (win_size, win_size), sigma) uxy = cv2.GaussianBlur(X * Y, (win_size, win_size), sigma) vx = cov_norm * (uxx - ux * ux) vy = cov_norm * (uyy - uy * uy) vxy = cov_norm * (uxy - ux * uy) A1 = 2 * ux * uy + C1 A2 = 2 * vxy + C2 B1 = ux ** 2 + uy ** 2 + C1 B2 = vx + vy + C2 D = B1 * B2 S = (A1 * A2) / D return np.mean(shave_margin(S, (win_size - 1) // 2)) def face_detect(im, biggest=False): side = math.sqrt(im.size) minlen = int(side / 20) maxlen = int(side / 2) flags = cv2.CASCADE_DO_CANNY_PRUNING # optimize queries when possible if biggest: flags |= cv2.CASCADE_FIND_BIGGEST_OBJECT # frontal faces cc = CASCADES['HAAR_FRONTALFACE_ALT2'] features = cc.detectMultiScale(im, 1.1, 4, flags, (minlen, minlen), (maxlen, maxlen)) return features def face_detect_file(path, biggest=False): im = cv2.imread(path, cv2.IMREAD_GRAYSCALE) if im is None: fatal("cannot load input image {}".format(path)) im = cv2.equalizeHist(im) features = face_detect(im, biggest) return im, features def pairwise_similarity(im, features, template, **mssim_args): template = np.float32(template) / 255 for rect in features: roi = norm_rect(im, rect) roi = np.float32(roi) / 255 yield mssim_norm(roi, template, **mssim_args) def __main__(): ap = argparse.ArgumentParser(description='A simple face detector for batch processing') ap.add_argument('--biggest', action="store_true", help='Extract only the biggest face') ap.add_argument('--best', action="store_true", help='Extract only the best matching face') ap.add_argument('-c', '--center', action="store_true", help='Print only the center coordinates') ap.add_argument('--data-dir', metavar='DIRECTORY', default=DATA_DIR, help='OpenCV data files directory') ap.add_argument('-q', '--query', action="store_true", help='Query only (exit 0: face detected, 2: no detection)') ap.add_argument('-s', '--search', metavar='FILE', help='Search for faces similar to the one supplied in FILE') ap.add_argument('--search-threshold', metavar='PERCENT', type=int, default=30, help='Face similarity threshold (default: 30%%)') ap.add_argument('-o', '--output', help='Image output file') ap.add_argument('-d', '--debug', action="store_true", help='Add debugging metrics in the image output file') ap.add_argument('file', help='Input image file') args = ap.parse_args() load_cascades(args.data_dir) # detect faces in input image im, features = face_detect_file(args.file, args.query or args.biggest) # match against the requested face sim_scores = None if args.search: s_im, s_features = face_detect_file(args.search, True) if len(s_features) == 0: fatal("cannot detect face in template") sim_scores = [] sim_features = [] sim_threshold = args.search_threshold / 100 sim_template = norm_rect(s_im, s_features[0]) for i, score in enumerate(pairwise_similarity(im, features, sim_template)): if score >= sim_threshold: sim_scores.append(score) sim_features.append(features[i]) features = sim_features # exit early if possible if args.query: return 0 if len(features) else 2 # compute scores scores = [] best = None if len(features) and (args.debug or args.best or args.biggest or sim_scores): scores, best = rank(im, features) if sim_scores: for i in range(len(features)): scores[i]['MSSIM'] = sim_scores[i] # debug features if args.output: im = cv2.imread(args.file) font = cv2.FONT_HERSHEY_SIMPLEX fontHeight = cv2.getTextSize("", font, 0.5, 1)[0][1] + 5 for i in range(len(features)): if best is not None and i != best and not args.debug: next rect = features[i] fg = (0, 255, 255) if i == best else (255, 255, 255) xy1 = (rect[0], rect[1]) xy2 = (rect[0] + rect[2], rect[1] + rect[3]) cv2.rectangle(im, xy1, xy2, (0, 0, 0), 4) cv2.rectangle(im, xy1, xy2, fg, 2) if args.debug: lines = [] for k, v in scores[i].iteritems(): lines.append("{}: {}".format(k, v)) h = rect[1] + rect[3] + fontHeight for line in lines: cv2.putText(im, line, (rect[0], h), font, 0.5, fg, 1, cv2.LINE_AA) h += fontHeight cv2.imwrite(args.output, im) # output if (args.best or args.biggest) and best is not None: features = [features[best]] if args.center: for rect in features: x = int(rect[0] + rect[2] / 2) y = int(rect[1] + rect[3] / 2) print("{} {}".format(x, y)) else: for rect in features: print("{} {} {} {}".format(*rect)) return 0 if __name__ == '__main__': sys.exit(__main__())