node-opencv/0000755000175000017500000000000013671667364011434 5ustar devdevnode-opencv/.editorconfig0000644000175000017500000000103613671667363014110 0ustar devdevroot = true # Unix-style newlines with a newline ending every file [*] end_of_line = lf insert_final_newline = true # Matches multiple files with brace expansion notation # Set default charset [*.{js,cpp}] charset = utf-8 # Tab indentation (no size specified) [Makefile] indent_style = tab indent_size = 2 # Indentation override for all JS under lib directory [*.{js,cpp}] indent_style = space indent_size = 2 # Matches the exact files either package.json or .travis.yml [{package.json,.travis.yml}] indent_style = space indent_size = 2 node-opencv/index.html0000755000175000017500000000047213671667364013437 0ustar devdev node-opencv/MIT-LICENSE.txt0000644000175000017500000000206313671667363013706 0ustar devdevCopyright 2012 Peter Braden and other contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. node-opencv/package.json0000644000175000017500000000124113671667364013720 0ustar devdev{ "name": "opencv", "version": "7.0.0", "description": "Node Bindings to OpenCV", "author": "Peter Braden ", "dependencies": { "buffers": "^0.1.1", "nan": "^2.14.0" }, "devDependencies": { "glob": "^5.0.3", "request": "^2.88.2", "tape": "^3.0.0" }, "license": "MIT", "scripts": { "build": "node-gyp build", "test": "node test/unit.js" }, "keywords": [ "opencv", "computer", "vision", "quadrocopter" ], "main": "./lib/opencv", "repository": { "type": "git", "url": "https://github.com/peterbraden/node-opencv.git" }, "engines": { "node": ">=0.12" } } node-opencv/examples/0000755000175000017500000000000013671667364013252 5ustar devdevnode-opencv/examples/car-detection.js0000644000175000017500000000112213671667364016325 0ustar devdev/* For some reason the cascade file is broken on linux :( var cv = require('../lib/opencv'); cv.readImage("./files/car1.jpg", function(err, im){ if (err) throw err; if (im.width() < 1 || im.height() < 1) throw new Error('Image has no size'); im.detectObject("../data/hogcascade_cars_sideview.xml", {}, function(err, cars){ if (err) throw err; for (var i=0; i < cars.length; i++){ var x = cars[i]; im.rectangle([x.x, x.y], [x.width, x.height]); } im.save('./tmp/car-detection.jpg'); console.log('Image saved to ./tmp/car-detection.jpg'); }); }); */ node-opencv/examples/mat-normalize.js0000644000175000017500000000116713671667364016374 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./examples/files/mona.png", function(err, orig) { /* if (err) throw err; orig.convertGrayscale(); var mask = new cv.Matrix(orig.height(),orig.width(),cv.Constants.CV_8UC1); var buf = Buffer(orig.height()*orig.width()); buf.fill(0); for(var i=0;i 0, then a reference ptr is present, and the value returned. // if -1, no reference pointer was present (i.e. the mat does not know about any data). var refcount = im.getrefCount(); console.log('initial refcount '+refcount); if (refcount !== 1) throw "refcountmismatch - initial should be 1"; im.release(); var refcount3 = im.getrefCount(); console.log('refcount after release (seems should be -1) '+refcount3); if (refcount3 !== -1) throw "refcountmismatch - after release should be -1"; // data is now still there somewhere, but lost to us - this is NOT a good situation. // should not fail - but will do absolutely nothing im.release(); console.log('did all refcount tests'); }); node-opencv/examples/bgsubtractor.js0000644000175000017500000000501513671667364016312 0ustar devdevvar path = require('path'), cv = require('../lib/opencv'); var bg = null; // gc stuff which we shojld not need with release() begin used //require("v8").setFlagsFromString('--expose_gc'); //var gc = require("vm").runInNewContext('gc'); var do_sync = function(done){ // When opening a file, the full path must be passed to opencv var vid = new cv.VideoCapture(path.join(__dirname, 'files', 'motion.mov')); var x = 0; // synchronous var iter = function(){ vid.read(function(err, m2){ if (err) throw err; var mat = bg.apply(m2); m2.release(); delete m2; // mat is a monochrome img where moving objects are white // do something with the return data // we are done with the return data mat.release(); delete mat; if (x++<100){ //console.log("iter "+x); setTimeout(iter, 1); } else { delete vid; console.log("bg sync done"); // gc(); - no need to gc if we release both m2 and mat if (undefined !== done) done(); } }) }; iter(); }; var do_async = function(done){ // restart video read var vid = new cv.VideoCapture(path.join(__dirname, 'files', 'motion.mov')); var x = 0; // asynchronous var iter = function(){ vid.read(function(err, m2){ if (err) throw err; bg.apply(m2, function(err, mat){ // do something with the return data // we are done with the return data mat.release(); delete mat; if (err) throw err; // mat is a monochrome img where moving objects are white if (x++<100){ //console.log("iter "+x); iter(); } else { console.log("bg async done"); delete vid; // gc(); - no need to gc if we release both m2 and mat if (undefined !== done) done(); } }); m2.release(); }) }; iter(); }; var do_default = function( done ){ console.log("doing Default"); bg = new cv.BackgroundSubtractor(); do_sync( function(){ do_async(done); }); } var do_gmg = function( done ){ console.log("doing GMG"); bg = cv.BackgroundSubtractor.createGMG(); do_sync( function(){ //console.log("not doing GMG Async - crashes my pi"); do_async(done); }); } var do_mog = function( done ){ console.log("doing MOG"); bg = cv.BackgroundSubtractor.createMOG(); do_sync( function(){ do_async(done); }); } var do_mog2 = function( done ){ console.log("doing MOG2"); bg = cv.BackgroundSubtractor.createMOG2(); do_sync( function(){ do_async(done); }); } do_default(function(){ do_mog(function(){ do_mog2(function(){ do_gmg(function(){ }); }); }); }); node-opencv/examples/face-detection.js0000755000175000017500000000112113671667364016460 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./files/mona.png", function(err, im){ if (err) throw err; if (im.width() < 1 || im.height() < 1) throw new Error('Image has no size'); im.detectObject("../data/haarcascade_frontalface_alt.xml", {}, function(err, faces){ if (err) throw err; for (var i = 0; i < faces.length; i++){ var face = faces[i]; im.ellipse(face.x + face.width / 2, face.y + face.height / 2, face.width / 2, face.height / 2); } im.save('./tmp/face-detection.png'); console.log('Image saved to ./tmp/face-detection.png'); }); }); node-opencv/examples/take-face-pics.js0000644000175000017500000000157513671667364016374 0ustar devdevvar cv = require('../lib/opencv'); try { var vid = new cv.VideoCapture(0); vid.read(function(err, im){ if (err) throw err; if (im.size()[0] > 0 && im.size()[1] > 0){ im.detectObject(cv.FACE_CASCADE, {}, function(err, faces){ if (err) throw err; if (!faces.length) return console.log("No Faces"); var face = faces[0]; var ims = im.size(); var im2 = im.roi(face.x, face.y, face.width, face.height) /* im.adjustROI( -face.y , (face.y + face.height) - ims[0] , -face.x , (face.x + face.width) - ims[1]) */ im2.save('./examples/tmp/take-face-pics.jpg') console.log('Image saved to ./tmp/take-face-pics.jpg'); }) } else { console.log("Camera didn't return image") } }); } catch (e){ console.log("Couldn't start camera", e) } node-opencv/examples/face-detection-rectangle.js0000755000175000017500000000123013671667364020423 0ustar devdevvar cv = require('../lib/opencv'); var COLOR = [0, 255, 0]; // default red var thickness = 2; // default 1 cv.readImage('./files/mona.png', function(err, im) { if (err) throw err; if (im.width() < 1 || im.height() < 1) throw new Error('Image has no size'); im.detectObject('../data/haarcascade_frontalface_alt2.xml', {}, function(err, faces) { if (err) throw err; for (var i = 0; i < faces.length; i++) { var face = faces[i]; im.rectangle([face.x, face.y], [face.width, face.height], COLOR, 2); } im.save('./tmp/face-detection-rectangle.png'); console.log('Image saved to ./tmp/face-detection-rectangle.png'); }); }); node-opencv/examples/face-proxy.js0000755000175000017500000000215613671667364015674 0ustar devdev// Face recognition proxy var http = require('http'), request = require('request'), cv = require('../lib/opencv'); var server = http.createServer(function(req, resp){ var url = req.url.slice(1); request({uri:url, encoding:'binary'}, function(err, r, body){ if (err) return resp.end(err.stack); if (!/image\//.test(r.headers['content-type'])) return resp.end('Not an image'); cv.readImage(new Buffer(body, 'binary'), function(err, im){ if (err) return resp.end(err.stack); if (im.width() < 1 || im.height() < 1) return resp.end('Image has no size'); im.detectObject('../data/haarcascade_frontalface_alt.xml', {}, function(err, faces) { if (err) return resp.end(err.stack); for (var i = 0; i < faces.length; i++){ var face = faces[i]; im.ellipse(face.x + face.width / 2, face.y + face.height / 2, face.width / 2, face.height / 2); } resp.writeHead(200, {'Content-Type': 'image/jpeg'}); resp.end(im.toBuffer()); }); }); }); }) //server.listen(3000, function(){ console.log('Listening on http://localhost:3000'); }) node-opencv/examples/mat-put.js0000644000175000017500000000060213671667364015175 0ustar devdevvar opencv = require('../lib/opencv'); var mat = new opencv.Matrix(256,256,opencv.Constants.CV_8UC1); var buf = Buffer(256*256); buf.fill(0); for(var i=0;i<256*256;i++){ buf[i] = (i%2===1)?230:0; } mat.put(buf); /* var window = new opencv.NamedWindow('Mat-Put', 0); window.show(mat); window.blockingWaitKey(0, 50); setTimeout(function(){ // keep the windows 30sec open },30000); */ node-opencv/examples/dissimilarity.js0000644000175000017500000000074613671667364016505 0ustar devdevvar cv = require('../lib/opencv'); if (cv.ImageSimilarity === undefined) { console.log('TODO: Please port Features2d.cc to OpenCV 3') process.exit(0); } cv.readImage("./files/car1.jpg", function(err, car1) { if (err) throw err; cv.readImage("./files/car2.jpg", function(err, car2) { if (err) throw err; cv.ImageSimilarity(car1, car2, function (err, dissimilarity) { if (err) throw err; console.log('Dissimilarity: ', dissimilarity); }); }); }); node-opencv/examples/emd.js0000644000175000017500000001136213671667364014360 0ustar devdevvar cv = require('../lib/opencv'); // // Example of use of EMD distance using histograms // 1. Build 2 histograms from images using calcHist // 2. Transform each histogram to a 64 x 4 (hist, b, g, r) x 1 normalized signatures in BGR space // 3. Compute the cost matrix (64 x 64 x 1), calculating the cost in LUV space // 4. Run EMD algorithm // /// Useful flatten function for step 2 function flatten(array, accu) { if(!accu){ accu = []; } array.forEach(function(a){ if(Array.isArray(a)) { flatten(a, accu) } else { accu.push(a) } }); return accu }; cv.readImage('./files/car1.jpg', function(err, im1) { if (err) throw err; if (im1.width() < 1 || im1.height() < 1) throw new Error('Image has no size'); cv.readImage('./files/car2.jpg', function(err, im2) { if (err) throw err; if (im2.width() < 1 || im2.height() < 1) throw new Error('Image has no size'); /////////////////// // 1. Build 2 histograms from images using calcHist ////////////////// var size = [4, 4, 4], channels = [0, 1, 2], range = [[0, 256], [0, 256], [0, 256]], uniform = true, accumulate = true, histFile = 'files/chart2.png'; /// Compute 64 (=4^3) histograms: var firstImageHist64 = cv.histogram.calcHist(im1, channels, size, range, uniform); var secondImageHist64 = cv.histogram.calcHist(im2, channels, size, range, uniform); ////////////// // 2. Transform each histogram to a 64 x 4 (hist, b, g, r) x 1 normalized signatures in BGR space //////////////// var step = 256/4; var halfStep = Math.round(step/2); var sum1 = 0; var sum2 = 0; firstImageHist64.map(function(bHist, bIndex){ return bHist.map(function(bgHist, gIndex){ return bgHist.map(function(bgrHist, rIndex){ sum1 += bgrHist; }) }) }) var sig1 = flatten(firstImageHist64.map(function(bHist, bIndex){ return bHist.map(function(bgHist, gIndex){ return bgHist.map(function(bgrHist, rIndex){ return { data :[ [bgrHist/sum1], [bIndex*step + halfStep], [gIndex*step + halfStep], [rIndex*step + halfStep] ] } }) }) })).map(function(a){ // trick to avoid flattening and get a 64 x 4 x 1 image as needed return a.data; }); secondImageHist64.map(function(bHist, bIndex){ return bHist.map(function(bgHist, gIndex){ return bgHist.map(function(bgrHist, rIndex){ sum2 += bgrHist; }) }) }) var sig2 = flatten(secondImageHist64.map(function(bHist, bIndex){ return bHist.map(function(bgHist, gIndex){ return bgHist.map(function(bgrHist, rIndex){ return { data : [ [bgrHist/sum2], [bIndex*step + halfStep], [gIndex*step + halfStep], [rIndex*step + halfStep] ] }; }) }) })).map(function(a){ // trick to avoid flattening and get a 64 x 4 x 1 image as needed return a.data; }); ///////////// // 3. Compute the cost matrix (64 x 64 x 1), calculating the cost in LUV space ///////////// //middles is a 1 x 64 x 3 array of the middles positions in RGB used to change to LUV var middles = [flatten(firstImageHist64.map(function(bHist, bIndex){ return bHist.map(function(bgHist, gIndex){ return bgHist.map(function(bgrHist, rIndex){ return { data : [ bIndex*step + halfStep, gIndex*step + halfStep, rIndex*step + halfStep ] } }) }) })).map(function(a){ // trick to avoid flattening and get a 1 x 64 x 3 image as needed return a.data; })]; var mat = cv.Matrix.fromArray(middles, cv.Constants.CV_8UC3); mat.cvtColor("CV_BGR2Luv"); //luvValues is a 1 x 64 x 3 array of the middles positions in LUV var luvMiddles = mat.toArray(); var distance = function(luv1, luv2){ return Math.sqrt((luv1[0]-luv2[0])*(luv1[0]-luv2[0]) + (luv1[1]-luv2[1])*(luv1[1]-luv2[1]) + (luv1[2]-luv2[2])*(luv1[2]-luv2[2])); }; var costs = luvMiddles[0].map(function(luvMiddle1){ return luvMiddles[0].map(function(luvMiddle2){ return [distance(luvMiddle1, luvMiddle2)]; }) }); ////// // 4. Run EMD algorithm ///// var matCosts = cv.Matrix.fromArray(costs, cv.Constants.CV_32FC1); var matSig1 = cv.Matrix.fromArray(sig1, cv.Constants.CV_32FC1); var matSig2 = cv.Matrix.fromArray(sig2, cv.Constants.CV_32FC1); var dist = cv.Constants.CV_DIST_L2; var emd = cv.histogram.emd(matSig1, matSig2, dist);//, matCosts); console.log("EMD is ", emd) }); }); node-opencv/examples/motion-track.js0000644000175000017500000000126213671667364016220 0ustar devdevvar path = require('path'), cv = require('../lib/opencv'); // When opening a file, the full path must be passed to opencv var vid = new cv.VideoCapture(path.join(__dirname, 'files', 'motion.mov')); vid.read(function(err, mat){ if (err) throw err; var track = new cv.TrackedObject(mat, [420, 110, 490, 170], {channel: 'value'}); var x = 0; var iter = function(){ vid.read(function(err, m2){ x++; var rec = track.track(m2) console.log('>>', x, ':' , rec) if (x % 10 == 0){ m2.rectangle([rec[0], rec[1]], [rec[2], rec[3]]) // m2.save('./out-motiontrack-' + x + '.jpg') } if (x<100) iter(); }) } iter(); }) node-opencv/examples/optical-flow.js0000755000175000017500000000307513671667364016220 0ustar devdevvar cv = require('../lib/opencv'); var path = require('path'); var cap = new cv.VideoCapture(path.join(__dirname, 'files', 'motion.mov')); // Parameters for lucas kanade optical flow var lk_params = { winSize: [15, 15], maxLevel: 2, criteria: [cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 30, 0.03] }; feature_params = { maxCorners: 100, qualityLevel: 0.1, minDistance: 10 }; // Create some random colors var color = [255, 0, 0]; /* var window = new cv.NamedWindow('Video', 0); */ // Take first frame and find corners in it cap.read(function(err, firstFrame) { if (err) throw err; var old_frame = firstFrame; // Create a mask image for drawing purposes function read() { var out = old_frame.copy(); cap.read(function(err, newFrame) { if (err) throw err; var frameSize = newFrame.size(); if ( frameSize[0] > 0 && frameSize[1] > 0) { var goodFeatures = old_frame.goodFeaturesToTrack(feature_params.maxCorners, feature_params.qualityLevel, feature_params.minDistance); // calculate optical flow var flow = old_frame.calcOpticalFlowPyrLK(newFrame, goodFeatures, lk_params.winSize, lk_params.maxLevel, lk_params.criteria); // Select good points // draw the tracks for(var i = 0; i < flow.old_points.length; i++){ if(flow.found[i]){ out.line(flow.old_points[i], flow.new_points[i], color); } } /* window.show(out); window.blockingWaitKey(0, 50); */ old_frame = newFrame.copy(); read(); } }); } read(); });node-opencv/examples/async-resize.js0000644000175000017500000000175613671667364016235 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./files/mona.png", function(err, im) { if (err) throw err; var width = im.width(); var height = im.height(); if (width < 1 || height < 1) throw new Error('Image has no size'); console.log('Image loaded from ./files/mona.png at '+im.width()+'x'+im.height()); var AfterResize = function(err, img){ if (err){ console.log('Error in resize:' + err); return; } img.save("./tmp/resize-async-image.png"); console.log('Image saved to ./tmp/resize-async-image.png at '+img.width()+'x'+img.height()); img.release(); delete img; }; var newwidth = width*0.95; var newheight = height*0.95; var Async = true; if (Async){ // note - generates a new image im.resize(newwidth, newheight, AfterResize); im.release(); // test release image before resize if done delete im; } else { // sync - note - modifies the input image im.resize(newwidth, newheight); AfterResize(null, im); } }); node-opencv/examples/contours.js0000755000175000017500000000247413671667364015476 0ustar devdevvar cv = require('../lib/opencv'); var lowThresh = 0; var highThresh = 100; var nIters = 2; var maxArea = 2500; var GREEN = [0, 255, 0]; // B, G, R var WHITE = [255, 255, 255]; // B, G, R var RED = [0, 0, 255]; // B, G, R cv.readImage('./files/stuff.png', function(err, im) { if (err) throw err; var width = im.width(); var height = im.height(); if (width < 1 || height < 1) throw new Error('Image has no size'); var big = new cv.Matrix(height, width); var all = new cv.Matrix(height, width); im.convertGrayscale(); var im_canny = im.copy(); im_canny.canny(lowThresh, highThresh); im_canny.dilate(nIters); var contours = im_canny.findContours(); const lineType = 8; const maxLevel = 0; const thickness = 1; for(i = 0; i < contours.size(); i++) { if(contours.area(i) > maxArea) { var moments = contours.moments(i); var cgx = Math.round(moments.m10 / moments.m00); var cgy = Math.round(moments.m01 / moments.m00); big.drawContour(contours, i, GREEN, thickness, lineType, maxLevel, [0, 0]); big.line([cgx - 5, cgy], [cgx + 5, cgy], RED); big.line([cgx, cgy - 5], [cgx, cgy + 5], RED); } } all.drawAllContours(contours, WHITE); big.save('./tmp/big.png'); all.save('./tmp/all.png'); console.log('Image saved to ./tmp/big.png && ./tmp/all.png'); }); node-opencv/examples/detect-shapes.js0000755000175000017500000000233413671667364016346 0ustar devdev// Detects triangles and quadrilaterals var cv = require('../lib/opencv'); var lowThresh = 0; var highThresh = 100; var nIters = 2; var minArea = 2000; var BLUE = [255, 0, 0]; // B, G, R var RED = [0, 0, 255]; // B, G, R var GREEN = [0, 255, 0]; // B, G, R var WHITE = [255, 255, 255]; // B, G, R cv.readImage('./files/shapes.jpg', function(err, im) { if (err) throw err; width = im.width() height = im.height() if (width < 1 || height < 1) throw new Error('Image has no size'); var out = new cv.Matrix(height, width); im.convertGrayscale(); im_canny = im.copy(); im_canny.canny(lowThresh, highThresh); im_canny.dilate(nIters); contours = im_canny.findContours(); for (i = 0; i < contours.size(); i++) { if (contours.area(i) < minArea) continue; var arcLength = contours.arcLength(i, true); contours.approxPolyDP(i, 0.01 * arcLength, true); switch(contours.cornerCount(i)) { case 3: out.drawContour(contours, i, GREEN); break; case 4: out.drawContour(contours, i, RED); break; default: out.drawContour(contours, i, WHITE); } } out.save('./tmp/detect-shapes.png'); console.log('Image saved to ./tmp/detect-shapes.png'); }); node-opencv/examples/camera.js0000755000175000017500000000070213671667364015042 0ustar devdevvar cv = require('../lib/opencv'); try { var camera = new cv.VideoCapture(0); var window = new cv.NamedWindow('Video', 0) /* setInterval(function() { camera.read(function(err, im) { if (err) throw err; console.log(im.size()) if (im.size()[0] > 0 && im.size()[1] > 0){ window.show(im); } window.blockingWaitKey(0, 50); }); }, 20); */ } catch (e){ console.log("Couldn't start camera:", e) } node-opencv/examples/files/0000755000175000017500000000000013671667364014354 5ustar devdevnode-opencv/examples/files/car1.jpg0000644000175000017500000103432213671667364015711 0ustar devdevJFIFExifII*HPhotoshop 3.08BIMZ%G8BIM%ȷx/4b4XwC C      Y!1A"Qa2q#BR$3b CSr%4cD&6'5sEFVd;!1AQ"a2qBR#CbS3 ?A@X_|<M~`d~5Gm V&>yps(ހ۾:8Eѡ˫G.iw7،CgD^{tbG/$[)J<=QiasP7T`_\aZY2}E=8|fk8/9 4J5)=v67rVPp\0k!u.0aqJ6a%a~ %EH;nzuqM0rlE6eZwU ؑ~&9i(PϦq}OȒJ+ fCFzuKF.ʉ39x%NK)Ѥ1&iGR4rOm;m9cI k|S`lHk6j1,,NmQcGjuZ mfY13*eF#MH1=gL( *P|ٽ[ZޘΈNUW_JDUi0 oUˡy U<HL#YP-qhCuHF0_{8aMU o#h,5H64Z%46&0?&p,jrj/m[[VesZH,P'kbbj#3W%;DI Rp@4ߣ81COUТMG*SyQU|o&WNR%h4RJP=.=ݕ-$UFUc$;z1n}O!Z73::bw BܙeC \v Oyvi5>uǙG|=fyVuCSJ EuXZ|c88{TlJcNSdRiѦ+(N|fn<}9˓6_F͛Q Ȟd; 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// Load the image cv.readImage('./files/note.png', function(err, im) { if (err) { throw err; } if (im.width() < 1 || im.height() < 1) { throw new Error('Image has no size'); } im.cvtColor('CV_BGR2GRAY'); var bw = im.adaptiveThreshold(255, 0, 0, 15, 2); bw.bitwiseNot(bw); var vertical = bw.clone(); var verticalsize = vertical.size()[0] / 30; var verticalStructure = cv.imgproc.getStructuringElement(1, [1, verticalsize]); // Apply morphology operations vertical.erode(1, verticalStructure); vertical.dilate(1, verticalStructure); vertical.bitwiseNot(vertical); vertical.gaussianBlur([3, 3]); // Save output image vertical.save('./tmp/note.png'); }); node-opencv/examples/make-example-files.sh0000755000175000017500000000012713671667364017257 0ustar devdevmencoder examples/motion.mov -ovc raw -vf format=i420 -nosound -o examples/motion.avi node-opencv/examples/readimage.js0000644000175000017500000001045013671667364015526 0ustar devdevvar cv = require('../lib/opencv'); // read as a buffer for decode operations later var fs = require('fs'); var imgdata = fs.readFileSync("./files/mona.png"); var img = cv.readImage("./files/mona.png"); console.log('Synchronous readImage("./files/mona.png")'+img.width()+'x'+img.height()); cv.readImage("./files/mona.png", function(err, im){ console.log('callback readImage("./files/mona.png", fn(){})'+im.width()+'x'+im.height()); }); img = cv.readImage( 100, 100 ); console.log('Synchronous readImage(100, 100) (create mat)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImage(100, 100, function(err, im){ console.log('callback readImage(100, 100, fn(){}) (create mat)'+im.width()+'x'+im.height()); }); img = cv.readImage(imgdata); console.log('Synchronous readImage(imgdata:Buffer)'+img.width()+'x'+img.height()); cv.readImage(imgdata, function(err, im){ console.log('callback readImage(imgdata:Buffer, fn(){})'+im.width()+'x'+im.height()); }); // try with flags now console.log('Now with flags'); img = cv.readImage("./files/mona.png", 0); console.log('Synchronous readImage("./files/mona.png", 0) (monochrome)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImage("./files/mona.png", 1, function(err, im){ console.log('callback readImage("./files/mona.png", 1, fn(){}) (colour)'+im.width()+'x'+im.height()+' type '+im.type()); }); img = cv.readImage( 100, 100, cv.Constants.CV_8UC3 ); console.log('Synchronous readImage(100, 100, cv.Constants.CV_8UC3) (create 8 bit 3 channel mat)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImage(100, 100, cv.Constants.CV_8UC1, function(err, im){ console.log('callback readImage(100, 100, cv.Constants.CV_8UC1, fn(){}) (create mat)'+im.width()+'x'+im.height()+' type '+im.type()); }); img = cv.readImage(imgdata, 0); console.log('Synchronous readImage(imgdata:Buffer, 0) (monochrome)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImage(imgdata, 1, function(err, im){ console.log('callback readImage(imgdata:Buffer, 1, fn(){}) (colour)'+im.width()+'x'+im.height()+' type '+im.type()); }); // try with readImageAsync console.log('Now with readImageAsync'); img = cv.readImageAsync("./files/mona.png"); console.log('Synchronous readImageAsync("./files/mona.png")'+img.width()+'x'+img.height()); cv.readImageAsync("./files/mona.png", function(err, im){ console.log('Asynchronous callback readImageAsync("./files/mona.png", fn(){})'+im.width()+'x'+im.height()); }); img = cv.readImageAsync( 100, 100 ); console.log('Synchronous readImageAsync(100, 100) (create mat)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImageAsync(100, 100, function(err, im){ console.log('!!Synchronous!! callback readImageAsync(100, 100, fn(){}) (create mat)'+im.width()+'x'+im.height()); }); img = cv.readImageAsync(imgdata); console.log('Synchronous readImageAsync(imgdata:Buffer)'+img.width()+'x'+img.height()); cv.readImageAsync(imgdata, function(err, im){ console.log('Asynchronous callback readImageAsync(imgdata:Buffer, fn(){})'+im.width()+'x'+im.height()); }); // try with flags now console.log('Now Async with flags'); img = cv.readImageAsync("./files/mona.png", 0); console.log('Synchronous readImageAsync("./files/mona.png", 0) (monochrome)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImageAsync("./files/mona.png", 1, function(err, im){ console.log('Asynchronous callback readImageAsync("./files/mona.png", 1, fn(){}) (colour)'+im.width()+'x'+im.height()+' type '+im.type()); }); img = cv.readImageAsync( 100, 100, cv.Constants.CV_8UC3 ); console.log('Synchronous readImageAsync(100, 100, cv.Constants.CV_8UC3) (create 8 bit 3 channel mat)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImageAsync(100, 100, cv.Constants.CV_8UC1, function(err, im){ console.log('!!Synchronous!! callback readImageAsync(100, 100, cv.Constants.CV_8UC1, fn(){}) (create mat)'+im.width()+'x'+im.height()+' type '+im.type()); }); img = cv.readImageAsync(imgdata, 0); console.log('Synchronous readImageAsync(imgdata:Buffer, 0) (monochrome)'+img.width()+'x'+img.height()+' type '+img.type()); cv.readImageAsync(imgdata, 1, function(err, im){ console.log('Asynchronous callback readImageAsync(imgdata:Buffer, 1, fn(){}) (colour)'+im.width()+'x'+im.height()+' type '+im.type()); }); console.log('\nTruely Async callbacks should be after this line\n'); node-opencv/examples/addweighted.js0000755000175000017500000000064513671667364016071 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./files/mona.png", function(err, orig) { if (err) throw err; cv.readImage("./files/over_text.png", function(err, over_text) { if (err) throw err; var result = new cv.Matrix(orig.width(), orig.height()); result.addWeighted(orig, 0.7, over_text, 0.9); result.save("./tmp/weighted.png"); console.log('Image saved to ./tmp/weighted.png'); }); }); node-opencv/examples/mat-array-conversion.js0000644000175000017500000000101213671667364017662 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./files/mona.png", function(err, orig) { if (err) throw err; var a = orig.toArray(); var type = orig.type(); var doubleConversion = cv.Matrix.fromArray(a, type).toArray(); for(var i = 0 ; i < a.length; i++){ for(var j = 0 ; j < a[i].length; j++){ for(var k = 0 ; k < a[i][j].length; k++){ if(a[i][j][k] !== doubleConversion[i][j][k]){ throw(new Error("double conversion is not equal to original")); } } } } }); node-opencv/examples/mat-dct.js0000644000175000017500000000107513671667364015144 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage("./files/mona.png", function(err, orig) { if (err) throw err; var chan1 = orig.split()[0]; var floatInputMatrix = new cv.Matrix(); chan1.convertTo(floatInputMatrix, cv.Constants.CV_32F) var foatAfterDct = floatInputMatrix.dct(); // for inverse dct, set argument to "true" var afterDoubleDct = foatAfterDct.dct(true); var intOutImage = new cv.Matrix(); afterDoubleDct.convertTo(intOutImage, cv.Constants.CV_8U); intOutImage.save("./tmp/dct.png"); console.log('Image saved to ./tmp/dct.png'); }); node-opencv/examples/convert-image.js0000755000175000017500000000103113671667364016346 0ustar devdevvar cv = require('../lib/opencv'); cv.readImage('./files/mona.png', function(err, im) { if (err) throw err; if (im.width() < 1 || im.height() < 1) throw new Error('Image has no size'); img_hsv = im.copy(); img_gray = im.copy(); img_hsv.convertHSVscale(); img_gray.convertGrayscale(); im.save('./tmp/nor.png'); img_hsv.save('./tmp/hsv.png'); img_gray.save('./tmp/gray.png'); img_crop = im.crop(50,50,250,250); img_crop.save('./tmp/crop.png'); console.log('Image saved to ./tmp/{crop|nor|hsv|gray}.png'); }); node-opencv/examples/quad-crosses.js0000755000175000017500000000247113671667364016230 0ustar devdev// Finds quadrilaterals and fills them with an X var cv = require('../lib/opencv'); var lowThresh = 0; var highThresh = 100; var nIters = 2; var minArea = 2000; var maxArea = 100000; var BLUE = [0, 255, 0]; //B, G, R var RED = [0, 0, 255]; //B, G, R var GREEN = [0, 255, 0]; //B, G, R var WHITE = [255, 255, 255]; //B, G, R cv.readImage('./files/quads.jpg', function(err, im) { if (err) throw err; if (im.width() < 1 || im.height() < 1) throw new Error('Image has no size'); var out = im.copy(); im.convertGrayscale(); im_canny = im.copy(); im_canny.canny(lowThresh, highThresh); im_canny.dilate(nIters); contours = im_canny.findContours(); for (i = 0; i < contours.size(); i++) { var area = contours.area(i); if (area < minArea || area > maxArea) continue; var arcLength = contours.arcLength(i, true); contours.approxPolyDP(i, 0.01 * arcLength, true); if (contours.cornerCount(i) != 4) continue; var points = [ contours.point(i, 0), contours.point(i, 1), contours.point(i, 2), contours.point(i, 3) ] out.line([points[0].x,points[0].y], [points[2].x, points[2].y], RED); out.line([points[1].x,points[1].y], [points[3].x, points[3].y], RED); } out.save('./tmp/quad-crosses.png'); console.log('Image saved to ./tmp/quad-crosses.png'); }); node-opencv/.gitignore0000755000175000017500000000021213671667363013421 0ustar devdevbuild .lock-wscript .DS_Store node_modules npm-debug.log out*.jpg out*.png examples/*.avi examples/tmp/* vagrant/.vagrant coverage/ .idea node-opencv/include_dirs.js0000644000175000017500000000102513671667364014434 0ustar devdev// Outputs the path to public node-opencv header files. This is used when // building other native node modules that require access to the // node-opencv Matrix base class. // // To use this file, add something like the following to your binding.gyp: // // "include_dirs": [ // "(Nan::To(info[0]).ToLocalChecked())->mat; // ... } */ #ifndef NODE_OPENCV_MATRIX_H #define NODE_OPENCV_MATRIX_H #include #include namespace node_opencv { class Matrix: public Nan::ObjectWrap { public: cv::Mat mat; protected: Matrix(): Nan::ObjectWrap() {}; }; } #endif // NODE_OPENCV_MATRIX_H node-opencv/src/0000755000175000017500000000000013671667364012223 5ustar devdevnode-opencv/src/OpenCV.cc0000755000175000017500000002517113671667364013675 0ustar devdev#include "OpenCV.h" #include "Matrix.h" #include void OpenCV::Init(Local target) { Nan::HandleScope scope; // Version string. char out [21]; int n = sprintf(out, "%i.%i", CV_MAJOR_VERSION, CV_MINOR_VERSION); target->Set(Nan::New("version").ToLocalChecked(), Nan::New(out, n).ToLocalChecked()); Nan::SetMethod(target, "readImage", ReadImage); Nan::SetMethod(target, "readImageAsync", ReadImageAsync); Nan::SetMethod(target, "readImageMulti", ReadImageMulti); } // worker which decodes an image from data. class AsyncImDecodeWorker: public Nan::AsyncWorker { public: AsyncImDecodeWorker(Nan::Callback *callback, uint8_t *buf, unsigned len, int flags = -1): Nan::AsyncWorker(callback), buf(buf), len(len), flags(flags), success(0){ } ~AsyncImDecodeWorker() { } void Execute() { try{ // don't new; just have a local class which will be removed. cv::Mat mbuf(len, 1, CV_64FC1, buf); outputmat = cv::imdecode(mbuf, flags); success = 1; } catch(...){ success = 0; } } void HandleOKCallback() { if (success){ Nan::HandleScope scope; try{ Local im_to_return = Matrix::CreateWrappedFromMat(outputmat); outputmat.release(); Local argv[] = { Nan::Null(), im_to_return }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } return; } catch (...){ } } // fall through here is !success or failed trying to callback. Local argv[] = { Nan::New("C++ exception executing imdecode").ToLocalChecked(), // err Nan::Null() }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: uint8_t *buf; unsigned len; int flags; cv::Mat outputmat; int success; //Matrix *img; }; // worker which reads an image from a file. class AsyncImReadWorker: public Nan::AsyncWorker { public: AsyncImReadWorker(Nan::Callback *callback, std::string filename, int flags = CV_LOAD_IMAGE_UNCHANGED): Nan::AsyncWorker(callback), filename(filename), flags(flags) { } ~AsyncImReadWorker() { } void Execute() { outputmat = cv::imread(filename, CV_LOAD_IMAGE_UNCHANGED); } void HandleOKCallback() { Nan::HandleScope scope; Local im_to_return = Matrix::CreateWrappedFromMat(outputmat); outputmat.release(); Local argv[] = { Nan::Null(), im_to_return }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: std::string filename; int flags; cv::Mat outputmat; }; NAN_METHOD(OpenCV::ReadImageAsync) { Nan::EscapableHandleScope scope; Local argv[2]; argv[0] = Nan::Null(); argv[1] = Nan::Null(); int callback_arg = -1; int numargs = info.Length(); Local cb; // deal with situation where we have int, int, cb if (info[numargs-1]->IsFunction()){ callback_arg = numargs-1; cb = Local::Cast(info[callback_arg]); } try { if (info[0]->IsNumber() && info[1]->IsNumber()) { ////////////////////////////// // create image from a filename // always do sync as this take no time? int width, height; int type = CV_64FC1; // if we have a type arg if ((numargs > 2) && info[2]->IsNumber()){ type = info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } width = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); height = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); Local img_to_return = Matrix::CreateWrappedFromMat(cv::Mat(width, height, type)); if (callback_arg < 0){ info.GetReturnValue().Set(img_to_return); return; } else { argv[0] = Nan::Null(); argv[1] = img_to_return; cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); return; } // WILL have returned by here unless exception ////////////////////////////// } else if (info[0]->IsString()) { ////////////////////////////// // read image from a filename std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); int flags = CV_LOAD_IMAGE_UNCHANGED; if (numargs > 1){ if (info[1]->IsNumber()){ flags = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } } if (callback_arg < 0){ Local img_to_return = Matrix::CreateWrappedFromMat(cv::imread(filename, flags)); info.GetReturnValue().Set(img_to_return); return; } else { Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncImReadWorker(callback, filename, flags)); return; } // WILL have returned by here unless exception ////////////////////////////// } else if (Buffer::HasInstance(info[0])) { ////////////////////////////// // read image from a buffer // if sync int flags = CV_LOAD_IMAGE_UNCHANGED; if (numargs > 1){ if (info[1]->IsNumber()){ flags = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } } if (callback_arg < 0){ uint8_t *buf = (uint8_t *) Buffer::Data(Nan::To(info[0]).ToLocalChecked()); unsigned len = Buffer::Length(Nan::To(info[0]).ToLocalChecked()); cv::Mat *mbuf = new cv::Mat(len, 1, CV_64FC1, buf); Local img_to_return = Matrix::CreateWrappedFromMat(cv::imdecode(*mbuf, flags)); info.GetReturnValue().Set(img_to_return); return; } else { // async uint8_t *buf = (uint8_t *) Buffer::Data(Nan::To(info[0]).ToLocalChecked()); unsigned len = Buffer::Length(Nan::To(info[0]).ToLocalChecked()); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncImDecodeWorker(callback, buf, len, flags)); return; } // WILL have returned by here unless exception ////////////////////////////// } } catch (cv::Exception& e) { argv[0] = Nan::Error(e.what()); argv[1] = Nan::Null(); } Nan::TryCatch try_catch; // if we got a callback if (callback_arg >= 0){ // if using callback cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); } else { // can only get here by exception info.GetReturnValue().Set(Nan::New(false)); } if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } return; } NAN_METHOD(OpenCV::ReadImage) { Nan::EscapableHandleScope scope; Local argv[2]; argv[0] = Nan::Null(); argv[1] = Nan::Null(); int callback_arg = -1; int numargs = info.Length(); int success = 1; Local cb; // deal with situation where we have int, int, cb if (info[numargs-1]->IsFunction()){ callback_arg = numargs-1; cb = Local::Cast(info[callback_arg]); } try { cv::Mat mat; if (info[0]->IsNumber() && info[1]->IsNumber()) { int width, height; int type = CV_64FC1; // if we have a type arg if ((numargs > 2) && info[2]->IsNumber()){ type = info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } width = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); height = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); mat = cv::Mat(width, height, type); } else if (info[0]->IsString()) { std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); int flags = CV_LOAD_IMAGE_UNCHANGED; if (numargs > 1){ if (info[1]->IsNumber()){ flags = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } } mat = cv::imread(filename, flags); } else if (Buffer::HasInstance(info[0])) { uint8_t *buf = (uint8_t *) Buffer::Data(Nan::To(info[0]).ToLocalChecked()); unsigned len = Buffer::Length(Nan::To(info[0]).ToLocalChecked()); int flags = CV_LOAD_IMAGE_UNCHANGED; if (numargs > 1){ if (info[1]->IsNumber()){ flags = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } } cv::Mat *mbuf = new cv::Mat(len, 1, CV_64FC1, buf); mat = cv::imdecode(*mbuf, flags); if (mat.empty()) { success = 0; argv[0] = Nan::Error("Error loading file"); } } argv[1] = Matrix::CreateWrappedFromMat(mat); } catch (cv::Exception& e) { argv[0] = Nan::Error(e.what()); argv[1] = Nan::Null(); success = 0; } Nan::TryCatch try_catch; // if we got a callback if (callback_arg >= 0){ // if using callback cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); } else { // if to return the mat if (success) info.GetReturnValue().Set(argv[1]); else info.GetReturnValue().Set(Nan::New(false)); } if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } return; } #if CV_MAJOR_VERSION >= 3 NAN_METHOD(OpenCV::ReadImageMulti) { Nan::EscapableHandleScope scope; REQ_FUN_ARG(1, cb); Local argv[2]; argv[0] = Nan::Null(); std::vector mats; try { if (info[0]->IsString()) { std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); cv::imreadmulti(filename, mats); if (mats.empty()) { argv[0] = Nan::Error("Error loading file"); } } } catch (cv::Exception& e) { argv[0] = Nan::Error(e.what()); argv[1] = Nan::Null(); } Local output = Nan::New(mats.size()); argv[1] = output; for (std::vector::size_type i = 0; i < mats.size(); i ++) { Local im_h = Matrix::CreateWrappedFromMat(mats[i]); output->Set(i, im_h); } Nan::TryCatch try_catch; cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } return; } #else NAN_METHOD(OpenCV::ReadImageMulti) { info.GetReturnValue().Set(Nan::New(false)); return; } #endif node-opencv/src/Constants.cc0000644000175000017500000000570013671667364014510 0ustar devdev#include "OpenCV.h" #include "Constants.h" #if CV_MAJOR_VERSION >= 4 #include #endif #define CONST(C) \ obj->Set(Nan::New(#C).ToLocalChecked(), Nan::New(C)); #define CONST_INT(C) \ obj->Set(Nan::New(#C).ToLocalChecked(), Nan::New((int)C)); #define CONST_DOUBLE(C) \ obj->Set(Nan::New(#C).ToLocalChecked(), Nan::New(C)); #define CONST_ENUM(C) \ obj->Set(Nan::New(#C).ToLocalChecked(), Nan::New((int)(cv::C))); void Constants::Init(Local target) { Nan::Persistent inner; Local obj = Nan::New(); inner.Reset(obj); CONST(CV_8U); CONST(CV_8S); CONST(CV_16U); CONST(CV_16S); CONST(CV_32S); CONST(CV_32F); CONST(CV_64F); #if CV_MAJOR_VERSION <= 3 CONST(CV_USRTYPE1); #endif CONST(CV_8UC1); CONST(CV_8UC2); CONST(CV_8UC3); CONST(CV_8UC4); CONST(CV_8SC1); CONST(CV_8SC2); CONST(CV_8SC3); CONST(CV_8SC4); CONST(CV_16UC1); CONST(CV_16UC2); CONST(CV_16UC3); CONST(CV_16UC4); CONST(CV_16SC1); CONST(CV_16SC2); CONST(CV_16SC3); CONST(CV_16SC4); CONST(CV_32SC1); CONST(CV_32SC2); CONST(CV_32SC3); CONST(CV_32SC4); CONST(CV_32FC1); CONST(CV_32FC2); CONST(CV_32FC3); CONST(CV_32FC4); CONST(CV_64FC1); CONST(CV_64FC2); CONST(CV_64FC3); CONST(CV_64FC4); CONST(CV_LOAD_IMAGE_UNCHANGED); CONST(CV_LOAD_IMAGE_GRAYSCALE); CONST(CV_LOAD_IMAGE_COLOR); CONST(CV_LOAD_IMAGE_ANYDEPTH); CONST(CV_LOAD_IMAGE_ANYCOLOR); // this is 3.2+ only #if ((CV_MAJOR_VERSION == 3) && (CV_MINOR_VERSION >=2)) CONST(CV_LOAD_IMAGE_IGNORE_ORIENTATION); #endif CONST_DOUBLE(CV_PI); CONST(CV_FILLED); CONST_ENUM(BORDER_DEFAULT); CONST_ENUM(BORDER_REPLICATE); CONST_ENUM(BORDER_REFLECT); CONST_ENUM(BORDER_REFLECT_101); CONST_ENUM(BORDER_WRAP); CONST_ENUM(BORDER_CONSTANT); CONST_ENUM(INTER_NEAREST); CONST_ENUM(INTER_LINEAR); CONST_ENUM(INTER_AREA); CONST_ENUM(INTER_CUBIC); CONST_ENUM(INTER_LANCZOS4); CONST_ENUM(NORM_MINMAX); CONST_ENUM(NORM_INF); CONST_ENUM(NORM_L1); CONST_ENUM(NORM_L2); CONST_ENUM(NORM_L2SQR); CONST_ENUM(NORM_HAMMING); CONST_ENUM(NORM_HAMMING2); CONST_ENUM(NORM_RELATIVE); CONST_ENUM(NORM_TYPE_MASK); CONST_ENUM(RETR_EXTERNAL); CONST_ENUM(RETR_LIST); CONST_ENUM(RETR_CCOMP); CONST_ENUM(RETR_TREE); CONST_INT(CV_DIST_C); CONST_INT(CV_DIST_L1); CONST_INT(CV_DIST_L2); CONST_INT(CV_DIST_USER); CONST_INT(CV_DIST_MASK_3); CONST_INT(CV_DIST_MASK_5); CONST_INT(CV_DIST_MASK_PRECISE); CONST_ENUM(CMP_EQ) CONST_ENUM(CMP_GT) CONST_ENUM(CMP_GE) CONST_ENUM(CMP_LT) CONST_ENUM(CMP_LE) CONST_ENUM(CMP_NE) target->Set(Nan::New("TERM_CRITERIA_EPS").ToLocalChecked(), Nan::New((int)cv::TermCriteria::EPS)); target->Set(Nan::New("TERM_CRITERIA_COUNT").ToLocalChecked(), Nan::New((int)cv::TermCriteria::COUNT)); target->Set(Nan::New("Constants").ToLocalChecked(), obj); } #undef CONST node-opencv/src/FaceRecognizer.cc0000644000175000017500000003425213671667364015426 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_FACE #include "FaceRecognizer.h" #include "Matrix.h" #include #include #if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 3 && CV_MINOR_VERSION < 3) namespace cv { using std::vector; using cv::face::createEigenFaceRecognizer; using cv::face::createFisherFaceRecognizer; using cv::face::createLBPHFaceRecognizer; } #else // version 3.3 and higher namespace cv { using std::vector; using cv::face::EigenFaceRecognizer; using cv::face::FisherFaceRecognizer; using cv::face::LBPHFaceRecognizer; } #endif #define EIGEN 0 #define LBPH 1 #define FISHER 2 // Todo, move somewhere useful // Note: References to the returned object here should not be retained past the end of the calling function. // Otherwise, node might not keep track of external memory usage correctly. cv::Mat fromMatrixOrFilename(Local v) { cv::Mat im; if (v->IsString()) { std::string filename = std::string(*Nan::Utf8String(v->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); im = cv::imread(filename); // std::cout<< im.size(); } else { Matrix *img = Nan::ObjectWrap::Unwrap(v->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); im = img->mat; } return im; } // Note: Use this function when you might need to retain the returned object past the end of the calling function, // such as in asynchronous methods Matrix *CreateFromMatrixOrFilename(Local v) { if (v->IsString()) { Matrix *im = new Matrix(); std::string filename = std::string(*Nan::Utf8String(v->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); im->setMat(cv::imread(filename)); return im; // std::cout<< im.size(); } else { return new Matrix(Nan::ObjectWrap::Unwrap(v->ToObject(Nan::GetCurrentContext()).ToLocalChecked())); } } Nan::Persistent FaceRecognizerWrap::constructor; void FaceRecognizerWrap::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(FaceRecognizerWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("FaceRecognizer").ToLocalChecked()); Nan::SetMethod(ctor, "createLBPHFaceRecognizer", CreateLBPH); Nan::SetMethod(ctor, "createEigenFaceRecognizer", CreateEigen); Nan::SetMethod(ctor, "createFisherFaceRecognizer", CreateFisher); Nan::SetPrototypeMethod(ctor, "trainSync", TrainSync); Nan::SetPrototypeMethod(ctor, "train", Train); Nan::SetPrototypeMethod(ctor, "updateSync", UpdateSync); Nan::SetPrototypeMethod(ctor, "predictSync", PredictSync); Nan::SetPrototypeMethod(ctor, "predict", Predict); Nan::SetPrototypeMethod(ctor, "saveSync", SaveSync); Nan::SetPrototypeMethod(ctor, "loadSync", LoadSync); Nan::SetPrototypeMethod(ctor, "getMat", GetMat); target->Set(Nan::New("FaceRecognizer").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(FaceRecognizerWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { JSTHROW_TYPE("Cannot Instantiate without new") } // By default initialize LBPH #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) cv::Ptr f = cv::LBPHFaceRecognizer::create(1, 8, 8, 8, 80.0); #else cv::Ptr f = cv::createLBPHFaceRecognizer(1, 8, 8, 8, 80.0); #endif FaceRecognizerWrap *pt = new FaceRecognizerWrap(f, LBPH); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } NAN_METHOD(FaceRecognizerWrap::CreateLBPH) { Nan::HandleScope scope; int radius = 1; int neighbors = 8; int grid_x = 8; int grid_y = 8; double threshold = 80; INT_FROM_ARGS(radius, 0) INT_FROM_ARGS(neighbors, 1) INT_FROM_ARGS(grid_x, 2) INT_FROM_ARGS(grid_y, 3) DOUBLE_FROM_ARGS(threshold, 4) Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(FaceRecognizerWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) cv::Ptr f = cv::LBPHFaceRecognizer::create(radius, neighbors, grid_x, grid_y, threshold); #else cv::Ptr f = cv::createLBPHFaceRecognizer(radius, neighbors, grid_x, grid_y, threshold); #endif FaceRecognizerWrap *pt = new FaceRecognizerWrap(f, LBPH); pt->Wrap(n); info.GetReturnValue().Set( n ); } NAN_METHOD(FaceRecognizerWrap::CreateEigen) { Nan::HandleScope scope; int components = 0; double threshold = DBL_MAX; INT_FROM_ARGS(components, 0) DOUBLE_FROM_ARGS(threshold, 1) Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(FaceRecognizerWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) cv::Ptr f = cv::EigenFaceRecognizer::create(components, threshold); #else cv::Ptr f = cv::createEigenFaceRecognizer(components, threshold); #endif FaceRecognizerWrap *pt = new FaceRecognizerWrap(f, EIGEN); pt->Wrap(n); info.GetReturnValue().Set( n ); } NAN_METHOD(FaceRecognizerWrap::CreateFisher) { Nan::HandleScope scope; int components = 0; double threshold = DBL_MAX; INT_FROM_ARGS(components, 0) DOUBLE_FROM_ARGS(threshold, 1) Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(FaceRecognizerWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) cv::Ptr f = cv::FisherFaceRecognizer::create(components, threshold); #else cv::Ptr f = cv::createFisherFaceRecognizer(components, threshold); #endif FaceRecognizerWrap *pt = new FaceRecognizerWrap(f, FISHER); pt->Wrap(n); info.GetReturnValue().Set( n ); } FaceRecognizerWrap::FaceRecognizerWrap(cv::Ptr f, int type) { rec = f; typ = type; } Local UnwrapTrainingData(Nan::NAN_METHOD_ARGS_TYPE info, cv::vector* images, cv::vector* labels) { if (info.Length() < 1 || !info[0]->IsArray()) { JSTHROW("FaceRecognizer.train takes a list of [ label, image] tuples") } // Iterate through [[label, image], ...] etc, and add matrix / label to vectors // const Local tuples = v8::Array::Cast(*info[0]); const Local tuples = Local::Cast(info[0]); const uint32_t length = tuples->Length(); for (uint32_t i = 0; i < length; ++i) { const Local val = tuples->Get(i); if (!val->IsArray()) { JSTHROW("train takes a list of [label, image] tuples") } Local valarr = Local::Cast(val); if (valarr->Length() != 2 || !valarr->Get(0)->IsInt32()) { JSTHROW("train takes a list of [label, image] tuples") } int label = valarr->Get(0)->Uint32Value(Nan::GetCurrentContext()).ToChecked(); cv::Mat im = fromMatrixOrFilename(valarr->Get(1)); //this is ok because we clone the image im = im.clone(); if (im.channels() == 3) { cv::cvtColor(im, im, CV_RGB2GRAY); } labels->push_back(label); images->push_back(im); } return Nan::Undefined(); } NAN_METHOD(FaceRecognizerWrap::TrainSync) { SETUP_FUNCTION(FaceRecognizerWrap) cv::vector images; cv::vector labels; Local exception = UnwrapTrainingData(info, &images, &labels); if (!exception->IsUndefined()) { // FIXME: not too sure about returning exceptions like this info.GetReturnValue().Set(exception); } self->rec->train(images, labels); return; } class TrainASyncWorker: public Nan::AsyncWorker { public: TrainASyncWorker(Nan::Callback *callback, cv::Ptr rec, cv::vector images, cv::vector labels) : Nan::AsyncWorker(callback), rec(rec), images(images), labels(labels) { } ~TrainASyncWorker() { } void Execute() { this->rec->train(this->images, this->labels); } private: cv::Ptr rec; cv::vector images; cv::vector labels; }; NAN_METHOD(FaceRecognizerWrap::Train) { SETUP_FUNCTION(FaceRecognizerWrap) if (info.Length() < 2 || !(info[1]->IsFunction())) { Nan::ThrowTypeError("Invalid number of arguments or invalid callback"); } cv::vector images; cv::vector labels; REQ_FUN_ARG(1, cb); Local exception = UnwrapTrainingData(info, &images, &labels); if (!exception->IsUndefined()) { // FIXME: not too sure about returning exceptions like this info.GetReturnValue().Set(exception); } Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new TrainASyncWorker(callback, self->rec, images, labels)); return; } NAN_METHOD(FaceRecognizerWrap::UpdateSync) { SETUP_FUNCTION(FaceRecognizerWrap) if (self->typ == EIGEN) { JSTHROW("Eigen Recognizer does not support update") } if (self->typ == FISHER) { JSTHROW("Fisher Recognizer does not support update") } cv::vector images; cv::vector labels; Local exception = UnwrapTrainingData(info, &images, &labels); if (!exception->IsUndefined()) { JSTHROW(exception); } self->rec->update(images, labels); return; } NAN_METHOD(FaceRecognizerWrap::PredictSync) { SETUP_FUNCTION(FaceRecognizerWrap) if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } cv::Mat im = fromMatrixOrFilename(info[0]); // TODO CHECK! if (im.channels() == 3) { cv::Mat previous = im; im = cv::Mat(); cv::cvtColor(previous, im, CV_RGB2GRAY); } int predictedLabel = -1; double confidence = 0.0; self->rec->predict(im, predictedLabel, confidence); #if CV_MAJOR_VERSION >= 3 // Older versions of OpenCV3 incorrectly returned label=0 at // confidence=DBL_MAX instead of label=-1 on failure. This can be removed // once the fix* becomes more widespread. // // * https://github.com/Itseez/opencv_contrib/commit/0aa58ae9b30a017b356a86d29453c0b56ed9e625#diff-d9c561bf45c255c5951ff1ab55e80473 if (predictedLabel == 0 && confidence == DBL_MAX) { predictedLabel = -1; } #endif v8::Local res = Nan::New(); res->Set(Nan::New("id").ToLocalChecked(), Nan::New(predictedLabel)); res->Set(Nan::New("confidence").ToLocalChecked(), Nan::New(confidence)); info.GetReturnValue().Set(res); } class PredictASyncWorker: public Nan::AsyncWorker { public: PredictASyncWorker(Nan::Callback *callback, cv::Ptr rec, Matrix *matrix_in) : Nan::AsyncWorker(callback), rec(rec), matrix(new Matrix(matrix_in)) { predictedLabel = -1; confidence = 0.0; } ~PredictASyncWorker() { } void Execute() { rec->predict(matrix->mat, predictedLabel, confidence); #if CV_MAJOR_VERSION >= 3 // Older versions of OpenCV3 incorrectly returned label=0 at // confidence=DBL_MAX instead of label=-1 on failure. This can be removed // once the fix* becomes more widespread. // // * https://github.com/Itseez/opencv_contrib/commit/0aa58ae9b30a017b356a86d29453c0b56ed9e625#diff-d9c561bf45c255c5951ff1ab55e80473 if (this->predictedLabel == 0 && this->confidence == DBL_MAX) { this->predictedLabel = -1; } #endif } void HandleOKCallback() { Nan::HandleScope scope; delete matrix; matrix = NULL; v8::Local res = Nan::New(); res->Set(Nan::New("id").ToLocalChecked(), Nan::New(predictedLabel)); res->Set(Nan::New("confidence").ToLocalChecked(), Nan::New(confidence)); Local argv[] = { res }; Nan::TryCatch try_catch; callback->Call(1, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: cv::Ptr rec; Matrix *matrix; int predictedLabel; double confidence; }; NAN_METHOD(FaceRecognizerWrap::Predict) { SETUP_FUNCTION(FaceRecognizerWrap) if (info.Length() < 2 || !(info[1]->IsFunction())) { Nan::ThrowTypeError("Invalid number of arguments or invalid callback"); } REQ_FUN_ARG(1, cb); Matrix *m = CreateFromMatrixOrFilename(info[0]); if (m->mat.channels() == 3) { cv::Mat grayMat; cv::cvtColor(m->mat, grayMat, CV_RGB2GRAY); m->setMat(grayMat); } Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new PredictASyncWorker(callback, self->rec, m)); delete m; return; } NAN_METHOD(FaceRecognizerWrap::SaveSync) { SETUP_FUNCTION(FaceRecognizerWrap) if (!info[0]->IsString()) { JSTHROW("Save takes a filename") } std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) self->rec->write(filename); #else self->rec->save(filename); #endif return; } NAN_METHOD(FaceRecognizerWrap::LoadSync) { SETUP_FUNCTION(FaceRecognizerWrap) if (!info[0]->IsString()) { JSTHROW("Load takes a filename") } std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); #if CV_MAJOR_VERSION >= 4 || (CV_MAJOR_VERSION >= 3 && CV_MINOR_VERSION >= 3) self->rec->read(filename); #else self->rec->load(filename); #endif return; } NAN_METHOD(FaceRecognizerWrap::GetMat) { SETUP_FUNCTION(FaceRecognizerWrap) if (!info[0]->IsString()) { JSTHROW("getMat takes a key") } std::string key = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); cv::Mat m; #if CV_MAJOR_VERSION >= 3 cv::face::BasicFaceRecognizer *bfr = dynamic_cast(self->rec.get()); if (bfr == NULL) { Nan::ThrowTypeError("getMat not supported"); return; } if (key.compare("mean") == 0) { m = bfr->getMean(); } else if (key.compare("eigenvectors") == 0) { m = bfr->getEigenVectors(); } else if (key.compare("eigenvalues") == 0) { m = bfr->getEigenValues(); } else { Nan::ThrowTypeError("Unknown getMat keyname"); return; } #else m = self->rec->getMat(key); #endif Local im = Matrix::CreateWrappedFromMat(m); info.GetReturnValue().Set(im); } #endif // End version > 2.4 node-opencv/src/HighGUI.h0000644000175000017500000000064313671667364013623 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_HIGHGUI class NamedWindow: public Nan::ObjectWrap { public: std::string winname; int flags; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); NamedWindow(const std::string& winname, int flags); JSFUNC(Show) ;JSFUNC(Destroy) ;JSFUNC(BlockingWaitKey) ;JSFUNC(ResizeWindow) ; }; #endif node-opencv/src/Features2d.cc0000644000175000017500000000621113671667364014536 0ustar devdev#include "OpenCV.h" #if ((CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION >=4)) #include "Features2d.h" #include "Matrix.h" #include #include #ifdef HAVE_OPENCV_FEATURES2D void Features::Init(Local target) { Nan::HandleScope scope; Nan::SetMethod(target, "ImageSimilarity", Similarity); } class AsyncDetectSimilarity: public Nan::AsyncWorker { public: AsyncDetectSimilarity(Nan::Callback *callback, Matrix *image1, Matrix *image2) : Nan::AsyncWorker(callback), image1(new Matrix(image1)), image2(new Matrix(image2)), dissimilarity(0) { } ~AsyncDetectSimilarity() { } void Execute() { cv::Ptr detector = cv::FeatureDetector::create("ORB"); cv::Ptr extractor = cv::DescriptorExtractor::create("ORB"); cv::Ptr matcher = cv::DescriptorMatcher::create( "BruteForce-Hamming"); std::vector matches; cv::Mat descriptors1 = cv::Mat(); cv::Mat descriptors2 = cv::Mat(); std::vector keypoints1; std::vector keypoints2; detector->detect(image1->mat, keypoints1); detector->detect(image2->mat, keypoints2); extractor->compute(image1->mat, keypoints1, descriptors1); extractor->compute(image2->mat, keypoints2, descriptors2); matcher->match(descriptors1, descriptors2, matches); double max_dist = 0; double min_dist = 100; //-- Quick calculation of max and min distances between keypoints for (int i = 0; i < descriptors1.rows; i++) { double dist = matches[i].distance; if (dist < min_dist) { min_dist = dist; } if (dist > max_dist) { max_dist = dist; } } //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist, //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very //-- small) //-- PS.- radiusMatch can also be used here. std::vector good_matches; double good_matches_sum = 0.0; for (int i = 0; i < descriptors1.rows; i++) { double distance = matches[i].distance; if (distance <= std::max(2 * min_dist, 0.02)) { good_matches.push_back(matches[i]); good_matches_sum += distance; } } dissimilarity = (double) good_matches_sum / (double) good_matches.size(); } void HandleOKCallback() { Nan::HandleScope scope; delete image1; delete image2; image1 = NULL; image2 = NULL; Local argv[2]; argv[0] = Nan::Null(); argv[1] = Nan::New(dissimilarity); callback->Call(2, argv); } private: Matrix *image1; Matrix *image2; double dissimilarity; }; NAN_METHOD(Features::Similarity) { Nan::HandleScope scope; REQ_FUN_ARG(2, cb); Matrix *image1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Matrix *image2 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker( new AsyncDetectSimilarity(callback, image1, image2) ); return; } #endif #endif node-opencv/src/VideoWriterWrap.h0000755000175000017500000000075713671667364015505 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_VIDEOIO class VideoWriterWrap: public Nan::ObjectWrap { public: cv::VideoWriter writer; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); VideoWriterWrap(const std::string& filename, int fourcc, double fps, cv::Size frameSize, bool isColor=true); static NAN_METHOD(Write); static NAN_METHOD(WriteSync); // release the stream static NAN_METHOD(Release); }; #endif node-opencv/src/VideoWriterWrap.cc0000755000175000017500000001111513671667364015631 0ustar devdev#include "VideoWriterWrap.h" #include "Matrix.h" #include "OpenCV.h" #if CV_MAJOR_VERSION >= 4 #include #endif #include #ifdef HAVE_OPENCV_VIDEOIO Nan::Persistent VideoWriterWrap::constructor; struct videowriter_baton { Nan::Persistent cb; VideoWriterWrap *vc; Matrix *im; uv_work_t request; }; void VideoWriterWrap::Init(Local target) { Nan::HandleScope scope; //Class Local ctor = Nan::New(VideoWriterWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("VideoWriter").ToLocalChecked()); Nan::SetPrototypeMethod(ctor, "write", Write); Nan::SetPrototypeMethod(ctor, "writeSync", WriteSync); Nan::SetPrototypeMethod(ctor, "release", Release); target->Set(Nan::New("VideoWriter").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(VideoWriterWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) return Nan::ThrowTypeError("Cannot Instantiate without new"); VideoWriterWrap *v; // Arg 0 is the output filename std::string filename = std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); // Arg 1 is the fourcc code const char *fourccStr = std::string(*Nan::Utf8String(info[1]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))).c_str(); int fourcc = CV_FOURCC(fourccStr[0],fourccStr[1],fourccStr[2],fourccStr[3]); // Arg 2 is the output fps double fps = Nan::To(info[2]).FromJust(); // Arg 3 is the image size cv::Size imageSize; if (info[3]->IsArray()) { Local v8sz = info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); imageSize = cv::Size(v8sz->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8sz->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { JSTHROW_TYPE("Must pass image size"); } // Arg 4 is the color flag bool isColor = info[4]->BooleanValue( Nan::GetCurrentContext() ).FromJust(); v = new VideoWriterWrap(filename, fourcc, fps, imageSize, isColor); v->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } VideoWriterWrap::VideoWriterWrap(const std::string& filename, int fourcc, double fps, cv::Size frameSize, bool isColor) { Nan::HandleScope scope; writer.open(filename, fourcc, fps, frameSize, isColor); if(!writer.isOpened()) { Nan::ThrowError("Writer could not be opened"); } } NAN_METHOD(VideoWriterWrap::Release) { Nan::HandleScope scope; VideoWriterWrap *v = Nan::ObjectWrap::Unwrap(info.This()); v->writer.release(); return; } class AsyncVWWorker: public Nan::AsyncWorker { public: AsyncVWWorker(Nan::Callback *callback, VideoWriterWrap *vw, Matrix *matrix_in) : Nan::AsyncWorker(callback), vw(vw), matrix(new Matrix(matrix_in)) { } ~AsyncVWWorker() { } // Executed inside the worker-thread. // It is not safe to access V8, or V8 data structures // here, so everything we need for input and output // should go on `this`. void Execute() { this->vw->writer.write(matrix->mat); } // Executed when the async work is complete // this function will be run inside the main event loop // so it is safe to use V8 again void HandleOKCallback() { Nan::HandleScope scope; delete matrix; matrix = NULL; Local argv[] = { Nan::Null() }; Nan::TryCatch try_catch; callback->Call(1, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: VideoWriterWrap *vw; Matrix *matrix; }; NAN_METHOD(VideoWriterWrap::Write) { Nan::HandleScope scope; VideoWriterWrap *v = Nan::ObjectWrap::Unwrap(info.This()); Matrix *im = Nan::ObjectWrap::Unwrap < Matrix > (Nan::To(info[0]).ToLocalChecked()); REQ_FUN_ARG(1, cb); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncVWWorker(callback, v, im)); return; } NAN_METHOD(VideoWriterWrap::WriteSync) { Nan::HandleScope scope; VideoWriterWrap *v = Nan::ObjectWrap::Unwrap(info.This()); Matrix *im = Nan::ObjectWrap::Unwrap < Matrix > (Nan::To(info[0]).ToLocalChecked()); v->writer.write(im->mat); info.GetReturnValue().Set(Nan::Null()); } #endif node-opencv/src/Stereo.h0000644000175000017500000000236413671667364013642 0ustar devdev#ifndef __NODE_STEREO_H #define __NODE_STEREO_H #include "OpenCV.h" #if CV_MAJOR_VERSION < 3 class StereoBM: public Nan::ObjectWrap { public: cv::StereoBM stereo; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); StereoBM(int preset = cv::StereoBM::BASIC_PRESET, int ndisparities = 0, int SADWindowSize = 21); JSFUNC(Compute) ; }; class StereoSGBM: public Nan::ObjectWrap { public: cv::StereoSGBM stereo; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); StereoSGBM(); StereoSGBM(int minDisparity, int ndisparities, int SADWindowSize, int p1 = 0, int p2 = 0, int disp12MaxDiff = 0, int preFilterCap = 0, int uniquenessRatio = 0, int speckleWindowSize = 0, int speckleRange = 0, bool fullDP = false); JSFUNC(Compute); }; struct CvStereoGCState; class StereoGC: public Nan::ObjectWrap { public: CvStereoGCState *stereo; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); StereoGC(int numberOfDisparities = 16, int maxIterations = 2); JSFUNC(Compute); }; #endif #endif // __NODE_STEREO_H node-opencv/src/FaceRecognizer.h0000644000175000017500000000155013671667364015263 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_FACE #if CV_MAJOR_VERSION >= 3 #include namespace cv { using cv::face::FaceRecognizer; } #else #warning using opencv2 contrib #include "opencv2/contrib/contrib.hpp" #endif class FaceRecognizerWrap: public Nan::ObjectWrap { public: cv::Ptr rec; int typ; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); FaceRecognizerWrap(cv::Ptr f, int type); JSFUNC(CreateLBPH) JSFUNC(CreateEigen) JSFUNC(CreateFisher) JSFUNC(TrainSync) JSFUNC(Train) JSFUNC(UpdateSync) //JSFUNC(Update) JSFUNC(PredictSync) JSFUNC(Predict) //static void EIO_Predict(eio_req *req); //static int EIO_AfterPredict(eio_req *req); JSFUNC(SaveSync) JSFUNC(LoadSync) JSFUNC(GetMat) }; #endif node-opencv/src/Contours.cc0000755000175000017500000003170113671667364014353 0ustar devdev#include "Contours.h" #include "OpenCV.h" #include #include Nan::Persistent Contour::constructor; void Contour::Init(Local target) { Nan::HandleScope scope; // Class/contructor Local ctor = Nan::New(Contour::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("Contours").ToLocalChecked()); // Prototype // Local proto = constructor->PrototypeTemplate(); Nan::SetPrototypeMethod(ctor, "point", Point); Nan::SetPrototypeMethod(ctor, "points", Points); Nan::SetPrototypeMethod(ctor, "size", Size); Nan::SetPrototypeMethod(ctor, "cornerCount", CornerCount); Nan::SetPrototypeMethod(ctor, "area", Area); Nan::SetPrototypeMethod(ctor, "arcLength", ArcLength); Nan::SetPrototypeMethod(ctor, "approxPolyDP", ApproxPolyDP); Nan::SetPrototypeMethod(ctor, "convexHull", ConvexHull); Nan::SetPrototypeMethod(ctor, "boundingRect", BoundingRect); Nan::SetPrototypeMethod(ctor, "minAreaRect", MinAreaRect); Nan::SetPrototypeMethod(ctor, "fitEllipse", FitEllipse); Nan::SetPrototypeMethod(ctor, "isConvex", IsConvex); Nan::SetPrototypeMethod(ctor, "moments", Moments); Nan::SetPrototypeMethod(ctor, "hierarchy", Hierarchy); Nan::SetPrototypeMethod(ctor, "serialize", Serialize); Nan::SetPrototypeMethod(ctor, "deserialize", Deserialize); target->Set(Nan::New("Contours").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(Contour::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { Nan::ThrowTypeError("Cannot instantiate without new"); } Contour *contours; contours = new Contour; contours->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } Contour::Contour() : Nan::ObjectWrap() { } NAN_METHOD(Contour::Point) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); int index = info[1].As()->Value(); cv::Point point = self->contours[pos][index]; Local data = Nan::New(); data->Set(Nan::New("x").ToLocalChecked(), Nan::New(point.x)); data->Set(Nan::New("y").ToLocalChecked(), Nan::New(point.y)); info.GetReturnValue().Set(data); } NAN_METHOD(Contour::Points) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); std::vector points = self->contours[pos]; Local data = Nan::New(points.size()); for (std::vector::size_type i = 0; i != points.size(); i++) { Local point_data = Nan::New(); point_data->Set(Nan::New("x").ToLocalChecked(), Nan::New(points[i].x)); point_data->Set(Nan::New("y").ToLocalChecked(), Nan::New(points[i].y)); data->Set(i, point_data); } info.GetReturnValue().Set(data); } // FIXME: this should better be called "Length" as ``Contours`` is an Array like // structure also, this would allow to use ``Size`` for the function returning // the number of corners in the contour for better consistency with OpenCV. NAN_METHOD(Contour::Size) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); info.GetReturnValue().Set(Nan::New(self->contours.size())); } NAN_METHOD(Contour::CornerCount) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); info.GetReturnValue().Set(Nan::New(self->contours[pos].size())); } NAN_METHOD(Contour::Area) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); bool orientation = (info.Length() > 1 && info[1]->BooleanValue( Nan::GetCurrentContext() ).FromJust()); // info.GetReturnValue().Set(Nan::New(contourArea(self->contours))); info.GetReturnValue().Set(Nan::New(contourArea(cv::Mat(self->contours[pos]), orientation))); } NAN_METHOD(Contour::ArcLength) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); bool isClosed = info[1]->BooleanValue( Nan::GetCurrentContext() ).FromJust(); info.GetReturnValue().Set(Nan::New(arcLength(cv::Mat(self->contours[pos]), isClosed))); } NAN_METHOD(Contour::ApproxPolyDP) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); double epsilon = info[1].As()->Value(); bool isClosed = info[2]->BooleanValue( Nan::GetCurrentContext() ).FromJust(); cv::Mat approxed; approxPolyDP(cv::Mat(self->contours[pos]), approxed, epsilon, isClosed); approxed.copyTo(self->contours[pos]); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Contour::ConvexHull) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); bool clockwise = info[1]->BooleanValue( Nan::GetCurrentContext() ).FromJust(); cv::Mat hull; cv::convexHull(cv::Mat(self->contours[pos]), hull, clockwise); hull.copyTo(self->contours[pos]); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Contour::BoundingRect) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); cv::Rect bounding = cv::boundingRect(cv::Mat(self->contours[pos])); Local rect = Nan::New(); rect->Set(Nan::New("x").ToLocalChecked(), Nan::New(bounding.x)); rect->Set(Nan::New("y").ToLocalChecked(), Nan::New(bounding.y)); rect->Set(Nan::New("width").ToLocalChecked(), Nan::New(bounding.width)); rect->Set(Nan::New("height").ToLocalChecked(), Nan::New(bounding.height)); info.GetReturnValue().Set(rect); } NAN_METHOD(Contour::MinAreaRect) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); cv::RotatedRect minimum = cv::minAreaRect(cv::Mat(self->contours[pos])); Local rect = Nan::New(); rect->Set(Nan::New("angle").ToLocalChecked(), Nan::New(minimum.angle)); Local size = Nan::New(); size->Set(Nan::New("height").ToLocalChecked(), Nan::New(minimum.size.height)); size->Set(Nan::New("width").ToLocalChecked(), Nan::New(minimum.size.width)); rect->Set(Nan::New("size").ToLocalChecked(), size); Local center = Nan::New(); center->Set(Nan::New("x").ToLocalChecked(), Nan::New(minimum.center.x)); center->Set(Nan::New("y").ToLocalChecked(), Nan::New(minimum.center.y)); v8::Local points = Nan::New(4); cv::Point2f rect_points[4]; minimum.points(rect_points); for (unsigned int i=0; i<4; i++) { Local point = Nan::New(); point->Set(Nan::New("x").ToLocalChecked(), Nan::New(rect_points[i].x)); point->Set(Nan::New("y").ToLocalChecked(), Nan::New(rect_points[i].y)); points->Set(i, point); } rect->Set(Nan::New("points").ToLocalChecked(), points); info.GetReturnValue().Set(rect); } NAN_METHOD(Contour::FitEllipse) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); if (self->contours[pos].size() >= 5) { // Minimum number for an ellipse cv::RotatedRect ellipse = cv::fitEllipse(cv::Mat(self->contours[pos])); Local jsEllipse = Nan::New(); jsEllipse->Set(Nan::New("angle").ToLocalChecked(), Nan::New(ellipse.angle)); Local size = Nan::New(); size->Set(Nan::New("height").ToLocalChecked(), Nan::New(ellipse.size.height)); size->Set(Nan::New("width").ToLocalChecked(), Nan::New(ellipse.size.width)); jsEllipse->Set(Nan::New("size").ToLocalChecked(), size); Local center = Nan::New(); center->Set(Nan::New("x").ToLocalChecked(), Nan::New(ellipse.center.x)); center->Set(Nan::New("y").ToLocalChecked(), Nan::New(ellipse.center.y)); jsEllipse->Set(Nan::New("center").ToLocalChecked(), center); info.GetReturnValue().Set(jsEllipse); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Contour::IsConvex) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); info.GetReturnValue().Set(Nan::New(isContourConvex(cv::Mat(self->contours[pos])))); } NAN_METHOD(Contour::Moments) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0].As()->Value(); // Get the moments cv::Moments mu = moments( self->contours[pos], false ); Local res = Nan::New(); res->Set(Nan::New("m00").ToLocalChecked(), Nan::New(mu.m00)); res->Set(Nan::New("m10").ToLocalChecked(), Nan::New(mu.m10)); res->Set(Nan::New("m01").ToLocalChecked(), Nan::New(mu.m01)); res->Set(Nan::New("m11").ToLocalChecked(), Nan::New(mu.m11)); info.GetReturnValue().Set(res); } NAN_METHOD(Contour::Hierarchy) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); int pos = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::Vec4i hierarchy = self->hierarchy[pos]; Local res = Nan::New(4); res->Set(0, Nan::New(hierarchy[0])); res->Set(1, Nan::New(hierarchy[1])); res->Set(2, Nan::New(hierarchy[2])); res->Set(3, Nan::New(hierarchy[3])); info.GetReturnValue().Set(res); } NAN_METHOD(Contour::Serialize) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); Local contours_data = Nan::New(self->contours.size()); for (std::vector::size_type i = 0; i != self->contours.size(); i++) { std::vector points = self->contours[i]; Local contour_data = Nan::New(points.size()); for (std::vector::size_type j = 0; j != points.size(); j++) { Local point_data = Nan::New(2); point_data->Set(0, Nan::New(points[j].x)); point_data->Set(1, Nan::New(points[j].y)); contour_data->Set(j, point_data); } contours_data->Set(i, contour_data); } Local hierarchy_data = Nan::New(self->hierarchy.size()); for (std::vector::size_type i = 0; i != self->hierarchy.size(); i++) { Local contour_data = Nan::New(4); contour_data->Set(0, Nan::New(self->hierarchy[i][0])); contour_data->Set(1, Nan::New(self->hierarchy[i][1])); contour_data->Set(2, Nan::New(self->hierarchy[i][2])); contour_data->Set(3, Nan::New(self->hierarchy[i][3])); hierarchy_data->Set(i, contour_data); } Local data = Nan::New(); data->Set(Nan::New("contours").ToLocalChecked(), contours_data); data->Set(Nan::New("hierarchy").ToLocalChecked(), hierarchy_data); info.GetReturnValue().Set(data); } NAN_METHOD(Contour::Deserialize) { Nan::HandleScope scope; Contour *self = Nan::ObjectWrap::Unwrap(info.This()); Local data = Local::Cast(info[0]); Local contours_data = Local::Cast(data->Get(Nan::New("contours").ToLocalChecked())); Local hierarchy_data = Local::Cast(data->Get(Nan::New("hierarchy").ToLocalChecked())); std::vector > contours_res; int contours_length = contours_data->Length(); for (int i = 0; i < contours_length; i++) { Local contour_data = Local::Cast(contours_data->Get(i)); std::vector points; int contour_length = contour_data->Length(); for (int j = 0; j < contour_length; j++) { Local point_data = Local::Cast(contour_data->Get(j)); int x = point_data->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int y = point_data->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); points.push_back(cv::Point(x, y)); } contours_res.push_back(points); } std::vector hierarchy_res; int hierarchy_length = hierarchy_data->Length(); for (int i = 0; i < hierarchy_length; i++) { Local contour_data = Local::Cast(hierarchy_data->Get(i)); int a = contour_data->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int b = contour_data->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int c = contour_data->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int d = contour_data->Get(3)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); hierarchy_res.push_back(cv::Vec4i(a, b, c, d)); } self->contours = contours_res; self->hierarchy = hierarchy_res; info.GetReturnValue().Set(Nan::Null()); } node-opencv/src/Matrix.cc0000755000175000017500000033304513671667364014011 0ustar devdev#include "Contours.h" #include "Matrix.h" #include "OpenCV.h" #include #include #if CV_MAJOR_VERSION >= 4 #include #include #endif Nan::Persistent Matrix::constructor; cv::Scalar setColor(Local objColor); cv::Point setPoint(Local objPoint); cv::Rect* setRect(Local objRect, cv::Rect &result); void Matrix::Init(Local target) { Nan::HandleScope scope; //Class Local ctor = Nan::New(Matrix::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("Matrix").ToLocalChecked()); // Prototype Nan::SetPrototypeMethod(ctor, "copyMakeBorder", CopyMakeBorder); Nan::SetPrototypeMethod(ctor, "row", Row); Nan::SetPrototypeMethod(ctor, "col", Col); Nan::SetPrototypeMethod(ctor, "pixelRow", PixelRow); Nan::SetPrototypeMethod(ctor, "pixelCol", PixelCol); Nan::SetPrototypeMethod(ctor, "empty", Empty); Nan::SetPrototypeMethod(ctor, "get", Get); Nan::SetPrototypeMethod(ctor, "getPixel", GetPixel); Nan::SetPrototypeMethod(ctor, "set", Set); Nan::SetPrototypeMethod(ctor, "put", Put); Nan::SetPrototypeMethod(ctor, "brightness", Brightness); Nan::SetPrototypeMethod(ctor, "normalize", Normalize); Nan::SetPrototypeMethod(ctor, "norm", Norm); Nan::SetPrototypeMethod(ctor, "getData", GetData); Nan::SetPrototypeMethod(ctor, "pixel", Pixel); Nan::SetPrototypeMethod(ctor, "width", Width); Nan::SetPrototypeMethod(ctor, "height", Height); Nan::SetPrototypeMethod(ctor, "type", Type); Nan::SetPrototypeMethod(ctor, "size", Size); Nan::SetPrototypeMethod(ctor, "clone", Clone); Nan::SetPrototypeMethod(ctor, "crop", Crop); Nan::SetPrototypeMethod(ctor, "toBuffer", ToBuffer); Nan::SetPrototypeMethod(ctor, "toBufferAsync", ToBufferAsync); Nan::SetPrototypeMethod(ctor, "ellipse", Ellipse); Nan::SetPrototypeMethod(ctor, "rectangle", Rectangle); Nan::SetPrototypeMethod(ctor, "line", Line); Nan::SetPrototypeMethod(ctor, "fillPoly", FillPoly); Nan::SetPrototypeMethod(ctor, "save", Save); Nan::SetPrototypeMethod(ctor, "saveAsync", SaveAsync); Nan::SetPrototypeMethod(ctor, "resize", Resize); Nan::SetPrototypeMethod(ctor, "rotate", Rotate); Nan::SetPrototypeMethod(ctor, "warpAffine", WarpAffine); Nan::SetPrototypeMethod(ctor, "copyTo", CopyTo); Nan::SetPrototypeMethod(ctor, "convertTo", ConvertTo); Nan::SetPrototypeMethod(ctor, "pyrDown", PyrDown); Nan::SetPrototypeMethod(ctor, "pyrUp", PyrUp); Nan::SetPrototypeMethod(ctor, "channels", Channels); Nan::SetPrototypeMethod(ctor, "convertGrayscale", ConvertGrayscale); Nan::SetPrototypeMethod(ctor, "convertHSVscale", ConvertHSVscale); Nan::SetPrototypeMethod(ctor, "gaussianBlur", GaussianBlur); Nan::SetPrototypeMethod(ctor, "medianBlur", MedianBlur); Nan::SetPrototypeMethod(ctor, "bilateralFilter", BilateralFilter); Nan::SetPrototypeMethod(ctor, "sobel", Sobel); Nan::SetPrototypeMethod(ctor, "copy", Copy); Nan::SetPrototypeMethod(ctor, "flip", Flip); Nan::SetPrototypeMethod(ctor, "roi", ROI); Nan::SetPrototypeMethod(ctor, "ptr", Ptr); Nan::SetPrototypeMethod(ctor, "absDiff", AbsDiff); Nan::SetPrototypeMethod(ctor, "dct", Dct); Nan::SetPrototypeMethod(ctor, "idct", Idct); Nan::SetPrototypeMethod(ctor, "addWeighted", AddWeighted); Nan::SetPrototypeMethod(ctor, "add", Add); Nan::SetPrototypeMethod(ctor, "bitwiseXor", BitwiseXor); Nan::SetPrototypeMethod(ctor, "bitwiseNot", BitwiseNot); Nan::SetPrototypeMethod(ctor, "bitwiseAnd", BitwiseAnd); Nan::SetPrototypeMethod(ctor, "countNonZero", CountNonZero); Nan::SetPrototypeMethod(ctor, "moments", Moments); Nan::SetPrototypeMethod(ctor, "canny", Canny); Nan::SetPrototypeMethod(ctor, "dilate", Dilate); Nan::SetPrototypeMethod(ctor, "erode", Erode); Nan::SetPrototypeMethod(ctor, "findContours", FindContours); Nan::SetPrototypeMethod(ctor, "drawContour", DrawContour); Nan::SetPrototypeMethod(ctor, "drawAllContours", DrawAllContours); Nan::SetPrototypeMethod(ctor, "goodFeaturesToTrack", GoodFeaturesToTrack); #ifdef HAVE_OPENCV_VIDEO Nan::SetPrototypeMethod(ctor, "calcOpticalFlowPyrLK", CalcOpticalFlowPyrLK); #endif Nan::SetPrototypeMethod(ctor, "houghLinesP", HoughLinesP); Nan::SetPrototypeMethod(ctor, "houghCircles", HoughCircles); Nan::SetPrototypeMethod(ctor, "inRange", inRange); Nan::SetPrototypeMethod(ctor, "adjustROI", AdjustROI); Nan::SetPrototypeMethod(ctor, "locateROI", LocateROI); Nan::SetPrototypeMethod(ctor, "threshold", Threshold); Nan::SetPrototypeMethod(ctor, "adaptiveThreshold", AdaptiveThreshold); Nan::SetPrototypeMethod(ctor, "meanStdDev", MeanStdDev); Nan::SetPrototypeMethod(ctor, "cvtColor", CvtColor); Nan::SetPrototypeMethod(ctor, "split", Split); Nan::SetPrototypeMethod(ctor, "merge", Merge); Nan::SetPrototypeMethod(ctor, "equalizeHist", EqualizeHist); Nan::SetPrototypeMethod(ctor, "floodFill", FloodFill); Nan::SetPrototypeMethod(ctor, "matchTemplate", MatchTemplate); Nan::SetPrototypeMethod(ctor, "matchTemplateByMatrix", MatchTemplateByMatrix); Nan::SetPrototypeMethod(ctor, "templateMatches", TemplateMatches); Nan::SetPrototypeMethod(ctor, "minMaxLoc", MinMaxLoc); Nan::SetPrototypeMethod(ctor, "pushBack", PushBack); Nan::SetPrototypeMethod(ctor, "putText", PutText); Nan::SetPrototypeMethod(ctor, "getPerspectiveTransform", GetPerspectiveTransform); Nan::SetPrototypeMethod(ctor, "warpPerspective", WarpPerspective); Nan::SetMethod(ctor, "Zeros", Zeros); Nan::SetMethod(ctor, "Ones", Ones); Nan::SetMethod(ctor, "Eye", Eye); Nan::SetMethod(ctor, "getRotationMatrix2D", GetRotationMatrix2D); Nan::SetPrototypeMethod(ctor, "copyWithMask", CopyWithMask); Nan::SetPrototypeMethod(ctor, "setWithMask", SetWithMask); Nan::SetPrototypeMethod(ctor, "meanWithMask", MeanWithMask); Nan::SetPrototypeMethod(ctor, "mean", Mean); Nan::SetPrototypeMethod(ctor, "shift", Shift); Nan::SetPrototypeMethod(ctor, "reshape", Reshape); // leave this out - can't see a way it could be useful to us, as release() always completely forgets the data // Nan::SetPrototypeMethod(ctor, "addref", Addref); Nan::SetPrototypeMethod(ctor, "release", Release); Nan::SetPrototypeMethod(ctor, "getrefCount", GetrefCount); Nan::SetPrototypeMethod(ctor, "subtract", Subtract); Nan::SetPrototypeMethod(ctor, "compare", Compare); Nan::SetPrototypeMethod(ctor, "mul", Mul); Nan::SetPrototypeMethod(ctor, "div", Div); Nan::SetPrototypeMethod(ctor, "pow", Pow); target->Set(Nan::New("Matrix").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(Matrix::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { Nan::ThrowTypeError("Cannot instantiate without new"); } Matrix *mat; if (info.Length() == 0) { mat = new Matrix; } else if (info.Length() == 2 && info[0]->IsInt32() && info[1]->IsInt32()) { mat = new Matrix(Nan::To(info[0]).FromJust(), Nan::To(info[1]).FromJust()); } else if (info.Length() == 3 && info[0]->IsInt32() && info[1]->IsInt32() && info[2]->IsInt32()) { mat = new Matrix(Nan::To(info[0]).FromJust(), Nan::To(info[1]).FromJust(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else if (info.Length() == 4 && info[0]->IsInt32() && info[1]->IsInt32() && info[2]->IsInt32() && info[3]->IsArray()) { mat = new Matrix(Nan::To(info[0]).FromJust(), Nan::To(info[1]).FromJust(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); } else { // if (info.Length() == 5) { Matrix *other = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); int x = Nan::To(info[1]).FromJust(); int y = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int w = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int h = info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); mat = new Matrix(other->mat, cv::Rect(x, y, w, h)); } mat->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } //Convenience factory method for creating a wrapped Matrix from a cv::Mat and tracking external memory correctly. // Always tracks the referenced matrix as external memory. Local Matrix::CreateWrappedFromMat(cv::Mat mat){ Local < Object > result = Nan::NewInstance(Nan::GetFunction(Nan::New(Matrix::constructor)).ToLocalChecked()).ToLocalChecked(); Matrix *m = Nan::ObjectWrap::Unwrap(result); m->setMat(mat); return result; } //Convenience factory method for creating a wrapped Matrix from a cv::Mat and tracking external memory correctly. // Only tracks the referenced matrix as external memory if the refcount does not exceed the base refcount. // Useful for creating a wrapper Matrix around a Mat that is also referenced by another wrapper Matrix Local Matrix::CreateWrappedFromMatIfNotReferenced(cv::Mat mat, int baseRefCount){ Local < Object > result = Nan::NewInstance(Nan::GetFunction(Nan::New(Matrix::constructor)).ToLocalChecked()).ToLocalChecked(); Matrix *m = Nan::ObjectWrap::Unwrap(result); m->mat = mat; if (m->getWrappedRefCount() <= 2 + baseRefCount){ //one reference in m, one on the stack Nan::AdjustExternalMemory(m->mat.dataend - m->mat.datastart); } return result; } Matrix::Matrix() : node_opencv::Matrix() { mat = cv::Mat(); } Matrix::Matrix(int rows, int cols) : node_opencv::Matrix() { mat = cv::Mat(rows, cols, CV_32FC3); Nan::AdjustExternalMemory(mat.dataend - mat.datastart); } Matrix::Matrix(int rows, int cols, int type) : node_opencv::Matrix() { mat = cv::Mat(rows, cols, type); Nan::AdjustExternalMemory(mat.dataend - mat.datastart); } Matrix::Matrix(Matrix *m) : node_opencv::Matrix() { mat = cv::Mat(m->mat); } Matrix::Matrix(cv::Mat m, cv::Rect roi) : node_opencv::Matrix() { mat = cv::Mat(m, roi); } Matrix::Matrix(int rows, int cols, int type, Local scalarObj) { mat = cv::Mat(rows, cols, type); Nan::AdjustExternalMemory(mat.dataend - mat.datastart); if (mat.channels() == 3) { mat.setTo(cv::Scalar(scalarObj->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), scalarObj->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), scalarObj->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked())); } else if (mat.channels() == 2) { mat.setTo(cv::Scalar(scalarObj->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), scalarObj->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked())); } else if (mat.channels() == 1) { mat.setTo(cv::Scalar(scalarObj->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked())); } else { Nan::ThrowError("Only 1-3 channels are supported"); } } Matrix::~Matrix(){ if(getWrappedRefCount() == 1){ //if this holds the last reference to the Mat int size = mat.dataend - mat.datastart; Nan::AdjustExternalMemory(-1 * size); } } // Set the wrapped Mat with correct memory tracking // For this to work correctly, there should be no external references held to our previous // wrapped Mat, and no other Matrix objects should wrap a Mat pointing at the same // memory as our new Mat. void Matrix::setMat(cv::Mat m){ int oldSize = 0; if(getWrappedRefCount() == 1){ //if this holds the last reference to the Mat oldSize = mat.dataend - mat.datastart; } mat = m; int newSize = mat.dataend - mat.datastart; Nan::AdjustExternalMemory(newSize - oldSize); } NAN_METHOD(Matrix::Empty) { SETUP_FUNCTION(Matrix) info.GetReturnValue().Set(Nan::New(self->mat.empty())); } double Matrix::DblGet(cv::Mat mat, int i, int j) { double val = 0; cv::Vec3b pix; unsigned int pint = 0; switch (mat.type()) { case CV_32FC3: pix = mat.at(i, j); pint |= (uchar) pix.val[2]; pint |= ((uchar) pix.val[1]) << 8; pint |= ((uchar) pix.val[0]) << 16; val = (double) pint; break; case CV_64FC1: val = mat.at(i, j); break; case CV_32FC1: val = mat.at(i, j); break; default: val = mat.at(i, j); break; } return val; } NAN_METHOD(Matrix::Pixel) { SETUP_FUNCTION(Matrix) int y = Nan::To(info[0]).FromJust(); int x = Nan::To(info[1]).FromJust(); // cv::Scalar scal = self->mat.at(y, x); if (info.Length() == 3) { Local < Object > objColor = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); if (self->mat.channels() == 3) { self->mat.at(y, x)[0] = (uchar) objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); self->mat.at(y, x)[1] = (uchar) objColor->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); self->mat.at(y, x)[2] = (uchar) objColor->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } else if (self->mat.channels() == 1) self->mat.at(y, x) = (uchar) objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); info.GetReturnValue().Set(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); } else { if (self->mat.channels() == 3) { cv::Vec3b intensity = self->mat.at(y, x); v8::Local < v8::Array > arr = Nan::New(3); arr->Set(0, Nan::New(intensity[0])); arr->Set(1, Nan::New(intensity[1])); arr->Set(2, Nan::New(intensity[2])); info.GetReturnValue().Set(arr); } else if (self->mat.channels() == 1) { uchar intensity = self->mat.at(y, x); info.GetReturnValue().Set(Nan::New(intensity)); } } return; // double val = Matrix::DblGet(t, i, j); // info.GetReturnValue().Set(Nan::New(val)); } NAN_METHOD(Matrix::Get) { SETUP_FUNCTION(Matrix) int i = Nan::To(info[0]).FromJust(); int j = Nan::To(info[1]).FromJust(); double val = Matrix::DblGet(self->mat, i, j); info.GetReturnValue().Set(Nan::New(val)); } NAN_METHOD(Matrix::GetPixel) { SETUP_FUNCTION(Matrix) int y = Nan::To(info[0]).FromJust(); int x = Nan::To(info[1]).FromJust(); if (self->mat.channels() == 4) { v8::Local < v8::Array > arr = Nan::New(4); cv::Vec4b pixel = self->mat.at(y, x); arr->Set(0, Nan::New((double) pixel.val[0])); arr->Set(1, Nan::New((double) pixel.val[1])); arr->Set(2, Nan::New((double) pixel.val[2])); arr->Set(3, Nan::New((double) pixel.val[3])); info.GetReturnValue().Set(arr); } else if (self->mat.channels() == 3) { v8::Local < v8::Array > arr = Nan::New(3); cv::Vec3b pixel = self->mat.at(y, x); arr->Set(0, Nan::New((double) pixel.val[0])); arr->Set(1, Nan::New((double) pixel.val[1])); arr->Set(2, Nan::New((double) pixel.val[2])); info.GetReturnValue().Set(arr); } else if(self->mat.channels() == 1) { int pixel = (int)self->mat.at(y, x); info.GetReturnValue().Set(pixel); } else { Nan::ThrowTypeError("Only 4, 3 and 1 channel matrix are supported"); } } NAN_METHOD(Matrix::Set) { SETUP_FUNCTION(Matrix) int i = Nan::To(info[0]).FromJust(); int j = Nan::To(info[1]).FromJust(); double val = info[2].As()->Value(); int vint = 0; if (info.Length() == 4) { self->mat.at(i, j)[info[3].As()->Value()] = val; } else if (info.Length() == 3) { switch (self->mat.type()) { case CV_32FC3: vint = static_cast(val + 0.5); self->mat.at(i, j)[0] = (uchar) (vint >> 16) & 0xff; self->mat.at(i, j)[1] = (uchar) (vint >> 8) & 0xff; self->mat.at(i, j)[2] = (uchar) (vint) & 0xff; // printf("!!!i %x, %x, %x", (vint >> 16) & 0xff, (vint >> 8) & 0xff, (vint) & 0xff); break; case CV_32FC1: self->mat.at(i, j) = val; break; default: self->mat.at(i, j) = val; } } else { Nan::ThrowTypeError("Invalid number of arguments"); } return; } // @author tualo // put node buffer directly into the image data // img.put(new Buffer([0,100,0,100,100...])); NAN_METHOD(Matrix::Put) { SETUP_FUNCTION(Matrix) if (!Buffer::HasInstance(info[0])) { Nan::ThrowTypeError("Not a buffer"); } const char* buffer_data = Buffer::Data(info[0]); size_t buffer_length = Buffer::Length(info[0]); memcpy(self->mat.data, buffer_data, buffer_length); return; } // @author tualo // getData getting node buffer of image data NAN_METHOD(Matrix::GetData) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int size = self->mat.rows * self->mat.cols * self->mat.elemSize(); Local buf = Nan::NewBuffer(size).ToLocalChecked(); uchar* data = (uchar*) Buffer::Data(buf); // if there is padding after each row, clone first to get rid of it if (self->mat.dims == 2 && self->mat.step[0] != size_t(self->mat.size[1])) { cv::Mat copy = self->mat.clone(); memcpy(data, copy.data, size); } else { memcpy(data, self->mat.data, size); } v8::Local globalObj = Nan::GetCurrentContext()->Global(); v8::Local bufferConstructor = v8::Local::Cast(globalObj->Get(Nan::New("Buffer").ToLocalChecked())); v8::Local constructorArgs[3] = {buf, Nan::New((unsigned) size), Nan::New(0)}; v8::Local actualBuffer = Nan::NewInstance(bufferConstructor, 3, constructorArgs).ToLocalChecked(); info.GetReturnValue().Set(actualBuffer); } NAN_METHOD(Matrix::Brightness) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if (info.Length() == 2) { cv::Mat image; if (self->mat.channels() == 3) { image = self->mat; } else if (self->mat.channels() == 1) { cv::Mat myimg = self->mat; cv::cvtColor(myimg, image, CV_GRAY2RGB); } else { Nan::ThrowError("those channels are not supported"); } cv::Mat new_image = cv::Mat::zeros( image.size(), image.type() ); double alpha = info[0].As()->Value(); int beta = Nan::To(info[1]).FromJust(); // Do the operation new_image(i,j) = alpha*image(i,j) + beta for (int y = 0; y < image.rows; y++ ) { for (int x = 0; x < image.cols; x++ ) { for (int c = 0; c < 3; c++ ) { new_image.at(y,x)[c] = cv::saturate_cast(alpha*( image.at(y,x)[c] ) + beta); } } } if (self->mat.channels() == 3) { new_image.copyTo(self->mat); } else if (self->mat.channels() == 1) { cv::Mat gray; cv::cvtColor(new_image, gray, CV_BGR2GRAY); gray.copyTo(self->mat); } } else { if (info.Length() == 1) { int diff = Nan::To(info[0]).FromJust(); cv::Mat img = self->mat + diff; img.copyTo(self->mat); } else { info.GetReturnValue().Set(Nan::New("Insufficient or wrong arguments").ToLocalChecked()); } } info.GetReturnValue().Set(Nan::Null()); } int getNormType(int type) { if ((type != cv::NORM_MINMAX) && (type != cv::NORM_INF) && (type != cv::NORM_L1) && (type != cv::NORM_L2) && (type != cv::NORM_L2SQR) && (type != cv::NORM_HAMMING) && (type != cv::NORM_HAMMING2) && (type != cv::NORM_RELATIVE) && (type != cv::NORM_TYPE_MASK)) { Nan::ThrowTypeError("type value must be NORM_INF=1, NORM_L1=2, NORM_L2=4," " NORM_L2SQR=5, NORM_HAMMING=6, NORM_HAMMING2=7, NORM_TYPE_MASK=7, " "NORM_RELATIVE=8, NORM_MINMAX=32 "); } return type; } // @author tualo // normalize wrapper NAN_METHOD(Matrix::Normalize) { if (!info[0]->IsNumber()) { Nan::ThrowTypeError("min is required (argument 1)"); } if (!info[1]->IsNumber()) { Nan::ThrowTypeError("max is required (argument 2)"); } int type = cv::NORM_MINMAX; if (info[2]->IsNumber()) { type = getNormType(info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked()); } int dtype = -1; if (info[3]->IsNumber()) { dtype = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } double min = info[0].As()->Value(); double max = info[1].As()->Value(); Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat norm; cv::Mat mask; if (info[4]->IsObject()) { Matrix *mmask = Nan::ObjectWrap::Unwrap(info[4]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); mask = mmask->mat; } cv::normalize(self->mat, norm, min, max, type, dtype, mask); norm.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } /** * Calculates an absolute array norm, an absolute difference norm, or a relative * difference norm. * * Reference: http://docs.opencv.org/2.4/modules/core/doc/ * operations_on_arrays.html?highlight=normalize#norm */ NAN_METHOD(Matrix::Norm) { Matrix *src2 = NULL; int normType = cv::NORM_L2; cv::Mat mask; int infoCount = 0; Matrix *src1 = Nan::ObjectWrap::Unwrap(info.This()); // If src2 is specified calculate absolute or relative difference norm if (!info[infoCount]->IsUndefined() && info[infoCount]->IsObject()) { src2 = Nan::ObjectWrap::Unwrap(info[infoCount]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); infoCount++; } // NORM_TYPE if (!info[infoCount]->IsUndefined() && info[infoCount]->IsInt32()) { normType = getNormType(info[infoCount]->Uint32Value(Nan::GetCurrentContext()).ToChecked()); infoCount++; } // Mask if (!info[infoCount]->IsUndefined() && info[infoCount]->IsObject()) { Matrix *mmask = Nan::ObjectWrap::Unwrap(info[infoCount]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); mask = mmask->mat; infoCount++; } double res = 0; if (src2) { res = cv::norm(src1->mat, src2->mat, normType, mask); } else { res = cv::norm(src1->mat, normType, mask); } info.GetReturnValue().Set(Nan::New(res)); } NAN_METHOD(Matrix::Size) { SETUP_FUNCTION(Matrix) v8::Local < v8::Array > arr = Nan::New(2); arr->Set(0, Nan::New(self->mat.size().height)); arr->Set(1, Nan::New(self->mat.size().width)); info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::Type) { SETUP_FUNCTION(Matrix) info.GetReturnValue().Set(Nan::New(self->mat.type())); } NAN_METHOD(Matrix::Clone) { SETUP_FUNCTION(Matrix) Local im_h = Matrix::CreateWrappedFromMat(self->mat.clone()); info.GetReturnValue().Set(im_h); } NAN_METHOD(Matrix::Crop) { SETUP_FUNCTION(Matrix) if ((info.Length() == 4) && (info[0]->IsNumber()) && (info[1]->IsNumber()) && (info[2]->IsNumber()) && (info[3]->IsNumber())) { int x = Nan::To(info[0]).FromJust(); int y = Nan::To(info[1]).FromJust(); int width = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int height = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::Mat mat(self->mat, cv::Rect(x,y,width,height)); Local < Object > im_h = Matrix::CreateWrappedFromMatIfNotReferenced(mat, 1); info.GetReturnValue().Set(im_h); } else { info.GetReturnValue().Set(Nan::New("Insufficient or wrong arguments").ToLocalChecked()); } } NAN_METHOD(Matrix::Row) { SETUP_FUNCTION(Matrix) int width = self->mat.size().width; int y = Nan::To(info[0]).FromJust(); v8::Local < v8::Array > arr = Nan::New(width); for (int x = 0; x < width; x++) { double v = Matrix::DblGet(self->mat, y, x); arr->Set(x, Nan::New(v)); } info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::PixelRow) { SETUP_FUNCTION(Matrix) int width = self->mat.size().width; int y = Nan::To(info[0]).FromJust(); v8::Local < v8::Array > arr; if (self->mat.channels() == 4) { arr = Nan::New(width * 4); for (int x = 0; x < width; x++) { cv::Vec4b pixel = self->mat.at(y, x); int offset = x * 4; arr->Set(offset, Nan::New((double) pixel.val[0])); arr->Set(offset + 1, Nan::New((double) pixel.val[1])); arr->Set(offset + 2, Nan::New((double) pixel.val[2])); arr->Set(offset + 3, Nan::New((double) pixel.val[3])); } } else if(self->mat.channels() == 3){ arr = Nan::New(width * 3); for (int x = 0; x < width; x++) { cv::Vec3b pixel = self->mat.at(y, x); int offset = x * 3; arr->Set(offset, Nan::New((double) pixel.val[0])); arr->Set(offset + 1, Nan::New((double) pixel.val[1])); arr->Set(offset + 2, Nan::New((double) pixel.val[2])); } } else if(self->mat.channels() == 1){ arr = Nan::New(width); for (int x = 0; x < width; x++) { int pixel = (int)self->mat.at(y, x); arr->Set(x, Nan::New(pixel)); } } else { Nan::ThrowTypeError("Only 4, 3 and 1 channel matrix are supported"); } info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::Col) { SETUP_FUNCTION(Matrix) int height = self->mat.size().height; int x = Nan::To(info[0]).FromJust(); v8::Local < v8::Array > arr = Nan::New(height); for (int y = 0; y < height; y++) { double v = Matrix::DblGet(self->mat, y, x); arr->Set(y, Nan::New(v)); } info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::PixelCol) { SETUP_FUNCTION(Matrix) int height = self->mat.size().height; int x = Nan::To(info[0]).FromJust(); v8::Local < v8::Array > arr; if (self->mat.channels() == 4) { arr = Nan::New(height * 4); for (int y = 0; y < height; y++) { cv::Vec4b pixel = self->mat.at(y, x); int offset = y * 4; arr->Set(offset, Nan::New((double) pixel.val[0])); arr->Set(offset + 1, Nan::New((double) pixel.val[1])); arr->Set(offset + 2, Nan::New((double) pixel.val[2])); arr->Set(offset + 3, Nan::New((double) pixel.val[3])); } } else if (self->mat.channels() == 3) { arr = Nan::New(height * 3); for (int y = 0; y < height; y++) { cv::Vec3b pixel = self->mat.at(y, x); int offset = y * 3; arr->Set(offset, Nan::New((double) pixel.val[0])); arr->Set(offset + 1, Nan::New((double) pixel.val[1])); arr->Set(offset + 2, Nan::New((double) pixel.val[2])); } } else if(self->mat.channels() == 1) { arr = Nan::New(height); for (int y = 0; y < height; y++) { int pixel = (int)self->mat.at(y, x); arr->Set(y, Nan::New(pixel)); } } else { Nan::ThrowTypeError("Only 4, 3 and 1 channel matrix are supported"); } info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::Width) { SETUP_FUNCTION(Matrix) info.GetReturnValue().Set(Nan::New(self->mat.size().width)); } NAN_METHOD(Matrix::Height) { SETUP_FUNCTION(Matrix) info.GetReturnValue().Set(Nan::New(self->mat.size().height)); } NAN_METHOD(Matrix::Channels) { SETUP_FUNCTION(Matrix) info.GetReturnValue().Set(Nan::New(self->mat.channels())); } NAN_METHOD(Matrix::ToBuffer) { SETUP_FUNCTION(Matrix) if ((info.Length() > 0) && (info[0]->IsFunction())) { return Matrix::ToBufferAsync(info); } // SergeMv changes // img.toBuffer({ext: ".png", pngCompression: 9}); // default png compression is 3 // img.toBuffer({ext: ".jpg", jpegQuality: 80}); // img.toBuffer(); // creates Jpeg with quality of 95 (Opencv default quality) // via the ext you can do other image formats too (like tiff), see // http://docs.opencv.org/modules/highgui/doc/reading_and_writing_images_and_video.html#imencode //--------------------------- // Provide default value const char *ext = ".jpg"; std::string optExt; std::vector params; // See if the options argument is passed if ((info.Length() > 0) && (info[0]->IsObject())) { // Get this options argument v8::Local < v8::Object > options = v8::Local::Cast(info[0]); // If the extension (image format) is provided if (options->Has( Nan::GetCurrentContext(), Nan::New("ext").ToLocalChecked()).ToChecked() ) { v8::String::Utf8Value str(v8::Isolate::GetCurrent(), options->Get(Nan::New("ext").ToLocalChecked())->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())); optExt = *str; ext = (const char *) optExt.c_str(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("jpegQuality").ToLocalChecked()).ToChecked() ) { int compression = options->Get(Nan::New("jpegQuality").ToLocalChecked())->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); params.push_back(CV_IMWRITE_JPEG_QUALITY); params.push_back(compression); } if (options->Has( Nan::GetCurrentContext(),Nan::New("pngCompression").ToLocalChecked()).ToChecked() ) { int compression = options->Get(Nan::New("pngCompression").ToLocalChecked())->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); params.push_back(CV_IMWRITE_PNG_COMPRESSION); params.push_back(compression); } } //--------------------------- std::vector vec(0); cv::imencode(ext, self->mat, vec, params); Local < Object > buf = Nan::NewBuffer(vec.size()).ToLocalChecked(); uchar* data = (uchar*) Buffer::Data(buf); memcpy(data, &vec[0], vec.size()); v8::Local < v8::Object > globalObj = Nan::GetCurrentContext()->Global(); v8::Local < v8::Function > bufferConstructor = v8::Local < v8::Function > ::Cast(globalObj->Get(Nan::New("Buffer").ToLocalChecked())); v8::Local constructorArgs[3] = {buf, Nan::New((unsigned)vec.size()), Nan::New(0)}; v8::Local < v8::Object > actualBuffer = Nan::NewInstance(bufferConstructor, 3, constructorArgs).ToLocalChecked(); info.GetReturnValue().Set(actualBuffer); } class AsyncToBufferWorker: public Nan::AsyncWorker { public: AsyncToBufferWorker(Nan::Callback *callback, Matrix *matrix, std::string ext, std::vector params) : Nan::AsyncWorker(callback), matrix(new Matrix(matrix)), // dulipcate matrix, adding ref, but not copying data ext(ext), params(params) { } ~AsyncToBufferWorker() { // mat is released, decrementing refcount } void Execute() { std::vector vec(0); // std::vector params(0);//CV_IMWRITE_JPEG_QUALITY 90 cv::imencode(ext, matrix->mat, vec, this->params); res = vec; } void HandleOKCallback() { Nan::HandleScope scope; delete matrix; matrix = NULL; Local buf = Nan::NewBuffer(res.size()).ToLocalChecked(); uchar* data = (uchar*) Buffer::Data(buf); memcpy(data, &res[0], res.size()); v8::Local globalObj = Nan::GetCurrentContext()->Global(); v8::Local bufferConstructor = v8::Local::Cast(globalObj->Get(Nan::New("Buffer").ToLocalChecked())); v8::Local constructorArgs[3] = {buf, Nan::New((unsigned)res.size()), Nan::New(0)}; v8::Local actualBuffer = Nan::NewInstance(bufferConstructor, 3, constructorArgs).ToLocalChecked();; Local argv[] = { Nan::Null(), actualBuffer }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: Matrix *matrix; std::string ext; std::vector params; std::vector res; }; NAN_METHOD(Matrix::ToBufferAsync) { SETUP_FUNCTION(Matrix) REQ_FUN_ARG(0, cb); std::string ext = std::string(".jpg"); std::vector params; // See if the options argument is passed if ((info.Length() > 1) && (info[1]->IsObject())) { // Get this options argument v8::Local < v8::Object > options = v8::Local::Cast(info[1]); // If the extension (image format) is provided if (options->Has( Nan::GetCurrentContext(),Nan::New("ext").ToLocalChecked()).ToChecked() ) { v8::String::Utf8Value str(v8::Isolate::GetCurrent(), options->Get(Nan::New("ext").ToLocalChecked())->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())); std::string str2 = std::string(*str); ext = str2; } if (options->Has( Nan::GetCurrentContext(),Nan::New("jpegQuality").ToLocalChecked()).ToChecked() ) { int compression = options->Get(Nan::New("jpegQuality").ToLocalChecked())->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); params.push_back(CV_IMWRITE_JPEG_QUALITY); params.push_back(compression); } if (options->Has( Nan::GetCurrentContext(),Nan::New("pngCompression").ToLocalChecked()).ToChecked() ) { int compression = options->Get(Nan::New("pngCompression").ToLocalChecked())->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); params.push_back(CV_IMWRITE_PNG_COMPRESSION); params.push_back(compression); } } Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncToBufferWorker(callback, self, ext, params)); return; } NAN_METHOD(Matrix::Ellipse) { SETUP_FUNCTION(Matrix) int x = 0; int y = 0; int width = 0; int height = 0; cv::Scalar color(0, 0, 255); int thickness = 1; double angle = 0; double startAngle = 0; double endAngle = 360; int lineType = 8; int shift = 0; if (info[0]->IsObject()) { v8::Local < v8::Object > options = v8::Local::Cast(info[0]); if (options->Has( Nan::GetCurrentContext(),Nan::New("center").ToLocalChecked()).ToChecked() ) { Local < Object > center = options->Get(Nan::New("center").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); x = center->Get(Nan::New("x").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); y = center->Get(Nan::New("y").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("axes").ToLocalChecked()).ToChecked() ) { Local < Object > axes = options->Get(Nan::New("axes").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); width = axes->Get(Nan::New("width").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); height = axes->Get(Nan::New("height").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("thickness").ToLocalChecked()).ToChecked() ) { thickness = options->Get(Nan::New("thickness").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("angle").ToLocalChecked()).ToChecked() ) { angle = options->Get(Nan::New("angle").ToLocalChecked()).As()->Value(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("startAngle").ToLocalChecked()).ToChecked() ) { startAngle = options->Get(Nan::New("startAngle").ToLocalChecked()).As()->Value(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("endAngle").ToLocalChecked()).ToChecked() ) { endAngle = options->Get(Nan::New("endAngle").ToLocalChecked()).As()->Value(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("lineType").ToLocalChecked()).ToChecked() ) { lineType = options->Get(Nan::New("lineType").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("shift").ToLocalChecked()).ToChecked() ) { shift = options->Get(Nan::New("shift").ToLocalChecked())->Uint32Value(Nan::GetCurrentContext()).ToChecked(); } if (options->Has( Nan::GetCurrentContext(),Nan::New("color").ToLocalChecked()).ToChecked() ) { Local < Object > objColor = options->Get(Nan::New("color").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } } else { x = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); y = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); width = info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); height = info[3]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); if (info[4]->IsArray()) { Local < Object > objColor = info[4]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } if (info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()) thickness = info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } cv::ellipse(self->mat, cv::Point(x, y), cv::Size(width, height), angle, startAngle, endAngle, color, thickness, lineType, shift); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Rectangle) { SETUP_FUNCTION(Matrix) if (info[0]->IsArray() && info[1]->IsArray()) { Local < Object > xy = Nan::To(info[0]).ToLocalChecked(); Local < Object > width_height = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); cv::Scalar color(0, 0, 255); if (info[2]->IsArray()) { Local < Object > objColor = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } int x = xy->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int y = xy->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int width = width_height->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int height = width_height->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int thickness = 1; if (info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()) thickness = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::rectangle(self->mat, cv::Point(x, y), cv::Point(x + width, y + height), color, thickness); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Line) { SETUP_FUNCTION(Matrix) if (info[0]->IsArray() && info[1]->IsArray()) { Local < Object > xy1 = Nan::To(info[0]).ToLocalChecked(); Local < Object > xy2 = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); cv::Scalar color(0, 0, 255); if (info[2]->IsArray()) { Local < Object > objColor = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } int x1 = xy1->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int y1 = xy1->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int x2 = xy2->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int y2 = xy2->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int thickness = 1; if (info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()) thickness = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::line(self->mat, cv::Point(x1, y1), cv::Point(x2, y2), color, thickness); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::FillPoly) { SETUP_FUNCTION(Matrix) if (info[0]->IsArray()) { Local < Array > polyArray = Local < Array > ::Cast(Nan::To(info[0]).ToLocalChecked()); cv::Point **polygons = new cv::Point*[polyArray->Length()]; int *polySizes = new int[polyArray->Length()]; for (unsigned int i = 0; i < polyArray->Length(); i++) { Local singlePoly = Local ::Cast(polyArray->Get(i)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); polygons[i] = new cv::Point[singlePoly->Length()]; polySizes[i] = singlePoly->Length(); for (unsigned int j = 0; j < singlePoly->Length(); j++) { Local point = Local ::Cast(singlePoly->Get(j)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); polygons[i][j].x = point->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); polygons[i][j].y = point->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } } cv::Scalar color(0, 0, 255); if (info[1]->IsArray()) { Local objColor = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } cv::fillPoly(self->mat, (const cv::Point **) polygons, polySizes, polyArray->Length(), color); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Save) { SETUP_FUNCTION(Matrix) if (info.Length() > 1) { return SaveAsync(info); } if (!info[0]->IsString()) { Nan::ThrowTypeError("filename required"); } Nan::Utf8String filename(info[0]); int res = cv::imwrite(*filename, self->mat); info.GetReturnValue().Set(Nan::New(res)); } // All this is for async save, see here for nan example: // https://github.com/rvagg/nan/blob/c579ae858ae3208d7e702e8400042ba9d48fa64b/examples/async_pi_estimate/async.cc class AsyncSaveWorker: public Nan::AsyncWorker { public: AsyncSaveWorker(Nan::Callback *callback, Matrix *matrix, char* filename) : Nan::AsyncWorker(callback), matrix(new Matrix(matrix)), filename(filename) { } ~AsyncSaveWorker() { } // Executed inside the worker-thread. // It is not safe to access V8, or V8 data structures // here, so everything we need for input and output // should go on `this`. void Execute() { res = cv::imwrite(this->filename, matrix->mat); } // Executed when the async work is complete // this function will be run inside the main event loop // so it is safe to use V8 again void HandleOKCallback() { Nan::HandleScope scope; delete matrix; matrix = NULL; Local argv[] = { Nan::Null(), Nan::New(res) }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: Matrix *matrix; std::string filename; int res; }; NAN_METHOD(Matrix::SaveAsync) { SETUP_FUNCTION(Matrix) if (!info[0]->IsString()) { Nan::ThrowTypeError("filename required"); } Nan::Utf8String filename(info[0]); REQ_FUN_ARG(1, cb); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncSaveWorker(callback, self, *filename)); return; } NAN_METHOD(Matrix::Zeros) { Nan::HandleScope scope; int h = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int w = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int type = (info.Length() > 2) ? info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : CV_64FC1; cv::Mat mat = cv::Mat::zeros(h, w, type); Local im_h = Matrix::CreateWrappedFromMat(mat); info.GetReturnValue().Set(im_h); } NAN_METHOD(Matrix::Ones) { Nan::HandleScope scope; int h = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int w = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int type = (info.Length() > 2) ? info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : CV_64FC1; cv::Mat mat = cv::Mat::ones(h, w, type); Local im_h = Matrix::CreateWrappedFromMat(mat); info.GetReturnValue().Set(im_h); } NAN_METHOD(Matrix::Eye) { Nan::HandleScope scope; int h = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int w = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int type = (info.Length() > 2) ? info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : CV_64FC1; cv::Mat mat = cv::Mat::eye(h, w, type); Local im_h = Matrix::CreateWrappedFromMat(mat); info.GetReturnValue().Set(im_h); } NAN_METHOD(Matrix::ConvertGrayscale) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if (self->mat.channels() != 3) { Nan::ThrowError("Image is no 3-channel"); } int oldSize = self->mat.dataend - self->mat.datastart; cv::cvtColor(self->mat, self->mat, CV_BGR2GRAY); int newSize = self->mat.dataend - self->mat.datastart; Nan::AdjustExternalMemory(newSize - oldSize); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::ConvertHSVscale) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if (self->mat.channels() != 3) { Nan::ThrowError("Image is no 3-channel"); } cv::Mat hsv; cv::cvtColor(self->mat, hsv, CV_BGR2HSV); hsv.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::GaussianBlur) { Nan::HandleScope scope; cv::Size ksize; cv::Mat blurred; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); double sigmaX = 0; double sigmaY = 0; int borderType = cv::BORDER_DEFAULT; if (info.Length() < 1) { ksize = cv::Size(5, 5); } else { if (!info[0]->IsArray()) { Nan::ThrowTypeError("'ksize' argument must be a 2 double array"); } Local array = Nan::To(info[0]).ToLocalChecked(); // TODO: Length check Local x = array->Get(0); Local y = array->Get(1); if (!x->IsNumber() || !y->IsNumber()) { Nan::ThrowTypeError("'ksize' argument must be a 2 double array"); } ksize = cv::Size(x.As()->Value(), y.As()->Value()); sigmaX = info.Length() < 2 ? 0 : info[1].As()->Value(); sigmaY = info.Length() < 3 ? 0 : info[2].As()->Value(); if (info.Length() == 4) { borderType = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } } cv::GaussianBlur(self->mat, blurred, ksize, sigmaX, sigmaY, borderType); blurred.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::MedianBlur) { Nan::HandleScope scope; cv::Mat blurred; int ksize = 3; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if (info[0]->IsNumber()) { ksize = Nan::To(info[0]).FromJust(); if ((ksize % 2) == 0) { Nan::ThrowTypeError("'ksize' argument must be a positive odd integer"); } } else { Nan::ThrowTypeError("'ksize' argument must be a positive odd integer"); } cv::medianBlur(self->mat, blurred, ksize); blurred.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::BilateralFilter) { Nan::HandleScope scope; cv::Mat filtered; int d = 15; double sigmaColor = 80; double sigmaSpace = 80; int borderType = cv::BORDER_DEFAULT; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if (info.Length() != 0) { if (info.Length() < 3 || info.Length() > 4) { Nan::ThrowTypeError("BilateralFilter takes 0, 3, or 4 arguments"); } else { d = Nan::To(info[0]).FromJust(); sigmaColor = info[1].As()->Value(); sigmaSpace = info[2].As()->Value(); if (info.Length() == 4) { borderType = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } } } cv::bilateralFilter(self->mat, filtered, d, sigmaColor, sigmaSpace, borderType); filtered.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Sobel) { Nan::HandleScope scope; if (info.Length() < 3) Nan::ThrowError("Need more arguments: sobel(ddepth, xorder, yorder, ksize=3, scale=1.0, delta=0.0, borderType=CV_BORDER_DEFAULT)"); int ddepth = Nan::To(info[0]).FromJust(); int xorder = Nan::To(info[1]).FromJust(); int yorder = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int ksize = 3; if (info.Length() > 3) ksize = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); double scale = 1; if (info.Length() > 4) scale = info[4].As()->Value(); double delta = 0; if (info.Length() > 5) delta = info[5].As()->Value(); int borderType = cv::BORDER_DEFAULT; if (info.Length() > 6) borderType = info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat result; cv::Sobel(self->mat, result, ddepth, xorder, yorder, ksize, scale, delta, borderType); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } NAN_METHOD(Matrix::Copy) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat result; self->mat.copyTo(result); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } NAN_METHOD(Matrix::Flip) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if ( info.Length() < 1 || !info[0]->IsInt32() ) { Nan::ThrowTypeError("Flip requires an integer flipCode argument " "(0 = X axis, positive = Y axis, negative = both axis)"); } int flipCode = Nan::To(info[0]).FromJust(); cv::Mat result; cv::flip(self->mat, result, flipCode); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } NAN_METHOD(Matrix::ROI) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if ( info.Length() != 4 ) { Nan::ThrowTypeError("ROI requires x,y,w,h arguments"); } // Although it's an image to return, it is in fact a pointer to ROI of parent matrix int x = Nan::To(info[0]).FromJust(); int y = Nan::To(info[1]).FromJust(); int w = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int h = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::Mat roi(self->mat, cv::Rect(x,y,w,h)); // Although it's an image to return, it is in fact a pointer to ROI of parent matrix Local img_to_return = Matrix::CreateWrappedFromMatIfNotReferenced(roi, 1); info.GetReturnValue().Set(img_to_return); } NAN_METHOD(Matrix::Ptr) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int line = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); char* data = self->mat.ptr(line); // uchar* data = self->mat.data; // char *mydata = "Random raw data\0"; Local return_buffer = Nan::NewBuffer((char*)data, self->mat.step).ToLocalChecked(); info.GetReturnValue().Set( return_buffer ); // return; } NAN_METHOD(Matrix::AbsDiff) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *src1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Matrix *src2 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::absdiff(src1->mat, src2->mat, self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Dct) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int cols = self->mat.cols; int rows = self->mat.rows; cv::Mat result(cols, rows, CV_32F); cv::dct(self->mat, result); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } NAN_METHOD(Matrix::Idct) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int cols = self->mat.cols; int rows = self->mat.rows; cv::Mat result(cols, rows, CV_32F); cv::idct(self->mat, result); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } NAN_METHOD(Matrix::AddWeighted) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *src1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Matrix *src2 = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); float alpha = info[1].As()->Value(); float beta = info[3].As()->Value(); int gamma = 0; try { cv::addWeighted(src1->mat, alpha, src2->mat, beta, gamma, self->mat); } catch(cv::Exception& e ) { const char* err_msg = e.what(); Nan::ThrowError(err_msg); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Add) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int cols = self->mat.cols; int rows = self->mat.rows; Matrix *src1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); try { cv::Mat outputmat = cv::Mat(cols, rows, self->mat.type()); cv::add(self->mat, src1->mat, outputmat); Local out = CreateWrappedFromMat(outputmat); info.GetReturnValue().Set(out); } catch(cv::Exception& e ) { const char* err_msg = e.what(); Nan::ThrowError(err_msg); } } NAN_METHOD(Matrix::BitwiseXor) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *src1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Matrix *src2 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); if (info.Length() == 3) { Matrix *mask = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::bitwise_xor(src1->mat, src2->mat, self->mat, mask->mat); } else { cv::bitwise_xor(src1->mat, src2->mat, self->mat); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::BitwiseNot) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *dst = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); if (info.Length() == 2) { Matrix *mask = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::bitwise_not(self->mat, dst->mat, mask->mat); } else { cv::bitwise_not(self->mat, dst->mat); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::BitwiseAnd) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *src1 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Matrix *src2 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); if (info.Length() == 3) { Matrix *mask = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::bitwise_and(src1->mat, src2->mat, self->mat, mask->mat); } else { cv::bitwise_and(src1->mat, src2->mat, self->mat); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::CountNonZero) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); double count = (double)cv::countNonZero(self->mat); info.GetReturnValue().Set(Nan::New(count)); } /* NAN_METHOD(Matrix::Split) { Nan::HandleScope scope; //Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); info.GetReturnValue().Set(Nan::Null()); } */ NAN_METHOD(Matrix::Moments) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Moments mo = moments( self->mat, false ); Local res = Nan::New(); res->Set(Nan::New("m00").ToLocalChecked(), Nan::New(mo.m00)); res->Set(Nan::New("m10").ToLocalChecked(), Nan::New(mo.m10)); res->Set(Nan::New("m01").ToLocalChecked(), Nan::New(mo.m01)); res->Set(Nan::New("m20").ToLocalChecked(), Nan::New(mo.m20)); res->Set(Nan::New("m11").ToLocalChecked(), Nan::New(mo.m11)); res->Set(Nan::New("m02").ToLocalChecked(), Nan::New(mo.m02)); res->Set(Nan::New("m30").ToLocalChecked(), Nan::New(mo.m30)); res->Set(Nan::New("m21").ToLocalChecked(), Nan::New(mo.m21)); res->Set(Nan::New("m12").ToLocalChecked(), Nan::New(mo.m12)); res->Set(Nan::New("m03").ToLocalChecked(), Nan::New(mo.m03)); res->Set(Nan::New("mu20").ToLocalChecked(), Nan::New(mo.mu20)); res->Set(Nan::New("mu11").ToLocalChecked(), Nan::New(mo.mu11)); res->Set(Nan::New("mu02").ToLocalChecked(), Nan::New(mo.mu02)); res->Set(Nan::New("mu30").ToLocalChecked(), Nan::New(mo.mu30)); res->Set(Nan::New("mu21").ToLocalChecked(), Nan::New(mo.mu21)); res->Set(Nan::New("mu12").ToLocalChecked(), Nan::New(mo.mu12)); res->Set(Nan::New("mu03").ToLocalChecked(), Nan::New(mo.mu03)); res->Set(Nan::New("nu20").ToLocalChecked(), Nan::New(mo.nu20)); res->Set(Nan::New("nu11").ToLocalChecked(), Nan::New(mo.nu11)); res->Set(Nan::New("nu02").ToLocalChecked(), Nan::New(mo.nu02)); res->Set(Nan::New("nu30").ToLocalChecked(), Nan::New(mo.nu30)); res->Set(Nan::New("nu21").ToLocalChecked(), Nan::New(mo.nu21)); res->Set(Nan::New("nu12").ToLocalChecked(), Nan::New(mo.nu12)); res->Set(Nan::New("nu03").ToLocalChecked(), Nan::New(mo.nu03)); info.GetReturnValue().Set(res); } NAN_METHOD(Matrix::Canny) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int lowThresh = info[0].As()->Value(); int highThresh = info[1].As()->Value(); cv::Mat newMat; cv::Canny(self->mat, newMat, lowThresh, highThresh); newMat.copyTo(self->mat); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Dilate) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int niters = info[0].As()->Value(); cv::Mat kernel = cv::Mat(); if (info.Length() == 2) { Matrix *kernelMatrix = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); kernel = kernelMatrix->mat; } cv::dilate(self->mat, self->mat, kernel, cv::Point(-1, -1), niters); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::Erode) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int niters = info[0].As()->Value(); cv::Mat kernel = cv::Mat(); if (info.Length() == 2) { Matrix *kernelMatrix = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); kernel = kernelMatrix->mat; } cv::erode(self->mat, self->mat, kernel, cv::Point(-1, -1), niters); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::FindContours) { Nan::HandleScope scope; int mode = CV_RETR_LIST; int chain = CV_CHAIN_APPROX_SIMPLE; if (info.Length() > 0) { if (info[0]->IsNumber()) mode = Nan::To(info[0]).FromJust(); } if (info.Length() > 1) { if (info[1]->IsNumber()) chain = Nan::To(info[1]).FromJust(); } Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Local conts_to_return = Nan::NewInstance(Nan::GetFunction(Nan::New(Contour::constructor)).ToLocalChecked()).ToLocalChecked(); Contour *contours = Nan::ObjectWrap::Unwrap(conts_to_return); cv::findContours(self->mat, contours->contours, contours->hierarchy, mode, chain); info.GetReturnValue().Set(conts_to_return); } NAN_METHOD(Matrix::DrawContour) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Contour *cont = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); int pos = info[1].As()->Value(); cv::Scalar color(0, 0, 255); if (info[2]->IsArray()) { Local objColor = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } int thickness = info.Length() < 4 ? 1 : info[3].As()->Value(); int lineType = info.Length() < 5 ? 8 : info[4].As()->Value(); int maxLevel = info.Length() < 6 ? 0 : info[5].As()->Value(); cv::Point offset; if (info.Length() == 6) { Local _offset = Local::Cast(info[5]); offset = cv::Point( Nan::To(Nan::Get(_offset, 0).ToLocalChecked()).FromJust(), Nan::To(Nan::Get(_offset, 1).ToLocalChecked()).FromJust() ); } cv::drawContours(self->mat, cont->contours, pos, color, thickness, lineType, cont->hierarchy, maxLevel, offset); return; } NAN_METHOD(Matrix::DrawAllContours) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Contour *cont = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Scalar color(0, 0, 255); if (info[1]->IsArray()) { Local objColor = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } int thickness = info.Length() < 3 ? 1 : info[2].As()->Value(); cv::drawContours(self->mat, cont->contours, -1, color, thickness); return; } NAN_METHOD(Matrix::GoodFeaturesToTrack) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int maxCorners = info.Length() >= 1 ? Nan::To(info[0]).FromJust() : 500; double qualityLevel = info.Length() >= 2 ? (double) info[1].As()->Value() : 0.01; double minDistance = info.Length() >= 3 ? (double) info[2].As()->Value() : 10; int blockSize = info.Length() >= 4 ? info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 3; bool useHarrisDetector = info.Length() >= 5 ? info[4]->BooleanValue( Nan::GetCurrentContext() ).FromJust() : false; double k = info.Length() >= 6 ? (double) info[5].As()->Value() : 0.04; std::vector corners; cv::Mat gray; if (self->mat.channels() == 1) { gray = self->mat; } else { cvtColor(self->mat, gray, CV_BGR2GRAY); equalizeHist(gray, gray); } cv::goodFeaturesToTrack(gray, corners, maxCorners, qualityLevel, minDistance, cv::noArray(), blockSize, useHarrisDetector, k); v8::Local arr = Nan::New(corners.size()); for (unsigned int i=0; i pt = Nan::New(2); pt->Set(0, Nan::New((double) corners[i].x)); pt->Set(1, Nan::New((double) corners[i].y)); arr->Set(i, pt); } info.GetReturnValue().Set(arr); } #ifdef HAVE_OPENCV_VIDEO NAN_METHOD(Matrix::CalcOpticalFlowPyrLK) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *newMatrix = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Local points = Local::Cast(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); std::vector old_points; for (unsigned int i=0; iLength(); i++) { Local pt = points->Get(i)->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); old_points.push_back(cv::Point2f(pt->Get(0).As()->Value(), pt->Get(1).As()->Value())); } cv::Size winSize; if (info.Length() >= 3 && info[2]->IsArray()) { Local winSizeObj = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); winSize = cv::Size(winSizeObj->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), winSizeObj->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { winSize = cv::Size(21, 21); } int maxLevel = info.Length() >= 4 ? info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 3; cv::TermCriteria criteria; if (info.Length() >= 5 && info[4]->IsArray()) { Local criteriaObj = info[4]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); criteria = cv::TermCriteria(criteriaObj->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), criteriaObj->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), (double) criteriaObj->Get(2).As()->Value()); } else { criteria = cv::TermCriteria(cv::TermCriteria::COUNT + cv::TermCriteria::EPS, 30, 0.01); } int flags = info.Length() >= 6 ? info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 0; double minEigThreshold = info.Length() >= 7 ? info[6].As()->Value() : 1e-4; cv::Mat old_gray; cv::cvtColor(self->mat, old_gray, CV_BGR2GRAY); cv::Mat new_gray; cv::cvtColor(newMatrix->mat, new_gray, CV_BGR2GRAY); std::vector new_points; std::vector status; std::vector err; cv::calcOpticalFlowPyrLK(old_gray, new_gray, old_points, new_points, status, err, winSize, maxLevel, criteria, flags, minEigThreshold); v8::Local old_arr = Nan::New(old_points.size()); v8::Local new_arr = Nan::New(new_points.size()); v8::Local found = Nan::New(status.size()); for (unsigned int i=0; i pt = Nan::New(2); pt->Set(0, Nan::New((double) old_points[i].x)); pt->Set(1, Nan::New((double) old_points[i].y)); old_arr->Set(i, pt); } for (unsigned int i=0; i pt = Nan::New(2); pt->Set(0, Nan::New((double) new_points[i].x)); pt->Set(1, Nan::New((double) new_points[i].y)); new_arr->Set(i, pt); } for (unsigned int i=0; i pt = Nan::New((int)status[i]); found->Set(i, pt); } Local data = Nan::New(); data->Set(Nan::New("old_points").ToLocalChecked(), old_arr); data->Set(Nan::New("new_points").ToLocalChecked(), new_arr); data->Set(Nan::New("found").ToLocalChecked(), found); info.GetReturnValue().Set(data); } #endif NAN_METHOD(Matrix::HoughLinesP) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); double rho = info.Length() < 1 ? 1 : info[0].As()->Value(); double theta = info.Length() < 2 ? CV_PI/180 : info[1].As()->Value(); int threshold = info.Length() < 3 ? 80 : info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); double minLineLength = info.Length() < 4 ? 30 : info[3].As()->Value(); double maxLineGap = info.Length() < 5 ? 10 : info[4].As()->Value(); std::vector lines; cv::Mat gray; equalizeHist(self->mat, gray); // cv::Canny(gray, gray, 50, 200, 3); cv::HoughLinesP(gray, lines, rho, theta, threshold, minLineLength, maxLineGap); v8::Local arr = Nan::New(lines.size()); for (unsigned int i=0; i pt = Nan::New(4); pt->Set(0, Nan::New((double) lines[i][0])); pt->Set(1, Nan::New((double) lines[i][1])); pt->Set(2, Nan::New((double) lines[i][2])); pt->Set(3, Nan::New((double) lines[i][3])); arr->Set(i, pt); } info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::HoughCircles) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); double dp = info.Length() < 1 ? 1 : info[0].As()->Value(); double minDist = info.Length() < 2 ? 1 : info[1].As()->Value(); double higherThreshold = info.Length() < 3 ? 100 : info[2].As()->Value(); double accumulatorThreshold = info.Length() < 4 ? 100 : info[3].As()->Value(); int minRadius = info.Length() < 5 ? 0 : info[4]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int maxRadius = info.Length() < 6 ? 0 : info[5]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); std::vector circles; cv::Mat gray; equalizeHist(self->mat, gray); cv::HoughCircles(gray, circles, CV_HOUGH_GRADIENT, dp, minDist, higherThreshold, accumulatorThreshold, minRadius, maxRadius); v8::Local arr = Nan::New(circles.size()); for (unsigned int i=0; i < circles.size(); i++) { v8::Local pt = Nan::New(3); pt->Set(0, Nan::New((double) circles[i][0])); // center x pt->Set(1, Nan::New((double) circles[i][1]));// center y pt->Set(2, Nan::New((double) circles[i][2]));// radius arr->Set(i, pt); } info.GetReturnValue().Set(arr); } cv::Scalar setColor(Local objColor) { int64_t channels[4] = { 0, 0, 0, 0 }; // We'll accomodate a channel count up to 4 and fall back to the old // "assume it's always 3" in the default case if (!objColor->HasRealIndexedProperty( Nan::GetCurrentContext() , 1).ToChecked()) { channels[0] = objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } else if (!objColor->HasRealIndexedProperty( Nan::GetCurrentContext() , 2).ToChecked()) { channels[0] = objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[1] = objColor->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } else if (!objColor->HasRealIndexedProperty( Nan::GetCurrentContext() , 4).ToChecked()) { channels[0] = objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[1] = objColor->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[2] = objColor->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[3] = objColor->Get(3)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } else { channels[0] = objColor->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[1] = objColor->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); channels[2] = objColor->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } return cv::Scalar(channels[0], channels[1], channels[2], channels[3]); } cv::Point setPoint(Local objPoint) { return cv::Point(objPoint->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), objPoint->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } cv::Rect* setRect(Local objRect, cv::Rect &result) { if (!objRect->IsArray() || !objRect->Get(0)->IsArray() || !objRect->Get(0)->IsArray()) { printf("error"); return 0; }; Local < Object > point = objRect->Get(0)->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); Local < Object > size = objRect->Get(1)->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); result.x = point->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); result.y = point->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); result.width = size->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); result.height = size->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); return &result; } class ResizeASyncWorker: public Nan::AsyncWorker { public: ResizeASyncWorker(Nan::Callback *callback, Matrix *image, cv::Size size, double fx, double fy, int interpolation) : Nan::AsyncWorker(callback), image(new Matrix(image)), dest(cv::Mat()), size(size), fx(fx), fy(fy), interpolation(interpolation), success(0) { } ~ResizeASyncWorker() { // Any cleanup we needed to do could be done here. // Clean up of the input image Matrix and the destination cv::Mat // should be handled automatically by destructors. } void Execute() { try { cv::resize(image->mat, dest, size, fx, fy, interpolation); success = 1; } catch(...){ success = 0; } } void HandleOKCallback() { Nan::HandleScope scope; delete image; image = NULL; if (success){ try{ Local im_to_return = Matrix::CreateWrappedFromMat(dest); dest.release(); //release our refcount before handing it back to the callback Local argv[] = { Nan::Null(), // err im_to_return //result }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } catch (...){ Local argv[] = { Nan::New("C++ exception wrapping response").ToLocalChecked(), // err Nan::Null() // result }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } } else { Local argv[] = { Nan::New("C++ exception").ToLocalChecked(), // err Nan::Null() //result }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } } private: Matrix *image; cv::Mat dest; cv::Size size; double fx; double fy; int interpolation; int success; }; NAN_METHOD(Matrix::Resize) { SETUP_FUNCTION(Matrix) if (info.Length() < 2) { return Nan::ThrowError("Matrix.resize requires at least 2 argument2"); } //im.resize( width, height ); //im.resize( width, height, fx, fy ); //im.resize( width, height, interpolation ); //im.resize( width, height, fx, fy, interpolation ); // if fn is added on the end, makes it Async int numargs = info.Length(); int isAsync = 0; if (info[numargs-1]->IsFunction()){ isAsync = 1; } if (info.Length() < 2+isAsync) { return Nan::ThrowError("Matrix.resize requires at least x and y size argument2"); } int x = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int y = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); cv::Size size(x, y); if (size.area() == 0) { return Nan::ThrowError("Area of size must be > 0"); } double fx = 0; double fy = 0; int interpolation = cv::INTER_LINEAR; // if 4 or more args, then expect fx, fy next if (numargs >= 4+isAsync) { DOUBLE_FROM_ARGS(fx, 2) DOUBLE_FROM_ARGS(fy, 3) if (numargs == 5+isAsync) { INT_FROM_ARGS(interpolation, 5) } } else { // if 3 args after possible function, expect interpolation if (numargs == 3+isAsync) { INT_FROM_ARGS(interpolation, 3) } } // if async if (isAsync){ REQ_FUN_ARG(numargs-1, cb); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new ResizeASyncWorker(callback, self, size, fx, fy, interpolation)); info.GetReturnValue().Set(Nan::Null()); } else { try{ Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat res = cv::Mat(x, y, CV_32FC3); cv::resize(self->mat, res, cv::Size(x, y), 0, 0, interpolation); self->setMat(res); } catch (...){ return Nan::ThrowError("c++ Exception processing resize"); } } } NAN_METHOD(Matrix::Rotate) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat rotMatrix(2, 3, CV_32FC1); cv::Mat res; float angle = Nan::To(info[0]).FromJust(); // Modification by SergeMv //------------- // If you provide only the angle argument and the angle is multiple of 90, then // we do a fast thing bool rightOrStraight = (ceil(angle) == angle) && (!((int)angle % 90)) && (info.Length() == 1); if (rightOrStraight) { int angle2 = ((int)angle) % 360; if (!angle2) {return;} if (angle2 < 0) {angle2 += 360;} // See if we do right angle rotation, we transpose the matrix: if (angle2 % 180) { cv::transpose(self->mat, res); self->setMat(res); } // Now flip the image int mode = -1;// flip around both axes // If counterclockwise, flip around the x-axis if (angle2 == 90) {mode = 0;} // If clockwise, flip around the y-axis if (angle2 == 270) {mode = 1;} cv::flip(self->mat, self->mat, mode); return; } //------------- int x = info[1]->IsUndefined() ? round(self->mat.size().width / 2) : info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int y = info[1]->IsUndefined() ? round(self->mat.size().height / 2) : info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); cv::Point center = cv::Point(x,y); rotMatrix = getRotationMatrix2D(center, angle, 1.0); cv::warpAffine(self->mat, res, rotMatrix, self->mat.size()); self->setMat(res); return; } NAN_METHOD(Matrix::GetRotationMatrix2D) { Nan::HandleScope scope; if (info.Length() < 3) { JSTHROW("Invalid number of arguments"); } float angle = Nan::To(info[0]).FromJust(); int x = Nan::To(info[1]).FromJust(); int y = Nan::To(info[2]).FromJust(); double scale = Nan::To(info[3]).FromMaybe(1.0); cv::Point center = cv::Point(x,y); Local img_to_return = Matrix::CreateWrappedFromMat(getRotationMatrix2D(center, angle, scale)); info.GetReturnValue().Set(img_to_return); } NAN_METHOD(Matrix::WarpAffine) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat res; Matrix *rotMatrix = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // Resize the image if size is specified int dstRows = info[1]->IsUndefined() ? self->mat.rows : info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int dstCols = info[2]->IsUndefined() ? self->mat.cols : info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); cv::Size resSize = cv::Size(dstRows, dstCols); cv::warpAffine(self->mat, res, rotMatrix->mat, resSize); self->setMat(res); return; } NAN_METHOD(Matrix::PyrDown) { SETUP_FUNCTION(Matrix) int oldSize = self->mat.dataend - self->mat.datastart; cv::pyrDown(self->mat, self->mat); int newSize = self->mat.dataend - self->mat.datastart; Nan::AdjustExternalMemory(newSize - oldSize); return; } NAN_METHOD(Matrix::PyrUp) { SETUP_FUNCTION(Matrix) int oldSize = self->mat.dataend - self->mat.datastart; cv::pyrUp(self->mat, self->mat); int newSize = self->mat.dataend - self->mat.datastart; Nan::AdjustExternalMemory(newSize - oldSize); return; } NAN_METHOD(Matrix::inRange) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); /*if (self->mat.channels() != 3) Nan::ThrowError(String::New("Image is no 3-channel"));*/ if (info[0]->IsArray() && info[1]->IsArray()) { Local args_lowerb = Nan::To(info[0]).ToLocalChecked(); Local args_upperb = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); cv::Scalar lowerb(0, 0, 0); cv::Scalar upperb(0, 0, 0); lowerb = setColor(args_lowerb); upperb = setColor(args_upperb); cv::Mat mask; cv::inRange(self->mat, lowerb, upperb, mask); mask.copyTo(self->mat); } info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::AdjustROI) { SETUP_FUNCTION(Matrix) int dtop = info[0]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int dbottom = info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int dleft = info[2]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); int dright = info[3]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); self->mat.adjustROI(dtop, dbottom, dleft, dright); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::LocateROI) { SETUP_FUNCTION(Matrix) cv::Size wholeSize; cv::Point ofs; self->mat.locateROI(wholeSize, ofs); v8::Local < v8::Array > arr = Nan::New(4); arr->Set(0, Nan::New(wholeSize.width)); arr->Set(1, Nan::New(wholeSize.height)); arr->Set(2, Nan::New(ofs.x)); arr->Set(3, Nan::New(ofs.y)); info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::Threshold) { SETUP_FUNCTION(Matrix) double threshold = info[0].As()->Value(); double maxVal = info[1].As()->Value(); int typ = cv::THRESH_BINARY; if (info.Length() >= 3) { Nan::Utf8String typstr(info[2]); if (strcmp(*typstr, "Binary") == 0) { // Uses default value } else if (strcmp(*typstr, "Binary Inverted") == 0) { typ = cv::THRESH_BINARY_INV; } else if (strcmp(*typstr, "Threshold Truncated") == 0) { typ = cv::THRESH_TRUNC; } else if (strcmp(*typstr, "Threshold to Zero") == 0) { typ = cv::THRESH_TOZERO; } else if (strcmp(*typstr, "Threshold to Zero Inverted") == 0) { typ = cv::THRESH_TOZERO_INV; } else { char *typeString = *typstr; char text[] = "\" is no supported binarization technique. " "Use \"Binary\" (default), \"Binary Inverted\", " "\"Threshold Truncated\", \"Threshold to Zero\" " "or \"Threshold to Zero Inverted\""; char *errorMessage; errorMessage = new char[strlen(typeString) + strlen(text) + 2]; strcpy(errorMessage, "\""); strcat(errorMessage, typeString); strcat(errorMessage, text); Nan::ThrowError(errorMessage); return; } } if (info.Length() >= 4) { Nan::Utf8String algorithm(info[3]); if (strcmp(*algorithm, "Simple") == 0) { // Uses default } else if (strcmp(*algorithm, "Otsu") == 0) { typ += 8; } else { char *algo = *algorithm; char text[] = "\" is no supported threshold algorithm. " "Use \"Simple\" (default) or \"Otsu\"."; char *errorMessage; errorMessage = new char[strlen(algo) + strlen(text) + 2]; strcpy(errorMessage, "\""); strcat(errorMessage, algo); strcat(errorMessage, text); Nan::ThrowError(errorMessage); return; } } cv::Mat outputmat = cv::Mat(); self->mat.copyTo(outputmat); cv::threshold(self->mat, outputmat, threshold, maxVal, typ); Local < Object > img_to_return = CreateWrappedFromMat(outputmat); info.GetReturnValue().Set(img_to_return); } NAN_METHOD(Matrix::AdaptiveThreshold) { SETUP_FUNCTION(Matrix) double maxVal = info[0].As()->Value(); double adaptiveMethod = info[1].As()->Value(); double thresholdType = info[2].As()->Value(); double blockSize = info[3].As()->Value(); double C = info[4].As()->Value(); cv::Mat outputmat = cv::Mat(); self->mat.copyTo(outputmat); cv::adaptiveThreshold(self->mat, outputmat, maxVal, adaptiveMethod, thresholdType, blockSize, C); Local < Object > img_to_return = CreateWrappedFromMat(outputmat); info.GetReturnValue().Set(img_to_return); } NAN_METHOD(Matrix::MeanStdDev) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat meanMat = cv::Mat(); cv::Mat stddevMat = cv::Mat(); cv::meanStdDev(self->mat, meanMat, stddevMat); Local data = Nan::New(); data->Set(Nan::New("mean").ToLocalChecked(), CreateWrappedFromMat(meanMat)); data->Set(Nan::New("stddev").ToLocalChecked(), CreateWrappedFromMat(stddevMat)); info.GetReturnValue().Set(data); } // @author SergeMv // Copies our (small) image into a ROI of another (big) image // @param Object another image (destination) // @param Number Destination x (where our image is to be copied) // @param Number Destination y (where our image is to be copied) // Example: smallImg.copyTo(bigImg, 50, 50); // Note, x,y and width and height of our image must be so that // our.width + x <= destination.width (and the same for y and height) // both x and y must be >= 0 NAN_METHOD(Matrix::CopyTo) { Nan::HandleScope scope; Matrix * self = Nan::ObjectWrap::Unwrap(info.This()); int width = self->mat.size().width; int height = self->mat.size().height; // param 0 - destination image: Matrix *dest = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // param 1 - x coord of the destination int x = Nan::To(info[1]).FromJust(); // param 2 - y coord of the destination int y = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::Mat dstROI = cv::Mat(dest->mat, cv::Rect(x, y, width, height)); self->mat.copyTo(dstROI); return; } /** * Converts an array to another data type with optional scaling * Reference: http://docs.opencv.org/2.4/modules/core/doc/basic_structures.html#mat-convertto */ NAN_METHOD(Matrix::ConvertTo) { SETUP_FUNCTION(Matrix) if (info.Length() < 2) { JSTHROW("Invalid number of arguments"); } // param 0 - destination image Matrix *dest = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // param 1 - desired matrix type int rtype = -1; INT_FROM_ARGS(rtype, 1); // param 2 - alpha double alpha = 1; if (info.Length() >= 3) { DOUBLE_FROM_ARGS(alpha, 2); } // param 3 - beta double beta = 0; if (info.Length() >= 4) { DOUBLE_FROM_ARGS(beta, 3); } self->mat.convertTo(dest->mat, rtype, alpha, beta); return; } // @author SergeMv // Does in-place color transformation // img.cvtColor('CV_BGR2YCrCb'); NAN_METHOD(Matrix::CvtColor) { Nan::HandleScope scope; Matrix * self = Nan::ObjectWrap::Unwrap(info.This()); if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } // Get transform string v8::String::Utf8Value str (v8::Isolate::GetCurrent(),info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())); std::string str2 = std::string(*str); const char * sTransform = (const char *) str2.c_str(); int iTransform; if (!strcmp(sTransform, "CV_BGR2GRAY")) { iTransform = CV_BGR2GRAY; } else if (!strcmp(sTransform, "CV_GRAY2BGR")) { iTransform = CV_GRAY2BGR; } else if (!strcmp(sTransform, "CV_BGR2XYZ")) { iTransform = CV_BGR2XYZ; } else if (!strcmp(sTransform, "CV_XYZ2BGR")) { iTransform = CV_XYZ2BGR; } else if (!strcmp(sTransform, "CV_BGR2YCrCb")) { iTransform = CV_BGR2YCrCb; } else if (!strcmp(sTransform, "CV_YCrCb2BGR")) { iTransform = CV_YCrCb2BGR; } else if (!strcmp(sTransform, "CV_BGR2HSV")) { iTransform = CV_BGR2HSV; } else if (!strcmp(sTransform, "CV_HSV2BGR")) { iTransform = CV_HSV2BGR; } else if (!strcmp(sTransform, "CV_BGR2HLS")) { iTransform = CV_BGR2HLS; } else if (!strcmp(sTransform, "CV_HLS2BGR")) { iTransform = CV_HLS2BGR; } else if (!strcmp(sTransform, "CV_BGR2Lab")) { iTransform = CV_BGR2Lab; } else if (!strcmp(sTransform, "CV_Lab2BGR")) { iTransform = CV_Lab2BGR; } else if (!strcmp(sTransform, "CV_BGR2Luv")) { iTransform = CV_BGR2Luv; } else if (!strcmp(sTransform, "CV_Luv2BGR")) { iTransform = CV_Luv2BGR; } else if (!strcmp(sTransform, "CV_BayerBG2BGR")) { iTransform = CV_BayerBG2BGR; } else if (!strcmp(sTransform, "CV_BayerGB2BGR")) { iTransform = CV_BayerGB2BGR; } else if (!strcmp(sTransform, "CV_BayerRG2BGR")) { iTransform = CV_BayerRG2BGR; } else if (!strcmp(sTransform, "CV_BayerGR2BGR")) { iTransform = CV_BayerGR2BGR; } else if (!strcmp(sTransform, "CV_BGR2RGB")) { iTransform = CV_BGR2RGB; } else if (!strcmp(sTransform, "CV_BGRA2BGR")) { iTransform = CV_BGRA2BGR; } else { iTransform = 0; // to avoid compiler warning Nan::ThrowTypeError("Conversion code is unsupported"); } int oldSize = self->mat.dataend - self->mat.datastart; cv::cvtColor(self->mat, self->mat, iTransform); int newSize = self->mat.dataend - self->mat.datastart; if(oldSize != newSize){ Nan::AdjustExternalMemory(newSize - oldSize); } return; } // @author SergeMv // arrChannels = img.split(); NAN_METHOD(Matrix::Split) { Nan::HandleScope scope; Matrix * self = Nan::ObjectWrap::Unwrap(info.This()); unsigned int size = self->mat.channels(); std::vector channels; // Split doesn't seem to work on empty vectors for (unsigned int i = 0; i < size; i++) { channels.push_back(cv::Mat()); } cv::split(self->mat, channels); size = channels.size(); v8::Local arrChannels = Nan::New(size); for (unsigned int i = 0; i < size; i++) { Local matObject = Matrix::CreateWrappedFromMatIfNotReferenced(channels[i], 1); arrChannels->Set(i, matObject); } info.GetReturnValue().Set(arrChannels); } // @author SergeMv // img.merge(arrChannels); NAN_METHOD(Matrix::Merge) { Nan::HandleScope scope; Matrix * self = Nan::ObjectWrap::Unwrap(info.This()); if (!info[0]->IsArray()) { Nan::ThrowTypeError("The argument must be an array"); } int oldSize = self->mat.dataend - self->mat.datastart; v8::Local jsChannels = v8::Local::Cast(info[0]); unsigned int L = jsChannels->Length(); std::vector vChannels(L); for (unsigned int i = 0; i < L; i++) { Matrix * matObject = Nan::ObjectWrap::Unwrap(jsChannels->Get(i)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); vChannels[i] = matObject->mat; } cv::merge(vChannels, self->mat); int newSize = self->mat.dataend - self->mat.datastart; Nan::AdjustExternalMemory(newSize - oldSize); return; } // @author SergeMv // Equalizes histogram // img.equalizeHist() NAN_METHOD(Matrix::EqualizeHist) { Nan::HandleScope scope; Matrix * self = Nan::ObjectWrap::Unwrap(info.This()); cv::equalizeHist(self->mat, self->mat); return; } NAN_METHOD(Matrix::FloodFill) { SETUP_FUNCTION(Matrix) // obj->Get(Nan::New("x").ToLocalChecked()) // int cv::floodFill(cv::InputOutputArray, cv::Point, cv::Scalar, cv::Rect*, cv::Scalar, cv::Scalar, int) /* mat.floodFill( {seedPoint: [1,1] , newColor: [255,0,0] , rect:[[0,2],[30,40]] , loDiff : [8,90,60], upDiff:[10,100,70] }); */ if (info.Length() < 1 || !info[0]->IsObject()) { // error } Local < Object > obj = Nan::To(info[0]).ToLocalChecked(); cv::Rect rect; int ret = cv::floodFill(self->mat, setPoint(obj->Get(Nan::New("seedPoint").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked()), setColor(obj->Get(Nan::New("newColor").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked()), obj->Get(Nan::New("rect").ToLocalChecked())->IsUndefined() ? 0 : setRect(obj->Get(Nan::New("rect").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked(), rect), setColor(obj->Get(Nan::New("loDiff").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked()), setColor(obj->Get(Nan::New("upDiff").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked()), 4); // Documentation notes that parameter "rect" is an optional output // parameter which will hold the smallest possible bounding box of // affected pixels. If "rect" was provided, let's update the values. // (https://docs.opencv.org/2.4/modules/imgproc/doc/miscellaneous_transformations.html#floodfill) if (!obj->Get(Nan::New("rect").ToLocalChecked())->IsUndefined()) { Local< Object > rectArgument = obj->Get(Nan::New("rect").ToLocalChecked())->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); rectArgument->Get(0)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()->Set(0, Nan::New(rect.x)); rectArgument->Get(0)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()->Set(1, Nan::New(rect.y)); rectArgument->Get(1)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()->Set(0, Nan::New(rect.width)); rectArgument->Get(1)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()->Set(1, Nan::New(rect.height)); } info.GetReturnValue().Set(Nan::New(ret)); } // @author olfox // Returns an array of the most probable positions // Usage: output = input.templateMatches(min_probability, max_probability, limit, ascending, min_x_distance, min_y_distance); NAN_METHOD(Matrix::TemplateMatches) { SETUP_FUNCTION(Matrix) bool filter_min_probability = (info.Length() >= 1) ? info[0]->IsNumber() : false; bool filter_max_probability = (info.Length() >= 2) ? info[1]->IsNumber() : false; double min_probability = filter_min_probability ? info[0].As()->Value() : 0; double max_probability = filter_max_probability ? info[1].As()->Value() : 0; int limit = (info.Length() >= 3) ? info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 0; bool ascending = (info.Length() >= 4) ? info[3]->BooleanValue( Nan::GetCurrentContext() ).FromJust() : false; int min_x_distance = (info.Length() >= 5) ? info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 0; int min_y_distance = (info.Length() >= 6) ? info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked() : 0; cv::Mat_ indices; if (ascending) { cv::sortIdx(self->mat.reshape(0, 1), indices, CV_SORT_ASCENDING + CV_SORT_EVERY_ROW); } else { cv::sortIdx(self->mat.reshape(0, 1), indices, CV_SORT_DESCENDING + CV_SORT_EVERY_ROW); } cv::Mat hit_mask = cv::Mat::zeros(self->mat.size(), CV_64F); v8::Local < v8::Array > probabilites_array = Nan::New(limit); cv::Mat_::const_iterator begin = self->mat.begin(); cv::Mat_::const_iterator it = indices.begin(); cv::Mat_::const_iterator end = indices.end(); int index = 0; for (; (limit == 0 || index < limit) && it != end; ++it) { cv::Point pt = (begin + *it).pos(); float probability = self->mat.at(pt.y, pt.x); if (filter_min_probability && probability < min_probability) { if (ascending) { continue; } else { break; } } if (filter_max_probability && probability > max_probability) { if (ascending) break; else continue; } if (min_x_distance != 0 || min_y_distance != 0) { // Check hit mask color for for every corner cv::Size maxSize = hit_mask.size(); int max_x = maxSize.width - 1; int max_y = maxSize.height - 1; cv::Point top_left = cv::Point(std::max(0, pt.x - min_x_distance), std::max(0, pt.y - min_y_distance)); cv::Point top_right = cv::Point(std::min(max_x, pt.x + min_x_distance), std::max(0, pt.y - min_y_distance)); cv::Point bottom_left = cv::Point(std::max(0, pt.x - min_x_distance), std::min(max_y, pt.y + min_y_distance)); cv::Point bottom_right = cv::Point(std::min(max_x, pt.x + min_x_distance), std::min(max_y, pt.y + min_y_distance)); if (hit_mask.at(top_left.y, top_left.x) > 0) continue; if (hit_mask.at(top_right.y, top_right.x) > 0) continue; if (hit_mask.at(bottom_left.y, bottom_left.x) > 0) continue; if (hit_mask.at(bottom_right.y, bottom_right.x) > 0) continue; cv::Scalar color(255.0); cv::rectangle(hit_mask, top_left, bottom_right, color, CV_FILLED); } Local x_value = Nan::New(pt.x); Local y_value = Nan::New(pt.y); Local probability_value = Nan::New(probability); Local < Object > probability_object = Nan::New(); probability_object->Set(Nan::New("x").ToLocalChecked(), x_value); probability_object->Set(Nan::New("y").ToLocalChecked(), y_value); probability_object->Set(Nan::New("probability").ToLocalChecked(), probability_value); probabilites_array->Set(index, probability_object); index++; } info.GetReturnValue().Set(probabilites_array); } // @author Evilcat325 // MatchTemplate accept a Matrix // Usage: output = input.matchTemplateByMatrix(matrix. method); NAN_METHOD(Matrix::MatchTemplateByMatrix) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *templ = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); int cols = self->mat.cols - templ->mat.cols + 1; int rows = self->mat.rows - templ->mat.rows + 1; cv::Mat result(cols, rows, CV_32FC1); /* TM_SQDIFF =0 TM_SQDIFF_NORMED =1 TM_CCORR =2 TM_CCORR_NORMED =3 TM_CCOEFF =4 TM_CCOEFF_NORMED =5 */ int method = (info.Length() < 2) ? (int)cv::TM_CCORR_NORMED : info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); if (!(method >= 0 && method <= 5)) method = (int)cv::TM_CCORR_NORMED; cv::matchTemplate(self->mat, templ->mat, result, method); info.GetReturnValue().Set(CreateWrappedFromMat(result)); } // @author ytham // Match Template filter // Usage: output = input.matchTemplate("templateFileString", method); NAN_METHOD(Matrix::MatchTemplate) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); v8::String::Utf8Value args0(v8::Isolate::GetCurrent(), info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())); std::string filename = std::string(*args0); cv::Mat templ; templ = cv::imread(filename, -1); int cols = self->mat.cols - templ.cols + 1; int rows = self->mat.rows - templ.rows + 1; cv::Mat result(cols, rows, CV_32FC1); /* TM_SQDIFF =0 TM_SQDIFF_NORMED =1 TM_CCORR =2 TM_CCORR_NORMED =3 TM_CCOEFF =4 TM_CCOEFF_NORMED =5 */ int method = (info.Length() < 2) ? (int)cv::TM_CCORR_NORMED : info[1]->Uint32Value(Nan::GetCurrentContext()).ToChecked(); cv::matchTemplate(self->mat, templ, result, method); cv::normalize(result, result, 0, 1, cv::NORM_MINMAX, -1, cv::Mat()); double minVal; double maxVal; cv::Point minLoc; cv::Point maxLoc; cv::Point matchLoc; minMaxLoc(result, &minVal, &maxVal, &minLoc, &maxLoc, cv::Mat()); if(method == CV_TM_SQDIFF || method == CV_TM_SQDIFF_NORMED) { matchLoc = minLoc; } else { matchLoc = maxLoc; } //detected ROI unsigned int roi_x = matchLoc.x; unsigned int roi_y = matchLoc.y; unsigned int roi_width = templ.cols; unsigned int roi_height = templ.rows; //draw rectangle if(info.Length() >= 3) { cv::Rect roi(roi_x,roi_y,roi_width,roi_height); cv::rectangle(self->mat, roi, cv::Scalar(0,0,255)); } result.convertTo(result, CV_8UC1, 255, 0); v8::Local arr = Nan::New(5); arr->Set(0, CreateWrappedFromMat(result)); arr->Set(1, Nan::New(roi_x)); arr->Set(2, Nan::New(roi_y)); arr->Set(3, Nan::New(roi_width)); arr->Set(4, Nan::New(roi_height)); info.GetReturnValue().Set(arr); } // @author ytham // Min/Max location NAN_METHOD(Matrix::MinMaxLoc) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); double minVal; double maxVal; cv::Point minLoc; cv::Point maxLoc; cv::minMaxLoc(self->mat, &minVal, &maxVal, &minLoc, &maxLoc, cv::Mat() ); Local v_minVal = Nan::New(minVal); Local v_maxVal = Nan::New(maxVal); Local v_minLoc_x = Nan::New(minLoc.x); Local v_minLoc_y = Nan::New(minLoc.y); Local v_maxLoc_x = Nan::New(maxLoc.x); Local v_maxLoc_y = Nan::New(maxLoc.y); Local o_minLoc = Nan::New(); o_minLoc->Set(Nan::New("x").ToLocalChecked(), v_minLoc_x); o_minLoc->Set(Nan::New("y").ToLocalChecked(), v_minLoc_y); Local o_maxLoc = Nan::New(); o_maxLoc->Set(Nan::New("x").ToLocalChecked(), v_maxLoc_x); o_maxLoc->Set(Nan::New("y").ToLocalChecked(), v_maxLoc_y); // Output result object Local result = Nan::New(); result->Set(Nan::New("minVal").ToLocalChecked(), v_minVal); result->Set(Nan::New("maxVal").ToLocalChecked(), v_maxVal); result->Set(Nan::New("minLoc").ToLocalChecked(), o_minLoc); result->Set(Nan::New("maxLoc").ToLocalChecked(), o_maxLoc); info.GetReturnValue().Set(result); } // @author ytham // Pushes some matrix (argument) the back of a matrix (self) NAN_METHOD(Matrix::PushBack) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Matrix *m_input = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); self->mat.push_back(m_input->mat); info.GetReturnValue().Set(info.This()); } NAN_METHOD(Matrix::PutText) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); Nan::Utf8String textString(info[0]); //FIXME: might cause issues, see here https://github.com/rvagg/nan/pull/152 char *text = *textString;//(char *) malloc(textString.length() + 1); //strcpy(text, *textString); int x = Nan::To(info[1]).FromJust(); int y = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); Nan::Utf8String fontString(info[3]); char *font = *fontString;//(char *) malloc(fontString.length() + 1); //strcpy(font, *fontString); int constFont = cv::FONT_HERSHEY_SIMPLEX; if (!strcmp(font, "HERSEY_SIMPLEX")) {constFont = cv::FONT_HERSHEY_SIMPLEX;} else if (!strcmp(font, "HERSEY_PLAIN")) {constFont = cv::FONT_HERSHEY_PLAIN;} else if (!strcmp(font, "HERSEY_DUPLEX")) {constFont = cv::FONT_HERSHEY_DUPLEX;} else if (!strcmp(font, "HERSEY_COMPLEX")) {constFont = cv::FONT_HERSHEY_COMPLEX;} else if (!strcmp(font, "HERSEY_TRIPLEX")) {constFont = cv::FONT_HERSHEY_TRIPLEX;} else if (!strcmp(font, "HERSEY_COMPLEX_SMALL")) {constFont = cv::FONT_HERSHEY_COMPLEX_SMALL;} else if (!strcmp(font, "HERSEY_SCRIPT_SIMPLEX")) {constFont = cv::FONT_HERSHEY_SCRIPT_SIMPLEX;} else if (!strcmp(font, "HERSEY_SCRIPT_COMPLEX")) {constFont = cv::FONT_HERSHEY_SCRIPT_COMPLEX;} else if (!strcmp(font, "HERSEY_SCRIPT_SIMPLEX")) {constFont = cv::FONT_HERSHEY_SCRIPT_SIMPLEX;} cv::Scalar color(0, 0, 255); if (info[4]->IsArray()) { Local objColor = info[4]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); color = setColor(objColor); } double scale = info.Length() < 6 ? 1 : info[5].As()->Value(); double thickness = info.Length() < 7 ? 1 : info[6].As()->Value(); cv::putText(self->mat, text, cv::Point(x, y), constFont, scale, color, thickness); return; } NAN_METHOD(Matrix::GetPerspectiveTransform) { Nan::HandleScope scope; // extract quad info Local srcArray = Nan::To(info[0]).ToLocalChecked(); Local tgtArray = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); std::vector src_corners(4); std::vector tgt_corners(4); for (unsigned int i = 0; i < 4; i++) { src_corners[i] = cvPoint(srcArray->Get(i*2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(),srcArray->Get(i*2+1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); tgt_corners[i] = cvPoint(tgtArray->Get(i*2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(),tgtArray->Get(i*2+1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } Local xfrm = Matrix::CreateWrappedFromMat(cv::getPerspectiveTransform(src_corners, tgt_corners)); info.GetReturnValue().Set(xfrm); } NAN_METHOD(Matrix::WarpPerspective) { SETUP_FUNCTION(Matrix) Matrix *xfrm = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); int width = Nan::To(info[1]).FromJust(); int height = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int flags = cv::INTER_LINEAR; int borderMode = cv::BORDER_REPLICATE; cv::Scalar borderColor(0, 0, 255); if (info[3]->IsArray()) { Local < Object > objColor = info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); borderColor = setColor(objColor); } cv::Mat res = cv::Mat(width, height, CV_32FC3); cv::warpPerspective(self->mat, res, xfrm->mat, cv::Size(width, height), flags, borderMode, borderColor); self->setMat(res); info.GetReturnValue().Set(Nan::Null()); } NAN_METHOD(Matrix::CopyWithMask) { SETUP_FUNCTION(Matrix) // param 0 - destination image: Matrix *dest = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // param 1 - mask. same size as src and dest Matrix *mask = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); self->mat.copyTo(dest->mat, mask->mat); return; } NAN_METHOD(Matrix::SetWithMask) { SETUP_FUNCTION(Matrix) // param 0 - target value: Local < Object > valArray = Nan::To(info[0]).ToLocalChecked(); cv::Scalar newvals; newvals.val[0] = valArray->Get(0).As()->Value(); newvals.val[1] = valArray->Get(1).As()->Value(); newvals.val[2] = valArray->Get(2).As()->Value(); // param 1 - mask. same size as src and dest Matrix *mask = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); self->mat.setTo(newvals, mask->mat); return; } NAN_METHOD(Matrix::MeanWithMask) { SETUP_FUNCTION(Matrix) // param 0 - mask. same size as src and dest Matrix *mask = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Scalar means = cv::mean(self->mat, mask->mat); v8::Local < v8::Array > arr = Nan::New(4); arr->Set(0, Nan::New(means[0])); arr->Set(1, Nan::New(means[1])); arr->Set(2, Nan::New(means[2])); arr->Set(3, Nan::New(means[3])); info.GetReturnValue().Set(arr); } NAN_METHOD(Matrix::Mean) { SETUP_FUNCTION(Matrix) cv::Scalar means = cv::mean(self->mat); v8::Local arr = Nan::New(4); arr->Set(0, Nan::New(means[0])); arr->Set(1, Nan::New(means[1])); arr->Set(2, Nan::New(means[2])); arr->Set(3, Nan::New(means[3])); info.GetReturnValue().Set(arr); } // http://docs.opencv.org/2.4/modules/imgproc/doc/filtering.html#copymakeborder NAN_METHOD(Matrix::CopyMakeBorder) { SETUP_FUNCTION(Matrix) double t = info[0].As()->Value(); double b = info[1].As()->Value(); double l = info[2].As()->Value(); double r = info[3].As()->Value(); int borderType = cv::BORDER_DEFAULT; cv::Scalar value; if (info.Length() > 4) { borderType = info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); value = cv::Scalar(0, 0, 0, 0); if (borderType == cv::BORDER_CONSTANT) { if (!info[5]->IsArray()) { Nan::ThrowTypeError("The argument must be an array"); } v8::Local objColor = v8::Local::Cast(info[5]); unsigned int length = objColor->Length(); Local valB = objColor->Get(0); Local valG = objColor->Get(1); Local valR = objColor->Get(2); if (length == 3) { value = cv::Scalar( valB->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), valG->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), valR->IntegerValue( Nan::GetCurrentContext() ).ToChecked() ); } else if (length == 4) { Local valA = objColor->Get(3); value = cv::Scalar( Nan::To(valB).FromJust(), Nan::To(valG).FromJust(), Nan::To(valR).FromJust(), //valA->IntegerValue( Nan::GetCurrentContext() ).ToChecked() Nan::To(valA).FromJust() ); } else { Nan::ThrowError("Fill must include 3 or 4 colors"); } } } cv::Mat padded; cv::copyMakeBorder(self->mat, padded, t, b, l, r, borderType, value); ~self->mat; self->mat = padded; return; } NAN_METHOD(Matrix::Shift) { SETUP_FUNCTION(Matrix) cv::Mat res; double tx = info[0].As()->Value(); double ty = info[1].As()->Value(); // get the integer values of info cv::Point2i deltai(ceil(tx), ceil(ty)); int fill = cv::BORDER_REPLICATE; cv::Scalar value = cv::Scalar(0, 0, 0, 0); // INTEGER SHIFT // first create a border around the parts of the Mat that will be exposed int t = 0, b = 0, l = 0, r = 0; if (deltai.x > 0) { l = deltai.x; } if (deltai.x < 0) { r = -deltai.x; } if (deltai.y > 0) { t = deltai.y; } if (deltai.y < 0) { b = -deltai.y; } cv::Mat padded; cv::copyMakeBorder(self->mat, padded, t, b, l, r, fill, value); // construct the region of interest around the new matrix cv::Rect roi = cv::Rect(std::max(-deltai.x, 0), std::max(-deltai.y, 0), 0, 0) + self->mat.size(); res = padded(roi); self->setMat(res); return; } /** * Changes the shape and/or the number of channels of a 2D matrix without * copying the data. * Reference:http://docs.opencv.org/2.4/modules/core/doc/basic_structures.html#mat-reshape */ NAN_METHOD(Matrix::Reshape) { SETUP_FUNCTION(Matrix) int cn = 0; int rows = 0; if (info.Length() == 2) { INT_FROM_ARGS(cn, 0); INT_FROM_ARGS(rows, 1); } else if (info.Length() == 1) { INT_FROM_ARGS(cn, 0); } else { JSTHROW("Invalid number of arguments"); } Local img_to_return = Matrix::CreateWrappedFromMatIfNotReferenced(self->mat.reshape(cn, rows),0); info.GetReturnValue().Set(img_to_return); } NAN_METHOD(Matrix::Release) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); if(self->getWrappedRefCount() == 1){ int size = self->mat.dataend - self->mat.datastart; Nan::AdjustExternalMemory(-1 * size); } self->mat.release(); return; } // leave this out - can't see a way it could be useful to us, as release() always completely forgets the data //NAN_METHOD(Matrix::Addref) { // Nan::HandleScope scope; // // Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); // self->mat.addref(); // // return; //} int Matrix::getWrappedRefCount(){ int refcount = -1; #if CV_MAJOR_VERSION >= 3 if (mat.u){ refcount = mat.u->refcount; } else { refcount = -1; // indicates no reference ptr } #else if (mat.refcount){ refcount = *(mat.refcount); } else { refcount = -1; // indicates no reference ptr } #endif return refcount; } NAN_METHOD(Matrix::GetrefCount) { Nan::HandleScope scope; Matrix *self = Nan::ObjectWrap::Unwrap(info.This()); int refcount = self->getWrappedRefCount(); info.GetReturnValue().Set(Nan::New(refcount)); return; } NAN_METHOD(Matrix::Subtract) { SETUP_FUNCTION(Matrix) if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } Matrix *other = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); self->mat -= other->mat; return; } NAN_METHOD(Matrix::Compare) { SETUP_FUNCTION(Matrix) if (info.Length() < 2) { Nan::ThrowTypeError("Invalid number of arguments"); } Matrix *other = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); int cmpop = Nan::To(info[1]).FromJust(); int width = self->mat.size().width; int height = self->mat.size().height; cv::Mat res = cv::Mat(width, height, CV_8UC1); cv::compare(self->mat, other->mat, res, cmpop); Local out = Matrix::CreateWrappedFromMat(res); info.GetReturnValue().Set(out); return; } NAN_METHOD(Matrix::Mul) { SETUP_FUNCTION(Matrix) if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } Matrix *other = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); double scale = 1; if (info.Length() > 1) scale = info[1].As()->Value(); cv::Mat res = self->mat.mul(other->mat, scale); Local out = Matrix::CreateWrappedFromMat(res); info.GetReturnValue().Set(out); return; } NAN_METHOD(Matrix::Div) { SETUP_FUNCTION(Matrix) if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } Matrix *other = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::divide(self->mat, other->mat, self->mat); return; } NAN_METHOD(Matrix::Pow) { SETUP_FUNCTION(Matrix) if (info.Length() < 1) { Nan::ThrowTypeError("Invalid number of arguments"); } double power = info[0].As()->Value(); cv::pow(self->mat, power, self->mat); return; } node-opencv/src/OpenCV.h0000755000175000017500000000465313671667364013541 0ustar devdev#ifndef __NODE_OPENCV_H__ #define __NODE_OPENCV_H__ #ifdef WIN /* This is needed on Windows for Visual Studio to not throw an error in the build/include/opencv2/flann/any.h file in OpenCV. */ namespace std{ typedef type_info type_info; } #endif #include #if !((((CV_MAJOR_VERSION <= 2) && (CV_MINOR_VERSION <= 4)) && (CV_MINOR_VERSION < 13)) || ((CV_MAJOR_VERSION >= 3) && (CV_MINOR_VERSION < 1))) #define INCLUDE_AVAILABLE_MODULES_ONLY #endif #include #include #include #include #include #include #if ((CV_MAJOR_VERSION <= 2) && (CV_MINOR_VERSION <= 4)) #include #elif CV_MAJOR_VERSION >= 4 #include #else #include #endif #ifndef INCLUDE_AVAILABLE_MODULES_ONLY #define HAVE_OPENCV_CALIB3D #define HAVE_OPENCV_FEATURES2D #define HAVE_OPENCV_FLANN #define HAVE_OPENCV_HIGHGUI // #define HAVE_OPENCV_IMGCODECS #define HAVE_OPENCV_IMGPROC #define HAVE_OPENCV_ML #define HAVE_OPENCV_OBJDETECT #define HAVE_OPENCV_PHOTO #define HAVE_OPENCV_SHAPE #define HAVE_OPENCV_STITCHING #define HAVE_OPENCV_SUPERRES #define HAVE_OPENCV_VIDEO #define HAVE_OPENCV_VIDEOIO #define HAVE_OPENCV_VIDEOSTAB #define HAVE_OPENCV_VIZ #endif #include #include using namespace v8; using namespace node; #define REQ_FUN_ARG(I, VAR) \ if (info.Length() <= (I) || !info[I]->IsFunction()) \ return Nan::ThrowTypeError("Argument " #I " must be a function"); \ Local VAR = Local::Cast(info[I]); #define SETUP_FUNCTION(TYP) \ Nan::HandleScope scope; \ TYP *self = Nan::ObjectWrap::Unwrap(info.This()); #define JSFUNC(NAME) \ static NAN_METHOD(NAME); #define JSTHROW_TYPE(ERR) \ Nan::ThrowTypeError( ERR ); #define JSTHROW(ERR) \ Nan::ThrowError( ERR ); #define INT_FROM_ARGS(NAME, IND) \ if (info[IND]->IsInt32()){ \ NAME = info[IND].As()->Value(); \ } #define DOUBLE_FROM_ARGS(NAME, IND) \ if (info[IND]->IsNumber()){ \ NAME = info[IND].As()->Value(); \ } class OpenCV: public Nan::ObjectWrap { public: static void Init(Local target); static NAN_METHOD(ReadImage); static NAN_METHOD(ReadImageAsync); static NAN_METHOD(ReadImageMulti); }; #endif node-opencv/src/VideoCaptureWrap.h0000755000175000017500000000171313671667364015625 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_VIDEOIO class VideoCaptureWrap: public Nan::ObjectWrap { public: cv::VideoCapture cap; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); VideoCaptureWrap(const std::string& filename); VideoCaptureWrap(int device); static NAN_METHOD(Read); static NAN_METHOD(ReadSync); static NAN_METHOD(Grab); static NAN_METHOD(Retrieve); // For getting width and height of the input video stream static NAN_METHOD(GetWidth); static NAN_METHOD(GetHeight); // (Optional) For setting width and height of the input video stream static NAN_METHOD(SetWidth); static NAN_METHOD(SetHeight); // to set frame position static NAN_METHOD(SetPosition); static NAN_METHOD(GetFrameCount); static NAN_METHOD(GetFPS); static NAN_METHOD(SetFPS); static NAN_METHOD(GetFrameAt); // release the stream static NAN_METHOD(Release); }; #endif node-opencv/src/init.cc0000755000175000017500000000320213671667364013475 0ustar devdev#include "OpenCV.h" #include "Point.h" #include "Matrix.h" #include "CascadeClassifierWrap.h" #include "VideoCaptureWrap.h" #include "VideoWriterWrap.h" #include "Contours.h" #include "CamShift.h" #include "HighGUI.h" #include "FaceRecognizer.h" #include "Features2d.h" #include "Constants.h" #include "Calib3D.h" #include "ImgProc.h" #include "Stereo.h" #include "BackgroundSubtractor.h" #include "LDAWrap.h" #include "Histogram.h" #define STR_HELPER(x) #x #define STR(x) STR_HELPER(x) #pragma message ( "Building with OpenCV" STR(CV_MAJOR_VERSION) "." STR(CV_MINOR_VERSION) ) extern "C" void init(Local target) { Nan::HandleScope scope; OpenCV::Init(target); Point::Init(target); Matrix::Init(target); #ifdef HAVE_OPENCV_OBJDETECT CascadeClassifierWrap::Init(target); #endif #ifdef HAVE_OPENCV_VIDEOIO VideoCaptureWrap::Init(target); VideoWriterWrap::Init(target); #endif Contour::Init(target); #ifdef HAVE_OPENCV_VIDEO TrackedObject::Init(target); #endif #ifdef HAVE_OPENCV_HIGHGUI NamedWindow::Init(target); #endif Constants::Init(target); #ifdef HAVE_OPENCV_CALIB3D Calib3D::Init(target); #endif #ifdef HAVE_OPENCV_IMGPROC ImgProc::Init(target); Histogram::Init(target); #endif #if CV_MAJOR_VERSION < 3 StereoBM::Init(target); StereoSGBM::Init(target); StereoGC::Init(target); #if CV_MAJOR_VERSION == 2 && CV_MINOR_VERSION >=4 #ifdef HAVE_OPENCV_FEATURES2D Features::Init(target); #endif LDAWrap::Init(target); #endif #endif #ifdef HAVE_BACKGROUNDSUBTRACTOR BackgroundSubtractorWrap::Init(target); #endif #ifdef HAVE_OPENCV_FACE FaceRecognizerWrap::Init(target); #endif }; NODE_MODULE(opencv, init) node-opencv/src/ImgProc.cc0000644000175000017500000001562213671667364014100 0ustar devdev#include "ImgProc.h" #include "Matrix.h" #ifdef HAVE_OPENCV_IMGPROC void ImgProc::Init(Local target) { Nan::Persistent inner; Local obj = Nan::New(); inner.Reset(obj); Nan::SetMethod(obj, "undistort", Undistort); Nan::SetMethod(obj, "initUndistortRectifyMap", InitUndistortRectifyMap); Nan::SetMethod(obj, "remap", Remap); Nan::SetMethod(obj, "distanceTransform", DistanceTransform); Nan::SetMethod(obj, "getStructuringElement", GetStructuringElement); target->Set(Nan::New("imgproc").ToLocalChecked(), obj); } // cv::distanceTransform NAN_METHOD(ImgProc::DistanceTransform) { Nan::EscapableHandleScope scope; try { // Arg 0 is the image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat inputImage = m0->mat; // Arg 1 is the distance type (CV_DIST_L1, CV_DIST_L2, etc.) int distType = info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked();; // Make a mat to hold the result image cv::Mat outputImage; // Perform distance transform cv::distanceTransform(inputImage, outputImage, distType, 0); // Wrap the output image Local outMatrixWrap = Matrix::CreateWrappedFromMat(outputImage); // Return the output image info.GetReturnValue().Set(outMatrixWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::undistort NAN_METHOD(ImgProc::Undistort) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0 is the image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat inputImage = m0->mat; // Arg 1 is the camera matrix Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat K = m1->mat; // Arg 2 is the distortion coefficents Matrix* m2 = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat dist = m2->mat; // Make an mat to hold the result image cv::Mat outputImage; // Undistort cv::undistort(inputImage, outputImage, K, dist); // Wrap the output image Local outMatrixWrap = Matrix::CreateWrappedFromMat(outputImage); // Return the output image info.GetReturnValue().Set(outMatrixWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::initUndistortRectifyMap NAN_METHOD(ImgProc::InitUndistortRectifyMap) { Nan::EscapableHandleScope scope; try { // Arg 0 is the camera matrix Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat K = m0->mat; // Arg 1 is the distortion coefficents Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat dist = m1->mat; // Arg 2 is the recification transformation Matrix* m2 = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat R = m2->mat; // Arg 3 is the new camera matrix Matrix* m3 = Nan::ObjectWrap::Unwrap(info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat newK = m3->mat; // Arg 4 is the image size cv::Size imageSize; if (info[4]->IsArray()) { Local v8sz = info[4]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); imageSize = cv::Size(v8sz->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8sz->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { JSTHROW_TYPE("Must pass image size"); } // Arg 5 is the first map type, skip for now int m1type = info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); // Make matrices to hold the output maps cv::Mat map1, map2; // Compute the rectification map cv::initUndistortRectifyMap(K, dist, R, newK, imageSize, m1type, map1, map2); // Wrap the output maps Local map1Wrap = Matrix::CreateWrappedFromMat(map1); Local map2Wrap = Matrix::CreateWrappedFromMat(map2); // Make a return object with the two maps Local ret = Nan::New(); ret->Set(Nan::New("map1").ToLocalChecked(), map1Wrap); ret->Set(Nan::New("map2").ToLocalChecked(), map2Wrap); // Return the maps info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::remap NAN_METHOD(ImgProc::Remap) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0 is the image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat inputImage = m0->mat; // Arg 1 is the first map Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat map1 = m1->mat; // Arg 2 is the second map Matrix* m2 = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat map2 = m2->mat; // Arg 3 is the interpolation mode int interpolation = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); // Args 4, 5 border settings, skipping for now // Output image cv::Mat outputImage; // Remap cv::remap(inputImage, outputImage, map1, map2, interpolation); // Wrap the output image Local outMatrixWrap = Matrix::CreateWrappedFromMat(outputImage); // Return the image info.GetReturnValue().Set(outMatrixWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::getStructuringElement NAN_METHOD(ImgProc::GetStructuringElement) { Nan::EscapableHandleScope scope; try { // Get the arguments if (info.Length() != 2) { Nan::ThrowTypeError("Invalid number of arguments"); } // Arg 0 is the element shape if (!info[0]->IsNumber()) { JSTHROW_TYPE("'shape' argument must be a number"); } int shape = info[0].As()->Value(); // Arg 1 is the size of the structuring element cv::Size ksize; if (!info[1]->IsArray()) { JSTHROW_TYPE("'ksize' argument must be a 2 double array"); } Local v8sz = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); ksize = cv::Size(v8sz->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8sz->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); // GetStructuringElement cv::Mat mat = cv::getStructuringElement(shape, ksize); // Wrap the output image Local outMatrixWrap = Matrix::CreateWrappedFromMat(mat); // Return the image info.GetReturnValue().Set(outMatrixWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); JSTHROW(err_msg); return; } } #endif node-opencv/src/Stereo.cc0000644000175000017500000003162213671667364013777 0ustar devdev #include "Stereo.h" #if CV_MAJOR_VERSION >= 3 #ifdef __GNUC__ #warning TODO: port me to OpenCV 3 #else // vs style message pragma #pragma message ( "TODO: port me to OpenCV 3" ) #endif #endif #if CV_MAJOR_VERSION < 3 #include "Matrix.h" #include // Block matching Nan::Persistent StereoBM::constructor; void StereoBM::Init(Local target) { Nan::HandleScope scope; Local ctor = Nan::New(StereoBM::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("StereoBM").ToLocalChecked()); Nan::SetPrototypeMethod(ctor, "compute", Compute); ctor->Set(Nan::New("BASIC_PRESET").ToLocalChecked(), Nan::New((int)cv::StereoBM::BASIC_PRESET)); ctor->Set(Nan::New("FISH_EYE_PRESET").ToLocalChecked(), Nan::New((int)cv::StereoBM::FISH_EYE_PRESET)); ctor->Set(Nan::New("NARROW_PRESET").ToLocalChecked(), Nan::New((int)cv::StereoBM::NARROW_PRESET)); target->Set(Nan::New("StereoBM").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(StereoBM::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { Nan::ThrowTypeError("Cannot instantiate without new"); } StereoBM *stereo; if (info.Length() == 0) { stereo = new StereoBM(); } else if (info.Length() == 1) { // preset stereo = new StereoBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else if (info.Length() == 2) { // preset, disparity search range stereo = new StereoBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { stereo = new StereoBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), // preset, disparity search range, sum of absolute differences window size info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } stereo->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } StereoBM::StereoBM(int preset, int ndisparities, int SADWindowSize) : Nan::ObjectWrap(), stereo(preset, ndisparities, SADWindowSize) { } // TODO make this async NAN_METHOD(StereoBM::Compute) { SETUP_FUNCTION(StereoBM) try { // Get the arguments // Arg 0, the 'left' image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat left = m0->mat; // Arg 1, the 'right' image Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat right = m1->mat; // Optional 3rd arg, the disparty depth int type = CV_16S; if (info.Length() > 2) { type = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } // Compute stereo using the block matching algorithm cv::Mat disparity; self->stereo(left, right, disparity, type); // Wrap the returned disparity map Local < Object > disparityWrap = Matrix::CreateWrappedFromMat(disparity); info.GetReturnValue().Set(disparityWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // Semi-Global Block matching Nan::Persistent StereoSGBM::constructor; void StereoSGBM::Init(Local target) { Nan::HandleScope scope; Local ctor = Nan::New(StereoSGBM::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("StereoSGBM").ToLocalChecked()); Nan::SetPrototypeMethod(ctor, "compute", Compute); target->Set(Nan::New("StereoSGBM").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(StereoSGBM::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { Nan::ThrowTypeError("Cannot instantiate without new"); } StereoSGBM *stereo; if (info.Length() == 0) { stereo = new StereoSGBM(); } else { // If passing arguments, must pass the first 3 at least if (info.Length() >= 3) { switch (info.Length()) { case 3: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 4: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 5: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 6: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 7: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 8: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[7]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 9: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[7]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[8]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; case 10: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[7]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[8]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[9]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); break; default: stereo = new StereoSGBM(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[6]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[7]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[8]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[9]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[10]->ToBoolean( v8::Isolate::GetCurrent() )->Value()); break; } } else { Nan::ThrowError("If overriding default settings, must pass minDisparity, numDisparities, and SADWindowSize"); return; } } stereo->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } StereoSGBM::StereoSGBM() : Nan::ObjectWrap(), stereo() { } StereoSGBM::StereoSGBM(int minDisparity, int ndisparities, int SADWindowSize, int p1, int p2, int disp12MaxDiff, int preFilterCap, int uniquenessRatio, int speckleWindowSize, int speckleRange, bool fullDP) : Nan::ObjectWrap(), stereo(minDisparity, ndisparities, SADWindowSize, p1, p2, disp12MaxDiff, preFilterCap, uniquenessRatio, speckleWindowSize, speckleRange, fullDP) { } // TODO make this async NAN_METHOD(StereoSGBM::Compute) { SETUP_FUNCTION(StereoSGBM) try { // Get the arguments // Arg 0, the 'left' image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat left = m0->mat; // Arg 1, the 'right' image Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat right = m1->mat; // Compute stereo using the block matching algorithm cv::Mat disparity; self->stereo(left, right, disparity); // Wrap the returned disparity map Local < Object > disparityWrap = Matrix::CreateWrappedFromMat(disparity); info.GetReturnValue().Set(disparityWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // Graph cut Nan::Persistent StereoGC::constructor; void StereoGC::Init(Local target) { Nan::HandleScope scope; Local ctor = Nan::New(StereoGC::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("StereoGC").ToLocalChecked()); Nan::SetPrototypeMethod(ctor, "compute", Compute); target->Set(Nan::New("StereoGC").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(StereoGC::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) Nan::ThrowTypeError("Cannot instantiate without new"); StereoGC *stereo; if (info.Length() == 0) { stereo = new StereoGC(); } else if (info.Length() == 1) { // numberOfDisparities stereo = new StereoGC(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { // max iterations stereo = new StereoGC(info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } stereo->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } StereoGC::StereoGC(int numberOfDisparities, int maxIters) : Nan::ObjectWrap() { stereo = cvCreateStereoGCState(numberOfDisparities, maxIters); } // TODO make this async NAN_METHOD(StereoGC::Compute) { SETUP_FUNCTION(StereoGC) try { // Get the arguments // Arg 0, the 'left' image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat left = m0->mat; // Arg 1, the 'right' image Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat right = m1->mat; // Compute stereo using the block matching algorithm CvMat left_leg = left, right_leg = right; CvMat *disp_left = cvCreateMat(left.rows, left.cols, CV_16S), *disp_right = cvCreateMat(right.rows, right.cols, CV_16S); cvFindStereoCorrespondenceGC(&left_leg, &right_leg, disp_left, disp_right, self->stereo, 0); cv::Mat disp16 = disp_left; cv::Mat disparity(disp16.rows, disp16.cols, CV_8U); disp16.convertTo(disparity, CV_8U, -16); // Wrap the returned disparity map Local < Object > disparityWrap = Matrix::CreateWrappedFromMat(disparity); info.GetReturnValue().Set(disparityWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } #endif node-opencv/src/Features2d.h0000644000175000017500000000060413671667364014400 0ustar devdev#include "OpenCV.h" #if ((CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION >=4)) #ifdef HAVE_OPENCV_FEATURES2D #include #include class Features: public Nan::ObjectWrap { public: static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(Similarity); }; #endif #endif node-opencv/src/ImgProc.h0000644000175000017500000000070213671667364013733 0ustar devdev#ifndef __NODE_IMGPROC_H #define __NODE_IMGPROC_H #include "OpenCV.h" #ifdef HAVE_OPENCV_IMGPROC /** * Implementation of imgproc.hpp functions */ class ImgProc: public Nan::ObjectWrap { public: static void Init(Local target); static NAN_METHOD(DistanceTransform); static NAN_METHOD(Undistort); static NAN_METHOD(InitUndistortRectifyMap); static NAN_METHOD(Remap); static NAN_METHOD(GetStructuringElement); }; #endif #endif node-opencv/src/CascadeClassifierWrap.cc0000755000175000017500000001106213671667364016717 0ustar devdev#include "CascadeClassifierWrap.h" #include "OpenCV.h" #include "Matrix.h" #include #if CV_MAJOR_VERSION >= 4 #include #endif #ifdef HAVE_OPENCV_OBJDETECT Nan::Persistent CascadeClassifierWrap::constructor; void CascadeClassifierWrap::Init(Local target) { Nan::HandleScope scope; Local ctor = Nan::New (CascadeClassifierWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("CascadeClassifier").ToLocalChecked()); // Prototype // Local proto = constructor->PrototypeTemplate(); Nan::SetPrototypeMethod(ctor, "detectMultiScale", DetectMultiScale); target->Set(Nan::New("CascadeClassifier").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(CascadeClassifierWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { Nan::ThrowTypeError("Cannot instantiate without new"); } CascadeClassifierWrap *pt = new CascadeClassifierWrap(*info[0]); pt->Wrap(info.This()); info.GetReturnValue().Set( info.This() ); } CascadeClassifierWrap::CascadeClassifierWrap(v8::Value* fileName) { std::string filename; filename = std::string(*Nan::Utf8String(fileName->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))); if (!cc.load(filename.c_str())) { Nan::ThrowTypeError("Error loading file"); } } class AsyncDetectMultiScale: public Nan::AsyncWorker { public: AsyncDetectMultiScale(Nan::Callback *callback, CascadeClassifierWrap *cc, Matrix* im, double scale, int neighbors, int minw, int minh) : Nan::AsyncWorker(callback), cc(cc), im(new Matrix(im)), //copy the matrix so we aren't affected if the original is released scale(scale), neighbors(neighbors), minw(minw), minh(minh) { } ~AsyncDetectMultiScale() { } void Execute() { try { std::vector < cv::Rect > objects; cv::Mat gray; if (this->im->mat.channels() != 1) { cvtColor(this->im->mat, gray, CV_BGR2GRAY); equalizeHist(gray, gray); } else { gray = this->im->mat; } this->cc->cc.detectMultiScale(gray, objects, this->scale, this->neighbors, 0 | cv::CASCADE_SCALE_IMAGE, cv::Size(this->minw, this->minh)); res = objects; } catch (cv::Exception& e) { SetErrorMessage(e.what()); } } void HandleOKCallback() { Nan::HandleScope scope; delete im; im = NULL; Local < Value > argv[2]; v8::Local < v8::Array > arr = Nan::New < v8::Array > (this->res.size()); for (unsigned int i = 0; i < this->res.size(); i++) { v8::Local < v8::Object > x = Nan::New(); x->Set(Nan::New("x").ToLocalChecked(), Nan::New < Number > (this->res[i].x)); x->Set(Nan::New("y").ToLocalChecked(), Nan::New < Number > (this->res[i].y)); x->Set(Nan::New("width").ToLocalChecked(), Nan::New < Number > (this->res[i].width)); x->Set(Nan::New("height").ToLocalChecked(), Nan::New < Number > (this->res[i].height)); arr->Set(i, x); } argv[0] = Nan::Null(); argv[1] = arr; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: CascadeClassifierWrap *cc; Matrix* im; double scale; int neighbors; int minw; int minh; std::vector res; }; NAN_METHOD(CascadeClassifierWrap::DetectMultiScale) { Nan::HandleScope scope; CascadeClassifierWrap *self = Nan::ObjectWrap::Unwrap (info.This()); if (info.Length() < 2) { Nan::ThrowTypeError("detectMultiScale takes at least 2 info"); } Matrix *im = Nan::ObjectWrap::Unwrap < Matrix > (Nan::To(info[0]).ToLocalChecked()); REQ_FUN_ARG(1, cb); double scale = 1.1; if (info.Length() > 2 && info[2]->IsNumber()) { scale = info[2].As()->Value(); } int neighbors = 2; if (info.Length() > 3 && info[3]->IsInt32()) { neighbors = info[3]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } int minw = 30; int minh = 30; if (info.Length() > 5 && info[4]->IsInt32() && info[5]->IsInt32()) { minw = info[4]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); minh = info[5]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker( new AsyncDetectMultiScale(callback, self, im, scale, neighbors, minw, minh)); return; } #endif node-opencv/src/CamShift.h0000644000175000017500000000066013671667364014074 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_VIDEO class TrackedObject: public Nan::ObjectWrap { public: int channel; cv::Mat hsv; cv::Mat hue; cv::Mat mask; cv::Mat prob; cv::Mat hist; cv::Rect prev_rect; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); TrackedObject(cv::Mat image, cv::Rect rect, int channel); JSFUNC(Track); }; #endif node-opencv/src/VideoCaptureWrap.cc0000755000175000017500000002062013671667364015761 0ustar devdev#include "VideoCaptureWrap.h" #include "Matrix.h" #include "OpenCV.h" #include #if CV_MAJOR_VERSION >= 4 #include #endif #ifdef HAVE_OPENCV_VIDEOIO Nan::Persistent VideoCaptureWrap::constructor; struct videocapture_baton { Nan::Persistent cb; VideoCaptureWrap *vc; Matrix *im; uv_work_t request; }; void VideoCaptureWrap::Init(Local target) { Nan::HandleScope scope; //Class Local ctor = Nan::New(VideoCaptureWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("VideoCapture").ToLocalChecked()); // Prototype //Local proto = constructor->PrototypeTemplate(); Nan::SetPrototypeMethod(ctor, "read", Read); Nan::SetPrototypeMethod(ctor, "setWidth", SetWidth); Nan::SetPrototypeMethod(ctor, "setHeight", SetHeight); Nan::SetPrototypeMethod(ctor, "getWidth", GetWidth); Nan::SetPrototypeMethod(ctor, "getHeight", GetHeight); Nan::SetPrototypeMethod(ctor, "setPosition", SetPosition); Nan::SetPrototypeMethod(ctor, "getFrameAt", GetFrameAt); Nan::SetPrototypeMethod(ctor, "getFrameCount", GetFrameCount); Nan::SetPrototypeMethod(ctor, "getFPS", GetFPS); Nan::SetPrototypeMethod(ctor, "setFPS", SetFPS); Nan::SetPrototypeMethod(ctor, "release", Release); Nan::SetPrototypeMethod(ctor, "readSync", ReadSync); Nan::SetPrototypeMethod(ctor, "grab", Grab); Nan::SetPrototypeMethod(ctor, "retrieve", Retrieve); target->Set(Nan::New("VideoCapture").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(VideoCaptureWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) return Nan::ThrowTypeError("Cannot Instantiate without new"); VideoCaptureWrap *v; if (info[0]->IsNumber()) { v = new VideoCaptureWrap(info[0].As()->Value()); } else { //TODO - assumes that we have string, verify v = new VideoCaptureWrap(std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())))); } v->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } VideoCaptureWrap::VideoCaptureWrap(int device) { Nan::HandleScope scope; cap.open(device); if(!cap.isOpened()) { Nan::ThrowError("Camera could not be opened"); } } VideoCaptureWrap::VideoCaptureWrap(const std::string& filename) { Nan::HandleScope scope; cap.open(filename); // TODO! At the moment this only takes a full path - do relative too. if(!cap.isOpened()) { Nan::ThrowError("Video file could not be opened (opencv reqs. non relative paths)"); } } NAN_METHOD(VideoCaptureWrap::GetWidth) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); int cnt = int(v->cap.get(CV_CAP_PROP_FRAME_WIDTH)); info.GetReturnValue().Set(Nan::New(cnt)); } NAN_METHOD(VideoCaptureWrap::SetWidth) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); if(info.Length() != 1) return; int w = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); if(v->cap.isOpened()) v->cap.set(CV_CAP_PROP_FRAME_WIDTH, w); return; } NAN_METHOD(VideoCaptureWrap::GetFrameCount) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); int cnt = int(v->cap.get(CV_CAP_PROP_FRAME_COUNT)); info.GetReturnValue().Set(Nan::New(cnt)); } NAN_METHOD(VideoCaptureWrap::GetFPS) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); int fps = int(v->cap.get(CV_CAP_PROP_FPS)); info.GetReturnValue().Set(Nan::New(fps)); } NAN_METHOD(VideoCaptureWrap::SetFPS) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); if (info.Length() > 0) { if (info[0]->IsNumber()) { int fps = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); v->cap.set(CV_CAP_PROP_FPS, fps); } } int fps = int(v->cap.get(CV_CAP_PROP_FPS)); info.GetReturnValue().Set(Nan::New(fps)); } NAN_METHOD(VideoCaptureWrap::GetHeight) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); int cnt = int(v->cap.get(CV_CAP_PROP_FRAME_HEIGHT)); info.GetReturnValue().Set(Nan::New(cnt)); } NAN_METHOD(VideoCaptureWrap::SetHeight) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); if(info.Length() != 1) return; int h = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); v->cap.set(CV_CAP_PROP_FRAME_HEIGHT, h); return; } NAN_METHOD(VideoCaptureWrap::SetPosition) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); if(info.Length() != 1) return; int pos = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); v->cap.set(CV_CAP_PROP_POS_FRAMES, pos); return; } NAN_METHOD(VideoCaptureWrap::GetFrameAt) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); if(info.Length() != 1) return; int pos = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); v->cap.set(CV_CAP_PROP_POS_MSEC, pos); return; } NAN_METHOD(VideoCaptureWrap::Release) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); v->cap.release(); return; } class AsyncVCWorker: public Nan::AsyncWorker { public: AsyncVCWorker(Nan::Callback *callback, VideoCaptureWrap* vc, bool retrieve = false, int channel = 0) : Nan::AsyncWorker(callback), vc(vc), retrieve(retrieve), channel(channel) { } ~AsyncVCWorker() { } // Executed inside the worker-thread. // It is not safe to access V8, or V8 data structures // here, so everything we need for input and output // should go on `this`. void Execute() { if (retrieve) { if (!this->vc->cap.retrieve(mat, channel)) { SetErrorMessage("retrieve failed"); } return; } this->vc->cap.read(mat); } // Executed when the async work is complete // this function will be run inside the main event loop // so it is safe to use V8 again void HandleOKCallback() { Nan::HandleScope scope; Local im_to_return = Matrix::CreateWrappedFromMat(mat); mat.release(); Local argv[] = { Nan::Null() , im_to_return }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: VideoCaptureWrap *vc; cv::Mat mat; bool retrieve; int channel; }; NAN_METHOD(VideoCaptureWrap::Read) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); REQ_FUN_ARG(0, cb); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncVCWorker(callback, v)); return; } NAN_METHOD(VideoCaptureWrap::ReadSync) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); cv::Mat outputmat = cv::Mat(); v->cap.read(outputmat); Local im_to_return = Matrix::CreateWrappedFromMat(outputmat); info.GetReturnValue().Set(im_to_return); } class AsyncGrabWorker: public Nan::AsyncWorker { public: AsyncGrabWorker(Nan::Callback *callback, VideoCaptureWrap* vc) : Nan::AsyncWorker(callback), vc(vc) { } ~AsyncGrabWorker() { } void Execute() { if (!this->vc->cap.grab()) { SetErrorMessage("grab failed"); } } private: VideoCaptureWrap *vc; }; NAN_METHOD(VideoCaptureWrap::Grab) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); REQ_FUN_ARG(0, cb); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncGrabWorker(callback, v)); return; } NAN_METHOD(VideoCaptureWrap::Retrieve) { Nan::HandleScope scope; VideoCaptureWrap *v = Nan::ObjectWrap::Unwrap(info.This()); int channel = 0; REQ_FUN_ARG(0, cb); INT_FROM_ARGS(channel, 1); Nan::Callback *callback = new Nan::Callback(cb.As()); Nan::AsyncQueueWorker(new AsyncVCWorker(callback, v, true, channel)); return; } #endif node-opencv/src/LDAWrap.cc0000644000175000017500000000540413671667364013767 0ustar devdev#include "OpenCV.h" #if CV_MAJOR_VERSION >= 3 #ifdef __GNUC__ #warning TODO: port me to OpenCV 3 #else // vs style message pragma #pragma message ( "TODO: port me to OpenCV 3" ) #endif #endif #if ((CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION >=4) && (CV_SUBMINOR_VERSION>=4)) #include "LDAWrap.h" #include "Matrix.h" #include Nan::Persistent LDAWrap::constructor; void LDAWrap::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(LDAWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("LDA").ToLocalChecked()); Nan::SetMethod(ctor, "subspaceProject", SubspaceProject); Nan::SetMethod(ctor, "subspaceReconstruct", SubspaceReconstruct); target->Set(Nan::New("LDA").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(LDAWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { JSTHROW_TYPE("Cannot Instantiate without new") } LDAWrap *pt = new LDAWrap(); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } LDAWrap::LDAWrap() { } /** * http://docs.opencv.org/master/db/d59/classcv_1_1LDA.html#a69c7019a344edc5c55799c16a9fc9ada */ NAN_METHOD(LDAWrap::SubspaceProject) { if (info.Length() != 3) { JSTHROW("SubspaceProject function requires 3 arguments") } // param 0 - eigenvectors Matrix *w = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // param 1 - mean Matrix *mean = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); // param 2 - src Matrix *src = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat m = cv::subspaceProject(w->mat, mean->mat, src->mat); Local im = Matrix::CreateWrappedFromMat(m); info.GetReturnValue().Set(im); } /** * http://docs.opencv.org/master/db/d59/classcv_1_1LDA.html#acbf9949e7567e23af5f71c29fcef76d4 */ NAN_METHOD(LDAWrap::SubspaceReconstruct) { if (info.Length() != 3) { JSTHROW("SubspaceReconstruct function requires 3 arguments") } // param 0 - eigenvectors Matrix *w = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // param 1 - mean Matrix *mean = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); // param 2 - src Matrix *src = Nan::ObjectWrap::Unwrap(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat m = cv::subspaceReconstruct(w->mat, mean->mat, src->mat); Local im = Matrix::CreateWrappedFromMat(m); info.GetReturnValue().Set(im); } #endif // End version > 2.4 node-opencv/src/Point.cc0000755000175000017500000000410213671667364013623 0ustar devdev#include "Point.h" #include "OpenCV.h" Nan::Persistent Point::constructor; void Point::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(Point::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("Point").ToLocalChecked()); // Prototype Local proto = ctor->PrototypeTemplate(); Nan::SetAccessor(proto, Nan::New("x").ToLocalChecked(), GetX, RaiseImmutable); Nan::SetAccessor(proto, Nan::New("y").ToLocalChecked(), GetY, RaiseImmutable); Nan::SetPrototypeMethod(ctor, "dot", Dot); target->Set(Nan::New("Point").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(Point::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { return Nan::ThrowTypeError("Cannot Instantiate without new"); } double x = 0, y = 0; if (info[0]->IsNumber()) { x = info[0].As()->Value(); } if (info[1]->IsNumber()) { y = info[1].As()->Value(); } Point *pt = new Point(x, y); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } NAN_GETTER(Point::GetX) { Nan::HandleScope scope; Point *pt = Nan::ObjectWrap::Unwrap(info.This()); info.GetReturnValue().Set(Nan::New(pt->point.x)); } NAN_GETTER(Point::GetY) { Nan::HandleScope scope; Point *pt = Nan::ObjectWrap::Unwrap(info.This()); info.GetReturnValue().Set(Nan::New(pt->point.y)); } NAN_SETTER(Point::RaiseImmutable) { Nan::ThrowTypeError("Point is immutable"); } NAN_METHOD(Point::Dot) { Nan::HandleScope scope; Point *p1 = Nan::ObjectWrap::Unwrap(info.This()); Point *p2 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); // Since V 2.3 Native Dot no longer supported info.GetReturnValue().Set(Nan::New(p1->point.x * p2->point.x + p1->point.y * p2->point.y)); } Point::Point(double x, double y) : Nan::ObjectWrap() { point = cvPoint2D32f(x, y); } node-opencv/src/Histogram.cc0000644000175000017500000001150413671667364014470 0ustar devdev#include "Histogram.h" #include "Matrix.h" void Histogram::Init(Local target) { Nan::Persistent inner; Local obj = Nan::New(); inner.Reset(obj); Nan::SetMethod(obj, "calcHist", CalcHist); Nan::SetMethod(obj, "emd", Emd); target->Set(Nan::New("histogram").ToLocalChecked(), obj); } NAN_METHOD(Histogram::CalcHist) { Nan::EscapableHandleScope scope; try { // Arg 0 is the image Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat inputImage = m0->mat; // Arg 1 is the channel Local nodeChannels = Local::Cast(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); // vs does not like this const, no need for it? /*const */unsigned int dims = nodeChannels->Length(); if(dims < 1 || dims > 3){ return Nan::ThrowTypeError("OPENCV nodejs binding error : only dimensions from 1 to 3 are allowed"); } // in vs, can't create an array of non-constant size; but since we have dims<3, just use 3.. int channels[3]; for (unsigned int i = 0; i < dims; i++) { channels[i] = nodeChannels->Get(i)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } // Arg 2 is histogram sizes in each dimension Local nodeHistSizes = Local::Cast(info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); int histSize[3]; for (unsigned int i = 0; i < dims; i++) { histSize[i] = nodeHistSizes->Get(i)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } // Arg 3 is array of the histogram bin boundaries in each dimension Local nodeRanges = Local::Cast(info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); /// Set the ranges ( for B,G,R) ) float histRanges[3][2]; const float* ranges[3]; for (unsigned int i = 0; i < dims; i++) { Local nodeRange = Local::Cast(nodeRanges->Get(i)->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); float lower = nodeRange->Get(0).As()->Value(); float higher = nodeRange->Get(1).As()->Value(); histRanges[i][0] = lower; histRanges[i][1] = higher; ranges[i] = histRanges[i]; } // Arg 4 is uniform flag bool uniform = info[4]->BooleanValue( Nan::GetCurrentContext() ).FromJust(); // Make a mat to hold the result image cv::Mat outputHist; // Perform calcHist cv::calcHist(&inputImage, 1, channels, cv::Mat(), outputHist, dims, histSize, ranges, uniform); v8::Local arr = Nan::New(histSize[0]); for (unsigned int i=0; i < (unsigned int) histSize[0]; i++) { if(dims <= 1){ arr->Set(i, Nan::New(outputHist.at(i))); } else { v8::Local arr2 = Nan::New(dims); for (unsigned int j=0; j < (unsigned int) histSize[1]; j++) { if(dims <= 2){ arr2->Set(j, Nan::New(outputHist.at(i,j))); } else { v8::Local arr3 = Nan::New(dims); for (unsigned int k=0; k < (unsigned int) histSize[1]; k++) { arr3->Set(k, Nan::New(outputHist.at(i,j,k))); } arr2->Set(j, arr3); } } arr->Set(i, arr2); } } info.GetReturnValue().Set(arr); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::distanceTransform NAN_METHOD(Histogram::Emd) { Nan::EscapableHandleScope scope; try { // Arg 0 is the first signature //std::vector> sig1 = nodeArrayToVec(Nan::To(info[0]).ToLocalChecked()); Matrix* m0 = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Mat sig1 = m0->mat; // Arg 1 is the second signature //std::vector> sig2 = nodeArrayToVec(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); Matrix* m1 = Nan::ObjectWrap::Unwrap(info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat sig2 = m1->mat; // Arg 2 is the distance type int distType = info[2]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); float emd; // Arg 3 is the cost matrix if (info.Length() > 3) { Matrix* m3 = Nan::ObjectWrap::Unwrap(info[3]->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); cv::Mat costs = m3->mat; emd = cv::EMD(sig1, sig2, distType, costs); info.GetReturnValue().Set(emd); } else { emd = cv::EMD(sig1, sig2, distType); } //printf("similarity %5.5f %%\n, DistanceType is %i\n", (1-emd)*100, distType); info.GetReturnValue().Set(emd); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } node-opencv/src/CascadeClassifierWrap.h0000755000175000017500000000115113671667364016557 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_OBJDETECT #if CV_MAJOR_VERSION >= 3 #include #endif class CascadeClassifierWrap: public Nan::ObjectWrap { public: cv::CascadeClassifier cc; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); CascadeClassifierWrap(v8::Value* fileName); //static Handle LoadHaarClassifierCascade(const v8::Arguments&); static NAN_METHOD(DetectMultiScale); static void EIO_DetectMultiScale(uv_work_t *req); static int EIO_AfterDetectMultiScale(uv_work_t *req); }; #endif node-opencv/src/Calib3D.h0000644000175000017500000000126413671667364013600 0ustar devdev#ifndef __NODE_CALIB3D_H #define __NODE_CALIB3D_H #include "OpenCV.h" #ifdef HAVE_OPENCV_CALIB3D #if CV_MAJOR_VERSION >= 3 #include #endif /** * Implementation of calib3d.hpp functions */ class Calib3D: public Nan::ObjectWrap { public: static void Init(Local target); static NAN_METHOD(FindChessboardCorners); static NAN_METHOD(DrawChessboardCorners); static NAN_METHOD(CalibrateCamera); static NAN_METHOD(SolvePnP); static NAN_METHOD(GetOptimalNewCameraMatrix); static NAN_METHOD(StereoCalibrate); static NAN_METHOD(StereoRectify); static NAN_METHOD(ComputeCorrespondEpilines); static NAN_METHOD(ReprojectImageTo3D); }; #endif #endif node-opencv/src/Calib3D.cc0000644000175000017500000004141713671667364013742 0ustar devdev#include "Calib3D.h" #include "Matrix.h" #ifdef HAVE_OPENCV_CALIB3D inline cv::Mat matFromMatrix(Local matrix) { Matrix* m = Nan::ObjectWrap::Unwrap(matrix->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); return m->mat; } inline cv::Size sizeFromArray(Local jsArray) { cv::Size patternSize; if (jsArray->IsArray()) { Local v8sz = jsArray->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); patternSize = cv::Size(v8sz->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8sz->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { JSTHROW_TYPE("Size is not a valid array"); } return patternSize; } inline std::vector points2fFromArray(Local array) { std::vector points; if (array->IsArray()) { Local pointsArray = Local::Cast(array->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); for (unsigned int i = 0; i < pointsArray->Length(); i++) { Local pt = Nan::Get(pointsArray, i).ToLocalChecked()->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); points.push_back( cv::Point2f( Nan::To(Nan::Get(pt, Nan::New("x").ToLocalChecked()).ToLocalChecked()).FromJust(), Nan::To(Nan::Get(pt, Nan::New("y").ToLocalChecked()).ToLocalChecked()).FromJust() ) ); } } else { JSTHROW_TYPE("Points not a valid array"); } return points; } inline std::vector points3fFromArray(Local array) { std::vector points; if (array->IsArray()) { Local pointsArray = Local::Cast(array->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); for (unsigned int i = 0; i < pointsArray->Length(); i++) { Local pt = pointsArray->Get(i)->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); points.push_back( cv::Point3f( Nan::To(Nan::Get(pt, Nan::New("x").ToLocalChecked()).ToLocalChecked()).FromJust(), Nan::To(Nan::Get(pt, Nan::New("y").ToLocalChecked()).ToLocalChecked()).FromJust(), Nan::To(Nan::Get(pt, Nan::New("z").ToLocalChecked()).ToLocalChecked()).FromJust() ) ); } } else { JSTHROW_TYPE("Must pass array of object points for each frame") } return points; } inline std::vector > points2fFromArrayOfArrays( Local array) { std::vector > points; if (array->IsArray()) { Local pointsArray = Local::Cast(array->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); for (unsigned int i = 0; i < pointsArray->Length(); i++) { points.push_back(points2fFromArray(pointsArray->Get(i))); } } else { JSTHROW_TYPE("Must pass array of object points for each frame") } return points; } inline std::vector > points3fFromArrayOfArrays( Local array) { std::vector > points; if (array->IsArray()) { Local pointsArray = Local::Cast(array->ToObject(Nan::GetCurrentContext()).ToLocalChecked()); for (unsigned int i = 0; i < pointsArray->Length(); i++) { points.push_back(points3fFromArray(pointsArray->Get(i))); } } else { JSTHROW_TYPE("Must pass array of object points for each frame") } return points; } void Calib3D::Init(Local target) { Nan::Persistent inner; Local obj = Nan::New(); inner.Reset(obj); Nan::SetMethod(obj, "findChessboardCorners", FindChessboardCorners); Nan::SetMethod(obj, "drawChessboardCorners", DrawChessboardCorners); Nan::SetMethod(obj, "calibrateCamera", CalibrateCamera); Nan::SetMethod(obj, "solvePnP", SolvePnP); Nan::SetMethod(obj, "getOptimalNewCameraMatrix", GetOptimalNewCameraMatrix); Nan::SetMethod(obj, "stereoCalibrate", StereoCalibrate); Nan::SetMethod(obj, "stereoRectify", StereoRectify); Nan::SetMethod(obj, "computeCorrespondEpilines", ComputeCorrespondEpilines); Nan::SetMethod(obj, "reprojectImageTo3d", ReprojectImageTo3D); target->Set(Nan::New("calib3d").ToLocalChecked(), obj); } // cv::findChessboardCorners NAN_METHOD(Calib3D::FindChessboardCorners) { Nan::EscapableHandleScope scope; try { // Get the arguments from javascript // Arg 0 is the image cv::Mat mat = matFromMatrix(info[0]); // Arg 1 is the pattern size cv::Size patternSize = sizeFromArray(info[1]); // Arg 2 would normally be the flags, ignoring this for now and using the // default flags // Find the corners std::vector corners; bool found = cv::findChessboardCorners(mat, patternSize, corners); // Make the return value Local ret = Nan::New(); ret->Set(Nan::New("found").ToLocalChecked(), Nan::New(found)); Local cornersArray = Nan::New(corners.size()); for (unsigned int i = 0; i < corners.size(); i++) { Local point_data = Nan::New(); point_data->Set(Nan::New("x").ToLocalChecked(), Nan::New(corners[i].x)); point_data->Set(Nan::New("y").ToLocalChecked(), Nan::New(corners[i].y)); cornersArray->Set(Nan::New(i), point_data); } ret->Set(Nan::New("corners").ToLocalChecked(), cornersArray); info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::drawChessboardCorners NAN_METHOD(Calib3D::DrawChessboardCorners) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0 is the image cv::Mat mat = matFromMatrix(info[0]); // Arg 1 is the pattern size cv::Size patternSize = sizeFromArray(info[1]); // Arg 2 is the corners array std::vector corners = points2fFromArray(info[2]); // Arg 3, pattern found boolean bool patternWasFound = info[3]->ToBoolean( v8::Isolate::GetCurrent() )->Value(); // Draw the corners cv::drawChessboardCorners(mat, patternSize, corners, patternWasFound); // Return the passed image, now with corners drawn on it info.GetReturnValue().Set(info[0]); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::calibrateCamera NAN_METHOD(Calib3D::CalibrateCamera) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0, the array of object points, an array of arrays std::vector > objectPoints = points3fFromArrayOfArrays(info[0]); // Arg 1, the image points, another array of arrays std::vector > imagePoints = points2fFromArrayOfArrays(info[1]); // Arg 2, the image size cv::Size imageSize = sizeFromArray(info[2]); // Arg 3, 4, input guesses for the camrea matrix and distortion coefficients, // skipping for now cv::Mat K, dist; // Arg 5, 6 flags and termination criteria, skipping for now // Calibrate the camera std::vector rvecs, tvecs; double error = cv::calibrateCamera(objectPoints, imagePoints, imageSize, K, dist, rvecs, tvecs); // make the return values Local ret = Nan::New(); // Reprojection error ret->Set(Nan::New("reprojectionError").ToLocalChecked(), Nan::New(error)); // K Local KMatrixWrap = Matrix::CreateWrappedFromMat(K); ret->Set(Nan::New("K").ToLocalChecked(), KMatrixWrap); // dist Local distMatrixWrap = Matrix::CreateWrappedFromMat(dist); ret->Set(Nan::New("distortion").ToLocalChecked(), distMatrixWrap); // Per frame R and t, skiping for now // Return info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::solvePnP NAN_METHOD(Calib3D::SolvePnP) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0, the array of object points std::vector objectPoints = points3fFromArray(info[0]); // Arg 1, the image points std::vector imagePoints = points2fFromArray(info[1]); // Arg 2, the camera matrix cv::Mat K = matFromMatrix(info[2]); // Arg 3, the distortion coefficients cv::Mat dist = matFromMatrix(info[3]); // Arg 4, use extrinsic guess, skipped for now // Arg 5, flags, skip for now // solve for r and t cv::Mat rvec, tvec; cv::solvePnP(objectPoints, imagePoints, K, dist, rvec, tvec); // make the return values Local ret = Nan::New(); // rvec Local rMatrixWrap = Matrix::CreateWrappedFromMat(rvec); ret->Set(Nan::New("rvec").ToLocalChecked(), rMatrixWrap); // tvec Local tMatrixWrap = Matrix::CreateWrappedFromMat(tvec); ret->Set(Nan::New("tvec").ToLocalChecked(), tMatrixWrap); // Return info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::getOptimalNewCameraMAtrix NAN_METHOD(Calib3D::GetOptimalNewCameraMatrix) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0 is the original camera matrix cv::Mat Kin = matFromMatrix(info[0]); // Arg 1 is the distortion coefficients cv::Mat dist = matFromMatrix(info[1]); // Arg 2, the image size cv::Size imageSize = sizeFromArray(info[2]); // Arg 3 is the alpha free scaling parameter double alpha = Nan::To(info[3]).FromJust(); // Arg 4, the new image size cv::Size newImageSize = sizeFromArray(info[4]); // Arg 5, valid ROI, skip for now // Arg 6, center principal point, skip for now // Get the optimal new camera matrix cv::Mat Kout = cv::getOptimalNewCameraMatrix(Kin, dist, imageSize, alpha, newImageSize); // Wrap the output K Local KMatrixWrap = Matrix::CreateWrappedFromMat(Kout); // Return the new K matrix info.GetReturnValue().Set(KMatrixWrap); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::stereoCalibrate NAN_METHOD(Calib3D::StereoCalibrate) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg 0, the array of object points, an array of arrays std::vector > objectPoints = points3fFromArrayOfArrays(info[0]); // Arg 1, the image points1, another array of arrays std::vector > imagePoints1 = points2fFromArrayOfArrays(info[1]); // Arg 2, the image points2, another array of arrays =( std::vector > imagePoints2 = points2fFromArrayOfArrays(info[2]); // Arg 3 is the image size (follows the PYTHON api not the C++ api since all // following arguments are optional or outputs) cv::Size imageSize = sizeFromArray(info[3]); // Arg 4,5,6,7 is the camera matrix and distortion coefficients // (optional but must pass all 4 or none) cv::Mat k1, d1, k2, d2; if (info.Length() >= 8) { k1 = matFromMatrix(info[4]); d1 = matFromMatrix(info[5]); k2 = matFromMatrix(info[6]); d2 = matFromMatrix(info[7]); } // Last argument is flags, skipping for now // Output mats cv::Mat R, t, E, F; // Do the stereo calibration cv::stereoCalibrate(objectPoints, imagePoints1, imagePoints2, k1, d1, k2, d2, imageSize, R, t, E, F); // make the return value Local ret = Nan::New(); // Make the output arguments // k1 Local K1MatrixWrap = Matrix::CreateWrappedFromMat(k1); // d1 Local d1MatrixWrap = Matrix::CreateWrappedFromMat(d1); // k2 Local K2MatrixWrap = Matrix::CreateWrappedFromMat(k2); // d2 Local d2MatrixWrap = Matrix::CreateWrappedFromMat(d2); // R Local RMatrixWrap = Matrix::CreateWrappedFromMat(R); // t Local tMatrixWrap = Matrix::CreateWrappedFromMat(t); // E Local EMatrixWrap = Matrix::CreateWrappedFromMat(E); // F Local FMatrixWrap = Matrix::CreateWrappedFromMat(F); // Add to return object ret->Set(Nan::New("K1").ToLocalChecked(), K1MatrixWrap); ret->Set(Nan::New("distortion1").ToLocalChecked(), d1MatrixWrap); ret->Set(Nan::New("K2").ToLocalChecked(), K2MatrixWrap); ret->Set(Nan::New("distortion2").ToLocalChecked(), d2MatrixWrap); ret->Set(Nan::New("R").ToLocalChecked(), RMatrixWrap); ret->Set(Nan::New("t").ToLocalChecked(), tMatrixWrap); ret->Set(Nan::New("E").ToLocalChecked(), EMatrixWrap); ret->Set(Nan::New("F").ToLocalChecked(), FMatrixWrap); // Return info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::stereoRectify NAN_METHOD(Calib3D::StereoRectify) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg0, the first camera matrix cv::Mat K1 = matFromMatrix(info[0]); // Arg1, the first distortion coefficients cv::Mat d1 = matFromMatrix(info[1]); // Arg2, the second camera matrix cv::Mat K2 = matFromMatrix(info[2]); // Arg3, the second distortion coefficients cv::Mat d2 = matFromMatrix(info[3]); // Arg4, the image size cv::Size imageSize = sizeFromArray(info[4]); // arg5, the intercamera rotation matrix cv::Mat R = matFromMatrix(info[5]); // Arg6, the intercamera translation vector cv::Mat t = matFromMatrix(info[6]); // Arg8, flags, skipping for now // Arg9, freescaling paremeter, skipping for now // Arg10, new image size, skipping for now to fix at original image size // Make output matrics cv::Mat R1, R2, P1, P2, Q; // Do the stereo rectification cv::stereoRectify(K1, d1, K2, d2, imageSize, R, t, R1, R2, P1, P2, Q); // Make the return object Local ret = Nan::New(); ret->Set(Nan::New("R1").ToLocalChecked(), Matrix::CreateWrappedFromMat(R1)); ret->Set(Nan::New("R2").ToLocalChecked(), Matrix::CreateWrappedFromMat(R2)); ret->Set(Nan::New("P1").ToLocalChecked(), Matrix::CreateWrappedFromMat(P1)); ret->Set(Nan::New("P2").ToLocalChecked(), Matrix::CreateWrappedFromMat(P2)); ret->Set(Nan::New("Q").ToLocalChecked(), Matrix::CreateWrappedFromMat(Q)); // Return the rectification parameters info.GetReturnValue().Set(ret); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::computeCorrespondEpilines NAN_METHOD(Calib3D::ComputeCorrespondEpilines) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg0, the image points std::vector points = points2fFromArray(info[0]); // Arg1, the image index (1 or 2) int whichImage = Nan::To(info[1]).FromJust(); // Arg2, the fundamental matrix cv::Mat F = matFromMatrix(info[2]); // compute the lines std::vector lines; cv::computeCorrespondEpilines(points, whichImage, F, lines); // Convert the lines to an array of objects (ax + by + c = 0) Local linesArray = Nan::New(lines.size()); for(unsigned int i = 0; i < lines.size(); i++) { Local line_data = Nan::New(); line_data->Set(Nan::New("a").ToLocalChecked(), Nan::New(lines[i][0])); line_data->Set(Nan::New("b").ToLocalChecked(), Nan::New(lines[i][1])); line_data->Set(Nan::New("c").ToLocalChecked(), Nan::New(lines[i][2])); linesArray->Set(Nan::New(i), line_data); } // Return the lines info.GetReturnValue().Set(linesArray); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } // cv::reprojectImageTo3D NAN_METHOD(Calib3D::ReprojectImageTo3D) { Nan::EscapableHandleScope scope; try { // Get the arguments // Arg0, the disparity image cv::Mat disparity = matFromMatrix(info[0]); // Arg1, the depth-to-disparity transformation Q cv::Mat Q = matFromMatrix(info[1]); // Arg 2, handle missing values, skipped for now // Arg3, output bit depth, skipped for now // Compute the depth image cv::Mat depthImage; cv::reprojectImageTo3D(disparity, depthImage, Q); // Wrap the depth image Local depthImageMatrix = Matrix::CreateWrappedFromMat(depthImage); info.GetReturnValue().Set(depthImageMatrix); } catch (cv::Exception &e) { const char *err_msg = e.what(); Nan::ThrowError(err_msg); return; } } #endif node-opencv/src/Point.h0000755000175000017500000000072213671667364013471 0ustar devdev// Template class for 2D points #include "OpenCV.h" #if CV_MAJOR_VERSION >= 4 #include #endif class Point: public Nan::ObjectWrap { public: CvPoint2D32f point; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); Point(double x, double y); static NAN_GETTER(GetX); static NAN_GETTER(GetY); static NAN_SETTER(RaiseImmutable); static NAN_METHOD(Dot); }; node-opencv/src/Contours.h0000755000175000017500000000116413671667364014215 0ustar devdev#include "OpenCV.h" class Contour: public Nan::ObjectWrap { public: cv::Mat mat; std::vector > contours; std::vector hierarchy; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); Contour(); JSFUNC(Point) JSFUNC(Points) JSFUNC(Size) JSFUNC(CornerCount) JSFUNC(Area) JSFUNC(ArcLength) JSFUNC(ApproxPolyDP) JSFUNC(ConvexHull) JSFUNC(BoundingRect) JSFUNC(MinAreaRect) JSFUNC(FitEllipse) JSFUNC(IsConvex) JSFUNC(Moments) JSFUNC(Hierarchy) JSFUNC(Serialize) JSFUNC(Deserialize) }; node-opencv/src/BackgroundSubtractor.cc0000644000175000017500000003456713671667364016701 0ustar devdev#include "BackgroundSubtractor.h" #include "Matrix.h" #include #include #ifdef HAVE_BACKGROUNDSUBTRACTOR #ifdef HAVE_OPENCV_BGSEGM #ifdef __GNUC__ #warning Building with HAVE_OPENCV_BGSEGM #else // vs style message pragma #pragma message ( "Building with HAVE_OPENCV_BGSEGM" ) #endif cv::bgsegm::BackgroundSubtractorMOG* getMOG(BackgroundSubtractorWrap *wrap) { return dynamic_cast(wrap->subtractor.get()); } #else #ifdef __GNUC__ #warning Building without HAVE_OPENCV_BGSEGM #else // vs style message pragma #pragma message ( "Building without HAVE_OPENCV_BGSEGM - only MOG2 will be available" ) #endif // without bgsem, it can't be MOG anyway void* getMOG(BackgroundSubtractorWrap *wrap) { return NULL; } #endif class BGAutoMutex { public: BGAutoMutex(cv::Mutex &m){ mutex = &m; mutex->lock(); }; ~BGAutoMutex(){ mutex->unlock(); mutex = NULL; }; private: cv::Mutex *mutex; }; Nan::Persistent BackgroundSubtractorWrap::constructor; void BackgroundSubtractorWrap::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(BackgroundSubtractorWrap::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("BackgroundSubtractor").ToLocalChecked()); Nan::SetMethod(ctor, "createMOG", CreateMOG); Nan::SetMethod(ctor, "createMOG2", CreateMOG2); Nan::SetMethod(ctor, "createGMG", CreateGMG); Nan::SetPrototypeMethod(ctor, "applyMOG", ApplyMOG); Nan::SetPrototypeMethod(ctor, "apply", Apply); Nan::SetPrototypeMethod(ctor, "history", History); Nan::SetPrototypeMethod(ctor, "nmixtures", Mixtures); Nan::SetPrototypeMethod(ctor, "noiseSigma", NoiseSigma); Nan::SetPrototypeMethod(ctor, "backgroundRatio", BackgroundRatio); target->Set(Nan::New("BackgroundSubtractor").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(BackgroundSubtractorWrap::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { JSTHROW_TYPE("Cannot Instantiate without new") } // Create MOG by default #if CV_MAJOR_VERSION >= 3 #ifdef HAVE_OPENCV_BGSEGM cv::Ptr bg = cv::bgsegm::createBackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from cv::bgsegm::createBackgroundSubtractorMOG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); #else // if no bgsem, then default to MOG2 cv::Ptr bg = cv::createBackgroundSubtractorMOG2(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from cv::createBackgroundSubtractorMOG2()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); #endif #else cv::Ptr bg = new cv::BackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from new cv::BackgroundSubtractorMOG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(info.This()); info.GetReturnValue().Set(info.This()); #endif } NAN_METHOD(BackgroundSubtractorWrap::CreateMOG) { Nan::HandleScope scope; // int history = 200; // int nmixtures = 5; // double backgroundRatio = 0.7; // double noiseSigma = 0; // // if(info.Length() > 1){ // INT_FROM_ARGS(history, 0) // INT_FROM_ARGS(nmixtures, 1) // DOUBLE_FROM_ARGS(backgroundRatio, 2) // DOUBLE_FROM_ARGS(noiseSigma, 3) // } Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(BackgroundSubtractorWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 3 #ifdef HAVE_OPENCV_BGSEGM cv::Ptr bg = cv::bgsegm::createBackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from cv::bgsegm::createBackgroundSubtractorMOG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(n); info.GetReturnValue().Set( n ); #else JSTHROW_TYPE("OpenCV built without bgsem (opencv_contrib)") #endif #else cv::Ptr bg = new cv::BackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from new cv::BackgroundSubtractorMOG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(n); info.GetReturnValue().Set( n ); #endif } NAN_METHOD(BackgroundSubtractorWrap::CreateMOG2) { Nan::HandleScope scope; // int history = 200; // int nmixtures = 5; // double backgroundRatio = 0.7; // double noiseSigma = 0; // // if(info.Length() > 1){ // INT_FROM_ARGS(history, 0) // INT_FROM_ARGS(nmixtures, 1) // DOUBLE_FROM_ARGS(backgroundRatio, 2) // DOUBLE_FROM_ARGS(noiseSigma, 3) // } Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(BackgroundSubtractorWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 3 cv::Ptr bg = cv::createBackgroundSubtractorMOG2(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from cv::createBackgroundSubtractorMOG2()"); } #else cv::Ptr bg = new cv::BackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from new cv::BackgroundSubtractorMOG()"); } #endif BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(n); info.GetReturnValue().Set( n ); } NAN_METHOD(BackgroundSubtractorWrap::CreateGMG) { Nan::HandleScope scope; // int history = 200; // int nmixtures = 5; // double backgroundRatio = 0.7; // double noiseSigma = 0; // // if(info.Length() > 1){ // INT_FROM_ARGS(history, 0) // INT_FROM_ARGS(nmixtures, 1) // DOUBLE_FROM_ARGS(backgroundRatio, 2) // DOUBLE_FROM_ARGS(noiseSigma, 3) // } Local n = Nan::NewInstance(Nan::GetFunction(Nan::New(BackgroundSubtractorWrap::constructor)).ToLocalChecked()).ToLocalChecked(); #if CV_MAJOR_VERSION >= 3 #ifdef HAVE_OPENCV_BGSEGM cv::Ptr bg = cv::bgsegm::createBackgroundSubtractorGMG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from cv::bgsegm::createBackgroundSubtractorGMG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(n); info.GetReturnValue().Set( n ); #else JSTHROW_TYPE("OpenCV built without bgsem (opencv_contrib) - use MOG2") #endif #else cv::Ptr bg = new cv::BackgroundSubtractorMOG(); if (NULL == bg){ JSTHROW_TYPE("OpenCV NULL from new cv::BackgroundSubtractorMOG()"); } BackgroundSubtractorWrap *pt = new BackgroundSubtractorWrap(bg); pt->Wrap(n); info.GetReturnValue().Set( n ); #endif } // Fetch foreground mask NAN_METHOD(BackgroundSubtractorWrap::ApplyMOG) { SETUP_FUNCTION(BackgroundSubtractorWrap); REQ_FUN_ARG(1, cb); Local argv[2]; if (info.Length() == 0) { argv[0] = Nan::New("Input image missing").ToLocalChecked(); argv[1] = Nan::Null(); cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); return; } if (NULL == self->subtractor){ argv[0] = Nan::New("BackgroundSubtractor not created").ToLocalChecked(); argv[1] = Nan::Null(); cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); return; } try { cv::Mat mat; if (Buffer::HasInstance(info[0])) { uint8_t *buf = (uint8_t *) Buffer::Data(Nan::To(info[0]).ToLocalChecked()); unsigned len = Buffer::Length(Nan::To(info[0]).ToLocalChecked()); cv::Mat *mbuf = new cv::Mat(len, 1, CV_64FC1, buf); mat = cv::imdecode(*mbuf, -1); //mbuf->release(); } else { Matrix *_img = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); mat = (_img->mat).clone(); } if (mat.empty()) { return Nan::ThrowTypeError("Error loading file"); } cv::Mat _fgMask; { // wait here if already in apply - auto-release on scope exit BGAutoMutex(self->applymutex); #if CV_MAJOR_VERSION >= 3 self->subtractor->apply(mat, _fgMask); #else self->subtractor->operator()(mat, _fgMask); #endif } Local fgMask = Matrix::CreateWrappedFromMat(_fgMask.clone()); mat.release(); argv[0] = Nan::Null(); argv[1] = fgMask; Nan::TryCatch try_catch; cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } return; } catch (cv::Exception& e) { const char* err_msg = e.what(); Nan::ThrowError(err_msg); return; } } class AsyncBackgroundSubtractorWorker: public Nan::AsyncWorker { public: AsyncBackgroundSubtractorWorker( Nan::Callback *callback, BackgroundSubtractorWrap *bg, Matrix *matrix_in): Nan::AsyncWorker(callback), bg(bg), matrix(new Matrix(matrix_in)) { } ~AsyncBackgroundSubtractorWorker() { } // Executed inside the worker-thread. // It is not safe to access V8, or V8 data structures // here, so everything we need for input and output // should go on `this`. void Execute() { // wait here if already in apply - auto-release on scope exit BGAutoMutex(bg->applymutex); #if CV_MAJOR_VERSION >= 3 bg->subtractor->apply(matrix->mat, _fgMask); #else bg->subtractor->operator()(matrix->mat, _fgMask); #endif } // Executed when the async work is complete // this function will be run inside the main event loop // so it is safe to use V8 again void HandleOKCallback() { Nan::HandleScope scope; delete matrix; matrix = NULL; Local im_to_return = Matrix::CreateWrappedFromMat(_fgMask.clone()); Local argv[] = { Nan::Null() , im_to_return }; Nan::TryCatch try_catch; callback->Call(2, argv); if (try_catch.HasCaught()) { Nan::FatalException(try_catch); } } private: BackgroundSubtractorWrap *bg; Matrix *matrix; cv::Mat _fgMask; }; // Fetch foreground mask NAN_METHOD(BackgroundSubtractorWrap::Apply) { SETUP_FUNCTION(BackgroundSubtractorWrap); int callback_arg = -1; int numargs = info.Length(); Local cb; if (info[numargs-1]->IsFunction()){ callback_arg = numargs-1; cb = Local::Cast(info[callback_arg]); } // if async if (callback_arg > 0){ Local argv[2]; if (info.Length() == 0) { argv[0] = Nan::New("Input image missing").ToLocalChecked(); argv[1] = Nan::Null(); cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); return; } if (NULL == self->subtractor){ argv[0] = Nan::New("BackgroundSubtractor not created").ToLocalChecked(); argv[1] = Nan::Null(); cb->Call( Nan::GetCurrentContext(), Nan::GetCurrentContext()->Global(), 2, argv); return; } Nan::Callback *callback = new Nan::Callback(cb.As()); Matrix *_img = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); Nan::AsyncQueueWorker(new AsyncBackgroundSubtractorWorker( callback, self, _img)); return; } else { //synchronous - return the image try { Local fgMask; cv::Mat mat; if (Buffer::HasInstance(info[0])) { uint8_t *buf = (uint8_t *) Buffer::Data(Nan::To(info[0]).ToLocalChecked()); unsigned len = Buffer::Length(Nan::To(info[0]).ToLocalChecked()); cv::Mat *mbuf = new cv::Mat(len, 1, CV_64FC1, buf); mat = cv::imdecode(*mbuf, -1); //mbuf->release(); } else { Matrix *_img = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); mat = (_img->mat).clone(); } if (mat.empty()) { return Nan::ThrowTypeError("Empty matrix?"); } { // wait here if already in apply - auto-release on scope exit BGAutoMutex(self->applymutex); cv::Mat _fgMask; #if CV_MAJOR_VERSION >= 3 self->subtractor->apply(mat, _fgMask); #else self->subtractor->operator()(mat, _fgMask); #endif fgMask = Matrix::CreateWrappedFromMat(_fgMask.clone()); } mat.release(); info.GetReturnValue().Set(fgMask); return; } catch (cv::Exception& e) { const char* err_msg = e.what(); Nan::ThrowError(err_msg); return; } } } NAN_METHOD(BackgroundSubtractorWrap::History) { SETUP_FUNCTION(BackgroundSubtractorWrap); auto mog = getMOG(self); if (!mog) { Nan::ThrowError("Not using a BackgroundSubtractorMOG"); } // only support for V3+ with opencv-contrib #ifdef HAVE_OPENCV_BGSEGM if (info.Length() > 0) { mog->setHistory(info[0].As()->Value()); } info.GetReturnValue().Set(mog->getHistory()); #else info.GetReturnValue().Set(Nan::Null()); #endif } NAN_METHOD(BackgroundSubtractorWrap::BackgroundRatio) { SETUP_FUNCTION(BackgroundSubtractorWrap); auto mog = getMOG(self); if (!mog) { Nan::ThrowError("Not using a BackgroundSubtractorMOG"); } // only support for V3+ with opencv-contrib #ifdef HAVE_OPENCV_BGSEGM if (info.Length() > 0) { mog->setBackgroundRatio(info[0].As()->Value()); } info.GetReturnValue().Set(mog->getBackgroundRatio()); #else info.GetReturnValue().Set(Nan::Null()); #endif } NAN_METHOD(BackgroundSubtractorWrap::NoiseSigma) { SETUP_FUNCTION(BackgroundSubtractorWrap); auto mog = getMOG(self); if (!mog) { Nan::ThrowError("Not using a BackgroundSubtractorMOG"); } // only support for V3+ with opencv-contrib #ifdef HAVE_OPENCV_BGSEGM if (info.Length() > 0) { mog->setNoiseSigma(info[0].As()->Value()); } info.GetReturnValue().Set(mog->getNoiseSigma()); #else info.GetReturnValue().Set(Nan::Null()); #endif } NAN_METHOD(BackgroundSubtractorWrap::Mixtures) { SETUP_FUNCTION(BackgroundSubtractorWrap); auto mog = getMOG(self); if (!mog) { Nan::ThrowError("Not using a BackgroundSubtractorMOG"); } // only support for V3+ with opencv-contrib #ifdef HAVE_OPENCV_BGSEGM if (info.Length() > 0) { mog->setNMixtures(info[0].As()->Value()); } info.GetReturnValue().Set(mog->getNMixtures()); #else info.GetReturnValue().Set(Nan::Null()); #endif } BackgroundSubtractorWrap::BackgroundSubtractorWrap( cv::Ptr _subtractor) { subtractor = _subtractor; } #endif node-opencv/src/Matrix.h0000755000175000017500000001067513671667364013654 0ustar devdev #include "OpenCV.h" #include "../inc/Matrix.h" class Matrix: public node_opencv::Matrix{ public: static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); static Local CreateWrappedFromMat(cv::Mat mat); static Local CreateWrappedFromMatIfNotReferenced(cv::Mat mat, int baseRefCount); int getWrappedRefCount(); void setMat(cv::Mat mat); Matrix(); Matrix(Matrix *other); Matrix(cv::Mat other, cv::Rect roi); Matrix(int rows, int cols); Matrix(int rows, int cols, int type); Matrix(int rows, int cols, int type, Local scalarObj); ~Matrix(); static double DblGet(cv::Mat mat, int i, int j); JSFUNC(Zeros) // factory JSFUNC(Ones) // factory JSFUNC(Eye) // factory JSFUNC(Get) // at JSFUNC(GetPixel) JSFUNC(Set) JSFUNC(Put) JSFUNC(GetData) JSFUNC(Normalize) JSFUNC(Brightness) JSFUNC(Norm) JSFUNC(CopyMakeBorder) JSFUNC(Row) JSFUNC(PixelRow) JSFUNC(Col) JSFUNC(PixelCol) JSFUNC(Size) JSFUNC(Width) JSFUNC(Height) JSFUNC(Type) JSFUNC(Channels) JSFUNC(Clone) JSFUNC(Ellipse) JSFUNC(Rectangle) JSFUNC(Line) JSFUNC(Empty) JSFUNC(FillPoly) JSFUNC(Save) JSFUNC(SaveAsync) JSFUNC(ToBuffer) JSFUNC(ToBufferAsync) JSFUNC(Resize) JSFUNC(Rotate) JSFUNC(GetRotationMatrix2D) JSFUNC(WarpAffine) JSFUNC(PyrDown) JSFUNC(PyrUp) JSFUNC(ConvertGrayscale) JSFUNC(ConvertHSVscale) JSFUNC(GaussianBlur) JSFUNC(MedianBlur) JSFUNC(BilateralFilter) JSFUNC(Sobel) JSFUNC(Copy) JSFUNC(Flip) JSFUNC(ROI) JSFUNC(Ptr) JSFUNC(AbsDiff) JSFUNC(Dct) JSFUNC(Idct) JSFUNC(AddWeighted) JSFUNC(Add) JSFUNC(BitwiseXor) JSFUNC(BitwiseNot) JSFUNC(BitwiseAnd) JSFUNC(CountNonZero) //JSFUNC(Split) JSFUNC(Moments) JSFUNC(Canny) JSFUNC(Dilate) JSFUNC(Erode) JSFUNC(FindContours) JSFUNC(DrawContour) JSFUNC(DrawAllContours) // Feature Detection JSFUNC(GoodFeaturesToTrack) #ifdef HAVE_OPENCV_VIDEO JSFUNC(CalcOpticalFlowPyrLK) #endif JSFUNC(HoughLinesP) JSFUNC(HoughCircles) JSFUNC(Crop) JSFUNC(inRange) JSFUNC(LocateROI) JSFUNC(AdjustROI) JSFUNC(Threshold) JSFUNC(AdaptiveThreshold) JSFUNC(MeanStdDev) JSFUNC(CopyTo) JSFUNC(ConvertTo) JSFUNC(CvtColor) JSFUNC(Split) JSFUNC(Merge) JSFUNC(EqualizeHist) JSFUNC(Pixel) JSFUNC(FloodFill) JSFUNC(MatchTemplate) JSFUNC(MatchTemplateByMatrix) JSFUNC(TemplateMatches) JSFUNC(MinMaxLoc) JSFUNC(PushBack) JSFUNC(PutText) JSFUNC(GetPerspectiveTransform) JSFUNC(WarpPerspective) JSFUNC(CopyWithMask) JSFUNC(SetWithMask) JSFUNC(MeanWithMask) JSFUNC(Mean) JSFUNC(Shift) JSFUNC(Reshape) // leave this out - can't see a way it could be useful to us, as release() always completely forgets the data //JSFUNC(Addref) JSFUNC(Release) JSFUNC(GetrefCount) JSFUNC(Subtract) JSFUNC(Compare) JSFUNC(Mul) JSFUNC(Div) JSFUNC(Pow) /* static Handle Val(const Arguments& info); static Handle RowRange(const Arguments& info); static Handle ColRange(const Arguments& info); static Handle Diag(const Arguments& info); static Handle Clone(const Arguments& info); static Handle CopyTo(const Arguments& info); static Handle ConvertTo(const Arguments& info); static Handle AssignTo(const Arguments& info); static Handle SetTo(const Arguments& info); static Handle Reshape(const Arguments& info); static Handle Transpose(const Arguments& info); static Handle Invert(const Arguments& info); static Handle Multiply(const Arguments& info); static Handle Cross(const Arguments& info); static Handle Dot(const Arguments& info); static Handle Zeroes(const Arguments& info); static Handle Ones(const Arguments& info); // create, increment, release static Handle PushBack(const Arguments& info); static Handle PopBack(const Arguments& info); static Handle Total(const Arguments& info); static Handle IsContinous(const Arguments& info); static Handle Type(const Arguments& info); static Handle Depth(const Arguments& info); static Handle Channels(const Arguments& info); static Handle StepOne(const Arguments& info); static Handle GetPerspectiveTransform(const Arguments& info); static Handle WarpPerspective(const Arguments& info); */ }; node-opencv/src/BackgroundSubtractor.h0000644000175000017500000000162413671667364016527 0ustar devdev#include "OpenCV.h" #ifdef HAVE_OPENCV_VIDEO #if ((CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION >=4)) || (CV_MAJOR_VERSION >= 3) #define HAVE_BACKGROUNDSUBTRACTOR #include #ifdef HAVE_OPENCV_BGSEGM #include #endif class BackgroundSubtractorWrap: public Nan::ObjectWrap { public: cv::Ptr subtractor; cv::Mutex applymutex; static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); BackgroundSubtractorWrap(cv::Ptr bg); static NAN_METHOD(CreateMOG); static NAN_METHOD(CreateMOG2); static NAN_METHOD(CreateGMG); static NAN_METHOD(ApplyMOG); static NAN_METHOD(Apply); static NAN_METHOD(History); static NAN_METHOD(Mixtures); static NAN_METHOD(NoiseSigma); static NAN_METHOD(BackgroundRatio); }; #endif #endif node-opencv/src/CamShift.cc0000644000175000017500000001320413671667364014230 0ustar devdev#include "CamShift.h" #include "OpenCV.h" #include "Matrix.h" #ifdef HAVE_OPENCV_VIDEO #if CV_MAJOR_VERSION >= 3 #include #endif #if CV_MAJOR_VERSION >= 4 #include #endif #define CHANNEL_HUE 0 #define CHANNEL_SATURATION 1 #define CHANNEL_VALUE 2 Nan::Persistent TrackedObject::constructor; void TrackedObject::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(TrackedObject::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("TrackedObject").ToLocalChecked()); // Prototype // Local proto = constructor->PrototypeTemplate(); Nan::SetPrototypeMethod(ctor, "track", Track); target->Set(Nan::New("TrackedObject").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NAN_METHOD(TrackedObject::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { JSTHROW_TYPE("Cannot Instantiate without new") } Matrix* m = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::Rect r; int channel = CHANNEL_HUE; if (info[1]->IsArray()) { Local v8rec = info[1]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); r = cv::Rect( v8rec->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8rec->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8rec->Get(2)->IntegerValue( Nan::GetCurrentContext() ).ToChecked() - v8rec->Get(0)->IntegerValue( Nan::GetCurrentContext() ).ToChecked(), v8rec->Get(3)->IntegerValue( Nan::GetCurrentContext() ).ToChecked() - v8rec->Get(1)->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } else { JSTHROW_TYPE("Must pass rectangle to track") } if (info[2]->IsObject()) { Local opts = info[2]->ToObject(Nan::GetCurrentContext()).ToLocalChecked(); if (opts->Get(Nan::New("channel").ToLocalChecked())->IsString()) { v8::String::Utf8Value c(v8::Isolate::GetCurrent(),opts->Get(Nan::New("channel").ToLocalChecked())->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local())); std::string cc = std::string(*c); if (cc == "hue" || cc == "h") { channel = CHANNEL_HUE; } if (cc == "saturation" || cc == "s") { channel = CHANNEL_SATURATION; } if (cc == "value" || cc == "v") { channel = CHANNEL_VALUE; } } } TrackedObject *to = new TrackedObject(m->mat, r, channel); to->Wrap(info.This()); info.GetReturnValue().Set(info.This()); } void update_chann_image(TrackedObject* t, cv::Mat image) { // Store HSV Hue Image cv::cvtColor(image, t->hsv, CV_BGR2HSV); // convert to HSV space // Mask out-of-range values int vmin = 65, vmax = 256, smin = 55; cv::inRange(t->hsv, //source cv::Scalar(0, smin, MIN(vmin, vmax), 0), //lower bound cv::Scalar(180, 256, MAX(vmin, vmax), 0), //upper bound t->mask); //destination // Extract the hue channel, split: src, dest channels std::vector hsvplanes; cv::split(t->hsv, hsvplanes); t->hue = hsvplanes[t->channel]; } TrackedObject::TrackedObject(cv::Mat image, cv::Rect rect, int chan) { channel = chan; update_chann_image(this, image); prev_rect = rect; // Calculate Histogram int hbins = 30, sbins = 32; int histSizes[] = { hbins, sbins }; // float hranges[] = { 0, 180 }; // saturation varies from 0 (black-gray-white) to // 255 (pure spectrum color) float sranges[] = { 0, 256 }; const float* ranges[] = { sranges }; cv::Mat hue_roi = hue(rect); cv::Mat mask_roi = mask(rect); cv::calcHist(&hue_roi, 1, 0, mask_roi, hist, 1, histSizes, ranges, true, false); } NAN_METHOD(TrackedObject::Track) { SETUP_FUNCTION(TrackedObject) if (info.Length() != 1) { Nan::ThrowTypeError("track takes an image param"); return; } Matrix *im = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); cv::RotatedRect r; if ((self->prev_rect.x < 0) || (self->prev_rect.y < 0) || (self->prev_rect.width <= 1) || (self->prev_rect.height <= 1)) { return Nan::ThrowTypeError("OPENCV ERROR: prev rectangle is illogical"); } update_chann_image(self, im->mat); cv::Rect backup_prev_rect = cv::Rect(self->prev_rect.x, self->prev_rect.y, self->prev_rect.width, self->prev_rect.height); float sranges[] = { 0, 256 }; const float* ranges[] = { sranges }; int channel = 0; cv::calcBackProject(&self->hue, 1, &channel, self->hist, self->prob, ranges); r = cv::CamShift(self->prob, self->prev_rect, cv::TermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1)); cv::Rect bounds = r.boundingRect(); if (bounds.x >= 0 && bounds.y >= 0 && bounds.width > 1 && bounds.height > 1) { self->prev_rect = bounds; } else { // printf("CRAP> %i %i %i %i ;", self->prev_rect.x, self->prev_rect.y, // self->prev_rect.width, self->prev_rect.height); // We have encountered a bug in opencv. Somehow the prev_rect has got // mangled, so we must reset it to a good value. self->prev_rect = backup_prev_rect; } v8::Local arr = Nan::New(4); arr->Set(0, Nan::New(bounds.x)); arr->Set(1, Nan::New(bounds.y)); arr->Set(2, Nan::New(bounds.x + bounds.width)); arr->Set(3, Nan::New(bounds.y + bounds.height)); /* cv::Point2f pts[4]; r.points(pts); for (int i = 0; i < 8; i += 2) { arr->Set(i, Nan::New(pts[i].x)); arr->Set(i + 1, Nan::New(pts[i].y)); } */ info.GetReturnValue().Set(arr); } #endif node-opencv/src/HighGUI.cc0000644000175000017500000000555513671667364013770 0ustar devdev#include "HighGUI.h" #include "OpenCV.h" #include "Matrix.h" #ifdef HAVE_OPENCV_HIGHGUI Nan::Persistent NamedWindow::constructor; void NamedWindow::Init(Local target) { Nan::HandleScope scope; // Constructor Local ctor = Nan::New(NamedWindow::New); constructor.Reset(ctor); ctor->InstanceTemplate()->SetInternalFieldCount(1); ctor->SetClassName(Nan::New("NamedWindow").ToLocalChecked()); // Prototype Nan::SetPrototypeMethod(ctor, "show", Show); Nan::SetPrototypeMethod(ctor, "destroy", Destroy); Nan::SetPrototypeMethod(ctor, "blockingWaitKey", BlockingWaitKey); Nan::SetPrototypeMethod(ctor, "resizeWindow", ResizeWindow); target->Set(Nan::New("NamedWindow").ToLocalChecked(), ctor->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); }; NAN_METHOD(NamedWindow::New) { Nan::HandleScope scope; if (info.This()->InternalFieldCount() == 0) { JSTHROW_TYPE("Cannot Instantiate without new") } NamedWindow* win; if (info.Length() == 1) { win = new NamedWindow(std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))), 0); } else { //if (info.Length() == 2){ win = new NamedWindow(std::string(*Nan::Utf8String(info[0]->ToString(Nan::GetCurrentContext()).FromMaybe(v8::Local()))), info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked()); } win->Wrap(info.Holder()); info.GetReturnValue().Set(info.Holder()); } NamedWindow::NamedWindow(const std::string& name, int f) { winname = std::string(name); flags = f; cv::namedWindow(winname, flags); } NAN_METHOD(NamedWindow::Show) { SETUP_FUNCTION(NamedWindow) Matrix *im = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked()); try { cv::imshow(self->winname, im->mat); } catch (cv::Exception& e) { const char* err_msg = e.what(); Nan::ThrowError(err_msg); } info.GetReturnValue().Set(info.Holder()); } NAN_METHOD(NamedWindow::Destroy) { SETUP_FUNCTION(NamedWindow) cv::destroyWindow(self->winname); info.GetReturnValue().Set(info.Holder()); } NAN_METHOD(NamedWindow::BlockingWaitKey) { int time = 0; if (info.Length() > 1) { time = info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } else { if (info.Length() > 0) { time = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); } } int res = cv::waitKey(time); info.GetReturnValue().Set(Nan::New(res)); } NAN_METHOD(NamedWindow::ResizeWindow) { SETUP_FUNCTION(NamedWindow) if (info.Length() != 2) { throw "expected 2 argurments: width, height"; }//otherwise int width = info[0]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); int height = info[1]->IntegerValue( Nan::GetCurrentContext() ).ToChecked(); cv::resizeWindow(self->winname, width, height); } #endif node-opencv/src/Histogram.h0000644000175000017500000000044313671667364014332 0ustar devdev#ifndef __NODE_HISTOGRAM_H #define __NODE_HISTOGRAM_H #include "OpenCV.h" /** * Implementation of histogram.hpp functions */ class Histogram: public Nan::ObjectWrap { public: static void Init(Local target); static NAN_METHOD(CalcHist); static NAN_METHOD(Emd); }; #endif node-opencv/src/Constants.h0000644000175000017500000000016413671667364014351 0ustar devdev#include "OpenCV.h" class Constants: public Nan::ObjectWrap { public: static void Init(Local target); }; node-opencv/src/LDAWrap.h0000644000175000017500000000056413671667364013633 0ustar devdev#include "OpenCV.h" #if ((CV_MAJOR_VERSION == 2) && (CV_MINOR_VERSION >=4)) #include "opencv2/contrib/contrib.hpp" class LDAWrap: public Nan::ObjectWrap { public: static Nan::Persistent constructor; static void Init(Local target); static NAN_METHOD(New); LDAWrap(); JSFUNC(SubspaceProject) JSFUNC(SubspaceReconstruct) }; #endif node-opencv/test/0000755000175000017500000000000013671667364012413 5ustar devdevnode-opencv/test/nativemat.cc0000644000175000017500000000132613671667364014714 0ustar devdev#include #include void Size(const Nan::FunctionCallbackInfo& info) { // Unwrap the node-opencv Matrix object into a normal cv::Mat cv::Mat mat = Nan::ObjectWrap::Unwrap(Nan::To(info[0]).ToLocalChecked())->mat; v8::Local < v8::Array > arr = Nan::New(2); arr->Set(0, Nan::New(mat.size().height)); arr->Set(1, Nan::New(mat.size().width)); info.GetReturnValue().Set(arr); } void Init(v8::Local exports) { exports->Set(Nan::New("size").ToLocalChecked(), Nan::New(Size)->GetFunction( Nan::GetCurrentContext() ).ToLocalChecked()); } NODE_MODULE(test_nativemat, Init) node-opencv/test/memory.js0000644000175000017500000007561613671667364014300 0ustar devdevrequire("v8").setFlagsFromString('--expose_gc'); var gc = require("vm").runInNewContext('gc'); var fs = require('fs') , path = require('path') , test = require('tape') , cv = require('../lib/opencv'); var IMAGE_PATH = path.resolve(__dirname, '../examples/files', 'mona.png'); var VIDEO_PATH = path.resolve(__dirname, '../examples/files', 'motion.mov'); var TEMP_SAVE_PATH = path.resolve(__dirname, '../examples/tmp', 'out.jpg'); var TEMP_VIDEO_PATH = path.resolve(__dirname, '../examples/tmp', 'out.mp4'); // These tests check that every function that creates or modifies a Matrix handles its externally tracked memory correctly. // Since the memory tracker uses OpenCV's reference counting to determine when to tell Node about memory changes, // it is important that only Matrix objects that Javascript knows about retain references to internal OpenCV Mat objects. // Reference counts for newly created objects should ususally therefore be 1, and releasing them should alter the // externally tracked memory appropriately. // Note that garbage collection could run at any time, which could interfere with measurements of external memory, // so these tests manually run garbage collection as part of their setup to minimize this possibility. //******************** // Image Reading //******************** test("readImage", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = cv.readImage(IMAGE_PATH); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("readImage creating a new 100x100 matrix", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = cv.readImage(100,100); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("readImage from data buffer", t=>{ gc(); var buffer = fs.readFileSync(IMAGE_PATH); var startingMemory = process.memoryUsage().external; var image = cv.readImage(buffer); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("readImage (callback pattern)", t=>{ gc(); var startingMemory = process.memoryUsage().external; cv.readImage(IMAGE_PATH, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("readImage creating a new 100x100 matrix (callback pattern)", t=>{ gc(); var startingMemory = process.memoryUsage().external; cv.readImage(100, 100, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("readImage from data buffer (callback pattern)", t=>{ gc(); var buffer = fs.readFileSync(IMAGE_PATH); var startingMemory = process.memoryUsage().external; cv.readImage(buffer, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("readImageAsync", t=>{ gc(); var startingMemory = process.memoryUsage().external; cv.readImageAsync(IMAGE_PATH, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("readImageAsync creating a new 100x100 matrix", t=>{ gc(); var startingMemory = process.memoryUsage().external; cv.readImageAsync(100, 100, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("readImageAsync from data buffer", t=>{ gc(); var buffer = fs.readFileSync(IMAGE_PATH); var startingMemory = process.memoryUsage().external; cv.readImageAsync(buffer, (err, image) => { t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 1134000); //image is tracked as external memory image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("video capture async", t=>{ gc(); var vid = new cv.VideoCapture(path.resolve(__dirname, '../examples/files', 'motion.mov')); var startingMemory = process.memoryUsage().external; vid.read( (err, im) => { t.equal(im.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 545280); //image is tracked as external memory im.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("video capture sync", t=>{ gc(); var vid = new cv.VideoCapture(path.resolve(__dirname, '../examples/files', 'motion.mov')); var startingMemory = process.memoryUsage().external; var im = vid.readSync(); t.equal(im.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 545280); im.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); //******************** // Matrix Constructors //******************** // Base constructor, doesn't actually allocate any memory test("Matrix()", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(); t.equal(image.getrefCount(), -1); t.equal(process.memoryUsage().external - startingMemory, 0); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); // Constructor with a size test("Matrix(int, int)", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 120000); //100 * 100 * size of CV_32FC3 (12) image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); // Constructor with a size and type test("Matrix(int, int, type)", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC1); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 10000); //100 * 100 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); // Constructor with a size, type, and initial values test("Matrix(int, int, type, [values])", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); // Constructor with an existing matrix and a region of interest test("Matrix(Matrix, x, y, w, h)", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(originalImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var image = new cv.Matrix(originalImage, 25, 25, 50, 50); //this should share memory with the original t.equal(image.getrefCount(), 2); //so the refcount goes up t.equal(process.memoryUsage().external - startingMemory, 30000); //but the memory usage does not originalImage.release(); t.equal(originalImage.getrefCount(), -1); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix.Zeros", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = cv.Matrix.Zeros(100, 100); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //100 * 100 * size of CV_64FC1 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix.Ones", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = cv.Matrix.Ones(100, 100); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //100 * 100 * size of CV_64FC1 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix.Eye", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = cv.Matrix.Eye(100, 100); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 80000); //100 * 100 * size of CV_64FC1 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); //******************** // Matrix Functions //******************** test("Matrix clone", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(originalImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var image = originalImage.clone(); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 60000); originalImage.release(); t.equal(originalImage.getrefCount(), -1); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix crop", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(originalImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 t.equal(originalImage.height(), 100); var image = originalImage.crop(25, 25, 50, 50); //crops share memory with the original t.equal(originalImage.height(), 100); t.equal(image.height(), 50); t.equal(image.getrefCount(), 2); t.equal(process.memoryUsage().external - startingMemory, 30000); originalImage.release(); t.equal(originalImage.getrefCount(), -1); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); //ROI in this implementation is basically the same thing as crop test("Matrix roi", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(originalImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 t.equal(originalImage.height(), 100); var image = originalImage.roi(25, 25, 50, 50); //ROIs share memory with the original t.equal(originalImage.height(), 100); t.equal(image.height(), 50); t.equal(image.getrefCount(), 2); t.equal(process.memoryUsage().external - startingMemory, 30000); originalImage.release(); t.equal(originalImage.getrefCount(), -1); t.equal(image.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000); image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix convertGrayscale", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 originalImage.convertGrayscale(); t.equal(process.memoryUsage().external - startingMemory, 10000); //grayscale takes less space t.equal(originalImage.getrefCount(), 1); originalImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix sobel", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var resultImage = originalImage.sobel(cv.Constants.CV_16S, 1, 1); t.equal(process.memoryUsage().external - startingMemory, 90000); //our original 30k image plus our new 60k one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix copy", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var resultImage = originalImage.copy(0); t.equal(process.memoryUsage().external - startingMemory, 60000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix flip", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var resultImage = originalImage.flip(0); t.equal(process.memoryUsage().external - startingMemory, 60000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix dct", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_32F); t.equal(process.memoryUsage().external - startingMemory, 40000); //100 * 100 * 4 var resultImage = originalImage.dct(); t.equal(process.memoryUsage().external - startingMemory, 80000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix idct", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_32F); t.equal(process.memoryUsage().external - startingMemory, 40000); //100 * 100 * 4 var resultImage = originalImage.idct(); t.equal(process.memoryUsage().external - startingMemory, 80000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix add", t=>{ gc(); var startingMemory = process.memoryUsage().external; var src1 = new cv.Matrix.Ones(100, 100); var src2 = new cv.Matrix.Ones(100, 100); t.equal(process.memoryUsage().external - startingMemory, 160000); var resultMatrix = src1.add(src2); t.equal(resultMatrix.get(0,0), 2); //just making sure the result is correct t.equal(process.memoryUsage().external - startingMemory, 240000); t.equal(src1.getrefCount(), 1); t.equal(src2.getrefCount(), 1); t.equal(resultMatrix.getrefCount(), 1); src1.release(); src2.release(); resultMatrix.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix resize", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.resize(50, 50); t.equal(process.memoryUsage().external - startingMemory, 7500); //50 * 50 * 3 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix resize async", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.resize(50, 50, (err, resizedImage)=>{ t.equal(image.height(), 100); //we have both the original and the resized image t.equal(resizedImage.height(), 50); t.equal(image.getrefCount(), 1); t.equal(resizedImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 37500); image.release(); resizedImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("Matrix resize async edge case", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 image.resize(50, 50, (err, resizedImage)=>{ t.equal(image.getrefCount(), -1); //this happens second, image should have been released already t.equal(resizedImage.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 7500); resizedImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); image.release(); //this happens first }); test("Matrix threshold", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8U); t.equal(process.memoryUsage().external - startingMemory, 10000); //100 * 100 var resultImage = originalImage.threshold(1,1); t.equal(process.memoryUsage().external - startingMemory, 20000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix adaptiveThreshold", t=>{ gc(); var startingMemory = process.memoryUsage().external; var originalImage = new cv.Matrix(100, 100, cv.Constants.CV_8U); t.equal(process.memoryUsage().external - startingMemory, 10000); //100 * 100 var resultImage = originalImage.adaptiveThreshold(255, 0, 0, 15, 2); t.equal(process.memoryUsage().external - startingMemory, 20000); //our original image plus our new one t.equal(originalImage.getrefCount(), 1); t.equal(resultImage.getrefCount(), 1); originalImage.release(); resultImage.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix meanStdDev", t=>{ gc(); var startingMemory = process.memoryUsage().external; var matrix = new cv.Matrix.Ones(100, 100, cv.Constants.CV_8UC3); t.equal(process.memoryUsage().external - startingMemory, 30000); var result = matrix.meanStdDev(); t.equal(result.mean.getrefCount(), 1); t.equal(result.stddev.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30048); matrix.release(); result.mean.release(); result.stddev.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix copyTo", t=>{ gc(); var startingMemory = process.memoryUsage().external; var smallImg = new cv.Matrix.Ones(25, 25, cv.Constants.CV_8UC3); var bigImg = cv.Matrix.Zeros(100, 100, cv.Constants.CV_8UC3); t.equal(process.memoryUsage().external - startingMemory, 30000 + 1875); smallImg.copyTo(bigImg, 0, 0); t.equal(smallImg.getrefCount(), 1); t.equal(bigImg.getrefCount(), 1); t.equal(process.memoryUsage().external - startingMemory, 30000 + 1875); smallImg.release(); bigImg.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix cvtColor", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.cvtColor("CV_BGR2GRAY"); t.equal(process.memoryUsage().external - startingMemory, 10000); //grayscale is smaller image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix split", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var result = image.split(); t.equal(process.memoryUsage().external - startingMemory, 60000); image.release(); t.equal(process.memoryUsage().external - startingMemory, 30000); result.forEach(m => m.release()); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix merge", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image1 = new cv.Matrix(100, 100, cv.Constants.CV_8UC1, [0]); var image2 = new cv.Matrix(100, 100, cv.Constants.CV_8UC1, [0]); var image3 = new cv.Matrix(100, 100, cv.Constants.CV_8UC1, [0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var result = new cv.Matrix(10,10); t.equal(process.memoryUsage().external - startingMemory, 30000 + 1200); result.merge([image1, image2, image3]); t.equal(process.memoryUsage().external - startingMemory, 60000); result.release(); image1.release(); image2.release(); image3.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix reshape", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 var result = image.reshape(2); t.equal(process.memoryUsage().external - startingMemory, 30000); //reshape does not copy data, so the allocated size hasn't changed image.release(); t.equal(process.memoryUsage().external - startingMemory, 30000); result.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix pyrDown", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.pyrDown(); t.equal(process.memoryUsage().external - startingMemory, 7500); //50 * 50 * 3 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); test("Matrix pyrUp", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(100, 100, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 30000); //100 * 100 * 3 image.pyrUp(); t.equal(process.memoryUsage().external - startingMemory, 120000); //200 * 200 * 3 image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); //******************** // Additional Asynchronous Matrix Functions //******************** test("Matrix toBuffer async edge case", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 image.toBufferAsync((err, buffer)=>{ t.equal(image.getrefCount(), -1); //this happens second, image should have been released already var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 33006); //the size of the buffer, which hasn't been released yet t.end(); }); image.release(); }); test("Matrix save async edge case", t=>{ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 image.saveAsync(TEMP_SAVE_PATH, (err, buffer)=>{ t.equal(image.getrefCount(), -1); //this happens second, image should have been released already var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); fs.unlinkSync(TEMP_SAVE_PATH); t.end(); }); image.release(); }); //******************** // Background Subtractors //******************** function testSyncBackgroundSubtractor(t, subtractor){ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 var output = subtractor.apply(image); t.equal(image.getrefCount(), 1); t.equal(output.getrefCount(), 1); image.release(); output.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); } function testAsyncBackgroundSubtractor(t, subtractor){ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 subtractor.apply(image, (err, output) => { t.equal(image.getrefCount(), 1); t.equal(output.getrefCount(), 1); image.release(); output.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); } function testAsyncBackgroundSubtractorEarlyRelease(t, subtractor){ gc(); var startingMemory = process.memoryUsage().external; var image = new cv.Matrix(1000, 1000, cv.Constants.CV_8UC3, [0,0,0]); t.equal(process.memoryUsage().external - startingMemory, 3000000); //100 * 100 * 3 subtractor.apply(image, (err, output) => { t.equal(image.getrefCount(), -1); t.equal(output.getrefCount(), 1); output.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); image.release(); } test("default background subtractor", t=>{ testSyncBackgroundSubtractor(t, new cv.BackgroundSubtractor()); }); test("MOG background subtractor", t=>{ testSyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createMOG()); }); test("MOG2 background subtractor", t=>{ testSyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createMOG2()); }); test("GMG background subtractor", t=>{ testSyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createGMG()); }); test("default background subtractor async", t=>{ testAsyncBackgroundSubtractor(t, new cv.BackgroundSubtractor()); }); test("MOG background subtractor async", t=>{ testAsyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createMOG()); }); test("MOG2 background subtractor async", t=>{ testAsyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createMOG2()); }); test("GMG background subtractor async", t=>{ testAsyncBackgroundSubtractor(t, cv.BackgroundSubtractor.createGMG()); }); test("default background subtractor async early release", t=>{ testAsyncBackgroundSubtractorEarlyRelease(t, new cv.BackgroundSubtractor()); }); test("MOG background subtractor async early release", t=>{ testAsyncBackgroundSubtractorEarlyRelease(t, cv.BackgroundSubtractor.createMOG()); }); test("MOG2 background subtractor async early release", t=>{ testAsyncBackgroundSubtractorEarlyRelease(t, cv.BackgroundSubtractor.createMOG2()); }); test("GMG background subtractor async early release", t=>{ testAsyncBackgroundSubtractorEarlyRelease(t, cv.BackgroundSubtractor.createGMG()); }); //******************** // cascade classifier //******************** test("cascade classifier async", t=> { gc(); var startingMemory = process.memoryUsage().external; var image = new cv.readImage(path.resolve(__dirname, '../examples/files', 'mona.png')); t.equal(process.memoryUsage().external - startingMemory, 1134000); //100 * 100 * 3 var classifier = new cv.CascadeClassifier(cv.FACE_CASCADE); classifier.detectMultiScale(image, (err, faces) =>{ t.equal(image.getrefCount(), 1); image.release(); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); }); test("cascade classifier async early release", t=> { gc(); var startingMemory = process.memoryUsage().external; var image = new cv.readImage(path.resolve(__dirname, '../examples/files', 'mona.png')); t.equal(process.memoryUsage().external - startingMemory, 1134000); var classifier = new cv.CascadeClassifier(cv.FACE_CASCADE); classifier.detectMultiScale(image, (err, faces) =>{ t.equal(image.getrefCount(), -1); var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); t.end(); }); image.release(); }); //******************** // VideoWriter //******************** test("Video writer async edge case", t=>{ gc(); var startingMemory = process.memoryUsage().external; var reader = new cv.VideoCapture(VIDEO_PATH); reader.read((err, image)=>{ t.equal(process.memoryUsage().external - startingMemory, 545280); var writer = new cv.VideoWriter(TEMP_VIDEO_PATH, 'mp4v', 1, image.size(), true); writer.write(image, err=>{ t.equal(image.getrefCount(), -1); //this happens second, image should have been released already var endingMemory = process.memoryUsage().external; t.equal(endingMemory - startingMemory, 0); writer.release(); fs.unlinkSync(TEMP_VIDEO_PATH); t.end(); }); image.release(); }); }); //******************** // Face Recognizer //******************** node-opencv/test/Dockerfile-ubuntu-14-040000644000175000017500000000104213671667364016365 0ustar devdev# This is a dockerfile to test the build on ubuntu 14.04 from ubuntu:14.04 run apt-get update -qq run apt-get install -y software-properties-common python-software-properties run add-apt-repository -y ppa:kubuntu-ppa/backports run apt-get update run apt-get install -y libcv-dev libcvaux-dev libhighgui-dev libopencv-dev run curl -sL https://deb.nodesource.com/setup_12.x | bash - run apt-get install -y nodejs WORKDIR /root/node-opencv add . /root/node-opencv run npm install --unsafe-perm --build-from-source || cat npm-debug.log run make test node-opencv/test/examples.js0000644000175000017500000000077413671667364014577 0ustar devdevvar test = require('tape') , glob = require('glob') , exec = require('child_process').exec , path = require('path') module.exports = function(){ glob.sync('./examples/*.js').forEach(function(example){ test("Example: " + example, function(assert){ var fullName = path.resolve(example) , examples = path.resolve('./examples') exec('node ' + fullName, {cwd: examples}, function(error, stdout, stderr){ assert.error(error) assert.end() }) }) }) } node-opencv/test/unit.js0000755000175000017500000004052013671667364013734 0ustar devdevvar fs = require('fs') , test = require('tape') , cv = null test("Smoke tests / Can Import", function(t){ cv = require('../lib/opencv') t.ok(cv, "imported fine") t.ok(cv.version, "version is there:" + cv.version) t.ok(cv.Point, "point is there") t.ok(cv.Matrix, "matrix is there") t.end() }) test('importing library multiple times is ok', function(t){ var cv1 = require('../lib/opencv') , cv2 = require('../lib/opencv') cv1.readImage('./examples/files/mona.png', function(err, im){ t.error(err) cv2.readImage('./examples/files/mona.png', function(err, im){ t.error(err) t.end(); }); }); }) test('Point', function(t){ t.ok(new cv.Point(1, 2)) t.throws(function () { cv.Point(1, 2)}, TypeError, "cannot call without new") t.equal(new cv.Point(1, 2).x, 1) t.equal(new cv.Point(1, 2).y, 2) t.equal(Math.round(new cv.Point(1.1, 2).x * 100), 110) t.equal(Math.round(new cv.Point(1.2, 2.75).y *100), 275) t.throws(function () {new cv.Point(1.1, 2).x = 5}, Error, "Points are immutable") t.throws(function () {new cv.Point(1.1, 2).y = 5}, Error, "Points are immutable") var p1 = new cv.Point(3, 6) , p2 = new cv.Point(5, 7) t.ok(p1.dot); t.equal(p1.dot(p2), 57); t.end() }) test('Matrix constructor', function(assert){ assert.ok(cv.Matrix); assert.ok(new cv.Matrix); assert.ok(new cv.Matrix(1, 2)); assert.ok(new cv.Matrix(1, 2, cv.Constants.CV_8U)); assert.ok(new cv.Matrix(1, 2, cv.Constants.CV_8U, [1])); assert.end() }) test('Matrix accessors', function(assert){ var mat = new cv.Matrix(1, 2); mat.set(0,0,3); mat.set(0,1,5000); assert.deepEqual(mat.row(0), [3,5000]); mat = new cv.Matrix(1,2); assert.equal(mat.set(0,0,3), undefined); assert.equal(mat.get(0,0), 3); mat = new cv.Matrix(6,7); assert.equal(mat.width(), 7); mat = new cv.Matrix(6,7); assert.equal(mat.height(), 6); mat = new cv.Matrix(6,7); assert.deepEqual(mat.size(), [6, 7]); mat = new cv.Matrix(6,7); assert.equal(mat.width(), 7); mat.resize(8,9); assert.equal(mat.width(), 8); mat = new cv.Matrix.Eye(4,4) assert.deepEqual(mat.row(1), [0,1,0,0]) assert.deepEqual(mat.row(2), [0,0,1,0]) mat = new cv.Matrix.Eye(4,4); assert.deepEqual(mat.col(1), [0,1,0,0]) assert.deepEqual(mat.col(2), [0,0,1,0]) assert.equal(new cv.Matrix().empty(), true); mat = new cv.Matrix(1, 2, cv.Constants.CV_8UC3, [1, 2, 3]); assert.deepEqual(mat.pixelRow(0), [1, 2, 3, 1, 2, 3]); assert.end() }) test('Matrix functions', function(assert) { // convertTo var mat = new cv.Matrix(75, 75, cv.Constants.CV_32F, [2.0]); var matNew = new cv.Matrix(75, 75, cv.Constants.CV_8U); var alpha = 2; var beta = 1; mat.convertTo(matNew, cv.Constants.CV_8U, alpha, beta); assert.equal(matNew.pixel(0, 0), mat.get(0, 0)*alpha + beta); // reshape mat = new cv.Matrix(75, 75, cv.Constants.CV_8UC1); matNew = mat.reshape(1, 1); assert.equal(matNew.height(), 1); assert.equal(matNew.width(), 5625); // GetRotationMatrix2D mat = cv.Matrix.getRotationMatrix2D(0, 0, 90, 1.0); assert.deepEqual(mat.size(), [2,3], 'GetRotationMatrix2D'); assert.end(); }) test(".norm", function(assert){ cv.readImage("./examples/files/coin1.jpg", function(err, im) { cv.readImage("./examples/files/coin2.jpg", function(err, im2){ assert.ok(im); assert.ok(im2); var errorL2 = im.norm(im2, cv.Constants.NORM_L2); assert.equal(errorL2, 7295.591339980605); errorL2 = im.norm(im, cv.Constants.NORM_L2); assert.equal(errorL2, 0); assert.end(); }); }); }) test("Matrix toBuffer", function(assert){ var buf = fs.readFileSync('./examples/files/mona.png') cv.readImage(buf.slice(0), function(err, mat){ var buf0 = mat.toBuffer() assert.ok(buf0); assert.end() }) }) test("Matrix toBuffer Async", function(assert){ var buf = fs.readFileSync('./examples/files/mona.png') cv.readImage(buf.slice(0), function(err, mat){ mat.toBuffer(function(err, buff){ assert.error(err) assert.ok(buf) assert.ok(buf.length > 100) assert.end() }) }) }) test("detectObject", function(assert){ cv.readImage("./examples/files/mona.png", function(err, im){ im.detectObject(cv.FACE_CASCADE, {}, function(err, faces){ assert.error(err) assert.ok(faces) assert.equal(faces.length, 1) assert.end() }) }) }) test(".absDiff and .countNonZero", function(assert){ cv.readImage("./examples/files/mona.png", function(err, im) { cv.readImage("./examples/files/mona.png", function(err, im2){ assert.ok(im); assert.ok(im2); var diff = new cv.Matrix(im.width(), im.height()); diff.absDiff(im, im2); diff.convertGrayscale(); assert.equal(diff.countNonZero(), 0); assert.end() }); }); }) test(".bitwiseXor", function(assert){ var mat1 = new cv.Matrix(1,1); mat1.set(0,0, 1); var mat2 = new cv.Matrix(1,1); mat2.set(0,0, 1); var xored = new cv.Matrix(1,1); xored.bitwiseXor(mat1, mat2); assert.equal(xored.get(0,0), 0); assert.end() }) test("Image read from file", function(assert){ cv.readImage("./examples/files/opencv.png", function(err, im){ assert.ok(im); assert.equal(im.width(), 82); assert.equal(im.height(), 99); assert.equal(im.channels(), 4); assert.equal(im.empty(), false); assert.end(); }) }) test("Multi-page image read from file", function(assert){ if (parseInt(cv.version) >= 3) { cv.readImageMulti("./examples/files/multipage.tif", function(err, imgs){ assert.ok(imgs); assert.equal(imgs.length, 10); for (var i = 0; i < imgs.length; i++) { assert.ok(imgs[i]); assert.equal(imgs[i].width(), 800); assert.equal(imgs[i].height(), 600); assert.equal(imgs[i].channels(), 3); assert.equal(imgs[i].empty(), false); } assert.end(); }) } else { assert.equal(cv.readImageMulti("./examples/files/multipage.tif"), false); assert.end(); } }) test("Distance transform", function(assert){ cv.readImage("./examples/files/distanceTransform.png", function(err, img){ assert.ok(img); // 50px image with single black pixel on right side var result = cv.imgproc.distanceTransform(img, cv.Constants.CV_DIST_L2, cv.Constants.CV_DIST_MASK_PRECISE); assert.equal(result.get(0,0), 49); assert.end(); }) }) test("read Image from buffer", function(assert){ cv.readImage(fs.readFileSync('./examples/files/opencv.png'), function(err, im){ assert.ok(im); assert.equal(im.width(), 82); assert.equal(im.height(), 99); assert.equal(im.channels(), 4); assert.equal(im.empty(), false); assert.end(); }) }) test("Cascade Classifier", function(assert){ assert.ok(new cv.CascadeClassifier("./data/haarcascade_frontalface_alt.xml"), 'test constructor') cv.readImage("./examples/files/mona.png", function(err, im){ cascade = new cv.CascadeClassifier("./data/haarcascade_frontalface_alt.xml"); cascade.detectMultiScale(im, function(err, faces){//, 1.1, 2, [30, 30]); assert.error(err); assert.equal(typeof faces, typeof []); assert.equal(faces.length, 1) assert.end() }) }) }) test("ImageDataStream", function(assert){ var s = new cv.ImageDataStream() s.on('load', function(im){ assert.ok(im) assert.equal(im.empty(), false); assert.end() }) fs.createReadStream('./examples/files/mona.png').pipe(s); }) test("ImageStream", function(assert){ var s = new cv.ImageStream() , im = fs.readFileSync('./examples/files/mona.png') s.on('data', function(mat){ assert.deepEqual(mat.size(), [756,500]) assert.end() }) s.write(im); }) test("CamShift", function(assert){ cv.readImage('./examples/files/coin1.jpg', function(e, im){ cv.readImage('./examples/files/coin2.jpg', function(e, im2){ var tracked = new cv.TrackedObject(im, [420, 110, 490, 170], {channel: 'v'}); assert.ok(tracked); var res = tracked.track(im2) assert.ok(res); assert.ok(res[0] < 396) assert.ok(res[0] > 376) assert.ok(res[1] < 122) assert.ok(res[1] > 102) assert.ok(res[2] < 469) assert.ok(res[2] > 449) assert.ok(res[3] < 176) assert.ok(res[3] > 156) assert.end() }) }) }) test("fonts", function(t) { function rnd() { return Math.round(Math.random() * 255); }; cv.readImage("./examples/files/coin1.jpg", function(e, im){ var y = 0; var scale = 1; ([ "HERSEY_SIMPLEX", "HERSEY_PLAIN", "HERSEY_DUPLEX", "HERSEY_COMPLEX", "HERSEY_TRIPLEX", "HERSEY_COMPLEX_SMALL", "HERSEY_SCRIPT_SIMPLEX", "HERSEY_SCRIPT_COMPLEX", "HERSEY_SCRIPT_SIMPLEX" ]).forEach(function(font) { im.putText("Some text", 0, y += 20, font, [rnd(), rnd(), rnd()], 2, scale++); }); t.ok(im, "image is ok"); im.save("./examples/tmp/coin1-with-text.jpg"); t.end(); }); }) test('LDA Wrap', function(assert) { if (cv.LDA === undefined) { console.log('TODO: Please port LDAWrap.cc to OpenCV 3') assert.end(); return; } // subspaceProject var mat = cv.LDA.subspaceProject(new cv.Matrix(1, 2, cv.Constants.CV_64F), new cv.Matrix(), new cv.Matrix(2, 1, cv.Constants.CV_8UC1)); assert.deepEqual(mat.size(), [2,2], 'subspaceProject'); // subspaceReconstruct mat = cv.LDA.subspaceReconstruct(new cv.Matrix(1, 2, cv.Constants.CV_64F), new cv.Matrix(), new cv.Matrix(1, 2, cv.Constants.CV_8UC1)); assert.deepEqual(mat.size(), [1,1], 'subspaceReconstruct'); assert.end(); }) test('Native Matrix', function(assert) { var nativemat = require('../build/' + (!!process.env.NODE_OPENCV_DEBUG ? 'Debug' : 'Release') + '/test_nativemat.node'); var mat = new cv.Matrix(42, 8); assert.deepEqual(mat.size(), nativemat.size(mat), 'nativemat'); assert.end(); }) test('Subtract', function(assert) { var a = new cv.Matrix.Zeros(1,1); a.set(0, 0, 3); var b = new cv.Matrix.Zeros(1,1); b.set(0, 0, 1); a.subtract(b); assert.deepEqual(a.get(0, 0), 2); assert.end(); }); test('Mean', function(assert) { var a = new cv.Matrix.Zeros(2, 2, cv.Constants.CV_8UC3); // Set [0, 0] element to 1 for all three channels a.set(0, 0, 1, 0); a.set(0, 0, 1, 1); a.set(0, 0, 1, 2); var means = a.mean(); assert.deepEqual(means, [0.25, 0.25, 0.25, 0]); assert.end(); }); test('Compare', function(assert) { var a = cv.Matrix.fromArray([[[0],[-20]],[[2],[-18]]], cv.Constants.CV_8SC1); var b = cv.Matrix.fromArray([[[3],[-20]],[[0],[-16]]], cv.Constants.CV_8SC1); var compare1 = a.compare(b, cv.Constants.CMP_EQ); var compare2 = a.compare(b, cv.Constants.CMP_GT); var compare3 = a.compare(b, cv.Constants.CMP_LE); assert.deepEqual(compare1.toArray(), [[[0],[255]],[[0],[0]]]); assert.deepEqual(compare2.toArray(), [[[0],[0]],[[255],[0]]]); assert.deepEqual(compare3.toArray(), [[[255],[255]],[[0],[255]]]); assert.end(); }); test('MatchTemplateByMatrix', function(assert) { var cv = require('../lib/opencv'); var targetFilename = "./examples/files/car1.jpg"; var templateFilename = "./examples/files/car1_template.jpg"; cv.readImage(targetFilename, function(err, target){ cv.readImage(templateFilename, function(err, template){ var TM_CCORR_NORMED = 3; var res = target.matchTemplateByMatrix(template, TM_CCORR_NORMED); var minMax = res.minMaxLoc(); var topLeft = minMax.maxLoc; assert.ok(topLeft, "RGB Found Match"); assert.equal(topLeft.x, 42, "match location x === 42"); assert.equal(topLeft.y, 263, "match location y === 263"); target.canny(5,300); template.canny(5,300); res = target.matchTemplateByMatrix(template, TM_CCORR_NORMED); minMax = res.minMaxLoc(); topLeft = minMax.maxLoc; assert.ok(topLeft, "Canny edge Found Match"); assert.equal(topLeft.x, 42, "match location x === 42"); assert.equal(topLeft.y, 263, "match location y === 263"); assert.end(); }); }) }); test('setColor works will alpha channels', function(assert) { var cv = require('../lib/opencv'); var mat = new cv.Matrix(100, 100, cv.Constants.CV_8UC4); var SQUARE = [ 50, 50 ]; mat.rectangle([ 0, 0 ], SQUARE, [ 0, 187, 255, 255 ], -1); mat.rectangle([ 0, 50 ], SQUARE, [ 0, 187, 124, 200 ], -1); mat.rectangle([ 50, 0 ], SQUARE, [ 241, 161, 0, 128 ], -1); mat.rectangle([ 50, 50 ], SQUARE, [ 20, 83, 246, 70 ], -1); cv.readImage('./examples/files/alpha-test.png', function(err, imgMat) { if (!err) { var diff = new cv.Matrix(); diff.absDiff(mat, imgMat); // We'll verify that each channel is 0 var channels = diff.split(); for (var i = 0; i < 4; i++) { assert.equal(channels[i].countNonZero(), 0); } } else { throw err; } assert.end(); }); }); test('getPixel',function(assert){ cv.readImage('./examples/files/alpha-test.png', function(err, imgMat) { if (!err) { assert.equal(imgMat.channels(),4); assert.deepEqual(imgMat.getPixel(10,10),[0,187,255,255]); assert.deepEqual(imgMat.getPixel(30,80),[241,161,0,128]); assert.deepEqual(imgMat.getPixel(80,30),[0,187,124,200]); assert.deepEqual(imgMat.getPixel(80,80),[20,83,246,70]); var channels = imgMat.split(); imgMat.merge([channels[0],channels[1],channels[2]]); assert.equal(imgMat.channels(),3); assert.deepEqual(imgMat.getPixel(10,10),[0,187,255]); assert.deepEqual(imgMat.getPixel(30,80),[241,161,0]); assert.deepEqual(imgMat.getPixel(80,30),[0,187,124]); assert.deepEqual(imgMat.getPixel(80,80),[20,83,246]); imgMat.merge([channels[3]]); assert.equal(imgMat.channels(),1); assert.deepEqual(imgMat.getPixel(0,0),255); assert.deepEqual(imgMat.getPixel(30,80),128); assert.deepEqual(imgMat.getPixel(80,30),200); assert.deepEqual(imgMat.getPixel(80,80),70); } else { throw err; } assert.end(); }); }); test('pixelRow pixelCol 4 channel image ',function(assert){ cv.readImage('./examples/files/alpha-small.png', function(err, imgMat) { if (!err) { assert.equal(imgMat.channels(),4); assert.deepEqual(imgMat.pixelCol(0), [ 0, 0, 0, 255, 0, 255, 0, 88, 0, 0, 0, 88 ]); assert.deepEqual(imgMat.pixelCol(1), [ 255, 255, 255, 255, 0, 0, 255, 88, 255, 255, 255, 88 ]); assert.deepEqual(imgMat.pixelRow(0), [ 0, 0, 0, 255, 255, 255, 255, 255, 0, 255, 0, 255, 255, 0, 0, 255, 0, 0, 255, 255, 229, 194, 201, 155 ]); assert.deepEqual(imgMat.pixelRow(1), [ 0, 255, 0, 88, 0, 0, 255, 88, 255, 0, 0, 88, 47, 40, 42, 155, 145, 66, 125, 227, 47, 100, 163, 72 ]); } else { throw err; } assert.end(); }); }); test('toArray/fromArray working in both ways', function(assert) { var cv = require('../lib/opencv'); cv.readImage("./examples/files/mona.png", function(err, orig) { if (err) throw err; var a = orig.toArray(); var type = orig.type(); var doubleConversion = cv.Matrix.fromArray(a, type).toArray(); var randomI = Math.floor(Math.random()*a.length) var randomJ = Math.floor(Math.random()*a[randomI].length) var randomK = Math.floor(Math.random()*a[randomI][randomJ].length) assert.equal(a[randomI][randomJ][randomK], doubleConversion[randomI][randomJ][randomK]); assert.end(); }); }); test('floodFill optional returned bounding rect', function(assert) { var cv = require('../lib/opencv'); var solidImage = new cv.Matrix( 10, 15, // Create 15px wide by 10px tall image cv.Constants.CV_8U); // Define output rect var rect = [ [0, 0], // x, y [0, 0] // width, height ]; // Set all pixels to black/0 for (var y=0; y{ cv.readImage(f, function (err, im) { // Assume all training photo are named as id_xxx.jpg let labelNumber = parseInt(path.basename(f).substring(3)); samples.push([labelNumber, im]); }) }) if ( samples.length > 3 ) { // There are async and sync version of training method: // .train(info, cb) // cb : standard Nan::Callback // info : [[intLabel,matrixImage],...]) // .trainSync(info) fr.trainSync(samples); fr.saveSync('./trained.xml'); }else { console.log('Not enough images uploaded yet', cvImages) } } function predictIt(fr, f){ cv.readImage(f, function (err, im) { let result = fr.predictSync(im); console.log(`recognize result:(${f}) id=${result.id} conf=${100.0-result.confidence}`); }); } //using defaults: .createLBPHFaceRecognizer(radius=1, neighbors=8, grid_x=8, grid_y=8, threshold=80) const fr = new cv.FaceRecognizer(); trainIt(fr); forEachFileInDir('./_bench', (f) => predictIt(fr, f)); ``` ## Test Using [tape](https://github.com/substack/tape). Run with command: `npm test`. ## Contributing I (@peterbraden) don't spend much time maintaining this library, it runs primarily on contributor support. I'm happy to accept most PR's if the tests run green, all new functionality is tested, and there are no objections in the PR. Because I haven't got much time for maintenance, I'd prefer to keep an absolute minimum of dependencies. ## MIT License The library is distributed under the MIT License - if for some reason that doesn't work for you please get in touch. node-opencv/.github/0000755000175000017500000000000013671667363012773 5ustar devdevnode-opencv/.github/workflows/0000755000175000017500000000000013671667363015030 5ustar devdevnode-opencv/.github/workflows/test.yml0000644000175000017500000000035313671667363016533 0ustar devdevname: CI on: [push] jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v1 - name: Test Ubuntu run: docker build -t peterbraden/node-opencv-ubuntu-14-04 -f test/Dockerfile-ubuntu-14-04 . node-opencv/.github/ISSUE_TEMPLATE/0000755000175000017500000000000013671667363015156 5ustar devdevnode-opencv/.github/ISSUE_TEMPLATE/feature_request.md0000644000175000017500000000112313671667363020700 0ustar devdev--- name: Feature request about: Suggest an idea for this project title: '' labels: '' assignees: '' --- **Is your feature request related to a problem? Please describe.** A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] **Describe the solution you'd like** A clear and concise description of what you want to happen. **Describe alternatives you've considered** A clear and concise description of any alternative solutions or features you've considered. **Additional context** Add any other context or screenshots about the feature request here. node-opencv/.github/ISSUE_TEMPLATE/bug_report.md0000644000175000017500000000114313671667363017647 0ustar devdev--- name: Bug report about: Please only create if you are using the latest version. title: "[BUG]" labels: bug assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: **Expected behavior** A clear and concise description of what you expected to happen. **Screenshots** If applicable, add screenshots to help explain your problem. **OS / Libraries** - Operating System - OpenCV Version - ONLY CREATE IF YOU ARE USING THE LATEST VERSION OF NODE-OPENCV **Additional context** Add any other context about the problem here. node-opencv/utils/0000755000175000017500000000000013671667364012574 5ustar devdevnode-opencv/utils/find-opencv.js0000644000175000017500000000517113671667364015346 0ustar devdev"use strict"; var exec = require("child_process").exec; var fs = require("fs"); var flags = { '--cflags' : '--cflags', '--libs' : '--libs' } var flag = flags[process.argv[2]] || '--exists' // Normally |pkg-config opencv ...| could report either OpenCV 2.x or OpenCV 3.y // depending on what is installed. To enable both 2.x and 3.y to co-exist on // the same machine, the opencv.pc for 3.y can be installed as opencv3.pc and // then selected by |export PKG_CONFIG_OPENCV3=1| before building node-opencv. var opencv = process.env.PKG_CONFIG_OPENCV3 === "1" ? "opencv3" : ' "opencv >= 2.3.1"'; opencv = process.env.PKG_CONFIG_OPENCV4 === "1" ? "opencv4" : opencv; function main(){ //Try using pkg-config, but if it fails and it is on Windows, try the fallback exec("pkg-config " + opencv + " " + flag, function(error, stdout, stderr){ if(error){ if(process.platform === "win32"){ fallback(); } else{ throw new Error("ERROR: failed to run: pkg-config" + opencv + " " + flag + " - Is OpenCV installed?"); } } else{ console.log(stdout); } }); } //======================Windows Specific======================================= function fallback(){ exec("echo %OPENCV_DIR%", function(error, stdout, stderr){ stdout = cleanupEchoOutput(stdout); if(error){ throw new Error("ERROR: There was an error reading OPENCV_DIR"); } else if(stdout === "%OPENCV_DIR%") { throw new Error("ERROR: OPENCV_DIR doesn't seem to be defined"); } else { printPaths(stdout); } }); } function printPaths(opencvPath){ if(flag === "--cflags") { console.log("\"" + opencvPath + "\\..\\..\\include\""); console.log("\"" + opencvPath + "\\..\\..\\include\\opencv\""); } else if(flag === "--libs") { var libPath = opencvPath + "\\lib\\"; fs.readdir(libPath, function(err, files){ if(err){ throw new Error("ERROR: couldn't read the lib directory " + err); } var libs = ""; for(var i = 0; i < files.length; i++){ if(getExtension(files[i]) === "lib"){ libs = libs + " \"" + libPath + files[i] + "\" \r\n "; } } console.log(libs); }); } else { throw new Error("Error: unknown argument '" + flag + "'"); } } function cleanupEchoOutput(s){ return s.slice(0, s.length - 2); } function getExtension(s){ return s.substr(s.lastIndexOf(".") + 1); } main(); node-opencv/utils/opencv_x86.pc0000644000175000017500000000151613671667364015122 0ustar devdev# Package Information for pkg-config opencv_prefix=C:/OpenCV249/opencv/build/x86/vc12 exec_prefix=${opencv_prefix}/bin libdir=${opencv_prefix}/lib includedir=C:/OpenCV249/opencv/build/include Name: OpenCV Description: Open Source Computer Vision Library Version: 2.4.9 Cflags: ${includedir} ${includedir}/opencv Libs: ${libdir}/opencv_calib3d249 ${libdir}/opencv_contrib249 ${libdir}/opencv_core249 ${libdir}/opencv_features2d249 ${libdir}/opencv_flann249 ${libdir}/opencv_gpu249 ${libdir}/opencv_highgui249 ${libdir}/opencv_imgproc249 ${libdir}/opencv_legacy249 ${libdir}/opencv_ml249 ${libdir}/opencv_nonfree249 ${libdir}/opencv_objdetect249 ${libdir}/opencv_ocl249 ${libdir}/opencv_photo249 ${libdir}/opencv_stitching249 ${libdir}/opencv_superres249 ${libdir}/opencv_ts249 ${libdir}/opencv_video249 ${libdir}/opencv_videostab249 node-opencv/utils/opencv_x64.pc0000644000175000017500000000152013671667364015111 0ustar devdev# Package Information for pkg-config opencv_prefix=C:/OpenCV249/opencv/build/x64/vc12 exec_prefix=${opencv_prefix}/bin libdir=${opencv_prefix}/lib includedir=C:/OpenCV249/opencv/build/include Name: OpenCV Description: Open Source Computer Vision Library Version: 2.4.9 Cflags: ${includedir} ${includedir}/opencv Libs: ${libdir}/opencv_calib3d249 ${libdir}/opencv_contrib249 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devdevnode-opencv/lib/opencv.js0000755000175000017500000001565113671667364014045 0ustar devdevvar Stream = require('stream').Stream , Buffers = require('buffers') , util = require('util') , path = require('path') , os = require('os'); var cv = module.exports = require('./bindings'); var Matrix = cv.Matrix , ImageStream , ImageDataStream , ObjectDetectionStream , VideoStream; if (cv.CascadeClassifier) { Matrix.prototype.detectObject = function(classifier, opts, cb){ var face_cascade; opts = opts || {}; cv._detectObjectClassifiers = cv._detectObjectClassifiers || {}; if (!(face_cascade = cv._detectObjectClassifiers[classifier])){ face_cascade = new cv.CascadeClassifier(classifier); cv._detectObjectClassifiers[classifier] = face_cascade; } face_cascade.detectMultiScale(this, cb, opts.scale, opts.neighbors , opts.min && opts.min[0], opts.min && opts.min[1]); } } else { cv.Matrix.prototype.detectObject = function() { throw new Error("You need to install OpenCV with OBJDETECT module"); } } cv.Matrix.prototype.inspect = function(){ var size = (this.size()||[]).join('x'); return "[ Matrix " + size + " ]"; } // we use the Opencv constants naming convention to extract the number of bytes (8, 16, 32, 64), and the number of channels from constants names var getNumberOfBytesAndChannelsPerType = function(type){ var regExp = /CV_([0-9]+)([A-Z]+)([0-9]+)/; for(var k in cv.Constants) if(cv.Constants.hasOwnProperty(k) && k.match(regExp) && cv.Constants[k] === type){ var bytes, channels, dataType; k.replace(regExp, function(all, b, l, c){ bytes = b; channels = c; dataType = l[0] }); return { bytes : parseInt(bytes), channels : !isNaN(parseInt(channels)) && parseInt(channels), dataType : dataType, label : k }; } }; var getBufferMethodName = function(bytes, dataType, endianness, read){ var fnName; if(read){ fnName = "read"; } else { fnName = "write"; } if (bytes === 32 && (dataType === "F" || dataType === "S")){ if(dataType === "F"){ fnName += "Float"+endianness; } else {//dataType === "S" fnName += "Int32"+endianness; } } else if(bytes === 8){ fnName += (dataType === "U" ? "U" : "")+"Int8"; } else if(bytes === 16){ fnName += (dataType === "U" ? "U" : "")+"Int16"+endianness; } else { throw("This matrix type (CV_"+bytes+dataType+") is not compatible with fromArray/toArray") } return fnName; }; Matrix.fromArray = function(arr, type){ var n_bytes; var bytesAndChannels = getNumberOfBytesAndChannelsPerType(type); var bytes = bytesAndChannels.bytes; var channels = bytesAndChannels.channels; var dataType = bytesAndChannels.dataType; var label = bytesAndChannels.label; if(!Array.isArray(arr) ||!Array.isArray(arr[0]) || !Array.isArray(arr[0][0]) || (channels && arr[0][0].length !== channels)) { throw(new Error("Input array must be a 3-level array/matrix with size rows x cols x channels corresponding to dataType ("+label+")")); } if(!channels){ channels = 1; } var rows = arr.length; var cols = arr[0].length; var mat = new cv.Matrix(rows, cols, type); var n_bytes = bytes/8; var buf = new Buffer(rows * cols * channels * n_bytes); buf.fill(0); var fnName = getBufferMethodName(bytes, dataType, os.endianness(), false) for(var i=0;i 0.4.0 ( missing description... ) #### 0.0.13 - V Early support for face recognition - API is _likely_ to change. Have fun! - *API Change*: VideoCapture.read now calls callback(err, im) instead of callback(im) #### 0.0.12 - Matrix clone() - NamedWindow Support #### 0.0.11 - Bug Fixes - ImageStream becomes ImageDataStream, and new ImageStream allows multiple images to be streamed as matrices, for example, with an object detection stream. - @ryansouza improved documentation - Correcting matrix constructor (thanks @gluxon) - @Michael Smith expanded Contours functionality. Thanks all! #### 0.0.10 - Bug Fixes - @Contra added code that allows thickness and color args for ellipse - Camshift Support - @jtlebi added bindings for erode, gaussianBlur, arcLength, approxPolyDP, isConvex, cornerCount - @gluxon added bindings for inRange Thanks everyone! #### 0.0.9 - toBuffer can now take a callback and be run async (re #21) node-opencv/package-lock.json0000644000175000017500000004551613671667364014663 0ustar devdev{ "name": "opencv", "version": "6.3.0", "lockfileVersion": 1, "requires": true, "dependencies": { "ajv": { "version": "6.12.0", "resolved": "https://registry.npmjs.org/ajv/-/ajv-6.12.0.tgz", "integrity": "sha512-D6gFiFA0RRLyUbvijN74DWAjXSFxWKaWP7mldxkVhyhAV3+SWA9HEJPHQ2c9soIeTFJqcSdFDGFgdqs1iUU2Hw==", "dev": true, "requires": { "fast-deep-equal": "3.1.1", "fast-json-stable-stringify": "2.1.0", "json-schema-traverse": "0.4.1", "uri-js": "4.2.2" } }, "asn1": { "version": "0.2.4", "resolved": "https://registry.npmjs.org/asn1/-/asn1-0.2.4.tgz", "integrity": 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docker build -t peterbraden/node-opencv-ubuntu-14-04 -f test/Dockerfile-ubuntu-14-04 . .PHONY: travis-build node-opencv/CONTRIBUTORS0000644000175000017500000000356613671667363013325 0ustar devdevOrdered by date of first contribution. [Auto-generated](https://github.com/xingrz/node-contributors) on Thu, 26 Nov 2015 18:05:16 GMT. - Peter Braden - [John Hurliman](https://github.com/jhurliman) - Tomas Alvarez - [Felix Geisendörfer](https://github.com/felixge) - [Wade Simmons](https://github.com/wadey) - rhasson - Camilo Aguilar - Contra - [Prabir Shrestha](https://github.com/prabirshrestha) - Jean-Tiare LE BIGOT - Brandon Cheng - [Michael Smith](https://github.com/spinda) - Ryan Souza - Jeremy Raadt - [Thorsten Ball](https://github.com/mrnugget) - [Carter Thaxton](https://github.com/carter-thaxton) - Solomon Hykes - Ryan Lord - [Matthew Hollings](https://github.com/mattholl) - Alessio Caiazza - Ray Smoodiver - magicode - [Loren M. Lang](https://github.com/penguin359) - carthagan - [Tim Park](https://github.com/timfpark) - Jakob Krigovsky - Paul - SergeMv - [Laurent Eschenauer](https://github.com/eschnou) - Sebastian Fastner - Edgar O Silva - [Andrew Stewart](https://github.com/stewart) - [Ron Evans](https://github.com/deadprogram) - Yu Jiang Tham - [Chia-liang Kao](https://github.com/clkao) - Morgan 'ARR\!' Allen - Jeff Crouse - Yu Jiang Tham - John Ludwig - Salman - [Gregory Ray](https://github.com/f3z0) - psayre23 - Mark kaosat-dev Moissette - John Cao - Oskar Dahlberg - J David Smith - Marc Bachmann - Edgar O Silva - madshall - Pieter De Bruyne - [Micah Elizabeth Scott](https://github.com/scanlime) - Fille - Max Ehrlich - AVVS - [tualo solutions GmbH](https://github.com/tualo) - [Adrian Zankich](https://github.com/zankich) - Paul Morris-Hill - Emanuel - dropfen - Michael Vines - Reski - Anshul Jain - [TJ Borromeo](https://github.com/borromeotlhs) - Keegan Brown - [Adrian Sieber](https://github.com/adius) - [vyacheslav](https://github.com/vyacheslav-lonschakov) - vyacheslav - [Harold Ozouf](https://github.com/jspdown) - [Dan Schultzer](https://github.com/danschultzer) node-opencv/binding.gyp0000755000175000017500000001006213671667363013570 0ustar devdev{ "targets": [{ "target_name": "opencv", "sources": [ "src/init.cc", "src/Matrix.cc", "src/OpenCV.cc", "src/CascadeClassifierWrap.cc", "src/Contours.cc", "src/Point.cc", "src/VideoCaptureWrap.cc", "src/VideoWriterWrap.cc", "src/CamShift.cc", "src/HighGUI.cc", "src/FaceRecognizer.cc", "src/Features2d.cc", "src/BackgroundSubtractor.cc", "src/Constants.cc", "src/Calib3D.cc", "src/ImgProc.cc", "src/Stereo.cc", "src/LDAWrap.cc", "src/Histogram.cc", ], "libraries": [ "