├── LICENSE ├── README.md ├── bower.json ├── build ├── jsfeat-min.js └── jsfeat.js ├── cascades ├── bbf_face.js ├── eye.js ├── frontalface.js ├── handfist.js ├── handopen.js ├── mouth.js ├── profileface.js └── upperbody.js ├── compile ├── build.xml └── yuicompressor-2.4.2.jar ├── package.json └── src ├── jsfeat.js ├── jsfeat_bbf.js ├── jsfeat_cache.js ├── jsfeat_export.js ├── jsfeat_fast_corners.js ├── jsfeat_haar.js ├── jsfeat_imgproc.js ├── jsfeat_linalg.js ├── jsfeat_mat_math.js ├── jsfeat_math.js ├── jsfeat_motion_estimator.js ├── jsfeat_optical_flow_lk.js ├── jsfeat_orb.js ├── jsfeat_struct.js ├── jsfeat_transform.js ├── jsfeat_yape.js └── jsfeat_yape06.js /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License 2 | 3 | Copyright (c) 2012 Eugene Zatepyakin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in 13 | all copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 21 | THE SOFTWARE. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | jsfeat 2 | ====== 3 | 4 | ### JavaScript Computer Vision library ### 5 | 6 | The project aim is to explore JS/HTML5 possibilities using modern & state-of-art computer vision algorithms. 7 | 8 | [Examples and Documentation](https://inspirit.github.io/jsfeat/) 9 | 10 | ### Features ### 11 | 12 | * Custom data structures 13 | * Basic image processing methods (grayscale, derivatives, box-blur, resample, etc.) 14 | * grayscale ([Demo](https://inspirit.github.io/jsfeat/sample_grayscale.html)) 15 | * box blur ([Demo](https://inspirit.github.io/jsfeat/sample_boxblur.html)) 16 | * gaussian blur ([Demo](https://inspirit.github.io/jsfeat/sample_gaussblur.html)) 17 | * equalize histogram ([Demo](https://inspirit.github.io/jsfeat/sample_equalize_hist.html)) 18 | * canny edges ([Demo](https://inspirit.github.io/jsfeat/sample_canny_edge.html)) 19 | * sobel deriv ([Demo](https://inspirit.github.io/jsfeat/sample_sobel.html)) 20 | * scharr deriv ([Demo](https://inspirit.github.io/jsfeat/sample_scharr.html)) 21 | * find more at Examples and Documentation page 22 | * Linear Algebra module 23 | * LU (Gaussian elimination) solver 24 | * Cholesky solver 25 | * SVD decomposition, solver and pseudo-inverse 26 | * Eigen Vectors and Values 27 | * Multiview module ([Demo](https://inspirit.github.io/jsfeat/sample_videostab.html)) 28 | * Affine2D motion kernel 29 | * Homography2D motion kernel 30 | * RANSAC motion estimator 31 | * LMEDS motion estimator 32 | * Matrix Math module for various matrix operation such as traspose, multiply etc. 33 | * Features 2D 34 | * Fast Corners feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_fast_corners.html)) 35 | * YAPE06 feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_yape06.html)) 36 | * YAPE feature detector ([Demo](https://inspirit.github.io/jsfeat/sample_yape.html)) 37 | * ORB feature descriptor ([Demo](https://inspirit.github.io/jsfeat/sample_orb.html)) 38 | * Lucas-Kanade optical flow ([Demo](https://inspirit.github.io/jsfeat/sample_oflow_lk.html) - click to add points) 39 | * HAAR object detector ([Demo](https://inspirit.github.io/jsfeat/sample_haar_face.html)) 40 | * BBF object detector ([Demo](https://inspirit.github.io/jsfeat/sample_bbf_face.html)) 41 | -------------------------------------------------------------------------------- /bower.json: -------------------------------------------------------------------------------- 1 | { 2 | 3 | "name": "jsfeat", 4 | "version" : "0.0.8", 5 | "description" : "JavaScript Computer Vision library", 6 | "author" : "Eugene Zatepyakin (http://www.inspirit.ru/)", 7 | "main" : "build/jsfeat-min.js", 8 | 9 | "ignore": [ 10 | "src", 11 | "compile", 12 | "node_modules" 13 | ] 14 | } 15 | -------------------------------------------------------------------------------- /cascades/handfist.js: -------------------------------------------------------------------------------- 1 | /** 2 | * this cascade is derived from https://github.com/mtschirs/js-objectdetect implementation 3 | * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t 4 | */ 5 | (function(global) { 6 | global.handfist = 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7 | })(jsfeat.haar); -------------------------------------------------------------------------------- /compile/build.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | -------------------------------------------------------------------------------- /compile/yuicompressor-2.4.2.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/inspirit/jsfeat/4c7b336bbeeb26e6cd4cdf3c7d414abe273846f3/compile/yuicompressor-2.4.2.jar -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name" : "jsfeat", 3 | "version" : "0.0.8", 4 | "description" : "JavaScript Computer Vision library", 5 | "author" : "Eugene Zatepyakin (http://www.inspirit.ru/)", 6 | "files" : "build/jsfeat.js", 7 | "main" : "build/jsfeat" 8 | } 9 | -------------------------------------------------------------------------------- /src/jsfeat.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | // namespace ? 6 | var jsfeat = jsfeat || { REVISION: 'ALPHA' }; 7 | -------------------------------------------------------------------------------- /src/jsfeat_bbf.js: -------------------------------------------------------------------------------- 1 | /** 2 | * BBF: Brightness Binary Feature 3 | * 4 | * @author Eugene Zatepyakin / http://inspirit.ru/ 5 | * 6 | * this code is a rewrite from https://github.com/liuliu/ccv implementation 7 | * @author Liu Liu / http://liuliu.me/ 8 | * 9 | * The original paper refers to: YEF∗ Real-Time Object Detection, Yotam Abramson and Bruno Steux 10 | */ 11 | 12 | (function(global) { 13 | "use strict"; 14 | // 15 | var bbf = (function() { 16 | 17 | var _group_func = function(r1, r2) { 18 | var distance = (r1.width * 0.25 + 0.5)|0; 19 | 20 | return r2.x <= r1.x + distance && 21 | r2.x >= r1.x - distance && 22 | r2.y <= r1.y + distance && 23 | r2.y >= r1.y - distance && 24 | r2.width <= (r1.width * 1.5 + 0.5)|0 && 25 | (r2.width * 1.5 + 0.5)|0 >= r1.width; 26 | } 27 | 28 | var img_pyr = new jsfeat.pyramid_t(1); 29 | 30 | return { 31 | 32 | interval: 4, 33 | scale: 1.1486, 34 | next: 5, 35 | scale_to: 1, 36 | 37 | // make features local copy 38 | // to avoid array allocation with each scale 39 | // this is strange but array works faster than Int32 version??? 40 | prepare_cascade: function(cascade) { 41 | var sn = cascade.stage_classifier.length; 42 | for (var j = 0; j < sn; j++) { 43 | var orig_feature = cascade.stage_classifier[j].feature; 44 | var f_cnt = cascade.stage_classifier[j].count; 45 | var feature = cascade.stage_classifier[j]._feature = new Array(f_cnt); 46 | for (var k = 0; k < f_cnt; k++) { 47 | feature[k] = {"size" : orig_feature[k].size, 48 | "px" : new Array(orig_feature[k].size), 49 | "pz" : new Array(orig_feature[k].size), 50 | "nx" : new Array(orig_feature[k].size), 51 | "nz" : new Array(orig_feature[k].size)}; 52 | } 53 | } 54 | }, 55 | 56 | build_pyramid: function(src, min_width, min_height, interval) { 57 | if (typeof interval === "undefined") { interval = 4; } 58 | 59 | var sw=src.cols,sh=src.rows; 60 | var i=0,nw=0,nh=0; 61 | var new_pyr=false; 62 | var src0=src,src1=src; 63 | var data_type = jsfeat.U8_t | jsfeat.C1_t; 64 | 65 | this.interval = interval; 66 | this.scale = Math.pow(2, 1 / (this.interval + 1)); 67 | this.next = (this.interval + 1)|0; 68 | this.scale_to = (Math.log(Math.min(sw / min_width, sh / min_height)) / Math.log(this.scale))|0; 69 | 70 | var pyr_l = ((this.scale_to + this.next * 2) * 4) | 0; 71 | if(img_pyr.levels != pyr_l) { 72 | img_pyr.levels = pyr_l; 73 | img_pyr.data = new Array(pyr_l); 74 | new_pyr = true; 75 | img_pyr.data[0] = src; // first is src 76 | } 77 | 78 | for (i = 1; i <= this.interval; ++i) { 79 | nw = (sw / Math.pow(this.scale, i))|0; 80 | nh = (sh / Math.pow(this.scale, i))|0; 81 | src0 = img_pyr.data[i<<2]; 82 | if(new_pyr || nw != src0.cols || nh != src0.rows) { 83 | img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); 84 | src0 = img_pyr.data[i<<2]; 85 | } 86 | jsfeat.imgproc.resample(src, src0, nw, nh); 87 | } 88 | for (i = this.next; i < this.scale_to + this.next * 2; ++i) { 89 | src1 = img_pyr.data[(i << 2) - (this.next << 2)]; 90 | src0 = img_pyr.data[i<<2]; 91 | nw = src1.cols >> 1; 92 | nh = src1.rows >> 1; 93 | if(new_pyr || nw != src0.cols || nh != src0.rows) { 94 | img_pyr.data[i<<2] = new jsfeat.matrix_t(nw, nh, data_type); 95 | src0 = img_pyr.data[i<<2]; 96 | } 97 | jsfeat.imgproc.pyrdown(src1, src0); 98 | } 99 | for (i = this.next * 2; i < this.scale_to + this.next * 2; ++i) { 100 | src1 = img_pyr.data[(i << 2) - (this.next << 2)]; 101 | nw = src1.cols >> 1; 102 | nh = src1.rows >> 1; 103 | src0 = img_pyr.data[(i<<2)+1]; 104 | if(new_pyr || nw != src0.cols || nh != src0.rows) { 105 | img_pyr.data[(i<<2)+1] = new jsfeat.matrix_t(nw, nh, data_type); 106 | src0 = img_pyr.data[(i<<2)+1]; 107 | } 108 | jsfeat.imgproc.pyrdown(src1, src0, 1, 0); 109 | // 110 | src0 = img_pyr.data[(i<<2)+2]; 111 | if(new_pyr || nw != src0.cols || nh != src0.rows) { 112 | img_pyr.data[(i<<2)+2] = new jsfeat.matrix_t(nw, nh, data_type); 113 | src0 = img_pyr.data[(i<<2)+2]; 114 | } 115 | jsfeat.imgproc.pyrdown(src1, src0, 0, 1); 116 | // 117 | src0 = img_pyr.data[(i<<2)+3]; 118 | if(new_pyr || nw != src0.cols || nh != src0.rows) { 119 | img_pyr.data[(i<<2)+3] = new jsfeat.matrix_t(nw, nh, data_type); 120 | src0 = img_pyr.data[(i<<2)+3]; 121 | } 122 | jsfeat.imgproc.pyrdown(src1, src0, 1, 1); 123 | } 124 | return img_pyr; 125 | }, 126 | 127 | detect: function(pyramid, cascade) { 128 | var interval = this.interval; 129 | var scale = this.scale; 130 | var next = this.next; 131 | var scale_upto = this.scale_to; 132 | var i=0,j=0,k=0,n=0,x=0,y=0,q=0,sn=0,f_cnt=0,q_cnt=0,p=0,pmin=0,nmax=0,f=0,i4=0,qw=0,qh=0; 133 | var sum=0.0, alpha, feature, orig_feature, feature_k, feature_o, flag = true, shortcut=true; 134 | var scale_x = 1.0, scale_y = 1.0; 135 | var dx = [0, 1, 0, 1]; 136 | var dy = [0, 0, 1, 1]; 137 | var seq = []; 138 | var pyr=pyramid.data, bpp = 1, bpp2 = 2, bpp4 = 4; 139 | 140 | var u8 = [], u8o = [0,0,0]; 141 | var step = [0,0,0]; 142 | var paddings = [0,0,0]; 143 | 144 | for (i = 0; i < scale_upto; i++) { 145 | i4 = (i<<2); 146 | qw = pyr[i4 + (next << 3)].cols - (cascade.width >> 2); 147 | qh = pyr[i4 + (next << 3)].rows - (cascade.height >> 2); 148 | step[0] = pyr[i4].cols * bpp; 149 | step[1] = pyr[i4 + (next << 2)].cols * bpp; 150 | step[2] = pyr[i4 + (next << 3)].cols * bpp; 151 | paddings[0] = (pyr[i4].cols * bpp4) - (qw * bpp4); 152 | paddings[1] = (pyr[i4 + (next << 2)].cols * bpp2) - (qw * bpp2); 153 | paddings[2] = (pyr[i4 + (next << 3)].cols * bpp) - (qw * bpp); 154 | sn = cascade.stage_classifier.length; 155 | for (j = 0; j < sn; j++) { 156 | orig_feature = cascade.stage_classifier[j].feature; 157 | feature = cascade.stage_classifier[j]._feature; 158 | f_cnt = cascade.stage_classifier[j].count; 159 | for (k = 0; k < f_cnt; k++) { 160 | feature_k = feature[k]; 161 | feature_o = orig_feature[k]; 162 | q_cnt = feature_o.size|0; 163 | for (q = 0; q < q_cnt; q++) { 164 | feature_k.px[q] = (feature_o.px[q] * bpp) + feature_o.py[q] * step[feature_o.pz[q]]; 165 | feature_k.pz[q] = feature_o.pz[q]; 166 | feature_k.nx[q] = (feature_o.nx[q] * bpp) + feature_o.ny[q] * step[feature_o.nz[q]]; 167 | feature_k.nz[q] = feature_o.nz[q]; 168 | } 169 | } 170 | } 171 | u8[0] = pyr[i4].data; u8[1] = pyr[i4 + (next<<2)].data; 172 | for (q = 0; q < 4; q++) { 173 | u8[2] = pyr[i4 + (next<<3) + q].data; 174 | u8o[0] = (dx[q]*bpp2) + dy[q] * (pyr[i4].cols*bpp2); 175 | u8o[1] = (dx[q]*bpp) + dy[q] * (pyr[i4 + (next<<2)].cols*bpp); 176 | u8o[2] = 0; 177 | for (y = 0; y < qh; y++) { 178 | for (x = 0; x < qw; x++) { 179 | sum = 0; 180 | flag = true; 181 | sn = cascade.stage_classifier.length; 182 | for (j = 0; j < sn; j++) { 183 | sum = 0; 184 | alpha = cascade.stage_classifier[j].alpha; 185 | feature = cascade.stage_classifier[j]._feature; 186 | f_cnt = cascade.stage_classifier[j].count; 187 | for (k = 0; k < f_cnt; k++) { 188 | feature_k = feature[k]; 189 | pmin = u8[feature_k.pz[0]][u8o[feature_k.pz[0]] + feature_k.px[0]]; 190 | nmax = u8[feature_k.nz[0]][u8o[feature_k.nz[0]] + feature_k.nx[0]]; 191 | if (pmin <= nmax) { 192 | sum += alpha[k << 1]; 193 | } else { 194 | shortcut = true; 195 | q_cnt = feature_k.size; 196 | for (f = 1; f < q_cnt; f++) { 197 | if (feature_k.pz[f] >= 0) { 198 | p = u8[feature_k.pz[f]][u8o[feature_k.pz[f]] + feature_k.px[f]]; 199 | if (p < pmin) { 200 | if (p <= nmax) { 201 | shortcut = false; 202 | break; 203 | } 204 | pmin = p; 205 | } 206 | } 207 | if (feature_k.nz[f] >= 0) { 208 | n = u8[feature_k.nz[f]][u8o[feature_k.nz[f]] + feature_k.nx[f]]; 209 | if (n > nmax) { 210 | if (pmin <= n) { 211 | shortcut = false; 212 | break; 213 | } 214 | nmax = n; 215 | } 216 | } 217 | } 218 | sum += (shortcut) ? alpha[(k << 1) + 1] : alpha[k << 1]; 219 | } 220 | } 221 | if (sum < cascade.stage_classifier[j].threshold) { 222 | flag = false; 223 | break; 224 | } 225 | } 226 | if (flag) { 227 | seq.push({"x" : (x * 4 + dx[q] * 2) * scale_x, 228 | "y" : (y * 4 + dy[q] * 2) * scale_y, 229 | "width" : cascade.width * scale_x, 230 | "height" : cascade.height * scale_y, 231 | "neighbor" : 1, 232 | "confidence" : sum}); 233 | ++x; 234 | u8o[0] += bpp4; 235 | u8o[1] += bpp2; 236 | u8o[2] += bpp; 237 | } 238 | u8o[0] += bpp4; 239 | u8o[1] += bpp2; 240 | u8o[2] += bpp; 241 | } 242 | u8o[0] += paddings[0]; 243 | u8o[1] += paddings[1]; 244 | u8o[2] += paddings[2]; 245 | } 246 | } 247 | scale_x *= scale; 248 | scale_y *= scale; 249 | } 250 | 251 | return seq; 252 | }, 253 | 254 | // OpenCV method to group detected rectangles 255 | group_rectangles: function(rects, min_neighbors) { 256 | if (typeof min_neighbors === "undefined") { min_neighbors = 1; } 257 | var i, j, n = rects.length; 258 | var node = []; 259 | for (i = 0; i < n; ++i) { 260 | node[i] = {"parent" : -1, 261 | "element" : rects[i], 262 | "rank" : 0}; 263 | } 264 | for (i = 0; i < n; ++i) { 265 | if (!node[i].element) 266 | continue; 267 | var root = i; 268 | while (node[root].parent != -1) 269 | root = node[root].parent; 270 | for (j = 0; j < n; ++j) { 271 | if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { 272 | var root2 = j; 273 | 274 | while (node[root2].parent != -1) 275 | root2 = node[root2].parent; 276 | 277 | if(root2 != root) { 278 | if(node[root].rank > node[root2].rank) 279 | node[root2].parent = root; 280 | else { 281 | node[root].parent = root2; 282 | if (node[root].rank == node[root2].rank) 283 | node[root2].rank++; 284 | root = root2; 285 | } 286 | 287 | /* compress path from node2 to the root: */ 288 | var temp, node2 = j; 289 | while (node[node2].parent != -1) { 290 | temp = node2; 291 | node2 = node[node2].parent; 292 | node[temp].parent = root; 293 | } 294 | 295 | /* compress path from node to the root: */ 296 | node2 = i; 297 | while (node[node2].parent != -1) { 298 | temp = node2; 299 | node2 = node[node2].parent; 300 | node[temp].parent = root; 301 | } 302 | } 303 | } 304 | } 305 | } 306 | var idx_seq = []; 307 | var class_idx = 0; 308 | for(i = 0; i < n; i++) { 309 | j = -1; 310 | var node1 = i; 311 | if(node[node1].element) { 312 | while (node[node1].parent != -1) 313 | node1 = node[node1].parent; 314 | if(node[node1].rank >= 0) 315 | node[node1].rank = ~class_idx++; 316 | j = ~node[node1].rank; 317 | } 318 | idx_seq[i] = j; 319 | } 320 | 321 | var comps = []; 322 | for (i = 0; i < class_idx+1; ++i) { 323 | comps[i] = {"neighbors" : 0, 324 | "x" : 0, 325 | "y" : 0, 326 | "width" : 0, 327 | "height" : 0, 328 | "confidence" : 0}; 329 | } 330 | 331 | // count number of neighbors 332 | for(i = 0; i < n; ++i) { 333 | var r1 = rects[i]; 334 | var idx = idx_seq[i]; 335 | 336 | if (comps[idx].neighbors == 0) 337 | comps[idx].confidence = r1.confidence; 338 | 339 | ++comps[idx].neighbors; 340 | 341 | comps[idx].x += r1.x; 342 | comps[idx].y += r1.y; 343 | comps[idx].width += r1.width; 344 | comps[idx].height += r1.height; 345 | comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); 346 | } 347 | 348 | var seq2 = []; 349 | // calculate average bounding box 350 | for(i = 0; i < class_idx; ++i) { 351 | n = comps[i].neighbors; 352 | if (n >= min_neighbors) 353 | seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), 354 | "y" : (comps[i].y * 2 + n) / (2 * n), 355 | "width" : (comps[i].width * 2 + n) / (2 * n), 356 | "height" : (comps[i].height * 2 + n) / (2 * n), 357 | "neighbors" : comps[i].neighbors, 358 | "confidence" : comps[i].confidence}); 359 | } 360 | 361 | var result_seq = []; 362 | n = seq2.length; 363 | // filter out small face rectangles inside large face rectangles 364 | for(i = 0; i < n; ++i) { 365 | var r1 = seq2[i]; 366 | var flag = true; 367 | for(j = 0; j < n; ++j) { 368 | var r2 = seq2[j]; 369 | var distance = (r2.width * 0.25 + 0.5)|0; 370 | 371 | if(i != j && 372 | r1.x >= r2.x - distance && 373 | r1.y >= r2.y - distance && 374 | r1.x + r1.width <= r2.x + r2.width + distance && 375 | r1.y + r1.height <= r2.y + r2.height + distance && 376 | (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { 377 | flag = false; 378 | break; 379 | } 380 | } 381 | 382 | if(flag) 383 | result_seq.push(r1); 384 | } 385 | return result_seq; 386 | } 387 | 388 | }; 389 | 390 | })(); 391 | 392 | global.bbf = bbf; 393 | 394 | })(jsfeat); 395 | -------------------------------------------------------------------------------- /src/jsfeat_cache.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | (function(global) { 6 | "use strict"; 7 | // 8 | 9 | var cache = (function() { 10 | 11 | // very primitive array cache, still need testing if it helps 12 | // of course V8 has its own powerful cache sys but i'm not sure 13 | // it caches several multichannel 640x480 buffer creations each frame 14 | 15 | var _pool_node_t = (function () { 16 | function _pool_node_t(size_in_bytes) { 17 | this.next = null; 18 | this.data = new jsfeat.data_t(size_in_bytes); 19 | this.size = this.data.size; 20 | this.buffer = this.data.buffer; 21 | this.u8 = this.data.u8; 22 | this.i32 = this.data.i32; 23 | this.f32 = this.data.f32; 24 | this.f64 = this.data.f64; 25 | } 26 | _pool_node_t.prototype.resize = function(size_in_bytes) { 27 | delete this.data; 28 | this.data = new jsfeat.data_t(size_in_bytes); 29 | this.size = this.data.size; 30 | this.buffer = this.data.buffer; 31 | this.u8 = this.data.u8; 32 | this.i32 = this.data.i32; 33 | this.f32 = this.data.f32; 34 | this.f64 = this.data.f64; 35 | } 36 | return _pool_node_t; 37 | })(); 38 | 39 | var _pool_head, _pool_tail; 40 | var _pool_size = 0; 41 | 42 | return { 43 | 44 | allocate: function(capacity, data_size) { 45 | _pool_head = _pool_tail = new _pool_node_t(data_size); 46 | for (var i = 0; i < capacity; ++i) { 47 | var node = new _pool_node_t(data_size); 48 | _pool_tail = _pool_tail.next = node; 49 | 50 | _pool_size++; 51 | } 52 | }, 53 | 54 | get_buffer: function(size_in_bytes) { 55 | // assume we have enough free nodes 56 | var node = _pool_head; 57 | _pool_head = _pool_head.next; 58 | _pool_size--; 59 | 60 | if(size_in_bytes > node.size) { 61 | node.resize(size_in_bytes); 62 | } 63 | 64 | return node; 65 | }, 66 | 67 | put_buffer: function(node) { 68 | _pool_tail = _pool_tail.next = node; 69 | _pool_size++; 70 | } 71 | }; 72 | })(); 73 | 74 | global.cache = cache; 75 | // for now we dont need more than 30 buffers 76 | // if having cache sys really helps we can add auto extending sys 77 | cache.allocate(30, 640*4); 78 | 79 | })(jsfeat); 80 | -------------------------------------------------------------------------------- /src/jsfeat_export.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | (function(lib) { 6 | "use strict"; 7 | 8 | if (typeof module === "undefined" || typeof module.exports === "undefined") { 9 | // in a browser, define its namespaces in global 10 | window.jsfeat = lib; 11 | } else { 12 | // in commonjs, or when AMD wrapping has been applied, define its namespaces as exports 13 | module.exports = lib; 14 | } 15 | })(jsfeat); 16 | -------------------------------------------------------------------------------- /src/jsfeat_fast_corners.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * This is FAST corner detector, contributed to OpenCV by the author, Edward Rosten. 5 | */ 6 | 7 | /* 8 | The references are: 9 | * Machine learning for high-speed corner detection, 10 | E. Rosten and T. Drummond, ECCV 2006 11 | * Faster and better: A machine learning approach to corner detection 12 | E. Rosten, R. Porter and T. Drummond, PAMI, 2009 13 | */ 14 | 15 | (function(global) { 16 | "use strict"; 17 | // 18 | var fast_corners = (function() { 19 | 20 | var offsets16 = new Int32Array([0, 3, 1, 3, 2, 2, 3, 1, 3, 0, 3, -1, 2, -2, 1, -3, 0, -3, -1, -3, -2, -2, -3, -1, -3, 0, -3, 1, -2, 2, -1, 3]); 21 | 22 | var threshold_tab = new Uint8Array(512); 23 | var pixel_off = new Int32Array(25); 24 | var score_diff = new Int32Array(25); 25 | 26 | // private functions 27 | var _cmp_offsets = function(pixel, step, pattern_size) { 28 | var k = 0; 29 | var offsets = offsets16; 30 | for( ; k < pattern_size; ++k ) { 31 | pixel[k] = offsets[k<<1] + offsets[(k<<1)+1] * step; 32 | } 33 | for( ; k < 25; ++k ) { 34 | pixel[k] = pixel[k - pattern_size]; 35 | } 36 | }, 37 | 38 | _cmp_score_16 = function(src, off, pixel, d, threshold) { 39 | var N = 25, k = 0, v = src[off]; 40 | var a0 = threshold,a=0,b0=0,b=0; 41 | 42 | for( ; k < N; ++k ) { 43 | d[k] = v - src[off+pixel[k]]; 44 | } 45 | 46 | for( k = 0; k < 16; k += 2 ) { 47 | a = Math.min(d[k+1], d[k+2]); 48 | a = Math.min(a, d[k+3]); 49 | 50 | if( a <= a0 ) continue; 51 | 52 | a = Math.min(a, d[k+4]); 53 | a = Math.min(a, d[k+5]); 54 | a = Math.min(a, d[k+6]); 55 | a = Math.min(a, d[k+7]); 56 | a = Math.min(a, d[k+8]); 57 | a0 = Math.max(a0, Math.min(a, d[k])); 58 | a0 = Math.max(a0, Math.min(a, d[k+9])); 59 | } 60 | 61 | b0 = -a0; 62 | for( k = 0; k < 16; k += 2 ) { 63 | b = Math.max(d[k+1], d[k+2]); 64 | b = Math.max(b, d[k+3]); 65 | b = Math.max(b, d[k+4]); 66 | b = Math.max(b, d[k+5]); 67 | 68 | if( b >= b0 ) continue; 69 | b = Math.max(b, d[k+6]); 70 | b = Math.max(b, d[k+7]); 71 | b = Math.max(b, d[k+8]); 72 | b0 = Math.min(b0, Math.max(b, d[k])); 73 | b0 = Math.min(b0, Math.max(b, d[k+9])); 74 | } 75 | 76 | return -b0-1; 77 | }; 78 | 79 | var _threshold = 20; 80 | 81 | return { 82 | set_threshold: function(threshold) { 83 | _threshold = Math.min(Math.max(threshold, 0), 255); 84 | for (var i = -255; i <= 255; ++i) { 85 | threshold_tab[(i + 255)] = (i < -_threshold ? 1 : (i > _threshold ? 2 : 0)); 86 | } 87 | return _threshold; 88 | }, 89 | 90 | detect: function(src, corners, border) { 91 | if (typeof border === "undefined") { border = 3; } 92 | 93 | var K = 8, N = 25; 94 | var img = src.data, w = src.cols, h = src.rows; 95 | var i=0, j=0, k=0, vt=0, x=0, m3=0; 96 | var buf_node = jsfeat.cache.get_buffer(3 * w); 97 | var cpbuf_node = jsfeat.cache.get_buffer(((w+1)*3)<<2); 98 | var buf = buf_node.u8; 99 | var cpbuf = cpbuf_node.i32; 100 | var pixel = pixel_off; 101 | var sd = score_diff; 102 | var sy = Math.max(3, border); 103 | var ey = Math.min((h-2), (h-border)); 104 | var sx = Math.max(3, border); 105 | var ex = Math.min((w - 3), (w - border)); 106 | var _count = 0, corners_cnt = 0, pt; 107 | var score_func = _cmp_score_16; 108 | var thresh_tab = threshold_tab; 109 | var threshold = _threshold; 110 | 111 | var v=0,tab=0,d=0,ncorners=0,cornerpos=0,curr=0,ptr=0,prev=0,pprev=0; 112 | var jp1=0,jm1=0,score=0; 113 | 114 | _cmp_offsets(pixel, w, 16); 115 | 116 | // local vars are faster? 117 | var pixel0 = pixel[0]; 118 | var pixel1 = pixel[1]; 119 | var pixel2 = pixel[2]; 120 | var pixel3 = pixel[3]; 121 | var pixel4 = pixel[4]; 122 | var pixel5 = pixel[5]; 123 | var pixel6 = pixel[6]; 124 | var pixel7 = pixel[7]; 125 | var pixel8 = pixel[8]; 126 | var pixel9 = pixel[9]; 127 | var pixel10 = pixel[10]; 128 | var pixel11 = pixel[11]; 129 | var pixel12 = pixel[12]; 130 | var pixel13 = pixel[13]; 131 | var pixel14 = pixel[14]; 132 | var pixel15 = pixel[15]; 133 | 134 | for(i = 0; i < w*3; ++i) { 135 | buf[i] = 0; 136 | } 137 | 138 | for(i = sy; i < ey; ++i) { 139 | ptr = ((i * w) + sx)|0; 140 | m3 = (i - 3)%3; 141 | curr = (m3*w)|0; 142 | cornerpos = (m3*(w+1))|0; 143 | for (j = 0; j < w; ++j) buf[curr+j] = 0; 144 | ncorners = 0; 145 | 146 | if( i < (ey - 1) ) { 147 | j = sx; 148 | 149 | for( ; j < ex; ++j, ++ptr ) { 150 | v = img[ptr]; 151 | tab = ( - v + 255 ); 152 | d = ( thresh_tab[tab+img[ptr+pixel0]] | thresh_tab[tab+img[ptr+pixel8]] ); 153 | 154 | if( d == 0 ) { 155 | continue; 156 | } 157 | 158 | d &= ( thresh_tab[tab+img[ptr+pixel2]] | thresh_tab[tab+img[ptr+pixel10]] ); 159 | d &= ( thresh_tab[tab+img[ptr+pixel4]] | thresh_tab[tab+img[ptr+pixel12]] ); 160 | d &= ( thresh_tab[tab+img[ptr+pixel6]] | thresh_tab[tab+img[ptr+pixel14]] ); 161 | 162 | if( d == 0 ) { 163 | continue; 164 | } 165 | 166 | d &= ( thresh_tab[tab+img[ptr+pixel1]] | thresh_tab[tab+img[ptr+pixel9]] ); 167 | d &= ( thresh_tab[tab+img[ptr+pixel3]] | thresh_tab[tab+img[ptr+pixel11]] ); 168 | d &= ( thresh_tab[tab+img[ptr+pixel5]] | thresh_tab[tab+img[ptr+pixel13]] ); 169 | d &= ( thresh_tab[tab+img[ptr+pixel7]] | thresh_tab[tab+img[ptr+pixel15]] ); 170 | 171 | if( d & 1 ) { 172 | vt = (v - threshold); 173 | _count = 0; 174 | 175 | for( k = 0; k < N; ++k ) { 176 | x = img[(ptr+pixel[k])]; 177 | if(x < vt) { 178 | ++_count; 179 | if( _count > K ) { 180 | ++ncorners; 181 | cpbuf[cornerpos+ncorners] = j; 182 | buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); 183 | break; 184 | } 185 | } 186 | else { 187 | _count = 0; 188 | } 189 | } 190 | } 191 | 192 | if( d & 2 ) { 193 | vt = (v + threshold); 194 | _count = 0; 195 | 196 | for( k = 0; k < N; ++k ) { 197 | x = img[(ptr+pixel[k])]; 198 | if(x > vt) { 199 | ++_count; 200 | if( _count > K ) { 201 | ++ncorners; 202 | cpbuf[cornerpos+ncorners] = j; 203 | buf[curr+j] = score_func(img, ptr, pixel, sd, threshold); 204 | break; 205 | } 206 | } 207 | else { 208 | _count = 0; 209 | } 210 | } 211 | } 212 | } 213 | } 214 | 215 | cpbuf[cornerpos+w] = ncorners; 216 | 217 | if ( i == sy ) { 218 | continue; 219 | } 220 | 221 | m3 = (i - 4 + 3)%3; 222 | prev = (m3*w)|0; 223 | cornerpos = (m3*(w+1))|0; 224 | m3 = (i - 5 + 3)%3; 225 | pprev = (m3*w)|0; 226 | 227 | ncorners = cpbuf[cornerpos+w]; 228 | 229 | for( k = 0; k < ncorners; ++k ) { 230 | j = cpbuf[cornerpos+k]; 231 | jp1 = (j+1)|0; 232 | jm1 = (j-1)|0; 233 | score = buf[prev+j]; 234 | if( (score > buf[prev+jp1] && score > buf[prev+jm1] && 235 | score > buf[pprev+jm1] && score > buf[pprev+j] && score > buf[pprev+jp1] && 236 | score > buf[curr+jm1] && score > buf[curr+j] && score > buf[curr+jp1]) ) { 237 | // save corner 238 | pt = corners[corners_cnt]; 239 | pt.x = j, pt.y = (i-1), pt.score = score; 240 | corners_cnt++; 241 | } 242 | } 243 | } // y loop 244 | jsfeat.cache.put_buffer(buf_node); 245 | jsfeat.cache.put_buffer(cpbuf_node); 246 | return corners_cnt; 247 | } 248 | }; 249 | })(); 250 | 251 | global.fast_corners = fast_corners; 252 | fast_corners.set_threshold(20); // set default 253 | 254 | })(jsfeat); 255 | -------------------------------------------------------------------------------- /src/jsfeat_haar.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * this code is a rewrite from https://github.com/mtschirs/js-objectdetect implementation 5 | * @author Martin Tschirsich / http://www.tu-darmstadt.de/~m_t 6 | */ 7 | 8 | (function(global) { 9 | "use strict"; 10 | // 11 | var haar = (function() { 12 | 13 | var _group_func = function(r1, r2) { 14 | var distance = (r1.width * 0.25 + 0.5)|0; 15 | 16 | return r2.x <= r1.x + distance && 17 | r2.x >= r1.x - distance && 18 | r2.y <= r1.y + distance && 19 | r2.y >= r1.y - distance && 20 | r2.width <= (r1.width * 1.5 + 0.5)|0 && 21 | (r2.width * 1.5 + 0.5)|0 >= r1.width; 22 | } 23 | 24 | return { 25 | 26 | edges_density: 0.07, 27 | 28 | detect_single_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale, classifier) { 29 | var win_w = (classifier.size[0] * scale)|0, 30 | win_h = (classifier.size[1] * scale)|0, 31 | step_x = (0.5 * scale + 1.5)|0, 32 | step_y = step_x; 33 | var i,j,k,x,y,ex=(width-win_w)|0,ey=(height-win_h)|0; 34 | var w1=(width+1)|0,edge_dens,mean,variance,std; 35 | var inv_area = 1.0 / (win_w * win_h); 36 | var stages,stage,trees,tree,sn,tn,fn,found=true,stage_thresh,stage_sum,tree_sum,feature,features; 37 | var fi_a,fi_b,fi_c,fi_d,fw,fh; 38 | 39 | var ii_a=0,ii_b=win_w,ii_c=win_h*w1,ii_d=ii_c+win_w; 40 | var edges_thresh = ((win_w*win_h) * 0xff * this.edges_density)|0; 41 | // if too much gradient we also can skip 42 | //var edges_thresh_high = ((win_w*win_h) * 0xff * 0.3)|0; 43 | 44 | var rects = []; 45 | for(y = 0; y < ey; y += step_y) { 46 | ii_a = y * w1; 47 | for(x = 0; x < ex; x += step_x, ii_a += step_x) { 48 | 49 | mean = int_sum[ii_a] 50 | - int_sum[ii_a+ii_b] 51 | - int_sum[ii_a+ii_c] 52 | + int_sum[ii_a+ii_d]; 53 | 54 | // canny prune 55 | if(int_canny_sum) { 56 | edge_dens = (int_canny_sum[ii_a] 57 | - int_canny_sum[ii_a+ii_b] 58 | - int_canny_sum[ii_a+ii_c] 59 | + int_canny_sum[ii_a+ii_d]); 60 | if(edge_dens < edges_thresh || mean < 20) { 61 | x += step_x, ii_a += step_x; 62 | continue; 63 | } 64 | } 65 | 66 | mean *= inv_area; 67 | variance = (int_sqsum[ii_a] 68 | - int_sqsum[ii_a+ii_b] 69 | - int_sqsum[ii_a+ii_c] 70 | + int_sqsum[ii_a+ii_d]) * inv_area - mean * mean; 71 | 72 | std = variance > 0. ? Math.sqrt(variance) : 1; 73 | 74 | stages = classifier.complexClassifiers; 75 | sn = stages.length; 76 | found = true; 77 | for(i = 0; i < sn; ++i) { 78 | stage = stages[i]; 79 | stage_thresh = stage.threshold; 80 | trees = stage.simpleClassifiers; 81 | tn = trees.length; 82 | stage_sum = 0; 83 | for(j = 0; j < tn; ++j) { 84 | tree = trees[j]; 85 | tree_sum = 0; 86 | features = tree.features; 87 | fn = features.length; 88 | if(tree.tilted === 1) { 89 | for(k=0; k < fn; ++k) { 90 | feature = features[k]; 91 | fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; 92 | fw = ~~(feature[2] * scale); 93 | fh = ~~(feature[3] * scale); 94 | fi_b = fw * w1; 95 | fi_c = fh * w1; 96 | 97 | tree_sum += (int_tilted[fi_a] 98 | - int_tilted[fi_a + fw + fi_b] 99 | - int_tilted[fi_a - fh + fi_c] 100 | + int_tilted[fi_a + fw - fh + fi_b + fi_c]) * feature[4]; 101 | } 102 | } else { 103 | for(k=0; k < fn; ++k) { 104 | feature = features[k]; 105 | fi_a = ~~(x + feature[0] * scale) + ~~(y + feature[1] * scale) * w1; 106 | fw = ~~(feature[2] * scale); 107 | fh = ~~(feature[3] * scale); 108 | fi_c = fh * w1; 109 | 110 | tree_sum += (int_sum[fi_a] 111 | - int_sum[fi_a+fw] 112 | - int_sum[fi_a+fi_c] 113 | + int_sum[fi_a+fi_c+fw]) * feature[4]; 114 | } 115 | } 116 | stage_sum += (tree_sum * inv_area < tree.threshold * std) ? tree.left_val : tree.right_val; 117 | } 118 | if (stage_sum < stage_thresh) { 119 | found = false; 120 | break; 121 | } 122 | } 123 | 124 | if(found) { 125 | rects.push({"x" : x, 126 | "y" : y, 127 | "width" : win_w, 128 | "height" : win_h, 129 | "neighbor" : 1, 130 | "confidence" : stage_sum}); 131 | x += step_x, ii_a += step_x; 132 | } 133 | } 134 | } 135 | return rects; 136 | }, 137 | 138 | detect_multi_scale: function(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, classifier, scale_factor, scale_min) { 139 | if (typeof scale_factor === "undefined") { scale_factor = 1.2; } 140 | if (typeof scale_min === "undefined") { scale_min = 1.0; } 141 | var win_w = classifier.size[0]; 142 | var win_h = classifier.size[1]; 143 | var rects = []; 144 | while (scale_min * win_w < width && scale_min * win_h < height) { 145 | rects = rects.concat(this.detect_single_scale(int_sum, int_sqsum, int_tilted, int_canny_sum, width, height, scale_min, classifier)); 146 | scale_min *= scale_factor; 147 | } 148 | return rects; 149 | }, 150 | 151 | // OpenCV method to group detected rectangles 152 | group_rectangles: function(rects, min_neighbors) { 153 | if (typeof min_neighbors === "undefined") { min_neighbors = 1; } 154 | var i, j, n = rects.length; 155 | var node = []; 156 | for (i = 0; i < n; ++i) { 157 | node[i] = {"parent" : -1, 158 | "element" : rects[i], 159 | "rank" : 0}; 160 | } 161 | for (i = 0; i < n; ++i) { 162 | if (!node[i].element) 163 | continue; 164 | var root = i; 165 | while (node[root].parent != -1) 166 | root = node[root].parent; 167 | for (j = 0; j < n; ++j) { 168 | if( i != j && node[j].element && _group_func(node[i].element, node[j].element)) { 169 | var root2 = j; 170 | 171 | while (node[root2].parent != -1) 172 | root2 = node[root2].parent; 173 | 174 | if(root2 != root) { 175 | if(node[root].rank > node[root2].rank) 176 | node[root2].parent = root; 177 | else { 178 | node[root].parent = root2; 179 | if (node[root].rank == node[root2].rank) 180 | node[root2].rank++; 181 | root = root2; 182 | } 183 | 184 | /* compress path from node2 to the root: */ 185 | var temp, node2 = j; 186 | while (node[node2].parent != -1) { 187 | temp = node2; 188 | node2 = node[node2].parent; 189 | node[temp].parent = root; 190 | } 191 | 192 | /* compress path from node to the root: */ 193 | node2 = i; 194 | while (node[node2].parent != -1) { 195 | temp = node2; 196 | node2 = node[node2].parent; 197 | node[temp].parent = root; 198 | } 199 | } 200 | } 201 | } 202 | } 203 | var idx_seq = []; 204 | var class_idx = 0; 205 | for(i = 0; i < n; i++) { 206 | j = -1; 207 | var node1 = i; 208 | if(node[node1].element) { 209 | while (node[node1].parent != -1) 210 | node1 = node[node1].parent; 211 | if(node[node1].rank >= 0) 212 | node[node1].rank = ~class_idx++; 213 | j = ~node[node1].rank; 214 | } 215 | idx_seq[i] = j; 216 | } 217 | 218 | var comps = []; 219 | for (i = 0; i < class_idx+1; ++i) { 220 | comps[i] = {"neighbors" : 0, 221 | "x" : 0, 222 | "y" : 0, 223 | "width" : 0, 224 | "height" : 0, 225 | "confidence" : 0}; 226 | } 227 | 228 | // count number of neighbors 229 | for(i = 0; i < n; ++i) { 230 | var r1 = rects[i]; 231 | var idx = idx_seq[i]; 232 | 233 | if (comps[idx].neighbors == 0) 234 | comps[idx].confidence = r1.confidence; 235 | 236 | ++comps[idx].neighbors; 237 | 238 | comps[idx].x += r1.x; 239 | comps[idx].y += r1.y; 240 | comps[idx].width += r1.width; 241 | comps[idx].height += r1.height; 242 | comps[idx].confidence = Math.max(comps[idx].confidence, r1.confidence); 243 | } 244 | 245 | var seq2 = []; 246 | // calculate average bounding box 247 | for(i = 0; i < class_idx; ++i) { 248 | n = comps[i].neighbors; 249 | if (n >= min_neighbors) 250 | seq2.push({"x" : (comps[i].x * 2 + n) / (2 * n), 251 | "y" : (comps[i].y * 2 + n) / (2 * n), 252 | "width" : (comps[i].width * 2 + n) / (2 * n), 253 | "height" : (comps[i].height * 2 + n) / (2 * n), 254 | "neighbors" : comps[i].neighbors, 255 | "confidence" : comps[i].confidence}); 256 | } 257 | 258 | var result_seq = []; 259 | n = seq2.length; 260 | // filter out small face rectangles inside large face rectangles 261 | for(i = 0; i < n; ++i) { 262 | var r1 = seq2[i]; 263 | var flag = true; 264 | for(j = 0; j < n; ++j) { 265 | var r2 = seq2[j]; 266 | var distance = (r2.width * 0.25 + 0.5)|0; 267 | 268 | if(i != j && 269 | r1.x >= r2.x - distance && 270 | r1.y >= r2.y - distance && 271 | r1.x + r1.width <= r2.x + r2.width + distance && 272 | r1.y + r1.height <= r2.y + r2.height + distance && 273 | (r2.neighbors > Math.max(3, r1.neighbors) || r1.neighbors < 3)) { 274 | flag = false; 275 | break; 276 | } 277 | } 278 | 279 | if(flag) 280 | result_seq.push(r1); 281 | } 282 | return result_seq; 283 | } 284 | }; 285 | 286 | })(); 287 | 288 | global.haar = haar; 289 | 290 | })(jsfeat); 291 | -------------------------------------------------------------------------------- /src/jsfeat_linalg.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | */ 5 | 6 | (function(global) { 7 | "use strict"; 8 | // 9 | 10 | var linalg = (function() { 11 | 12 | var swap = function(A, i0, i1, t) { 13 | t = A[i0]; 14 | A[i0] = A[i1]; 15 | A[i1] = t; 16 | } 17 | 18 | var hypot = function(a, b) { 19 | a = Math.abs(a); 20 | b = Math.abs(b); 21 | if( a > b ) { 22 | b /= a; 23 | return a*Math.sqrt(1.0 + b*b); 24 | } 25 | if( b > 0 ) { 26 | a /= b; 27 | return b*Math.sqrt(1.0 + a*a); 28 | } 29 | return 0.0; 30 | } 31 | 32 | var JacobiImpl = function(A, astep, W, V, vstep, n) { 33 | var eps = jsfeat.EPSILON; 34 | var i=0,j=0,k=0,m=0,l=0,idx=0,_in=0,_in2=0; 35 | var iters=0,max_iter=n*n*30; 36 | var mv=0.0,val=0.0,p=0.0,y=0.0,t=0.0,s=0.0,c=0.0,a0=0.0,b0=0.0; 37 | 38 | var indR_buff = jsfeat.cache.get_buffer(n<<2); 39 | var indC_buff = jsfeat.cache.get_buffer(n<<2); 40 | var indR = indR_buff.i32; 41 | var indC = indC_buff.i32; 42 | 43 | if(V) { 44 | for(; i < n; i++) { 45 | k = i*vstep; 46 | for(j = 0; j < n; j++) { 47 | V[k + j] = 0.0; 48 | } 49 | V[k + i] = 1.0; 50 | } 51 | } 52 | 53 | for(k = 0; k < n; k++) { 54 | W[k] = A[(astep + 1)*k]; 55 | if(k < n - 1) { 56 | for(m = k+1, mv = Math.abs(A[astep*k + m]), i = k+2; i < n; i++) { 57 | val = Math.abs(A[astep*k+i]); 58 | if(mv < val) 59 | mv = val, m = i; 60 | } 61 | indR[k] = m; 62 | } 63 | if(k > 0) { 64 | for(m = 0, mv = Math.abs(A[k]), i = 1; i < k; i++) { 65 | val = Math.abs(A[astep*i+k]); 66 | if(mv < val) 67 | mv = val, m = i; 68 | } 69 | indC[k] = m; 70 | } 71 | } 72 | 73 | if(n > 1) for( ; iters < max_iter; iters++) { 74 | // find index (k,l) of pivot p 75 | for(k = 0, mv = Math.abs(A[indR[0]]), i = 1; i < n-1; i++) { 76 | val = Math.abs(A[astep*i + indR[i]]); 77 | if( mv < val ) 78 | mv = val, k = i; 79 | } 80 | l = indR[k]; 81 | for(i = 1; i < n; i++) { 82 | val = Math.abs(A[astep*indC[i] + i]); 83 | if( mv < val ) 84 | mv = val, k = indC[i], l = i; 85 | } 86 | 87 | p = A[astep*k + l]; 88 | 89 | if(Math.abs(p) <= eps) break; 90 | 91 | y = (W[l] - W[k])*0.5; 92 | t = Math.abs(y) + hypot(p, y); 93 | s = hypot(p, t); 94 | c = t/s; 95 | s = p/s; t = (p/t)*p; 96 | if(y < 0) 97 | s = -s, t = -t; 98 | A[astep*k + l] = 0; 99 | 100 | W[k] -= t; 101 | W[l] += t; 102 | 103 | // rotate rows and columns k and l 104 | for (i = 0; i < k; i++) { 105 | _in = (astep * i + k); 106 | _in2 = (astep * i + l); 107 | a0 = A[_in]; 108 | b0 = A[_in2]; 109 | A[_in] = a0 * c - b0 * s; 110 | A[_in2] = a0 * s + b0 * c; 111 | } 112 | for (i = (k + 1); i < l; i++) { 113 | _in = (astep * k + i); 114 | _in2 = (astep * i + l); 115 | a0 = A[_in]; 116 | b0 = A[_in2]; 117 | A[_in] = a0 * c - b0 * s; 118 | A[_in2] = a0 * s + b0 * c; 119 | } 120 | i = l + 1; 121 | _in = (astep * k + i); 122 | _in2 = (astep * l + i); 123 | for (; i < n; i++, _in++, _in2++) { 124 | a0 = A[_in]; 125 | b0 = A[_in2]; 126 | A[_in] = a0 * c - b0 * s; 127 | A[_in2] = a0 * s + b0 * c; 128 | } 129 | 130 | // rotate eigenvectors 131 | if (V) { 132 | _in = vstep * k; 133 | _in2 = vstep * l; 134 | for (i = 0; i < n; i++, _in++, _in2++) { 135 | a0 = V[_in]; 136 | b0 = V[_in2]; 137 | V[_in] = a0 * c - b0 * s; 138 | V[_in2] = a0 * s + b0 * c; 139 | } 140 | } 141 | 142 | for(j = 0; j < 2; j++) { 143 | idx = j == 0 ? k : l; 144 | if(idx < n - 1) { 145 | for(m = idx+1, mv = Math.abs(A[astep*idx + m]), i = idx+2; i < n; i++) { 146 | val = Math.abs(A[astep*idx+i]); 147 | if( mv < val ) 148 | mv = val, m = i; 149 | } 150 | indR[idx] = m; 151 | } 152 | if(idx > 0) { 153 | for(m = 0, mv = Math.abs(A[idx]), i = 1; i < idx; i++) { 154 | val = Math.abs(A[astep*i+idx]); 155 | if( mv < val ) 156 | mv = val, m = i; 157 | } 158 | indC[idx] = m; 159 | } 160 | } 161 | } 162 | 163 | // sort eigenvalues & eigenvectors 164 | for(k = 0; k < n-1; k++) { 165 | m = k; 166 | for(i = k+1; i < n; i++) { 167 | if(W[m] < W[i]) 168 | m = i; 169 | } 170 | if(k != m) { 171 | swap(W, m, k, mv); 172 | if(V) { 173 | for(i = 0; i < n; i++) { 174 | swap(V, vstep*m + i, vstep*k + i, mv); 175 | } 176 | } 177 | } 178 | } 179 | 180 | 181 | jsfeat.cache.put_buffer(indR_buff); 182 | jsfeat.cache.put_buffer(indC_buff); 183 | } 184 | 185 | var JacobiSVDImpl = function(At, astep, _W, Vt, vstep, m, n, n1) { 186 | var eps = jsfeat.EPSILON * 2.0; 187 | var minval = jsfeat.FLT_MIN; 188 | var i=0,j=0,k=0,iter=0,max_iter=Math.max(m, 30); 189 | var Ai=0,Aj=0,Vi=0,Vj=0,changed=0; 190 | var c=0.0, s=0.0, t=0.0; 191 | var t0=0.0,t1=0.0,sd=0.0,beta=0.0,gamma=0.0,delta=0.0,a=0.0,p=0.0,b=0.0; 192 | var seed = 0x1234; 193 | var val=0.0,val0=0.0,asum=0.0; 194 | 195 | var W_buff = jsfeat.cache.get_buffer(n<<3); 196 | var W = W_buff.f64; 197 | 198 | for(; i < n; i++) { 199 | for(k = 0, sd = 0; k < m; k++) { 200 | t = At[i*astep + k]; 201 | sd += t*t; 202 | } 203 | W[i] = sd; 204 | 205 | if(Vt) { 206 | for(k = 0; k < n; k++) { 207 | Vt[i*vstep + k] = 0; 208 | } 209 | Vt[i*vstep + i] = 1; 210 | } 211 | } 212 | 213 | for(; iter < max_iter; iter++) { 214 | changed = 0; 215 | 216 | for(i = 0; i < n-1; i++) { 217 | for(j = i+1; j < n; j++) { 218 | Ai = (i*astep)|0, Aj = (j*astep)|0; 219 | a = W[i], p = 0, b = W[j]; 220 | 221 | k = 2; 222 | p += At[Ai]*At[Aj]; 223 | p += At[Ai+1]*At[Aj+1]; 224 | 225 | for(; k < m; k++) 226 | p += At[Ai+k]*At[Aj+k]; 227 | 228 | if(Math.abs(p) <= eps*Math.sqrt(a*b)) continue; 229 | 230 | p *= 2.0; 231 | beta = a - b, gamma = hypot(p, beta); 232 | if( beta < 0 ) { 233 | delta = (gamma - beta)*0.5; 234 | s = Math.sqrt(delta/gamma); 235 | c = (p/(gamma*s*2.0)); 236 | } else { 237 | c = Math.sqrt((gamma + beta)/(gamma*2.0)); 238 | s = (p/(gamma*c*2.0)); 239 | } 240 | 241 | a=0.0, b=0.0; 242 | 243 | k = 2; // unroll 244 | t0 = c*At[Ai] + s*At[Aj]; 245 | t1 = -s*At[Ai] + c*At[Aj]; 246 | At[Ai] = t0; At[Aj] = t1; 247 | a += t0*t0; b += t1*t1; 248 | 249 | t0 = c*At[Ai+1] + s*At[Aj+1]; 250 | t1 = -s*At[Ai+1] + c*At[Aj+1]; 251 | At[Ai+1] = t0; At[Aj+1] = t1; 252 | a += t0*t0; b += t1*t1; 253 | 254 | for( ; k < m; k++ ) 255 | { 256 | t0 = c*At[Ai+k] + s*At[Aj+k]; 257 | t1 = -s*At[Ai+k] + c*At[Aj+k]; 258 | At[Ai+k] = t0; At[Aj+k] = t1; 259 | 260 | a += t0*t0; b += t1*t1; 261 | } 262 | 263 | W[i] = a; W[j] = b; 264 | 265 | changed = 1; 266 | 267 | if(Vt) { 268 | Vi = (i*vstep)|0, Vj = (j*vstep)|0; 269 | 270 | k = 2; 271 | t0 = c*Vt[Vi] + s*Vt[Vj]; 272 | t1 = -s*Vt[Vi] + c*Vt[Vj]; 273 | Vt[Vi] = t0; Vt[Vj] = t1; 274 | 275 | t0 = c*Vt[Vi+1] + s*Vt[Vj+1]; 276 | t1 = -s*Vt[Vi+1] + c*Vt[Vj+1]; 277 | Vt[Vi+1] = t0; Vt[Vj+1] = t1; 278 | 279 | for(; k < n; k++) { 280 | t0 = c*Vt[Vi+k] + s*Vt[Vj+k]; 281 | t1 = -s*Vt[Vi+k] + c*Vt[Vj+k]; 282 | Vt[Vi+k] = t0; Vt[Vj+k] = t1; 283 | } 284 | } 285 | } 286 | } 287 | if(changed == 0) break; 288 | } 289 | 290 | for(i = 0; i < n; i++) { 291 | for(k = 0, sd = 0; k < m; k++) { 292 | t = At[i*astep + k]; 293 | sd += t*t; 294 | } 295 | W[i] = Math.sqrt(sd); 296 | } 297 | 298 | for(i = 0; i < n-1; i++) { 299 | j = i; 300 | for(k = i+1; k < n; k++) { 301 | if(W[j] < W[k]) 302 | j = k; 303 | } 304 | if(i != j) { 305 | swap(W, i, j, sd); 306 | if(Vt) { 307 | for(k = 0; k < m; k++) { 308 | swap(At, i*astep + k, j*astep + k, t); 309 | } 310 | 311 | for(k = 0; k < n; k++) { 312 | swap(Vt, i*vstep + k, j*vstep + k, t); 313 | } 314 | } 315 | } 316 | } 317 | 318 | for(i = 0; i < n; i++) { 319 | _W[i] = W[i]; 320 | } 321 | 322 | if(!Vt) { 323 | jsfeat.cache.put_buffer(W_buff); 324 | return; 325 | } 326 | 327 | for(i = 0; i < n1; i++) { 328 | 329 | sd = i < n ? W[i] : 0; 330 | 331 | while(sd <= minval) { 332 | // if we got a zero singular value, then in order to get the corresponding left singular vector 333 | // we generate a random vector, project it to the previously computed left singular vectors, 334 | // subtract the projection and normalize the difference. 335 | val0 = (1.0/m); 336 | for(k = 0; k < m; k++) { 337 | seed = (seed * 214013 + 2531011); 338 | val = (((seed >> 16) & 0x7fff) & 256) != 0 ? val0 : -val0; 339 | At[i*astep + k] = val; 340 | } 341 | for(iter = 0; iter < 2; iter++) { 342 | for(j = 0; j < i; j++) { 343 | sd = 0; 344 | for(k = 0; k < m; k++) { 345 | sd += At[i*astep + k]*At[j*astep + k]; 346 | } 347 | asum = 0.0; 348 | for(k = 0; k < m; k++) { 349 | t = (At[i*astep + k] - sd*At[j*astep + k]); 350 | At[i*astep + k] = t; 351 | asum += Math.abs(t); 352 | } 353 | asum = asum ? 1.0/asum : 0; 354 | for(k = 0; k < m; k++) { 355 | At[i*astep + k] *= asum; 356 | } 357 | } 358 | } 359 | sd = 0; 360 | for(k = 0; k < m; k++) { 361 | t = At[i*astep + k]; 362 | sd += t*t; 363 | } 364 | sd = Math.sqrt(sd); 365 | } 366 | 367 | s = (1.0/sd); 368 | for(k = 0; k < m; k++) { 369 | At[i*astep + k] *= s; 370 | } 371 | } 372 | 373 | jsfeat.cache.put_buffer(W_buff); 374 | } 375 | 376 | return { 377 | 378 | lu_solve: function(A, B) { 379 | var i=0,j=0,k=0,p=1,astep=A.cols; 380 | var ad=A.data, bd=B.data; 381 | var t,alpha,d,s; 382 | 383 | for(i = 0; i < astep; i++) { 384 | k = i; 385 | for(j = i+1; j < astep; j++) { 386 | if(Math.abs(ad[j*astep + i]) > Math.abs(ad[k*astep+i])) { 387 | k = j; 388 | } 389 | } 390 | 391 | if(Math.abs(ad[k*astep+i]) < jsfeat.EPSILON) { 392 | return 0; // FAILED 393 | } 394 | 395 | if(k != i) { 396 | for(j = i; j < astep; j++ ) { 397 | swap(ad, i*astep+j, k*astep+j, t); 398 | } 399 | 400 | swap(bd, i, k, t); 401 | p = -p; 402 | } 403 | 404 | d = -1.0/ad[i*astep+i]; 405 | 406 | for(j = i+1; j < astep; j++) { 407 | alpha = ad[j*astep+i]*d; 408 | 409 | for(k = i+1; k < astep; k++) { 410 | ad[j*astep+k] += alpha*ad[i*astep+k]; 411 | } 412 | 413 | bd[j] += alpha*bd[i]; 414 | } 415 | 416 | ad[i*astep+i] = -d; 417 | } 418 | 419 | for(i = astep-1; i >= 0; i--) { 420 | s = bd[i]; 421 | for(k = i+1; k < astep; k++) { 422 | s -= ad[i*astep+k]*bd[k]; 423 | } 424 | bd[i] = s*ad[i*astep+i]; 425 | } 426 | 427 | return 1; // OK 428 | }, 429 | 430 | cholesky_solve: function(A, B) { 431 | var col=0,row=0,col2=0,cs=0,rs=0,i=0,j=0; 432 | var size = A.cols; 433 | var ad=A.data, bd=B.data; 434 | var val,inv_diag; 435 | 436 | for (col = 0; col < size; col++) { 437 | inv_diag = 1.0; 438 | cs = (col * size); 439 | rs = cs; 440 | for (row = col; row < size; row++) 441 | { 442 | // correct for the parts of cholesky already computed 443 | val = ad[(rs+col)]; 444 | for (col2 = 0; col2 < col; col2++) { 445 | val -= ad[(col2*size+col)] * ad[(rs+col2)]; 446 | } 447 | if (row == col) { 448 | // this is the diagonal element so don't divide 449 | ad[(rs+col)] = val; 450 | if(val == 0) { 451 | return 0; 452 | } 453 | inv_diag = 1.0 / val; 454 | } else { 455 | // cache the value without division in the upper half 456 | ad[(cs+row)] = val; 457 | // divide my the diagonal element for all others 458 | ad[(rs+col)] = val * inv_diag; 459 | } 460 | rs = (rs + size); 461 | } 462 | } 463 | 464 | // first backsub through L 465 | cs = 0; 466 | for (i = 0; i < size; i++) { 467 | val = bd[i]; 468 | for (j = 0; j < i; j++) { 469 | val -= ad[(cs+j)] * bd[j]; 470 | } 471 | bd[i] = val; 472 | cs = (cs + size); 473 | } 474 | // backsub through diagonal 475 | cs = 0; 476 | for (i = 0; i < size; i++) { 477 | bd[i] /= ad[(cs + i)]; 478 | cs = (cs + size); 479 | } 480 | // backsub through L Transpose 481 | i = (size-1); 482 | for (; i >= 0; i--) { 483 | val = bd[i]; 484 | j = (i + 1); 485 | cs = (j * size); 486 | for (; j < size; j++) { 487 | val -= ad[(cs + i)] * bd[j]; 488 | cs = (cs + size); 489 | } 490 | bd[i] = val; 491 | } 492 | 493 | return 1; 494 | }, 495 | 496 | svd_decompose: function(A, W, U, V, options) { 497 | if (typeof options === "undefined") { options = 0; }; 498 | var at=0,i=0,j=0,_m=A.rows,_n=A.cols,m=_m,n=_n; 499 | var dt = A.type | jsfeat.C1_t; // we only work with single channel 500 | 501 | if(m < n) { 502 | at = 1; 503 | i = m; 504 | m = n; 505 | n = i; 506 | } 507 | 508 | var a_buff = jsfeat.cache.get_buffer((m*m)<<3); 509 | var w_buff = jsfeat.cache.get_buffer(n<<3); 510 | var v_buff = jsfeat.cache.get_buffer((n*n)<<3); 511 | 512 | var a_mt = new jsfeat.matrix_t(m, m, dt, a_buff.data); 513 | var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); 514 | var v_mt = new jsfeat.matrix_t(n, n, dt, v_buff.data); 515 | 516 | if(at == 0) { 517 | // transpose 518 | jsfeat.matmath.transpose(a_mt, A); 519 | } else { 520 | for(i = 0; i < _n*_m; i++) { 521 | a_mt.data[i] = A.data[i]; 522 | } 523 | for(; i < n*m; i++) { 524 | a_mt.data[i] = 0; 525 | } 526 | } 527 | 528 | JacobiSVDImpl(a_mt.data, m, w_mt.data, v_mt.data, n, m, n, m); 529 | 530 | if(W) { 531 | for(i=0; i < n; i++) { 532 | W.data[i] = w_mt.data[i]; 533 | } 534 | for(; i < _n; i++) { 535 | W.data[i] = 0; 536 | } 537 | } 538 | 539 | if (at == 0) { 540 | if(U && (options & jsfeat.SVD_U_T)) { 541 | i = m*m; 542 | while(--i >= 0) { 543 | U.data[i] = a_mt.data[i]; 544 | } 545 | } else if(U) { 546 | jsfeat.matmath.transpose(U, a_mt); 547 | } 548 | 549 | if(V && (options & jsfeat.SVD_V_T)) { 550 | i = n*n; 551 | while(--i >= 0) { 552 | V.data[i] = v_mt.data[i]; 553 | } 554 | } else if(V) { 555 | jsfeat.matmath.transpose(V, v_mt); 556 | } 557 | } else { 558 | if(U && (options & jsfeat.SVD_U_T)) { 559 | i = n*n; 560 | while(--i >= 0) { 561 | U.data[i] = v_mt.data[i]; 562 | } 563 | } else if(U) { 564 | jsfeat.matmath.transpose(U, v_mt); 565 | } 566 | 567 | if(V && (options & jsfeat.SVD_V_T)) { 568 | i = m*m; 569 | while(--i >= 0) { 570 | V.data[i] = a_mt.data[i]; 571 | } 572 | } else if(V) { 573 | jsfeat.matmath.transpose(V, a_mt); 574 | } 575 | } 576 | 577 | jsfeat.cache.put_buffer(a_buff); 578 | jsfeat.cache.put_buffer(w_buff); 579 | jsfeat.cache.put_buffer(v_buff); 580 | 581 | }, 582 | 583 | svd_solve: function(A, X, B) { 584 | var i=0,j=0,k=0; 585 | var pu=0,pv=0; 586 | var nrows=A.rows,ncols=A.cols; 587 | var sum=0.0,xsum=0.0,tol=0.0; 588 | var dt = A.type | jsfeat.C1_t; 589 | 590 | var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); 591 | var w_buff = jsfeat.cache.get_buffer(ncols<<3); 592 | var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); 593 | 594 | var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); 595 | var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); 596 | var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); 597 | 598 | var bd = B.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; 599 | 600 | this.svd_decompose(A, w_mt, u_mt, v_mt, 0); 601 | 602 | tol = jsfeat.EPSILON * wd[0] * ncols; 603 | 604 | for (; i < ncols; i++, pv += ncols) { 605 | xsum = 0.0; 606 | for(j = 0; j < ncols; j++) { 607 | if(wd[j] > tol) { 608 | for(k = 0, sum = 0.0, pu = 0; k < nrows; k++, pu += ncols) { 609 | sum += ud[pu + j] * bd[k]; 610 | } 611 | xsum += sum * vd[pv + j] / wd[j]; 612 | } 613 | } 614 | X.data[i] = xsum; 615 | } 616 | 617 | jsfeat.cache.put_buffer(u_buff); 618 | jsfeat.cache.put_buffer(w_buff); 619 | jsfeat.cache.put_buffer(v_buff); 620 | }, 621 | 622 | svd_invert: function(Ai, A) { 623 | var i=0,j=0,k=0; 624 | var pu=0,pv=0,pa=0; 625 | var nrows=A.rows,ncols=A.cols; 626 | var sum=0.0,tol=0.0; 627 | var dt = A.type | jsfeat.C1_t; 628 | 629 | var u_buff = jsfeat.cache.get_buffer((nrows*nrows)<<3); 630 | var w_buff = jsfeat.cache.get_buffer(ncols<<3); 631 | var v_buff = jsfeat.cache.get_buffer((ncols*ncols)<<3); 632 | 633 | var u_mt = new jsfeat.matrix_t(nrows, nrows, dt, u_buff.data); 634 | var w_mt = new jsfeat.matrix_t(1, ncols, dt, w_buff.data); 635 | var v_mt = new jsfeat.matrix_t(ncols, ncols, dt, v_buff.data); 636 | 637 | var id = Ai.data, ud = u_mt.data, wd = w_mt.data, vd = v_mt.data; 638 | 639 | this.svd_decompose(A, w_mt, u_mt, v_mt, 0); 640 | 641 | tol = jsfeat.EPSILON * wd[0] * ncols; 642 | 643 | for (; i < ncols; i++, pv += ncols) { 644 | for (j = 0, pu = 0; j < nrows; j++, pa++) { 645 | for (k = 0, sum = 0.0; k < ncols; k++, pu++) { 646 | if (wd[k] > tol) sum += vd[pv + k] * ud[pu] / wd[k]; 647 | } 648 | id[pa] = sum; 649 | } 650 | } 651 | 652 | jsfeat.cache.put_buffer(u_buff); 653 | jsfeat.cache.put_buffer(w_buff); 654 | jsfeat.cache.put_buffer(v_buff); 655 | }, 656 | 657 | eigenVV: function(A, vects, vals) { 658 | var n=A.cols,i=n*n; 659 | var dt = A.type | jsfeat.C1_t; 660 | 661 | var a_buff = jsfeat.cache.get_buffer((n*n)<<3); 662 | var w_buff = jsfeat.cache.get_buffer(n<<3); 663 | var a_mt = new jsfeat.matrix_t(n, n, dt, a_buff.data); 664 | var w_mt = new jsfeat.matrix_t(1, n, dt, w_buff.data); 665 | 666 | while(--i >= 0) { 667 | a_mt.data[i] = A.data[i]; 668 | } 669 | 670 | JacobiImpl(a_mt.data, n, w_mt.data, vects ? vects.data : null, n, n); 671 | 672 | if(vals) { 673 | while(--n >= 0) { 674 | vals.data[n] = w_mt.data[n]; 675 | } 676 | } 677 | 678 | jsfeat.cache.put_buffer(a_buff); 679 | jsfeat.cache.put_buffer(w_buff); 680 | } 681 | 682 | }; 683 | 684 | })(); 685 | 686 | global.linalg = linalg; 687 | 688 | })(jsfeat); -------------------------------------------------------------------------------- /src/jsfeat_mat_math.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | */ 5 | 6 | (function(global) { 7 | "use strict"; 8 | // 9 | 10 | var matmath = (function() { 11 | 12 | return { 13 | identity: function(M, value) { 14 | if (typeof value === "undefined") { value=1; } 15 | var src=M.data; 16 | var rows=M.rows, cols=M.cols, cols_1=(cols+1)|0; 17 | var len = rows * cols; 18 | var k = len; 19 | while(--len >= 0) src[len] = 0.0; 20 | len = k; 21 | k = 0; 22 | while(k < len) { 23 | src[k] = value; 24 | k = k + cols_1; 25 | } 26 | }, 27 | 28 | transpose: function(At, A) { 29 | var i=0,j=0,nrows=A.rows,ncols=A.cols; 30 | var Ai=0,Ati=0,pAt=0; 31 | var ad=A.data,atd=At.data; 32 | 33 | for (; i < nrows; Ati += 1, Ai += ncols, i++) { 34 | pAt = Ati; 35 | for (j = 0; j < ncols; pAt += nrows, j++) atd[pAt] = ad[Ai+j]; 36 | } 37 | }, 38 | 39 | // C = A * B 40 | multiply: function(C, A, B) { 41 | var i=0,j=0,k=0; 42 | var Ap=0,pA=0,pB=0,p_B=0,Cp=0; 43 | var ncols=A.cols,nrows=A.rows,mcols=B.cols; 44 | var ad=A.data,bd=B.data,cd=C.data; 45 | var sum=0.0; 46 | 47 | for (; i < nrows; Ap += ncols, i++) { 48 | for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { 49 | pB = p_B; 50 | pA = Ap; 51 | sum = 0.0; 52 | for (k = 0; k < ncols; pA++, pB += mcols, k++) { 53 | sum += ad[pA] * bd[pB]; 54 | } 55 | cd[Cp] = sum; 56 | } 57 | } 58 | }, 59 | 60 | // C = A * B' 61 | multiply_ABt: function(C, A, B) { 62 | var i=0,j=0,k=0; 63 | var Ap=0,pA=0,pB=0,Cp=0; 64 | var ncols=A.cols,nrows=A.rows,mrows=B.rows; 65 | var ad=A.data,bd=B.data,cd=C.data; 66 | var sum=0.0; 67 | 68 | for (; i < nrows; Ap += ncols, i++) { 69 | for (pB = 0, j = 0; j < mrows; Cp++, j++) { 70 | pA = Ap; 71 | sum = 0.0; 72 | for (k = 0; k < ncols; pA++, pB++, k++) { 73 | sum += ad[pA] * bd[pB]; 74 | } 75 | cd[Cp] = sum; 76 | } 77 | } 78 | }, 79 | 80 | // C = A' * B 81 | multiply_AtB: function(C, A, B) { 82 | var i=0,j=0,k=0; 83 | var Ap=0,pA=0,pB=0,p_B=0,Cp=0; 84 | var ncols=A.cols,nrows=A.rows,mcols=B.cols; 85 | var ad=A.data,bd=B.data,cd=C.data; 86 | var sum=0.0; 87 | 88 | for (; i < ncols; Ap++, i++) { 89 | for (p_B = 0, j = 0; j < mcols; Cp++, p_B++, j++) { 90 | pB = p_B; 91 | pA = Ap; 92 | sum = 0.0; 93 | for (k = 0; k < nrows; pA += ncols, pB += mcols, k++) { 94 | sum += ad[pA] * bd[pB]; 95 | } 96 | cd[Cp] = sum; 97 | } 98 | } 99 | }, 100 | 101 | // C = A * A' 102 | multiply_AAt: function(C, A) { 103 | var i=0,j=0,k=0; 104 | var pCdiag=0,p_A=0,pA=0,pB=0,pC=0,pCt=0; 105 | var ncols=A.cols,nrows=A.rows; 106 | var ad=A.data,cd=C.data; 107 | var sum=0.0; 108 | 109 | for (; i < nrows; pCdiag += nrows + 1, p_A = pA, i++) { 110 | pC = pCdiag; 111 | pCt = pCdiag; 112 | pB = p_A; 113 | for (j = i; j < nrows; pC++, pCt += nrows, j++) { 114 | pA = p_A; 115 | sum = 0.0; 116 | for (k = 0; k < ncols; k++) { 117 | sum += ad[pA++] * ad[pB++]; 118 | } 119 | cd[pC] = sum 120 | cd[pCt] = sum; 121 | } 122 | } 123 | }, 124 | 125 | // C = A' * A 126 | multiply_AtA: function(C, A) { 127 | var i=0,j=0,k=0; 128 | var p_A=0,pA=0,pB=0,p_C=0,pC=0,p_CC=0; 129 | var ncols=A.cols,nrows=A.rows; 130 | var ad=A.data,cd=C.data; 131 | var sum=0.0; 132 | 133 | for (; i < ncols; p_C += ncols, i++) { 134 | p_A = i; 135 | p_CC = p_C + i; 136 | pC = p_CC; 137 | for (j = i; j < ncols; pC++, p_CC += ncols, j++) { 138 | pA = p_A; 139 | pB = j; 140 | sum = 0.0; 141 | for (k = 0; k < nrows; pA += ncols, pB += ncols, k++) { 142 | sum += ad[pA] * ad[pB]; 143 | } 144 | cd[pC] = sum 145 | cd[p_CC] = sum; 146 | } 147 | } 148 | }, 149 | 150 | // various small matrix operations 151 | identity_3x3: function(M, value) { 152 | if (typeof value === "undefined") { value=1; } 153 | var dt=M.data; 154 | dt[0] = dt[4] = dt[8] = value; 155 | dt[1] = dt[2] = dt[3] = 0; 156 | dt[5] = dt[6] = dt[7] = 0; 157 | }, 158 | 159 | invert_3x3: function(from, to) { 160 | var A = from.data, invA = to.data; 161 | var t1 = A[4]; 162 | var t2 = A[8]; 163 | var t4 = A[5]; 164 | var t5 = A[7]; 165 | var t8 = A[0]; 166 | 167 | var t9 = t8*t1; 168 | var t11 = t8*t4; 169 | var t13 = A[3]; 170 | var t14 = A[1]; 171 | var t15 = t13*t14; 172 | var t17 = A[2]; 173 | var t18 = t13*t17; 174 | var t20 = A[6]; 175 | var t21 = t20*t14; 176 | var t23 = t20*t17; 177 | var t26 = 1.0/(t9*t2-t11*t5-t15*t2+t18*t5+t21*t4-t23*t1); 178 | invA[0] = (t1*t2-t4*t5)*t26; 179 | invA[1] = -(t14*t2-t17*t5)*t26; 180 | invA[2] = -(-t14*t4+t17*t1)*t26; 181 | invA[3] = -(t13*t2-t4*t20)*t26; 182 | invA[4] = (t8*t2-t23)*t26; 183 | invA[5] = -(t11-t18)*t26; 184 | invA[6] = -(-t13*t5+t1*t20)*t26; 185 | invA[7] = -(t8*t5-t21)*t26; 186 | invA[8] = (t9-t15)*t26; 187 | }, 188 | // C = A * B 189 | multiply_3x3: function(C, A, B) { 190 | var Cd=C.data, Ad=A.data, Bd=B.data; 191 | var m1_0 = Ad[0], m1_1 = Ad[1], m1_2 = Ad[2]; 192 | var m1_3 = Ad[3], m1_4 = Ad[4], m1_5 = Ad[5]; 193 | var m1_6 = Ad[6], m1_7 = Ad[7], m1_8 = Ad[8]; 194 | 195 | var m2_0 = Bd[0], m2_1 = Bd[1], m2_2 = Bd[2]; 196 | var m2_3 = Bd[3], m2_4 = Bd[4], m2_5 = Bd[5]; 197 | var m2_6 = Bd[6], m2_7 = Bd[7], m2_8 = Bd[8]; 198 | 199 | Cd[0] = m1_0 * m2_0 + m1_1 * m2_3 + m1_2 * m2_6; 200 | Cd[1] = m1_0 * m2_1 + m1_1 * m2_4 + m1_2 * m2_7; 201 | Cd[2] = m1_0 * m2_2 + m1_1 * m2_5 + m1_2 * m2_8; 202 | Cd[3] = m1_3 * m2_0 + m1_4 * m2_3 + m1_5 * m2_6; 203 | Cd[4] = m1_3 * m2_1 + m1_4 * m2_4 + m1_5 * m2_7; 204 | Cd[5] = m1_3 * m2_2 + m1_4 * m2_5 + m1_5 * m2_8; 205 | Cd[6] = m1_6 * m2_0 + m1_7 * m2_3 + m1_8 * m2_6; 206 | Cd[7] = m1_6 * m2_1 + m1_7 * m2_4 + m1_8 * m2_7; 207 | Cd[8] = m1_6 * m2_2 + m1_7 * m2_5 + m1_8 * m2_8; 208 | }, 209 | 210 | mat3x3_determinant: function(M) { 211 | var md=M.data; 212 | return md[0] * md[4] * md[8] - 213 | md[0] * md[5] * md[7] - 214 | md[3] * md[1] * md[8] + 215 | md[3] * md[2] * md[7] + 216 | md[6] * md[1] * md[5] - 217 | md[6] * md[2] * md[4]; 218 | }, 219 | 220 | determinant_3x3: function(M11, M12, M13, 221 | M21, M22, M23, 222 | M31, M32, M33) { 223 | return M11 * M22 * M33 - M11 * M23 * M32 - 224 | M21 * M12 * M33 + M21 * M13 * M32 + 225 | M31 * M12 * M23 - M31 * M13 * M22; 226 | } 227 | }; 228 | 229 | })(); 230 | 231 | global.matmath = matmath; 232 | 233 | })(jsfeat); -------------------------------------------------------------------------------- /src/jsfeat_math.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | (function(global) { 6 | "use strict"; 7 | // 8 | 9 | var math = (function() { 10 | 11 | var qsort_stack = new Int32Array(48*2); 12 | 13 | return { 14 | get_gaussian_kernel: function(size, sigma, kernel, data_type) { 15 | var i=0,x=0.0,t=0.0,sigma_x=0.0,scale_2x=0.0; 16 | var sum = 0.0; 17 | var kern_node = jsfeat.cache.get_buffer(size<<2); 18 | var _kernel = kern_node.f32;//new Float32Array(size); 19 | 20 | if((size&1) == 1 && size <= 7 && sigma <= 0) { 21 | switch(size>>1) { 22 | case 0: 23 | _kernel[0] = 1.0; 24 | sum = 1.0; 25 | break; 26 | case 1: 27 | _kernel[0] = 0.25, _kernel[1] = 0.5, _kernel[2] = 0.25; 28 | sum = 0.25+0.5+0.25; 29 | break; 30 | case 2: 31 | _kernel[0] = 0.0625, _kernel[1] = 0.25, _kernel[2] = 0.375, 32 | _kernel[3] = 0.25, _kernel[4] = 0.0625; 33 | sum = 0.0625+0.25+0.375+0.25+0.0625; 34 | break; 35 | case 3: 36 | _kernel[0] = 0.03125, _kernel[1] = 0.109375, _kernel[2] = 0.21875, 37 | _kernel[3] = 0.28125, _kernel[4] = 0.21875, _kernel[5] = 0.109375, _kernel[6] = 0.03125; 38 | sum = 0.03125+0.109375+0.21875+0.28125+0.21875+0.109375+0.03125; 39 | break; 40 | } 41 | } else { 42 | sigma_x = sigma > 0 ? sigma : ((size-1)*0.5 - 1.0)*0.3 + 0.8; 43 | scale_2x = -0.5/(sigma_x*sigma_x); 44 | 45 | for( ; i < size; ++i ) 46 | { 47 | x = i - (size-1)*0.5; 48 | t = Math.exp(scale_2x*x*x); 49 | 50 | _kernel[i] = t; 51 | sum += t; 52 | } 53 | } 54 | 55 | if(data_type & jsfeat.U8_t) { 56 | // int based kernel 57 | sum = 256.0/sum; 58 | for (i = 0; i < size; ++i) { 59 | kernel[i] = (_kernel[i] * sum + 0.5)|0; 60 | } 61 | } else { 62 | // classic kernel 63 | sum = 1.0/sum; 64 | for (i = 0; i < size; ++i) { 65 | kernel[i] = _kernel[i] * sum; 66 | } 67 | } 68 | 69 | jsfeat.cache.put_buffer(kern_node); 70 | }, 71 | 72 | // model is 3x3 matrix_t 73 | perspective_4point_transform: function(model, src_x0, src_y0, dst_x0, dst_y0, 74 | src_x1, src_y1, dst_x1, dst_y1, 75 | src_x2, src_y2, dst_x2, dst_y2, 76 | src_x3, src_y3, dst_x3, dst_y3) { 77 | var t1 = src_x0; 78 | var t2 = src_x2; 79 | var t4 = src_y1; 80 | var t5 = t1 * t2 * t4; 81 | var t6 = src_y3; 82 | var t7 = t1 * t6; 83 | var t8 = t2 * t7; 84 | var t9 = src_y2; 85 | var t10 = t1 * t9; 86 | var t11 = src_x1; 87 | var t14 = src_y0; 88 | var t15 = src_x3; 89 | var t16 = t14 * t15; 90 | var t18 = t16 * t11; 91 | var t20 = t15 * t11 * t9; 92 | var t21 = t15 * t4; 93 | var t24 = t15 * t9; 94 | var t25 = t2 * t4; 95 | var t26 = t6 * t2; 96 | var t27 = t6 * t11; 97 | var t28 = t9 * t11; 98 | var t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); 99 | var t32 = t1 * t15; 100 | var t35 = t14 * t11; 101 | var t41 = t4 * t1; 102 | var t42 = t6 * t41; 103 | var t43 = t14 * t2; 104 | var t46 = t16 * t9; 105 | var t48 = t14 * t9 * t11; 106 | var t51 = t4 * t6 * t2; 107 | var t55 = t6 * t14; 108 | var Hr0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; 109 | var Hr1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; 110 | var Hr2 = t1; 111 | var Hr3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; 112 | var Hr4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; 113 | var Hr5 = t14; 114 | var Hr6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; 115 | var Hr7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; 116 | 117 | t1 = dst_x0; 118 | t2 = dst_x2; 119 | t4 = dst_y1; 120 | t5 = t1 * t2 * t4; 121 | t6 = dst_y3; 122 | t7 = t1 * t6; 123 | t8 = t2 * t7; 124 | t9 = dst_y2; 125 | t10 = t1 * t9; 126 | t11 = dst_x1; 127 | t14 = dst_y0; 128 | t15 = dst_x3; 129 | t16 = t14 * t15; 130 | t18 = t16 * t11; 131 | t20 = t15 * t11 * t9; 132 | t21 = t15 * t4; 133 | t24 = t15 * t9; 134 | t25 = t2 * t4; 135 | t26 = t6 * t2; 136 | t27 = t6 * t11; 137 | t28 = t9 * t11; 138 | t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); 139 | t32 = t1 * t15; 140 | t35 = t14 * t11; 141 | t41 = t4 * t1; 142 | t42 = t6 * t41; 143 | t43 = t14 * t2; 144 | t46 = t16 * t9; 145 | t48 = t14 * t9 * t11; 146 | t51 = t4 * t6 * t2; 147 | t55 = t6 * t14; 148 | var Hl0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; 149 | var Hl1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; 150 | var Hl2 = t1; 151 | var Hl3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; 152 | var Hl4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; 153 | var Hl5 = t14; 154 | var Hl6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; 155 | var Hl7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; 156 | 157 | // the following code computes R = Hl * inverse Hr 158 | t2 = Hr4-Hr7*Hr5; 159 | t4 = Hr0*Hr4; 160 | t5 = Hr0*Hr5; 161 | t7 = Hr3*Hr1; 162 | t8 = Hr2*Hr3; 163 | t10 = Hr1*Hr6; 164 | var t12 = Hr2*Hr6; 165 | t15 = 1.0 / (t4-t5*Hr7-t7+t8*Hr7+t10*Hr5-t12*Hr4); 166 | t18 = -Hr3+Hr5*Hr6; 167 | var t23 = -Hr3*Hr7+Hr4*Hr6; 168 | t28 = -Hr1+Hr2*Hr7; 169 | var t31 = Hr0-t12; 170 | t35 = Hr0*Hr7-t10; 171 | t41 = -Hr1*Hr5+Hr2*Hr4; 172 | var t44 = t5-t8; 173 | var t47 = t4-t7; 174 | t48 = t2*t15; 175 | var t49 = t28*t15; 176 | var t50 = t41*t15; 177 | var mat = model.data; 178 | mat[0] = Hl0*t48+Hl1*(t18*t15)-Hl2*(t23*t15); 179 | mat[1] = Hl0*t49+Hl1*(t31*t15)-Hl2*(t35*t15); 180 | mat[2] = -Hl0*t50-Hl1*(t44*t15)+Hl2*(t47*t15); 181 | mat[3] = Hl3*t48+Hl4*(t18*t15)-Hl5*(t23*t15); 182 | mat[4] = Hl3*t49+Hl4*(t31*t15)-Hl5*(t35*t15); 183 | mat[5] = -Hl3*t50-Hl4*(t44*t15)+Hl5*(t47*t15); 184 | mat[6] = Hl6*t48+Hl7*(t18*t15)-t23*t15; 185 | mat[7] = Hl6*t49+Hl7*(t31*t15)-t35*t15; 186 | mat[8] = -Hl6*t50-Hl7*(t44*t15)+t47*t15; 187 | }, 188 | 189 | // The current implementation was derived from *BSD system qsort(): 190 | // Copyright (c) 1992, 1993 191 | // The Regents of the University of California. All rights reserved. 192 | qsort: function(array, low, high, cmp) { 193 | var isort_thresh = 7; 194 | var t,ta,tb,tc; 195 | var sp = 0,left=0,right=0,i=0,n=0,m=0,ptr=0,ptr2=0,d=0; 196 | var left0=0,left1=0,right0=0,right1=0,pivot=0,a=0,b=0,c=0,swap_cnt=0; 197 | 198 | var stack = qsort_stack; 199 | 200 | if( (high-low+1) <= 1 ) return; 201 | 202 | stack[0] = low; 203 | stack[1] = high; 204 | 205 | while( sp >= 0 ) { 206 | 207 | left = stack[sp<<1]; 208 | right = stack[(sp<<1)+1]; 209 | sp--; 210 | 211 | for(;;) { 212 | n = (right - left) + 1; 213 | 214 | if( n <= isort_thresh ) { 215 | //insert_sort: 216 | for( ptr = left + 1; ptr <= right; ptr++ ) { 217 | for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { 218 | t = array[ptr2]; 219 | array[ptr2] = array[ptr2-1]; 220 | array[ptr2-1] = t; 221 | } 222 | } 223 | break; 224 | } else { 225 | swap_cnt = 0; 226 | 227 | left0 = left; 228 | right0 = right; 229 | pivot = left + (n>>1); 230 | 231 | if( n > 40 ) { 232 | d = n >> 3; 233 | a = left, b = left + d, c = left + (d<<1); 234 | ta = array[a],tb = array[b],tc = array[c]; 235 | left = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) 236 | : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); 237 | 238 | a = pivot - d, b = pivot, c = pivot + d; 239 | ta = array[a],tb = array[b],tc = array[c]; 240 | pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) 241 | : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); 242 | 243 | a = right - (d<<1), b = right - d, c = right; 244 | ta = array[a],tb = array[b],tc = array[c]; 245 | right = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) 246 | : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); 247 | } 248 | 249 | a = left, b = pivot, c = right; 250 | ta = array[a],tb = array[b],tc = array[c]; 251 | pivot = cmp(ta, tb) ? (cmp(tb, tc) ? b : (cmp(ta, tc) ? c : a)) 252 | : (cmp(tc, tb) ? b : (cmp(ta, tc) ? a : c)); 253 | if( pivot != left0 ) { 254 | t = array[pivot]; 255 | array[pivot] = array[left0]; 256 | array[left0] = t; 257 | pivot = left0; 258 | } 259 | left = left1 = left0 + 1; 260 | right = right1 = right0; 261 | 262 | ta = array[pivot]; 263 | for(;;) { 264 | while( left <= right && !cmp(ta, array[left]) ) { 265 | if( !cmp(array[left], ta) ) { 266 | if( left > left1 ) { 267 | t = array[left1]; 268 | array[left1] = array[left]; 269 | array[left] = t; 270 | } 271 | swap_cnt = 1; 272 | left1++; 273 | } 274 | left++; 275 | } 276 | 277 | while( left <= right && !cmp(array[right], ta) ) { 278 | if( !cmp(ta, array[right]) ) { 279 | if( right < right1 ) { 280 | t = array[right1]; 281 | array[right1] = array[right]; 282 | array[right] = t; 283 | } 284 | swap_cnt = 1; 285 | right1--; 286 | } 287 | right--; 288 | } 289 | 290 | if( left > right ) break; 291 | 292 | t = array[left]; 293 | array[left] = array[right]; 294 | array[right] = t; 295 | swap_cnt = 1; 296 | left++; 297 | right--; 298 | } 299 | 300 | if( swap_cnt == 0 ) { 301 | left = left0, right = right0; 302 | //goto insert_sort; 303 | for( ptr = left + 1; ptr <= right; ptr++ ) { 304 | for( ptr2 = ptr; ptr2 > left && cmp(array[ptr2],array[ptr2-1]); ptr2--) { 305 | t = array[ptr2]; 306 | array[ptr2] = array[ptr2-1]; 307 | array[ptr2-1] = t; 308 | } 309 | } 310 | break; 311 | } 312 | 313 | n = Math.min( (left1 - left0), (left - left1) ); 314 | m = (left-n)|0; 315 | for( i = 0; i < n; ++i,++m ) { 316 | t = array[left0+i]; 317 | array[left0+i] = array[m]; 318 | array[m] = t; 319 | } 320 | 321 | n = Math.min( (right0 - right1), (right1 - right) ); 322 | m = (right0-n+1)|0; 323 | for( i = 0; i < n; ++i,++m ) { 324 | t = array[left+i]; 325 | array[left+i] = array[m]; 326 | array[m] = t; 327 | } 328 | n = (left - left1); 329 | m = (right1 - right); 330 | if( n > 1 ) { 331 | if( m > 1 ) { 332 | if( n > m ) { 333 | ++sp; 334 | stack[sp<<1] = left0; 335 | stack[(sp<<1)+1] = left0 + n - 1; 336 | left = right0 - m + 1, right = right0; 337 | } else { 338 | ++sp; 339 | stack[sp<<1] = right0 - m + 1; 340 | stack[(sp<<1)+1] = right0; 341 | left = left0, right = left0 + n - 1; 342 | } 343 | } else { 344 | left = left0, right = left0 + n - 1; 345 | } 346 | } 347 | else if( m > 1 ) 348 | left = right0 - m + 1, right = right0; 349 | else 350 | break; 351 | } 352 | } 353 | } 354 | }, 355 | 356 | median: function(array, low, high) { 357 | var w; 358 | var middle=0,ll=0,hh=0,median=(low+high)>>1; 359 | for (;;) { 360 | if (high <= low) return array[median]; 361 | if (high == (low + 1)) { 362 | if (array[low] > array[high]) { 363 | w = array[low]; 364 | array[low] = array[high]; 365 | array[high] = w; 366 | } 367 | return array[median]; 368 | } 369 | middle = ((low + high) >> 1); 370 | if (array[middle] > array[high]) { 371 | w = array[middle]; 372 | array[middle] = array[high]; 373 | array[high] = w; 374 | } 375 | if (array[low] > array[high]) { 376 | w = array[low]; 377 | array[low] = array[high]; 378 | array[high] = w; 379 | } 380 | if (array[middle] > array[low]) { 381 | w = array[middle]; 382 | array[middle] = array[low]; 383 | array[low] = w; 384 | } 385 | ll = (low + 1); 386 | w = array[middle]; 387 | array[middle] = array[ll]; 388 | array[ll] = w; 389 | hh = high; 390 | for (;;) { 391 | do ++ll; while (array[low] > array[ll]); 392 | do --hh; while (array[hh] > array[low]); 393 | if (hh < ll) break; 394 | w = array[ll]; 395 | array[ll] = array[hh]; 396 | array[hh] = w; 397 | } 398 | w = array[low]; 399 | array[low] = array[hh]; 400 | array[hh] = w; 401 | if (hh <= median) 402 | low = ll; 403 | else if (hh >= median) 404 | high = (hh - 1); 405 | } 406 | return 0; 407 | } 408 | }; 409 | 410 | })(); 411 | 412 | global.math = math; 413 | 414 | })(jsfeat); 415 | -------------------------------------------------------------------------------- /src/jsfeat_motion_estimator.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | */ 5 | 6 | (function(global) { 7 | "use strict"; 8 | // 9 | 10 | var motion_model = (function() { 11 | 12 | var sqr = function(x) { 13 | return x*x; 14 | } 15 | 16 | // does isotropic normalization 17 | var iso_normalize_points = function(from, to, T0, T1, count) { 18 | var i=0; 19 | var cx0=0.0, cy0=0.0, d0=0.0, s0=0.0; 20 | var cx1=0.0, cy1=0.0, d1=0.0, s1=0.0; 21 | var dx=0.0,dy=0.0; 22 | 23 | for (; i < count; ++i) { 24 | cx0 += from[i].x; 25 | cy0 += from[i].y; 26 | cx1 += to[i].x; 27 | cy1 += to[i].y; 28 | } 29 | 30 | cx0 /= count; cy0 /= count; 31 | cx1 /= count; cy1 /= count; 32 | 33 | for (i = 0; i < count; ++i) { 34 | dx = from[i].x - cx0; 35 | dy = from[i].y - cy0; 36 | d0 += Math.sqrt(dx*dx + dy*dy); 37 | dx = to[i].x - cx1; 38 | dy = to[i].y - cy1; 39 | d1 += Math.sqrt(dx*dx + dy*dy); 40 | } 41 | 42 | d0 /= count; d1 /= count; 43 | 44 | s0 = Math.SQRT2 / d0; s1 = Math.SQRT2 / d1; 45 | 46 | T0[0] = T0[4] = s0; 47 | T0[2] = -cx0*s0; 48 | T0[5] = -cy0*s0; 49 | T0[1] = T0[3] = T0[6] = T0[7] = 0.0; 50 | T0[8] = 1.0; 51 | 52 | T1[0] = T1[4] = s1; 53 | T1[2] = -cx1*s1; 54 | T1[5] = -cy1*s1; 55 | T1[1] = T1[3] = T1[6] = T1[7] = 0.0; 56 | T1[8] = 1.0; 57 | } 58 | 59 | var have_collinear_points = function(points, count) { 60 | var j=0,k=0,i=(count-1)|0; 61 | var dx1=0.0,dy1=0.0,dx2=0.0,dy2=0.0; 62 | 63 | // check that the i-th selected point does not belong 64 | // to a line connecting some previously selected points 65 | for(; j < i; ++j) { 66 | dx1 = points[j].x - points[i].x; 67 | dy1 = points[j].y - points[i].y; 68 | for(k = 0; k < j; ++k) { 69 | dx2 = points[k].x - points[i].x; 70 | dy2 = points[k].y - points[i].y; 71 | if( Math.abs(dx2*dy1 - dy2*dx1) <= jsfeat.EPSILON*(Math.abs(dx1) + Math.abs(dy1) + Math.abs(dx2) + Math.abs(dy2))) 72 | return true; 73 | } 74 | } 75 | return false; 76 | } 77 | 78 | var T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); 79 | var T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); 80 | var AtA = new jsfeat.matrix_t(6, 6, jsfeat.F32_t|jsfeat.C1_t); 81 | var AtB = new jsfeat.matrix_t(6, 1, jsfeat.F32_t|jsfeat.C1_t); 82 | 83 | var affine2d = (function () { 84 | 85 | function affine2d() { 86 | // empty constructor 87 | } 88 | 89 | affine2d.prototype.run = function(from, to, model, count) { 90 | var i=0,j=0; 91 | var dt=model.type|jsfeat.C1_t; 92 | var md=model.data, t0d=T0.data, t1d=T1.data; 93 | var pt0,pt1,px=0.0,py=0.0; 94 | 95 | iso_normalize_points(from, to, t0d, t1d, count); 96 | 97 | var a_buff = jsfeat.cache.get_buffer((2*count*6)<<3); 98 | var b_buff = jsfeat.cache.get_buffer((2*count)<<3); 99 | 100 | var a_mt = new jsfeat.matrix_t(6, 2*count, dt, a_buff.data); 101 | var b_mt = new jsfeat.matrix_t(1, 2*count, dt, b_buff.data); 102 | var ad=a_mt.data, bd=b_mt.data; 103 | 104 | for (; i < count; ++i) { 105 | pt0 = from[i]; 106 | pt1 = to[i]; 107 | 108 | px = t0d[0]*pt0.x + t0d[1]*pt0.y + t0d[2]; 109 | py = t0d[3]*pt0.x + t0d[4]*pt0.y + t0d[5]; 110 | 111 | j = i*2*6; 112 | ad[j]=px, ad[j+1]=py, ad[j+2]=1.0, ad[j+3]=0.0, ad[j+4]=0.0, ad[j+5]=0.0; 113 | 114 | j += 6; 115 | ad[j]=0.0, ad[j+1]=0.0, ad[j+2]=0.0, ad[j+3]=px, ad[j+4]=py, ad[j+5]=1.0; 116 | 117 | bd[i<<1] = t1d[0]*pt1.x + t1d[1]*pt1.y + t1d[2]; 118 | bd[(i<<1)+1] = t1d[3]*pt1.x + t1d[4]*pt1.y + t1d[5]; 119 | } 120 | 121 | jsfeat.matmath.multiply_AtA(AtA, a_mt); 122 | jsfeat.matmath.multiply_AtB(AtB, a_mt, b_mt); 123 | 124 | jsfeat.linalg.lu_solve(AtA, AtB); 125 | 126 | md[0] = AtB.data[0], md[1]=AtB.data[1], md[2]=AtB.data[2]; 127 | md[3] = AtB.data[3], md[4]=AtB.data[4], md[5]=AtB.data[5]; 128 | md[6] = 0.0, md[7] = 0.0, md[8] = 1.0; // fill last row 129 | 130 | // denormalize 131 | jsfeat.matmath.invert_3x3(T1, T1); 132 | jsfeat.matmath.multiply_3x3(model, T1, model); 133 | jsfeat.matmath.multiply_3x3(model, model, T0); 134 | 135 | // free buffer 136 | jsfeat.cache.put_buffer(a_buff); 137 | jsfeat.cache.put_buffer(b_buff); 138 | 139 | return 1; 140 | } 141 | 142 | affine2d.prototype.error = function(from, to, model, err, count) { 143 | var i=0; 144 | var pt0,pt1; 145 | var m=model.data; 146 | 147 | for (; i < count; ++i) { 148 | pt0 = from[i]; 149 | pt1 = to[i]; 150 | 151 | err[i] = sqr(pt1.x - m[0]*pt0.x - m[1]*pt0.y - m[2]) + 152 | sqr(pt1.y - m[3]*pt0.x - m[4]*pt0.y - m[5]); 153 | } 154 | } 155 | 156 | affine2d.prototype.check_subset = function(from, to, count) { 157 | return true; // all good 158 | } 159 | 160 | return affine2d; 161 | })(); 162 | 163 | var mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); 164 | var Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); 165 | 166 | var homography2d = (function () { 167 | 168 | function homography2d() { 169 | // empty constructor 170 | //this.T0 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); 171 | //this.T1 = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); 172 | //this.mLtL = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); 173 | //this.Evec = new jsfeat.matrix_t(9, 9, jsfeat.F32_t|jsfeat.C1_t); 174 | } 175 | 176 | homography2d.prototype.run = function(from, to, model, count) { 177 | var i=0,j=0; 178 | var md=model.data, t0d=T0.data, t1d=T1.data; 179 | var LtL=mLtL.data, evd=Evec.data; 180 | var x=0.0,y=0.0,X=0.0,Y=0.0; 181 | 182 | // norm 183 | var smx=0.0, smy=0.0, cmx=0.0, cmy=0.0, sMx=0.0, sMy=0.0, cMx=0.0, cMy=0.0; 184 | 185 | for(; i < count; ++i) { 186 | cmx += to[i].x; 187 | cmy += to[i].y; 188 | cMx += from[i].x; 189 | cMy += from[i].y; 190 | } 191 | 192 | cmx /= count; cmy /= count; 193 | cMx /= count; cMy /= count; 194 | 195 | for(i = 0; i < count; ++i) 196 | { 197 | smx += Math.abs(to[i].x - cmx); 198 | smy += Math.abs(to[i].y - cmy); 199 | sMx += Math.abs(from[i].x - cMx); 200 | sMy += Math.abs(from[i].y - cMy); 201 | } 202 | 203 | if( Math.abs(smx) < jsfeat.EPSILON 204 | || Math.abs(smy) < jsfeat.EPSILON 205 | || Math.abs(sMx) < jsfeat.EPSILON 206 | || Math.abs(sMy) < jsfeat.EPSILON ) return 0; 207 | 208 | smx = count/smx; smy = count/smy; 209 | sMx = count/sMx; sMy = count/sMy; 210 | 211 | t0d[0] = sMx; t0d[1] = 0; t0d[2] = -cMx*sMx; 212 | t0d[3] = 0; t0d[4] = sMy; t0d[5] = -cMy*sMy; 213 | t0d[6] = 0; t0d[7] = 0; t0d[8] = 1; 214 | 215 | t1d[0] = 1.0/smx; t1d[1] = 0; t1d[2] = cmx; 216 | t1d[3] = 0; t1d[4] = 1.0/smy; t1d[5] = cmy; 217 | t1d[6] = 0; t1d[7] = 0; t1d[8] = 1; 218 | // 219 | 220 | // construct system 221 | i = 81; 222 | while(--i >= 0) { 223 | LtL[i] = 0.0; 224 | } 225 | for(i = 0; i < count; ++i) { 226 | x = (to[i].x - cmx) * smx; 227 | y = (to[i].y - cmy) * smy; 228 | X = (from[i].x - cMx) * sMx; 229 | Y = (from[i].y - cMy) * sMy; 230 | 231 | LtL[0] += X*X; 232 | LtL[1] += X*Y; 233 | LtL[2] += X; 234 | 235 | LtL[6] += X*-x*X; 236 | LtL[7] += X*-x*Y; 237 | LtL[8] += X*-x; 238 | LtL[10] += Y*Y; 239 | LtL[11] += Y; 240 | 241 | LtL[15] += Y*-x*X; 242 | LtL[16] += Y*-x*Y; 243 | LtL[17] += Y*-x; 244 | LtL[20] += 1.0; 245 | 246 | LtL[24] += -x*X; 247 | LtL[25] += -x*Y; 248 | LtL[26] += -x; 249 | LtL[30] += X*X; 250 | LtL[31] += X*Y; 251 | LtL[32] += X; 252 | LtL[33] += X*-y*X; 253 | LtL[34] += X*-y*Y; 254 | LtL[35] += X*-y; 255 | LtL[40] += Y*Y; 256 | LtL[41] += Y; 257 | LtL[42] += Y*-y*X; 258 | LtL[43] += Y*-y*Y; 259 | LtL[44] += Y*-y; 260 | LtL[50] += 1.0; 261 | LtL[51] += -y*X; 262 | LtL[52] += -y*Y; 263 | LtL[53] += -y; 264 | LtL[60] += -x*X*-x*X + -y*X*-y*X; 265 | LtL[61] += -x*X*-x*Y + -y*X*-y*Y; 266 | LtL[62] += -x*X*-x + -y*X*-y; 267 | LtL[70] += -x*Y*-x*Y + -y*Y*-y*Y; 268 | LtL[71] += -x*Y*-x + -y*Y*-y; 269 | LtL[80] += -x*-x + -y*-y; 270 | } 271 | // 272 | 273 | // symmetry 274 | for(i = 0; i < 9; ++i) { 275 | for(j = 0; j < i; ++j) 276 | LtL[i*9+j] = LtL[j*9+i]; 277 | } 278 | 279 | jsfeat.linalg.eigenVV(mLtL, Evec); 280 | 281 | md[0]=evd[72], md[1]=evd[73], md[2]=evd[74]; 282 | md[3]=evd[75], md[4]=evd[76], md[5]=evd[77]; 283 | md[6]=evd[78], md[7]=evd[79], md[8]=evd[80]; 284 | 285 | // denormalize 286 | jsfeat.matmath.multiply_3x3(model, T1, model); 287 | jsfeat.matmath.multiply_3x3(model, model, T0); 288 | 289 | // set bottom right to 1.0 290 | x = 1.0/md[8]; 291 | md[0] *= x; md[1] *= x; md[2] *= x; 292 | md[3] *= x; md[4] *= x; md[5] *= x; 293 | md[6] *= x; md[7] *= x; md[8] = 1.0; 294 | 295 | return 1; 296 | } 297 | 298 | homography2d.prototype.error = function(from, to, model, err, count) { 299 | var i=0; 300 | var pt0,pt1,ww=0.0,dx=0.0,dy=0.0; 301 | var m=model.data; 302 | 303 | for (; i < count; ++i) { 304 | pt0 = from[i]; 305 | pt1 = to[i]; 306 | 307 | ww = 1.0/(m[6]*pt0.x + m[7]*pt0.y + 1.0); 308 | dx = (m[0]*pt0.x + m[1]*pt0.y + m[2])*ww - pt1.x; 309 | dy = (m[3]*pt0.x + m[4]*pt0.y + m[5])*ww - pt1.y; 310 | err[i] = (dx*dx + dy*dy); 311 | } 312 | } 313 | 314 | homography2d.prototype.check_subset = function(from, to, count) { 315 | // seems to reject good subsets actually 316 | //if( have_collinear_points(from, count) || have_collinear_points(to, count) ) { 317 | //return false; 318 | //} 319 | if( count == 4 ) { 320 | var negative = 0; 321 | 322 | var fp0=from[0],fp1=from[1],fp2=from[2],fp3=from[3]; 323 | var tp0=to[0],tp1=to[1],tp2=to[2],tp3=to[3]; 324 | 325 | // set1 326 | var A11=fp0.x, A12=fp0.y, A13=1.0; 327 | var A21=fp1.x, A22=fp1.y, A23=1.0; 328 | var A31=fp2.x, A32=fp2.y, A33=1.0; 329 | 330 | var B11=tp0.x, B12=tp0.y, B13=1.0; 331 | var B21=tp1.x, B22=tp1.y, B23=1.0; 332 | var B31=tp2.x, B32=tp2.y, B33=1.0; 333 | 334 | var detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); 335 | var detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); 336 | 337 | if(detA*detB < 0) negative++; 338 | 339 | // set2 340 | A11=fp1.x, A12=fp1.y; 341 | A21=fp2.x, A22=fp2.y; 342 | A31=fp3.x, A32=fp3.y; 343 | 344 | B11=tp1.x, B12=tp1.y; 345 | B21=tp2.x, B22=tp2.y; 346 | B31=tp3.x, B32=tp3.y; 347 | 348 | detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); 349 | detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); 350 | 351 | if(detA*detB < 0) negative++; 352 | 353 | // set3 354 | A11=fp0.x, A12=fp0.y; 355 | A21=fp2.x, A22=fp2.y; 356 | A31=fp3.x, A32=fp3.y; 357 | 358 | B11=tp0.x, B12=tp0.y; 359 | B21=tp2.x, B22=tp2.y; 360 | B31=tp3.x, B32=tp3.y; 361 | 362 | detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); 363 | detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); 364 | 365 | if(detA*detB < 0) negative++; 366 | 367 | // set4 368 | A11=fp0.x, A12=fp0.y; 369 | A21=fp1.x, A22=fp1.y; 370 | A31=fp3.x, A32=fp3.y; 371 | 372 | B11=tp0.x, B12=tp0.y; 373 | B21=tp1.x, B22=tp1.y; 374 | B31=tp3.x, B32=tp3.y; 375 | 376 | detA = jsfeat.matmath.determinant_3x3(A11,A12,A13, A21,A22,A23, A31,A32,A33); 377 | detB = jsfeat.matmath.determinant_3x3(B11,B12,B13, B21,B22,B23, B31,B32,B33); 378 | 379 | if(detA*detB < 0) negative++; 380 | 381 | if(negative != 0 && negative != 4) { 382 | return false; 383 | } 384 | } 385 | return true; // all good 386 | } 387 | 388 | return homography2d; 389 | })(); 390 | 391 | return { 392 | 393 | affine2d:affine2d, 394 | homography2d:homography2d 395 | 396 | }; 397 | 398 | })(); 399 | 400 | var ransac_params_t = (function () { 401 | function ransac_params_t(size, thresh, eps, prob) { 402 | if (typeof size === "undefined") { size=0; } 403 | if (typeof thresh === "undefined") { thresh=0.5; } 404 | if (typeof eps === "undefined") { eps=0.5; } 405 | if (typeof prob === "undefined") { prob=0.99; } 406 | 407 | this.size = size; 408 | this.thresh = thresh; 409 | this.eps = eps; 410 | this.prob = prob; 411 | }; 412 | ransac_params_t.prototype.update_iters = function(_eps, max_iters) { 413 | var num = Math.log(1 - this.prob); 414 | var denom = Math.log(1 - Math.pow(1 - _eps, this.size)); 415 | return (denom >= 0 || -num >= max_iters*(-denom) ? max_iters : Math.round(num/denom))|0; 416 | }; 417 | return ransac_params_t; 418 | })(); 419 | 420 | var motion_estimator = (function() { 421 | 422 | var get_subset = function(kernel, from, to, need_cnt, max_cnt, from_sub, to_sub) { 423 | var max_try = 1000; 424 | var indices = []; 425 | var i=0, j=0, ssiter=0, idx_i=0, ok=false; 426 | for(; ssiter < max_try; ++ssiter) { 427 | i = 0; 428 | for (; i < need_cnt && ssiter < max_try;) { 429 | ok = false; 430 | idx_i = 0; 431 | while (!ok) { 432 | ok = true; 433 | idx_i = indices[i] = Math.floor(Math.random() * max_cnt)|0; 434 | for (j = 0; j < i; ++j) { 435 | if (idx_i == indices[j]) 436 | { ok = false; break; } 437 | } 438 | } 439 | from_sub[i] = from[idx_i]; 440 | to_sub[i] = to[idx_i]; 441 | if( !kernel.check_subset( from_sub, to_sub, i+1 ) ) { 442 | ssiter++; 443 | continue; 444 | } 445 | ++i; 446 | } 447 | break; 448 | } 449 | 450 | return (i == need_cnt && ssiter < max_try); 451 | } 452 | 453 | var find_inliers = function(kernel, model, from, to, count, thresh, err, mask) { 454 | var numinliers = 0, i=0, f=0; 455 | var t = thresh*thresh; 456 | 457 | kernel.error(from, to, model, err, count); 458 | 459 | for(; i < count; ++i) { 460 | f = err[i] <= t; 461 | mask[i] = f; 462 | numinliers += f; 463 | } 464 | return numinliers; 465 | } 466 | 467 | return { 468 | 469 | ransac: function(params, kernel, from, to, count, model, mask, max_iters) { 470 | if (typeof max_iters === "undefined") { max_iters=1000; } 471 | 472 | if(count < params.size) return false; 473 | 474 | var model_points = params.size; 475 | var niters = max_iters, iter=0; 476 | var result = false; 477 | 478 | var subset0 = []; 479 | var subset1 = []; 480 | var found = false; 481 | 482 | var mc=model.cols,mr=model.rows; 483 | var dt = model.type | jsfeat.C1_t; 484 | 485 | var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); 486 | var ms_buff = jsfeat.cache.get_buffer(count); 487 | var err_buff = jsfeat.cache.get_buffer(count<<2); 488 | var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); 489 | var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8C1_t, ms_buff.data); 490 | 491 | var inliers_max = -1, numinliers=0; 492 | var nmodels = 0; 493 | 494 | var err = err_buff.f32; 495 | 496 | // special case 497 | if(count == model_points) { 498 | if(kernel.run(from, to, M, count) <= 0) { 499 | jsfeat.cache.put_buffer(m_buff); 500 | jsfeat.cache.put_buffer(ms_buff); 501 | jsfeat.cache.put_buffer(err_buff); 502 | return false; 503 | } 504 | 505 | M.copy_to(model); 506 | if(mask) { 507 | while(--count >= 0) { 508 | mask.data[count] = 1; 509 | } 510 | } 511 | jsfeat.cache.put_buffer(m_buff); 512 | jsfeat.cache.put_buffer(ms_buff); 513 | jsfeat.cache.put_buffer(err_buff); 514 | return true; 515 | } 516 | 517 | for (; iter < niters; ++iter) { 518 | // generate subset 519 | found = get_subset(kernel, from, to, model_points, count, subset0, subset1); 520 | if(!found) { 521 | if(iter == 0) { 522 | jsfeat.cache.put_buffer(m_buff); 523 | jsfeat.cache.put_buffer(ms_buff); 524 | jsfeat.cache.put_buffer(err_buff); 525 | return false; 526 | } 527 | break; 528 | } 529 | 530 | nmodels = kernel.run( subset0, subset1, M, model_points ); 531 | if(nmodels <= 0) 532 | continue; 533 | 534 | // TODO handle multimodel output 535 | 536 | numinliers = find_inliers(kernel, M, from, to, count, params.thresh, err, curr_mask.data); 537 | 538 | if( numinliers > Math.max(inliers_max, model_points-1) ) { 539 | M.copy_to(model); 540 | inliers_max = numinliers; 541 | if(mask) curr_mask.copy_to(mask); 542 | niters = params.update_iters((count - numinliers)/count, niters); 543 | result = true; 544 | } 545 | } 546 | 547 | jsfeat.cache.put_buffer(m_buff); 548 | jsfeat.cache.put_buffer(ms_buff); 549 | jsfeat.cache.put_buffer(err_buff); 550 | 551 | return result; 552 | }, 553 | 554 | lmeds: function(params, kernel, from, to, count, model, mask, max_iters) { 555 | if (typeof max_iters === "undefined") { max_iters=1000; } 556 | 557 | if(count < params.size) return false; 558 | 559 | var model_points = params.size; 560 | var niters = max_iters, iter=0; 561 | var result = false; 562 | 563 | var subset0 = []; 564 | var subset1 = []; 565 | var found = false; 566 | 567 | var mc=model.cols,mr=model.rows; 568 | var dt = model.type | jsfeat.C1_t; 569 | 570 | var m_buff = jsfeat.cache.get_buffer((mc*mr)<<3); 571 | var ms_buff = jsfeat.cache.get_buffer(count); 572 | var err_buff = jsfeat.cache.get_buffer(count<<2); 573 | var M = new jsfeat.matrix_t(mc, mr, dt, m_buff.data); 574 | var curr_mask = new jsfeat.matrix_t(count, 1, jsfeat.U8_t|jsfeat.C1_t, ms_buff.data); 575 | 576 | var numinliers=0; 577 | var nmodels = 0; 578 | 579 | var err = err_buff.f32; 580 | var min_median = 1000000000.0, sigma=0.0, median=0.0; 581 | 582 | params.eps = 0.45; 583 | niters = params.update_iters(params.eps, niters); 584 | 585 | // special case 586 | if(count == model_points) { 587 | if(kernel.run(from, to, M, count) <= 0) { 588 | jsfeat.cache.put_buffer(m_buff); 589 | jsfeat.cache.put_buffer(ms_buff); 590 | jsfeat.cache.put_buffer(err_buff); 591 | return false; 592 | } 593 | 594 | M.copy_to(model); 595 | if(mask) { 596 | while(--count >= 0) { 597 | mask.data[count] = 1; 598 | } 599 | } 600 | jsfeat.cache.put_buffer(m_buff); 601 | jsfeat.cache.put_buffer(ms_buff); 602 | jsfeat.cache.put_buffer(err_buff); 603 | return true; 604 | } 605 | 606 | for (; iter < niters; ++iter) { 607 | // generate subset 608 | found = get_subset(kernel, from, to, model_points, count, subset0, subset1); 609 | if(!found) { 610 | if(iter == 0) { 611 | jsfeat.cache.put_buffer(m_buff); 612 | jsfeat.cache.put_buffer(ms_buff); 613 | jsfeat.cache.put_buffer(err_buff); 614 | return false; 615 | } 616 | break; 617 | } 618 | 619 | nmodels = kernel.run( subset0, subset1, M, model_points ); 620 | if(nmodels <= 0) 621 | continue; 622 | 623 | // TODO handle multimodel output 624 | 625 | kernel.error(from, to, M, err, count); 626 | median = jsfeat.math.median(err, 0, count-1); 627 | 628 | if(median < min_median) { 629 | min_median = median; 630 | M.copy_to(model); 631 | result = true; 632 | } 633 | } 634 | 635 | if(result) { 636 | sigma = 2.5*1.4826*(1 + 5.0/(count - model_points))*Math.sqrt(min_median); 637 | sigma = Math.max(sigma, 0.001); 638 | 639 | numinliers = find_inliers(kernel, model, from, to, count, sigma, err, curr_mask.data); 640 | if(mask) curr_mask.copy_to(mask); 641 | 642 | result = numinliers >= model_points; 643 | } 644 | 645 | jsfeat.cache.put_buffer(m_buff); 646 | jsfeat.cache.put_buffer(ms_buff); 647 | jsfeat.cache.put_buffer(err_buff); 648 | 649 | return result; 650 | } 651 | 652 | }; 653 | 654 | })(); 655 | 656 | global.ransac_params_t = ransac_params_t; 657 | global.motion_model = motion_model; 658 | global.motion_estimator = motion_estimator; 659 | 660 | })(jsfeat); 661 | -------------------------------------------------------------------------------- /src/jsfeat_optical_flow_lk.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * this code is a rewrite from OpenCV's Lucas-Kanade optical flow implementation 5 | */ 6 | 7 | (function(global) { 8 | "use strict"; 9 | // 10 | var optical_flow_lk = (function() { 11 | 12 | // short link to shar deriv 13 | var scharr_deriv = jsfeat.imgproc.scharr_derivatives; 14 | 15 | return { 16 | track: function(prev_pyr, curr_pyr, prev_xy, curr_xy, count, win_size, max_iter, status, eps, min_eigen_threshold) { 17 | if (typeof max_iter === "undefined") { max_iter = 30; } 18 | if (typeof status === "undefined") { status = new Uint8Array(count); } 19 | if (typeof eps === "undefined") { eps = 0.01; } 20 | if (typeof min_eigen_threshold === "undefined") { min_eigen_threshold = 0.0001; } 21 | 22 | var half_win = (win_size-1)*0.5; 23 | var win_area = (win_size*win_size)|0; 24 | var win_area2 = win_area << 1; 25 | var prev_imgs = prev_pyr.data, next_imgs = curr_pyr.data; 26 | var img_prev=prev_imgs[0].data,img_next=next_imgs[0].data; 27 | var w0 = prev_imgs[0].cols, h0 = prev_imgs[0].rows,lw=0,lh=0; 28 | 29 | var iwin_node = jsfeat.cache.get_buffer(win_area<<2); 30 | var deriv_iwin_node = jsfeat.cache.get_buffer(win_area2<<2); 31 | var deriv_lev_node = jsfeat.cache.get_buffer((h0*(w0<<1))<<2); 32 | 33 | var deriv_m = new jsfeat.matrix_t(w0, h0, jsfeat.S32C2_t, deriv_lev_node.data); 34 | 35 | var iwin_buf = iwin_node.i32; 36 | var deriv_iwin = deriv_iwin_node.i32; 37 | var deriv_lev = deriv_lev_node.i32; 38 | 39 | var dstep=0,src=0,dsrc=0,iptr=0,diptr=0,jptr=0; 40 | var lev_sc=0.0,prev_x=0.0,prev_y=0.0,next_x=0.0,next_y=0.0; 41 | var prev_delta_x=0.0,prev_delta_y=0.0,delta_x=0.0,delta_y=0.0; 42 | var iprev_x=0,iprev_y=0,inext_x=0,inext_y=0; 43 | var i=0,j=0,x=0,y=0,level=0,ptid=0,iter=0; 44 | var brd_tl=0,brd_r=0,brd_b=0; 45 | var a=0.0,b=0.0,b1=0.0,b2=0.0; 46 | 47 | // fixed point math 48 | var W_BITS14 = 14; 49 | var W_BITS4 = 14; 50 | var W_BITS1m5 = W_BITS4 - 5; 51 | var W_BITS1m51 = (1 << ((W_BITS1m5) - 1)); 52 | var W_BITS14_ = (1 << W_BITS14); 53 | var W_BITS41 = (1 << ((W_BITS4) - 1)); 54 | var FLT_SCALE = 1.0/(1 << 20); 55 | var iw00=0,iw01=0,iw10=0,iw11=0,ival=0,ixval=0,iyval=0; 56 | var A11=0.0,A12=0.0,A22=0.0,D=0.0,min_eig=0.0; 57 | 58 | var FLT_EPSILON = 0.00000011920929; 59 | eps *= eps; 60 | 61 | // reset status 62 | for(; i < count; ++i) { 63 | status[i] = 1; 64 | } 65 | 66 | var max_level = (prev_pyr.levels - 1)|0; 67 | level = max_level; 68 | 69 | for(; level >= 0; --level) { 70 | lev_sc = (1.0/(1 << level)); 71 | lw = w0 >> level; 72 | lh = h0 >> level; 73 | dstep = lw << 1; 74 | img_prev = prev_imgs[level].data; 75 | img_next = next_imgs[level].data; 76 | 77 | brd_r = (lw - win_size)|0; 78 | brd_b = (lh - win_size)|0; 79 | 80 | // calculate level derivatives 81 | scharr_deriv(prev_imgs[level], deriv_m); 82 | 83 | // iterate through points 84 | for(ptid = 0; ptid < count; ++ptid) { 85 | i = ptid << 1; 86 | j = i + 1; 87 | prev_x = prev_xy[i]*lev_sc; 88 | prev_y = prev_xy[j]*lev_sc; 89 | 90 | if( level == max_level ) { 91 | next_x = prev_x; 92 | next_y = prev_y; 93 | } else { 94 | next_x = curr_xy[i]*2.0; 95 | next_y = curr_xy[j]*2.0; 96 | } 97 | curr_xy[i] = next_x; 98 | curr_xy[j] = next_y; 99 | 100 | prev_x -= half_win; 101 | prev_y -= half_win; 102 | iprev_x = prev_x|0; 103 | iprev_y = prev_y|0; 104 | 105 | // border check 106 | x = (iprev_x <= brd_tl)|(iprev_x >= brd_r)|(iprev_y <= brd_tl)|(iprev_y >= brd_b); 107 | if( x != 0 ) { 108 | if( level == 0 ) { 109 | status[ptid] = 0; 110 | } 111 | continue; 112 | } 113 | 114 | a = prev_x - iprev_x; 115 | b = prev_y - iprev_y; 116 | iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; 117 | iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; 118 | iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; 119 | iw11 = (W_BITS14_ - iw00 - iw01 - iw10); 120 | 121 | A11 = 0.0, A12 = 0.0, A22 = 0.0; 122 | 123 | // extract the patch from the first image, compute covariation matrix of derivatives 124 | for( y = 0; y < win_size; ++y ) { 125 | src = ( (y + iprev_y)*lw + iprev_x )|0; 126 | dsrc = src << 1; 127 | 128 | iptr = (y*win_size)|0; 129 | diptr = iptr << 1; 130 | for(x = 0 ; x < win_size; ++x, ++src, ++iptr, dsrc += 2) { 131 | ival = ( (img_prev[src])*iw00 + (img_prev[src+1])*iw01 + 132 | (img_prev[src+lw])*iw10 + (img_prev[src+lw+1])*iw11 ); 133 | ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); 134 | 135 | ixval = ( deriv_lev[dsrc]*iw00 + deriv_lev[dsrc+2]*iw01 + 136 | deriv_lev[dsrc+dstep]*iw10 + deriv_lev[dsrc+dstep+2]*iw11 ); 137 | ixval = (((ixval) + W_BITS41) >> (W_BITS4)); 138 | 139 | iyval = ( deriv_lev[dsrc+1]*iw00 + deriv_lev[dsrc+3]*iw01 + deriv_lev[dsrc+dstep+1]*iw10 + 140 | deriv_lev[dsrc+dstep+3]*iw11 ); 141 | iyval = (((iyval) + W_BITS41) >> (W_BITS4)); 142 | 143 | iwin_buf[iptr] = ival; 144 | deriv_iwin[diptr++] = ixval; 145 | deriv_iwin[diptr++] = iyval; 146 | 147 | A11 += ixval*ixval; 148 | A12 += ixval*iyval; 149 | A22 += iyval*iyval; 150 | } 151 | } 152 | 153 | A11 *= FLT_SCALE; A12 *= FLT_SCALE; A22 *= FLT_SCALE; 154 | 155 | D = A11*A22 - A12*A12; 156 | min_eig = (A22 + A11 - Math.sqrt((A11-A22)*(A11-A22) + 4.0*A12*A12)) / win_area2; 157 | 158 | if( min_eig < min_eigen_threshold || D < FLT_EPSILON ) 159 | { 160 | if( level == 0 ) { 161 | status[ptid] = 0; 162 | } 163 | continue; 164 | } 165 | 166 | D = 1.0/D; 167 | 168 | next_x -= half_win; 169 | next_y -= half_win; 170 | prev_delta_x = 0.0; 171 | prev_delta_y = 0.0; 172 | 173 | for( iter = 0; iter < max_iter; ++iter ) { 174 | inext_x = next_x|0; 175 | inext_y = next_y|0; 176 | 177 | x = (inext_x <= brd_tl)|(inext_x >= brd_r)|(inext_y <= brd_tl)|(inext_y >= brd_b); 178 | if( x != 0 ) { 179 | if( level == 0 ) { 180 | status[ptid] = 0; 181 | } 182 | break; 183 | } 184 | 185 | a = next_x - inext_x; 186 | b = next_y - inext_y; 187 | iw00 = (((1.0 - a)*(1.0 - b)*W_BITS14_) + 0.5)|0; 188 | iw01 = ((a*(1.0 - b)*W_BITS14_) + 0.5)|0; 189 | iw10 = (((1.0 - a)*b*W_BITS14_) + 0.5)|0; 190 | iw11 = (W_BITS14_ - iw00 - iw01 - iw10); 191 | b1 = 0.0, b2 = 0.0; 192 | 193 | for( y = 0; y < win_size; ++y ) { 194 | jptr = ( (y + inext_y)*lw + inext_x )|0; 195 | 196 | iptr = (y*win_size)|0; 197 | diptr = iptr << 1; 198 | for( x = 0 ; x < win_size; ++x, ++jptr, ++iptr ) { 199 | ival = ( (img_next[jptr])*iw00 + (img_next[jptr+1])*iw01 + 200 | (img_next[jptr+lw])*iw10 + (img_next[jptr+lw+1])*iw11 ); 201 | ival = (((ival) + W_BITS1m51) >> (W_BITS1m5)); 202 | ival = (ival - iwin_buf[iptr]); 203 | 204 | b1 += ival * deriv_iwin[diptr++]; 205 | b2 += ival * deriv_iwin[diptr++]; 206 | } 207 | } 208 | 209 | b1 *= FLT_SCALE; 210 | b2 *= FLT_SCALE; 211 | 212 | delta_x = ((A12*b2 - A22*b1) * D); 213 | delta_y = ((A12*b1 - A11*b2) * D); 214 | 215 | next_x += delta_x; 216 | next_y += delta_y; 217 | curr_xy[i] = next_x + half_win; 218 | curr_xy[j] = next_y + half_win; 219 | 220 | if( delta_x*delta_x + delta_y*delta_y <= eps ) { 221 | break; 222 | } 223 | 224 | if( iter > 0 && Math.abs(delta_x + prev_delta_x) < 0.01 && 225 | Math.abs(delta_y + prev_delta_y) < 0.01 ) { 226 | curr_xy[i] -= delta_x*0.5; 227 | curr_xy[j] -= delta_y*0.5; 228 | break; 229 | } 230 | 231 | prev_delta_x = delta_x; 232 | prev_delta_y = delta_y; 233 | } 234 | } // points loop 235 | } // levels loop 236 | 237 | jsfeat.cache.put_buffer(iwin_node); 238 | jsfeat.cache.put_buffer(deriv_iwin_node); 239 | jsfeat.cache.put_buffer(deriv_lev_node); 240 | } 241 | }; 242 | })(); 243 | 244 | global.optical_flow_lk = optical_flow_lk; 245 | 246 | })(jsfeat); 247 | -------------------------------------------------------------------------------- /src/jsfeat_orb.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * Original implementation derived from OpenCV, 5 | * @authors Ethan Rublee, Vincent Rabaud, Gary Bradski 6 | */ 7 | 8 | (function(global) { 9 | "use strict"; 10 | // 11 | 12 | var orb = (function() { 13 | 14 | var bit_pattern_31_ = new Int32Array([ 15 | 8,-3, 9,5/*mean (0), correlation (0)*/, 16 | 4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/, 17 | -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/, 18 | 7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/, 19 | 2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/, 20 | 1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/, 21 | -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/, 22 | -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/, 23 | -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/, 24 | 10,4, 11,9/*mean (0.122065), correlation (0.093285)*/, 25 | -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/, 26 | -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/, 27 | 7,7, 12,6/*mean (0.160583), correlation (0.130064)*/, 28 | -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/, 29 | -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/, 30 | -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/, 31 | 12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/, 32 | -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/, 33 | -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/, 34 | 11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/, 35 | 4,7, 5,1/*mean (0.205106), correlation (0.186848)*/, 36 | 5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/, 37 | 3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/, 38 | -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/, 39 | -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/, 40 | -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/, 41 | -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/, 42 | -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/, 43 | -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/, 44 | 5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/, 45 | 5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/, 46 | 1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/, 47 | 9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/, 48 | 4,7, 4,12/*mean (0.131005), correlation (0.257622)*/, 49 | 2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/, 50 | -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/, 51 | -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/, 52 | 4,11, 9,12/*mean (0.226226), correlation (0.258255)*/, 53 | 0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/, 54 | -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/, 55 | -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/, 56 | -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/, 57 | 8,12, 10,7/*mean (0.225337), correlation (0.282851)*/, 58 | 0,9, 1,3/*mean (0.226687), correlation (0.278734)*/, 59 | 7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/, 60 | -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/, 61 | 10,7, 12,1/*mean (0.125517), correlation (0.31089)*/, 62 | -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/, 63 | 10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/, 64 | -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/, 65 | -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/, 66 | 3,3, 7,8/*mean (0.177755), correlation (0.309394)*/, 67 | 5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/, 68 | -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/, 69 | 3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/, 70 | 2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/, 71 | -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/, 72 | -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/, 73 | -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/, 74 | -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/, 75 | 6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/, 76 | -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/, 77 | -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/, 78 | -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/, 79 | 3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/, 80 | -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/, 81 | -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/, 82 | 2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/, 83 | -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/, 84 | -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/, 85 | 5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/, 86 | -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/, 87 | -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/, 88 | -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/, 89 | 10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/, 90 | 7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/, 91 | -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/, 92 | -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/, 93 | 7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/, 94 | -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/, 95 | -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/, 96 | -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/, 97 | 7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/, 98 | -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/, 99 | 1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/, 100 | 2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/, 101 | -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/, 102 | -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/, 103 | 7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/, 104 | 1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/, 105 | 9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/, 106 | -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/, 107 | -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/, 108 | 7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/, 109 | 12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/, 110 | 6,3, 7,11/*mean (0.1074), correlation (0.413224)*/, 111 | 5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/, 112 | 2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/, 113 | 3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/, 114 | 2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/, 115 | 9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/, 116 | -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/, 117 | -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/, 118 | 1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/, 119 | 6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/, 120 | 2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/, 121 | 6,3, 11,0/*mean (0.204588), correlation (0.411762)*/, 122 | 3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/, 123 | 7,8, 9,3/*mean (0.213237), correlation (0.409306)*/, 124 | -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/, 125 | -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/, 126 | -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/, 127 | -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/, 128 | 8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/, 129 | 4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/, 130 | -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/, 131 | 4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/, 132 | -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/, 133 | -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/, 134 | 7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/, 135 | -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/, 136 | -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/, 137 | 8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/, 138 | -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/, 139 | 1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/, 140 | 7,-4, 9,1/*mean (0.132692), correlation (0.454)*/, 141 | -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/, 142 | 11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/, 143 | -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/, 144 | 3,7, 7,12/*mean (0.147627), correlation (0.456643)*/, 145 | 5,5, 10,8/*mean (0.152901), correlation (0.455036)*/, 146 | 0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/, 147 | -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/, 148 | 0,7, 2,12/*mean (0.18312), correlation (0.433855)*/, 149 | -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/, 150 | 5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/, 151 | 3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/, 152 | -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/, 153 | -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/, 154 | -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/, 155 | 6,5, 8,0/*mean (0.1972), correlation (0.450481)*/, 156 | -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/, 157 | -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/, 158 | 1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/, 159 | 4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/, 160 | -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/, 161 | 2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/, 162 | -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/, 163 | 4,1, 9,3/*mean (0.23962), correlation (0.444824)*/, 164 | -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/, 165 | -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/, 166 | 7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/, 167 | 4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/, 168 | -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/, 169 | 7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/, 170 | 7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/, 171 | -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/, 172 | -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/, 173 | -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/, 174 | 2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/, 175 | 10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/, 176 | -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/, 177 | 8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/, 178 | 2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/, 179 | -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/, 180 | -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/, 181 | -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/, 182 | 5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/, 183 | -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/, 184 | -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/, 185 | -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/, 186 | -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/, 187 | -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/, 188 | 2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/, 189 | -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/, 190 | -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/, 191 | -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/, 192 | -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/, 193 | 6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/, 194 | -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/, 195 | 11,11, 12,6/*mean (0.16652), correlation (0.497632)*/, 196 | 7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/, 197 | -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/, 198 | -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/, 199 | -7,1, -6,7/*mean (0.175), correlation (0.500024)*/, 200 | -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/, 201 | -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/, 202 | -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/, 203 | -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/, 204 | -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/, 205 | 1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/, 206 | 1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/, 207 | 9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/, 208 | 5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/, 209 | -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/, 210 | -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/, 211 | -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/, 212 | -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/, 213 | 8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/, 214 | 2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/, 215 | 7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/, 216 | -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/, 217 | -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/, 218 | 4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/, 219 | 3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/, 220 | -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/, 221 | 5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/, 222 | 4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/, 223 | -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/, 224 | 0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/, 225 | -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/, 226 | 3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/, 227 | -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/, 228 | 8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/, 229 | -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/, 230 | 2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/, 231 | 10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/, 232 | 6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/, 233 | -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/, 234 | -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/, 235 | -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/, 236 | -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/, 237 | -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/, 238 | 4,2, 12,12/*mean (0.01778), correlation (0.546921)*/, 239 | 2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/, 240 | 6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/, 241 | 3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/, 242 | 11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/, 243 | -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/, 244 | 4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/, 245 | 2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/, 246 | -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/, 247 | -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/, 248 | -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/, 249 | 6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/, 250 | 0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/, 251 | -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/, 252 | -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/, 253 | -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/, 254 | 5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/, 255 | 2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/, 256 | -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/, 257 | 9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/, 258 | 11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/, 259 | 3,0, 3,5/*mean (0.101147), correlation (0.525576)*/, 260 | -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/, 261 | 3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/, 262 | -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/, 263 | 5,8, 12,11/*mean (0.114181), correlation (0.555793)*/, 264 | 8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/, 265 | 7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/, 266 | -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/, 267 | 7,3, 12,4/*mean (0.122582), correlation (0.555825)*/, 268 | 9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/, 269 | 7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/, 270 | -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/ 271 | ]); 272 | 273 | var H = new jsfeat.matrix_t(3, 3, jsfeat.F32_t|jsfeat.C1_t); 274 | var patch_img = new jsfeat.matrix_t(32, 32, jsfeat.U8_t|jsfeat.C1_t); 275 | 276 | var rectify_patch = function(src, dst, angle, px, py, psize) { 277 | var cosine = Math.cos(angle); 278 | var sine = Math.sin(angle); 279 | 280 | H.data[0] = cosine, H.data[1] = -sine, H.data[2] = (-cosine + sine ) * psize*0.5 + px, 281 | H.data[3] = sine, H.data[4] = cosine, H.data[5] = (-sine - cosine) * psize*0.5 + py; 282 | 283 | jsfeat.imgproc.warp_affine(src, dst, H, 128); 284 | } 285 | 286 | return { 287 | 288 | describe: function(src, corners, count, descriptors) { 289 | var DESCR_SIZE = 32; // bytes; 290 | var i=0,b=0,px=0.0,py=0.0,angle=0.0; 291 | var t0=0, t1=0, val=0; 292 | var img = src.data, w = src.cols, h = src.rows; 293 | var patch_d = patch_img.data; 294 | var patch_off = 16*32 + 16; // center of patch 295 | var patt=0; 296 | 297 | if(!(descriptors.type&jsfeat.U8_t)) { 298 | // relocate to U8 type 299 | descriptors.type = jsfeat.U8_t; 300 | descriptors.cols = DESCR_SIZE; 301 | descriptors.rows = count; 302 | descriptors.channel = 1; 303 | descriptors.allocate(); 304 | } else { 305 | descriptors.resize(DESCR_SIZE, count, 1); 306 | } 307 | 308 | var descr_d = descriptors.data; 309 | var descr_off = 0; 310 | 311 | for(i = 0; i < count; ++i) { 312 | px = corners[i].x; 313 | py = corners[i].y; 314 | angle = corners[i].angle; 315 | 316 | rectify_patch(src, patch_img, angle, px, py, 32); 317 | 318 | // describe the patch 319 | patt = 0; 320 | for (b = 0; b < DESCR_SIZE; ++b) { 321 | 322 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 323 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 324 | val = (t0 < t1)|0; 325 | 326 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 327 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 328 | val |= (t0 < t1) << 1; 329 | 330 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 331 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 332 | val |= (t0 < t1) << 2; 333 | 334 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 335 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 336 | val |= (t0 < t1) << 3; 337 | 338 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 339 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 340 | val |= (t0 < t1) << 4; 341 | 342 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 343 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 344 | val |= (t0 < t1) << 5; 345 | 346 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 347 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 348 | val |= (t0 < t1) << 6; 349 | 350 | t0 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 351 | t1 = patch_d[patch_off + bit_pattern_31_[patt+1] * 32 + bit_pattern_31_[patt]]; patt += 2 352 | val |= (t0 < t1) << 7; 353 | 354 | descr_d[descr_off+b] = val; 355 | } 356 | descr_off += DESCR_SIZE; 357 | } 358 | } 359 | }; 360 | })(); 361 | 362 | global.orb = orb; 363 | 364 | })(jsfeat); 365 | -------------------------------------------------------------------------------- /src/jsfeat_struct.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | (function(global) { 6 | "use strict"; 7 | // 8 | 9 | // CONSTANTS 10 | var EPSILON = 0.0000001192092896; 11 | var FLT_MIN = 1E-37; 12 | 13 | // implementation from CCV project 14 | // currently working only with u8,s32,f32 15 | var U8_t = 0x0100, 16 | S32_t = 0x0200, 17 | F32_t = 0x0400, 18 | S64_t = 0x0800, 19 | F64_t = 0x1000; 20 | 21 | var C1_t = 0x01, 22 | C2_t = 0x02, 23 | C3_t = 0x03, 24 | C4_t = 0x04; 25 | 26 | var _data_type_size = new Int32Array([ -1, 1, 4, -1, 4, -1, -1, -1, 8, -1, -1, -1, -1, -1, -1, -1, 8 ]); 27 | 28 | var get_data_type = (function () { 29 | return function(type) { 30 | return (type & 0xFF00); 31 | } 32 | })(); 33 | 34 | var get_channel = (function () { 35 | return function(type) { 36 | return (type & 0xFF); 37 | } 38 | })(); 39 | 40 | var get_data_type_size = (function () { 41 | return function(type) { 42 | return _data_type_size[(type & 0xFF00) >> 8]; 43 | } 44 | })(); 45 | 46 | // color conversion 47 | var COLOR_RGBA2GRAY = 0; 48 | var COLOR_RGB2GRAY = 1; 49 | var COLOR_BGRA2GRAY = 2; 50 | var COLOR_BGR2GRAY = 3; 51 | 52 | // box blur option 53 | var BOX_BLUR_NOSCALE = 0x01; 54 | // svd options 55 | var SVD_U_T = 0x01; 56 | var SVD_V_T = 0x02; 57 | 58 | var data_t = (function () { 59 | function data_t(size_in_bytes, buffer) { 60 | // we need align size to multiple of 8 61 | this.size = ((size_in_bytes + 7) | 0) & -8; 62 | if (typeof buffer === "undefined") { 63 | this.buffer = new ArrayBuffer(this.size); 64 | } else { 65 | this.buffer = buffer; 66 | this.size = buffer.length; 67 | } 68 | this.u8 = new Uint8Array(this.buffer); 69 | this.i32 = new Int32Array(this.buffer); 70 | this.f32 = new Float32Array(this.buffer); 71 | this.f64 = new Float64Array(this.buffer); 72 | } 73 | return data_t; 74 | })(); 75 | 76 | var matrix_t = (function () { 77 | // columns, rows, data_type 78 | function matrix_t(c, r, data_type, data_buffer) { 79 | this.type = get_data_type(data_type)|0; 80 | this.channel = get_channel(data_type)|0; 81 | this.cols = c|0; 82 | this.rows = r|0; 83 | if (typeof data_buffer === "undefined") { 84 | this.allocate(); 85 | } else { 86 | this.buffer = data_buffer; 87 | // data user asked for 88 | this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); 89 | } 90 | } 91 | matrix_t.prototype.allocate = function() { 92 | // clear references 93 | delete this.data; 94 | delete this.buffer; 95 | // 96 | this.buffer = new data_t((this.cols * get_data_type_size(this.type) * this.channel) * this.rows); 97 | this.data = this.type&U8_t ? this.buffer.u8 : (this.type&S32_t ? this.buffer.i32 : (this.type&F32_t ? this.buffer.f32 : this.buffer.f64)); 98 | } 99 | matrix_t.prototype.copy_to = function(other) { 100 | var od = other.data, td = this.data; 101 | var i = 0, n = (this.cols*this.rows*this.channel)|0; 102 | for(; i < n-4; i+=4) { 103 | od[i] = td[i]; 104 | od[i+1] = td[i+1]; 105 | od[i+2] = td[i+2]; 106 | od[i+3] = td[i+3]; 107 | } 108 | for(; i < n; ++i) { 109 | od[i] = td[i]; 110 | } 111 | } 112 | matrix_t.prototype.resize = function(c, r, ch) { 113 | if (typeof ch === "undefined") { ch = this.channel; } 114 | // relocate buffer only if new size doesnt fit 115 | var new_size = (c * get_data_type_size(this.type) * ch) * r; 116 | if(new_size > this.buffer.size) { 117 | this.cols = c; 118 | this.rows = r; 119 | this.channel = ch; 120 | this.allocate(); 121 | } else { 122 | this.cols = c; 123 | this.rows = r; 124 | this.channel = ch; 125 | } 126 | } 127 | 128 | return matrix_t; 129 | })(); 130 | 131 | var pyramid_t = (function () { 132 | 133 | function pyramid_t(levels) { 134 | this.levels = levels|0; 135 | this.data = new Array(levels); 136 | this.pyrdown = jsfeat.imgproc.pyrdown; 137 | } 138 | 139 | pyramid_t.prototype.allocate = function(start_w, start_h, data_type) { 140 | var i = this.levels; 141 | while(--i >= 0) { 142 | this.data[i] = new matrix_t(start_w >> i, start_h >> i, data_type); 143 | } 144 | } 145 | 146 | pyramid_t.prototype.build = function(input, skip_first_level) { 147 | if (typeof skip_first_level === "undefined") { skip_first_level = true; } 148 | // just copy data to first level 149 | var i = 2, a = input, b = this.data[0]; 150 | if(!skip_first_level) { 151 | var j=input.cols*input.rows; 152 | while(--j >= 0) { 153 | b.data[j] = input.data[j]; 154 | } 155 | } 156 | b = this.data[1]; 157 | this.pyrdown(a, b); 158 | for(; i < this.levels; ++i) { 159 | a = b; 160 | b = this.data[i]; 161 | this.pyrdown(a, b); 162 | } 163 | } 164 | 165 | return pyramid_t; 166 | })(); 167 | 168 | var keypoint_t = (function () { 169 | function keypoint_t(x,y,score,level,angle) { 170 | if (typeof x === "undefined") { x=0; } 171 | if (typeof y === "undefined") { y=0; } 172 | if (typeof score === "undefined") { score=0; } 173 | if (typeof level === "undefined") { level=0; } 174 | if (typeof angle === "undefined") { angle=-1.0; } 175 | 176 | this.x = x; 177 | this.y = y; 178 | this.score = score; 179 | this.level = level; 180 | this.angle = angle; 181 | } 182 | return keypoint_t; 183 | })(); 184 | 185 | 186 | // data types 187 | global.U8_t = U8_t; 188 | global.S32_t = S32_t; 189 | global.F32_t = F32_t; 190 | global.S64_t = S64_t; 191 | global.F64_t = F64_t; 192 | // data channels 193 | global.C1_t = C1_t; 194 | global.C2_t = C2_t; 195 | global.C3_t = C3_t; 196 | global.C4_t = C4_t; 197 | 198 | // popular formats 199 | global.U8C1_t = U8_t | C1_t; 200 | global.U8C3_t = U8_t | C3_t; 201 | global.U8C4_t = U8_t | C4_t; 202 | 203 | global.F32C1_t = F32_t | C1_t; 204 | global.F32C2_t = F32_t | C2_t; 205 | global.S32C1_t = S32_t | C1_t; 206 | global.S32C2_t = S32_t | C2_t; 207 | 208 | // constants 209 | global.EPSILON = EPSILON; 210 | global.FLT_MIN = FLT_MIN; 211 | 212 | // color convert 213 | global.COLOR_RGBA2GRAY = COLOR_RGBA2GRAY; 214 | global.COLOR_RGB2GRAY = COLOR_RGB2GRAY; 215 | global.COLOR_BGRA2GRAY = COLOR_BGRA2GRAY; 216 | global.COLOR_BGR2GRAY = COLOR_BGR2GRAY; 217 | 218 | // options 219 | global.BOX_BLUR_NOSCALE = BOX_BLUR_NOSCALE; 220 | global.SVD_U_T = SVD_U_T; 221 | global.SVD_V_T = SVD_V_T; 222 | 223 | global.get_data_type = get_data_type; 224 | global.get_channel = get_channel; 225 | global.get_data_type_size = get_data_type_size; 226 | 227 | global.data_t = data_t; 228 | global.matrix_t = matrix_t; 229 | global.pyramid_t = pyramid_t; 230 | global.keypoint_t = keypoint_t; 231 | 232 | })(jsfeat); 233 | -------------------------------------------------------------------------------- /src/jsfeat_transform.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | */ 4 | 5 | (function(global) { 6 | "use strict"; 7 | // 8 | 9 | var transform = (function() { 10 | // 11 | return { 12 | affine_3point_transform: function(mat, src_x0, src_y0, dst_x0, dst_y0, 13 | src_x1, src_y1, dst_x1, dst_y1, 14 | src_x2, src_y2, dst_x2, dst_y2) { 15 | // we need linear algebra module first 16 | }, 17 | 18 | perspective_4point_transform: function(mat, src_x0, src_y0, dst_x0, dst_y0, 19 | src_x1, src_y1, dst_x1, dst_y1, 20 | src_x2, src_y2, dst_x2, dst_y2, 21 | src_x3, src_y3, dst_x3, dst_y3) { 22 | var t1 = src_x0; 23 | var t2 = src_x2; 24 | var t4 = src_y1; 25 | var t5 = t1 * t2 * t4; 26 | var t6 = src_y3; 27 | var t7 = t1 * t6; 28 | var t8 = t2 * t7; 29 | var t9 = src_y2; 30 | var t10 = t1 * t9; 31 | var t11 = src_x1; 32 | var t14 = src_y0; 33 | var t15 = src_x3; 34 | var t16 = t14 * t15; 35 | var t18 = t16 * t11; 36 | var t20 = t15 * t11 * t9; 37 | var t21 = t15 * t4; 38 | var t24 = t15 * t9; 39 | var t25 = t2 * t4; 40 | var t26 = t6 * t2; 41 | var t27 = t6 * t11; 42 | var t28 = t9 * t11; 43 | var t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); 44 | var t32 = t1 * t15; 45 | var t35 = t14 * t11; 46 | var t41 = t4 * t1; 47 | var t42 = t6 * t41; 48 | var t43 = t14 * t2; 49 | var t46 = t16 * t9; 50 | var t48 = t14 * t9 * t11; 51 | var t51 = t4 * t6 * t2; 52 | var t55 = t6 * t14; 53 | var Hr0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; 54 | var Hr1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; 55 | var Hr2 = t1; 56 | var Hr3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; 57 | var Hr4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; 58 | var Hr5 = t14; 59 | var Hr6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; 60 | var Hr7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; 61 | 62 | t1 = dst_x0; 63 | t2 = dst_x2; 64 | t4 = dst_y1; 65 | t5 = t1 * t2 * t4; 66 | t6 = dst_y3; 67 | t7 = t1 * t6; 68 | t8 = t2 * t7; 69 | t9 = dst_y2; 70 | t10 = t1 * t9; 71 | t11 = dst_x1; 72 | t14 = dst_y0; 73 | t15 = dst_x3; 74 | t16 = t14 * t15; 75 | t18 = t16 * t11; 76 | t20 = t15 * t11 * t9; 77 | t21 = t15 * t4; 78 | t24 = t15 * t9; 79 | t25 = t2 * t4; 80 | t26 = t6 * t2; 81 | t27 = t6 * t11; 82 | t28 = t9 * t11; 83 | t30 = 1.0 / (t21-t24 - t25 + t26 - t27 + t28); 84 | t32 = t1 * t15; 85 | t35 = t14 * t11; 86 | t41 = t4 * t1; 87 | t42 = t6 * t41; 88 | t43 = t14 * t2; 89 | t46 = t16 * t9; 90 | t48 = t14 * t9 * t11; 91 | t51 = t4 * t6 * t2; 92 | t55 = t6 * t14; 93 | var Hl0 = -(t8-t5 + t10 * t11 - t11 * t7 - t16 * t2 + t18 - t20 + t21 * t2) * t30; 94 | var Hl1 = (t5 - t8 - t32 * t4 + t32 * t9 + t18 - t2 * t35 + t27 * t2 - t20) * t30; 95 | var Hl2 = t1; 96 | var Hl3 = (-t9 * t7 + t42 + t43 * t4 - t16 * t4 + t46 - t48 + t27 * t9 - t51) * t30; 97 | var Hl4 = (-t42 + t41 * t9 - t55 * t2 + t46 - t48 + t55 * t11 + t51 - t21 * t9) * t30; 98 | var Hl5 = t14; 99 | var Hl6 = (-t10 + t41 + t43 - t35 + t24 - t21 - t26 + t27) * t30; 100 | var Hl7 = (-t7 + t10 + t16 - t43 + t27 - t28 - t21 + t25) * t30; 101 | 102 | // the following code computes R = Hl * inverse Hr 103 | t2 = Hr4-Hr7*Hr5; 104 | t4 = Hr0*Hr4; 105 | t5 = Hr0*Hr5; 106 | t7 = Hr3*Hr1; 107 | t8 = Hr2*Hr3; 108 | t10 = Hr1*Hr6; 109 | var t12 = Hr2*Hr6; 110 | t15 = 1.0 / (t4-t5*Hr7-t7+t8*Hr7+t10*Hr5-t12*Hr4); 111 | t18 = -Hr3+Hr5*Hr6; 112 | var t23 = -Hr3*Hr7+Hr4*Hr6; 113 | t28 = -Hr1+Hr2*Hr7; 114 | var t31 = Hr0-t12; 115 | t35 = Hr0*Hr7-t10; 116 | t41 = -Hr1*Hr5+Hr2*Hr4; 117 | var t44 = t5-t8; 118 | var t47 = t4-t7; 119 | t48 = t2*t15; 120 | var t49 = t28*t15; 121 | var t50 = t41*t15; 122 | mat[0] = Hl0*t48+Hl1*(t18*t15)-Hl2*(t23*t15); 123 | mat[1] = Hl0*t49+Hl1*(t31*t15)-Hl2*(t35*t15); 124 | mat[2] = -Hl0*t50-Hl1*(t44*t15)+Hl2*(t47*t15); 125 | mat[3] = Hl3*t48+Hl4*(t18*t15)-Hl5*(t23*t15); 126 | mat[4] = Hl3*t49+Hl4*(t31*t15)-Hl5*(t35*t15); 127 | mat[5] = -Hl3*t50-Hl4*(t44*t15)+Hl5*(t47*t15); 128 | mat[6] = Hl6*t48+Hl7*(t18*t15)-t23*t15; 129 | mat[7] = Hl6*t49+Hl7*(t31*t15)-t35*t15; 130 | mat[8] = -Hl6*t50-Hl7*(t44*t15)+t47*t15; 131 | }, 132 | 133 | invert_affine_transform: function(src, dst) { 134 | var m11 = src[0], m12 = src[1], m13 = src[2]; 135 | var m21 = src[3], m22 = src[4], m23 = src[5]; 136 | 137 | var det = 1.0 / (m11 * m22 - m12 * m21); 138 | 139 | dst[0] = det * m22; 140 | dst[1] = det * -m12; 141 | dst[2] = det * (m12*m23 - m13*m22); 142 | 143 | dst[3] = det * -m21; 144 | dst[4] = det * m11; 145 | dst[5] = det * (m13*m21 - m11*m23); 146 | }, 147 | 148 | invert_perspective_transform: function(src, dst) { 149 | var m11 = src[0], m12 = src[1], m13 = src[2]; 150 | var m21 = src[3], m22 = src[4], m23 = src[5]; 151 | var m31 = src[6], m32 = src[7], m33 = src[8]; 152 | 153 | var det = 1.0 / (m11 * (m22*m33 - m23*m32) - m12 * (m21*m33 - m23*m31) + m13 * (m21*m32 - m22*m31)); 154 | 155 | dst[0] = det * (m22*m33 - m23*m32); 156 | dst[1] = det * (m13*m32 - m12*m33); 157 | dst[2] = det * (m12*m23 - m13*m22); 158 | 159 | dst[3] = det * (m23*m31 - m21*m33); 160 | dst[4] = det * (m11*m33 - m13*m31); 161 | dst[5] = det * (m13*m21 - m11*m23); 162 | 163 | dst[6] = det * (m21*m32 - m22*m31); 164 | dst[7] = det * (m12*m31 - m11*m32); 165 | dst[8] = det * (m11*m22 - m12*m21); 166 | } 167 | }; 168 | })(); 169 | 170 | global.transform = transform; 171 | 172 | })(jsfeat); -------------------------------------------------------------------------------- /src/jsfeat_yape.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * Copyright 2007 Computer Vision Lab, 5 | * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. 6 | */ 7 | 8 | (function(global) { 9 | "use strict"; 10 | // 11 | 12 | var yape = (function() { 13 | 14 | var precompute_directions = function(step, dirs, R) { 15 | var i = 0; 16 | var x, y; 17 | 18 | x = R; 19 | for(y = 0; y < x; y++, i++) 20 | { 21 | x = (Math.sqrt((R * R - y * y)) + 0.5)|0; 22 | dirs[i] = (x + step * y); 23 | } 24 | for(x-- ; x < y && x >= 0; x--, i++) 25 | { 26 | y = (Math.sqrt((R * R - x * x)) + 0.5)|0; 27 | dirs[i] = (x + step * y); 28 | } 29 | for( ; -x < y; x--, i++) 30 | { 31 | y = (Math.sqrt((R * R - x * x)) + 0.5)|0; 32 | dirs[i] = (x + step * y); 33 | } 34 | for(y-- ; y >= 0; y--, i++) 35 | { 36 | x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; 37 | dirs[i] = (x + step * y); 38 | } 39 | for(; y > x; y--, i++) 40 | { 41 | x = (-Math.sqrt((R * R - y * y)) - 0.5)|0; 42 | dirs[i] = (x + step * y); 43 | } 44 | for(x++ ; x <= 0; x++, i++) 45 | { 46 | y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; 47 | dirs[i] = (x + step * y); 48 | } 49 | for( ; x < -y; x++, i++) 50 | { 51 | y = (-Math.sqrt((R * R - x * x)) - 0.5)|0; 52 | dirs[i] = (x + step * y); 53 | } 54 | for(y++ ; y < 0; y++, i++) 55 | { 56 | x = (Math.sqrt((R * R - y * y)) + 0.5)|0; 57 | dirs[i] = (x + step * y); 58 | } 59 | 60 | dirs[i] = dirs[0]; 61 | dirs[i + 1] = dirs[1]; 62 | return i; 63 | } 64 | 65 | var third_check = function (Sb, off, step) { 66 | var n = 0; 67 | if(Sb[off+1] != 0) n++; 68 | if(Sb[off-1] != 0) n++; 69 | if(Sb[off+step] != 0) n++; 70 | if(Sb[off+step+1] != 0) n++; 71 | if(Sb[off+step-1] != 0) n++; 72 | if(Sb[off-step] != 0) n++; 73 | if(Sb[off-step+1] != 0) n++; 74 | if(Sb[off-step-1] != 0) n++; 75 | 76 | return n; 77 | } 78 | 79 | var is_local_maxima = function(p, off, v, step, neighborhood) { 80 | var x, y; 81 | 82 | if (v > 0) { 83 | off -= step*neighborhood; 84 | for (y= -neighborhood; y<=neighborhood; ++y) { 85 | for (x= -neighborhood; x<=neighborhood; ++x) { 86 | if (p[off+x] > v) return false; 87 | } 88 | off += step; 89 | } 90 | } else { 91 | off -= step*neighborhood; 92 | for (y= -neighborhood; y<=neighborhood; ++y) { 93 | for (x= -neighborhood; x<=neighborhood; ++x) { 94 | if (p[off+x] < v) return false; 95 | } 96 | off += step; 97 | } 98 | } 99 | return true; 100 | } 101 | 102 | var perform_one_point = function(I, x, Scores, Im, Ip, dirs, opposite, dirs_nb) { 103 | var score = 0; 104 | var a = 0, b = (opposite - 1)|0; 105 | var A=0, B0=0, B1=0, B2=0; 106 | var state=0; 107 | 108 | // WE KNOW THAT NOT(A ~ I0 & B1 ~ I0): 109 | A = I[x+dirs[a]]; 110 | if ((A <= Ip)) { 111 | if ((A >= Im)) { // A ~ I0 112 | B0 = I[x+dirs[b]]; 113 | if ((B0 <= Ip)) { 114 | if ((B0 >= Im)) { Scores[x] = 0; return; } 115 | else { 116 | b++; B1 = I[x+dirs[b]]; 117 | if ((B1 > Ip)) { 118 | b++; B2 = I[x+dirs[b]]; 119 | if ((B2 > Ip)) state = 3; 120 | else if ((B2 < Im)) state = 6; 121 | else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 122 | } 123 | else/* if ((B1 < Im))*/ { 124 | b++; B2 = I[x+dirs[b]]; 125 | if ((B2 > Ip)) state = 7; 126 | else if ((B2 < Im)) state = 2; 127 | else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 128 | } 129 | //else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 130 | } 131 | } 132 | else { // B0 < I0 133 | b++; B1 = I[x+dirs[b]]; 134 | if ((B1 > Ip)) { 135 | b++; B2 = I[x+dirs[b]]; 136 | if ((B2 > Ip)) state = 3; 137 | else if ((B2 < Im)) state = 6; 138 | else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 139 | } 140 | else if ((B1 < Im)) { 141 | b++; B2 = I[x+dirs[b]]; 142 | if ((B2 > Ip)) state = 7; 143 | else if ((B2 < Im)) state = 2; 144 | else { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 145 | } 146 | else { Scores[x] = 0; return; } // A ~ I0, B1 ~ I0 147 | } 148 | } 149 | else { // A > I0 150 | B0 = I[x+dirs[b]]; 151 | if ((B0 > Ip)) { Scores[x] = 0; return; } 152 | b++; B1 = I[x+dirs[b]]; 153 | if ((B1 > Ip)) { Scores[x] = 0; return; } 154 | b++; B2 = I[x+dirs[b]]; 155 | if ((B2 > Ip)) { Scores[x] = 0; return; } 156 | state = 1; 157 | } 158 | } 159 | else // A < I0 160 | { 161 | B0 = I[x+dirs[b]]; 162 | if ((B0 < Im)) { Scores[x] = 0; return; } 163 | b++; B1 = I[x+dirs[b]]; 164 | if ((B1 < Im)) { Scores[x] = 0; return; } 165 | b++; B2 = I[x+dirs[b]]; 166 | if ((B2 < Im)) { Scores[x] = 0; return; } 167 | state = 0; 168 | } 169 | 170 | for(a = 1; a <= opposite; a++) 171 | { 172 | A = I[x+dirs[a]]; 173 | 174 | switch(state) 175 | { 176 | case 0: 177 | if ((A > Ip)) { 178 | B1 = B2; b++; B2 = I[x+dirs[b]]; 179 | if ((B2 < Im)) { Scores[x] = 0; return; } 180 | { score -= A + B1; state = 0; break; }; 181 | } 182 | if ((A < Im)) { 183 | if ((B1 > Ip)) { Scores[x] = 0; return; } 184 | if ((B2 > Ip)) { Scores[x] = 0; return; } 185 | B1 = B2; b++; B2 = I[x+dirs[b]]; 186 | if ((B2 > Ip)) { Scores[x] = 0; return; } 187 | { score -= A + B1; state = 8; break; }; 188 | } 189 | // A ~ I0 190 | if ((B1 <= Ip)) { Scores[x] = 0; return; } 191 | if ((B2 <= Ip)) { Scores[x] = 0; return; } 192 | B1 = B2; b++; B2 = I[x+dirs[b]]; 193 | if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; 194 | if ((B2 < Im)) { score -= A + B1; state = 6; break; }; 195 | { Scores[x] = 0; return; } 196 | 197 | case 1: 198 | if ((A < Im)) { 199 | B1 = B2; b++; B2 = I[x+dirs[b]]; 200 | if ((B2 > Ip)) { Scores[x] = 0; return; } 201 | { score -= A + B1; state = 1; break; }; 202 | } 203 | if ((A > Ip)) { 204 | if ((B1 < Im)) { Scores[x] = 0; return; } 205 | if ((B2 < Im)) { Scores[x] = 0; return; } 206 | B1 = B2; b++; B2 = I[x+dirs[b]]; 207 | if ((B2 < Im)) { Scores[x] = 0; return; } 208 | { score -= A + B1; state = 9; break; }; 209 | } 210 | // A ~ I0 211 | if ((B1 >= Im)) { Scores[x] = 0; return; } 212 | if ((B2 >= Im)) { Scores[x] = 0; return; } 213 | B1 = B2; b++; B2 = I[x+dirs[b]]; 214 | if ((B2 < Im)) { score -= A + B1; state = 2; break; }; 215 | if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; 216 | { Scores[x] = 0; return; } 217 | 218 | case 2: 219 | if ((A > Ip)) { Scores[x] = 0; return; } 220 | B1 = B2; b++; B2 = I[x+dirs[b]]; 221 | if ((A < Im)) 222 | { 223 | if ((B2 > Ip)) { Scores[x] = 0; return; } 224 | { score -= A + B1; state = 4; break; }; 225 | } 226 | // A ~ I0 227 | if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; 228 | if ((B2 < Im)) { score -= A + B1; state = 2; break; }; 229 | { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 230 | 231 | case 3: 232 | if ((A < Im)) { Scores[x] = 0; return; } 233 | B1 = B2; b++; B2 = I[x+dirs[b]]; 234 | if ((A > Ip)) { 235 | if ((B2 < Im)) { Scores[x] = 0; return; } 236 | { score -= A + B1; state = 5; break; }; 237 | } 238 | // A ~ I0 239 | if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; 240 | if ((B2 < Im)) { score -= A + B1; state = 6; break; }; 241 | { Scores[x] = 0; return; } 242 | 243 | case 4: 244 | if ((A > Ip)) { Scores[x] = 0; return; } 245 | if ((A < Im)) { 246 | B1 = B2; b++; B2 = I[x+dirs[b]]; 247 | if ((B2 > Ip)) { Scores[x] = 0; return; } 248 | { score -= A + B1; state = 1; break; }; 249 | } 250 | if ((B2 >= Im)) { Scores[x] = 0; return; } 251 | B1 = B2; b++; B2 = I[x+dirs[b]]; 252 | if ((B2 < Im)) { score -= A + B1; state = 2; break; }; 253 | if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; 254 | { Scores[x] = 0; return; } 255 | 256 | case 5: 257 | if ((A < Im)) { Scores[x] = 0; return; } 258 | if ((A > Ip)) { 259 | B1 = B2; b++; B2 = I[x+dirs[b]]; 260 | if ((B2 < Im)) { Scores[x] = 0; return; } 261 | { score -= A + B1; state = 0; break; }; 262 | } 263 | // A ~ I0 264 | if ((B2 <= Ip)) { Scores[x] = 0; return; } 265 | B1 = B2; b++; B2 = I[x+dirs[b]]; 266 | if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; 267 | if ((B2 < Im)) { score -= A + B1; state = 6; break; }; 268 | { Scores[x] = 0; return; } 269 | 270 | case 7: 271 | if ((A > Ip)) { Scores[x] = 0; return; } 272 | if ((A < Im)) { Scores[x] = 0; return; } 273 | B1 = B2; b++; B2 = I[x+dirs[b]]; 274 | // A ~ I0 275 | if ((B2 > Ip)) { score -= A + B1; state = 3; break; }; 276 | if ((B2 < Im)) { score -= A + B1; state = 6; break; }; 277 | { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 278 | 279 | case 6: 280 | if ((A > Ip)) { Scores[x] = 0; return; } 281 | if ((A < Im)) { Scores[x] = 0; return; } 282 | B1 = B2; b++; B2 = I[x+dirs[b]]; 283 | // A ~ I0 284 | if ((B2 < Im)) { score -= A + B1; state = 2; break; }; 285 | if ((B2 > Ip)) { score -= A + B1; state = 7; break; }; 286 | { Scores[x] = 0; return; } // A ~ I0, B2 ~ I0 287 | 288 | case 8: 289 | if ((A > Ip)) { 290 | if ((B2 < Im)) { Scores[x] = 0; return; } 291 | B1 = B2; b++; B2 = I[x+dirs[b]]; 292 | if ((B2 < Im)) { Scores[x] = 0; return; } 293 | { score -= A + B1; state = 9; break; }; 294 | } 295 | if ((A < Im)) { 296 | B1 = B2; b++; B2 = I[x+dirs[b]]; 297 | if ((B2 > Ip)) { Scores[x] = 0; return; } 298 | { score -= A + B1; state = 1; break; }; 299 | } 300 | { Scores[x] = 0; return; } 301 | 302 | case 9: 303 | if ((A < Im)) { 304 | if ((B2 > Ip)) { Scores[x] = 0; return; } 305 | B1 = B2; b++; B2 = I[x+dirs[b]]; 306 | if ((B2 > Ip)) { Scores[x] = 0; return; } 307 | { score -= A + B1; state = 8; break; }; 308 | } 309 | if ((A > Ip)) { 310 | B1 = B2; b++; B2 = I[x+dirs[b]]; 311 | if ((B2 < Im)) { Scores[x] = 0; return; } 312 | { score -= A + B1; state = 0; break; }; 313 | } 314 | { Scores[x] = 0; return; } 315 | 316 | default: 317 | //"PB default"; 318 | break; 319 | } // switch(state) 320 | } // for(a...) 321 | 322 | Scores[x] = (score + dirs_nb * I[x]); 323 | } 324 | 325 | var lev_table_t = (function () { 326 | function lev_table_t(w, h, r) { 327 | this.dirs = new Int32Array(1024); 328 | this.dirs_count = precompute_directions(w, this.dirs, r)|0; 329 | this.scores = new Int32Array(w*h); 330 | this.radius = r|0; 331 | } 332 | return lev_table_t; 333 | })(); 334 | 335 | return { 336 | 337 | level_tables: [], 338 | tau: 7, 339 | 340 | init: function(width, height, radius, pyramid_levels) { 341 | if (typeof pyramid_levels === "undefined") { pyramid_levels = 1; } 342 | var i; 343 | radius = Math.min(radius, 7); 344 | radius = Math.max(radius, 3); 345 | for(i = 0; i < pyramid_levels; ++i) { 346 | this.level_tables[i] = new lev_table_t(width>>i, height>>i, radius); 347 | } 348 | }, 349 | 350 | detect: function(src, points, border) { 351 | if (typeof border === "undefined") { border = 4; } 352 | var t = this.level_tables[0]; 353 | var R = t.radius|0, Rm1 = (R-1)|0; 354 | var dirs = t.dirs; 355 | var dirs_count = t.dirs_count|0; 356 | var opposite = dirs_count >> 1; 357 | var img = src.data, w=src.cols|0, h=src.rows|0,hw=w>>1; 358 | var scores = t.scores; 359 | var x=0,y=0,row=0,rowx=0,ip=0,im=0,abs_score=0, score=0; 360 | var tau = this.tau|0; 361 | var number_of_points = 0, pt; 362 | 363 | var sx = Math.max(R+1, border)|0; 364 | var sy = Math.max(R+1, border)|0; 365 | var ex = Math.min(w-R-2, w-border)|0; 366 | var ey = Math.min(h-R-2, h-border)|0; 367 | 368 | row = (sy*w+sx)|0; 369 | for(y = sy; y < ey; ++y, row+=w) { 370 | for(x = sx, rowx = row; x < ex; ++x, ++rowx) { 371 | ip = img[rowx] + tau, im = img[rowx] - tau; 372 | 373 | if (im= 3 && is_local_maxima(scores, rowx, score, hw, R)) { 392 | pt = points[number_of_points]; 393 | pt.x = x, pt.y = y, pt.score = abs_score; 394 | ++number_of_points; 395 | 396 | x += Rm1, rowx += Rm1; 397 | } 398 | } 399 | } 400 | } 401 | 402 | return number_of_points; 403 | } 404 | }; 405 | 406 | })(); 407 | 408 | global.yape = yape; 409 | 410 | })(jsfeat); -------------------------------------------------------------------------------- /src/jsfeat_yape06.js: -------------------------------------------------------------------------------- 1 | /** 2 | * @author Eugene Zatepyakin / http://inspirit.ru/ 3 | * 4 | * Copyright 2007 Computer Vision Lab, 5 | * Ecole Polytechnique Federale de Lausanne (EPFL), Switzerland. 6 | * @author Vincent Lepetit (http://cvlab.epfl.ch/~lepetit) 7 | */ 8 | 9 | (function(global) { 10 | "use strict"; 11 | // 12 | 13 | var yape06 = (function() { 14 | 15 | var compute_laplacian = function(src, dst, w, h, Dxx, Dyy, sx,sy, ex,ey) { 16 | var y=0,x=0,yrow=(sy*w+sx)|0,row=yrow; 17 | 18 | for(y = sy; y < ey; ++y, yrow+=w, row = yrow) { 19 | for(x = sx; x < ex; ++x, ++row) { 20 | dst[row] = -4 * src[row] + src[row+Dxx] + src[row-Dxx] + src[row+Dyy] + src[row-Dyy]; 21 | } 22 | } 23 | } 24 | 25 | var hessian_min_eigen_value = function(src, off, tr, Dxx, Dyy, Dxy, Dyx) { 26 | var Ixx = -2 * src[off] + src[off + Dxx] + src[off - Dxx]; 27 | var Iyy = -2 * src[off] + src[off + Dyy] + src[off - Dyy]; 28 | var Ixy = src[off + Dxy] + src[off - Dxy] - src[off + Dyx] - src[off - Dyx]; 29 | var sqrt_delta = ( Math.sqrt(((Ixx - Iyy) * (Ixx - Iyy) + 4 * Ixy * Ixy) ) )|0; 30 | 31 | return Math.min(Math.abs(tr - sqrt_delta), Math.abs(-(tr + sqrt_delta))); 32 | } 33 | 34 | return { 35 | 36 | laplacian_threshold: 30, 37 | min_eigen_value_threshold: 25, 38 | 39 | detect: function(src, points, border) { 40 | if (typeof border === "undefined") { border = 5; } 41 | var x=0,y=0; 42 | var w=src.cols, h=src.rows, srd_d=src.data; 43 | var Dxx = 5, Dyy = (5 * w)|0; 44 | var Dxy = (3 + 3 * w)|0, Dyx = (3 - 3 * w)|0; 45 | var lap_buf = jsfeat.cache.get_buffer((w*h)<<2); 46 | var laplacian = lap_buf.i32; 47 | var lv=0, row=0,rowx=0,min_eigen_value=0,pt; 48 | var number_of_points = 0; 49 | var lap_thresh = this.laplacian_threshold; 50 | var eigen_thresh = this.min_eigen_value_threshold; 51 | 52 | var sx = Math.max(5, border)|0; 53 | var sy = Math.max(3, border)|0; 54 | var ex = Math.min(w-5, w-border)|0; 55 | var ey = Math.min(h-3, h-border)|0; 56 | 57 | x = w*h; 58 | while(--x>=0) {laplacian[x]=0;} 59 | compute_laplacian(srd_d, laplacian, w, h, Dxx, Dyy, sx,sy, ex,ey); 60 | 61 | row = (sy*w+sx)|0; 62 | for(y = sy; y < ey; ++y, row += w) { 63 | for(x = sx, rowx=row; x < ex; ++x, ++rowx) { 64 | 65 | lv = laplacian[rowx]; 66 | if ((lv < -lap_thresh && 67 | lv < laplacian[rowx - 1] && lv < laplacian[rowx + 1] && 68 | lv < laplacian[rowx - w] && lv < laplacian[rowx + w] && 69 | lv < laplacian[rowx - w - 1] && lv < laplacian[rowx + w - 1] && 70 | lv < laplacian[rowx - w + 1] && lv < laplacian[rowx + w + 1]) 71 | || 72 | (lv > lap_thresh && 73 | lv > laplacian[rowx - 1] && lv > laplacian[rowx + 1] && 74 | lv > laplacian[rowx - w] && lv > laplacian[rowx + w] && 75 | lv > laplacian[rowx - w - 1] && lv > laplacian[rowx + w - 1] && 76 | lv > laplacian[rowx - w + 1] && lv > laplacian[rowx + w + 1]) 77 | ) { 78 | 79 | min_eigen_value = hessian_min_eigen_value(srd_d, rowx, lv, Dxx, Dyy, Dxy, Dyx); 80 | if (min_eigen_value > eigen_thresh) { 81 | pt = points[number_of_points]; 82 | pt.x = x, pt.y = y, pt.score = min_eigen_value; 83 | ++number_of_points; 84 | ++x, ++rowx; // skip next pixel since this is maxima in 3x3 85 | } 86 | } 87 | } 88 | } 89 | 90 | jsfeat.cache.put_buffer(lap_buf); 91 | 92 | return number_of_points; 93 | } 94 | 95 | }; 96 | })(); 97 | 98 | global.yape06 = yape06; 99 | 100 | })(jsfeat); 101 | --------------------------------------------------------------------------------