├── COFW68_Data ├── cofw68_test_bboxes.mat └── test_annotations │ ├── 100_points.mat │ ├── 101_points.mat │ ├── 102_points.mat │ ├── 103_points.mat │ ├── 104_points.mat │ ├── 105_points.mat │ ├── 106_points.mat │ ├── 107_points.mat │ ├── 108_points.mat │ ├── 109_points.mat │ ├── 10_points.mat │ ├── 110_points.mat │ ├── 111_points.mat │ ├── 112_points.mat │ ├── 113_points.mat │ ├── 114_points.mat │ ├── 115_points.mat │ ├── 116_points.mat │ ├── 117_points.mat │ ├── 118_points.mat │ ├── 119_points.mat │ ├── 11_points.mat │ ├── 120_points.mat │ ├── 121_points.mat │ ├── 122_points.mat │ ├── 123_points.mat │ ├── 124_points.mat │ ├── 125_points.mat │ ├── 126_points.mat │ ├── 127_points.mat │ ├── 128_points.mat │ ├── 129_points.mat │ ├── 12_points.mat │ ├── 130_points.mat │ ├── 131_points.mat │ ├── 132_points.mat │ ├── 133_points.mat │ ├── 134_points.mat │ ├── 135_points.mat │ ├── 136_points.mat │ ├── 137_points.mat │ ├── 138_points.mat │ ├── 139_points.mat │ ├── 13_points.mat │ ├── 140_points.mat │ ├── 141_points.mat │ ├── 142_points.mat │ ├── 143_points.mat │ ├── 144_points.mat │ ├── 145_points.mat │ ├── 146_points.mat │ ├── 147_points.mat │ ├── 148_points.mat │ ├── 149_points.mat │ ├── 14_points.mat │ ├── 150_points.mat │ ├── 151_points.mat │ ├── 152_points.mat │ ├── 153_points.mat │ ├── 154_points.mat │ ├── 155_points.mat │ ├── 156_points.mat │ ├── 157_points.mat │ ├── 158_points.mat │ ├── 159_points.mat │ ├── 15_points.mat │ ├── 160_points.mat │ ├── 161_points.mat │ ├── 162_points.mat │ ├── 163_points.mat │ ├── 164_points.mat │ ├── 165_points.mat │ ├── 166_points.mat │ ├── 167_points.mat │ ├── 168_points.mat │ ├── 169_points.mat │ ├── 16_points.mat │ ├── 170_points.mat │ ├── 171_points.mat │ ├── 172_points.mat │ ├── 173_points.mat │ ├── 174_points.mat │ ├── 175_points.mat │ ├── 176_points.mat │ ├── 177_points.mat │ ├── 178_points.mat │ ├── 179_points.mat │ ├── 17_points.mat │ ├── 180_points.mat │ ├── 181_points.mat │ ├── 182_points.mat │ ├── 183_points.mat │ ├── 184_points.mat │ ├── 185_points.mat │ ├── 186_points.mat │ ├── 187_points.mat │ ├── 188_points.mat │ ├── 189_points.mat │ ├── 18_points.mat │ ├── 190_points.mat │ ├── 191_points.mat │ ├── 192_points.mat │ ├── 193_points.mat │ ├── 194_points.mat │ ├── 195_points.mat │ ├── 196_points.mat │ ├── 197_points.mat │ ├── 198_points.mat │ ├── 199_points.mat │ ├── 19_points.mat │ ├── 1_points.mat │ ├── 200_points.mat │ ├── 201_points.mat │ ├── 202_points.mat │ ├── 203_points.mat │ ├── 204_points.mat │ ├── 205_points.mat │ ├── 206_points.mat │ ├── 207_points.mat │ ├── 208_points.mat │ ├── 209_points.mat │ ├── 20_points.mat │ ├── 210_points.mat │ ├── 211_points.mat │ ├── 212_points.mat │ ├── 213_points.mat │ ├── 214_points.mat │ ├── 215_points.mat │ ├── 216_points.mat │ ├── 217_points.mat │ ├── 218_points.mat │ ├── 219_points.mat │ ├── 21_points.mat │ ├── 220_points.mat │ ├── 221_points.mat │ ├── 222_points.mat │ ├── 223_points.mat │ ├── 224_points.mat │ ├── 225_points.mat │ ├── 226_points.mat │ ├── 227_points.mat │ ├── 228_points.mat │ ├── 229_points.mat │ ├── 22_points.mat │ ├── 230_points.mat │ ├── 231_points.mat │ ├── 232_points.mat │ ├── 233_points.mat │ ├── 234_points.mat │ ├── 235_points.mat │ ├── 236_points.mat │ ├── 237_points.mat │ ├── 238_points.mat │ ├── 239_points.mat │ ├── 23_points.mat │ ├── 240_points.mat │ ├── 241_points.mat │ ├── 242_points.mat │ ├── 243_points.mat │ ├── 244_points.mat │ ├── 245_points.mat │ ├── 246_points.mat │ ├── 247_points.mat │ ├── 248_points.mat │ ├── 249_points.mat │ ├── 24_points.mat │ ├── 250_points.mat │ ├── 251_points.mat │ ├── 252_points.mat │ ├── 253_points.mat │ ├── 254_points.mat │ ├── 255_points.mat │ ├── 256_points.mat │ ├── 257_points.mat │ ├── 258_points.mat │ ├── 259_points.mat │ ├── 25_points.mat │ ├── 260_points.mat │ ├── 261_points.mat │ ├── 262_points.mat │ ├── 263_points.mat │ ├── 264_points.mat │ ├── 265_points.mat │ ├── 266_points.mat │ ├── 267_points.mat │ ├── 268_points.mat │ ├── 269_points.mat │ ├── 26_points.mat │ ├── 270_points.mat │ ├── 271_points.mat │ ├── 272_points.mat │ ├── 273_points.mat │ ├── 274_points.mat │ ├── 275_points.mat │ ├── 276_points.mat │ ├── 277_points.mat │ ├── 278_points.mat │ ├── 279_points.mat │ ├── 27_points.mat │ ├── 280_points.mat │ ├── 281_points.mat │ ├── 282_points.mat │ ├── 283_points.mat │ ├── 284_points.mat │ ├── 285_points.mat │ ├── 286_points.mat │ ├── 287_points.mat │ ├── 288_points.mat │ ├── 289_points.mat │ ├── 28_points.mat │ ├── 290_points.mat │ ├── 291_points.mat │ ├── 292_points.mat │ ├── 293_points.mat │ ├── 294_points.mat │ ├── 295_points.mat │ ├── 296_points.mat │ ├── 297_points.mat │ ├── 298_points.mat │ ├── 299_points.mat │ ├── 29_points.mat │ ├── 2_points.mat │ ├── 300_points.mat │ ├── 301_points.mat │ ├── 302_points.mat │ ├── 303_points.mat │ ├── 304_points.mat │ ├── 305_points.mat │ ├── 306_points.mat │ ├── 307_points.mat │ ├── 308_points.mat │ ├── 309_points.mat │ ├── 30_points.mat │ ├── 310_points.mat │ ├── 311_points.mat │ ├── 312_points.mat │ ├── 313_points.mat │ ├── 314_points.mat │ ├── 315_points.mat │ ├── 316_points.mat │ ├── 317_points.mat │ ├── 318_points.mat │ ├── 319_points.mat │ ├── 31_points.mat │ ├── 320_points.mat │ ├── 321_points.mat │ ├── 322_points.mat │ ├── 323_points.mat │ ├── 324_points.mat │ ├── 325_points.mat │ ├── 326_points.mat │ ├── 327_points.mat │ ├── 328_points.mat │ ├── 329_points.mat │ ├── 32_points.mat │ ├── 330_points.mat │ ├── 331_points.mat │ ├── 332_points.mat │ ├── 333_points.mat │ ├── 334_points.mat │ ├── 335_points.mat │ ├── 336_points.mat │ ├── 337_points.mat │ ├── 338_points.mat │ ├── 339_points.mat │ ├── 33_points.mat │ ├── 340_points.mat │ ├── 341_points.mat │ ├── 342_points.mat │ ├── 343_points.mat │ ├── 344_points.mat │ ├── 345_points.mat │ ├── 346_points.mat │ ├── 347_points.mat │ ├── 348_points.mat │ ├── 349_points.mat │ ├── 34_points.mat │ ├── 350_points.mat │ ├── 351_points.mat │ ├── 352_points.mat │ ├── 353_points.mat │ ├── 354_points.mat │ ├── 355_points.mat │ ├── 356_points.mat │ ├── 357_points.mat │ ├── 358_points.mat │ ├── 359_points.mat │ ├── 35_points.mat │ ├── 360_points.mat │ ├── 361_points.mat │ ├── 362_points.mat │ ├── 363_points.mat │ ├── 364_points.mat │ ├── 365_points.mat │ ├── 366_points.mat │ ├── 367_points.mat │ ├── 368_points.mat │ ├── 369_points.mat │ ├── 36_points.mat │ ├── 370_points.mat │ ├── 371_points.mat │ ├── 372_points.mat │ ├── 373_points.mat │ ├── 374_points.mat │ ├── 375_points.mat │ ├── 376_points.mat │ ├── 377_points.mat │ ├── 378_points.mat │ ├── 379_points.mat │ ├── 37_points.mat │ ├── 380_points.mat │ ├── 381_points.mat │ ├── 382_points.mat │ ├── 383_points.mat │ ├── 384_points.mat │ ├── 385_points.mat │ ├── 386_points.mat │ ├── 387_points.mat │ ├── 388_points.mat │ ├── 389_points.mat │ ├── 38_points.mat │ ├── 390_points.mat │ ├── 391_points.mat │ ├── 392_points.mat │ ├── 393_points.mat │ ├── 394_points.mat │ ├── 395_points.mat │ ├── 396_points.mat │ ├── 397_points.mat │ ├── 398_points.mat │ ├── 399_points.mat │ ├── 39_points.mat │ ├── 3_points.mat │ ├── 400_points.mat │ ├── 401_points.mat │ ├── 402_points.mat │ ├── 403_points.mat │ ├── 404_points.mat │ ├── 405_points.mat │ ├── 406_points.mat │ ├── 407_points.mat │ ├── 408_points.mat │ ├── 409_points.mat │ ├── 40_points.mat │ ├── 410_points.mat │ ├── 411_points.mat │ ├── 412_points.mat │ ├── 413_points.mat │ ├── 414_points.mat │ ├── 415_points.mat │ ├── 416_points.mat │ ├── 417_points.mat │ ├── 418_points.mat │ ├── 419_points.mat │ ├── 41_points.mat │ ├── 420_points.mat │ ├── 421_points.mat │ ├── 422_points.mat │ ├── 423_points.mat │ ├── 424_points.mat │ ├── 425_points.mat │ ├── 426_points.mat │ ├── 427_points.mat │ ├── 428_points.mat │ ├── 429_points.mat │ ├── 42_points.mat │ ├── 430_points.mat │ ├── 431_points.mat │ ├── 432_points.mat │ ├── 433_points.mat │ ├── 434_points.mat │ ├── 435_points.mat │ ├── 436_points.mat │ ├── 437_points.mat │ ├── 438_points.mat │ ├── 439_points.mat │ ├── 43_points.mat │ ├── 440_points.mat │ ├── 441_points.mat │ ├── 442_points.mat │ ├── 443_points.mat │ ├── 444_points.mat │ ├── 445_points.mat │ ├── 446_points.mat │ ├── 447_points.mat │ ├── 448_points.mat │ ├── 449_points.mat │ ├── 44_points.mat │ ├── 450_points.mat │ ├── 451_points.mat │ ├── 452_points.mat │ ├── 453_points.mat │ ├── 454_points.mat │ ├── 455_points.mat │ ├── 456_points.mat │ ├── 457_points.mat │ ├── 458_points.mat │ ├── 459_points.mat │ ├── 45_points.mat │ ├── 460_points.mat │ ├── 461_points.mat │ ├── 462_points.mat │ ├── 463_points.mat │ ├── 464_points.mat │ ├── 465_points.mat │ ├── 466_points.mat │ ├── 467_points.mat │ ├── 468_points.mat │ ├── 469_points.mat │ ├── 46_points.mat │ ├── 470_points.mat │ ├── 471_points.mat │ ├── 472_points.mat │ ├── 473_points.mat │ ├── 474_points.mat │ ├── 475_points.mat │ ├── 476_points.mat │ ├── 477_points.mat │ ├── 478_points.mat │ ├── 479_points.mat │ ├── 47_points.mat │ ├── 480_points.mat │ ├── 481_points.mat │ ├── 482_points.mat │ ├── 483_points.mat │ ├── 484_points.mat │ ├── 485_points.mat │ ├── 486_points.mat │ ├── 487_points.mat │ ├── 488_points.mat │ ├── 489_points.mat │ ├── 48_points.mat │ ├── 490_points.mat │ ├── 491_points.mat │ ├── 492_points.mat │ ├── 493_points.mat │ ├── 494_points.mat │ ├── 495_points.mat │ ├── 496_points.mat │ ├── 497_points.mat │ ├── 498_points.mat │ ├── 499_points.mat │ ├── 49_points.mat │ ├── 4_points.mat │ ├── 500_points.mat │ ├── 501_points.mat │ ├── 502_points.mat │ ├── 503_points.mat │ ├── 504_points.mat │ ├── 505_points.mat │ ├── 506_points.mat │ ├── 507_points.mat │ ├── 50_points.mat │ ├── 51_points.mat │ ├── 52_points.mat │ ├── 53_points.mat │ ├── 54_points.mat │ ├── 55_points.mat │ ├── 56_points.mat │ ├── 57_points.mat │ ├── 58_points.mat │ ├── 59_points.mat │ ├── 5_points.mat │ ├── 60_points.mat │ ├── 61_points.mat │ ├── 62_points.mat │ ├── 63_points.mat │ ├── 64_points.mat │ ├── 65_points.mat │ ├── 66_points.mat │ ├── 67_points.mat │ ├── 68_points.mat │ ├── 69_points.mat │ ├── 6_points.mat │ ├── 70_points.mat │ ├── 71_points.mat │ ├── 72_points.mat │ ├── 73_points.mat │ ├── 74_points.mat │ ├── 75_points.mat │ ├── 76_points.mat │ ├── 77_points.mat │ ├── 78_points.mat │ ├── 79_points.mat │ ├── 7_points.mat │ ├── 80_points.mat │ ├── 81_points.mat │ ├── 82_points.mat │ ├── 83_points.mat │ ├── 84_points.mat │ ├── 85_points.mat │ ├── 86_points.mat │ ├── 87_points.mat │ ├── 88_points.mat │ ├── 89_points.mat │ ├── 8_points.mat │ ├── 90_points.mat │ ├── 91_points.mat │ ├── 92_points.mat │ ├── 93_points.mat │ ├── 94_points.mat │ ├── 95_points.mat │ ├── 96_points.mat │ ├── 97_points.mat │ ├── 98_points.mat │ ├── 99_points.mat │ └── 9_points.mat ├── COFW68_vis0_CED.pdf ├── COFW68_vis1_CED.pdf ├── LandmarkLocalizationEval.m ├── Main.m ├── PlotCED.m ├── README.md ├── ReadCofw68Data.m ├── ReadDetectionResults.m ├── Results ├── CFSS_CFOW68.txt ├── HPM_HelenLfpw_COFW68test.txt ├── RCPR-occ_300w.txt ├── SAPM_COFW68.txt └── TCDCN_CFOW68.txt ├── VisualizeAnnotations.m ├── VisualizeLocalizationRes.m └── 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-------------------------------------------------------------------------------- /LandmarkLocalizationEval.m: -------------------------------------------------------------------------------- 1 | function [errors, precision, recall] = LandmarkLocalizationEval( ... 2 | boxes, pts_name, occ_name, test, left_eye_inds, .... 3 | right_eye_inds, vis_subset) 4 | 5 | gr_occ = []; 6 | dt_occ = []; 7 | errors = nan(length(test), 1); 8 | for i = 1 : length(test) 9 | if isempty(boxes{i}) 10 | errors(i) = nan; 11 | continue; 12 | end 13 | 14 | pts_gt = test(i).pts; 15 | % Computes the distance between the centers of the eyes. 16 | p1 = mean(pts_gt(left_eye_inds, :), 1); 17 | p2 = mean(pts_gt(right_eye_inds, :), 1); 18 | normalization_dist = norm(p1 - p2); 19 | 20 | b = boxes{i}(1); 21 | pts_det = getfield(b, pts_name); 22 | occ_det = getfield(b, occ_name); 23 | 24 | if size(pts_det, 1) ~= size(pts_gt, 1) 25 | warning('The numbers of keypoints are different!'); 26 | errors(i) = nan; 27 | continue; 28 | end 29 | 30 | if vis_subset && isfield(test(i), 'occ') 31 | occ_gt = test(i).occ; 32 | pts_gt = pts_gt(occ_gt == 0, :); 33 | pts_det = pts_det(occ_gt == 0, :); 34 | end 35 | 36 | dif = pts_gt - pts_det; 37 | e = (dif(:,1) .^ 2 + dif(:,2) .^ 2) .^ 0.5; 38 | errors(i) = mean(e) / normalization_dist; 39 | 40 | if isfield(test(i), 'occ') 41 | occ_gt = test(i).occ; 42 | gr_occ = [gr_occ, occ_gt]; 43 | dt_occ = [dt_occ, occ_det']; 44 | end 45 | end 46 | 47 | precision = 0; 48 | recall = 0; 49 | if(isfield(test(1), 'occ')) 50 | gr_occ = gr_occ(:); 51 | dt_occ = dt_occ(:); 52 | C = confusionmat(gr_occ, dt_occ); 53 | recall = C(2, 2) / sum(C(2, :)) * 100; 54 | precision = C(2, 2) / sum(C(:, 2)) * 100; 55 | end 56 | -------------------------------------------------------------------------------- /Main.m: -------------------------------------------------------------------------------- 1 | function Main(file_name, method_name) 2 | 3 | %addpath ~/export_fig/ 4 | close all; 5 | 6 | % Normalizing errors with respect to the distance between the center of eyes. 7 | % Indexes of eyes keypoints in the ground-truth. 8 | left_eye_inds = 37 : 42; 9 | right_eye_inds = 43 : 48; 10 | 11 | cofw_test_images_dir = 'COFW68_Data/test_annotations/'; 12 | cofw_test_data_file = 'COFW68_Data/COFW_test.mat'; 13 | test_data_path = 'COFW68_Data/data.mat'; 14 | try 15 | load([test_data_path]); 16 | catch 17 | test = ReadCofw68Data(cofw_test_data_file, cofw_test_images_dir); 18 | save([test_data_path], 'test'); 19 | end 20 | 21 | file_names = cell(1, 0); 22 | method_names = cell(1, 0); 23 | 24 | % Golnaz Ghiasi, Charless Fowlkes, 25 | % Occlusion Coherence: Detecting and Localizing Occluded Faces, arXiv:1506.08347, 2015 26 | file_names{end+1} = 'Results/HPM_HelenLfpw_COFW68test.txt'; 27 | method_names{end+1} = 'HPM(HELEN,LFPW)'; 28 | 29 | % Ghiasi G, Fowlkes CC, Using segmentation to predict the absence of occluded parts, BMVC 2015 30 | file_names{end+1} = 'Results/SAPM_COFW68.txt'; 31 | method_names{end+1} = 'SAPM(HELEN)'; 32 | 33 | % Zhu, Shizhan and Li, Cheng and Loy, Chen Change and Tang, Xiaoou, 34 | % Face Alignment by Coarse-to-Fine Shape Searching, CVPR 2015 35 | file_names{end+1} = 'Results/CFSS_CFOW68.txt'; 36 | method_names{end+1} = 'CFSS(HELEN,LFPW,AFW)'; 37 | 38 | % Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang 39 | % Learning Deep Representation for Face Alignment with Auxiliary Attributes, PAMI 2016 40 | file_names{end+1} = 'Results/TCDCN_CFOW68.txt'; 41 | method_names{end+1} = 'TCDCN(HELEN,LFPW,AFW,MAFL)'; 42 | 43 | % X. P. Burgos-Artizzu, P. Perona and P. DollárA, Robust face landmark estimation under occlusion, ICCV 2013 44 | file_names{end+1} = 'Results/RCPR-occ_300w.txt'; 45 | method_names{end+1} = 'RCPR-occ(HELEN,LFPW)'; 46 | 47 | % Add other methods here. 48 | % file_names{end+1} = 'address of the detection info file'; 49 | % method_names{end+1} = 'name of the method'; 50 | 51 | % Adds the results of the input (if exists). 52 | if(exist('file_name', 'var')) 53 | file_names{end+1} = file_name; 54 | method_names{end+1} = method_name; 55 | end 56 | 57 | max_to_show = 20; 58 | crop_images = true; 59 | show_groundtruth = true; 60 | show_keypoint_num = 0; 61 | draw_line_between_gt_det = true; 62 | vis_predictions = false; 63 | if vis_predictions 64 | visdir = 'vis'; 65 | if ~exist(visdir, 'dir') 66 | mkdir(visdir); 67 | end 68 | end 69 | 70 | for eval_on_vis_keypoints = 0 : 1 71 | for i = 1 : length(file_names) 72 | fprintf('Reading detection results of "%s" from "%s".\n', ... 73 | method_names{i}, file_names{i}); 74 | boxes = ReadDetectionResults(file_names{i}); 75 | 76 | fprintf('Evaluating ...\n'); 77 | [errors{i}, precision{i}, recall{i}] = LandmarkLocalizationEval( ... 78 | boxes, 'pts68', 'occ68', test, left_eye_inds, right_eye_inds, eval_on_vis_keypoints); 79 | 80 | % Prints results. 81 | ave_errors{i} = nanmean(errors{i}); 82 | success_rate{i} = sum(errors{i} <= 0.1) / length(errors{i}) * 100; 83 | fprintf('Average Error: %.4f, Success rate(at the thresh 0.1): %.2f%% ', ave_errors{i}, success_rate{i}); 84 | fprintf('(normalized by the distance between the centers of eyes)\n'); 85 | fprintf('recall: %.3f precision %.3f\n', recall{i}, precision{i}); 86 | fprintf('number of nan: %d\n', sum(isnan(errors{i}))); 87 | 88 | % Visualize predictions 89 | if vis_predictions && eval_on_vis_keypoints == 0 90 | VisualizeLocalizationRes(boxes, 'pts68', 'occ68', test, 'cofw_68test_vis', ... 91 | visdir, crop_images, show_groundtruth, show_keypoint_num, ... 92 | errors{i}, draw_line_between_gt_det, method_names{i}, max_to_show); 93 | end 94 | end 95 | PlotCED(errors, ave_errors, success_rate, precision, recall, method_names, eval_on_vis_keypoints); 96 | %export_fig(['COFW68_vis' num2str(eval_on_vis_keypoints) '_CED'], '-pdf'); 97 | end 98 | 99 | -------------------------------------------------------------------------------- /PlotCED.m: -------------------------------------------------------------------------------- 1 | function PlotCED(errors, ave_errors, success_rate, precision, recall, method_names, eval_on_vis_keypoints) 2 | 3 | figure; hold on; grid on; 4 | colors = distinguishable_colors(length(method_names)); 5 | for i = 1 : length(method_names) 6 | [y, x] = ecdf(errors{i}); 7 | plot(x, y, 'color', colors(i, :), 'linewidth', 2); 8 | method_names{i} = [method_names{i} sprintf(', %.4f%% [%.2f%%]', ave_errors{i}, success_rate{i})]; 9 | if ~isnan(precision{i}) 10 | method_names{i} = [method_names{i} sprintf(', Precision: %.2f%%, Recall: %.2f%%', precision{i}, recall{i})]; 11 | end 12 | end 13 | 14 | fontsize = 10; 15 | ylabel('Proportion of faces', 'fontsize', fontsize); 16 | xlabel('Average localization error as fraction of interpupillary distance', 'fontsize', fontsize); 17 | if eval_on_vis_keypoints 18 | title('Evaluation on only the visible keypoints'); 19 | else 20 | title('Evaluation on all the keypoints'); 21 | end 22 | 23 | set(gca,'fontsize', fontsize); 24 | set(gcf,'color', 'w') 25 | legend(method_names, 'Location', 'SouthEast', 'FontSize', 8); 26 | xlim([0 0.3]); 27 | 28 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## 68 keypoint annotations for COFW test data 2 | 3 | This repository contains manually annotated 68 keypoints for COFW 4 | test data (original annotation of [CFOW dataset](http://www.vision.caltech.edu/xpburgos/ICCV13/) 5 | contains 29 keypoints). The annotations are stored at "COFW68_Data/test_annotations/". For each image, 6 | there is one .mat file which contains the location of the keypoints and their visibilities. 7 | Also, a face bounding box for each image is stored at "COFW68_Data/cofw68_test_bboxes.mat". 8 | These bounding boxes are calculated using similar detection method 9 | which is used for [300-W](http://ibug.doc.ic.ac.uk/resources/300-W/) datasets. So a 10 | landmark localization model trained on 300-W datasets can be tested on COFW68 dataset. 11 | 12 | ##### Download COFW Images: 13 | ``` sh 14 | cd COFW68_Data 15 | wget http://www.vision.caltech.edu/xpburgos/ICCV13/Data/COFW.zip 16 | unzip COFW.zip 17 | mv common/xpburgos/behavior/code/pose/COFW_test.mat . 18 | ``` 19 | 20 | To visualize the annotations execute "VisualizeAnnotations.m" 21 | 22 | #### Evaluate your method 23 | 24 | To evaluate your model, create a text file that contains the landmark localization 25 | predictions (and their visibilities). Then specify its address and name in Main.m and 26 | execute the Main file. 27 | For each image there should be 4 lines in the text file. The first line should contain 28 | the image index (in COFW_test.mat). The second, third and fouth lines should contains 29 | x, y and occlusion state of each keypoint, respectively. 30 | 31 | If you like to share your results with others, please send the file containing landmark 32 | localization results to gghiasi@uci.edu and it will be added it to the "Results" directory. 33 | 34 | ##### Please cite this paper if you use this benchmark for your research paper: 35 | [Golnaz Ghiasi, Charless Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces", arXiv:1506.08347](http://arxiv.org/pdf/1506.08347.pdf) 36 | 37 | #### Issues, Questions, etc 38 | 39 | Please contact "gghiasi @ ics.uci.edu" 40 | 41 | -------------------------------------------------------------------------------- /ReadCofw68Data.m: -------------------------------------------------------------------------------- 1 | function test = ReadCofw68Data(data_file_add, data_dir) 2 | 3 | fprintf('Reading COFW68 test data ...\n'); 4 | load(data_file_add, 'IsT', 'bboxesT'); 5 | 6 | for i = 1 : length(IsT) 7 | im_add = [data_dir '/im_' num2str(i) '.png']; 8 | pts_add = [data_dir num2str(i) '_points.mat']; 9 | try 10 | imread(im_add); 11 | catch 12 | I = IsT{i}; 13 | imwrite(I, im_add); 14 | end 15 | load(pts_add, 'Points', 'Occ'); 16 | test(i).id = num2str(i); 17 | test(i).im = im_add; 18 | test(i).pts = Points; 19 | test(i).occ = Occ; 20 | end 21 | -------------------------------------------------------------------------------- /ReadDetectionResults.m: -------------------------------------------------------------------------------- 1 | function boxes = ReadDetectionResults(filename) 2 | 3 | fid = fopen(filename, 'r'); 4 | boxes = cell(1, 507); 5 | while ~feof(fid) 6 | nextline = fgets(fid); 7 | imageid = sscanf(nextline, '%s', 1); 8 | 9 | nextline = fgets(fid); 10 | x = sscanf(nextline, '%f'); 11 | assert(length(x) == 68); 12 | 13 | nextline = fgets(fid); 14 | y = sscanf(nextline, '%f'); 15 | assert(length(y) == 68); 16 | 17 | nextline = fgets(fid); 18 | occ = sscanf(nextline, '%d'); 19 | assert(length(occ) == 68); 20 | 21 | id = str2num(imageid); 22 | assert(isempty(boxes{id})); 23 | boxes{id}.pts68 = [x y]; 24 | boxes{id}.occ68 = occ; 25 | end 26 | fclose(fid); 27 | -------------------------------------------------------------------------------- /VisualizeAnnotations.m: -------------------------------------------------------------------------------- 1 | load('COFW68_Data/COFW_test.mat', 'IsT', 'phisT'); 2 | load('COFW68_Data/cofw68_test_bboxes.mat', 'bboxes'); 3 | 4 | for i = 1 : length(IsT) 5 | clf; imshow(IsT{i}); axis 'equal'; hold on; 6 | load(['COFW68_Data/test_annotations/' num2str(i) '_points.mat'], 'Points', 'Occ'); 7 | plot(Points(Occ == 0, 1), Points(Occ == 0, 2), '.g', 'MarkerSize', 10); 8 | plot(Points(Occ == 1, 1), Points(Occ == 1, 2), '.r', 'MarkerSize', 10); 9 | 10 | rectangle('Position', bboxes(i, :), 'EdgeColor', 'b', 'LineWidth', 1); 11 | pause; 12 | end 13 | -------------------------------------------------------------------------------- /VisualizeLocalizationRes.m: -------------------------------------------------------------------------------- 1 | function [] = VisualizeLocalizationRes( ... 2 | boxes, pts_name, occ_name, test, testname, figdir, ... 3 | crop_images, show_groundtruth, show_keypoint_num, errors, ... 4 | draw_line_between_gt_det, method_name, max_to_show) 5 | if(~exist('max_to_show', 'var') || max_to_show > length(test)) 6 | max_to_show = length(test); 7 | end 8 | 9 | fprintf(['Visualizing landmark localization predication of first %d images' ... 10 | ' for %s method ...\n'], max_to_show, method_name); 11 | save_res = fullfile(figdir, method_name); 12 | if(~exist(save_res, 'dir')) 13 | mkdir(save_res); 14 | end 15 | for i = 1 : max_to_show 16 | if(isempty(boxes{i})) 17 | im = imread(test(i).im); 18 | clf; imagesc(im); axis off; axis image; hold on; 19 | 20 | if isfield(test(i), 'bbox') 21 | bbox = test(i).bbox; 22 | [im, offset] = CropImage(im, bbox, 0); 23 | clf; imagesc(im); axis off; axis image; hold on; 24 | end 25 | continue; 26 | end 27 | 28 | pts_gt = test(i).pts; 29 | if isfield(test(i), 'occ') && ~isempty(test(i).occ) 30 | occ_gt = test(i).occ; 31 | else 32 | occ_gt = zeros(1, size(pts_gt, 1)); 33 | end 34 | 35 | im = imread(test(i).im); 36 | b = boxes{i}(1); 37 | pts_det = getfield(b, pts_name); 38 | occ_det = getfield(b, occ_name); 39 | 40 | if crop_images 41 | if isfield(test(i), 'bbox') 42 | bbox = test(i).bbox; 43 | [im, offset] = CropImage(im, bbox, 0.3); 44 | else 45 | bbox = [min(pts_gt(:, 1)), min(pts_gt(:, 2)), ... 46 | max(pts_gt(:, 1)), max(pts_gt(:, 2))]; 47 | [im, offset] = CropImage(im, bbox, 0.3); 48 | end 49 | 50 | pts_gt(:, 1) = pts_gt(:, 1) - offset(1); 51 | pts_gt(:, 2) = pts_gt(:, 2) - offset(2); 52 | 53 | pts_det(:, 1) = pts_det(:, 1) - offset(1); 54 | pts_det(:, 2) = pts_det(:, 2) - offset(2); 55 | end 56 | 57 | clf; imagesc(im); axis off; axis image; hold on; 58 | if(size(im, 3) == 1) 59 | colormap(gray); 60 | end 61 | plot(pts_det(occ_det == 0, 1), pts_det(occ_det == 0, 2), '.g', ... 62 | 'MarkerSize', 20); 63 | plot(pts_det(occ_det == 1, 1), pts_det(occ_det == 1, 2), '.r', ... 64 | 'MarkerSize', 20); 65 | 66 | if show_keypoint_num 67 | ShowKeypointNums(pts_det, 'k'); 68 | end 69 | 70 | if show_groundtruth 71 | plot(pts_gt(occ_gt == 0, 1), pts_gt(occ_gt == 0, 2), '.b', ... 72 | 'MarkerSize', 20); 73 | plot(pts_gt(occ_gt == 1, 1), pts_gt(occ_gt == 1, 2), '.m', ... 74 | 'MarkerSize', 20); 75 | 76 | if show_keypoint_num 77 | ShowKeypointNums(pts_gt, 'm'); 78 | end 79 | end 80 | if draw_line_between_gt_det 81 | for k = 1 : size(pts_gt, 1) 82 | plot([pts_gt(k, 1) pts_det(k, 1)], [pts_gt(k, 2) pts_det(k, 2)], 'g'); 83 | end 84 | end 85 | title(sprintf('%.3f', errors(i))); 86 | pause; 87 | %export_fig(fullfile(save_res, num2str(i)), '-pdf'); 88 | end 89 | 90 | 91 | function [im, offset] = CropImage(im, box, pad_ratio) 92 | % Crops image around the bounding box. 93 | pad = pad_ratio * ((box(3) - box(1) + 1) + (box(4) - box(2) + 1)); 94 | x1 = max(1, round(box(1) - pad)); 95 | y1 = max(1, round(box(2) - pad)); 96 | x2 = min(size(im, 2), round(box(3) + pad)); 97 | y2 = min(size(im, 1), round(box(4) + pad)); 98 | 99 | im = im(y1:y2, x1:x2, :); 100 | 101 | offset(1) = x1 -1; 102 | offset(2) = y1 -1; 103 | 104 | function ShowKeypointNums(pts, color) 105 | for i = 1 : size(pts, 1) 106 | text(pts(i, 1), pts(i, 2), num2str(i), 'Color', color, 'FontSize', 10); 107 | end 108 | -------------------------------------------------------------------------------- /distinguishable_colors.m: -------------------------------------------------------------------------------- 1 | function colors = distinguishable_colors(n_colors,bg,func) 2 | % DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct 3 | % 4 | % When plotting a set of lines, you may want to distinguish them by color. 5 | % By default, Matlab chooses a small set of colors and cycles among them, 6 | % and so if you have more than a few lines there will be confusion about 7 | % which line is which. To fix this problem, one would want to be able to 8 | % pick a much larger set of distinct colors, where the number of colors 9 | % equals or exceeds the number of lines you want to plot. Because our 10 | % ability to distinguish among colors has limits, one should choose these 11 | % colors to be "maximally perceptually distinguishable." 12 | % 13 | % This function generates a set of colors which are distinguishable 14 | % by reference to the "Lab" color space, which more closely matches 15 | % human color perception than RGB. Given an initial large list of possible 16 | % colors, it iteratively chooses the entry in the list that is farthest (in 17 | % Lab space) from all previously-chosen entries. While this "greedy" 18 | % algorithm does not yield a global maximum, it is simple and efficient. 19 | % Moreover, the sequence of colors is consistent no matter how many you 20 | % request, which facilitates the users' ability to learn the color order 21 | % and avoids major changes in the appearance of plots when adding or 22 | % removing lines. 23 | % 24 | % Syntax: 25 | % colors = distinguishable_colors(n_colors) 26 | % Specify the number of colors you want as a scalar, n_colors. This will 27 | % generate an n_colors-by-3 matrix, each row representing an RGB 28 | % color triple. If you don't precisely know how many you will need in 29 | % advance, there is no harm (other than execution time) in specifying 30 | % slightly more than you think you will need. 31 | % 32 | % colors = distinguishable_colors(n_colors,bg) 33 | % This syntax allows you to specify the background color, to make sure that 34 | % your colors are also distinguishable from the background. Default value 35 | % is white. bg may be specified as an RGB triple or as one of the standard 36 | % "ColorSpec" strings. You can even specify multiple colors: 37 | % bg = {'w','k'} 38 | % or 39 | % bg = [1 1 1; 0 0 0] 40 | % will only produce colors that are distinguishable from both white and 41 | % black. 42 | % 43 | % colors = distinguishable_colors(n_colors,bg,rgb2labfunc) 44 | % By default, distinguishable_colors uses the image processing toolbox's 45 | % color conversion functions makecform and applycform. Alternatively, you 46 | % can supply your own color conversion function. 47 | % 48 | % Example: 49 | % c = distinguishable_colors(25); 50 | % figure 51 | % image(reshape(c,[1 size(c)])) 52 | % 53 | % Example using the file exchange's 'colorspace': 54 | % func = @(x) colorspace('RGB->Lab',x); 55 | % c = distinguishable_colors(25,'w',func); 56 | 57 | % Copyright 2010-2011 by Timothy E. Holy 58 | 59 | % Parse the inputs 60 | if (nargin < 2) 61 | bg = [1 1 1]; % default white background 62 | else 63 | if iscell(bg) 64 | % User specified a list of colors as a cell aray 65 | bgc = bg; 66 | for i = 1:length(bgc) 67 | bgc{i} = parsecolor(bgc{i}); 68 | end 69 | bg = cat(1,bgc{:}); 70 | else 71 | % User specified a numeric array of colors (n-by-3) 72 | bg = parsecolor(bg); 73 | end 74 | end 75 | 76 | % Generate a sizable number of RGB triples. This represents our space of 77 | % possible choices. By starting in RGB space, we ensure that all of the 78 | % colors can be generated by the monitor. 79 | n_grid = 30; % number of grid divisions along each axis in RGB space 80 | x = linspace(0,1,n_grid); 81 | [R,G,B] = ndgrid(x,x,x); 82 | rgb = [R(:) G(:) B(:)]; 83 | if (n_colors > size(rgb,1)/3) 84 | error('You can''t readily distinguish that many colors'); 85 | end 86 | 87 | % Convert to Lab color space, which more closely represents human 88 | % perception 89 | if (nargin > 2) 90 | lab = func(rgb); 91 | bglab = func(bg); 92 | else 93 | C = makecform('srgb2lab'); 94 | lab = applycform(rgb,C); 95 | bglab = applycform(bg,C); 96 | end 97 | 98 | % If the user specified multiple background colors, compute distances 99 | % from the candidate colors to the background colors 100 | mindist2 = inf(size(rgb,1),1); 101 | for i = 1:size(bglab,1)-1 102 | dX = bsxfun(@minus,lab,bglab(i,:)); % displacement all colors from bg 103 | dist2 = sum(dX.^2,2); % square distance 104 | mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color 105 | end 106 | 107 | % Iteratively pick the color that maximizes the distance to the nearest 108 | % already-picked color 109 | colors = zeros(n_colors,3); 110 | lastlab = bglab(end,:); % initialize by making the "previous" color equal to background 111 | for i = 1:n_colors 112 | dX = bsxfun(@minus,lab,lastlab); % displacement of last from all colors on list 113 | dist2 = sum(dX.^2,2); % square distance 114 | mindist2 = min(dist2,mindist2); % dist2 to closest previously-chosen color 115 | [~,index] = max(mindist2); % find the entry farthest from all previously-chosen colors 116 | colors(i,:) = rgb(index,:); % save for output 117 | lastlab = lab(index,:); % prepare for next iteration 118 | end 119 | end 120 | 121 | function c = parsecolor(s) 122 | if ischar(s) 123 | c = colorstr2rgb(s); 124 | elseif isnumeric(s) && size(s,2) == 3 125 | c = s; 126 | else 127 | error('MATLAB:InvalidColorSpec','Color specification cannot be parsed.'); 128 | end 129 | end 130 | 131 | function c = colorstr2rgb(c) 132 | % Convert a color string to an RGB value. 133 | % This is cribbed from Matlab's whitebg function. 134 | % Why don't they make this a stand-alone function? 135 | rgbspec = [1 0 0;0 1 0;0 0 1;1 1 1;0 1 1;1 0 1;1 1 0;0 0 0]; 136 | cspec = 'rgbwcmyk'; 137 | k = find(cspec==c(1)); 138 | if isempty(k) 139 | error('MATLAB:InvalidColorString','Unknown color string.'); 140 | end 141 | if k~=3 || length(c)==1, 142 | c = rgbspec(k,:); 143 | elseif length(c)>2, 144 | if strcmpi(c(1:3),'bla') 145 | c = [0 0 0]; 146 | elseif strcmpi(c(1:3),'blu') 147 | c = [0 0 1]; 148 | else 149 | error('MATLAB:UnknownColorString', 'Unknown color string.'); 150 | end 151 | end 152 | end 153 | --------------------------------------------------------------------------------