container-dp/resources/FairMOT/track.py [26:293]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def write_results(filename, results, data_type): if data_type == 'mot': save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n' elif data_type == 'kitti': save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' else: raise ValueError(data_type) with open(filename, 'w') as f: for frame_id, tlwhs, track_ids in results: if data_type == 'kitti': frame_id -= 1 for tlwh, track_id in zip(tlwhs, track_ids): if track_id < 0: continue x1, y1, w, h = tlwh x2, y2 = x1 + w, y1 + h line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h) f.write(line) logger.info('save results to {}'.format(filename)) def write_results_score(filename, results, data_type): if data_type == 'mot': save_format = '{frame},{id},{x1},{y1},{w},{h},{s},1,-1,-1,-1\n' elif data_type == 'kitti': save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' else: raise ValueError(data_type) with open(filename, 'w') as f: for frame_id, tlwhs, track_ids, scores in results: if data_type == 'kitti': frame_id -= 1 for tlwh, track_id, score in zip(tlwhs, track_ids, scores): if track_id < 0: continue x1, y1, w, h = tlwh x2, y2 = x1 + w, y1 + h line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, s=score) f.write(line) logger.info('save results to {}'.format(filename)) def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30, use_cuda=True): if save_dir: mkdir_if_missing(save_dir) tracker = JDETracker(opt, frame_rate=frame_rate) timer = Timer() results = [] frame_id = 0 #for path, img, img0 in dataloader: for i, (path, img, img0) in enumerate(dataloader): #if i % 8 != 0: #continue if frame_id % 20 == 0: logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time))) # run tracking timer.tic() if use_cuda: blob = torch.from_numpy(img).cuda().unsqueeze(0) else: blob = torch.from_numpy(img).unsqueeze(0) online_targets = tracker.update(blob, img0) online_tlwhs = [] online_ids = [] #online_scores = [] for t in online_targets: tlwh = t.tlwh tid = t.track_id vertical = tlwh[2] / tlwh[3] > 1.6 if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical: online_tlwhs.append(tlwh) online_ids.append(tid) #online_scores.append(t.score) timer.toc() # save results results.append((frame_id + 1, online_tlwhs, online_ids)) #results.append((frame_id + 1, online_tlwhs, online_ids, online_scores)) if show_image or save_dir is not None: online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id, fps=1. / timer.average_time) if show_image: cv2.imshow('online_im', online_im) if save_dir is not None: cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im) frame_id += 1 # save results write_results(result_filename, results, data_type) #write_results_score(result_filename, results, data_type) return frame_id, timer.average_time, timer.calls def print_summary(strsummary, epoch): lines = strsummary.split("\n") metric_names = lines[0][1:] metric_names = re.sub(' +', ' ', metric_names) metric_names = metric_names.split(' ') metric_values = lines[-1][1:] metric_values = re.sub(' +', ' ', metric_values) metric_values = metric_values.split(' ') metric_list = "epoch-{}: ".format(epoch) for k, v in zip(metric_names, metric_values): if '%' in v: v = v.replace('%', '') v = float(v)/100 metric_list += "{} {}|".format(k, v) print(metric_list) def evaluation(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo', save_images=False, save_videos=False, show_image=True, epoch=0, model=None): logger.setLevel(logging.INFO) result_root = os.path.join(data_root, '..', 'results', exp_name) mkdir_if_missing(result_root) data_type = 'mot' # run tracking accs = [] n_frame = 0 timer_avgs, timer_calls = [], [] for seq in seqs: output_dir = os.path.join(data_root, '..', 'outputs', exp_name, seq) if save_images or save_videos else None logger.info('start seq: {}'.format(seq)) dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size) result_filename = os.path.join(result_root, '{}.txt'.format(seq)) meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read() frame_rate = int(meta_info[meta_info.find('frameRate') + 10:meta_info.find('\nseqLength')]) nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename, save_dir=output_dir, show_image=show_image, frame_rate=frame_rate) n_frame += nf timer_avgs.append(ta) timer_calls.append(tc) # eval logger.info('Evaluate seq: {}'.format(seq)) evaluator = Evaluator(data_root, seq, data_type) accs.append(evaluator.eval_file(result_filename)) if save_videos: output_video_path = osp.join(output_dir, '{}.mp4'.format(seq)) cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path) os.system(cmd_str) timer_avgs = np.asarray(timer_avgs) timer_calls = np.asarray(timer_calls) all_time = np.dot(timer_avgs, timer_calls) avg_time = all_time / np.sum(timer_calls) logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time)) # get summary metrics = mm.metrics.motchallenge_metrics mh = mm.metrics.create() summary = Evaluator.get_summary(accs, seqs, metrics) strsummary = mm.io.render_summary( summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names ) #print(strsummary) print_summary(strsummary, epoch) #Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name))) if __name__ == '__main__': os.environ['CUDA_VISIBLE_DEVICES'] = '1' opt = opts().init() if not opt.val_mot16: seqs_str = '''KITTI-13 KITTI-17 ADL-Rundle-6 PETS09-S2L1 TUD-Campus TUD-Stadtmitte''' #seqs_str = '''TUD-Campus''' data_root = os.path.join(opt.data_dir, 'MOT15/images/train') else: seqs_str = '''MOT16-02 MOT16-04 MOT16-05 MOT16-09 MOT16-10 MOT16-11 MOT16-13''' data_root = os.path.join(opt.data_dir, 'MOT16/train') if opt.test_mot16: seqs_str = '''MOT16-01 MOT16-03 MOT16-06 MOT16-07 MOT16-08 MOT16-12 MOT16-14''' #seqs_str = '''MOT16-01 MOT16-07 MOT16-12 MOT16-14''' #seqs_str = '''MOT16-06 MOT16-08''' data_root = os.path.join(opt.data_dir, 'MOT16/test') if opt.test_mot15: seqs_str = '''ADL-Rundle-1 ADL-Rundle-3 AVG-TownCentre ETH-Crossing ETH-Jelmoli ETH-Linthescher KITTI-16 KITTI-19 PETS09-S2L2 TUD-Crossing Venice-1''' data_root = os.path.join(opt.data_dir, 'MOT15/images/test') if opt.test_mot17: seqs_str = '''MOT17-01-SDP MOT17-03-SDP MOT17-06-SDP MOT17-07-SDP MOT17-08-SDP MOT17-12-SDP MOT17-14-SDP''' data_root = os.path.join(opt.data_dir, 'MOT17/images/test') if opt.val_mot17: seqs_str = '''MOT17-02-SDP MOT17-04-SDP MOT17-05-SDP MOT17-09-SDP MOT17-10-SDP MOT17-11-SDP MOT17-13-SDP''' data_root = os.path.join(opt.data_dir, 'MOT17/images/train') if opt.val_mot15: seqs_str = '''Venice-2 KITTI-13 KITTI-17 ETH-Bahnhof ETH-Sunnyday PETS09-S2L1 TUD-Campus TUD-Stadtmitte ADL-Rundle-6 ADL-Rundle-8 ETH-Pedcross2 TUD-Stadtmitte''' data_root = os.path.join(opt.data_dir, 'MOT15/images/train') if opt.val_mot20: """ seqs_str = '''MOT20-01 MOT20-02 MOT20-03 MOT20-05 ''' """ seqs_str = "MOT20-05" data_root = os.path.join(opt.data_dir, 'MOT20/images/train') if opt.test_mot20: seqs_str = '''MOT20-04 MOT20-06 MOT20-07 MOT20-08 ''' data_root = os.path.join(opt.data_dir, 'MOT20/images/test') seqs = [seq.strip() for seq in seqs_str.split()] evaluation(opt, data_root=data_root, seqs=seqs, exp_name='MOT17_test_public_dla34', show_image=False, save_images=False, save_videos=False) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - container-serving/resources/FairMOT/track.py [26:293]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - def write_results(filename, results, data_type): if data_type == 'mot': save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n' elif data_type == 'kitti': save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' else: raise ValueError(data_type) with open(filename, 'w') as f: for frame_id, tlwhs, track_ids in results: if data_type == 'kitti': frame_id -= 1 for tlwh, track_id in zip(tlwhs, track_ids): if track_id < 0: continue x1, y1, w, h = tlwh x2, y2 = x1 + w, y1 + h line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h) f.write(line) logger.info('save results to {}'.format(filename)) def write_results_score(filename, results, data_type): if data_type == 'mot': save_format = '{frame},{id},{x1},{y1},{w},{h},{s},1,-1,-1,-1\n' elif data_type == 'kitti': save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' else: raise ValueError(data_type) with open(filename, 'w') as f: for frame_id, tlwhs, track_ids, scores in results: if data_type == 'kitti': frame_id -= 1 for tlwh, track_id, score in zip(tlwhs, track_ids, scores): if track_id < 0: continue x1, y1, w, h = tlwh x2, y2 = x1 + w, y1 + h line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, s=score) f.write(line) logger.info('save results to {}'.format(filename)) def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30, use_cuda=True): if save_dir: mkdir_if_missing(save_dir) tracker = JDETracker(opt, frame_rate=frame_rate) timer = Timer() results = [] frame_id = 0 #for path, img, img0 in dataloader: for i, (path, img, img0) in enumerate(dataloader): #if i % 8 != 0: #continue if frame_id % 20 == 0: logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time))) # run tracking timer.tic() if use_cuda: blob = torch.from_numpy(img).cuda().unsqueeze(0) else: blob = torch.from_numpy(img).unsqueeze(0) online_targets = tracker.update(blob, img0) online_tlwhs = [] online_ids = [] #online_scores = [] for t in online_targets: tlwh = t.tlwh tid = t.track_id vertical = tlwh[2] / tlwh[3] > 1.6 if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical: online_tlwhs.append(tlwh) online_ids.append(tid) #online_scores.append(t.score) timer.toc() # save results results.append((frame_id + 1, online_tlwhs, online_ids)) #results.append((frame_id + 1, online_tlwhs, online_ids, online_scores)) if show_image or save_dir is not None: online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id, fps=1. / timer.average_time) if show_image: cv2.imshow('online_im', online_im) if save_dir is not None: cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im) frame_id += 1 # save results write_results(result_filename, results, data_type) #write_results_score(result_filename, results, data_type) return frame_id, timer.average_time, timer.calls def print_summary(strsummary, epoch): lines = strsummary.split("\n") metric_names = lines[0][1:] metric_names = re.sub(' +', ' ', metric_names) metric_names = metric_names.split(' ') metric_values = lines[-1][1:] metric_values = re.sub(' +', ' ', metric_values) metric_values = metric_values.split(' ') metric_list = "epoch-{}: ".format(epoch) for k, v in zip(metric_names, metric_values): if '%' in v: v = v.replace('%', '') v = float(v)/100 metric_list += "{} {}|".format(k, v) print(metric_list) def evaluation(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo', save_images=False, save_videos=False, show_image=True, epoch=0, model=None): logger.setLevel(logging.INFO) result_root = os.path.join(data_root, '..', 'results', exp_name) mkdir_if_missing(result_root) data_type = 'mot' # run tracking accs = [] n_frame = 0 timer_avgs, timer_calls = [], [] for seq in seqs: output_dir = os.path.join(data_root, '..', 'outputs', exp_name, seq) if save_images or save_videos else None logger.info('start seq: {}'.format(seq)) dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size) result_filename = os.path.join(result_root, '{}.txt'.format(seq)) meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read() frame_rate = int(meta_info[meta_info.find('frameRate') + 10:meta_info.find('\nseqLength')]) nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename, save_dir=output_dir, show_image=show_image, frame_rate=frame_rate) n_frame += nf timer_avgs.append(ta) timer_calls.append(tc) # eval logger.info('Evaluate seq: {}'.format(seq)) evaluator = Evaluator(data_root, seq, data_type) accs.append(evaluator.eval_file(result_filename)) if save_videos: output_video_path = osp.join(output_dir, '{}.mp4'.format(seq)) cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path) os.system(cmd_str) timer_avgs = np.asarray(timer_avgs) timer_calls = np.asarray(timer_calls) all_time = np.dot(timer_avgs, timer_calls) avg_time = all_time / np.sum(timer_calls) logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time)) # get summary metrics = mm.metrics.motchallenge_metrics mh = mm.metrics.create() summary = Evaluator.get_summary(accs, seqs, metrics) strsummary = mm.io.render_summary( summary, formatters=mh.formatters, namemap=mm.io.motchallenge_metric_names ) #print(strsummary) print_summary(strsummary, epoch) #Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name))) if __name__ == '__main__': os.environ['CUDA_VISIBLE_DEVICES'] = '1' opt = opts().init() if not opt.val_mot16: seqs_str = '''KITTI-13 KITTI-17 ADL-Rundle-6 PETS09-S2L1 TUD-Campus TUD-Stadtmitte''' #seqs_str = '''TUD-Campus''' data_root = os.path.join(opt.data_dir, 'MOT15/images/train') else: seqs_str = '''MOT16-02 MOT16-04 MOT16-05 MOT16-09 MOT16-10 MOT16-11 MOT16-13''' data_root = os.path.join(opt.data_dir, 'MOT16/train') if opt.test_mot16: seqs_str = '''MOT16-01 MOT16-03 MOT16-06 MOT16-07 MOT16-08 MOT16-12 MOT16-14''' #seqs_str = '''MOT16-01 MOT16-07 MOT16-12 MOT16-14''' #seqs_str = '''MOT16-06 MOT16-08''' data_root = os.path.join(opt.data_dir, 'MOT16/test') if opt.test_mot15: seqs_str = '''ADL-Rundle-1 ADL-Rundle-3 AVG-TownCentre ETH-Crossing ETH-Jelmoli ETH-Linthescher KITTI-16 KITTI-19 PETS09-S2L2 TUD-Crossing Venice-1''' data_root = os.path.join(opt.data_dir, 'MOT15/images/test') if opt.test_mot17: seqs_str = '''MOT17-01-SDP MOT17-03-SDP MOT17-06-SDP MOT17-07-SDP MOT17-08-SDP MOT17-12-SDP MOT17-14-SDP''' data_root = os.path.join(opt.data_dir, 'MOT17/images/test') if opt.val_mot17: seqs_str = '''MOT17-02-SDP MOT17-04-SDP MOT17-05-SDP MOT17-09-SDP MOT17-10-SDP MOT17-11-SDP MOT17-13-SDP''' data_root = os.path.join(opt.data_dir, 'MOT17/images/train') if opt.val_mot15: seqs_str = '''Venice-2 KITTI-13 KITTI-17 ETH-Bahnhof ETH-Sunnyday PETS09-S2L1 TUD-Campus TUD-Stadtmitte ADL-Rundle-6 ADL-Rundle-8 ETH-Pedcross2 TUD-Stadtmitte''' data_root = os.path.join(opt.data_dir, 'MOT15/images/train') if opt.val_mot20: """ seqs_str = '''MOT20-01 MOT20-02 MOT20-03 MOT20-05 ''' """ seqs_str = "MOT20-05" data_root = os.path.join(opt.data_dir, 'MOT20/images/train') if opt.test_mot20: seqs_str = '''MOT20-04 MOT20-06 MOT20-07 MOT20-08 ''' data_root = os.path.join(opt.data_dir, 'MOT20/images/test') seqs = [seq.strip() for seq in seqs_str.split()] evaluation(opt, data_root=data_root, seqs=seqs, exp_name='MOT17_test_public_dla34', show_image=False, save_images=False, save_videos=False) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -