import os import os.path as osp import cv2 import logging import argparse import motmetrics as mm import torch from tracker.multitracker import JDETracker from utils import visualization as vis from utils.log import logger from utils.timer import Timer from utils.evaluation import Evaluator from utils.parse_config import parse_model_cfg import utils.datasets as datasets from utils.utils import * 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 eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30): 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: 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() blob = torch.from_numpy(img).cuda().unsqueeze(0) online_targets = tracker.update(blob, img0) online_tlwhs = [] online_ids = [] 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) timer.toc() # save results results.append((frame_id + 1, online_tlwhs, online_ids)) 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) return frame_id, timer.average_time, timer.calls def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo', save_images=False, save_videos=False, show_image=True): logger.setLevel(logging.INFO) result_root = os.path.join(data_root, '..', 'results', exp_name) mkdir_if_missing(result_root) data_type = 'mot' # Read config cfg_dict = parse_model_cfg(opt.cfg) opt.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])] # 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) Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name))) if __name__ == '__main__': parser = argparse.ArgumentParser(prog='track.py') parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file') parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected') parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold') parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression') parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes') parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer') parser.add_argument('--test-mot16', action='store_true', help='tracking buffer') parser.add_argument('--save-images', action='store_true', help='save tracking results (image)') parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)') opt = parser.parse_args() print(opt, end='\n\n') if not opt.test_mot16: #seqs_str = '''KITTI-13 # KITTI-17 # ADL-Rundle-6 # PETS09-S2L1 # TUD-Campus # TUD-Stadtmitte''' #data_root = '/home/wangzd/datasets/MOT/MOT15/train' seqs_str = '''MOT17-02-SDP MOT17-04-SDP MOT17-05-SDP MOT17-09-SDP MOT17-10-SDP MOT17-11-SDP MOT17-13-SDP ''' seqs_str = '''MOT17-02-SDP ''' data_root = '/home/wangzd/datasets/MOT/MOT17/images/train' else: seqs_str = '''MOT16-01 MOT16-03 MOT16-06 MOT16-07 MOT16-08 MOT16-12 MOT16-14''' data_root = '/home/wangzd/datasets/MOT/MOT16/images/test' seqs = [seq.strip() for seq in seqs_str.split()] main(opt, data_root=data_root, seqs=seqs, exp_name=opt.weights.split('/')[-2], show_image=False, save_images=opt.save_images, save_videos=opt.save_videos)