191 lines
7.7 KiB
Python
191 lines
7.7 KiB
Python
import os
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import os.path as osp
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import cv2
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import logging
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import argparse
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import motmetrics as mm
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import torch
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from tracker.multitracker import JDETracker
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from utils import visualization as vis
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from utils.log import logger
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from utils.timer import Timer
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from utils.evaluation import Evaluator
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from utils.parse_config import parse_model_cfg
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import utils.datasets as datasets
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from utils.utils import *
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def write_results(filename, results, data_type):
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if data_type == 'mot':
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save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
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elif data_type == 'kitti':
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save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
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else:
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raise ValueError(data_type)
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with open(filename, 'w') as f:
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for frame_id, tlwhs, track_ids in results:
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if data_type == 'kitti':
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frame_id -= 1
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for tlwh, track_id in zip(tlwhs, track_ids):
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if track_id < 0:
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continue
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x1, y1, w, h = tlwh
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x2, y2 = x1 + w, y1 + h
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line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
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f.write(line)
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logger.info('save results to {}'.format(filename))
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def eval_seq(opt, dataloader, data_type, result_filename, save_dir=None, show_image=True, frame_rate=30):
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if save_dir:
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mkdir_if_missing(save_dir)
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tracker = JDETracker(opt, frame_rate=frame_rate)
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timer = Timer()
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results = []
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frame_id = 0
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for path, img, img0 in dataloader:
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if frame_id % 20 == 0:
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logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1./max(1e-5, timer.average_time)))
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# run tracking
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timer.tic()
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blob = torch.from_numpy(img).cuda().unsqueeze(0)
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online_targets = tracker.update(blob, img0)
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online_tlwhs = []
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online_ids = []
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for t in online_targets:
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tlwh = t.tlwh
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tid = t.track_id
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vertical = tlwh[2] / tlwh[3] > 1.6
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if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
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online_tlwhs.append(tlwh)
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online_ids.append(tid)
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timer.toc()
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# save results
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results.append((frame_id + 1, online_tlwhs, online_ids))
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if show_image or save_dir is not None:
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online_im = vis.plot_tracking(img0, online_tlwhs, online_ids, frame_id=frame_id,
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fps=1. / timer.average_time)
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if show_image:
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cv2.imshow('online_im', online_im)
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if save_dir is not None:
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cv2.imwrite(os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), online_im)
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frame_id += 1
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# save results
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write_results(result_filename, results, data_type)
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return frame_id, timer.average_time, timer.calls
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def main(opt, data_root='/data/MOT16/train', det_root=None, seqs=('MOT16-05',), exp_name='demo',
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save_images=False, save_videos=False, show_image=True):
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logger.setLevel(logging.INFO)
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result_root = os.path.join(data_root, '..', 'results', exp_name)
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mkdir_if_missing(result_root)
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data_type = 'mot'
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# Read config
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cfg_dict = parse_model_cfg(opt.cfg)
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opt.img_size = [int(cfg_dict[0]['width']), int(cfg_dict[0]['height'])]
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# run tracking
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accs = []
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n_frame = 0
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timer_avgs, timer_calls = [], []
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for seq in seqs:
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output_dir = os.path.join(data_root, '..','outputs', exp_name, seq) if save_images or save_videos else None
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logger.info('start seq: {}'.format(seq))
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dataloader = datasets.LoadImages(osp.join(data_root, seq, 'img1'), opt.img_size)
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result_filename = os.path.join(result_root, '{}.txt'.format(seq))
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meta_info = open(os.path.join(data_root, seq, 'seqinfo.ini')).read()
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frame_rate = int(meta_info[meta_info.find('frameRate')+10:meta_info.find('\nseqLength')])
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nf, ta, tc = eval_seq(opt, dataloader, data_type, result_filename,
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save_dir=output_dir, show_image=show_image, frame_rate=frame_rate)
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n_frame += nf
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timer_avgs.append(ta)
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timer_calls.append(tc)
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# eval
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logger.info('Evaluate seq: {}'.format(seq))
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evaluator = Evaluator(data_root, seq, data_type)
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accs.append(evaluator.eval_file(result_filename))
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if save_videos:
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output_video_path = osp.join(output_dir, '{}.mp4'.format(seq))
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cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -c:v copy {}'.format(output_dir, output_video_path)
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os.system(cmd_str)
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timer_avgs = np.asarray(timer_avgs)
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timer_calls = np.asarray(timer_calls)
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all_time = np.dot(timer_avgs, timer_calls)
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avg_time = all_time / np.sum(timer_calls)
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logger.info('Time elapsed: {:.2f} seconds, FPS: {:.2f}'.format(all_time, 1.0 / avg_time))
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# get summary
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metrics = mm.metrics.motchallenge_metrics
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mh = mm.metrics.create()
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summary = Evaluator.get_summary(accs, seqs, metrics)
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strsummary = mm.io.render_summary(
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summary,
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formatters=mh.formatters,
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namemap=mm.io.motchallenge_metric_names
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)
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print(strsummary)
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Evaluator.save_summary(summary, os.path.join(result_root, 'summary_{}.xlsx'.format(exp_name)))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(prog='track.py')
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parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
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parser.add_argument('--weights', type=str, default='weights/latest.pt', help='path to weights file')
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parser.add_argument('--iou-thres', type=float, default=0.5, help='iou threshold required to qualify as detected')
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parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
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parser.add_argument('--nms-thres', type=float, default=0.4, help='iou threshold for non-maximum suppression')
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parser.add_argument('--min-box-area', type=float, default=200, help='filter out tiny boxes')
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parser.add_argument('--track-buffer', type=int, default=30, help='tracking buffer')
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parser.add_argument('--test-mot16', action='store_true', help='tracking buffer')
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parser.add_argument('--save-images', action='store_true', help='save tracking results (image)')
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parser.add_argument('--save-videos', action='store_true', help='save tracking results (video)')
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opt = parser.parse_args()
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print(opt, end='\n\n')
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if not opt.test_mot16:
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#seqs_str = '''KITTI-13
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# KITTI-17
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# ADL-Rundle-6
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# PETS09-S2L1
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# TUD-Campus
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# TUD-Stadtmitte'''
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#data_root = '/home/wangzd/datasets/MOT/MOT15/train'
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seqs_str = '''MOT17-02-SDP
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MOT17-04-SDP
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MOT17-05-SDP
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MOT17-09-SDP
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MOT17-10-SDP
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MOT17-11-SDP
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MOT17-13-SDP
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'''
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seqs_str = '''MOT17-02-SDP
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'''
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data_root = '/home/wangzd/datasets/MOT/MOT17/images/train'
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else:
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seqs_str = '''MOT16-01
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MOT16-03
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MOT16-06
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MOT16-07
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MOT16-08
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MOT16-12
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MOT16-14'''
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data_root = '/home/wangzd/datasets/MOT/MOT16/images/test'
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seqs = [seq.strip() for seq in seqs_str.split()]
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main(opt,
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data_root=data_root,
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seqs=seqs,
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exp_name=opt.weights.split('/')[-2],
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show_image=False,
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save_images=opt.save_images,
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save_videos=opt.save_videos)
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