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
#size = w * h
#if size > 7000:
#if size <= 7000 or size >= 15000:
#if size < 15000:
#continue
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:
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()
blob = torch.from_numpy(img).cuda().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 demo(opt):
result_root = opt.output_root if opt.output_root != '' else '.'
mkdir_if_missing(result_root)
logger.info('Starting tracking...')
dataloader = datasets.LoadVideo(opt.input_video, opt.img_size)
result_filename = os.path.join(result_root, 'results.txt')
frame_rate = dataloader.frame_rate
frame_dir = None if opt.output_format == 'text' else osp.join(
result_root, 'frame')
eval_seq(opt,
dataloader,
'mot',
result_filename,
save_dir=frame_dir,
show_image=False,
frame_rate=frame_rate)
if opt.output_format == 'video':
output_video_path = osp.join(result_root, 'MOT16-03-results.mp4')
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg -b 5000k -c:v mpeg4 {}'.format(
osp.join(result_root, 'frame'), output_video_path)
os.system(cmd_str)
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'
# 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)))