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
tid_max, tid_temp = 1, 1
for path, img, img0 in dataloader:
if frame_id < 300:
frame_id += 1
continue
elif frame_id > 302:
break
else:
print(frame_id)
filename = path.split('/')[-1]
if '0000001' in path:
tid_max = tid_temp + 1
print(path, tid_max)
tracker = JDETracker(opt, frame_rate=frame_rate)
if frame_id % 100 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1. / max(1e-5, timer.average_time)))
# if frame_id >20:
# break
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
online_targets = tracker.update_sep3(
blob,
img0,
conf_thres=[0.4, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5])
# print(online_targets)
online_tlwhs = []
online_ids = []
online_cids = [] #class id
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id + tid_max
tcid = t.class_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_cids.append(tcid)
tid_temp = max(tid, tid_temp)
timer.toc()
# save results
results.append((filename, online_tlwhs, online_ids, online_cids))
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 eval_seq(opt, dataloader, data_type, result_filename, gt_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 = []
gts = []
frame_id = 0
tid_max, tid_temp = 1, 1
for path, img, img0 in dataloader:
if frame_id % 100 == 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(frame_id, 1. / max(1e-5, timer.average_time)))
# if frame_id>400:
# break
if '0000001' in path:
tid_max = tid_temp
tracker = JDETracker(opt, frame_rate=frame_rate)
# run tracking
timer.tic()
blob = torch.from_numpy(img).cuda().unsqueeze(0)
# online_targets = tracker.update(blob, img0)
online_targets = tracker.update_sep3(blob, img0, conf_thres=[0.4, 0.3, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5]) ## use class-separated tracker
# print(online_targets)
online_tlwhs = []
online_ids = []
online_cids = [] #class id
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id + tid_max
tcid = t.class_id
# vertical = tlwh[2] / tlwh[3] > 1.6
# if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
if tlwh[2] * tlwh[3] > opt.min_box_area:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_cids.append(tcid)
tid_temp = max(tid, tid_temp)
timer.toc()
gt_tlwhs = []
gt_ids = []
gt_cids = []
gt_path = path.replace('images', 'labels_with_ids').replace('jpg', 'txt')
gt_targets = read_gt_txts(gt_path)
for (tlwh, tid, tcid) in gt_targets:
gt_tlwhs.append(tlwh)
gt_ids.append(tid)
gt_cids.append(tcid)
# save results
results.append((frame_id + 1, online_tlwhs, online_ids, online_cids))
# save gts
gts.append((frame_id + 1, gt_tlwhs, gt_ids, gt_cids))
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)
# save gts
write_results(gt_filename, gts, data_type)
return frame_id, timer.average_time, timer.calls
