def eval_seq(opt,
data_loader,
data_type,
result_f_name,
save_dir=None,
show_image=True,
frame_rate=30,
mode='track'):
"""
:param opt:
:param data_loader:
:param data_type:
:param result_f_name:
:param save_dir:
:param show_image:
:param frame_rate:
:param mode: track or detect
:return:
"""
if save_dir:
mkdir_if_missing(save_dir)
# tracker = JDETracker(opt, frame_rate)
tracker = MCJDETracker(opt, frame_rate)
timer = Timer()
results_dict = defaultdict(list)
frame_id = 0 # frame index
for path, img, img0 in data_loader:
if frame_id % 30 == 0 and frame_id != 0:
logger.info('Processing frame {} ({:.2f} fps)'.format(
frame_id, 1.0 / max(1e-5, timer.average_time)))
# --- run tracking
blob = torch.from_numpy(img).unsqueeze(0).to(opt.device)
if mode == 'track': # process tracking
# ----- track updates of each frame
timer.tic()
online_targets_dict = tracker.update_tracking(blob, img0)
timer.toc()
# -----
# collect current frame's result
online_tlwhs_dict = defaultdict(list)
online_ids_dict = defaultdict(list)
online_scores_dict = defaultdict(list)
for cls_id in range(opt.num_classes): # process each class id
online_targets = online_targets_dict[cls_id]
for track in online_targets:
tlwh = track.tlwh
t_id = track.track_id
score = track.score
if tlwh[2] * tlwh[
3] > opt.min_box_area: # and not vertical:
online_tlwhs_dict[cls_id].append(tlwh)
online_ids_dict[cls_id].append(t_id)
online_scores_dict[cls_id].append(score)
# collect result
for cls_id in range(opt.num_classes):
results_dict[cls_id].append(
(frame_id + 1, online_tlwhs_dict[cls_id],
online_ids_dict[cls_id], online_scores_dict[cls_id]))
# draw track/detection
if show_image or save_dir is not None:
if frame_id > 0:
online_im: ndarray = vis.plot_tracks(
image=img0,
tlwhs_dict=online_tlwhs_dict,
obj_ids_dict=online_ids_dict,
num_classes=opt.num_classes,
frame_id=frame_id,
fps=1.0 / timer.average_time)
elif mode == 'detect': # process detections
timer.tic()
# update detection results of this frame(or image)
dets_dict = tracker.update_detection(blob, img0)
timer.toc()
# plot detection results
if show_image or save_dir is not None:
online_im = vis.plot_detects(image=img0,
dets_dict=dets_dict,
num_classes=opt.num_classes,
frame_id=frame_id,
fps=1.0 /
max(1e-5, timer.average_time))
else:
print('[Err]: un-recognized mode.')
if frame_id > 0:
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)
# update frame id
frame_id += 1
# write track/detection results
write_results_dict(result_f_name, results_dict, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
data_loader,
data_type,
result_f_name,
save_dir=None,
show_image=True,
frame_rate=30,
mode='track'):
"""
:param opt:
:param data_loader:
:param data_type:
:param result_f_name:
:param save_dir:
:param show_image:
:param frame_rate:
:param mode: track or detect
:return:
"""
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results_dict = defaultdict(list)
frame_id = 0 # 帧编号
for path, img, img_0 in data_loader:
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)
blob = torch.from_numpy(img).to(opt.device).unsqueeze(0)
if mode == 'track': # process tracking
# --- track updates of each frame
online_targets_dict = tracker.update_tracking(blob, img_0)
# 聚合每一帧的结果
online_tlwhs_dict = defaultdict(list)
online_ids_dict = defaultdict(list)
for cls_id in range(opt.num_classes):
# 处理每一个目标检测类
online_targets = online_targets_dict[cls_id]
for track in online_targets:
tlwh = track.tlwh
t_id = track.track_id
# vertical = tlwh[2] / tlwh[3] > 1.6 # box宽高比判断:w/h不能超过1.6?
if tlwh[2] * tlwh[
3] > opt.min_box_area: # and not vertical:
online_tlwhs_dict[cls_id].append(tlwh)
online_ids_dict[cls_id].append(t_id)
timer.toc()
# 保存每一帧的结果
for cls_id in range(opt.num_classes):
results_dict[cls_id].append(
(frame_id + 1, online_tlwhs_dict[cls_id],
online_ids_dict[cls_id]))
# 绘制每一帧的结果
if show_image or save_dir is not None:
if frame_id > 0:
online_im: ndarray = vis.plot_tracks(
image=img_0,
tlwhs_dict=online_tlwhs_dict,
obj_ids_dict=online_ids_dict,
num_classes=opt.num_classes,
frame_id=frame_id,
fps=1.0 / timer.average_time)
elif mode == 'detect': # process detections
# update detection results of this frame(or image)
dets_dict = tracker.update_detection(blob, img_0)
timer.toc()
# plot detection results
if show_image or save_dir is not None:
online_im = vis.plot_detects(image=img_0,
dets_dict=dets_dict,
num_classes=opt.num_classes,
frame_id=frame_id,
fps=1.0 /
max(1e-5, timer.average_time))
else:
print('[Err]: un-recognized mode.')
# # 可视化中间结果
# if frame_id > 0:
# cv2.imshow('Frame {}'.format(str(frame_id)), online_im)
# cv2.waitKey()
if frame_id > 0:
# 是否显示中间结果
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)
# 处理完一帧, 更新frame_id
frame_id += 1
# 写入最终结果save results
write_results_dict(result_f_name, results_dict, data_type)
return frame_id, timer.average_time, timer.calls
def eval_seq(opt,
data_loader,
data_type,
result_f_name,
save_dir=None,
show_image=True,
frame_rate=30):
"""
对序列进行跟踪
:param opt:
:param data_loader:
:param data_type:
:param result_f_name:
:param save_dir:
:param show_image:
:param frame_rate:
:return:
"""
if save_dir:
mkdir_if_missing(save_dir)
tracker = JDETracker(opt, frame_rate=frame_rate)
timer = Timer()
results_dict = defaultdict(list)
frame_id = 0 # 帧编号
for path, img, img0 in data_loader:
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)
# --- 输出结果的核心函数: 更新跟踪状态
online_targets_dict = tracker.update(blob, img0)
# 聚合每一帧的结果
online_tlwhs_dict = defaultdict(list)
online_ids_dict = defaultdict(list)
for cls_id in range(opt.num_classes):
# 处理每一个目标检测类
online_targets = online_targets_dict[cls_id]
for track in online_targets:
tlwh = track.tlwh
t_id = track.track_id
# vertical = tlwh[2] / tlwh[3] > 1.6 # box宽高比判断:w/h不能超过1.6?
if tlwh[2] * tlwh[3] > opt.min_box_area: # and not vertical:
online_tlwhs_dict[cls_id].append(tlwh)
online_ids_dict[cls_id].append(t_id)
timer.toc()
# 保存每一帧的结果
for cls_id in range(opt.num_classes):
results_dict[cls_id].append(
(frame_id + 1, online_tlwhs_dict[cls_id],
online_ids_dict[cls_id]))
# 绘制每一帧的结果
if show_image or save_dir is not None:
if frame_id > 0:
online_im = vis.plot_tracks(image=img0,
tlwhs_dict=online_tlwhs_dict,
obj_ids_dict=online_ids_dict,
num_classes=opt.num_classes,
frame_id=frame_id,
fps=1.0 / timer.average_time)
# # 可视化中间结果
# if frame_id > 0:
# cv2.imshow('Frame {}'.format(str(frame_id)), online_im)
# cv2.waitKey()
if frame_id > 0:
# 是否显示中间结果
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)
# 处理完一帧, 更新frame_id
frame_id += 1
# 写入最终结果save results
write_results_dict(result_f_name, results_dict, data_type)
return frame_id, timer.average_time, timer.calls