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)
cv2.waitKey(1)
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,
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_and_output_dets(opt,
data_loader,
data_type,
result_f_name,
out_dir,
save_dir=None,
show_image=True):
"""
:param opt:
:param data_loader:
:param data_type:
:param result_f_name:
:param out_dir:
:param save_dir:
:param show_image:
:return:
"""
if save_dir:
mkdir_if_missing(save_dir)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
else:
shutil.rmtree(out_dir)
os.makedirs(out_dir)
tracker = JDETracker(opt, frame_rate=30)
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).to(opt.device).unsqueeze(0)
# 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))
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)
# ----- 格式化并输出detection结果(txt)到指定目录
# 格式化
dets_list = format_dets_dict2dets_list(dets_dict,
w=img_0.shape[1],
h=img_0.shape[0])
# 输出到指定目录
out_img_name = os.path.split(path)[-1]
# if out_img_name == '192.168.1.219_2_2018-02-13_14-46-00-688_3-1518504845.jpg':
# print('pause here')
out_f_name = out_img_name.replace('.jpg', '.txt')
out_f_path = out_dir + '/' + out_f_name
with open(out_f_path, 'w', encoding='utf-8') as w_h:
w_h.write('class prob x y w h total=' + str(len(dets_list)) + '\n')
for det in dets_list:
w_h.write('%d %f %f %f %f %f\n' %
(det[0], det[1], det[2], det[3], det[4], det[5]))
# print('{} written'.format(out_f_path))
# 处理完一帧, 更新frame_id
frame_id += 1
print('Total {:d} detection result output.\n'.format(frame_id))
# 写入最终结果save results
write_results_dict(result_f_name, results_dict, data_type)
# 返回结果
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