else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
runtime_profile = {'dt': [], 'pt': [], 'pn': []}
# Init data writer
writer = DataWriter(args.save_video).start()
# time.sleep(10)
data_len = dataset.__len__()
im_names_desc = tqdm(range(data_len))
for i in im_names_desc:
start_time = getTime()
with torch.no_grad():
(inp, orig_img, im_name, boxes, scores) = test_loader.read()
if boxes is None or boxes.nelement() == 0:
writer.save(None, None, None, None, None, orig_img,
im_name.split('/')[-1])
continue
print("test loader:", test_loader.len())
ckpt_time, det_time = getTime(start_time)
runtime_profile['dt'].append(det_time)
# Pose Estimation
inps, pt1, pt2 = crop_from_dets(inp, boxes)
inps = Variable(inps.cuda())
hm = pose_model(inps)
ckpt_time, pose_time = getTime(ckpt_time)
runtime_profile['pt'].append(pose_time)
else:
raise IOError('Error: must contain either --indir/--list')
# Load input images
data_loader = ImageLoader(im_names, batchSize=args.detbatch,
format='yolo').start()
# Load detection loader
print('Loading YOLO model..')
sys.stdout.flush()
det_loader = DetectionLoader(data_loader, batchSize=args.detbatch).start()
print('here will show the det_loader information')
data_loader_length = data_loader.length()
for i in range(data_loader_length):
(orig_img, im_name, boxes, scores, inps, pt1, pt2) = det_loader.read()
print('image_name', im_name)
print('boxes', boxes)
print('scores', scores)
print('inps', inps)
print('pt1', pt1)
print('pt2', pt2)
print('------------------------------------------------------------')
print('data_loader finish+++++++++++++++++++++++++++++++++++')
det_processor = DetectionProcessor(det_loader).start()
print('successful processed')
print('here is the result which is processed')
data_len = data_loader.length()
print('start shown ')
def loop():
n = 0
while True:
yield n
n += 1
print('Initing tracker...')
sys.stdout.flush()
im_names_desc = tqdm(loop())
store_tracker1 = for_store_tracker()
store_tracker2 = for_store_tracker()
for i in im_names_desc:
try:
with torch.no_grad():
(orig_img, frame_id, bbox, bbox_score, kp, kp_score,
imgs) = det_loader_1.read()
if bbox == None or len(bbox) == 0:
continue
if init_flag == 1:
init_info[0].append(frame_id)
init_info[1].append(bbox)
init_info[2].append(bbox_score)
init_info[3].append(imgs)
if len(init_info[0]) != 25: # 不满25帧，结束当前循环，进行积累
continue
else:
# 初始化追踪器 tracker / tracker_id
init_flag, Frames_Lib, tracker, tracker_id, tracker_cnt, feature_model = init_frames(
cam_number, init_info)
init_info = [[], [], [], []] # 记录器清零