# pose module
print('Loading pose model')
sys.stdout.flush()
pose_model = inference.pose_detection()
print('Starting webcam demo, press Ctrl + C to terminate...')
sys.stdout.flush()
im_names_desc = loop() # tqdm(loop())
for i in im_names_desc:
try:
start_time = getTime()
begin = time.time()
# ################################## Get Frames ####################################
print('\n******************* Frame:%d ********************' % i)
img_0 = fvs_0.read()
img_1 = fvs_1.read()
# 可视化显示变成1/2
fvis_0 = resize_vis(img_0)
fvis_1 = resize_vis(img_1)
# YOLO处理图片时变成1/4
frame_0 = resize_yolo(img_0)
frame_1 = resize_yolo(img_1)
single_height = frame_0.shape[
0] # print(frame_0.shape) # (432, 768, 3)
# ################################## Pre Process ####################################
img, orig_img, dim, im_dim_list = preprocess(frame_0, frame_1)
# ################################## Detection ####################################
with torch.no_grad():
pose_model = InferenNet_fast(4 * 1 + 1, pose_dataset)
else:
pose_model = InferenNet(4 * 1 + 1, pose_dataset)
pose_model.cuda()
pose_model.eval()
runtime_profile = {'ld': [], 'dt': [], 'dn': [], 'pt': [], 'pn': []}
print('Starting webcam demo, press Ctrl + C to terminate...')
sys.stdout.flush()
im_names_desc = tqdm(loop())
for i in im_names_desc:
try:
start_time = getTime()
(img, orig_img, inp, im_dim_list) = fvs.read()
ckpt_time, load_time = getTime(start_time)
runtime_profile['ld'].append(load_time)
with torch.no_grad():
# Human Detection
img = Variable(img).cuda()
im_dim_list = im_dim_list.cuda()
prediction = det_model(img, CUDA=True)
ckpt_time, det_time = getTime(ckpt_time)
runtime_profile['dt'].append(det_time)
# NMS process
dets = dynamic_write_results(prediction,
opt.confidence,
opt.num_classes,
nms=True,
pose_model.eval()
# reid module
# reid_model = reid_interface.ReID(is_folder=False)
# Running time of each module
runtime_profile = {'ld': [], 'dt': [], 'dn': [], 'pt': [], 'pn': []}
print('Starting webcam demo, press Ctrl + C to terminate...')
sys.stdout.flush()
im_names_desc = tqdm(loop())
for i in im_names_desc:
try:
begin = time.time()
start_time = getTime()
frame_0 = fvs_0.read()
frame_1 = fvs_1.read()
single_height = frame_0.shape[0]
print(frame_0.shape) # (432, 768, 3)
# pre-process
frame = np.concatenate([frame_0, frame_1], 0)
inp_dim = int(args.inp_dim) # default=608
img, orig_img, dim = prep_frame(frame, inp_dim)
#print('img:',img.shape) # torch.Size([1, 3, 608, 608])
# print('orig_img:',orig_img.shape) # (864, 768, 3)
# print('dim',dim) # (768, 864)
inp = im_to_torch(orig_img)
im_dim_list = torch.FloatTensor([dim]).repeat(1, 2)
# print(im_dim_list) # tensor([[768., 864., 768., 864.]])