def inference_image(model, parser):
print("image")
# preparing
list_image_paths = glob(os.path.join(parser.input, "*.jpg"))
preprocess = InferenceTransformation(320, 320)
pose_parser = Pose(image_scale=0.125)
hand_model = HandPoseDetector(static_model=True)
average_time = 0
for ind, image_path in tqdm.tqdm(enumerate(list_image_paths)):
origin_image = cv2.imread(image_path)
height, width, _ = origin_image.shape
ratio_scale = height / 320
add_width = (320 - int(320 * width / height)) // 2
current_time = cv2.getTickCount()
process_image = preprocess(origin_image)
image = preprocess_tensor(process_image.copy())
paf, heatmap = model(image)
# paf = paf[0]
# heatmap = heatmap[0]
paf = paf.reshape((38, 40, 40))
heatmap = heatmap.reshape((19, 40, 40))
pose_parser.parser_pose(paf, heatmap)
draw_pose(process_image, pose_parser.poses_list,
pose_parser.hand_window, body_edges)
hand_images = pose_parser.get_hand_head_images(origin_image,
ratio_scale, add_width)
if hand_images is not None:
image, _ = hand_model(hand_images)
current_time = (cv2.getTickCount() -
current_time) / cv2.getTickFrequency()
# cv2.imwrite("./_image/temp_hand_%d.jpg"%ind, image)
if hand_images is not None:
process_image[-200:, :100] = cv2.resize(image, (100, 200))
average_time += current_time
cv2.putText(process_image, 'parsing time: {}'.format(current_time),
(10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))
cv2.imwrite("./_image/img_with_pose_%d.jpg" % ind, process_image)
print("avg time: %f" % (average_time / len(list_image_paths)))
def inference_image(model, parser):
print("image")
# preparing
list_image_paths = glob(os.path.join(parser.input, "*.jpg"))
preprocess = InferenceTransformation(368, 368)
pose_parser = Pose(image_scale=0.125)
hand_model = HandPoseDetector(static_model=True)
face_model = FaceMeshDetector(static_model=True)
average_time = 0
for ind, image_path in tqdm.tqdm(enumerate(list_image_paths)):
origin_image = cv2.imread(image_path)
height, width,_ = origin_image.shape
ratio_scale = height / 368
add_width = (368 - int(368 * width / height))//2
current_time = cv2.getTickCount()
process_image = preprocess(origin_image)
image = torch.Tensor(preprocess_tensor(process_image.copy())).unsqueeze(0).cuda()
paf, heatmap = model(image)
paf = paf.detach().cpu().numpy()[0]
heatmap = heatmap.detach().cpu().numpy()[0]
pose_parser.parser_pose(paf, heatmap)
draw_pose(process_image, pose_parser.poses_list, pose_parser.hand_window, body_edges)
hand_images = pose_parser.get_hand_head_images(origin_image,ratio_scale, add_width)
for hand_ind, image in enumerate(hand_images):
image, _ = hand_model(image)
# cv2.imwrite("./_image/temp_hand_%d.jpg"%ind, image)
if hand_ind == 1:
process_image[-100:,:100] = cv2.resize(image,(100,100))
elif hand_ind == 0:
process_image[-100:,-100:] = cv2.resize(image,(100,100))
current_time = (cv2.getTickCount() - current_time) / cv2.getTickFrequency()
average_time += current_time
cv2.putText(process_image, 'parsing time: {}'.format(current_time),
(10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))
cv2.imwrite("./_image/img_with_pose_%d.jpg" % ind, process_image)
print("avg time: %f" % (average_time / len(list_image_paths)))
def inference_video(model, parser, is_optical_flow=False):
print("video")
# preparing
list_video_paths = glob(os.path.join(parser.input, "*.mp4"))
FIX_SIZE = 320
preprocess = InferenceTransformation(FIX_SIZE, FIX_SIZE)
mean_time = 0
pose_parser = Pose(image_scale=0.125)
hand_model = HandPoseDetector(static_model= True)
face_model = FaceMeshDetector(static_model= False)
lk_params = dict(winSize=(100, 100),
maxLevel=10,
criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 20, 0.03))
for ind, video_path in tqdm.tqdm(enumerate(list_video_paths)):
vidcap = cv2.VideoCapture(video_path)
success, image = vidcap.read()
height, width,_ = image.shape
ratio_scale = height / FIX_SIZE
add_width = (FIX_SIZE - int(FIX_SIZE * width / height))//2
count = 0
video = cv2.VideoWriter('_image/video_with_hand_optical%d.avi'%ind, cv2.VideoWriter_fourcc(*'DIVX'), 30, (FIX_SIZE, FIX_SIZE))
old_gray = None
num_frame = 0
while success:
success, origin_image = vidcap.read()
if success:
current_time = cv2.getTickCount()
# origin_image = cv2.rotate(origin_image, cv2.ROTATE_90_COUNTERCLOCKWISE)
process_image = preprocess(origin_image)
if is_optical_flow and 0<count <4 and len(pose_parser.poses_list) >0:
visible_mask = pose_parser.poses_list[0][:,2]>0
visible_point = pose_parser.poses_list[0][visible_mask][:,0:2] # get only coord
current_gray = cv2.cvtColor(process_image, cv2.COLOR_BGR2GRAY)
visible_point = np.expand_dims(visible_point.astype(np.float32), axis = 1)
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, current_gray, visible_point,
None, **lk_params)
old_gray = current_gray.copy()
visible_point[st==1] = p1[st==1]
visible_point = np.squeeze(visible_point, axis=1)
pose_parser.poses_list[0][np.where(visible_mask),:-1] = visible_point.copy()
pose_parser.get_hand_head_window()
else:
image = torch.Tensor(preprocess_tensor(process_image.copy())).unsqueeze(0)
image = image.cuda()
paf, heatmap = model(image)
paf = paf.detach().cpu().numpy()[0]
heatmap = heatmap.detach().cpu().numpy()[0]
pose_parser.parser_pose(paf, heatmap)
old_gray = cv2.cvtColor(process_image, cv2.COLOR_BGR2GRAY)
count = 0
draw_pose(process_image, pose_parser.poses_list, pose_parser.hand_window, body_edges)
hand_images = pose_parser.get_hand_head_images(origin_image,ratio_scale, add_width)
if hand_images is not None:
hand_images, _ = hand_model(hand_images)
current_time = (cv2.getTickCount() - current_time) / cv2.getTickFrequency()
if hand_images is not None:
process_image[-200:, :100] = cv2.resize(hand_images, (100, 200))
if mean_time == 0:
mean_time = current_time
else:
mean_time = mean_time * 0.95 + current_time * 0.05
cv2.putText(process_image, 'FPS: {}'.format(int(1 / mean_time * 10) / 10),
(10, 20), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255))
video.write(process_image)
count += 1
num_frame+=1
vidcap.release()
video.release()
print(count)
print(int(1/mean_time*10)/10)
