logger.debug('cam read+')
cam = cv2.VideoCapture('videos/' + args.video + '.mp4')
ret_val, image = cam.read()
logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))
logger.info('FPS: ' + str(cam.get(cv2.CAP_PROP_FPS)))
with open('videos/' + args.video + '.fps', 'w+') as f:
f.write(str(cam.get(cv2.CAP_PROP_FPS)))
n_frames = int(cam.get(cv2.CAP_PROP_FRAME_COUNT))
all_cords = []
for _ in tqdm(range(n_frames)):
ret_val, image = cam.read()
if not ret_val:
break
humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
all_cords.append(TfPoseEstimator.get_cords(image, humans, imgcopy=False))
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
print(len(all_cords))
print(cam.get(cv2.CAP_PROP_FRAME_COUNT))
with open('keypoints/' + args.video + '.keypoints.json', 'w+') as f:
json.dump(all_cords, f)
print('VIDEO PATH:', video_path)
print('KEYPOINTS PATH:', choreo_keypoints_path)
sec_start_time = time.time()
curr_cords = []
last_cords = [time.time(), [{}]]
part_linger_max = 3
part_linger = {}
while time.time() - start_time + 1 < len(choreo_json) / fps:
ret_val, image = cam.read()
humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=4.)
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
curr_cords.append([time.time(), TfPoseEstimator.get_cords(image, humans, imgcopy=False)])
print(last_cords, 'a')
if len(last_cords[1]) == 0:
last_cords[1].append({})
for key in last_cords[1][0].keys():
if len(curr_cords[-1][1]) == 0:
curr_cords[-1][1].append({})
if key not in curr_cords[-1][1][0]:
if key not in part_linger:
part_linger[key] = 0
part_linger[key] += 1
if part_linger[key] > 3:
part_linger[key] = 0
else:
curr_cords[-1][1][0][key] = last_cords[1][0][key]
last_cords = curr_cords[-1]
