e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
else:
e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368))
logger.debug('cam read+')
cam = cv2.VideoCapture(args.camera)
ret_val, image = cam.read()
logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))
while True:
ret_val, image = cam.read()
logger.debug('image process+')
humans = e.inference(image,
resize_to_default=(w > 0 and h > 0),
upsample_size=args.resize_out_ratio)
logger.debug('postprocess+')
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
image = TfPoseEstimator.evaluate_flexion(image, humans, imgcopy=False)
logger.debug('show+')
cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)),
(10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('tf-pose-estimation result', image)
fps_time = time.time()
if cv2.waitKey(1) == 27:
break
logger.debug('finished+')
cv2.destroyAllWindows()
cap = cv2.VideoCapture(args.video)
frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
FPS = cap.get(cv2.CAP_PROP_FPS)
if cap.isOpened() is False:
print("Error opening video stream or file")
while cap.isOpened():
for i in range(5): #SKIP FRAMES
cap.grab()
ret_val, image = cap.read()
logger.debug('image process+')
humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)
if not args.showBG:
image = np.zeros(image.shape)
logger.debug('postprocess+')
image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
TfPoseEstimator.evaluate_flexion(humans)
logger.debug('show+')
cv2.putText(image, "FPS: %f" % (1.0 / (time.time() - fps_time)), (10, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(0, 255, 0), 2)
cv2.imshow('tf-pose-estimation result', image)
fps_time = time.time()
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
logger.debug('finished+')
