def main(image, model='mobilenet_thin', resize='432x368'):
w, h = model_wh(resize)
if w == 0 or h == 0:
e = TfPoseEstimator(get_graph_path(model), target_size=(432, 368))
else:
e = TfPoseEstimator(get_graph_path(model), target_size=(w, h))
# estimate human poses from a single image !
image = common.read_imgfile(image, None, None)
if image is None:
sys.exit(-1)
humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=4.0)
points = TfPoseEstimator.get_points(image, humans)
return points
def single(video_path,
model='mobilenet_thin',
verbose=10,
model_instance=None):
if model not in [
'cmu', 'mobilenet_thin', 'mobilenet_v2_large',
'mobilenet_v2_small', None
]:
raise Exception(
'Incompatible model chosen! Available models: cmu, mobilenet_thin, mobilenet_v2_large, mobilenet_v2_small'
)
cap = cv2.VideoCapture(video_path)
width = cap.get(3)
height = cap.get(4)
fps = cap.get(cv2.CAP_PROP_FPS)
# future support for batch processing of videos using same instance of TfPoseEstimator
if model_instance == None:
model_instance = TfPoseEstimator(get_graph_path(model),
target_size=(result_width,
result_height))
points_per_frame = np.array([])
frameN = 1
if cap.isOpened() is False:
raise Exception('Error opening file!')
while cap.isOpened():
ret, image = cap.read()
if image is None:
break
image = resize_frame(image)
humans = model_instance.inference(image, upsample_size=4.0)
points = TfPoseEstimator.get_points(image, humans)
_zeros = np.full((points.shape[0], 1), frameN)
_points = np.c_[_zeros, points]
if _points.size != 0:
if points_per_frame.size == 0:
points_per_frame = _points
else:
points_per_frame = np.vstack((points_per_frame, _points))
# else:
# _zeros = np.array([[0, 0, 0, 0, 'NA', 0]])
# _zeros[0, 0] = int(_zeros[0, 0]) + frameN
# print(_zeros)
# points_per_frame = np.vstack((points_per_frame, _zeros))
# change level of verbosity
if verbose == 0:
print(f"Frame number {frameN} processed")
else:
if frameN % verbose == 0:
print(f"Frame number {frameN} processed")
frameN += 1
return points_per_frame