class ProcessFrame:
def __init__(self, args: Dict):
# todo proper handling of self.modes
self.ie = IECore()
self.modes = self.__determine_processing_mode(args)
net_face_detect = net_landmarks_detect = net_recognize_face = None
if not self.modes['detect']:
raise ValueError('detection model undefined')
# load networks from file
net_face_detect = self.__prepare_network(args['detection_model'])
# put it to corresponding class
# self.face_locator = FaceLocator(net_face_detect, args['detection_model_threshold'])
self.face_locator = FaceLocator(net_face_detect,
args['detection_model_threshold'],
NetworkType(args['detection_model']))
# setup device plugins
if next(iter(args['device'])) == 'CPU':
# CPU
self.ie.set_config(config={
"CPU_THROUGHPUT_STREAMS": "1",
"CPU_THREADS_NUM": "8",
},
device_name='CPU')
elif next(iter(args['device'])) == 'GPU':
# GPU
pass
self.ie.set_config(config={"GPU_THROUGHPUT_STREAMS": "1"},
device_name='GPU')
elif next(iter(args['device'])) == 'MYRIAD':
pass
# load to device for inferencing
self.face_locator.deploy_network(next(iter(args['device'])), self.ie)
if self.modes['landmark']:
net_landmarks_detect = self.__prepare_network(
args['landmarks_model'])
self.landmarks_locator = LandmarksLocator(net_landmarks_detect)
self.landmarks_locator.deploy_network(next(iter(args['device'])),
self.ie)
if self.modes['recognize']:
net_recognize_face = self.__prepare_network(
args['recognition_model'])
self.face_recognizer = FaceRecognizer(net_recognize_face)
self.face_recognizer.deploy_network(next(iter(args['device'])),
self.ie)
# todo other models or load separately
@staticmethod
def __determine_processing_mode(args: Dict) -> Dict[str, bool]:
ret = {}
if args['detection_model']:
ret['detect'] = True
else:
ret['detect'] = False
if args['landmarks_model']:
ret['landmark'] = True
else:
ret['landmark'] = False
if args['recognition_model']:
ret['recognize'] = True
else:
ret['recognize'] = False
return ret
def __prepare_network(self, model_path: str) -> IENetwork:
model_path = os.path.abspath(model_path)
model = self.ie.read_network(model=model_path,
weights=os.path.splitext(model_path)[0] +
".bin")
return model
def process_frame(
self, frame: np.ndarray
) -> List[
Union[List[FaceLocator.FacePosition],
List[LandmarksLocator.FaceLandmarks],
List[FaceRecognizer.FaceIdentity]]]: # todo uniton with None
faces_landmarks = faces_identities = None
face_positions = self.face_locator.get_face_positions(frame)
if self.modes['landmark']:
faces_landmarks = self.landmarks_locator.get_landmarks(
frame, face_positions)
if self.modes['recognize']:
faces_identities = self.face_recognizer.get_identities(
frame, face_positions, faces_landmarks)
return [face_positions, faces_landmarks, faces_identities]
