def __init__(
self,
min_face_size: int = 40,
steps_threshold: list = None,
scale_factor: float = 0.7,
):
"""
Initializes the MTCNN.
:param min_face_size: minimum size of the face to detect
:param steps_threshold: step's thresholds values
:param scale_factor: scale factor
"""
if steps_threshold is None:
steps_threshold = [0.6, 0.7,
0.7] # original mtcnn values [0.6, 0.7, 0.7]
self._min_face_size = min_face_size
self._steps_threshold = steps_threshold
self._scale_factor = scale_factor
self.p_net = tflite.Interpreter(
model_path=get_file(BASE_URL +
"p_net.tflite", FILE_HASHES["p_net"]))
self.r_net = tflite.Interpreter(
model_path=get_file(BASE_URL +
"r_net.tflite", FILE_HASHES["r_net"]))
self.o_net = tflite.Interpreter(
model_path=get_file(BASE_URL +
"o_net.tflite", FILE_HASHES["o_net"]))
def __init__(
self,
model_path: str = None,
model_type: str = "mobileNet",
):
if model_path is None:
model_path = get_file(BASE_URL + model_type + ".tflite", FILE_HASHES[model_type])
self.face_recognizer = tflite.Interpreter(model_path=model_path)
def __init__(self, gal_dir: str = None):
self.detector = FaceDetection()
self.recognizer = FaceRecognition()
self.gal_embs = []
self.gal_names = []
self.gal_faces = []
self.gal_dir = (gal_dir if gal_dir is not None else get_file(
BASE_URL + "sample_gallery.zip",
FILE_HASHES["sample_gallery"],
is_zip=True))
self.update_gallery()