def main(_):
args = parser.parse_args()
with tf.Session() as sess:
model = Face(sess, args)
if args.phase == "train":
model.train(args)
print('train mode')
def _read_face(self):
n = self._read_normal()
# get face-specific data
verts = struct.unpack("<4H", self.data.read(2 * 4))
texture = struct.unpack("<H", self.data.read(2))[0]
# skip unknown data
self.data.read(2)
# construct face dict
return Face(verts, n, n.group, texture)
def main(_):
args = parser.parse_args()
log.init("FaceNeural", logging.DEBUG, log_path="output/log.txt")
with tf.Session() as sess:
if args.phase == "train":
model = Face(sess, args)
model.train(args)
log.info('train mode')
elif args.phase == "inference":
log.info("inference")
model = Face(sess, args)
model.inference(args)
elif args.phase == "lightcnn":
log.info("light cnn test")
elif args.phase == "faceparsing":
log.info("faceparsing")
elif args.phase == "net":
log.info("net start with ports (%d, %d)", 5010, 5011)
net = Net(5010, 5011)
while True:
r_input = raw_input("command: \n")
if r_input == "s":
msg = raw_input("input: ")
net.only_send(msg)
elif r_input == 'r':
msg = raw_input("input: ")
net.send_recv(msg)
elif r_input == "q":
net.only_send("quit")
net.close()
break
else:
log.error("unknown code, quit")
net.close()
break
def detect(self, image):
"""
returns a Face object or None if a face does not found
"""
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = self.face_cascade.detectMultiScale(gray,
scaleFactor=1.1,
minNeighbors=5)
if len(faces) == 0:
if self.num_recovery >= self.max_recovery:
self.last_face = None
self.num_recovery = 0
if self.last_face is not None:
self.num_recovery = self.num_recovery + 1
return self.last_face
(x, y, w, h) = faces[0]
face = Face(x, y, w, h)
self.last_face = face
self.num_recovery = 0
return face
def recognize_face(self, img: np.ndarray, face_box: Box) -> Face:
shape: dlib.full_object_detection = self.face_shape_predictor(
img, face_box.to_dlib_rect())
descriptor: np.ndarray = np.asarray(
self.face_recognition_model.compute_face_descriptor(img, shape))
return Face(box=face_box, shape=shape, descriptor=descriptor)