def recognize(self):
classifier = HaarcascadeDetective().get_face_classifier()
while self.playing:
try:
if len(self.frames) == 0:
time.sleep(0.05)
continue
if self.flag_recognize:
frame = self.frames.pop()
faces = classifier.get_faces_position(frame)
self.recognized_faces.clear()
for (x, y, w, h) in faces:
face = frame[y:y + h, x:x + w]
if self.model is not None:
gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
params = self.model.predict(gray)
user = self.find_user_by_id(params[0])
if user is not None:
self.recognized_faces[user[1]] = ((x, y, w, h), user[2], user[3], int(params[1]))
else:
self.recognized_faces['-1'] = ((x, y, w, h), 'No this user', '255,0,0', 0)
else:
self.recognized_faces['-2'] = ((x, y, w, h), 'No model', '255,0,0', 0)
except Exception as e:
print(e)
def recognize(self):
while self.flag_recognize:
try:
if self.recognizing_frame is None:
time.sleep(0.05)
continue
self.recognized_faces.clear()
faces = HaarcascadeDetective.get_faces_position(
self.recognizing_frame)
for (x, y, w, h) in faces:
recognized_face = {'position': (x, y, w, h)}
face = self.recognizing_frame[y:y + h, x:x + w]
if self.model is not None:
gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
params = self.model.predict(gray)
if params[1] <= Config.threshold:
recognized_face['name'] = self.users[params[0]]
recognized_face['degree'] = int(params[1])
self.recognized_faces.append(recognized_face)
self.recognizing_frame = None
except Exception as e:
print(e)
def recognize(self):
classifier = HaarcascadeDetective().get_face_classifier()
while self.playing:
if len(self.frames) == 0:
continue
frame = self.frames.pop()
if self.flag_recognize:
faces = classifier.get_faces_position(frame)
self.recognized_faces.clear()
for (x, y, w, h) in faces:
face = frame[y:y + h, x:x + w]
if self.model is not None:
gray = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
params = self.model.predict(gray)
if params[1] <= Tool.config.getint(
'recognize', 'threshold', fallback=50):
self.recognized_faces[self.face_names[
params[0]]] = (x, y, w, h)
else:
self.recognized_faces['Unknown'] = (x, y, w, h)
else:
self.recognized_faces['Unknown'] = (x, y, w, h)