class FaceBlocking:
def __init__(self, detection_type, recognition_type, video_source=0):
self.COVER_COLOR = (0, 0, 0)
self.MIN_CONF = 40
self.detector = FaceDetector(detection_type)
self.face_recognizer = FaceRecognizer(recognition_type)
self.embedding_model = load_model(
os.path.join(MODELS_DIR, "facenet_keras.h5"))
self.labels = self.face_recognizer.labels
self.capture = cv2.VideoCapture(video_source)
if not self.capture.isOpened():
raise ValueError("Unable to open video source", video_source)
self.width = self.capture.get(cv2.CAP_PROP_FRAME_WIDTH)
self.height = self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
def __del__(self):
if self.capture.isOpened():
self.capture.release()
def get_processed_frame(self, _block_list=[], _debug=False):
val, frame = self.capture.read()
faces = self.detector.get_coordinates(frame, _multi_face=True)
faces = faces if faces is not None else []
for i in range(len(faces)):
start_x, start_y, end_x, end_y = faces[i]
roi_color = frame[start_y:end_y, start_x:end_x]
who_face, conf = self.face_recognizer.face_classification(
roi_color, self.embedding_model)
if conf >= self.MIN_CONF and who_face in _block_list:
self.block_face(frame, who_face, start_x, start_y, end_x,
end_y)
if _debug:
self.draw_debug(frame, who_face, start_x, start_y, end_x,
end_y)
return frame
def draw_debug(self, _frame, _name, _start_x, _start_y, _end_x, _end_y):
font = cv2.FONT_HERSHEY_SIMPLEX
color = (255, 255, 255)
stroke = 2
cv2.putText(_frame, _name, (_start_x, _start_y), font, 1, color,
stroke, cv2.LINE_AA)
cv2.rectangle(_frame, (_start_x, _start_y), (_end_x, _end_y), color,
stroke)
def block_face(self, _frame, _name, _start_x, _start_y, _end_x, _end_y):
cv2.rectangle(_frame, (_start_x, _start_y), (_end_x, _end_y),
self.COVER_COLOR, -1)
class FaceBlocking:
def __init__(self, video_source=0):
self.COVER_COLOR = (0, 0, 0)
self.detector = FaceDetector()
self.age_estimator = AgeEstimator()
self.capture = cv2.VideoCapture(video_source)
if not self.capture.isOpened():
raise ValueError("Unable to open video source", video_source)
self.width = self.capture.get(cv2.CAP_PROP_FRAME_WIDTH)
self.height = self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT)
def __del__(self):
if self.capture.isOpened():
self.capture.release()
def get_processed_frame(self, _age_restrictions=(), _debug=False):
val, frame = self.capture.read()
faces = self.detector.get_coordinates(frame, _multi_face=True)
faces = faces if faces is not None else []
for i in range(len(faces)):
start_x, start_y, end_x, end_y = faces[i]
roi_color = frame[start_y:end_y, start_x:end_x]
pred_gender, pred_age = self.age_estimator.estimate(roi_color)
estimated_age = np.argmax(pred_age)
if not _age_restrictions[0] <= estimated_age <= _age_restrictions[1]:
self.block_face(frame, start_x, start_y, end_x, end_y)
if _debug:
self.draw_debug(frame, estimated_age, start_x, start_y, end_x, end_y)
return frame
def draw_debug(self, _frame, _age, _start_x, _start_y, _end_x, _end_y):
font = cv2.FONT_HERSHEY_SIMPLEX
color = (0, 255, 0)
stroke = 2
cv2.putText(_frame, "Age: " + str(_age), (_start_x, _start_y), font, 1, color, stroke, cv2.LINE_AA)
cv2.rectangle(_frame, (_start_x, _start_y), (_end_x, _end_y), color, stroke)
def block_face(self, _frame, _start_x, _start_y, _end_x, _end_y):
cv2.rectangle(_frame, (_start_x, _start_y), (_end_x, _end_y), self.COVER_COLOR, -1)
def set_detection_type(self, detection_type):
self.detector.set_detection_type(detection_type)
def set_age_estimation_model(self, estimation_model):
self.age_estimator.switch_model(estimation_model)
num = 1
while True:
if dir_name is None:
print('You must pass your name as argument!')
subprocess.run(["python", "took_pictures.py", "-h"])
break
img_dir = f'images/{dir_name}/'
os.makedirs(img_dir, exist_ok=True)
detector = FaceDetector(detection_type)
# Capture frame-by-frame
val, frame = capture.read()
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
try:
start_x, start_y, end_x, end_y = detector.get_coordinates(frame)[0]
roi_ = frame[start_y:end_y, start_x:end_x]
cv2.rectangle(frame, (start_x, start_y), (end_x, end_y),
(255, 255, 255), 2)
if only_face:
cv2.imwrite(f'{img_dir}/{num}.png', roi_)
else:
cv2.imwrite(f'{img_dir}/{num}.png', frame)
print(f'face no. {num}')
num += 1
except TypeError:
pass
# Display the resulting frame
cv2.imshow('video', frame)