def get_detected_faces(self, bounding_boxes, points):
detected_faces = []
# the number of boxes indicates the number of faces detected
for i in range(len(bounding_boxes)):
bbox = bounding_boxes[i][:4]
score = bounding_boxes[i][4:]
detected_face = Face()
detected_face.face_rectangle = bbox.tolist()
landmarks = []
for p in range(len(points)):
landmarks.append(float(points[p][i]))
detected_face.face_landmarks = landmarks
detected_face.confidence = score[0]
detected_faces.append(detected_face)
return detected_faces
def get_frames(user_id):
"""Video streaming generator function."""
video = cv2.VideoCapture(0)
while True:
rval, frame = video.read()
if not rval:
load_faces()
break
else:
new_frame = frame[:, :, ::-1]
locations, face_encodings = get_face(new_frame)
print(locations)
f = pickle.dumps(face_encodings)
if face_encodings is not None:
face = Face(user_id, f)
db.session.add(face)
db.session.commit()
top, right, bottom, left = locations
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255),
2)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' +
cv2.imencode('.jpg', frame)[1].tostring() + b'\r\n')
