def enroll_face(image, label,
embeddings_path="/home/ubuntu/Downloads/snehafc/face-recognition-master/face_embeddings.npy",
labels_path="/home/ubuntu/Downloads/snehafc/face-recognition-master/labels.cpickle", down_scale=1.0):
faces = detect_faces(image, down_scale)
if len(faces)<1:
return False
if len(faces)>1:
print("ok")
face = faces[0]
face_embeddings = extract_face_embeddings(image, face, shape_predictor,
face_recognizer)
add_embeddings(face_embeddings, label, embeddings_path=embeddings_path,
labels_path=labels_path)
print("training done")
return True
def enroll_face(image,
label,
embeddings_path="face_embeddings.npy",
labels_path="labels.pickle",
down_scale=1.0):
faces = detect_faces(image, down_scale)
if len(faces) < 1:
return False
if len(faces) > 1:
raise ValueError("Multiple faces not allowed for enrolling")
face = faces[0]
face_embeddings = extract_face_embeddings(image, face, shape_predictor,
face_recognizer)
add_embeddings(face_embeddings,
label,
embeddings_path=embeddings_path,
labels_path=labels_path)
return True
def loop(cam_obj, servo):
i = 0
running = True
while running:
# pobierz obraz z kamery
camera = get_image(cam_obj)
faces = detect_faces(camera)
for face in faces:
add_filters(camera, i, face)
# wyswietl obraz
cv2.imshow("Smile!", camera)
if len(faces) == 1:
(camera_height, camera_width) = (camera.shape[0], camera.shape[1])
(face_x, face_y, face_width, face_height) = faces[0]
position = get_position(camera_width, face_x, face_width)
t = ServoThread(position, servo)
t.start()
else:
t = ServoThread(7.5, servo)
t.start()
key = cv2.waitKey(1)
if key == ord('n'):
i += 1
i %= f_length
if key == ord('b'):
i -= 1
if i < 0:
i = f_length - 1
# jesli odczytamy wcisniecie klawisza q, to wychodzimy
if key == ord('q'):
running = False
default="labels.pickle")
args = vars(ap.parse_args())
embeddings = np.load(args["embeddings"])
labels = cPickle.load(open(args["labels"]))
shape_predictor = dlib.shape_predictor(
"models/"
"shape_predictor_5_face_landmarks.dat")
face_recognizer = dlib.face_recognition_model_v1(
"models/"
"dlib_face_recognition_resnet_model_v1.dat")
image = cv2.imread(args["image"])
image_original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
faces = detect_faces(image)
for face in faces:
embedding = extract_face_embeddings(image, face, shape_predictor,
face_recognizer)
label = recognize_face(embedding, embeddings, labels)
(x1, y1, x2, y2) = face.left(), face.top(), face.right(), face.bottom()
cv2.rectangle(image_original, (x1, y1), (x2, y2), (255, 120, 120), 2,
cv2.CV_AA)
cv2.putText(image_original, label[0], (x1, y1 - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2)
cv2.imshow("Image", image_original)
cv2.waitKey(0)