opencv - 使用 DLIB 进行面部对齐检查

Question

我只是想知道是否可以使用 DLIB 和 OpenCV 检测面部是否正确对齐，直接对准相机？

我试过这段代码来检测形状并获取面部的点:

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

vs = VideoStream(0).start()

    while True:
        # grab the frame from the threaded video stream, resize it to
        # have a maximum width of 400 pixels, and convert it to
        # grayscale
        frame = vs.read()
        frame = imutils.resize(frame, width=400)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # detect faces in the grayscale frame
        rects = detector(gray, 0)

        # loop over the face detections
        for rect in rects:
            # determine the facial landmarks for the face region, then
            # convert the facial landmark (x, y)-coordinates to a NumPy
            # array
            shape = predictor(gray, rect)
            shape = face_utils.shape_to_np(shape)

            # loop over the (x, y)-coordinates for the facial landmarks
            # and draw them on the image
            for (x, y) in shape:
                print x,y
                cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)
            cv2.putText(frame, "Aptiktas veidas", (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

        # show the frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

Answer 1

下面是我编写的一个函数，用于查找人头朝向的姿势。这里 p1 和 p2 定义了位姿向量。计算它们之间的角度是微不足道的，您可以根据角度决定接受或丢弃哪个图像。

def pose_estimate(image, landmarks):
    """
    Given an image and a set of facial landmarks generates the direction of pose
    """
    size = image.shape
    image_points = np.array([
        (landmarks[33, 0], landmarks[33, 1]),     # Nose tip
        (landmarks[8, 0], landmarks[8, 1]),       # Chin
        (landmarks[36, 0], landmarks[36, 1]),     # Left eye left corner
        (landmarks[45, 0], landmarks[45, 1]),     # Right eye right corner
        (landmarks[48, 0], landmarks[48, 1]),     # Left Mouth corner
        (landmarks[54, 0], landmarks[54, 1])      # Right mouth corner
        ], dtype="double")

    model_points = np.array([
        (0.0, 0.0, 0.0),             # Nose tip
        (0.0, -330.0, -65.0),        # Chin
        (-225.0, 170.0, -135.0),     # Left eye left corner
        (225.0, 170.0, -135.0),      # Right eye right corner
        (-150.0, -150.0, -125.0),    # Left Mouth corner
        (150.0, -150.0, -125.0)      # Right mouth corner
        ])

    focal_length = size[1]
    center = (size[1]/2, size[0]/2)
    camera_matrix = np.array([
        [focal_length, 0, center[0]],
        [0, focal_length, center[1]],
        [0, 0, 1]
        ], dtype="double")

    dist_coeffs = np.zeros((4, 1))
    success, rotation_vector, translation_vector = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs)     
    (nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
    p1 = (int(image_points[0][0]), int(image_points[0][1]))
    p2 = (int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
    return p1, p2