DLIB的面部检查 [英] Face aligment check with DLIB

问题描述

我只是想知道是否可以通过DLIB和OpenCV检测到人脸是否正确对准了相机?

I am just wondering is it possible to detect is the face aligned correctly, straight to the camera with DLIB and OpenCV?

我尝试使用此代码来检测形状并获取脸部点:

I tried this code to detect shape and get the points of face:

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

推荐答案

下面是我编写的用于查找人头所面对姿势的函数.这里的p1和p2定义了姿势矢量.计算它们之间的角度是微不足道的，您可以根据角度确定要接受还是丢弃的图像.

Below is a function I wrote to find the pose where the person head is facing. here p1 and p2 define the vector of pose. Calculating the angle between them is trivial and based on angle you may decide which image to accept or discard.

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

这篇关于DLIB的面部检查的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！