Python imutils.face_utils.FaceAligner() Examples

The following are 3 code examples of imutils.face_utils.FaceAligner(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module imutils.face_utils , or try the search function .
Example #1
Source File: eval.py    From Age-Gender-Estimate-TF with MIT License 5 votes vote down vote up 
def load_image(image_path, shape_predictor):
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor(shape_predictor)
    fa = FaceAligner(predictor, desiredFaceWidth=160)
    image = cv2.imread(image_path, cv2.IMREAD_COLOR)
    # image = imutils.resize(image, width=256)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    rects = detector(gray, 2)
    rect_nums = len(rects)
    XY, aligned_images = [], []
    if rect_nums == 0:
        aligned_images.append(image)
        return aligned_images, image, rect_nums, XY
    else:
        for i in range(rect_nums):
            aligned_image = fa.align(image, gray, rects[i])
            aligned_images.append(aligned_image)
            (x, y, w, h) = rect_to_bb(rects[i])
            image = cv2.rectangle(image, (x, y), (x + w, y + h), color=(255, 0, 0), thickness=2)
            XY.append((x, y))
        return np.array(aligned_images), image, rect_nums, XY


# def draw_label(image, point, ages, genders, font=cv2.FONT_HERSHEY_COMPLEX, font_scale=1, thickness=1):
#     for i in range(len(point)):
#         label = "{}, {}".format(int(ages[i]), "F" if genders[i] == 0 else "M")
#         size = cv2.getTextSize(label, font, font_scale, thickness)[0]
#         x, y = point[i]
#         # cv2.rectangle(image, (x, y - size[1]), (x + size[0], y), (255, 0, 0), cv2.FILLED)
#         cv2.putText(image, label, (x, np.max(y - 5, 0)), font, font_scale, (255, 255, 255), thickness)
Example #2
Source File: face_detection.py    From Heart-rate-measurement-using-camera with Apache License 2.0 5 votes vote down vote up 
def __init__(self):
        self.detector = dlib.get_frontal_face_detector()
        self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
        self.fa = face_utils.FaceAligner(self.predictor, desiredFaceWidth=256)
Example #3
Source File: demo.py    From Age-Gender-Estimate-TF with MIT License 4 votes vote down vote up 
def main(sess,age,gender,train_mode,images_pl):
    args = get_args()
    depth = args.depth
    k = args.width

    # for face detection
    detector = dlib.get_frontal_face_detector()
    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
    fa = FaceAligner(predictor, desiredFaceWidth=160)

    # load model and weights
    img_size = 160

    # capture video
    cap = cv2.VideoCapture(0)
    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)

    while True:
        # get video frame
        ret, img = cap.read()

        if not ret:
            print("error: failed to capture image")
            return -1

        input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_h, img_w, _ = np.shape(input_img)

        # detect faces using dlib detector
        detected = detector(input_img, 1)
        faces = np.empty((len(detected), img_size, img_size, 3))

        for i, d in enumerate(detected):
            x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
            xw1 = max(int(x1 - 0.4 * w), 0)
            yw1 = max(int(y1 - 0.4 * h), 0)
            xw2 = min(int(x2 + 0.4 * w), img_w - 1)
            yw2 = min(int(y2 + 0.4 * h), img_h - 1)
            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
            # cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
            faces[i, :, :, :] = fa.align(input_img, gray, detected[i])
            # faces[i,:,:,:] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1, :], (img_size, img_size))
        #
        if len(detected) > 0:
            # predict ages and genders of the detected faces
            ages,genders = sess.run([age, gender], feed_dict={images_pl: faces, train_mode: False})

        # draw results
        for i, d in enumerate(detected):
            label = "{}, {}".format(int(ages[i]), "F" if genders[i] == 0 else "M")
            draw_label(img, (d.left(), d.top()), label)

        cv2.imshow("result", img)
        key = cv2.waitKey(1)

        if key == 27:
            break