def get_images_and_labels(path):
    image_paths = [os.path.join(path, f) for f in os.listdir(path)]
    # 新建连个list用于存放
    face_samples = []
    ids = []

    # 遍历图片路径，导入图片和id添加到list中
    for image_path in image_paths:

        # 通过图片路径将其转换为灰度图片
        img = Image.open(image_path).convert('L')

        # 将图片转化为数组
        img_np = np.array(img, 'uint8')

        if os.path.split(image_path)[-1].split(".")[-1] != 'jpg':
            continue

        # 为了获取id，将图片和路径分裂并获取
        id = int(os.path.split(image_path)[-1].split(".")[1])

        # 调用熟悉的人脸分类器
        detector = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

        faces = detector.detectMultiScale(img_np)

        # 将获取的图片和id添加到list中
        for (x, y, w, h) in faces:
            face_samples.append(img_np[y:y + h, x:x + w])
            ids.append(id)
    return face_samples, ids