import cv2
import os


def get_num_list(folder_path):
    """
    读取文件夹中的所有.png格式图片，并进行二值化处理，并返回包含二值化图片的列表。
    """
    binary_images = []
    # 遍历文件夹中的所有文件
    for filename in sorted(os.listdir(folder_path)):
        # 检查文件是否是.png格式
        if filename.endswith('.png'):
            # 构建图片的完整路径
            img_path = os.path.join(folder_path, filename)

            # 使用OpenCV读取图片为灰度图
            img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)

            # 检查图片是否成功读取
            if img is not None:
                # 使用阈值进行二值化处理
                # 假设我们使用127作为阈值，但这可以根据你的需求进行调整
                _, binary_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

                # 将二值化后的图片添加到列表中
                binary_images.append(binary_img.tolist())

    return binary_images


num_list = [
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
    [[0, 0, 255, 255], [255, 255, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255],
     [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255], [0, 0, 255, 255]],
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
     [0, 0, 0, 255, 255, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0], [255, 255, 0, 0, 0, 0],
     [255, 255, 0, 0, 0, 0], [255, 255, 255, 255, 255, 255]],
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
     [0, 0, 255, 255, 255, 0], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
    [[0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 255], [0, 0, 255, 255, 255, 255], [0, 0, 255, 255, 255, 255],
     [0, 255, 255, 0, 255, 255], [0, 255, 255, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 255, 255, 255, 255],
     [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255]],
    [[255, 255, 255, 255, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
     [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 0, 0], [255, 255, 0, 0, 0, 0],
     [255, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
    [[255, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 255, 255, 0], [0, 0, 0, 255, 255, 0],
     [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 0, 255, 255, 0, 0], [0, 255, 255, 0, 0, 0],
     [0, 255, 255, 0, 0, 0], [0, 255, 255, 0, 0, 0]],
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]],
    [[0, 255, 255, 255, 255, 0], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255], [255, 255, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 255], [0, 0, 0, 0, 255, 255], [0, 0, 0, 0, 255, 255],
     [255, 255, 0, 0, 255, 255], [0, 255, 255, 255, 255, 0]]]