我用ai生成的代码运行无结果

"""
    # -*- coding :utf-8 -*-
    # @Time:2022/3/129:05
    # @Author:WEIKETAN
    # @File:equalizeHIst.py
    # @Desc:灰度直方图、直方图均衡化、掩膜的应用，直方图规定化(TODO)
"""
import matplotlib
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

""" 1.draw and show grayscale_image"""
def dsGrayscaleimage():
    # 1.read grayscale_image directly
    """
    imread(imgpath, 0)
    :param
        imgpath
        gray or RGB: default RGB、 gray -- 0
    :return:the image arrays
    """
    img = cv.imread("F:\\Develop_Tools_Python\\Python_WorkSpace\\Notebook_Python_WorkSpace\\IMage_processing\\homeworks\\photo\\cat.jpeg", 0)
    plt.imshow(img, cmap=plt.cm.gray)  # default--热图(heatmap)、 another show_style: cv.imshow("grayImg",img)

    # 2.calculate grayscale_image
    """
    cv2.calcHist(images,channels,mask,histSize,ranges[,hist[,accumulate]])
    :param
        images:raw images eg:[img]
        channels:number [0]--grayImage [0][1][2]--BGR(26 letter order)
        mask:local or globel  default:globel(None)
        histSize:the number of BIN（直条或组距） eg:[256]
        ranges:the range of pixel eg:[0,255]
    :return:the histr arrays
    """
    histr = cv.calcHist([img], [0], None, [256], [0, 255])
    # print(histr, histr.shape)

    # 3.draw grayImage
    """
    pyplot.figsize()
    :param 
        figsize : (float, float), default: :rc:`figure.figsize`
            Width, height in inches.
        dpi : float, default: :rc:`figure.dpi`
        The resolution of the figure in dots-per-inch.
    """
    plt.figure(figsize=(10, 6), dpi=100)
    plt.plot(histr)
    plt.show()

""" 2.mask_ROI(Region of interest)：使用掩膜提取感兴趣区域"""
def mask_ROI():
    # 1. 直接以灰度图的方式读入
    img = cv.imread("F:\\Develop_Tools_Python\\Python_WorkSpace\\Notebook_Python_WorkSpace\\IMage_processing\\homeworks\\photo\\cat.jpeg", 0)
    # plt.imshow(img, cmap=plt.cm.gray)

    # 2. 创建膜板
    mask = np.zeros(img.shape[:2], np.uint8)  # 提取图片的长和宽
    mask[400:650, 200:500] = 1
    # plt.imshow(mask, cmap=plt.cm.gray)

    # 3. 掩膜后数据
    masked_img = cv.bitwise_and(img, img, mask=mask)
    # plt.imshow(masked_img, cmap=plt.cm.gray)

    # 4. 统计掩膜后图像的灰度图
    mask_histr = cv.calcHist([img], [0], mask, [256], [0, 255])

    # 5. 图像展示
    # 设置字体为楷体,解决显示字体乱码问题
    matplotlib.rcParams['font.sans-serif'] = ['KaiTi']

    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
    axes[0, 0].imshow(img, cmap=plt.cm.gray)
    axes[0, 0].set_title("原图")

    axes[0, 1].imshow(mask, cmap=plt.cm.gray)
    axes[0, 1].set_title("膜板数据")

    axes[1, 0].imshow(masked_img, cmap=plt.cm.gray)
    axes[1, 0].set_title("掩膜后数据")

    axes[1, 1].plot(mask_histr)
    axes[1, 1].grid()
    axes[1, 1].set_title('灰度直方图')
    plt.show()

""" 3.直方图均衡化"""
def equalizeHist():
    # 1. 读入图片
    img = cv.imread("F:\\Develop_Tools_Python\\Python_WorkSpace\\Notebook_Python_WorkSpace\\IMage_processing\\homeworks\\photo\\cat.jpeg", 0)
    # 2. 原图直方图
    histr = cv.calcHist([img], [0], None, [256], [0, 255])

    # 3. 均衡化处理
    dst = cv.equalizeHist(img)

    # 4. 均衡化后直方图
    histr1 = cv.calcHist([dst], [0], None, [256], [0, 255])


    # 4.  结果显示
    # 设置字体为楷体,解决显示字体乱码问题
    matplotlib.rcParams['font.sans-serif'] = ['KaiTi']
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
    axes[0, 0].imshow(img, cmap=plt.cm.gray)
    axes[0, 0].set_title("原图")

    axes[0, 1].imshow(dst, cmap=plt.cm.gray)
    axes[0, 1].set_title("均衡化过的图片")

    axes[1, 0].plot(histr)
    axes[1, 0].set_title("原图直方图")

    axes[1, 1].plot(histr1)
    axes[1, 1].grid()
    axes[1, 1].set_title('均衡化直方图')
    plt.show()


""" 4.自适应的直方图均衡化"""
def selfAdaptionQqualizeHist():
    # 1. 读取图片
    img = cv.imread("F:\\Develop_Tools_Python\\Python_WorkSpace\\Notebook_Python_WorkSpace\\IMage_processing\\homeworks\\photo\\cat.jpeg", 0)

    # 2. 原图直方图
    histr = cv.calcHist([img], [0], None, [256], [0, 255])

    # 3. 创建一个自适应均衡化的对象，并应用于图像
    # clipLimit:对比度限制，默认为 40
    # tileGridSize：分块大小，默认为 8 * 8
    clahe = cv.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
    cl1 = clahe.apply(img)

    # 4. 原图直方图
    histr1 = cv.calcHist([cl1], [0], None, [256], [0, 255])

    # 5. 图像显示
    # 设置字体为楷体,解决显示字体乱码问题
    matplotlib.rcParams['font.sans-serif'] = ['KaiTi']
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
    axes[0, 0].imshow(img, cmap=plt.cm.gray)
    axes[0, 0].set_title("原图")

    axes[0, 1].imshow(cl1, cmap=plt.cm.gray)
    axes[0, 1].set_title("均衡化过的图片")

    axes[1, 0].plot(histr)
    axes[1, 0].set_title("原图直方图")

    axes[1, 1].plot(histr1)
    axes[1, 1].set_title('均衡化直方图')
    plt.show()


if __name__ == '__main__':
    # 1.灰度直方图计算和显示
    # dsGrayscaleimage()

    # 2.使用掩膜提取感兴趣区域
    # mask_ROI()

    # 3.直方图均衡化
    # equalizeHist()

    # 4. 自适应直方图均衡化
    selfAdaptionQqualizeHist()

    # 5.直方图规定化
    # todo