用python写一个程序，要求为通过自适应中值滤波加局部自适应阈值对Canny算法改进的程序

好的，您的要求是编写一个通过自适应中值滤波加局部自适应阈值对Canny算法改进的程序，以下是一份可能符合您要求的Python程序：

import cv2
import numpy as np

# 读取图像
img = cv2.imread('input.jpg', 0)

# 自适应中值滤波
def adaptive_median_filter(img, size):
    height, width = img.shape
    result = np.zeros((height, width), dtype=np.uint8)
    for i in range(height):
        for j in range(width):
            filter_size = 3
            while filter_size <= size:
                offset = filter_size // 2
                roi = img[max(0, i-offset):min(height, i+offset+1), max(0, j-offset):min(width, j+offset+1)]
                median = np.median(roi)
                if img[i, j] > median and median > np.min(roi) and median < np.max(roi):
                    result[i, j] = img[i, j]
                    break
                else:
                    filter_size += 2
            else:
                result[i, j] = median
    return result

# 局部自适应阈值
def local_adaptive_threshold(img, ksize, c):
    img_mean = cv2.blur(img, (ksize, ksize))
    img_sub = img - img_mean
    img_sub_mean = cv2.blur(img_sub * img_sub, (ksize, ksize))
    img_std = np.sqrt(img_sub_mean)
    return cv2.threshold(img, img_mean - c * img_std, 255, cv2.THRESH_BINARY)[1]

# Canny算法改进
def canny_improved(img):
    img_median = adaptive_median_filter(img, 7)
    img_threshold = local_adaptive_threshold(img_median, 3, 0.1)
    return cv2.Canny(img_threshold, 100, 200)

# 显示结果
cv2.imshow('Input', img)
cv2.imshow('Output', canny_improved(img))
cv2.waitKey(0)
cv2.destroyAllWindows()

该程序使用OpenCV库实现了自适应中值滤波和局部自适应阈值，然后将处理后的图像输入到Canny算法中，得到改进后的边缘检测结果。您可以将程序中的`input.jpg`替换为您想要处理的图像，然后运行程序查看结果。