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建立阈值矩阵对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建立阈值矩阵对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 img_mean - c * img_std

# Canny算法改进
def canny_improved(img, threshold_matrix):
    img_median = adaptive_median_filter(img, 7)
    img_threshold = cv2.compare(img_median, threshold_matrix, cv2.CMP_GT)
    return cv2.Canny(img_threshold, 100, 200)

# 建立阈值矩阵
threshold_matrix = local_adaptive_threshold(img, 3, 0.1)

# 显示结果
cv2.imshow('Input', img)
cv2.imshow('Output', canny_improved(img, threshold_matrix))
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，然后建立阈值矩阵，并将处理后的图像和阈值矩阵输入到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。