用python写一个自适应中值滤波加局部阈值改进Canny算子的程序

以下是Python实现自适应中值滤波加局部阈值改进Canny算子的程序：

import cv2
import numpy as np

def adaptive_median_filter(img, max_window_size):
    h, w = img.shape
    filtered = np.zeros((h, w), np.uint8)
    for y in range(h):
        for x in range(w):
            filtered[y, x] = img[y, x]
            window_size = 3
            while window_size <= max_window_size:
                window = img[max(0, y - window_size // 2):min(h, y + window_size // 2 + 1),
                             max(0, x - window_size // 2):min(w, x + window_size // 2 + 1)]
                median = np.median(window)
                min_val, max_val = np.min(window), np.max(window)
                if min_val < median < max_val:
                    if min_val < img[y, x] < max_val:
                        filtered[y, x] = img[y, x]
                    else:
                        filtered[y, x] = median
                    break
                else:
                    window_size += 2
    return filtered

def local_threshold_canny(img, threshold_ratio=0.33, kernel_size=3):
    blurred = cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
    v = np.median(blurred)
    lower = int(max(0, (1.0 - threshold_ratio) * v))
    upper = int(min(255, (1.0 + threshold_ratio) * v))
    edged = cv2.Canny(blurred, lower, upper)
    return edged

def adaptive_median_filter_canny(img, max_window_size=15, threshold_ratio=0.33, kernel_size=3):
    filtered = adaptive_median_filter(img, max_window_size)
    edged = local_threshold_canny(filtered, threshold_ratio, kernel_size)
    return edged

其中，adaptive_median_filter_canny()函数是自适应中值滤波加局部阈值改进Canny算子的组合函数，有四个参数：img表示输入图像，max_window_size表示最大窗口大小（默认为15），threshold_ratio表示阈值比例（默认为0.33），kernel_size表示高斯滤波器的卷积核大小（默认为3）。程序首先对输入图像进行自适应中值滤波，然后对滤波后的图像进行局部阈值改进Canny算子，得到边缘图像。