首页用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

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

时间: 2024-03-31 18:38:51 AIGC 浏览: 86

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： ```python 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。

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