均值滤波-----python opencv

【均值滤波-----python】平滑线性滤波器的输出（响应）是包含在滤波器模板邻域内的像素简单平均值。这些滤波器有时也称为均值滤波器。它使用滤波器模板确定的邻域内像素的平均灰度值代替图像中每个像素的值，这种处理的结果降低了图像灰度的尖锐变化。由于典型的随机噪声由灰度级的急剧变化组成，因此，常见的平滑处理应用就是降低噪声。然而，由于图像边缘也是由图像灰度尖锐变化带来的特性，所以均值滤波处理还是存在着不希望有的边缘模糊的负面效应。
代码实现：

import cv2 as cv import numpy as np import math import copy from skimage import io import randomdef spilt(a): if a / 2 == 0: x1 = x2 = a / 2 else: x1 = math.floor(a / 2) x2 = a - x1 return -x1,x2def salt_and_pepper_noise(img, proportion=0.05): noise_img = img height, width = noise_img.shape[0], noise_img.shape[1] num = int(height * width * proportion)# 多少个像素点添加椒盐噪声 for i in range(num): w = random.randint(0, width - 1) h = random.randint(0, height - 1) if random.randint(0, 1) == 0: noise_img[h, w] = 0 else: noise_img[h, w] = 255 return noise_imgdef original(i, j, k, a, b, img): x1, x2 = spilt(a) y1, y2 = spilt(b) temp = np.zeros(a * b) count = 0 for m in range(x1, x2): for n in range(y1, y2): if i + m < 0 or i + m > img.shape[0] - 1 or j + n < 0 or j + n > img.shape[1] - 1: temp[count] = img[i, j, k] else: temp[count] = img[i + m, j + n, k] count += 1 return tempdef average_function(a, b, img): img0 = copy.copy(img) for i in range(0, img.shape[0]): for j in range(2, img.shape[1]): for k in range(img.shape[2]): temp = original(i, j, k, a, b, img0) img[i, j, k] = int(np.mean(temp)) return imgif __name__ == "__main__":img = io.imread(r"E:/pythontupian/5.jpg") cv.imshow("original", img) noise_img = salt_and_pepper_noise(img) cv.imshow("noise_img", noise_img) ave_img = average_function(3, 3, copy.copy( noise_img))# （3，3）滤波器大小 cv.imshow("ave_img", ave_img) cv.waitKey(0) cv.destroyAllWindows()

结果展示：
原图