中值平滑

最新推荐文章于 2023-06-28 17:47:35 发布

qq_41950131

最新推荐文章于 2023-06-28 17:47:35 发布

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分类专栏： OpenCV 文章标签： OpenCV 中值平滑

本文链接：https://blog.youkuaiyun.com/qq_41950131/article/details/94844243

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本文介绍了一种去除椒盐噪声的中值平滑方法，通过定义中值平滑函数并使用OpenCV进行对比，展示了如何在图像处理中有效去除噪声，保持边缘清晰。

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书中代码感觉有错误，理解的可以交流

#中值平滑，去除椒盐噪声，同时边缘锐利较好。如果取最大最小值相当于形态学
#添加椒盐噪声
import numpy as np
from scipy import signal
import cv2
def salt(image,number):
    rows,cols = image.shape
    saltImage = np.copy(image)
    for i in range(number):
        randR = random.randint(0,rows-1)
        randC = random.randint(0,cols-1)
        saltImage[randR][randC] = 255
    return saltImage

def medianBlur(image,winSize):
    rows,cols = image.shape
    winH,winW = winSize
    halfWinH = (winH-1)/2
    halfWinW = (winW-1)/2
    medianBlurImage = np.zeros(image.shape,image.dtype)
    for r in range(rows):
        for c in range(cols):
            rTop = 0 if r-halfWinH<0 else r-halfWinH
            rBottom = rows-1 if r+halfWinH>rows-1 else r+halfWinH
            cLeft = 0 if c-halfWinW<0 else c-halfWinW
            cRight = cols-1 if c+halfWinW>cols-1 else c+halfWinW
            print(rTop,rBottom,cLeft,cRight)
            #取邻域
            region = image[int(rTop):int(rBottom+1),int(cLeft):int(cRight+1)]
            print(region)
            #求中值
            medianBlurImage[r][c] = np.median(region)
    return medianBlurImage
if __name__ =='__main__':
    #image = cv2.imread('E:\\sy2\\5\\img6.jpg',cv2.IMREAD_GRAYSCALE)
    image = np.array([[1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7],
                      [1,2,3,4,5,6,7]],np.uint8)
    medianBlurImage = medianBlur(image,(3,3))
    print(medianBlurImage)
    #medianBlurImage = medianBlur(image,(3,3))
    #cv2.namedWindow("image",cv2.WINDOW_NORMAL)
    #cv2.imshow('image',image)
    #cv2.imshow('medianBlurImage',medianBlurImage)
    #cv2.waitKey(0)
    #cv2.destroyAllWindows()
    在这里插入代码片

运行结果

0 1.0 0 1.0
[[1 2]
 [1 2]]
0 1.0 0.0 2.0
[[1 2 3]
 [1 2 3]]
0 1.0 1.0 3.0
[[2 3 4]
 [2 3 4]]
0 1.0 2.0 4.0
[[3 4 5]
 [3 4 5]]
0 1.0 3.0 5.0
[[4 5 6]
 [4 5 6]]
0 1.0 4.0 6.0
[[5 6 7]
 [5 6 7]]
0 1.0 5.0 6
[[6 7]
 [6 7]]
0.0 2.0 0 1.0
[[1 2]
 [1 2]
 [1 2]]
0.0 2.0 0.0 2.0
[[1 2 3]
 [1 2 3]
 [1 2 3]]
0.0 2.0 1.0 3.0
[[2 3 4]
 [2 3 4]
 [2 3 4]]
0.0 2.0 2.0 4.0
[[3 4 5]
 [3 4 5]
 [3 4 5]]
0.0 2.0 3.0 5.0
[[4 5 6]
 [4 5 6]
 [4 5 6]]
0.0 2.0 4.0 6.0
[[5 6 7]
 [5 6 7]
 [5 6 7]]
0.0 2.0 5.0 6
[[6 7]
 [6 7]
 [6 7]]
1.0 3.0 0 1.0
[[1 2]
 [1 2]
 [1 2]]
1.0 3.0 0.0 2.0
[[1 2 3]
 [1 2 3]
 [1 2 3]]
1.0 3.0 1.0 3.0
[[2 3 4]
 [2 3 4]
 [2 3 4]]
1.0 3.0 2.0 4.0
[[3 4 5]
 [3 4 5]
 [3 4 5]]
1.0 3.0 3.0 5.0
[[4 5 6]
 [4 5 6]
 [4 5 6]]
1.0 3.0 4.0 6.0
[[5 6 7]
 [5 6 7]
 [5 6 7]]
1.0 3.0 5.0 6
[[6 7]
 [6 7]
 [6 7]]
2.0 4.0 0 1.0
[[1 2]
 [1 2]
 [1 2]]
2.0 4.0 0.0 2.0
[[1 2 3]
 [1 2 3]
 [1 2 3]]
2.0 4.0 1.0 3.0
[[2 3 4]
 [2 3 4]
 [2 3 4]]
2.0 4.0 2.0 4.0
[[3 4 5]
 [3 4 5]
 [3 4 5]]
2.0 4.0 3.0 5.0
[[4 5 6]
 [4 5 6]
 [4 5 6]]
2.0 4.0 4.0 6.0
[[5 6 7]
 [5 6 7]
 [5 6 7]]
2.0 4.0 5.0 6
[[6 7]
 [6 7]
 [6 7]]
3.0 5.0 0 1.0
[[1 2]
 [1 2]
 [1 2]]
3.0 5.0 0.0 2.0
[[1 2 3]
 [1 2 3]
 [1 2 3]]
3.0 5.0 1.0 3.0
[[2 3 4]
 [2 3 4]
 [2 3 4]]
3.0 5.0 2.0 4.0
[[3 4 5]
 [3 4 5]
 [3 4 5]]
3.0 5.0 3.0 5.0
[[4 5 6]
 [4 5 6]
 [4 5 6]]
3.0 5.0 4.0 6.0
[[5 6 7]
 [5 6 7]
 [5 6 7]]
3.0 5.0 5.0 6
[[6 7]
 [6 7]
 [6 7]]
4.0 6.0 0 1.0
[[1 2]
 [1 2]
 [1 2]]
4.0 6.0 0.0 2.0
[[1 2 3]
 [1 2 3]
 [1 2 3]]
4.0 6.0 1.0 3.0
[[2 3 4]
 [2 3 4]
 [2 3 4]]
4.0 6.0 2.0 4.0
[[3 4 5]
 [3 4 5]
 [3 4 5]]
4.0 6.0 3.0 5.0
[[4 5 6]
 [4 5 6]
 [4 5 6]]
4.0 6.0 4.0 6.0
[[5 6 7]
 [5 6 7]
 [5 6 7]]
4.0 6.0 5.0 6
[[6 7]
 [6 7]
 [6 7]]
5.0 6 0 1.0
[[1 2]
 [1 2]]
5.0 6 0.0 2.0
[[1 2 3]
 [1 2 3]]
5.0 6 1.0 3.0
[[2 3 4]
 [2 3 4]]
5.0 6 2.0 4.0
[[3 4 5]
 [3 4 5]]
5.0 6 3.0 5.0
[[4 5 6]
 [4 5 6]]
5.0 6 4.0 6.0
[[5 6 7]
 [5 6 7]]
5.0 6 5.0 6
[[6 7]
 [6 7]]
[[1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]
 [1 2 3 4 5 6 6]]在这里插入代码片

opencv提供中值平滑函数
medianBlur(src,dst,ksize)
src 输入
dst 输出
ksize 窗口（3，3）