✨我们将以下图为例，快速实践不同的形态学操作，如腐蚀 erode、膨胀 dilate、开 open、闭 close …

import cv2
import numpy as np

img = cv2.imread('D:\Desktop\csdn.png',0)
cv2.imshow('binary_Erode2', img) 
cv2.waitKey(0)

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💜 腐蚀 ：内核在图像中滑动，当内核下存在(原始图像中的)像素不为 1 时，内核下的区域将被腐蚀 （变为零）

erosion = cv2.erode(img,kernel) ## 白色变小

import cv2
import numpy as np

img = cv2.imread('D:\Desktop\csdn.png',0)

kernel = np.ones((10,10),np.uint8)
erosion = cv2.erode(img,kernel)  ### 

cv2.imshow('binary_Erode', erosion) 
cv2.waitKey(0)

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💜 膨胀 ：内核在图像中滑动，当内核下存在(原始图像中的)像素为 1 时，内核下的区域将被膨胀 （变为一）

dilation = cv2.dilate(img,kernel) ## 白色变大

import cv2
import numpy as np

img = cv2.imread('D:\Desktop\csdn.png',0)

kernel = np.ones((10,10),np.uint8)
dilation = cv2.dilate(img,kernel)   ### 

cv2.imshow('binary_Dilation', dilation) 
cv2.waitKey(0)

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💜 开运算 ：腐蚀再膨胀

opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)

import cv2
import numpy as np

img = cv2.imread('D:\Desktop\csdn.png',0)

kernel = np.ones((10,10),np.uint8)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)   ### 

cv2.imshow('binary_Opening ', opening) 
cv2.waitKey(0)

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💜 闭运算 ：膨胀再腐蚀

closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)

import cv2
import numpy as np

img = cv2.imread('D:\Desktop\csdn.png',0)

kernel = np.ones((10,10),np.uint8)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)   ### 

cv2.imshow('binary_Closing', closing) 
cv2.waitKey(0)