图像形态学处理

腐蚀操作

import cv2
import numpy as np
# 腐蚀前的图像
img = cv2.imread("../res/dige.png")
cv2.imshow("img",img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# 执行腐蚀操作并显示腐蚀后的图像
kernel = np.ones((5,5),np.uint8)
erosion=cv2.erode(img,kernel,iterations=1)
cv2.imshow("erosion", erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

执行腐蚀操作前的图像：

执行腐蚀操作后的图像：

膨胀操作

kernel=np.ones((3,3),np.uint8)
dige_dilate=cv2.dilate(dige_erosion,kernel,iterations=1)
cv2.imshow("dige",dige_dilate)
cv2.waitKey(0)
cv2.destroyAllWindows()

膨胀操作后的效果：

对圆进行不同迭代次数的膨胀操作：

pie=cv2.imread("../res/pie.png")
kernel=np.ones((30,30),np.uint8)
dilate_1=cv2.dilate(pie,kernel,iterations=1)
dilate_2=cv2.dilate(pie,kernel,iterations=3)
dilate_3=cv2.dilate(pie,kernel,iterations=3)
res=np.hstack((dilate_1,dilate_2,dilate_3))
cv2.imshow("result",res)
cv2.waitKey(0)
cv2.destroyAllWindows()

开运算与闭运算

开运算

先腐蚀，再膨胀

# 开运算
img = cv2.imread("../res/dige.png")

kernel=np.ones((5,5),np.uint8)
opening=cv2.morphologyEx(img,cv2.MORPH_OPEN,kernel)

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

闭运算

先膨胀，再腐蚀

# 闭运算
img = cv2.imread("../res/dige.png")

kernel=np.ones((5,5),np.uint8)
closing=cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel)

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

梯度运算

梯度=膨胀-腐蚀

# 梯度=膨胀-腐蚀
pie=cv2.imread("../res/pie.png")
kernel=np.ones((7,7),np.uint8)
dilate=cv2.dilate(pie,kernel,iterations=5)
erosion=cv2.erode(pie,kernel,iterations=5)

result=np.hstack((dilate,erosion))

cv2.imshow('result',result)
cv2.waitKey(0)
cv2.destroyAllWindows()

gradient=cv2.morphologyEx(pie,cv2.MORPH_GRADIENT,kernel)

cv2.imshow('gradient',gradient)
cv2.waitKey(0)
cv2.destroyAllWindows()

礼帽与黑帽

• 礼帽=原始输入-开运算结果
• 黑帽=闭运算-原始输入

# 礼帽
img=cv2.imread('../res/dige.png')
tophat=cv2.morphologyEx(img,cv2.MORPH_TOPHAT,kernel)
cv2.imshow('tophat',tophat)
cv2.waitKey(0)
cv2.destroyAllWindows()

礼帽结果：

# 黑帽
blackhat=cv2.morphologyEx(img,cv2.MORPH_BLACKHAT,kernel)
cv2.imshow('blackhat',blackhat)
cv2.waitKey(0)
cv2.destroyAllWindows()

黑帽结果：