OpenCV腐蚀操作、膨胀操作、开运算与闭运算

本文介绍了使用OpenCV进行图像处理的基本操作，包括腐蚀、膨胀、开运算和闭运算等，并通过实例展示了不同内核大小和迭代次数对图像的影响。

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import cv2 #opencv读取的格式是BGR
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline

1 腐蚀操作
img_dg = cv2.imread("dige.png")

cv2.imshow("img",img_dg)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(img_dg,kernel,iterations=1)

cv2.imshow("erosion",erosion)
cv2.waitKey(0)
cv2.destroyAllWindows()

pie = cv2.imread("pie.png")

cv2.imshow("pie",pie)
cv2.waitKey(0)
cv2.destroyAllWindows()

kernel2 = np.ones((30,30),np.uint8)
erosion_1 = cv2.erode(pie,kernel2,iterations=1)
erosion_2 = cv2.erode(pie,kernel2,iterations=2)
erosion_3 = cv2.erode(pie,kernel2,iterations=3)
res = np.hstack((erosion_1,erosion_2,erosion_3))

cv2.imshow("res",res)
cv2.waitKey(0)
cv2.destroyAllWindows()

2 膨胀操作

img_dg = cv2.imread("dige.png")

cv2.imshow("img",img_dg)
cv2.waitKey(0)

cv2.destroyAllWindows()

kernel = np.ones((3,3),np.uint8)

dige_erosion = cv2.erode(img_dg,kernel,iterations=1)

cv2.imshow("erosion",erosion)

cv2.waitKey(0)

cv2.destroyAllWindows()

kernel = np.ones((3,3),np.uint8)

dige_dilate = cv2.dilate(dige_erosion,kernel,iterations=1)

cv2.imshow("dilate",dige_dilate)

cv2.waitKey(0)

cv2.destroyAllWindows()

pie = cv2.imread("pie.png")

kernel3 = np.ones((30,30),np.uint8)

dilate_1 = cv2.dilate(pie,kernel3,iterations=1)

dilate_2 = cv2.dilate(pie,kernel3,iterations=2)

dilate_3 = cv2.dilate(pie,kernel3,iterations=3)

res = np.hstack((dilate_1,dilate_2,dilate_3))

cv2.imshow("res",res)

cv2.waitKey(0)

cv2.destroyAllWindows()

3 开运算与闭运算

#开:先腐蚀，再膨胀
img_k = cv2.imread("dige.png")
kernel_k = np.ones((5,5),np.uint8)
opening = cv2.morphologyEx(img_k,cv2.MORPH_OPEN,kernel_k)

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

#闭:先膨胀，再腐蚀
img_b = cv2.imread("dige.png")
kernel_b = np.ones((5,5),np.uint8)
closinging = cv2.morphologyEx(img_k,cv2.MORPH_CLOSE,kernel_k)

cv2.imshow("closinging",closinging)
cv2.waitKey(0)
cv2.destroyAllWindows()