图像分析之图像形态学处理

1.腐蚀操作

2.膨胀操作

3.开运算与闭运算

4.梯度运算（绘制图像的边框）

5.礼帽与黑帽

1.腐蚀操作

import cv2 #opencv读取的格式是BGR
import numpy as np
import matplotlib.pyplot as plt#Matplotlib是RGB

img = cv2.imread('dige.png')
cv2.imshow('img', img)

# 腐蚀操作
#kernel 腐蚀范围, iterations 迭代次数

kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(img, kernel, iterations = 1)

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

2.膨胀操作

img = cv2.imread('dige.png')
cv2.imshow('img', img)
# 膨胀操作
#kernel 腐蚀范围, iterations 迭代次数

kernel = np.ones((3,3),np.uint8)
dige_dilate = cv2.dilate(img,kernel,iterations = 1)

cv2.imshow('dilate', dige_dilate)
cv2.waitKey(0)
cv2.destroyAllWindows()

3.开运算与闭运算

# 开：先腐蚀，再膨胀
img = cv2.imread('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：先膨胀，再腐蚀
img = cv2.imread('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()

4.梯度运算（绘制图像的边框）

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

# 梯度运算
gradient = cv2.morphologyEx(pie, cv2.MORPH_GRADIENT, kernel)
cv2.imshow('gradient', gradient)

cv2.waitKey(0)
cv2.destroyAllWindows()

5.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

img = cv2.imread('dige.png')
kernel = np.ones((7, 7), np.uint8)

#礼帽
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
cv2.imshow('tophat', tophat)

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