图像形态学处理

腐蚀操作

import cv2 as cv
import numpy as np


img = cv.imread('dige.png')
kernel = np.ones((5, 5), np.uint8)
erosion = cv.erode(img, kernel, iterations = 1)

res = np.hstack((img, erosion))
cv.imshow('res', res)
cv.waitKey(0)
cv.destroyAllWindows()

 右边是处理后的结果，白色线条没有了，字体也更加苗条了。

 erosion = cv.erode(img, kernel, iterations = 1) ：腐蚀函数。 iterations = 1 表示迭代次数为1，迭代次数越高，腐蚀程度越厉害。

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再来看一个圆形腐蚀操作例子。

import cv2 as cv
import numpy as np

img = cv.imread('pie.png')
kernel = np.ones((30, 30), np.uint8)
erosion_1 = cv.erode(img, kernel, iterations = 1)
erosion_2 = cv.erode(img, kernel, iterations = 2)
erosion_3 = cv.erode(img, kernel, iterations = 3)

res = np.hstack((erosion_1, erosion_2, erosion_3))
cv.imshow('res', res)
cv.waitKey(0)
cv.destroyAllWindows()

膨胀操作

import cv2 as cv
import numpy as np


img = cv.imread('dige.png')
kernel = np.ones((3, 3), np.uint8)
erosion = cv.erode(img, kernel, iterations = 1) # 先腐蚀

dilate = cv.dilate(erosion, kernel, iterations = 1) # 再膨胀

res = np.hstack((img, erosion, dilate))
cv.imshow('res', res)
cv.waitKey(0)
cv.destroyAllWindows()

 从左到右，依次是：原图，腐蚀，膨胀。 dilate = cv.dilate(erosion, kernel, iterations = 1) 是膨胀函数。

由于腐蚀让原图线条变细了，膨胀又可以让线条还原。

开，闭运算

import cv2 as cv
import numpy as np


img = cv.imread('dige.png')
kernel = np.ones((5, 5), np.uint8)
opening = cv.morphologyEx(img, cv.MORPH_OPEN, kernel) # 开运算
closing = cv.morphologyEx(img, cv.MORPH_CLOSE, kernel) # 闭运算

res = np.hstack((img, opening, closing))
cv.imshow('res', res)
cv.waitKey(0)
cv.destroyAllWindows()

 从左到右依次是：原图，开运算，闭运算。

 opening = cv.morphologyEx(img, cv.MORPH_OPEN, kernel) ：开运算，先腐蚀，后膨胀

 closing = cv.morphologyEx(img, cv.MORPH_CLOSE, kernel) ：闭运算，先膨胀，后腐蚀

梯度运算

import cv2 as cv
import numpy as np


img = cv.imread('pie.png')
kernel = np.ones((3, 3), np.uint8)
gradient = cv.morphologyEx(img, cv.MORPH_GRADIENT, kernel)

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

 梯度运算简单来说，就是将一张图片的膨胀之后，再减去原来图片的腐蚀的结果，得到的就是上图。

礼帽，黑帽

import cv2 as cv
import numpy as np


img = cv.imread('dige.png')
kernel = np.ones((3, 3), np.uint8)
tophat = cv.morphologyEx(img, cv.MORPH_TOPHAT, kernel) # 礼帽
blackhat = cv.morphologyEx(img, cv.MORPH_BLACKHAT, kernel) # 黑帽

res = np.hstack((img, tophat, blackhat))
cv.imshow('res', res)
cv.waitKey(0)
cv.destroyAllWindows()

  tophat = cv.morphologyEx(img, cv.MORPH_TOPHAT, kernel) # 礼帽 ：礼帽=原始输入-开运算

 blackhat = cv.morphologyEx(img, cv.MORPH_BLACKHAT, kernel) # 黑帽 ：黑帽=闭运算-原始输入