Python+OpenCV之形態(tài)學操作詳解

更新時間：2022年09月28日 17:00:07 作者：錦鯉AI幸運

這篇文章主要為大家詳細介紹了Python?OpenCV中的形態(tài)學操作(開運算、閉運算)的實現(xiàn)，文中的示例代碼講解詳細，感興趣的小伙伴可以了解一下

一、腐蝕與膨脹

1.1 腐蝕操作

import cv2
import numpy as np

img = cv2.imread('DataPreprocessing/img/dige.png')

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

dige.png原圖1展示(注: 沒有原圖的可以截圖下來保存本地。):

腐蝕1輪次之后~ (iterations = 1)

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

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

腐蝕結果展示圖2：

腐蝕圓多次的效果，以及腐蝕原理

pie = cv2.imread('DataPreprocessing/img/pie.png')

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

pie.png原圖3:

腐蝕原理, 其中濾波器的大小越大腐蝕的程度越大圖4:

kernel = np.ones((30, 30), np.uint8)
erosion_1 = cv2.erode(pie, kernel, iterations=1)
erosion_2 = cv2.erode(pie, kernel, iterations=2)
erosion_3 = cv2.erode(pie, kernel, iterations=3)
res = np.hstack((erosion_1, erosion_2, erosion_3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

圓腐蝕三次結果展示圖5:

1.2 膨脹操作

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

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

膨脹之前圖2,發(fā)現(xiàn)線條變粗，跟原圖對比的線條相差無幾，但是沒了那些長須裝的噪音，圖6:

膨脹圓多次的效果，以及膨脹原理與腐蝕相反，有白色點的濾波器則濾波器內數(shù)據(jù)全變?yōu)榘咨?/p>

pie = cv2.imread('DataPreprocessing/img/pie.png')

kernel = np.ones((30, 30), np.uint8)
dilate_1 = cv2.dilate(pie, kernel, iterations=1)
dilate_2 = cv2.dilate(pie, kernel, iterations=2)
dilate_3 = cv2.dilate(pie, kernel, iterations=3)
res = np.hstack((dilate_1, dilate_2, dilate_3))
cv2.imshow('res', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

膨脹圓3次的結果展示,圖7:

二、開運算與閉運算

2.1 開運算

# 開：先腐蝕，再膨脹
img = cv2.imread('DataPreprocessing/img/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()

將原圖1，先腐蝕，再膨脹，得到開運算結果圖8:

2.2 閉運算

# 閉：先膨脹，再腐蝕
img = cv2.imread('DataPreprocessing/img/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()

將原圖1，先膨脹，再腐蝕，得到開運算結果圖9:

三、梯度運算

拿原圖3的圓，做5次膨脹，5次腐蝕，相減得到其輪廓。

# 梯度=膨脹-腐蝕
pie = cv2.imread('DataPreprocessing/img/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)
cv2.waitKey(0)
cv2.destroyAllWindows()

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

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

得到梯度運算結果圖10:

四、禮帽與黑帽

4.1 禮帽

禮帽 = 原始輸入-開運算結果

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

得到禮帽結果圖11:

4.2 黑帽

黑帽 = 閉運算-原始輸入

# 黑帽
img = cv2.imread('DataPreprocessing/img/dige.png')
blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)
cv2.imshow('blackhat ', blackhat)
cv2.waitKey(0)
cv2.destroyAllWindows()

得到禮帽結果圖12: