用膨胀腐蚀方法实现区域图像的提取

た入场券 2023-01-16 12:24 77阅读 0赞

# 用膨胀腐蚀方法实现 #

## （1）如下图片的表格线提取（2）如下图片的字母A提取 ##

![0e9cd810e1b9b214f24207c516458eea.png][]

（1）图片的表格线提取

import cv2
    
    import numpy as np
    
    import matplotlib.pyplot as plt
    
    #读入图片
    
    alphabeta = cv2.imread("pic/alphabeta.png", 0)
    
    #显示图片
    
    plt.imshow(alphabeta)
    
    #表格线提取
    
    kernel = np.ones((1,100), np.uint8)
    
    close1 = cv2.erode(cv2.dilate(alphabeta, kernel, iterations = 1), kernel, iterations = 1)
    
    cv2.imshow("close", close1)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![d30cf321bfb1dca16fd912cbd93452a7.png][]

（2）图片的字母A提取

#1.通过图像阈值转换高亮标注字母
    
    ret,close2 = cv2.threshold(alphabeta, 127, 255, cv2.THRESH_BINARY_INV)
    
    cv2.imshow("close", close2)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![afa3312add402c175dc63f0ea20edeb2.png][]

#2.提取字母A大小的矩阵块作为核
    
    kernel = close2[160:280,100:210];
    
    print(type(kernel))
    
    print(kernel.shape)
    
    cv2.imshow("kernel", kernel)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![b2d690958c15873fad0b823248391690.png][]

#3.对字母表进行腐蚀操作
    
    open1 = cv2.erode(close2, kernel, iterations = 1)
    
    cv2.imshow("open", open1)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![83d83ae766f27f1438b6ac8f95907091.png][]

#4.对字母表进行开运算，先腐蚀，后膨胀
    
    open1 = cv2.dilate(cv2.erode(close2, kernel, iterations = 1), kernel, iterations = 1)
    
    cv2.imshow("open", open1)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![410b7747ba80dc3c6ca22fd349b7fe39.png][]

#5.提取字母Aa大小的矩阵块作为核
    
    kernel = close2[160:280,100:280];
    
    print(type(kernel))
    
    print(kernel.shape)
    
    cv2.imshow("kernel", kernel)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![c1066e26a6e1dc13b82583fb2d8a0473.png][]

open1 = cv2.dilate(cv2.erode(close2, kernel, iterations = 1), kernel, iterations = 1)
    
    cv2.imshow("open", open1)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![05170bfa8cb07610e69ef86b077e6b3a.png][]

#腐蚀操作
    
    close2 = cv2.erode(close2, kernel, iterations = 1)
    
    #旋转内核
    
    rows,cols = kernel.shape[:2]
    
    M = cv2.getRotationMatrix2D((cols/2,rows/2),180,1) 
    
    kernel = cv2.warpAffine(kernel,M,(cols,rows))
    
    cv2.imshow("kernel", kernel)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![885c27267ccd56536c3ab35be9e93e86.png][]

#膨胀操作
    
    open1 = cv2.dilate(close2, kernel, iterations = 1)
    
    cv2.imshow("open", open1)
    
    cv2.waitKey(0)
    
    cv2.destroyAllWindows()

![5b80049939a0e2e5e4b10c9c452cadc2.png][]

结论：进行腐蚀和膨胀的对象的像素值要大于零，对原图进行操作时得到的是全白的图片，开运算的到的目标区域会旋转180度，在进行腐蚀操作后需要对内核进行旋转180度得到原始目标区域图像

[0e9cd810e1b9b214f24207c516458eea.png]: /images/20221022/0a84069c341048a68295f1cef31c58d7.png
[d30cf321bfb1dca16fd912cbd93452a7.png]: /images/20221022/64fc399053ca4b4cab64c48f6fbedad3.png
[afa3312add402c175dc63f0ea20edeb2.png]: /images/20221022/44bdd5d3e4fd47de9aacecd200c452ee.png
[b2d690958c15873fad0b823248391690.png]: /images/20221022/e6f19d3b1a1a46b786cd9fdaf95f8578.png
[83d83ae766f27f1438b6ac8f95907091.png]: /images/20221022/d71575d443864a2cae3d40f484c18db2.png
[410b7747ba80dc3c6ca22fd349b7fe39.png]: /images/20221022/e4dcd1d29819455c9cd0f436e9ce5a55.png
[c1066e26a6e1dc13b82583fb2d8a0473.png]: /images/20221022/73c29f82fa0d4e5dbacb39ced8d135ef.png
[05170bfa8cb07610e69ef86b077e6b3a.png]: /images/20221022/004bce5af8bc4f92b4c5d867b3f4b589.png
[885c27267ccd56536c3ab35be9e93e86.png]: /images/20221022/46f707ad19814e99b80ba7a1834182fe.png
[5b80049939a0e2e5e4b10c9c452cadc2.png]: /images/20221022/6acb1518ae7c42e4a5dca6bc8df848ae.png