如何從手繪電路的掃描圖像中檢測邏輯門？

我們從邏輯電路的正確繪製圖的掃描圖像開始，我們能夠將邏輯門與電路的掃描圖像分開，但是我們無法檢測到以及如何進一步操作，因此我們使用了python open cv，我們對上述代碼是如何從手繪電路的掃描圖像中檢測邏輯門？

import cv2 
import numpy as np 
from matplotlib import pyplot as plt 
img = cv2.imread('logic.png',0) 

ret,img2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV) # converting the image into binary image. 
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(100,3)) # kernel to detect vertical lines 
vertical = cv2.morphologyEx(img2, cv2.MORPH_OPEN, kernel) # applying morphological opening operation to detect vertical lines 
vertical = cv2.dilate(vertical,kernel,iterations = 1) #dilate the vertical lines obtained 

kernel2 = cv2.getStructuringElement(cv2.MORPH_RECT,(3,100)) # kernel to detect horizontal lines 
horizontal = cv2.morphologyEx(img2, cv2.MORPH_OPEN, kernel2) # applying morphological opening operation to detect horizontal lines 
horizontal = cv2.dilate(horizontal,kernel2,iterations = 1) #dilate the horizontal lines obtained 

cv2.imshow('d',vertical) # show the vertical imag 
cv2.imshow('b',horizontal) # show the horizontal image 

img = img2 -horizontal - vertical # subtracting horizontal and vertical lines from original image 

cv2.imwrite('horizontal.png',horizontal) 
cv2.imwrite('vertical.png',vertical) 
cv2.imwrite('result.png',img) 


cv2.imshow('last',img)  # show the resulted image after subtraction 

kerne = np.ones((3,3),np.uint8)    # kernel to remove the noise from the last image 
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kerne) # applying opening morphological operation to remove the noise from the image 
cv2.imshow('opening',opening)    # show the resulted image after removing noise 
cv2.imwrite('noise_removal.png',opening) 
cv2.waitKey(0)

檢查下面的結果和建議如何進一步從手繪電路的掃描圖像檢測邏輯門進行？

代碼的結果是如下：

1）輸入圖像：