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我们从正确绘制的逻辑电路图的扫描图像开始，我们能够将逻辑门从电路的扫描图像中分离出来，但是我们无法检测到以及如何进一步进行，我们为此使用了 python open cv，我们的代码因为上面是

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)

检查下面的结果并建议如何进一步从手绘电路的扫描图像中检测逻辑门？

代码结果如下：

1）输入图像：

2）输出图像（代码结果）：

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1 回答 1

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逻辑门都具有相同的尺寸。我会这样做：

  1. 白色区域的连接组件标签。
  2. 分开/隔离
  3. 按大小过滤标签。
  4. （可选）所有逻辑门都会接触到右侧的微小白色图案/标签。
于 2016-09-20T20:42:11.217 回答