├── detaction.py
└── detaction.rar


/detaction.py:
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 1 | # -*- coding: utf-8 -*-
 2 | import cv2
 3 | import numpy as np
 4 | 
 5 | img = cv2.imread("E://eraser.jpg",1)
 6 | 
 7 | kernel0 = np.ones((2,1),np.uint8)
 8 | kernel = np.ones((1,4),np.uint8)
 9 | 
10 | img1 = cv2.GaussianBlur(img,(5,5),0)
11 | 
12 | opening_img = cv2.morphologyEx(img1, cv2.MORPH_OPEN, kernel)#待处理边缘
13 | 
14 | #cv2.imshow("img",img1)
15 | canny_img = cv2.Canny(opening_img,50,150)
16 | 
17 | lines=cv2.HoughLinesP(canny_img, 1, np.pi/180,15,0,0)
18 | #for i in range(len(lines)-1):
19 |  #   for x1,y1,x2,y2 in lines[i]:
20 |   #      cv2.line(img, (x1,y1), (x2,y2), (0,0,255), 2)
21 | 
22 | #缺陷检测
23 | ret, thresh = cv2.threshold(canny_img, 127, 255,0)
24 | contours,hierarchy = cv2.findContours(thresh,2,1)  
25 | cnt = contours[0]  
26 | hull = cv2.convexHull(cnt,returnPoints = False) 
27 | defects = cv2.convexityDefects(cnt,hull)
28 | for i in range(defects.shape[0]-1): 
29 |     
30 |     s,e,f,d = defects[i,0] 
31 |     start = tuple(cnt[s][0]) 
32 |     end = tuple(cnt[e][0]) 
33 |     x1,y1 = cnt[s][0]
34 |     x2,y2 = cnt[e][0]
35 |     x3,y3 = cnt[s-1][0]
36 |     x4,y4 = cnt[e-1][0]
37 |     
38 |     if (abs(x1-x3) <= 20 or abs(x2 - x4) <= 20):
39 |         s = s + 1
40 |         e = e + 1
41 |     cv2.rectangle(img,((int)(x1+20),(int)(y1+20)),((int)(x2-20),(int)(y2-20)),(0,255,0),2)
42 |     
43 |     img_crop = img [(int)(y2-20) : (int)(y1+20),(int)(x2-20):(int)(x1+20)]#缺陷处放大    
44 |     img_bigger = cv2.resize(img_crop, (0, 0), fx=3, fy=3, 
45 |                               interpolation=cv2.INTER_NEAREST)
46 | 
47 | 
48 | cv2.imshow("img_output",img)
49 | #cv2.imshow('crop_img.jpg', img_crop)
50 | cv2.imshow("img_bigger",img_bigger)
51 | 
52 | cv2.waitKey()
53 | cv2.destroyAllWindows()
54 | 


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/detaction.rar:
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https://raw.githubusercontent.com/HANLIN-zeng/defect_detection/6f2e2a04235a0a1210e5bcab5f066d6a809023be/detaction.rar


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