python opencv实现图像配准与比较

本文实例为大家分享了python opencv实现图像配准与比较的具体代码，供大家参考，具体内容如下

代码

1. from skimage import io
2. import cv2 as cv
3. import numpy as np
4. import matplotlib.pyplot as plt
5.  
6. img_path1 = '2_HE_maxarea.png'
7. img_path2 = '2_IHC_maxarea.png'
8.  
9. img1 = io.imread(img_path1)
10. img2 = io.imread(img_path2)
11. img1 = np.uint8(img1)
12. img2 = np.uint8(img2)
13.  
14. # find the keypoints and descriptors with ORB
15. orb = cv.ORB_create()
16. kp1, des1 = orb.detectAndCompute(img1,None)
17. kp2, des2 = orb.detectAndCompute(img2,None)
18.  
19. # def get_good_match(des1,des2):
20. # bf = cv.BFMatcher()
21. # matches = bf.knnMatch(des1, des2, k=2)
22. # good = []
23. # for m, n in matches:
24. # if m.distance < 0.75 * n.distance:
25. # good.append(m)
26. # return good,matches
27. # goodMatch,matches = get_good_match(des1,des2)
28. # img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches[:20],None,flags=2)
29.  
30. # create BFMatcher object
31. bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
32. # Match descriptors.
33. matches = bf.match(des1,des2)
34. # Sort them in the order of their distance.
35. matches = sorted(matches, key = lambda x:x.distance)
36. # Draw first 20 matches.
37. img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:20],None, flags=2)
38.  
39. goodMatch = matches[:20]
40. if len(goodMatch) > 4:
41. ptsA= np.float32([kp1[m.queryIdx].pt for m in goodMatch]).reshape(-1, 1, 2)
42. ptsB = np.float32([kp2[m.trainIdx].pt for m in goodMatch]).reshape(-1, 1, 2)
43. ransacReprojThreshold = 4
44. H, status =cv.findHomography(ptsA,ptsB,cv.RANSAC,ransacReprojThreshold);
45. #其中H为求得的单应性矩阵矩阵
46. #status则返回一个列表来表征匹配成功的特征点。
47. #ptsA,ptsB为关键点
48. #cv2.RANSAC, ransacReprojThreshold这两个参数与RANSAC有关
49. imgOut = cv.warpPerspective(img2, H, (img1.shape[1],img1.shape[0]),flags=cv.INTER_LINEAR + cv.WARP_INVERSE_MAP)
50.  
51. # 叠加配准变换图与基准图
52. rate = 0.5
53. overlapping = cv.addWeighted(img1, rate, imgOut, 1-rate, 0)
54. io.imsave('HE_2_IHC.png', overlapping)
55. err = cv.absdiff(img1,imgOut)
56.  
57. # 显示对比
58. plt.subplot(221)
59. plt.title('orb')
60. plt.imshow(img3)
61.  
62. plt.subplot(222)
63. plt.title('imgOut')
64. plt.imshow(imgOut)
65.  
66. plt.subplot(223)
67. plt.title('overlapping')
68. plt.imshow(overlapping)
69.  
70. plt.subplot(224)
71. plt.title('diff')
72. plt.imshow(err)
73.  
74. plt.show()

结果：

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持我们。

原文链接：https://blog.csdn.net/Ericohe/article/details/113755837