python opencv实现图像配准与比较

╰+哭是因爲堅強的太久メ 2022-10-29 07:51 242阅读 0赞

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

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from skimage import io  
import cv2 as cv  
import numpy as np  
import matplotlib.pyplot as plt  
  
img\_path1 = '2\_HE\_maxarea.png'  
img\_path2 = '2\_IHC\_maxarea.png'  
  
img1 = io.imread(img\_path1)  
img2 = io.imread(img\_path2)  
img1 = np.uint8(img1)  
img2 = np.uint8(img2)  
  
\# find the keypoints and descriptors with ORB  
orb = cv.ORB\_create()  
kp1, des1 = orb.detectAndCompute(img1,None)  
kp2, des2 = orb.detectAndCompute(img2,None)  
  
\# def get\_good\_match(des1,des2):  
\# bf = cv.BFMatcher()  
\# matches = bf.knnMatch(des1, des2, k=2)  
\# good = \[\]  
\# for m, n in matches:  
\# if m.distance < 0.75 \* n.distance:  
\# good.append(m)  
\# return good,matches  
\# goodMatch,matches = get\_good\_match(des1,des2)  
\# img3 = cv.drawMatchesKnn(img1,kp1,img2,kp2,matches\[:20\],None,flags=2)  
  
\# create BFMatcher object  
bf = cv.BFMatcher(cv.NORM\_HAMMING, crossCheck=True)  
\# Match descriptors.  
matches = bf.match(des1,des2)  
\# Sort them in the order of their distance.  
matches = sorted(matches, key = lambda x:x.distance)  
\# Draw first 20 matches.  
img3 = cv.drawMatches(img1,kp1,img2,kp2,matches\[:20\],None, flags=2)  
  
  
goodMatch = matches\[:20\]  
if len(goodMatch) > 4:  
ptsA= np.float32(\[kp1\[m.queryIdx\].pt for m in goodMatch\]).reshape(-1, 1, 2)  
ptsB = np.float32(\[kp2\[m.trainIdx\].pt for m in goodMatch\]).reshape(-1, 1, 2)  
ransacReprojThreshold = 4  
H, status =cv.findHomography(ptsA,ptsB,cv.RANSAC,ransacReprojThreshold);  
\#其中H为求得的单应性矩阵矩阵  
\#status则返回一个列表来表征匹配成功的特征点。  
\#ptsA,ptsB为关键点  
\#cv2.RANSAC, ransacReprojThreshold这两个参数与RANSAC有关  
imgOut = cv.warpPerspective(img2, H, (img1.shape\[1\],img1.shape\[0\]),flags=cv.INTER\_LINEAR + cv.WARP\_INVERSE\_MAP)  
  
\# 叠加配准变换图与基准图  
rate = 0.5  
overlapping = cv.addWeighted(img1, rate, imgOut, 1-rate, 0)  
io.imsave('HE\_2\_IHC.png', overlapping)  
err = cv.absdiff(img1,imgOut)  
  
\# 显示对比  
plt.subplot(221)  
plt.title('orb')  
plt.imshow(img3)  
  
plt.subplot(222)  
plt.title('imgOut')  
plt.imshow(imgOut)  
  
plt.subplot(223)  
plt.title('overlapping')  
plt.imshow(overlapping)  
  
plt.subplot(224)  
plt.title('diff')  
plt.imshow(err)  
  
plt.show()  
结果：

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