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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
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:
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。
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