自学教程：python opencv实现图像配准与比较

from skimage import ioimport cv2 as cvimport numpy as npimport 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 ORBorb = 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 objectbf = 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 = 0.5overlapping = 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()