本文實例為大家分享了python opencv實現圖像配準與比較的具體代碼，供大家參考，具體內容如下

代碼

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為求得的單應性矩陣矩陣 #status則返回一個列表來表征匹配成功的特征點。 #ptsA,ptsB為關鍵點 #cv2.RANSAC, ransacReprojThreshold這兩個參數與RANSAC有關 imgOut = cv.warpPerspective(img2, H, (img1.shape[1],img1.shape[0]),flags=cv.INTER_LINEAR + cv.WARP_INVERSE_MAP) # 疊加配準變換圖與基準圖rate = 0.5overlapping = cv.addWeighted(img1, rate, imgOut, 1-rate, 0)io.imsave(’HE_2_IHC.png’, overlapping)err = cv.absdiff(img1,imgOut) # 顯示對比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()

結果：

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支持好吧啦網。