Python 如何使用opencv orb进行图像对齐？

我有两个图像一个是输入图像，另一个是模板图像。对于图像对齐，我使用ORB和单应矩阵。但输出图像并没有以完美的方式显示。 我的代码是

 def get(img1,img2):
   # Convert to grayscale. 
   img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) 
   img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) 
   height, width = img2.shape 

   # Create ORB detector with 5000 features. 
   orb_detector = cv2.ORB_create(1000) 
 
   # Find keypoints and descriptors. 
   # The first arg is the image, second arg is the mask 
   # (which is not reqiured in this case). 
   kp1, d1 = orb_detector.detectAndCompute(img1, None) 
   kp2, d2 = orb_detector.detectAndCompute(img2, None) 

   # Match features between the two images. 
  # We create a Brute Force matcher with 
   # Hamming distance as measurement mode. 
   matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck = True) 

   # Match the two sets of descriptors. 
   matches = matcher.match(d1, d2) 

   # Sort matches on the basis of their Hamming distance. 
   matches.sort(key = lambda x: x.distance) 
   #     print(len(matches))
   # Take the top 90 % matches forward. 
   matches = matches[:int(len(matches)*90)] 
   no_of_matches = len(matches) 


   # Define empty matrices of shape no_of_matches * 2. 
   p1 = np.zeros((no_of_matches, 2)) 
   p2 = np.zeros((no_of_matches, 2)) 

   for i in range(len(matches)): 
       p1[i, :] = kp1[matches[i].queryIdx].pt 
       p2[i, :] = kp2[matches[i].trainIdx].pt 

   # Find the homography matrix. 
   homography, mask = cv2.findHomography(p1, p2, cv2.RANSAC) 

   # Use this matrix to transform the 
   # colored image wrt the reference image. 
   transformed_img = cv2.warpPerspective(img1, 
                                        homography, (width, height)) 

   # Save the output.
   #     cv2.imwrite(r"output.jpg", transformed_img)
   return transformed_img

if __name__ == "__main__":

   img1_color = cv2.imread(r"1.jpg") # Image to be aligned. 
   img2_color = cv2.imread(r"reference.jpg") # Reference image.
   image_transform=get(img1_color, img2_color)

   plt.figure(figsize=(15,10))
   plt.imshow(image_transform)

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当我使用相同的文档时，这意味着一个模板（参考图像）和另一个是输入图像（任何倾斜图像）。该代码工作得更好。但当我拍摄不同的图像时，它与文档不同。这意味着在这类图像中有一些不同的东西。并运行代码，结果并不理想。我的问题是，对于不同的图像，我应该怎么做才能使我的图像与模板图像完全对齐？

matches=matches[：int（len（matches）*90）]

毫无意义。你是说

matches=matches[：90]

？还有，为什么这些图像会完全对齐？这不是同一页的图片好的，我看到了，如果同一个图像出现任何错误，我会通知您如果模板和输入图像不相同，这意味着以前的图像在这种情况下应该怎么做。如果我使用空白模板（目标图像），请告诉我是否有与此相关的帖子。对于这种情况，我将如何调整代码中的图像？