使用Sift在许多图像中查找重复项

Question

我有一个源图像和数千个上下文图像。我想知道源图像是任何目的地图像的裁剪，模糊还是旋转版本。我正在使用基于SIFT的图像匹配算法来识别重复的图像。它拍摄2张图像，并说出1张其他图像的裁剪，模糊或旋转版本是：

def get_sift_results(img1,img2):
  MIN_MATCH_COUNT = 1000

  img1 = cv2.imread(img1, 0)          # queryImage
  img2 = cv2.imread(img2, 0) # trainImage

  # Initiate SIFT detector
  sift = cv2.xfeatures2d.SIFT_create()

  # find the keypoints and descriptors with SIFT
  kp1, des1 = sift.detectAndCompute(img1,None)
  kp2, des2 = sift.detectAndCompute(img2,None)



  FLANN_INDEX_KDTREE = 0
  index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
  search_params = dict(checks = 50)

  flann = cv2.FlannBasedMatcher(index_params, search_params)

  matches = flann.knnMatch(des1, des2, k=2)

  # store all the good matches as per Lowe's ratio test.
  good = []
  for m,n in matches:
      if m.distance < 0.7*n.distance:
          good.append(m)


  if len(good)>MIN_MATCH_COUNT:
      src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
      dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

      M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
      matchesMask = mask.ravel().tolist()

      h,w, = img1.shape
      pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
      dst = cv2.perspectiveTransform(pts,M)

      img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)
      print("Duplictae Images")
      return True
  else:
      print ("Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT))
      matchesMask = None
      return False

该解决方案效果很好，并以每对图像的5-6秒为单位提供结果。如何优化它以从数千幅图像中找到重复项？我尝试为每个图像存储描述符，并将其传递给Flann，但这也需要大量时间和空间。任何帮助将不胜感激。谢谢！