title: OpenCV feature matching for multiple images
body: <p>How can I optimise the SIFT feature matching for many pictures using FLANN?</p>
  
  <p>I have a working example taken from the Python OpenCV docs. However this is comparing one image with another and it's slow. I need it to search for features matching in a series of images (a few thousands) and I need it to be faster.</p>
  
  <p>My current idea:</p>
  
  <ol>
  <li>Run through all the images and save the features. How?</li>
  <li>Compare an image from a camera with this above base, and find the correct one. How?</li>
  <li>Give me the result, matching image or something.</li>
  </ol>
  
  <p><a href="http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html" rel="noreferrer">http://docs.opencv.org/trunk/doc/py_tutorials/py_feature2d/py_feature_homography/py_feature_homography.html</a></p>
  
  <pre>
  import sys # For debugging only
  import numpy as np
  import cv2
  from matplotlib import pyplot as plt
  
  MIN_MATCH_COUNT = 10
  
  img1 = cv2.imread('image.jpg',0) # queryImage
  img2 = cv2.imread('target.jpg',0) # trainImage
  
  # Initiate SIFT detector
  sift = cv2.SIFT()
  
  # 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 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)
  
  else:
      print "Not enough matches are found - %d/%d" % (len(good),MIN_MATCH_COUNT)
      matchesMask = None
  
  draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                     singlePointColor = None,
                     matchesMask = matchesMask, # draw only inliers
                     flags = 2)
  
  img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
  
  plt.imshow(img3, 'gray'),plt.show()
  </pre>
  
  <hr>
  
  <p><strong>UPDATE</strong></p>
  
  <p>After trying out many things I might have come closer to the solution now. I hope it's possible to build the index and then search in it like this:</p>
  
  <pre>
  flann_params = dict(algorithm=1, trees=4)
  flann = cv2.flann_Index(npArray, flann_params)
  idx, dist = flann.knnSearch(queryDes, 1, params={})
  </pre>
  
  <p>However I still haven't managed to build an accepted npArray to the flann_Index parameter.</p>
  
  <pre>
  loop through all images as image:
    npArray.append(sift.detectAndCompute(image, None))
  npArray = np.array(npArray)
  </pre>