text: import numpy as np
  import cv2
  #from common import anorm
  from functools import partial
  
  help_message = '''SURF image match 
  
  USAGE: findobj.py [ <image1> <image2> ]
  '''
  
  FLANN_INDEX_KDTREE = 1  # bug: flann enums are missing
  
  flann_params = dict(algorithm = FLANN_INDEX_KDTREE,
                      trees = 4)
  
  
  def anorm2(a):
      return (a*a).sum(-1)
  def anorm(a):
      return np.sqrt( anorm2(a) )
  
  def match_bruteforce(desc1, desc2, r_threshold = 0.75):
      res = []
      for i in xrange(len(desc1)):
          dist = anorm( desc2 - desc1[i] )
          n1, n2 = dist.argsort()[:2]
          r = dist[n1] / dist[n2]
          if r < r_threshold:
              res.append((i, n1))
      return np.array(res)
  
  def match_flann(desc1, desc2, r_threshold = 0.6):
      flann = cv2.flann_Index(desc2, flann_params)
      idx2, dist = flann.knnSearch(desc1, 2, params = {}) # bug: need to provide empty dict
      mask = dist[:,0] / dist[:,1] < r_threshold
      idx1 = np.arange(len(desc1))
      pairs = np.int32( zip(idx1, idx2[:,0]) )
      return pairs[mask]
  
  def draw_match(img1, img2, p1, p2, status = None, H = None):
      h1, w1 = img1.shape[:2]
      h2, w2 = img2.shape[:2]
      vis = np.zeros((max(h1, h2), w1+w2), np.uint8)
      vis[:h1, :w1] = img1
      vis[:h2, w1:w1+w2] = img2
      vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR)
  
      if H is not None:
          corners = np.float32([[0, 0], [w1, 0], [w1, h1], [0, h1]])
          corners = np.int32( cv2.perspectiveTransform(corners.reshape(1, -1, 2), H).reshape(-1, 2) + (w1, 0) )
          cv2.polylines(vis, [corners], True, (255, 255, 255))
      
      if status is None:
          status = np.ones(len(p1), np.bool_)
      green = (0, 255, 0)
      red = (0, 0, 255)
      for (x1, y1), (x2, y2), inlier in zip(np.int32(p1), np.int32(p2), status):
          col = [red, green][inlier]
          if inlier:
              cv2.line(vis, (x1, y1), (x2+w1, y2), col)
              cv2.circle(vis, (x1, y1), 2, col, -1)
              cv2.circle(vis, (x2+w1, y2), 2, col, -1)
          else:
              r = 2
              thickness = 3
              cv2.line(vis, (x1-r, y1-r), (x1+r, y1+r), col, thickness)
              cv2.line(vis, (x1-r, y1+r), (x1+r, y1-r), col, thickness)
              cv2.line(vis, (x2+w1-r, y2-r), (x2+w1+r, y2+r), col, thickness)
              cv2.line(vis, (x2+w1-r, y2+r), (x2+w1+r, y2-r), col, thickness)
      return vis
  
  
  if __name__ == '__main__':
      import sys
      try: fn1, fn2 = sys.argv[1:3]
      except:
          fn1 = '../c/box.png'
          fn2 = '../c/box_in_scene.png'
      print help_message
  
      img1 = cv2.imread(fn1, 0)
      img2 = cv2.imread(fn2, 0)
  
      surf = cv2.SURF(400)
      kp1, desc1 = surf.detect(img1, None, False)
      kp2, desc2 = surf.detect(img2, None, False)
      desc1.shape = (-1, surf.descriptorSize())
      desc2.shape = (-1, surf.descriptorSize())
      print 'img1 - %d features, img2 - %d features' % (len(kp1), len(kp2))
  
      def match_and_draw(match, r_threshold):
          m = match(desc1, desc2, r_threshold)
          matched_p1 = np.array([kp1[i].pt for i, j in m])
          matched_p2 = np.array([kp2[j].pt for i, j in m])
          H, status = cv2.findHomography(matched_p1, matched_p2, cv2.RANSAC, 5.0)
          print '%d / %d  inliers/matched' % (np.sum(status), len(status))
  
          vis = draw_match(img1, img2, matched_p1, matched_p2, status, H)
          return vis
  
      print 'bruteforce match:',
      vis_brute = match_and_draw( match_bruteforce, 0.75 )
      print 'flann match:',
      vis_flann = match_and_draw( match_flann, 0.6 ) # flann tends to find more distant second
                                                     # neighbours, so r_threshold is decreased
      cv2.imshow('find_obj SURF', vis_brute)
      cv2.imshow('find_obj SURF flann', vis_flann)
      cv2.waitKey()