Opencv2.4 Python surf matching

opencv_haystack =cv2.imread('woman2.bmp')opencv_needle =cv2.imread('face.bmp')ngrey = cv2.cvtColor(opencv_needle, cv2.COLOR_BGR2GRAY)hgrey = cv2.cvtColor(opencv_haystack, cv2.COLOR_BGR2GRAY)# build feature detector and descriptor extractorhessian_threshold = 85detector = cv2.SURF(hessian_threshold)(hkeypoints, hdescriptors) = detector.detect(hgrey, None, useProvidedKeypoints = False)(nkeypoints, ndescriptors) = detector.detect(ngrey, None, useProvidedKeypoints = False)# extract vectors of size 64 from raw descriptors numpy arraysrowsize = len(hdescriptors) / len(hkeypoints)if rowsize > 1:    hrows = numpy.array(hdescriptors, dtype = numpy.float32).reshape((-1, rowsize))    nrows = numpy.array(ndescriptors, dtype = numpy.float32).reshape((-1, rowsize))    #print hrows.shape, nrows.shapeelse:    hrows = numpy.array(hdescriptors, dtype = numpy.float32)    nrows = numpy.array(ndescriptors, dtype = numpy.float32)    rowsize = len(hrows[0])# kNN training - learn mapping from hrow to hkeypoints indexsamples = hrowsresponses = numpy.arange(len(hkeypoints), dtype = numpy.float32)#print len(samples), len(responses)knn = cv2.KNearest()knn.train(samples,responses)# retrieve index and value through enumerationcount = 1for i, descriptor in enumerate(nrows):    descriptor = numpy.array(descriptor, dtype = numpy.float32).reshape((1, rowsize))    #print i, descriptor.shape, samples[0].shape    retval, results, neigh_resp, dists = knn.find_nearest(descriptor, 1)    res, dist =  int(results[0][0]), dists[0][0]    #print res, dist    if dist < 0.1:        count = count+1        # draw matched keypoints in red color        color = (0, 0, 255)#    else:#        # draw unmatched in blue color#        color = (255, 0, 0)    # draw matched key points on haystack image        x,y = hkeypoints[res].pt        center = (int(x),int(y))        cv2.circle(opencv_haystack,center,2,color,-1)        # draw matched key points on needle image        x,y = nkeypoints[i].pt        center = (int(x),int(y))        cv2.circle(opencv_needle,center,2,color,-1)cv.ShowImage("Input Image", opencv_haystack)cv.waitKey(0)cv.ShowImage("The match Result", opencv_needle)cv.waitKey(0)print countif count>40:    print "Yes Success!"else:    print "False Face!"#cv2.waitKey(0)#cv2.destroyAllWindows()

Compiling environment opencv2.4 python2.7

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