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I ended up implementing vl_ubcmatch in python. Short and sweet :)

def find_matches(template_descriptors, current_img_descriptors, match_thresh):

    flann_params = dict(algorithm=1, trees=4)
    flann = cv2.flann_Index(current_img_descriptors, flann_params)
    idx, dist = flann.knnSearch(template_descriptors, 2, params={})
    del flann
    matches = np.c_[np.arange(len(idx)), idx[:,0]]
    pass_filter = dist[:,0]*match_thresh < dist[:,1]
    matches = matches[pass_filter]

    return matches

I ended up implementing vl_ubcmatch in python. Short and sweet :)

def find_matches(template_descriptors, current_img_descriptors, match_thresh):

    flann_params = dict(algorithm=1, trees=4)
    flann = cv2.flann_Index(current_img_descriptors, flann_params)
    idx, dist = flann.knnSearch(template_descriptors, 2, params={})
    del flann
    matches = np.c_[np.arange(len(idx)), idx[:,0]]
    pass_filter = dist[:,0]*match_thresh < dist[:,1]
    matches = matches[pass_filter]

    return matches

I ended up implementing vl_ubcmatch in python. Short and sweet :)

def find_matches(template_descriptors, current_img_descriptors, match_thresh):

    flann_params = dict(algorithm=1, trees=4)
    flann = cv2.flann_Index(current_img_descriptors, flann_params)
    idx, dist = flann.knnSearch(template_descriptors, 2, params={})
    del flann
    matches = np.c_[np.arange(len(idx)), idx[:,0]]
    pass_filter = dist[:,0]*match_thresh < dist[:,1]
    matches = matches[pass_filter]

    return matches

If you want a c++ version, this guy wrote about it extracted the cpp code from vlfeat's mex code in his blog.

In the comments, he gives a link to his cpp file with the code (change the extension from txt to cpp).