match_binary_descriptors.py

def match_keypoints_brief(keypoints1, descriptors1, keypoints2,
                          descriptors2, threshold=0.40):
    """Match keypoints described using BRIEF descriptors in one image to
    those in second image.

    Parameters
    ----------
    keypoints1 : (M, 2) ndarray
        M Keypoints from the first image described using skimage.feature.brief
    descriptors1 : (M, P) ndarray
        BRIEF descriptors of size P about M keypoints in the first image.
    keypoints2 : (N, 2) ndarray
        N Keypoints from the second image described using skimage.feature.brief
    descriptors2 : (N, P) ndarray
        BRIEF descriptors of size P about N keypoints in the second image.
    threshold : float in range [0, 1]
        Maximum allowable hamming distance between descriptors of two keypoints
        in separate images to be regarded as a match.

    Returns
    -------
    match_keypoints_brief : (Q, 2, 2) ndarray
        Location of Q matched keypoint pairs from two images.

    """
    if (keypoints1.shape[0] != descriptors1.shape[0]
    or keypoints2.shape[0] != descriptors2.shape[0]):
        raise ValueError("The number of keypoints and number of described "
                         "keypoints do not match.")

    if descriptors1.shape[1] != descriptors2.shape[1]:
        raise ValueError("Descriptor sizes for matching keypoints in both "
                         "the images should be equal.")

    # Get hamming distances between keypoints1 and keypoints2
    distance = pairwise_hamming_distance(descriptors1, descriptors2)

    matched_keypoints1_index = np.argmin(distance, axis=0)
    matched_keypoints2_index = np.argmin(distance, axis=1)
    matched_index = []
    for i in range(len(matched_keypoints1_index)):
        if (matched_keypoints2_index[matched_keypoints1_index[i]] == i and
            distance[i, matched_keypoints1_index[i]] < threshold):
            matched_index.append([i, matched_keypoints1_index[i]])

    matched_index = np.asarray(matched_index)

    matched_keypoint_pairs = np.zeros((matched_index.shape[0], 2, 2), dtype=np.intp)
    matched_keypoint_pairs[:, 0, :] = keypoints1[matched_index[:, 0]]
    matched_keypoint_pairs[:, 1, :] = keypoints2[matched_index[:, 1]]
    return matched_keypoint_pairs