Created new homography branch without outlier changes

                                i.distance))

        return pd.DataFrame(matched, columns=['matched_to', 'queryIdx',

                                              'trainIdx', 'distance'])

#don't throw anything out, just have dataframes and masks

#TODO: decide on a consistent mask format to output. Do we want to also accept existing masks and just mask more things?

#consider passing in the matches and source_node to __init__

class MatchOutlierDetector(object):

    """

    Documentation

    """

    def __init__(self, ratio=0.8):

        #0.8 is Lowe's paper value -- can be changed.

        self.distance_ratio = ratio

    # return mask with self-neighbors set to zero. (query only takes care of literal self-matches on a keypoint basis, not self-matches for the whole image)

    #TODO: turn this into a mask-style thing. just returns a mask of bad values

    def find_self_neighbors(self, source_node, matches):

        """

        Returns a df containing self-neighbors that must be removed.

        (temporary return val?)

        Parameters

        ----------

        matches : dataframe

                  The pandas dataframe output by FlannMatcher.query()

                  containing matched points with columns containing:

                  matched image name, query index, train index, and

                  descriptor distance

        source_node: a string used as the key of the matched node

        Returns

        -------

        """

        mask = []

        self_matches = matches.loc[matches['matched_to'] == source_node]

        return mask

    def distance_ratio_test(self, matches):

        """

        Compute and return a mask for the matches dataframe returned by FlannMatcher.query()

        using the ratio test and distance_ratio set during initialization.

        Parameters

        ----------

        matches : dataframe

                  The pandas dataframe output by FlannMatcher.query()

                  containing matched points with columns containing:

                  matched image name, query index, train index, and

                  descriptor distance

        Returns

        -------

        mask : list

               a list of the same size as the matches dataframe

               with value = [1] if that entry in the df should be included

               and [0] if that entry in the df should be excluded

        """

        mask = []

        for key, group in matches.groupBy('queryIdx'):

            #won't work if there's only 1 match for each queryIdx

            if len(group) < 2:

                pass

            else:

                if group['distance'].iloc[0] < self.distance_ratio * group['distance'].iloc[1]:

                    mask.append([1])

                else:

                    mask.append([0])

        return mask