minor updates to outlier_detector

                  the matches dataframe stored along the edge of the graph

                  containing matched points with columns containing:

                  matched image name, query index, train index, and

                  descriptor distance

                  descriptor distance. ***Will only work as expected if matches already has dropped duplicates***

        ratio: float

               the ratio between the first and second-best match distances

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

        mask = []

        temp_matches = matches.drop_duplicates() #don't want to deal with duplicates...

        for key, group in temp_matches.groupby('source_idx'): #change to searchId?

        for key, group in temp_matches.groupby('source_idx'):

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

            if len(group) < 2:

                mask.append(True)

        self.outliers = matcher.OutlierDetector()

    def test_distance_ratio(self):

        #TODO: write real test

        self.assertTrue(len(self.outliers.distance_ratio(self.matches)), 13)

    def test_self_neighbors(self):