Commit b61fe765 authored by Adam Paquette's avatar Adam Paquette
Browse files

Changed the name subpixel to subpixel_matches.

parent aba0b032

autocnet/graph/edge.py

+7 −8
Original line number Diff line number Diff line
@@ -45,7 +45,7 @@ class Edge(dict, MutableMapping): 
        self['fundamental_matrix'] = None

        self.matches = pd.DataFrame()

        self.masks = pd.DataFrame()

        self.subpixel = pd.DataFrame()

        self.subpixel_matches = pd.DataFrame()

        self['weights'] = {}

        self['source_mbr'] = None

        self['destin_mbr'] = None
@@ -299,8 +299,8 @@ class Edge(dict, MutableMapping): 
                      without being considered an outlier

        """

        for column, default in {'x_offset': 0, 'y_offset': 0, 'correlation': 0, 'reference': -1}.items():

            if column not in self.subpixel.columns:

                self.subpixel[column] = default

            if column not in self.subpixel_matches.columns:

                self.subpixel_matches[column] = default



        # Build up a composite mask from all of the user specified masks

        matches, mask = self.clean(clean_keys)
@@ -316,7 +316,6 @@ class Edge(dict, MutableMapping): 
        source_image = (matches.iloc[0]['source_image'])



        # for each edge, calculate this for each keypoint pair

        subpixel_offests = []

        for i, (idx, row) in enumerate(matches.iterrows()):

            s_idx = int(row['source_idx'])

            d_idx = int(row['destination_idx'])
@@ -329,17 +328,17 @@ class Edge(dict, MutableMapping): 
            d_search = sp.clip_roi(d_img, d_keypoint, search_size)

            try:

                x_offset, y_offset, strength = sp.subpixel_offset(s_template, d_search, **kwargs)

                self.subpixel.loc[idx, ('x_offset', 'y_offset', 'correlation', 'reference')]= [x_offset, y_offset, strength, source_image]

                self.subpixel_matches.loc[idx, ('x_offset', 'y_offset', 'correlation', 'reference')]= [x_offset, y_offset, strength, source_image]

            except:

                warnings.warn('Template-Search size mismatch, failing for this correspondence point.')



        # Compute the mask for correlations less than the threshold

        threshold_mask = self.subpixel['correlation'] >= threshold

        threshold_mask = self.subpixel_matches['correlation'] >= threshold



        # Compute the mask for the point shifts that are too large

        query_string = 'x_offset <= -{0} or x_offset >= {0} or y_offset <= -{1} or y_offset >= {1}'.format(max_x_shift,max_y_shift)

        sp_shift_outliers = self.subpixel.query(query_string)

        shift_mask = pd.Series(True, index=self.subpixel.index)

        sp_shift_outliers = self.subpixel_matches.query(query_string)

        shift_mask = pd.Series(True, index=self.subpixel_matches.index)

        shift_mask.loc[sp_shift_outliers.index] = False



        # Generate the composite mask and write the masks to the mask data structure