Commit 2adac495 authored by Jason R Laura's avatar Jason R Laura
Browse files

Fixes suppression

parent e13d668a

autocnet/matcher/outlier_detector.py

+9 −9
Changes for autocnet/matcher/outlier_detector.py: 9 added lines, 9 removed lines.
Original line number Diff line number Diff line
@@ -166,7 +166,6 @@ class SpatialSuppression(Observable): 
    def nvalid(self):

        return self.mask.sum()





    @property

    def error_k(self):

        return self._error_k
@@ -186,14 +185,21 @@ class SpatialSuppression(Observable): 
            self.k = len(self.df)

            result = self.df.index

            process = False

        search_space = np.linspace(self.min_radius, self.max_radius, 100)

        search_space = np.linspace(self.min_radius, self.max_radius, 1000)

        cell_sizes = search_space / math.sqrt(2)

        min_idx = 0

        max_idx = len(search_space) - 1



        prev_min = None

        prev_max = None



        while process:

            mid_idx = int((min_idx + max_idx) / 2)



            if min_idx == mid_idx or mid_idx == max_idx:

                warnings.warn('Unable to optimally solve.  Returning with {} points'.format(len(result)))

                process = False



            cell_size = cell_sizes[mid_idx]

            n_x_cells = int(self.domain[0] / cell_size)

            n_y_cells = int(self.domain[1] / cell_size)
@@ -245,11 +251,10 @@ class SpatialSuppression(Observable): 
                    grid[y_min: y_max,

                         x_min: x_max] = True





            #  Check break conditions

            if self.k - self.k * self.error_k <= len(result) <= self.k + self.k * self.error_k:

                process = False

            elif len(result) < self.k:

            elif len(result) < self.k - self.k * self.error_k:

                # The radius is too large

                max_idx = mid_idx

                if max_idx == 0:
@@ -258,10 +263,6 @@ class SpatialSuppression(Observable): 
                    process = False

                if min_idx == max_idx:

                    process = False

            elif min_idx == mid_idx or mid_idx == max_idx:

                warnings.warn('Unable to optimally solve.  Returning with {} points'.format(len(result)))

                process = False



        self.mask = pd.Series(False, self.df.index)

        self.mask.loc[list(result)] = True

        state_package = {'mask': self.mask,
@@ -319,4 +320,3 @@ def mirroring_test(matches): 
    """

    duplicate_mask = matches.duplicated(subset=['source_idx', 'destination_idx', 'distance'], keep='last')

    return duplicate_mask

autocnet/matcher/suppression_funcs.py

+1 −1
Changes for autocnet/matcher/suppression_funcs.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -29,6 +29,6 @@ def error(row, edge): 
    """

    key = row.name

    try:

        return 1 / edge.fundamental_matrix.error.iloc[key]

        return 1 / edge.fundamental_matrix.error.loc[key]

    except:

        return np.NaN