Loading autocnet/transformation/transformations.py +5 −1 Original line number Diff line number Diff line Loading @@ -192,6 +192,10 @@ class FundamentalMatrix(TransformationMatrix): method : object A function that accepts and ndarray and returns an object with a bins attribute df : dataframe Dataframe (from which a ndarray will be extracted) to pass to the method. bin_id : int The index into the bins object. Data classified > this id is masked Loading @@ -214,7 +218,7 @@ class FundamentalMatrix(TransformationMatrix): fj = method(df.values.ravel(), **kwargs) bins = fj.bins # Mask the data that falls outside the provided bins mask = df <= bins[bin_id] mask = df.iloc[:, 0] <= bins[bin_id] new_x1 = self.x1.iloc[mask[mask == True].index] new_x2 = self.x2.iloc[mask[mask == True].index] fmatrix, new_mask = compute_fundamental_matrix(new_x1.values, new_x2.values) Loading Loading
autocnet/transformation/transformations.py +5 −1 Original line number Diff line number Diff line Loading @@ -192,6 +192,10 @@ class FundamentalMatrix(TransformationMatrix): method : object A function that accepts and ndarray and returns an object with a bins attribute df : dataframe Dataframe (from which a ndarray will be extracted) to pass to the method. bin_id : int The index into the bins object. Data classified > this id is masked Loading @@ -214,7 +218,7 @@ class FundamentalMatrix(TransformationMatrix): fj = method(df.values.ravel(), **kwargs) bins = fj.bins # Mask the data that falls outside the provided bins mask = df <= bins[bin_id] mask = df.iloc[:, 0] <= bins[bin_id] new_x1 = self.x1.iloc[mask[mask == True].index] new_x2 = self.x2.iloc[mask[mask == True].index] fmatrix, new_mask = compute_fundamental_matrix(new_x1.values, new_x2.values) Loading