Unverified Commit 2af7e3d9 authored by Jesse Mapel's avatar Jesse Mapel Committed by GitHub
Browse files

Added ISIS outlier detection notebook (#505) 

* Added ISIS outlier detection notebook

* Cleaned up notebook

* Fixed docs spacing

* Fixed docs typo
parent 9ae825e5

autocnet/graph/edge.py

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@@ -757,6 +757,67 @@ class NetworkEdge(Edge): 
            session.expunge_all()

        return res



    def compute_homography(self, method='ransac', maskname='homography', **kwargs):

        """

        Estimate the homography and reprojective error on this edge of the graph.



        Parameters

        ----------

        method : {'ransac', 'lmeds', 'normal'}

                 The method that will be used when computing the homography.



        maskname : str

                   The column that the mask will be saved under in the masks dataframe.

                   If the column already exists, then the mask in that column will be overwritten.



        kwargs : dict

                 Extra arguments that will be passed to the homography computation.



        See Also

        --------

        autocnet.transformation.transformations.homography.compute_homography

        """

        matches = self.matches



        s_keypoints = matches[["source_x", "source_y"]].values.astype(np.float64)

        d_keypoints = matches[["destination_x", "destination_y"]].values.astype(np.float64)



        self['homography'], hmask = hm.compute_homography(s_keypoints, d_keypoints, method=method, **kwargs)



        self.masks[maskname] = hmask



    def compute_fundamental_matrix(self, method='ransac', maskname='fundamental', **kwargs):

        """

        Estimate the fundamental matrix (F) using the correspondences tagged to this

        edge.





        Parameters

        ----------

        method : {'ransac', 'lmeds', 'normal', '8point'}

                 The method that will be used when computing the homography.



        maskname : str

                   The column that the mask will be saved under in the masks dataframe.

                   If the column already exists, then the mask in that column will be overwritten.



        kwargs : dict

                 Extra arguments that will be passed to the fundamental matrix computation.



        See Also

        --------

        autocnet.transformation.transformations.fundamental_matrix.compute_fundamental_matrix



        """



        matches = self.matches



        s_keypoints = matches[["source_x", "source_y"]].values.astype(np.float64)

        d_keypoints = matches[["destination_x", "destination_y"]].values.astype(np.float64)

        self.fundamental_matrix, fmask = fm.compute_fundamental_matrix(s_keypoints, d_keypoints, method=method, **kwargs)



        self.masks[maskname] = fmask



    @property

    def weights(self):

        with self.parent.session_scope() as session:

docs/users/tutorials/index.rst

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@@ -9,6 +9,7 @@ Working With ISIS Control Networks 
   :maxdepth: 1



    Ingesting from ISIS control network<isis_ingest/from_cnet.ipynb>

    Using outlier detection to check for bad measures<outlier_detection/outlier_detection.ipynb>





Working With Apollo Pan

docs/users/tutorials/outlier_detection/outlier_detection.ipynb

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