Unverified Commit 5fdb5d0f authored by jlaura's avatar jlaura Committed by GitHub
Browse files

Updates Matches and other minor fix (#490) 

* Updates matches for edges

* Small fix for reprojetive error
parent 9fc28a27

autocnet/graph/edge.py

+12 −6
Original line number Diff line number Diff line
@@ -1010,7 +1010,9 @@ class NetworkEdge(Edge): 
                      Measures.sample,

                      Measures.line,

                      Measures.measuretype,

                      Measures.imageid).\

                      Measures.imageid,

                      Measures.apriorisample,

                      Measures.aprioriline).\

                filter(Points.ignore==ignore_point,

                       Measures.ignore==ignore_measure,

                       Measures.jigreject==rejected_jigsaw,
@@ -1019,9 +1021,12 @@ class NetworkEdge(Edge): 


            df = pd.read_sql(q.statement, self.parent.engine)

            matches = []

            columns = ['point_id', 'source_measure_id', 'destin_measure_id', 'source', 'source_idx', 'destination', 'destination_idx',

                   'lat', 'lon', 'geom', 'source_x', 'source_y', 'destination_x',

                   'destination_y', 'shift_x', 'shift_y', 'original_destination_x',

            columns = ['point_id', 'source_measure_id', 'destin_measure_id', 'source',

                       'source_idx', 'destination', 'destination_idx',

                       'lat', 'lon', 'geom',

                       'source_x', 'source_y', 'source_apriori_x', 'source_apriori_y',

                       'destination_x','destination_y', 'destination_apriori_x', 'destination_apriori_y', 

                       'shift_x', 'shift_y', 'original_destination_x',

                       'original_destination_y']



        def net2matches(grp, matches, source, destin):
@@ -1035,7 +1040,8 @@ class NetworkEdge(Edge): 
            match = [int(imagea.id), int(imagea.mid), int(imageb.mid),

                     source, 0, destin, 0,

                     None, None, None,

                     imagea['sample'], imagea['line'], imageb['sample'], imageb['line'],

                     imagea['sample'], imagea['line'], imagea['apriorisample'], imagea['aprioriline'],

                     imageb['sample'], imageb['line'], imageb['apriorisample'], imageb['aprioriline'],

                     None, None, None, None]

            matches.append(match)

autocnet/io/db/model.py

+4 −0
Original line number Diff line number Diff line
@@ -150,8 +150,12 @@ class Matches(BaseMixin, Base): 
    _geom = Column("geom", Geometry('POINT', dimension=2, srid=latitudinal_srid, spatial_index=True))

    source_x = Column(Float)

    source_y = Column(Float)

    source_apriori_x = Column(Float)

    source_apriori_y = Column(Float)

    destination_x = Column(Float)

    destination_y = Column(Float)

    destination_apriori_x = Column(Float)

    destination_apriori_y = Column(Float)

    shift_x = Column(Float)

    shift_y = Column(Float)

    original_destination_x = Column(Float)

autocnet/transformation/fundamental_matrix.py

+5 −3
Original line number Diff line number Diff line
@@ -98,14 +98,16 @@ def compute_reprojection_error(F, x, x1, index=None): 
              n,1 vector of reprojection errors

    """



    if not x.shape[1] == 3 or not x1.shape[1] == 3:

        raise ValueError('The input points must be homogenous with shape (n,3)')



    if isinstance(x, (pd.Series, pd.DataFrame)):

        x = x.values

    if isinstance(x1, (pd.Series, pd.DataFrame)):

        x1 = x1.values



    if x.shape[1] != 3:

        x = make_homogeneous(x)

    if x1.shape[1] != 3:

        x1 = make_homogeneous(x1)



    # Compute the epipolar lines

    lines1 = compute_epipolar_lines(F,x)

    lines2 = compute_epipolar_lines(F.T, x1)