Commit fef1f3b3 authored by Jay's avatar Jay
Browse files

Merge remote-tracking branch 'upstream/dev' into footprints

parents f988c884 1b940d5c

autocnet/__init__.py

+2 −2
Original line number Diff line number Diff line
@@ -38,7 +38,7 @@ def cuda(enable=False, gpu=0): 
            Node._extract_features = staticmethod(extract_features)



            from autocnet.matcher.cuda_matcher import match

            Edge.match = match

            Edge._match = staticmethod(match)



            from autocnet.matcher.cuda_decompose import decompose_and_match

            Edge.decompose_and_match = decompose_and_match
@@ -52,7 +52,7 @@ def cuda(enable=False, gpu=0): 
    Node._extract_features = staticmethod(extract_features)



    from autocnet.matcher.cpu_matcher import match

    Edge.match = match

    Edge._match = staticmethod(match)



    from autocnet.matcher.cpu_decompose import decompose_and_match

    Edge.decompose_and_match = decompose_and_match

autocnet/graph/edge.py

+15 −4
Original line number Diff line number Diff line
@@ -107,11 +107,22 @@ class Edge(dict, MutableMapping): 
        ----------

        k : int

            The number of neighbors to find

        """

        Edge._match(self, k, **kwargs)



    @staticmethod

    def _match(edge, k=2, **kwargs):

        """

        Patches the static cpu_matcher.match(edge) or cuda_match.match(edge)

        into the member method Edge.match()



        overlap : boolean

                  Apply the matcher only to the overlapping area defined by

                  the source_mbr and destin_mbr attributes (stored in the

                  edge dict).

        Parameters

        ----------

        edge : Edge

               The edge object to compute matches for; Edge.match() calls this

               with self

        k : int

            The number of neighbors to find

        """

        pass

autocnet/graph/network.py

+11 −7
Original line number Diff line number Diff line
@@ -799,11 +799,11 @@ class CandidateGraph(nx.Graph): 


        for s, d, edge in self.edges_iter(data=True):

            source_node = edge.source

            intersect_gdf = self.compute_intersection(source_node, clean_keys = clean_keys)

            overlap, _ = self.compute_intersection(source_node, clean_keys = clean_keys)



            matches, _ = edge.clean(clean_keys)

            kps = edge.get_keypoints(edge.source, index=matches['source_idx'])[['x', 'y']]

            reproj_geom = source_node.reproject_geom(intersect_gdf.query("overlaps_all == True").geometry.values[0].__geo_interface__['coordinates'][0])

            reproj_geom = source_node.reproject_geom(overlap.geometry.values[0].__geo_interface__['coordinates'][0])

            initial_mask = geom_mask(kps, reproj_geom)



            if (len(kps[initial_mask]) <= 0):
@@ -858,17 +858,21 @@ class CandidateGraph(nx.Graph): 
                proj_node_list.append(s)



        proj_gdf = gpd.GeoDataFrame({"geometry": proj_poly_list, "proj_node": proj_node_list})

        # Overlay the all geometry and find the one geometry element that overlaps all of the images

        # Overlay all geometry and find the one geometry element that overlaps all of the images

        intersect_gdf = gpd.overlay(source_gdf, proj_gdf, how='intersection')

        intersect_gdf['overlaps_all'] = intersect_gdf.geometry.apply(lambda x:proj_gdf.geometry.contains(shapely.affinity.scale(x, .9, .9)).all())

        if len(intersect_gdf) == 0:

            raise ValueError('Node ' + str(source['node_id']) +  ' does not overlap with any other images in the candidate graph.')

        overlaps_mask = intersect_gdf.geometry.apply(lambda x:proj_gdf.geometry.contains(shapely.affinity.scale(x, .9, .9)).all())

        overlaps_all = intersect_gdf[overlaps_mask]



        # If there is no intersection polygon that overlaps all of the images, union all of the intersection

        # polygons into one large polygon that does overlap all of the images

        if len(intersect_gdf.query("overlaps_all == True")) <= 0:

        if len(overlaps_all) <= 0:

            new_poly = shapely.ops.unary_union(intersect_gdf.geometry)

            intersect_gdf.loc[len(intersect_gdf)] = [source['node_id'], source['node_id'], new_poly, True]

            overlaps_all = gpd.GeoDataFrame({'source_node': source['node_id'], 'proj_node': source['node_id'],

                                             'geometry': [new_poly]})



        return intersect_gdf

        return overlaps_all, intersect_gdf



    def is_complete(self):

        """

autocnet/graph/tests/test_network.py

+5 −5
Original line number Diff line number Diff line
@@ -220,16 +220,16 @@ def test_intersection(): 
        e.source = cang.node[s]

        e.destination = cang.node[d]



    intersect_gdf = cang.compute_intersection(3)

    overlap, intersect_gdf = cang.compute_intersection(3)



    # Test the correct areas were found

    # Test the correct areas were found for the overlap and

    # the intersect_gdf

    print(overlap.geometry.area)

    assert intersect_gdf.geometry[0].area == 7.5

    assert intersect_gdf.geometry[1].area == 5

    assert intersect_gdf.geometry[2].area == 5

    assert intersect_gdf.geometry[3].area == 3.75

    assert intersect_gdf.geometry[4].area == 21.25

    # Check if the correct poly was determined to overlap all other images

    assert intersect_gdf.overlaps_all[4] == True

    assert overlap.geometry.area.values == 21.25





def test_set_maxsize(graph):

autocnet/matcher/cpu_matcher.py

+14 −20
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ FLANN_INDEX_KDTREE = 1 # Algorithm to set centers, 
DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE, trees=3)





def match(self, k=2, **kwargs):

def match(edge, k=2, **kwargs):

    """

    Given two sets of descriptors, utilize a FLANN (Approximate Nearest

    Neighbor KDTree) matcher to find the k nearest matches.  Nearness is
@@ -32,11 +32,11 @@ def match(self, k=2, **kwargs): 
        matches : dataframe

                  A dataframe of matches

        """

        if self.matches is None:

            self.matches = matches

        if edge.matches.empty:

            edge.matches = matches

        else:

            df = self.matches

            self.matches = df.append(matches,

            df = edge.matches

            edge.matches = df.append(matches,

                                     ignore_index=True,

                                     verify_integrity=True)
@@ -78,25 +78,19 @@ def match(self, k=2, **kwargs): 


    fl = FlannMatcher()



    # Get the correct descriptors

    # TODO: Extract into a helper function

    if 'aidx' in kwargs.keys():

        aidx = kwargs['aidx']

        kwargs.pop('aidx')

    else:

        aidx = None

    # Reset the edge.masks attrib; New matches would mean masks have to be

    # re-calculated

    edge.masks = pd.DataFrame()

    

    if 'bidx' in kwargs.keys():

        bidx = kwargs['bidx']

        kwargs.pop('bidx')

    else:

        bidx = None

    # Get the correct descriptors

    aidx = kwargs.pop('aidx', None)

    bidx = kwargs.pop('bidx', None)



    mono_matches(self.source, self.destination, aidx=aidx, bidx=bidx, **kwargs)

    mono_matches(edge.source, edge.destination, aidx=aidx, bidx=bidx)

    # Swap the indices since mono_matches is generic and source/destin are

    # swapped

    mono_matches(self.destination, self.source, aidx=bidx, bidx=aidx, **kwargs)

    self.matches.sort_values(by=['distance'])

    mono_matches(edge.destination, edge.source, aidx=bidx, bidx=aidx)

    edge.matches.sort_values(by=['distance'])





class FlannMatcher(object):