Commit d5fbb483 authored by Jay's avatar Jay
Browse files

Edge matching to overlap if footprints present.

parent 4fe32abb

autocnet/__init__.py

+6 −0
Original line number Diff line number Diff line
@@ -43,6 +43,9 @@ def cuda(enable=False, gpu=0): 
            from autocnet.matcher.cuda_decompose import decompose_and_match

            Edge.decompose_and_match = decompose_and_match



            from autocnet.matcher.cuda_outlier_detector import distance_ratio

            Edge._ratio_check = staticmethod(distance_ratio)



        except Exception:

            warnings.warn('Failed to enable Cuda')

        return
@@ -57,4 +60,7 @@ def cuda(enable=False, gpu=0): 
    from autocnet.matcher.cpu_decompose import decompose_and_match

    Edge.decompose_and_match = decompose_and_match



    from autocnet.matcher.cpu_outlier_detector import  distance_ratio

    Edge._ratio_check = staticmethod(distance_ratio)



cuda()

autocnet/graph/edge.py

+36 −15
Original line number Diff line number Diff line
@@ -48,8 +48,10 @@ class Edge(dict, MutableMapping): 
        self.masks = pd.DataFrame()

        self.subpixel_matches = pd.DataFrame()

        self['weights'] = {}



        self['source_mbr'] = None

        self['destin_mbr'] = None

        self['overlap_latlon_coords'] = None



    def __repr__(self):

        return """
@@ -76,7 +78,11 @@ class Edge(dict, MutableMapping): 
        k : int

            The number of neighbors to find

        """

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

        # Reset the edge masks because matching is happening (again)

        self.masks = pd.DataFrame()

        kwargs['aidx'] = self.get_keypoints('source', overlap=True).index

        kwargs['bidx'] = self.get_keypoints('destination', overlap=True).index

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



    @staticmethod

    def _match(edge, k=2, **kwargs):
@@ -128,7 +134,12 @@ class Edge(dict, MutableMapping): 


    def ratio_check(self, clean_keys=[], maskname='ratio', **kwargs):

        matches, mask = self.clean(clean_keys)

        self.masks[maskname] = od.distance_ratio(matches, **kwargs)

        self.masks[maskname] = self._ratio_check(self, matches, **kwargs)



    @staticmethod

    def _ratio_check(edge, matches, **kwargs):

        pass

        #return.masks[maskname] = od.distance_ratio(matches, **kwargs)



    def compute_fundamental_matrix(self, clean_keys=[], maskname='fundamental', **kwargs):

        """
@@ -178,13 +189,10 @@ class Edge(dict, MutableMapping): 
        # If we only want keypoints in the overlap

        if overlap:

            # Can't use overlap if we haven't computed MBRs

            if not (self["source_mbr"] and self["destin_mbr"]):

                warnings.warn(

                    "Cannot use overlap constraint, minimum bounding rectangles"

                    " have not been computed for one or more Nodes")

            if self['overlap_latlon_coords'] is None:

                return keypts

            # Create overlap's bounding polygon in pixel space

            bounds_poly = node.reproject_geom(self.overlap_latlon_coords)

            bounds_poly = node.reproject_geom(self['overlap_latlon_coords'])

            # Mask for node keypts based on bounding poly

            overlap_mask = cg.geom_mask(node.keypoints, bounds_poly)

            # Return masked keypts
@@ -497,18 +505,31 @@ class Edge(dict, MutableMapping): 
        voronoi = cg.vor(self, clean_keys, **kwargs)

        self.matches = pd.concat([self.matches, voronoi[1]['vor_weights']], axis=1)



    def compute_overlap(self, **kwargs):

    def compute_overlap(self, buffer_dist=0, **kwargs):

        """

        Estimate a source and destination minimum bounding rectangle, in

        pixel space

        pixel space.

        """

        try:

            self.overlap_latlon_coords, self["source_mbr"], self["destin_mbr"] = self.source.geodata.compute_overlap(self.destination.geodata, **kwargs)

        except Exception as e:

            raise Exception("Overlap between {} and {} could not be "

                            "computed: {}".format(self.source['image_name'],

                                                  self.destination['image_name'],

                                                  type(e)))

            self['overlap_latlon_coords'], smbr, dmbr = self.source.geodata.compute_overlap(self.destination.geodata, **kwargs)

            smbr = list(smbr)

            dmbr = list(dmbr)

            for i in range(4):

                if i % 2:

                    buf = buffer_dist

                else:

                    buf = -buffer_dist

                smbr[i] += buf

                dmbr[i] += buf

        except:

            smbr = self.source.geodata.xy_extent

            dmbr = self.source.geodata.xy_extent

            warnings.warn("Overlap between {} and {} could not be "

                            "computed.  Using the full image extents".format(self.source['image_name'],

                                                  self.destination['image_name']))



        self['source_mbr'] = smbr

        self['destin_mbr'] = dmbr



    def get_matches(self): # pragma: no cover

        if self.matches.empty:

autocnet/graph/network.py

+1 −0
Original line number Diff line number Diff line
@@ -88,6 +88,7 @@ class CandidateGraph(nx.Graph): 


        self.graph['creationdate'] = strftime("%Y-%m-%d %H:%M:%S", gmtime())

        self.graph['modifieddate'] = strftime("%Y-%m-%d %H:%M:%S", gmtime())

        self.compute_overlaps()



    def __eq__(self, other):

        # Check the nodes

autocnet/matcher/cpu_matcher.py

+0 −4
Original line number Diff line number Diff line
@@ -78,10 +78,6 @@ def match(edge, k=2, **kwargs): 


    fl = FlannMatcher()



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

    # re-calculated

    edge.masks = pd.DataFrame()

    

    # Get the correct descriptors

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

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

autocnet/matcher/cpu_outlier_detector.py

+1 −1
Original line number Diff line number Diff line
@@ -6,7 +6,7 @@ import numpy as np 
import pandas as pd





def distance_ratio(matches, ratio=0.8, single=False):

def distance_ratio(edge, matches, ratio=0.8, single=False):

    """

    Compute and return a mask for a matches dataframe

    using Lowe's ratio test.  If keypoints have a single