Commit ae8f9b51 authored by jay's avatar jay
Browse files

Cleanup on the open PR when testing on various CTX images

parent fd597beb

autocnet/__init__.py

+0 −2
Original line number Diff line number Diff line
@@ -43,8 +43,6 @@ def cuda(enable=False, gpu=0): 
            from autocnet.matcher.cuda_decompose import decompose_and_match

            Edge.decompose_and_match = decompose_and_match



            # Outlier Detectors



        except Exception:

            warnings.warn('Failed to enable Cuda')

        return

autocnet/graph/edge.py

+6 −2
Original line number Diff line number Diff line
@@ -92,7 +92,7 @@ class Edge(dict, MutableMapping): 
        boolean_mask = v[1]

        self.masks[column_name] = boolean_mask"""



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

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



        """

        Given two sets of descriptors, utilize a FLANN (Approximate Nearest
@@ -105,6 +105,11 @@ class Edge(dict, MutableMapping): 
        ----------

        k : int

            The number of neighbors to find



        overlap : boolean

                  Apply the matcher only to the overlapping area defined by

                  the source_mbr and destin_mbr attributes (stored in the

                  edge dict).

        """

        pass
@@ -498,4 +503,3 @@ class Edge(dict, MutableMapping): 
        pixel space

        """

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

autocnet/graph/network.py

+2 −0
Original line number Diff line number Diff line 
import itertools

import math

import os

from time import gmtime, strftime

import warnings
@@ -247,6 +248,7 @@ class CandidateGraph(nx.Graph): 


    def extract_features_with_tiling(self, tilesize=1000, overlap=500, *args, **kwargs): #pragma: no cover

        for i, node in self.nodes_iter(data=True):

            print('Processing {}'.format(node['image_name']))

            node.extract_features_with_tiling(tilesize=tilesize, overlap=overlap, *args, **kwargs)



    def extract_subsets(self, *args, **kwargs):

autocnet/graph/node.py

+7 −1
Original line number Diff line number Diff line
@@ -340,12 +340,18 @@ class Node(dict, MutableMapping): 
        stepsize = tilesize - overlap

        if stepsize < 0:

            raise ValueError('Overlap can not be greater than tilesize.')



        # Compute the tiles

        if tilesize >= array_size[1]:

            ytiles = [(0, array_size[1])]

        else:

            ystarts = range(0, array_size[1], stepsize)

            ystops = range(tilesize, array_size[1], stepsize)

            ytiles = list(zip(ystarts, ystops))

            ytiles.append((ytiles[-1][0] + stepsize, array_size[1]))

        

        if tilesize >= array_size[0]:

            xtiles = [(0, array_size[0])]

        else:

            xstarts = range(0, array_size[0], stepsize)

            xstops = range(tilesize, array_size[0], stepsize)

            xtiles = list(zip(xstarts, xstops))

autocnet/matcher/cpu_matcher.py

+29 −3
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(self, k=2, overlap=False, **kwargs):

    """

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

    Neighbor KDTree) matcher to find the k nearest matches.  Nearness is
@@ -77,8 +77,34 @@ def match(self, k=2, **kwargs): 
        fl.clear()



    fl = FlannMatcher()

    mono_matches(self.source, self.destination, **kwargs)

    mono_matches(self.destination, self.source, **kwargs)

    

    # Get the correct descriptors

    # TODO: Extract into a helper function

    if 'aidx' in kwargs.keys():

        aidx = kwargs['aidx']

        kwargs.pop('aidx')

    elif overlap:

        # Query the source keypoints for those in the MBR

        source_mbr = self['source_mbr']

        query_result = self.source.keypoints.query()

        aidx = query_result.index

    else:

        aidx = None

    

    if 'bidx' in kwargs.keys():

        bidx = kwargs['bidx']

        kwargs.pop('bidx')

    elif overlap:

        destin_mbr = self['destin_mbr']

        query_result = self.destination.keypoints.query()

        bidx = query_result.index

    else:

        bidx = None



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

    # 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'])