Commit 9fbd0ba9 authored by Jay's avatar Jay Committed by jay
Browse files

Updates to the function tests.

parent 4fc0d376

autocnet/graph/network.py

+1 −0
Original line number Diff line number Diff line
@@ -59,3 +59,4 @@ class CandidateGraph(DiGraph): 
        adjacency_dict = io_json.read_json(inputfile)

        return cls(adjacency_dict)

functional_tests/test_two_image.py

+12 −4
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ from autocnet.examples import get_path 
from autocnet.fileio.io_gdal import GeoDataset

from autocnet.fileio.io_controlnetwork import to_isis

from autocnet.graph.network import CandidateGraph



from autocnet.matcher.matcher import FlannMatcher



class TestTwoImageMatching(unittest.TestCase):

    """
@@ -22,8 +22,8 @@ class TestTwoImageMatching(unittest.TestCase): 
            Then extract image data and attribute nodes

            And find features and descriptors

            Then tag these to the graph nodes

            Then apply a FLANN matcher

            And create a C object

            And apply a FLANN matcher

            Then create a C object from the graph matches

            Then output a control network

    """
@@ -45,10 +45,18 @@ class TestTwoImageMatching(unittest.TestCase): 


        # Step: And tag these to the graph nodes

        for node, attributes in cg.nodes_iter(data=True):

            attributes['features'] = 'a'  # Will be our feature/descriptor data structure

            attributes['keypoints'] = 'a'  # Will be our feature/descriptor data structure

            attributes['descriptors'] = 'b'



        # Step: Then apply a FLANN matcher

        fl = FlannMatcher()

        for node, attributes in cg.nodes_iter(data=True):

            fl.add(attributes['descriptors'])

        fl.train()



        for node, attributes in cg.nodes_iter(data=True):

            descriptors = attributes['descriptors']

            attributes['matches'] = fl.query(descriptors, k=2)



        # Step: And create a C object

        cnet = C()