Commit 26671018 authored by Adam Paquette's avatar Adam Paquette
Browse files

fixed merge conflicts

parents baf06a7d 61e54f1d

autocnet/graph/network.py

+13 −6
Original line number Diff line number Diff line
@@ -47,7 +47,7 @@ class CandidateGraph(nx.Graph): 
        self.node_counter = 0

        node_labels = {}

        self.node_name_map = {}

        self.graph_masks = {}

        self.graph_masks = pd.DataFrame()



        # the node_name is the relative path for the image

        for node_name, node in self.nodes_iter(data=True):
@@ -368,15 +368,21 @@ class CandidateGraph(nx.Graph): 
                          of keys in graph_masks

        """



        if graph_mask_keys:

            merged_graph_mask = self.graph_masks[graph_mask_keys].all(axis=1)

            edges_to_iter = merged_graph_mask[merged_graph_mask].index

        else:

            edges_to_iter = self.edges()



        if not isinstance(func, str):

            func = func.__name__



        for s, d in edges_to_iter:

            curr_edge = self.get_edge_data(s, d)

            try:

                function = getattr(curr_edge, func)

            except:

                raise AttributeError('The passed function is not an attribute of Edge')

                raise AttributeError(func, ' is not an attribute of Edge')

            else:

                function(*args, **kwargs)
@@ -391,7 +397,9 @@ class CandidateGraph(nx.Graph): 
           boolean mask for edges in the minimum spanning tree

        """



        self.graph_masks = pd.DataFrame(False, index=self.edges(), columns=['mst'])

        graph_mask = pd.Series(False, index=self.edges())

        self.graph_masks['mst'] = graph_mask



        mst = nx.minimum_spanning_tree(self)

        self.graph_masks['mst'][mst.edges()] = True
@@ -503,7 +511,6 @@ class CandidateGraph(nx.Graph): 
             : C

               the cleaned control network

            """



            mask = np.zeros(len(cnet), dtype=bool)

            counter = 0

            for i, group in cnet.groupby('pid'):

functional_tests/test_three_image.py

+2 −1
Original line number Diff line number Diff line
@@ -52,7 +52,8 @@ class TestThreeImageMatching(unittest.TestCase): 
        for source, destination, edge in cg.edges_iter(data=True):

            edge.symmetry_check()

            edge.ratio_check(clean_keys=['symmetry'], ratio=0.99)

        cg.compute_homographies(clean_keys=['symmetry', 'ratio'])



        cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio'])



        # Step: And create a C object

        cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac'])

functional_tests/test_two_image.py

+2 −2
Original line number Diff line number Diff line
@@ -73,14 +73,14 @@ class TestTwoImageMatching(unittest.TestCase): 
            self.assertIn(edge.masks['ratio'].sum(), range(40, 100))



        # Step: Compute the homographies and apply RANSAC

        cg.compute_homographies(clean_keys=['symmetry', 'ratio'])

        cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio'])



        # Step: Compute the overlap ratio and coverage ratio

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

            edge.coverage_ratio(clean_keys=['symmetry', 'ratio'])



        # Step: Compute subpixel offsets for candidate points

        cg.subpixel_register(clean_keys=['ransac'])

        cg.apply_func_to_edges("subpixel_register", clean_keys=['ransac'])



        # Step: And create a C object

        cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel'])

notebooks/Tutorial with Visualization.ipynb

+61 −35

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