Loading autocnet/graph/network.py +57 −0 Original line number Diff line number Diff line Loading @@ -395,6 +395,63 @@ class CandidateGraph(nx.Graph): else: func(*args, **kwargs) def symmetry_checks(self): ''' Apply a symmetry check to all edges in the graph ''' self.apply_func_to_edges('symmetry_check') def ratio_checks(self, *args, **kwargs): ''' Apply a ratio check to all edges in the graph See Also -------- autocnet.matcher.outlier_detector.DistanceRatio.compute ''' self.apply_func_to_edges('ratio_check', *args, **kwargs) def compute_homographies(self, *args, **kwargs): ''' Compute homographies for all edges using identical parameters See Also -------- autocnet.graph.edge.Edge.compute_homography autocnet.matcher.outlier_detector.compute_homography ''' self.apply_func_to_edges('compute_homography', *args, **kwargs) def compute_fundamental_matrices(self, *args, **kwargs): ''' Compute fundmental matrices for all edges using identical parameters See Also -------- autocnet.matcher.outlier_detector.compute_fundamental_matrix ''' self.apply_func_to_edges('compute_fundamental_matrix', *args, **kwargs) def subpixel_register(self, *args, **kwargs): ''' Compute subpixel offsets for all edges using identical parameters See Also -------- autocnet.graph.edge.Edge.subpixel_register ''' self.apply_func_to_edges('subpixel_register', *args, **kwargs) def suppress(self, *args, **kwargs): ''' Apply a metric of point suppression to the graph See Also -------- autocnet.matcher.outlier_detector.SpatialSuppression ''' self.apply_func_to_edges('suppress', *args, **kwargs) def minimum_spanning_tree(self): """ Calculates the minimum spanning tree of the graph Loading functional_tests/test_two_image.py +17 −5 Original line number Diff line number Diff line Loading @@ -62,25 +62,37 @@ class TestTwoImageMatching(unittest.TestCase): cg.match_features(k=2) # Perform the symmetry check cg.symmetry_checks() # Perform the ratio check cg.ratio_checks(clean_keys = ['symmetry']) # Create fundamental matrix cg.compute_fundamental_matrices(clean_keys = ['symmetry', 'ratio']) for source, destination, edge in cg.edges_iter(data=True): # Perform the symmetry check edge.symmetry_check() self.assertIn(edge.masks['symmetry'].sum(), range(400, 600)) # Perform the ratio test edge.ratio_check(clean_keys=['symmetry']) self.assertIn(edge.masks['ratio'].sum(), range(30, 100)) # Range needs to be set self.assertIn(edge.masks['fundamental'].sum(), range(25, 50)) # Step: Compute the homographies and apply RANSAC cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio']) cg.compute_homographies(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.apply_func_to_edges("subpixel_register", clean_keys=['ransac']) cg.subpixel_register(clean_keys=['ransac']) # Step: cg.suppress() # Step: And create a C object cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel']) Loading Loading
autocnet/graph/network.py +57 −0 Original line number Diff line number Diff line Loading @@ -395,6 +395,63 @@ class CandidateGraph(nx.Graph): else: func(*args, **kwargs) def symmetry_checks(self): ''' Apply a symmetry check to all edges in the graph ''' self.apply_func_to_edges('symmetry_check') def ratio_checks(self, *args, **kwargs): ''' Apply a ratio check to all edges in the graph See Also -------- autocnet.matcher.outlier_detector.DistanceRatio.compute ''' self.apply_func_to_edges('ratio_check', *args, **kwargs) def compute_homographies(self, *args, **kwargs): ''' Compute homographies for all edges using identical parameters See Also -------- autocnet.graph.edge.Edge.compute_homography autocnet.matcher.outlier_detector.compute_homography ''' self.apply_func_to_edges('compute_homography', *args, **kwargs) def compute_fundamental_matrices(self, *args, **kwargs): ''' Compute fundmental matrices for all edges using identical parameters See Also -------- autocnet.matcher.outlier_detector.compute_fundamental_matrix ''' self.apply_func_to_edges('compute_fundamental_matrix', *args, **kwargs) def subpixel_register(self, *args, **kwargs): ''' Compute subpixel offsets for all edges using identical parameters See Also -------- autocnet.graph.edge.Edge.subpixel_register ''' self.apply_func_to_edges('subpixel_register', *args, **kwargs) def suppress(self, *args, **kwargs): ''' Apply a metric of point suppression to the graph See Also -------- autocnet.matcher.outlier_detector.SpatialSuppression ''' self.apply_func_to_edges('suppress', *args, **kwargs) def minimum_spanning_tree(self): """ Calculates the minimum spanning tree of the graph Loading
functional_tests/test_two_image.py +17 −5 Original line number Diff line number Diff line Loading @@ -62,25 +62,37 @@ class TestTwoImageMatching(unittest.TestCase): cg.match_features(k=2) # Perform the symmetry check cg.symmetry_checks() # Perform the ratio check cg.ratio_checks(clean_keys = ['symmetry']) # Create fundamental matrix cg.compute_fundamental_matrices(clean_keys = ['symmetry', 'ratio']) for source, destination, edge in cg.edges_iter(data=True): # Perform the symmetry check edge.symmetry_check() self.assertIn(edge.masks['symmetry'].sum(), range(400, 600)) # Perform the ratio test edge.ratio_check(clean_keys=['symmetry']) self.assertIn(edge.masks['ratio'].sum(), range(30, 100)) # Range needs to be set self.assertIn(edge.masks['fundamental'].sum(), range(25, 50)) # Step: Compute the homographies and apply RANSAC cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio']) cg.compute_homographies(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.apply_func_to_edges("subpixel_register", clean_keys=['ransac']) cg.subpixel_register(clean_keys=['ransac']) # Step: cg.suppress() # Step: And create a C object cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel']) Loading
