Loading functional_tests/test_two_image.py +2 −2 Original line number Diff line number Diff line Loading @@ -73,14 +73,14 @@ class TestTwoImageMatching(unittest.TestCase): self.assertIn(edge.masks['ratio'].sum(), range(30, 100)) # 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: And create a C object cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel']) Loading Loading
functional_tests/test_two_image.py +2 −2 Original line number Diff line number Diff line Loading @@ -73,14 +73,14 @@ class TestTwoImageMatching(unittest.TestCase): self.assertIn(edge.masks['ratio'].sum(), range(30, 100)) # 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: And create a C object cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel']) Loading