Loading functional_tests/test_save_load.py +1 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ def test_save_project(tmpdir): cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath) #Apply SIFT to extract features cg.extract_features(method='sift', extractor_parameters={'nfeatures':500}) cg.extract_features(extractor_method='sift', extractor_parameters={'nfeatures':500}) #Match cg.match() Loading functional_tests/test_three_image.py +2 −2 Original line number Diff line number Diff line Loading @@ -54,14 +54,14 @@ class TestThreeImageMatching(unittest.TestCase): cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'], reproj_threshold=3.0, method='ransac') # Step: And create a C object cg.generate_cnet(clean_keys=['symmetry', 'ratio', 'ransac']) cg.generate_cnet(clean_keys=['symmetry', 'ratio', 'fundamental']) # Step: Create a fromlist to go with the cnet and write it to a file filelist = cg.to_filelist() write_filelist(filelist, 'TestThreeImageMatching_fromlist.lis') # Step: Create a correspondence network cg.generate_cnet(clean_keys=['ransac'], deepen=True) cg.generate_cnet(clean_keys=['fundamental'], deepen=True) to_isis('TestThreeImageMatching.net', cg.cn, mode='wb', networkid='TestThreeImageMatching', targetname='Moon') Loading functional_tests/test_two_image.py +1 −1 Original line number Diff line number Diff line Loading @@ -48,7 +48,7 @@ class TestTwoImageMatching(unittest.TestCase): self.assertEqual(1, cg.number_of_edges()) # Step: Extract image data and attribute nodes cg.extract_features(method='sift', extractor_parameters={"nfeatures":500}) cg.extract_features(extractor_method='sift', extractor_parameters={"nfeatures":500}) for i, node in cg.nodes_iter(data=True): self.assertIn(node.nkeypoints, range(490, 510)) Loading Loading
functional_tests/test_save_load.py +1 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ def test_save_project(tmpdir): cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath) #Apply SIFT to extract features cg.extract_features(method='sift', extractor_parameters={'nfeatures':500}) cg.extract_features(extractor_method='sift', extractor_parameters={'nfeatures':500}) #Match cg.match() Loading
functional_tests/test_three_image.py +2 −2 Original line number Diff line number Diff line Loading @@ -54,14 +54,14 @@ class TestThreeImageMatching(unittest.TestCase): cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'], reproj_threshold=3.0, method='ransac') # Step: And create a C object cg.generate_cnet(clean_keys=['symmetry', 'ratio', 'ransac']) cg.generate_cnet(clean_keys=['symmetry', 'ratio', 'fundamental']) # Step: Create a fromlist to go with the cnet and write it to a file filelist = cg.to_filelist() write_filelist(filelist, 'TestThreeImageMatching_fromlist.lis') # Step: Create a correspondence network cg.generate_cnet(clean_keys=['ransac'], deepen=True) cg.generate_cnet(clean_keys=['fundamental'], deepen=True) to_isis('TestThreeImageMatching.net', cg.cn, mode='wb', networkid='TestThreeImageMatching', targetname='Moon') Loading
functional_tests/test_two_image.py +1 −1 Original line number Diff line number Diff line Loading @@ -48,7 +48,7 @@ class TestTwoImageMatching(unittest.TestCase): self.assertEqual(1, cg.number_of_edges()) # Step: Extract image data and attribute nodes cg.extract_features(method='sift', extractor_parameters={"nfeatures":500}) cg.extract_features(extractor_method='sift', extractor_parameters={"nfeatures":500}) for i, node in cg.nodes_iter(data=True): self.assertIn(node.nkeypoints, range(490, 510)) Loading