Commit 656252cf authored by Jay's avatar Jay
Browse files

More key issues with the move to float

parent 91e093c0

autocnet/matcher/cpu_decompose.py

+1 −1
Changes for autocnet/matcher/cpu_decompose.py: 1 added line, 1 removed line.
Original line number Diff line number Diff line
@@ -163,7 +163,7 @@ def decompose_and_match(self, k=2, maxiteration=3, size=18, buf_dist=3,**kwargs) 
                # Apply Lowe's ratio test to try to find a 'good' starting point

                mask = matches.groupby('source_idx')['distance'].transform(func).astype('bool')

                candidate_matches = matches[mask]

                match_idx = candidate_matches['source_idx']

                match_idx = candidate_matches['source_idx'].astype(np.int)



                # Extract those matches that pass the ratio check

                sub_skp = skp.iloc[match_idx]

functional_tests/test_three_image.py

+2 −2
Changes for functional_tests/test_three_image.py: 2 added lines, 2 removed lines.
Original line number Diff line number Diff line
@@ -47,11 +47,11 @@ class TestThreeImageMatching(unittest.TestCase): 
            self.assertIn(node.nkeypoints, range(490, 511))



        cg.match(k=5)

        cg.symmetry_checks()

        cg.symmetry_checks(single=False)

        cg.ratio_checks()



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

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

        cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'], reproj_threshold=3.0)



        # Step: And create a C object

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

functional_tests/test_two_image.py

+3 −22
Changes for functional_tests/test_two_image.py: 3 added lines, 22 removed lines.
Original line number Diff line number Diff line
@@ -50,37 +50,18 @@ class TestTwoImageMatching(unittest.TestCase): 
        # Step: Extract image data and attribute nodes

        cg.extract_features(method='sift', extractor_parameters={"nfeatures":500})

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

            self.assertIn(node.nkeypoints, range(490, 511))

            self.assertIn(node.nkeypoints, range(490, 510))



        # Step: Compute the coverage ratios

        truth_ratios = [0.95351579,

                        0.93595664]

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

            ratio = node.coverage_ratio()

            self.assertIn(round(ratio, 8), truth_ratios)

            self.assertTrue(0.93 < round(ratio, 8) < 0.96)



        cg.decompose_and_match(k=2, maxiteration=2)

        self.assertTrue(isinstance(cg.edge[0][1].smembership, np.ndarray))



        # Perform the symmetry check

        cg.symmetry_checks()

        # Perform the ratio check

        cg.ratio_checks(clean_keys=['symmetry'], single=True)

        # 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

            self.assertIn(edge.masks['symmetry'].sum(), range(200, 400))

            # Perform the ratio test

            self.assertIn(edge.masks['ratio'].sum(), range(200, 300))



            # Range needs to be set

            self.assertIn(edge.masks['fundamental'].sum(), range(200, 300))



        # Step: Compute the homographies and apply RANSAC

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

        cg.compute_fundamental_matrices()



        # Apply AMNS

        cg.suppress(k=30, suppression_func=error)