Loading autocnet/control/tests/test_control.py +21 −0 Original line number Diff line number Diff line Loading @@ -54,3 +54,24 @@ class TestC(unittest.TestCase): def test_creation_date(self): self.assertEqual(self.C.creationdate, strftime("%Y-%m-%d %H:%M:%S", gmtime())) def test_point_subpixel(self): for k, v in self.C.point_to_correspondence.items(): self.assertFalse(k.subpixel) k.subpixel = True self.assertTrue(k.subpixel) break def test_equalities(self): points = [] correspondences = [] for k, v in self.C.point_to_correspondence.items(): points.append(k) correspondences.extend(v) self.assertEqual(points[0], points[0]) self.assertNotEqual(points[-1], points[1]) self.assertEqual(correspondences[0][0], correspondences[0][0]) def test_to_dataframe(self): self.C.to_dataframe() autocnet/graph/network.py +0 −136 Original line number Diff line number Diff line Loading @@ -530,142 +530,6 @@ class CandidateGraph(nx.Graph): matches = edge.matches self.cn.add_correspondences(edge, matches) def to_cnet(self, clean_keys=[], isis_serials=False): """ Generate a control network (C) object from a graph Parameters ---------- clean_keys : list of strings identifying the masking arrays to use, e.g. ratio, symmetry isis_serials : bool Replace the node ID (nid) values with an ISIS serial number. Default False Returns ------- merged_cnet : C A control network object """ def _validate_cnet(cnet): """ Once the control network is aggregated from graph edges, ensure that a given correspondence in a given image does not match multiple correspondences in a different image. Parameters ---------- cnet : C control network object Returns ------- : C the cleaned control network """ mask = np.zeros(len(cnet), dtype=bool) counter = 0 for i, group in cnet.groupby('pid'): group_size = len(group) if len(group) != len(group['nid'].unique()): mask[counter: counter + group_size] = False else: mask[counter: counter + group_size] = True counter += group_size return cnet[mask] merged_cnet = None for source, destination, edge in self.edges_iter(data=True): matches = edge.matches # Merge all of the masks if clean_keys: matches, mask = edge._clean(clean_keys) subpixel = False point_type = 2 if 'subpixel' in clean_keys: subpixel = True point_type = 3 kp1 = self.node[source].get_keypoints() kp2 = self.node[destination].get_keypoints() pt_idx = 0 values = [] for i, (idx, row) in enumerate(matches.iterrows()): # Composite matching key (node_id, point_id) m1_pid = int(row['source_idx']) m2_pid = int(row['destination_idx']) m1 = (source, int(row['source_idx'])) m2 = (destination, int(row['destination_idx'])) values.append([kp1.loc[m1_pid]['x'], kp1.loc[m1_pid]['y'], m1, pt_idx, source, idx, point_type]) if subpixel: kp2x = kp2.loc[m2_pid]['x'] + row['x_offset'] kp2y = kp2.loc[m2_pid]['y'] + row['y_offset'] else: kp2x = kp2.loc[m2_pid]['x'] kp2y = kp2.loc[m2_pid]['y'] values.append([kp2x, kp2y, m2, pt_idx, destination, idx, point_type]) pt_idx += 1 columns = ['x', 'y', 'idx', 'pid', 'nid', 'mid', 'point_type'] cnet = CorrespondenceNetwork(values, columns=columns) if merged_cnet is None: merged_cnet = cnet.copy(deep=True) else: pid_offset = merged_cnet['pid'].max() + 1 # Get the current max point index cnet[['pid']] += pid_offset # Inner merge on the dataframe identifies common points common = pd.merge(merged_cnet, cnet, how='inner', on='idx', left_index=True, suffixes=['_r', '_l']) # Iterate over the points to be merged and merge them in. for i, r in common.iterrows(): new_pid = r['pid_r'] update_pid = r['pid_l'] cnet.loc[cnet['pid'] == update_pid, ['pid']] = new_pid # Update the point ids # Perform the concat merged_cnet = pd.concat([merged_cnet, cnet]) merged_cnet.drop_duplicates(['idx', 'pid'], keep='first', inplace=True) # Final validation to remove any correspondence with multiple correspondences in the same image merged_cnet = _validate_cnet(merged_cnet) # If the user wants ISIS serial numbers, replace the nid with the serial. if isis_serials is True: nid_to_serial = {} for i, node in self.nodes_iter(data=True): nid_to_serial[i] = node.isis_serial merged_cnet.replace({'nid': nid_to_serial}, inplace=True) return merged_cnet def to_json_file(self, outputfile): """ Write the edge structure to a JSON adjacency list Loading autocnet/graph/tests/test_network.py +10 −6 Original line number Diff line number Diff line Loading @@ -53,17 +53,20 @@ class TestCandidateGraph(unittest.TestCase): except AttributeError: pass mst_graph.extract_features(extractor_parameters={'nfeatures': 500}) mst_graph.extract_features(extractor_parameters={'nfeatures': 50}) mst_graph.match_features() mst_graph.apply_func_to_edges("symmetry_check") # Test passing the func by signature mst_graph.apply_func_to_edges(graph[0][1].symmetry_check) self.assertFalse(graph[0][2].masks['symmetry'].all()) self.assertFalse(graph[0][1].masks['symmetry'].all()) try: self.assertTrue(graph[1][2].masks['symmetry'].all()) except: pass except: pass def test_connected_subgraphs(self): subgraph_list = self.disconnected_graph.connected_subgraphs() Loading Loading @@ -178,6 +181,7 @@ class TestCandidateGraph(unittest.TestCase): self.assertEqual(sorted(mst_graph.nodes()), sorted(graph.nodes())) self.assertEqual(len(mst_graph.edges()), len(graph.edges())-5) def tearDown(self): pass def test_triangular_cycles(self): cycles = self.graph.compute_triangular_cycles() # Node order is variable, length is not self.assertEqual(len(cycles), 1) autocnet/matcher/outlier_detector.py +2 −4 Original line number Diff line number Diff line Loading @@ -160,10 +160,8 @@ class SpatialSuppression(Observable): @property def nvalid(self): try: return self.mask.sum() except: return None @property def error_k(self): Loading autocnet/matcher/tests/test_outlier_detector.py +8 −0 Original line number Diff line number Diff line Loading @@ -94,3 +94,11 @@ class TestSpatialSuppression(unittest.TestCase): self.suppression_obj.suppress() self.assertIn(self.suppression_obj.mask.sum(), list(range(27, 34))) with warnings.catch_warnings(record=True) as w: self.suppression_obj.k = 101 self.suppression_obj.suppress() self.assertEqual(len(w), 1) self.assertTrue(issubclass(w[0].category, UserWarning)) Loading
autocnet/control/tests/test_control.py +21 −0 Original line number Diff line number Diff line Loading @@ -54,3 +54,24 @@ class TestC(unittest.TestCase): def test_creation_date(self): self.assertEqual(self.C.creationdate, strftime("%Y-%m-%d %H:%M:%S", gmtime())) def test_point_subpixel(self): for k, v in self.C.point_to_correspondence.items(): self.assertFalse(k.subpixel) k.subpixel = True self.assertTrue(k.subpixel) break def test_equalities(self): points = [] correspondences = [] for k, v in self.C.point_to_correspondence.items(): points.append(k) correspondences.extend(v) self.assertEqual(points[0], points[0]) self.assertNotEqual(points[-1], points[1]) self.assertEqual(correspondences[0][0], correspondences[0][0]) def test_to_dataframe(self): self.C.to_dataframe()
autocnet/graph/network.py +0 −136 Original line number Diff line number Diff line Loading @@ -530,142 +530,6 @@ class CandidateGraph(nx.Graph): matches = edge.matches self.cn.add_correspondences(edge, matches) def to_cnet(self, clean_keys=[], isis_serials=False): """ Generate a control network (C) object from a graph Parameters ---------- clean_keys : list of strings identifying the masking arrays to use, e.g. ratio, symmetry isis_serials : bool Replace the node ID (nid) values with an ISIS serial number. Default False Returns ------- merged_cnet : C A control network object """ def _validate_cnet(cnet): """ Once the control network is aggregated from graph edges, ensure that a given correspondence in a given image does not match multiple correspondences in a different image. Parameters ---------- cnet : C control network object Returns ------- : C the cleaned control network """ mask = np.zeros(len(cnet), dtype=bool) counter = 0 for i, group in cnet.groupby('pid'): group_size = len(group) if len(group) != len(group['nid'].unique()): mask[counter: counter + group_size] = False else: mask[counter: counter + group_size] = True counter += group_size return cnet[mask] merged_cnet = None for source, destination, edge in self.edges_iter(data=True): matches = edge.matches # Merge all of the masks if clean_keys: matches, mask = edge._clean(clean_keys) subpixel = False point_type = 2 if 'subpixel' in clean_keys: subpixel = True point_type = 3 kp1 = self.node[source].get_keypoints() kp2 = self.node[destination].get_keypoints() pt_idx = 0 values = [] for i, (idx, row) in enumerate(matches.iterrows()): # Composite matching key (node_id, point_id) m1_pid = int(row['source_idx']) m2_pid = int(row['destination_idx']) m1 = (source, int(row['source_idx'])) m2 = (destination, int(row['destination_idx'])) values.append([kp1.loc[m1_pid]['x'], kp1.loc[m1_pid]['y'], m1, pt_idx, source, idx, point_type]) if subpixel: kp2x = kp2.loc[m2_pid]['x'] + row['x_offset'] kp2y = kp2.loc[m2_pid]['y'] + row['y_offset'] else: kp2x = kp2.loc[m2_pid]['x'] kp2y = kp2.loc[m2_pid]['y'] values.append([kp2x, kp2y, m2, pt_idx, destination, idx, point_type]) pt_idx += 1 columns = ['x', 'y', 'idx', 'pid', 'nid', 'mid', 'point_type'] cnet = CorrespondenceNetwork(values, columns=columns) if merged_cnet is None: merged_cnet = cnet.copy(deep=True) else: pid_offset = merged_cnet['pid'].max() + 1 # Get the current max point index cnet[['pid']] += pid_offset # Inner merge on the dataframe identifies common points common = pd.merge(merged_cnet, cnet, how='inner', on='idx', left_index=True, suffixes=['_r', '_l']) # Iterate over the points to be merged and merge them in. for i, r in common.iterrows(): new_pid = r['pid_r'] update_pid = r['pid_l'] cnet.loc[cnet['pid'] == update_pid, ['pid']] = new_pid # Update the point ids # Perform the concat merged_cnet = pd.concat([merged_cnet, cnet]) merged_cnet.drop_duplicates(['idx', 'pid'], keep='first', inplace=True) # Final validation to remove any correspondence with multiple correspondences in the same image merged_cnet = _validate_cnet(merged_cnet) # If the user wants ISIS serial numbers, replace the nid with the serial. if isis_serials is True: nid_to_serial = {} for i, node in self.nodes_iter(data=True): nid_to_serial[i] = node.isis_serial merged_cnet.replace({'nid': nid_to_serial}, inplace=True) return merged_cnet def to_json_file(self, outputfile): """ Write the edge structure to a JSON adjacency list Loading
autocnet/graph/tests/test_network.py +10 −6 Original line number Diff line number Diff line Loading @@ -53,17 +53,20 @@ class TestCandidateGraph(unittest.TestCase): except AttributeError: pass mst_graph.extract_features(extractor_parameters={'nfeatures': 500}) mst_graph.extract_features(extractor_parameters={'nfeatures': 50}) mst_graph.match_features() mst_graph.apply_func_to_edges("symmetry_check") # Test passing the func by signature mst_graph.apply_func_to_edges(graph[0][1].symmetry_check) self.assertFalse(graph[0][2].masks['symmetry'].all()) self.assertFalse(graph[0][1].masks['symmetry'].all()) try: self.assertTrue(graph[1][2].masks['symmetry'].all()) except: pass except: pass def test_connected_subgraphs(self): subgraph_list = self.disconnected_graph.connected_subgraphs() Loading Loading @@ -178,6 +181,7 @@ class TestCandidateGraph(unittest.TestCase): self.assertEqual(sorted(mst_graph.nodes()), sorted(graph.nodes())) self.assertEqual(len(mst_graph.edges()), len(graph.edges())-5) def tearDown(self): pass def test_triangular_cycles(self): cycles = self.graph.compute_triangular_cycles() # Node order is variable, length is not self.assertEqual(len(cycles), 1)
autocnet/matcher/outlier_detector.py +2 −4 Original line number Diff line number Diff line Loading @@ -160,10 +160,8 @@ class SpatialSuppression(Observable): @property def nvalid(self): try: return self.mask.sum() except: return None @property def error_k(self): Loading
autocnet/matcher/tests/test_outlier_detector.py +8 −0 Original line number Diff line number Diff line Loading @@ -94,3 +94,11 @@ class TestSpatialSuppression(unittest.TestCase): self.suppression_obj.suppress() self.assertIn(self.suppression_obj.mask.sum(), list(range(27, 34))) with warnings.catch_warnings(record=True) as w: self.suppression_obj.k = 101 self.suppression_obj.suppress() self.assertEqual(len(w), 1) self.assertTrue(issubclass(w[0].category, UserWarning))
