def test_three_image(self):
# Step: Create an adjacency graph
adjacency = get_path('three_image_adjacency.json')
basepath = get_path('Apollo15')
cg = CandidateGraph.from_adjacency(adjacency, basepath)
self.assertEqual(3, cg.number_of_nodes())
self.assertEqual(3, cg.number_of_edges())
# Step: Extract image data and attribute nodes
cg.extract_features(extractor_parameters={'nfeatures':500})
for i, node, in cg.nodes_iter(data=True):
self.assertIn(node.nkeypoints, range(490, 511))
cg.match_features(k=5)
cg.symmetry_checks()
cg.ratio_checks()
cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio'])
cg.compute_fundamental_matrices(clean_keys=['symmetry', 'ratio'])
# Step: And create a C object
cg.generate_cnet(clean_keys=['symmetry', 'ratio', 'ransac'])
# 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)
to_isis('TestThreeImageMatching.net', cg.cn, mode='wb',
networkid='TestThreeImageMatching', targetname='Moon')
def test_two_image(self):
# Step: Create an adjacency graph
adjacency = get_path('two_image_adjacency.json')
basepath = get_path('Apollo15')
cg = CandidateGraph.from_adjacency(adjacency, basepath=basepath)
self.assertEqual(2, cg.number_of_nodes())
self.assertEqual(1, cg.number_of_edges())
# 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))
# 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)
cg.match_features(k=2)
# 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(400, 600))
# Perform the ratio test
self.assertIn(edge.masks['ratio'].sum(), range(225, 275))
# Range needs to be set
self.assertIn(edge.masks['fundamental'].sum(), range(200, 250))
# Step: Compute the homographies and apply RANSAC
cg.compute_homographies(clean_keys=['symmetry', 'ratio'])
# Apply AMNS
cg.suppress(k=30, suppression_func=error)
# Step: Compute subpixel offsets for candidate points
cg.subpixel_register(clean_keys=['suppression'])
# Step: And create a C object
cg.generate_cnet(clean_keys=['subpixel'])
# Step: Create a fromlist to go with the cnet and write it to a file
filelist = cg.to_filelist()
write_filelist(filelist, path="fromlis.lis")
# Step: Output a control network
to_isis('TestTwoImageMatching.net', cg.cn, mode='wb',
networkid='TestTwoImageMatching', targetname='Moon')
