def symmetry_check(self):
if hasattr(self, 'matches'):
mask = od.mirroring_test(self.matches)
self.masks = ('symmetry', mask)
else:
raise AttributeError(
'No matches have been computed for this edge.')
def test_two_image(self):
# Step: Create an adjacency graph
adjacency = get_path('two_image_adjacency.json')
cg = CandidateGraph.from_adjacency(adjacency)
self.assertEqual(2, cg.number_of_nodes())
self.assertEqual(1, cg.number_of_edges())
# Step: Extract image data and attribute nodes
cg.extract_features(extractor_parameters={"nfeatures":500})
for node, attributes in cg.nodes_iter(data=True):
self.assertIn(len(attributes['keypoints']), range(490, 511))
# Step: Then apply a FLANN matcher
fl = FlannMatcher()
for node, attributes in cg.nodes_iter(data=True):
fl.add(attributes['descriptors'], key=node)
fl.train()
for node, attributes in cg.nodes_iter(data=True):
descriptors = attributes['descriptors']
matches = fl.query(descriptors, node, k=5)
cg.add_matches(matches)
for source, destination, attributes in cg.edges_iter(data=True):
matches = attributes['matches']
# Perform the symmetry check
symmetry_mask = od.mirroring_test(matches)
self.assertIn(symmetry_mask.sum(), range(430, 461))
attributes['symmetry'] = symmetry_mask
# Perform the ratio test
ratio_mask = od.distance_ratio(matches, ratio=0.95)
self.assertIn(ratio_mask.sum(), range(390, 451))
attributes['ratio'] = ratio_mask
mask = np.array(ratio_mask * symmetry_mask)
self.assertIn(len(matches.loc[mask]), range(75,101))
# Step: Compute the homographies and apply RANSAC
cg.compute_homographies(clean_keys=['symmetry', 'ratio'])
# Step: Compute subpixel offsets for candidate points
cg.compute_subpixel_offsets(clean_keys=['symmetry', 'ratio', 'ransac'])
# Step: And create a C object
cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel'])
# Step update the serial numbers
nid_to_serial = {}
for node, attributes in cg.nodes_iter(data=True):
nid_to_serial[node] = self.serial_numbers[attributes['image_name']]
cnet.replace({'nid': nid_to_serial}, inplace=True)
# Step: Output a control network
to_isis('TestTwoImageMatching.net', cnet, mode='wb',
networkid='TestTwoImageMatching', targetname='Moon')
def test_two_image(self):
# Step: Create an adjacency graph
adjacency = get_path("two_image_adjacency.json")
cg = CandidateGraph.from_adjacency(adjacency)
self.assertEqual(2, cg.number_of_nodes())
self.assertEqual(1, cg.number_of_edges())
# Step: Extract image data and attribute nodes
cg.extract_features(500)
for node, attributes in cg.nodes_iter(data=True):
self.assertIn(len(attributes["keypoints"]), range(490, 511))
# Step: Then apply a FLANN matcher
fl = FlannMatcher()
for node, attributes in cg.nodes_iter(data=True):
fl.add(attributes["descriptors"], key=node)
fl.train()
for node, attributes in cg.nodes_iter(data=True):
descriptors = attributes["descriptors"]
matches = fl.query(descriptors, node, k=5)
cg.add_matches(matches)
for source, destination, attributes in cg.edges_iter(data=True):
matches = attributes["matches"]
# Perform the symmetry check
symmetry_mask = od.mirroring_test(matches)
self.assertIn(symmetry_mask.sum(), range(430, 461))
attributes["symmetry"] = symmetry_mask
# Perform the ratio test
ratio_mask = od.distance_ratio(matches, ratio=0.95)
self.assertIn(ratio_mask.sum(), range(400, 451))
attributes["ratio"] = ratio_mask
mask = np.array(ratio_mask * symmetry_mask)
self.assertIn(len(matches.loc[mask]), range(75, 101))
cg.compute_homographies(clean_keys=["symmetry", "ratio"])
# Step: And create a C object
cnet = cg.to_cnet(clean_keys=["symmetry", "ratio", "ransac"])
# Step update the serial numbers
nid_to_serial = {}
for node, attributes in cg.nodes_iter(data=True):
nid_to_serial[node] = self.serial_numbers[attributes["image_name"]]
cnet.replace({"nid": nid_to_serial}, inplace=True)
# Step: Output a control network
to_isis("TestTwoImageMatching.net", cnet, mode="wb", networkid="TestTwoImageMatching", targetname="Moon")
def test_three_image(self):
# Step: Create an adjacency graph
adjacency = get_path('three_image_adjacency.json')
cg = CandidateGraph.from_adjacency(adjacency)
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 node, attributes in cg.nodes_iter(data=True):
self.assertIn(len(attributes['keypoints']), range(490, 511))
fl = FlannMatcher()
for node, attributes in cg.nodes_iter(data=True):
fl.add(attributes['descriptors'], key=node)
fl.train()
for node, attributes in cg.nodes_iter(data=True):
descriptors = attributes['descriptors']
matches = fl.query(descriptors, node, k=5)
cg.add_matches(matches)
for source, destination, attributes in cg.edges_iter(data=True):
matches = attributes['matches']
# Perform the symmetry check
symmetry_mask = od.mirroring_test(matches)
attributes['symmetry'] = symmetry_mask
# Perform the ratio test
ratio_mask = od.distance_ratio(matches, ratio=0.8)
attributes['ratio'] = ratio_mask
cg.compute_homographies(clean_keys=['symmetry', 'ratio'])
# Step: And create a C object
cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac'])
# Step update the serial numbers
nid_to_serial = {}
for node, attributes in cg.nodes_iter(data=True):
nid_to_serial[node] = self.serial_numbers[attributes['image_name']]
cnet.replace({'nid': nid_to_serial}, inplace=True)
# Step: Output a control network
to_isis('TestThreeImageMatching.net', cnet, mode='wb',
networkid='TestThreeImageMatching', targetname='Moon')
def symmetry_check(self):
if hasattr(self, 'matches'):
mask = od.mirroring_test(self.matches)
self.masks = ('symmetry', mask)
else:
raise AttributeError('No matches have been computed for this edge.')