Merged with master and finished fix in image_match (3f41be77) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/network.py

+18 −0

Original line number	Diff line number	Diff line
		@@ -698,3 +698,21 @@ class CandidateGraph(nx.Graph):

		H.graph = self.graph
		return H

		def subgraph_from_matches(self):
		"""
		Returns a sub-graph where all edges have matches.
		(i.e. images with no matches are removed)

		Returns
		-------
		: Object
		A networkX graph object
		"""

		# get all edges that have matches
		matches = [(u, v) for u, v, edge in self.edges_iter(data=True)
		if hasattr(edge, 'matches') and
		not edge.matches.empty]

		return self.create_edge_subgraph(matches)

autocnet/graph/tests/test_network.py

+9 −0

Original line number	Diff line number	Diff line
		@@ -114,6 +114,15 @@ class TestCandidateGraph(unittest.TestCase):
		node_sub = g.create_node_subgraph([0,1])
		self.assertEqual(len(node_sub), 2)

		def test_subgraph_from_matches(self):
		test_sub_graph = self.graph.create_node_subgraph([0, 1])
		test_sub_graph.extract_features(extractor_parameters={'nfeatures': 500})
		test_sub_graph.match_features(k=2)

		sub_graph_from_matches = self.graph.subgraph_from_matches()

		self.assertEqual(test_sub_graph.edges(), sub_graph_from_matches.edges())

		def tearDown(self):
		pass

bin/image_match.py

+9 −57

Original line number	Diff line number	Diff line
		@@ -20,63 +20,9 @@ def parse_arguments():
		return args

		def match_images(args, config_dict):
		print(find_in_dict(config_dict, 'ratio'))
		# Matches the images in the input file using various candidate graph methods
		# produces two files usable in isis

		try:
		cg = CandidateGraph.from_adjacency(find_in_dict(config_dict, 'inputfile_path') +
		args.input_file, basepath=find_in_dict(config_dict, 'basepath'))
		except:
		cg = CandidateGraph.from_filelist(find_in_dict(config_dict, 'inputfile_path') + args.input_file)

		# Apply SIFT to extract features
		cg.extract_features(method=config_dict['extract_features']['method'],
		extractor_parameters=find_in_dict(config_dict, 'extractor_parameters'))

		# Match
		cg.match_features(k=config_dict['match_features']['k'])

		# Apply outlier detection
		cg.apply_func_to_edges('symmetry_check')
		cg.apply_func_to_edges('ratio_check',
		ratio=find_in_dict(config_dict, 'ratio'),
		mask_name=find_in_dict(config_dict, 'mask_name'),
		single=find_in_dict(config_dict, 'single'))

		# Compute a homography and apply RANSAC
		cg.apply_func_to_edges('compute_fundamental_matrix', clean_keys=find_in_dict(config_dict, 'fundamental_matrices')['clean_keys'],
		method=find_in_dict(config_dict, 'fundamental_matrices')['method'],
		reproj_threshold=find_in_dict(config_dict, 'reproj_threshold'),
		confidence=find_in_dict(config_dict, 'confidence'))

		cg.apply_func_to_edges('subpixel_register', clean_keys=['fundamental', 'symmetry', 'ratio'], template_size=5, search_size=15)

		cg.apply_func_to_edges('suppress', clean_keys=find_in_dict(config_dict, 'suppress')['clean_keys'],
		k=find_in_dict(config_dict, 'suppress')['k'],
		min_radius=find_in_dict(config_dict, 'min_radius'),
		error_k=find_in_dict(config_dict, 'error_k'))

		cnet = cg.to_cnet(clean_keys=find_in_dict(config_dict, 'cnet_conversion')['clean_keys'],
		isis_serials=True)

		filelist = cg.to_filelist()

		write_filelist(filelist, find_in_dict(config_dict, 'outputfile_path') + args.output_file + '.lis')

		to_isis(find_in_dict(config_dict, 'outputfile_path') + args.output_file + '.net', cnet,
		mode='wb',
		networkid=find_in_dict(config_dict, 'networkid'),
		targetname=find_in_dict(config_dict, 'targetname'),
		description=find_in_dict(config_dict, 'description'),
		username=find_in_dict(config_dict, 'username'))

		if __name__ == '__main__':
		config = read_yaml('image_match_config.yml')
		command_line_args = parse_arguments()
		match_images(command_line_args, config)

		'''
		try:
		cg = CandidateGraph.from_adjacency(find_in_dict(config_dict, 'inputfile_path') +
		args.input_file, basepath=find_in_dict(config_dict, 'basepath'))
		@@ -105,11 +51,13 @@ try:

		cg.apply_func_to_edges('subpixel_register', clean_keys=find_in_dict(config_dict, 'subpixel_register')['clean_keys'],
		template_size=find_in_dict(config_dict, 'template_size'),
		threshold=find_in_dict(config_dict, 'threshold_size'),
		threshold=find_in_dict(config_dict, 'threshold'),
		search_size=find_in_dict(config_dict, 'search_size'),
		max_x_shift=find_in_dict(config_dict, 'max_x_shift'),
		max_y_shift=find_in_dict(config_dict, 'max_y_shift'),
		tiled=find_in_dict(config_dict, 'tiled'))
		tiled=find_in_dict(config_dict, 'tiled'),
		upsampling = find_in_dict(config_dict, 'upsampling'),
		error_check = find_in_dict(config_dict, 'error_check'))

		cg.apply_func_to_edges('suppress', clean_keys=find_in_dict(config_dict, 'suppress')['clean_keys'],
		k=find_in_dict(config_dict, 'suppress')['k'],
		@@ -129,4 +77,8 @@ try:
		targetname=find_in_dict(config_dict, 'targetname'),
		description=find_in_dict(config_dict, 'description'),
		username=find_in_dict(config_dict, 'username'))
		'''

		if __name__ == '__main__':
		config = read_yaml('image_match_config.yml')
		command_line_args = parse_arguments()
		match_images(command_line_args, config)
		No newline at end of file

image_match_config.yml

+2 −1

Original line number	Diff line number	Diff line
		@@ -47,8 +47,9 @@ subpixel_register:
		tiled: False

		# Keyword arguments
		upsampling:
		upsampling: 16
		error_check: False
		subpixel_func: cv2.TM_CCOEFF_NORMED

		suppress:
		clean_keys: