Merge pull request #82 from Kelvinrr/edge_func_refactor (61e54f1d) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/network.py

+13 −62

Original line number	Diff line number	Diff line
		@@ -47,7 +47,7 @@ class CandidateGraph(nx.Graph):
		self.node_counter = 0
		node_labels = {}
		self.node_name_map = {}
		self.graph_masks = {}
		self.graph_masks = pd.DataFrame()

		# the node_name is the relative path for the image
		for node_name, node in self.nodes_iter(data=True):
		@@ -368,15 +368,21 @@ class CandidateGraph(nx.Graph):
		of keys in graph_masks
		"""

		if graph_mask_keys:
		merged_graph_mask = self.graph_masks[graph_mask_keys].all(axis=1)
		edges_to_iter = merged_graph_mask[merged_graph_mask].index
		else:
		edges_to_iter = self.edges()

		if not isinstance(func, str):
		func = func.__name__

		for s, d in edges_to_iter:
		curr_edge = self.get_edge_data(s, d)
		try:
		function = getattr(curr_edge, func)
		except:
		raise AttributeError('The passed function is not an attribute of Edge')
		raise AttributeError(func, ' is not an attribute of Edge')
		else:
		function(args, *kwargs)

		@@ -391,66 +397,12 @@ class CandidateGraph(nx.Graph):
		boolean mask for edges in the minimum spanning tree
		"""

		self.graph_masks = pd.DataFrame(False, index=self.edges(), columns=['mst'])
		graph_mask = pd.Series(False, index=self.edges())
		self.graph_masks['mst'] = graph_mask

		mst = nx.minimum_spanning_tree(self)
		self.graph_masks['mst'][mst.edges()] = True

		def symmetry_checks(self):
		"""
		Perform a symmetry check on all edges in the graph
		"""
		for s, d, edge in self.edges_iter(data=True):
		edge.symmetry_check()

		def ratio_checks(self, clean_keys=[], **kwargs):
		"""
		Perform a ratio check on all edges in the graph
		"""
		for s, d, edge in self.edges_iter(data=True):
		edge.ratio_check(clean_keys=clean_keys)

		def compute_homographies(self, clean_keys=[], **kwargs):
		"""
		Compute homographies for all edges using identical parameters

		Parameters
		----------
		clean_keys : list
		Of keys in the mask dict

		"""

		for s, d, edge in self.edges_iter(data=True):
		edge.compute_homography(clean_keys=clean_keys, **kwargs)

		def compute_fundamental_matrices(self, clean_keys=[], **kwargs):
		"""
		Compute fundamental matrices for all edges using identical parameters

		Parameters
		----------
		clean_keys : list
		Of keys in the mask dict

		"""

		for s, d, edge in self.edges_iter(data=True):
		edge.compute_fundamental_matrix(clean_keys=clean_keys, **kwargs)

		def subpixel_register(self, clean_keys=[], threshold=0.8, upsampling=10,
		template_size=9, search_size=27, tiled=False, **kwargs):
		"""
		Compute subpixel offsets for all edges using identical parameters
		"""
		for s, d, edge in self.edges_iter(data=True):
		edge.subpixel_register(clean_keys=clean_keys, threshold=threshold,
		upsampling=upsampling, template_size=template_size,
		search_size=search_size, tiled=tiled, **kwargs)

		def suppress(self, clean_keys=[], func=spf.correlation, **kwargs):
		for s, d, e in self.edges_iter(data=True):
		e.suppress(clean_keys=clean_keys, func=func, **kwargs)

		def to_filelist(self):
		"""
		Generate a file list for the entire graph.
		@@ -503,7 +455,6 @@ class CandidateGraph(nx.Graph):
		: C
		the cleaned control network
		"""

		mask = np.zeros(len(cnet), dtype=bool)
		counter = 0
		for i, group in cnet.groupby('pid'):

functional_tests/test_three_image.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -52,7 +52,8 @@ class TestThreeImageMatching(unittest.TestCase):
		for source, destination, edge in cg.edges_iter(data=True):
		edge.symmetry_check()
		edge.ratio_check(clean_keys=['symmetry'], ratio=0.99)
		cg.compute_homographies(clean_keys=['symmetry', 'ratio'])

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

		# Step: And create a C object
		cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac'])

functional_tests/test_two_image.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -73,14 +73,14 @@ class TestTwoImageMatching(unittest.TestCase):
		self.assertIn(edge.masks['ratio'].sum(), range(40, 100))

		# Step: Compute the homographies and apply RANSAC
		cg.compute_homographies(clean_keys=['symmetry', 'ratio'])
		cg.apply_func_to_edges("compute_homography", clean_keys=['symmetry', 'ratio'])

		# Step: Compute the overlap ratio and coverage ratio
		for s, d, edge in cg.edges_iter(data=True):
		edge.coverage_ratio(clean_keys=['symmetry', 'ratio'])

		# Step: Compute subpixel offsets for candidate points
		cg.subpixel_register(clean_keys=['ransac'])
		cg.apply_func_to_edges("subpixel_register", clean_keys=['ransac'])

		# Step: And create a C object
		cnet = cg.to_cnet(clean_keys=['symmetry', 'ratio', 'ransac', 'subpixel'])

notebooks/Tutorial with Visualization.ipynb

+61 −35

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