Removed stateful fundamental matrix lookup from Edge.masks(); Updated refs to... (9fee1d07) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/edge.py

+8 −19

Changes for autocnet/graph/edge.py: 8 added lines, 19 removed lines.
Original line number	Diff line number	Diff line
		@@ -74,22 +74,11 @@ class Edge(dict, MutableMapping):
		def masks(self):
		mask_lookup = {'fundamental': 'fundamental_matrix'}
		if not hasattr(self, '_masks'):
		if self.matches is not None:
		if isinstance(self.matches, pd.DataFrame):
		self._masks = pd.DataFrame(True, columns=['symmetry'],
		index=self.matches.index)
		else:
		self._masks = pd.DataFrame()
		# If the mask is coming form another object that tracks
		# state, dynamically draw the mask from the object.
		for c in self._masks.columns:
		if c in mask_lookup:
		try:
		truncated_mask = getattr(self, mask_lookup[c]).mask
		self._masks[c] = False
		self._masks[c].iloc[truncated_mask.index] = truncated_mask
		except Exception:
		#TODO: Get rid of state
		pass
		return self._masks

		@masks.setter
		@@ -118,14 +107,14 @@ class Edge(dict, MutableMapping):
		pass

		def symmetry_check(self):
		if self.matches:
		if isinstance(self.matches, pd.DataFrame):
		mask = od.mirroring_test(self.matches)
		self.masks = ('symmetry', mask)
		else:
		raise AttributeError('No matches have been computed for this edge.')

		def ratio_check(self, clean_keys=[], **kwargs):
		if self.matches:
		if isinstance(self.matches, pd.DataFrame):
		matches, mask = self.clean(clean_keys)
		distance_mask = od.distance_ratio(matches, **kwargs)
		self.masks = ('ratio', distance_mask)
		@@ -152,7 +141,7 @@ class Edge(dict, MutableMapping):
		autocnet.transformation.transformations.FundamentalMatrix

		"""
		if not self.matches:
		if not isinstance(self.matches, pd.DataFrame):
		raise AttributeError('Matches have not been computed for this edge')
		return
		matches, mask = self.clean(clean_keys)
		@@ -197,7 +186,7 @@ class Edge(dict, MutableMapping):
		Boolean array of the outliers
		"""

		if self.matches:
		if isinstance(self.matches, pd.DataFrame):
		matches = self.matches
		else:
		raise AttributeError('Matches have not been computed for this edge')
		@@ -321,7 +310,7 @@ class Edge(dict, MutableMapping):
		of mask keys to be used to reduce the total size
		of the matches dataframe.
		"""
		if not self.matches:
		if not isinstance(self.matches, pd.DataFrame):
		raise AttributeError('This edge does not yet have any matches computed.')

		matches, mask = self.clean(clean_keys)
		@@ -418,7 +407,7 @@ class Edge(dict, MutableMapping):
		returns the overlap area
		covered by the keypoints
		"""
		if self.matches is None:
		if not isinstance(self.matches, pd.DataFrame):
		raise AttributeError('Edge needs to have features extracted and matched')
		return
		matches, mask = self.clean(clean_keys)
		@@ -454,7 +443,7 @@ class Edge(dict, MutableMapping):
		Of strings used to apply masks to omit correspondences

		"""
		if self.matches is None:
		if not isinstance(self.matches, pd.DataFrame):
		raise AttributeError('Matches have not been computed for this edge')
		voronoi = cg.vor(self, clean_keys, **kwargs)
		self.matches = pd.concat([self.matches, voronoi[1]['vor_weights']], axis=1)

autocnet/graph/tests/test_edge.py

+43 −12

Changes for autocnet/graph/tests/test_edge.py: 43 added lines, 12 removed lines.
Original line number	Diff line number	Diff line
		@@ -7,6 +7,7 @@ import numpy as np
		import pandas as pd
		from plio.io import io_gdal

		from autocnet.matcher import outlier_detector as od
		from autocnet.examples import get_path
		from autocnet.graph.network import CandidateGraph
		from autocnet.utils.utils import array_to_poly
		@@ -45,24 +46,33 @@ class TestEdge(unittest.TestCase):

		def test_masks(self):
		self.assertIsInstance(self.edge.masks, pd.DataFrame)

		keypoint_matches = [[0, 0, 1, 4],
		[0, 1, 1, 3],
		matches = [[0, 0, 1, 0],
		[0, 1, 1, 1],
		[0, 2, 1, 2],
		[0, 3, 1, 1],
		[0, 4, 1, 0]]

		# Test masks returns properly
		matches_df = pd.DataFrame(data=keypoint_matches, columns=['source_image', 'source_idx',
		'destination_image', 'destination_idx'])
		[0, 3, 1, 3],
		[0, 4, 1, 4]]
		matches_df = pd.DataFrame(data=matches,
		columns=['source_image', 'source_idx',
		'destination_image',
		'destination_idx'])
		e = edge.Edge()
		e.matches = matches_df

		# Test empty masks df on an edge with computed matches
		expected = pd.DataFrame(True, columns=['symmetry'],
		index=matches_df.index)
		self.assertTrue(e.masks.equals(expected))
		self.assertTrue(expected.equals(e.masks))

		# Test the masks setter, changing a given row
		new_symmetry_rows = [True, False, True, False, True]
		e.masks = "symmetry", new_symmetry_rows

		self.assertEqual(new_symmetry_rows, list(e.masks.loc[:, "symmetry"]))

		# Test the masks setter, inserting a new row
		e.masks = "fundamental", new_symmetry_rows
		self.assertEqual(new_symmetry_rows, list(e.masks.loc[:, "fundamental"]))

		def test_masks_setter(self):
		e = edge.Edge()


		def test_compute_fundamental_matrix(self):
		@@ -397,3 +407,24 @@ class TestEdge(unittest.TestCase):
		# If there are no matches, should raise attrib err
		with (self.assertRaises(AttributeError)):
		e.symmetry_check()

		def test_ratio_check(self):
		# Matches is init to None
		e = edge.Edge()
		# If there are no matches, should raise attrib err
		with (self.assertRaises(AttributeError)):
		e.ratio_check()

		# If there are matches...
		keypoint_matches = [[0, 0, 1, 4, 5],
		[0, 1, 1, 3, 5],
		[0, 2, 1, 2, 5],
		[0, 3, 1, 1, 5],
		[0, 4, 1, 0, 5]]

		matches_df = pd.DataFrame(data=keypoint_matches, columns=['source_image', 'source_idx',
		'destination_image', 'destination_idx', 'distance'])
		e.matches = matches_df
		expected = list(od.distance_ratio(matches_df))
		e.ratio_check()
		self.assertEqual(expected, list(e.masks["ratio"]))