Merge pull request #188 from evindunn/dev (8a4dbd85) · Commits · aflab / astrogeology / Autocnet

autocnet/control/tests/test_control.py

+20 −0

Original line number	Diff line number	Diff line
		@@ -75,3 +75,23 @@ class TestC(unittest.TestCase):
		def test_to_dataframe(self):
		self.C.to_dataframe()

		def test_point_repr(self):
		expected = 0
		p = control.Point(expected)
		self.assertEqual(str(expected), p.__repr__())

		def test_correspondence_repr(self):
		expected = 0
		c = control.Correspondence(expected, 1, 1)
		self.assertEqual(str(expected), c.__repr__())

		def test_correspondence_eq(self):
		expected = 0
		c = control.Correspondence(expected, 1, 1)
		self.assertTrue(c == expected)

		def test_correspondence_hash(self):
		expected = 200
		c = control.Correspondence(expected, 1, 1)
		self.assertEqual(hash(expected), hash(c))

autocnet/graph/edge.py

+8 −19

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 hasattr(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 hasattr(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 hasattr(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 hasattr(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 hasattr(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

+107 −0

Original line number	Diff line number	Diff line
		@@ -3,9 +3,11 @@ from unittest.mock import Mock
		from unittest.mock import MagicMock

		import ogr
		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
		@@ -44,6 +46,33 @@ class TestEdge(unittest.TestCase):

		def test_masks(self):
		self.assertIsInstance(self.edge.masks, pd.DataFrame)
		matches = [[0, 0, 1, 0],
		[0, 1, 1, 1],
		[0, 2, 1, 2],
		[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(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_compute_fundamental_matrix(self):
		@@ -321,3 +350,81 @@ class TestEdge(unittest.TestCase):
		# Check key error thrown when string arg != "source" or "destination"
		with self.assertRaises(KeyError):
		e.get_keypoints("string", clean_keys)

		def test_eq(self):
		edge1 = edge.Edge()
		edge2 = edge.Edge()
		edge3 = edge.Edge()

		# Test edges w/ different keys are not equal, ones with same keys are
		edge1.__dict__["key"] = 1
		edge2.__dict__["key"] = 1
		edge3.__dict__["not_key"] = 1

		self.assertTrue(edge1 == edge2)
		self.assertFalse(edge1 == edge3)

		# Test edges with same keys, but diff df values
		edge1.__dict__["key"] = pd.DataFrame({'x': (0, 1, 2, 3, 4)})
		edge2.__dict__["key"] = pd.DataFrame({'x': (0, 1, 2, 3, 4)})
		edge3.__dict__["key"] = pd.DataFrame({'x': (0, 1, 2, 3, 5)})

		self.assertTrue(edge1 == edge2)
		self.assertFalse(edge1 == edge3)

		# Test edges with same keys, but diff np array vals
		# edge.__eq__ calls ndarray.all(), which checks that
		# all values in an array eval to true
		edge1.__dict__["key"] = np.array([True, True, True], dtype=np.bool)
		edge2.__dict__["key"] = np.array([True, True, True], dtype=np.bool)
		edge3.__dict__["key"] = np.array([True, True, False], dtype=np.bool)

		self.assertTrue(edge1 == edge2)
		self.assertFalse(edge1 == edge3)

		def test_repr(self):
		src = node.Node()
		dst = node.Node()
		masks = pd.DataFrame()

		e = edge.Edge()
		e.source = src
		e.destination = dst

		expected = """
		Source Image Index: {}
		Destination Image Index: {}
		Available Masks: {}
		""".format(src, dst, masks)

		self.assertEqual(expected, e.__repr__())

		def test_symmetry_check(self):
		# Matches is init to None
		e = edge.Edge()
		e.source = node.Node()
		e.destination = node.Node()
		# 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"]))