Merge pull request #144 from jlaura/master (37f4818d) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/node.py

+9 −3

Original line number	Diff line number	Diff line
		@@ -251,8 +251,10 @@ class Node(dict, MutableMapping):

		allkps = pd.DataFrame(data=clean_kps, columns=columns, index=index)

		if 'response' in allkps.columns:
		self._keypoints = allkps.sort_values(by='response', ascending=False)

		elif 'size' in allkps.columns:
		self._keypoints = allkps.sort_values(by='size', ascending=False)
		if isinstance(in_path, str):
		hdf = None

		@@ -299,7 +301,7 @@ class Node(dict, MutableMapping):
		if isinstance(out_path, str):
		hdf = None

		def group_correspondences(self, cg, args, clean_keys=['fundamental'], deepen=False, *kwargs):
		def group_correspondences(self, cg, args, deepen=False, *kwargs):
		"""

		Parameters
		@@ -319,6 +321,11 @@ class Node(dict, MutableMapping):
		# TODO: Add dangling correspondences to control network anyway. Subgraphs handle this segmentation if req.
		return

		try:
		clean_keys = kwargs['clean_keys']
		except:
		clean_keys = []

		# Grab all the incident edge matches and concatenate into a group match set.
		# All share the same source node
		edge_matches = []
		@@ -460,4 +467,3 @@ class Node(dict, MutableMapping):
		mask = panel[clean_keys].all(axis=1)
		matches = self._keypoints[mask]
		return matches, mask

autocnet/matcher/outlier_detector.py

+13 −12

Original line number	Diff line number	Diff line
		@@ -166,7 +166,6 @@ class SpatialSuppression(Observable):
		def nvalid(self):
		return self.mask.sum()


		@property
		def error_k(self):
		return self._error_k
		@@ -186,22 +185,30 @@ class SpatialSuppression(Observable):
		self.k = len(self.df)
		result = self.df.index
		process = False
		search_space = np.linspace(self.min_radius, self.max_radius, 100)
		nsteps = max(self.domain) * 0.95
		search_space = np.linspace(self.min_radius, self.max_radius, nsteps)
		cell_sizes = search_space / math.sqrt(2)
		min_idx = 0
		max_idx = len(search_space) - 1

		prev_min = None
		prev_max = None

		while process:
		# Setup to store results
		result = []

		mid_idx = int((min_idx + max_idx) / 2)

		if min_idx == mid_idx or mid_idx == max_idx:
		warnings.warn('Unable to optimally solve. Returning with {} points'.format(len(result)))
		process = False

		cell_size = cell_sizes[mid_idx]
		n_x_cells = int(self.domain[0] / cell_size)
		n_y_cells = int(self.domain[1] / cell_size)
		grid = np.zeros((n_x_cells, n_y_cells), dtype=np.bool)

		# Setup to store results
		result = []

		# Assign all points to bins
		x_edges = np.linspace(0, self.domain[0], n_x_cells)
		y_edges = np.linspace(0, self.domain[1], n_y_cells)
		@@ -245,11 +252,10 @@ class SpatialSuppression(Observable):
		grid[y_min: y_max,
		x_min: x_max] = True


		# Check break conditions
		if self.k - self.k * self.error_k <= len(result) <= self.k + self.k * self.error_k:
		process = False
		elif len(result) < self.k:
		elif len(result) < self.k - self.k * self.error_k:
		# The radius is too large
		max_idx = mid_idx
		if max_idx == 0:
		@@ -258,10 +264,6 @@ class SpatialSuppression(Observable):
		process = False
		if min_idx == max_idx:
		process = False
		elif min_idx == mid_idx or mid_idx == max_idx:
		warnings.warn('Unable to optimally solve. Returning with {} points'.format(len(result)))
		process = False

		self.mask = pd.Series(False, self.df.index)
		self.mask.loc[list(result)] = True
		state_package = {'mask': self.mask,
		@@ -319,4 +321,3 @@ def mirroring_test(matches):
		"""
		duplicate_mask = matches.duplicated(subset=['source_idx', 'destination_idx', 'distance'], keep='last')
		return duplicate_mask

autocnet/matcher/suppression_funcs.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -29,6 +29,6 @@ def error(row, edge):
		"""
		key = row.name
		try:
		return 1 / edge.fundamental_matrix.error.iloc[key]
		return 1 / edge.fundamental_matrix.error.loc[key]
		except:
		return np.NaN

autocnet/matcher/tests/test_outlier_detector.py

+1 −6

Original line number	Diff line number	Diff line
		@@ -95,11 +95,6 @@ class testSuppressionRanges(unittest.TestCase):
		def setUpClass(cls):
		cls.r = np.random.RandomState(12345)

		def test_one_by_one(self):
		df = pd.DataFrame(self.r.uniform(0,1,(500, 3)), columns=['x', 'y', 'strength'])
		sup = SpatialSuppression(df, (1,1), k = 1)
		self.assertRaises(ValueError, sup.suppress())

		def test_min_max(self):
		df = pd.DataFrame(self.r.uniform(0,2,(500, 3)), columns=['x', 'y', 'strength'])
		sup = SpatialSuppression(df, (1.5,1.5), k = 1)
		@@ -110,7 +105,7 @@ class testSuppressionRanges(unittest.TestCase):
		df = pd.DataFrame(self.r.uniform(0,15,(500, 3)), columns=['x', 'y', 'strength'])
		sup = SpatialSuppression(df, (15,15), k = 200)
		sup.suppress()
		self.assertEqual(len(df[sup.mask]), 70)
		self.assertEqual(len(df[sup.mask]), 69)

		def test_small_distribution(self):
		df = pd.DataFrame(self.r.uniform(0,25,(500, 3)), columns=['x', 'y', 'strength'])