Three image support added (6a473e31) · Commits · aflab / astrogeology / Autocnet

autocnet/examples/Apollo15/three_image_adjacency.json

0 → 100644

+6 −0

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		{"autocnet/examples/Apollo15/AS15-M-0297_SML.png": ["autocnet/examples/Apollo15/AS15-M-0298_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0299_SML.png"],
		"autocnet/examples/Apollo15/AS15-M-0298_SML.png": ["autocnet/examples/Apollo15/AS15-M-0297_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0299_SML.png"],
		"autocnet/examples/Apollo15/AS15-M-0299_SML.png": ["autocnet/examples/Apollo15/AS15-M-0297_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0298_SML.png"]}

autocnet/fileio/io_controlnetwork.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -80,6 +80,7 @@ def to_isis(path, C, mode='w', version=VERSION,

		store.write(header)


		class IsisStore(object):
		"""
		Class to manage IO of an ISIS control network (version 2).

autocnet/graph/network.py

+39 −5

Original line number	Diff line number	Diff line
		from functools import reduce
		import operator as op
		import os

		import networkx as nx
		@@ -15,6 +13,7 @@ from autocnet.fileio.io_gdal import GeoDataset
		from autocnet.matcher import feature_extractor as fe # extract features from image
		from autocnet.matcher import outlier_detector as od


		class CandidateGraph(nx.Graph):
		"""
		A NetworkX derived directed graph to store candidate overlap images.
		@@ -252,14 +251,46 @@ class CandidateGraph(nx.Graph):
		"""
		Generate a control network (C) object from a graph

		Parameters
		----------
		clean_keys : list
		of strings identifying the masking arrays to use, e.g. ratio, symmetry

		Returns
		-------
		merged_cnet : C
		A control network object
		"""

		clean_keys : list
		of strings identifying the masking arrays to use, e.g. ratio, symmetry
		def _validate_cnet(cnet):
		"""
		Once the control network is aggregated from graph edges,
		ensure that a given correspondence in a given image does
		not match multiple correspondences in a different image.

		Parameters
		----------
		cnet : C
		control network object

		Returns
		-------
		: C
		the cleaned control network
		"""

		mask = np.zeros(len(cnet), dtype=bool)
		counter = 0
		for i, group in cnet.groupby('pid'):
		group_size = len(group)
		if len(group) != len(group['nid'].unique()):
		mask[counter: counter + group_size] = False
		else:
		mask[counter: counter + group_size] = True
		counter += group_size

		return cnet[mask]

		merged_cnet = None

		for source, destination, attributes in self.edges_iter(data=True):
		@@ -317,6 +348,9 @@ class CandidateGraph(nx.Graph):
		merged_cnet = pd.concat([merged_cnet, cnet])
		merged_cnet.drop_duplicates(['idx', 'pid'], keep='first', inplace=True)

		# Final validation to remove any correspondence with multiple correspondences in the same image
		merged_cnet = _validate_cnet(merged_cnet)

		return merged_cnet

		def to_json_file(self, outputfile):

autocnet/matcher/matcher.py

+0 −3

Original line number	Diff line number	Diff line
		@@ -41,9 +41,6 @@ def pattern_match(template, image, upsampling=10,

		strength : float
		The strength of the correlation in the range [-1, 1].



		"""
		if upsampling < 1:
		raise ValueError

autocnet/matcher/tests/test_feature_extractor.py

+4 −2

Original line number	Diff line number	Diff line
		@@ -12,8 +12,10 @@ from autocnet.fileio import io_gdal


		class TestFeatureExtractor(unittest.TestCase):
		def setUp(self):
		self.dataset = io_gdal.GeoDataset(get_path('Mars_MGS_MOLA_ClrShade_MAP2_0.0N0.0_MERC.tif'))

		@classmethod
		def setUpClass(self):
		self.dataset = io_gdal.GeoDataset(get_path('AS15-M-0295_SML.png'))
		self.data_array = self.dataset.read_array()
		self.parameters = {"nfeatures" : 10,
		"nOctaveLayers" : 3,