Merge pull request #19 from jlaura/functional_test (558e71b8) · Commits · aflab / astrogeology / Autocnet

autocnet/control/control.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -2,6 +2,8 @@ from time import gmtime, strftime

		import pandas as pd

		POINT_TYPE = 2
		MEASURE_TYPE = 2

		class CSeries(pd.Series):
		"""

autocnet/examples/Apollo15/two_image_adjacency.json

0 → 100644

+2 −0

Original line number	Diff line number	Diff line
		{"AS15-M-0297_SML.png": ["AS15-M-0298_SML.png"],
		"AS15-M-0298_SML.png": ["AS15-M-0297_SML.png"]}

autocnet/fileio/io_controlnetwork.py

+11 −5

Original line number	Diff line number	Diff line
		@@ -144,18 +144,24 @@ class IsisStore(object):
		# Get the subset of the dataframe
		point = cnet.loc[point_id]

		try:
		point_spec.id = point_id
		except:
		point_spec.id = str(point_id)
		point_spec.type = FREEPOINT # Hard coded to free

		# A single extend call is cheaper than many add calls to pack points
		measure_iterable = []

		for m in point.iterrows():
		for name, measure in point.iterrows():
		measure_spec = point_spec.Measure()
		serialnumber = m[0][1]
		mtype = m[0][2]
		measure_spec.serialnumber = serialnumber
		serial_number = name[1]
		mtype = name[2]
		measure_spec.serialnumber = serial_number
		measure_spec.type = mtype
		measure_spec.sample = measure.x
		measure_spec.line = measure.y

		measure_iterable.append(measure_spec)
		point_spec.measures.extend(measure_iterable)

autocnet/fileio/tests/test_io_controlnetwork.py

+8 −8

Original line number	Diff line number	Diff line
		@@ -38,17 +38,17 @@ class TestWriteIsisControlNetwork(unittest.TestCase):
		multi_index = pd.MultiIndex.from_tuples(list(zip(ids, ptype, serials, mtype)),
		names=['Id', 'Type', 'Serial Number', 'Measure Type'])

		columns = ['Random Number']
		columns = ['x', 'y']
		self.data_length = 5
		data = np.random.randn(self.data_length)
		data = np.random.random((self.data_length, 2))

		self.creation_time = strftime("%Y-%m-%d %H:%M:%S", gmtime())
		cnet = C(data, index=multi_index, columns=columns)

		io_controlnetwork.to_isis('test.net', cnet, mode='wb', targetname='Moon')

		self.header_message_size = 84
		self.point_start_byte = 65620
		self.header_message_size = 85
		self.point_start_byte = 65621

		def test_create_buffer_header(self):
		with open('test.net', 'rb') as f:
		@@ -68,14 +68,14 @@ class TestWriteIsisControlNetwork(unittest.TestCase):
		self.assertEqual('Not modified', header_protocol.lastModified)

		#Repeating
		self.assertEqual([145, 99], header_protocol.pointMessageSizes)
		self.assertEqual([199, 135], header_protocol.pointMessageSizes)

		def test_create_point(self):
		with open('test.net', 'rb') as f:

		with open('test.net', 'rb') as f:
		f.seek(self.point_start_byte)
		for i, length in enumerate([145, 99]):
		for i, length in enumerate([199, 135]):
		point_protocol = cnf.ControlPointFileEntryV0002()
		raw_point = f.read(length)
		point_protocol.ParseFromString(raw_point)
		@@ -95,10 +95,10 @@ class TestWriteIsisControlNetwork(unittest.TestCase):
		self.assertEqual(5, mpoints)

		points_bytes = find_in_dict(pvl_header, 'PointsBytes')
		self.assertEqual(244, points_bytes)
		self.assertEqual(334, points_bytes)

		points_start_byte = find_in_dict(pvl_header, 'PointsStartByte')
		self.assertEqual(65620, points_start_byte)
		self.assertEqual(65621, points_start_byte)

		def tearDown(self):
		os.remove('test.net')
		No newline at end of file

autocnet/graph/network.py

+84 −1

Original line number	Diff line number	Diff line
		from collections import Hashable

		import networkx as nx
		import pandas as pd

		from autocnet.control.control import C, POINT_TYPE, MEASURE_TYPE
		from autocnet.fileio import io_json

		class CandidateGraph(nx.DiGraph):


		class CandidateGraph(nx.Graph):
		"""
		A NetworkX derived directed graph to store candidate overlap images.

		@@ -44,6 +49,83 @@ class CandidateGraph(nx.DiGraph):
		adjacency_dict[n] = self.neighbors(n)
		io_json.write_json(adjacency_dict, outputfile)

		def add_matches(self, source_node, matches):
		"""

		Adds match data to a node and attributes the data to the
		appropriate edges, e.g. if A-B have a match, edge A-B is attributes
		with the pandas dataframe.

		Parameters
		----------
		source_node : str
		The identifier for the node

		matches : dataframe
		The pandas dataframe containing the matches
		"""

		#TODO: This really belongs in an outlier detection matcher class, not here.
		# Remove erroneous self neighbors
		matches = matches.loc[matches['matched_to'] != source_node]

		groups = matches.groupby('matched_to')
		for destination_node, group in groups:
		try:
		edge = self[source_node][destination_node]
		except:
		edge = self[destination_node][source_node]

		if 'matches' in edge.keys():
		df = edge['matches']
		edge['matches'] = pd.merge(df, matches, left_on='queryIdx', right_on='trainIdx')
		else:
		edge['matches'] = matches

		def to_cnet(self):
		"""
		Generate a control network (C) object from a graph

		Returns
		-------
		cnet : C
		A control network object
		"""
		data = []
		point_ids = []
		serials = []
		for source, destination, attributes in self.edges_iter(data=True):

		for i, row in attributes['matches'].iterrows():

		# Get the source and destination x,y coordinates for matches
		source_idx = row['queryIdx_x']
		source_keypoints = (self.node[source]['keypoints'][source_idx].pt[0],
		self.node[source]['keypoints'][source_idx].pt[1])

		destination_idx = row['queryIdx_y']
		destination_keypoints = (self.node[destination]['keypoints'][destination_idx].pt[0],
		self.node[destination]['keypoints'][destination_idx].pt[1])

		data.append(source_keypoints)
		data.append(destination_keypoints)
		serials.append(source)
		serials.append(destination)
		point_ids.append(i)
		point_ids.append(i)

		point_types = [2] * len(point_ids)
		measure_types = [2] * len(point_ids)
		multi_index = pd.MultiIndex.from_tuples(list(zip(point_ids,point_types,
		serials, measure_types)))

		columns = ['x', 'y']
		cnet = C(data, index=multi_index, columns=columns)

		# TODO: This method assumes a 2 image match and should be generalized to build an n-image C object

		return cnet

		@classmethod
		def from_adjacency(cls, inputfile):
		"""
		@@ -68,3 +150,4 @@ class CandidateGraph(nx.DiGraph):
		adjacency_dict = io_json.read_json(inputfile)
		return cls(adjacency_dict)