Merge pull request #25 from jcwbacker/graphAccessors (2cb0fe08) · Commits · aflab / astrogeology / Autocnet

autocnet/examples/Apollo15/adjacency.json

+25 −1

Original line number	Diff line number	Diff line
		{"AS15-M-0297_SML.png": ["AS15-M-0298_SML.png", "AS15-M-0300_SML.png", "AS15-M-0299_SML.png", "AS15-M-0296_SML.png"], "AS15-M-0300_SML.png": ["AS15-M-0298_SML.png", "AS15-M-0297_SML.png", "AS15-M-0299_SML.png"], "AS15-M-0299_SML.png": ["AS15-M-0298_SML.png", "AS15-M-0297_SML.png", "AS15-M-0300_SML.png", "AS15-M-0296_SML.png"], "AS15-M-0295_SML.png": [], "AS15-M-0296_SML.png": ["AS15-M-0298_SML.png", "AS15-M-0297_SML.png", "AS15-M-0299_SML.png"], "AS15-M-0298_SML.png": ["AS15-M-0297_SML.png", "AS15-M-0300_SML.png", "AS15-M-0299_SML.png", "AS15-M-0296_SML.png"]}
		No newline at end of file
		{"autocnet/examples/Apollo15/AS15-M-0297_SML.png"
		: ["autocnet/examples/Apollo15/AS15-M-0298_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0300_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0299_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0296_SML.png"],
		"autocnet/examples/Apollo15/AS15-M-0300_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-0298_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0297_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0300_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0296_SML.png"],
		"autocnet/examples/Apollo15/AS15-M-0295_SML.png"
		: [],
		"autocnet/examples/Apollo15/AS15-M-0296_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-0298_SML.png"
		: ["autocnet/examples/Apollo15/AS15-M-0297_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0300_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0299_SML.png",
		"autocnet/examples/Apollo15/AS15-M-0296_SML.png"]}

autocnet/examples/Apollo15/two_image_adjacency.json

+4 −2

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

autocnet/graph/network.py

+124 −33

Original line number	Diff line number	Diff line
		@@ -5,9 +5,12 @@ import pandas as pd
		import cv2
		import numpy as np

		from scipy.misc import bytescale # store image array

		from autocnet.control.control import C
		from autocnet.fileio import io_json

		from autocnet.fileio.io_gdal import GeoDataset
		from autocnet.matcher import feature_extractor as fe # extract features from image

		class CandidateGraph(nx.Graph):
		"""
		@@ -17,6 +20,12 @@ class CandidateGraph(nx.Graph):
		----------

		Attributes
		node_counter : int
		The number of nodes in the graph.
		node_name_map : dict
		The mapping of image labels (i.e. file base names) to their
		corresponding node indices.

		----------
		"""

		@@ -24,47 +33,138 @@ class CandidateGraph(nx.Graph):
		super(CandidateGraph, self).__init__(args, *kwargs)
		self.node_counter = 0
		node_labels = {}
		self.node_name_map = {}

		# the node_name is the relative path for the image
		for node_name, node_attributes in self.nodes_iter(data=True):

		if os.path.isabs(node_name):
		node_attributes['image_name'] = os.path.basename(node_name)
		node_attributes['image_path'] = node_name
		else:
		node_attributes['image_name'] = node_name
		node_attributes['image_path'] = None
		node_attributes['image_name'] = os.path.basename(os.path.abspath(node_name))
		node_attributes['image_path'] = os.path.abspath(node_name)

		node_labels[node_attributes['image_name']] = self.node_counter
		# fill the dictionary used for relabelling nodes with relative path keys
		node_labels[node_name] = self.node_counter
		# fill the dictionary used for mapping base name to node index
		self.node_name_map[node_attributes['image_name']] = self.node_counter
		self.node_counter += 1

		nx.relabel_nodes(self, node_labels, copy=False)

		@classmethod
		def from_adjacency_file(cls, inputfile):
		"""
		Instantiate the class using an adjacency file. This file must contain relative or
		absolute paths to image files.

		Parameters
		----------
		inputfile : str
		The input file containing the graph representation

		Returns
		-------
		: object
		A Network graph object

		Examples
		--------
		>>> from autocnet.examples import get_path
		>>> inputfile = get_path('adjacency.json')
		>>> candidate_graph = network.CandidateGraph.from_adjacency_file(inputfile)
		"""
		adjacency_dict = io_json.read_json(inputfile)
		return cls(adjacency_dict)

		def get_name(self, nodeIndex):
		"""
		Get the image name for the given node.

		Parameters
		----------
		nodeIndex : int
		The index of the node.

		Returns
		-------
		: str
		The name of the image attached to the given node.


		"""
		return self.node[nodeIndex]['image_name']

		def get_keypoints(self, nodeIndex):
		"""
		Get the list of keypoints for the given node.

		Parameters
		----------
		nodeIndex : int
		The index of the node.

		Returns
		-------
		: list
		The list of keypoints for the given node.

		"""
		return self.node[nodeIndex]['keypoints']

		def add_image(self, args, *kwargs):
		"""
		Adds an image node to the graph.

		Parameters
		==========
		----------

		"""

		raise NotImplementedError
		self.add_node(self.node_counter, args, *kwargs)
		#self.node_labels[self.node[self.node_counter]['image_name']] = self.node_counter
		self.node_counter += 1

		def adjacency_to_json(self, outputfile):
		def get_geodataset(self, nodeIndex):
		"""
		Write the edge structure to a JSON adjacency list
		Constructs a GeoDataset object from the given node image and assigns the
		dataset and its NumPy array to the 'handle' and 'image' node attributes.

		Parameters
		==========
		----------
		nodeIndex : int
		The index of the node.

		outputfile : str
		PATH where the JSON will be written
		"""
		adjacency_dict = {}
		for n in self.nodes():
		adjacency_dict[n] = self.neighbors(n)
		io_json.write_json(adjacency_dict, outputfile)
		self.node[nodeIndex]['handle'] = GeoDataset(self.node[nodeIndex]['image_path'])
		self.node[nodeIndex]['image'] = bytescale(self.node[nodeIndex]['handle'].read_array())

		def add_matches(self, matches):
		def extract_features(self, nfeatures) :
		"""
		Extracts features from each image in the graph and uses the result to assign the
		node attributes for 'handle', 'image', 'keypoints', and 'descriptors'.

		Parameters
		----------
		nfeatures : int
		The number of features to be extracted.

		"""
		# Loop through the nodes (i.e. images) on the graph and fill in their attributes.
		# These attributes are...
		# geo dataset (handle and image)
		# features (keypoints and descriptors)
		for node, attributes in self.nodes_iter(data=True):

		self.get_geodataset(node)
		extraction_params = {'nfeatures' : nfeatures}
		attributes['keypoints'], attributes['descriptors'] = fe.extract_features(attributes['image'],
		extraction_params)

		def add_matches(self, 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.
		@@ -199,28 +299,19 @@ class CandidateGraph(nx.Graph):

		return merged_cnet

		@classmethod
		def from_adjacency(cls, inputfile):
		def to_json_file(self, outputfile):
		"""
		Instantiate the class using an adjacency list
		Write the edge structure to a JSON adjacency list

		Parameters
		----------
		inputfile : str
		The input file containing the graph representation

		Returns
		-------
		: object
		A Network graph object
		==========

		Examples
		--------
		>>> from autocnet.examples import get_path
		>>> inputfile = get_path('adjacency.json')
		>>> candidate_graph = network.CandidateGraph.from_adjacency(inputfile)
		outputfile : str
		PATH where the JSON will be written
		"""
		adjacency_dict = io_json.read_json(inputfile)
		return cls(adjacency_dict)
		adjacency_dict = {}
		for n in self.nodes():
		adjacency_dict[n] = self.neighbors(n)
		io_json.write_json(adjacency_dict, outputfile)

autocnet/graph/tests/test_network.py

+30 −10

Original line number	Diff line number	Diff line
		import os
		import sys
		sys.path.insert(0, os.path.abspath('..'))

		import cv2
		import numpy as np
		import unittest

		from autocnet.examples import get_path

		import sys
		sys.path.insert(0, os.path.abspath('..'))

		from .. import network


		class TestCandidateGraph(unittest.TestCase):

		def setUp(self):
		self.graph = network.CandidateGraph.from_adjacency(get_path('adjacency.json'))
		self.graph = network.CandidateGraph.from_adjacency_file(get_path('adjacency.json'))

		def test_get_name(self):
		node_number = self.graph.node_name_map['AS15-M-0297_SML.png']
		name = self.graph.get_name(node_number)
		self.assertEquals(name, 'AS15-M-0297_SML.png')

		def test_add_image(self):
		with self.assertRaises(NotImplementedError):
		self.graph.add_image()
		self.assertEqual(self.graph.node_counter, 7)

		def test_adjacency_to_json(self):
		self.graph.adjacency_to_json('test_adjacency_to_json.json')
		self.assertTrue(os.path.exists('test_adjacency_to_json.json'))
		def test_to_json_file(self):
		self.graph.to_json_file('test_graph_to_json.json')
		self.assertTrue(os.path.exists('test_graph_to_json.json'))

		def test_extract_features(self):
		# also tests get_geodataset() and get_keypoints
		self.graph.extract_features(10)
		node_number = self.graph.node_name_map['AS15-M-0297_SML.png']
		node = self.graph.node[node_number]
		self.assertEquals(len(node['image']), 1012)
		self.assertEquals(len(node['keypoints']), 10)
		self.assertEquals(len(node['descriptors']), 10)
		self.assertIsInstance(node['keypoints'][0], type(cv2.KeyPoint()))
		self.assertIsInstance(node['descriptors'][0], np.ndarray)
		self.assertEquals(self.graph.get_keypoints(node_number), node['keypoints'])


		def tearDown(self):
		try:
		os.remove('test_adjacency_to_json.json')
		os.remove('test_graph_to_json.json')
		except:
		pass

functional_tests/test_two_image.py

+5 −17

Original line number	Diff line number	Diff line
		import os
		import unittest

		import pandas as pd
		from scipy.misc import bytescale

		from autocnet.examples import get_path
		from autocnet.fileio.io_gdal import GeoDataset
		from autocnet.fileio.io_controlnetwork import to_isis
		from autocnet.fileio.io_gdal import GeoDataset
		from autocnet.graph.network import CandidateGraph
		from autocnet.matcher import feature_extractor as fe
		from autocnet.matcher.matcher import FlannMatcher
		@@ -42,20 +43,13 @@ class TestTwoImageMatching(unittest.TestCase):
		# Step: Create an adjacency graph
		adjacency = get_path('two_image_adjacency.json')
		basepath = os.path.dirname(adjacency)
		cg = CandidateGraph.from_adjacency(adjacency)
		cg = CandidateGraph.from_adjacency_file(adjacency)
		self.assertEqual(2, cg.number_of_nodes())
		self.assertEqual(1, cg.number_of_edges())

		# Step: Extract image data and attribute nodes
		cg.extract_features(25)
		for node, attributes in cg.nodes_iter(data=True):
		dataset = GeoDataset(os.path.join(basepath, attributes['image_name']))
		attributes['handle'] = dataset
		img = bytescale(dataset.read_array())
		attributes['image'] = img

		# Step: Then find features and descriptors
		attributes['keypoints'], attributes['descriptors'] = fe.extract_features(attributes['image'],
		{'nfeatures':25})
		self.assertIn(len(attributes['keypoints']), [24, 25, 26])

		# Step: Then apply a FLANN matcher
		@@ -66,15 +60,9 @@ class TestTwoImageMatching(unittest.TestCase):

		for node, attributes in cg.nodes_iter(data=True):
		descriptors = attributes['descriptors']
		matches = fl.query(descriptors, node, k=3)
		matches = fl.query(descriptors, node, k=2)
		cg.add_matches(matches)

		# Step: Compute Homography
		transformation_matrix, mask = cg.compute_homography(0, 1)
		self.assertEquals(len(transformation_matrix), 3)
		#TODO: write better test
		#self.assertEquals(len(mask), 19)

		# Step: And create a C object
		cnet = cg.to_cnet()