Prototype matcher class (c30e43a9) · Commits · aflab / astrogeology / Autocnet

autocnet/fileio/io_gdal.py

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		@@ -350,6 +350,7 @@ class GeoDataset(object):
		xextent, yextent).astype(dtype)
		return array


		def array_to_raster(array, file_name, projection=None,
		geotransform=None, outformat='GTiff',
		ndv=None):

autocnet/graph/network.py

+16 −7

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		from collections import Hashable
		from networkx import DiGraph
		import networkx as nx

		from autocnet.fileio import io_json
		class CandidateGraph(DiGraph):

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

		@@ -12,8 +13,6 @@ class CandidateGraph(DiGraph):
		Attributes
		----------
		"""
		#TODO: This would be better with composition, and then dispatch the
		# network X calls to the graph object.

		def __init__(self,args, *kwargs):
		super(CandidateGraph, self).__init__(args, *kwargs)
		@@ -36,6 +35,7 @@ class CandidateGraph(DiGraph):

		Parameters
		==========

		outputfile : str
		PATH where the JSON will be written
		"""
		@@ -50,12 +50,21 @@ class CandidateGraph(DiGraph):
		Instantiate the class using an 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)
		"""
		#TODO: This is better as a generic reader that tries drivers until
		# a valid dict is returned.
		adjacency_dict = io_json.read_json(inputfile)
		return cls(adjacency_dict)

autocnet/matcher/matcher.py

0 → 100644

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		import cv2
		import numpy as np

		from autocnet.graph.network import CandidateGraph

		FLANN_INDEX_KDTREE = 1
		DEFAULT_FLANN_PARAMETERS = dict(algorithm=FLANN_INDEX_KDTREE,
		trees=3)


		class FlannMatcher(object):
		"""
		A wrapper to the OpenCV Flann based matcher class that adds
		metadata tracking attributes and methods.

		Attributes
		----------
		image_indices : dict
		with key equal to the train image idx (returned by the DMatch object)
		and value equal to the image identifier, e.g. the name

		image_index_counter : int
		The current number of images loaded into the matcher
		"""

		def __init__(self, flann_parameters=DEFAULT_FLANN_PARAMETERS):
		self._flann_matcher = cv2.FlannBasedMatcher(flann_parameters, {})
		self.image_indices = {}
		self.image_index_counter = 0 # OpenCv DMatch.imgIdx are one based

		def add(self, descriptor, key):
		"""
		Add a set of descriptors to the matcher and add the image
		index key to the image_indices attribute

		Parameters
		----------
		descriptor : ndarray
		The descriptor to be added

		key : hashable
		The identifier for this image, e.g. the image name
		"""
		self._flann_matcher.add([descriptor])
		self.image_indices[self.image_index_counter] = key
		self.image_index_counter += 1

		def train(self):
		"""
		Using the descriptors, generate the KDTree
		"""
		self._flann_matcher.train()

		def query(self, descriptor, k=3):
		"""

		Parameters
		----------
		descriptor : ndarray
		The query descriptor to search for

		k : int
		The number of nearest neighbors to search for

		Returns
		-------
		:
		"""
		print(self.image_indices)
		matches = self._flann_matcher.knnMatch(descriptor, k=k)
		print(dir(self._flann_matcher))
		print(matches)
		for m in matches:
		print(m[0].imgIdx, m[0].queryIdx, m[0].trainIdx)
		#print(self.image_indices[m[0].imgIdx])


		def match_features(iterable, flann_parameters=DEFAULT_FLANN_PARAMETERS):
		"""
		Iterate over all nodes in the graph, create a single KDTree,
		and then apply a FLANN matcher.

		Parameters
		----------
		iterable : iterable
		An iterable of descriptors

		flann_parameters : dict
		of parameters for the FLANN matcher


		Returns
		-------

		"""
		flann_matcher = create_flann_matcher(iterable, flann_parameters)


		def create_flann_matcher(iterable, flann_parameters):

		flann_matcher = cv2.FlannBasedMatcher(flann_parameters, {})

		try:
		if isinstance(iterable, CandidateGraph):
		training_descriptors = [i[1]['descriptors'] for i in iterable.nodes_iter(data=True)]
		elif isinstance(iterable[0], np.ndarray):
		training_descriptors = iterable
		else:
		raise(TypeError, 'Unsupported iterable passed to match_features')
		except TypeError:
		print('Object is not iterable')

		flann_matcher.add(training_descriptors)
		flann_matcher.train()
		return flann_matcher
		No newline at end of file

autocnet/matcher/nimage.py

deleted100644 → 0

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Empty file deleted.

autocnet/fileio/tests/io_controlnetwork.py→autocnet/matcher/tests/init.py

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File moved.