Merge pull request #16 from USGS-Astrogeology/revert-15-revert-14-master (236d69cc) · Commits · aflab / astrogeology / Autocnet

.travis.yml

+2 −0

Original line number	Diff line number	Diff line
		@@ -9,6 +9,7 @@ python:
		- "3.5"

		before_install:

		install:
		# We do this conditionally because it saves us some downloading if the
		# version is the same.
		@@ -31,6 +32,7 @@ install:
		- source activate test-env

		# Install the non-conda packages if required, requirements.txt duplicates are ignored
		- conda install -c https://conda.binstar.org/menpo opencv3
		- conda install -c osgeo proj4
		- pip install -r requirements.txt
		- pip install coverage

autocnet/fileio/io_gdal.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -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

Original line number	Diff line number	Diff line
		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

+92 −0

Original line number	Diff line number	Diff line
		import cv2
		import numpy as np
		import pandas as pd
		from autocnet.graph.network import CandidateGraph


		FLANN_INDEX_KDTREE = 1 # Algorithm to set centers,
		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. This takes arbitrary
		descriptors and so should be available for use with any
		descriptor data stored as an ndarray.

		Attributes
		----------
		image_indices : dict
		with key equal to the train image index (returned by the DMatch object),
		e.g. an integer array index
		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

		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, self_neighbor=True):
		"""

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

		k : int
		The number of nearest neighbors to search for

		self_neighbor : bool
		If the query descriptor is also a member
		of the KDTree avoid self neighbor, default True.

		Returns
		-------
		matched : dataframe
		containing matched points with columns containg:
		matched image name, query index, train index, and
		descriptor distance
		"""
		idx = 0
		if self_neighbor:
		idx = 1
		matches = self._flann_matcher.knnMatch(descriptor, k=k)
		matched = []
		for m in matches:
		for i in m[idx:]:
		matched.append((self.image_indices[i.imgIdx],
		i.queryIdx,
		i.trainIdx,
		i.distance))
		return pd.DataFrame(matched, columns=['matched_to', 'queryIdx',
		'trainIdx', 'distance'])

autocnet/matcher/nimage.py

deleted100644 → 0

+0 −0

Empty file deleted.