HDF reads/writes of keypoints and descriptors. (3d2d42a4) · Commits · aflab / astrogeology / Autocnet

.travis.yml

+1 −1

Original line number	Diff line number	Diff line
		@@ -27,7 +27,7 @@ install:
		- conda info -a

		# Create a virtual env and install dependencies
		- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image sqlalchemy numexpr dill
		- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image sqlalchemy numexpr dill cython
		# Activate the env
		- source activate test-env

AUTHORS.rst

+1 −0

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		@@ -10,6 +10,7 @@ Development Team
		* Jeannie Backer <jwbacker@usgs.gov>
		* Dyer Lytle <dmlytle@usgs.gov>
		* Kelvin Rodriguez <krodriguez@usgs.gov>
		* Adam Paquette <acpaquette@usgs.gov>

		Contributors
		------------

autocnet/fileio/io_hdf.py

+6 −1

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		@@ -3,6 +3,9 @@ import numpy as np
		import pandas as pd


		DEFAULT_COMPRESSION = 'gzip'
		DEFAULT_COMPRESSION_VALUE = 8 # 0 - 9

		class HDFDataset(h5.File):
		"""
		Read / Write an HDF5 dataset using h5py. If HDF5 is compiled with
		@@ -35,7 +38,6 @@ class HDFDataset(h5.File):
		z : ndarray
		a numpy structured array representation of df
		"""

		v = df.values
		cols = df.columns
		types = [(cols[i], df[k].dtype.type) for (i, k) in enumerate(cols)]
		@@ -55,6 +57,9 @@ class HDFDataset(h5.File):
		sarray : array
		numpy structured array

		index_column : str
		The name of the index column. Default: 'index'

		Returns
		-------
		: dataframe

autocnet/fileio/tests/test_io_hdf.py

+19 −2

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		import os
		import unittest

		import numpy as np
		@@ -8,8 +9,24 @@ from .. import io_hdf

		class TestHDF(unittest.TestCase):

		@classmethod
		def setUpClass(cls):
		cls.hdf = io_hdf.HDFDataset('test_io_hdf.hdf', mode='w')
		cls.x = np.array([(0 ,2.,'String'), (1, 3.,"String2")],
		dtype=[('index', 'i8'),('bar', 'f4'), ('baz', 'O')])
		cls.df = pd.DataFrame(cls.x[['bar', 'baz']], index=cls.x['index'],
		columns=['bar', 'baz'])

		@classmethod
		def tearDownClass(cls):
		os.remove('test_io_hdf.hdf')

		def test_df_sarray(self):
		self.assertTrue(False)
		converted = self.hdf.df_to_sarray(self.df.reset_index())
		np.testing.assert_array_equal(converted, self.x)

		def test_sarray_df(self):
		self.assertTrue(False)
		converted = self.hdf.sarray_to_df(self.x)
		self.assertTrue((self.df == converted).all().all())

autocnet/graph/network.py

+60 −1

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		import itertools
		import os
		import dill as pickle
		import warnings

		import networkx as nx
		import numpy as np
		import pandas as pd

		from autocnet.fileio.io_gdal import GeoDataset
		from autocnet.fileio import io_hdf
		from autocnet.control.control import C
		from autocnet.fileio import io_json
		from autocnet.matcher.matcher import FlannMatcher
		@@ -241,6 +243,63 @@ class CandidateGraph(nx.Graph):
		node.extract_features(image, method=method,
		extractor_parameters=extractor_parameters)

		def save_features(self, out_path, nodes=[]):
		"""

		Save the features (keypoints and descriptors) for the
		specified nodes.

		Parameters
		----------
		out_path : str
		Location of the output file. If the file exists,
		features are appended. Otherwise, the file is created.

		nodes : list
		of nodes to save features for. If empty, save for all nodes
		"""

		if os.path.exists(out_path):
		mode = 'a'
		else:
		mode = 'w'
		hdf = io_hdf.HDFDataset(out_path, mode=mode)

		# Cleaner way to do this?
		if nodes:
		for i, n in self.nodes_iter(nodes, data=True):
		n.save_features(hdf)
		else:
		for i, n in self.nodes_iter(data=True):
		n.save_features(hdf)

		hdf = None

		def load_features(self, in_path, nodes=[]):
		"""
		Load features (keypoints and descriptors) for the
		specified nodes.

		Parameters
		----------
		in_path : str
		Location of the input file.

		nodes : list
		of nodes to load features for. If empty, load features
		for all nodes
		"""
		hdf = io_hdf.HDFDataset(in_path, 'r')

		if nodes:
		for i, n in self.nodes_iter(nodes, data=True):
		n.load_features(hdf)
		else:
		for i, n in self.nodes_iter(data=True):
		n.load_features(hdf)

		hdf = None

		def match_features(self, k=None):
		"""
		For all connected edges in the graph, apply feature matching