Adds ability to save features to npy or hdf5 (521444b9) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/node.py

+18 −47

Changes for autocnet/graph/node.py: 18 added lines, 47 removed lines.
Original line number	Diff line number	Diff line
		@@ -5,13 +5,14 @@ import warnings
		import numpy as np
		import pandas as pd
		from plio.io.io_gdal import GeoDataset
		from plio.io import io_hdf
		from plio.io.isis_serial_number import generate_serial_number
		from scipy.misc import bytescale

		from autocnet.cg import cg
		from autocnet.control.control import Correspondence, Point

		from autocnet.io import keypoints as io_keypoints

		from autocnet.matcher.add_depth import deepen_correspondences
		from autocnet.matcher import feature_extractor as fe
		from autocnet.matcher import outlier_detector as od
		@@ -295,7 +296,7 @@ class Node(dict, MutableMapping):
		def extract_features(self, args, *kwargs):
		self._keypoints, self.descriptors = Node._extract_features(args, *kwargs)

		def load_features(self, in_path):
		def load_features(self, in_path, format='npy'):
		"""
		Load keypoints and descriptors for the given image
		from a HDF file.
		@@ -304,28 +305,16 @@ class Node(dict, MutableMapping):
		----------
		in_path : str or object
		PATH to the hdf file or a HDFDataset object handle
		"""
		if isinstance(in_path, str):
		hdf = io_hdf.HDFDataset(in_path, mode='r')
		else:
		hdf = in_path

		self.descriptors = hdf['{}/descriptors'.format(self['image_name'])][:]
		raw_kps = hdf['{}/keypoints'.format(self['image_name'])][:]
		index = raw_kps['index']
		clean_kps = utils.remove_field_name(raw_kps, 'index')
		columns = clean_kps.dtype.names

		allkps = pd.DataFrame(data=clean_kps, columns=columns, index=index)
		format : {'npy', 'hdf5'}
		"""
		if format == 'npy':
		io_keypoints.from_npy(in_path, self)
		elif format == 'hdf5':
		io_keypoints.from_hdf(in_path, self)

		if 'response' in allkps.columns:
		self._keypoints = allkps.sort_values(by='response', ascending=False)
		elif 'size' in allkps.columns:
		self._keypoints = allkps.sort_values(by='size', ascending=False)
		if isinstance(in_path, str):
		hdf = None

		def save_features(self, out_path):
		def save_features(self, out_path, format='npy'):
		"""
		Save the extracted keypoints and descriptors to
		the given HDF5 file.
		@@ -334,39 +323,21 @@ class Node(dict, MutableMapping):
		----------
		out_path : str or object
		PATH to the hdf file or a HDFDataset object handle

		format : {'npy', 'hdf5'}
		The desired output format.
		"""

		if not hasattr(self, '_keypoints'):
		warnings.warn('Node {} has not had features extracted.'.format(i))
		return

		# If the out_path is a string, access the HDF5 file
		if isinstance(out_path, str):
		if os.path.exists(out_path):
		mode = 'a'
		else:
		mode = 'w'
		hdf = io_hdf.HDFDataset(out_path, mode=mode)
		if format == 'hdf':
		io_keypoints.to_hdf(out_path, self)
		elif format == 'npy':
		io_keypoints.to_npy(out_path, self)
		else:
		hdf = out_path

		#try:
		hdf.create_dataset('{}/descriptors'.format(self['image_name']),
		data=self.descriptors,
		compression=io_hdf.DEFAULT_COMPRESSION,
		compression_opts=io_hdf.DEFAULT_COMPRESSION_VALUE)
		hdf.create_dataset('{}/keypoints'.format(self['image_name']),
		data=hdf.df_to_sarray(self._keypoints.reset_index()),
		compression=io_hdf.DEFAULT_COMPRESSION,
		compression_opts=io_hdf.DEFAULT_COMPRESSION_VALUE)
		#except:
		#warnings.warn('Descriptors for the node {} are already stored'.format(self['image_name']))

		# If the out_path is a string, assume this method is being called as a singleton
		# and close the hdf file gracefully. If an object, let the instantiator of the
		# object close the file
		if isinstance(out_path, str):
		hdf = None
		warnings.warn('Unknown keypoint output format.')

		def group_correspondences(self, cg, args, deepen=False, *kwargs):
		"""

autocnet/io/init.py

0 → 100644

+0 −0

Empty file added.

autocnet/io/keypoints.py

0 → 100644

+65 −0

Changes for autocnet/io/keypoints.py: 65 added lines, 0 removed lines.
Original line number	Diff line number	Diff line
		import os

		import numpy as np
		from plio.io import io_hdf

		def from_hdf(in_path, node):
		if isinstance(in_path, str):
		hdf = io_hdf.HDFDataset(in_path, mode='r')
		else:
		hdf = in_path

		node.descriptors = hdf['{}/descriptors'.format(node['image_name'])][:]
		raw_kps = hdf['{}/keypoints'.format(node['image_name'])][:]
		index = raw_kps['index']
		clean_kps = utils.remove_field_name(raw_kps, 'index')
		columns = clean_kps.dtype.names

		allkps = pd.DataFrame(data=clean_kps, columns=columns, index=index)

		if 'response' in allkps.columns:
		node._keypoints = allkps.sort_values(by='response', ascending=False)
		elif 'size' in allkps.columns:
		node._keypoints = allkps.sort_values(by='size', ascending=False)
		if isinstance(in_path, str):
		hdf = None

		def to_hdf(out_path, node):
		# If the out_path is a string, access the HDF5 file
		if isinstance(out_path, str):
		if os.path.exists(out_path):
		mode = 'a'
		else:
		mode = 'w'
		hdf = io_hdf.HDFDataset(out_path, mode=mode)
		else:
		hdf = out_path

		#try:
		hdf.create_dataset('{}/descriptors'.format(node['image_name']),
		data=node.descriptors,
		compression=io_hdf.DEFAULT_COMPRESSION,
		compression_opts=io_hdf.DEFAULT_COMPRESSION_VALUE)
		hdf.create_dataset('{}/keypoints'.format(node['image_name']),
		data=hdf.df_to_sarray(node._keypoints.reset_index()),
		compression=io_hdf.DEFAULT_COMPRESSION,
		compression_opts=io_hdf.DEFAULT_COMPRESSION_VALUE)
		#except:
		#warnings.warn('Descriptors for the node {} are already stored'.format(self['image_name']))

		# If the out_path is a string, assume this method is being called as a singleton
		# and close the hdf file gracefully. If an object, let the instantiator of the
		# object close the file
		if isinstance(out_path, str):
		hdf = None

		def from_npy(in_path, node):
		nzf = np.load(in_path)
		node.descriptors = nzf['descriptors']
		node._keypoints = pd.DataFrame(nzf['_keypoints'], index=nzf['_keypoints_idx'], columns=nzf['_keypoints_columns'])

		def to_npy(out_path, node):
		np.savez(out_path, descriptors=node.descriptors,
		_keypoints=data._keypoints,
		_keypoints_idx=data._keypoints.index,
		_keypoints_columns=data._keypoints.columns)