Standardize the interface to the feature extractor (e13d668a) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/node.py

+1 −23

Changes for autocnet/graph/node.py: 1 added line, 23 removed lines.
Original line number	Diff line number	Diff line
		@@ -226,29 +226,7 @@ class Node(dict, MutableMapping):
		kwargs passed to autocnet.feature_extractor.extract_features

		"""
		keypoint_objs, self.descriptors = fe.extract_features(array, **kwargs)
		if self.descriptors.dtype != np.float32:
		self.descriptors = self.descriptors.astype(np.float32)

		# OpenCV returned keypoint objects
		if isinstance(keypoint_objs, list):
		keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
		for i, kpt in enumerate(keypoint_objs):
		octave = kpt.octave & 8
		layer = (kpt.octave >> 8) & 255
		if octave < 128:
		octave = octave
		else:
		octave = (-128 \| octave)
		keypoints[i] = kpt.pt[0], kpt.pt[1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
		self._keypoints = pd.DataFrame(keypoints, columns=['x', 'y', 'response', 'size',
		'angle', 'octave', 'layer'])

		# VLFeat returned keypoint objects
		elif isinstance(keypoint_objs, np.ndarray):
		# Swap columns for value style access, vl_feat returns y, x
		keypoint_objs[:, 0], keypoint_objs[:, 1] = keypoint_objs[:, 1], keypoint_objs[:, 0].copy()
		self._keypoints = pd.DataFrame(keypoint_objs, columns=['x', 'y', 'size', 'angle'])
		self._keypoints, self.descriptors = fe.extract_features(array, **kwargs)

		def load_features(self, in_path):
		"""

autocnet/matcher/feature_extractor.py

+33 −4

Changes for autocnet/matcher/feature_extractor.py: 33 added lines, 4 removed lines.
Original line number	Diff line number	Diff line
		import cv2
		import numpy as np
		import pandas as pd

		from scipy.misc import bytescale

		@@ -30,8 +32,11 @@ def extract_features(array, method='orb', extractor_parameters={}):

		Returns
		-------
		: tuple
		in the form ([list of OpenCV KeyPoints], [NumPy array of descriptors as geometric vectors])
		keypoints : DataFrame
		data frame of coordinates ('x', 'y', 'size', 'angle', and other available information)

		descriptors : ndarray
		Of descriptors
		"""

		detectors = {'fast': cv2.FastFeatureDetector_create,
		@@ -42,10 +47,34 @@ def extract_features(array, method='orb', extractor_parameters={}):
		detectors['vl_sift'] = vl.sift.sift

		if 'vl_' in method:
		return detectors[method](array, compute_descriptor=True, float_descriptors=True, **extractor_parameters)
		keypoint_objs, descriptors = detectors[method](array,
		compute_descriptor=True,
		float_descriptors=True,
		**extractor_parameters)
		# Swap columns for value style access, vl_feat returns y, x
		keypoint_objs[:, 0], keypoint_objs[:, 1] = keypoint_objs[:, 1], keypoint_objs[:, 0].copy()
		keypoints = pd.DataFrame(keypoint_objs, columns=['x', 'y', 'size', 'angle'])

		else:
		# OpenCV requires the input images to be 8-bit
		if not array.dtype == 'int8':
		array = bytescale(array)
		detector = detectors[method](**extractor_parameters)
		return detector.detectAndCompute(array, None)
		keypoint_objs, descriptors = detector.detectAndCompute(array, None)

		keypoints = np.empty((len(keypoint_objs), 7), dtype=np.float32)
		for i, kpt in enumerate(keypoint_objs):
		octave = kpt.octave & 8
		layer = (kpt.octave >> 8) & 255
		if octave < 128:
		octave = octave
		else:
		octave = (-128 \| octave)
		keypoints[i] = kpt.pt[0], kpt.pt[1], kpt.response, kpt.size, kpt.angle, octave, layer # y, x
		keypoints = pd.DataFrame(keypoints, columns=['x', 'y', 'response', 'size',
		'angle', 'octave', 'layer'])

		if descriptors.dtype != np.float32:
		descriptors = descriptors.astype(np.float32)

		return keypoints, descriptors
		No newline at end of file

autocnet/matcher/tests/test_feature_extractor.py

+4 −6

Changes for autocnet/matcher/tests/test_feature_extractor.py: 4 added lines, 6 removed lines.
Original line number	Diff line number	Diff line
		import os
		import numpy as np
		import pandas as pd
		import unittest
		from autocnet.examples import get_path
		import cv2
		@@ -25,17 +26,14 @@ class TestFeatureExtractor(unittest.TestCase):
		"sigma": 1.6}

		def test_extract_features(self):
		features = feature_extractor.extract_features(self.data_array,
		features, descriptors = feature_extractor.extract_features(self.data_array,
		method='sift',
		extractor_parameters=self.parameters)
		self.assertEquals(len(features), 2)
		self.assertIn(len(features[0]), range(8, 12))
		self.assertIsInstance(features[0][0], type(cv2.KeyPoint()))
		self.assertIsInstance(features[1][0], np.ndarray)
		self.assertEquals(len(features), 10)

		def test_extract_vlfeat(self):
		kps, descriptors = feature_extractor.extract_features(self.data_array,
		method='vl_sift',
		extractor_parameters={})
		self.assertIsInstance(kps, np.ndarray)
		self.assertIsInstance(kps, pd.DataFrame)
		self.assertEqual(descriptors.dtype, np.float32)