Merge pull request #18 from jcwbacker/master (e0a3af7a) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/feature_extractor.py

+9 −9

Original line number	Diff line number	Diff line
		import cv2
		from scipy import misc

		def extract_features(image_array, num_nodes=500):
		def extract_features(image_array, extractor_parameters):
		"""
		Extracts keypoints and corresponding descriptors from a numpy array
		that represents an image. The extracted information is returned as a tuple whose first element is
		a list of KeyPoints and whose second element is a numpy array of geometric vector descriptors.
		This method finds and extracts features from an image using the given dictionary of keyword arguments.
		The input image is represented as NumPy array and the output features are represented as keypoint IDs
		with corresponding descriptors.

		Parameters
		----------
		image_array : ndarray
		a numpy array that represents an image
		num_nodes : int
		the number of best features to retain
		a NumPy array that represents an image
		extractor_parameters : dict
		A dictionary containing OpenCV SIFT parameters names and values.

		Returns
		-------
		: tuple
		This tuple is in the form (list of KeyPoints, array of descriptors)
		in the form ([list of OpenCV KeyPoints], [NumPy array of descriptors as geometric vectors])
		"""
		sift = cv2.xfeatures2d.SIFT_create(num_nodes)
		sift = cv2.xfeatures2d.SIFT_create(**extractor_parameters)
		converted_array = misc.bytescale(image_array)
		return sift.detectAndCompute(converted_array, None)

+6 −1

Original line number	Diff line number	Diff line
		@@ -15,9 +15,14 @@ class TestFeatureExtractor(unittest.TestCase):
		def setUp(self):
		self.dataset = io_gdal.GeoDataset(get_path('Mars_MGS_MOLA_ClrShade_MAP2_0.0N0.0_MERC.tif'))
		self.data_array = self.dataset.read_array()
		self.parameters = {"nfeatures" : 10,
		"nOctaveLayers" : 3,
		"contrastThreshold" : 0.02,
		"edgeThreshold" : 10,
		"sigma" : 1.6}

		def test_extract_features(self):
		features = feature_extractor.extract_features(self.data_array, 10)
		features = feature_extractor.extract_features(self.data_array, self.parameters)
		self.assertEquals(len(features), 2)
		self.assertIsInstance(features[0][0], type(cv2.KeyPoint()))
		self.assertIsInstance(features[1][0], np.ndarray)

+2 −2

Original line number	Diff line number	Diff line
		@@ -293,8 +293,8 @@ class Mock(MagicMock):
		def __getter__(cls, name):
		return Mock()


		MOCK_MODULES = ['proj4', 'numpy', 'pandas', 'scipy', 'osgeo']
		# All imported libraries should be added to this mock modules list.
		MOCK_MODULES = ['proj4', 'numpy', 'pandas', 'scipy', 'osgeo', 'cv2']
		sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

		# NumpyDoc Options

+4 −1

Original line number	Diff line number	Diff line
		:mod:`fileio.io_gdal` --- Geospatial Data Abstraction Library
		=============================================================

		The :mod:`fileio.io_gdal` module provides a convenience wrapper to GDAL
		The :mod:`fileio.io_gdal` and :mod:`fileio.extract_metadata` modules provide convenience wrappers to GDAL.

		.. versionadded:: 0.1.0

		@@ -9,3 +9,6 @@ The :mod:`fileio.io_gdal` module provides a convenience wrapper to GDAL
		:synopsis: This is the synopsis
		:members:

		.. automodule:: autocnet.fileio.extract_metadata
		:synopsis: This is the synopsis
		:members:

0 → 100644

+10 −0

Original line number	Diff line number	Diff line
		:mod:`matcher.feature_extractor` --- Extracting Features from Images
		====================================================================

		The :mod:`matcher.feature_extractor` module

		.. versionadded:: 0.1.0

		.. automodule:: autocnet.matcher.feature_extractor
		:synopsis:
		:members: