Loading autocnet/graph/node.py 0 → 100644 +174 −0 Original line number Diff line number Diff line from collections import MutableMapping import numpy as np import pandas as pd from scipy.misc import bytescale from autocnet.fileio.io_gdal import GeoDataset from autocnet.matcher import feature_extractor as fe from autocnet.matcher import outlier_detector as od from autocnet.cg.cg import convex_hull_ratio from autocnet.utils.isis_serial_numbers import generate_serial_number from autocnet.vis.graph_view import plot_node class Node(dict, MutableMapping): """ Attributes ---------- image_name : str Name of the image, with extension image_path : str Relative or absolute PATH to the image handle : object File handle to the object keypoints : dataframe With columns, x, y, and response nkeypoints : int The number of keypoints found for this image descriptors : ndarray 32-bit array of feature descriptors returned by OpenCV masks : set A list of the available masking arrays isis_serial : str If the input images have PVL headers, generate an ISIS compatible serial number provenance : dict With key equal to an autoincrementing integer and value equal to a dict of parameters used to generate this realization. """ def __init__(self, image_name=None, image_path=None): self.image_name = image_name self.image_path = image_path self._masks = set() self._mask_arrays = {} self.provenance = {} self._pid = 0 def __repr__(self): return """ NodeID: {} Image Name: {} Image PATH: {} Number Keypoints: {} Available Masks : {} """.format(None, self.image_name, self.image_path, self.nkeypoints, self.masks) @property def handle(self): if not getattr(self, '_handle', None): self._handle = GeoDataset(self.image_path) return self._handle @property def nkeypoints(self): if hasattr(self, '_nkeypoints'): return self._nkeypoints else: return 0 @nkeypoints.setter def nkeypoints(self, v): self._nkeypoints = v @property def masks(self): return self._masks @masks.setter def masks(self, v): self._masks.add(v[0]) self._mask_arrays[v[0]] = v[1] def get_array(self, band=1): """ Get a band as a 32-bit numpy array Parameters ---------- band : int The band to read, default 1 """ array = self.handle.read_array(band=band) return bytescale(array) def extract_features(self, array, **kwargs): """ Extract features for the node Parameters ---------- array : ndarray kwargs : dict KWargs passed to autocnet.feature_extractor.extract_features """ keypoint_objs, descriptors = fe.extract_features(array, **kwargs) 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']) self._nkeypoints = len(self.keypoints) self.descriptors = descriptors.astype(np.float32) self.provenance[self._pid] = {'detector': 'sift', 'parameters':kwargs} self._pid += 1 def anms(self, nfeatures=100, robust=0.9): mask = od.adaptive_non_max_suppression(self.keypoints,nfeatures,robust) self.masks = ('anms', mask) def coverage_ratio(self, clean_keys=[]): """ Compute the ratio $area_{convexhull} / area_{total}$ Returns ------- ratio : float The ratio of convex hull area to total area. """ ideal_area = self.handle.pixel_area if not hasattr(self, 'keypoints'): raise AttributeError('Keypoints must be extracted already, they have not been.') if clean_keys: mask = np.prod([self._mask_arrays[i] for i in clean_keys], axis=0, dtype=np.bool) keypoints = self.keypoints[mask] keypoints = self.keypoints[['x', 'y']].values ratio = convex_hull_ratio(keypoints, ideal_area) return ratio def plot(self, clean_keys=[], **kwargs): # pragma: no cover return plot_node(self, clean_keys=clean_keys, **kwargs) @property def isis_serial(self): """ Generate an ISIS compatible serial number using the data file associated with this node. This assumes that the data file has a PVL header. """ if not hasattr(self, '_isis_serial'): try: self._isis_serial = generate_serial_number(self.image_path) except: self._isis_serial = None return self._isis_serial No newline at end of file autocnet/graph/tests/test_network.py +0 −25 Original line number Diff line number Diff line Loading @@ -47,29 +47,4 @@ class TestCandidateGraph(unittest.TestCase): pass class TestNode(unittest.TestCase): @classmethod def setUpClass(cls): cls.graph = network.CandidateGraph.from_adjacency(get_path('adjacency.json')) def test_get_handle(self): self.assertIsInstance(self.graph.node[0].handle, GeoDataset) def test_get_array(self): image = self.graph.node[0].get_array() self.assertEqual((1012, 1012), image.shape) self.assertEqual(np.uint8, image.dtype) def test_extract_features(self): node = self.graph.node[0] image = node.get_array() node.extract_features(image, extractor_parameters={'nfeatures':10}) self.assertEquals(len(node.keypoints), 10) self.assertEquals(len(node.descriptors), 10) self.assertIsInstance(node.descriptors[0], np.ndarray) def test_convex_hull_ratio_fail(self): # Convex hull computation is checked lower in the hull computation node = self.graph.node[0] self.assertRaises(AttributeError, node.coverage_ratio) autocnet/graph/tests/test_node.py 0 → 100644 +62 −0 Original line number Diff line number Diff line import os import sys sys.path.insert(0, os.path.abspath('..')) import numpy as np import unittest from autocnet.examples import get_path from autocnet.fileio.io_gdal import GeoDataset from autocnet.utils.utils import find_in_dict from .. import node class TestNode(unittest.TestCase): def setUp(self): img = get_path('AS15-M-0295_SML.png') self.node = node.Node(image_name='AS15-M-0295_SML', image_path=img) def test_get_handle(self): self.assertIsInstance(self.node.handle, GeoDataset) def test_get_array(self): image = self.node.get_array() self.assertEqual((1012, 1012), image.shape) self.assertEqual(np.uint8, image.dtype) def test_extract_features(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':10}) self.assertEquals(len(self.node.keypoints), 10) self.assertEquals(len(self.node.descriptors), 10) self.assertIsInstance(self.node.descriptors[0], np.ndarray) self.assertEqual(10, self.node.nkeypoints) def test_convex_hull_ratio_fail(self): # Convex hull computation is checked lower in the hull computation self.assertRaises(AttributeError, self.node.coverage_ratio) def test_provenance(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':10}) self.node.extract_features(image, extractor_parameters={'nfeatures':15}) p0 = self.node.provenance[0] p1 = self.node.provenance[1] print(self.node.provenance) self.assertEqual(len(self.node.provenance.keys()), 2) self.assertNotEqual(find_in_dict(p0, 'nfeatures'), find_in_dict(p1, 'nfeatures')) def test_anms(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':100}) self.node.anms(nfeatures=10) self.assertIn('anms', self.node.masks) self.assertTrue(sum(self.node._mask_arrays['anms']), 10) def test_isis_serial(self): serial = self.node.isis_serial self.assertEqual(None, serial) Loading
autocnet/graph/node.py 0 → 100644 +174 −0 Original line number Diff line number Diff line from collections import MutableMapping import numpy as np import pandas as pd from scipy.misc import bytescale from autocnet.fileio.io_gdal import GeoDataset from autocnet.matcher import feature_extractor as fe from autocnet.matcher import outlier_detector as od from autocnet.cg.cg import convex_hull_ratio from autocnet.utils.isis_serial_numbers import generate_serial_number from autocnet.vis.graph_view import plot_node class Node(dict, MutableMapping): """ Attributes ---------- image_name : str Name of the image, with extension image_path : str Relative or absolute PATH to the image handle : object File handle to the object keypoints : dataframe With columns, x, y, and response nkeypoints : int The number of keypoints found for this image descriptors : ndarray 32-bit array of feature descriptors returned by OpenCV masks : set A list of the available masking arrays isis_serial : str If the input images have PVL headers, generate an ISIS compatible serial number provenance : dict With key equal to an autoincrementing integer and value equal to a dict of parameters used to generate this realization. """ def __init__(self, image_name=None, image_path=None): self.image_name = image_name self.image_path = image_path self._masks = set() self._mask_arrays = {} self.provenance = {} self._pid = 0 def __repr__(self): return """ NodeID: {} Image Name: {} Image PATH: {} Number Keypoints: {} Available Masks : {} """.format(None, self.image_name, self.image_path, self.nkeypoints, self.masks) @property def handle(self): if not getattr(self, '_handle', None): self._handle = GeoDataset(self.image_path) return self._handle @property def nkeypoints(self): if hasattr(self, '_nkeypoints'): return self._nkeypoints else: return 0 @nkeypoints.setter def nkeypoints(self, v): self._nkeypoints = v @property def masks(self): return self._masks @masks.setter def masks(self, v): self._masks.add(v[0]) self._mask_arrays[v[0]] = v[1] def get_array(self, band=1): """ Get a band as a 32-bit numpy array Parameters ---------- band : int The band to read, default 1 """ array = self.handle.read_array(band=band) return bytescale(array) def extract_features(self, array, **kwargs): """ Extract features for the node Parameters ---------- array : ndarray kwargs : dict KWargs passed to autocnet.feature_extractor.extract_features """ keypoint_objs, descriptors = fe.extract_features(array, **kwargs) 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']) self._nkeypoints = len(self.keypoints) self.descriptors = descriptors.astype(np.float32) self.provenance[self._pid] = {'detector': 'sift', 'parameters':kwargs} self._pid += 1 def anms(self, nfeatures=100, robust=0.9): mask = od.adaptive_non_max_suppression(self.keypoints,nfeatures,robust) self.masks = ('anms', mask) def coverage_ratio(self, clean_keys=[]): """ Compute the ratio $area_{convexhull} / area_{total}$ Returns ------- ratio : float The ratio of convex hull area to total area. """ ideal_area = self.handle.pixel_area if not hasattr(self, 'keypoints'): raise AttributeError('Keypoints must be extracted already, they have not been.') if clean_keys: mask = np.prod([self._mask_arrays[i] for i in clean_keys], axis=0, dtype=np.bool) keypoints = self.keypoints[mask] keypoints = self.keypoints[['x', 'y']].values ratio = convex_hull_ratio(keypoints, ideal_area) return ratio def plot(self, clean_keys=[], **kwargs): # pragma: no cover return plot_node(self, clean_keys=clean_keys, **kwargs) @property def isis_serial(self): """ Generate an ISIS compatible serial number using the data file associated with this node. This assumes that the data file has a PVL header. """ if not hasattr(self, '_isis_serial'): try: self._isis_serial = generate_serial_number(self.image_path) except: self._isis_serial = None return self._isis_serial No newline at end of file
autocnet/graph/tests/test_network.py +0 −25 Original line number Diff line number Diff line Loading @@ -47,29 +47,4 @@ class TestCandidateGraph(unittest.TestCase): pass class TestNode(unittest.TestCase): @classmethod def setUpClass(cls): cls.graph = network.CandidateGraph.from_adjacency(get_path('adjacency.json')) def test_get_handle(self): self.assertIsInstance(self.graph.node[0].handle, GeoDataset) def test_get_array(self): image = self.graph.node[0].get_array() self.assertEqual((1012, 1012), image.shape) self.assertEqual(np.uint8, image.dtype) def test_extract_features(self): node = self.graph.node[0] image = node.get_array() node.extract_features(image, extractor_parameters={'nfeatures':10}) self.assertEquals(len(node.keypoints), 10) self.assertEquals(len(node.descriptors), 10) self.assertIsInstance(node.descriptors[0], np.ndarray) def test_convex_hull_ratio_fail(self): # Convex hull computation is checked lower in the hull computation node = self.graph.node[0] self.assertRaises(AttributeError, node.coverage_ratio)
autocnet/graph/tests/test_node.py 0 → 100644 +62 −0 Original line number Diff line number Diff line import os import sys sys.path.insert(0, os.path.abspath('..')) import numpy as np import unittest from autocnet.examples import get_path from autocnet.fileio.io_gdal import GeoDataset from autocnet.utils.utils import find_in_dict from .. import node class TestNode(unittest.TestCase): def setUp(self): img = get_path('AS15-M-0295_SML.png') self.node = node.Node(image_name='AS15-M-0295_SML', image_path=img) def test_get_handle(self): self.assertIsInstance(self.node.handle, GeoDataset) def test_get_array(self): image = self.node.get_array() self.assertEqual((1012, 1012), image.shape) self.assertEqual(np.uint8, image.dtype) def test_extract_features(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':10}) self.assertEquals(len(self.node.keypoints), 10) self.assertEquals(len(self.node.descriptors), 10) self.assertIsInstance(self.node.descriptors[0], np.ndarray) self.assertEqual(10, self.node.nkeypoints) def test_convex_hull_ratio_fail(self): # Convex hull computation is checked lower in the hull computation self.assertRaises(AttributeError, self.node.coverage_ratio) def test_provenance(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':10}) self.node.extract_features(image, extractor_parameters={'nfeatures':15}) p0 = self.node.provenance[0] p1 = self.node.provenance[1] print(self.node.provenance) self.assertEqual(len(self.node.provenance.keys()), 2) self.assertNotEqual(find_in_dict(p0, 'nfeatures'), find_in_dict(p1, 'nfeatures')) def test_anms(self): image = self.node.get_array() self.node.extract_features(image, extractor_parameters={'nfeatures':100}) self.node.anms(nfeatures=10) self.assertIn('anms', self.node.masks) self.assertTrue(sum(self.node._mask_arrays['anms']), 10) def test_isis_serial(self): serial = self.node.isis_serial self.assertEqual(None, serial)
