Fixed network extract_features to take extraction parameters, not just nfeatures. (00b61096) · Commits · aflab / astrogeology / Autocnet

autocnet/graph/network.py

+35 −20

Original line number	Diff line number	Diff line
		@@ -5,7 +5,8 @@ import pandas as pd
		import cv2
		import numpy as np

		from scipy.misc import bytescale # store image array
		from scipy.misc import bytescale, imresize


		from autocnet.control.control import C
		from autocnet.fileio import io_json
		@@ -151,41 +152,59 @@ class CandidateGraph(nx.Graph):
		#self.node_labels[self.node[self.node_counter]['image_name']] = self.node_counter
		self.node_counter += 1

		def get_geodataset(self, nodeIndex):
		def get_geodataset(self, nodeindex):
		"""
		Constructs a GeoDataset object from the given node image and assigns the
		dataset and its NumPy array to the 'handle' and 'image' node attributes.

		Parameters
		----------
		nodeIndex : int
		nodeindex : int
		The index of the node.

		"""
		self.node[nodeIndex]['handle'] = GeoDataset(self.node[nodeIndex]['image_path'])
		self.node[nodeIndex]['image'] = bytescale(self.node[nodeIndex]['handle'].read_array())
		self.node[nodeindex]['handle'] = GeoDataset(self.node[nodeindex]['image_path'])

		def get_array(self, nodeindex, downsampling=1):
		"""
		Downsample the input image file by some amount using bicubic interpolation
		in order to reduce data sizes for visualization and analysis, e.g. feature detection

		Parameters
		----------
		nodeindex : hashable
		The index into the node containing a geodataset object

		def extract_features(self, nfeatures) :
		downsampling : int
		[1, infinity] downsampling
		"""

		array = self.node[nodeindex]['handle'].read_array()
		newx_size = int(array.shape[0] / downsampling)
		newy_size = int(array.shape[1] / downsampling)

		resized_array = imresize(array, (newx_size, newy_size), interp='bicubic')
		self.node[nodeindex]['image'] = bytescale(resized_array)
		self.node[nodeindex]['image_downsampling'] = downsampling

		def extract_features(self, extractor_parameters={}, downsampling=1):
		"""
		Extracts features from each image in the graph and uses the result to assign the
		node attributes for 'handle', 'image', 'keypoints', and 'descriptors'.

		Parameters
		----------
		nfeatures : int
		The number of features to be extracted.
		extractor_parameters : dict
		A dictionary containing OpenCV SIFT parameters names and values.

		downsampling : int
		The divisor to image_size to down sample the input image.
		"""
		# Loop through the nodes (i.e. images) on the graph and fill in their attributes.
		# These attributes are...
		# geo dataset (handle and image)
		# features (keypoints and descriptors)
		for node, attributes in self.nodes_iter(data=True):

		self.get_geodataset(node)
		extraction_params = {'nfeatures' : nfeatures}
		self.get_array(node, downsampling=downsampling)
		attributes['keypoints'], attributes['descriptors'] = fe.extract_features(attributes['image'],
		extraction_params)
		extractor_parameters)

		def add_matches(self, matches):
		"""
		@@ -394,7 +413,6 @@ class CandidateGraph(nx.Graph):

		if 'subpixel' in clean_keys:
		offsets = attributes['subpixel_offsets'][attributes['subpixel']]
		print(offsets)
		kp1 = self.node[source]['keypoints']
		kp2 = self.node[destination]['keypoints']

		@@ -415,11 +433,8 @@ class CandidateGraph(nx.Graph):
		kp2y = kp2[m2[1]].pt[1]

		if 'subpixel' in clean_keys:
		print(idx)
		print(kp2x, kp2y)
		kp2x += offsets['x_offset'].values[i]
		kp2y += offsets['y_offset'].values[i]
		print(kp2x, kp2y)
		values.append([kp2x,
		kp2y,
		m2,

autocnet/graph/tests/test_network.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -36,7 +36,7 @@ class TestCandidateGraph(unittest.TestCase):

		def test_extract_features(self):
		# also tests get_geodataset() and get_keypoints
		self.graph.extract_features(10)
		self.graph.extract_features(extractor_parameters={'nfeatures':10})
		node_number = self.graph.node_name_map['AS15-M-0297_SML.png']
		node = self.graph.node[node_number]
		self.assertEquals(len(node['image']), 1012)

autocnet/matcher/feature_extractor.py

+5 −7

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


		def extract_features(image_array, extractor_parameters):
		def extract_features(array, extractor_parameters):
		"""
		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
		@@ -10,8 +9,9 @@ def extract_features(image_array, extractor_parameters):

		Parameters
		----------
		image_array : ndarray
		array : ndarray
		a NumPy array that represents an image

		extractor_parameters : dict
		A dictionary containing OpenCV SIFT parameters names and values.

		@@ -22,6 +22,4 @@ def extract_features(image_array, extractor_parameters):
		"""

		sift = cv2.xfeatures2d.SIFT_create(**extractor_parameters)
		converted_array = misc.bytescale(image_array)

		return sift.detectAndCompute(converted_array, None)
		return sift.detectAndCompute(array, None)

autocnet/matcher/subpixel.py

+13 −13

Original line number	Diff line number	Diff line
		import pandas as pd
		from autocnet.matcher import matcher

		from scipy.misc import imresize

		# TODO: look into KeyPoint.size and perhaps use to determine an appropriately-sized search/template.
		# TODO: do not allow even sizes


		def subpixel_offset(template_kp, search_kp, template_img, search_img,
		template_size=9, search_size=27, upsampling=10):
		"""
		Uses a pattern-matcher on subsets of two images determined from the passed-in keypoints and optional sizes to
		compute an x and y offset from the search keypoint to the template keypoint and an associated strength.
		@@ -33,7 +32,8 @@ Parameters
		The returned tuple is of form: (x_offset, y_offset, strength). The offsets are from the search to the template
		keypoint.
		"""
		def subpixel_offset(template_kp, search_kp, template_img, search_img, template_size=9, search_size=27, upsampling=10):
		if template_size % 2 == 0 or search_size %2 == 0:
		raise ValueError('The search and template images must have an odd number of lines and samples')
		# Get the x,y coordinates
		temp_x, temp_y = map(int, template_kp.pt)
		search_x, search_y = map(int, search_kp.pt)

autocnet/matcher/tests/test_feature_extractor.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ class TestFeatureExtractor(unittest.TestCase):
		@classmethod
		def setUpClass(self):
		self.dataset = io_gdal.GeoDataset(get_path('AS15-M-0295_SML.png'))
		self.data_array = self.dataset.read_array()
		self.data_array = self.dataset.read_array(dtype='uint8')
		self.parameters = {"nfeatures" : 10,
		"nOctaveLayers" : 3,
		"contrastThreshold" : 0.02,