Merge pull request #32 from jlaura/subpixel (8e862b85) · Commits · aflab / astrogeology / Autocnet

.travis.yml

+7 −5

Original line number	Diff line number	Diff line
		@@ -27,13 +27,15 @@ install:
		- conda info -a

		# Create a virtual env and install dependencies
		- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose gdal numpy pillow scipy pandas networkx scikit-image
		- conda create -y -q -n test-env python=$TRAVIS_PYTHON_VERSION nose numpy pillow scipy pandas networkx scikit-image
		# Activate the env
		- source activate test-env

		# Install the non-conda packages if required, requirements.txt duplicates are ignored
		- conda install -c https://conda.anaconda.org/jlaura opencv3=3.0.0
		- conda install -c https://conda.anaconda.org/osgeo gdal
		- conda install -c osgeo proj4
		- conda upgrade numpy
		- pip install -r requirements.txt
		- pip install coverage
		- pip install coveralls
		@@ -41,6 +43,9 @@ install:
		script:
		- nosetests --with-coverage --cover-package=autocnet

		after_success:
		- coveralls

		notifications:
		email:
		recipients:
		@@ -50,6 +55,3 @@ notifications:
		- jwbacker@usgs.gov
		on_success: always
		on_failure: always
		No newline at end of file

		after_success:
		- coveralls

autocnet/fileio/io_gdal.py

+4 −2

Original line number	Diff line number	Diff line
		import os

		import numpy as np
		from osgeo import gdal
		from osgeo import osr
		import gdal
		import osr

		from autocnet.fileio import extract_metadata

		gdal.UseExceptions()

		NP2GDAL_CONVERSION = {
		"uint8": 1,
		"int8": 1,

autocnet/graph/network.py

+73 −18

Original line number	Diff line number	Diff line
		@@ -258,7 +258,6 @@ class CandidateGraph(nx.Graph):

		transformation_matrix, ransac_mask = od.compute_homography(s_coords,
		d_coords)

		ransac_mask = ransac_mask.ravel()
		# Convert the truncated RANSAC mask back into a full length mask
		if clean_keys:
		@@ -266,34 +265,78 @@ class CandidateGraph(nx.Graph):
		else:
		mask = ransac_mask

		# Compute the error in the homography
		s_trans = np.hstack((s_coords[::-1],np.ones(s_coords.shape[0]).reshape(-1,1))).T
		s_trans = np.dot(transformation_matrix, s_trans).T

		#Normalize the error
		for i in range(3):
		s_trans[i] /= s_trans[2]

		d_trans = np.hstack((d_coords[::-1], np.ones(d_coords.shape[0]).reshape(-1,1)))

		attributes['homography_error'] = np.sqrt(np.sum((d_trans - s_trans)**2, axis=1))
		attributes['homography'] = transformation_matrix
		attributes['ransac'] = mask

		def compute_subpixel_offsets(self):
		def compute_subpixel_offsets(self, clean_keys=[], threshold=0.8, upsampling=10):
		"""
		For the entire graph, compute the subpixel offsets using pattern-matching and add the result
		as an attribute to each edge of the graph.

		Returns
		-------
		subpixel_offsets : ndarray
		A numpy array containing all the subpixel offsets for the entire graph.
		Parameters
		----------
		clean_keys : list
		of string keys to masking arrays
		(created by calling outlier detection)

		threshold : float
		On the range [-1, 1]. Values less than or equal to
		this threshold are masked and can be considered
		outliers
		"""
		subpixel_offsets = []
		for source, destination, attributes in self.edges_iter(data=True): #for each edge
		matches = attributes['matches'] #grab the matches

		for source, destination, attributes in self.edges_iter(data=True):
		matches = attributes['matches']

		full_offsets = np.zeros((len(matches), 3))

		# Build up a composite mask from all of the user specified masks
		if clean_keys:
		mask = np.prod([attributes[i] for i in clean_keys], axis=0, dtype=np.bool)
		matches = matches[mask]
		full_mask = np.where(mask == True)

		src_image = self.node[source]['image']
		dest_image = self.node[destination]['image']
		edge_offsets = []
		for i, (idx, row) in enumerate(matches.iterrows()): #for each edge, calculate this for each keypoint pair

		# Preallocate the numpy array to avoid appending and type conversion
		edge_offsets = np.empty((len(matches),3))

		# for each edge, calculate this for each keypoint pair
		for i, (idx, row) in enumerate(matches.iterrows()):
		s_idx = int(row['source_idx'])
		d_idx = int(row['destination_idx'])
		src_keypoint = self.node[source]['keypoints'][s_idx]
		dest_keypoint = self.node[destination]['keypoints'][d_idx]
		edge_offsets.append(sp.subpixel_offset(src_keypoint, dest_keypoint, src_image, dest_image))
		attributes['subpixel_offsets'] = np.array(edge_offsets)
		subpixel_offsets.append(np.array(edge_offsets))
		return subpixel_offsets

		# Compute the subpixel offset
		edge_offsets[i] = sp.subpixel_offset(src_keypoint, dest_keypoint, src_image, dest_image, upsampling=upsampling)

		# Compute the mask for correlations less than the threshold
		threshold_mask = edge_offsets[edge_offsets[:,-1] >= threshold]

		# Convert the truncated mask back into a full length mask
		if clean_keys:
		mask[full_mask] = threshold_mask
		full_offsets[full_mask] = edge_offsets
		else:
		mask = threshold_mask

		attributes['subpixel_offsets'] = pd.DataFrame(full_offsets, columns=['x_offset',
		'y_offset',
		'correlation'])
		attributes['subpixel'] = mask

		def to_cnet(self, clean_keys=[]):
		"""
		@@ -349,12 +392,15 @@ class CandidateGraph(nx.Graph):
		mask = np.prod([attributes[i] for i in clean_keys], axis=0, dtype=np.bool)
		matches = matches[mask]

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

		pt_idx = 0
		values = []
		for idx, row in matches.iterrows():
		for i, (idx, row) in enumerate(matches.iterrows()):
		# Composite matching key (node_id, point_id)
		m1 = (source, int(row['source_idx']))
		m2 = (destination, int(row['destination_idx']))
		@@ -365,8 +411,17 @@ class CandidateGraph(nx.Graph):
		pt_idx,
		source])

		values.append([kp2[m2[1]].pt[0],
		kp2[m2[1]].pt[1],
		kp2x = kp2[m2[1]].pt[0]
		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,
		pt_idx,
		destination])

autocnet/graph/tests/test_network.py

+9 −7

Original line number	Diff line number	Diff line
		@@ -13,8 +13,9 @@ from .. import network

		class TestCandidateGraph(unittest.TestCase):

		def setUp(self):
		self.graph = network.CandidateGraph.from_adjacency(get_path('adjacency.json'))
		@classmethod
		def setUpClass(cls):
		cls.graph = network.CandidateGraph.from_adjacency(get_path('adjacency.json'))

		def test_get_name(self):
		node_number = self.graph.node_name_map['AS15-M-0297_SML.png']
		@@ -28,6 +29,10 @@ class TestCandidateGraph(unittest.TestCase):
		def test_to_json_file(self):
		self.graph.to_json_file('test_graph_to_json.json')
		self.assertTrue(os.path.exists('test_graph_to_json.json'))
		try:
		os.remove('test_graph_to_json.json')
		except:
		pass

		def test_extract_features(self):
		# also tests get_geodataset() and get_keypoints
		@@ -42,7 +47,4 @@ class TestCandidateGraph(unittest.TestCase):
		self.assertEquals(self.graph.get_keypoints(node_number), node['keypoints'])

		def tearDown(self):
		try:
		os.remove('test_graph_to_json.json')
		except:
		pass

autocnet/matcher/outlier_detector.py

+2 −0

Original line number	Diff line number	Diff line
		@@ -148,3 +148,5 @@ def compute_homography(kp1, kp2, outlier_algorithm=cv2.RANSAC, reproj_threshold=
		reproj_threshold)
		mask = mask.astype(bool)
		return transformation_matrix, mask