Loading autocnet/graph/network.py +0 −1 Original line number Diff line number Diff line Loading @@ -177,7 +177,6 @@ class CandidateGraph(nx.Graph): matches : dataframe The pandas dataframe containing the matches """ source_groups = matches.groupby('source_image') for i, source_group in source_groups: for j, dest_group in source_group.groupby('destination_image'): Loading autocnet/matcher/matcher.py +100 −0 Original line number Diff line number Diff line Loading @@ -101,3 +101,103 @@ class FlannMatcher(object): 'destination_image', 'destination_idx', 'distance']) class OutlierDetector(object): """ A class which contains several outlier detection methods which all return True/False masks as pandas data series, which can be used as masks for the "matches" pandas dataframe which stores match information for each edge of the graph. Attributes ---------- """ def __init__(self): pass # (query only takes care of literal self-matches on a keypoint basis, not self-matches for the whole image) def self_neighbors(self, matches): """ Returns a pandas data series intended to be used as a mask. Each row is True if it is not matched to a point in the same image (good) and False if it is (bad.) Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance Returns ------- : dataseries Intended to mask the matches dataframe. True means the row is not matched to a point in the same image and false the row is. """ return matches.source_image != matches.destination_image def distance_ratio(self, matches, ratio=0.8): """ Compute and return a mask for the matches dataframe stored on each edge of the graph using the ratio test and distance_ratio set during initialization. Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance. ***Will only work as expected if matches already has dropped duplicates*** ratio: float the ratio between the first and second-best match distances for each keypoint to use as a bound for marking the first keypoint as "good." Returns ------- : dataseries Intended to mask the matches dataframe. Rows are True if the associated keypoint passes the ratio test and false otherwise. Keypoints without more than one match are True by default, since the ratio test will not work for them. """ #0.8 is Lowe's paper value -- can be changed. mask = [] temp_matches = matches.drop_duplicates() #don't want to deal with duplicates... for key, group in temp_matches.groupby('source_idx'): #won't work if there's only 1 match for each queryIdx if len(group) < 2: mask.append(True) else: if group['distance'].iloc[0] < ratio * group['distance'].iloc[1]: #this means distance _0_ is good and can drop all other distances mask.append(True) for i in range(len(group['distance']-1)): mask.append(False) else: for i in range(len(group['distance'])): mask.append(False) return pd.Series(mask) def mirroring_test(self, matches): """ Compute and return a mask for the matches dataframe on each edge of the graph which will keep only entries in which there is both a source -> destination match and a destination -> source match. Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance Returns ------- : dataseries Intended to mask the matches dataframe. Rows are True if the associated keypoint passes the mirroring test and false otherwise. That is, if 1->2, 2->1, both rows will be True, otherwise, they will be false. Keypoints with only one match will be False. Removes duplicate rows. """ return matches.duplicated(keep='first') autocnet/matcher/tests/test_matcher.py +44 −0 Original line number Diff line number Diff line Loading @@ -67,3 +67,47 @@ class TestMatcher(unittest.TestCase): def tearDown(self): pass class TestOutlierDetector(unittest.TestCase): def setUp(self): #actually set up everything for matches im1 = cv2.imread(get_path('AS15-M-0296_SML.png')) im2 = cv2.imread(get_path('AS15-M-0297_SML.png')) fd = {} sift = cv2.xfeatures2d.SIFT_create(10) fd['AS15-M-0296_SML.png'] = sift.detectAndCompute(im1, None) fd['AS15-M-0297_SML.png'] = sift.detectAndCompute(im2, None) fmatcher = matcher.FlannMatcher() truth_image_indices = {} counter = 0 for imageid, (keypoint, descriptor) in fd.items(): truth_image_indices[counter] = imageid fmatcher.add(descriptor, imageid) counter += 1 fmatcher.train() self.matches = fmatcher.query(fd['AS15-M-0296_SML.png'][1],'AS15-M-0296_SML.png', k=3) self.outliers = matcher.OutlierDetector() def test_distance_ratio(self): self.assertTrue(len(self.outliers.distance_ratio(self.matches)), 13) def test_self_neighbors(self): print(self.matches[self.outliers.self_neighbors(self.matches)]) #returned mask should be same length as input df self.assertEquals(len(self.outliers.self_neighbors(self.matches)), len(self.matches)) def test_mirroring_test(self): #returned mask should be same length as input df self.assertEquals(len(self.outliers.mirroring_test(self.matches)), len(self.matches)) def tearDown(self): pass functional_tests/test_two_image.py +3 −1 Original line number Diff line number Diff line Loading @@ -10,6 +10,7 @@ from autocnet.fileio.io_gdal import GeoDataset from autocnet.graph.network import CandidateGraph from autocnet.matcher import feature_extractor as fe from autocnet.matcher.matcher import FlannMatcher from autocnet.matcher.matcher import OutlierDetector class TestTwoImageMatching(unittest.TestCase): Loading Loading @@ -60,7 +61,8 @@ class TestTwoImageMatching(unittest.TestCase): for node, attributes in cg.nodes_iter(data=True): descriptors = attributes['descriptors'] matches = fl.query(descriptors, node, k=2) matches = fl.query(descriptors, node, k=3) #had to increase from 2 to test distance ratio test detectme = OutlierDetector() cg.add_matches(matches) # Step: And create a C object Loading Loading
autocnet/graph/network.py +0 −1 Original line number Diff line number Diff line Loading @@ -177,7 +177,6 @@ class CandidateGraph(nx.Graph): matches : dataframe The pandas dataframe containing the matches """ source_groups = matches.groupby('source_image') for i, source_group in source_groups: for j, dest_group in source_group.groupby('destination_image'): Loading
autocnet/matcher/matcher.py +100 −0 Original line number Diff line number Diff line Loading @@ -101,3 +101,103 @@ class FlannMatcher(object): 'destination_image', 'destination_idx', 'distance']) class OutlierDetector(object): """ A class which contains several outlier detection methods which all return True/False masks as pandas data series, which can be used as masks for the "matches" pandas dataframe which stores match information for each edge of the graph. Attributes ---------- """ def __init__(self): pass # (query only takes care of literal self-matches on a keypoint basis, not self-matches for the whole image) def self_neighbors(self, matches): """ Returns a pandas data series intended to be used as a mask. Each row is True if it is not matched to a point in the same image (good) and False if it is (bad.) Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance Returns ------- : dataseries Intended to mask the matches dataframe. True means the row is not matched to a point in the same image and false the row is. """ return matches.source_image != matches.destination_image def distance_ratio(self, matches, ratio=0.8): """ Compute and return a mask for the matches dataframe stored on each edge of the graph using the ratio test and distance_ratio set during initialization. Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance. ***Will only work as expected if matches already has dropped duplicates*** ratio: float the ratio between the first and second-best match distances for each keypoint to use as a bound for marking the first keypoint as "good." Returns ------- : dataseries Intended to mask the matches dataframe. Rows are True if the associated keypoint passes the ratio test and false otherwise. Keypoints without more than one match are True by default, since the ratio test will not work for them. """ #0.8 is Lowe's paper value -- can be changed. mask = [] temp_matches = matches.drop_duplicates() #don't want to deal with duplicates... for key, group in temp_matches.groupby('source_idx'): #won't work if there's only 1 match for each queryIdx if len(group) < 2: mask.append(True) else: if group['distance'].iloc[0] < ratio * group['distance'].iloc[1]: #this means distance _0_ is good and can drop all other distances mask.append(True) for i in range(len(group['distance']-1)): mask.append(False) else: for i in range(len(group['distance'])): mask.append(False) return pd.Series(mask) def mirroring_test(self, matches): """ Compute and return a mask for the matches dataframe on each edge of the graph which will keep only entries in which there is both a source -> destination match and a destination -> source match. Parameters ---------- matches : dataframe the matches dataframe stored along the edge of the graph containing matched points with columns containing: matched image name, query index, train index, and descriptor distance Returns ------- : dataseries Intended to mask the matches dataframe. Rows are True if the associated keypoint passes the mirroring test and false otherwise. That is, if 1->2, 2->1, both rows will be True, otherwise, they will be false. Keypoints with only one match will be False. Removes duplicate rows. """ return matches.duplicated(keep='first')
autocnet/matcher/tests/test_matcher.py +44 −0 Original line number Diff line number Diff line Loading @@ -67,3 +67,47 @@ class TestMatcher(unittest.TestCase): def tearDown(self): pass class TestOutlierDetector(unittest.TestCase): def setUp(self): #actually set up everything for matches im1 = cv2.imread(get_path('AS15-M-0296_SML.png')) im2 = cv2.imread(get_path('AS15-M-0297_SML.png')) fd = {} sift = cv2.xfeatures2d.SIFT_create(10) fd['AS15-M-0296_SML.png'] = sift.detectAndCompute(im1, None) fd['AS15-M-0297_SML.png'] = sift.detectAndCompute(im2, None) fmatcher = matcher.FlannMatcher() truth_image_indices = {} counter = 0 for imageid, (keypoint, descriptor) in fd.items(): truth_image_indices[counter] = imageid fmatcher.add(descriptor, imageid) counter += 1 fmatcher.train() self.matches = fmatcher.query(fd['AS15-M-0296_SML.png'][1],'AS15-M-0296_SML.png', k=3) self.outliers = matcher.OutlierDetector() def test_distance_ratio(self): self.assertTrue(len(self.outliers.distance_ratio(self.matches)), 13) def test_self_neighbors(self): print(self.matches[self.outliers.self_neighbors(self.matches)]) #returned mask should be same length as input df self.assertEquals(len(self.outliers.self_neighbors(self.matches)), len(self.matches)) def test_mirroring_test(self): #returned mask should be same length as input df self.assertEquals(len(self.outliers.mirroring_test(self.matches)), len(self.matches)) def tearDown(self): pass
functional_tests/test_two_image.py +3 −1 Original line number Diff line number Diff line Loading @@ -10,6 +10,7 @@ from autocnet.fileio.io_gdal import GeoDataset from autocnet.graph.network import CandidateGraph from autocnet.matcher import feature_extractor as fe from autocnet.matcher.matcher import FlannMatcher from autocnet.matcher.matcher import OutlierDetector class TestTwoImageMatching(unittest.TestCase): Loading Loading @@ -60,7 +61,8 @@ class TestTwoImageMatching(unittest.TestCase): for node, attributes in cg.nodes_iter(data=True): descriptors = attributes['descriptors'] matches = fl.query(descriptors, node, k=2) matches = fl.query(descriptors, node, k=3) #had to increase from 2 to test distance ratio test detectme = OutlierDetector() cg.add_matches(matches) # Step: And create a C object Loading
