def test_all(self):
"""Test everything in DFSMap."""
def key(index_pair):
return index_pair.i(), index_pair.j()
dsf = gtsam.DSFMapIndexPair()
pair1 = gtsam.IndexPair(1, 18)
self.assertEqual(key(dsf.find(pair1)), key(pair1))
pair2 = gtsam.IndexPair(2, 2)
dsf.merge(pair1, pair2)
self.assertTrue(dsf.find(pair1), dsf.find(pair1))
def test_all(self) -> None:
"""Test everything in DFSMap."""
def key(index_pair) -> Tuple[int, int]:
return index_pair.i(), index_pair.j()
dsf = gtsam.DSFMapIndexPair()
pair1 = gtsam.IndexPair(1, 18)
self.assertEqual(key(dsf.find(pair1)), key(pair1))
pair2 = gtsam.IndexPair(2, 2)
# testing the merge feature of dsf
dsf.merge(pair1, pair2)
self.assertEqual(key(dsf.find(pair1)), key(dsf.find(pair2)))
def filter_bad_landmarks(self, landmark_map, dsf, enable):
"""Filter bad landmarks:
1. landmark observations<3
2. landmarks with more than one observations in an image.
3. Features with more than one landmark correspondences"""
# filter bad matches
if enable is True:
bad_matches = self.find_bad_matches()
else:
bad_matches = {}
bad_key_list = set()
for bad_match in bad_matches:
landmark_representative = dsf.find(
gtsam.IndexPair(bad_match[0], bad_match[1]))
key = (landmark_representative.i(), landmark_representative.j())
bad_key_list.add(key)
for key in bad_key_list:
del landmark_map[key]
landmark_map_values = [
landmark_map[key] for key in sorted(landmark_map.keys())
]
landmark_map_new = copy.copy(landmark_map_values)
for observation_list in landmark_map_values:
if len(observation_list) < self._seen:
landmark_map_new.remove(observation_list)
return landmark_map_new
def test_find_dsf(self):
"""Test functions related to DSF"""
# """Test find bad matches."""
data_directory = 'mapping/datasets/dsf_test_data/'
num_images = 3
filter_bad_landmarks_enable = False
min_obersvation_number = 3
back_end = MappingBackEnd(
data_directory, num_images, gtsam.Cal3_S2(), [], [], [], filter_bad_landmarks_enable, min_obersvation_number)
bad_matches = back_end.find_bad_matches()
self.assertEqual(bad_matches, {(2, 6), (0, 4)})
# """Test create landmark map."""
actual_landmark_map, dsf = back_end.create_landmark_map(False)
expected_landmark_map = {(0, 1): [(0, Point2(1, 1)), (1, Point2(2, 1)), (2, Point2(3, 1))], (2, 0): [(2, Point2(0, 0))], (0, 0): [(0, Point2(0, 0))], (0, 5): [(0, Point2(1, 5)), (0, Point2(1, 6)), (2, Point2(3, 6))], (0, 4): [
(0, Point2(1, 4)), (2, Point2(3, 3)), (2, Point2(3, 5))], (1, 0): [(1, Point2(0, 0))], (0, 3): [(0, Point2(1, 3)), (1, Point2(2, 2)), (2, Point2(3, 2))], (0, 2): [(0, Point2(1, 2)), (2, Point2(3, 4))]}
self.assert_landmark_map_equal(
actual_landmark_map, expected_landmark_map)
# """Test generate dsf."""
landmark_representative = dsf.find(gtsam.IndexPair(2, 1))
key = (landmark_representative.i(), landmark_representative.j())
self.assertEqual(key, (0, 1))
# """Test filter bad landmarks."""
actual_landmark_map = back_end.filter_bad_landmarks(
expected_landmark_map, dsf, True)
expected_landmark_map = [[(0, Point2(1, 1)), (1, Point2(2, 1)), (2, Point2(3, 1))], [
(0, Point2(1, 3)), (1, Point2(2, 2)), (2, Point2(3, 2))]]
# self.assert_landmark_map_equal(actual_landmark_map, expected_landmark_map)
for i, observations in enumerate(expected_landmark_map):
for j, observation in enumerate(observations):
self.assertEqual(actual_landmark_map[i][j][0], observation[0])
self.gtsamAssertEquals(
actual_landmark_map[i][j][1], observation[1])
def generate_dsf(self, enable=True):
"""Use dsf to find data association between landmark and landmark observation(features)"""
dsf = gtsam.DSFMapIndexPair()
for i in range(0, self._nrimages - 1):
for j in range(i + 1, self._nrimages):
matches = self.load_matches(i, j)
if enable:
bad_essential, matches = self.ransac_filter_keypoints(
matches, i, j)
if bad_essential:
print(
"Not enough points to generate essential matrix for image_",
i, " and image_", j)
continue
for frame_1, keypt_1, frame_2, keypt_2 in matches:
dsf.merge(gtsam.IndexPair(frame_1, keypt_1),
gtsam.IndexPair(frame_2, keypt_2))
return dsf
def create_landmark_map(self, enable=True):
"""Create a list to map landmarks and their correspondences.
[Landmark_i:[(i,Point2()), (j,Point2())...]...]"""
dsf = self.generate_dsf(enable)
landmark_map = defaultdict(list)
for img_index, feature_list in enumerate(self._image_features):
for feature_index, feature in enumerate(feature_list):
landmark_representative = dsf.find(
gtsam.IndexPair(img_index, feature_index))
key = (landmark_representative.i(),
landmark_representative.j())
landmark_map[key].append((img_index, feature))
return landmark_map, dsf
def generate_tracks_from_pairwise_matches(
matches_dict: Dict[Tuple[int, int], np.ndarray],
keypoints_list: List[Keypoints],
) -> List["SfmTrack2d"]:
"""Factory function that creates a list of tracks from 2d point correspondences.
Creates a disjoint-set forest (DSF) and 2d tracks from pairwise matches. We create a singleton for union-find
set elements from camera index of a detection and the index of that detection in that camera's keypoint list,
i.e. (i,k).
Args:
matches_dict: Dict of pairwise matches of type:
key: pose indices for the matched pair of images
val: feature indices, as array of Nx2 shape; N being nb of features. A row is (feature_idx1,
feature_idx2).
keypoints_list: List of keypoints for each image.
Returns:
list of all valid SfmTrack2d generated by the matches.
"""
# check to ensure dimensions of coordinates are correct
dims_valid = all([kps.coordinates.ndim == 2 for kps in keypoints_list])
if not dims_valid:
raise Exception(
"Dimensions for Keypoint coordinates incorrect. Array needs to be 2D"
)
# Generate the DSF to form tracks
dsf = gtsam.DSFMapIndexPair()
track_2d_list = []
# for DSF finally
# measurement_idxs represented by ks
for (i1, i2), k_pairs in matches_dict.items():
for (k1, k2) in k_pairs:
dsf.merge(gtsam.IndexPair(i1, k1), gtsam.IndexPair(i2, k2))
key_set = dsf.sets()
# create a landmark map: a list of tracks
# Each track is represented as a list of (camera_idx, measurements)
for set_id in key_set:
index_pair_set = key_set[
set_id] # key_set is a wrapped C++ map, so this unusual syntax is required
# Initialize track from measurements
track_measurements = []
for index_pair in gtsam.IndexPairSetAsArray(index_pair_set):
# camera_idx is represented by i
# measurement_idx is represented by k
i = index_pair.i()
k = index_pair.j()
# add measurement in this track
track_measurements += [
SfmMeasurement(i, keypoints_list[i].coordinates[k])
]
track_2d = SfmTrack2d(track_measurements)
if track_2d.validate_unique_cameras():
track_2d_list += [track_2d]
return track_2d_list
def run(self, matches_dict: Dict[Tuple[int, int], np.ndarray],
keypoints_list: List[Keypoints]) -> List[SfmTrack2d]:
"""Estimate tracks from feature correspondences.
Creates a disjoint-set forest (DSF) and 2d tracks from pairwise matches. We create a singleton for union-find
set elements from camera index of a detection and the index of that detection in that camera's keypoint list,
i.e. (i,k).
Args:
matches_dict: Dict of pairwise matches of type:
key: indices for the matched pair of images
val: feature indices, as array of Nx2 shape; N being number of features. A row is (feature_idx1,
feature_idx2).
keypoints_list: List of keypoints for each image.
Returns:
list of all valid SfmTrack2d generated by the matches.
"""
# check to ensure dimensions of coordinates are correct
dims_valid = all([kps.coordinates.ndim == 2 for kps in keypoints_list])
if not dims_valid:
raise Exception(
"Dimensions for Keypoint coordinates incorrect. Array needs to be 2D"
)
# Generate the DSF to form tracks
dsf = gtsam.DSFMapIndexPair()
track_2d_list = []
# for DSF finally
# measurement_idxs represented by ks
for (i1, i2), k_pairs in matches_dict.items():
for (k1, k2) in k_pairs:
dsf.merge(gtsam.IndexPair(i1, k1), gtsam.IndexPair(i2, k2))
key_set = dsf.sets()
erroneous_track_count = 0
# create a landmark map: a list of tracks
# Each track is represented as a list of (camera_idx, measurements)
for set_id in key_set:
index_pair_set = key_set[
set_id] # key_set is a wrapped C++ map, so this unusual syntax is required
# Initialize track from measurements
track_measurements = []
for index_pair in gtsam.IndexPairSetAsArray(index_pair_set):
# camera_idx is represented by i
# measurement_idx is represented by k
i = index_pair.i()
k = index_pair.j()
# add measurement in this track
track_measurements += [
SfmMeasurement(i, keypoints_list[i].coordinates[k])
]
track_2d = SfmTrack2d(track_measurements)
# Skip erroneous track that had repeated measurements within the same image (i.e., violates transitivity).
# This is an expected result from an incorrect correspondence slipping through.
if track_2d.validate_unique_cameras():
track_2d_list += [track_2d]
else:
erroneous_track_count += 1
erroneous_track_pct = erroneous_track_count / len(
key_set) * 100 if len(key_set) > 0 else np.NaN
logger.info(
f"DSF Union-Find: {erroneous_track_pct:.2f}% of tracks discarded from multiple obs. in a single image."
)
return track_2d_list
