def test_eq_check_with_missing_measurements(self) -> None:
"""Tests the __eq__ function with one track having subset of measurements of the other."""
track_1 = SfmTrack2d(SAMPLE_MEASUREMENTS)
# dropping the last measurement
track_2 = SfmTrack2d(SAMPLE_MEASUREMENTS[:3])
self.assertNotEqual(track_1, track_2)
self.assertNotEqual(track_2, track_1)
def test_eq_check_with_different_measurements(self) -> None:
"""Tests the __eq__ function with one measurement having different value of the 2d point."""
track_1 = SfmTrack2d(SAMPLE_MEASUREMENTS)
# changing the value of the last measurement
old_measurement = SAMPLE_MEASUREMENTS[-1]
track_2 = SfmTrack2d(
SAMPLE_MEASUREMENTS[:3] +
[SfmMeasurement(old_measurement.i, np.random.rand(2))])
self.assertNotEqual(track_1, track_2)
self.assertNotEqual(track_2, track_1)
def test_eq_check_with_same_measurements(self) -> None:
"""Tests the __eq__ function with the same set of measurements but with different ordering."""
# construct two tracks with different ordering of measurements
track_1 = SfmTrack2d(SAMPLE_MEASUREMENTS)
track_2 = SfmTrack2d([
SAMPLE_MEASUREMENTS[0],
SAMPLE_MEASUREMENTS[3],
SAMPLE_MEASUREMENTS[1],
SAMPLE_MEASUREMENTS[2],
])
self.assertEqual(track_1, track_2)
def __runWithCheiralityException(self, obj: Point3dInitializer) -> bool:
"""Run the initialization in a a-cheiral setup, and check that the result is a None track."""
cameras = obj.track_camera_dict
# flip the cameras first
yaw = np.pi
camera_flip_pose = Pose3(Rot3.RzRyRx(yaw, 0, 0), np.zeros((3, 1)))
flipped_cameras = {
i: PinholeCameraCal3Bundler(cam.pose().compose(camera_flip_pose),
cam.calibration())
for i, cam in cameras.items()
}
obj_with_flipped_cameras = Point3dInitializer(
flipped_cameras,
obj.mode,
obj.reproj_error_thresh,
obj.num_ransac_hypotheses,
)
sfm_track = obj_with_flipped_cameras.triangulate(
SfmTrack2d(MEASUREMENTS))
return sfm_track is None
def classify_tracks3d_with_gt_cameras(
tracks: List[SfmTrack], cameras_gt: List[PinholeCameraCal3Bundler], reproj_error_thresh_px: float = 3
) -> List[TriangulationExitCode]:
"""Classifies the 3D tracks w.r.t ground truth cameras by performing triangulation and collecting exit codes.
Args:
tracks: list of 3d tracks, of length J.
cameras_gt: cameras with GT params.
reproj_error_thresh_px (optional): Reprojection error threshold (in pixels) for a track to be considered an
all-inlier one. Defaults to 3.
Returns:
The triangulation exit code for each input track, as list of length J (same as input).
"""
# convert the 3D tracks to 2D tracks
tracks_2d: List[SfmTrack2d] = []
for track_3d in tracks:
num_measurements = track_3d.numberMeasurements()
measurements: List[SfmMeasurement] = []
for k in range(num_measurements):
i, uv = track_3d.measurement(k)
measurements.append(SfmMeasurement(i, uv))
tracks_2d.append(SfmTrack2d(measurements))
return classify_tracks2d_with_gt_cameras(tracks_2d, cameras_gt, reproj_error_thresh_px)
def __runWithSingleOutlier(self, obj: Point3dInitializer) -> bool:
"""Run the initialization for a track with all inlier measurements except one, and checks for correctness of
the estimated point."""
sfm_track = obj.triangulate(SfmTrack2d(get_track_with_one_outlier()))
point3d = sfm_track.point3()
return np.array_equal(point3d, LANDMARK_POINT)
def testSimpleTriangulationOnDoorDataset(self):
"""Test the tracks of the door dataset using simple triangulation initialization. Using computed tracks with
ground truth camera params.
Expecting failures on 2 tracks which have incorrect matches."""
with open(DOOR_TRACKS_PATH, "rb") as handle:
tracks = pickle.load(handle)
loader = FolderLoader(DOOR_DATASET_PATH, image_extension="JPG")
camera_dict = {
i: PinholeCameraCal3Bundler(loader.get_camera_pose(i),
loader.get_camera_intrinsics(i))
for i in range(len(loader))
}
initializer = Point3dInitializer(camera_dict,
TriangulationParam.NO_RANSAC,
reproj_error_thresh=1e5)
# tracks which have expected failures
# (both tracks have incorrect measurements)
expected_failures = [
SfmTrack2d(measurements=[
SfmMeasurement(i=1,
uv=np.array([1252.22729492, 1487.29431152])),
SfmMeasurement(i=2,
uv=np.array([1170.96679688, 1407.35876465])),
SfmMeasurement(i=4, uv=np.array([263.32104492, 1489.76965332
])),
]),
SfmTrack2d(measurements=[
SfmMeasurement(i=6, uv=np.array([1142.34545898, 735.92169189
])),
SfmMeasurement(i=7, uv=np.array([1179.84155273, 763.04095459
])),
SfmMeasurement(i=9, uv=np.array([216.54107666, 774.74017334])),
]),
]
for track_2d in tracks:
triangulated_track = initializer.triangulate(track_2d)
if triangulated_track is None:
# assert we have failures which are already expected
self.assertIn(track_2d, expected_failures)
def __runWithTwoMeasurements(self, obj: Point3dInitializer) -> bool:
"""Run the initialization with a track with all correct measurements, and checks for correctness of the
recovered 3D point."""
sfm_track = obj.triangulate(SfmTrack2d(MEASUREMENTS[:2]))
point3d = sfm_track.point3()
return np.allclose(point3d, LANDMARK_POINT)
def test_generate_tracks_from_pairwise_matches_no_duplicates(self) -> None:
"""Tests that the tracks are being merged and mapped correctly."""
dummy_keypoints_list = get_dummy_keypoints_list()
dummy_matches_dict = get_dummy_matches()
tracks = SfmTrack2d.generate_tracks_from_pairwise_matches(
dummy_matches_dict, dummy_keypoints_list)
# len(track) value for toy case strictly
self.assertEqual(len(tracks), 4, "tracks incorrectly mapped")
def __runWithDuplicateMeasurements(self, obj: Point3dInitializer) -> bool:
"""Run the initialization for a track with all inlier measurements except one, and checks for correctness of
the estimated point."""
sfm_track = obj.triangulate(
SfmTrack2d(get_track_with_duplicate_measurements()))
point3d = sfm_track.point3()
return np.allclose(point3d, LANDMARK_POINT, atol=1, rtol=1e-1)
def generate_measurement_pairs(track: SfmTrack2d) -> List[Tuple[int, ...]]:
"""
Extract all possible measurement pairs in a track for triangulation.
Args:
track: feature track from which measurements are to be extracted
Returns:
measurement_idxs: all possible matching measurement indices in a given track
"""
num_track_measurements = track.number_measurements()
all_measurement_idxs = range(num_track_measurements)
measurement_pair_idxs = list(
itertools.combinations(all_measurement_idxs,
NUM_SAMPLES_PER_RANSAC_HYPOTHESIS))
return measurement_pair_idxs
def test_generate_tracks_from_pairwise_matches_with_duplicates(
self, ) -> None:
"""Tests that the tracks are being filtered correctly. Removes tracks that have two measurements in a single
image.
"""
dummy_keypoints_list = get_dummy_keypoints_list()
dummy_matches_dict = get_dummy_matches()
malformed_matches_dict = copy.deepcopy(dummy_matches_dict)
# add erroneous correspondence
malformed_matches_dict[(1, 1)] = np.array([[0, 3]])
tracks = SfmTrack2d.generate_tracks_from_pairwise_matches(
malformed_matches_dict, dummy_keypoints_list)
# check that the length of the observation list corresponding to each key
# is the same. Only good tracks will remain
self.assertEqual(len(tracks), 4, "Tracks not filtered correctly")
def triangulate(
self, track_2d: SfmTrack2d
) -> Tuple[Optional[SfmTrack], Optional[float], TriangulationExitCode]:
"""Triangulates 3D point according to the configured triangulation mode.
Args:
track: feature track from which measurements are to be extracted
Returns:
track with inlier measurements and 3D landmark. None returned if triangulation fails or has high error.
avg_track_reproj_error: reprojection error of 3d triangulated point to each image plane
Note: this may be "None" if the 3d point could not be triangulated successfully
due to a cheirality exception or insufficient number of RANSAC inlier measurements
is_cheirality_failure: boolean representing whether the selected 2d measurements lead
to a cheirality exception upon triangulation
"""
# Check if we will run RANSAC, or not.
if self.options.mode in [
TriangulationSamplingMode.RANSAC_SAMPLE_UNIFORM,
TriangulationSamplingMode.RANSAC_SAMPLE_BIASED_BASELINE,
TriangulationSamplingMode.RANSAC_TOPK_BASELINES,
]:
best_inliers = self.execute_ransac_variant(track_2d)
elif self.options.mode == TriangulationSamplingMode.NO_RANSAC:
best_inliers = np.ones(len(track_2d.measurements),
dtype=bool) # all marked as inliers
# Verify we have at least 2 inliers.
inlier_idxs = (np.where(best_inliers)[0]).tolist()
if len(inlier_idxs) < 2:
return None, None, TriangulationExitCode.INLIERS_UNDERCONSTRAINED
# Extract keypoint measurements corresponding to inlier indices.
inlier_track = track_2d.select_subset(inlier_idxs)
track_cameras, track_measurements = self.extract_measurements(
inlier_track)
# Exit if we do not have at least 2 measurements in cameras with estimated poses.
if track_cameras is None:
return None, None, TriangulationExitCode.POSES_UNDERCONSTRAINED
# Triangulate and check for cheirality failure from GTSAM.
try:
triangulated_pt = gtsam.triangulatePoint3(
track_cameras,
track_measurements,
rank_tol=SVD_DLT_RANK_TOL,
optimize=True,
)
except RuntimeError:
return None, None, TriangulationExitCode.CHEIRALITY_FAILURE
# Compute reprojection errors for each measurement.
reproj_errors, avg_track_reproj_error = reproj_utils.compute_point_reprojection_errors(
self.track_camera_dict, triangulated_pt, inlier_track.measurements)
# Check that all measurements are within reprojection error threshold.
if not np.all(
reproj_errors.flatten() < self.options.reproj_error_threshold):
return None, avg_track_reproj_error, TriangulationExitCode.EXCEEDS_REPROJ_THRESH
# Create a gtsam.SfmTrack with the triangulated 3d point and associated 2d measurements.
track_3d = SfmTrack(triangulated_pt)
for i, uv in inlier_track.measurements:
track_3d.addMeasurement(i, uv)
return track_3d, avg_track_reproj_error, TriangulationExitCode.SUCCESS
def testSimpleTriangulationWithOutlierMeasurements(self):
sfm_track = self.simple_triangulation_initializer.triangulate(
SfmTrack2d(get_track_with_one_outlier()))
self.assertIsNone(sfm_track)
def __runWithOneMeasurement(self, obj: Point3dInitializer) -> bool:
"""Run the initialization with a track with all correct measurements, and checks for a None track as a result."""
sfm_track = obj.triangulate(SfmTrack2d(MEASUREMENTS[:1]))
return sfm_track is None
def run(
self,
cameras: Dict[int, PinholeCameraCal3Bundler],
corr_idxs_dict: Dict[Tuple[int, int], np.ndarray],
keypoints_list: List[Keypoints],
) -> Tuple[SfmData, Dict[str, Any]]:
"""Perform the data association.
Args:
cameras: dictionary, with image index -> camera mapping.
corr_idxs_dict: dictionary, with key as image pair (i1,i2) and value as matching keypoint indices.
keypoints_list: keypoints for each image.
Returns:
cameras and tracks as SfmData.
"""
# generate tracks for 3D points using pairwise correspondences
tracks_2d = SfmTrack2d.generate_tracks_from_pairwise_matches(
corr_idxs_dict, keypoints_list)
# metrics on tracks w/o triangulation check
num_tracks_2d = len(tracks_2d)
track_lengths = list(map(lambda x: x.number_measurements(), tracks_2d))
mean_2d_track_length = np.mean(track_lengths)
logger.debug("[Data association] input number of tracks: %s",
num_tracks_2d)
logger.debug("[Data association] input avg. track length: %s",
mean_2d_track_length)
# initializer of 3D landmark for each track
point3d_initializer = Point3dInitializer(
cameras,
self.mode,
self.reproj_error_thresh,
self.num_ransac_hypotheses,
)
num_tracks_w_cheirality_exceptions = 0
per_accepted_track_avg_errors = []
per_rejected_track_avg_errors = []
# form SFMdata object after triangulation
triangulated_data = SfmData()
for track_2d in tracks_2d:
# triangulate and filter based on reprojection error
sfm_track, avg_track_reproj_error, is_cheirality_failure = point3d_initializer.triangulate(
track_2d)
if is_cheirality_failure:
num_tracks_w_cheirality_exceptions += 1
if sfm_track is not None and self.__validate_track(sfm_track):
triangulated_data.add_track(sfm_track)
per_accepted_track_avg_errors.append(avg_track_reproj_error)
else:
per_rejected_track_avg_errors.append(avg_track_reproj_error)
num_accepted_tracks = triangulated_data.number_tracks()
accepted_tracks_ratio = num_accepted_tracks / len(tracks_2d)
track_cheirality_failure_ratio = num_tracks_w_cheirality_exceptions / len(
tracks_2d)
# TODO: improve dropped camera handling
num_cameras = len(cameras.keys())
expected_camera_indices = np.arange(num_cameras)
# add cameras to landmark_map
for i, cam in cameras.items():
if i != expected_camera_indices[i]:
raise RuntimeError("Some cameras must have been dropped ")
triangulated_data.add_camera(cam)
mean_3d_track_length, median_3d_track_length, track_lengths_3d = SfmResult(
triangulated_data, None).get_track_length_statistics()
logger.debug("[Data association] output number of tracks: %s",
num_accepted_tracks)
logger.debug("[Data association] output avg. track length: %s",
mean_3d_track_length)
# dump the 3d point cloud before Bundle Adjustment for offline visualization
points_3d = [
list(triangulated_data.track(j).point3())
for j in range(num_accepted_tracks)
]
# bin edges are halfway between each integer
track_lengths_histogram, _ = np.histogram(track_lengths_3d,
bins=np.linspace(
-0.5, 10.5, 12))
# min possible track len is 2, above 10 is improbable
histogram_dict = {
f"num_len_{i}_tracks": int(track_lengths_histogram[i])
for i in range(2, 11)
}
data_assoc_metrics = {
"mean_2d_track_length":
np.round(mean_2d_track_length, 3),
"accepted_tracks_ratio":
np.round(accepted_tracks_ratio, 3),
"track_cheirality_failure_ratio":
np.round(track_cheirality_failure_ratio, 3),
"num_accepted_tracks":
num_accepted_tracks,
"3d_tracks_length": {
"median": median_3d_track_length,
"mean": mean_3d_track_length,
"min": int(track_lengths_3d.min()),
"max": int(track_lengths_3d.max()),
"track_lengths_histogram": histogram_dict,
},
"mean_accepted_track_avg_error":
np.array(per_accepted_track_avg_errors).mean(),
"per_rejected_track_avg_errors":
per_rejected_track_avg_errors,
"per_accepted_track_avg_errors":
per_accepted_track_avg_errors,
"points_3d":
points_3d,
}
return triangulated_data, data_assoc_metrics
def triangulate(
self, track_2d: SfmTrack2d
) -> Tuple[Optional[SfmTrack], Optional[float], bool]:
"""Triangulates 3D point according to the configured triangulation mode.
Args:
track: feature track from which measurements are to be extracted
Returns:
track with inlier measurements and 3D landmark. None returned if triangulation fails or has high error.
avg_track_reproj_error: reprojection error of 3d triangulated point to each image plane
Note: this may be "None" if the 3d point could not be triangulated successfully
due to a cheirality exception or insufficient number of RANSAC inlier measurements
is_cheirality_failure: boolean representing whether the selected 2d measurements lead
to a cheirality exception upon triangulation
"""
if self.mode in [
TriangulationParam.RANSAC_SAMPLE_UNIFORM,
TriangulationParam.RANSAC_SAMPLE_BIASED_BASELINE,
TriangulationParam.RANSAC_TOPK_BASELINES,
]:
best_inliers = self.execute_ransac_variant(track_2d)
elif self.mode == TriangulationParam.NO_RANSAC:
best_inliers = np.ones(len(track_2d.measurements),
dtype=bool) # all marked as inliers
inlier_idxs = (np.where(best_inliers)[0]).tolist()
is_cheirality_failure = False
if len(inlier_idxs) < 2:
return None, None, is_cheirality_failure
inlier_track = track_2d.select_subset(inlier_idxs)
camera_track, measurement_track = self.extract_measurements(
inlier_track)
try:
triangulated_pt = gtsam.triangulatePoint3(
camera_track,
measurement_track,
rank_tol=SVD_DLT_RANK_TOL,
optimize=True,
)
except RuntimeError:
is_cheirality_failure = True
return None, None, is_cheirality_failure
# compute reprojection errors for each measurement
reproj_errors = self.compute_track_reprojection_errors(
inlier_track.measurements, triangulated_pt)
# all the measurements should have error < threshold
if not np.all(reproj_errors < self.reproj_error_thresh):
return None, reproj_errors.mean(), is_cheirality_failure
track_3d = SfmTrack(triangulated_pt)
for i, uv in inlier_track.measurements:
track_3d.add_measurement(i, uv)
avg_track_reproj_error = reproj_errors.mean()
return track_3d, avg_track_reproj_error, is_cheirality_failure
def run(
self,
num_images: int,
cameras: Dict[int, PinholeCameraCal3Bundler],
corr_idxs_dict: Dict[Tuple[int, int], np.ndarray],
keypoints_list: List[Keypoints],
images: Optional[List[Image]] = None
) -> Tuple[GtsfmData, Dict[str, Any]]:
"""Perform the data association.
Args:
num_images: Number of images in the scene.
cameras: dictionary, with image index -> camera mapping.
corr_idxs_dict: dictionary, with key as image pair (i1,i2) and value as matching keypoint indices.
keypoints_list: keypoints for each image.
images: a list of all images in scene (optional and only for track patch visualization)
viz_patch_sz: width and height of patches, if if dumping/visualizing a patch for each 2d track measurement
Returns:
Cameras and tracks as GtsfmData.
"""
# generate tracks for 3D points using pairwise correspondences
tracks_2d = SfmTrack2d.generate_tracks_from_pairwise_matches(
corr_idxs_dict, keypoints_list)
if self.save_track_patches_viz and images is not None:
io_utils.save_track_visualizations(tracks_2d,
images,
save_dir=os.path.join(
"plots", "tracks_2d"))
# metrics on tracks w/o triangulation check
num_tracks_2d = len(tracks_2d)
track_lengths = list(map(lambda x: x.number_measurements(), tracks_2d))
mean_2d_track_length = np.mean(track_lengths)
logger.debug("[Data association] input number of tracks: %s",
num_tracks_2d)
logger.debug("[Data association] input avg. track length: %s",
mean_2d_track_length)
# initializer of 3D landmark for each track
point3d_initializer = Point3dInitializer(
cameras,
self.mode,
self.reproj_error_thresh,
self.num_ransac_hypotheses,
)
num_tracks_w_cheirality_exceptions = 0
per_accepted_track_avg_errors = []
per_rejected_track_avg_errors = []
# form GtsfmData object after triangulation
triangulated_data = GtsfmData(num_images)
# add all cameras
for i, camera in cameras.items():
triangulated_data.add_camera(i, camera)
# add valid tracks where triangulation is successful
for track_2d in tracks_2d:
# triangulate and filter based on reprojection error
sfm_track, avg_track_reproj_error, is_cheirality_failure = point3d_initializer.triangulate(
track_2d)
if is_cheirality_failure:
num_tracks_w_cheirality_exceptions += 1
if avg_track_reproj_error is not None:
# need no more than 3 significant figures in json report
avg_track_reproj_error = np.round(avg_track_reproj_error, 3)
if sfm_track is not None and self.__validate_track(sfm_track):
triangulated_data.add_track(sfm_track)
per_accepted_track_avg_errors.append(avg_track_reproj_error)
else:
per_rejected_track_avg_errors.append(avg_track_reproj_error)
track_cheirality_failure_ratio = num_tracks_w_cheirality_exceptions / len(
tracks_2d)
# pick only the largest connected component
connected_data = triangulated_data.select_largest_connected_component()
num_accepted_tracks = connected_data.number_tracks()
accepted_tracks_ratio = num_accepted_tracks / len(tracks_2d)
mean_3d_track_length, median_3d_track_length = connected_data.get_track_length_statistics(
)
track_lengths_3d = connected_data.get_track_lengths()
logger.debug("[Data association] output number of tracks: %s",
num_accepted_tracks)
logger.debug("[Data association] output avg. track length: %s",
np.round(mean_3d_track_length, 2))
# dump the 3d point cloud before Bundle Adjustment for offline visualization
points_3d = [
list(connected_data.get_track(j).point3())
for j in range(num_accepted_tracks)
]
# bin edges are halfway between each integer
track_lengths_histogram, _ = np.histogram(track_lengths_3d,
bins=np.linspace(
-0.5, 10.5, 12))
# min possible track len is 2, above 10 is improbable
histogram_dict = {
f"num_len_{i}_tracks": int(track_lengths_histogram[i])
for i in range(2, 11)
}
data_assoc_metrics = {
"mean_2d_track_length":
np.round(mean_2d_track_length, 3),
"accepted_tracks_ratio":
np.round(accepted_tracks_ratio, 3),
"track_cheirality_failure_ratio":
np.round(track_cheirality_failure_ratio, 3),
"num_accepted_tracks":
num_accepted_tracks,
"3d_tracks_length": {
"median":
median_3d_track_length,
"mean":
mean_3d_track_length,
"min":
int(track_lengths_3d.min())
if track_lengths_3d.size > 0 else None,
"max":
int(track_lengths_3d.max())
if track_lengths_3d.size > 0 else None,
"track_lengths_histogram":
histogram_dict,
},
"mean_accepted_track_avg_error":
np.array(per_accepted_track_avg_errors).mean(),
"per_rejected_track_avg_errors":
per_rejected_track_avg_errors,
"per_accepted_track_avg_errors":
per_accepted_track_avg_errors,
"points_3d":
points_3d,
}
return connected_data, data_assoc_metrics
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
