Merge pull request #1615 from borglab/fix-dsf-transitivity

Fix DSF bug where tracks failing transitivity check were returned
release/4.3a0
Frank Dellaert 2023-08-30 18:30:46 -07:00 committed by GitHub
commit f7a6312345
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2 changed files with 94 additions and 2 deletions

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@ -130,7 +130,7 @@ std::vector<SfmTrack2d> tracksFromPairwiseMatches(
}
// TODO(johnwlambert): return the Transitivity failure percentage here.
return tracks2d;
return validTracks;
}
} // namespace gtsfm

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@ -4,6 +4,7 @@ Authors: John Lambert
"""
import unittest
from typing import Dict, List, Tuple
import numpy as np
from gtsam.gtsfm import Keypoints
@ -16,6 +17,29 @@ from gtsam import IndexPair, Point2, SfmTrack2d
class TestDsfTrackGenerator(GtsamTestCase):
"""Tests for DsfTrackGenerator."""
def test_generate_tracks_from_pairwise_matches_nontransitive(
self,
) -> None:
"""Tests DSF for non-transitive matches.
Test will result in no tracks since nontransitive tracks are naively discarded by DSF.
"""
keypoints_list = get_dummy_keypoints_list()
nontransitive_matches_dict = get_nontransitive_matches() # contains one non-transitive track
# For each image pair (i1,i2), we provide a (K,2) matrix
# of corresponding keypoint indices (k1,k2).
matches_dict = {}
for (i1,i2), corr_idxs in nontransitive_matches_dict.items():
matches_dict[IndexPair(i1, i2)] = corr_idxs
tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
matches_dict,
keypoints_list,
verbose=True,
)
self.assertEqual(len(tracks), 0, "Tracks not filtered correctly")
def test_track_generation(self) -> None:
"""Ensures that DSF generates three tracks from measurements
in 3 images (H=200,W=400)."""
@ -29,7 +53,7 @@ class TestDsfTrackGenerator(GtsamTestCase):
keypoints_list.append(kps_i2)
# For each image pair (i1,i2), we provide a (K,2) matrix
# of corresponding image indices (k1,k2).
# of corresponding keypoint indices (k1,k2).
matches_dict = {}
matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
@ -93,5 +117,73 @@ class TestSfmTrack2d(GtsamTestCase):
assert track.numberMeasurements() == 1
def get_dummy_keypoints_list() -> List[Keypoints]:
""" """
img1_kp_coords = np.array([[1, 1], [2, 2], [3, 3.]])
img1_kp_scale = np.array([6.0, 9.0, 8.5])
img2_kp_coords = np.array(
[
[1, 1.],
[2, 2],
[3, 3],
[4, 4],
[5, 5],
[6, 6],
[7, 7],
[8, 8],
]
)
img3_kp_coords = np.array(
[
[1, 1.],
[2, 2],
[3, 3],
[4, 4],
[5, 5],
[6, 6],
[7, 7],
[8, 8],
[9, 9],
[10, 10],
]
)
img4_kp_coords = np.array(
[
[1, 1.],
[2, 2],
[3, 3],
[4, 4],
[5, 5],
]
)
keypoints_list = [
Keypoints(coordinates=img1_kp_coords),
Keypoints(coordinates=img2_kp_coords),
Keypoints(coordinates=img3_kp_coords),
Keypoints(coordinates=img4_kp_coords),
]
return keypoints_list
def get_nontransitive_matches() -> Dict[Tuple[int, int], np.ndarray]:
"""Set up correspondences for each (i1,i2) pair that violates transitivity.
(i=0, k=0) (i=0, k=1)
| \\ |
| \\ |
(i=1, k=2)--(i=2,k=3)--(i=3, k=4)
Transitivity is violated due to the match between frames 0 and 3.
"""
nontransitive_matches_dict = {
(0, 1): np.array([[0, 2]]),
(1, 2): np.array([[2, 3]]),
(0, 2): np.array([[0, 3]]),
(0, 3): np.array([[1, 4]]),
(2, 3): np.array([[3, 4]]),
}
return nontransitive_matches_dict
if __name__ == "__main__":
unittest.main()