Merge pull request #1615 from borglab/fix-dsf-transitivity
Fix DSF bug where tracks failing transitivity check were returnedrelease/4.3a0
commit
f7a6312345
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@ -130,7 +130,7 @@ std::vector<SfmTrack2d> tracksFromPairwiseMatches(
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}
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// TODO(johnwlambert): return the Transitivity failure percentage here.
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return tracks2d;
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return validTracks;
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}
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} // namespace gtsfm
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@ -4,6 +4,7 @@ Authors: John Lambert
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"""
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import unittest
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from typing import Dict, List, Tuple
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import numpy as np
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from gtsam.gtsfm import Keypoints
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@ -16,6 +17,29 @@ from gtsam import IndexPair, Point2, SfmTrack2d
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class TestDsfTrackGenerator(GtsamTestCase):
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"""Tests for DsfTrackGenerator."""
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def test_generate_tracks_from_pairwise_matches_nontransitive(
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self,
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) -> None:
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"""Tests DSF for non-transitive matches.
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Test will result in no tracks since nontransitive tracks are naively discarded by DSF.
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"""
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keypoints_list = get_dummy_keypoints_list()
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nontransitive_matches_dict = get_nontransitive_matches() # contains one non-transitive track
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# For each image pair (i1,i2), we provide a (K,2) matrix
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# of corresponding keypoint indices (k1,k2).
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matches_dict = {}
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for (i1,i2), corr_idxs in nontransitive_matches_dict.items():
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matches_dict[IndexPair(i1, i2)] = corr_idxs
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tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
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matches_dict,
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keypoints_list,
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verbose=True,
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)
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self.assertEqual(len(tracks), 0, "Tracks not filtered correctly")
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def test_track_generation(self) -> None:
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"""Ensures that DSF generates three tracks from measurements
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in 3 images (H=200,W=400)."""
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@ -29,7 +53,7 @@ class TestDsfTrackGenerator(GtsamTestCase):
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keypoints_list.append(kps_i2)
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# For each image pair (i1,i2), we provide a (K,2) matrix
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# of corresponding image indices (k1,k2).
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# of corresponding keypoint indices (k1,k2).
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matches_dict = {}
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matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
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matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
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@ -93,5 +117,73 @@ class TestSfmTrack2d(GtsamTestCase):
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assert track.numberMeasurements() == 1
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def get_dummy_keypoints_list() -> List[Keypoints]:
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""" """
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img1_kp_coords = np.array([[1, 1], [2, 2], [3, 3.]])
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img1_kp_scale = np.array([6.0, 9.0, 8.5])
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img2_kp_coords = np.array(
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[
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[1, 1.],
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[2, 2],
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[3, 3],
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[4, 4],
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[5, 5],
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[6, 6],
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[7, 7],
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[8, 8],
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]
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)
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img3_kp_coords = np.array(
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[
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[1, 1.],
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[2, 2],
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[3, 3],
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[4, 4],
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[5, 5],
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[6, 6],
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[7, 7],
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[8, 8],
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[9, 9],
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[10, 10],
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]
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)
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img4_kp_coords = np.array(
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[
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[1, 1.],
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[2, 2],
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[3, 3],
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[4, 4],
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[5, 5],
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]
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)
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keypoints_list = [
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Keypoints(coordinates=img1_kp_coords),
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Keypoints(coordinates=img2_kp_coords),
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Keypoints(coordinates=img3_kp_coords),
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Keypoints(coordinates=img4_kp_coords),
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]
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return keypoints_list
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def get_nontransitive_matches() -> Dict[Tuple[int, int], np.ndarray]:
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"""Set up correspondences for each (i1,i2) pair that violates transitivity.
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(i=0, k=0) (i=0, k=1)
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| \\ |
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| \\ |
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(i=1, k=2)--(i=2,k=3)--(i=3, k=4)
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Transitivity is violated due to the match between frames 0 and 3.
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"""
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nontransitive_matches_dict = {
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(0, 1): np.array([[0, 2]]),
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(1, 2): np.array([[2, 3]]),
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(0, 2): np.array([[0, 3]]),
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(0, 3): np.array([[1, 4]]),
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(2, 3): np.array([[3, 4]]),
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}
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return nontransitive_matches_dict
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if __name__ == "__main__":
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unittest.main()
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