add another unit test

gtsam/sfm/DsfTrackGenerator.cpp

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

python/gtsam/tests/test_DsfTrackGenerator.py

@@ -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).
+        for (i1,i2), corr_idxs in nontransitive_matches_dict.items():
+            matches_dict = {}
+            matches_dict[IndexPair(i1, i2)] = corr_idxs
+
+        tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
+            matches_dict,
+            keypoints_list,
+            verbose=False,
+        )
+        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 +117,74 @@ 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, scales=img1_kp_scale),
+        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()