Fix 4.3 python style

python/gtsam/tests/test_DsfTrackGenerator.py

@@ -4,14 +4,15 @@ Authors: John Lambert
 """
 
 import unittest
-from typing import Dict, List, Tuple
+from typing import Dict, Tuple
 
 import numpy as np
 from gtsam.gtsfm import Keypoints
 from gtsam.utils.test_case import GtsamTestCase
 
 import gtsam
-from gtsam import IndexPair, Point2, SfmTrack2d
+from gtsam import (IndexPair, KeypointsVector, MatchIndicesMap, Point2,
+                   SfmMeasurementVector, SfmTrack2d)
 
 
 class TestDsfTrackGenerator(GtsamTestCase):
@@ -22,20 +23,21 @@ class TestDsfTrackGenerator(GtsamTestCase):
     ) -> None:
         """Tests DSF for non-transitive matches.
 
-        Test will result in no tracks since nontransitive tracks are naively discarded by DSF.
+        Test will result in no tracks since nontransitive tracks are naively
+        discarded by DSF.
         """
-        keypoints_list = get_dummy_keypoints_list()
+        keypoints = get_dummy_keypoints_list()
-        nontransitive_matches_dict = get_nontransitive_matches()  # contains one non-transitive track
+        nontransitive_matches = get_nontransitive_matches()
 
         # For each image pair (i1,i2), we provide a (K,2) matrix
         # of corresponding keypoint indices (k1,k2).
-        matches_dict = {}
+        matches = MatchIndicesMap()
-        for (i1,i2), corr_idxs in nontransitive_matches_dict.items():
+        for (i1, i2), correspondences in nontransitive_matches.items():
-            matches_dict[IndexPair(i1, i2)] = corr_idxs
+            matches[IndexPair(i1, i2)] = correspondences
 
         tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
-            matches_dict,
+            matches,
-            keypoints_list,
+            keypoints,
             verbose=True,
         )
         self.assertEqual(len(tracks), 0, "Tracks not filtered correctly")
@@ -47,20 +49,20 @@ class TestDsfTrackGenerator(GtsamTestCase):
         kps_i1 = Keypoints(np.array([[50.0, 60], [70, 80], [90, 100]]))
         kps_i2 = Keypoints(np.array([[110.0, 120], [130, 140]]))
 
-        keypoints_list = []
+        keypoints = KeypointsVector()
-        keypoints_list.append(kps_i0)
+        keypoints.append(kps_i0)
-        keypoints_list.append(kps_i1)
+        keypoints.append(kps_i1)
-        keypoints_list.append(kps_i2)
+        keypoints.append(kps_i2)
 
         # For each image pair (i1,i2), we provide a (K,2) matrix
-        # of corresponding keypoint indices (k1,k2).
+        # of corresponding image indices (k1,k2).
-        matches_dict = {}
+        matches = MatchIndicesMap()
-        matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
+        matches[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
-        matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
+        matches[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
 
         tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
-            matches_dict,
+            matches,
-            keypoints_list,
+            keypoints,
             verbose=False,
         )
         assert len(tracks) == 3
@@ -110,17 +112,16 @@ class TestSfmTrack2d(GtsamTestCase):
 
     def test_sfm_track_2d_constructor(self) -> None:
         """Test construction of 2D SfM track."""
-        measurements = []
+        measurements = SfmMeasurementVector()
         measurements.append((0, Point2(10, 20)))
         track = SfmTrack2d(measurements=measurements)
         track.measurement(0)
         assert track.numberMeasurements() == 1
 
 
-def get_dummy_keypoints_list() -> List[Keypoints]:
+def get_dummy_keypoints_list() -> KeypointsVector:
-    """ """
+    """Generate a list of dummy keypoints for testing."""
     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.],
@@ -156,33 +157,32 @@ def get_dummy_keypoints_list() -> List[Keypoints]:
             [5, 5],
         ]
     )
-    keypoints_list = [
+    keypoints = KeypointsVector()
-        Keypoints(coordinates=img1_kp_coords),
+    keypoints.append(Keypoints(coordinates=img1_kp_coords))
-        Keypoints(coordinates=img2_kp_coords),
+    keypoints.append(Keypoints(coordinates=img2_kp_coords))
-        Keypoints(coordinates=img3_kp_coords),
+    keypoints.append(Keypoints(coordinates=img3_kp_coords))
-        Keypoints(coordinates=img4_kp_coords),
+    keypoints.append(Keypoints(coordinates=img4_kp_coords))
-    ]
+    return keypoints
-    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. 
+    Transitivity is violated due to the match between frames 0 and 3.
     """
-    nontransitive_matches_dict = {
+    nontransitive_matches = {
         (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
+    return nontransitive_matches
 
 
 if __name__ == "__main__":