undo changes to tests to ensure backwards compatibility

python/gtsam/tests/test_Cal3Fisheye.py

@@ -11,11 +11,12 @@ Refactored: Roderick Koehle
 """
 import unittest
 
-import gtsam
 import numpy as np
 from gtsam.symbol_shorthand import K, L, P
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+
 
 def ulp(ftype=np.float64):
     """
@@ -26,7 +27,7 @@ def ulp(ftype=np.float64):
 
 
 class TestCal3Fisheye(GtsamTestCase):
-    
+
     @classmethod
     def setUpClass(cls):
         """
@@ -50,10 +51,11 @@ class TestCal3Fisheye(GtsamTestCase):
         camera1 = gtsam.PinholeCameraCal3Fisheye(pose1)
         camera2 = gtsam.PinholeCameraCal3Fisheye(pose2)
         cls.origin = np.array([0.0, 0.0, 0.0])
-        cls.poses = [pose1, pose2]
+        cls.poses = gtsam.Pose3Vector([pose1, pose2])
-        cls.cameras = [camera1, camera2]
+        cls.cameras = gtsam.CameraSetCal3Fisheye([camera1, camera2])
-        cls.measurements = [k.project(cls.origin) for k in cls.cameras]
+        cls.measurements = gtsam.Point2Vector(
-        
+            [k.project(cls.origin) for k in cls.cameras])
+
     def test_Cal3Fisheye(self):
         K = gtsam.Cal3Fisheye()
         self.assertEqual(K.fx(), 1.)
@@ -62,7 +64,7 @@ class TestCal3Fisheye(GtsamTestCase):
     def test_distortion(self):
         """Fisheye distortion and rectification"""
         equidistant = gtsam.Cal3Fisheye()
-        perspective_pt = self.obj_point[0:2]/self.obj_point[2]
+        perspective_pt = self.obj_point[0:2] / self.obj_point[2]
         distorted_pt = equidistant.uncalibrate(perspective_pt)
         rectified_pt = equidistant.calibrate(distorted_pt)
         self.gtsamAssertEquals(distorted_pt, self.img_point)
@@ -166,7 +168,7 @@ class TestCal3Fisheye(GtsamTestCase):
         pose = gtsam.Pose3()
         camera = gtsam.Cal3Fisheye()
         state = gtsam.Values()
-        camera_key, pose_key, landmark_key = K(0), P(0), L(0)
+        pose_key, landmark_key = P(0), L(0)
         state.insert_point3(landmark_key, obj_point)
         state.insert_pose3(pose_key, pose)
         g = gtsam.NonlinearFactorGraph()

python/gtsam/tests/test_Cal3Unified.py

@@ -10,11 +10,12 @@ Author: Frank Dellaert & Duy Nguyen Ta (Python)
 """
 import unittest
 
-import gtsam
 import numpy as np
 from gtsam.symbol_shorthand import K, L, P
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+
 
 class TestCal3Unified(GtsamTestCase):
 
@@ -47,9 +48,10 @@ class TestCal3Unified(GtsamTestCase):
         camera1 = gtsam.PinholeCameraCal3Unified(pose1, cls.stereographic)
         camera2 = gtsam.PinholeCameraCal3Unified(pose2, cls.stereographic)
         cls.origin = np.array([0.0, 0.0, 0.0])
-        cls.poses = [pose1, pose2]
+        cls.poses = gtsam.Pose3Vector([pose1, pose2])
-        cls.cameras = [camera1, camera2]
+        cls.cameras = gtsam.CameraSetCal3Unified([camera1, camera2])
-        cls.measurements = [k.project(cls.origin) for k in cls.cameras]
+        cls.measurements = gtsam.Point2Vector(
+            [k.project(cls.origin) for k in cls.cameras])
 
     def test_Cal3Unified(self):
         K = gtsam.Cal3Unified()
@@ -106,7 +108,7 @@ class TestCal3Unified(GtsamTestCase):
         state = gtsam.Values()
         measured = self.img_point
         noise_model = gtsam.noiseModel.Isotropic.Sigma(2, 1)
-        camera_key, pose_key, landmark_key = K(0), P(0), L(0)      
+        camera_key, pose_key, landmark_key = K(0), P(0), L(0)
         k = self.stereographic
         state.insert_cal3unified(camera_key, k)
         state.insert_pose3(pose_key, gtsam.Pose3())
@@ -141,7 +143,7 @@ class TestCal3Unified(GtsamTestCase):
         Dcal = np.zeros((2, 10), order='F')
         Dp = np.zeros((2, 2), order='F')
         camera.calibrate(img_point, Dcal, Dp)
-        
+
         self.gtsamAssertEquals(Dcal, np.array(
             [[ 0.,  0.,  0., -1.,  0.,  0.,  0.,  0.,  0.,  0.],
             [ 0.,  0.,  0.,  0., -1.,  0.,  0.,  0.,  0.,  0.]]))
@@ -157,7 +159,7 @@ class TestCal3Unified(GtsamTestCase):
 
     def test_triangulation_rectify(self):
         """Estimate spatial point from image measurements using rectification"""
-        rectified = [k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.measurements)]
+        rectified = gtsam.Point2Vector([k.calibration().calibrate(pt) for k, pt in zip(self.cameras, self.measurements)])
         shared_cal = gtsam.Cal3_S2()
         triangulated = gtsam.triangulatePoint3(self.poses, shared_cal, rectified, rank_tol=1e-9, optimize=False)
         self.gtsamAssertEquals(triangulated, self.origin)

python/gtsam/tests/test_DsfTrackGenerator.py

@@ -23,14 +23,14 @@ 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_list = gtsam.KeypointsVector()
         keypoints_list.append(kps_i0)
         keypoints_list.append(kps_i1)
         keypoints_list.append(kps_i2)
 
         # For each image pair (i1,i2), we provide a (K,2) matrix
         # of corresponding image indices (k1,k2).
-        matches_dict = {}
+        matches_dict = gtsam.MatchIndicesMap()
         matches_dict[IndexPair(0, 1)] = np.array([[0, 0], [1, 1]])
         matches_dict[IndexPair(1, 2)] = np.array([[2, 0], [1, 1]])
 
@@ -86,7 +86,7 @@ class TestSfmTrack2d(GtsamTestCase):
 
     def test_sfm_track_2d_constructor(self) -> None:
         """Test construction of 2D SfM track."""
-        measurements = []
+        measurements = gtsam.SfmMeasurementVector()
         measurements.append((0, Point2(10, 20)))
         track = SfmTrack2d(measurements=measurements)
         track.measurement(0)

python/gtsam/tests/test_FixedLagSmootherExample.py

@@ -37,7 +37,7 @@ class TestFixedLagSmootherExample(GtsamTestCase):
         # that will be sent to the smoothers
         new_factors = gtsam.NonlinearFactorGraph()
         new_values = gtsam.Values()
-        new_timestamps = {}
+        new_timestamps = gtsam.FixedLagSmootherKeyTimestampMap()
 
         # Create  a prior on the first pose, placing it at the origin
         prior_mean = gtsam.Pose2(0, 0, 0)
@@ -48,7 +48,7 @@ class TestFixedLagSmootherExample(GtsamTestCase):
             gtsam.PriorFactorPose2(
                 X1, prior_mean, prior_noise))
         new_values.insert(X1, prior_mean)
-        new_timestamps[X1] = 0.0
+        new_timestamps.insert((X1, 0.0))
 
         delta_time = 0.25
         time = 0.25
@@ -78,7 +78,7 @@ class TestFixedLagSmootherExample(GtsamTestCase):
             current_key = int(1000 * time)
 
             # assign current key to the current timestamp
-            new_timestamps[current_key] = time
+            new_timestamps.insert((current_key, time))
 
             # Add a guess for this pose to the new values
             # Assume that the robot moves at 2 m/s. Position is time[s] *

python/gtsam/tests/test_GaussianFactorGraph.py

@@ -14,11 +14,12 @@ from __future__ import print_function
 
 import unittest
 
-import gtsam
 import numpy as np
 from gtsam.symbol_shorthand import X
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+
 
 def create_graph():
     """Create a basic linear factor graph for testing"""
@@ -98,7 +99,7 @@ class TestGaussianFactorGraph(GtsamTestCase):
         bn = gfg.eliminateSequential(ordering)
         self.assertEqual(bn.size(), 3)
 
-        keyVector = []
+        keyVector = gtsam.KeyVector()
         keyVector.append(keys[2])
         #TODO(Varun) Below code causes segfault in Debug config
         # ordering = gtsam.Ordering.ColamdConstrainedLastGaussianFactorGraph(gfg, keyVector)

python/gtsam/tests/test_KarcherMeanFactor.py

@@ -13,11 +13,12 @@ Author: Frank Dellaert
 
 import unittest
 
-import gtsam
 import numpy as np
-from gtsam import Rot3
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+from gtsam import Rot3
+
 KEY = 0
 MODEL = gtsam.noiseModel.Unit.Create(3)
 
@@ -29,9 +30,11 @@ R = Rot3.Expmap(np.array([0.1, 0, 0]))
 class TestKarcherMean(GtsamTestCase):
 
     def test_find(self):
-        # Check that optimizing for Karcher mean (which minimizes Between distance)
+        """
-        # gets correct result.
+        Check that optimizing for Karcher mean (which minimizes Between distance)
-        rotations = [R, R.inverse()]
+        gets correct result.
+        """
+        rotations = gtsam.Rot3Vector([R, R.inverse()])
         expected = Rot3()
         actual = gtsam.FindKarcherMean(rotations)
         self.gtsamAssertEquals(expected, actual)
@@ -42,7 +45,7 @@ class TestKarcherMean(GtsamTestCase):
         a2Rb2 = Rot3()
         a3Rb3 = Rot3()
 
-        aRb_list = [a1Rb1, a2Rb2, a3Rb3]
+        aRb_list = gtsam.Rot3Vector([a1Rb1, a2Rb2, a3Rb3])
         aRb_expected = Rot3()
 
         aRb = gtsam.FindKarcherMean(aRb_list)
@@ -58,7 +61,9 @@ class TestKarcherMean(GtsamTestCase):
         graph = gtsam.NonlinearFactorGraph()
         R12 = R.compose(R.compose(R))
         graph.add(gtsam.BetweenFactorRot3(1, 2, R12, MODEL))
-        keys = [1, 2]
+        keys = gtsam.KeyVector()
+        keys.append(1)
+        keys.append(2)
         graph.add(gtsam.KarcherMeanFactorRot3(keys))
 
         initial = gtsam.Values()
@@ -67,7 +72,9 @@ class TestKarcherMean(GtsamTestCase):
         expected = Rot3()
 
         result = gtsam.GaussNewtonOptimizer(graph, initial).optimize()
-        actual = gtsam.FindKarcherMean([result.atRot3(1), result.atRot3(2)])
+        actual = gtsam.FindKarcherMean(
+            gtsam.Rot3Vector([result.atRot3(1),
+                              result.atRot3(2)]))
         self.gtsamAssertEquals(expected, actual)
         self.gtsamAssertEquals(
             R12, result.atRot3(1).between(result.atRot3(2)))

python/gtsam/tests/test_Pose2.py

@@ -12,9 +12,10 @@ import math
 import unittest
 
 import numpy as np
-from gtsam import Point2, Pose2
 from gtsam.utils.test_case import GtsamTestCase
 
+from gtsam import Point2, Point2Pairs, Pose2
+
 
 class TestPose2(GtsamTestCase):
     """Test selected Pose2 methods."""
@@ -82,7 +83,7 @@ class TestPose2(GtsamTestCase):
         ]
 
         # fmt: on
-        ab_pairs = list(zip(pts_a, pts_b))
+        ab_pairs = Point2Pairs(list(zip(pts_a, pts_b)))
         aTb = Pose2.Align(ab_pairs)
         self.assertIsNotNone(aTb)
 

python/gtsam/tests/test_Pose3.py

@@ -12,11 +12,12 @@ Author: Frank Dellaert, Duy Nguyen Ta
 import math
 import unittest
 
-import gtsam
 import numpy as np
-from gtsam import Point3, Pose3, Rot3
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+from gtsam import Point3, Pose3, Rot3
+
 
 def numerical_derivative_pose(pose, method, delta=1e-5):
     jacobian = np.zeros((6, 6))
@@ -222,7 +223,7 @@ class TestPose3(GtsamTestCase):
         sTt = Pose3(Rot3.Rodrigues(0, 0, -math.pi), Point3(2, 4, 0))
         transformed = sTt.transformTo(square)
 
-        st_pairs = []
+        st_pairs = gtsam.Point3Pairs()
         for j in range(4):
             st_pairs.append((square[:,j], transformed[:,j]))
 

python/gtsam/tests/test_ShonanAveraging.py

@@ -12,12 +12,13 @@ Author: Frank Dellaert
 
 import unittest
 
-import gtsam
 import numpy as np
+from gtsam.utils.test_case import GtsamTestCase
+
+import gtsam
 from gtsam import (BetweenFactorPose2, LevenbergMarquardtParams, Pose2, Rot2,
                    ShonanAveraging2, ShonanAveraging3,
                    ShonanAveragingParameters2, ShonanAveragingParameters3)
-from gtsam.utils.test_case import GtsamTestCase
 
 DEFAULT_PARAMS = ShonanAveragingParameters3(
     gtsam.LevenbergMarquardtParams.CeresDefaults()
@@ -176,7 +177,7 @@ class TestShonanAveraging(GtsamTestCase):
         shonan_params.setCertifyOptimality(True)
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
-        between_factors = []
+        between_factors = gtsam.BetweenFactorPose2s()
         for (i1, i2), i2Ri1 in i2Ri1_dict.items():
             i2Ti1 = Pose2(i2Ri1, np.zeros(2))
             between_factors.append(

python/gtsam/tests/test_Sim2.py

@@ -12,9 +12,10 @@ Author: John Lambert
 import unittest
 
 import numpy as np
-from gtsam import Pose2, Rot2, Similarity2
 from gtsam.utils.test_case import GtsamTestCase
 
+from gtsam import Pose2, Pose2Pairs, Rot2, Similarity2
+
 
 class TestSim2(GtsamTestCase):
     """Test selected Sim2 methods."""
@@ -46,7 +47,7 @@ class TestSim2(GtsamTestCase):
 
         wToi_list = [wTo0, wTo1, wTo2]
 
-        we_pairs = list(zip(wToi_list, eToi_list))
+        we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         wSe = Similarity2.Align(we_pairs)
@@ -81,7 +82,7 @@ class TestSim2(GtsamTestCase):
 
         wToi_list = [wTo0, wTo1, wTo2]
 
-        we_pairs = list(zip(wToi_list, eToi_list))
+        we_pairs = Pose2Pairs(list(zip(wToi_list, eToi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         wSe = Similarity2.Align(we_pairs)
@@ -119,7 +120,7 @@ class TestSim2(GtsamTestCase):
 
         bTi_list = [bTi0, bTi1, bTi2, bTi3]
 
-        ab_pairs = list(zip(aTi_list, bTi_list))
+        ab_pairs = Pose2Pairs(list(zip(aTi_list, bTi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         aSb = Similarity2.Align(ab_pairs)

python/gtsam/tests/test_Sim3.py

@@ -12,11 +12,12 @@ Author: John Lambert
 import unittest
 from typing import List, Optional
 
-import gtsam
 import numpy as np
-from gtsam import Point3, Pose3, Rot3, Similarity3
 from gtsam.utils.test_case import GtsamTestCase
 
+import gtsam
+from gtsam import Point3, Pose3, Rot3, Similarity3
+
 
 class TestSim3(GtsamTestCase):
     """Test selected Sim3 methods."""
@@ -48,7 +49,7 @@ class TestSim3(GtsamTestCase):
 
         wToi_list = [wTo0, wTo1, wTo2]
 
-        we_pairs = list(zip(wToi_list, eToi_list))
+        we_pairs = gtsam.Pose3Pairs(list(zip(wToi_list, eToi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         wSe = Similarity3.Align(we_pairs)
@@ -83,7 +84,7 @@ class TestSim3(GtsamTestCase):
 
         wToi_list = [wTo0, wTo1, wTo2]
 
-        we_pairs = list(zip(wToi_list, eToi_list))
+        we_pairs = gtsam.Pose3Pairs(list(zip(wToi_list, eToi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         wSe = Similarity3.Align(we_pairs)
@@ -121,7 +122,7 @@ class TestSim3(GtsamTestCase):
 
         bTi_list = [bTi0, bTi1, bTi2, bTi3]
 
-        ab_pairs = list(zip(aTi_list, bTi_list))
+        ab_pairs = gtsam.Pose3Pairs(list(zip(aTi_list, bTi_list)))
 
         # Recover the transformation wSe (i.e. world_S_egovehicle)
         aSb = Similarity3.Align(ab_pairs)
@@ -688,7 +689,7 @@ def align_poses_sim3(aTi_list: List[Pose3], bTi_list: List[Pose3]) -> Similarity
     assert len(aTi_list) == len(bTi_list)
     assert n_to_align >= 2, "SIM(3) alignment uses at least 2 frames"
 
-    ab_pairs = list(zip(aTi_list, bTi_list))
+    ab_pairs = gtsam.Pose3Pairs(list(zip(aTi_list, bTi_list)))
 
     aSb = Similarity3.Align(ab_pairs)
 

python/gtsam/tests/test_Triangulation.py

@@ -12,13 +12,14 @@ Authors: Frank Dellaert & Fan Jiang (Python) & Sushmita Warrier & John Lambert
 import unittest
 from typing import Iterable, List, Optional, Tuple, Union
 
-import gtsam
 import numpy as np
+from gtsam.utils.test_case import GtsamTestCase
+
+import gtsam
 from gtsam import (Cal3_S2, Cal3Bundler, CameraSetCal3_S2,
                    CameraSetCal3Bundler, PinholeCameraCal3_S2,
                    PinholeCameraCal3Bundler, Point2, Point3, Pose3, Rot3,
                    TriangulationParameters, TriangulationResult)
-from gtsam.utils.test_case import GtsamTestCase
 
 UPRIGHT = Rot3.Ypr(-np.pi / 2, 0.0, -np.pi / 2)
 
@@ -34,7 +35,9 @@ class TestTriangulationExample(GtsamTestCase):
         # create second camera 1 meter to the right of first camera
         pose2 = pose1.compose(Pose3(Rot3(), Point3(1, 0, 0)))
         # twoPoses
-        self.poses = [pose1, pose2]
+        self.poses = gtsam.Pose3Vector()
+        self.poses.append(pose1)
+        self.poses.append(pose2)
 
         # landmark ~5 meters infront of camera
         self.landmark = Point3(5, 0.5, 1.2)
@@ -64,7 +67,7 @@ class TestTriangulationExample(GtsamTestCase):
             cameras = camera_set()
         else:
             cameras = []
-        measurements = []
+        measurements = gtsam.Point2Vector()
 
         for k, pose in zip(cal_params, self.poses):
             K = calibration(*k)
@@ -93,7 +96,7 @@ class TestTriangulationExample(GtsamTestCase):
         self.gtsamAssertEquals(self.landmark, triangulated_landmark, 1e-9)
 
         # Add some noise and try again: result should be ~ (4.995, 0.499167, 1.19814)
-        measurements_noisy = []
+        measurements_noisy = gtsam.Point2Vector()
         measurements_noisy.append(measurements[0] - np.array([0.1, 0.5]))
         measurements_noisy.append(measurements[1] - np.array([-0.2, 0.3]))
 
@@ -160,8 +163,8 @@ class TestTriangulationExample(GtsamTestCase):
         z2: Point2 = camera2.project(landmark)
         z3: Point2 = camera3.project(landmark)
 
-        poses = [pose1, pose2, pose3]
+        poses = gtsam.Pose3Vector([pose1, pose2, pose3])
-        measurements = [z1, z2, z3]
+        measurements = gtsam.Point2Vector([z1, z2, z3])
 
         # noise free, so should give exactly the landmark
         actual = gtsam.triangulatePoint3(poses,
@@ -226,7 +229,7 @@ class TestTriangulationExample(GtsamTestCase):
         cameras.append(camera1)
         cameras.append(camera2)
 
-        measurements = []
+        measurements = gtsam.Point2Vector()
         measurements.append(z1)
         measurements.append(z2)