Add iou_complement as a configurable alternative distance function to center_distance (for detection and tracking evaluation)

python-sdk/nuscenes/eval/common/utils.py

@@ -5,12 +5,57 @@
 
 import numpy as np
 from pyquaternion import Quaternion
-
+from shapely import affinity
+from shapely.geometry import Polygon
 from nuscenes.eval.common.data_classes import EvalBox
 from nuscenes.utils.data_classes import Box
 
 DetectionBox = Any  # Workaround as direct imports lead to cyclic dependencies.
 
+def create_polygon_from_box(bbox: EvalBox):
+    """
+    Convert an EvalBox into a Polygon
+    :param bbox: A EvalBox describing center, rotation and size.
+    :return: A Polygon describing the xy vertices.
+    """
+    l = bbox.size[0]
+    w = bbox.size[1]
+    poly_veh = Polygon(((0.5*l,0.5*w),(-0.5*l,0.5*w),(-0.5*l,-0.5*w),(0.5*l,-0.5*w),(0.5*l,0.5*w)))
+    poly_rot = affinity.rotate(poly_veh,quaternion_yaw(Quaternion(bbox.rotation)),use_radians=True)
+    poly_glob = affinity.translate(poly_rot,bbox.translation[0],bbox.translation[1])
+    return poly_glob
+
+def intersection_over_union(gt_poly: Polygon, pred_poly: Polygon):
+    """
+    IOU percentage between two input polygons (xy only).
+    :param gt_poly: GT annotation sample.
+    :param pred_poly: Predicted sample.
+    :return: IOU.
+    """
+    intersection = gt_poly.intersection(pred_poly).area
+    iou = intersection/(gt_poly.area + pred_poly.area - intersection)
+
+     # Guard against machine precision (i.e. when dealing with perfect overlap)
+    iou = min(iou,1.0)
+    return iou
+
+def iou_complement(gt_box: EvalBox, pred_box: EvalBox) -> float:
+    """
+    1 - IOU percentage between two input boxes (xy only).
+    :param gt_box: GT annotation sample.
+    :param pred_box: Predicted sample.
+    :return: 1 - IOU.
+    """
+    # Do a cheaper first pass before calculating IOU i.e. check if the circles that enclose the two
+    # boxes overlap
+    gt_radius = np.linalg.norm(0.5*np.array([gt_box.size[0],gt_box.size[1]]))
+    pred_radius = np.linalg.norm(0.5*np.array([pred_box.size[0],pred_box.size[1]]))
+    if (center_distance(gt_box,pred_box) >= pred_radius + gt_radius):
+        iou_complement = 1.0
+    else:
+        iou_complement = 1.0 - intersection_over_union(create_polygon_from_box(gt_box),
+                                      create_polygon_from_box(pred_box))
+    return iou_complement
 
 def center_distance(gt_box: EvalBox, pred_box: EvalBox) -> float:
     """

python-sdk/nuscenes/eval/detection/data_classes.py

@@ -7,7 +7,7 @@
 import numpy as np
 
 from nuscenes.eval.common.data_classes import MetricData, EvalBox
-from nuscenes.eval.common.utils import center_distance
+from nuscenes.eval.common.utils import center_distance, iou_complement
 from nuscenes.eval.detection.constants import DETECTION_NAMES, ATTRIBUTE_NAMES, TP_METRICS
 
 
@@ -74,6 +74,8 @@ def dist_fcn_callable(self):
         """ Return the distance function corresponding to the dist_fcn string. """
         if self.dist_fcn == 'center_distance':
             return center_distance
+        elif self.dist_fcn == "iou_complement":
+            return iou_complement
         else:
             raise Exception('Error: Unknown distance function %s!' % self.dist_fcn)
 

python-sdk/nuscenes/eval/detection/tests/test_evaluate.py

@@ -149,5 +149,16 @@ def test_delta(self, eval_split, mock__get_custom_splits_file_path):
         # 10. Score = 0.19449091580477748. Changed to use v1.0 mini_val split, and the equal mini_custom_val split.
         self.assertAlmostEqual(metrics.nd_score, 0.19449091580477748)
 
+        # Evaluate again but use the iou_complement distance function
+        # 1. Score = 0.16651633528966858. Measured on forked repo sbarkby/nuscenes-devkit April 22nd 2024.
+        cfg.dist_fcn = "iou_complement"
+        cfg.dist_ths = [0,0.999999]
+        cfg.dist_th_tp = 0.999999
+
+        nusc_eval = DetectionEval(nusc, cfg, self.res_mockup, eval_set=eval_split, output_dir=self.res_eval_folder,
+                                  verbose=False)
+        metrics, md_list = nusc_eval.evaluate()
+        self.assertAlmostEqual(metrics.nd_score, 0.16651633528966858)
+
 if __name__ == '__main__':
     unittest.main()

python-sdk/nuscenes/eval/detection/tests/test_utils.py

@@ -7,8 +7,8 @@
 from numpy.testing import assert_array_almost_equal
 from pyquaternion import Quaternion
 
-from nuscenes.eval.common.utils import attr_acc, scale_iou, yaw_diff, angle_diff, center_distance, velocity_l2, \
-    cummean
+from nuscenes.eval.common.utils import attr_acc, scale_iou, yaw_diff, angle_diff, iou_complement, center_distance, \
+    velocity_l2, cummean
 from nuscenes.eval.detection.data_classes import DetectionBox
 
 
@@ -128,6 +128,19 @@ def rad(x):
         period = 360
         self.assertAlmostEqual(rad(180), abs(angle_diff(rad(a), rad(b), rad(period))))
 
+    def test_iou_complement_no_overlap(self):
+        # Two boxes specified, no overlap
+        sa = DetectionBox(translation=(1.0, 0.0, 1.0), size=(2,1,1))
+        sr = DetectionBox(translation=(3.5, 0.0, 1.0), size=(3,1,2))
+        self.assertAlmostEqual(iou_complement(sa, sr), 1.0)
+
+    def test_iou_complement_overlap(self):
+        # Two boxes specified, one rotated by 90 degrees in z axis, should attain 25% overlap
+        sa = DetectionBox(rotation=(0,0,0,0), translation=(1.0, 0.5, 2.0), size=(2,1,1))
+        sr = DetectionBox(rotation=(0.70710678118,0,0,0.70710678118),
+                          translation=(0.5, 1.5, 1), size=(3,1,2))
+        self.assertAlmostEqual(iou_complement(sa, sr), 0.75)
+
     def test_center_distance(self):
         """Test for center_distance()."""
 

python-sdk/nuscenes/eval/tracking/algo.py

@@ -23,6 +23,7 @@
 except ModuleNotFoundError:
     raise unittest.SkipTest('Skipping test as pandas was not found!')
 
+from nuscenes.eval.common.utils import iou_complement
 from nuscenes.eval.tracking.constants import MOT_METRIC_MAP, TRACKING_METRICS
 from nuscenes.eval.tracking.data_classes import TrackingBox, TrackingMetricData
 from nuscenes.eval.tracking.mot import MOTAccumulatorCustom
@@ -257,13 +258,19 @@ def accumulate_threshold(self, threshold: float = None) -> Tuple[pandas.DataFram
 
                 # Calculate distances.
                 # Note that the distance function is hard-coded to achieve significant speedups via vectorization.
-                assert self.dist_fcn.__name__ == 'center_distance'
                 if len(frame_gt) == 0 or len(frame_pred) == 0:
                     distances = np.ones((0, 0))
-                else:
+                elif self.dist_fcn.__name__ == 'center_distance':
                     gt_boxes = np.array([b.translation[:2] for b in frame_gt])
                     pred_boxes = np.array([b.translation[:2] for b in frame_pred])
                     distances = sklearn.metrics.pairwise.euclidean_distances(gt_boxes, pred_boxes)
+                elif self.dist_fcn.__name__ == 'iou_complement':
+                    distances = np.zeros((len(frame_gt),len(frame_pred)))
+                    for i in range(len(frame_gt)):
+                        for j in range(len(frame_pred)):
+                             distances[i,j] = iou_complement(frame_gt[i],frame_pred[j])
+                else:
+                    raise Exception('Error: Unknown distance function %s!' % self.dist_fcn.__name__)
 
                 # Distances that are larger than the threshold won't be associated.
                 assert len(distances) == 0 or not np.all(np.isnan(distances))

python-sdk/nuscenes/eval/tracking/data_classes.py

@@ -6,7 +6,7 @@
 import numpy as np
 
 from nuscenes.eval.common.data_classes import MetricData, EvalBox
-from nuscenes.eval.common.utils import center_distance
+from nuscenes.eval.common.utils import center_distance, iou_complement
 from nuscenes.eval.tracking.constants import TRACKING_METRICS, AMOT_METRICS
 
 
@@ -86,6 +86,8 @@ def dist_fcn_callable(self):
         """ Return the distance function corresponding to the dist_fcn string. """
         if self.dist_fcn == 'center_distance':
             return center_distance
+        elif self.dist_fcn == "iou_complement":
+            return iou_complement
         else:
             raise Exception('Error: Unknown distance function %s!' % self.dist_fcn)
 

python-sdk/nuscenes/eval/tracking/tests/test_evaluate.py

@@ -148,7 +148,9 @@ def random_id(instance_token: str, _add_errors: bool = False) -> str:
     def basic_test(self,
                    eval_set: str = 'mini_val',
                    add_errors: bool = False,
-                   render_curves: bool = False) -> Dict[str, Any]:
+                   render_curves: bool = False,
+                   dist_fcn: str = '',
+                   dist_th_tp: float = 0.0) -> Dict[str, Any]:
         """
         Run the evaluation with fixed randomness on the specified subset, with or without introducing errors in the
         submission.
@@ -174,6 +176,11 @@ def basic_test(self,
             json.dump(mock, f, indent=2)
 
         cfg = config_factory('tracking_nips_2019')
+
+        # Update dist fcn and threshold with those specified
+        cfg.dist_fcn = dist_fcn
+        cfg.dist_th_tp = dist_th_tp
+
         nusc_eval = TrackingEval(cfg, self.res_mockup, eval_set=eval_set, output_dir=self.res_eval_folder,
                                  nusc_version=version, nusc_dataroot=os.environ['NUSCENES'], verbose=False)
         metrics = nusc_eval.main(render_curves=render_curves)
@@ -192,7 +199,8 @@ def test_delta_mock(self,
         :param render_curves: Whether to render stats curves to disk.
         """
         # Run the evaluation with errors.
-        metrics = self.basic_test(eval_set, add_errors=True, render_curves=render_curves)
+        metrics = self.basic_test(eval_set, add_errors=True, render_curves=render_curves,
+                                dist_fcn='center_distance', dist_th_tp=2.0)
 
         # Compare metrics to known solution.
         if eval_set == 'mini_val':
@@ -204,6 +212,20 @@ def test_delta_mock(self,
         else:
             print('Skipping checks due to choice of custom eval_set: %s' % eval_set)
 
+        # Run again with the alternative iou_complement dist_fcn
+        metrics = self.basic_test(eval_set, add_errors=True, render_curves=render_curves,
+                                  dist_fcn='iou_complement', dist_th_tp=0.999999)
+
+        # Compare metrics to known solution.
+        if eval_set == 'mini_val':
+            self.assertAlmostEqual(metrics['amota'], 0.231839679131956)
+            self.assertAlmostEqual(metrics['amotp'], 1.3629342647309446)
+            self.assertAlmostEqual(metrics['motar'], 0.27918315466340504)
+            self.assertAlmostEqual(metrics['mota'], 0.22922560056448252)
+            self.assertAlmostEqual(metrics['motp'], 0.7541595548820258)
+        else:
+            print('Skipping checks due to choice of custom eval_set: %s' % eval_set)
+
     @parameterized.expand([
         ('mini_val',),
         ('mini_custom_train',)
@@ -224,7 +246,8 @@ def test_delta_gt(self,
         mock__get_custom_splits_file_path.return_value = self.splits_file_mockup
 
         # Run the evaluation without errors.
-        metrics = self.basic_test(eval_set, add_errors=False, render_curves=render_curves)
+        metrics = self.basic_test(eval_set, add_errors=False, render_curves=render_curves,
+                                  dist_fcn='center_distance', dist_th_tp=2.0)
 
         # Compare metrics to known solution. Do not check:
         # - MT/TP (hard to figure out here).
@@ -247,6 +270,30 @@ def test_delta_gt(self,
         else:
             print('Skipping checks due to choice of custom eval_set: %s' % eval_set)
 
+        # Run again with the alternative iou_complement dist_fcn (and a very precise threshold)
+        metrics = self.basic_test(eval_set, add_errors=False, render_curves=render_curves,
+                                  dist_fcn='iou_complement', dist_th_tp=1e-6)
+
+        # Compare metrics to known solution. Do not check:
+        # - MT/TP (hard to figure out here).
+        # - AMOTA/AMOTP (unachieved recall values lead to hard unintuitive results).
+        if eval_set in ['mini_val', 'mini_custom_train']:
+            self.assertAlmostEqual(metrics['amota'], 1.0)
+            self.assertAlmostEqual(metrics['amotp'], 0.0, delta=1e-5)
+            self.assertAlmostEqual(metrics['motar'], 1.0)
+            self.assertAlmostEqual(metrics['recall'], 1.0)
+            self.assertAlmostEqual(metrics['mota'], 1.0)
+            self.assertAlmostEqual(metrics['motp'], 0.0, delta=1e-5)
+            self.assertAlmostEqual(metrics['faf'], 0.0)
+            self.assertAlmostEqual(metrics['ml'], 0.0)
+            self.assertAlmostEqual(metrics['fp'], 0.0)
+            self.assertAlmostEqual(metrics['fn'], 0.0)
+            self.assertAlmostEqual(metrics['ids'], 0.0)
+            self.assertAlmostEqual(metrics['frag'], 0.0)
+            self.assertAlmostEqual(metrics['tid'], 0.0)
+            self.assertAlmostEqual(metrics['lgd'], 0.0)
+        else:
+            print('Skipping checks due to choice of custom eval_set: %s' % eval_set)
 
 if __name__ == '__main__':
     unittest.main()