added keep_det_order to keeep Tracks as detection order

Makefile

@@ -5,7 +5,7 @@ install-develop:
 	python setup.py develop
 
 test:
-	python -m pytest ./tests
+	python -m pytest -vvs ./tests
 
 env-create:
 	conda create --name env_motpy python=3.7 -y

motpy/tracker.py

@@ -363,18 +363,32 @@ def __init__(self, dt: float,
     def active_tracks(self,
                       max_staleness_to_positive_ratio: float = 3.0,
                       max_staleness: float = 999,
-                      min_steps_alive: int = -1) -> List[Track]:
-        """ returns all active tracks after optional filtering by tracker steps count and staleness """
+                      min_steps_alive: int = -1,
+                      return_indices: bool = False) -> Union[List[Track], Tuple[List[Track], List[int]]]:
+        """ returns all active tracks after optional filtering by tracker steps count and staleness 
+            returns indices array from last step, -1 is a body not in passed detected boxes
+        """
 
         tracks: List[Track] = []
+        if return_indices:
+            tracks_indices: List[int] = []
+
         for tracker in self.trackers:
             cond1 = tracker.staleness / tracker.steps_positive < max_staleness_to_positive_ratio  # early stage
             cond2 = tracker.staleness < max_staleness
             cond3 = tracker.steps_alive >= min_steps_alive
             if cond1 and cond2 and cond3:
                 tracks.append(Track(id=tracker.id, box=tracker.box(), score=tracker.score, class_id=tracker.class_id))
+                if return_indices:
+                    try:
+                        tracks_indices.append(self.detections_matched_ids.index(tracker))
+                    except ValueError:
+                        tracks_indices.append(-1)
 
         logger.debug('active/all tracks: %d/%d' % (len(self.trackers), len(tracks)))
+        if return_indices:
+            return tracks, tracks_indices
+
         return tracks
 
     def cleanup_trackers(self) -> None:
@@ -407,7 +421,7 @@ def step(self, detections: Sequence[Detection]) -> List[Track]:
         for match in matches:
             track_idx, det_idx = match[0], match[1]
             self.trackers[track_idx].update(detection=detections[det_idx])
-            self.detections_matched_ids[det_idx] = self.trackers[track_idx].id
+            self.detections_matched_ids[det_idx] = self.trackers[track_idx]
 
         # not assigned detections: create new trackers POF
         assigned_det_idxs = set(matches[:, 1]) if len(matches) > 0 else []
@@ -417,7 +431,7 @@ def step(self, detections: Sequence[Detection]) -> List[Track]:
                                         score0=det.score,
                                         class_id0=det.class_id,
                                         **self.tracker_kwargs)
-            self.detections_matched_ids[det_idx] = tracker.id
+            self.detections_matched_ids[det_idx] = tracker
             self.trackers.append(tracker)
 
         # unassigned trackers

tests/test_tracker.py

@@ -2,6 +2,7 @@
 
 import numpy as np
 import pytest
+from motpy import ModelPreset
 from motpy.core import Detection, setup_logger
 from motpy.testing import data_generator
 from motpy.tracker import (IOUAndFeatureMatchingFunction, MultiObjectTracker,
@@ -94,6 +95,23 @@ def test_tracker_diverges():
     assert len(mot.trackers) == 1
     assert mot.active_tracks()[0].id != first_track_id
 
+def test_tracker_det_indices():
+    mot = MultiObjectTracker(dt=5)
+    box0 = np.array([0, 0, 10, 10])
+    box1 = np.array([20, 20, 30, 30])
+    mot.step([Detection(box=box) for box in [box0, box1]])
+    track_ids = [t.id for t in mot.active_tracks()]
+    assert len(track_ids) == 2
+    _, indices = mot.active_tracks(return_indices=True)
+    assert indices == [0, 1]
+    mot.step([Detection(box=box) for box in [box1, box0]])
+    assert track_ids == [t.id for t in mot.active_tracks()]
+    track_ids_idx, indices = mot.active_tracks(return_indices=True)
+    assert track_ids == [t.id for t in track_ids_idx]
+    assert indices == [1, 0]
+    mot.step([Detection(box=box) for box in []])
+    _, indices = mot.active_tracks(return_indices=True)
+    assert indices == [-1, -1]
 
 def test_class_smoothing():
     box = np.array([0, 0, 10, 10])