added filter + tracking tests

Author: tobias-gp-porsche

simple_filters/tracker.py

@@ -64,14 +64,17 @@ def update(self, states):
 
         states = np.array(states)
 
-        # check if array is 2d, otherwise make it so
+        # check if the states array is 2d, otherwise make it so
         if len(states.shape) == 1: 
             states = np.array([states])
 
-        # set initial properties
-        number_of_states = states.shape[0]
+        # check if the states array is empty
+        if states.size == 0: 
+            number_of_states = 0
+        else: 
+            number_of_states = states.shape[0]
+        
         number_of_tracked_objects = len(self.__tracked_objects)
-
         objects_to_match = [i for i in range(0, number_of_tracked_objects)] 
         states_to_match = [i for i in range(0, number_of_states)]
 

tests/test_single_object_tracking.py

@@ -1,18 +1,17 @@
 from unittest import TestCase
 
-from ..simple_filters import Tracker, TrackedObject, Filter, PolynomialFilterStrategy
+from ..simple_filters import Tracker, TrackedObject, Filter, PolynomialFilterStrategy, DummyFilterStrategy
 
 import pytest
 import numpy as np
 
 class TestSingleStepSingleObjectTracking(TestCase):
 
-    def setUp(self):
-        strategy = PolynomialFilterStrategy()
-        filter_prototype = Filter(strategy, history_size=1)
-        self.tracker = Tracker(filter_prototype, distance_threshold=1.)
-    
-    def test_main(self):
+    def test_new_obj(self):
+        strategy = DummyFilterStrategy()
+        filter_prototype = Filter(strategy, history_size=5)
+        tracker = Tracker(filter_prototype, distance_threshold=1.)
+
         states = [
             np.array([1.0, 1.0]),
             np.array([1.5, 1.5]),
@@ -26,8 +25,33 @@ def test_main(self):
 
         for i, (state, expected_tracking_id) in enumerate(zip(states, expected_tracking_ids)):
             print("timestep", i)
-            self.tracker.update(state)
-            tracked_state = self.tracker.get_tracked_objects()
+            tracker.update(state)
+            tracked_state = tracker.get_tracked_objects()
 
             self.assertEqual(1, len(tracked_state))
             self.assertEqual(tracked_state[0].id, expected_tracking_id)
+
+    def test_interpolate_object_with_ttl(self):
+        strategy = PolynomialFilterStrategy(poly_degree=1, reject_outliers=False)
+        filter_prototype = Filter(strategy, history_size=3)
+        tracker = Tracker(filter_prototype, distance_threshold=1., time_to_live=1)
+
+        states = [
+            np.array([[1.0, 1.0]]),
+            np.array([[1.5, 1.5]]),
+            np.array([[2.0, 2.0]]),
+            np.array([]),
+            np.array([[3.0, 3.0]]),
+            np.array([[3.5, 3.5]]),
+            np.array([[4.0, 4.0]])
+        ]
+        
+        expected_tracking_ids = [1, 1, 1, 1, 1, 1, 1]
+
+        for i, (state, expected_tracking_id) in enumerate(zip(states, expected_tracking_ids)):
+            print("timestep", i)
+            tracker.update(state)
+            tracked_state = tracker.get_tracked_objects()
+
+            self.assertEqual(1, len(tracked_state))
+            self.assertEqual(tracked_state[0].id, expected_tracking_id)