feat: enhance LineCrossCounter to deduplicate re-entries and add WindowIdentityResolver for person tracking

chore: update docker-compose for additional host configuration
test: add unit tests for LineCrossCounter and WindowIdentityResolver functionality

managed/people_flow_project/tests/test_counting.py

@@ -0,0 +1,43 @@
+import numpy as np
+
+from src.people_flow.counting import LineCrossCounter
+from src.people_flow.models import CountingConfig, TrackObservation
+from src.people_flow.window_identity import WindowIdentityResolver
+
+
+def _observation(track_id: int, center_y: float) -> TrackObservation:
+    return TrackObservation(
+        track_id=track_id,
+        bbox=(0, 0, 10, 10),
+        confidence=0.9,
+        center=(5.0, center_y),
+    )
+
+
+def test_line_cross_counter_dedupes_reentry_with_same_person_key():
+    counter = LineCrossCounter(
+        (0.0, 5.0, 10.0, 5.0), CountingConfig(crossing_tolerance=0.0)
+    )
+
+    counter.update([_observation(1, 2.0)], person_keys={1: "person:1"})
+    first_events = counter.update([_observation(1, 8.0)], person_keys={1: "person:1"})
+    counter.update([_observation(2, 2.0)], person_keys={2: "person:1"})
+    second_events = counter.update([_observation(2, 8.0)], person_keys={2: "person:1"})
+
+    assert len(first_events) == 1
+    assert second_events == []
+    assert counter.total_people == 1
+    assert len(counter.crossings) == 1
+    assert counter.crossings[0].track_id == 1
+
+
+def test_window_identity_resolver_matches_reentry_after_track_switch():
+    resolver = WindowIdentityResolver(similarity_threshold=0.97)
+    frame = np.zeros((16, 16, 3), dtype=np.uint8)
+    frame[:, :] = (30, 60, 180)
+
+    first_keys = resolver.resolve(frame, [_observation(1, 2.0)])
+    resolver.resolve(frame, [])
+    second_keys = resolver.resolve(frame, [_observation(2, 8.0)])
+
+    assert first_keys[1] == second_keys[2]