Update backend/evaluation.py

backend/evaluation.py

@@ -1346,11 +1346,14 @@ def evaluate_architecture_fixes(perc_results: dict, risk_results: dict) -> dict:
     risk = RiskAgent()
 
     # Build frames with known densities
+    _fr_seq = [0]
     def _make_fr(density: float, count: int, spacing: float = 200.0) -> FrameResult:
+        _fr_seq[0] += 1
         return FrameResult(
-            frame_id=0, timestamp=time.time(),
+            frame_id=_fr_seq[0], timestamp=time.time(),
             person_count=count, density_score=density,
             avg_spacing=spacing, boxes=[], annotated=None, guardrail_flags=[],
+            track_ids=list(range(count)),  # unique IDs 0..count-1 for K-window density
         )
 
     density_cases = [
@@ -1361,9 +1364,11 @@ def evaluate_architecture_fixes(perc_results: dict, risk_results: dict) -> dict:
 
     fix4_pass = 0
     for density, count, label, expected_trend in density_cases:
-        # Fill the window with identical frames to stabilise score
+        # Fill window (K_WINDOW warmup) + stabilization (STABLE_FRAMES) to confirm level
         risk2 = RiskAgent()
-        for _ in range(risk2.WINDOW_SIZE):
+        _fr_seq[0] = 0
+        n_iters = risk2.K_WINDOW + risk2.STABLE_FRAMES
+        for _ in range(n_iters):
             rr = risk2.process_frame(_make_fr(density, count))
         got_level = rr.risk_level
         # For density=1.0 → d_score=1.0, t_score=0.4 → raw≥0.50 → MEDIUM or HIGH