Improved + Stable + Confidence (#15)

- Improved + Stable + Confidence (9c32c096d1949df40ca23c870d86970a8799f79c)

app.py

@@ -1,5 +1,5 @@
 # ============================================================
-# 🚦 Stage 3 — Wrong Direction Detection (Improved + Toggle)
+# 🚦 Stage 3 — Wrong Direction Detection (Improved + Stable + Confidence)
 # ============================================================
 
 import os, cv2, json, tempfile, numpy as np, gradio as gr
@@ -32,6 +32,8 @@ class Track:
         self.history = []
         self.frames_seen = 0
         self.status = "OK"
+        self.status_history = []  # 🔸 maintain recent decision history
+        self.confidence = 1.0
 
     def update(self, bbox):
         self.kf.predict()
@@ -43,8 +45,20 @@ class Track:
         self.frames_seen += 1
         return [x, y]
 
+    def stable_status(self, new_status, new_conf, window=6, agree_ratio=0.7):
+        """Debounce flicker using recent window consensus."""
+        self.status_history.append(new_status)
+        if len(self.status_history) > window:
+            self.status_history.pop(0)
+        # majority consensus
+        if self.status_history.count(new_status) >= int(agree_ratio * len(self.status_history)):
+            self.status = new_status
+            self.confidence = new_conf
+        return self.status, self.confidence
+
+
 # ============================================================
-# ⚙️ Utility Functions
+# ⚙️ Utilities
 # ============================================================
 def compute_cosine_similarity(v1, v2):
     v1 = v1 / (np.linalg.norm(v1) + 1e-6)
@@ -120,29 +134,25 @@ def process_video(video_file, stage2_json, show_only_wrong=False):
                 for i in range(1, len(pts)):
                     cv2.line(frame, tuple(np.int32(pts[i-1])), tuple(np.int32(pts[i])), (0, 0, 255), 1)
 
-            # compute smooth direction
             motion = smooth_direction(pts)
             if motion is None:
                 continue
             if np.linalg.norm(motion) < MIN_FLOW_SPEED:
                 continue
 
-            # cosine similarity to closest lane flow
+            # cosine similarity vs lane flows
             sims = [compute_cosine_similarity(motion, f) for f in lane_flows]
             best_sim = max(sims)
 
-            # only classify after some frames
             if trk.frames_seen > DELAY_FRAMES:
-                if best_sim < SIM_THRESH:
-                    trk.status = "WRONG"
-                else:
-                    trk.status = "OK"
+                new_status = "WRONG" if best_sim < SIM_THRESH else "OK"
+                trk.stable_status(new_status, new_conf=best_sim)
 
-                # draw depending on toggle
+                # draw if toggle allows
                 if (not show_only_wrong) or (trk.status == "WRONG"):
                     color = (0, 0, 255) if trk.status == "WRONG" else (0, 255, 0)
-                    cv2.putText(frame, f"ID:{tid} {trk.status}",
-                                tuple(np.int32(pos)),
+                    label = f"ID:{tid} {trk.status} ({trk.confidence:.2f})"
+                    cv2.putText(frame, label, tuple(np.int32(pos)),
                                 cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
 
         out.write(frame)
@@ -155,11 +165,13 @@ def process_video(video_file, stage2_json, show_only_wrong=False):
 # 🎛️ Gradio Interface
 # ============================================================
 description = """
-### 🚦 Stage 3 — Wrong Direction Detection (Improved)
+### 🚦 Stage 3 — Wrong Direction Detection (Improved + Stable + Confidence)
 - ✅ Cosine similarity instead of raw angle  
 - ✅ Lane-wise flow support for curved roads  
 - ✅ Temporal smoothing & delayed classification  
-- ✅ Toggle to show only WRONG vehicles  
+- ✅ Flicker-free labels using consensus window  
+- ✅ Confidence score display (cosine similarity)
+- ✅ Toggle to show only WRONG overlays  
 """
 
 demo = gr.Interface(
@@ -169,8 +181,8 @@ demo = gr.Interface(
         gr.File(label="Stage 2 Flow JSON"),
         gr.Checkbox(label="Show ONLY Wrong Labels Overlay", value=False)
     ],
-    outputs=gr.Video(label="Output (with WRONG/OK labels)"),
-    title="🚗 Stage 3 – Improved Wrong-Direction Detection",
+    outputs=gr.Video(label="Output Video"),
+    title="🚗 Stage 3 – Stable Wrong-Direction Detection (with Confidence)",
     description=description
 )