update

app.py

@@ -1,24 +1,25 @@
-import gradio as gr
-import cv2, os, numpy as np, json, tempfile, time
+import os, cv2, json, time, tempfile, numpy as np, gradio as gr
 from ultralytics import YOLO
 from filterpy.kalman import KalmanFilter
 from scipy.optimize import linear_sum_assignment
 
 # ------------------------------------------------------------
-# ⚙️ Load YOLO and flow centers
+# 🔧 Safe-load fix for PyTorch 2.6
+# ------------------------------------------------------------
+import torch
+import ultralytics.nn.tasks as ultralytics_tasks
+torch.serialization.add_safe_globals([ultralytics_tasks.DetectionModel])
+
+# ------------------------------------------------------------
+# ⚙️ Model + constants
 # ------------------------------------------------------------
 MODEL_PATH = "yolov8n.pt"
 model = YOLO(MODEL_PATH)
 VEHICLE_CLASSES = [2, 3, 5, 7]  # car, motorbike, bus, truck
 
-def load_flow_centers(flow_json):
-    data = json.load(open(flow_json))
-    centers = np.array(data["flow_centers"])
-    centers = centers / (np.linalg.norm(centers, axis=1, keepdims=True) + 1e-6)
-    return centers
 
 # ------------------------------------------------------------
-# 🧩 Simple Kalman Tracker
+# 🧩 Kalman Tracker
 # ------------------------------------------------------------
 class Track:
     def __init__(self, bbox, tid):
@@ -31,7 +32,7 @@ class Track:
         self.kf.x[:2] = np.array(self.centroid(bbox)).reshape(2,1)
         self.trace = []
 
-    def centroid(self,b):
+    def centroid(self, b):
         x1,y1,x2,y2=b
         return [(x1+x2)/2,(y1+y2)/2]
     def predict(self): self.kf.predict(); return self.kf.x[:2].reshape(2)
@@ -42,47 +43,60 @@ class Track:
         self.trace.append((float(cx),float(cy)))
         return (cx,cy)
 
+
 # ------------------------------------------------------------
-# 🚦 Wrong-Direction Analyzer
+# 🧮 Direction check
 # ------------------------------------------------------------
 def analyze_direction(trace, centers):
-    if len(trace)<3: return "NA",1.0
-    v = np.array(trace[-1]) - np.array(trace[-3])     # motion vector
-    if np.linalg.norm(v)<1e-6: return "NA",1.0
+    if len(trace) < 3:
+        return "NA", 1.0
+    v = np.array(trace[-1]) - np.array(trace[-3])
+    if np.linalg.norm(v) < 1e-6:
+        return "NA", 1.0
     v = v / np.linalg.norm(v)
     sims = np.dot(centers, v)
     max_sim = np.max(sims)
-    if max_sim < 0: return "WRONG", float(max_sim)
+    if max_sim < 0:
+        return "WRONG", float(max_sim)
     return "OK", float(max_sim)
 
+
 # ------------------------------------------------------------
-# 🎥 Process Video
+# 🚦 Main Processing
 # ------------------------------------------------------------
+def load_flow_centers(flow_json):
+    data = json.load(open(flow_json))
+    centers = np.array(data["flow_centers"])
+    centers = centers / (np.linalg.norm(centers, axis=1, keepdims=True) + 1e-6)
+    return centers
+
 def process_video(video_path, flow_json):
     centers = load_flow_centers(flow_json)
     cap = cv2.VideoCapture(video_path)
     fps = cap.get(cv2.CAP_PROP_FPS) or 25
     w,h = int(cap.get(3)), int(cap.get(4))
 
-    tmp_out = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False)
-    out = cv2.VideoWriter(tmp_out.name, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w,h))
+    out_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
+    fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+    out = cv2.VideoWriter(out_path, fourcc, fps, (w,h))
 
-    tracks, next_id = [], 0
-    log = []
+    tracks, next_id, log = [], 0, []
 
     while True:
         ret, frame = cap.read()
-        if not ret: break
+        if not ret:
+            break
         results = model(frame, verbose=False)[0]
         detections=[]
         for box in results.boxes:
             if int(box.cls) in VEHICLE_CLASSES and box.conf>0.3:
                 detections.append(box.xyxy[0].cpu().numpy())
 
-        # predict existing tracks
-        predicted = [trk.predict() for trk in tracks]
-        predicted = np.array(predicted) if len(predicted)>0 else np.empty((0,2))
+        # Predict existing
+        predicted=[t.predict() for t in tracks]
+        predicted=np.array(predicted) if len(predicted)>0 else np.empty((0,2))
 
+        # Assign detections
         assigned=set()
         if len(predicted)>0 and len(detections)>0:
             cost=np.zeros((len(predicted),len(detections)))
@@ -96,21 +110,21 @@ def process_video(video_path, flow_json):
                     assigned.add(j)
                     tracks[i].update(detections[j])
 
-        # new tracks
+        # New tracks
         for j,d in enumerate(detections):
             if j not in assigned:
-                trk=Track(d,next_id); next_id+=1
-                trk.update(d)
-                tracks.append(trk)
+                t=Track(d,next_id); next_id+=1
+                t.update(d); tracks.append(t)
 
-        # draw
+        # Draw results
         for trk in tracks:
             if len(trk.trace)<3: continue
             status, sim = analyze_direction(trk.trace, centers)
             x,y=map(int,trk.trace[-1])
-            color=(0,255,0) if status=="OK" else ((0,0,255) if status=="WRONG" else (255,255,255))
+            color=(0,255,0) if status=="OK" else ((0,0,255) if status=="WRONG" else (200,200,200))
             cv2.circle(frame,(x,y),4,color,-1)
-            cv2.putText(frame,f"ID:{trk.id} {status}",(x-20,y-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,color,1)
+            cv2.putText(frame,f"ID:{trk.id} {status}",(x-20,y-10),
+                        cv2.FONT_HERSHEY_SIMPLEX,0.5,color,1)
             for i in range(1,len(trk.trace)):
                 cv2.line(frame,(int(trk.trace[i-1][0]),int(trk.trace[i-1][1])),
                                 (int(trk.trace[i][0]),int(trk.trace[i][1])),color,1)
@@ -119,12 +133,14 @@ def process_video(video_path, flow_json):
         out.write(frame)
 
     cap.release(); out.release()
+
     log_path = tempfile.NamedTemporaryFile(suffix=".json", delete=False).name
     with open(log_path,"w") as f: json.dump(log,f,indent=2)
-    return tmp_out.name, log_path
+    return out_path, log_path
+
 
 # ------------------------------------------------------------
-# 🖥️ Gradio Interface
+# 🖥️ Gradio UI
 # ------------------------------------------------------------
 def run_app(video, flow_file):
     out_path, log_path = process_video(video, flow_file)
@@ -135,7 +151,7 @@ def run_app(video, flow_file):
 description_text = """
 ### 🚦 Wrong-Direction Detection (Stage 3)
 Uploads your traffic video and the **flow_stats.json** from Stage 2.  
-Outputs an annotated video with ✅ OK / 🚫 WRONG labels per vehicle, plus a JSON log.
+Outputs an annotated video with ✅ OK / 🚫 WRONG labels and a JSON log.
 """
 
 example_vid = "10.mp4" if os.path.exists("10.mp4") else None
@@ -143,19 +159,16 @@ example_flow = "flow_stats.json" if os.path.exists("flow_stats.json") else None
 
 demo = gr.Interface(
     fn=run_app,
-    inputs=[
-        gr.Video(label="Upload Traffic Video (.mp4)"),
-        gr.File(label="Upload flow_stats.json (Stage 2 Output)")
-    ],
-    outputs=[
-        gr.Video(label="Violation Output Video"),
-        gr.JSON(label="Per-Vehicle Log"),
-        gr.JSON(label="Summary")
-    ],
+    inputs=[gr.Video(label="Upload Traffic Video (.mp4)"),
+            gr.File(label="Upload flow_stats.json (Stage 2 Output)")],
+    outputs=[gr.Video(label="Violation Output Video"),
+             gr.JSON(label="Per-Vehicle Log"),
+             gr.JSON(label="Summary")],
     title="🚗 Wrong-Direction Detection – Stage 3",
     description=description_text,
-    examples=[[example_vid, example_flow]] if example_vid and example_flow else None,
+    examples=None,     # disables example caching
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False, show_api=False)