Update app.py

app.py

@@ -262,7 +262,7 @@ class IntrusionDetection:
         self.max_intrusion_time = max_intrusion_time
         self.iou_threshold = iou_threshold
         self.conf_threshold = conf_threshold
-        @spaces.GPU
+    @spaces.GPU
 
     def detect_intrusion(self, video_path):
         try:
@@ -320,6 +320,82 @@ class IntrusionDetection:
         except Exception as e:
             raise ValueError(f"Error in detect_intrusion: {str(e)}")
 
+
+class LoiteringDetection:
+    def __init__(self, model_path="yolov8n.pt", loitering_threshold=10, conf_threshold=0.5):
+        self.model_path = model_path
+        self.loitering_threshold = loitering_threshold
+        self.conf_threshold = conf_threshold
+        self.entry_time = {}
+        self.area = [(153, 850), (139, 535), (239, 497), (291, 857)]
+    @spaces.GPU
+    def load_model(self):
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        model = YOLO(self.model_path).to(device)
+        return model
+
+    def calculate_center(self, box):
+        x1, y1, x2, y2 = box
+        return int((x1 + x2) / 2), int((y1 + y2) / 2)
+
+    def track_time(self, id, frame_duration):
+        if id not in self.entry_time:
+            self.entry_time[id] = {'duration': 0, 'loitering': False}
+        else:
+            self.entry_time[id]['duration'] += frame_duration
+        if self.entry_time[id]['duration'] > self.loitering_threshold:
+            self.entry_time[id]['loitering'] = True
+
+    def detect_loitering(self, video_path):
+        try:
+            model = self.load_model()
+            cap = cv2.VideoCapture(video_path)
+            if not cap.isOpened():
+                raise ValueError(f"❌ Failed to open video: {video_path}")
+
+            fps = int(cap.get(cv2.CAP_PROP_FPS)) or 30
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            output_path = "output_loitering.mp4"
+            out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
+            frame_duration = 1 / fps
+            frame_count = 0
+
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                frame_count += 1
+
+                results = model.track(frame, conf=self.conf_threshold, iou=0.1, classes=[0], persist=True)
+                boxes = results[0].boxes.xyxy.cpu().tolist()
+                ids = results[0].boxes.id.cpu().tolist()
+
+                ids_in_area = []
+                for box, id in zip(boxes, ids):
+                    center = self.calculate_center(box)
+                    if cv2.pointPolygonTest(np.array(self.area, np.int32), center, False) >= 0:
+                        ids_in_area.append(id)
+                        self.track_time(id, frame_duration)
+
+                for id in ids_in_area:
+                    color = (0, 0, 255) if self.entry_time.get(id, {}).get('loitering', False) else (0, 255, 0)
+                    cv2.putText(frame, f"ID {id}, Time: {self.entry_time[id]['duration']:.1f}s", (15, 30 + id * 30),
+                                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+                
+                pts = np.array(self.area, np.int32).reshape((-1, 1, 2))
+                color = (0, 0, 255) if any(self.entry_time.get(id, {}).get('loitering', False) for id in ids_in_area) else (152, 251, 152)
+                cv2.polylines(frame, [pts], isClosed=True, color=color, thickness=3)
+                out.write(frame)
+
+            cap.release()
+            out.release()
+            if frame_count == 0 or not os.path.exists(output_path):
+                raise ValueError("❌ Processing failed: No frames processed or output not created")
+            return output_path
+        except Exception as e:
+            raise ValueError(f"Error in detect_loitering: {str(e)}")
+
 # Unified processing function with status output
 def process_video(feature, video):
     detectors = {
@@ -327,7 +403,8 @@ def process_video(feature, video):
         "People Tracking": PeopleTracking,
         "Fall Detection": FallDetection,
         "Fight Detection": FightDetection,
-        "Intrusion Detection" : IntrusionDetection
+        "Intrusion Detection" : IntrusionDetection,
+        "Loitering Detection" : LoiteringDetection
     }
     try:
         detector = detectors[feature]()
@@ -341,7 +418,7 @@ def process_video(feature, video):
 interface = gr.Interface(
     fn=process_video,
     inputs=[
-        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection", "Intrusion Detection"], label="Select Feature"),
+        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection", "Intrusion Detection", "Loitering Detection"], label="Select Feature"),
         gr.Video(label="Upload Video")
     ],
     outputs=[