Update app.py

app.py

@@ -256,13 +256,77 @@ class FightDetection:
         except Exception as e:
             raise ValueError(f"Error in fight_detection: {str(e)}")
 
+class IntrusionDetection:
+    def __init__(self, model_path="yolov8n.pt", max_intrusion_time=300, iou_threshold=0.5, conf_threshold=0.5):
+        self.model_path = model_path
+        self.max_intrusion_time = max_intrusion_time
+        self.iou_threshold = iou_threshold
+        self.conf_threshold = conf_threshold
+
+    def detect_intrusion(self, video_path):
+        try:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            if not os.path.exists(self.model_path):
+                model = YOLO("yolov8n.pt")
+                model.save(self.model_path)
+            else:
+                model = YOLO(self.model_path)
+            model.to(device)
+
+            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))
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+            output_path = "output_intrusion.mp4"
+            fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+            out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+            if not out.isOpened():
+                cap.release()
+                raise ValueError(f"❌ Failed to initialize video writer")
+
+            frame_count = 0
+
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                frame_count += 1
+
+                results = model(frame)
+                for result in results:
+                    boxes = result.boxes.xyxy.cpu().numpy()
+                    classes = result.boxes.cls.cpu().numpy()
+                    confidences = result.boxes.conf.cpu().numpy()
+                    for box, cls, conf in zip(boxes, classes, confidences):
+                        if int(cls) == 0 and conf > self.conf_threshold:  # Person class with confidence filter
+                            x1, y1, x2, y2 = map(int, box)
+                            label = "Intruder"
+                            color = (0, 0, 255)
+                            cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
+                            cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+                
+                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_intrusion: {str(e)}")
+
 # Unified processing function with status output
 def process_video(feature, video):
     detectors = {
         "Crowd Detection": CrowdDetection,
         "People Tracking": PeopleTracking,
         "Fall Detection": FallDetection,
-        "Fight Detection": FightDetection
+        "Fight Detection": FightDetection,
+        "Intrusion Detection" : IntrusionDetection
     }
     try:
         detector = detectors[feature]()
@@ -276,7 +340,7 @@ def process_video(feature, video):
 interface = gr.Interface(
     fn=process_video,
     inputs=[
-        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"),
+        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection", "Intrusion Detection"], label="Select Feature"),
         gr.Video(label="Upload Video")
     ],
     outputs=[