Update app.py

app.py

@@ -8,7 +8,67 @@ import spaces
 
 @spaces.GPU
 
-# Define People Tracking
+class CrowdDetection:
+    def __init__(self, model_path="yolov8n.pt", crowd_threshold=10):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🔍 Using device: {self.device}")
+        self.model = YOLO(model_path).to(self.device)
+        self.crowd_threshold = crowd_threshold
+
+    def detect_crowd(self, video_path):
+        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))
+        print(f"🎥 Video details - FPS: {fps}, Width: {width}, Height: {height}")
+
+        output_path = "output_crowd.mp4"
+        fourcc = cv2.VideoWriter_fourcc(*"mp4v")
+        out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
+
+        frame_count = 0
+
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            
+            frame_count += 1
+            results = self.model(frame)
+            person_count = 0
+
+            for result in results:
+                boxes = result.boxes.xyxy.cpu().numpy()
+                classes = result.boxes.cls.cpu().numpy()
+
+                for box, cls in zip(boxes, classes):
+                    if int(cls) == 0:  # Class ID 0 = "person"
+                        person_count += 1
+                        x1, y1, x2, y2 = map(int, box)
+                        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+                        cv2.putText(frame, "Person", (x1, y1 - 10),
+                                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+            alert_text = "Crowd Alert!" if person_count > self.crowd_threshold else f"People: {person_count}"
+            cv2.putText(frame, alert_text, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1,
+                        (0, 0, 255) if person_count > self.crowd_threshold else (0, 255, 0), 2)
+            out.write(frame)
+
+        cap.release()
+        out.release()
+
+        if frame_count == 0:
+            raise ValueError("❌ No frames were processed!")
+
+        if not os.path.exists(output_path):
+            raise FileNotFoundError(f"❌ Output video not found: {output_path}")
+
+        print(f"✅ Processed video saved at: {output_path}")
+        return output_path
+
 class PeopleTracking:
     def __init__(self, yolo_model_path="yolov8n.pt"):
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -128,6 +188,7 @@ class FightDetection:
 # Function to process video based on selected feature
 def process_video(feature, video):
     detectors = {
+        "Crowd Detection": CrowdDetection,
         "People Tracking": PeopleTracking,
         "Fall Detection": FallDetection,
         "Fight Detection": FightDetection
@@ -141,7 +202,7 @@ def process_video(feature, video):
 interface = gr.Interface(
     fn=process_video,
     inputs=[
-        gr.Dropdown(choices=["People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"),
+        gr.Dropdown(choices=["Crowd Detection", "People Tracking", "Fall Detection", "Fight Detection"], label="Select Feature"),
         gr.Video(label="Upload Video")
     ],
     outputs=gr.Video(label="Processed Video"),