Update app.py

app.py

@@ -131,23 +131,18 @@ class PeopleTracking:
 class FallDetection:
     def __init__(self, yolo_model_path="yolov8l.pt"):
         self.model_path = yolo_model_path
-    
-    @spaces.GPU
+    spaces@GPU
     def fall_detect(self, video_path):
         try:
-            import torch
-            import os
-            import cv2
-            import numpy as np
-            from ultralytics import YOLO
-
             device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
+            
+            # Load YOLOv8 model
             if not os.path.exists(self.model_path):
                 model = YOLO("yolov8l.pt")
                 model.save(self.model_path)
             else:
                 model = YOLO(self.model_path)
+            
             model.to(device)
 
             cap = cv2.VideoCapture(video_path)
@@ -159,19 +154,29 @@ class FallDetection:
             height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * 0.5)
             output_path = "output_fall.mp4"
             out = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height))
+            
             if not out.isOpened():
                 cap.release()
                 raise ValueError(f"❌ Failed to initialize video writer")
 
+            frame_skip = 3  # Process every 3rd frame to optimize performance
+            frame_count = 0
+
             while cap.isOpened():
                 ret, frame = cap.read()
                 if not ret:
+                    print("⚠️ No more frames to read. Exiting loop.")
                     break
-                
+
+                frame_count += 1
+                if frame_count % frame_skip != 0:
+                    continue  # Skip frames to optimize performance
+
                 frame = cv2.resize(frame, (width, height))
 
-                # ✅ FIX: Use `.predict()` to ensure proper YOLO inference
-                results = model.predict(frame, imgsz=640, device=device)  
+                # Ensure YOLO runs without unnecessary graph tracking
+                with torch.no_grad():
+                    results = model.predict(frame, imgsz=640, device=device)
 
                 for result in results:
                     boxes = result.boxes.xyxy.cpu().numpy()
@@ -180,9 +185,9 @@ class FallDetection:
                     for box, cls in zip(boxes, classes):
                         if int(cls) == 0:
                             x1, y1, x2, y2 = map(int, box)
-                            width = x2 - x1
-                            height = y2 - y1
-                            aspect_ratio = width / height if height > 0 else float('inf')
+                            obj_width = x2 - x1
+                            obj_height = y2 - y1
+                            aspect_ratio = obj_width / obj_height if obj_height > 0 else float('inf')
 
                             if aspect_ratio > 0.55:
                                 color = (0, 0, 255)
@@ -196,14 +201,19 @@ class FallDetection:
 
                 out.write(frame)
 
+            # ✅ Release resources after processing
             cap.release()
             out.release()
+
             if not os.path.exists(output_path):
                 raise ValueError("❌ Processing failed")
+
             return output_path
+
         except Exception as e:
             raise ValueError(f"Error in fall_detection: {str(e)}")
 
+
 class FightDetection:
     def __init__(self, yolo_model_path="yolov8n-pose.pt"):
         self.model_path = yolo_model_path