changes num_frames

app.py

@@ -19,7 +19,7 @@ from pathlib import Path
 # 1. MODEL CONFIGURATION (Giữ nguyên logic của bạn)
 # ============================================================
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-MODEL_PATH = "best_model_efficientnet_lstm_224_16.pth"
+MODEL_PATH = "best_model_efficientnet_lstm_v2.pth"
 
 class EfficientNetLSTM(nn.Module):
     def __init__(self, hidden_size=256, num_layers=2, dropout=0.5):
@@ -71,7 +71,7 @@ transform = A.Compose([
 class FallDetectionSystem:
     def __init__(self):
         # Config
-        self.num_frames = 16
+        self.num_frames = 32
         self.conf_thres = 0.5
         self.output_dir = Path("fall_videos")
         self.output_dir.mkdir(exist_ok=True)
@@ -111,8 +111,8 @@ class FallDetectionSystem:
         total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
         
         # Logic lấy 16 frames (như code cũ)
-        if total_frames >= 16:
-            indices = np.linspace(0, total_frames - 1, 16, dtype=int)
+        if total_frames >= 32:
+            indices = np.linspace(0, total_frames - 1, 32, dtype=int)
         else:
             indices = np.arange(total_frames)
         
@@ -125,7 +125,7 @@ class FallDetectionSystem:
         cap.release()
         
         # Pad frame nếu thiếu
-        while len(frames) < 16: frames.append(frames[-1])
+        while len(frames) < 32: frames.append(frames[-1])
         
         # Predict
         video_tensor = torch.stack(frames).unsqueeze(0).to(DEVICE)
@@ -231,7 +231,7 @@ class FallDetectionSystem:
                     self._stop_recording_if_active(save=True)
 
             else:
-                cv2.putText(frame_bgr, f"Buffering: {len(self.buffer)}/16", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
+                cv2.putText(frame_bgr, f"Buffering: {len(self.buffer)}/32", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 0), 2)
 
         # Cập nhật Log
         if log_entry: