more stuff

vr180_streaming/frame_writer.py

@@ -132,13 +132,22 @@ class StreamingFrameWriter:
                 bufsize=10**8  # Large buffer for performance
             )
             
+            # Test if ffmpeg starts successfully (quick check)
+            import time
+            time.sleep(0.1)  # Give ffmpeg time to fail if it's going to
+            
+            if self.ffmpeg_process.poll() is not None:
+                # Process already died - read error
+                stderr = self.ffmpeg_process.stderr.read().decode()
+                raise RuntimeError(f"FFmpeg failed immediately: {stderr}")
+            
             # Set process to ignore SIGINT (Ctrl+C) - we'll handle it
             if hasattr(signal, 'pthread_sigmask'):
                 signal.pthread_sigmask(signal.SIG_BLOCK, [signal.SIGINT])
                 
         except Exception as e:
             # Try CPU fallback if GPU encoding fails
-            if 'nvenc' in self.ffmpeg_cmd:
+            if 'nvenc' in ' '.join(self.ffmpeg_cmd):
                 print(f"⚠️  GPU encoding failed, trying CPU fallback...")
                 self.ffmpeg_cmd = self._build_ffmpeg_command('libx264', 'medium', 18)
                 self._start_ffmpeg()

vr180_streaming/sam2_streaming.py

@@ -34,6 +34,11 @@ class SAM2StreamingProcessor:
         self.config = config
         self.device = torch.device(config.get('hardware', {}).get('device', 'cuda'))
         
+        # Processing parameters (set before _init_predictor)
+        self.memory_offload = config.get('matting', {}).get('memory_offload', True)
+        self.fp16 = config.get('matting', {}).get('fp16', True)
+        self.correction_interval = config.get('matting', {}).get('correction_interval', 300)
+        
         # SAM2 model configuration
         model_cfg = config.get('matting', {}).get('sam2_model_cfg', 'sam2.1_hiera_l')
         checkpoint = config.get('matting', {}).get('sam2_checkpoint', 
@@ -43,11 +48,6 @@ class SAM2StreamingProcessor:
         self.predictor = None
         self._init_predictor(model_cfg, checkpoint)
         
-        # Processing parameters
-        self.memory_offload = config.get('matting', {}).get('memory_offload', True)
-        self.fp16 = config.get('matting', {}).get('fp16', True)
-        self.correction_interval = config.get('matting', {}).get('correction_interval', 300)
-        
         # State management
         self.states = {}  # eye -> inference state
         self.object_ids = []