new buffer paradigm

services/stream_connection_manager.py

@@ -42,6 +42,7 @@ class TrackingResult:
     tracked_objects: List  # List of TrackedObject from TrackingController
     detections: List  # Raw detections
     frame_shape: Tuple[int, int, int]
+    frame_tensor: Optional[torch.Tensor]  # GPU tensor of the frame (C, H, W)
     metadata: Dict
 
 
@@ -158,6 +159,9 @@ class StreamConnection:
 
             # Submit to model controller for batched inference
             # Pass the FrameReference in metadata so we can free it later
+            logger.debug(
+                f"[{self.stream_id}] Submitting frame {self.frame_count} to model controller"
+            )
             self.model_controller.submit_frame(
                 stream_id=self.stream_id,
                 frame=cloned_tensor,  # Use cloned tensor, not original
@@ -167,6 +171,9 @@ class StreamConnection:
                     "frame_ref": frame_ref,  # Store reference for later cleanup
                 },
             )
+            logger.debug(
+                f"[{self.stream_id}] Frame {self.frame_count} submitted, queue size: {len(self.model_controller.frame_queue)}"
+            )
 
             # Update connection status based on decoder status
             if (
@@ -211,6 +218,12 @@ class StreamConnection:
             frame_shape = result["metadata"].get("shape")
             tracked_objects = self._run_tracking_sync(detections, frame_shape)
 
+            # Get ORIGINAL frame tensor from metadata (not the preprocessed one in result["frame"])
+            # The frame in result["frame"] is preprocessed (resized, normalized)
+            # We need the original frame for visualization
+            frame_ref = result["metadata"].get("frame_ref")
+            frame_tensor = frame_ref.rgb_tensor if frame_ref else None
+
             # Create tracking result
             tracking_result = TrackingResult(
                 stream_id=self.stream_id,
@@ -218,6 +231,7 @@ class StreamConnection:
                 tracked_objects=tracked_objects,
                 detections=detections,
                 frame_shape=result["metadata"].get("shape"),
+                frame_tensor=frame_tensor,  # Original frame, not preprocessed
                 metadata=result["metadata"],
             )
 
@@ -328,7 +342,7 @@ class StreamConnectionManager:
     Args:
         gpu_id: GPU device ID (default: 0)
         batch_size: Maximum batch size for inference (default: 16)
-        force_timeout: Force buffer switch timeout in seconds (default: 0.05)
+        max_queue_size: Maximum frames in queue before dropping (default: 100)
         poll_interval: Frame polling interval in seconds (default: 0.01)
 
     Example:
@@ -343,14 +357,14 @@ class StreamConnectionManager:
         self,
         gpu_id: int = 0,
         batch_size: int = 16,
-        force_timeout: float = 0.05,
+        max_queue_size: int = 100,
         poll_interval: float = 0.01,
         enable_pt_conversion: bool = True,
         backend: str = "tensorrt",  # "tensorrt" or "ultralytics"
     ):
         self.gpu_id = gpu_id
         self.batch_size = batch_size
-        self.force_timeout = force_timeout
+        self.max_queue_size = max_queue_size
         self.poll_interval = poll_interval
         self.backend = backend.lower()
 
@@ -449,7 +463,7 @@ class StreamConnectionManager:
                 inference_engine=self.inference_engine,
                 model_id=model_id,
                 batch_size=self.batch_size,
-                force_timeout=self.force_timeout,
+                max_queue_size=self.max_queue_size,
                 preprocess_fn=preprocess_fn,
                 postprocess_fn=postprocess_fn,
             )
@@ -473,7 +487,7 @@ class StreamConnectionManager:
                 model_repository=self.model_repository,
                 model_id=model_id,
                 batch_size=self.batch_size,
-                force_timeout=self.force_timeout,
+                max_queue_size=self.max_queue_size,
                 preprocess_fn=preprocess_fn,
                 postprocess_fn=postprocess_fn,
             )
@@ -656,7 +670,7 @@ class StreamConnectionManager:
                 "gpu_id": self.gpu_id,
                 "num_connections": len(self.connections),
                 "batch_size": self.batch_size,
-                "force_timeout": self.force_timeout,
+                "max_queue_size": self.max_queue_size,
                 "poll_interval": self.poll_interval,
             },
             "model_controller": self.model_controller.get_stats()