profiling

ISSUES.md

@@ -2,4 +2,8 @@
 
 - It doesn't really care what pt file is included and it always use YOLO's model id, for example if id 1 is apple, it still say person. maybe extract class list from yolo's .pt somehow?
 
+- It read frame a bit too fast. it say it's infering at 20-ish fps but the actual camera is only 5 fps or so
+
 - Potential race condition issue when multiple camera try to init with the same unconverted model.
+
+- Blurry asyncio archtecture, require documentations

scripts/profiling.py

@@ -0,0 +1,165 @@
+"""
+Profiling script for the real-time object tracking pipeline.
+
+This script runs the single-stream example from test_tracking_realtime.py
+under the Python profiler (cProfile) to identify performance bottlenecks.
+
+Usage:
+    python scripts/profiling.py
+
+The script will print a summary of the most time-consuming functions
+at the end of the run.
+"""
+
+import asyncio
+import cProfile
+import pstats
+import io
+import time
+import os
+import torch
+import cv2
+from dotenv import load_dotenv
+
+# Add project root to path to allow imports from services
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+
+from services import (
+    StreamConnectionManager,
+    YOLOv8Utils,
+)
+
+# Load environment variables
+load_dotenv()
+
+
+async def profiled_main():
+    """
+    Single stream example with event-driven architecture, adapted for profiling.
+    This function is a modified version of main_single_stream from test_tracking_realtime.py
+    """
+    print("=" * 80)
+    print("Profiling: Event-Driven GPU-Accelerated Object Tracking")
+    print("=" * 80)
+
+    # Configuration
+    GPU_ID = 0
+    MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"
+    STREAM_URL = os.getenv('CAMERA_URL_1', 'rtsp://localhost:8554/test')
+    BATCH_SIZE = 4
+    FORCE_TIMEOUT = 0.05
+    # NOTE: Display is disabled for profiling to isolate pipeline performance
+    ENABLE_DISPLAY = False
+    # Run for a limited number of frames to get a representative profile
+    MAX_FRAMES = int(os.getenv('MAX_FRAMES', '300'))
+
+    print(f"\nConfiguration:")
+    print(f"  GPU: {GPU_ID}")
+    print(f"  Model: {MODEL_PATH}")
+    print(f"  Stream: {STREAM_URL}")
+    print(f"  Batch size: {BATCH_SIZE}")
+    print(f"  Force timeout: {FORCE_TIMEOUT}s")
+    print(f"  Display: Disabled for profiling")
+    print(f"  Max frames: {MAX_FRAMES}\n")
+
+    # Create StreamConnectionManager
+    print("[1/3] Creating StreamConnectionManager...")
+    manager = StreamConnectionManager(
+        gpu_id=GPU_ID,
+        batch_size=BATCH_SIZE,
+        force_timeout=FORCE_TIMEOUT,
+        enable_pt_conversion=True
+    )
+    print("✓ Manager created")
+
+    # Initialize with PT model
+    print("\n[2/3] Initializing with PT model...")
+    try:
+        await manager.initialize(
+            model_path=MODEL_PATH,
+            model_id="detector",
+            preprocess_fn=YOLOv8Utils.preprocess,
+            postprocess_fn=YOLOv8Utils.postprocess,
+            num_contexts=4,
+            pt_input_shapes={"images": (1, 3, 640, 640)},
+            pt_precision=torch.float16
+        )
+        print("✓ Manager initialized")
+    except Exception as e:
+        print(f"✗ Failed to initialize: {e}")
+        return
+
+    # Connect stream
+    print("\n[3/3] Connecting to stream...")
+    try:
+        connection = await manager.connect_stream(
+            rtsp_url=STREAM_URL,
+            stream_id="camera_1",
+            buffer_size=30
+        )
+        print(f"✓ Stream connected: camera_1")
+    except Exception as e:
+        print(f"✗ Failed to connect stream: {e}")
+        await manager.shutdown()
+        return
+
+    print(f"\n{'=' * 80}")
+    print(f"Profiling is running for {MAX_FRAMES} frames...")
+    print(f"{ '=' * 80}\n")
+
+    result_count = 0
+    start_time = time.time()
+
+    try:
+        async for result in connection.tracking_results():
+            result_count += 1
+            if result_count >= MAX_FRAMES:
+                print(f"\n✓ Reached max frames limit ({MAX_FRAMES})")
+                break
+
+            if result_count % 50 == 0:
+                print(f"  Processed {result_count}/{MAX_FRAMES} frames...")
+
+    except KeyboardInterrupt:
+        print(f"\n✓ Interrupted by user")
+
+    # Cleanup
+    print(f"\n{'=' * 80}")
+    print("Cleanup")
+    print(f"{ '=' * 80}")
+
+    await connection.stop()
+    await manager.shutdown()
+    print("✓ Stopped")
+
+    # Final stats
+    elapsed = time.time() - start_time
+    avg_fps = result_count / elapsed if elapsed > 0 else 0
+    print(f"\nFinal: {result_count} results in {elapsed:.1f}s ({avg_fps:.1f} FPS)")
+
+
+if __name__ == "__main__":
+    # Create a profiler object
+    profiler = cProfile.Profile()
+
+    # Run the async main function under the profiler
+    print("Starting profiler...")
+    profiler.enable()
+
+    asyncio.run(profiled_main())
+
+    profiler.disable()
+    print("Profiling complete.")
+
+    # Print the stats
+    s = io.StringIO()
+    # Sort stats by cumulative time
+    sortby = pstats.SortKey.CUMULATIVE
+    ps = pstats.Stats(profiler, stream=s).sort_stats(sortby)
+    ps.print_stats(30)  # Print top 30 functions
+
+    print("\n" + "="*80)
+    print("PROFILING RESULTS (Top 30, sorted by cumulative time)")
+    print("="*80)
+    print(s.getvalue())

services/__init__.py

@@ -5,8 +5,7 @@ Services package for RTSP stream processing with GPU acceleration.
 from .stream_decoder import StreamDecoderFactory, StreamDecoder, ConnectionStatus
 from .jpeg_encoder import JPEGEncoderFactory, encode_frame_to_jpeg
 from .model_repository import TensorRTModelRepository, ModelMetadata, ExecutionContext, SharedEngine
-from .tracking_controller import TrackingController, TrackedObject
-from .tracking_factory import TrackingFactory
+from .tracking_controller import ObjectTracker, TrackedObject, Detection
 from .yolo import YOLOv8Utils, COCO_CLASSES
 from .model_controller import ModelController, BatchFrame, BufferState
 from .stream_connection_manager import StreamConnectionManager, StreamConnection, TrackingResult
@@ -23,9 +22,9 @@ __all__ = [
     'ModelMetadata',
     'ExecutionContext',
     'SharedEngine',
-    'TrackingController',
+    'ObjectTracker',
     'TrackedObject',
-    'TrackingFactory',
+    'Detection',
     'YOLOv8Utils',
     'COCO_CLASSES',
     'ModelController',

services/stream_connection_manager.py

@@ -16,7 +16,6 @@ import torch
 
 from .model_controller import ModelController
 from .stream_decoder import StreamDecoderFactory
-from .tracking_factory import TrackingFactory
 from .model_repository import TensorRTModelRepository
 
 logger = logging.getLogger(__name__)
@@ -133,28 +132,32 @@ class StreamConnection:
 
     async def _frame_poller(self):
         """Poll frames from threaded decoder and submit to model controller"""
-        last_frame_ptr = None
+        last_decoder_frame_count = -1
 
         while self.running:
             try:
-                # Poll frame from decoder (runs in thread)
-                frame = self.decoder.get_latest_frame(rgb=True)
+                # Get current decoder frame count (no data transfer, just counter)
+                decoder_frame_count = self.decoder.get_frame_count()
 
-                # Check if we got a new frame (avoid reprocessing same frame)
-                if frame is not None and frame.data_ptr() != last_frame_ptr:
-                    last_frame_ptr = frame.data_ptr()
-                    self.last_frame_time = time.time()
-                    self.frame_count += 1
+                # Check if decoder has a new frame (avoid reprocessing same frame)
+                if decoder_frame_count > last_decoder_frame_count:
+                    # Poll frame from decoder (zero-copy - stays in VRAM)
+                    frame = self.decoder.get_latest_frame(rgb=True)
 
-                    # Submit to model controller for batched inference
-                    await self.model_controller.submit_frame(
-                        stream_id=self.stream_id,
-                        frame=frame,
-                        metadata={
-                            "frame_number": self.frame_count,
-                            "shape": tuple(frame.shape),
-                        }
-                    )
+                    if frame is not None:
+                        last_decoder_frame_count = decoder_frame_count
+                        self.last_frame_time = time.time()
+                        self.frame_count += 1
+
+                        # Submit to model controller for batched inference
+                        await self.model_controller.submit_frame(
+                            stream_id=self.stream_id,
+                            frame=frame,
+                            metadata={
+                                "frame_number": self.frame_count,
+                                "shape": tuple(frame.shape),
+                            }
+                        )
 
                 # Check decoder status
                 if not self.decoder.is_connected():
@@ -211,53 +214,37 @@ class StreamConnection:
             logger.error(f"Error handling inference result for {self.stream_id}: {e}", exc_info=True)
             await self.error_queue.put(e)
 
-    def _run_tracking_sync(self, detections):
+    def _run_tracking_sync(self, detections, min_confidence=0.7):
         """
         Run tracking synchronously (called from executor).
 
         Args:
             detections: Detection tensor (N, 6) [x1, y1, x2, y2, conf, class_id]
+            min_confidence: Minimum confidence threshold for detections
 
         Returns:
             List of TrackedObject instances
         """
-        # Use the TrackingController's internal tracking with detections
-        # We need to manually update tracks since we already have detections
-        import torch
+        # Convert tensor detections to Detection objects, filtering by confidence
+        from .tracking_controller import Detection
 
-        with self.tracking_controller._lock:
-            self.tracking_controller._frame_count += 1
+        detection_list = []
+        for det in detections:
+            confidence = float(det[4])
 
-            # If no detections, just cleanup and return current tracks
-            if len(detections) == 0:
-                self.tracking_controller._cleanup_stale_tracks()
-                return list(self.tracking_controller._tracks.values())
+            # Filter by confidence threshold (prevents track accumulation)
+            if confidence < min_confidence:
+                continue
 
-            # Run IoU tracking to associate detections with existing tracks
-            associations = self.tracking_controller._iou_tracking(detections)
+            detection_list.append(Detection(
+                bbox=det[:4].cpu().tolist(),
+                confidence=confidence,
+                class_id=int(det[5]) if det.shape[0] > 5 else 0,
+                class_name=f"class_{int(det[5])}" if det.shape[0] > 5 else "unknown"
+            ))
 
-            # Update or create tracks
-            for (det_idx, track_id), detection in zip(associations, detections):
-                bbox = detection[:4].cpu().tolist()
-                confidence = float(detection[4])
-                class_id = int(detection[5]) if detection.shape[0] > 5 else 0
-
-                if track_id == -1:
-                    # Create new track
-                    new_track = self.tracking_controller._create_track(
-                        bbox, confidence, class_id, self.tracking_controller._frame_count
-                    )
-                    self.tracking_controller._tracks[new_track.track_id] = new_track
-                else:
-                    # Update existing track
-                    self.tracking_controller._tracks[track_id].update(
-                        bbox, confidence, self.tracking_controller._frame_count
-                    )
-
-            # Cleanup stale tracks
-            self.tracking_controller._cleanup_stale_tracks()
-
-            return list(self.tracking_controller._tracks.values())
+        # Update tracker with detections (lightweight, no model dependency!)
+        return self.tracking_controller.update(detection_list)
 
     async def tracking_results(self) -> AsyncIterator[TrackingResult]:
         """
@@ -341,7 +328,6 @@ class StreamConnectionManager:
 
         # Factories
         self.decoder_factory = StreamDecoderFactory(gpu_id=gpu_id)
-        self.tracking_factory = TrackingFactory(gpu_id=gpu_id)
         self.model_repository = TensorRTModelRepository(
             gpu_id=gpu_id,
             enable_pt_conversion=enable_pt_conversion
@@ -349,7 +335,6 @@ class StreamConnectionManager:
 
         # Controllers
         self.model_controller: Optional[ModelController] = None
-        self.tracking_controller = None
 
         # Connections
         self.connections: Dict[str, StreamConnection] = {}
@@ -454,17 +439,16 @@ class StreamConnectionManager:
         # Create decoder
         decoder = self.decoder_factory.create_decoder(rtsp_url, buffer_size=buffer_size)
 
-        # Create dedicated tracking controller for THIS stream
-        # This prevents track accumulation across multiple streams
-        tracking_controller = self.tracking_factory.create_controller(
-            model_repository=self.model_repository,
-            model_id=self.model_id_for_tracking,
+        # Create lightweight tracker (NO model_repository dependency!)
+        from .tracking_controller import ObjectTracker
+        tracking_controller = ObjectTracker(
+            gpu_id=self.gpu_id,
             tracker_type="iou",
             max_age=30,
-            min_confidence=0.5,
             iou_threshold=0.3,
+            class_names=None  # TODO: pass class names if available
         )
-        logger.info(f"Created dedicated TrackingController for stream {stream_id}")
+        logger.info(f"Created lightweight ObjectTracker for stream {stream_id}")
 
         # Create connection
         connection = StreamConnection(

services/stream_decoder.py

@@ -448,6 +448,10 @@ class StreamDecoder:
         with self._buffer_lock:
             return len(self.frame_buffer)
 
+    def get_frame_count(self) -> int:
+        """Get total number of frames decoded since start"""
+        return self.frame_count
+
     def is_connected(self) -> bool:
         """Check if stream is actively connected"""
         return self.get_status() == ConnectionStatus.CONNECTED

services/tracking_controller.py

@@ -5,7 +5,6 @@ from collections import defaultdict, deque
 import time
 import torch
 import numpy as np
-from .model_repository import TensorRTModelRepository
 
 
 @dataclass
@@ -61,78 +60,81 @@ class TrackedObject:
         }
 
 
-class TrackingController:
+@dataclass
+class Detection:
     """
-    GPU-accelerated object tracking controller that wraps TensorRTModelRepository.
+    Represents a single detection from object detection model.
 
-    Architecture:
-    - Wraps model repository for dependency injection
-    - Maintains CUDA state for bbox tracking operations
-    - Stores persistent tracking data (track IDs, histories, states)
-    - Processes GPU tensor frames directly (zero-copy pipeline)
-    - Thread-safe for concurrent tracking operations
+    Attributes:
+        bbox: Bounding box [x1, y1, x2, y2]
+        confidence: Detection confidence (0-1)
+        class_id: Object class ID
+        class_name: Object class name (optional)
+    """
+    bbox: List[float]
+    confidence: float
+    class_id: int
+    class_name: str = "unknown"
+
+
+class ObjectTracker:
+    """
+    Lightweight GPU-accelerated object tracker (decoupled from inference).
+
+    This class only handles tracking logic - associating detections with existing tracks,
+    maintaining track IDs, and managing track lifecycle. It does NOT perform inference.
+
+    Architecture (Event-Driven Mode):
+    - Receives pre-computed detections (from ModelController)
+    - Maintains persistent tracking state (track IDs, histories)
+    - GPU-accelerated IoU computation for track association
+    - Thread-safe for concurrent operations
 
     Tracking Flow:
-        GPU Frame → Model Inference (GPU) → Detections (GPU)
-              ↓
-        Tracking Algorithm (GPU/CPU) → Track Assignment
-              ↓
-        Update Persistent Tracks → Return Tracked Objects
+        Detections → Track Association (GPU IoU) → Update Tracks → Return Tracked Objects
 
     Features:
-    - GPU-first: All tensor operations stay on GPU until final results
+    - Lightweight: No model_repository dependency (zero VRAM overhead)
+    - GPU-accelerated: IoU computation on GPU for performance
     - Persistent IDs: Tracks maintain consistent IDs across frames
     - Track History: Maintains trajectory history for each object
-    - Configurable: Supports custom tracking algorithms via callbacks
     - Thread-safe: Mutex-based locking for concurrent access
 
     Example:
-        # Initialize with DI
-        repo = TensorRTModelRepository(gpu_id=0)
-        factory = TrackingFactory(gpu_id=0)
-        controller = factory.create_controller(
-            model_repository=repo,
-            model_id="yolov8_detector",
-            tracker_type="iou"
+        # Event-driven mode (no model dependency)
+        tracker = ObjectTracker(
+            gpu_id=0,
+            tracker_type="iou",
+            max_age=30,
+            iou_threshold=0.3,
+            class_names=COCO_CLASSES
         )
 
-        # Track objects in frame
-        rgb_frame = decoder.get_latest_frame()  # GPU tensor
-        tracked_objects = controller.track(rgb_frame)
-
-        # Get all tracked objects
-        all_tracks = controller.get_all_tracks()
+        # Update with pre-computed detections
+        detections = [Detection(bbox=[x1,y1,x2,y2], confidence=0.9, class_id=0)]
+        tracked_objects = tracker.update(detections)
     """
 
     def __init__(self,
-                 model_repository: TensorRTModelRepository,
-                 model_id: str,
                  gpu_id: int = 0,
                  tracker_type: str = "iou",
                  max_age: int = 30,
-                 min_confidence: float = 0.5,
                  iou_threshold: float = 0.3,
                  class_names: Optional[Dict[int, str]] = None):
         """
-        Initialize TrackingController.
+        Initialize ObjectTracker (no model dependency).
 
         Args:
-            model_repository: TensorRT model repository (dependency injection)
-            model_id: Model ID in repository to use for detection
-            gpu_id: GPU device ID
-            tracker_type: Tracking algorithm type ("iou", "sort", "deepsort", "bytetrack")
+            gpu_id: GPU device ID for IoU computation
+            tracker_type: Tracking algorithm type ("iou")
             max_age: Maximum frames to keep track without detection
-            min_confidence: Minimum confidence threshold for detections
             iou_threshold: IoU threshold for track association
             class_names: Optional mapping of class IDs to names
         """
-        self.model_repository = model_repository
-        self.model_id = model_id
         self.gpu_id = gpu_id
         self.device = torch.device(f'cuda:{gpu_id}')
         self.tracker_type = tracker_type
         self.max_age = max_age
-        self.min_confidence = min_confidence
         self.iou_threshold = iou_threshold
         self.class_names = class_names or {}
 
@@ -146,19 +148,6 @@ class TrackingController:
         self._total_detections = 0
         self._total_tracks_created = 0
 
-        # Verify model exists in repository
-        metadata = self.model_repository.get_metadata(model_id)
-        if metadata is None:
-            raise ValueError(f"Model '{model_id}' not found in repository")
-
-        print(f"TrackingController initialized:")
-        print(f"  Model ID: {model_id}")
-        print(f"  GPU: {gpu_id}")
-        print(f"  Tracker: {tracker_type}")
-        print(f"  Max age: {max_age} frames")
-        print(f"  Min confidence: {min_confidence}")
-        print(f"  IoU threshold: {iou_threshold}")
-
     def _compute_iou_gpu(self, boxes1: torch.Tensor, boxes2: torch.Tensor) -> torch.Tensor:
         """
         Compute IoU between two sets of boxes on GPU.
@@ -283,97 +272,51 @@ class TrackingController:
         for tid in stale_track_ids:
             del self._tracks[tid]
 
-    def track(self, frame: torch.Tensor,
-              preprocess_fn: Optional[callable] = None,
-              postprocess_fn: Optional[callable] = None) -> List[TrackedObject]:
+    def update(self, detections: List[Detection]) -> List[TrackedObject]:
         """
-        Track objects in a GPU tensor frame.
+        Update tracker with new detections (decoupled from inference).
 
         Args:
-            frame: RGB frame as GPU tensor, shape (3, H, W) or (1, 3, H, W)
-            preprocess_fn: Optional preprocessing function (frame -> model_input)
-            postprocess_fn: Optional postprocessing function (model_output -> detections)
-                          Should return tensor of shape (N, 6): [x1, y1, x2, y2, conf, class_id]
+            detections: List of Detection objects from model inference
 
         Returns:
             List of currently tracked objects
         """
         with self._lock:
             self._frame_count += 1
-
-            # Ensure frame is on correct device
-            if not frame.is_cuda:
-                frame = frame.to(self.device)
-            elif frame.device != self.device:
-                frame = frame.to(self.device)
-
-            # Preprocess frame for model
-            if preprocess_fn is not None:
-                model_input = preprocess_fn(frame)
-            else:
-                # Default: add batch dimension if needed
-                if frame.dim() == 3:
-                    model_input = frame.unsqueeze(0)  # (1, 3, H, W)
-                else:
-                    model_input = frame
-
-            # Run inference (GPU-to-GPU)
-            # Assuming model expects input named "images" or "input"
-            metadata = self.model_repository.get_metadata(self.model_id)
-            input_name = metadata.input_names[0] if metadata else "images"
-
-            outputs = self.model_repository.infer(
-                model_id=self.model_id,
-                inputs={input_name: model_input},
-                synchronize=True
-            )
-
-            # Postprocess model output to get detections
-            if postprocess_fn is not None:
-                detections = postprocess_fn(outputs)
-            else:
-                # Default: assume output is already in correct format
-                # Get first output tensor
-                output_name = list(outputs.keys())[0]
-                detections = outputs[output_name]
-
-                # Reshape if needed: (1, N, 6) -> (N, 6)
-                if detections.dim() == 3:
-                    detections = detections.squeeze(0)
-
-            # Filter by confidence
-            if detections.dim() == 2 and detections.shape[1] >= 5:
-                conf_mask = detections[:, 4] >= self.min_confidence
-                detections = detections[conf_mask]
-
             self._total_detections += len(detections)
 
-            # Track objects
+            # No detections, just cleanup stale tracks
             if len(detections) == 0:
-                # No detections, just cleanup stale tracks
                 self._cleanup_stale_tracks()
                 return list(self._tracks.values())
 
+            # Convert detections to tensor for GPU processing
+            det_tensor = torch.tensor(
+                [[*det.bbox, det.confidence, det.class_id] for det in detections],
+                dtype=torch.float32,
+                device=self.device
+            )
+
             # Run tracking algorithm
             if self.tracker_type == "iou":
-                associations = self._iou_tracking(detections)
+                associations = self._iou_tracking(det_tensor)
             else:
                 raise NotImplementedError(f"Tracker type '{self.tracker_type}' not implemented")
 
             # Update tracks based on associations
             for det_idx, track_id in associations:
-                detection = detections[det_idx]
-                bbox = detection[:4].cpu().tolist()
-                confidence = float(detection[4])
-                class_id = int(detection[5]) if detection.shape[0] > 5 else 0
+                det = detections[det_idx]
 
                 if track_id == -1:
                     # Create new track
-                    new_track = self._create_track(bbox, confidence, class_id, self._frame_count)
+                    new_track = self._create_track(
+                        det.bbox, det.confidence, det.class_id, self._frame_count
+                    )
                     self._tracks[new_track.track_id] = new_track
                 else:
                     # Update existing track
-                    self._tracks[track_id].update(bbox, confidence, self._frame_count)
+                    self._tracks[track_id].update(det.bbox, det.confidence, self._frame_count)
 
             # Cleanup stale tracks
             self._cleanup_stale_tracks()
@@ -476,7 +419,6 @@ class TrackingController:
                 'total_tracks_created': self._total_tracks_created,
                 'total_detections': self._total_detections,
                 'avg_detections_per_frame': self._total_detections / max(self._frame_count, 1),
-                'model_id': self.model_id,
                 'tracker_type': self.tracker_type,
                 'class_counts': self.get_class_counts(active_only=True)
             }
@@ -518,7 +460,6 @@ class TrackingController:
 
     def __repr__(self):
         with self._lock:
-            return (f"TrackingController(model={self.model_id}, "
-                   f"tracker={self.tracker_type}, "
+            return (f"ObjectTracker(tracker={self.tracker_type}, "
                    f"frame={self._frame_count}, "
                    f"tracks={len(self._tracks)})")

services/tracking_factory.py

@@ -1,8 +1,11 @@
 import threading
 from typing import Optional, Dict
-from .tracking_controller import TrackingController
+from .tracking_controller import ObjectTracker
 from .model_repository import TensorRTModelRepository
 
+# Backward compatibility alias (TrackingFactory is deprecated in event-driven mode)
+TrackingController = ObjectTracker
+
 
 class TrackingFactory:
     """

test_tracking_realtime.py

@@ -13,6 +13,8 @@ import asyncio
 import time
 import os
 import torch
+import cv2
+import numpy as np
 from dotenv import load_dotenv
 from services import (
     StreamConnectionManager,
@@ -32,17 +34,21 @@ async def main_single_stream():
 
     # Configuration
     GPU_ID = 0
-    MODEL_PATH = "models/yolov8n.pt"  # PT file will be auto-converted
+    MODEL_PATH = "bangchak/models/frontal_detection_v5.pt"  # PT file will be auto-converted
     STREAM_URL = os.getenv('CAMERA_URL_1', 'rtsp://localhost:8554/test')
     BATCH_SIZE = 4
     FORCE_TIMEOUT = 0.05
+    ENABLE_DISPLAY = os.getenv('ENABLE_DISPLAY', 'false').lower() == 'true'  # Set to 'true' to enable OpenCV display
+    MAX_FRAMES = int(os.getenv('MAX_FRAMES', '300'))  # Stop after N frames (0 = unlimited)
 
     print(f"\nConfiguration:")
     print(f"  GPU: {GPU_ID}")
     print(f"  Model: {MODEL_PATH}")
     print(f"  Stream: {STREAM_URL}")
     print(f"  Batch size: {BATCH_SIZE}")
-    print(f"  Force timeout: {FORCE_TIMEOUT}s\n")
+    print(f"  Force timeout: {FORCE_TIMEOUT}s")
+    print(f"  Display: {'Enabled' if ENABLE_DISPLAY else 'Disabled (inference only)'}")
+    print(f"  Max frames: {MAX_FRAMES if MAX_FRAMES > 0 else 'Unlimited'}\n")
 
     # Create StreamConnectionManager with PT conversion enabled
     print("[1/3] Creating StreamConnectionManager...")
@@ -94,14 +100,68 @@ async def main_single_stream():
     print("Press Ctrl+C to stop")
     print(f"{'=' * 80}\n")
 
-    # Stream results
+    # Stream results with optional OpenCV visualization
     result_count = 0
     start_time = time.time()
 
+    # Create window only if display is enabled
+    if ENABLE_DISPLAY:
+        cv2.namedWindow("Object Tracking", cv2.WINDOW_NORMAL)
+        cv2.resizeWindow("Object Tracking", 1280, 720)
+
     try:
         async for result in connection.tracking_results():
             result_count += 1
 
+            # Check if we've reached max frames
+            if MAX_FRAMES > 0 and result_count >= MAX_FRAMES:
+                print(f"\n✓ Reached max frames limit ({MAX_FRAMES})")
+                break
+
+            # OpenCV visualization (only if enabled)
+            if ENABLE_DISPLAY:
+                # Get latest frame from decoder (as CPU numpy array)
+                frame = connection.decoder.get_latest_frame_cpu(rgb=True)
+
+                if frame is not None:
+                    # Convert to BGR for OpenCV
+                    frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+
+                    # Draw tracked objects
+                    for obj in result.tracked_objects:
+                        # Get bbox coordinates
+                        x1, y1, x2, y2 = map(int, obj.bbox)
+
+                        # Draw bounding box
+                        cv2.rectangle(frame_bgr, (x1, y1), (x2, y2), (0, 255, 0), 2)
+
+                        # Draw track ID and class name
+                        label = f"ID:{obj.track_id} {obj.class_name} {obj.confidence:.2f}"
+                        label_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+
+                        # Draw label background
+                        cv2.rectangle(frame_bgr, (x1, y1 - label_size[1] - 10),
+                                    (x1 + label_size[0], y1), (0, 255, 0), -1)
+
+                        # Draw label text
+                        cv2.putText(frame_bgr, label, (x1, y1 - 5),
+                                  cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
+
+                    # Draw FPS and object count
+                    elapsed = time.time() - start_time
+                    fps = result_count / elapsed if elapsed > 0 else 0
+                    info_text = f"FPS: {fps:.1f} | Objects: {len(result.tracked_objects)} | Frame: {result_count}"
+                    cv2.putText(frame_bgr, info_text, (10, 30),
+                              cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+
+                    # Display frame
+                    cv2.imshow("Object Tracking", frame_bgr)
+
+                    # Check for 'q' key to quit
+                    if cv2.waitKey(1) & 0xFF == ord('q'):
+                        print(f"\n✓ Quit by user (pressed 'q')")
+                        break
+
             # Print stats every 30 results
             if result_count % 30 == 0:
                 elapsed = time.time() - start_time
@@ -125,6 +185,10 @@ async def main_single_stream():
     print("Cleanup")
     print(f"{'=' * 80}")
 
+    # Close OpenCV window if it was opened
+    if ENABLE_DISPLAY:
+        cv2.destroyAllWindows()
+
     await connection.stop()
     await manager.shutdown()
     print("✓ Stopped")