Remove shared tracker between cameras, fix task warning, add export script

- Remove shared DetectionTracker across cameras to prevent state leakage
- Add task="detect" to YOLO constructor to suppress warning
- Add export_engine.py script for .pt to .engine conversion
- Regenerate ONNX and TensorRT engine with latest settings

export_engine.py

@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+"""Export a YOLO .pt model to TensorRT .engine.
+
+Usage:
+    python3 export_engine.py chicken-detection-model-v26n-300e-best-2026-05-02-NEW.pt
+    python3 export_engine.py model.pt --imgsz 640 --half --workspace 4
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+
+def export_engine(
+    model_path: str | Path,
+    *,
+    imgsz: int = 640,
+    half: bool = True,
+    int8: bool = False,
+    batch: int = 1,
+    workspace: int = 4,     # GB
+    simplify: bool = True,
+    opset: int = 17,
+    verbose: bool = True,
+) -> str:
+    from ultralytics import YOLO
+
+    model = YOLO(model_path, task="detect")
+
+    output = model.export(
+        format="engine",
+        imgsz=imgsz,
+        half=half,
+        int8=int8,
+        batch=batch,
+        workspace=workspace,
+        simplify=simplify,
+        opset=opset,
+        verbose=verbose,
+    )
+
+    print(f"\nExported to: {output}")
+    return str(output)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Export YOLO .pt → TensorRT .engine")
+    parser.add_argument("model", help="Path to .pt model file")
+    parser.add_argument("--imgsz", type=int, default=640, help="Input image size (default: 640)")
+    parser.add_argument("--half", action="store_true", default=True, help="FP16 precision (default: on)")
+    parser.add_argument("--no-half", dest="half", action="store_false", help="FP32 precision")
+    parser.add_argument("--int8", action="store_true", help="INT8 quantization (needs calibration)")
+    parser.add_argument("--batch", type=int, default=1, help="Batch size (default: 1)")
+    parser.add_argument("--workspace", type=int, default=4, help="GPU workspace in GB (default: 4)")
+    parser.add_argument("--opset", type=int, default=17, help="ONNX opset version (default: 17)")
+    parser.add_argument("--quiet", action="store_true", help="Suppress verbose output")
+    args = parser.parse_args()
+
+    if not Path(args.model).exists():
+        print(f"error: model file not found: {args.model}", file=sys.stderr)
+        sys.exit(1)
+
+    export_engine(
+        args.model,
+        imgsz=args.imgsz,
+        half=args.half,
+        int8=args.int8,
+        batch=args.batch,
+        workspace=args.workspace,
+        opset=args.opset,
+        verbose=not args.quiet,
+    )
+
+
+if __name__ == "__main__":
+    main()

src/chicken_counter/batch_runner.py

@@ -10,7 +10,6 @@ from chicken_counter.compress import compress_video_to_target
 from chicken_counter.config import BatchSettings, build_camera_config_from_batch
 from chicken_counter.pipeline import run_pipeline
 from chicken_counter.report import build_batch_report, persist_batch_reports
-from chicken_counter.tracking import DetectionTracker
 from chicken_counter.types import CameraBatchResult
 
 
@@ -29,20 +28,6 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None, *, verbose
     discovery = discover_camera_videos(day_dir, settings)
     camera_order = sorted(settings.cameras.items(), key=lambda item: item[1].camera_num)
 
-    first_camera_id = next(
-        camera_id for camera_id, _preset in camera_order if camera_id in discovery.found
-    )
-    first_source = discovery.found[first_camera_id]
-    init_output_path = output_dir / f"{first_camera_id}_vis.mp4" if not no_video else None
-    init_config = build_camera_config_from_batch(
-        settings,
-        first_camera_id,
-        source=first_source,
-        output_path=init_output_path,
-        checkpoint_dir=output_dir / "checkpoints" / first_camera_id,
-    )
-    shared_tracker = DetectionTracker(init_config)
-
     camera_results: list[CameraBatchResult] = []
     report_path = output_dir / f"counts_{run_date}.json"
 
@@ -73,7 +58,7 @@ def run_daily_batch(settings: BatchSettings, date: str | None = None, *, verbose
             checkpoint_dir=checkpoint_dir,
         )
         camera_config.performance.verbose = verbose
-        pipeline_result = run_pipeline(camera_config, tracker=shared_tracker, show_progress=show_progress)
+        pipeline_result = run_pipeline(camera_config, show_progress=show_progress)
         camera_results.append(
             CameraBatchResult(
                 camera_id=camera_id,

src/chicken_counter/tracking.py

@@ -17,7 +17,7 @@ class DetectionTracker:
         self.config = config
         model_path = Path(config.detection.model_path)
         self.model_kind = model_path.suffix.lower().lstrip(".") or "unknown"
-        self.model = YOLO(config.detection.model_path)
+        self.model = YOLO(config.detection.model_path, task="detect")
         self.tracker_config_path = str(Path(config.tracker.tracker_config_path))
         self.verbose = config.performance.verbose
         self._infer_count = 0
@@ -47,7 +47,6 @@ class DetectionTracker:
             offset_x, offset_y = x1, y1
 
         track_kwargs: dict = {
-            "task": "detect",
             "source": source,
             "persist": self.config.tracker.persist,
             "tracker": self.tracker_config_path,