Dashboard: auto-focus running camera, show date + all-camera totals

- Auto-detect active camera by polling frame_index
- Show All Cameras Total accumulation in sidebar
- Show per-camera totals in camera list
- Add run_date to stats.json and display in dashboard
- Pipeline: thread run_date through build/run/batch

src/chicken_counter/batch_runner.py

@@ -58,7 +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, show_progress=show_progress)
+        pipeline_result = run_pipeline(camera_config, show_progress=show_progress, run_date=run_date)
         camera_results.append(
             CameraBatchResult(
                 camera_id=camera_id,

src/chicken_counter/pipeline.py

@@ -90,11 +90,14 @@ class PipelineArtifacts:
     total_source_frames: int | None
     owns_tracker: bool
     detection_zone_rect: tuple[int, int, int, int] | None = None
+    run_date: str = ""
 
 
 def build_pipeline(
     config: CameraConfig,
     tracker: DetectionTracker | None = None,
+    *,
+    run_date: str = "",
 ) -> PipelineArtifacts:
     capture = open_capture(config.source)
     owns_tracker = tracker is None
@@ -157,6 +160,7 @@ def build_pipeline(
         total_source_frames=total_source_frames,
         owns_tracker=owns_tracker,
         detection_zone_rect=detection_zone_rect,
+        run_date=run_date,
     )
 
 
@@ -165,12 +169,13 @@ def run_pipeline(
     tracker: DetectionTracker | None = None,
     *,
     show_progress: bool = False,
+    run_date: str = "",
 ) -> PipelineResult:
     if tracker is not None:
         tracker.config = config
         tracker.reset_tracking()
 
-    artifacts = build_pipeline(config, tracker=tracker)
+    artifacts = build_pipeline(config, tracker=tracker, run_date=run_date)
     inference_stride = max(1, config.performance.inference_stride)
     print(
         f"[perf] inference_stride={inference_stride} "
@@ -252,7 +257,7 @@ def run_pipeline(
             last_annotated = annotated
 
             if config.stream.enabled and frame_index % max(1, config.stream.interval_frames) == 0:
-                _write_stream_frame(config.stream.shm_dir, config.camera_id, annotated, result)
+                _write_stream_frame(config.stream.shm_dir, config.camera_id, annotated, result, run_date=artifacts.run_date)
 
             if verbose:
                 t_write = time.monotonic()
@@ -366,7 +371,7 @@ def _consume_result(
         _emit_periodic_feedback(config, artifacts, annotated, result, progress)
 
 
-def _write_stream_frame(shm_dir: str, camera_id: str, frame: np.ndarray, result: FrameResult) -> None:
+def _write_stream_frame(shm_dir: str, camera_id: str, frame: np.ndarray, result: FrameResult, *, run_date: str = "") -> None:
     cam_dir = Path(shm_dir) / f"chicken_counter_{camera_id}"
     cam_dir.mkdir(parents=True, exist_ok=True)
 
@@ -383,6 +388,7 @@ def _write_stream_frame(shm_dir: str, camera_id: str, frame: np.ndarray, result:
         "backward_active": result.motion_state.backward_active,
         "smoothed_speed": round(result.motion_state.smoothed_speed, 1),
         "count_events": len(result.count_events),
+        "run_date": run_date,
     }
     stats_path = cam_dir / "stats.json"
     stats_tmp = cam_dir / ".stats_tmp.json"