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track(...) and evaluate(...)

These two functions are the public composable entry points for running sources and summarizing results.

Create a lazy streaming tracking result iterator.

Source code in boxmot/api/_facade.py 
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def track(source, detector, reid=None, tracker=None, *, verbose: bool = True, drawer=None) -> Results:
    """Create a lazy streaming tracking result iterator."""
    if tracker is None:
        raise ValueError("A tracker instance is required.")
    return Results(source, detector, reid, tracker, verbose=verbose, drawer=drawer)

Aggregate run metrics over one or more tracking results.

Source code in boxmot/api/_facade.py 
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def evaluate(
    data,
    detector=None,
    reid=None,
    tracker=None,
    *,
    metrics: bool = True,
    speed: bool = True,
    verbose: bool = False,
) -> dict[str, Any]:
    """Aggregate run metrics over one or more tracking results."""
    runs = _coerce_results(
        data,
        detector=detector,
        reid=reid,
        tracker=tracker,
        verbose=verbose,
        track_fn=track,
    )
    summaries = [run.summary() for run in runs]

    total_frames = sum(summary["frames"] for summary in summaries)
    total_detections = sum(summary["detections"] for summary in summaries)
    total_tracks = sum(summary["tracks"] for summary in summaries)
    total_det_ms = sum(summary["timings_ms"]["det"] for summary in summaries)
    total_reid_ms = sum(summary["timings_ms"]["reid"] for summary in summaries)
    total_track_ms = sum(summary["timings_ms"]["track"] for summary in summaries)
    total_ms = sum(summary["timings_ms"]["total"] for summary in summaries)

    response: dict[str, Any] = {
        "sources": len(summaries),
        "runs": summaries,
    }

    if metrics:
        response["metrics"] = {
            "frames": total_frames,
            "detections": total_detections,
            "tracks": total_tracks,
            "avg_tracks_per_frame": (total_tracks / total_frames) if total_frames else 0.0,
        }

    if speed:
        response["speed"] = {
            "det_ms": total_det_ms,
            "reid_ms": total_reid_ms,
            "track_ms": total_track_ms,
            "total_ms": total_ms,
            "avg_total_ms": (total_ms / total_frames) if total_frames else 0.0,
            "fps": (1000.0 * total_frames / total_ms) if total_ms else 0.0,
        }

    return response