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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| from functools import partial | |
| from pathlib import Path | |
| import torch | |
| from ultralytics.utils import IterableSimpleNamespace, yaml_load | |
| from ultralytics.utils.checks import check_yaml | |
| from .bot_sort import BOTSORT | |
| from .byte_tracker import BYTETracker | |
| # A mapping of tracker types to corresponding tracker classes | |
| TRACKER_MAP = {"bytetrack": BYTETracker, "botsort": BOTSORT} | |
| def on_predict_start(predictor: object, persist: bool = False) -> None: | |
| """ | |
| Initialize trackers for object tracking during prediction. | |
| Args: | |
| predictor (object): The predictor object to initialize trackers for. | |
| persist (bool): Whether to persist the trackers if they already exist. | |
| Raises: | |
| AssertionError: If the tracker_type is not 'bytetrack' or 'botsort'. | |
| Examples: | |
| Initialize trackers for a predictor object: | |
| >>> predictor = SomePredictorClass() | |
| >>> on_predict_start(predictor, persist=True) | |
| """ | |
| if hasattr(predictor, "trackers") and persist: | |
| return | |
| tracker = check_yaml(predictor.args.tracker) | |
| cfg = IterableSimpleNamespace(**yaml_load(tracker)) | |
| if cfg.tracker_type not in {"bytetrack", "botsort"}: | |
| raise AssertionError(f"Only 'bytetrack' and 'botsort' are supported for now, but got '{cfg.tracker_type}'") | |
| trackers = [] | |
| for _ in range(predictor.dataset.bs): | |
| tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30) | |
| trackers.append(tracker) | |
| if predictor.dataset.mode != "stream": # only need one tracker for other modes. | |
| break | |
| predictor.trackers = trackers | |
| predictor.vid_path = [None] * predictor.dataset.bs # for determining when to reset tracker on new video | |
| def on_predict_postprocess_end(predictor: object, persist: bool = False) -> None: | |
| """ | |
| Postprocess detected boxes and update with object tracking. | |
| Args: | |
| predictor (object): The predictor object containing the predictions. | |
| persist (bool): Whether to persist the trackers if they already exist. | |
| Examples: | |
| Postprocess predictions and update with tracking | |
| >>> predictor = YourPredictorClass() | |
| >>> on_predict_postprocess_end(predictor, persist=True) | |
| """ | |
| path, im0s = predictor.batch[:2] | |
| is_obb = predictor.args.task == "obb" | |
| is_stream = predictor.dataset.mode == "stream" | |
| for i in range(len(im0s)): | |
| tracker = predictor.trackers[i if is_stream else 0] | |
| vid_path = predictor.save_dir / Path(path[i]).name | |
| if not persist and predictor.vid_path[i if is_stream else 0] != vid_path: | |
| tracker.reset() | |
| predictor.vid_path[i if is_stream else 0] = vid_path | |
| det = (predictor.results[i].obb if is_obb else predictor.results[i].boxes).cpu().numpy() | |
| if len(det) == 0: | |
| continue | |
| tracks = tracker.update(det, im0s[i]) | |
| if len(tracks) == 0: | |
| continue | |
| idx = tracks[:, -1].astype(int) | |
| predictor.results[i] = predictor.results[i][idx] | |
| update_args = {"obb" if is_obb else "boxes": torch.as_tensor(tracks[:, :-1])} | |
| predictor.results[i].update(**update_args) | |
| def register_tracker(model: object, persist: bool) -> None: | |
| """ | |
| Register tracking callbacks to the model for object tracking during prediction. | |
| Args: | |
| model (object): The model object to register tracking callbacks for. | |
| persist (bool): Whether to persist the trackers if they already exist. | |
| Examples: | |
| Register tracking callbacks to a YOLO model | |
| >>> model = YOLOModel() | |
| >>> register_tracker(model, persist=True) | |
| """ | |
| model.add_callback("on_predict_start", partial(on_predict_start, persist=persist)) | |
| model.add_callback("on_predict_postprocess_end", partial(on_predict_postprocess_end, persist=persist)) | |