Function create_tracking_dataset

deeplabcut/compat.py:1001–1136

Creates a tracking dataset to train a ReID tracklet stitcher. Parameters ---------- config: str Full path of the config.yaml file. videos: list[str] A list of strings containing the full paths to videos from which to create a tracking dataset, or a path to t

(
    config: str,
    videos: list[str],
    track_method: str,
    video_extensions: str | Sequence[str] | None = None,
    shuffle: int = 1,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    destfolder: str | None = None,
    batch_size: int | None = None,
    cropping: list[int] | None = None,
    TFGPUinference: bool = True,
    modelprefix: str = "",
    robust_nframes: bool = False,
    n_triplets: int = 1000,
    engine: Engine | None = None,
)

@renamed_parameter(old="batchsize", new="batch_size", since="3.0.0")
@renamed_parameter(old="videotype", new="video_extensions", since="3.0.0")
def create_tracking_dataset(
    config: str,
    videos: list[str],
    track_method: str,
    video_extensions: str | Sequence[str] | None = None,
    shuffle: int = 1,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    destfolder: str | None = None,
    batch_size: int | None = None,
    cropping: list[int] | None = None,
    TFGPUinference: bool = True,
    modelprefix: str = "",
    robust_nframes: bool = False,
    n_triplets: int = 1000,
    engine: Engine | None = None,
) -> str:
    """Creates a tracking dataset to train a ReID tracklet stitcher.

    Parameters
    ----------
    config: str
        Full path of the config.yaml file.

    videos: list[str]
        A list of strings containing the full paths to videos from which to create a
        tracking dataset, or a path to the directory where all the videos with same
        extension are stored.

    track_method: str
        Specifies the tracker used to generate the pose estimation data. Must be either
        'box', 'skeleton', or 'ellipse'.

    video_extensions : str | Sequence[str] | None, optional, default=None
        Controls how ``videos`` are filtered, based on file extension.
        File paths and directory contents are treated differently:
        - ``None`` (default): file paths are accepted as-is; directories are
          scanned for files with a recognized video extension.
        - ``str`` or ``Sequence[str]`` (e.g. ``"mp4"`` or ``["mp4", "avi"]``):
          both file paths and directory contents are filtered by the given
          extension(s).

    shuffle: int, optional, default=1
        An integer specifying the shuffle index of the training dataset used for
        training the network.

    trainingsetindex: int, optional, default=0
        Integer specifying which TrainingsetFraction to use.
        By default the first (note that TrainingFraction is a list in config.yaml).

    gputouse: int or None, optional, default=None
        Only for the TensorFlow engine (for the PyTorch engine use ``device``).
        Indicates the GPU to use (see number in ``nvidia-smi``). If you do not have a
        GPU put ``None``. See:
            https://nvidia.custhelp.com/app/answers/detail/a_id/3751/~/useful-nvidia-smi-queries

    TFGPUinference: bool, optional, default=True
        Only for the TensorFlow engine.