Function analyze_time_lapse_frames

deeplabcut/compat.py:1315–1425

Analyzed all images (of type = frametype) in a folder and stores the output in one file. You can crop the frames (before analysis), by changing 'cropping'=True and setting 'x1','x2','y1','y2' in the config file. Output: The labels are stored as MultiIndex Pandas Array, which contai

(
    config: str,
    directory: str,
    frametype: str = ".png",
    shuffle: int = 1,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    device: str | None = None,
    save_as_csv: bool = False,
    modelprefix: str = "",
    engine: Engine | None = None,
)

def analyze_time_lapse_frames(
    config: str,
    directory: str,
    frametype: str = ".png",
    shuffle: int = 1,
    trainingsetindex: int = 0,
    gputouse: int | None = None,
    device: str | None = None,
    save_as_csv: bool = False,
    modelprefix: str = "",
    engine: Engine | None = None,
):
    """Analyzed all images (of type = frametype) in a folder and stores the output in
    one file.

    You can crop the frames (before analysis), by changing 'cropping'=True and setting
    'x1','x2','y1','y2' in the config file.

    Output: The labels are stored as MultiIndex Pandas Array, which contains the name
    of the network, body part name, (x, y) label position in pixels, and the likelihood
    for each frame per body part. These arrays are stored in an efficient Hierarchical
    Data Format (HDF) in the same directory, where the video is stored. However, if the
    flag save_as_csv is set to True, the data can also be exported in comma-separated
    values format (.csv), which in turn can be imported in many programs, such as
    MATLAB, R, Prism, etc.

    Parameters
    ----------
    config : string
        Full path of the config.yaml file as a string.

    directory: string
        Full path to directory containing the frames that shall be analyzed

    frametype: string, optional
        Checks for the file extension of the frames. Only images with this extension are
        analyzed. The default is ``.png``

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

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

    gputouse: int, optional.
        Only for TensorFlow models. For PyTorch models, please use `device`. Natural
        number indicating the number of your GPU (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

    device: str, optional
        The CUDA device to use for training. If None, the device will be taken from the
        ``pytorch_config.yaml`` file. Examples: {"cpu", "cuda", "cuda:0", "cuda:1"}. For
        more information, see https://pytorch.org/docs/stable/notes/cuda.html

    save_as_csv: bool, optional