Function triangulate

deeplabcut/pose_estimation_3d/triangulation.py:27–469

This function triangulates the detected DLC-keypoints from the two camera views using the camera matrices (derived from calibration) to calculate 3D predictions. Parameters ---------- config : string Full path of the config.yaml file as a string. video_path : string/lis

(
    config,
    video_path,
    videotype="",
    filterpredictions=True,
    filtertype="median",
    gputouse=None,
    destfolder=None,
    save_as_csv=False,
    track_method="",
)

# other API (i.e. videotype: str -> video_extensions: str | Sequence[str] | None)
# this requires updating get_camerawise_videos (matching `collect_video_paths`)
def triangulate(
    config,
    video_path,
    videotype="",
    filterpredictions=True,
    filtertype="median",
    gputouse=None,
    destfolder=None,
    save_as_csv=False,
    track_method="",
):
    """This function triangulates the detected DLC-keypoints from the two camera views
    using the camera matrices (derived from calibration) to calculate 3D predictions.

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

    video_path : string/list of list
        Full path of the directory where videos are saved. If the user wants to analyze
        only a pair of videos, the user needs to pass them as a list of list of videos,
        i.e. [['video1-camera-1.avi','video1-camera-2.avi']]

    videotype: string, optional
        Checks for the extension of the video in case the input to the video is a directory.\n
        Only videos with this extension are analyzed.
        If left unspecified, videos with common extensions ('avi', 'mp4', 'mov', 'mpeg', 'mkv') are kept.


    filterpredictions: Bool, optional
        Filter the predictions with filter specified by "filtertype". If specified it
        should be either ``True`` or ``False``.

    filtertype: string
        Select which filter, 'arima' or 'median' filter (currently supported).

    gputouse: int, optional. 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

    destfolder: string, optional
        Specifies the destination folder for analysis data (default is the path of the video)

    save_as_csv: bool, optional
        Saves the predictions in a .csv file. The default is ``False``

    Example
    -------
    Linux/MacOS
    To analyze all the videos in the directory:
    >>> deeplabcut.triangulate(config,'/data/project1/videos/')

    To analyze only a few pairs of videos:
    >>> deeplabcut.triangulate(config,[['/data/project1/videos/video1-camera-1.avi',
    ... '/data/project1/videos/video1-camera-2.avi'],['/data/project1/videos/video2-camera-1.avi',
    ... '/data/project1/videos/video2-camera-2.avi']])