from_dlc_style_df

movement.io.load_poses.from_dlc_style_df(df, fps=None, source_software='DeepLabCut')

Create a movement poses dataset from a DeepLabCut-style DataFrame.

Parameters:

• df (pandas.DataFrame) – DataFrame containing the pose tracks and confidence scores. Must be formatted as in DeepLabCut output files (see Notes).

• fps (float, optional) – The number of frames per second in the video. If None (default), the time coordinates will be in frame numbers.

• source_software (str, optional) – Name of the pose estimation software from which the data originate. Defaults to “DeepLabCut”, but it can also be “LightningPose” (because they use the same DataFrame format).

Returns:

movement dataset containing the pose tracks, confidence scores, and associated metadata.

Return type:

xarray.Dataset

Notes

The DataFrame must have a multi-index column with the following levels: “scorer”, (“individuals”), “bodyparts”, “coords”. The “individuals” level may be omitted if there is only one individual in the video. The “coords” level contains either:

• the spatial coordinates “x”, “y”, and “likelihood” (point-wise confidence scores), or

• the spatial coordinates “x”, “y”, and “z” (3D poses estimated by triangulating 2D poses from multiple DeepLabCut output files).

The row index corresponds to the frame number.

See also

movement.io.load_poses.from_dlc_file