deepof.annotation_utils.supervised_tagging

deepof.annotation_utils.supervised_tagging(coord_object: deepof_coordinates, raw_coords: deepof_table_dict, coords: deepof_table_dict, dists: deepof_table_dict, angles: deepof_table_dict, speeds: deepof_table_dict, full_features: dict, key: str, immobility_estimator: str | None = None, center: str = 'Center', params: dict = {}, run_numba: bool = False) → DataFrame

Output a dataframe with the registered motives per frame.

If specified, produces a labeled video displaying the information in real time

Parameters:

• coord_object (deepof.data.coordinates) – coordinates object containing the project information

• raw_coords (deepof.data.table_dict) – table_dict with raw coordinates

• coords (deepof.data.table_dict) – table_dict with already processed (centered and aligned) coordinates

• dists (deepof.data.table_dict) – table_dict with already processed distances

• angles (deepof.data.table_dict) – table_dict with already processed angles

• speeds (deepof.data.table_dict) – table_dict with already processed speeds

• full_features (dict) – A dictionary of aligned kinematics, where the keys are the names of the experimental conditions. The values are the aligned kinematics for each condition.

• key (str) – key to the experiment to tag and current set of objects (videos, tables, distances etc.)

• immobility_estimator (str) – classifier to determine if a mouse is immobile or not.

• center (str) – Body part to center coordinates on. “Center” by default.

• params (dict) – dictionary to overwrite the default values of the parameters of the functions that the rule-based pose estimation utilizes. See documentation for details.

• run_numba (bool) – Determines if numba versions of functions should be used (run faster but require initial compilation time on first run)

Returns:

table with traits as columns and frames as rows. Each value is a boolean indicating trait detection at a given time

Return type:

tag_df (pandas.DataFrame)