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, speeds: deepof_table_dict, full_features: dict, video: str, trained_model_path: str | None = None, center: str = 'Center', params: dict = {}) → 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

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

• full_features (dict) – dictionary with

• video (str) – string name of the experiment to tag

• trained_model_path (str) – path indicating where all pretrained models are located

• 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.

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)