deepof.annotation_utils

Functions and general utilities for supervised pose estimation. See documentation for details.

Functions

augment_with_neighbors(X_huddle[, window, ...])	Expands a given set of features with leading and lagging features on the time axis.
calculate_close_range(df, mouse_id, ...)	Detects for a given set of mouse coordinates if the selected bodypart of the selected mouse is close to any bodypart of any other mouse for each frame.
climb_arena(arena_type, arena, pos_dict, ...)	Return True if the specified mouse is climbing the wall.
close_double_contact(pos_dframe, left1, ...)	Return a boolean array that's True if the specified body parts are closer than tol.
close_single_contact(pos_dframe, left, ...)	Return a boolean array that's True if the specified body parts are closer than tol.
detect_activity(speed_dframe, ...[, ...])	Return true when the mouse is either moving (moving), standing still and either moving (active) or not moving (passive).
digging(speed_dframe, dist_dframe, ...[, ...])	Return true when the mouse is digging.
following_path(distance_dframe, ...[, ...])	Return True if 'follower' is closer than tol to the path that followed has walked over the last specified number of frames.
frame_corners(w, h[, corners])	Return a dictionary with the corner positions of the video frame.
immobility(X_huddle, huddle_estimator[, ...])	Return true when the mouse is huddling a pretrained model.
max_behaviour(behaviour_dframe[, ...])	Return the most frequent behaviour in a window of window_size frames.
outside_ellipse(x, y, e_center, e_axes, e_angle)	Auxiliar function to climb_wall and sniff_object.
rotate(origin, point, ang)	Auxiliar function to climb_wall and sniff_object.
sniff_around(speed_dframe, ...[, ...])	Return true when the mouse is sniffing around using simple rules.
sniff_object(speed_dframe, arena, pos_dict, ...)	Return True if the specified mouse is sniffing an object.
stationary_lookaround(speed_dframe, ...[, ...])	Return true when the mouse is standing still and looking around (moving nose without head being tilted too much).
supervised_tagging(coord_object, raw_coords, ...)	Output a dataframe with the registered motives per frame.