pose_utils.py 36.6 KB
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# @author lucasmiranda42
# encoding: utf-8
# module deepof

"""

Functions and general utilities for rule-based pose estimation. See documentation for details

"""

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from itertools import combinations
from scipy import stats
from typing import Any, List, NewType
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import cv2
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import deepof.utils
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import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import regex as re
import seaborn as sns
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import warnings
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# Ignore warning with no downstream effect
warnings.filterwarnings("ignore", message="All-NaN slice encountered")

# Create custom string type
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Coordinates = NewType("Coordinates", Any)
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def close_single_contact(
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    pos_dframe: pd.DataFrame,
    left: str,
    right: str,
    tol: float,
    arena_abs: int,
    arena_rel: int,
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) -> np.array:
    """Returns a boolean array that's True if the specified body parts are closer than tol.

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    Parameters:
        - pos_dframe (pandas.DataFrame): DLC output as pandas.DataFrame; only applicable
        to two-animal experiments.
        - left (string): First member of the potential contact
        - right (string): Second member of the potential contact
        - tol (float): maximum distance for which a contact is reported
        - arena_abs (int): length in mm of the diameter of the real arena
        - arena_rel (int): length in pixels of the diameter of the arena in the video
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    Returns:
        - contact_array (np.array): True if the distance between the two specified points
        is less than tol, False otherwise"""
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    close_contact = None

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    if isinstance(right, str):
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        close_contact = (
            np.linalg.norm(pos_dframe[left] - pos_dframe[right], axis=1) * arena_abs
        ) / arena_rel < tol

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    elif isinstance(right, list):
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        close_contact = np.any(
            [
                (np.linalg.norm(pos_dframe[left] - pos_dframe[r], axis=1) * arena_abs)
                / arena_rel
                < tol
                for r in right
            ],
            axis=0,
        )
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    return close_contact


def close_double_contact(
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    pos_dframe: pd.DataFrame,
    left1: str,
    left2: str,
    right1: str,
    right2: str,
    tol: float,
    arena_abs: int,
    arena_rel: int,
    rev: bool = False,
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) -> np.array:
    """Returns a boolean array that's True if the specified body parts are closer than tol.

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    Parameters:
        - pos_dframe (pandas.DataFrame): DLC output as pandas.DataFrame; only applicable
        to two-animal experiments.
        - left1 (string): First contact point of animal 1
        - left2 (string): Second contact point of animal 1
        - right1 (string): First contact point of animal 2
        - right2 (string): Second contact point of animal 2
        - tol (float): maximum distance for which a contact is reported
        - arena_abs (int): length in mm of the diameter of the real arena
        - arena_rel (int): length in pixels of the diameter of the arena in the video
        - rev (bool): reverses the default behaviour (nose2tail contact for both mice)
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    Returns:
        - double_contact (np.array): True if the distance between the two specified points
        is less than tol, False otherwise"""
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    if rev:
        double_contact = (
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            (np.linalg.norm(pos_dframe[right1] - pos_dframe[left2], axis=1) * arena_abs)
            / arena_rel
            < tol
        ) & (
            (np.linalg.norm(pos_dframe[right2] - pos_dframe[left1], axis=1) * arena_abs)
            / arena_rel
            < tol
        )
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    else:
        double_contact = (
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            (np.linalg.norm(pos_dframe[right1] - pos_dframe[left1], axis=1) * arena_abs)
            / arena_rel
            < tol
        ) & (
            (np.linalg.norm(pos_dframe[right2] - pos_dframe[left2], axis=1) * arena_abs)
            / arena_rel
            < tol
        )
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    return double_contact


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def rotate(origin, point, ang):
    """Auxiliar function to climb_wall and sniff_object. Rotates x,y coordinates over a pivot"""

    ox, oy = origin
    px, py = point

    qx = ox + np.cos(ang) * (px - ox) - np.sin(ang) * (py - oy)
    qy = oy + np.sin(ang) * (px - ox) + np.cos(ang) * (py - oy)
    return qx, qy


def outside_ellipse(x, y, e_center, e_axes, e_angle, threshold=0.0):
    """Auxiliar function to climb_wall and sniff_object. Returns True if the passed x, y coordinates
    are outside the ellipse denoted by e_center, e_axes and e_angle, with a certain threshold"""

    x, y = rotate(e_center, (x, y), np.radians(e_angle))

    term_x = (x - e_center[0]) ** 2 / (e_axes[0] + threshold) ** 2
    term_y = (y - e_center[1]) ** 2 / (e_axes[1] + threshold) ** 2
    return term_x + term_y > 1


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def climb_wall(
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    arena_type: str,
    arena: np.array,
    pos_dict: pd.DataFrame,
    tol: float,
    nose: str,
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    centered_data: bool = False,
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) -> np.array:
    """Returns True if the specified mouse is climbing the wall

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    Parameters:
        - arena_type (str): arena type; must be one of ['circular']
        - arena (np.array): contains arena location and shape details
        - pos_dict (table_dict): position over time for all videos in a project
        - tol (float): minimum tolerance to report a hit
        - nose (str): indicates the name of the body part representing the nose of
        the selected animal
        - arena_dims (int): indicates radius of the real arena in mm
        - centered_data (bool): indicates whether the input data is centered
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    Returns:
        - climbing (np.array): boolean array. True if selected animal
        is climbing the walls of the arena"""
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    nose = pos_dict[nose]

    if arena_type == "circular":
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        center = np.zeros(2) if centered_data else np.array(arena[0])
        axes = arena[1]
        angle = arena[2]
        climbing = outside_ellipse(
            x=nose["x"],
            y=nose["y"],
            e_center=center,
            e_axes=axes,
            e_angle=-angle,
            threshold=tol,
        )
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    else:
        raise NotImplementedError("Supported values for arena_type are ['circular']")

    return climbing


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def sniff_object(
    speed_dframe: pd.DataFrame,
    arena_type: str,
    arena: np.array,
    pos_dict: pd.DataFrame,
    tol: float,
    tol_speed: float,
    nose: str,
    centered_data: bool = False,
    object: str = "arena",
    animal_id: str = "",
):
    """Returns True if the specified mouse is sniffing an object

    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - arena_type (str): arena type; must be one of ['circular']
        - arena (np.array): contains arena location and shape details
        - pos_dict (table_dict): position over time for all videos in a project
        - tol (float): minimum tolerance to report a hit
        - nose (str): indicates the name of the body part representing the nose of
        the selected animal
        - arena_dims (int): indicates radius of the real arena in mm
        - centered_data (bool): indicates whether the input data is centered
        - object (str): indicates the object that the animal is sniffing.
        Can be one of ['arena', 'partner']

    Returns:
        - sniffing (np.array): boolean array. True if selected animal
        is sniffing the selected object"""

    nose, nosing = pos_dict[nose], True

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    if animal_id != "":
        animal_id += "_"

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    if object == "arena":
        if arena_type == "circular":
            center = np.zeros(2) if centered_data else np.array(arena[0])
            axes = arena[1]
            angle = arena[2]

            nosing_min = outside_ellipse(
                x=nose["x"],
                y=nose["y"],
                e_center=center,
                e_axes=axes,
                e_angle=-angle,
                threshold=-tol,
            )
            nosing_max = outside_ellipse(
                x=nose["x"],
                y=nose["y"],
                e_center=center,
                e_axes=axes,
                e_angle=-angle,
                threshold=tol,
            )
            nosing = nosing_min & (~nosing_max)

    elif object == "partner":
        raise NotImplementedError

    else:
        raise ValueError("object should be one of [arena, partner]")

    speed = speed_dframe[animal_id + "Center"] < tol_speed
    sniffing = nosing & speed

    return sniffing


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def huddle(
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    pos_dframe: pd.DataFrame,
    speed_dframe: pd.DataFrame,
    tol_forward: float,
    tol_speed: float,
    animal_id: str = "",
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) -> np.array:
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    """Returns true when the mouse is huddling using simple rules.
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    Parameters:
        - pos_dframe (pandas.DataFrame): position of body parts over time
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - tol_forward (float): Maximum tolerated distance between ears and
        forward limbs
        - tol_rear (float): Maximum tolerated average distance between spine
        body parts
        - tol_speed (float): Maximum tolerated speed for the center of the mouse

    Returns:
        hudd (np.array): True if the animal is huddling, False otherwise
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    """
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    if animal_id != "":
        animal_id += "_"

    forward = (
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        np.linalg.norm(
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            pos_dframe[animal_id + "Left_bhip"] - pos_dframe[animal_id + "Left_fhip"],
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            axis=1,
        )
        < tol_forward
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    ) | (
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        np.linalg.norm(
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            pos_dframe[animal_id + "Right_bhip"] - pos_dframe[animal_id + "Right_fhip"],
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            axis=1,
        )
        < tol_forward
    )
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    speed = speed_dframe[animal_id + "Center"] < tol_speed
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    hudd = forward & speed
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    return hudd


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def dig(
    speed_dframe: pd.DataFrame,
    likelihood_dframe: pd.DataFrame,
    tol_speed: float,
    tol_likelihood: float,
    animal_id: str = "",
):
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    """Returns true when the mouse is digging using simple rules.

    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - likelihood_dframe (pandas.DataFrame): likelihood of body part tracker over time,
        as directly obtained from DeepLabCut
        - tol_speed (float): Maximum tolerated speed for the center of the mouse
        - tol_likelihood (float): Maximum tolerated likelihood for the nose (if the animal
        is digging, the nose is momentarily occluded).

    Returns:
        dig (np.array): True if the animal is digging, False otherwise
    """

    if animal_id != "":
        animal_id += "_"

    speed = speed_dframe[animal_id + "Center"] < tol_speed
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    nose_likelihood = likelihood_dframe[animal_id + "Nose"] < tol_likelihood
    digging = speed & nose_likelihood
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    return digging
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def look_around(
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    speed_dframe: pd.DataFrame,
    likelihood_dframe: pd.DataFrame,
    tol_speed: float,
    tol_likelihood: float,
    animal_id: str = "",
):
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    """Returns true when the mouse is digging using simple rules.
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    Parameters:
        - speed_dframe (pandas.DataFrame): speed of body parts over time
        - likelihood_dframe (pandas.DataFrame): likelihood of body part tracker over time,
        as directly obtained from DeepLabCut
        - tol_speed (float): Maximum tolerated speed for the center of the mouse
        - tol_likelihood (float): Maximum tolerated likelihood for the nose (if the animal
        is digging, the nose is momentarily occluded).
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    Returns:
        lookaround (np.array): True if the animal is standing still and looking around, False otherwise
    """

    if animal_id != "":
        animal_id += "_"

    speed = speed_dframe[animal_id + "Center"] < tol_speed
    nose_speed = speed_dframe[animal_id + "Center"] < speed_dframe[animal_id + "Nose"]
    nose_likelihood = likelihood_dframe[animal_id + "Nose"] > tol_likelihood

    lookaround = speed & nose_likelihood & nose_speed

    return lookaround
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def following_path(
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    distance_dframe: pd.DataFrame,
    position_dframe: pd.DataFrame,
    follower: str,
    followed: str,
    frames: int = 20,
    tol: float = 0,
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) -> np.array:
    """For multi animal videos only. Returns True if 'follower' is closer than tol to the path that
    followed has walked over the last specified number of frames

        Parameters:
            - distance_dframe (pandas.DataFrame): distances between bodyparts; generated by the preprocess module
            - position_dframe (pandas.DataFrame): position of bodyparts; generated by the preprocess module
            - follower (str) identifier for the animal who's following
            - followed (str) identifier for the animal who's followed
            - frames (int) frames in which to track whether the process consistently occurs,
            - tol (float) Maximum distance for which True is returned

        Returns:
            - follow (np.array): boolean sequence, True if conditions are fulfilled, False otherwise"""

    # Check that follower is close enough to the path that followed has passed though in the last frames
    shift_dict = {
        i: position_dframe[followed + "_Tail_base"].shift(i) for i in range(frames)
    }
    dist_df = pd.DataFrame(
        {
            i: np.linalg.norm(
                position_dframe[follower + "_Nose"] - shift_dict[i], axis=1
            )
            for i in range(frames)
        }
    )

    # Check that the animals are oriented follower's nose -> followed's tail
    right_orient1 = (
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        distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Tail_base"]))]
        < distance_dframe[
            tuple(sorted([follower + "_Tail_base", followed + "_Tail_base"]))
        ]
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    )

    right_orient2 = (
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        distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Tail_base"]))]
        < distance_dframe[tuple(sorted([follower + "_Nose", followed + "_Nose"]))]
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    )

    follow = np.all(
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        np.array([(dist_df.min(axis=1) < tol), right_orient1, right_orient2]),
        axis=0,
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    )

    return follow


def single_behaviour_analysis(
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    behaviour_name: str,
    treatment_dict: dict,
    behavioural_dict: dict,
    plot: int = 0,
    stat_tests: bool = True,
    save: str = None,
    ylim: float = None,
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) -> list:
    """Given the name of the behaviour, a dictionary with the names of the groups to compare, and a dictionary
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    with the actual tags, outputs a box plot and a series of significance tests amongst the groups
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     Parameters:
         - behaviour_name (str): name of the behavioural trait to analize
         - treatment_dict (dict): dictionary containing video names as keys and experimental conditions as values
         - behavioural_dict (dict): tagged dictionary containing video names as keys and annotations as values
         - plot (int): Silent if 0; otherwise, indicates the dpi of the figure to plot
         - stat_tests (bool): performs FDR corrected Mann-U non-parametric tests among all groups if True
         - save (str): Saves the produced figure to the specified file
         - ylim (float): y-limit for the boxplot. Ignored if plot == False
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     Returns:
         - beh_dict (dict): dictionary containing experimental conditions as keys and video names as values
         - stat_dict (dict): dictionary containing condition pairs as keys and stat results as values"""
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    beh_dict = {condition: [] for condition in treatment_dict.keys()}

    for condition in beh_dict.keys():
        for ind in treatment_dict[condition]:
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            beh_dict[condition] += np.sum(behavioural_dict[ind][behaviour_name]) / len(
                behavioural_dict[ind][behaviour_name]
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            )

    return_list = [beh_dict]

    if plot > 0:

        fig, ax = plt.subplots(dpi=plot)

        sns.boxplot(
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            x=list(beh_dict.keys()),
            y=list(beh_dict.values()),
            orient="vertical",
            ax=ax,
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        )

        ax.set_title("{} across groups".format(behaviour_name))
        ax.set_ylabel("Proportion of frames")

        if ylim is not None:
            ax.set_ylim(ylim)

        if save is not None:  # pragma: no cover
            plt.savefig(save)

        return_list.append(fig)

    if stat_tests:
        stat_dict = {}
        for i in combinations(treatment_dict.keys(), 2):
            # Solves issue with automatically generated examples
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            if np.any(
                np.array(
                    [
                        beh_dict[i[0]] == beh_dict[i[1]],
                        np.var(beh_dict[i[0]]) == 0,
                        np.var(beh_dict[i[1]]) == 0,
                    ]
                )
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            ):
                stat_dict[i] = "Identical sources. Couldn't run"
            else:
                stat_dict[i] = stats.mannwhitneyu(
                    beh_dict[i[0]], beh_dict[i[1]], alternative="two-sided"
                )
        return_list.append(stat_dict)

    return return_list


def max_behaviour(
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    behaviour_dframe: pd.DataFrame, window_size: int = 10, stepped: bool = False
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) -> np.array:
    """Returns the most frequent behaviour in a window of window_size frames

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    Parameters:
            - behaviour_dframe (pd.DataFrame): boolean matrix containing occurrence
            of tagged behaviours per frame in the video
            - window_size (int): size of the window to use when computing
            the maximum behaviour per time slot
            - stepped (bool): sliding windows don't overlap if True. False by default
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    Returns:
        - max_array (np.array): string array with the most common behaviour per instance
        of the sliding window"""
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    speeds = [col for col in behaviour_dframe.columns if "speed" in col.lower()]

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    behaviour_dframe = behaviour_dframe.drop(speeds, axis=1).astype("float")
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    win_array = behaviour_dframe.rolling(window_size, center=True).sum()
    if stepped:
        win_array = win_array[::window_size]
    max_array = win_array[1:].idxmax(axis=1)

    return np.array(max_array)


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# noinspection PyDefaultArgument
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def get_hparameters(hparams: dict = {}) -> dict:
    """Returns the most frequent behaviour in a window of window_size frames

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    Parameters:
        - hparams (dict): dictionary containing hyperparameters to overwrite
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    Returns:
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        - defaults (dict): dictionary with overwritten parameters. Those not
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        specified in the input retain their default values"""
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    defaults = {
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        "speed_pause": 5,
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        "climb_tol": 10,
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        "close_contact_tol": 35,
        "side_contact_tol": 80,
        "follow_frames": 10,
        "follow_tol": 5,
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        "huddle_forward": 15,
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        "huddle_speed": 1,
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        "nose_likelihood": 0.85,
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        "fps": 24,
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    }
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    for k, v in hparams.items():
        defaults[k] = v
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    return defaults


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# noinspection PyDefaultArgument
def frame_corners(w, h, corners: dict = {}):
    """Returns a dictionary with the corner positions of the video frame

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    Parameters:
        - w (int): width of the frame in pixels
        - h (int): height of the frame in pixels
        - corners (dict): dictionary containing corners to overwrite
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    Returns:
        - defaults (dict): dictionary with overwriten parameters. Those not
        specified in the input retain their default values"""
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    defaults = {
        "downleft": (int(w * 0.3 / 10), int(h / 1.05)),
        "downright": (int(w * 6.5 / 10), int(h / 1.05)),
        "upleft": (int(w * 0.3 / 10), int(h / 20)),
        "upright": (int(w * 6.3 / 10), int(h / 20)),
    }

    for k, v in corners.items():
        defaults[k] = v

    return defaults


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# noinspection PyDefaultArgument,PyProtectedMember
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def rule_based_tagging(
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    tracks: List,
    videos: List,
    coordinates: Coordinates,
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    coords: Any,
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    dists: Any,
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    speeds: Any,
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    vid_index: int,
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    arena_type: str,
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    recog_limit: int = 100,
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    path: str = os.path.join("."),
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    params: dict = {},
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) -> pd.DataFrame:
    """Outputs a dataframe with the registered motives per frame. If specified, produces a labeled
    video displaying the information in real time

    Parameters:
        - tracks (list): list containing experiment IDs as strings
        - videos (list): list of videos to load, in the same order as tracks
        - coordinates (deepof.preprocessing.coordinates): coordinates object containing the project information
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        - coords (deepof.preprocessing.table_dict): table_dict with already processed coordinates
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        - dists (deepof.preprocessing.table_dict): table_dict with already processed distances
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        - speeds (deepof.preprocessing.table_dict): table_dict with already processed speeds
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        - vid_index (int): index in videos of the experiment to annotate
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        - path (str): directory in which the experimental data is stored
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        - recog_limit (int): number of frames to use for arena recognition (100 by default)
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        - 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.
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    Returns:
        - tag_df (pandas.DataFrame): table with traits as columns and frames as rows. Each
        value is a boolean indicating trait detection at a given time"""

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    params = get_hparameters(params)
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    animal_ids = coordinates._animal_ids
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    undercond = "_" if len(animal_ids) > 1 else ""
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    try:
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        vid_name = re.findall("(.*)DLC", tracks[vid_index])[0]
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    except IndexError:
        vid_name = tracks[vid_index]
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    coords = coords[vid_name]
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    dists = dists[vid_name]
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    speeds = speeds[vid_name]
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    likelihoods = coordinates.get_quality()[vid_name]
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    arena_abs = coordinates.get_arenas[1][0]
    arena, h, w = deepof.utils.recognize_arena(
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        videos, vid_index, path, recog_limit, coordinates._arena
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    )

    # Dictionary with motives per frame
    tag_dict = {}

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    # Bulk body parts
    main_body = [
        "Left_ear",
        "Right_ear",
        "Spine_1",
        "Center",
        "Spine_2",
        "Left_fhip",
        "Right_fhip",
        "Left_bhip",
        "Right_bhip",
    ]

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    def onebyone_contact(bparts: List):
        """Returns a smooth boolean array with 1to1 contacts between two mice"""
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        nonlocal coords, animal_ids, params, arena_abs, arena
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        try:
            left = animal_ids[0] + bparts[0]
        except TypeError:
            left = [animal_ids[0] + "_" + suffix for suffix in bparts[0]]

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        try:
            right = animal_ids[1] + bparts[-1]
        except TypeError:
            right = [animal_ids[1] + "_" + suffix for suffix in bparts[-1]]

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        return deepof.utils.smooth_boolean_array(
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            close_single_contact(
                coords,
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                (left if not isinstance(left, list) else right),
                (right if not isinstance(left, list) else left),
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                params["close_contact_tol"],
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                arena_abs,
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                arena[1][1],
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            )
        )
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    def twobytwo_contact(rev):
        """Returns a smooth boolean array with side by side contacts between two mice"""

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        nonlocal coords, animal_ids, params, arena_abs, arena
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        return deepof.utils.smooth_boolean_array(
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            close_double_contact(
                coords,
                animal_ids[0] + "_Nose",
                animal_ids[0] + "_Tail_base",
                animal_ids[1] + "_Nose",
                animal_ids[1] + "_Tail_base",
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                params["side_contact_tol"],
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                rev=rev,
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                arena_abs=arena_abs,
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                arena_rel=arena[1][1],
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            )
        )
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    def overall_speed(ovr_speeds, _id, ucond):
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        bparts = [
            "Center",
            "Spine_1",
            "Spine_2",
            "Nose",
            "Left_ear",
            "Right_ear",
            "Left_fhip",
            "Right_fhip",
            "Left_bhip",
            "Right_bhip",
            "Tail_base",
        ]
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        array = ovr_speeds[[_id + ucond + bpart for bpart in bparts]]
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        avg_speed = np.nanmedian(array[1:], axis=1)
        return np.insert(avg_speed, 0, np.nan, axis=0)

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    if len(animal_ids) == 2:
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        # Define behaviours that can be computed on the fly from the distance matrix
        tag_dict["nose2nose"] = onebyone_contact(bparts=["_Nose"])

        tag_dict["sidebyside"] = twobytwo_contact(rev=False)

        tag_dict["sidereside"] = twobytwo_contact(rev=True)

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        tag_dict[animal_ids[0] + "_nose2tail"] = onebyone_contact(
            bparts=["_Nose", "_Tail_base"]
        )
        tag_dict[animal_ids[1] + "_nose2tail"] = onebyone_contact(
            bparts=["_Tail_base", "_Nose"]
        )
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        tag_dict[animal_ids[0] + "_nose2body"] = onebyone_contact(
            bparts=[
                "_Nose",
                main_body,
            ]
        )
        tag_dict[animal_ids[1] + "_nose2body"] = onebyone_contact(
            bparts=[
                main_body,
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                "_Nose",
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            ]
        )
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        for _id in animal_ids:
            tag_dict[_id + "_following"] = deepof.utils.smooth_boolean_array(
                following_path(
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                    dists,
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                    coords,
                    follower=_id,
                    followed=[i for i in animal_ids if i != _id][0],
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                    frames=params["follow_frames"],
                    tol=params["follow_tol"],
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                )
            )

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    for _id in animal_ids:
        tag_dict[_id + undercond + "climbing"] = deepof.utils.smooth_boolean_array(
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            climb_wall(
                arena_type,
                arena,
                coords,
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                params["climb_tol"],
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                _id + undercond + "Nose",
            )
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        )
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        tag_dict[_id + undercond + "sniffing"] = deepof.utils.smooth_boolean_array(
            sniff_object(
                speeds,
                arena_type,
                arena,
                coords,
                params["climb_tol"],
                params["huddle_speed"],
                _id + undercond + "Nose",
                object="arena",
                animal_id=_id,
            )
        )
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        tag_dict[_id + undercond + "speed"] = overall_speed(speeds, _id, undercond)
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        tag_dict[_id + undercond + "huddle"] = deepof.utils.smooth_boolean_array(
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            huddle(
                coords,
                speeds,
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                params["huddle_forward"],
                params["huddle_speed"],
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                animal_id=_id,
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            )
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        )
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        tag_dict[_id + undercond + "dig"] = deepof.utils.smooth_boolean_array(
            dig(
                speeds,
                likelihoods,
                params["huddle_speed"],
                params["nose_likelihood"],
                animal_id=_id,
            )
        )
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        tag_dict[_id + undercond + "lookaround"] = deepof.utils.smooth_boolean_array(
            look_around(
                speeds,
                likelihoods,
                params["huddle_speed"],
                params["nose_likelihood"],
                animal_id=_id,
            )
        )
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    tag_df = pd.DataFrame(tag_dict)

    return tag_df


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def tag_rulebased_frames(
    frame,
    font,
    frame_speeds,
    animal_ids,
    corners,
    tag_dict,
    fnum,
    undercond,
    hparams,
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    arena,
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    debug,
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    coords,
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):
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    """Helper function for rule_based_video. Annotates a given frame with on-screen information
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    about the recognised patterns"""

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    arena, w, h = arena
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    def write_on_frame(text, pos, col=(255, 255, 255)):
        """Partial closure over cv2.putText to avoid code repetition"""
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        return cv2.putText(frame, text, pos, font, 0.75, col, 2)
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    def conditional_flag():
        """Returns a tag depending on a condition"""
        if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
            return left_flag
        else:
            return right_flag

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    def conditional_pos():
        """Returns a position depending on a condition"""
        if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
            return corners["downleft"]
        else:
            return corners["downright"]

    def conditional_col(cond=None):
        """Returns a colour depending on a condition"""
        if cond is None:
            cond = frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]
        if cond:
            return 150, 255, 150
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        else:
            return 150, 150, 255
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    # Keep track of space usage in the output video
    # The flags are set to False as soon as the lower
    # corners are occupied with text
    left_flag, right_flag = True, True

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    if debug:
        # Print arena for debugging
        cv2.ellipse(frame, arena[0], arena[1], arena[2], 0, 360, (0, 255, 0), 3)
        # Print body parts for debuging
        for bpart in coords.columns.levels[0]:
            if not np.isnan(coords[bpart]["x"][fnum]):
                cv2.circle(
                    frame,
                    (int(coords[bpart]["x"][fnum]), int(coords[bpart]["y"][fnum])),
                    radius=3,
                    color=(
                        (255, 0, 0) if bpart.startswith(animal_ids[0]) else (0, 0, 255)
                    ),
                    thickness=-1,
                )
        # Print frame number
        write_on_frame("Frame " + str(fnum), (int(w * 0.3 / 10), int(h / 1.15)))
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    if len(animal_ids) > 1:
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        if tag_dict["nose2nose"][fnum]:
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            write_on_frame("Nose-Nose", conditional_pos())
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            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

        if tag_dict[animal_ids[0] + "_nose2body"][fnum] and left_flag:
            write_on_frame("nose2body", corners["downleft"])
            left_flag = False

        if tag_dict[animal_ids[1] + "_nose2body"][fnum] and right_flag:
            write_on_frame("nose2body", corners["downright"])
            right_flag = False

        if tag_dict[animal_ids[0] + "_nose2tail"][fnum] and left_flag:
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            write_on_frame("Nose-Tail", corners["downleft"])
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            left_flag = False

        if tag_dict[animal_ids[1] + "_nose2tail"][fnum] and right_flag:
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            write_on_frame("Nose-Tail", corners["downright"])
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            right_flag = False

        if tag_dict["sidebyside"][fnum] and left_flag and conditional_flag():
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            write_on_frame(
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                "Side-side",
                conditional_pos(),
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            )
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            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

        if tag_dict["sidereside"][fnum] and left_flag and conditional_flag():
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            write_on_frame(
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                "Side-Rside",
                conditional_pos(),
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            )
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            if frame_speeds[animal_ids[0]] > frame_speeds[animal_ids[1]]:
                left_flag = False
            else:
                right_flag = False

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    zipped_pos = list(
        zip(
            animal_ids,
            [corners["downleft"], corners["downright"]],
            [corners["upleft"], corners["upright"]],
            [left_flag, right_flag],
        )
    )

    for _id, down_pos, up_pos, flag in zipped_pos:

        if flag:

            if tag_dict[_id + undercond + "climbing"][fnum]:
                write_on_frame("climbing", down_pos)
            elif tag_dict[_id + undercond + "huddle"][fnum]:
                write_on_frame("huddling", down_pos)
            elif tag_dict[_id + undercond + "sniffing"][fnum]:
                write_on_frame("sniffing", down_pos)
            elif tag_dict[_id + undercond + "dig"][fnum]:
                write_on_frame("digging", down_pos)
            elif tag_dict[_id + undercond + "lookaround"][fnum]:
                write_on_frame("lookaround", down_pos)

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        #     if (
        #         tag_dict[_id + "_following"][fnum]
        #         and not tag_dict[_id + "_climbing"][fnum]
        #     ):
        #         write_on_frame(
        #             "*f",
        #             (int(w * 0.3 / 10), int(h / 10)),
        #             conditional_col(),
        #         )
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        # Define the condition controlling the colour of the speed display
        if len(animal_ids) > 1:
            colcond = frame_speeds[_id] == max(list(frame_speeds.values()))
        else:
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            colcond = hparams["huddle_speed"] < frame_speeds
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        write_on_frame(
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            str(
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                np.round(
                    (frame_speeds if len(animal_ids) == 1 else frame_speeds[_id]), 2
                )
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            )
            + " mmpf",
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            up_pos,
            conditional_col(cond=colcond),
        )


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# noinspection PyProtectedMember,PyDefaultArgument
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def rule_based_video(
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    coordinates: Coordinates,
    tracks: List,
    videos: List,
    vid_index: int,
    tag_dict: pd.DataFrame,
    frame_limit: int = np.inf,
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    recog_limit: int = 100,
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    path: str = os.path.join("."),
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    params: dict = {},
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    debug: bool = False,
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) -> True:
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    """Renders a version of the input video with all rule-based taggings in place.

    Parameters:
        - tracks (list): list containing experiment IDs as strings
        - videos (list): list of videos to load, in the same order as tracks
        - coordinates (deepof.preprocessing.coordinates): coordinates object containing the project information
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        - debug (bool): if True, several debugging attributes (such as used body parts and arena) are plotted in
        the output video
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        - vid_index (int): index in videos of the experiment to annotate
        - fps (float): frames per second of the analysed video. Same as input by default
        - path (str): directory in which the experimental data is stored
        - frame_limit (float): limit the number of frames to output. Generates all annotated frames by default
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        - recog_limit (int): number of frames to use for arena recognition (100 by default)
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        - params (dict): dictionary to overwrite the default values of the hyperparameters of the functions
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        that the rule-based pose estimation utilizes. Values can be:
            - speed_pause (int): size of the rolling window to use when computing speeds
            - close_contact_tol (int): maximum distance between single bodyparts that can be used to report the trait
            - side_contact_tol (int): maximum distance between single bodyparts that can be used to report the trait
            - follow_frames (int): number of frames during which the following trait is tracked
            - follow_tol (int): maximum distance between follower and followed's path during the last follow_frames,
            in order to report a detection
            - huddle_forward (int): maximum distance between ears and forward limbs to report a huddle detection
            - huddle_speed (int): maximum speed to report a huddle detection

    Returns:
        True

    """

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    # DATA OBTENTION AND PREPARATION
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    params = get_hparameters(params)
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    animal_ids = coordinates._animal_ids
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    undercond = "_" if len(animal_ids) > 1 else ""
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    try:
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        vid_name = re.findall("(.*)DLC", tracks[vid_index])[0]
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    except IndexError:
        vid_name = tracks[vid_index]
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    arena, h, w = deepof.utils.recognize_arena(
        videos, vid_index, path, recog_limit, coordinates._arena
    )
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    corners = frame_corners(h, w)
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    cap = cv2.VideoCapture(os.path.join(path, videos[vid_index]))
    # Keep track of the frame number, to align with the tracking data
    fnum = 0
    writer = None
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    frame_speeds = (
        {_id: -np.inf for _id in animal_ids} if len(animal_ids) > 1 else -np.inf
    )
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    # Loop over the frames in the video
    while cap.isOpened() and fnum < frame_limit:
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        ret, frame = cap.read()
        # if frame is read correctly ret is True
        if not ret:  # pragma: no cover
            print("Can't receive frame (stream end?). Exiting ...")
            break
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        font = cv2.FONT_HERSHEY_DUPLEX
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        # Capture speeds
        try:
            if (
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                list(frame_speeds.values())[0] == -np.inf
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                or fnum % params["speed_pause"] == 0
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            ):
                for _id in animal_ids:
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                    frame_speeds[_id] = tag_dict[_id + undercond + "speed"][fnum]
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        except AttributeError:
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            if frame_speeds == -np.inf or fnum % params["speed_pause"] == 0:
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                frame_speeds = tag_dict["speed"][fnum]
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        # Display all annotations in the output video
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        tag_rulebased_frames(
            frame,
            font,
            frame_speeds,
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            animal_ids,
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            corners,
            tag_dict,
            fnum,
            undercond,
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            params,
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            (arena, h, w),
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            debug,
            coordinates.get_coords(center=False)[vid_name],
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        )

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        if writer is None:
            # Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file.
            # Define the FPS. Also frame size is passed.
            writer = cv2.VideoWriter()
            writer.open(
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                vid_name + "_tagged.avi",
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                cv2.VideoWriter_fourcc(*"MJPG"),
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                params["fps"],
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                (frame.shape[1], frame.shape[0]),
                True,
            )
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        writer.write(frame)
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        fnum += 1
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    cap.release()
    cv2.destroyAllWindows()
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    return True
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# TODO:
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#    - Is border sniffing anything you might consider interesting?