__author__ = "Simon Nilsson"


import glob
import os
from copy import deepcopy
from typing import Dict, List, Optional, Union

import cv2
import numpy as np
from PIL import Image

from simba.mixins.config_reader import ConfigReader
from simba.mixins.plotting_mixin import PlottingMixin
from simba.mixins.train_model_mixin import TrainModelMixin
from simba.utils.checks import (check_file_exist_and_readable, check_float,
                                check_int)
from simba.utils.data import create_color_palette
from simba.utils.enums import ConfigKey, Dtypes, Formats
from simba.utils.errors import NoSpecifiedOutputError
from simba.utils.printing import stdout_success
from simba.utils.read_write import (get_fn_ext, get_video_meta_data,
                                    read_config_entry, read_df)


class PlotSklearnResultsSingleCore(ConfigReader, TrainModelMixin, PlottingMixin):
    """
    Plot classification results overlays on videos. Results are stored in the
    `project_folder/frames/output/sklearn_results` directory of the SimBA project.

    .. note::
       For improved run-time, see :meth:`simba.plotting.plot_clf_results_mp.PlotSklearnResultsMultiProcess` for multiprocess class.
       Scikit visualization documentation <https://github.com/sgoldenlab/simba/blob/master/docs/tutorial.md#step-10-sklearn-visualization__.

    :param str config_path: path to SimBA project config file in Configparser format
    :param bool rotate: If True, the output video will be rotated 90 degrees from the input.
    :param bool video_setting: If True, SimBA will create compressed videos.
    :param bool frame_setting: If True, SimBA will create individual frames.
    :param Optional[dict] text_settings:
    :param str video_file_path: path to video file to create classification visualizations for.

    Examples
    ----------
    >>> clf_plotter = PlotSklearnResultsSingleCore(config_path='MyProjectConfig', video_setting=True, frame_setting=False, rotate=False, video_file_path='VideoPath')
    >>> clf_plotter.run()
    """

    def __init__(
        self,
        config_path: str,
        video_setting: bool,
        frame_setting: bool,
        text_settings: Optional[Union[Dict[str, float], bool]] = False,
        video_file_path: Optional[List] = None,
        rotate: bool = False,
        print_timers: bool = True,
    ):
        ConfigReader.__init__(self, config_path=config_path)
        TrainModelMixin.__init__(self)
        PlottingMixin.__init__(self)

        if (not video_setting) and (not frame_setting):
            raise NoSpecifiedOutputError(
                msg="Please choose to create a video and/or frames. SimBA found that you ticked neither video and/or frames"
            )
        self.video_file_path, self.print_timers, self.text_settings = (
            video_file_path,
            print_timers,
            text_settings,
        )
        self.video_setting, self.frame_setting = video_setting, frame_setting
        if video_file_path is not None:
            check_file_exist_and_readable(os.path.join(self.video_dir, video_file_path))
        if not os.path.exists(self.sklearn_plot_dir):
            os.makedirs(self.sklearn_plot_dir)
        self.pose_threshold = read_config_entry(
            self.config,
            ConfigKey.THRESHOLD_SETTINGS.value,
            ConfigKey.SKLEARN_BP_PROB_THRESH.value,
            Dtypes.FLOAT.value,
            0.00,
        )
        self.clr_lst = create_color_palette(
            pallete_name="Set1", increments=self.clf_cnt
        )
        self.files_found = glob.glob(self.machine_results_dir + "/*." + self.file_type)
        self.model_dict = self.get_model_info(self.config, self.clf_cnt)
        self.fourcc = cv2.VideoWriter_fourcc(*Formats.MP4_CODEC.value)
        self.rotate = rotate
        self.a = np.deg2rad(90)
        print(f"Processing {str(len(self.files_found))} videos...")

    def __get_print_settings(self):
        if self.text_settings is False:
            self.space_scale, self.radius_scale, self.res_scale, self.font_scale = (
                60,
                12,
                1500,
                1.1,
            )
            self.max_dim = max(
                self.video_meta_data["width"], self.video_meta_data["height"]
            )
            self.circle_scale = int(self.radius_scale / (self.res_scale / self.max_dim))
            self.font_size = float(self.font_scale / (self.res_scale / self.max_dim))
            self.spacing_scale = int(self.space_scale / (self.res_scale / self.max_dim))
            self.text_thickness = 2
        else:
            check_float(name="ERROR: TEXT SIZE", value=self.text_settings["font_size"])
            check_int(name="ERROR: SPACE SIZE", value=self.text_settings["space_size"])
            check_int(
                name="ERROR: TEXT THICKNESS", value=self.text_settings["text_thickness"]
            )
            check_int(
                name="ERROR: CIRCLE SIZE", value=self.text_settings["circle_size"]
            )
            self.font_size = float(self.text_settings["font_size"])
            self.spacing_scale = int(self.text_settings["space_size"])
            self.text_thickness = int(self.text_settings["text_thickness"])
            self.circle_scale = int(self.text_settings["circle_size"])

    def create_visualizations(self):
        _, self.video_name, _ = get_fn_ext(self.file_path)
        self.data_df = read_df(self.file_path, self.file_type).reset_index(drop=True)
        self.video_settings, _, self.fps = self.read_video_info(
            video_name=self.video_name
        )
        self.video_path = self.find_video_of_file(self.video_dir, self.video_name)
        self.cap = cv2.VideoCapture(self.video_path)
        self.save_path = os.path.join(self.sklearn_plot_dir, self.video_name + ".mp4")
        self.video_meta_data = get_video_meta_data(self.video_path)
        height, width = deepcopy(self.video_meta_data["height"]), deepcopy(
            self.video_meta_data["width"]
        )
        if self.frame_setting:
            self.video_frame_dir = os.path.join(self.sklearn_plot_dir, self.video_name)
            if not os.path.exists(self.video_frame_dir):
                os.makedirs(self.video_frame_dir)
        if self.rotate:
            self.video_meta_data["height"], self.video_meta_data["width"] = (
                width,
                height,
            )
        self.writer = cv2.VideoWriter(
            self.save_path,
            self.fourcc,
            self.fps,
            (self.video_meta_data["width"], self.video_meta_data["height"]),
        )
        self.__get_print_settings()
        self.video_model_dict = deepcopy(self.model_dict)
        for model in self.video_model_dict:
            self.video_model_dict[model]["time"] = 0

        row_n = 0
        while self.cap.isOpened():
            ret, self.frame = self.cap.read()
            try:
                if ret:
                    self.id_flag_cords = {}
                    for animal_name, animal_data in self.animal_bp_dict.items():
                        animal_clr = animal_data["colors"]
                        ID_flag = False
                        for bp_no in range(len(animal_data["X_bps"])):
                            bp_clr = animal_clr[bp_no]
                            x_bp, y_bp = (
                                animal_data["X_bps"][bp_no],
                                animal_data["Y_bps"][bp_no],
                            )
                            p_bp = x_bp[:-2] + "_p"
                            bp_cords = self.data_df.loc[row_n, [x_bp, y_bp, p_bp]]
                            if bp_cords[p_bp] > self.pose_threshold:
                                cv2.circle(
                                    self.frame,
                                    (int(bp_cords[x_bp]), int(bp_cords[y_bp])),
                                    0,
                                    bp_clr,
                                    self.circle_scale,
                                )
                                if ("centroid" in x_bp.lower()) or (
                                    "center" in x_bp.lower()
                                ):
                                    self.id_flag_cords[animal_name] = (
                                        int(bp_cords[x_bp]),
                                        int(bp_cords[y_bp]),
                                    )
                                    ID_flag = True

                        if not ID_flag:
                            self.id_flag_cords[animal_name] = (
                                int(bp_cords[x_bp]),
                                int(bp_cords[y_bp]),
                            )

                    for animal_name, animal_cords in self.id_flag_cords.items():
                        cv2.putText(
                            self.frame,
                            animal_name,
                            animal_cords,
                            self.font,
                            self.font_size,
                            self.animal_bp_dict[animal_name]["colors"][0],
                            2,
                        )

                    if self.rotate:
                        self.frame = np.array(
                            Image.fromarray(self.frame).rotate(
                                90, Image.BICUBIC, expand=True
                            )
                        )
                    if self.print_timers:
                        cv2.putText(
                            self.frame,
                            str("Timers"),
                            (
                                10,
                                (
                                    (
                                        self.video_meta_data["height"]
                                        - self.video_meta_data["height"]
                                    )
                                    + self.spacing_scale
                                ),
                            ),
                            self.font,
                            self.font_size,
                            (0, 255, 0),
                            self.text_thickness,
                        )
                    self.add_spacer = 2
                    for model_no, model_info in self.video_model_dict.items():
                        frame_results = self.data_df.loc[
                            row_n, model_info["model_name"]
                        ]
                        self.video_model_dict[model_no]["frame_results"] = frame_results
                        self.video_model_dict[model_no]["time"] += (
                            frame_results / self.fps
                        )
                        if self.print_timers:
                            cv2.putText(
                                self.frame,
                                model_info["model_name"]
                                + " "
                                + str(round(self.video_model_dict[model_no]["time"], 2))
                                + str("s"),
                                (
                                    10,
                                    (
                                        self.video_meta_data["height"]
                                        - self.video_meta_data["height"]
                                    )
                                    + self.spacing_scale * self.add_spacer,
                                ),
                                self.font,
                                self.font_size,
                                (255, 0, 0),
                                self.text_thickness,
                            )
                            self.add_spacer += 1
                    cv2.putText(
                        self.frame,
                        str("Ensemble prediction"),
                        (
                            10,
                            (
                                self.video_meta_data["height"]
                                - self.video_meta_data["height"]
                            )
                            + self.spacing_scale * self.add_spacer,
                        ),
                        self.font,
                        self.font_size,
                        (0, 255, 0),
                        self.text_thickness,
                    )
                    self.add_spacer += 1

                    for model_cnt, model_info in self.video_model_dict.items():
                        if self.video_model_dict[model_cnt]["frame_results"] == 1:
                            cv2.putText(
                                self.frame,
                                model_info["model_name"],
                                (
                                    10,
                                    (
                                        self.video_meta_data["height"]
                                        - self.video_meta_data["height"]
                                    )
                                    + self.spacing_scale * self.add_spacer,
                                ),
                                self.font,
                                self.font_size,
                                self.clr_lst[model_cnt],
                                self.text_thickness,
                            )
                            self.add_spacer += 1
                    if self.video_setting:
                        self.writer.write(self.frame)
                    if self.frame_setting:
                        frame_save_name = os.path.join(
                            self.video_frame_dir, str(row_n) + ".png"
                        )
                        cv2.imwrite(frame_save_name, self.frame)
                    print(
                        f'Frame: {row_n} / {self.video_meta_data["frame_count"]}. Video: {self.video_name} ({self.file_cnt + 1}/{len(self.files_found)})'
                    )
                    row_n += 1

                else:
                    print("Video {} saved...".format(self.video_name))
                    self.cap.release()
                    self.writer.release()
            except KeyError as e:
                print(e.args, e)
                print(
                    "SIMBA INDEX WARNING: Some frames appears to be missing in the dataframe and could not be created"
                )
                print("Video {} saved...".format(self.video_name))
                self.cap.release()
                self.writer.release()

    def run(self):
        if self.video_file_path is None:
            for file_cnt, file_path in enumerate(self.files_found):
                self.file_cnt, self.file_path = file_cnt, file_path
                self.create_visualizations()
        else:
            self.file_cnt, file_path = 0, self.video_file_path
            _, file_name, _ = get_fn_ext(file_path)
            self.file_path = os.path.join(
                self.machine_results_dir, file_name + "." + self.file_type
            )
            self.files_found = [self.file_path]
            check_file_exist_and_readable(self.file_path)
            self.create_visualizations()

        self.timer.stop_timer()
        stdout_success(
            msg="All visualizations created in project_folder/frames/output/sklearn_results directory",
            elapsed_time=self.timer.elapsed_time_str,
        )


# test = PlotSklearnResultsSingleCore(config_path='/Users/simon/Desktop/envs/troubleshooting/two_black_animals_14bp/project_folder/project_config.ini',
#                                       video_setting=True,
#                                       frame_setting=False,
#                                       video_file_path='/Users/simon/Desktop/envs/troubleshooting/two_black_animals_14bp/project_folder/videos/Together_1.avi',
#                                       print_timers=True,
#                                       text_settings=False,
#                                       rotate=False)
# test.run()
