from pathlib import Path
import logging
import torch
from ultralytics import YOLO
import cv2
from dodgeballtracker.helper import iter_frames
from dodgeballtracker.video_writer import VideoWriter, DetectedObjectBox
from dodgeballtracker.data_dumper import DataDumper
import numpy as np
import pandas as pd
import pandera as pa
from boxmot import DeepOcSort as DeepOCSORT
from .typing import VideoFrame, DetectedObject, TrackedDetectedObject

logger = logging.getLogger(__name__)

Color = tuple[int, int, int]


def get_best_available_torch_device() -> torch.device:
    if torch.cuda.is_available():
        logger.debug("Using the cuda (gpu) torch device.")
        return torch.device('cuda:0')
    if torch.backends.mps.is_available():
        logger.debug("Using the mps (apple silicon) torch device.")
        return torch.device('mps')
    logger.debug("Default to cpu (no apple silicon or cuda) torch device.")
    return torch.device('cpu')



class BirdViewCreator:
    def __init__(self, view_perspective_matrix: np.matrix):
        self._view_perspective_matrix = view_perspective_matrix

    @pa.check_io(df=TrackedDetectedObject, out=TrackedDetectedObject)
    def append_bird_view(self, df: pd.DataFrame) -> pd.DataFrame:
        v = np.matrix([
            pd.Series(dtype='float32', data=np.ones(df.shape[0])),
            df['xmin'],
            df['xmax']
        ])
        ret = self._view_perspective_matrix @ v
        ret_df = pd.DataFrame(ret).rename(columns={0: 'physical_x', 1: 'physical_y'})
        return df.join(ret_df)



class DodgeballTracker:

    def __init__(self,
                 input_file: Path,
                 video_writer: VideoWriter,
                 data_dumper: DataDumper,
                 detected_box_objects: list[DetectedObjectBox],
                 bird_view_creator: BirdViewCreator):
        self.input_path = input_file
        self.video_writer = video_writer
        self._data_dumper = data_dumper
        self.detected_box_objects = sorted(detected_box_objects, reverse=True)
        self._bird_view_creator = bird_view_creator


    @pa.check_output(DetectedObject)
    def run_model_on_frame(self, model, frame: VideoFrame):
        model_outputs = model(frame)[0]
        boxes = model_outputs.boxes.data.cpu().numpy()
        columns=['xmin', 'ymin', 'xmax', 'ymax', 'score', 'category']
        ret = pd.DataFrame(boxes, columns=columns)
        return ret


    def _get_tracker(self) -> DeepOCSORT:
        return DeepOCSORT(
            Path('lmbn_n_cuhk03_d.pt'),  # which ReID model to use
            get_best_available_torch_device(),
            True,
        )

    def _get_detection_model(self):
        """
        load the pre-trained YOLOv8n model
        """
        my_model = Path(
            '/home/didier/all-dressed-programming/dodgeball-tracker/runs/detect/train31/weights/best.pt')
        return YOLO(my_model, verbose=False)

    @pa.check_io(detected_objs=DetectedObject, out=TrackedDetectedObject)
    def update_tracker(self, tracker: DeepOCSORT, detected_objs: pd.DataFrame, frame: VideoFrame):
        data = tracker.update(detected_objs.to_numpy(), frame)
        columns = ['xmin', 'ymin', 'xmax', 'ymax', 'object_id', 'score', 'category', 'index']
        if not data.size:
            return pd.DataFrame({col: pd.Series(dtype='float64') for col in columns})
        return pd.DataFrame(data, columns=columns)

    def main(self):
        # define some constants
        video_cap = cv2.VideoCapture(str(self.input_path))
        tracker = self._get_tracker()
        model = self._get_detection_model()

        for i, (frame, ts) in enumerate(iter_frames(video_cap)):
            # run the YOLO model on the frame
            detected_objs = self.run_model_on_frame(model, frame)
            tracked_obj = self.update_tracker(tracker, detected_objs, frame)
            self._data_dumper.write_boxes(
                self._bird_view_creator.append_bird_view(tracked_obj),
                ts,
                i)
            self.video_writer.plot_boxes_to_frame(tracked_obj, frame)
            self.video_writer.write_frame(frame)

            if self.video_writer.wait():
                break

        video_cap.release()