import json
from pathlib import Path
import logging
import torch
from ultralytics import YOLO
import cv2
from dodgeballtracker.helper import create_video_writer, iter_frames
import matplotlib
import numpy as np
from boxmot import DeepOcSort as DeepOCSORT


logger = logging.getLogger(__name__)

Color = tuple[int, int, int]


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


class DetectedObjectBox:
    def __init__(self, min_threshold: float, color: Color):
        self.min_threshold = min_threshold
        self.color = color

    def __lt__(self, other):
        return self.min_threshold < other.min_threshold

    @classmethod
    def from_viridis(cls, min_threshold):
        cm = matplotlib.colormaps['viridis']
        color: Color = tuple((int(255 * cm(min_threshold)[i]) for i in range(3)))
        return cls(min_threshold, color)


def get_court_perspective_transform():
    """
    """
    empirical_top_left = np.float32([416, 175])
    empirical_top_right = np.float32([905, 173])
    empirical_bottom_left = np.float32([51, 702])
    empirical_bottom_right = np.float32([1188, 695])

    court_top_left = np.float32([0, 0])
    court_top_right = np.float32([9, 0])
    court_bottom_left = np.float32([0, 18])
    court_bottom_right = np.float32([9, 18])

    empirical_source = np.float32([empirical_top_left,
                                   empirical_top_right,
                                   empirical_bottom_left,
                                   empirical_bottom_right])
    pixel_per_meter = 50
    empirical_target = np.float32([court_top_left,
                                   court_top_right,
                                   court_bottom_left,
                                   court_bottom_right]) * pixel_per_meter
    return cv2.getPerspectiveTransform(empirical_source, empirical_target)


class DodgeballTracker:

    def __init__(self,
                 input_file: Path,
                 output_file: Path,
                 detected_box_objects: list[DetectedObjectBox],
                 transformation_matrix):
        self.input_path = input_file
        self.output_path = output_file
        self.output_json_path = output_file.with_suffix(".json")
        self.detected_box_objects = sorted(detected_box_objects, reverse=True)
        self.transformation_matrix = transformation_matrix
        self.points = []

    def write_frame(self, writer, frame) -> None:
        cv2.imshow("Frame", frame)
        writer.write(frame)

    def extract_box(self, data) -> tuple[int, int, int, int]:
        return int(data[0]), int(data[1]), int(data[2]), int(data[3])

    def print_box(self, data, frame_idx, frame_ts):
        xmin, ymin, xmax, ymax = self.extract_box(data)
        xavg = (xmax + xmin) / 2
        v = np.array([xavg, ymax, 1])
        ret = self.transformation_matrix @ v
        x, y = ret[0] / ret[2], ret[1] / ret[2]
        object_id = data[-1]
        self.points.append({"x": x,
                            "y": y,
                            "frameIndex": frame_idx,
                            "frameTimestamp": frame_ts,
                            "objectId": int(object_id)
                            })

    def print_tracker_data(self, tracker_data, frame_idx, frame_ts):
        for d in tracker_data:
            self.print_box(d, frame_idx, frame_ts)

    def plot_box_to_frame(self, data, frame):
        confidence = float(data[4])
        for detected_box_object in self.detected_box_objects:

            if detected_box_object.min_threshold < confidence:
                xmin, ymin, xmax, ymax = self.extract_box(data)
                cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), detected_box_object.color, 2)
                return True
        return False

    def run_model_on_frame(self, model, frame):
        return model(frame)[0]

    def crazy_experiment(self, frame):
        cv2.putText(frame,
                    "Hello world",
                    (50, 50),
                    cv2.FONT_HERSHEY_SIMPLEX,
                    2,
                    (0, 255, 0), 8)
        cv2.line(frame, (0, 0), (511, 511), (255, 0, 0), 5)
        pts = np.array([[50, 50], [500, 50], [500, 500], [50, 500], [50, 50]], np.int32)
        pts = pts.reshape((-1, 1, 2))

        cv2.polylines(frame, pts, True, (0, 255, 255), 8)

    def persist_points(self):
        with open(self.output_json_path, "wt") as file_:
            json.dump({"points": self.points}, file_, indent=4)

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

    def _get_detection_model(self):
        """
        load the pre-trained YOLOv8n model
        """
        return YOLO("yolo11n.pt", verbose=True)

    def main(self):
        # define some constants
        print("started")

        video_cap = cv2.VideoCapture(str(self.input_path))
        writer = create_video_writer(video_cap, str(self.output_path))
        tracker = self._get_tracker()

        # load the pre-trained YOLOv8n model
        model = self._get_detection_model()

        for i, (frame, ts) in enumerate(iter_frames(video_cap)):
            # run the YOLO model on the frame
            res = []
            detections = self.run_model_on_frame(model, frame)
            for data in detections.boxes.data.tolist():
                is_good_box = self.plot_box_to_frame(data, frame)
                if is_good_box:
                    res.append(data)

            # show the frame to our screen
            print("tracker?")
            data_ = tracker.update(np.array(res), frame)
            self.print_tracker_data(data_, i, ts)

            self.write_frame(writer, frame)

            if cv2.waitKey(1) == ord("q"):
                break
        print("fini")

        video_cap.release()
        writer.release()
        cv2.destroyAllWindows()
        self.persist_points()
        print("finished")