import json
from pathlib import Path

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



Color = tuple[int, int, int]


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)


class DodgeballTracker:

    def __init__(self,
                 input_file: Path,
                 output_file: Path,
                 detected_box_objects: list[DetectedObjectBox]):
        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 = self.get_court_perspective_transform()
        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 get_court_perspective_transform(self):
        """
        """
        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)

    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]
        self.points.append({"x": x, "y": y, "frameIndex": frame_idx, "frameTimestamp": frame_ts})

    def plot_boxe_to_frame(self, data, frame, frame_idx, frame_ts):
        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)
                self.print_box(data, frame_idx, frame_ts)
                cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), detected_box_object.color, 2)
                return data  # [[xmin, ymin, xmax - xmin, ymax - ymin], confidence, int(data[5])]

    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 main(self):
        # define some constants

        video_cap = cv2.VideoCapture(str(self.input_path))
        writer = create_video_writer(video_cap, str(self.output_path))
        tracker = DeepOCSORT(
            model_weights=Path('osnet_x0_25_msmt17.pt'),  # which ReID model to use
            device=torch.device("mps"),
            fp16=True,
        )

        # load the pre-trained YOLOv8n model
        model = YOLO("yolov8n.pt")

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

            tracks = tracker.update(np.array([r for r in results if r is not None]), img=frame)

            # show the frame to our screen
            self.write_frame(writer, frame)

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

        video_cap.release()
        writer.release()
        cv2.destroyAllWindows()
        self.persist_points()