import queue
import re
import threading
from concurrent.futures import ThreadPoolExecutor
from typing import Dict, Optional

import cv2
import easyocr
import numpy as np
import torch
import torch.nn as nn


class LicensePlateReader(nn.Module):
    def __init__(
        self,
        model,
        char_to_num_mappings: Optional[Dict[str, str]] = None,
        num_to_char_mappings: Optional[Dict[str, str]] = None,
        confidence: float = 0.30,
        queue_size: int = 10,
    ):
        """
        Initialize the LicensePlateReader with the given model and mappings.
        Args:.
            model OCR model for reading text.
            char_to_num_mappings Mappings from characters to numbers.
            num_to_char_mappings Mappings from numbers to characters.
            confidence threshold for accepting OCR results.
        """
        super(LicensePlateReader, self).__init__()

        # Initializing
        self.char_to_num_mappings = char_to_num_mappings or {
            "L": "4",
            "D": "0",
            "S": "5",
            "Z": "2",
            "B": "8",
            "C": "0",
        }
        self.num_to_char_mappings = num_to_char_mappings or {
            "2": "Z",
            "4": "A",
            "6": "G",
            "5": "S",
            "0": "D",
            "7": "T",
            "8": "B",
        }
        self.model = model
        self.confidence = confidence

        self.input_queue = queue.Queue(maxsize=queue_size)
        self.output_queue = queue.Queue(maxsize=queue_size)
        self.executor = ThreadPoolExecutor(max_workers=2)
        self.processing_thread = threading.Thread(
            target=self._process_queue, daemon=True
        )
        self.processing_thread.start()

    def forward(self, numbers_side: np.ndarray, letters_side: np.ndarray) -> str:
        self.input_queue.put((numbers_side, letters_side))
        return self.output_queue.get()

    def _process_queue(self):
        while True:
            numbers_side, letters_side = self.input_queue.get()
            result = self._process_single_plate(numbers_side, letters_side)
            self.output_queue.put(result)
            self.input_queue.task_done()

    def _process_single_plate(
        self, numbers_side: np.ndarray, letters_side: np.ndarray
    ) -> str:
        future_preprocessed_numbers = self.executor.submit(
            self._pre_process, numbers_side
        )
        future_preprocessed_letters = self.executor.submit(
            self._pre_process, letters_side
        )

        preprocessed_numbers_side = future_preprocessed_numbers.result()
        preprocessed_letters_side = future_preprocessed_letters.result()

        future_extracted_numbers = self.executor.submit(
            self.predict, preprocessed_numbers_side
        )
        future_extracted_letters = self.executor.submit(
            self.predict, preprocessed_letters_side
        )

        extracted_numbers_side = future_extracted_numbers.result()
        extracted_letters_side = future_extracted_letters.result()

        future_postprocessed_numbers = self.executor.submit(
            self._post_process, extracted_numbers_side, True
        )
        future_postprocessed_letters = self.executor.submit(
            self._post_process, extracted_letters_side, False
        )

        postprocessed_numbers_side = future_postprocessed_numbers.result()
        postprocessed_letters_side = future_postprocessed_letters.result()

        return postprocessed_numbers_side + "" + postprocessed_letters_side

    def _pre_process(self, frame: np.ndarray) -> np.ndarray:
        """
        Preprocess the input frame by blurring, grayscaling, and thresholding.
        Args:
            frame Input image frame.
        Returns:
            Preprocessed binary image.
        """
        # Blurring
        # blurred_frame = cv2.GaussianBlur(frame, (3, 3), 0)

        # greyscaleing
        greyscaled_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # thresholding white and black
        _, binary_frame = cv2.threshold(
            greyscaled_frame, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU
        )
        return binary_frame

    def predict(self, frame: np.ndarray) -> str:
        """
        Predict text from the preprocessed frame using the OCR model.
        Args:
            frame Preprocessed image frame.
        Returns:
            Extracted text if confidence is above threshold, else an empty string.
        """
        # OCR model extraction
        extraction = self.model.readtext(frame)

        # loop all text extractions that above the confidence
        for _, text, confidence in extraction:
            if confidence > self.confidence:
                return extraction[-1][1]

        # Error handling
        return ""  # raise Exception No OCR reading

    def _post_process(self, extracted_text: str, is_numbers: bool) -> str:
        if not extracted_text:
            return ""

        if is_numbers:
            result = extracted_text.strip()
            result = "".join(
                self.char_to_num_mappings.get(char, char) for char in result
            )

            result = "".join(re.findall(r"\b([0-9]{1,4})\b", result))
            # if not result.isdigit() or len(result) > 4:
            #     return ""
            return result
        else:
            result = extracted_text.strip().upper()
            result = "".join(
                self.num_to_char_mappings.get(char, char) for char in result
            )
            result = "".join(re.findall(r"[A-Z]{3}", result))
            if len(result) != 3:
                return ""
            return result

    def annotate_frame(self, frame, bbox, extracted_text):
        if bbox is not None:
            color = (0, 255, 0) if extracted_text else (0, 0, 255)
            label = "No Extraction" if not extracted_text else extracted_text
            cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color, 2)
            cv2.putText(
                frame,
                f"{label}",
                (bbox[0], bbox[1] - 10),  # Top left above the bounding box
                cv2.FONT_HERSHEY_SIMPLEX,
                0.9,
                color,
                2,
            )
        return frame