from __future__ import annotations

from dataclasses import dataclass, field
from typing import Protocol

import cv2
import numpy as np


@dataclass
class OcrLine:
    text: str
    confidence: float | None = None
    bbox: list[list[float]] | None = None


@dataclass
class OcrResult:
    text: str = ""
    confidence: float | None = None
    lines: list[OcrLine] = field(default_factory=list)
    error: str | None = None
    elapsed_ms: float = 0.0
    engine: str = "none"


class OcrEngine(Protocol):
    name: str

    def read_label(self, frame_bgr: np.ndarray, bbox: tuple[int, int, int, int]) -> OcrResult:
        ...


def crop_bbox(frame_bgr: np.ndarray, bbox: tuple[int, int, int, int], margin: int = 0) -> np.ndarray | None:
    x1, y1, x2, y2 = bbox
    h, w = frame_bgr.shape[:2]
    x1, y1 = max(0, x1 - margin), max(0, y1 - margin)
    x2, y2 = min(w, x2 + margin), min(h, y2 + margin)
    if x2 <= x1 or y2 <= y1:
        return None
    return frame_bgr[y1:y2, x1:x2]


def prepare_ocr_image(image_bgr: np.ndarray, config: dict) -> np.ndarray:
    scale = float(config.get("scale", 1.0))
    if scale != 1.0:
        image_bgr = cv2.resize(image_bgr, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC)

    if not config.get("threshold", False):
        return image_bgr

    gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (3, 3), 0)
    return cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]