"""二维码工具。

基于 OpenCV 的 QRCodeDetector 完成二维码检测、裁剪与解码。
"""

from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
from typing import Iterable

import cv2
import numpy as np


@dataclass
class QrCodeResult:
    """单个二维码识别结果。"""

    text: str
    points: tuple[tuple[int, int], ...]
    crop: np.ndarray | None = None


@dataclass
class QrScanResult:
    """二维码扫描结果。"""

    has_qr: bool
    items: list[QrCodeResult]


def _load_image(image: bytes | np.ndarray | str | Path) -> np.ndarray:
    """把输入转成 BGR 图像。"""
    if isinstance(image, np.ndarray):
        if image.ndim == 2:
            return cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        return image.copy()

    if isinstance(image, (str, Path)):
        data = Path(image).read_bytes()
    else:
        data = image

    arr = np.frombuffer(data, dtype=np.uint8)
    img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
    if img is None:
        raise ValueError("Failed to decode image data.")
    return img


def _order_points(points: np.ndarray) -> np.ndarray:
    """把四个角点整理成左上、右上、右下、左下。"""
    pts = np.asarray(points, dtype=np.float32).reshape(4, 2)
    rect = np.zeros((4, 2), dtype=np.float32)

    s = pts.sum(axis=1)
    diff = np.diff(pts, axis=1)

    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect


def _warp_qr_image(img: np.ndarray, points: np.ndarray) -> np.ndarray:
    """按四边形点做透视矫正，截取二维码区域。"""
    rect = _order_points(points)
    (tl, tr, br, bl) = rect

    width_a = np.linalg.norm(br - bl)
    width_b = np.linalg.norm(tr - tl)
    height_a = np.linalg.norm(tr - br)
    height_b = np.linalg.norm(tl - bl)

    width = max(int(round(max(width_a, width_b))), 1)
    height = max(int(round(max(height_a, height_b))), 1)

    dst = np.array(
        [
            [0, 0],
            [width - 1, 0],
            [width - 1, height - 1],
            [0, height - 1],
        ],
        dtype=np.float32,
    )

    matrix = cv2.getPerspectiveTransform(rect, dst)
    return cv2.warpPerspective(img, matrix, (width, height))


def _normalize_points(points: np.ndarray | None) -> list[tuple[tuple[int, int], ...]]:
    if points is None:
        return []

    arr = np.asarray(points)
    if arr.ndim == 2 and arr.shape == (4, 2):
        arr = arr[None, ...]
    elif arr.ndim == 3 and arr.shape[-2:] == (4, 2):
        pass
    else:
        return []

    result: list[tuple[tuple[int, int], ...]] = []
    for item in arr:
        quad = tuple((int(round(x)), int(round(y))) for x, y in item)
        result.append(quad)
    return result


def _decode_with_multi_detector(img: np.ndarray) -> tuple[list[str], list[tuple[tuple[int, int], ...]], list[np.ndarray]]:
    detector = cv2.QRCodeDetector()

    texts: list[str] = []
    points_list: list[tuple[tuple[int, int], ...]] = []
    crops: list[np.ndarray] = []

    if hasattr(detector, "detectAndDecodeMulti"):
        ok, decoded_info, points, _ = detector.detectAndDecodeMulti(img)
        if ok and points is not None:
            normalized_points = _normalize_points(points)
            if isinstance(decoded_info, Iterable) and not isinstance(
                decoded_info, (str, bytes)
            ):
                decoded_iter = [item or "" for item in decoded_info]
            else:
                decoded_iter = [decoded_info or ""]

            for idx, quad in enumerate(normalized_points):
                text = decoded_iter[idx] if idx < len(decoded_iter) else ""
                crop = _warp_qr_image(img, np.array(quad, dtype=np.float32))
                if not text:
                    fallback_text, _, _ = detector.detectAndDecode(crop)
                    text = fallback_text or ""
                texts.append(text)
                points_list.append(quad)
                crops.append(crop)

            return texts, points_list, crops

    text, points, _ = detector.detectAndDecode(img)
    normalized_points = _normalize_points(points)
    if normalized_points:
        crop = _warp_qr_image(img, np.array(normalized_points[0], dtype=np.float32))
        if not text:
            fallback_text, _, _ = detector.detectAndDecode(crop)
            text = fallback_text or ""
        return [text or ""], [normalized_points[0]], [crop]

    return [], [], []


def scan_qr(image: bytes | np.ndarray | str | Path) -> QrScanResult:
    """扫描图片中的二维码，返回是否存在、位置和文本。"""
    img = _load_image(image)
    texts, points_list, crops = _decode_with_multi_detector(img)

    items = [
        QrCodeResult(text=text, points=points, crop=crop)
        for text, points, crop in zip(texts, points_list, crops)
    ]
    return QrScanResult(has_qr=bool(items), items=items)


def has_qr(image: bytes | np.ndarray | str | Path) -> bool:
    """判断图片里有没有二维码。"""
    return scan_qr(image).has_qr


def crop_qr(image: bytes | np.ndarray | str | Path) -> list[np.ndarray]:
    """裁剪出图片中的二维码区域。"""
    return [item.crop for item in scan_qr(image).items if item.crop is not None]


def decode_qr(image: bytes | np.ndarray | str | Path) -> list[str]:
    """识别图片中的二维码内容。"""
    return [item.text for item in scan_qr(image).items if item.text]