"""二维码识别工具。"""

from __future__ import annotations

from pathlib import Path

import cv2
import numpy as np

from .cv import _load_image, _normalize_points, _warp_qr_image


def _decode_with_detector(img: np.ndarray) -> list[str]:
    detector = cv2.QRCodeDetector()
    texts: list[str] = []

    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, (list, tuple)):
                decoded_iter = [item or "" for item in decoded_info]
            else:
                decoded_iter = [decoded_info or ""]

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

    text, points, _ = detector.detectAndDecode(img)
    if text:
        return [text]

    normalized_points = _normalize_points(points)
    if normalized_points:
        crop = _warp_qr_image(img, np.array(normalized_points[0], dtype=np.float32))
        fallback_text, _, _ = detector.detectAndDecode(crop)
        if fallback_text:
            return [fallback_text]

    return []


def decode_qr(image: bytes | np.ndarray | str | Path) -> list[str]:
    """识别图片中的二维码内容。"""
    img = _load_image(image)
    return _decode_with_detector(img)