"""二维码识别工具。

多级管线，从快到慢逐级重试，命中即止：
    1. zxing-cpp 原图（默认已开反色 / 旋转 / 缩放重试）
    2. zxing-cpp 换二值化算法（救低对比度、带纹理背景）
    3. zxing-cpp 放大图（救长图里的小码、低分辨率码）
    4. OpenCV 检测器 + 反色重试（zxing-cpp 缺失时的主路径）
    5. 先裁剪二维码区域、补静默区再放大解码（救贴边、占比极小的码）
"""

from __future__ import annotations

from pathlib import Path

import cv2
import numpy as np

from app.core.logger import log

from .cv import (
    _HAS_ZXING,
    _load_image,
    _normalize_points,
    _warp_qr_image,
    pad_quiet_zone,
    zxing_read,
    zxing_variants,
    zxingcpp,
)

# 裁剪区域重试时，把二维码放大到的目标边长
_CROP_TARGET_SIDE = 480


def _dedupe(texts: list[str]) -> list[str]:
    """去重且保持顺序。"""
    seen: set[str] = set()
    result: list[str] = []
    for text in texts:
        if text and text not in seen:
            seen.add(text)
            result.append(text)
    return result


def _texts_of(barcodes: list) -> list[str]:
    return _dedupe([getattr(item, "text", "") or "" for item in barcodes])


def _stage_zxing_plain(img: np.ndarray) -> list[str]:
    """原图直接交给 zxing-cpp。"""
    return _texts_of(zxing_read(img))


def _stage_zxing_binarizers(img: np.ndarray) -> list[str]:
    """换二值化算法重试，应对低对比度和带图案的背景。"""
    if not _HAS_ZXING:
        return []

    for binarizer in (
        zxingcpp.Binarizer.GlobalHistogram,
        zxingcpp.Binarizer.FixedThreshold,
        zxingcpp.Binarizer.BoolCast,
    ):
        texts = _texts_of(zxing_read(img, binarizer=binarizer))
        if texts:
            log.debug(f"二维码解码命中二值化算法: {binarizer}")
            return texts
    return []


def _stage_zxing_upscaled(img: np.ndarray) -> list[str]:
    """放大后重试，救小尺寸二维码。"""
    for name, variant in zxing_variants(img):
        if name == "原图":
            continue
        texts = _texts_of(zxing_read(variant, try_downscale=False))
        if texts:
            log.debug(f"二维码解码命中变体: {name}")
            return texts
    return []


def _decode_with_opencv(img: np.ndarray) -> list[str]:
    """OpenCV 检测器解码，含反色重试。"""
    texts: list[str] = []

    for candidate in (img, cv2.bitwise_not(img)):
        for detector in (cv2.QRCodeDetector(), cv2.QRCodeDetectorAruco()):
            try:
                ok, infos, points, _ = detector.detectAndDecodeMulti(candidate)
            except cv2.error:
                ok, infos, points = False, None, None

            if ok and infos is not None:
                found = [item for item in infos if item]
                if found:
                    texts.extend(found)

            if texts:
                return _dedupe(texts)

            try:
                text, points, _ = detector.detectAndDecode(candidate)
            except cv2.error:
                continue

            if text:
                return [text]

            # 检测到位置但没解出内容时，裁出区域再试一次
            for quad in _normalize_points(points):
                crop = _warp_qr_image(candidate, np.array(quad, dtype=np.float32))
                if crop.size == 0:
                    continue
                try:
                    fallback, _, _ = detector.detectAndDecode(crop)
                except cv2.error:
                    continue
                if fallback:
                    texts.append(fallback)

            if texts:
                return _dedupe(texts)

    return _dedupe(texts)


def _stage_opencv(img: np.ndarray) -> list[str]:
    return _decode_with_opencv(img)


def _stage_crop_retry(img: np.ndarray) -> list[str]:
    """先定位并裁出二维码，补静默区放大后再解码。

    针对二维码在长图里占比极小、或紧贴边缘没有留白的情况。
    """
    from .cv import _detect_regions  # 局部导入，避免循环依赖

    texts: list[str] = []
    for region in _detect_regions(img):
        if region.text:
            texts.append(region.text)
            continue

        crop = region.crop
        if crop is None or crop.size == 0:
            continue

        padded = pad_quiet_zone(crop)
        longest = max(padded.shape[:2])
        if longest and longest < _CROP_TARGET_SIDE:
            scale = _CROP_TARGET_SIDE / longest
            padded = cv2.resize(
                padded, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
            )

        found = _texts_of(zxing_read(padded, try_downscale=False))
        if not found:
            found = _decode_with_opencv(padded)
        texts.extend(found)

    return _dedupe(texts)


# 管线顺序：命中即返回
_STAGES = (
    ("zxing原图", _stage_zxing_plain),
    ("zxing二值化", _stage_zxing_binarizers),
    ("zxing放大", _stage_zxing_upscaled),
    ("opencv", _stage_opencv),
    ("裁剪重试", _stage_crop_retry),
)


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

    Args:
        image: 图片字节流、BGR 图像数组或图片路径。

    Returns:
        二维码内容列表，已去重；没识别出来时返回空列表。
    """
    img = _load_image(image)

    for name, stage in _STAGES:
        try:
            texts = stage(img)
        except Exception as exc:  # 单级失败不影响后续重试
            log.debug(f"二维码解码阶段异常({name}): {exc}")
            continue

        if texts:
            if name != "zxing原图":
                log.debug(f"二维码解码命中阶段: {name}")
            return texts

    return []