200 lines
5.7 KiB
Python
200 lines
5.7 KiB
Python
"""二维码识别工具。
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多级管线，从快到慢逐级重试，命中即止：
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1. zxing-cpp 原图（默认已开反色 / 旋转 / 缩放重试）
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2. zxing-cpp 换二值化算法（救低对比度、带纹理背景）
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3. zxing-cpp 放大图（救长图里的小码、低分辨率码）
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4. OpenCV 检测器 + 反色重试（zxing-cpp 缺失时的主路径）
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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）
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"""
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from __future__ import annotations
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from pathlib import Path
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import cv2
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import numpy as np
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from app.core.logger import log
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from .cv import (
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_HAS_ZXING,
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_load_image,
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_normalize_points,
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_warp_qr_image,
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pad_quiet_zone,
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zxing_read,
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zxing_variants,
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zxingcpp,
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)
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# 裁剪区域重试时，把二维码放大到的目标边长
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_CROP_TARGET_SIDE = 480
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def _dedupe(texts: list[str]) -> list[str]:
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"""去重且保持顺序。"""
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seen: set[str] = set()
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result: list[str] = []
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for text in texts:
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if text and text not in seen:
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seen.add(text)
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result.append(text)
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return result
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def _texts_of(barcodes: list) -> list[str]:
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return _dedupe([getattr(item, "text", "") or "" for item in barcodes])
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def _stage_zxing_plain(img: np.ndarray) -> list[str]:
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"""原图直接交给 zxing-cpp。"""
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return _texts_of(zxing_read(img))
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def _stage_zxing_binarizers(img: np.ndarray) -> list[str]:
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"""换二值化算法重试，应对低对比度和带图案的背景。"""
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if not _HAS_ZXING:
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return []
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for binarizer in (
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zxingcpp.Binarizer.GlobalHistogram,
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zxingcpp.Binarizer.FixedThreshold,
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zxingcpp.Binarizer.BoolCast,
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):
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texts = _texts_of(zxing_read(img, binarizer=binarizer))
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if texts:
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log.debug(f"二维码解码命中二值化算法: {binarizer}")
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return texts
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return []
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def _stage_zxing_upscaled(img: np.ndarray) -> list[str]:
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"""放大后重试，救小尺寸二维码。"""
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for name, variant in zxing_variants(img):
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if name == "原图":
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continue
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texts = _texts_of(zxing_read(variant, try_downscale=False))
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if texts:
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log.debug(f"二维码解码命中变体: {name}")
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return texts
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return []
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def _decode_with_opencv(img: np.ndarray) -> list[str]:
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"""OpenCV 检测器解码，含反色重试。"""
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texts: list[str] = []
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for candidate in (img, cv2.bitwise_not(img)):
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for detector in (cv2.QRCodeDetector(), cv2.QRCodeDetectorAruco()):
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try:
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ok, infos, points, _ = detector.detectAndDecodeMulti(candidate)
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except cv2.error:
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ok, infos, points = False, None, None
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if ok and infos is not None:
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found = [item for item in infos if item]
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if found:
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texts.extend(found)
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if texts:
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return _dedupe(texts)
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try:
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text, points, _ = detector.detectAndDecode(candidate)
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except cv2.error:
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continue
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if text:
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return [text]
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# 检测到位置但没解出内容时，裁出区域再试一次
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for quad in _normalize_points(points):
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crop = _warp_qr_image(candidate, np.array(quad, dtype=np.float32))
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if crop.size == 0:
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continue
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try:
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fallback, _, _ = detector.detectAndDecode(crop)
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except cv2.error:
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continue
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if fallback:
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texts.append(fallback)
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if texts:
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return _dedupe(texts)
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return _dedupe(texts)
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def _stage_opencv(img: np.ndarray) -> list[str]:
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return _decode_with_opencv(img)
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def _stage_crop_retry(img: np.ndarray) -> list[str]:
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"""先定位并裁出二维码，补静默区放大后再解码。
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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。
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"""
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from .cv import _detect_regions # 局部导入，避免循环依赖
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texts: list[str] = []
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for region in _detect_regions(img):
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if region.text:
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texts.append(region.text)
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continue
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crop = region.crop
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if crop is None or crop.size == 0:
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continue
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padded = pad_quiet_zone(crop)
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longest = max(padded.shape[:2])
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if longest and longest < _CROP_TARGET_SIDE:
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scale = _CROP_TARGET_SIDE / longest
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padded = cv2.resize(
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padded, None, fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
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)
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found = _texts_of(zxing_read(padded, try_downscale=False))
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if not found:
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found = _decode_with_opencv(padded)
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texts.extend(found)
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return _dedupe(texts)
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# 管线顺序：命中即返回
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_STAGES = (
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("zxing原图", _stage_zxing_plain),
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("zxing二值化", _stage_zxing_binarizers),
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("zxing放大", _stage_zxing_upscaled),
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("opencv", _stage_opencv),
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("裁剪重试", _stage_crop_retry),
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)
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def decode_qr(image: bytes | np.ndarray | str | Path) -> list[str]:
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"""识别图片中的二维码内容。
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Args:
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image: 图片字节流、BGR 图像数组或图片路径。
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Returns:
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二维码内容列表，已去重；没识别出来时返回空列表。
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"""
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img = _load_image(image)
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for name, stage in _STAGES:
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try:
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texts = stage(img)
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except Exception as exc: # 单级失败不影响后续重试
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log.debug(f"二维码解码阶段异常({name}): {exc}")
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continue
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if texts:
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if name != "zxing原图":
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log.debug(f"二维码解码命中阶段: {name}")
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return texts
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return []
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