优化定二维码识别逻辑
This commit is contained in:
zk
+178
-20
@@ -1,4 +1,8 @@
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"""二维码检测与裁剪工具。"""
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"""二维码检测与裁剪工具。
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识别引擎优先用 zxing-cpp（对反色、旋转、小尺寸、艺术化二维码的容错明显更好），
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拿不到时退回 OpenCV 自带检测器，保证不装 zxing-cpp 也能跑。
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"""
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from __future__ import annotations
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@@ -8,6 +12,34 @@ 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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try: # zxing-cpp 是可选依赖，缺失时自动退化为纯 OpenCV
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import zxingcpp
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_HAS_ZXING = True
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except ImportError: # pragma: no cover - 取决于部署环境
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zxingcpp = None
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_HAS_ZXING = False
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log.warning("未安装 zxing-cpp，二维码识别将退化为 OpenCV 检测器，识别率会明显下降")
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# 要识别的二维码类型：标准 QR + 微型 QR + 矩形 QR
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_QR_FORMATS = (
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[
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zxingcpp.BarcodeFormat.QRCode,
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zxingcpp.BarcodeFormat.MicroQRCode,
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zxingcpp.BarcodeFormat.RMQRCode,
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]
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if _HAS_ZXING
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else []
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)
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# 放大重试的尺寸上限，避免长图被放大到内存爆掉
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_MAX_UPSCALE_SIDE = 2600
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# 裁剪区域重试时补的静默区宽度（像素）
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_QUIET_ZONE = 16
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@dataclass
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class QrRegion:
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@@ -15,6 +47,7 @@ class QrRegion:
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points: tuple[tuple[int, int], ...]
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crop: np.ndarray | None = None
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text: str = ""
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@dataclass
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@@ -105,34 +138,159 @@ def _normalize_points(points: np.ndarray | None) -> list[tuple[tuple[int, int],
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return result
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def _detect_points(img: np.ndarray) -> list[tuple[tuple[int, int], ...]]:
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detector = cv2.QRCodeDetector()
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def _clip_points(
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quad: tuple[tuple[int, int], ...], shape: tuple[int, ...]
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) -> tuple[tuple[int, int], ...]:
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"""把角点裁进图像范围内，避免透视变换取到界外。"""
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height, width = shape[:2]
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return tuple(
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(min(max(x, 0), width - 1), min(max(y, 0), height - 1)) for x, y in quad
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)
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if hasattr(detector, "detectMulti"):
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ok, points = detector.detectMulti(img)
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if ok:
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normalized_points = _normalize_points(points)
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if normalized_points:
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return normalized_points
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ok, points = detector.detect(img)
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if ok:
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normalized_points = _normalize_points(points)
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if normalized_points:
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return normalized_points
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def zxing_read(
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img: np.ndarray,
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*,
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binarizer: object | None = None,
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try_downscale: bool = True,
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) -> list:
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"""用 zxing-cpp 识别图中所有二维码，失败返回空列表。
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zxing-cpp 默认已开启 try_invert（反色）和 try_rotate（旋转），
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这是它比 OpenCV 检测器兼容性好的主要原因。
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"""
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if not _HAS_ZXING:
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return []
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kwargs = {
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"formats": _QR_FORMATS,
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"try_rotate": True,
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"try_invert": True,
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"try_downscale": try_downscale,
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}
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if binarizer is not None:
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kwargs["binarizer"] = binarizer
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try:
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return list(zxingcpp.read_barcodes(img, **kwargs))
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except Exception as exc: # zxing 内部异常不应该打断整个流程
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log.debug(f"zxing-cpp 识别异常: {exc}")
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return []
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def zxing_variants(img: np.ndarray) -> list[tuple[str, np.ndarray]]:
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"""构造 zxing 的重试图像变体：原图之外再补一版放大图。
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放大主要救「长图里的小二维码」和「低分辨率二维码」两类。
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"""
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variants: list[tuple[str, np.ndarray]] = [("原图", img)]
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longest = max(img.shape[:2])
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if longest and longest * 2 <= _MAX_UPSCALE_SIDE:
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variants.append(
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("放大2倍", cv2.resize(img, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC))
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)
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return variants
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def pad_quiet_zone(img: np.ndarray, border: int = _QUIET_ZONE) -> np.ndarray:
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"""给裁剪出来的二维码补静默区。
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有些海报把二维码贴边放，裁出来没有留白，补一圈能提高解码成功率。
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边框颜色取图像四角的中位数，反色码补深色、正常码补浅色，避免破坏极性。
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"""
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corners = np.array(
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[img[0, 0], img[0, -1], img[-1, 0], img[-1, -1]], dtype=np.float32
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)
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color = np.median(corners, axis=0)
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value = tuple(int(round(c)) for c in np.atleast_1d(color))
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if len(value) == 1:
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value = value * 3
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return cv2.copyMakeBorder(
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img, border, border, border, border, cv2.BORDER_CONSTANT, value=value
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)
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def _points_from_zxing(barcode: object) -> tuple[tuple[int, int], ...] | None:
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"""把 zxing 的 position 转成四角点。"""
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position = getattr(barcode, "position", None)
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if position is None:
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return None
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quad: list[tuple[int, int]] = []
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for name in ("top_left", "top_right", "bottom_right", "bottom_left"):
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point = getattr(position, name, None)
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if point is None:
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return None
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quad.append((int(round(point.x)), int(round(point.y))))
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return tuple(quad)
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def _detect_by_opencv(img: np.ndarray) -> list[tuple[tuple[int, int], ...]]:
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"""OpenCV 检测器兜底，额外补一次反色重试。"""
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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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if hasattr(detector, "detectMulti"):
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ok, points = detector.detectMulti(candidate)
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if ok:
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normalized = _normalize_points(points)
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if normalized:
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return normalized
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ok, points = detector.detect(candidate)
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if ok:
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normalized = _normalize_points(points)
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if normalized:
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return normalized
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except cv2.error:
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continue
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return []
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def _detect_regions(img: np.ndarray) -> list[QrRegion]:
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"""检测二维码区域：zxing 优先（顺带拿到内容），OpenCV 兜底。"""
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for name, variant in zxing_variants(img):
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barcodes = zxing_read(variant)
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if not barcodes:
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continue
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scale = variant.shape[1] / img.shape[1] if img.shape[1] else 1
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regions: list[QrRegion] = []
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for barcode in barcodes:
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quad = _points_from_zxing(barcode)
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if quad is None:
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continue
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if scale != 1:
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quad = tuple(
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(int(round(x / scale)), int(round(y / scale))) for x, y in quad
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)
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quad = _clip_points(quad, img.shape)
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regions.append(
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QrRegion(
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points=quad,
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crop=_warp_qr_image(img, np.array(quad, dtype=np.float32)),
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text=getattr(barcode, "text", "") or "",
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)
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)
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if regions:
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if name != "原图":
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log.debug(f"二维码检测命中变体: {name}")
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return regions
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return [
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QrRegion(
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points=quad,
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crop=_warp_qr_image(img, np.array(quad, dtype=np.float32)),
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)
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for quad in _detect_by_opencv(img)
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]
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def scan_qr(image: bytes | np.ndarray | str | Path) -> QrDetectResult:
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"""扫描图片中是否存在二维码，并返回二维码区域。"""
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img = _load_image(image)
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points_list = _detect_points(img)
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items = [
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QrRegion(points=points, crop=_warp_qr_image(img, np.array(points, dtype=np.float32)))
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for points in points_list
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]
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items = _detect_regions(img)
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return QrDetectResult(has_qr=bool(items), items=items)
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+179
-34
@@ -1,4 +1,12 @@
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"""二维码识别工具。"""
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"""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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@@ -7,48 +15,185 @@ from pathlib import Path
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import cv2
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import numpy as np
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from .cv import _load_image, _normalize_points, _warp_qr_image
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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 _decode_with_detector(img: np.ndarray) -> list[str]:
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detector = cv2.QRCodeDetector()
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texts: list[str] = []
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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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if hasattr(detector, "detectAndDecodeMulti"):
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ok, decoded_info, points, _ = detector.detectAndDecodeMulti(img)
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if ok and points is not None:
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normalized_points = _normalize_points(points)
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if isinstance(decoded_info, (list, tuple)):
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decoded_iter = [item or "" for item in decoded_info]
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else:
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decoded_iter = [decoded_info or ""]
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for idx, points_item in enumerate(normalized_points):
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text = decoded_iter[idx] if idx < len(decoded_iter) else ""
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if not text:
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crop = _warp_qr_image(img, np.array(points_item, dtype=np.float32))
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fallback_text, _, _ = detector.detectAndDecode(crop)
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text = fallback_text or ""
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if text:
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texts.append(text)
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if texts:
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return texts
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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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text, points, _ = detector.detectAndDecode(img)
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if text:
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return [text]
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normalized_points = _normalize_points(points)
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if normalized_points:
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crop = _warp_qr_image(img, np.array(normalized_points[0], dtype=np.float32))
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fallback_text, _, _ = detector.detectAndDecode(crop)
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if fallback_text:
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return [fallback_text]
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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]:
|
||||
"""识别图片中的二维码内容。"""
|
||||
"""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)
|
||||
return _decode_with_detector(img)
|
||||
|
||||
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 []
|
||||
|
||||
Reference in New Issue
Block a user