添加二维码处理能力

requirements.txt

@@ -1,3 +1,4 @@
 cryptography>=41
 rapidocr>=3.9
 onnxruntime>=1.17
+opencv-python>=4.5

tool/cv.py

@@ -1,33 +1,28 @@
-"""二维码工具。
-
-基于 OpenCV 的 QRCodeDetector 完成二维码检测、裁剪与解码。
-"""
+"""二维码检测与裁剪工具。"""
 
 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。"""
+class QrRegion:
+    """单个二维码区域。"""
 
-    text: str
     points: tuple[tuple[int, int], ...]
     crop: np.ndarray | None = None
 
 
 @dataclass
-class QrScanResult:
-    """二维码扫描结果。"""
+class QrDetectResult:
+    """二维码检测结果。"""
 
     has_qr: bool
-    items: list[QrCodeResult]
+    items: list[QrRegion]
 
 
 def _load_image(image: bytes | np.ndarray | str | Path) -> np.ndarray:
@@ -110,58 +105,35 @@ def _normalize_points(points: np.ndarray | None) -> list[tuple[tuple[int, int],
     return result
 
 
-def _decode_with_multi_detector(img: np.ndarray) -> tuple[list[str], list[tuple[tuple[int, int], ...]], list[np.ndarray]]:
+def _detect_points(img: np.ndarray) -> list[tuple[tuple[int, int], ...]]:
     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)
+    if hasattr(detector, "detectMulti"):
+        ok, points = detector.detectMulti(img)
+        if ok:
             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 normalized_points
 
-    return [], [], []
+    ok, points = detector.detect(img)
+    if ok:
+        normalized_points = _normalize_points(points)
+        if normalized_points:
+            return normalized_points
+
+    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。"""
+def scan_qr(image: bytes | np.ndarray | str | Path) -> QrDetectResult:
+    """扫描图片中是否存在二维码，并返回二维码区域。"""
     img = _load_image(image)
-    texts, points_list, crops = _decode_with_multi_detector(img)
+    points_list = _detect_points(img)
 
     items = [
-        QrCodeResult(text=text, points=points, crop=crop)
-        for text, points, crop in zip(texts, points_list, crops)
+        QrRegion(points=points, crop=_warp_qr_image(img, np.array(points, dtype=np.float32)))
+        for points in points_list
     ]
-    return QrScanResult(has_qr=bool(items), items=items)
+    return QrDetectResult(has_qr=bool(items), items=items)
 
 
 def has_qr(image: bytes | np.ndarray | str | Path) -> bool:
@@ -172,8 +144,3 @@ def has_qr(image: bytes | np.ndarray | str | Path) -> bool:
 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]

tool/qr_decode.py

@@ -0,0 +1,54 @@
+"""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)