299 lines
10 KiB
Python
299 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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"""二维码处理核心类 - 基于 OpenCV QRCodeDetector
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本模块使用 OpenCV 的 QRCodeDetector 进行二维码识别，
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配合多种图像预处理策略，确保高识别率和跨平台兼容性。
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"""
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import base64
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import io
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from typing import Optional, Callable, Tuple
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import cv2
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import httpx
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import numpy as np
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import qrcode
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from PIL import Image
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class QRCodeProcessor:
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"""二维码处理器 - 负责二维码的生成、解析和图像预处理"""
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# 预处理策略映射
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PREPROCESSING_STRATEGIES = {
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"original": ("原图", lambda img, gray: img),
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"grayscale": ("灰度图", lambda img, gray: cv2.cvtColor(gray, cv2.COLOR_GRAY2BGR)),
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"clahe": ("CLAHE增强", lambda img, gray: cv2.cvtColor(
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cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)).apply(gray),
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cv2.COLOR_GRAY2BGR
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)),
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"adaptive_threshold": ("自适应二值化", lambda img, gray: cv2.cvtColor(
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cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2),
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cv2.COLOR_GRAY2BGR
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)),
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"otsu": ("Otsu二值化", lambda img, gray: cv2.cvtColor(
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cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1],
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cv2.COLOR_GRAY2BGR
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)),
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"denoise": ("去噪+二值化", lambda img, gray: cv2.cvtColor(
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cv2.threshold(
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cv2.fastNlMeansDenoising(gray, None, 10, 7, 21),
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0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU
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)[1],
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cv2.COLOR_GRAY2BGR
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)),
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"sharpen": ("锐化", lambda img, gray: cv2.cvtColor(
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cv2.filter2D(gray, -1, np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])),
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cv2.COLOR_GRAY2BGR
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)),
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"morphology": ("形态学处理", lambda img, gray: cv2.cvtColor(
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cv2.morphologyEx(
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cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2),
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cv2.MORPH_CLOSE,
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cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
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),
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cv2.COLOR_GRAY2BGR
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)),
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"scale_0.5": ("0.5x缩放", lambda img, gray: cv2.resize(
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img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA
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)),
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"scale_1.5": ("1.5x缩放", lambda img, gray: cv2.resize(
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img, None, fx=1.5, fy=1.5, interpolation=cv2.INTER_CUBIC
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)),
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"scale_2.0": ("2.0x缩放", lambda img, gray: cv2.resize(
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img, None, fx=2.0, fy=2.0, interpolation=cv2.INTER_CUBIC
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)),
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}
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# 策略组合映射
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STRATEGY_MAP = {
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"basic": ["original", "grayscale"],
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"enhanced": ["original", "grayscale", "clahe", "adaptive_threshold", "otsu", "denoise", "sharpen", "morphology"],
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"all": ["original", "grayscale", "clahe", "adaptive_threshold", "otsu", "denoise", "sharpen", "morphology", "scale_0.5", "scale_1.5", "scale_2.0"],
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}
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@staticmethod
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def generate(
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content: str,
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size: int = 300,
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error_correction: str = "H",
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box_size: int = 10,
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border: int = 4
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) -> str:
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"""
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生成二维码
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Args:
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content: 二维码内容
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size: 图片大小（像素）
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error_correction: 纠错级别 (L/M/Q/H)
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box_size: 每个格子的像素大小
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border: 边框大小（格子数）
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Returns:
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base64编码的图片数据 (data:image/png;base64,...)
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Raises:
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ValueError: 参数错误时抛出异常
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"""
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# 验证纠错级别
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error_levels = {
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"L": qrcode.constants.ERROR_CORRECT_L, # 7% 容错
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"M": qrcode.constants.ERROR_CORRECT_M, # 15% 容错
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"Q": qrcode.constants.ERROR_CORRECT_Q, # 25% 容错
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"H": qrcode.constants.ERROR_CORRECT_H, # 30% 容错
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}
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if error_correction not in error_levels:
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raise ValueError(f"无效的纠错级别: {error_correction}，支持: L/M/Q/H")
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# 创建二维码对象
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qr = qrcode.QRCode(
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version=None, # 自动确定版本
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error_correction=error_levels[error_correction],
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box_size=box_size,
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border=border,
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)
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# 添加数据并生成
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qr.add_data(content)
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qr.make(fit=True)
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# 生成图片
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img = qr.make_image(fill_color="black", back_color="white")
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# 调整到指定大小
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img = img.resize((size, size), Image.Resampling.LANCZOS)
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# 转换为base64
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buffer = io.BytesIO()
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img.save(buffer, format="PNG")
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img_base64 = base64.b64encode(buffer.getvalue()).decode()
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return f"data:image/png;base64,{img_base64}"
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@staticmethod
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async def load_image(image_source: str) -> np.ndarray:
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"""
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加载图片（支持URL、base64、本地路径）
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Args:
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image_source: 图片来源
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- URL: http://... 或 https://...
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- base64: data:image/...;base64,...
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- 本地路径: /path/to/image.png
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Returns:
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OpenCV图片对象 (BGR格式)
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Raises:
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ValueError: 图片加载失败时抛出异常
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"""
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try:
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# 检查是否为base64
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if image_source.startswith("data:image"):
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# 提取base64数据
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base64_data = image_source.split(",")[1]
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img_data = base64.b64decode(base64_data)
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img_array = np.frombuffer(img_data, np.uint8)
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img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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elif image_source.startswith("http://") or image_source.startswith("https://"):
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# 下载图片
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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.get(image_source)
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response.raise_for_status()
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img_array = np.frombuffer(response.content, np.uint8)
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img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
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else:
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# 本地文件
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img = cv2.imread(image_source)
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if img is None:
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raise ValueError("无法解析图片数据")
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return img
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except Exception as e:
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raise ValueError(f"图片加载失败: {str(e)}")
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@staticmethod
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async def search_qrcode(img: np.ndarray)-> list[np.ndarray]:
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"""
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搜索二维码，只搜索不解析
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Args:
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img: OpenCV图像对象
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Returns:
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二维码列表
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"""
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detector = cv2.QRCodeDetector()
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imgs = detector.detect(img)
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@staticmethod
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def decode(img: np.ndarray) -> Optional[str]:
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"""
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使用 OpenCV QRCodeDetector 解码二维码
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Args:
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img: OpenCV图像对象
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Returns:
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二维码内容，如果没有检测到返回None
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"""
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detector = cv2.QRCodeDetector()
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data, bbox, _ = detector.detectAndDecode(img)
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if data:
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return data
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return None
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@staticmethod
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async def parse(
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image_source: str,
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strategy: str = "auto"
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) -> dict:
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"""
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解析二维码（使用 OpenCV + 按需预处理策略）
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解码策略：
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1. 根据 strategy 参数选择预处理步骤列表
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2. 按需应用每种预处理算法并立即尝试解码
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3. 一旦成功立即返回，避免不必要的计算
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Args:
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image_source: 图片来源（URL/base64/本地路径）
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strategy: 预处理策略
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- basic: 原图 + 灰度图（2种）
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- enhanced: basic + 6种增强算法（8种）
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- all: auto + 多尺度（11种）
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Returns:
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解析结果字典:
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{
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"success": bool,
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"content": str or None,
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"strategy_used": str, # 使用的预处理策略名称
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"preprocessing_index": int, # 预处理索引
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"total_attempts": int,
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"message": str # 仅失败时有
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}
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"""
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# 验证策略参数
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if strategy not in QRCodeProcessor.STRATEGY_MAP:
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raise ValueError(f"无效的策略: {strategy}，支持: {list(QRCodeProcessor.STRATEGY_MAP.keys())}")
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# 加载原始图片
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img = await QRCodeProcessor.load_image(image_source)
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# 预先生成灰度图（很多预处理都需要）
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# 获取该策略对应的预处理步骤列表
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preprocessing_steps = QRCodeProcessor.STRATEGY_MAP[strategy]
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# 依次应用每种预处理并尝试解码
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for idx, step_key in enumerate(preprocessing_steps):
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strategy_name, preprocess_func = QRCodeProcessor.PREPROCESSING_STRATEGIES[step_key]
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try:
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# 按需处理图像
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processed_img = preprocess_func(img, gray)
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# 立即尝试解码
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result = QRCodeProcessor.decode(processed_img)
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if result:
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# 解码成功，立即返回
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return {
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"success": True,
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"content": result,
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"strategy_used": f"opencv_{strategy_name}",
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"preprocessing_index": idx,
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"total_attempts": idx + 1
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}
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except Exception as e:
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# 某个预处理步骤失败，继续尝试下一个
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continue
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# 所有预处理方法都失败
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return {
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"success": False,
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"content": None,
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"message": f"未检测到二维码或二维码损坏（已尝试 {len(preprocessing_steps)} 种预处理）",
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"total_attempts": len(preprocessing_steps)
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}
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if __name__ == "__main__":
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import asyncio
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async def main():
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# 示例用法
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result = await QRCodeProcessor.parse("F:/Project/urbanLifeline/docs/qrcode.png", "enhanced")
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print(result)
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asyncio.run(main())
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