614 lines
16 KiB
Python
614 lines
16 KiB
Python
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"""
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OCR 模块
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提供文字识别功能，支持：
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- 本地 PaddleOCR 识别
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- 云端 OCR API 扩展
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- 图片预处理增强
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- 多语言支持（中/英/混合）
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"""
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import List, Optional, Dict, Any, Tuple
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from dataclasses import dataclass
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from enum import Enum
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import logging
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try:
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from PIL import Image, ImageEnhance, ImageFilter
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import numpy as np
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except ImportError:
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raise ImportError(
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"请安装图像处理库: pip install pillow numpy"
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)
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try:
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from paddleocr import PaddleOCR
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except ImportError:
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PaddleOCR = None
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logging.warning("PaddleOCR 未安装，本地 OCR 功能不可用")
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# 配置日志
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logger = logging.getLogger(__name__)
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class OCRLanguage(str, Enum):
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"""OCR 支持的语言"""
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CHINESE = "ch" # 中文
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ENGLISH = "en" # 英文
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MIXED = "chinese_chinese" # 中英文混合
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@dataclass
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class OCRResult:
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"""
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OCR 识别结果
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Attributes:
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text: 识别的文本内容
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confidence: 置信度 (0-1)
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bbox: 文本框坐标 [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
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line_index: 行索引（从0开始）
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"""
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text: str
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confidence: float
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bbox: Optional[List[List[float]]] = None
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line_index: int = 0
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def __repr__(self) -> str:
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return f"OCRResult(text='{self.text[:30]}...', confidence={self.confidence:.2f})"
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@dataclass
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class OCRBatchResult:
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"""
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OCR 批量识别结果
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Attributes:
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results: 所有的识别结果列表
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full_text: 完整文本（所有行拼接）
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total_confidence: 平均置信度
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success: 是否识别成功
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error_message: 错误信息（如果失败）
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"""
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results: List[OCRResult]
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full_text: str
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total_confidence: float
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success: bool = True
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error_message: Optional[str] = None
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def __repr__(self) -> str:
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return f"OCRBatchResult(lines={len(self.results)}, confidence={self.total_confidence:.2f})"
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class ImagePreprocessor:
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"""
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图像预处理器
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提供常见的图像增强和预处理功能，提高 OCR 识别准确率
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"""
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@staticmethod
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def load_image(image_path: str) -> Image.Image:
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"""
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加载图像
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Args:
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image_path: 图像文件路径
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Returns:
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PIL Image 对象
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"""
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image = Image.open(image_path)
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# 转换为 RGB 模式
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if image.mode != 'RGB':
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image = image.convert('RGB')
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return image
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@staticmethod
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def resize_image(image: Image.Image, max_width: int = 2000) -> Image.Image:
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"""
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调整图像大小（保持宽高比）
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Args:
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image: PIL Image 对象
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max_width: 最大宽度
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Returns:
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调整后的图像
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"""
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if image.width > max_width:
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ratio = max_width / image.width
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new_height = int(image.height * ratio)
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image = image.resize((max_width, new_height), Image.Resampling.LANCZOS)
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return image
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@staticmethod
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def enhance_contrast(image: Image.Image, factor: float = 1.5) -> Image.Image:
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"""
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增强对比度
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Args:
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image: PIL Image 对象
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factor: 增强因子，1.0 表示原始，>1.0 增强，<1.0 减弱
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Returns:
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处理后的图像
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"""
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enhancer = ImageEnhance.Contrast(image)
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return enhancer.enhance(factor)
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@staticmethod
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def enhance_sharpness(image: Image.Image, factor: float = 1.5) -> Image.Image:
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"""
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增强锐度
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Args:
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image: PIL Image 对象
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factor: 锐化因子
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Returns:
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处理后的图像
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"""
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enhancer = ImageEnhance.Sharpness(image)
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return enhancer.enhance(factor)
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@staticmethod
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def enhance_brightness(image: Image.Image, factor: float = 1.1) -> Image.Image:
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"""
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调整亮度
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Args:
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image: PIL Image 对象
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factor: 亮度因子
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Returns:
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处理后的图像
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"""
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enhancer = ImageEnhance.Brightness(image)
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return enhancer.enhance(factor)
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@staticmethod
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def denoise(image: Image.Image) -> Image.Image:
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"""
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去噪（使用中值滤波）
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Args:
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image: PIL Image 对象
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Returns:
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处理后的图像
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"""
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return image.filter(ImageFilter.MedianFilter(size=3))
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@staticmethod
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def binarize(image: Image.Image, threshold: int = 127) -> Image.Image:
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"""
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二值化（转换为黑白图像）
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Args:
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image: PIL Image 对象
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threshold: 二值化阈值
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Returns:
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处理后的图像
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"""
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# 先转为灰度图
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gray = image.convert('L')
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# 二值化
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binary = gray.point(lambda x: 0 if x < threshold else 255, '1')
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# 转回 RGB
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return binary.convert('RGB')
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@staticmethod
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def preprocess(
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image: Image.Image,
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resize: bool = True,
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enhance_contrast: bool = True,
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enhance_sharpness: bool = True,
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denoise: bool = False,
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binarize: bool = False
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) -> Image.Image:
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"""
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综合预处理（根据指定选项）
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Args:
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image: PIL Image 对象
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resize: 是否调整大小
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enhance_contrast: 是否增强对比度
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enhance_sharpness: 是否增强锐度
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denoise: 是否去噪
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binarize: 是否二值化
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Returns:
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处理后的图像
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"""
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result = image.copy()
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if resize:
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result = ImagePreprocessor.resize_image(result)
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if enhance_contrast:
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result = ImagePreprocessor.enhance_contrast(result)
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if enhance_sharpness:
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result = ImagePreprocessor.enhance_sharpness(result)
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if denoise:
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result = ImagePreprocessor.denoise(result)
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if binarize:
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result = ImagePreprocessor.binarize(result)
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return result
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@staticmethod
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def preprocess_from_path(
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image_path: str,
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**kwargs
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) -> Image.Image:
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"""
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从文件路径加载并预处理图像
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Args:
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image_path: 图像文件路径
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**kwargs: preprocess 方法的参数
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Returns:
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处理后的图像
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"""
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image = ImagePreprocessor.load_image(image_path)
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return ImagePreprocessor.preprocess(image, **kwargs)
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class BaseOCREngine(ABC):
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"""
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OCR 引擎基类
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所有 OCR 实现必须继承此类并实现 recognize 方法
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"""
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def __init__(self, config: Optional[Dict[str, Any]] = None):
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"""
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初始化 OCR 引擎
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Args:
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config: OCR 配置字典
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"""
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self.config = config or {}
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self.preprocessor = ImagePreprocessor()
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@abstractmethod
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def recognize(
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self,
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image,
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preprocess: bool = True,
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**kwargs
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) -> OCRBatchResult:
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"""
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识别图像中的文本
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Args:
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image: 图像（可以是路径、PIL Image 或 numpy 数组）
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preprocess: 是否预处理图像
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**kwargs: 其他参数
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Returns:
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OCRBatchResult: 识别结果
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"""
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pass
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def _load_image(self, image) -> Image.Image:
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"""
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加载图像（支持多种输入格式）
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Args:
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image: 图像（路径、PIL Image 或 numpy 数组）
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Returns:
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PIL Image 对象
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"""
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if isinstance(image, str) or isinstance(image, Path):
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return self.preprocessor.load_image(str(image))
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elif isinstance(image, Image.Image):
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return image
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elif isinstance(image, np.ndarray):
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return Image.fromarray(image)
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else:
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raise ValueError(f"不支持的图像类型: {type(image)}")
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def _calculate_total_confidence(self, results: List[OCRResult]) -> float:
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"""
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计算平均置信度
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Args:
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results: OCR 结果列表
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Returns:
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平均置信度 (0-1)
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"""
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if not results:
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return 0.0
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return sum(r.confidence for r in results) / len(results)
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class PaddleOCREngine(BaseOCREngine):
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"""
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PaddleOCR 本地识别引擎
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使用 PaddleOCR 进行本地文字识别
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"""
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def __init__(self, config: Optional[Dict[str, Any]] = None):
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"""
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初始化 PaddleOCR 引擎
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Args:
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config: 配置字典，支持:
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- use_gpu: 是否使用 GPU (默认 False)
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- lang: 语言 (默认 "ch"，支持 ch/en/chinese_chinese)
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- show_log: 是否显示日志 (默认 False)
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"""
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super().__init__(config)
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if PaddleOCR is None:
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raise ImportError(
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"PaddleOCR 未安装。请运行: pip install paddleocr paddlepaddle"
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)
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# 解析配置
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self.use_gpu = self.config.get('use_gpu', False)
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self.lang = self.config.get('lang', 'ch')
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self.show_log = self.config.get('show_log', False)
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# 初始化 PaddleOCR
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logger.info(f"初始化 PaddleOCR (lang={self.lang}, gpu={self.use_gpu})")
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self.ocr = PaddleOCR(
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use_angle_cls=True, # 使用方向分类器
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lang=self.lang,
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use_gpu=self.use_gpu,
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show_log=self.show_log
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)
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def recognize(
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self,
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image,
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|||
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preprocess: bool = False,
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**kwargs
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|||
|
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) -> OCRBatchResult:
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"""
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使用 PaddleOCR 识别图像中的文本
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|||
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Args:
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|||
|
|
image: 图像（路径、PIL Image 或 numpy 数组）
|
|||
|
|
preprocess: 是否预处理图像
|
|||
|
|
**kwargs: 其他参数（未使用）
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
OCRBatchResult: 识别结果
|
|||
|
|
"""
|
|||
|
|
try:
|
|||
|
|
# 加载图像
|
|||
|
|
pil_image = self._load_image(image)
|
|||
|
|
|
|||
|
|
# 预处理（如果启用）
|
|||
|
|
if preprocess:
|
|||
|
|
pil_image = self.preprocessor.preprocess(pil_image)
|
|||
|
|
|
|||
|
|
# 转换为 numpy 数组（PaddleOCR 需要）
|
|||
|
|
img_array = np.array(pil_image)
|
|||
|
|
|
|||
|
|
# 执行 OCR
|
|||
|
|
result = self.ocr.ocr(img_array, cls=True)
|
|||
|
|
|
|||
|
|
# 解析结果
|
|||
|
|
ocr_results = []
|
|||
|
|
full_lines = []
|
|||
|
|
|
|||
|
|
if result and result[0]:
|
|||
|
|
for line_index, line in enumerate(result[0]):
|
|||
|
|
if line:
|
|||
|
|
# PaddleOCR 返回格式: [[bbox], (text, confidence)]
|
|||
|
|
bbox = line[0]
|
|||
|
|
text_info = line[1]
|
|||
|
|
text = text_info[0]
|
|||
|
|
confidence = float(text_info[1])
|
|||
|
|
|
|||
|
|
ocr_result = OCRResult(
|
|||
|
|
text=text,
|
|||
|
|
confidence=confidence,
|
|||
|
|
bbox=bbox,
|
|||
|
|
line_index=line_index
|
|||
|
|
)
|
|||
|
|
ocr_results.append(ocr_result)
|
|||
|
|
full_lines.append(text)
|
|||
|
|
|
|||
|
|
# 计算平均置信度
|
|||
|
|
total_confidence = self._calculate_total_confidence(ocr_results)
|
|||
|
|
|
|||
|
|
# 拼接完整文本
|
|||
|
|
full_text = '\n'.join(full_lines)
|
|||
|
|
|
|||
|
|
logger.info(f"OCR 识别完成: {len(ocr_results)} 行, 平均置信度 {total_confidence:.2f}")
|
|||
|
|
|
|||
|
|
return OCRBatchResult(
|
|||
|
|
results=ocr_results,
|
|||
|
|
full_text=full_text,
|
|||
|
|
total_confidence=total_confidence,
|
|||
|
|
success=True
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"OCR 识别失败: {e}", exc_info=True)
|
|||
|
|
return OCRBatchResult(
|
|||
|
|
results=[],
|
|||
|
|
full_text="",
|
|||
|
|
total_confidence=0.0,
|
|||
|
|
success=False,
|
|||
|
|
error_message=str(e)
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
|
|||
|
|
class CloudOCREngine(BaseOCREngine):
|
|||
|
|
"""
|
|||
|
|
云端 OCR 引擎（适配器）
|
|||
|
|
|
|||
|
|
预留接口，用于扩展云端 OCR 服务
|
|||
|
|
支持：百度 OCR、腾讯 OCR、阿里云 OCR 等
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
|||
|
|
"""
|
|||
|
|
初始化云端 OCR 引擎
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
config: 配置字典，支持:
|
|||
|
|
- api_endpoint: API 端点
|
|||
|
|
- api_key: API 密钥
|
|||
|
|
- provider: 提供商 (baidu/tencent/aliyun/custom)
|
|||
|
|
- timeout: 超时时间（秒）
|
|||
|
|
"""
|
|||
|
|
super().__init__(config)
|
|||
|
|
|
|||
|
|
self.api_endpoint = self.config.get('api_endpoint', '')
|
|||
|
|
self.api_key = self.config.get('api_key', '')
|
|||
|
|
self.provider = self.config.get('provider', 'custom')
|
|||
|
|
self.timeout = self.config.get('timeout', 30)
|
|||
|
|
|
|||
|
|
if not self.api_endpoint:
|
|||
|
|
logger.warning("云端 OCR: api_endpoint 未配置")
|
|||
|
|
|
|||
|
|
def recognize(
|
|||
|
|
self,
|
|||
|
|
image,
|
|||
|
|
preprocess: bool = False,
|
|||
|
|
**kwargs
|
|||
|
|
) -> OCRBatchResult:
|
|||
|
|
"""
|
|||
|
|
使用云端 API 识别图像中的文本
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
image: 图像（路径、PIL Image）
|
|||
|
|
preprocess: 是否预处理图像
|
|||
|
|
**kwargs: 其他参数
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
OCRBatchResult: 识别结果
|
|||
|
|
"""
|
|||
|
|
# 这是一个占位实现
|
|||
|
|
# 实际使用时需要根据具体的云端 OCR API 实现
|
|||
|
|
logger.warning("云端 OCR 尚未实现，请使用本地 PaddleOCR 或自行实现")
|
|||
|
|
|
|||
|
|
return OCRBatchResult(
|
|||
|
|
results=[],
|
|||
|
|
full_text="",
|
|||
|
|
total_confidence=0.0,
|
|||
|
|
success=False,
|
|||
|
|
error_message="云端 OCR 尚未实现"
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
def _send_request(self, image_data: bytes) -> Dict[str, Any]:
|
|||
|
|
"""
|
|||
|
|
发送 API 请求（待实现）
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
image_data: 图像二进制数据
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
API 响应
|
|||
|
|
"""
|
|||
|
|
raise NotImplementedError("请根据具体云服务 API 实现此方法")
|
|||
|
|
|
|||
|
|
|
|||
|
|
class OCRFactory:
|
|||
|
|
"""
|
|||
|
|
OCR 引擎工厂
|
|||
|
|
|
|||
|
|
根据配置创建对应的 OCR 引擎实例
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def create_engine(
|
|||
|
|
mode: str = "local",
|
|||
|
|
config: Optional[Dict[str, Any]] = None
|
|||
|
|
) -> BaseOCREngine:
|
|||
|
|
"""
|
|||
|
|
创建 OCR 引擎
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
mode: OCR 模式 ("local" 或 "cloud")
|
|||
|
|
config: 配置字典
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
BaseOCREngine: OCR 引擎实例
|
|||
|
|
|
|||
|
|
Raises:
|
|||
|
|
ValueError: 不支持的 OCR 模式
|
|||
|
|
"""
|
|||
|
|
if mode == "local":
|
|||
|
|
return PaddleOCREngine(config)
|
|||
|
|
elif mode == "cloud":
|
|||
|
|
return CloudOCREngine(config)
|
|||
|
|
else:
|
|||
|
|
raise ValueError(f"不支持的 OCR 模式: {mode}")
|
|||
|
|
|
|||
|
|
|
|||
|
|
# 便捷函数
|
|||
|
|
def recognize_text(
|
|||
|
|
image,
|
|||
|
|
mode: str = "local",
|
|||
|
|
lang: str = "ch",
|
|||
|
|
use_gpu: bool = False,
|
|||
|
|
preprocess: bool = False,
|
|||
|
|
**kwargs
|
|||
|
|
) -> OCRBatchResult:
|
|||
|
|
"""
|
|||
|
|
快捷识别文本
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
image: 图像（路径、PIL Image）
|
|||
|
|
mode: OCR 模式 ("local" 或 "cloud")
|
|||
|
|
lang: 语言 (ch/en/chinese_chinese)
|
|||
|
|
use_gpu: 是否使用 GPU（仅本地模式）
|
|||
|
|
preprocess: 是否预处理图像
|
|||
|
|
**kwargs: 其他配置
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
OCRBatchResult: 识别结果
|
|||
|
|
"""
|
|||
|
|
config = {
|
|||
|
|
'lang': lang,
|
|||
|
|
'use_gpu': use_gpu,
|
|||
|
|
**kwargs
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
engine = OCRFactory.create_engine(mode, config)
|
|||
|
|
return engine.recognize(image, preprocess=preprocess)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def preprocess_image(
|
|||
|
|
image_path: str,
|
|||
|
|
output_path: Optional[str] = None,
|
|||
|
|
**kwargs
|
|||
|
|
) -> Image.Image:
|
|||
|
|
"""
|
|||
|
|
快捷预处理图像
|
|||
|
|
|
|||
|
|
Args:
|
|||
|
|
image_path: 输入图像路径
|
|||
|
|
output_path: 输出图像路径（如果指定，则保存）
|
|||
|
|
**kwargs: 预处理参数
|
|||
|
|
|
|||
|
|
Returns:
|
|||
|
|
PIL Image: 处理后的图像
|
|||
|
|
"""
|
|||
|
|
processed = ImagePreprocessor.preprocess_from_path(image_path, **kwargs)
|
|||
|
|
|
|||
|
|
if output_path:
|
|||
|
|
processed.save(output_path)
|
|||
|
|
logger.info(f"预处理图像已保存到: {output_path}")
|
|||
|
|
|
|||
|
|
return processed
|