194 lines
6.4 KiB
Python
194 lines
6.4 KiB
Python
"""
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OCR服务模块
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---------
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提供OCR识别服务，协调OCR流程。
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"""
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from typing import Dict, List, Optional, Tuple, Union, Any, Callable
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import os
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from ..config.settings import ConfigManager
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from ..core.utils.log_utils import get_logger
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from ..core.ocr.table_ocr import OCRProcessor
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logger = get_logger(__name__)
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class OCRService:
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"""
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OCR识别服务：协调OCR流程
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"""
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def __init__(self, config: Optional[ConfigManager] = None):
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"""
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初始化OCR服务
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Args:
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config: 配置管理器，如果为None则创建新的
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"""
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logger.info("初始化OCRService")
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self.config = config or ConfigManager()
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# 创建OCR处理器
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self.ocr_processor = OCRProcessor(self.config)
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logger.info("OCRService初始化完成")
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def get_unprocessed_images(self) -> List[str]:
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"""
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获取待处理的图片列表
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Returns:
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待处理图片路径列表
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"""
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return self.ocr_processor.get_unprocessed_images()
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def process_image(self, image_path: str) -> Optional[str]:
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"""
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处理单个图片文件
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Args:
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image_path: 图片文件路径
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Returns:
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生成的Excel文件路径，如果处理失败则返回None
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"""
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try:
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# 检查文件是否存在
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if not os.path.exists(image_path):
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logger.error(f"文件不存在: {image_path}")
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return None
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# 检查文件类型
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if not self._is_valid_image(image_path):
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logger.error(f"不支持的文件类型: {image_path}")
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return None
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# 检查是否已处理
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excel_file = self._get_excel_path(image_path)
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if os.path.exists(excel_file):
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logger.info(f"文件已处理过，跳过OCR识别: {image_path}")
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return excel_file
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# 执行OCR识别
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result = self.ocr_processor.process_image(image_path)
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if not result:
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logger.error(f"OCR识别失败: {image_path}")
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return None
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# 生成Excel文件
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excel_file = self._generate_excel(result, image_path)
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if not excel_file:
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logger.error(f"生成Excel文件失败: {image_path}")
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return None
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logger.info(f"处理完成: {image_path} -> {excel_file}")
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return excel_file
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except Exception as e:
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logger.error(f"处理图片时发生错误: {e}", exc_info=True)
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return None
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def process_images_batch(self, batch_size: int = None, max_workers: int = None, progress_cb: Optional[Callable[[int], None]] = None) -> Tuple[int, int]:
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"""
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批量处理图片
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Args:
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batch_size: 批处理大小
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max_workers: 最大线程数
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Returns:
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(总处理数, 成功处理数)元组
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"""
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logger.info(f"OCRService开始批量处理图片, batch_size={batch_size}, max_workers={max_workers}")
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return self.ocr_processor.process_images_batch(batch_size, max_workers, progress_cb)
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# 添加batch_process作为process_images_batch的别名，确保兼容性
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def batch_process(self, batch_size: int = None, max_workers: int = None, progress_cb: Optional[Callable[[int], None]] = None) -> Tuple[int, int]:
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"""
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批量处理图片（别名方法，与process_images_batch功能相同）
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Args:
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batch_size: 批处理大小
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max_workers: 最大线程数
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Returns:
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(总处理数, 成功处理数)元组
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"""
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logger.info(f"OCRService.batch_process被调用，转发到process_images_batch")
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return self.process_images_batch(batch_size, max_workers, progress_cb)
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def validate_image(self, image_path: str) -> bool:
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"""
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验证图片是否有效
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Args:
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image_path: 图片路径
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Returns:
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图片是否有效
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"""
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return self.ocr_processor.validate_image(image_path)
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def _is_valid_image(self, image_path: str) -> bool:
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"""
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检查文件是否为有效的图片格式
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Args:
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image_path: 图片文件路径
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Returns:
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是否为有效图片格式
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"""
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return self.validate_image(image_path)
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def _get_excel_path(self, image_path: str) -> str:
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"""
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根据图片路径生成对应的Excel文件路径
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Args:
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image_path: 图片文件路径
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Returns:
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Excel文件路径
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"""
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# 获取文件名（不含扩展名）
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base_name = os.path.splitext(os.path.basename(image_path))[0]
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# 生成Excel文件路径
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output_dir = self.config.get('Paths', 'output_folder', fallback='data/output')
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excel_path = os.path.join(output_dir, f"{base_name}.xlsx")
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return excel_path
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def _generate_excel(self, ocr_result: dict, image_path: str) -> Optional[str]:
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"""
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根据OCR结果生成Excel文件
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Args:
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ocr_result: OCR识别结果
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image_path: 原始图片路径
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Returns:
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生成的Excel文件路径，失败返回None
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"""
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try:
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excel_path = self._get_excel_path(image_path)
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# 确保输出目录存在
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os.makedirs(os.path.dirname(excel_path), exist_ok=True)
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# 调用OCR处理器的Excel生成功能
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if hasattr(self.ocr_processor, 'generate_excel'):
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success = self.ocr_processor.generate_excel(ocr_result, excel_path)
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if success:
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return excel_path
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else:
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# 如果OCR处理器没有generate_excel方法，直接返回路径
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# 假设OCR处理器已经生成了Excel文件
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if os.path.exists(excel_path):
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return excel_path
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return None
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except Exception as e:
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logger.error(f"生成Excel文件时发生错误: {e}", exc_info=True)
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return None
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