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# -*- coding: utf-8 -*-
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"""
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OCR 路由模块
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提供 OCR 识别和快递单解析端点
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"""
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from typing import Optional
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from fastapi import APIRouter, Depends, File, Request, UploadFile
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from api.dependencies import (
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build_pipeline_config,
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decode_image_bytes,
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encode_image_base64,
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get_ocr_pipeline,
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parse_multipart_image,
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parse_multipart_params,
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)
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from api.exceptions import OCRProcessingError
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from api.schemas.request import OCRRequestBase64, OCRRequestParams, ROIParams
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from api.schemas.response import (
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ErrorDetail,
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ExpressInfoData,
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ExpressPersonData,
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ExpressResponse,
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ExpressResultData,
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OCRResponse,
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OCRResultData,
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TextBlockData,
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)
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from api.security import decode_base64_image
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from ocr.pipeline import OCRPipeline, OCRResult
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from utils.config import OCRConfig, PipelineConfig, ROIConfig
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from visualize.draw import OCRVisualizer
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from utils.config import VisualizeConfig
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router = APIRouter(prefix="/ocr", tags=["OCR 识别"])
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# 模块级别的 Visualizer 实例（避免重复创建）
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_visualizer: Optional[OCRVisualizer] = None
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def _get_visualizer() -> OCRVisualizer:
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"""
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获取 Visualizer 实例（单例模式，避免重复创建）
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Returns:
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OCRVisualizer 实例
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"""
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global _visualizer
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if _visualizer is None:
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_visualizer = OCRVisualizer(VisualizeConfig())
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return _visualizer
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def _convert_ocr_result_to_response(
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result: OCRResult,
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annotated_image_base64: Optional[str] = None,
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) -> OCRResultData:
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"""
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将 OCRResult 转换为响应数据模型
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Args:
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result: OCR 处理结果
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annotated_image_base64: 标注图片的 Base64 编码
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Returns:
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响应数据模型
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"""
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text_blocks = [
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TextBlockData(
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text=block.text,
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confidence=block.confidence,
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bbox=block.bbox,
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bbox_with_offset=block.bbox_with_offset,
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center=list(block.center),
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width=block.width,
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height=block.height,
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)
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for block in result.text_blocks
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]
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return OCRResultData(
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processing_time_ms=result.processing_time_ms,
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text_count=result.text_count,
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average_confidence=result.average_confidence,
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roi_applied=result.roi_applied,
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roi_rect=list(result.roi_rect) if result.roi_rect else None,
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text_blocks=text_blocks,
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annotated_image_base64=annotated_image_base64,
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)
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def _convert_express_result_to_response(
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result: OCRResult,
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annotated_image_base64: Optional[str] = None,
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) -> ExpressResultData:
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"""
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将 OCRResult 转换为快递单响应数据模型
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Args:
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result: OCR 处理结果
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annotated_image_base64: 标注图片的 Base64 编码
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Returns:
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快递单响应数据模型
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"""
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# 解析快递单信息
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express_info = result.parse_express()
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merged_text = result.merge_text()
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return ExpressResultData(
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processing_time_ms=result.processing_time_ms,
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express_info=ExpressInfoData(
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tracking_number=express_info.tracking_number,
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sender=ExpressPersonData(
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name=express_info.sender_name,
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phone=express_info.sender_phone,
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address=express_info.sender_address,
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),
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receiver=ExpressPersonData(
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name=express_info.receiver_name,
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phone=express_info.receiver_phone,
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address=express_info.receiver_address,
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),
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courier_company=express_info.courier_company,
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confidence=express_info.confidence,
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extra_fields=express_info.extra_fields,
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raw_text=express_info.raw_text,
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),
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merged_text=merged_text,
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annotated_image_base64=annotated_image_base64,
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)
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def _process_ocr(
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image_bytes: bytes,
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pipeline: OCRPipeline,
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roi: Optional[ROIParams] = None,
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drop_score: Optional[float] = None,
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return_annotated_image: bool = False,
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) -> tuple[OCRResult, Optional[str]]:
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"""
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执行 OCR 处理（线程安全）
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Args:
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image_bytes: 图片字节数据
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pipeline: OCR 管道
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roi: ROI 参数
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drop_score: 置信度阈值，低于此值的结果将被过滤
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return_annotated_image: 是否返回标注图片
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Returns:
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(OCR 结果, 标注图片 Base64)
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"""
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# 解码图片
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image = decode_image_bytes(image_bytes)
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# 构建管道配置（每次请求独立的配置，线程安全）
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pipeline_config = build_pipeline_config(roi)
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try:
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# 执行 OCR（传递临时配置，不修改共享状态）
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result = pipeline.process(
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image=image,
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pipeline_config=pipeline_config,
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drop_score=drop_score,
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)
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except Exception as e:
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raise OCRProcessingError(f"OCR 处理失败: {str(e)}")
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# 生成标注图片
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annotated_image_base64 = None
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if return_annotated_image and result.text_count > 0:
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annotated = _get_visualizer().draw_result(image, result)
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annotated_image_base64 = encode_image_base64(annotated)
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return result, annotated_image_base64
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# ============================================================
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# multipart/form-data 端点
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# ============================================================
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@router.post(
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"/recognize",
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response_model=OCRResponse,
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summary="OCR 识别 (文件上传)",
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description="上传图片文件进行 OCR 识别，支持 jpg/png/bmp/webp 格式",
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)
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async def recognize_multipart(
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request: Request,
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file: UploadFile = File(..., description="图片文件"),
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params: OCRRequestParams = Depends(parse_multipart_params),
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pipeline: OCRPipeline = Depends(get_ocr_pipeline),
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) -> OCRResponse:
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"""
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OCR 识别端点 (multipart/form-data)
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上传图片文件进行文字识别
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"""
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try:
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# 验证并读取文件
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image_bytes = await parse_multipart_image(file)
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# 执行 OCR
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result, annotated_base64 = _process_ocr(
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image_bytes=image_bytes,
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pipeline=pipeline,
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roi=params.get_roi(),
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drop_score=params.drop_score,
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return_annotated_image=params.return_annotated_image,
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)
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# 构建响应
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return OCRResponse(
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success=True,
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data=_convert_ocr_result_to_response(result, annotated_base64),
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)
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except Exception as e:
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return OCRResponse(
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success=False,
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error=ErrorDetail(
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code=type(e).__name__,
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message=str(e),
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),
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)
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@router.post(
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"/express",
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response_model=ExpressResponse,
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summary="快递单解析 (文件上传)",
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description="上传快递单图片进行 OCR 识别并解析结构化信息",
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)
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async def express_multipart(
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request: Request,
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file: UploadFile = File(..., description="快递单图片"),
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params: OCRRequestParams = Depends(parse_multipart_params),
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pipeline: OCRPipeline = Depends(get_ocr_pipeline),
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) -> ExpressResponse:
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"""
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快递单解析端点 (multipart/form-data)
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上传快递单图片，自动识别并提取结构化信息
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"""
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try:
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# 验证并读取文件
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image_bytes = await parse_multipart_image(file)
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# 执行 OCR
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result, annotated_base64 = _process_ocr(
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image_bytes=image_bytes,
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pipeline=pipeline,
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roi=params.get_roi(),
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drop_score=params.drop_score,
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return_annotated_image=params.return_annotated_image,
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)
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# 构建响应
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return ExpressResponse(
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success=True,
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data=_convert_express_result_to_response(result, annotated_base64),
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)
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except Exception as e:
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return ExpressResponse(
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success=False,
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error=ErrorDetail(
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code=type(e).__name__,
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message=str(e),
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),
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)
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# ============================================================
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# JSON (Base64) 端点
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# ============================================================
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@router.post(
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"/recognize/base64",
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response_model=OCRResponse,
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summary="OCR 识别 (Base64)",
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description="提交 Base64 编码的图片进行 OCR 识别",
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)
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async def recognize_base64(
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request: Request,
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body: OCRRequestBase64,
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pipeline: OCRPipeline = Depends(get_ocr_pipeline),
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) -> OCRResponse:
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"""
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OCR 识别端点 (JSON Base64)
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提交 Base64 编码的图片进行文字识别
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"""
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try:
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# 解码并验证 Base64 图片
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image_bytes = decode_base64_image(body.image_base64)
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# 执行 OCR
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result, annotated_base64 = _process_ocr(
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image_bytes=image_bytes,
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pipeline=pipeline,
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roi=body.roi,
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drop_score=body.drop_score,
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return_annotated_image=body.return_annotated_image,
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)
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# 构建响应
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return OCRResponse(
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success=True,
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data=_convert_ocr_result_to_response(result, annotated_base64),
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)
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except Exception as e:
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return OCRResponse(
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success=False,
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error=ErrorDetail(
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code=type(e).__name__,
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message=str(e),
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),
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)
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@router.post(
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"/express/base64",
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response_model=ExpressResponse,
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summary="快递单解析 (Base64)",
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description="提交 Base64 编码的快递单图片进行解析",
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)
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async def express_base64(
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request: Request,
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body: OCRRequestBase64,
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pipeline: OCRPipeline = Depends(get_ocr_pipeline),
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) -> ExpressResponse:
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"""
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快递单解析端点 (JSON Base64)
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提交 Base64 编码的快递单图片，自动识别并提取结构化信息
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"""
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try:
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# 解码并验证 Base64 图片
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image_bytes = decode_base64_image(body.image_base64)
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# 执行 OCR
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result, annotated_base64 = _process_ocr(
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image_bytes=image_bytes,
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pipeline=pipeline,
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roi=body.roi,
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drop_score=body.drop_score,
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return_annotated_image=body.return_annotated_image,
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)
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# 构建响应
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return ExpressResponse(
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success=True,
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data=_convert_express_result_to_response(result, annotated_base64),
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)
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except Exception as e:
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return ExpressResponse(
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success=False,
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error=ErrorDetail(
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code=type(e).__name__,
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message=str(e),
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),
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)
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