417 lines
12 KiB
Python
417 lines
12 KiB
Python
"""
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人脸特征API路由
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"""
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from typing import List, Optional
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from datetime import datetime
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from fastapi import APIRouter, Depends, HTTPException, status, Query, BackgroundTasks
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from fastapi.responses import JSONResponse
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from schemas.face_feature import (
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FaceFeatureCreate,
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FaceFeatureUpdate,
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FaceFeatureQuery,
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FaceFeatureResponse,
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FaceFeatureListResponse,
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FaceFeatureStatsResponse,
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BatchFaceFeatureCreate,
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FeatureStatus
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)
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from api.dependencies import (
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get_face_feature_service,
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get_face_feature_by_id
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)
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from services.face_feature_service import FaceFeatureService
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from api.errors import (
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FaceFeatureProcessingError,
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FeatureNotFoundError,
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DuplicateFeatureError
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)
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from config import settings
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# 创建路由器
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router = APIRouter(
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prefix="/face-features",
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tags=["人脸特征管理"],
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responses={
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404: {"description": "资源不存在"},
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400: {"description": "请求参数错误"},
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500: {"description": "服务器内部错误"}
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}
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)
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@router.post(
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"/",
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response_model=FaceFeatureResponse,
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status_code=status.HTTP_201_CREATED,
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summary="创建人脸特征记录",
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description="创建新的人脸特征记录"
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)
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async def create_face_feature(
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feature_data: FaceFeatureCreate,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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创建人脸特征记录
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- **person_id**: 人员ID (必须，大于0)
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- **feature_type**: 特征类型 (可选，大于等于0)
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- **pic_id**: 图片ID (可选)
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- **status**: 计算状态 (默认: NOT_STARTED)
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- **feature_data**: 特征数据 (可选，二进制)
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"""
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try:
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return service.create_feature(feature_data)
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except ValueError as e:
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if "already exists" in str(e):
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# 解析错误信息中的person_id和feature_type
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raise DuplicateFeatureError(
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person_id=feature_data.person_id,
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feature_type=feature_data.feature_type or 0
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)
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raise FaceFeatureProcessingError(detail=str(e))
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@router.get(
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"/{feature_id}",
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response_model=FaceFeatureResponse,
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summary="获取人脸特征记录",
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description="根据ID获取人脸特征记录"
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)
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async def get_face_feature(
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feature: FaceFeatureResponse = Depends(get_face_feature_by_id)
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):
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"""
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根据ID获取人脸特征记录
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- **feature_id**: 特征记录ID (路径参数)
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"""
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return feature
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@router.get(
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"/",
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response_model=FaceFeatureListResponse,
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summary="查询人脸特征记录",
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description="查询人脸特征记录列表，支持分页和过滤"
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)
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async def list_face_features(
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person_id: Optional[int] = Query(None, description="人员ID", gt=0),
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feature_type: Optional[int] = Query(None, description="特征类型", ge=0),
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status: Optional[FeatureStatus] = Query(None, description="计算状态"),
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start_date: Optional[datetime] = Query(None, description="开始时间"),
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end_date: Optional[datetime] = Query(None, description="结束时间"),
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has_feature_data: Optional[bool] = Query(None, description="是否有特征数据"),
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page: int = Query(1, description="页码", ge=1),
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page_size: int = Query(20, description="每页数量", ge=1, le=100),
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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查询人脸特征记录
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- **person_id**: 按人员ID过滤 (可选)
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- **feature_type**: 按特征类型过滤 (可选)
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- **status**: 按计算状态过滤 (可选)
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- **start_date**: 开始时间过滤 (可选)
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- **end_date**: 结束时间过滤 (可选)
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- **has_feature_data**: 是否有特征数据过滤 (可选)
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- **page**: 页码 (默认: 1)
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- **page_size**: 每页数量 (默认: 20, 最大: 100)
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"""
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# 构建查询参数
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query = FaceFeatureQuery(
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person_id=person_id,
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feature_type=feature_type,
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status=status,
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start_date=start_date,
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end_date=end_date,
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has_feature_data=has_feature_data
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)
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return service.query_features(
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query=query,
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page=page,
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page_size=page_size,
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order_by="created_time",
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desc_order=True
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)
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@router.put(
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"/{feature_id}",
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response_model=FaceFeatureResponse,
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summary="更新人脸特征记录",
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description="更新指定ID的人脸特征记录"
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)
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async def update_face_feature(
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feature_id: int,
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update_data: FaceFeatureUpdate,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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更新人脸特征记录
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- **feature_id**: 特征记录ID (路径参数)
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- **update_data**: 更新数据 (请求体)
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"""
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try:
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result = service.update_feature(feature_id, update_data)
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if not result:
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raise FeatureNotFoundError(feature_id)
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return result
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except ValueError as e:
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raise FaceFeatureProcessingError(detail=str(e), feature_id=feature_id)
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@router.delete(
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"/{feature_id}",
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status_code=status.HTTP_204_NO_CONTENT,
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summary="删除人脸特征记录",
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description="删除指定ID的人脸特征记录"
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)
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async def delete_face_feature(
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feature_id: int,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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删除人脸特征记录
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- **feature_id**: 特征记录ID (路径参数)
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"""
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success = service.delete_feature(feature_id)
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if not success:
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raise FeatureNotFoundError(feature_id)
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return JSONResponse(
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status_code=status.HTTP_204_NO_CONTENT,
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content=None
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)
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@router.post(
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"/{feature_id}/start-processing",
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response_model=FaceFeatureResponse,
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summary="开始处理人脸特征",
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description="开始计算指定ID的人脸特征值"
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)
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async def start_face_feature_processing(
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feature_id: int,
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background_tasks: BackgroundTasks,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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开始处理人脸特征计算
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- **feature_id**: 特征记录ID (路径参数)
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注意：这是一个异步处理接口，会立即返回开始状态，
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实际特征计算可能在后台进行。
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"""
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try:
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# 先获取特征记录
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feature = service.get_feature(feature_id)
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if not feature:
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raise FeatureNotFoundError(feature_id)
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# 检查是否可以开始处理
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if feature.status != FeatureStatus.NOT_STARTED:
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raise FaceFeatureProcessingError(
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detail=f"特征记录状态为 {feature.status_name}，无法开始处理",
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feature_id=feature_id
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)
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# 开始处理
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success = service.start_processing(feature_id)
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if not success:
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raise FaceFeatureProcessingError(
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detail="开始处理失败",
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feature_id=feature_id
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)
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# 异步任务：模拟特征计算过程
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# 在实际应用中，这里应该调用实际的特征计算服务
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background_tasks.add_task(
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simulate_feature_processing,
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feature_id=feature_id,
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service=service
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)
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# 返回更新后的特征记录
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return service.get_feature(feature_id)
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except ValueError as e:
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raise FaceFeatureProcessingError(detail=str(e), feature_id=feature_id)
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@router.post(
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"/{feature_id}/finish-processing",
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response_model=FaceFeatureResponse,
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summary="完成人脸特征处理",
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description="完成指定ID的人脸特征值计算"
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)
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async def finish_face_feature_processing(
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feature_id: int,
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success: bool = Query(True, description="是否成功完成"),
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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完成人脸特征计算
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- **feature_id**: 特征记录ID (路径参数)
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- **success**: 是否成功完成 (查询参数，默认: true)
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"""
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try:
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# 检查特征记录
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feature = service.get_feature(feature_id)
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if not feature:
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raise FeatureNotFoundError(feature_id)
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# 检查是否可以完成处理
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if feature.status != FeatureStatus.PROCESSING:
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raise FaceFeatureProcessingError(
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detail=f"特征记录状态为 {feature.status_name}，无法完成处理",
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feature_id=feature_id
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)
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# 完成处理
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finish_success = service.finish_processing(feature_id, success)
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if not finish_success:
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raise FaceFeatureProcessingError(
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detail="完成处理失败",
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feature_id=feature_id
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)
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return service.get_feature(feature_id)
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except ValueError as e:
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raise FaceFeatureProcessingError(detail=str(e), feature_id=feature_id)
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@router.post(
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"/batch",
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response_model=List[FaceFeatureResponse],
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status_code=status.HTTP_201_CREATED,
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summary="批量创建人脸特征记录",
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description="批量创建多个人脸特征记录"
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)
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async def batch_create_face_features(
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batch_data: BatchFaceFeatureCreate,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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批量创建人脸特征记录
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- **items**: 特征记录列表 (必须，1-1000条)
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"""
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try:
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return service.create_features_batch(batch_data)
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except ValueError as e:
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raise FaceFeatureProcessingError(detail=str(e))
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@router.get(
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"/person/{person_id}",
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response_model=List[FaceFeatureResponse],
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summary="获取人员的人脸特征记录",
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description="根据人员ID获取所有相关的人脸特征记录"
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)
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async def get_face_features_by_person(
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person_id: int,
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limit: int = Query(100, description="返回数量限制", ge=1, le=1000),
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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获取人员的人脸特征记录
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- **person_id**: 人员ID (路径参数)
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- **limit**: 返回数量限制 (查询参数，默认: 100, 最大: 1000)
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"""
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return service.list_features_by_person(person_id, limit)
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@router.get(
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"/stats/summary",
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response_model=FaceFeatureStatsResponse,
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summary="获取特征记录统计信息",
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description="获取人脸特征记录的统计摘要"
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)
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async def get_face_features_stats(
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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获取特征记录统计信息
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"""
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return service.get_statistics()
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@router.get(
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"/person/{person_id}/stats",
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summary="获取人员特征统计信息",
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description="获取指定人员的特征记录统计信息"
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)
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async def get_person_face_features_stats(
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person_id: int,
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service: FaceFeatureService = Depends(get_face_feature_service)
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):
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"""
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获取人员特征统计信息
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- **person_id**: 人员ID (路径参数)
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"""
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try:
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stats = service.get_person_statistics(person_id)
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return {
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"person_id": person_id,
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"total_features": stats["total_features"],
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"status_summary": stats["status_summary"],
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"feature_types": stats["feature_types"],
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"avg_processing_time": stats["avg_processing_time"],
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"successful_count": stats["successful_count"]
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}
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except Exception as e:
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raise FaceFeatureProcessingError(detail=str(e))
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async def simulate_feature_processing(
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feature_id: int,
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service: FaceFeatureService
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):
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"""
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模拟人脸特征计算过程
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在实际应用中，这里应该调用实际的特征计算算法
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例如：使用InsightFace、OpenCV等库进行人脸特征提取
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Args:
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feature_id: 特征记录ID
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service: 人脸特征服务
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"""
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import asyncio
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import random
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try:
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# 模拟计算延迟 (3-10秒)
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delay = random.uniform(3, 10)
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await asyncio.sleep(delay)
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# 模拟成功或失败 (90%成功率)
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success = random.random() < 0.9
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# 完成处理
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service.finish_processing(feature_id, success)
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# 如果成功，添加模拟的特征数据
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if success:
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# 生成模拟的512维特征向量 (float32)
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import numpy as np
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feature_data = np.random.randn(512).astype(np.float32).tobytes()
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service.update_feature_data(feature_id, feature_data)
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except Exception as e:
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# 如果发生异常，标记为失败
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service.finish_processing(feature_id, False)
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# 记录日志
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import logging
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logger = logging.getLogger(__name__)
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logger.error(f"特征计算失败 (ID: {feature_id}): {e}") |