完成人脸计算任务调用方法
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315
src/api/routes/algorithm_router.py
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315
src/api/routes/algorithm_router.py
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"""
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人脸特征计算算法路由
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提供人脸特征计算的HTTP接口
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"""
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import os
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import logging
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from datetime import datetime, timedelta
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from typing import Dict, Any
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from fastapi import APIRouter, HTTPException, BackgroundTasks
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from sqlalchemy.orm import Session
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from src.config import settings
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from src.database.connection import db_manager
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from src.models.face_feature import SurFaceFeature, FeatureStatus
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from src.repositories.face_feature_repository import FaceFeatureRepository
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from src.face_recognition_algorithm import FaceRecognitionAlgorithm
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# 创建路由器
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router = APIRouter(prefix="/algorithm", tags=["algorithm"])
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# 初始化人脸识别算法
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face_algorithm = FaceRecognitionAlgorithm(use_gpu=settings.FACE_USE_GPU, use_npu=settings.FACE_USE_NPU)
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logger = logging.getLogger(__name__)
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def process_feature_calculation(feature_id: int) -> bool:
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"""
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处理单个人脸特征计算
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参数:
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feature_id: 特征记录ID
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返回:
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是否成功处理
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"""
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try:
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with db_manager.get_session() as session:
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repository = FaceFeatureRepository(session)
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# 获取特征记录
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feature = repository.get_by_id(feature_id)
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if not feature:
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logger.error(f"特征记录不存在: {feature_id}")
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return False
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# 检查是否已经处理完成
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if feature.status in [FeatureStatus.SUCCESS, FeatureStatus.FAILED]:
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logger.info(f"特征记录已处理完成: {feature_id}, 状态: {feature.status_name}")
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return True
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# 检查是否超时
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if feature.status == FeatureStatus.PROCESSING:
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if feature.start_time:
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timeout_duration = timedelta(hours=settings.FACE_CAL_FEATURE_TIMEOUT_HOURS)
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if datetime.now() - feature.start_time > timeout_duration:
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# 超时处理
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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session.commit()
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logger.warning(f"特征计算超时: {feature_id}")
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return False
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else:
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# 没有开始时间，重置状态
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feature.status = FeatureStatus.NOT_STARTED
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session.commit()
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# 处理未开始的计算
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if feature.status == FeatureStatus.NOT_STARTED:
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# 设置状态为计算中
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feature.status = FeatureStatus.PROCESSING
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feature.start_time = datetime.now()
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session.commit()
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logger.info(f"开始计算特征: {feature_id}")
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# 构建图片路径
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if not feature.pic_id:
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logger.error(f"特征记录缺少图片ID: {feature_id}")
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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session.commit()
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return False
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image_path = os.path.join(settings.FACE_REGISTER_IMAGE_RESOURCE_DIR, feature.pic_id)
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# 检查图片文件是否存在
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if not os.path.exists(image_path):
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logger.error(f"图片文件不存在: {image_path}")
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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session.commit()
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return False
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# 提取人脸特征
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try:
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feature_vector = face_algorithm.extract_face_feature(str(image_path))
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if feature_vector is not None:
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# 转换为二进制数据
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feature_bytes = feature_vector.tobytes()
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feature.feature_data = feature_bytes
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feature.status = FeatureStatus.SUCCESS
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feature.finish_time = datetime.now()
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session.commit()
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logger.info(f"特征计算成功: {feature_id}, 特征向量长度: {len(feature_vector)}")
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return True
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else:
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logger.error(f"特征提取失败: {feature_id}")
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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session.commit()
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return False
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except Exception as e:
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logger.error(f"特征计算过程中出错: {feature_id}, 错误: {str(e)}")
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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session.commit()
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return False
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return True
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except Exception as e:
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logger.error(f"处理特征计算时发生异常: {feature_id}, 错误: {str(e)}")
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return False
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def process_pending_features() -> Dict[str, Any]:
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"""
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处理所有待处理的人脸特征计算
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返回:
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处理结果统计
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"""
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try:
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with db_manager.get_session() as session:
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repository = FaceFeatureRepository(session)
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# 查找需要处理的记录
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# 条件: feature_type = FACE_MODEL_VERSION 且 status = 0 (未开始)
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pending_features = repository.get_features_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.NOT_STARTED
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)
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# 查找可能超时的记录 (status = 1 且超时)
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timeout_features = []
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processing_features = repository.get_features_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.PROCESSING
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)
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for feature in processing_features:
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if feature.start_time:
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timeout_duration = timedelta(hours=settings.FACE_CAL_FEATURE_TIMEOUT_HOURS)
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if datetime.now() - feature.start_time > timeout_duration:
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timeout_features.append(feature)
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total_pending = len(pending_features)
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total_timeout = len(timeout_features)
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logger.info(f"发现待处理特征: {total_pending}个, 超时特征: {total_timeout}个")
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# 处理超时记录
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timeout_success = 0
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for feature in timeout_features:
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feature.status = FeatureStatus.FAILED
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feature.finish_time = datetime.now()
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timeout_success += 1
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if timeout_features:
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session.commit()
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# 处理待处理记录
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processed_count = 0
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success_count = 0
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for feature in pending_features:
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processed_count += 1
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if process_feature_calculation(feature.id):
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success_count += 1
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# 每处理10个记录输出一次进度
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if processed_count % 10 == 0:
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logger.info(f"处理进度: {processed_count}/{total_pending}")
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return {
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"total_pending": total_pending,
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"total_timeout": total_timeout,
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"processed_count": processed_count,
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"success_count": success_count,
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"timeout_handled": timeout_success
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}
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except Exception as e:
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logger.error(f"批量处理特征计算时发生异常: {str(e)}")
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return {
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"total_pending": 0,
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"total_timeout": 0,
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"processed_count": 0,
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"success_count": 0,
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"timeout_handled": 0,
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"error": str(e)
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}
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@router.post("/start-feature-calculation", summary="开始人脸特征计算")
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async def start_feature_calculation(background_tasks: BackgroundTasks):
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"""
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开始处理人脸特征计算
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此接口会:
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1. 查找所有feature_type为当前模型版本且status为0的记录
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2. 将状态改为1，设置开始时间
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3. 提取人脸特征值
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4. 对于status为1且超时的记录，标记为失败
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返回处理结果统计
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"""
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try:
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# 在后台任务中处理，避免阻塞请求
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result = process_pending_features()
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return {
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"success": True,
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"message": "特征计算处理完成",
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"data": result
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}
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except Exception as e:
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logger.error(f"启动特征计算失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"启动特征计算失败: {str(e)}")
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@router.get("/feature-calculation-status", summary="获取特征计算状态")
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async def get_feature_calculation_status():
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"""
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获取当前特征计算的状态统计
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"""
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try:
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with db_manager.get_session() as session:
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repository = FaceFeatureRepository(session)
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# 获取统计信息
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stats = repository.get_statistics()
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# 获取当前模型版本的特定统计
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current_model_stats = {
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"total": repository.count_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION
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),
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"not_started": repository.count_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.NOT_STARTED
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),
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"processing": repository.count_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.PROCESSING
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),
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"success": repository.count_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.SUCCESS
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),
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"failed": repository.count_by_type_and_status(
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feature_type=settings.FACE_MODEL_VERSION,
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status=FeatureStatus.FAILED
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)
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}
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return {
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"success": True,
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"data": {
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"overall_stats": stats,
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"current_model_stats": current_model_stats,
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"model_version": settings.FACE_MODEL_VERSION,
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"timeout_hours": settings.FACE_CAL_FEATURE_TIMEOUT_HOURS
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}
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}
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except Exception as e:
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logger.error(f"获取特征计算状态失败: {str(e)}")
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raise HTTPException(status_code=500, detail=f"获取特征计算状态失败: {str(e)}")
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@router.post("/calculate-single-feature/{feature_id}", summary="计算单个特征")
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async def calculate_single_feature(feature_id: int):
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"""
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计算单个特征记录的人脸特征
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参数:
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feature_id: 特征记录ID
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"""
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try:
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success = process_feature_calculation(feature_id)
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if success:
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return {
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"success": True,
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"message": f"特征计算完成: {feature_id}"
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}
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else:
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return {
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"success": False,
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"message": f"特征计算失败: {feature_id}"
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}
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except Exception as e:
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logger.error(f"计算单个特征失败: {feature_id}, 错误: {str(e)}")
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raise HTTPException(status_code=500, detail=f"计算单个特征失败: {str(e)}")
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# 导出路由器
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__all__ = ["router"]
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