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213
schemas/face_feature.py
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213
schemas/face_feature.py
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"""
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人脸特征值的Pydantic模型
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用于数据验证和序列化
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"""
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from datetime import datetime
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from typing import Optional, List, Dict, Any
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from pydantic import BaseModel, Field, field_validator, ConfigDict, model_validator
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from enum import IntEnum
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# 枚举定义（与数据库模型一致）
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class FeatureStatus(IntEnum):
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NOT_STARTED = 0
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PROCESSING = 1
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SUCCESS = 2
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FAILED = 3
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# 基础模型
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class FaceFeatureBase(BaseModel):
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"""基础模型，包含所有字段"""
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person_id: int = Field(..., description="人员ID", gt=0)
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feature_type: Optional[int] = Field(None, description="模型版本", ge=0)
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feature_data: Optional[bytes] = Field(None, description="特征值（二进制）")
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pic_id: Optional[str] = Field(None, description="图片ID", max_length=255)
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status: FeatureStatus = Field(
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default=FeatureStatus.NOT_STARTED,
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description="计算状态"
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)
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start_time: Optional[datetime] = Field(None, description="计算开始时间")
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finish_time: Optional[datetime] = Field(None, description="计算结束时间")
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@field_validator('feature_data', mode='before')
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@classmethod
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def validate_feature_data(cls, v):
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"""验证特征数据"""
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if v is not None and not isinstance(v, bytes):
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if isinstance(v, str):
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# 尝试从hex字符串转换
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try:
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return bytes.fromhex(v)
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except ValueError:
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raise ValueError("feature_data must be valid hex string or bytes")
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else:
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raise ValueError("feature_data must be bytes or hex string")
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return v
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# 创建模型
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class FaceFeatureCreate(FaceFeatureBase):
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"""创建特征记录模型"""
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# 创建时不指定ID和时间
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"person_id": 1001,
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"feature_type": 1,
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"pic_id": "img_20250101_001",
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"status": 0
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}
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}
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)
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class FaceFeatureUpdate(BaseModel):
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"""更新特征记录模型"""
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feature_type: Optional[int] = Field(None, description="模型版本", ge=0)
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feature_data: Optional[bytes] = Field(None, description="特征值（二进制）")
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pic_id: Optional[str] = Field(None, description="图片ID", max_length=255)
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status: Optional[FeatureStatus] = Field(None, description="计算状态")
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start_time: Optional[datetime] = Field(None, description="计算开始时间")
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finish_time: Optional[datetime] = Field(None, description="计算结束时间")
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"status": 2,
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"finish_time": "2024-01-01T12:00:00Z"
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}
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}
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)
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# 查询参数模型
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class FaceFeatureQuery(BaseModel):
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"""特征记录查询参数"""
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person_id: Optional[int] = Field(None, description="人员ID", gt=0)
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feature_type: Optional[int] = Field(None, description="模型版本", ge=0)
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status: Optional[FeatureStatus] = Field(None, description="计算状态")
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start_date: Optional[datetime] = Field(None, description="开始时间")
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end_date: Optional[datetime] = Field(None, description="结束时间")
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has_feature_data: Optional[bool] = Field(None, description="是否有特征数据")
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"person_id": 1001,
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"status": 2,
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"start_date": "2024-01-01T00:00:00Z"
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}
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}
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)
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# 响应模型
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class FaceFeatureResponse(FaceFeatureBase):
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"""特征记录响应模型"""
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id: int
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created_time: datetime
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# 计算字段（将在验证后设置）
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status_name: Optional[str] = None
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is_completed: Optional[bool] = None
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processing_time: Optional[float] = None
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has_feature_data: Optional[bool] = None
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@model_validator(mode='after')
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def set_computed_fields(self):
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"""设置所有计算字段"""
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# 状态名称
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try:
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self.status_name = FeatureStatus(self.status).name
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except ValueError:
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self.status_name = f"未知状态({self.status})"
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# 是否完成
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self.is_completed = self.status in [FeatureStatus.SUCCESS, FeatureStatus.FAILED]
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# 处理时间
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if self.start_time and self.finish_time:
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self.processing_time = (self.finish_time - self.start_time).total_seconds()
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# 是否有特征数据
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self.has_feature_data = self.feature_data is not None and len(self.feature_data) > 0
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return self
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model_config = ConfigDict(
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from_attributes=True,
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populate_by_name=True,
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json_schema_extra={
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"example": {
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"id": 1,
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"person_id": 1001,
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"feature_type": 1,
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"status": 2,
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"status_name": "SUCCESS",
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"created_time": "2024-01-01T10:00:00Z",
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"pic_id": "img_20250101_001",
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"start_time": "2024-01-01T10:00:00Z",
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"finish_time": "2024-01-01T10:00:05Z",
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"is_completed": True,
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"processing_time": 5.0,
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"has_feature_data": True
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}
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}
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)
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class FaceFeatureListResponse(BaseModel):
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"""特征记录列表响应"""
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total: int = Field(..., description="总记录数")
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items: List[FaceFeatureResponse] = Field(..., description="记录列表")
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page: Optional[int] = Field(None, description="当前页码")
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page_size: Optional[int] = Field(None, description="每页数量")
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"total": 100,
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"page": 1,
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"page_size": 20,
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"items": []
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}
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}
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)
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# 批量操作模型
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class BatchFaceFeatureCreate(BaseModel):
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"""批量创建特征记录"""
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items: List[FaceFeatureCreate] = Field(..., description="特征记录列表", min_items=1, max_items=1000)
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"items": [
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{"person_id": 1001, "feature_type": 1},
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{"person_id": 1002, "feature_type": 1}
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]
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}
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}
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)
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class FaceFeatureStatsResponse(BaseModel):
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"""特征记录统计响应"""
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total_count: int = Field(..., description="总记录数")
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by_status: Dict[str, int] = Field(..., description="按状态统计")
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by_feature_type: Dict[str, int] = Field(..., description="按特征类型统计")
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avg_processing_time: Optional[float] = Field(None, description="平均处理时间（秒）")
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model_config = ConfigDict(
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json_schema_extra={
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"example": {
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"total_count": 1000,
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"by_status": {"SUCCESS": 800, "PROCESSING": 100, "FAILED": 100},
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"by_feature_type": {"1": 500, "2": 500},
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"avg_processing_time": 5.2
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}
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}
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)
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