admin
afcddfaeb9
- 新增基于 JWT 当前用户的登录恢复、角色权限、用户管理、审计日志和演示出厂重置后台接口与前端管理页。 - 重串 GT_label 导出和 GT Mask 导入逻辑：导出保留类别真实 maskid，导入仅接受灰度或 RGB 等通道 maskid 图，支持未知 maskid 策略、尺寸最近邻拉伸和导入预览。 - 统一分割结果导出体验：默认当前帧，按项目抽帧顺序和 XhXXmXXsXXXms 时间戳命名 ZIP 与图片，补齐 GT/Pro/Mix/分开 Mask 输出和映射 JSON。 - 调整工作区左侧工具栏：移除创建点/线段入口，新增画笔、橡皮擦及尺寸控制，并按绘制、布尔、导入/AI 工具分组分隔。 - 扩展 Canvas 编辑能力：画笔按语义分类绘制并可自动并入连通选中 mask，橡皮擦对选中区域扣除，优化布尔操作、选区、撤销重做和保存状态联动。 - 优化自动传播时间轴显示：同一蓝色系按传播新旧递进变暗，老传播记录达到阈值后统一旧记录色，并维护范围选择与清空后的历史显示。 - 将 AI 智能分割入口替换为更明确的 AI 元素图标，并同步侧栏、工作区和 AI 页面入口表现。 - 完善模板分类、maskid 工具函数、分类树联动、遮罩透明度、边缘平滑和传播链同步相关前端状态。 - 扩展后端项目、媒体、任务、Dashboard、模板和传播 runner 的用户隔离、任务控制、进度事件与兼容处理。 - 补充前后端测试，覆盖用户管理、GT_label 往返导入导出、GT Mask 校验和预览、画笔/橡皮擦、时间轴传播历史、导出范围、WebSocket 与 API 封装。 - 更新 AGENTS、README 和 doc 文档，记录当前接口契约、实现状态、测试计划、安装说明和 maskid/GT_label 规则。
378 lines
9.6 KiB
Python
378 lines
9.6 KiB
Python
"""Pydantic schemas for request/response validation."""
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from datetime import datetime
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from typing import Optional, Any
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from pydantic import BaseModel, ConfigDict
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# ---------------------------------------------------------------------------
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# Auth / user schemas
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# ---------------------------------------------------------------------------
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class UserOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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username: str
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role: str
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is_active: int
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class LoginResponse(BaseModel):
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token: str
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token_type: str = "bearer"
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username: str
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user: UserOut
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class AdminUserCreate(BaseModel):
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username: str
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password: str
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role: str = "annotator"
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is_active: bool = True
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class AdminUserUpdate(BaseModel):
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username: Optional[str] = None
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password: Optional[str] = None
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role: Optional[str] = None
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is_active: Optional[bool] = None
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class AuditLogOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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actor_user_id: Optional[int] = None
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action: str
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target_type: Optional[str] = None
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target_id: Optional[str] = None
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detail: Optional[dict[str, Any]] = None
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created_at: datetime
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class DemoFactoryResetRequest(BaseModel):
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confirmation: str
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# ---------------------------------------------------------------------------
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# Project schemas
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# ---------------------------------------------------------------------------
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class ProjectBase(BaseModel):
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name: str
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description: Optional[str] = None
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video_path: Optional[str] = None
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thumbnail_url: Optional[str] = None
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status: Optional[str] = "pending"
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source_type: Optional[str] = "video"
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original_fps: Optional[float] = None
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parse_fps: Optional[float] = 30.0
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class ProjectCreate(ProjectBase):
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pass
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class ProjectUpdate(BaseModel):
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name: Optional[str] = None
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description: Optional[str] = None
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video_path: Optional[str] = None
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thumbnail_url: Optional[str] = None
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status: Optional[str] = None
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source_type: Optional[str] = None
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original_fps: Optional[float] = None
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parse_fps: Optional[float] = None
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class ProjectOut(ProjectBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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owner_user_id: Optional[int] = None
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created_at: datetime
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updated_at: datetime
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frame_count: int = 0
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class DemoFactoryResetOut(BaseModel):
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admin_user: UserOut
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project: ProjectOut
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deleted_counts: dict[str, int]
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message: str
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# ---------------------------------------------------------------------------
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# Frame schemas
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# ---------------------------------------------------------------------------
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class FrameBase(BaseModel):
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frame_index: int
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image_url: str
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width: Optional[int] = None
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height: Optional[int] = None
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timestamp_ms: Optional[float] = None
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source_frame_number: Optional[int] = None
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class FrameCreate(FrameBase):
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project_id: int
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class FrameOut(FrameBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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project_id: int
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Template schemas
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# ---------------------------------------------------------------------------
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class TemplateBase(BaseModel):
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name: str
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description: Optional[str] = None
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color: str
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z_index: int = 0
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mapping_rules: Optional[dict[str, Any]] = None
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classes: Optional[list[dict[str, Any]]] = None
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rules: Optional[list[dict[str, Any]]] = None
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class TemplateCreate(TemplateBase):
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pass
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class TemplateUpdate(BaseModel):
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name: Optional[str] = None
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description: Optional[str] = None
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color: Optional[str] = None
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z_index: Optional[int] = None
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mapping_rules: Optional[dict[str, Any]] = None
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classes: Optional[list[dict[str, Any]]] = None
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rules: Optional[list[dict[str, Any]]] = None
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class TemplateOut(TemplateBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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owner_user_id: Optional[int] = None
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Annotation schemas
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# ---------------------------------------------------------------------------
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class AnnotationBase(BaseModel):
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project_id: int
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frame_id: Optional[int] = None
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template_id: Optional[int] = None
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mask_data: Optional[dict[str, Any]] = None
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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class AnnotationCreate(AnnotationBase):
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pass
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class AnnotationUpdate(BaseModel):
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mask_data: Optional[dict[str, Any]] = None
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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template_id: Optional[int] = None
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class AnnotationOut(AnnotationBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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updated_at: datetime
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# ---------------------------------------------------------------------------
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# Mask schemas
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# ---------------------------------------------------------------------------
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class MaskBase(BaseModel):
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annotation_id: int
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mask_url: str
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format: str = "png"
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class MaskCreate(MaskBase):
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pass
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class MaskOut(MaskBase):
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model_config = ConfigDict(from_attributes=True)
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id: int
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created_at: datetime
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# ---------------------------------------------------------------------------
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# Processing task schemas
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# ---------------------------------------------------------------------------
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class ProcessingTaskOut(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: int
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task_type: str
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status: str
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progress: int
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message: Optional[str] = None
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project_id: Optional[int] = None
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celery_task_id: Optional[str] = None
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payload: Optional[dict[str, Any]] = None
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result: Optional[dict[str, Any]] = None
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error: Optional[str] = None
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created_at: datetime
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started_at: Optional[datetime] = None
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finished_at: Optional[datetime] = None
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updated_at: datetime
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# ---------------------------------------------------------------------------
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# AI schemas
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# ---------------------------------------------------------------------------
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class PredictRequest(BaseModel):
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image_id: int
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prompt_type: str # point / box / semantic
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prompt_data: Any
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model: Optional[str] = None
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options: Optional[dict[str, Any]] = None
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class PredictResponse(BaseModel):
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polygons: list[list[list[float]]]
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scores: Optional[list[float]] = None
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class MaskAnalysisRequest(BaseModel):
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frame_id: Optional[int] = None
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mask_data: dict[str, Any]
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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extract_skeleton: bool = False
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class MaskAnalysisResponse(BaseModel):
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confidence: Optional[float] = None
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confidence_source: str
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topology_anchor_count: int
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topology_anchors: list[list[float]]
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area: float
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bbox: Optional[list[float]] = None
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source: Optional[str] = None
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message: str
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class SmoothMaskRequest(BaseModel):
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frame_id: Optional[int] = None
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mask_data: dict[str, Any]
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points: Optional[list[list[float]]] = None
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bbox: Optional[list[float]] = None
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strength: float = 0.0
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method: str = "chaikin"
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class SmoothMaskResponse(BaseModel):
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polygons: list[list[list[float]]]
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topology_anchor_count: int
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topology_anchors: list[list[float]]
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area: float
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bbox: Optional[list[float]] = None
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smoothing: dict[str, Any]
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message: str
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class PropagationSeed(BaseModel):
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polygons: Optional[list[list[list[float]]]] = None
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bbox: Optional[list[float]] = None
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points: Optional[list[list[float]]] = None
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labels: Optional[list[int]] = None
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label: Optional[str] = None
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color: Optional[str] = None
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class_metadata: Optional[dict[str, Any]] = None
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template_id: Optional[int] = None
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source_mask_id: Optional[str] = None
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source_annotation_id: Optional[int] = None
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propagation_seed_signature: Optional[str] = None
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smoothing: Optional[dict[str, Any]] = None
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class PropagateRequest(BaseModel):
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project_id: int
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frame_id: int
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model: Optional[str] = "sam2.1_hiera_tiny"
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seed: PropagationSeed
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direction: str = "forward"
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max_frames: int = 30
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include_source: bool = False
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save_annotations: bool = True
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class PropagateResponse(BaseModel):
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model: str
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direction: str
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source_frame_id: int
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processed_frame_count: int
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created_annotation_count: int
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annotations: list[AnnotationOut]
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class PropagateTaskStep(BaseModel):
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seed: PropagationSeed
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direction: str = "forward"
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max_frames: int = 30
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class PropagateTaskRequest(BaseModel):
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project_id: int
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frame_id: int
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model: Optional[str] = "sam2.1_hiera_tiny"
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steps: list[PropagateTaskStep]
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include_source: bool = False
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save_annotations: bool = True
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class AiModelStatus(BaseModel):
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id: str
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label: str
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available: bool
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loaded: bool = False
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device: str
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supports: list[str]
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message: str
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package_available: bool = False
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checkpoint_exists: bool = False
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checkpoint_path: Optional[str] = None
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python_ok: bool = True
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torch_ok: bool = True
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cuda_required: bool = False
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external_available: bool = False
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external_python: Optional[str] = None
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class GpuStatus(BaseModel):
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available: bool
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device: str
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name: Optional[str] = None
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torch_available: bool
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torch_version: Optional[str] = None
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cuda_version: Optional[str] = None
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class AiRuntimeStatus(BaseModel):
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selected_model: str
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gpu: GpuStatus
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models: list[AiModelStatus]
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# ---------------------------------------------------------------------------
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# Export schemas
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# ---------------------------------------------------------------------------
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class ExportStatus(BaseModel):
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url: str
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format: str
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