后端能力: - 新增 Celery app、worker task、ProcessingTask 模型、/api/tasks 查询接口和 media_task_runner,将 /api/media/parse 改为创建后台任务并由 worker 执行 FFmpeg/OpenCV/pydicom 拆帧。 - 新增 Redis 进度事件模块和 FastAPI Redis pub/sub 订阅,将 worker 任务进度广播到 /ws/progress;Dashboard 后端概览接口改为聚合 projects/frames/annotations/templates/processing_tasks。 - 统一项目状态为 pending/parsing/ready/error,新增共享 status 常量,并让前端兼容归一化旧状态值。 - 扩展 AI 后端:新增 SAM registry、SAM2 真实运行状态、SAM3 状态检测与文本语义推理适配入口,以及 /api/ai/models/status GPU/模型状态接口。 - 补齐标注保存/更新/删除、COCO/PNG mask 导出相关后端契约和模板 mapping_rules 打包/解包行为。 前端能力: - 新增运行时 API/WS 地址推导配置,前端 API 封装对齐 FastAPI 路由、字段映射、任务轮询、标注归档、导出下载和 AI 预测响应转换。 - Dashboard 改为读取 /api/dashboard/overview,并订阅 WebSocket progress/complete/error/status 更新解析队列和实时流转记录。 - 项目库导入视频/DICOM 后创建项目、上传媒体、触发异步解析并刷新真实项目列表。 - 工作区加载真实帧、无帧时触发解析任务、回显已保存标注、保存未归档 mask、更新 dirty mask、清空当前帧后端标注、导出 COCO JSON。 - Canvas 支持当前帧点/框提示调用后端 AI、渲染推理/已保存 mask、应用模板分类并维护保存状态计数;时间轴按项目 fps 播放。 - AI 页面新增 SAM2/SAM3 模型选择,预测请求携带 model;侧边栏和工作区新增真实 GPU/SAM 状态徽标。 - 模板库和本体面板接入真实模板 CRUD、分类编辑、拖拽排序、JSON 导入、默认腹腔镜分类和本地自定义分类选择。 测试与文档: - 新增 Vitest 配置、前端测试 setup、API/config/websocket/store/组件测试,覆盖登录、项目库、Dashboard、Canvas、工作区、模型状态、时间轴、本体和模板库。 - 新增 pytest 后端测试夹具和 auth/projects/templates/media/AI/export/dashboard/tasks/progress 测试,使用 SQLite、fake MinIO、fake SAM registry 和 Redis monkeypatch 隔离外部服务。 - 新增 doc/ 文档结构,冻结当前需求、设计、接口契约、测试计划、前端逐元素审计、实现地图和后续实施计划,并同步更新 README 与 AGENTS。 验证: - conda run -n seg_server pytest backend/tests:27 passed。 - npm run test:run:54 passed。 - npm run lint、npm run build、compileall、git diff --check 均通过;Vite 仅提示大 chunk 警告。
227 lines
5.8 KiB
Python
227 lines
5.8 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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# 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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created_at: datetime
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updated_at: datetime
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frame_count: int = 0
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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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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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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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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 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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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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