Files
Pre_Seg_Server/backend/schemas.py
admin f020ff3b4f feat: 打通全栈标注闭环、异步拆帧与模型状态
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。
2026-05-01 13:29:14 +08:00

227 lines
5.8 KiB
Python

"""Pydantic schemas for request/response validation."""
from datetime import datetime
from typing import Optional, Any
from pydantic import BaseModel, ConfigDict
# ---------------------------------------------------------------------------
# Project schemas
# ---------------------------------------------------------------------------
class ProjectBase(BaseModel):
name: str
description: Optional[str] = None
video_path: Optional[str] = None
thumbnail_url: Optional[str] = None
status: Optional[str] = "pending"
source_type: Optional[str] = "video"
original_fps: Optional[float] = None
parse_fps: Optional[float] = 30.0
class ProjectCreate(ProjectBase):
pass
class ProjectUpdate(BaseModel):
name: Optional[str] = None
description: Optional[str] = None
video_path: Optional[str] = None
thumbnail_url: Optional[str] = None
status: Optional[str] = None
source_type: Optional[str] = None
original_fps: Optional[float] = None
parse_fps: Optional[float] = None
class ProjectOut(ProjectBase):
model_config = ConfigDict(from_attributes=True)
id: int
created_at: datetime
updated_at: datetime
frame_count: int = 0
# ---------------------------------------------------------------------------
# Frame schemas
# ---------------------------------------------------------------------------
class FrameBase(BaseModel):
frame_index: int
image_url: str
width: Optional[int] = None
height: Optional[int] = None
class FrameCreate(FrameBase):
project_id: int
class FrameOut(FrameBase):
model_config = ConfigDict(from_attributes=True)
id: int
project_id: int
created_at: datetime
# ---------------------------------------------------------------------------
# Template schemas
# ---------------------------------------------------------------------------
class TemplateBase(BaseModel):
name: str
description: Optional[str] = None
color: str
z_index: int = 0
mapping_rules: Optional[dict[str, Any]] = None
classes: Optional[list[dict[str, Any]]] = None
rules: Optional[list[dict[str, Any]]] = None
class TemplateCreate(TemplateBase):
pass
class TemplateUpdate(BaseModel):
name: Optional[str] = None
description: Optional[str] = None
color: Optional[str] = None
z_index: Optional[int] = None
mapping_rules: Optional[dict[str, Any]] = None
classes: Optional[list[dict[str, Any]]] = None
rules: Optional[list[dict[str, Any]]] = None
class TemplateOut(TemplateBase):
model_config = ConfigDict(from_attributes=True)
id: int
created_at: datetime
# ---------------------------------------------------------------------------
# Annotation schemas
# ---------------------------------------------------------------------------
class AnnotationBase(BaseModel):
project_id: int
frame_id: Optional[int] = None
template_id: Optional[int] = None
mask_data: Optional[dict[str, Any]] = None
points: Optional[list[list[float]]] = None
bbox: Optional[list[float]] = None
class AnnotationCreate(AnnotationBase):
pass
class AnnotationUpdate(BaseModel):
mask_data: Optional[dict[str, Any]] = None
points: Optional[list[list[float]]] = None
bbox: Optional[list[float]] = None
template_id: Optional[int] = None
class AnnotationOut(AnnotationBase):
model_config = ConfigDict(from_attributes=True)
id: int
created_at: datetime
updated_at: datetime
# ---------------------------------------------------------------------------
# Mask schemas
# ---------------------------------------------------------------------------
class MaskBase(BaseModel):
annotation_id: int
mask_url: str
format: str = "png"
class MaskCreate(MaskBase):
pass
class MaskOut(MaskBase):
model_config = ConfigDict(from_attributes=True)
id: int
created_at: datetime
# ---------------------------------------------------------------------------
# Processing task schemas
# ---------------------------------------------------------------------------
class ProcessingTaskOut(BaseModel):
model_config = ConfigDict(from_attributes=True)
id: int
task_type: str
status: str
progress: int
message: Optional[str] = None
project_id: Optional[int] = None
celery_task_id: Optional[str] = None
payload: Optional[dict[str, Any]] = None
result: Optional[dict[str, Any]] = None
error: Optional[str] = None
created_at: datetime
started_at: Optional[datetime] = None
finished_at: Optional[datetime] = None
updated_at: datetime
# ---------------------------------------------------------------------------
# AI schemas
# ---------------------------------------------------------------------------
class PredictRequest(BaseModel):
image_id: int
prompt_type: str # point / box / semantic
prompt_data: Any
model: Optional[str] = None
class PredictResponse(BaseModel):
polygons: list[list[list[float]]]
scores: Optional[list[float]] = None
class AiModelStatus(BaseModel):
id: str
label: str
available: bool
loaded: bool = False
device: str
supports: list[str]
message: str
package_available: bool = False
checkpoint_exists: bool = False
checkpoint_path: Optional[str] = None
python_ok: bool = True
torch_ok: bool = True
cuda_required: bool = False
class GpuStatus(BaseModel):
available: bool
device: str
name: Optional[str] = None
torch_available: bool
torch_version: Optional[str] = None
cuda_version: Optional[str] = None
class AiRuntimeStatus(BaseModel):
selected_model: str
gpu: GpuStatus
models: list[AiModelStatus]
# ---------------------------------------------------------------------------
# Export schemas
# ---------------------------------------------------------------------------
class ExportStatus(BaseModel):
url: str
format: str