Files
Pre_Seg_Server/backend/schemas.py
admin 689a9ba283 feat: 建立 SAM2 标注闭环基线
- 打通工作区真实标注闭环:支持手工多边形、矩形、圆形、点区域和线段生成 mask,并可保存、回显、更新和删除后端 annotation。

- 增强 polygon 编辑器:支持顶点拖动、顶点删除、边中点插入、多 polygon 子区域选择编辑,以及区域合并和区域去除。

- 接入 GT mask 导入:后端支持二值/多类别 mask 拆分、contour 转 polygon、distance transform seed point,前端支持导入、回显和 seed point 拖动编辑。

- 完善导出能力:COCO JSON 导出对齐前端,PNG mask ZIP 同时包含单标注 mask、按 zIndex 融合的 semantic_frame 和 semantic_classes.json。

- 打通异步任务管理:新增任务取消、重试、失败详情接口与 Dashboard 控件,worker 支持取消状态检查并通过 Redis/WebSocket 推送 cancelled 事件。

- 对接 Dashboard 后端数据:概览统计、解析队列和实时流转记录从 FastAPI 聚合接口与 WebSocket 更新。

- 增强 AI 推理参数:前端发送 crop_to_prompt、auto_filter_background 和 min_score,后端支持点/框 prompt 局部裁剪推理、结果回映射和负向点/低分过滤。

- 接入 SAM3 基础设施:新增独立 Python 3.12 sam3 环境安装脚本、外部 worker helper、后端桥接和真实 Python/CUDA/包/HF checkpoint access 状态检测。

- 保留 SAM3 授权边界:当前官方 facebook/sam3 gated 权重未授权时状态接口会返回不可用,不伪装成可推理。

- 增强前端状态管理:新增 mask undo/redo 历史栈、AI 模型选择状态、保存状态 dirty/draft/saved 流转和项目状态归一化。

- 更新前端 API 封装:补充 annotation CRUD、GT mask import、mask ZIP export、task cancel/retry/detail、AI runtime status 和 prediction options。

- 更新 UI 控件:ToolsPalette、AISegmentation、VideoWorkspace 和 CanvasArea 接入真实操作、导入导出、撤销重做、任务控制和模型状态。

- 新增 polygon-clipping 依赖,用于前端区域 union/difference 几何运算。

- 完善后端 schemas/status/progress:补充 AI 模型外部状态字段、任务 cancelled 状态和进度事件 payload。

- 补充测试覆盖:新增后端任务控制、SAM3 桥接、GT mask、导出融合、AI options 测试;补充前端 Canvas、Dashboard、VideoWorkspace、ToolsPalette、API 和 store 测试。

- 更新 README、AGENTS 和 doc 文档:冻结当前需求/设计/测试计划,标注真实功能、剩余 Mock、SAM3 授权边界和后续实施顺序。
2026-05-01 15:26:25 +08:00

230 lines
5.9 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
options: Optional[dict[str, Any]] = 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
external_available: bool = False
external_python: Optional[str] = None
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