From f020ff3b4f9c405c55e21b9fc9c4390e3cd1149a Mon Sep 17 00:00:00 2001 From: admin <572701190@qq.com> Date: Fri, 1 May 2026 13:29:14 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=89=93=E9=80=9A=E5=85=A8=E6=A0=88?= =?UTF-8?q?=E6=A0=87=E6=B3=A8=E9=97=AD=E7=8E=AF=E3=80=81=E5=BC=82=E6=AD=A5?= =?UTF-8?q?=E6=8B=86=E5=B8=A7=E4=B8=8E=E6=A8=A1=E5=9E=8B=E7=8A=B6=E6=80=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 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。 --- .env.example | 13 + AGENTS.md | 322 ++++-- README.md | 128 ++- backend/celery_app.py | 21 + backend/config.py | 4 +- backend/main.py | 56 +- backend/models.py | 33 +- backend/progress_events.py | 64 ++ backend/requirements-dev.txt | 2 + backend/routers/ai.py | 165 ++- backend/routers/dashboard.py | 137 +++ backend/routers/media.py | 142 +-- backend/routers/tasks.py | 37 + backend/routers/templates.py | 4 +- backend/schemas.py | 58 +- backend/services/media_task_runner.py | 220 ++++ backend/services/sam2_engine.py | 71 +- backend/services/sam3_engine.py | 148 +++ backend/services/sam_registry.py | 80 ++ backend/statuses.py | 11 + backend/tests/conftest.py | 72 ++ backend/tests/test_ai.py | 248 +++++ backend/tests/test_auth.py | 15 + backend/tests/test_dashboard.py | 69 ++ backend/tests/test_export.py | 66 ++ backend/tests/test_main.py | 15 + backend/tests/test_media.py | 142 +++ backend/tests/test_progress_events.py | 42 + backend/tests/test_projects.py | 56 + backend/tests/test_templates.py | 39 + backend/worker_tasks.py | 22 + doc/01-purpose-and-word-summary.md | 58 ++ doc/02-current-implementation-map.md | 104 ++ doc/03-frontend-element-audit.md | 146 +++ doc/04-api-contracts.md | 193 ++++ doc/05-implementation-plan.md | 115 ++ doc/06-fastapi-docs-explained.md | 103 ++ doc/07-current-requirements-freeze.md | 120 +++ doc/08-current-design-freeze.md | 155 +++ doc/09-test-plan.md | 49 + doc/README.md | 32 + package-lock.json | 1154 ++++++++++++++++++++- package.json | 11 +- src/components/AISegmentation.test.tsx | 43 + src/components/AISegmentation.tsx | 100 +- src/components/CanvasArea.test.tsx | 130 +++ src/components/CanvasArea.tsx | 108 +- src/components/Dashboard.test.tsx | 115 ++ src/components/Dashboard.tsx | 132 ++- src/components/FrameTimeline.test.tsx | 62 ++ src/components/FrameTimeline.tsx | 38 +- src/components/Login.test.tsx | 42 + src/components/ModelStatusBadge.test.tsx | 45 + src/components/ModelStatusBadge.tsx | 56 + src/components/OntologyInspector.test.tsx | 60 ++ src/components/OntologyInspector.tsx | 35 +- src/components/ProjectLibrary.test.tsx | 92 ++ src/components/ProjectLibrary.tsx | 6 +- src/components/Sidebar.tsx | 5 +- src/components/TemplateRegistry.test.tsx | 85 ++ src/components/ToolsPalette.test.tsx | 30 + src/components/ToolsPalette.tsx | 2 +- src/components/VideoWorkspace.test.tsx | 259 +++++ src/components/VideoWorkspace.tsx | 221 +++- src/lib/api.test.ts | 361 +++++++ src/lib/api.ts | 409 +++++++- src/lib/config.test.ts | 38 + src/lib/config.ts | 29 + src/lib/templateSelection.ts | 15 + src/lib/websocket.test.ts | 46 + src/lib/websocket.ts | 8 +- src/store/useStore.test.ts | 56 + src/store/useStore.ts | 36 +- src/test/setup.tsx | 66 ++ src/test/storeTestUtils.ts | 23 + src/vite-env.d.ts | 6 + start_services.sh | 20 +- vitest.config.ts | 24 + 78 files changed, 7089 insertions(+), 456 deletions(-) create mode 100644 backend/celery_app.py create mode 100644 backend/progress_events.py create mode 100644 backend/requirements-dev.txt create mode 100644 backend/routers/dashboard.py create mode 100644 backend/routers/tasks.py create mode 100644 backend/services/media_task_runner.py create mode 100644 backend/services/sam3_engine.py create mode 100644 backend/services/sam_registry.py create mode 100644 backend/statuses.py create mode 100644 backend/tests/conftest.py create mode 100644 backend/tests/test_ai.py create mode 100644 backend/tests/test_auth.py create mode 100644 backend/tests/test_dashboard.py create mode 100644 backend/tests/test_export.py create mode 100644 backend/tests/test_main.py create mode 100644 backend/tests/test_media.py create mode 100644 backend/tests/test_progress_events.py create mode 100644 backend/tests/test_projects.py create mode 100644 backend/tests/test_templates.py create mode 100644 backend/worker_tasks.py create mode 100644 doc/01-purpose-and-word-summary.md create mode 100644 doc/02-current-implementation-map.md create mode 100644 doc/03-frontend-element-audit.md create mode 100644 doc/04-api-contracts.md create mode 100644 doc/05-implementation-plan.md create mode 100644 doc/06-fastapi-docs-explained.md create mode 100644 doc/07-current-requirements-freeze.md create mode 100644 doc/08-current-design-freeze.md create mode 100644 doc/09-test-plan.md create mode 100644 doc/README.md create mode 100644 src/components/AISegmentation.test.tsx create mode 100644 src/components/CanvasArea.test.tsx create mode 100644 src/components/Dashboard.test.tsx create mode 100644 src/components/FrameTimeline.test.tsx create mode 100644 src/components/Login.test.tsx create mode 100644 src/components/ModelStatusBadge.test.tsx create mode 100644 src/components/ModelStatusBadge.tsx create mode 100644 src/components/OntologyInspector.test.tsx create mode 100644 src/components/ProjectLibrary.test.tsx create mode 100644 src/components/TemplateRegistry.test.tsx create mode 100644 src/components/ToolsPalette.test.tsx create mode 100644 src/components/VideoWorkspace.test.tsx create mode 100644 src/lib/api.test.ts create mode 100644 src/lib/config.test.ts create mode 100644 src/lib/config.ts create mode 100644 src/lib/templateSelection.ts create mode 100644 src/lib/websocket.test.ts create mode 100644 src/store/useStore.test.ts create mode 100644 src/test/setup.tsx create mode 100644 src/test/storeTestUtils.ts create mode 100644 src/vite-env.d.ts create mode 100644 vitest.config.ts diff --git a/.env.example b/.env.example index 7a550fe..5de43e1 100644 --- a/.env.example +++ b/.env.example @@ -7,3 +7,16 @@ GEMINI_API_KEY="MY_GEMINI_API_KEY" # AI Studio automatically injects this at runtime with the Cloud Run service URL. # Used for self-referential links, OAuth callbacks, and API endpoints. APP_URL="MY_APP_URL" + +# Frontend API address. If unset, the app infers http://:8000. +VITE_API_BASE_URL="http://192.168.3.11:8000" + +# Optional WebSocket override. If unset, it is derived from VITE_API_BASE_URL. +VITE_WS_PROGRESS_URL="ws://192.168.3.11:8000/ws/progress" + +# Backend SAM runtime defaults. SAM 3 additionally requires the official sam3 +# package, Python 3.12+, PyTorch 2.7+, and a CUDA-capable GPU per Meta's repo. +sam_default_model="sam2" +sam_model_path="/home/wkmgc/Desktop/Seg_Server/models/sam2_hiera_tiny.pt" +sam_model_config="configs/sam2/sam2_hiera_t.yaml" +sam3_model_version="sam3.1" diff --git a/AGENTS.md b/AGENTS.md index 3f4e9d3..eccb264 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,17 +1,21 @@ # AGENTS.md — AI 编码助手项目指南 -> 本文件面向 AI 编码助手。阅读者应假设对该项目一无所知。以下所有信息均基于项目实际内容，不做假设性推断。 +> 本文件面向 AI 编码助手。阅读者应假设对该项目一无所知。以下信息基于当前仓库实际文件、脚本和源码；不要把早期设计目标当作已实现事实。任何代码和功能修改都要落实到文档和测试上，如果生成git commit信息，要逐个列点把所有修改都列上，重要的、大的修改放前面，不重要的、小的修改列在后面。 --- ## 项目概述 -本项目是一个**语义分割系统**（Semantic Segmentation System）的 Web 前端应用，用于 AI 驱动的图像/视频分割与标注。它提供了一个深色主题（Dark Mode）的单页应用（SPA），包含项目管理、分割工作区、AI 智能分割引擎和模板库四大核心模块。 +本项目是一个**语义分割系统**（Semantic Segmentation System），当前形态是 React 前端 + FastAPI 后端的全栈 Web 应用，用于视频/DICOM 医学影像上传、服务器端拆帧、交互式 Canvas 标注、SAM 2/SAM 3 可选辅助分割、模板分类管理和标注导出。 -- **项目名称**: `react-example`（package.json 中的 name） -- **部署目标**: Google AI Studio（Cloud Run） -- **AI Studio 应用链接**: https://ai.studio/apps/2707f0e1-d453-4594-a618-fba53cb937c4 -- **业务文档**: `语义分割系统构建方案.docx`（项目根目录，未解析内容） +- **项目名称**: `react-example`（`package.json` 中的 `name`） +- **前端入口**: `src/main.tsx` → `src/App.tsx` +- **前端服务入口**: `server.ts`（Express + Vite 中间件 / 生产静态服务，并保留少量旧版 mock API） +- **后端入口**: `backend/main.py`（FastAPI） +- **默认前端地址**: `http://localhost:3000` +- **默认后端地址**: `http://localhost:8000` +- **前端 API 配置**: `src/lib/config.ts`，优先读取 `VITE_API_BASE_URL`，未配置时按当前浏览器 hostname 推导 `http://:8000` +- **业务文档**: `语义分割系统构建方案.docx`（项目根目录） --- @@ -21,15 +25,22 @@ |------|------| | 前端框架 | React 19 + TypeScript 5.8 | | 构建工具 | Vite 6 | -| 样式方案 | TailwindCSS 4 + 自定义深色主题 | -| 状态管理 | React `useState`（无外部状态库） | -| 路由 | 无路由库，基于 React 状态切换模块 | -| Canvas 渲染 | Konva + react-konva | +| 前端样式 | TailwindCSS 4 + 自定义深色主题 | +| 前端状态 | Zustand（`src/store/useStore.ts`） | +| 前端请求 | Axios（`src/lib/api.ts`） | +| 实时通信 | WebSocket 客户端（`src/lib/websocket.ts`） | +| Canvas 渲染 | Konva + react-konva + use-image | | 图标库 | lucide-react | -| 动画 | motion | -| AI SDK | @google/genai（Gemini API） | -| 后端/服务器 | Express 4（单文件 `server.ts`） | -| 运行时 | Node.js，ES Modules（`"type": "module"`） | +| 动画依赖 | motion（在 `package.json` 中声明） | +| AI SDK 依赖 | `@google/genai`（在 `package.json` 中声明；当前业务源码未直接调用） | +| 后端框架 | FastAPI + Uvicorn | +| ORM / 数据库 | SQLAlchemy + PostgreSQL | +| 缓存 / 队列 Broker | Redis | +| 后台任务 | Celery worker | +| 对象存储 | MinIO | +| AI 推理 | SAM 2 / SAM 3 可选模型 + PyTorch；`GET /api/ai/models/status` 返回真实 GPU/模型状态 | +| 视频 / 影像处理 | FFmpeg / OpenCV / pydicom | +| 运行时 | Node.js ES Modules；Python 3.11 后端环境 | --- @@ -37,159 +48,244 @@ ``` Seg_Server/ -├── server.ts # Express 服务端入口（开发服务器 + 生产静态文件服务） -├── index.html # SPA HTML 入口 -├── vite.config.ts # Vite 构建配置 -├── tsconfig.json # TypeScript 配置 -├── package.json # 依赖与脚本 -├── .env.example # 环境变量模板 -├── metadata.json # AI Studio 元数据（目前为空） -├── src/ -│ ├── main.tsx # React 应用挂载点（StrictMode） -│ ├── App.tsx # 根组件：模块路由 + 登录鉴权 -│ ├── index.css # TailwindCSS 导入 + 自定义工具类 -│ ├── lib/ -│ │ └── utils.ts # `cn()` 工具函数（clsx + tailwind-merge） -│ └── components/ -│ ├── auth/ -│ │ └── Login.tsx # 登录页 -│ ├── layout/ -│ │ └── Sidebar.tsx # 左侧导航栏（w-16） -│ ├── dashboard/ -│ │ └── Dashboard.tsx # 总体概况仪表盘 -│ ├── projects/ -│ │ └── ProjectLibrary.tsx # 项目库列表 -│ ├── workspace/ -│ │ ├── VideoWorkspace.tsx # 核心分割工作区布局 -│ │ ├── CanvasArea.tsx # Konva 画布（缩放/平移/选点） -│ │ ├── ToolsPalette.tsx # 左侧工具栏 -│ │ ├── OntologyInspector.tsx # 右侧本体/属性检查面板 -│ │ └── FrameTimeline.tsx # 底部时间轴 -│ ├── ai/ -│ │ └── AISegmentation.tsx # AI 智能分割引擎界面 -│ └── templates/ -│ └── TemplateRegistry.tsx # 模板库管理 +├── server.ts # Express + Vite 前端入口；保留 /api/login、/api/projects、/api/templates mock +├── index.html # SPA HTML 入口 +├── vite.config.ts # Vite 配置；含 @/* 路径别名与 DISABLE_HMR 逻辑 +├── tsconfig.json # TypeScript 配置；@/* 映射到项目根目录 +├── package.json # npm 依赖与脚本 +├── .env.example # AI Studio/Gemini 前端环境变量模板 +├── metadata.json # AI Studio 元数据 +├── public/ +│ └── logo.png # Sidebar 使用的 /logo.png +├── doc/ # 当前实现审计、接口契约和后续实施文档 +├── start_services.sh # 本地一键启动 PostgreSQL/Redis/MinIO/FastAPI/Celery/前端 +├── backend/ # FastAPI 后端 +│ ├── main.py # 应用入口、lifespan、CORS、路由注册、WebSocket +│ ├── config.py # Pydantic Settings；读取 backend/.env +│ ├── database.py # SQLAlchemy Engine / Session +│ ├── models.py # Project/Frame/Template/Annotation/Mask/ProcessingTask ORM +│ ├── schemas.py # Pydantic 请求/响应模型 +│ ├── minio_client.py # MinIO 上传、下载、预签名 URL +│ ├── redis_client.py # Redis 连接封装 +│ ├── celery_app.py # Celery app 配置 +│ ├── worker_tasks.py # Celery 任务入口 +│ ├── download_sam2.py # SAM 2 权重下载脚本 +│ ├── requirements.txt # Python 依赖 +│ ├── routers/ +│ │ ├── auth.py # /api/auth/login +│ │ ├── projects.py # /api/projects 与 /api/projects/{id}/frames +│ │ ├── templates.py # /api/templates +│ │ ├── media.py # /api/media/upload、/upload/dicom、/parse +│ │ ├── ai.py # /api/ai/predict、/models/status、/auto、/annotate +│ │ └── export.py # /api/export/{project_id}/coco、/masks +│ └── services/ +│ ├── frame_parser.py # FFmpeg/OpenCV 拆帧、pydicom 读片、帧上传 +│ ├── sam2_engine.py # SAM 2 懒加载推理封装和 fallback +│ ├── sam3_engine.py # SAM 3 状态检测与文本语义推理适配器 +│ └── sam_registry.py # SAM 模型选择、GPU 状态与推理分发 +└── src/ # React 前端 + ├── main.tsx # React StrictMode 挂载 + ├── App.tsx # 登录拦截 + 模块切换 + ├── index.css # TailwindCSS 导入 + 全局样式 + ├── store/useStore.ts # Zustand 全局状态 + ├── lib/api.ts # Axios API 封装 + ├── lib/websocket.ts # 解析进度 WebSocket 客户端 + ├── lib/utils.ts # cn() 工具函数 + └── components/ # 扁平化组件目录 + ├── Login.tsx + ├── Sidebar.tsx + ├── Dashboard.tsx + ├── ProjectLibrary.tsx + ├── VideoWorkspace.tsx + ├── CanvasArea.tsx + ├── ToolsPalette.tsx + ├── OntologyInspector.tsx + ├── FrameTimeline.tsx + ├── AISegmentation.tsx + └── TemplateRegistry.tsx ``` +以下目录/文件通常是运行产物或本地数据，已在 `.gitignore` 中忽略：`node_modules/`、`dist/`、`models/`、`uploads/`、`frames/`、`Data_*/`、`*.mp4`、`*.dcm`、`*.7z`、`backend/.env`、日志文件等。 + +`doc/` 目录是当前项目的事实文档入口。修改功能前优先查看： + +- `doc/03-frontend-element-audit.md`：哪些前端元素是真功能，哪些是 Mock/UI-only。 +- `doc/04-api-contracts.md`：前后端接口契约，以及当前不一致点。 +- `doc/05-implementation-plan.md`：建议的后续实施顺序。 + --- ## 构建与运行命令 +### 前端 / Node 入口 + ```bash -# 安装依赖 npm install -# 开发模式（启动 Express + Vite 中间件，端口 3000） +# 开发模式：运行 tsx server.ts，Express 集成 Vite middleware，端口 3000 npm run dev -# 生产构建（输出到 dist/） +# 生产构建：输出 dist/ npm run build -# 预览生产构建 +# Vite 预览 npm run preview -# 生产模式启动（Node 直接运行 server.ts，需先 build） +# 生产模式运行 server.ts，服务 dist/；仍保留 server.ts 中的旧版 mock API npm start -# 类型检查（不输出文件） +# TypeScript 类型检查 npm run lint -# 清理构建产物 +# 删除 dist/ npm run clean ``` -**开发服务器地址**: `http://localhost:3000` +### FastAPI 后端 -**环境变量**（复制 `.env.example` 为 `.env.local`）： -- `GEMINI_API_KEY` — Gemini AI API 密钥（AI Studio 会自动注入） -- `APP_URL` — 应用托管 URL（AI Studio 自动注入 Cloud Run 地址） +```bash +cd backend +uvicorn main:app --host 0.0.0.0 --port 8000 --reload +``` + +### 一键启动 + +```bash +./start_services.sh +``` + +该脚本会依次检查/启动 PostgreSQL、Redis、MinIO、FastAPI 后端、Celery worker 和前端。 --- ## 运行时架构 ### 前端 -- 单页应用，React 19 `StrictMode` 挂载。 -- 模块切换通过 `App.tsx` 中的 `activeModule` 状态控制，可选值： - `'dashboard' | 'projects' | 'ai' | 'workspace' | 'templates'` -- 默认进入 `workspace`（分割工作区）。 -- 未登录时全局拦截，显示 `Login` 组件。 -### 后端 (`server.ts`) -- Express 服务器，端口 `3000`。 -- **开发模式**: 集成 Vite 中间件（`middlewareMode: true`）。 -- **生产模式**: 静态文件服务 `dist/`，所有路由回退到 `index.html`。 -- **API 端点**（内存数据存储，无数据库）： - - `POST /api/login` — 认证（固定用户名 `admin`，密码 `123456`） - - `GET /api/projects` — 返回项目列表 - - `GET /api/templates` — 返回模板列表 +- 单页应用，无路由库；模块切换由 `useStore().activeModule` 控制。 +- 模块值包括：`dashboard`、`projects`、`ai`、`workspace`、`templates`。 +- 默认模块是 `workspace`。 +- 未登录时渲染 `Login`。 +- 登录成功后 token 写入 `localStorage`，Axios request interceptor 会附加 `Authorization: Bearer `。 +- `App.tsx` 在登录后调用 `getProjects()` 初始化项目列表。 -### 部署 -- 面向 **Google AI Studio** / **Cloud Run** 部署。 -- `metadata.json` 用于 AI Studio 元数据配置（当前为空）。 -- `vite.config.ts` 中 HMR 可通过环境变量 `DISABLE_HMR=true` 关闭（AI Studio 环境下文件监听被禁用以防止 agent 编辑时闪烁）。 +### 后端 + +- 主后端是 `backend/main.py` 的 FastAPI 服务。 +- `lifespan` 启动时会： + - 创建数据库表； + - 检查/创建 MinIO bucket `seg-media`； + - 测试 Redis 连接； + - 后台 seed 默认模板； + - 如果本地存在 `Data_MyVideo_1.mp4`，后台 seed 默认演示项目并拆前 100 帧。 +- API 路由包括： + - `POST /api/auth/login` + - `GET/POST/PATCH/DELETE /api/projects` + - `GET/POST /api/projects/{project_id}/frames` + - `GET/POST/PATCH/DELETE /api/templates` + - `POST /api/media/upload` + - `POST /api/media/upload/dicom` + - `POST /api/media/parse` + - `GET /api/tasks` + - `GET /api/tasks/{task_id}` + - `POST /api/ai/predict` + - `GET /api/ai/models/status` + - `POST /api/ai/auto` + - `POST /api/ai/annotate` + - `GET /api/ai/annotations` + - `PATCH/DELETE /api/ai/annotations/{annotation_id}` + - `GET /api/dashboard/overview` + - `GET /api/export/{project_id}/coco` + - `GET /api/export/{project_id}/masks` + - `GET /health` + - `WS /ws/progress` + +### 存储 + +- PostgreSQL 存储项目、帧、模板、标注、mask 和后台任务元数据。 +- MinIO 存储上传视频、DICOM、拆出的帧、缩略图等对象；前端展示使用预签名 URL。 +- Redis 当前作为 Celery broker/result backend，并用于连接检查。 + +--- + +## 主要业务流程 + +1. 登录：`Login.tsx` 调用 `POST /api/auth/login`，默认开发凭证为 `admin / 123456`。 +2. 项目管理：`ProjectLibrary.tsx` 调用项目 API 创建项目、拉取列表。 +3. 上传资源：视频走 `/api/media/upload`；DICOM 批量走 `/api/media/upload/dicom`。 +4. 拆帧入队：前端调用 `/api/media/parse`；后端创建 `ProcessingTask` 并投递 Celery。 +5. worker 执行：Celery worker 用 FFmpeg 优先拆视频帧，失败后用 OpenCV fallback，DICOM 使用 pydicom，并持续更新任务进度。 +6. 帧展示：`VideoWorkspace.tsx` 调用 `/api/projects/{id}/frames`，`CanvasArea.tsx` 和 `FrameTimeline.tsx` 显示当前帧与时间轴缩略图。 +7. AI 分割：前端工具包括正向点、反向点和框选；后端 `ai.py` 期望按 `image_id`、`prompt_type`、`prompt_data`、`model` 调用 SAM registry。SAM 2 支持点/框/自动分割；SAM 3 入口支持文本语义推理，运行时不满足官方要求时会在状态接口中标为不可用。 +8. 模板管理：`TemplateRegistry.tsx` 管理分类、颜色和 z-index；`OntologyInspector.tsx` 在工作区显示当前模板分类树。 +9. 导出：后端支持 COCO JSON 和 PNG mask ZIP 导出。 + +--- + +## 当前实现注意事项 + +- `src/lib/config.ts` 会优先读取 `VITE_API_BASE_URL` 和 `VITE_WS_PROGRESS_URL`；未配置时按当前浏览器 hostname 推导后端 `:8000` 地址。 +- 前端 `predictMask()` 已按后端 `PredictRequest` 发送 `image_id`、`prompt_type`、`prompt_data`、`model`，并将后端 `polygons` 转成 Konva 可渲染的 `pathData`。 +- 前端 `exportCoco()` 已对齐后端 `/api/export/{project_id}/coco`；工作区“导出 JSON 标注集”按钮已绑定下载流程，导出前会先保存当前待归档 mask。 +- 工作区“结构化归档保存”按钮已接入 `POST /api/ai/annotate` 和 `PATCH /api/ai/annotations/{id}`；加载工作区时会通过 `GET /api/ai/annotations` 回显已保存标注。 +- 工作区“清空遮罩”会调用 `DELETE /api/ai/annotations/{id}` 删除当前帧已保存标注，并清空当前帧本地 mask。 +- 项目状态已统一为 `pending`、`parsing`、`ready`、`error`；前端 `src/lib/api.ts` 会兼容归一化旧库中可能存在的 `Ready`、`Parsing`、`Error`。 +- `server.ts` 仍有旧版 `/api/login`、`/api/projects`、`/api/templates` mock；当前前端真实 API 调用主要走 FastAPI 的 `/api/auth/*`、`/api/projects`、`/api/templates` 等接口。 +- `Dashboard.tsx` 初始统计、队列和活动日志来自 `GET /api/dashboard/overview`；解析队列来自 `processing_tasks`，Celery worker 通过 Redis pub/sub 的 `seg:progress` 频道推送细粒度进度，再由 FastAPI 广播到 `/ws/progress`。 --- ## 代码风格与约定 ### 样式规范 -- **深色主题**: 全局背景色以 `#0a0a0a`、`#111`、`#0d0d0d`、`#151515`、`#1e1e1e` 为主。 -- **强调色**: 青色（`cyan-400`/`cyan-500`）用于激活状态、按钮和关键指示器。 -- **工具类优先**: 全面使用 TailwindCSS 工具类，通过 `cn()` 合并条件类名。 -- **自定义工具类**: `index.css` 中定义 `.no-scrollbar` 用于隐藏滚动条。 + +- 深色主题为主，常见背景色包括 `#0a0a0a`、`#111`、`#0d0d0d`、`#151515`、`#1e1e1e`。 +- 青色（如 `cyan-400` / `cyan-500`）用于激活状态、主按钮和关键指标。 +- 前端样式主要使用 TailwindCSS 工具类，通过 `cn()` 合并条件类名。 +- `src/index.css` 使用 TailwindCSS 4 的 `@import "tailwindcss";`。 ### 组件规范 -- 所有组件使用 **函数组件 + Hooks**，无类组件。 -- 组件按功能模块分目录存放在 `src/components/{module}/` 下。 -- Props 类型使用 TypeScript `interface` 定义。 -- 导入排序：React → 第三方库 → 内部模块 → 类型。 + +- 组件使用函数组件 + Hooks。 +- 当前组件目录是扁平结构：`src/components/*.tsx`，不是按模块子目录分层。 +- Props 类型优先使用 TypeScript `interface`。 +- UI 文本保持中文。 +- 代码与注释优先使用英文。 ### 命名规范 -- 组件文件使用 **PascalCase**（如 `AISegmentation.tsx`）。 -- 工具文件使用 **camelCase**（如 `utils.ts`）。 -- 类型/接口使用 **PascalCase**。 -### 语言约定 -- **界面文本**: 全部使用 **中文**（如 "核心分割工作区"、"AI智能分割引擎"、"导出 JSON 标注集"）。 -- **代码与注释**: 使用英文。 -- 添加新 UI 文本时，**必须保持中文**。 +- 组件文件使用 PascalCase，例如 `AISegmentation.tsx`。 +- 工具文件使用 camelCase，例如 `utils.ts`。 +- 类型和接口使用 PascalCase。 --- ## 测试策略 -**当前状态：无测试文件。** +当前仓库已配置前端 Vitest 测试和后端 pytest 测试。测试依据 `doc/07-current-requirements-freeze.md`、`doc/08-current-design-freeze.md` 和 `doc/09-test-plan.md`。 -- 项目中不存在 `.test.` 或 `.spec.` 文件。 -- 无测试框架配置（如 Jest、Vitest、Playwright）。 -- 若需添加测试，建议在前端引入 Vitest（与 Vite 同生态）进行单元测试，或使用 Playwright 进行 E2E 测试。 +- 前端测试配置：`vitest.config.ts`，共享 setup 在 `src/test/setup.tsx`。 +- 前端测试命令：`npm run test:run`。 +- 后端测试依赖：`backend/requirements-dev.txt`。 +- 后端测试命令：`pytest backend/tests`，或在 `backend/` 目录执行 `pytest tests`。 +- 基础静态校验：`npm run lint`、`npm run build`、`python -m py_compile backend/routers/ai.py backend/routers/templates.py backend/schemas.py`。 +- 后端测试使用内存 SQLite、fake MinIO 和 fake SAM registry，不依赖真实 PostgreSQL、MinIO、Redis 或模型权重。 --- ## 安全注意事项 -- **硬编码凭证**: `server.ts` 中登录验证使用硬编码凭据（`admin` / `123456`），生产环境必须替换为真实的身份验证机制。 -- **Mock JWT**: 登录成功返回固定的 `fake-jwt-token-for-admin`，无实际的 JWT 签名验证。 -- **内存数据存储**: 所有项目/模板数据存储在内存中，服务重启后数据丢失。无持久化层。 -- **环境变量**: `GEMINI_API_KEY` 通过 `.env.local` 管理，已加入 `.gitignore`，不会误提交。 -- **CORS / 安全头**: Express 服务器目前未配置 CORS 策略或安全响应头（如 Helmet）。 +- FastAPI 登录是开发用硬编码凭证：`admin / 123456`。 +- 登录成功返回固定 token：`fake-jwt-token-for-admin`，没有真实 JWT 签名校验。 +- Axios 会附加 Bearer token，但后端大多数业务路由当前没有鉴权依赖。 +- `backend/.env` 被 `.gitignore` 忽略；不要提交真实数据库、MinIO、Redis、模型路径等敏感配置。 +- `start_services.sh` 中包含本机路径和 sudo 启动逻辑，迁移机器时要审查。 +- Express `server.ts` 的旧版 mock API 只适合开发/兼容场景，不能当生产鉴权或持久化方案。 --- -## 关键依赖与注意事项 +## AI Studio / Vite 特定配置 -- **React 19**: 使用 `createRoot` API，注意与 React 18 的部分差异。 -- **TailwindCSS 4**: 使用 `@import "tailwindcss"` 语法（非 v3 的 `@tailwind` 指令）。 -- **react-konva**: Canvas 交互核心，所有画布相关操作（缩放、选点、遮罩）均依赖此库。 -- **use-image**: 用于异步加载图片到 Konva 画布。 -- **路径别名**: `@/*` 映射到项目根目录（由 `vite.config.ts` 和 `tsconfig.json` 共同配置）。 -- **缺失资源**: `Sidebar.tsx` 引用了 `/Logo.png`，但项目根目录无此文件，运行时会 404。 - ---- - -## AI Studio 特定配置 - -- `vite.config.ts` 中通过 `loadEnv` 加载环境变量，并将 `GEMINI_API_KEY` 注入到 `process.env.GEMINI_API_KEY`。 -- AI Studio 会在部署时自动注入 `GEMINI_API_KEY` 和 `APP_URL`。 -- `DISABLE_HMR` 环境变量用于在 AI Studio agent 编辑模式下关闭 HMR，避免界面闪烁。**请勿修改此逻辑。** +- `.env.example` 包含 `GEMINI_API_KEY` 和 `APP_URL`，说明这些值由 AI Studio 注入。 +- `vite.config.ts` 通过 `loadEnv` 把 `GEMINI_API_KEY` 注入到 `process.env.GEMINI_API_KEY`。 +- `vite.config.ts` 中的 `DISABLE_HMR` 逻辑用于关闭 HMR，避免 AI Studio agent 编辑时闪烁。**不要随意修改该逻辑。** diff --git a/README.md b/README.md index 974698f..afe1a93 100644 --- a/README.md +++ b/README.md @@ -4,16 +4,16 @@ # 语义分割系统（SegServer） -> 基于 React + FastAPI + SAM 2 的全栈交互式图像/视频语义分割与标注平台。 +> 基于 React + FastAPI + 可选 SAM 2 / SAM 3 的全栈交互式图像/视频语义分割与标注平台。 > -> 支持本地多媒体资产上传、服务器端按帧解析、AI 视觉大模型实时推理（正反向选点、框选生成分割 Mask）、动态图层状态管理及最终标注数据结构化导出。 +> 支持本地多媒体资产上传、服务器端按帧解析、交互式 Canvas 标注、模板分类管理和标注数据结构化导出；工作区点/框 AI 推理默认走 SAM 2，语义文本可选择 SAM 3，前端会显示真实 GPU/模型状态。 --- ## 核心功能 - **多媒体资产管理** — 支持视频（MP4/AVI/MOV）和 DICOM 医学影像的上传、存储与解析 -- **AI 智能分割引擎** — 集成 SAM 2 模型，支持点分割（point）、框分割（box）、语义分割（semantic）和自动分割（auto） +- **AI 智能分割引擎** — 后端提供 SAM 2 / SAM 3 模型选择；SAM 2 支持点分割（point）、框分割（box）和自动分割（auto），SAM 3 入口支持文本语义提示并按真实运行环境显示可用性 - **交互式画布标注** — 基于 Konva 的高性能 Canvas，支持缩放/平移/选点/框选，实时渲染 Mask 遮罩 - **本体字典管理** — 可配置的分类体系、颜色映射、图层优先级（z-index） - **项目工作区** — 项目创建、帧浏览、多图层标注、进度追踪 @@ -37,15 +37,16 @@ │ ├── /api/auth 登录认证 │ │ ├── /api/projects 项目 & 视频帧 CRUD │ │ ├── /api/templates 本体字典（分类/颜色/z-index） │ -│ ├── /api/media 文件上传 & FFmpeg/pydicom 帧解析 │ -│ ├── /api/ai SAM 2 推理（点/框/语义/自动分割） │ +│ ├── /api/media 文件上传 & 异步拆帧任务创建 │ +│ ├── /api/tasks Celery 后台任务状态 │ +│ ├── /api/ai SAM 2 / SAM 3 推理与模型状态 │ │ └── /api/export COCO JSON / PNG Masks 导出 │ └──────────────────────────┬──────────────────────────────────┘ │ SQLAlchemy 2.0 ┌──────────────────────────▼──────────────────────────────────┐ │ 数据持久化层 │ -│ PostgreSQL 14 — 项目/帧/标注/Mask 元数据 │ -│ Redis 6 — 缓存 & 任务队列状态 │ +│ PostgreSQL 14 — 项目/帧/标注/Mask/Task 元数据 │ +│ Redis 6 — Celery broker/result backend + 进度 pub/sub │ │ MinIO — 对象存储（原始视频/解析帧/Mask图像） │ └─────────────────────────────────────────────────────────────┘ ``` @@ -63,11 +64,12 @@ | Canvas 渲染 | Konva + react-konva | - | | HTTP 客户端 | Axios | - | | 后端框架 | FastAPI | v0.136+ | -| 数据库 ORM | SQLAlchemy + Alembic | 2.0+ | +| 数据库 ORM | SQLAlchemy（依赖中包含 Alembic） | 2.0+ | | 数据库 | PostgreSQL | 14 | -| 缓存 | Redis | 6 | +| 队列 Broker | Redis | 6 | +| 后台任务 | Celery worker | 5.6+ | | 对象存储 | MinIO | 2025+ | -| AI 推理 | SAM 2 (Meta) + PyTorch | - | +| AI 推理 | SAM 2 / SAM 3 (Meta) + PyTorch | - | | 视频处理 | FFmpeg + OpenCV | 4.4+ | | DICOM 处理 | pydicom | 3.0+ | @@ -78,13 +80,17 @@ ``` Seg_Server/ ├── backend/ # FastAPI 后端 -│ ├── main.py # 应用入口（CORS/生命周期/路由注册） +│ ├── main.py # 应用入口（CORS/生命周期/路由注册/WebSocket） │ ├── config.py # 环境变量配置（Pydantic Settings） │ ├── database.py # SQLAlchemy 引擎 + Session -│ ├── models.py # ORM 模型（Project/Frame/Template/Annotation/Mask） +│ ├── models.py # ORM 模型（Project/Frame/Template/Annotation/Mask/ProcessingTask） │ ├── schemas.py # Pydantic 请求/响应校验模型 │ ├── minio_client.py # MinIO 上传/下载/预签名URL封装 │ ├── redis_client.py # Redis 连接封装 +│ ├── progress_events.py # 任务进度事件 payload 与 Redis 发布 +│ ├── statuses.py # 项目/任务状态常量 +│ ├── celery_app.py # Celery app 配置 +│ ├── worker_tasks.py # Celery 任务入口 │ ├── download_sam2.py # SAM 2 模型权重自动下载脚本 │ ├── requirements.txt # Python 依赖 │ ├── routers/ # API 路由 @@ -92,10 +98,12 @@ Seg_Server/ │ │ ├── projects.py # 项目 & 帧 CRUD │ │ ├── templates.py # 本体字典管理 │ │ ├── media.py # 上传 & 解析 -│ │ ├── ai.py # SAM 2 推理接口 +│ │ ├── ai.py # SAM 推理与模型状态接口 │ │ └── export.py # 数据导出 │ └── services/ # 业务服务 │ ├── sam2_engine.py # SAM 2 推理引擎（懒加载 + stub降级） +│ ├── sam3_engine.py # SAM 3 状态检测与文本语义推理适配器 +│ ├── sam_registry.py # SAM 模型选择、GPU 状态与推理分发 │ └── frame_parser.py # FFmpeg 拆帧 / pydicom 读片 ├── src/ # React 前端 │ ├── main.tsx # 应用挂载点 @@ -121,8 +129,11 @@ Seg_Server/ ├── models/ # SAM 2 模型权重（.pt 文件） ├── uploads/ # 临时上传目录 ├── frames/ # 临时帧目录 +├── doc/ # 当前实现审计、接口契约与后续实施文档 +├── public/ +│ └── logo.png # 侧边栏 Logo 静态资源 ├── start_services.sh # 一键启动所有服务脚本 -├── server.ts # 旧版 Express 入口（已弃用） +├── server.ts # Express + Vite 前端入口（也保留少量旧版 mock API） ├── index.html # SPA HTML 入口 ├── vite.config.ts # Vite 构建配置 ├── package.json # npm 依赖与脚本 @@ -131,12 +142,23 @@ Seg_Server/ --- +## 项目文档 + +当前实现审计与接口契约文档在 `doc/` 目录： + +- `doc/01-purpose-and-word-summary.md` — 项目目的、Word 方案摘要与当前落地程度 +- `doc/03-frontend-element-audit.md` — 前端逐元素功能审计，标注真实可用、部分可用、Mock/UI-only、接口不通 +- `doc/04-api-contracts.md` — 前后端接口契约和已知不一致 +- `doc/06-fastapi-docs-explained.md` — `http://192.168.3.11:8000/docs` 的作用说明 + +--- + ## 环境准备 ### 系统要求 - **OS**: Ubuntu 22.04 LTS -- **GPU**: NVIDIA GPU（推荐 RTX 4090 或同等算力），用于 SAM 2 推理 +- **GPU**: NVIDIA GPU（推荐 RTX 4090 或同等算力），用于 SAM 推理；SAM 3 官方要求 Python 3.12+、PyTorch 2.7+ 和 CUDA 12.6+ 环境 - **CUDA**: 12.x / 13.x - **Node.js**: 22.x+ - **Python**: 3.11（通过 Miniconda/Anaconda 管理） @@ -223,19 +245,32 @@ python download_sam2.py ### 步骤 5: 配置环境变量 -项目根目录已提供默认配置，如需修改请编辑以下文件： +后端通过 `backend/config.py` 中的 Pydantic Settings 读取 `backend/.env`。如需覆盖默认值，请编辑以下文件： **backend/.env**（数据库/Redis/MinIO/SAM 路径）: ```ini -DATABASE_URL=postgresql://seguser:segpass123@localhost:5432/segserver -REDIS_URL=redis://localhost:6379/0 -MINIO_ENDPOINT=localhost:9000 -MINIO_ACCESS_KEY=minioadmin -MINIO_SECRET_KEY=minioadmin -MINIO_BUCKET_NAME=seg-media -SAM2_MODEL_PATH=/home/wkmgc/Desktop/Seg_Server/models/sam2_hiera_tiny.pt +db_url=postgresql://seguser:segpass123@localhost:5432/segserver +redis_url=redis://localhost:6379/0 +minio_endpoint=192.168.3.11:9000 +minio_access_key=minioadmin +minio_secret_key=minioadmin +minio_secure=false +sam_model_path=/home/wkmgc/Desktop/Seg_Server/models/sam2_hiera_tiny.pt +sam_model_config=configs/sam2/sam2_hiera_t.yaml +sam_default_model=sam2 +sam3_model_version=sam3.1 +cors_origins=["http://localhost:3000","http://192.168.3.11:3000"] ``` +前端根目录的 `.env.example` 包含 AI Studio 注入变量和前端 API 配置： + +```ini +VITE_API_BASE_URL=http://192.168.3.11:8000 +VITE_WS_PROGRESS_URL=ws://192.168.3.11:8000/ws/progress +``` + +如果未配置 `VITE_API_BASE_URL`，前端会按当前浏览器 hostname 推导 `http://:8000`。 + ### 步骤 6: 启动后端服务 ```bash @@ -252,7 +287,21 @@ nohup uvicorn main:app --host 0.0.0.0 --port 8000 > /tmp/fastapi.log 2>&1 & - 创建数据库表（如果不存在） - 检查 MinIO bucket 是否存在 - 测试 Redis 连接 -- 懒加载 SAM 2 模型（权重存在且 sam2 包已安装时） +- 懒加载 SAM 模型；`GET /api/ai/models/status` 会返回 SAM 2、SAM 3 与 GPU 的真实可用状态 + +### 步骤 6.1: 启动 Celery Worker + +```bash +cd ~/Desktop/Seg_Server/backend +source ~/miniconda3/etc/profile.d/conda.sh +conda activate seg_server +celery -A celery_app:celery_app worker --loglevel=info --concurrency=1 + +# 或使用后台模式 +nohup celery -A celery_app:celery_app worker --loglevel=info --concurrency=1 > /tmp/celery.log 2>&1 & +``` + +`POST /api/media/parse` 只创建 `processing_tasks` 记录并把任务投递给 Celery；真正的 FFmpeg/OpenCV/pydicom 拆帧由 worker 执行。worker 每次更新任务状态后会发布到 Redis `seg:progress` 频道，FastAPI 订阅后转发到 `/ws/progress`，前端 Dashboard 可实时更新。 ### 步骤 7: 安装前端依赖并构建 @@ -286,7 +335,7 @@ cd ~/Desktop/Seg_Server ./start_services.sh ``` -脚本将依次检查并启动：PostgreSQL → Redis → MinIO → FastAPI 后端 → 前端。 +脚本将依次检查并启动：PostgreSQL → Redis → MinIO → FastAPI 后端 → Celery Worker → 前端。 --- @@ -307,10 +356,12 @@ cd ~/Desktop/Seg_Server ```bash npm install # 安装依赖 -npm run dev # Vite 开发模式（端口 5173） +npm run dev # 运行 tsx server.ts，Express + Vite 中间件（端口 3000） npm run build # 生产构建（输出到 dist/） npm run lint # TypeScript 类型检查 -npm start # Node.js 运行 server.ts（旧版） +npm run test # Vitest watch 模式 +npm run test:run # Vitest 单次运行 +npm start # Node.js 运行 server.ts（生产静态服务 / 旧版 mock API） ``` ### 后端 @@ -318,8 +369,11 @@ npm start # Node.js 运行 server.ts（旧版） ```bash # 在 conda seg_server 环境中 cd backend +pip install -r requirements-dev.txt # 安装后端测试依赖 +pytest tests # 后端接口测试 uvicorn main:app --host 0.0.0.0 --port 8000 --reload # 开发模式（热重载） uvicorn main:app --host 0.0.0.0 --port 8000 # 生产模式 +celery -A celery_app:celery_app worker --loglevel=info --concurrency=1 # 后台任务 worker ``` --- @@ -375,7 +429,25 @@ pip install -e . --no-build-isolation **检查清单**: 1. 后端是否已启动（`curl http://localhost:8000/health`） 2. `backend/.env` 中的 `cors_origins` 是否包含 `http://localhost:3000` -3. 前端 `src/lib/api.ts` 中的 `baseURL` 是否为 `http://localhost:8000` +3. 前端是否配置了正确的 `VITE_API_BASE_URL`；未配置时会按当前浏览器 hostname 推导 `http://:8000` + +### Q5: 如何验证 AI 推理或 COCO 导出接口 + +**当前状态**: + +- 前端 `predictMask()` 已发送后端需要的 `image_id`、`prompt_type`、`prompt_data`，并把后端 `polygons` 转成 Konva `pathData`。 +- 工作区点选/框选会使用当前帧的数据库 `frame.id` 调用 `/api/ai/predict`。 +- 前端 `exportCoco()` 已对齐到 `/api/export/{projectId}/coco`。 +- 工作区“导出 JSON 标注集”按钮已绑定下载流程；导出前会先保存当前待归档的前端 mask。 +- 工作区“结构化归档保存”按钮会把当前项目未保存 mask 写入 `POST /api/ai/annotate`，并把 dirty mask 写入 `PATCH /api/ai/annotations/{id}`。 +- 工作区“清空遮罩”会通过 `DELETE /api/ai/annotations/{id}` 删除当前帧已保存标注，并清空当前帧本地 mask。 + +**验证**: + +```bash +curl http://localhost:8000/health +curl http://localhost:8000/api/export/1/coco +``` --- diff --git a/backend/celery_app.py b/backend/celery_app.py new file mode 100644 index 0000000..d7ced3d --- /dev/null +++ b/backend/celery_app.py @@ -0,0 +1,21 @@ +"""Celery application for background processing.""" + +from celery import Celery + +from config import settings + +celery_app = Celery( + "seg_server", + broker=settings.redis_url, + backend=settings.redis_url, + include=["worker_tasks"], +) + +celery_app.conf.update( + task_serializer="json", + result_serializer="json", + accept_content=["json"], + timezone="Asia/Shanghai", + enable_utc=True, + task_track_started=True, +) diff --git a/backend/config.py b/backend/config.py index 712e3b0..f55c266 100644 --- a/backend/config.py +++ b/backend/config.py @@ -18,9 +18,11 @@ class Settings(BaseSettings): minio_secret_key: str = "minioadmin" minio_secure: bool = False - # SAM2 + # SAM + sam_default_model: str = "sam2" sam_model_path: str = "/home/wkmgc/Desktop/Seg_Server/models/sam2_hiera_tiny.pt" sam_model_config: str = "configs/sam2/sam2_hiera_t.yaml" + sam3_model_version: str = "sam3.1" # App app_env: str = "development" diff --git a/backend/main.py b/backend/main.py index ba88450..9ddc5fc 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,11 +1,13 @@ """FastAPI application entrypoint.""" import asyncio +import json import logging import os import shutil import tempfile -from contextlib import asynccontextmanager +from contextlib import asynccontextmanager, suppress +from datetime import datetime, timezone from fastapi import FastAPI, WebSocket, WebSocketDisconnect from fastapi.middleware.cors import CORSMiddleware @@ -13,9 +15,11 @@ from fastapi.middleware.cors import CORSMiddleware from config import settings from database import Base, engine, SessionLocal from minio_client import ensure_bucket_exists, upload_file -from redis_client import ping as redis_ping +from progress_events import PROGRESS_CHANNEL +from redis_client import get_redis_client, ping as redis_ping +from statuses import PROJECT_STATUS_PENDING, PROJECT_STATUS_READY -from routers import projects, templates, media, ai, export, auth +from routers import projects, templates, media, ai, export, auth, dashboard, tasks logging.basicConfig( level=logging.INFO, @@ -45,7 +49,7 @@ def _seed_default_project_sync() -> None: project = Project( name="Data_MyVideo_1", description="默认演示视频", - status="pending", + status=PROJECT_STATUS_PENDING, source_type="video", parse_fps=30.0, ) @@ -98,7 +102,7 @@ def _seed_default_project_sync() -> None: ) db.add(frame) - project.status = "ready" + project.status = PROJECT_STATUS_READY db.commit() logger.info("Seeded default project id=%s with %d frames", project.id, len(object_names)) finally: @@ -165,6 +169,7 @@ def _seed_default_templates_sync() -> None: @asynccontextmanager async def lifespan(app: FastAPI): """Application lifespan: startup and shutdown hooks.""" + progress_listener: asyncio.Task | None = None # Startup logger.info("Starting up SegServer backend...") @@ -187,6 +192,11 @@ async def lifespan(app: FastAPI): else: logger.warning("Redis connection failed.") + try: + progress_listener = asyncio.create_task(_progress_pubsub_loop()) + except Exception as exc: # noqa: BLE001 + logger.error("Failed to start Redis progress subscription: %s", exc) + # Seed default templates try: asyncio.create_task(asyncio.to_thread(_seed_default_templates_sync)) @@ -203,6 +213,10 @@ async def lifespan(app: FastAPI): # Shutdown logger.info("Shutting down SegServer backend...") + if progress_listener is not None: + progress_listener.cancel() + with suppress(asyncio.CancelledError): + await progress_listener engine.dispose() @@ -229,6 +243,8 @@ app.include_router(templates.router) app.include_router(media.router) app.include_router(ai.router) app.include_router(export.router) +app.include_router(dashboard.router) +app.include_router(tasks.router) @app.get("/health", tags=["Health"]) @@ -269,6 +285,34 @@ class ConnectionManager: manager = ConnectionManager() +async def _progress_pubsub_loop() -> None: + """Forward Redis task-progress events to connected WebSocket clients.""" + while True: + pubsub = None + try: + pubsub = get_redis_client().pubsub() + await asyncio.to_thread(pubsub.subscribe, PROGRESS_CHANNEL) + logger.info("Subscribed to Redis progress channel: %s", PROGRESS_CHANNEL) + while True: + message = await asyncio.to_thread(pubsub.get_message, True, 1.0) + if message is None: + await asyncio.sleep(0) + continue + raw_data = message.get("data") + payload = json.loads(raw_data) if isinstance(raw_data, str) else raw_data + if isinstance(payload, dict): + await manager.broadcast(payload) + except asyncio.CancelledError: + raise + except Exception as exc: # noqa: BLE001 + logger.error("Redis progress subscription failed: %s", exc) + await asyncio.sleep(5) + finally: + if pubsub is not None: + with suppress(Exception): + await asyncio.to_thread(pubsub.close) + + @app.websocket("/ws/progress") async def websocket_progress(websocket: WebSocket): """WebSocket endpoint for real-time parsing/AI progress updates.""" @@ -284,7 +328,7 @@ async def websocket_progress(websocket: WebSocket): "type": "status", "status": "connected", "message": "Progress stream active", - "timestamp": str(logging.time.time() if hasattr(logging, 'time') else __import__('time').time()), + "timestamp": datetime.now(timezone.utc).isoformat(), }) except WebSocketDisconnect: manager.disconnect(websocket) diff --git a/backend/models.py b/backend/models.py index 8b778a8..9dbf08d 100644 --- a/backend/models.py +++ b/backend/models.py @@ -14,6 +14,7 @@ from sqlalchemy.orm import relationship from sqlalchemy.sql import func from database import Base +from statuses import PROJECT_STATUS_PENDING class Project(Base): @@ -26,7 +27,7 @@ class Project(Base): description = Column(Text, nullable=True) video_path = Column(String(512), nullable=True) thumbnail_url = Column(String(512), nullable=True) - status = Column(String(50), default="Ready", nullable=False) + status = Column(String(50), default=PROJECT_STATUS_PENDING, nullable=False) source_type = Column(String(20), default="video", nullable=False) # video | dicom original_fps = Column(Float, nullable=True) parse_fps = Column(Float, default=30.0, nullable=False) @@ -39,6 +40,9 @@ class Project(Base): annotations = relationship( "Annotation", back_populates="project", cascade="all, delete-orphan" ) + tasks = relationship( + "ProcessingTask", back_populates="project", cascade="all, delete-orphan" + ) class Frame(Base): @@ -121,3 +125,30 @@ class Mask(Base): created_at = Column(DateTime(timezone=True), server_default=func.now()) annotation = relationship("Annotation", back_populates="masks") + + +class ProcessingTask(Base): + """Background task state persisted for dashboard and polling.""" + + __tablename__ = "processing_tasks" + + id = Column(Integer, primary_key=True, index=True) + task_type = Column(String(80), nullable=False) + status = Column(String(40), default="queued", nullable=False) + progress = Column(Integer, default=0, nullable=False) + message = Column(Text, nullable=True) + project_id = Column( + Integer, ForeignKey("projects.id", ondelete="CASCADE"), nullable=True + ) + celery_task_id = Column(String(255), nullable=True) + payload = Column(JSON, nullable=True) + result = Column(JSON, nullable=True) + error = Column(Text, nullable=True) + created_at = Column(DateTime(timezone=True), server_default=func.now()) + started_at = Column(DateTime(timezone=True), nullable=True) + finished_at = Column(DateTime(timezone=True), nullable=True) + updated_at = Column( + DateTime(timezone=True), server_default=func.now(), onupdate=func.now() + ) + + project = relationship("Project", back_populates="tasks") diff --git a/backend/progress_events.py b/backend/progress_events.py new file mode 100644 index 0000000..6b5a1c0 --- /dev/null +++ b/backend/progress_events.py @@ -0,0 +1,64 @@ +"""Progress event payloads and Redis publication helpers.""" + +from __future__ import annotations + +import json +import logging +from datetime import datetime, timezone +from typing import Any + +from redis_client import get_redis_client +from statuses import TASK_STATUS_FAILED, TASK_STATUS_SUCCESS + +logger = logging.getLogger(__name__) + +PROGRESS_CHANNEL = "seg:progress" + + +def _iso_now() -> str: + return datetime.now(timezone.utc).isoformat() + + +def _event_type(task_status: str) -> str: + if task_status == TASK_STATUS_SUCCESS: + return "complete" + if task_status == TASK_STATUS_FAILED: + return "error" + return "progress" + + +def task_progress_payload(task: Any) -> dict[str, Any]: + """Build the WebSocket payload from a persisted processing task.""" + project = getattr(task, "project", None) + project_name = getattr(project, "name", None) + status = getattr(task, "status", "") + updated_at = getattr(task, "updated_at", None) + timestamp = updated_at.isoformat() if updated_at is not None else _iso_now() + message = getattr(task, "message", None) + + return { + "type": _event_type(status), + "taskId": f"task-{task.id}", + "task_id": task.id, + "project_id": getattr(task, "project_id", None), + "projectName": project_name, + "filename": project_name, + "progress": getattr(task, "progress", 0), + "status": message or status, + "message": message, + "error": getattr(task, "error", None), + "timestamp": timestamp, + } + + +def publish_progress_event(payload: dict[str, Any]) -> None: + """Publish a JSON progress event without failing the worker on Redis errors.""" + try: + get_redis_client().publish(PROGRESS_CHANNEL, json.dumps(payload, ensure_ascii=False)) + except Exception as exc: # noqa: BLE001 + logger.warning("Failed to publish progress event: %s", exc) + + +def publish_task_progress_event(task: Any) -> None: + """Publish a progress event for a ProcessingTask ORM object.""" + publish_progress_event(task_progress_payload(task)) diff --git a/backend/requirements-dev.txt b/backend/requirements-dev.txt new file mode 100644 index 0000000..5769ad4 --- /dev/null +++ b/backend/requirements-dev.txt @@ -0,0 +1,2 @@ +pytest +httpx diff --git a/backend/routers/ai.py b/backend/routers/ai.py index d4ea034..29a7096 100644 --- a/backend/routers/ai.py +++ b/backend/routers/ai.py @@ -1,18 +1,25 @@ -"""AI inference endpoints using SAM 2.""" +"""AI inference endpoints using selectable SAM runtimes.""" import logging from typing import Any, List import cv2 import numpy as np -from fastapi import APIRouter, Depends, HTTPException, status +from fastapi import APIRouter, Depends, HTTPException, Response, status from sqlalchemy.orm import Session from database import get_db from minio_client import download_file -from models import Frame, Annotation -from schemas import PredictRequest, PredictResponse, AnnotationOut, AnnotationCreate -from services.sam2_engine import sam_engine +from models import Project, Frame, Template, Annotation +from schemas import ( + AiRuntimeStatus, + PredictRequest, + PredictResponse, + AnnotationOut, + AnnotationCreate, + AnnotationUpdate, +) +from services.sam_registry import ModelUnavailableError, sam_registry logger = logging.getLogger(__name__) router = APIRouter(prefix="/api/ai", tags=["AI"]) @@ -35,14 +42,15 @@ def _load_frame_image(frame: Frame) -> np.ndarray: @router.post( "/predict", response_model=PredictResponse, - summary="Run SAM 2 inference with a prompt", + summary="Run SAM inference with a prompt", ) def predict(payload: PredictRequest, db: Session = Depends(get_db)) -> dict: - """Execute SAM 2 segmentation given an image and a prompt. + """Execute selected SAM segmentation given an image and a prompt. - - **point**: `prompt_data` is a list of `[[x, y], ...]` normalized coordinates. + - **point**: `prompt_data` is either a list of `[[x, y], ...]` normalized + coordinates or `{ "points": [[x, y], ...], "labels": [1, 0, ...] }`. - **box**: `prompt_data` is `[x1, y1, x2, y2]` normalized coordinates. - - **semantic**: Not yet implemented; falls back to auto segmentation. + - **semantic**: SAM 3 text prompt when model=`sam3`; SAM 2 falls back to auto. """ frame = db.query(Frame).filter(Frame.id == payload.image_id).first() if not frame: @@ -54,30 +62,57 @@ def predict(payload: PredictRequest, db: Session = Depends(get_db)) -> dict: polygons: List[List[List[float]]] = [] scores: List[float] = [] - if prompt_type == "point": - points = payload.prompt_data - if not isinstance(points, list) or len(points) == 0: - raise HTTPException(status_code=400, detail="Invalid point prompt data") - labels = [1] * len(points) - polygons, scores = sam_engine.predict_points(image, points, labels) + try: + if prompt_type == "point": + point_payload = payload.prompt_data + if isinstance(point_payload, dict): + points = point_payload.get("points") + labels = point_payload.get("labels") + else: + points = point_payload + labels = None - elif prompt_type == "box": - box = payload.prompt_data - if not isinstance(box, list) or len(box) != 4: - raise HTTPException(status_code=400, detail="Invalid box prompt data") - polygons, scores = sam_engine.predict_box(image, box) + if not isinstance(points, list) or len(points) == 0: + raise HTTPException(status_code=400, detail="Invalid point prompt data") + if not isinstance(labels, list) or len(labels) != len(points): + labels = [1] * len(points) + polygons, scores = sam_registry.predict_points(payload.model, image, points, labels) - elif prompt_type == "semantic": - # Placeholder: use auto segmentation for now - logger.info("Semantic prompt not implemented; using auto segmentation") - polygons, scores = sam_engine.predict_auto(image) + elif prompt_type == "box": + box = payload.prompt_data + if not isinstance(box, list) or len(box) != 4: + raise HTTPException(status_code=400, detail="Invalid box prompt data") + polygons, scores = sam_registry.predict_box(payload.model, image, box) - else: - raise HTTPException(status_code=400, detail=f"Unsupported prompt_type: {prompt_type}") + elif prompt_type == "semantic": + text = payload.prompt_data if isinstance(payload.prompt_data, str) else "" + polygons, scores = sam_registry.predict_semantic(payload.model, image, text) + + else: + raise HTTPException(status_code=400, detail=f"Unsupported prompt_type: {prompt_type}") + except ModelUnavailableError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc + except NotImplementedError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc return {"polygons": polygons, "scores": scores} +@router.get( + "/models/status", + response_model=AiRuntimeStatus, + summary="Get SAM model and GPU runtime status", +) +def model_status(selected_model: str | None = None) -> dict: + """Return real runtime availability for GPU, SAM 2, and SAM 3.""" + try: + return sam_registry.runtime_status(selected_model) + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + @router.post( "/auto", response_model=PredictResponse, @@ -90,7 +125,10 @@ def auto_segment(image_id: int, db: Session = Depends(get_db)) -> dict: raise HTTPException(status_code=404, detail="Frame not found") image = _load_frame_image(frame) - polygons, scores = sam_engine.predict_auto(image) + try: + polygons, scores = sam_registry.predict_auto(None, image) + except ModelUnavailableError as exc: + raise HTTPException(status_code=503, detail=str(exc)) from exc return {"polygons": polygons, "scores": scores} @@ -106,7 +144,7 @@ def save_annotation( db: Session = Depends(get_db), ) -> Annotation: """Persist an annotation (mask, points, bbox) into the database.""" - project = db.query(Frame).filter(Frame.id == payload.project_id).first() + project = db.query(Project).filter(Project.id == payload.project_id).first() if not project: raise HTTPException(status_code=404, detail="Project not found") @@ -121,3 +159,74 @@ def save_annotation( db.refresh(annotation) logger.info("Saved annotation id=%s project_id=%s", annotation.id, annotation.project_id) return annotation + + +@router.get( + "/annotations", + response_model=List[AnnotationOut], + summary="List saved annotations for a project", +) +def list_annotations( + project_id: int, + frame_id: int | None = None, + db: Session = Depends(get_db), +) -> List[Annotation]: + """Return persisted annotations for a project, optionally scoped to one frame.""" + project = db.query(Project).filter(Project.id == project_id).first() + if not project: + raise HTTPException(status_code=404, detail="Project not found") + + query = db.query(Annotation).filter(Annotation.project_id == project_id) + if frame_id is not None: + query = query.filter(Annotation.frame_id == frame_id) + return query.order_by(Annotation.id).all() + + +@router.patch( + "/annotations/{annotation_id}", + response_model=AnnotationOut, + summary="Update a saved annotation", +) +def update_annotation( + annotation_id: int, + payload: AnnotationUpdate, + db: Session = Depends(get_db), +) -> Annotation: + """Update mutable annotation fields persisted in the database.""" + annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first() + if not annotation: + raise HTTPException(status_code=404, detail="Annotation not found") + + updates = payload.model_dump(exclude_unset=True) + if "template_id" in updates and updates["template_id"] is not None: + template = db.query(Template).filter(Template.id == updates["template_id"]).first() + if not template: + raise HTTPException(status_code=404, detail="Template not found") + + for field, value in updates.items(): + setattr(annotation, field, value) + + db.commit() + db.refresh(annotation) + logger.info("Updated annotation id=%s", annotation.id) + return annotation + + +@router.delete( + "/annotations/{annotation_id}", + status_code=status.HTTP_204_NO_CONTENT, + summary="Delete a saved annotation", +) +def delete_annotation( + annotation_id: int, + db: Session = Depends(get_db), +) -> Response: + """Delete an annotation and its derived mask rows through ORM cascade.""" + annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first() + if not annotation: + raise HTTPException(status_code=404, detail="Annotation not found") + + db.delete(annotation) + db.commit() + logger.info("Deleted annotation id=%s", annotation_id) + return Response(status_code=status.HTTP_204_NO_CONTENT) diff --git a/backend/routers/dashboard.py b/backend/routers/dashboard.py new file mode 100644 index 0000000..6bd5a6d --- /dev/null +++ b/backend/routers/dashboard.py @@ -0,0 +1,137 @@ +"""Dashboard overview endpoints.""" + +import os +from datetime import datetime, timezone +from typing import Any + +from fastapi import APIRouter, Depends +from sqlalchemy import func +from sqlalchemy.orm import Session + +from database import get_db +from models import Annotation, Frame, ProcessingTask, Project, Template + +router = APIRouter(prefix="/api/dashboard", tags=["Dashboard"]) + +ACTIVE_TASK_STATUSES = {"queued", "running"} + + +def _system_load_percent() -> int: + """Return a real host load estimate without adding a psutil dependency.""" + try: + load_1m = os.getloadavg()[0] + cpu_count = os.cpu_count() or 1 + return min(100, max(0, round((load_1m / cpu_count) * 100))) + except (AttributeError, OSError): + return 0 + + +def _iso_or_none(value: datetime | None) -> str | None: + if value is None: + return None + if value.tzinfo is None: + value = value.replace(tzinfo=timezone.utc) + return value.isoformat() + + +def _task_payload(task: ProcessingTask) -> dict[str, Any]: + return { + "id": f"task-{task.id}", + "task_id": task.id, + "project_id": task.project_id or 0, + "name": task.project.name if task.project else f"任务 {task.id}", + "progress": task.progress, + "status": task.message or task.status, + "frame_count": (task.result or {}).get("frames_extracted", 0), + "updated_at": _iso_or_none(task.updated_at), + } + + +@router.get("/overview", summary="Get dashboard overview") +def get_dashboard_overview(db: Session = Depends(get_db)) -> dict[str, Any]: + """Return live dashboard data derived from persisted backend records.""" + project_count = db.query(func.count(Project.id)).scalar() or 0 + frame_count = db.query(func.count(Frame.id)).scalar() or 0 + annotation_count = db.query(func.count(Annotation.id)).scalar() or 0 + template_count = db.query(func.count(Template.id)).scalar() or 0 + active_task_count = ( + db.query(func.count(ProcessingTask.id)) + .filter(ProcessingTask.status.in_(ACTIVE_TASK_STATUSES)) + .scalar() + or 0 + ) + + projects = db.query(Project).order_by(Project.updated_at.desc()).all() + recent_tasks = ( + db.query(ProcessingTask) + .order_by(ProcessingTask.created_at.desc()) + .limit(50) + .all() + ) + tasks = [_task_payload(task) for task in recent_tasks if task.status in ACTIVE_TASK_STATUSES] + + activities: list[dict[str, Any]] = [] + for task in recent_tasks[:10]: + project_name = task.project.name if task.project else f"项目 {task.project_id}" + activities.append({ + "id": f"task-{task.id}", + "kind": "task", + "time": _iso_or_none(task.updated_at), + "message": task.message or f"任务状态: {task.status}", + "project": project_name, + }) + + for project in projects[:10]: + activities.append({ + "id": f"project-{project.id}", + "kind": "project", + "time": _iso_or_none(project.updated_at), + "message": f"项目状态: {project.status}", + "project": project.name, + }) + + recent_annotations = ( + db.query(Annotation) + .order_by(Annotation.updated_at.desc()) + .limit(10) + .all() + ) + for annotation in recent_annotations: + project_name = annotation.project.name if annotation.project else f"项目 {annotation.project_id}" + activities.append({ + "id": f"annotation-{annotation.id}", + "kind": "annotation", + "time": _iso_or_none(annotation.updated_at), + "message": f"标注已更新 #{annotation.id}", + "project": project_name, + }) + + recent_templates = ( + db.query(Template) + .order_by(Template.created_at.desc()) + .limit(10) + .all() + ) + for template in recent_templates: + activities.append({ + "id": f"template-{template.id}", + "kind": "template", + "time": _iso_or_none(template.created_at), + "message": f"模板可用: {template.name}", + "project": "系统", + }) + + activities.sort(key=lambda item: item["time"] or "", reverse=True) + + return { + "summary": { + "project_count": project_count, + "parsing_task_count": active_task_count, + "annotation_count": annotation_count, + "frame_count": frame_count, + "template_count": template_count, + "system_load_percent": _system_load_percent(), + }, + "tasks": tasks, + "activity": activities[:10], + } diff --git a/backend/routers/media.py b/backend/routers/media.py index c18bb87..3ec77ff 100644 --- a/backend/routers/media.py +++ b/backend/routers/media.py @@ -1,10 +1,6 @@ """Media upload and parsing endpoints.""" import logging -import os -import shutil -import subprocess -import tempfile from pathlib import Path from typing import List, Optional @@ -12,13 +8,12 @@ from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, s from sqlalchemy.orm import Session from database import get_db -from minio_client import upload_file, get_presigned_url, download_file -from models import Project, Frame -from schemas import FrameOut -from services.frame_parser import ( - parse_video, parse_dicom, upload_frames_to_minio, - extract_thumbnail, get_video_fps, -) +from minio_client import upload_file, get_presigned_url +from models import ProcessingTask, Project +from progress_events import publish_task_progress_event +from schemas import ProcessingTaskOut +from statuses import PROJECT_STATUS_PARSING, PROJECT_STATUS_PENDING, TASK_STATUS_QUEUED +from worker_tasks import parse_project_media logger = logging.getLogger(__name__) router = APIRouter(prefix="/api/media", tags=["Media"]) @@ -79,7 +74,7 @@ async def upload_media( project = Project( name=file.filename, description="Auto-created from upload", - status="pending", + status=PROJECT_STATUS_PENDING, video_path=object_name, source_type="video", ) @@ -135,7 +130,7 @@ async def upload_dicom_batch( project = Project( name=first_name, description=f"DICOM series with {len(files)} files", - status="pending", + status=PROJECT_STATUS_PENDING, source_type="dicom", ) db.add(project) @@ -168,19 +163,18 @@ async def upload_dicom_batch( @router.post( "/parse", status_code=status.HTTP_202_ACCEPTED, + response_model=ProcessingTaskOut, summary="Trigger frame extraction", ) def parse_media( project_id: int, source_type: Optional[str] = None, db: Session = Depends(get_db), -) -> dict: - """Trigger frame extraction for a project's uploaded media. +) -> ProcessingTask: + """Create a background task for media frame extraction. - * video: uses FFmpeg or OpenCV fallback, extracts thumbnail. - * dicom: uses pydicom to read DCM frames. - - Extracted frames are uploaded to MinIO and registered in the database. + The Celery worker performs the heavy FFmpeg/OpenCV/pydicom work and + updates the persisted task record as it progresses. """ project = db.query(Project).filter(Project.id == project_id).first() if not project: @@ -190,100 +184,24 @@ def parse_media( raise HTTPException(status_code=400, detail="Project has no media uploaded") effective_source = source_type or project.source_type or "video" - parse_fps = project.parse_fps or 30.0 - - tmp_dir = tempfile.mkdtemp(prefix=f"seg_parse_{project_id}_") - output_dir = os.path.join(tmp_dir, "frames") - os.makedirs(output_dir, exist_ok=True) - - try: - if effective_source == "dicom": - # Download all dicom files from MinIO - dcm_dir = os.path.join(tmp_dir, "dcm") - os.makedirs(dcm_dir, exist_ok=True) - - from minio_client import get_minio_client, BUCKET_NAME - client = get_minio_client() - prefix = project.video_path - objects = list(client.list_objects(BUCKET_NAME, prefix=prefix, recursive=True)) - for obj in objects: - if obj.object_name.lower().endswith(".dcm"): - data = download_file(obj.object_name) - local_dcm = os.path.join(dcm_dir, os.path.basename(obj.object_name)) - with open(local_dcm, "wb") as f: - f.write(data) - - frame_files = parse_dicom(dcm_dir, output_dir) - else: - # Video: download and parse - media_bytes = download_file(project.video_path) - local_path = os.path.join(tmp_dir, Path(project.video_path).name) - with open(local_path, "wb") as f: - f.write(media_bytes) - - frame_files, original_fps = parse_video(local_path, output_dir, fps=int(parse_fps)) - project.original_fps = original_fps - - # Extract thumbnail from first frame - thumbnail_path = os.path.join(tmp_dir, "thumbnail.jpg") - try: - extract_thumbnail(local_path, thumbnail_path) - with open(thumbnail_path, "rb") as f: - thumb_data = f.read() - thumb_object = f"projects/{project_id}/thumbnail.jpg" - upload_file(thumb_object, thumb_data, content_type="image/jpeg", length=len(thumb_data)) - project.thumbnail_url = thumb_object - logger.info("Uploaded thumbnail for project_id=%s", project_id) - except Exception as exc: # noqa: BLE001 - logger.warning("Thumbnail extraction failed: %s", exc) - except Exception as exc: # noqa: BLE001 - logger.error("Frame extraction failed: %s", exc) - shutil.rmtree(tmp_dir, ignore_errors=True) - raise HTTPException(status_code=500, detail="Frame extraction failed") from exc - - # Upload frames to MinIO - try: - object_names = upload_frames_to_minio(frame_files, project_id) - except Exception as exc: # noqa: BLE001 - logger.error("Frame upload failed: %s", exc) - shutil.rmtree(tmp_dir, ignore_errors=True) - raise HTTPException(status_code=500, detail="Frame upload to storage failed") from exc - - # Register frames in DB - frames_out = [] - for idx, obj_name in enumerate(object_names): - local_frame = frame_files[idx] - try: - import cv2 - img = cv2.imread(local_frame) - h, w = img.shape[:2] if img is not None else (None, None) - except Exception: # noqa: BLE001 - h, w = None, None - - frame = Frame( - project_id=project_id, - frame_index=idx, - image_url=obj_name, - width=w, - height=h, - ) - db.add(frame) - frames_out.append(frame) - + task = ProcessingTask( + task_type=f"parse_{effective_source}", + status=TASK_STATUS_QUEUED, + progress=0, + message="解析任务已入队", + project_id=project_id, + payload={"source_type": effective_source}, + ) + project.status = PROJECT_STATUS_PARSING + db.add(task) db.commit() - for f in frames_out: - db.refresh(f) + db.refresh(task) + publish_task_progress_event(task) - # Cleanup temp files - shutil.rmtree(tmp_dir, ignore_errors=True) - - project.status = "ready" + async_result = parse_project_media.delay(task.id) + task.celery_task_id = async_result.id db.commit() + db.refresh(task) - logger.info("Parsed %d frames for project_id=%s", len(frames_out), project_id) - return { - "project_id": project_id, - "frames_extracted": len(frames_out), - "status": "ready", - "message": "Frame extraction completed successfully.", - } + logger.info("Queued parse task id=%s project_id=%s celery_id=%s", task.id, project_id, async_result.id) + return task diff --git a/backend/routers/tasks.py b/backend/routers/tasks.py new file mode 100644 index 0000000..9c1d335 --- /dev/null +++ b/backend/routers/tasks.py @@ -0,0 +1,37 @@ +"""Processing task query endpoints.""" + +from typing import List + +from fastapi import APIRouter, Depends, HTTPException +from sqlalchemy.orm import Session + +from database import get_db +from models import ProcessingTask +from schemas import ProcessingTaskOut + +router = APIRouter(prefix="/api/tasks", tags=["Tasks"]) + + +@router.get("", response_model=List[ProcessingTaskOut], summary="List processing tasks") +def list_tasks( + project_id: int | None = None, + status: str | None = None, + limit: int = 50, + db: Session = Depends(get_db), +) -> List[ProcessingTask]: + """Return recent background processing tasks.""" + query = db.query(ProcessingTask) + if project_id is not None: + query = query.filter(ProcessingTask.project_id == project_id) + if status is not None: + query = query.filter(ProcessingTask.status == status) + return query.order_by(ProcessingTask.created_at.desc()).limit(limit).all() + + +@router.get("/{task_id}", response_model=ProcessingTaskOut, summary="Get processing task") +def get_task(task_id: int, db: Session = Depends(get_db)) -> ProcessingTask: + """Return one background task by id.""" + task = db.query(ProcessingTask).filter(ProcessingTask.id == task_id).first() + if not task: + raise HTTPException(status_code=404, detail="Task not found") + return task diff --git a/backend/routers/templates.py b/backend/routers/templates.py index 99aab13..df8caf3 100644 --- a/backend/routers/templates.py +++ b/backend/routers/templates.py @@ -18,9 +18,9 @@ def _pack_mapping_rules(data: dict) -> dict: """Pack classes/rules into mapping_rules for DB storage.""" mapping = data.get("mapping_rules") or {} if "classes" in data and data["classes"] is not None: - mapping["classes"] = data["classes"] + mapping["classes"] = data.pop("classes") if "rules" in data and data["rules"] is not None: - mapping["rules"] = data["rules"] + mapping["rules"] = data.pop("rules") data["mapping_rules"] = mapping return data diff --git a/backend/schemas.py b/backend/schemas.py index 1e1bebb..ac74557 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -70,6 +70,7 @@ class FrameOut(FrameBase): # --------------------------------------------------------------------------- class TemplateBase(BaseModel): name: str + description: Optional[str] = None color: str z_index: int = 0 mapping_rules: Optional[dict[str, Any]] = None @@ -83,6 +84,7 @@ class TemplateCreate(TemplateBase): 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 @@ -115,7 +117,7 @@ class AnnotationCreate(AnnotationBase): class AnnotationUpdate(BaseModel): mask_data: Optional[dict[str, Any]] = None - points: Optional[list[float]] = None + points: Optional[list[list[float]]] = None bbox: Optional[list[float]] = None template_id: Optional[int] = None @@ -148,6 +150,28 @@ class MaskOut(MaskBase): 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 # --------------------------------------------------------------------------- @@ -155,6 +179,7 @@ class PredictRequest(BaseModel): image_id: int prompt_type: str # point / box / semantic prompt_data: Any + model: Optional[str] = None class PredictResponse(BaseModel): @@ -162,6 +187,37 @@ class PredictResponse(BaseModel): 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 # --------------------------------------------------------------------------- diff --git a/backend/services/media_task_runner.py b/backend/services/media_task_runner.py new file mode 100644 index 0000000..396d376 --- /dev/null +++ b/backend/services/media_task_runner.py @@ -0,0 +1,220 @@ +"""Background media parsing runner used by Celery workers.""" + +import logging +import os +import shutil +import tempfile +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + +from sqlalchemy.orm import Session + +from minio_client import BUCKET_NAME, download_file, get_minio_client, upload_file +from models import Frame, ProcessingTask, Project +from progress_events import publish_task_progress_event +from services.frame_parser import ( + extract_thumbnail, + parse_dicom, + parse_video, + upload_frames_to_minio, +) +from statuses import ( + PROJECT_STATUS_ERROR, + PROJECT_STATUS_PARSING, + PROJECT_STATUS_READY, + TASK_STATUS_FAILED, + TASK_STATUS_RUNNING, + TASK_STATUS_SUCCESS, +) + +logger = logging.getLogger(__name__) + + +def _now() -> datetime: + return datetime.now(timezone.utc) + + +def _set_task_state( + db: Session, + task: ProcessingTask, + *, + status: str | None = None, + progress: int | None = None, + message: str | None = None, + result: dict[str, Any] | None = None, + error: str | None = None, + started: bool = False, + finished: bool = False, +) -> None: + if status is not None: + task.status = status + if progress is not None: + task.progress = max(0, min(100, progress)) + if message is not None: + task.message = message + if result is not None: + task.result = result + if error is not None: + task.error = error + if started: + task.started_at = _now() + if finished: + task.finished_at = _now() + db.commit() + db.refresh(task) + publish_task_progress_event(task) + + +def run_parse_media_task(db: Session, task_id: int) -> dict[str, Any]: + """Parse one project's media and update task progress in the database.""" + task = db.query(ProcessingTask).filter(ProcessingTask.id == task_id).first() + if not task: + raise ValueError(f"Task not found: {task_id}") + + if task.project_id is None: + _set_task_state( + db, + task, + status=TASK_STATUS_FAILED, + progress=100, + message="任务缺少 project_id", + error="Task has no project_id", + finished=True, + ) + raise ValueError("Task has no project_id") + + project = db.query(Project).filter(Project.id == task.project_id).first() + if not project: + _set_task_state( + db, + task, + status=TASK_STATUS_FAILED, + progress=100, + message="项目不存在", + error="Project not found", + finished=True, + ) + raise ValueError(f"Project not found: {task.project_id}") + + if not project.video_path: + _set_task_state( + db, + task, + status=TASK_STATUS_FAILED, + progress=100, + message="项目没有可解析媒体", + error="Project has no media uploaded", + finished=True, + ) + project.status = PROJECT_STATUS_ERROR + db.commit() + raise ValueError("Project has no media uploaded") + + project.status = PROJECT_STATUS_PARSING + _set_task_state(db, task, status=TASK_STATUS_RUNNING, progress=5, message="后台解析已启动", started=True) + + effective_source = (task.payload or {}).get("source_type") or project.source_type or "video" + parse_fps = project.parse_fps or 30.0 + tmp_dir = tempfile.mkdtemp(prefix=f"seg_parse_{project.id}_") + output_dir = os.path.join(tmp_dir, "frames") + os.makedirs(output_dir, exist_ok=True) + + try: + _set_task_state(db, task, progress=15, message="正在下载媒体文件") + if effective_source == "dicom": + dcm_dir = os.path.join(tmp_dir, "dcm") + os.makedirs(dcm_dir, exist_ok=True) + + client = get_minio_client() + objects = list(client.list_objects(BUCKET_NAME, prefix=project.video_path, recursive=True)) + for obj in objects: + if obj.object_name.lower().endswith(".dcm"): + data = download_file(obj.object_name) + local_dcm = os.path.join(dcm_dir, os.path.basename(obj.object_name)) + with open(local_dcm, "wb") as f: + f.write(data) + + _set_task_state(db, task, progress=35, message="正在解析 DICOM 序列") + frame_files = parse_dicom(dcm_dir, output_dir) + else: + media_bytes = download_file(project.video_path) + local_path = os.path.join(tmp_dir, Path(project.video_path).name) + with open(local_path, "wb") as f: + f.write(media_bytes) + + _set_task_state(db, task, progress=35, message="正在使用 FFmpeg/OpenCV 拆帧") + frame_files, original_fps = parse_video(local_path, output_dir, fps=int(parse_fps)) + project.original_fps = original_fps + + thumbnail_path = os.path.join(tmp_dir, "thumbnail.jpg") + try: + extract_thumbnail(local_path, thumbnail_path) + with open(thumbnail_path, "rb") as f: + thumb_data = f.read() + thumb_object = f"projects/{project.id}/thumbnail.jpg" + upload_file(thumb_object, thumb_data, content_type="image/jpeg", length=len(thumb_data)) + project.thumbnail_url = thumb_object + except Exception as exc: # noqa: BLE001 + logger.warning("Thumbnail extraction failed: %s", exc) + + _set_task_state(db, task, progress=70, message="正在上传帧到对象存储") + object_names = upload_frames_to_minio(frame_files, project.id) + + _set_task_state(db, task, progress=85, message="正在写入帧索引") + frames_out = [] + for idx, obj_name in enumerate(object_names): + local_frame = frame_files[idx] + try: + import cv2 + + img = cv2.imread(local_frame) + h, w = img.shape[:2] if img is not None else (None, None) + except Exception: # noqa: BLE001 + h, w = None, None + + frame = Frame( + project_id=project.id, + frame_index=idx, + image_url=obj_name, + width=w, + height=h, + ) + db.add(frame) + frames_out.append(frame) + + project.status = PROJECT_STATUS_READY + db.commit() + + result = { + "project_id": project.id, + "frames_extracted": len(frames_out), + "status": PROJECT_STATUS_READY, + "message": "Frame extraction completed successfully.", + } + _set_task_state( + db, + task, + status=TASK_STATUS_SUCCESS, + progress=100, + message="解析完成", + result=result, + finished=True, + ) + logger.info("Parsed %d frames for project_id=%s", len(frames_out), project.id) + return result + except Exception as exc: # noqa: BLE001 + project.status = PROJECT_STATUS_ERROR + _set_task_state( + db, + task, + status=TASK_STATUS_FAILED, + progress=100, + message="解析失败", + error=str(exc), + finished=True, + ) + logger.error("Frame extraction failed: %s", exc) + raise + finally: + shutil.rmtree(tmp_dir, ignore_errors=True) diff --git a/backend/services/sam2_engine.py b/backend/services/sam2_engine.py index bb670cf..fb25a8a 100644 --- a/backend/services/sam2_engine.py +++ b/backend/services/sam2_engine.py @@ -1,4 +1,4 @@ -"""SAM 2 engine wrapper with lazy loading and fallback stubs.""" +"""SAM 2 engine wrapper with lazy loading and explicit runtime status.""" import logging import os @@ -11,10 +11,18 @@ from config import settings logger = logging.getLogger(__name__) # --------------------------------------------------------------------------- -# Attempt to import SAM 2; fall back to stubs if unavailable. +# Attempt to import PyTorch and SAM 2; fall back to stubs if unavailable. # --------------------------------------------------------------------------- try: import torch + + TORCH_AVAILABLE = True +except Exception as exc: # noqa: BLE001 + TORCH_AVAILABLE = False + torch = None # type: ignore[assignment] + logger.warning("PyTorch import failed (%s). SAM2 will be unavailable.", exc) + +try: from sam2.build_sam import build_sam2 from sam2.sam2_image_predictor import SAM2ImagePredictor @@ -31,6 +39,8 @@ class SAM2Engine: def __init__(self) -> None: self._predictor: Optional[SAM2ImagePredictor] = None self._model_loaded = False + self._loaded_device: str | None = None + self._last_error: str | None = None # ----------------------------------------------------------------------- # Internal helpers @@ -40,34 +50,87 @@ class SAM2Engine: if self._model_loaded: return + if not TORCH_AVAILABLE: + self._last_error = "PyTorch is not installed." + logger.warning("PyTorch not available; skipping SAM2 model load.") + self._model_loaded = True + return + if not SAM2_AVAILABLE: + self._last_error = "sam2 package is not installed." logger.warning("SAM2 not available; skipping model load.") self._model_loaded = True return if not os.path.isfile(settings.sam_model_path): + self._last_error = f"SAM2 checkpoint not found: {settings.sam_model_path}" logger.error("SAM checkpoint not found at %s", settings.sam_model_path) self._model_loaded = True return try: + device = self._best_device() model = build_sam2( settings.sam_model_config, settings.sam_model_path, - device="cuda", + device=device, ) self._predictor = SAM2ImagePredictor(model) self._model_loaded = True - logger.info("SAM 2 model loaded from %s", settings.sam_model_path) + self._loaded_device = device + self._last_error = None + logger.info("SAM 2 model loaded from %s on %s", settings.sam_model_path, device) except Exception as exc: # noqa: BLE001 + self._last_error = str(exc) logger.error("Failed to load SAM 2 model: %s", exc) self._model_loaded = True # Prevent repeated load attempts + def _best_device(self) -> str: + if TORCH_AVAILABLE and torch is not None and torch.cuda.is_available(): + return "cuda" + return "cpu" + def _ensure_ready(self) -> bool: """Ensure the model is loaded; return whether it is usable.""" self._load_model() return SAM2_AVAILABLE and self._predictor is not None + def status(self) -> dict: + """Return lightweight, real runtime status without forcing model load.""" + checkpoint_exists = os.path.isfile(settings.sam_model_path) + device = self._loaded_device or self._best_device() + available = bool(TORCH_AVAILABLE and SAM2_AVAILABLE and checkpoint_exists) + if self._predictor is not None: + message = "SAM 2 model loaded and ready." + elif available: + message = "SAM 2 dependencies and checkpoint are present; model will load on first inference." + else: + missing = [] + if not TORCH_AVAILABLE: + missing.append("PyTorch") + if not SAM2_AVAILABLE: + missing.append("sam2 package") + if not checkpoint_exists: + missing.append("checkpoint") + message = f"SAM 2 unavailable: missing {', '.join(missing)}." + if self._last_error and not self._predictor: + message = self._last_error + return { + "id": "sam2", + "label": "SAM 2", + "available": available, + "loaded": self._predictor is not None, + "device": device, + "supports": ["point", "box", "auto"], + "message": message, + "package_available": SAM2_AVAILABLE, + "checkpoint_exists": checkpoint_exists, + "checkpoint_path": settings.sam_model_path, + "python_ok": True, + "torch_ok": TORCH_AVAILABLE, + "cuda_required": False, + } + # ----------------------------------------------------------------------- # Public API # ----------------------------------------------------------------------- diff --git a/backend/services/sam3_engine.py b/backend/services/sam3_engine.py new file mode 100644 index 0000000..7f71d64 --- /dev/null +++ b/backend/services/sam3_engine.py @@ -0,0 +1,148 @@ +"""SAM 3 engine adapter and runtime status. + +The official facebookresearch/sam3 package currently targets Python 3.12+ +and CUDA-capable PyTorch. This adapter reports those requirements honestly and +only performs inference when the local runtime can actually import and execute +the package. +""" + +from __future__ import annotations + +import importlib.util +import logging +import sys +from typing import Any + +import numpy as np +from PIL import Image + +from config import settings +from services.sam2_engine import SAM2Engine + +logger = logging.getLogger(__name__) + +try: + import torch + + TORCH_AVAILABLE = True +except Exception as exc: # noqa: BLE001 + TORCH_AVAILABLE = False + torch = None # type: ignore[assignment] + logger.warning("PyTorch import failed (%s). SAM3 will be unavailable.", exc) + +SAM3_PACKAGE_AVAILABLE = importlib.util.find_spec("sam3") is not None + + +class SAM3Engine: + """Lazy SAM 3 image inference adapter.""" + + def __init__(self) -> None: + self._model: Any | None = None + self._processor: Any | None = None + self._model_loaded = False + self._last_error: str | None = None + + def _python_ok(self) -> bool: + return sys.version_info >= (3, 12) + + def _gpu_ok(self) -> bool: + return bool(TORCH_AVAILABLE and torch is not None and torch.cuda.is_available()) + + def _can_load(self) -> bool: + return bool(SAM3_PACKAGE_AVAILABLE and TORCH_AVAILABLE and self._python_ok() and self._gpu_ok()) + + def _load_model(self) -> None: + if self._model_loaded: + return + if not self._can_load(): + self._last_error = self._status_message() + self._model_loaded = True + return + + try: + from sam3.model.sam3_image_processor import Sam3Processor + from sam3.model_builder import build_sam3_image_model + + self._model = build_sam3_image_model() + self._processor = Sam3Processor(self._model) + self._model_loaded = True + self._last_error = None + logger.info("SAM 3 image model loaded with version setting %s", settings.sam3_model_version) + except Exception as exc: # noqa: BLE001 + self._last_error = str(exc) + self._model_loaded = True + logger.error("Failed to load SAM 3 model: %s", exc) + + def _ensure_ready(self) -> bool: + self._load_model() + return self._processor is not None + + def _status_message(self) -> str: + missing = [] + if not SAM3_PACKAGE_AVAILABLE: + missing.append("sam3 package") + if not self._python_ok(): + missing.append("Python 3.12+ runtime") + if not TORCH_AVAILABLE: + missing.append("PyTorch") + if not self._gpu_ok(): + missing.append("CUDA GPU") + if missing: + return f"SAM 3 unavailable: missing {', '.join(missing)}." + return "SAM 3 dependencies are present; model will load on first inference." + + def status(self) -> dict: + available = self._can_load() + return { + "id": "sam3", + "label": "SAM 3", + "available": available, + "loaded": self._processor is not None, + "device": "cuda" if self._gpu_ok() else "unavailable", + "supports": ["semantic"], + "message": "SAM 3 model loaded and ready." if self._processor is not None else (self._last_error or self._status_message()), + "package_available": SAM3_PACKAGE_AVAILABLE, + "checkpoint_exists": SAM3_PACKAGE_AVAILABLE, + "checkpoint_path": f"official/HuggingFace ({settings.sam3_model_version})", + "python_ok": self._python_ok(), + "torch_ok": TORCH_AVAILABLE, + "cuda_required": True, + } + + def predict_semantic(self, image: np.ndarray, text: str) -> tuple[list[list[list[float]]], list[float]]: + if not text.strip(): + raise ValueError("SAM 3 semantic prompt requires non-empty text.") + if not self._ensure_ready(): + raise RuntimeError(self.status()["message"]) + + pil_image = Image.fromarray(image) + with torch.inference_mode(): # type: ignore[union-attr] + state = self._processor.set_image(pil_image) + output = self._processor.set_text_prompt(state=state, prompt=text.strip()) + + masks = output.get("masks", []) + scores = output.get("scores", []) + polygons = [] + for mask in masks: + if hasattr(mask, "detach"): + mask = mask.detach().cpu().numpy() + if mask.ndim == 3: + mask = mask[0] + poly = SAM2Engine._mask_to_polygon(mask) + if poly: + polygons.append(poly) + + if hasattr(scores, "detach"): + scores = scores.detach().cpu().tolist() + elif hasattr(scores, "tolist"): + scores = scores.tolist() + return polygons, list(scores) + + def predict_points(self, *_args: Any, **_kwargs: Any) -> tuple[list[list[list[float]]], list[float]]: + raise NotImplementedError("This backend currently exposes SAM 3 semantic text inference; use SAM 2 for point prompts.") + + def predict_box(self, *_args: Any, **_kwargs: Any) -> tuple[list[list[list[float]]], list[float]]: + raise NotImplementedError("This backend currently exposes SAM 3 semantic text inference; use SAM 2 for box prompts.") + + +sam3_engine = SAM3Engine() diff --git a/backend/services/sam_registry.py b/backend/services/sam_registry.py new file mode 100644 index 0000000..8907991 --- /dev/null +++ b/backend/services/sam_registry.py @@ -0,0 +1,80 @@ +"""Model registry for SAM runtimes and GPU status.""" + +from __future__ import annotations + +from typing import Any + +from config import settings +from services.sam2_engine import TORCH_AVAILABLE, sam_engine as sam2_engine +from services.sam3_engine import sam3_engine + +try: + import torch +except Exception: # noqa: BLE001 + torch = None # type: ignore[assignment] + + +class ModelUnavailableError(RuntimeError): + """Raised when a selected model cannot run in this environment.""" + + +class SAMRegistry: + """Dispatch predictions to the selected SAM backend.""" + + def __init__(self) -> None: + self._engines = { + "sam2": sam2_engine, + "sam3": sam3_engine, + } + + def normalize_model_id(self, model_id: str | None) -> str: + selected = (model_id or settings.sam_default_model or "sam2").lower() + if selected not in self._engines: + raise ValueError(f"Unsupported model: {model_id}") + return selected + + def runtime_status(self, selected_model: str | None = None) -> dict[str, Any]: + return { + "selected_model": self.normalize_model_id(selected_model), + "gpu": self.gpu_status(), + "models": [engine.status() for engine in self._engines.values()], + } + + def gpu_status(self) -> dict[str, Any]: + cuda_available = bool(TORCH_AVAILABLE and torch is not None and torch.cuda.is_available()) + return { + "available": cuda_available, + "device": "cuda" if cuda_available else "cpu", + "name": torch.cuda.get_device_name(0) if cuda_available else None, + "torch_available": bool(TORCH_AVAILABLE), + "torch_version": getattr(torch, "__version__", None) if torch is not None else None, + "cuda_version": getattr(torch.version, "cuda", None) if torch is not None else None, + } + + def _engine(self, model_id: str | None) -> Any: + return self._engines[self.normalize_model_id(model_id)] + + def _ensure_available(self, model_id: str | None) -> Any: + engine = self._engine(model_id) + status = engine.status() + if not status["available"]: + raise ModelUnavailableError(status["message"]) + return engine + + def predict_points(self, model_id: str | None, image: Any, points: list[list[float]], labels: list[int]): + return self._ensure_available(model_id).predict_points(image, points, labels) + + def predict_box(self, model_id: str | None, image: Any, box: list[float]): + return self._ensure_available(model_id).predict_box(image, box) + + def predict_auto(self, model_id: str | None, image: Any): + return self._ensure_available(model_id).predict_auto(image) + + def predict_semantic(self, model_id: str | None, image: Any, text: str): + model = self.normalize_model_id(model_id) + if model == "sam3": + return self._ensure_available(model).predict_semantic(image, text) + return self._ensure_available(model).predict_auto(image) + + +sam_registry = SAMRegistry() diff --git a/backend/statuses.py b/backend/statuses.py new file mode 100644 index 0000000..1cec48f --- /dev/null +++ b/backend/statuses.py @@ -0,0 +1,11 @@ +"""Shared status constants used across backend project/task flows.""" + +PROJECT_STATUS_PENDING = "pending" +PROJECT_STATUS_PARSING = "parsing" +PROJECT_STATUS_READY = "ready" +PROJECT_STATUS_ERROR = "error" + +TASK_STATUS_QUEUED = "queued" +TASK_STATUS_RUNNING = "running" +TASK_STATUS_SUCCESS = "success" +TASK_STATUS_FAILED = "failed" diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py new file mode 100644 index 0000000..cbc3a77 --- /dev/null +++ b/backend/tests/conftest.py @@ -0,0 +1,72 @@ +"""Shared pytest fixtures for backend API tests.""" + +from __future__ import annotations + +import sys +from pathlib import Path +from typing import Iterator + +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient +from sqlalchemy import create_engine +from sqlalchemy.orm import Session, sessionmaker +from sqlalchemy.pool import StaticPool + +BACKEND_DIR = Path(__file__).resolve().parents[1] +if str(BACKEND_DIR) not in sys.path: + sys.path.insert(0, str(BACKEND_DIR)) + +from database import Base, get_db # noqa: E402 +from main import websocket_progress # noqa: E402 +from routers import ai, auth, dashboard, export, media, projects, tasks, templates # noqa: E402 + + +@pytest.fixture() +def db_session() -> Iterator[Session]: + engine = create_engine( + "sqlite://", + connect_args={"check_same_thread": False}, + poolclass=StaticPool, + ) + TestingSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + Base.metadata.create_all(bind=engine) + session = TestingSessionLocal() + try: + yield session + finally: + session.close() + Base.metadata.drop_all(bind=engine) + engine.dispose() + + +@pytest.fixture() +def app(db_session: Session) -> FastAPI: + test_app = FastAPI() + + def override_get_db() -> Iterator[Session]: + yield db_session + + test_app.dependency_overrides[get_db] = override_get_db + test_app.include_router(auth.router) + test_app.include_router(projects.router) + test_app.include_router(templates.router) + test_app.include_router(media.router) + test_app.include_router(ai.router) + test_app.include_router(export.router) + test_app.include_router(dashboard.router) + test_app.include_router(tasks.router) + + @test_app.get("/health") + def health_check() -> dict[str, str]: + return {"status": "ok", "service": "SegServer"} + + test_app.add_api_websocket_route("/ws/progress", websocket_progress) + + return test_app + + +@pytest.fixture() +def client(app: FastAPI) -> Iterator[TestClient]: + with TestClient(app) as test_client: + yield test_client diff --git a/backend/tests/test_ai.py b/backend/tests/test_ai.py new file mode 100644 index 0000000..3d22f15 --- /dev/null +++ b/backend/tests/test_ai.py @@ -0,0 +1,248 @@ +import numpy as np + + +def _create_project_and_frame(client): + project = client.post("/api/projects", json={"name": "AI Project"}).json() + frame = client.post(f"/api/projects/{project['id']}/frames", json={ + "project_id": project["id"], + "frame_index": 0, + "image_url": "frames/0.jpg", + "width": 640, + "height": 360, + }).json() + template = client.post("/api/templates", json={ + "name": "Template", + "color": "#06b6d4", + "z_index": 0, + "classes": [], + "rules": [], + }).json() + return project, frame, template + + +def test_predict_accepts_point_object_with_labels(client, monkeypatch): + _, frame, _ = _create_project_and_frame(client) + calls = {} + + monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8)) + + def fake_predict_points(image, points, labels): + calls["args"] = (points, labels) + return ( + [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]], + [0.95], + ) + + monkeypatch.setattr("routers.ai.sam_registry.predict_points", lambda model, image, points, labels: fake_predict_points(image, points, labels)) + + response = client.post("/api/ai/predict", json={ + "image_id": frame["id"], + "prompt_type": "point", + "prompt_data": {"points": [[0.5, 0.5], [0.1, 0.1]], "labels": [1, 0]}, + }) + + assert response.status_code == 200 + assert response.json()["scores"] == [0.95] + assert calls["args"] == ([[0.5, 0.5], [0.1, 0.1]], [1, 0]) + + +def test_predict_box_and_semantic_fallback(client, monkeypatch): + _, frame, _ = _create_project_and_frame(client) + monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8)) + monkeypatch.setattr("routers.ai.sam_registry.predict_box", lambda model, image, box: ( + [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]], + [0.8], + )) + monkeypatch.setattr("routers.ai.sam_registry.predict_semantic", lambda model, image, text: ( + [[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0]]], + [0.5], + )) + + box_response = client.post("/api/ai/predict", json={ + "image_id": frame["id"], + "prompt_type": "box", + "prompt_data": [0.2, 0.2, 0.8, 0.8], + }) + semantic_response = client.post("/api/ai/predict", json={ + "image_id": frame["id"], + "prompt_type": "semantic", + "prompt_data": "胆囊", + }) + + assert box_response.status_code == 200 + assert box_response.json()["scores"] == [0.8] + assert semantic_response.status_code == 200 + assert semantic_response.json()["scores"] == [0.5] + + +def test_model_status_reports_runtime(client, monkeypatch): + monkeypatch.setattr("routers.ai.sam_registry.runtime_status", lambda selected_model=None: { + "selected_model": selected_model or "sam2", + "gpu": { + "available": False, + "device": "cpu", + "name": None, + "torch_available": True, + "torch_version": "2.x", + "cuda_version": None, + }, + "models": [ + { + "id": "sam2", + "label": "SAM 2", + "available": True, + "loaded": False, + "device": "cpu", + "supports": ["point", "box", "auto"], + "message": "ready", + "package_available": True, + "checkpoint_exists": True, + "checkpoint_path": "model.pt", + "python_ok": True, + "torch_ok": True, + "cuda_required": False, + }, + { + "id": "sam3", + "label": "SAM 3", + "available": False, + "loaded": False, + "device": "unavailable", + "supports": ["semantic"], + "message": "missing Python 3.12+ runtime", + "package_available": False, + "checkpoint_exists": False, + "checkpoint_path": None, + "python_ok": False, + "torch_ok": True, + "cuda_required": True, + }, + ], + }) + + response = client.get("/api/ai/models/status?selected_model=sam3") + + assert response.status_code == 200 + body = response.json() + assert body["selected_model"] == "sam3" + assert body["models"][1]["id"] == "sam3" + assert body["models"][1]["available"] is False + + +def test_predict_validation_errors(client, monkeypatch): + project, _, _ = _create_project_and_frame(client) + + assert client.post("/api/ai/predict", json={ + "image_id": 999, + "prompt_type": "point", + "prompt_data": [[0.5, 0.5]], + }).status_code == 404 + + frame = client.post(f"/api/projects/{project['id']}/frames", json={ + "project_id": project["id"], + "frame_index": 1, + "image_url": "frames/1.jpg", + }).json() + monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8)) + assert client.post("/api/ai/predict", json={ + "image_id": frame["id"], + "prompt_type": "box", + "prompt_data": [0.1, 0.2], + }).status_code == 400 + + +def test_save_annotation_validates_project_and_frame(client): + project, frame, template = _create_project_and_frame(client) + + saved = client.post("/api/ai/annotate", json={ + "project_id": project["id"], + "frame_id": frame["id"], + "template_id": template["id"], + "mask_data": {"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]]}, + "points": [[0.5, 0.5]], + "bbox": [0.1, 0.1, 0.8, 0.8], + }) + assert saved.status_code == 201 + assert saved.json()["project_id"] == project["id"] + + listing = client.get(f"/api/ai/annotations?project_id={project['id']}") + assert listing.status_code == 200 + assert listing.json()[0]["id"] == saved.json()["id"] + + frame_listing = client.get(f"/api/ai/annotations?project_id={project['id']}&frame_id={frame['id']}") + assert frame_listing.status_code == 200 + assert len(frame_listing.json()) == 1 + + missing_project = client.post("/api/ai/annotate", json={"project_id": 999}) + assert missing_project.status_code == 404 + + missing_frame = client.post("/api/ai/annotate", json={ + "project_id": project["id"], + "frame_id": 999, + }) + assert missing_frame.status_code == 404 + + missing_project_list = client.get("/api/ai/annotations?project_id=999") + assert missing_project_list.status_code == 404 + + +def test_update_and_delete_annotation(client): + project, frame, template = _create_project_and_frame(client) + saved = client.post("/api/ai/annotate", json={ + "project_id": project["id"], + "frame_id": frame["id"], + "template_id": template["id"], + "mask_data": { + "polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]], + "label": "AI Mask", + "color": "#06b6d4", + }, + "points": [[0.5, 0.5]], + "bbox": [0.1, 0.1, 0.8, 0.8], + }).json() + + updated = client.patch(f"/api/ai/annotations/{saved['id']}", json={ + "template_id": template["id"], + "mask_data": { + "polygons": [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]], + "label": "胆囊", + "color": "#ff0000", + "class": {"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 20}, + }, + "points": [[0.4, 0.4]], + "bbox": [0.2, 0.2, 0.6, 0.6], + }) + + assert updated.status_code == 200 + body = updated.json() + assert body["mask_data"]["label"] == "胆囊" + assert body["mask_data"]["class"]["id"] == "c1" + assert body["points"] == [[0.4, 0.4]] + assert body["bbox"] == [0.2, 0.2, 0.6, 0.6] + + listing = client.get(f"/api/ai/annotations?project_id={project['id']}") + assert listing.status_code == 200 + assert listing.json()[0]["mask_data"]["class"]["name"] == "胆囊" + + deleted = client.delete(f"/api/ai/annotations/{saved['id']}") + assert deleted.status_code == 204 + + empty_listing = client.get(f"/api/ai/annotations?project_id={project['id']}") + assert empty_listing.status_code == 200 + assert empty_listing.json() == [] + + +def test_update_and_delete_annotation_validation(client): + project, frame, template = _create_project_and_frame(client) + saved = client.post("/api/ai/annotate", json={ + "project_id": project["id"], + "frame_id": frame["id"], + "template_id": template["id"], + }).json() + + assert client.patch("/api/ai/annotations/999", json={"bbox": [0, 0, 1, 1]}).status_code == 404 + assert client.delete("/api/ai/annotations/999").status_code == 404 + assert client.patch( + f"/api/ai/annotations/{saved['id']}", + json={"template_id": 999}, + ).status_code == 404 diff --git a/backend/tests/test_auth.py b/backend/tests/test_auth.py new file mode 100644 index 0000000..27f298f --- /dev/null +++ b/backend/tests/test_auth.py @@ -0,0 +1,15 @@ +def test_login_success(client): + response = client.post("/api/auth/login", json={"username": "admin", "password": "123456"}) + + assert response.status_code == 200 + assert response.json() == { + "token": "fake-jwt-token-for-admin", + "username": "admin", + } + + +def test_login_rejects_invalid_credentials(client): + response = client.post("/api/auth/login", json={"username": "admin", "password": "wrong"}) + + assert response.status_code == 401 + assert response.json()["detail"] == "Invalid credentials" diff --git a/backend/tests/test_dashboard.py b/backend/tests/test_dashboard.py new file mode 100644 index 0000000..aabc75c --- /dev/null +++ b/backend/tests/test_dashboard.py @@ -0,0 +1,69 @@ +def test_dashboard_overview_uses_persisted_records(client, db_session): + from models import ProcessingTask + + project_pending = client.post("/api/projects", json={ + "name": "Pending Project", + "status": "pending", + }).json() + project_ready = client.post("/api/projects", json={ + "name": "Ready Project", + "status": "ready", + }).json() + frame = client.post(f"/api/projects/{project_pending['id']}/frames", json={ + "project_id": project_pending["id"], + "frame_index": 0, + "image_url": "frames/0.jpg", + "width": 640, + "height": 360, + }).json() + template = client.post("/api/templates", json={ + "name": "Dashboard Template", + "color": "#06b6d4", + "z_index": 0, + "classes": [], + "rules": [], + }).json() + annotation = client.post("/api/ai/annotate", json={ + "project_id": project_pending["id"], + "frame_id": frame["id"], + "template_id": template["id"], + "mask_data": {"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]]}, + }) + assert annotation.status_code == 201 + task = ProcessingTask( + task_type="parse_video", + status="running", + progress=35, + message="正在使用 FFmpeg/OpenCV 拆帧", + project_id=project_pending["id"], + payload={"source_type": "video"}, + ) + db_session.add(task) + db_session.commit() + db_session.refresh(task) + + response = client.get("/api/dashboard/overview") + + assert response.status_code == 200 + body = response.json() + assert body["summary"]["project_count"] == 2 + assert body["summary"]["frame_count"] == 1 + assert body["summary"]["annotation_count"] == 1 + assert body["summary"]["template_count"] == 1 + assert body["summary"]["parsing_task_count"] == 1 + assert body["tasks"] == [ + { + "id": f"task-{task.id}", + "task_id": task.id, + "project_id": project_pending["id"], + "name": "Pending Project", + "progress": 35, + "status": "正在使用 FFmpeg/OpenCV 拆帧", + "frame_count": 0, + "updated_at": body["tasks"][0]["updated_at"], + }, + ] + assert any(item["kind"] == "task" for item in body["activity"]) + assert any(item["kind"] == "annotation" for item in body["activity"]) + assert any(item["kind"] == "template" for item in body["activity"]) + assert all(item["name"] != "Ready Project" for item in body["tasks"]) diff --git a/backend/tests/test_export.py b/backend/tests/test_export.py new file mode 100644 index 0000000..898ba4e --- /dev/null +++ b/backend/tests/test_export.py @@ -0,0 +1,66 @@ +import zipfile +from io import BytesIO + + +def _seed_export_data(client): + project = client.post("/api/projects", json={"name": "Export Project"}).json() + frame = client.post(f"/api/projects/{project['id']}/frames", json={ + "project_id": project["id"], + "frame_index": 0, + "image_url": "frames/0.jpg", + "width": 100, + "height": 50, + }).json() + template = client.post("/api/templates", json={ + "name": "Category", + "color": "#06b6d4", + "z_index": 0, + "classes": [], + "rules": [], + }).json() + annotation = client.post("/api/ai/annotate", json={ + "project_id": project["id"], + "frame_id": frame["id"], + "template_id": template["id"], + "mask_data": {"polygons": [[[0.1, 0.2], [0.9, 0.2], [0.9, 0.8], [0.1, 0.8]]]}, + "points": [[0.5, 0.5]], + "bbox": [0.1, 0.2, 0.8, 0.6], + }).json() + return project, frame, template, annotation + + +def test_export_coco_json_structure(client): + project, frame, _, _ = _seed_export_data(client) + + response = client.get(f"/api/export/{project['id']}/coco") + + assert response.status_code == 200 + assert response.headers["content-type"].startswith("application/json") + data = response.json() + assert data["info"]["description"] == "Annotations for Export Project" + assert data["images"][0] == { + "id": frame["id"], + "file_name": "frames/0.jpg", + "width": 100, + "height": 50, + "frame_index": 0, + } + assert data["annotations"][0]["segmentation"] == [[10.0, 10.0, 90.0, 10.0, 90.0, 40.0, 10.0, 40.0]] + assert data["annotations"][0]["bbox"] == [10.0, 10.0, 80.0, 30.000000000000004] + assert data["categories"][0]["name"] == "Category" + + +def test_export_masks_zip(client): + project, _, _, annotation = _seed_export_data(client) + + response = client.get(f"/api/export/{project['id']}/masks") + + assert response.status_code == 200 + assert response.headers["content-type"].startswith("application/zip") + with zipfile.ZipFile(BytesIO(response.content)) as archive: + assert archive.namelist() == [f"mask_{annotation['id']:06d}.png"] + + +def test_export_missing_project_returns_404(client): + assert client.get("/api/export/999/coco").status_code == 404 + assert client.get("/api/export/999/masks").status_code == 404 diff --git a/backend/tests/test_main.py b/backend/tests/test_main.py new file mode 100644 index 0000000..3c6da47 --- /dev/null +++ b/backend/tests/test_main.py @@ -0,0 +1,15 @@ +def test_health_endpoint(client): + response = client.get("/health") + + assert response.status_code == 200 + assert response.json() == {"status": "ok", "service": "SegServer"} + + +def test_websocket_progress_heartbeat(client): + with client.websocket_connect("/ws/progress") as websocket: + websocket.send_text("ping") + data = websocket.receive_json() + + assert data["type"] == "status" + assert data["status"] == "connected" + assert data["message"] == "Progress stream active" diff --git a/backend/tests/test_media.py b/backend/tests/test_media.py new file mode 100644 index 0000000..e3ecce7 --- /dev/null +++ b/backend/tests/test_media.py @@ -0,0 +1,142 @@ +def test_upload_rejects_unsupported_file_type(client): + response = client.post( + "/api/media/upload", + files={"file": ("notes.txt", b"text", "text/plain")}, + ) + + assert response.status_code == 400 + assert "Unsupported file type" in response.json()["detail"] + + +def test_upload_auto_creates_project(client, monkeypatch): + uploaded = [] + monkeypatch.setattr("routers.media.upload_file", lambda object_name, data, content_type, length: uploaded.append(object_name)) + monkeypatch.setattr("routers.media.get_presigned_url", lambda object_name, expires=3600: f"http://storage/{object_name}") + + response = client.post( + "/api/media/upload", + files={"file": ("clip.mp4", b"video", "video/mp4")}, + ) + + assert response.status_code == 201 + data = response.json() + assert data["project_id"] is not None + assert data["object_name"] == f"uploads/{data['project_id']}/clip.mp4" + assert uploaded == ["uploads/general/clip.mp4", f"uploads/{data['project_id']}/clip.mp4"] + + +def test_upload_links_existing_project(client, monkeypatch): + project = client.post("/api/projects", json={"name": "Existing"}).json() + monkeypatch.setattr("routers.media.upload_file", lambda *args, **kwargs: None) + monkeypatch.setattr("routers.media.get_presigned_url", lambda object_name, expires=3600: f"http://storage/{object_name}") + + response = client.post( + "/api/media/upload", + data={"project_id": str(project["id"])}, + files={"file": ("clip.mp4", b"video", "video/mp4")}, + ) + + assert response.status_code == 201 + detail = client.get(f"/api/projects/{project['id']}").json() + assert detail["video_path"] == f"uploads/{project['id']}/clip.mp4" + + +def test_upload_dicom_batch_filters_files_and_creates_project(client, monkeypatch): + uploaded = [] + monkeypatch.setattr("routers.media.upload_file", lambda object_name, data, content_type, length: uploaded.append(object_name)) + + response = client.post( + "/api/media/upload/dicom", + files=[ + ("files", ("a.dcm", b"dcm", "application/dicom")), + ("files", ("skip.txt", b"text", "text/plain")), + ], + ) + + assert response.status_code == 201 + data = response.json() + assert data["uploaded_count"] == 1 + assert uploaded == [f"uploads/{data['project_id']}/dicom/a.dcm"] + + +def test_parse_media_queues_background_task(client, monkeypatch): + project = client.post("/api/projects", json={ + "name": "Parse Me", + "video_path": "uploads/1/clip.mp4", + "source_type": "video", + "parse_fps": 5, + }).json() + + class FakeAsyncResult: + id = "celery-1" + + queued = [] + monkeypatch.setattr("routers.media.parse_project_media.delay", lambda task_id: queued.append(task_id) or FakeAsyncResult()) + published = [] + monkeypatch.setattr("routers.media.publish_task_progress_event", lambda task: published.append(task.id)) + + response = client.post(f"/api/media/parse?project_id={project['id']}") + + assert response.status_code == 202 + data = response.json() + assert data["task_type"] == "parse_video" + assert data["status"] == "queued" + assert data["progress"] == 0 + assert data["project_id"] == project["id"] + assert data["celery_task_id"] == "celery-1" + assert queued == [data["id"]] + assert published == [data["id"]] + + detail = client.get(f"/api/tasks/{data['id']}") + assert detail.status_code == 200 + assert detail.json()["status"] == "queued" + project_detail = client.get(f"/api/projects/{project['id']}").json() + assert project_detail["status"] == "parsing" + + +def test_parse_task_runner_registers_frames(client, db_session, monkeypatch, tmp_path): + from models import ProcessingTask + from services.media_task_runner import run_parse_media_task + + project = client.post("/api/projects", json={ + "name": "Parse Me", + "video_path": "uploads/1/clip.mp4", + "source_type": "video", + "parse_fps": 5, + }).json() + task = ProcessingTask( + task_type="parse_video", + status="queued", + progress=0, + project_id=project["id"], + payload={"source_type": "video"}, + ) + db_session.add(task) + db_session.commit() + db_session.refresh(task) + frame_file = tmp_path / "frame_000001.jpg" + frame_file.write_bytes(b"fake image") + + monkeypatch.setattr("services.media_task_runner.download_file", lambda object_name: b"video") + monkeypatch.setattr("services.media_task_runner.parse_video", lambda local_path, output_dir, fps: ([str(frame_file)], 25.0)) + monkeypatch.setattr("services.media_task_runner.extract_thumbnail", lambda local_path, thumbnail_path: open(thumbnail_path, "wb").write(b"thumb")) + monkeypatch.setattr("services.media_task_runner.upload_file", lambda *args, **kwargs: None) + monkeypatch.setattr("services.media_task_runner.upload_frames_to_minio", lambda frame_files, project_id: [f"projects/{project_id}/frames/frame_000001.jpg"]) + published = [] + monkeypatch.setattr( + "services.media_task_runner.publish_task_progress_event", + lambda event_task: published.append((event_task.status, event_task.progress, event_task.message)), + ) + + result = run_parse_media_task(db_session, task.id) + + assert result["frames_extracted"] == 1 + db_session.refresh(task) + assert task.status == "success" + assert task.progress == 100 + assert ("running", 5, "后台解析已启动") in published + assert ("success", 100, "解析完成") in published + project_detail = client.get(f"/api/projects/{project['id']}").json() + assert project_detail["status"] == "ready" + frames = client.get(f"/api/projects/{project['id']}/frames").json() + assert "frame_000001.jpg" in frames[0]["image_url"] diff --git a/backend/tests/test_progress_events.py b/backend/tests/test_progress_events.py new file mode 100644 index 0000000..171f27a --- /dev/null +++ b/backend/tests/test_progress_events.py @@ -0,0 +1,42 @@ +from types import SimpleNamespace + +from progress_events import PROGRESS_CHANNEL, publish_progress_event, task_progress_payload + + +def test_task_progress_payload_uses_dashboard_task_id_and_project_name(): + task = SimpleNamespace( + id=12, + project_id=7, + project=SimpleNamespace(name="demo.mp4"), + status="success", + progress=100, + message="解析完成", + error=None, + updated_at=None, + ) + + payload = task_progress_payload(task) + + assert payload["type"] == "complete" + assert payload["taskId"] == "task-12" + assert payload["task_id"] == 12 + assert payload["project_id"] == 7 + assert payload["filename"] == "demo.mp4" + assert payload["projectName"] == "demo.mp4" + assert payload["status"] == "解析完成" + + +def test_publish_progress_event_writes_json_to_redis(monkeypatch): + calls = [] + + class FakeRedis: + def publish(self, channel, payload): + calls.append((channel, payload)) + + monkeypatch.setattr("progress_events.get_redis_client", lambda: FakeRedis()) + + publish_progress_event({"type": "progress", "message": "正在下载媒体文件"}) + + assert calls + assert calls[0][0] == PROGRESS_CHANNEL + assert "正在下载媒体文件" in calls[0][1] diff --git a/backend/tests/test_projects.py b/backend/tests/test_projects.py new file mode 100644 index 0000000..da70e85 --- /dev/null +++ b/backend/tests/test_projects.py @@ -0,0 +1,56 @@ +def test_project_crud_and_frames(client, monkeypatch): + monkeypatch.setattr("routers.projects.get_presigned_url", lambda key, expires=3600: f"http://storage/{key}") + + created = client.post("/api/projects", json={ + "name": "Demo", + "description": "desc", + "thumbnail_url": "thumb.jpg", + "parse_fps": 12, + }) + assert created.status_code == 201 + project_id = created.json()["id"] + + frame = client.post(f"/api/projects/{project_id}/frames", json={ + "project_id": project_id, + "frame_index": 0, + "image_url": "frames/0.jpg", + "width": 640, + "height": 360, + }) + assert frame.status_code == 201 + frame_id = frame.json()["id"] + + listing = client.get("/api/projects") + assert listing.status_code == 200 + assert listing.json()[0]["frame_count"] == 1 + assert listing.json()[0]["thumbnail_url"] == "http://storage/thumb.jpg" + + frames = client.get(f"/api/projects/{project_id}/frames") + assert frames.status_code == 200 + assert frames.json()[0]["image_url"] == "http://storage/frames/0.jpg" + + single_frame = client.get(f"/api/projects/{project_id}/frames/{frame_id}") + assert single_frame.status_code == 200 + assert single_frame.json()["frame_index"] == 0 + + updated = client.patch(f"/api/projects/{project_id}", json={"name": "Renamed", "status": "ready"}) + assert updated.status_code == 200 + assert updated.json()["name"] == "Renamed" + assert updated.json()["status"] == "ready" + + deleted = client.delete(f"/api/projects/{project_id}") + assert deleted.status_code == 204 + assert client.get(f"/api/projects/{project_id}").status_code == 404 + + +def test_project_and_frame_404s(client): + assert client.get("/api/projects/999").status_code == 404 + assert client.patch("/api/projects/999", json={"name": "x"}).status_code == 404 + assert client.delete("/api/projects/999").status_code == 404 + assert client.post("/api/projects/999/frames", json={ + "project_id": 999, + "frame_index": 0, + "image_url": "missing.jpg", + }).status_code == 404 + assert client.get("/api/projects/999/frames").status_code == 404 + assert client.get("/api/projects/999/frames/1").status_code == 404 diff --git a/backend/tests/test_templates.py b/backend/tests/test_templates.py new file mode 100644 index 0000000..ef8c917 --- /dev/null +++ b/backend/tests/test_templates.py @@ -0,0 +1,39 @@ +def test_template_crud_packs_and_unpacks_mapping_rules(client): + payload = { + "name": "Template", + "color": "#06b6d4", + "z_index": 0, + "classes": [{"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 10}], + "rules": [{"id": "r1", "name": "rule"}], + } + + created = client.post("/api/templates", json=payload) + assert created.status_code == 201 + template_id = created.json()["id"] + assert created.json()["classes"][0]["name"] == "胆囊" + assert created.json()["rules"][0]["id"] == "r1" + + listing = client.get("/api/templates") + assert listing.status_code == 200 + assert listing.json()[0]["classes"][0]["name"] == "胆囊" + + detail = client.get(f"/api/templates/{template_id}") + assert detail.status_code == 200 + assert detail.json()["name"] == "Template" + + updated = client.patch(f"/api/templates/{template_id}", json={ + "classes": [{"id": "c2", "name": "肝脏", "color": "#00ff00", "zIndex": 20}], + "rules": [], + }) + assert updated.status_code == 200 + assert updated.json()["classes"][0]["name"] == "肝脏" + + deleted = client.delete(f"/api/templates/{template_id}") + assert deleted.status_code == 204 + assert client.get(f"/api/templates/{template_id}").status_code == 404 + + +def test_template_404s(client): + assert client.get("/api/templates/999").status_code == 404 + assert client.patch("/api/templates/999", json={"name": "x"}).status_code == 404 + assert client.delete("/api/templates/999").status_code == 404 diff --git a/backend/worker_tasks.py b/backend/worker_tasks.py new file mode 100644 index 0000000..2f06b4c --- /dev/null +++ b/backend/worker_tasks.py @@ -0,0 +1,22 @@ +"""Celery task definitions.""" + +import logging + +from celery_app import celery_app +from database import SessionLocal +from services.media_task_runner import run_parse_media_task + +logger = logging.getLogger(__name__) + + +@celery_app.task(name="media.parse_project") +def parse_project_media(task_id: int) -> dict: + """Run media parsing for one queued task.""" + db = SessionLocal() + try: + return run_parse_media_task(db, task_id) + except Exception as exc: # noqa: BLE001 + logger.exception("Parse media task failed: task_id=%s", task_id) + raise exc + finally: + db.close() diff --git a/doc/01-purpose-and-word-summary.md b/doc/01-purpose-and-word-summary.md new file mode 100644 index 0000000..93fe322 --- /dev/null +++ b/doc/01-purpose-and-word-summary.md @@ -0,0 +1,58 @@ +# 目的与 Word 方案摘要 + +## 为什么要做这个系统 + +Word 文档《语义分割系统构建方案.docx》的核心目标是建设一个面向视频和连续帧的智能语义分割标注系统，解决传统标注工具在以下场景中的痛点： + +- 视频或连续帧数量大，逐帧人工画 mask 成本高。 +- 高分辨率图像上同时存在底图、点、框、多边形和遮罩，DOM 渲染难以支撑重交互。 +- AI 分割需要低延迟点选/框选反馈，普通 REST 往返在密集交互场景下体验较差。 +- 语义分割要求一个像素只能归属一个类别，因此需要模板、颜色、z-index 和类别优先级来解决遮罩重叠。 +- 历史 GT mask 如果只是作为静态像素图层叠加，后续修改不灵活；Word 方案希望把 mask 降维成可编辑的点区域。 + +所以这个系统的业务目的不是单纯播放视频，而是把“视频/DICOM 数据接入、拆帧、AI 辅助分割、语义分类、标注导出”串成一个工作台。 + +## Word 中的目标架构 + +Word 方案描述的理想系统包含： + +- React/Vue + Konva 的高性能 Canvas 工作台。 +- FastAPI 后端，使用 WebSocket 处理实时交互与任务进度。 +- Celery + Redis 处理视频拆帧等长任务。 +- FFmpeg/OpenCV 解析视频，pydicom 解析医学影像。 +- 本地 CUDA 上的 SAM 3 推理。 +- GT mask 导入后通过距离变换、骨架提取、聚类等算法降维为点区域。 +- 模板库管理分类、颜色和 z-index，用于语义分割遮罩重叠裁决。 +- PostgreSQL 存储项目、帧、模板和点区域数据。 + +## 当前代码已落地的部分 + +| 目标 | 当前代码状态 | 依据 | +|------|--------------|------| +| React 前端工作台 | 已落地 | `src/App.tsx`、`src/components/*.tsx` | +| Konva Canvas | 已落地 | `CanvasArea.tsx`、`AISegmentation.tsx` 使用 `react-konva` | +| FastAPI 后端 | 已落地 | `backend/main.py` | +| PostgreSQL ORM | 已落地 | `backend/database.py`、`backend/models.py` | +| MinIO 对象存储 | 已落地 | `backend/minio_client.py` | +| Redis 连接 | 已落地 | 用于 Celery broker/result backend，并通过 `seg:progress` pub/sub 转发任务进度 | +| 视频拆帧 | 已落地 | `backend/services/frame_parser.py`、`backend/routers/media.py` | +| DICOM 批量导入 | 部分落地 | 上传和解析存在，项目级体验还需完善 | +| WebSocket 进度 | 已落地 | 拆帧进度写入任务表后发布到 Redis `seg:progress`，FastAPI 广播到 `/ws/progress` | +| SAM 推理 | 部分落地 | 后端已有 SAM 2 / SAM 3 选择和真实模型状态接口；SAM 3 依赖官方运行环境，当前环境不满足时会标为不可用 | +| 模板库 | 部分落地 | 分类、颜色、z-index 能存储和编辑；重叠裁决算法未落地 | +| 标注持久化 | 部分落地 | 后端有 `Annotation` 表，前端已接入新增、回显、分类更新和当前帧删除；逐点几何编辑未落地 | +| COCO / Mask 导出 | 部分落地 | `backend/routers/export.py`；COCO JSON 前端按钮已接入，PNG mask ZIP 尚未提供前端按钮 | + +## 当前代码尚未落地的目标 + +- SAM 3：当前已提供 `sam3_engine.py` 适配入口和状态检测；要实际运行仍需安装官方 `facebookresearch/sam3` 依赖并满足 Python 3.12+、PyTorch 2.7+、CUDA 12.6+。 +- Celery 异步任务队列：已注册 Celery app 和拆帧 worker task，`/api/media/parse` 会创建任务表记录并入队。 +- GT mask 导入：当前前端没有 GT Label 导入入口，后端也没有对应路由。 +- Mask 到点区域的拓扑降维：当前没有距离变换、骨架提取、HDBSCAN 等实现。 +- 类别优先级融合：模板有 z-index，但没有后端融合算法。 +- 撤销/重做：工具栏有按钮，但没有历史栈。 +- 结构化归档保存：工作区按钮已调用 `POST /api/ai/annotate` 保存当前未归档 mask，并通过 `PATCH /api/ai/annotations/{id}` 更新 dirty mask。 + +## 结论 + +当前项目已经从 UI 原型推进到“可上传、可异步拆帧、可实时查看任务进度、可浏览项目帧、可维护模板、可点/框 AI 推理、可保存标注、可导出 COCO、可查看 Dashboard 后端概览”的全栈雏形，但离 Word 中描述的完整智能标注系统还有明显差距。下一阶段最重要的是继续补齐手工绘制、撤销重做和真实语义文本分割。 diff --git a/doc/02-current-implementation-map.md b/doc/02-current-implementation-map.md new file mode 100644 index 0000000..981029c --- /dev/null +++ b/doc/02-current-implementation-map.md @@ -0,0 +1,104 @@ +# 当前实现地图 + +## 运行入口 + +### 前端入口 + +- React 挂载：`src/main.tsx` +- 根组件：`src/App.tsx` +- 前端服务：`server.ts` +- 默认访问：`http://localhost:3000` + +`server.ts` 的角色比较特殊：它既负责在开发模式下创建 Vite middleware，也在生产模式下服务 `dist/`。同时它还保留了旧版 mock API：`/api/login`、`/api/projects`、`/api/templates`。当前前端业务 API 主要不走这些 mock，而是走 `src/lib/api.ts` 指向的 FastAPI。 + +### 后端入口 + +- FastAPI 应用：`backend/main.py` +- 默认访问：`http://localhost:8000` +- API 文档：`http://localhost:8000/docs` +- 健康检查：`GET /health` + +后端启动时会通过 lifespan 执行： + +- 创建数据库表。 +- 检查 MinIO bucket。 +- 测试 Redis。 +- Seed 默认模板。 +- 如果存在 `Data_MyVideo_1.mp4`，创建默认项目并拆前 100 帧。 + +## 前端模块切换 + +`App.tsx` 使用 Zustand 中的 `activeModule` 做模块切换，没有使用路由库。 + +| activeModule | 组件 | 页面 | +|--------------|------|------| +| `dashboard` | `Dashboard` | 系统概况 | +| `projects` | `ProjectLibrary` | 项目库 | +| `workspace` | `VideoWorkspace` | 分割工作区 | +| `ai` | `AISegmentation` | AI 智能分割页 | +| `templates` | `TemplateRegistry` | 模板库 | + +未登录时，`App.tsx` 直接渲染 `Login`。 + +## 全局状态 + +全局状态在 `src/store/useStore.ts` 中，主要包括： + +- 登录状态：`isAuthenticated`、`token` +- 项目：`projects`、`currentProject` +- 工作区：`activeModule`、`activeTool`、`frames`、`currentFrameIndex` +- 标注与 mask：`annotations`、`masks` +- 模板：`templates`、`activeTemplateId` +- UI：`isLoading`、`error` + +当前状态管理是前端内存状态，没有持久化到 localStorage，除了登录 token。 + +## 数据流 + +### 登录 + +1. `Login.tsx` 调用 `login()`。 +2. `src/lib/api.ts` 请求 `POST /api/auth/login`。 +3. FastAPI `backend/routers/auth.py` 校验 `admin / 123456`。 +4. 前端把返回 token 写入 localStorage。 + +### 项目与拆帧 + +1. `ProjectLibrary.tsx` 调用 `getProjects()` 获取项目。 +2. 上传视频时先 `createProject()`，再 `uploadMedia()`，再 `parseMedia()`。 +3. 后端 `media.py` 把原始文件上传到 MinIO。 +4. `parseMedia()` 创建 `processing_tasks` 记录并投递 Celery worker。 +5. Celery worker 下载 MinIO 文件，调用 `frame_parser.py` 拆帧。 +6. worker 把拆出的帧重新上传 MinIO，写入 `frames` 表，并更新任务状态。 +7. 工作区通过 `GET /api/tasks/{id}` 等待任务完成，再通过 `GET /api/projects/{id}/frames` 获取预签名图片 URL。 + +### 工作区浏览 + +1. `VideoWorkspace.tsx` 根据 `currentProject.id` 加载帧。 +2. `CanvasArea.tsx` 用当前帧 URL 加载底图。 +3. `FrameTimeline.tsx` 显示缩略图和当前帧索引。 +4. 播放按钮会推进 `currentFrameIndex`，从而更新画布底图。 + +### 模板管理 + +1. `TemplateRegistry.tsx` 调用模板 API。 +2. 后端 `templates.py` 把 `classes` 和 `rules` 打包进 `mapping_rules` JSON 字段。 +3. `OntologyInspector.tsx` 读取全局 `templates` 和 `activeTemplateId` 展示分类树。 + +## 后端数据模型 + +| 模型 | 表 | 用途 | +|------|----|------| +| `Project` | `projects` | 项目元数据，包含视频路径、缩略图、状态、fps | +| `Frame` | `frames` | 拆帧后的图片记录 | +| `Template` | `templates` | 模板、本体类别、颜色、z-index、mapping_rules | +| `Annotation` | `annotations` | 标注数据、点、bbox、mask_data | +| `Mask` | `masks` | mask 文件元数据 | + +## 当前主要风险点 + +- 前端 API/WS 地址虽然已支持环境变量和 hostname 推导，但部署时仍需要确认浏览器可访问 `:8000` 后端。 +- AI 语义文本提示在选择 SAM 3 且运行环境满足官方依赖时走 SAM 3；当前环境若不满足会在模型状态中标明不可用。 +- 工作区顶部“导出 JSON 标注集”和“结构化归档保存”已接入导出、标注新增和 dirty 标注更新；清空当前帧遮罩会删除对应后端标注。撤销重做和手工绘制仍未持久化。 +- Dashboard 初始统计、队列和活动日志来自后端聚合接口；解析队列来自 `processing_tasks`，worker 进度通过 Redis `seg:progress` 转发到 WebSocket。 +- 后端路由大多未做真实鉴权。 diff --git a/doc/03-frontend-element-audit.md b/doc/03-frontend-element-audit.md new file mode 100644 index 0000000..cb9a5e5 --- /dev/null +++ b/doc/03-frontend-element-audit.md @@ -0,0 +1,146 @@ +# 前端逐元素审计 + +状态说明： + +- 真实可用：接真实状态或后端接口，可以完成主要动作。 +- 部分可用：能展示或完成一部分，但存在关键缺口。 +- Mock / UI-only：只有展示或本地状态变化，没有真实业务效果。 +- 接口不通：前端调用与后端接口不一致，按当前代码大概率失败。 + +## App 与导航 + +| 元素 | 位置 | 状态 | 说明 | +|------|------|------|------| +| 登录拦截 | `App.tsx` | 真实可用 | 未登录显示 `Login`，登录后显示主界面 | +| 模块切换 | `Sidebar.tsx` + `App.tsx` | 真实可用 | 切换 `dashboard/projects/workspace/ai/templates` | +| Logo | `Sidebar.tsx` | 真实可用 | 使用 `/logo.png`，文件存在于 `public/logo.png` | +| GPU 状态圆标 | `Sidebar.tsx` | 真实可用 | 通过 `GET /api/ai/models/status` 显示 GPU/CPU 和当前模型可用性 | + +## 登录页 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 用户名/密码输入 | 真实可用 | 默认填入 `admin / 123456` | +| 安全登录按钮 | 真实可用 | 调用 `POST /api/auth/login` | +| 错误提示 | 真实可用 | 捕获后端错误并显示 | +| 安全审计说明文字 | Mock / UI-only | UI 文案，没有真实审计功能 | + +## Dashboard 系统概况 + +| 元素 | 状态 | 说明 | +|------|------|------| +| WebSocket 连接状态 | 真实可用 | 前端通过 `src/lib/config.ts` 推导或读取 `VITE_WS_PROGRESS_URL`，后端有 `/ws/progress` | +| 解析队列任务 | 真实可用 | 初始数据来自 `GET /api/dashboard/overview`，按 `processing_tasks` queued/running 任务生成 | +| WebSocket 更新任务 | 真实可用 | Celery worker 更新 `processing_tasks` 后发布 Redis `seg:progress`，FastAPI 广播 progress/complete/error | +| 项目、任务、标注、系统负载统计 | 真实可用 | 初始数据来自 `GET /api/dashboard/overview`，系统负载按主机 load average 估算 | +| 近期实时流转记录 | 真实可用 | 初始数据来自任务、项目、标注和模板记录；WebSocket status/complete/error 会继续追加 | + +## 项目库 ProjectLibrary + +| 元素 | 状态 | 说明 | +|------|------|------| +| 项目列表 | 真实可用 | 调用 `GET /api/projects` | +| 项目卡片缩略图 | 真实可用 | 后端返回 MinIO 预签名 `thumbnail_url` 时显示 | +| 点击项目进入工作区 | 真实可用 | 设置 `currentProject` 后切到 `workspace` | +| 新建项目 | 真实可用 | 调用 `POST /api/projects` | +| 导入视频文件 | 真实可用 | 创建项目、上传文件、触发拆帧、刷新项目列表 | +| 解析 FPS 滑块 | 真实可用 | 值传入 `createProject({ parse_fps })` | +| 导入 DICOM 序列 | 部分可用 | 可上传 `.dcm` 并触发解析；体验和错误反馈较粗 | +| 项目状态徽标 | 真实可用 | 项目状态统一为 `pending/parsing/ready/error`，前端兼容归一化旧状态值 | +| 更多按钮 | Mock / UI-only | 有图标，没有菜单或事件 | +| alert 成功/失败提示 | 真实可用但粗糙 | 使用浏览器 `alert` | + +## 工作区 VideoWorkspace + +| 元素 | 状态 | 说明 | +|------|------|------| +| 当前项目名 | 真实可用 | 读取 `currentProject.name` | +| 自动加载项目帧 | 真实可用 | 调用 `GET /api/projects/{id}/frames` | +| 无帧时触发解析 | 真实可用 | 如果 `video_path` 存在会调用 `parseMedia()` 创建异步任务，并轮询 `GET /api/tasks/{id}` 等待完成 | +| SAM 模型状态徽标 | 真实可用 | 调用 `GET /api/ai/models/status`，显示当前选择的 SAM 2/SAM 3 是否可用 | +| 已保存标注回显 | 真实可用 | 加载工作区帧后调用 `GET /api/ai/annotations` 并渲染已保存 mask | +| “导出 JSON 标注集”按钮 | 真实可用 | 导出前会保存未归档 mask，然后调用 `exportCoco()` 下载 JSON | +| “结构化归档保存”按钮 | 真实可用 | 未保存 mask 写入 `POST /api/ai/annotate`；dirty mask 写入 `PATCH /api/ai/annotations/{id}` | + +## CanvasArea 画布 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 当前帧底图显示 | 真实可用 | `useImage(frameUrl)` 加载当前帧 URL | +| 滚轮缩放 | 真实可用 | 改变 Konva Stage scale | +| 拖拽平移 | 真实可用 | activeTool 为 `move` 时 Stage draggable | +| 光标坐标显示 | 真实可用 | 根据 pointer position 计算 | +| 正向/反向选点 | 部分可用 | UI 能加点，并按当前帧 `frame.id` 调用 `/api/ai/predict`；需点击归档保存才持久化 | +| 框选 | 部分可用 | UI 能画框，并把框坐标归一化后调用后端推理；需点击归档保存才持久化 | +| AI 推理中提示 | 真实可用 | 请求期间会显示 | +| Mask 渲染 | 部分可用 | 前端会把推理/已保存标注转成 Konva `pathData` 渲染 | +| 应用分类 | 真实可用 | 将当前选择的模板分类应用到本帧 mask；已保存 mask 会标为 dirty，归档保存时更新后端 | +| 清空遮罩 | 真实可用 | 工作区中会删除当前帧已保存标注并清空当前帧本地 mask | +| 保存状态计数 | 真实可用 | 底部显示已保存、未保存、待更新数量 | +| 当前图层树文字 | Mock / UI-only | 固定显示 `OBJECT_VEHICLE_01` | + +## ToolsPalette 工具栏 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 拖拽/选择 | 真实可用 | 控制 Canvas 是否可拖拽 | +| 多边形/矩形/圆/点/线 | Mock / UI-only | 只切换 activeTool，没有对应绘制逻辑 | +| 区域合并/去除 | Mock / UI-only | 只切换 activeTool，没有后端或前端算法 | +| 正向选点/反向选点/框选 | 部分可用 | 会影响 Canvas 交互，并能触发已对齐的 AI 推理接口 | +| 魔法棒 SAM 触发 | 部分可用 | 切到 AI 页面；不是直接执行推理 | +| 撤销/重做 | Mock / UI-only | 按钮无事件 | + +## FrameTimeline 时间轴 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 帧缩略图 | 真实可用 | 使用 `frames[].url` | +| 点击缩略图跳帧 | 真实可用 | 调用 `setCurrentFrame(idx)` | +| 顶部 range 拖动 | 真实可用 | 改变当前帧 | +| 播放/暂停 | 真实可用 | 当前代码按 `parse_fps/original_fps` 推进帧，最多 30fps | +| 方向键切帧 | Mock / UI-only | Word 提到，但当前没有键盘监听 | + +## OntologyInspector 本体面板 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 模板选择 | 部分可用 | 读取全局 templates，可切换 activeTemplateId | +| 分类树展示 | 真实可用 | 显示模板 classes 和本地 customClasses | +| 添加自定义分类 | 部分可用 | 只存在组件本地状态，不保存到后端 | +| 置信度条 | Mock / UI-only | 固定 `0.9412` | +| 拓扑锚点数量 | Mock / UI-only | 固定 `12 节点` | +| 重新提取骨架按钮 | Mock / UI-only | 无事件 | + +## AISegmentation 独立 AI 页 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 模型选择 SAM2/SAM3 | 真实可用 | 选择写入 Zustand，`predictMask()` 会把 `model` 传给后端 SAM registry | +| 正向/反向点 | 部分可用 | 可在当前项目帧上加点，并可调用 AI 推理接口 | +| 语义文本输入 | 部分可用 | 纯文本会以 `semantic` prompt 调用后端；选择 SAM 3 且运行环境满足官方依赖时走 SAM 3 文本语义推理，否则状态接口会标明不可用 | +| 参数开关 | Mock / UI-only | `cropMode`、`autoDeleteBg` 只改本地状态 | +| 执行高精度语义分割 | 部分可用 | 使用当前项目帧调用 `/api/ai/predict`；没有当前帧时按钮禁用 | +| 上传替换底图 | Mock / UI-only | 按钮无事件 | +| 清空全体锚点 | 部分可用 | 清空前端 points 和 masks | +| 退档推送至工作区重组 | 部分可用 | 只切回工作区，共用 masks store，但没有保存/确认流程 | +| 背景图 | 部分可用 | 优先显示当前项目帧；没有项目帧时仍回退到 Unsplash 演示图 | + +## TemplateRegistry 模板库 + +| 元素 | 状态 | 说明 | +|------|------|------| +| 模板列表 | 真实可用 | 调用 `GET /api/templates` | +| 新建方案 | 真实可用 | 调用 `POST /api/templates` | +| 编辑模板 | 真实可用 | 调用 `PATCH /api/templates/{id}` | +| 删除模板 | 真实可用 | 调用 `DELETE /api/templates/{id}` | +| 添加/删除分类 | 真实可用 | 保存在模板 `mapping_rules.classes` | +| 拖拽排序 | 真实可用 | 重算 zIndex，保存时写后端 | +| JSON 批量导入 | 部分可用 | 前端解析 JSON 并加入编辑态，保存后才落库 | +| 载入腹腔镜 35 分类 | 真实可用 | 前端内置数据；后端也 seed 默认模板 | +| mapping rules | 部分可用 | 可存 `rules`，但无实际映射执行引擎 | + +## 总体结论 + +当前前端真实可用的主链路是：登录、Dashboard 后端概览、项目列表、新建项目、上传视频/DICOM、拆帧、浏览帧、播放帧、工作区点/框 AI 推理、标注保存/回显、COCO 导出、模板 CRUD。 + +当前最主要的 Mock 或未打通链路是：撤销重做、手工几何绘制、GT 导入、mask 降维点区域、真正的文本语义分割和语义优先级融合。 diff --git a/doc/04-api-contracts.md b/doc/04-api-contracts.md new file mode 100644 index 0000000..c4108dc --- /dev/null +++ b/doc/04-api-contracts.md @@ -0,0 +1,193 @@ +# 接口契约清单 + +## 前端 API 基础配置 + +位置：`src/lib/config.ts`、`src/lib/api.ts` + +```ts +API_BASE_URL = import.meta.env.VITE_API_BASE_URL || 'http://:8000' +timeout: 30000 +``` + +前端 request interceptor 会从 localStorage 读取 `token`，附加： + +```http +Authorization: Bearer +``` + +当前后端多数接口没有鉴权依赖，所以这个 header 主要是前端侧行为。 + +## 前端封装的 API + +| 函数 | 方法与路径 | 状态 | 说明 | +|------|------------|------|------| +| `login(username, password)` | `POST /api/auth/login` | 对齐 | 后端返回 `{ token, username }`，前端只使用 token | +| `getProjects()` | `GET /api/projects` | 对齐 | 前端映射 `frame_count`、`thumbnail_url` 等字段 | +| `createProject(payload)` | `POST /api/projects` | 对齐 | 支持 `name`、`description`、`parse_fps` | +| `updateProject(id, payload)` | `PATCH /api/projects/{id}` | 对齐 | 后端是 `PATCH /api/projects/{id}` | +| `deleteProject(id)` | `DELETE /api/projects/{id}` | 对齐 | 当前 UI 未明显接入 | +| `getTemplates()` | `GET /api/templates` | 对齐 | 前端从 `mapping_rules` 取 classes/rules | +| `createTemplate(payload)` | `POST /api/templates` | 对齐 | 后端会打包 classes/rules 到 mapping_rules | +| `updateTemplate(id, payload)` | `PATCH /api/templates/{id}` | 对齐 | 模板编辑页使用 | +| `deleteTemplate(id)` | `DELETE /api/templates/{id}` | 对齐 | 模板编辑页使用 | +| `uploadMedia(file, projectId)` | `POST /api/media/upload` | 对齐 | multipart form-data | +| `uploadDicomBatch(files, projectId)` | `POST /api/media/upload/dicom` | 对齐 | multipart form-data | +| `parseMedia(projectId)` | `POST /api/media/parse?project_id=...` | 对齐 | 创建异步拆帧任务并返回 task | +| `getTask(taskId)` | `GET /api/tasks/{task_id}` | 对齐 | 查询异步任务状态 | +| `getProjectFrames(projectId)` | `GET /api/projects/{id}/frames` | 对齐 | 后端返回预签名 image_url | +| `predictMask(payload)` | `POST /api/ai/predict` | 对齐 | 前端发送 `image_id/prompt_type/prompt_data/model`，并把后端 `polygons` 转为 `masks[].pathData` | +| `getAiModelStatus(selectedModel?)` | `GET /api/ai/models/status` | 对齐 | 返回 GPU、SAM 2、SAM 3 的真实运行状态 | +| `getProjectAnnotations(projectId, frameId?)` | `GET /api/ai/annotations` | 对齐 | 前端加载工作区时用于回显已保存标注 | +| `saveAnnotation(payload)` | `POST /api/ai/annotate` | 对齐 | 工作区归档保存当前项目未保存 mask | +| `updateAnnotation(annotationId, payload)` | `PATCH /api/ai/annotations/{annotation_id}` | 对齐 | 工作区归档保存 dirty mask | +| `deleteAnnotation(annotationId)` | `DELETE /api/ai/annotations/{annotation_id}` | 对齐 | 工作区清空当前帧已保存标注 | +| `getDashboardOverview()` | `GET /api/dashboard/overview` | 对齐 | Dashboard 初始统计、队列和活动日志 | +| `exportCoco(projectId)` | `GET /api/export/{projectId}/coco` | 对齐 | 后端实际是 `GET /api/export/{project_id}/coco` | + +## 后端 FastAPI 接口 + +以下列表来自当前运行的 OpenAPI： + +| 方法 | 路径 | 用途 | +|------|------|------| +| POST | `/api/auth/login` | 登录 | +| POST | `/api/projects` | 创建项目 | +| GET | `/api/projects` | 项目列表 | +| GET | `/api/projects/{project_id}` | 项目详情 | +| PATCH | `/api/projects/{project_id}` | 更新项目 | +| DELETE | `/api/projects/{project_id}` | 删除项目 | +| POST | `/api/projects/{project_id}/frames` | 添加帧记录 | +| GET | `/api/projects/{project_id}/frames` | 项目帧列表 | +| GET | `/api/projects/{project_id}/frames/{frame_id}` | 单帧详情 | +| POST | `/api/templates` | 创建模板 | +| GET | `/api/templates` | 模板列表 | +| GET | `/api/templates/{template_id}` | 模板详情 | +| PATCH | `/api/templates/{template_id}` | 更新模板 | +| DELETE | `/api/templates/{template_id}` | 删除模板 | +| POST | `/api/media/upload` | 上传视频/图片/DICOM 单文件 | +| POST | `/api/media/upload/dicom` | 批量上传 DICOM | +| POST | `/api/media/parse` | 创建 Celery 拆帧任务 | +| GET | `/api/tasks` | 查询后台任务列表 | +| GET | `/api/tasks/{task_id}` | 查询单个后台任务 | +| POST | `/api/ai/predict` | SAM 2 / SAM 3 可选推理 | +| GET | `/api/ai/models/status` | GPU 和 SAM 模型状态 | +| POST | `/api/ai/auto` | 自动分割 | +| POST | `/api/ai/annotate` | 保存 AI 标注 | +| GET | `/api/ai/annotations` | 查询项目标注，可选按帧过滤 | +| PATCH | `/api/ai/annotations/{annotation_id}` | 更新已保存标注 | +| DELETE | `/api/ai/annotations/{annotation_id}` | 删除已保存标注 | +| GET | `/api/dashboard/overview` | Dashboard 聚合快照 | +| GET | `/api/export/{project_id}/coco` | 导出 COCO JSON | +| GET | `/api/export/{project_id}/masks` | 导出 PNG mask ZIP | +| GET | `/health` | 健康检查 | +| WS | `/ws/progress` | WebSocket 进度通道，未出现在 OpenAPI paths 中 | + +## 关键请求体 + +### 登录 + +```json +{ + "username": "admin", + "password": "123456" +} +``` + +### 创建项目 + +```json +{ + "name": "example.mp4", + "description": "导入说明", + "parse_fps": 30 +} +``` + +### 创建/更新模板 + +```json +{ + "name": "腹腔镜胆囊切除术", + "color": "#06b6d4", + "z_index": 0, + "classes": [ + { + "id": "cls-1", + "name": "胆囊", + "color": "#ffae00", + "zIndex": 280, + "category": "腹腔镜胆囊切除术" + } + ], + "rules": [] +} +``` + +### AI 推理请求体 + +前端 `predictMask()` 当前已适配后端 `PredictRequest`： + +```json +{ + "image_id": 123, + "model": "sam2", + "prompt_type": "point", + "prompt_data": { + "points": [[0.5, 0.5]], + "labels": [1] + } +} +``` + +`prompt_type` 支持： + +- `point` +- `box` +- `semantic`，选择 `sam3` 时进入 SAM 3 文本语义推理；选择 `sam2` 时仍回退到 auto segmentation + +后端响应： + +```json +{ + "polygons": [ + [[0.25, 0.25], [0.75, 0.25], [0.75, 0.75], [0.25, 0.75]] + ], + "scores": [0.5] +} +``` + +前端会把上面的 `polygons` 转成： + +```json +{ + "masks": [ + { + "pathData": "M 160 90 L 480 90 L 480 270 L 160 270 Z", + "segmentation": [[160, 90, 480, 90, 480, 270, 160, 270]], + "bbox": [160, 90, 320, 180] + } + ] +} +``` + +## 已完成的接口对齐 + +- `updateProject()` 已从 `PUT` 改为 `PATCH`。 +- `exportCoco()` 已从 `/api/export/coco/{projectId}` 改为 `/api/export/{projectId}/coco`。 +- Canvas 已使用真实 `frame.id` 作为 `image_id`。 +- 点和框坐标已转成后端需要的归一化坐标。 +- 后端 `polygons` 已在前端转成 Konva 可渲染的 path。 +- `saveAnnotation()` 已接入 `POST /api/ai/annotate`。 +- `getProjectAnnotations()` 已接入 `GET /api/ai/annotations`。 +- `updateAnnotation()` 已接入 `PATCH /api/ai/annotations/{annotationId}`。 +- `deleteAnnotation()` 已接入 `DELETE /api/ai/annotations/{annotationId}`。 +- `parseMedia()` 已改为创建 Celery 后台任务，并返回 `ProcessingTask`。 +- `getTask()` 已接入 `GET /api/tasks/{taskId}`。 +- `getDashboardOverview()` 已从 `processing_tasks` 聚合解析队列。 +- 工作区导出按钮已调用 `exportCoco()`，并会先保存未归档 mask。 + +## 仍需处理的接口问题 + +- WebSocket 地址已从 `VITE_WS_PROGRESS_URL` 读取，未配置时从 `API_BASE_URL` 推导；部署时仍要确认浏览器能访问该地址。 +- Celery worker 进度会写 PostgreSQL 任务表，同时发布到 Redis `seg:progress`；FastAPI 订阅后广播到 `/ws/progress`。 +- 已保存标注目前支持分类级更新和整帧清空删除；逐点几何编辑器尚未实现。 diff --git a/doc/05-implementation-plan.md b/doc/05-implementation-plan.md new file mode 100644 index 0000000..08b0450 --- /dev/null +++ b/doc/05-implementation-plan.md @@ -0,0 +1,115 @@ +# 后续实施建议 + +目标是把当前“能看、能上传、能拆帧”的系统推进到“能真实完成标注闭环”的系统。 + +## 阶段 1：先修接口契约（已完成基础对齐） + +优先级最高。AI 点/框推理和 COCO 导出的基础契约已经按当前代码完成对齐。 + +已完成： + +1. `src/lib/api.ts` 的 `updateProject()` 已改为 `PATCH`。 +2. `exportCoco()` 路径已改为 `/api/export/{projectId}/coco`。 +3. Canvas 调 AI 时已使用当前帧真实 `frame.id` 作为 `image_id`。 +4. Canvas 点/框坐标已转成后端需要的归一化坐标。 +5. 后端 `polygons` 已转成前端可渲染的 Konva path。 + +剩余边界： + +1. SAM 3 真实推理需要独立满足官方 Python 3.12+、PyTorch 2.7+、CUDA 12.6+ 环境。 +2. 标注删除/更新接口已打通基础能力；逐点几何编辑器尚未实现。 + +## 阶段 2：打通标注保存（已完成基础闭环） + +当前工作区可将未保存 mask 写入后端标注表，并在加载项目帧后回显。 + +已完成： + +1. 前端根据 `Mask.segmentation` 构造后端需要的 normalized `mask_data.polygons`。 +2. 用户点击“结构化归档保存”后，未保存 mask 调用 `POST /api/ai/annotate`，dirty mask 调用 `PATCH /api/ai/annotations/{annotation_id}`。 +3. 后端保存或更新 `project_id`、`frame_id`、`template_id`、`mask_data`、`bbox`；具体分类写入 `mask_data.class`。 +4. 工作区加载帧后调用 `GET /api/ai/annotations` 回显已保存标注。 +5. 工作区“清空遮罩”调用 `DELETE /api/ai/annotations/{annotation_id}` 删除当前帧已保存标注。 + +剩余建议： + +1. 加入保存冲突处理和批量保存错误提示。 +2. 增加逐点几何编辑器，让已保存 mask 的 polygon 本身可以被修改后 PATCH。 + +## 阶段 3：接入导出按钮（已完成 COCO JSON） + +当前工作区“导出 JSON 标注集”会先保存未归档 mask，再调用 COCO 导出接口。 + +建议： + +1. 增加“导出 PNG Mask ZIP”按钮，调用 `/api/export/{projectId}/masks`。 +2. 无标注时给出更明确的空导出提示。 + +## 阶段 4：替换 Dashboard mock + +当前 Dashboard 已通过 `GET /api/dashboard/overview` 读取后端聚合快照，不再使用硬编码初始统计、队列或活动日志。 + +已完成： + +- 聚合项目、帧、标注、模板数量和主机 load average。 +- 按 `processing_tasks` queued/running 任务生成解析队列。 +- 按最近任务、项目、标注、模板记录生成活动流。 + +剩余建议： + +1. 为任务增加取消、重试和失败详情 UI。 +2. 为 Dashboard 增加任务历史筛选和失败详情入口。 + +## 阶段 5：异步拆帧和进度 + +Word 方案中提到 Celery + Redis。当前已经有 Celery app、worker task 和 `processing_tasks` 表。 + +已完成： + +1. 新建 Celery app。 +2. `POST /api/media/parse` 只创建任务并立即返回 task id。 +3. worker 执行 FFmpeg/OpenCV/pydicom。 +4. worker 写 PostgreSQL 任务进度。 +5. worker 发布 Redis `seg:progress`，FastAPI 广播到 `/ws/progress`。 + +剩余建议： + +1. 为任务增加取消、重试和失败详情接口。 +2. 前端 Dashboard 保留轮询兜底，并补充失败详情 UI。 + +Dashboard 的解析队列现在已经从“项目状态派生”升级为任务表驱动，实时推送也已通过 Redis/WebSocket 打通；剩余重点是任务控制。 + +## 阶段 6：GT 导入与点区域 + +这是 Word 方案中最复杂的部分，当前完全未实现。 + +建议拆成小步： + +1. 先支持上传二值/多类别 mask。 +2. 后端按类别提取 connected components。 +3. 用 OpenCV distance transform 找正向点。 +4. 暂时不做骨架/HDBSCAN，先生成最小可用点集。 +5. 前端以可拖拽点显示并保存。 +6. 后续再做骨架和聚类增强。 + +## 阶段 7：模板优先级融合 + +当前模板有 z-index，但没有真正用于语义冲突裁决。 + +建议： + +1. 标注保存时记录 template class id / name / zIndex。 +2. 导出 mask 时按 zIndex 从低到高覆盖。 +3. 同类 mask 做 union。 +4. 跨类重叠由高 zIndex 覆盖低 zIndex。 + +这一步完成后，系统才真正符合“语义分割一个像素一个类别”的目标。 + +## 阶段 8：清理 UI 文案与 Mock + +建议统一这些文案和真实能力： + +- SAM/GPU 状态已改为 `GET /api/ai/models/status` 驱动。 +- 撤销/重做按钮接历史栈，否则隐藏。 +- “重新提取内侧中轴树骨架”接真实接口，否则标为未实现。 +- AI 独立页不要固定 Unsplash 图，应从当前项目帧或上传文件进入。 diff --git a/doc/06-fastapi-docs-explained.md b/doc/06-fastapi-docs-explained.md new file mode 100644 index 0000000..8d5a2b8 --- /dev/null +++ b/doc/06-fastapi-docs-explained.md @@ -0,0 +1,103 @@ +# `/docs` 是什么 + +地址： + +- 本机：`http://localhost:8000/docs` +- 局域网：`http://192.168.3.11:8000/docs` + +这个页面不是文件列表，也不是项目文档目录。它是 FastAPI 自动生成的 Swagger UI，用来展示和调试后端 HTTP API。 + +## 为什么会自动出现 + +FastAPI 会根据代码里的路由和 Pydantic schema 自动生成 OpenAPI 描述，然后用 Swagger UI 展示出来。 + +相关代码在： + +- `backend/main.py` 创建 `FastAPI(...)` +- `backend/routers/*.py` 定义 `@router.get(...)`、`@router.post(...)` 等接口 +- `backend/schemas.py` 定义请求体和响应体 + +## 页面上 GET / POST / PATCH / DELETE 是什么 + +这些是 HTTP 方法，不是文件。 + +| 方法 | 含义 | 例子 | +|------|------|------| +| GET | 读取数据 | `GET /api/projects` 获取项目列表 | +| POST | 创建或触发动作 | `POST /api/media/upload` 上传文件 | +| PATCH | 局部更新 | `PATCH /api/templates/{template_id}` 更新模板 | +| DELETE | 删除 | `DELETE /api/projects/{project_id}` 删除项目 | + +你看到的每一行，都是后端暴露给前端调用的一个接口。 + +## `/docs` 能做什么 + +可以： + +- 查看后端目前有哪些接口。 +- 展开接口查看参数、请求体和响应格式。 +- 点击 `Try it out` 直接发请求测试后端。 +- 检查接口返回错误，比如 400、401、404、500。 + +不能： + +- 查看前端页面源码。 +- 直接代表某个功能已经完整可用。 +- 展示 WebSocket 的完整交互，因为 OpenAPI 主要描述 HTTP 接口。 + +## 和前端有什么关系 + +前端的 `src/lib/api.ts` 会调用这些接口。例如： + +- 登录页调用 `/api/auth/login` +- 项目库调用 `/api/projects` +- 上传视频调用 `/api/media/upload` +- 拆帧调用 `/api/media/parse` +- 模板库调用 `/api/templates` + +所以 `/docs` 是检查“后端提供了什么”的地方；前端是否真的用对了，还要对照 `src/lib/api.ts`。 + +## 目前通过 `/docs` 能看到的接口 + +当前后端接口包括： + +- Auth：登录 +- Projects：项目 CRUD、项目帧 CRUD +- Templates：模板 CRUD +- Media：上传视频/DICOM、触发拆帧 +- AI：SAM 2 / SAM 3 可选推理、模型状态、自动分割、保存标注 +- Export：导出 COCO JSON、导出 PNG masks +- Health：健康检查 + +## 为什么看起来像“列举文件和请求” + +因为 Swagger UI 默认按接口分组，把每个 endpoint 展开成一行。它列举的是“后端可被调用的功能入口”，不是项目文件。 + +真正的项目文件在本地目录里，例如： + +- 前端：`src/components/*.tsx` +- 后端路由：`backend/routers/*.py` +- 后端模型：`backend/models.py` + +## 如何用 `/docs` 验证一个接口 + +以项目列表为例： + +1. 打开 `/docs`。 +2. 找到 `GET /api/projects`。 +3. 点开。 +4. 点击 `Try it out`。 +5. 点击 `Execute`。 +6. 查看 Response body。 + +如果这里能返回数据，但前端项目库加载失败，那问题多半在前端 API 地址、CORS、字段映射或浏览器网络请求。 + +## 另一个机器可读入口 + +OpenAPI JSON 在： + +```text +http://localhost:8000/openapi.json +``` + +这是给工具读取的接口描述，Swagger UI 就是基于它渲染出来的。 diff --git a/doc/07-current-requirements-freeze.md b/doc/07-current-requirements-freeze.md new file mode 100644 index 0000000..609f0c4 --- /dev/null +++ b/doc/07-current-requirements-freeze.md @@ -0,0 +1,120 @@ +# 当前需求冻结文档 + +冻结日期：2026-05-01 + +本文档描述当前仓库已经实现或明确保留为占位的需求。测试用例以本文档为准，不把早期设想或 Word 文档中的远期能力当作当前版本必须实现的功能。 + +## R1 登录与会话 + +- 系统提供登录页。 +- 默认开发凭证为 `admin / 123456`。 +- 登录成功后前端保存 token，并进入主应用。 +- 登录失败时显示错误信息。 +- 当前 token 是开发用固定 token，不做真实 JWT 校验。 + +## R2 项目管理 + +- 前端展示项目库，并从 `GET /api/projects` 获取项目列表。 +- 用户可以新建项目，前端调用 `POST /api/projects`。 +- 用户可以选择项目，进入工作区。 +- 用户可以导入视频文件，前端创建项目、上传文件、触发拆帧、刷新项目列表。 +- 用户可以导入 DICOM 序列，前端上传 DICOM、触发拆帧、刷新项目列表。 +- 后端支持项目创建、列表、详情、局部更新和删除。 +- 后端支持项目帧创建、列表和单帧查询。 + +## R3 媒体上传与拆帧 + +- 后端允许上传视频、图片、DICOM 文件，其他扩展名返回 400。 +- 未提供项目 ID 上传时，后端自动创建项目。 +- 提供项目 ID 上传时，后端把上传对象关联到该项目。 +- 拆帧接口根据项目 `source_type` 处理视频或 DICOM。 +- 拆帧完成后写入 `frames` 记录，并把项目状态设为 `ready`。 +- 拆帧接口会创建 `processing_tasks` 记录并投递 Celery worker。 +- 前端可通过 `GET /api/tasks/{task_id}` 查询任务状态。 + +## R4 工作区与帧浏览 + +- 工作区根据当前项目加载帧列表。 +- 若项目有媒体但无帧，工作区会尝试触发拆帧后重新加载。 +- Canvas 显示当前帧图片。 +- Canvas 支持滚轮缩放、移动工具拖拽、鼠标坐标显示。 +- 时间轴支持缩略图点击切帧、range 拖动切帧、播放/暂停顺序推进帧。 +- 播放帧率使用项目 `parse_fps` 或 `original_fps`，限制在 1 到 30 FPS。 + +## R5 工具栏 + +- 工具栏可以切换当前 active tool。 +- 正向点、反向点、框选工具会影响 Canvas 交互。 +- 魔法棒按钮切换到 AI 页面。 +- 多边形、矩形、圆、点、线、合并、去除、撤销、重做当前只提供 UI 状态或占位按钮，不完成真实绘制/算法。 + +## R6 AI 推理 + +- 前端可以在 AI 页面选择 `sam2` 或 `sam3`，选择结果存放在全局 store。 +- 前端和工作区通过 `GET /api/ai/models/status` 展示 GPU、SAM 2 和 SAM 3 的真实运行状态。 +- 前端 `predictMask()` 调用 `POST /api/ai/predict`。 +- 前端发送后端契约：`image_id`、`prompt_type`、`prompt_data`、`model`。 +- 点提示传 `{ points, labels }`，正向点 label 为 1，反向点 label 为 0。 +- 框选提示传归一化 `[x1, y1, x2, y2]`。 +- 语义文本提示传 `semantic`；选择 `sam3` 且环境满足依赖时走 SAM 3 文本语义推理，选择 `sam2` 时回退到自动分割。 +- 后端返回 `polygons` 和 `scores`。 +- 前端把后端 `polygons` 转成 Konva `pathData`、`segmentation`、`bbox`、`area`。 +- AI 推理结果先存放在前端 store 的 `masks` 中，点击“结构化归档保存”后持久化到后端标注表。 + +## R7 标注保存 + +- 后端提供 `POST /api/ai/annotate` 保存标注。 +- 保存时必须存在项目；如果传入 `frame_id`，帧也必须存在。 +- 后端提供 `GET /api/ai/annotations` 查询项目标注，可选按 `frame_id` 过滤。 +- 后端提供 `PATCH /api/ai/annotations/{annotation_id}` 更新已保存标注的 `mask_data`、`points`、`bbox` 和 `template_id`。 +- 后端提供 `DELETE /api/ai/annotations/{annotation_id}` 删除已保存标注。 +- 当前前端“结构化归档保存”会保存当前项目未保存 mask，并会更新已标记为 dirty 的已保存 mask。 +- 工作区“清空遮罩”会删除当前帧已保存标注，并清空当前帧未保存 mask。 +- 工作区加载项目帧后会查询已保存标注并回显。 + +## R8 模板库 + +- 前端展示模板列表，调用 `GET /api/templates`。 +- 用户可以新建、编辑、删除模板。 +- 模板分类存放在 `mapping_rules.classes`，规则存放在 `mapping_rules.rules`。 +- 前端支持添加/删除分类、拖拽排序后重算 `zIndex`、JSON 批量导入、加载腹腔镜默认分类。 +- 后端支持模板创建、列表、详情、局部更新和删除。 + +## R9 本体检查面板 + +- 工作区右侧可以选择模板。 +- 面板显示模板分类和组件本地自定义分类。 +- 用户可以选择具体分类；新 AI mask 会记录 `classId`、`className`、`classZIndex`，并在保存时写入 `mask_data.class`。 +- 添加自定义分类只存在组件本地状态，不保存到后端。 +- 置信度、拓扑锚点和重新提取骨架按钮当前为展示/占位。 + +## R10 Dashboard 与 WebSocket + +- Dashboard 显示基础统计、解析队列和活动日志。 +- Dashboard 初始数据来自 `GET /api/dashboard/overview`。 +- 后端聚合项目数、处理中任务数、标注数、帧数、模板数和主机 load average。 +- 解析队列由 `processing_tasks` 中的 queued/running 任务生成；活动日志由最近任务、项目、标注和模板记录生成。 +- Dashboard 会连接 `/ws/progress`。 +- 收到 progress、complete、error、status 消息时，前端会更新队列或日志。 +- Celery worker 每次更新 `processing_tasks` 后会发布 Redis `seg:progress` 事件，FastAPI 订阅并广播给 `/ws/progress` 客户端。 +- 后端 WebSocket 接收到客户端消息后返回 status heartbeat。 + +## R11 导出 + +- 后端支持 `GET /api/export/{project_id}/coco` 导出 COCO JSON。 +- 后端支持 `GET /api/export/{project_id}/masks` 导出 PNG mask ZIP。 +- 当前前端 `exportCoco()` API 封装已对齐后端路径。 +- 工作区“导出 JSON 标注集”按钮已绑定下载事件；导出前会先保存当前未归档 mask。 + +## R12 配置 + +- 前端 API 地址由 `src/lib/config.ts` 统一推导。 +- `VITE_API_BASE_URL` 优先级高于自动推导。 +- `VITE_WS_PROGRESS_URL` 优先级高于从 API 地址推导 WebSocket 地址。 +- 未设置环境变量时，前端按当前浏览器 hostname 推导 `http://:8000`。 + +## R13 文档与测试 + +- `doc/` 目录保存当前实现审计、接口契约、需求冻结、设计冻结和测试计划。 +- 测试应覆盖当前冻结需求中的真实功能、半可用行为和明确占位行为。 +- 对外部服务依赖 PostgreSQL、MinIO、Redis、SAM 模型的测试应使用 mock 或测试替身，不依赖真实服务可用性。 diff --git a/doc/08-current-design-freeze.md b/doc/08-current-design-freeze.md new file mode 100644 index 0000000..5aebebd --- /dev/null +++ b/doc/08-current-design-freeze.md @@ -0,0 +1,155 @@ +# 当前设计冻结文档 + +冻结日期：2026-05-01 + +本文档描述当前代码结构、数据流、接口契约和测试边界。后续实现如果改变这些设计，应同步更新本文档和测试。 + +## 总体架构 + +当前系统由三层组成： + +- React + TypeScript 前端 SPA。 +- FastAPI 后端 API。 +- PostgreSQL、MinIO、Redis、SAM 2 / SAM 3 等外部基础设施。 + +开发时前端通过 `server.ts` 启动 Express + Vite middleware；后端通过 `backend/main.py` 启动 FastAPI。前端业务接口主要访问 FastAPI，不依赖 `server.ts` 中保留的旧 mock API。 + +## 前端模块 + +| 模块 | 文件 | 设计职责 | +|------|------|----------| +| 应用入口 | `src/App.tsx` | 根据登录状态和 `activeModule` 切换页面 | +| 全局状态 | `src/store/useStore.ts` | Zustand store，保存项目、帧、模板、mask、工具状态 | +| API 封装 | `src/lib/api.ts` | Axios 客户端、字段映射、AI 响应转换 | +| 配置 | `src/lib/config.ts` | 推导 API 和 WebSocket 地址 | +| WebSocket | `src/lib/websocket.ts` | 进度流连接、订阅和重连 | +| 模型状态 | `src/components/ModelStatusBadge.tsx` | 展示 GPU 与当前 SAM 模型真实可用状态 | +| 登录页 | `src/components/Login.tsx` | 调用登录 API，写入 store | +| Dashboard | `src/components/Dashboard.tsx` | 展示统计和 WebSocket 进度消息 | +| 项目库 | `src/components/ProjectLibrary.tsx` | 项目列表、新建、导入视频/DICOM | +| 工作区 | `src/components/VideoWorkspace.tsx` | 加载帧和模板，组织工具栏、Canvas、本体面板、时间轴 | +| Canvas | `src/components/CanvasArea.tsx` | 显示帧、缩放平移、点/框提示、渲染 mask | +| 工具栏 | `src/components/ToolsPalette.tsx` | 切换工具和跳转 AI 页面 | +| 时间轴 | `src/components/FrameTimeline.tsx` | 帧导航和播放 | +| 本体面板 | `src/components/OntologyInspector.tsx` | 模板选择、分类树、本地自定义分类 | +| AI 页面 | `src/components/AISegmentation.tsx` | 独立 AI 推理视图，使用当前项目帧 | +| 模板库 | `src/components/TemplateRegistry.tsx` | 模板 CRUD、分类编辑、导入、排序 | + +## 后端模块 + +| 模块 | 文件 | 设计职责 | +|------|------|----------| +| 应用入口 | `backend/main.py` | FastAPI app、CORS、路由注册、健康检查、WebSocket | +| 配置 | `backend/config.py` | Pydantic settings | +| 数据库 | `backend/database.py` | SQLAlchemy engine、session、Base | +| 模型 | `backend/models.py` | Project、Frame、Template、Annotation、Mask、ProcessingTask | +| Schema | `backend/schemas.py` | Pydantic 请求/响应模型 | +| Auth | `backend/routers/auth.py` | 开发登录 | +| Projects | `backend/routers/projects.py` | 项目与帧 CRUD | +| Templates | `backend/routers/templates.py` | 模板 CRUD 和 mapping_rules 打包/解包 | +| Media | `backend/routers/media.py` | 上传媒体和拆帧 | +| AI | `backend/routers/ai.py` | SAM 2 / SAM 3 可选推理、模型状态和标注保存 | +| Export | `backend/routers/export.py` | COCO 和 PNG mask 导出 | +| SAM 2 | `backend/services/sam2_engine.py` | SAM 2 懒加载、状态检测和点/框/自动推理 | +| SAM 3 | `backend/services/sam3_engine.py` | SAM 3 状态检测和文本语义推理适配 | +| SAM Registry | `backend/services/sam_registry.py` | 模型选择、GPU 状态和推理分发 | + +## 状态模型 + +前端 store 的核心对象： + +- `Project`：项目基本信息、状态、帧数、fps、媒体路径。 +- `Frame`：帧 ID、项目 ID、索引、图片 URL、宽高。 +- `Template` / `TemplateClass`：模板和分类定义。 +- `Mask`：前端渲染用 mask，包含 `pathData`、`segmentation`、`bbox`、`area`。 +- `activeModule`：当前页面。 +- `activeTool`：当前工具。 +- `aiModel`：当前选择的 AI 模型，取值为 `sam2` 或 `sam3`。 + +## 关键数据流 + +### 登录 + +1. `Login` 收集用户名和密码。 +2. `login()` 调用 `POST /api/auth/login`。 +3. 成功后 store 写入 token，App 渲染主界面。 + +### 项目导入 + +1. `ProjectLibrary` 创建项目。 +2. 上传视频或 DICOM 到 `/api/media/upload` 或 `/api/media/upload/dicom`。 +3. 调用 `/api/media/parse` 创建异步拆帧任务。 +4. Celery worker 执行 FFmpeg/OpenCV/pydicom 拆帧，持续更新 `processing_tasks`，并发布 Redis `seg:progress`。 +5. 刷新项目列表。 + +### 工作区加载 + +1. `VideoWorkspace` 根据 `currentProject.id` 调用 `getProjectFrames()`。 +2. 若无帧但项目有 `video_path`，触发 `parseMedia()`，通过 `getTask()` 轮询任务完成后重新取帧。 +3. 帧数据映射为 store `Frame[]`。 +4. 当前帧传入 `CanvasArea`。 + +### AI 点/框推理 + +1. 用户在 Canvas 选择正向点、反向点或框选。 +2. `CanvasArea` 读取当前帧 ID 和宽高。 +3. `predictMask()` 归一化坐标并携带当前 `model` 调用 `/api/ai/predict`。 +4. 后端加载帧图片并通过 SAM registry 分发到 SAM 2 或 SAM 3。 +5. 前端把 `polygons` 转为 mask，写入 store。 +6. Canvas 按当前帧过滤并渲染 mask。 +7. 新 mask 会带上当前选择的模板分类元数据，包括 `classId`、`className`、`classZIndex` 和保存状态 `draft`。 +8. 用户点击“结构化归档保存”后，前端将像素 `segmentation` 转成 normalized `mask_data.polygons`；未保存 mask 调用 `POST /api/ai/annotate`，dirty mask 调用 `PATCH /api/ai/annotations/{annotation_id}`。 +9. 工作区加载项目帧后通过 `GET /api/ai/annotations` 取回已保存标注并转成前端 mask。 +10. 工作区“清空遮罩”删除当前帧已保存标注，并清除当前帧本地 mask。 + +### 模板管理 + +1. `TemplateRegistry` 从后端读取模板。 +2. 编辑态在组件本地维护分类列表。 +3. 保存时调用 `createTemplate()` 或 `updateTemplate()`。 +4. 后端把 `classes`、`rules` 打包进 `mapping_rules`。 +5. 返回时再解包给前端。 +6. `OntologyInspector` 可以选择具体分类；选择结果进入全局 store，供 `CanvasArea` 和 `AISegmentation` 新建/更新 mask 时使用。 + +### 导出 + +1. 后端根据项目、帧、标注和模板生成 COCO JSON。 +2. PNG mask 导出会把 normalized polygon 渲染为二值 mask 并打包 ZIP。 +3. 前端“导出 JSON 标注集”按钮会在导出前保存待归档标注，然后下载 COCO JSON。 + +## 接口契约 + +接口详情见 `doc/04-api-contracts.md`。测试中重点固定以下契约： + +- `updateProject()` 使用 `PATCH /api/projects/{id}`。 +- `exportCoco()` 使用 `GET /api/export/{projectId}/coco`。 +- `predictMask()` 使用 `POST /api/ai/predict`，请求体为 `image_id`、`prompt_type`、`prompt_data`、`model`。 +- `saveAnnotation()` 使用 `POST /api/ai/annotate`。 +- `getProjectAnnotations()` 使用 `GET /api/ai/annotations`。 +- `updateAnnotation()` 使用 `PATCH /api/ai/annotations/{annotationId}`。 +- `deleteAnnotation()` 使用 `DELETE /api/ai/annotations/{annotationId}`。 +- 后端 `/api/ai/predict` 支持 point、box、semantic 三种 prompt_type，并通过 `model` 选择 SAM 2 或 SAM 3。 +- 后端 `/api/ai/models/status` 返回 GPU、SAM 2、SAM 3 的真实运行状态。 +- point prompt 支持旧数组形式和 `{ points, labels }` 对象形式。 + +## 外部依赖边界 + +测试不直接依赖以下真实服务： + +- PostgreSQL：后端测试使用内存 SQLite。 +- MinIO：上传、下载、预签名 URL 使用 monkeypatch。 +- Redis：单测使用 monkeypatch 验证进度事件发布，不依赖真实 Redis 服务。 +- SAM：AI 推理测试使用 fake registry。 +- 浏览器 Canvas/Konva 图片加载：前端测试 mock `react-konva` 和 `use-image`。 + +## 已知占位设计 + +以下能力属于当前冻结版本的占位或半可用功能： + +- Dashboard 初始快照来自 `GET /api/dashboard/overview`；解析队列由 `processing_tasks` queued/running 任务生成。 +- 多边形、矩形、圆、点、线手工绘制未实现。 +- 合并、去除、撤销、重做未实现。 +- 工作区导出 PNG mask ZIP 按钮尚未提供。 +- 已保存标注支持通过“应用分类”进入 dirty 状态并归档更新；暂未提供逐点几何编辑器。 +- SAM 3 文本语义分割取决于官方依赖和 GPU 运行环境；状态接口会暴露真实可用性。 +- 自定义分类只存在本地组件状态。 diff --git a/doc/09-test-plan.md b/doc/09-test-plan.md new file mode 100644 index 0000000..1bf11b7 --- /dev/null +++ b/doc/09-test-plan.md @@ -0,0 +1,49 @@ +# 当前测试计划 + +本文档把 `doc/07-current-requirements-freeze.md` 中的冻结需求映射到测试。测试目标是覆盖当前真实行为和明确占位行为。 + +## 测试分层 + +| 层级 | 工具 | 覆盖范围 | +|------|------|----------| +| 前端单元/组件 | Vitest + Testing Library | API 封装、store、组件交互、Mock/UI-only 状态 | +| 后端路由 | pytest + FastAPI TestClient | Auth、Projects、Templates、AI、Export、Media 的接口契约 | +| 静态契约 | TypeScript / py_compile | 类型和 Python 语法 | + +## 覆盖矩阵 + +| 需求 | 测试文件 | 覆盖点 | +|------|----------|--------| +| R1 登录与会话 | `src/components/Login.test.tsx`, `backend/tests/test_auth.py` | 成功登录、失败提示、后端 401 | +| R2 项目管理 | `src/lib/api.test.ts`, `backend/tests/test_projects.py` | 前端字段映射、PATCH 更新、后端 CRUD、帧列表 | +| R3 媒体上传与拆帧 | `backend/tests/test_media.py` | 扩展名校验、自动建项目、关联项目、创建异步任务、worker 注册帧 | +| R4 工作区与帧浏览 | `src/components/VideoWorkspace.test.tsx`, `src/components/FrameTimeline.test.tsx` | 加载帧、无帧触发解析、切帧、播放 | +| R5 工具栏 | `src/components/ToolsPalette.test.tsx` | 工具切换、AI 跳转、占位按钮存在 | +| R6 AI 推理 | `src/lib/api.test.ts`, `src/components/CanvasArea.test.tsx`, `src/components/AISegmentation.test.tsx`, `src/components/ModelStatusBadge.test.tsx`, `backend/tests/test_ai.py` | 点/框/semantic 契约、模型选择、GPU/SAM 状态、状态徽标、坐标归一化、正负点 labels、polygons 转 path、后端 fake registry | +| R7 标注保存 | `src/components/VideoWorkspace.test.tsx`, `src/components/CanvasArea.test.tsx`, `src/lib/api.test.ts`, `backend/tests/test_ai.py` | 保存标注、加载回显、更新 dirty 标注、清空删除已保存标注、项目不存在、帧不存在 | +| R8 模板库 | `src/lib/api.test.ts`, `backend/tests/test_templates.py` | mapping_rules 解包/打包、模板 CRUD | +| R9 本体检查面板 | `src/components/OntologyInspector.test.tsx` | 模板选择、分类展示、具体分类选择、自定义分类本地添加 | +| R10 Dashboard 与 WebSocket | `src/lib/api.test.ts`, `src/lib/websocket.test.ts`, `src/components/Dashboard.test.tsx`, `backend/tests/test_dashboard.py`, `backend/tests/test_main.py`, `backend/tests/test_progress_events.py` | 后端概览接口、任务表驱动队列、Redis 进度事件 payload/发布、地址推导、消息订阅、队列更新、heartbeat | +| R11 导出 | `src/components/VideoWorkspace.test.tsx`, `src/lib/api.test.ts`, `backend/tests/test_export.py` | COCO 按钮下载、导出前自动保存、COCO 路径、JSON 结构、mask ZIP | +| R12 配置 | `src/lib/config.test.ts` | env 优先、hostname 推导、WS 推导 | +| R13 文档与测试 | `doc/09-test-plan.md` | 测试覆盖矩阵 | + +## 运行命令 + +```bash +npm run test +npm run test:run +npm run lint +npm run build + +pip install -r backend/requirements-dev.txt +pytest backend/tests +python -m py_compile backend/routers/ai.py backend/routers/templates.py backend/schemas.py +``` + +## 当前不做的测试 + +- 不启动真实 PostgreSQL、MinIO、Redis 或 SAM 模型。 +- 不做真实视频大文件拆帧性能测试。 +- 不用浏览器 E2E 验证视觉细节。 +- 不把当前明确 Mock/UI-only 的按钮当成真实业务成功路径测试。 diff --git a/doc/README.md b/doc/README.md new file mode 100644 index 0000000..f323266 --- /dev/null +++ b/doc/README.md @@ -0,0 +1,32 @@ +# 项目文档索引 + +本目录用于记录当前代码库的真实状态、目标设计与实现差距。文档依据包括： + +- 根目录 Word 文档：`语义分割系统构建方案.docx` +- 前端源码：`src/App.tsx`、`src/components/*.tsx`、`src/lib/api.ts`、`src/store/useStore.ts` +- 后端源码：`backend/main.py`、`backend/routers/*.py`、`backend/schemas.py`、`backend/models.py` +- 运行时 OpenAPI：`http://localhost:8000/openapi.json` + +## 文档结构 + +| 文档 | 内容 | +|------|------| +| [01-purpose-and-word-summary.md](./01-purpose-and-word-summary.md) | 为什么要做这个系统，Word 方案中的目标，以及当前代码的落地程度 | +| [02-current-implementation-map.md](./02-current-implementation-map.md) | 当前系统怎么运行，前后端、存储、数据流具体怎么串起来 | +| [03-frontend-element-audit.md](./03-frontend-element-audit.md) | 前端逐页面/逐元素审计：真实可用、半可用、Mock/UI-only、接口不通 | +| [04-api-contracts.md](./04-api-contracts.md) | 前端 API 封装、后端 FastAPI 接口、已完成对齐项和剩余接口问题 | +| [05-implementation-plan.md](./05-implementation-plan.md) | 后续要把 Mock 变成真实功能的建议实施顺序 | +| [06-fastapi-docs-explained.md](./06-fastapi-docs-explained.md) | `http://192.168.3.11:8000/docs` 是什么，怎么看和怎么用 | +| [07-current-requirements-freeze.md](./07-current-requirements-freeze.md) | 当前版本需求冻结，测试以此为准 | +| [08-current-design-freeze.md](./08-current-design-freeze.md) | 当前版本设计冻结，记录模块、数据流和接口边界 | +| [09-test-plan.md](./09-test-plan.md) | 需求到测试文件的覆盖矩阵和运行命令 | + +## 状态标记 + +| 标记 | 含义 | +|------|------| +| 真实可用 | 已接真实前端状态或后端 API，按当前代码能完成主要动作 | +| 部分可用 | 有真实数据或真实 UI，但存在关键缺口，例如只读、不能持久化、缺少错误处理 | +| Mock / UI-only | 只有展示或本地状态变化，没有真实业务效果 | +| 接口不通 | 前端调用和后端接口契约不一致，按当前代码大概率失败 | +| 目标设计 | Word 方案中提出，但当前代码尚未实现 | diff --git a/package-lock.json b/package-lock.json index 1f1d6c7..51f889c 100644 --- a/package-lock.json +++ b/package-lock.json @@ -27,15 +27,78 @@ "zustand": "^5.0.12" }, "devDependencies": { + "@testing-library/jest-dom": "^6.9.1", + "@testing-library/react": "^16.3.2", + "@testing-library/user-event": "^14.6.1", "@types/express": "^4.17.21", "@types/node": "^22.14.0", "autoprefixer": "^10.4.21", + "jsdom": "^29.1.1", "tailwindcss": "^4.1.14", "tsx": "^4.21.0", "typescript": "~5.8.2", - "vite": "^6.2.0" + "vite": "^6.2.0", + "vitest": "^4.1.5" } }, + "node_modules/@adobe/css-tools": { + "version": "4.4.4", + "resolved": "https://registry.npmjs.org/@adobe/css-tools/-/css-tools-4.4.4.tgz", + "integrity": "sha512-Elp+iwUx5rN5+Y8xLt5/GRoG20WGoDCQ/1Fb+1LiGtvwbDavuSk0jhD/eZdckHAuzcDzccnkv+rEjyWfRx18gg==", + "dev": true, + "license": "MIT" + }, + "node_modules/@asamuzakjp/css-color": { + "version": "5.1.11", + "resolved": "https://registry.npmjs.org/@asamuzakjp/css-color/-/css-color-5.1.11.tgz", + "integrity": "sha512-KVw6qIiCTUQhByfTd78h2yD1/00waTmm9uy/R7Ck/ctUyAPj+AEDLkQIdJW0T8+qGgj3j5bpNKK7Q3G+LedJWg==", + "dev": true, + "license": "MIT", + "dependencies": { + "@asamuzakjp/generational-cache": "^1.0.1", + "@csstools/css-calc": "^3.2.0", + "@csstools/css-color-parser": "^4.1.0", + "@csstools/css-parser-algorithms": "^4.0.0", + "@csstools/css-tokenizer": "^4.0.0" + }, + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, + "node_modules/@asamuzakjp/dom-selector": { + "version": "7.1.1", + "resolved": "https://registry.npmjs.org/@asamuzakjp/dom-selector/-/dom-selector-7.1.1.tgz", + "integrity": "sha512-67RZDnYRc8H/8MLDgQCDE//zoqVFwajkepHZgmXrbwybzXOEwOWGPYGmALYl9J2DOLfFPPs6kKCqmbzV895hTQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "@asamuzakjp/generational-cache": "^1.0.1", + "@asamuzakjp/nwsapi": "^2.3.9", + "bidi-js": "^1.0.3", + "css-tree": "^3.2.1", + "is-potential-custom-element-name": "^1.0.1" + }, + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, + "node_modules/@asamuzakjp/generational-cache": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/@asamuzakjp/generational-cache/-/generational-cache-1.0.1.tgz", + "integrity": "sha512-wajfB8KqzMCN2KGNFdLkReeHncd0AslUSrvHVvvYWuU8ghncRJoA50kT3zP9MVL0+9g4/67H+cdvBskj9THPzg==", + "dev": true, + "license": "MIT", + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, + "node_modules/@asamuzakjp/nwsapi": { + "version": "2.3.9", + "resolved": "https://registry.npmjs.org/@asamuzakjp/nwsapi/-/nwsapi-2.3.9.tgz", + "integrity": "sha512-n8GuYSrI9bF7FFZ/SjhwevlHc8xaVlb/7HmHelnc/PZXBD2ZR49NnN9sMMuDdEGPeeRQ5d0hqlSlEpgCX3Wl0Q==", + "dev": true, + "license": "MIT" + }, "node_modules/@babel/code-frame": { "version": "7.29.0", "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz", @@ -254,6 +317,16 @@ "@babel/core": "^7.0.0-0" } }, + "node_modules/@babel/runtime": { + "version": "7.29.2", + "resolved": "https://registry.npmjs.org/@babel/runtime/-/runtime-7.29.2.tgz", + "integrity": "sha512-JiDShH45zKHWyGe4ZNVRrCjBz8Nh9TMmZG1kh4QTK8hCBTWBi8Da+i7s1fJw7/lYpM4ccepSNfqzZ/QvABBi5g==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=6.9.0" + } + }, "node_modules/@babel/template": { "version": "7.28.6", "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.28.6.tgz", @@ -299,6 +372,159 @@ "node": ">=6.9.0" } }, + "node_modules/@bramus/specificity": { + "version": "2.4.2", + "resolved": "https://registry.npmjs.org/@bramus/specificity/-/specificity-2.4.2.tgz", + "integrity": "sha512-ctxtJ/eA+t+6q2++vj5j7FYX3nRu311q1wfYH3xjlLOsczhlhxAg2FWNUXhpGvAw3BWo1xBcvOV6/YLc2r5FJw==", + "dev": true, + "license": "MIT", + "dependencies": { + "css-tree": "^3.0.0" + }, + "bin": { + "specificity": "bin/cli.js" + } + }, + "node_modules/@csstools/color-helpers": { + "version": "6.0.2", + "resolved": "https://registry.npmjs.org/@csstools/color-helpers/-/color-helpers-6.0.2.tgz", + "integrity": "sha512-LMGQLS9EuADloEFkcTBR3BwV/CGHV7zyDxVRtVDTwdI2Ca4it0CCVTT9wCkxSgokjE5Ho41hEPgb8OEUwoXr6Q==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT-0", + "engines": { + "node": ">=20.19.0" + } + }, + "node_modules/@csstools/css-calc": { + "version": "3.2.0", + "resolved": "https://registry.npmjs.org/@csstools/css-calc/-/css-calc-3.2.0.tgz", + "integrity": "sha512-bR9e6o2BDB12jzN/gIbjHa5wLJ4UjD1CB9pM7ehlc0ddk6EBz+yYS1EV2MF55/HUxrHcB/hehAyt5vhsA3hx7w==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT", + "engines": { + "node": ">=20.19.0" + }, + "peerDependencies": { + "@csstools/css-parser-algorithms": "^4.0.0", + "@csstools/css-tokenizer": "^4.0.0" + } + }, + "node_modules/@csstools/css-color-parser": { + "version": "4.1.0", + "resolved": "https://registry.npmjs.org/@csstools/css-color-parser/-/css-color-parser-4.1.0.tgz", + "integrity": "sha512-U0KhLYmy2GVj6q4T3WaAe6NPuFYCPQoE3b0dRGxejWDgcPp8TP7S5rVdM5ZrFaqu4N67X8YaPBw14dQSYx3IyQ==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT", + "dependencies": { + "@csstools/color-helpers": "^6.0.2", + "@csstools/css-calc": "^3.2.0" + }, + "engines": { + "node": ">=20.19.0" + }, + "peerDependencies": { + "@csstools/css-parser-algorithms": "^4.0.0", + "@csstools/css-tokenizer": "^4.0.0" + } + }, + "node_modules/@csstools/css-parser-algorithms": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/@csstools/css-parser-algorithms/-/css-parser-algorithms-4.0.0.tgz", + "integrity": "sha512-+B87qS7fIG3L5h3qwJ/IFbjoVoOe/bpOdh9hAjXbvx0o8ImEmUsGXN0inFOnk2ChCFgqkkGFQ+TpM5rbhkKe4w==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT", + "engines": { + "node": ">=20.19.0" + }, + "peerDependencies": { + "@csstools/css-tokenizer": "^4.0.0" + } + }, + "node_modules/@csstools/css-syntax-patches-for-csstree": { + "version": "1.1.3", + "resolved": "https://registry.npmjs.org/@csstools/css-syntax-patches-for-csstree/-/css-syntax-patches-for-csstree-1.1.3.tgz", + "integrity": "sha512-SH60bMfrRCJF3morcdk57WklujF4Jr/EsQUzqkarfHXEFcAR1gg7fS/chAE922Sehgzc1/+Tz5H3Ypa1HiEKrg==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT-0", + "peerDependencies": { + "css-tree": "^3.2.1" + }, + "peerDependenciesMeta": { + "css-tree": { + "optional": true + } + } + }, + "node_modules/@csstools/css-tokenizer": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/@csstools/css-tokenizer/-/css-tokenizer-4.0.0.tgz", + "integrity": "sha512-QxULHAm7cNu72w97JUNCBFODFaXpbDg+dP8b/oWFAZ2MTRppA3U00Y2L1HqaS4J6yBqxwa/Y3nMBaxVKbB/NsA==", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/csstools" + }, + { + "type": "opencollective", + "url": "https://opencollective.com/csstools" + } + ], + "license": "MIT", + "engines": { + "node": ">=20.19.0" + } + }, "node_modules/@esbuild/aix-ppc64": { "version": "0.27.7", "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.27.7.tgz", @@ -715,6 +941,24 @@ "node": ">=18" } }, + "node_modules/@exodus/bytes": { + "version": "1.15.0", + "resolved": "https://registry.npmjs.org/@exodus/bytes/-/bytes-1.15.0.tgz", + "integrity": "sha512-UY0nlA+feH81UGSHv92sLEPLCeZFjXOuHhrIo0HQydScuQc8s0A7kL/UdgwgDq8g8ilksmuoF35YVTNphV2aBQ==", + "dev": true, + "license": "MIT", + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + }, + "peerDependencies": { + "@noble/hashes": "^1.8.0 || ^2.0.0" + }, + "peerDependenciesMeta": { + "@noble/hashes": { + "optional": true + } + } + }, "node_modules/@google/genai": { "version": "1.50.1", "resolved": "https://registry.npmjs.org/@google/genai/-/genai-1.50.1.tgz", @@ -1178,6 +1422,13 @@ "win32" ] }, + "node_modules/@standard-schema/spec": { + "version": "1.1.0", + "resolved": "https://registry.npmjs.org/@standard-schema/spec/-/spec-1.1.0.tgz", + "integrity": "sha512-l2aFy5jALhniG5HgqrD6jXLi/rUWrKvqN/qJx6yoJsgKhblVd+iqqU4RCXavm/jPityDo5TCvKMnpjKnOriy0w==", + "dev": true, + "license": "MIT" + }, "node_modules/@tailwindcss/node": { "version": "4.2.4", "resolved": "https://registry.npmjs.org/@tailwindcss/node/-/node-4.2.4.tgz", @@ -1435,6 +1686,104 @@ "vite": "^5.2.0 || ^6 || ^7 || ^8" } }, + "node_modules/@testing-library/dom": { + "version": "10.4.1", + "resolved": "https://registry.npmjs.org/@testing-library/dom/-/dom-10.4.1.tgz", + "integrity": "sha512-o4PXJQidqJl82ckFaXUeoAW+XysPLauYI43Abki5hABd853iMhitooc6znOnczgbTYmEP6U6/y1ZyKAIsvMKGg==", + "dev": true, + "license": "MIT", + "peer": true, + "dependencies": { + "@babel/code-frame": "^7.10.4", + "@babel/runtime": "^7.12.5", + "@types/aria-query": "^5.0.1", + "aria-query": "5.3.0", + "dom-accessibility-api": "^0.5.9", + "lz-string": "^1.5.0", + "picocolors": "1.1.1", + "pretty-format": "^27.0.2" + }, + "engines": { + "node": ">=18" + } + }, + "node_modules/@testing-library/jest-dom": { + "version": "6.9.1", + "resolved": "https://registry.npmjs.org/@testing-library/jest-dom/-/jest-dom-6.9.1.tgz", + "integrity": "sha512-zIcONa+hVtVSSep9UT3jZ5rizo2BsxgyDYU7WFD5eICBE7no3881HGeb/QkGfsJs6JTkY1aQhT7rIPC7e+0nnA==", + "dev": true, + "license": "MIT", + "dependencies": { + "@adobe/css-tools": "^4.4.0", + "aria-query": "^5.0.0", + "css.escape": "^1.5.1", + "dom-accessibility-api": "^0.6.3", + "picocolors": "^1.1.1", + "redent": "^3.0.0" + }, + "engines": { + "node": ">=14", + "npm": ">=6", + "yarn": ">=1" + } + }, + "node_modules/@testing-library/jest-dom/node_modules/dom-accessibility-api": { + "version": "0.6.3", + "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.6.3.tgz", + "integrity": "sha512-7ZgogeTnjuHbo+ct10G9Ffp0mif17idi0IyWNVA/wcwcm7NPOD/WEHVP3n7n3MhXqxoIYm8d6MuZohYWIZ4T3w==", + "dev": true, + "license": "MIT" + }, + "node_modules/@testing-library/react": { + "version": "16.3.2", + "resolved": "https://registry.npmjs.org/@testing-library/react/-/react-16.3.2.tgz", + "integrity": "sha512-XU5/SytQM+ykqMnAnvB2umaJNIOsLF3PVv//1Ew4CTcpz0/BRyy/af40qqrt7SjKpDdT1saBMc42CUok5gaw+g==", + "dev": true, + "license": "MIT", + "dependencies": { + "@babel/runtime": "^7.12.5" + }, + "engines": { + "node": ">=18" + }, + "peerDependencies": { + "@testing-library/dom": "^10.0.0", + "@types/react": "^18.0.0 || ^19.0.0", + "@types/react-dom": "^18.0.0 || ^19.0.0", + "react": "^18.0.0 || ^19.0.0", + "react-dom": "^18.0.0 || ^19.0.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "@types/react-dom": { + "optional": true + } + } + }, + "node_modules/@testing-library/user-event": { + "version": "14.6.1", + "resolved": "https://registry.npmjs.org/@testing-library/user-event/-/user-event-14.6.1.tgz", + "integrity": "sha512-vq7fv0rnt+QTXgPxr5Hjc210p6YKq2kmdziLgnsZGgLJ9e6VAShx1pACLuRjd/AS/sr7phAR58OIIpf0LlmQNw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12", + "npm": ">=6" + }, + "peerDependencies": { + "@testing-library/dom": ">=7.21.4" + } + }, + "node_modules/@types/aria-query": { + "version": "5.0.4", + "resolved": "https://registry.npmjs.org/@types/aria-query/-/aria-query-5.0.4.tgz", + "integrity": "sha512-rfT93uj5s0PRL7EzccGMs3brplhcrghnDoV26NqKhCAS1hVo+WdNsPvE/yb6ilfr5hi2MEk6d5EWJTKdxg8jVw==", + "dev": true, + "license": "MIT", + "peer": true + }, "node_modules/@types/babel__core": { "version": "7.20.5", "resolved": "https://registry.npmjs.org/@types/babel__core/-/babel__core-7.20.5.tgz", @@ -1487,6 +1836,17 @@ "@types/node": "*" } }, + "node_modules/@types/chai": { + "version": "5.2.3", + "resolved": "https://registry.npmjs.org/@types/chai/-/chai-5.2.3.tgz", + "integrity": "sha512-Mw558oeA9fFbv65/y4mHtXDs9bPnFMZAL/jxdPFUpOHHIXX91mcgEHbS5Lahr+pwZFR8A7GQleRWeI6cGFC2UA==", + "dev": true, + "license": "MIT", + "dependencies": { + "@types/deep-eql": "*", + "assertion-error": "^2.0.1" + } + }, "node_modules/@types/connect": { "version": "3.4.38", "resolved": "https://registry.npmjs.org/@types/connect/-/connect-3.4.38.tgz", @@ -1497,6 +1857,13 @@ "@types/node": "*" } }, + "node_modules/@types/deep-eql": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/@types/deep-eql/-/deep-eql-4.0.2.tgz", + "integrity": "sha512-c9h9dVVMigMPc4bwTvC5dxqtqJZwQPePsWjPlpSOnojbor6pGqdk541lfA7AqFQr5pB1BRdq0juY9db81BwyFw==", + "dev": true, + "license": "MIT" + }, "node_modules/@types/estree": { "version": "1.0.8", "resolved": "https://registry.npmjs.org/@types/estree/-/estree-1.0.8.tgz", @@ -1644,6 +2011,119 @@ "vite": "^4.2.0 || ^5.0.0 || ^6.0.0 || ^7.0.0 || ^8.0.0" } }, + "node_modules/@vitest/expect": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/expect/-/expect-4.1.5.tgz", + "integrity": "sha512-PWBaRY5JoKuRnHlUHfpV/KohFylaDZTupcXN1H9vYryNLOnitSw60Mw9IAE2r67NbwwzBw/Cc/8q9BK3kIX8Kw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@standard-schema/spec": "^1.1.0", + "@types/chai": "^5.2.2", + "@vitest/spy": "4.1.5", + "@vitest/utils": "4.1.5", + "chai": "^6.2.2", + "tinyrainbow": "^3.1.0" + }, + "funding": { + "url": "https://opencollective.com/vitest" + } + }, + "node_modules/@vitest/mocker": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/mocker/-/mocker-4.1.5.tgz", + "integrity": "sha512-/x2EmFC4mT4NNzqvC3fmesuV97w5FC903KPmey4gsnJiMQ3Be1IlDKVaDaG8iqaLFHqJ2FVEkxZk5VmeLjIItw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/spy": "4.1.5", + "estree-walker": "^3.0.3", + "magic-string": "^0.30.21" + }, + "funding": { + "url": "https://opencollective.com/vitest" + }, + "peerDependencies": { + "msw": "^2.4.9", + "vite": "^6.0.0 || ^7.0.0 || ^8.0.0" + }, + "peerDependenciesMeta": { + "msw": { + "optional": true + }, + "vite": { + "optional": true + } + } + }, + "node_modules/@vitest/pretty-format": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/pretty-format/-/pretty-format-4.1.5.tgz", + "integrity": "sha512-7I3q6l5qr03dVfMX2wCo9FxwSJbPdwKjy2uu/YPpU3wfHvIL4QHwVRp57OfGrDFeUJ8/8QdfBKIV12FTtLn00g==", + "dev": true, + "license": "MIT", + "dependencies": { + "tinyrainbow": "^3.1.0" + }, + "funding": { + "url": "https://opencollective.com/vitest" + } + }, + "node_modules/@vitest/runner": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/runner/-/runner-4.1.5.tgz", + "integrity": "sha512-2D+o7Pr82IEO46YPpoA/YU0neeyr6FTerQb5Ro7BUnBuv6NQtT/kmVnczngiMEBhzgqz2UZYl5gArejsyERDSQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/utils": "4.1.5", + "pathe": "^2.0.3" + }, + "funding": { + "url": "https://opencollective.com/vitest" + } + }, + "node_modules/@vitest/snapshot": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/snapshot/-/snapshot-4.1.5.tgz", + "integrity": "sha512-zypXEt4KH/XgKGPUz4eC2AvErYx0My5hfL8oDb1HzGFpEk1P62bxSohdyOmvz+d9UJwanI68MKwr2EquOaOgMQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/pretty-format": "4.1.5", + "@vitest/utils": "4.1.5", + "magic-string": "^0.30.21", + "pathe": "^2.0.3" + }, + "funding": { + "url": "https://opencollective.com/vitest" + } + }, + "node_modules/@vitest/spy": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/spy/-/spy-4.1.5.tgz", + "integrity": "sha512-2lNOsh6+R2Idnf1TCZqSwYlKN2E/iDlD8sgU59kYVl+OMDmvldO1VDk39smRfpUNwYpNRVn3w4YfuC7KfbBnkQ==", + "dev": true, + "license": "MIT", + "funding": { + "url": "https://opencollective.com/vitest" + } + }, + "node_modules/@vitest/utils": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/@vitest/utils/-/utils-4.1.5.tgz", + "integrity": "sha512-76wdkrmfXfqGjueGgnb45ITPyUi1ycZ4IHgC2bhPDUfWHklY/q3MdLOAB+TF1e6xfl8NxNY0ZYaPCFNWSsw3Ug==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/pretty-format": "4.1.5", + "convert-source-map": "^2.0.0", + "tinyrainbow": "^3.1.0" + }, + "funding": { + "url": "https://opencollective.com/vitest" + } + }, "node_modules/accepts": { "version": "1.3.8", "resolved": "https://registry.npmjs.org/accepts/-/accepts-1.3.8.tgz", @@ -1666,12 +2146,57 @@ "node": ">= 14" } }, + "node_modules/ansi-regex": { + "version": "5.0.1", + "resolved": "https://registry.npmjs.org/ansi-regex/-/ansi-regex-5.0.1.tgz", + "integrity": "sha512-quJQXlTSUGL2LH9SUXo8VwsY4soanhgo6LNSm84E1LBcE8s3O0wpdiRzyR9z/ZZJMlMWv37qOOb9pdJlMUEKFQ==", + "dev": true, + "license": "MIT", + "peer": true, + "engines": { + "node": ">=8" + } + }, + "node_modules/ansi-styles": { + "version": "5.2.0", + "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-5.2.0.tgz", + "integrity": "sha512-Cxwpt2SfTzTtXcfOlzGEee8O+c+MmUgGrNiBcXnuWxuFJHe6a5Hz7qwhwe5OgaSYI0IJvkLqWX1ASG+cJOkEiA==", + "dev": true, + "license": "MIT", + "peer": true, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/chalk/ansi-styles?sponsor=1" + } + }, + "node_modules/aria-query": { + "version": "5.3.0", + "resolved": "https://registry.npmjs.org/aria-query/-/aria-query-5.3.0.tgz", + "integrity": "sha512-b0P0sZPKtyu8HkeRAfCq0IfURZK+SuwMjY1UXGBU27wpAiTwQAIlq56IbIO+ytk/JjS1fMR14ee5WBBfKi5J6A==", + "dev": true, + "license": "Apache-2.0", + "dependencies": { + "dequal": "^2.0.3" + } + }, "node_modules/array-flatten": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/array-flatten/-/array-flatten-1.1.1.tgz", "integrity": "sha512-PCVAQswWemu6UdxsDFFX/+gVeYqKAod3D3UVm91jHwynguOwAvYPhx8nNlM++NqRcK6CxxpUafjmhIdKiHibqg==", "license": "MIT" }, + "node_modules/assertion-error": { + "version": "2.0.1", + "resolved": "https://registry.npmjs.org/assertion-error/-/assertion-error-2.0.1.tgz", + "integrity": "sha512-Izi8RQcffqCeNVgFigKli1ssklIbpHnCYc6AknXGYoB6grJqyeby7jv12JUQgmTAnIDnbck1uxksT4dzN3PWBA==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=12" + } + }, "node_modules/asynckit": { "version": "0.4.0", "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz", @@ -1758,6 +2283,16 @@ "node": ">=6.0.0" } }, + "node_modules/bidi-js": { + "version": "1.0.3", + "resolved": "https://registry.npmjs.org/bidi-js/-/bidi-js-1.0.3.tgz", + "integrity": "sha512-RKshQI1R3YQ+n9YJz2QQ147P66ELpa1FQEg20Dk8oW9t2KgLbpDLLp9aGZ7y8WHSshDknG0bknqGw5/tyCs5tw==", + "dev": true, + "license": "MIT", + "dependencies": { + "require-from-string": "^2.0.2" + } + }, "node_modules/bignumber.js": { "version": "9.3.1", "resolved": "https://registry.npmjs.org/bignumber.js/-/bignumber.js-9.3.1.tgz", @@ -1918,6 +2453,16 @@ ], "license": "CC-BY-4.0" }, + "node_modules/chai": { + "version": "6.2.2", + "resolved": "https://registry.npmjs.org/chai/-/chai-6.2.2.tgz", + "integrity": "sha512-NUPRluOfOiTKBKvWPtSD4PhFvWCqOi0BGStNWs57X9js7XGTprSmFoz5F0tWhR4WPjNeR9jXqdC7/UpSJTnlRg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=18" + } + }, "node_modules/clsx": { "version": "2.1.1", "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz", @@ -1981,6 +2526,27 @@ "integrity": "sha512-NXdYc3dLr47pBkpUCHtKSwIOQXLVn8dZEuywboCOJY/osA0wFSLlSawr3KN8qXJEyX66FcONTH8EIlVuK0yyFA==", "license": "MIT" }, + "node_modules/css-tree": { + "version": "3.2.1", + "resolved": "https://registry.npmjs.org/css-tree/-/css-tree-3.2.1.tgz", + "integrity": "sha512-X7sjQzceUhu1u7Y/ylrRZFU2FS6LRiFVp6rKLPg23y3x3c3DOKAwuXGDp+PAGjh6CSnCjYeAul8pcT8bAl+lSA==", + "dev": true, + "license": "MIT", + "dependencies": { + "mdn-data": "2.27.1", + "source-map-js": "^1.2.1" + }, + "engines": { + "node": "^10 || ^12.20.0 || ^14.13.0 || >=15.0.0" + } + }, + "node_modules/css.escape": { + "version": "1.5.1", + "resolved": "https://registry.npmjs.org/css.escape/-/css.escape-1.5.1.tgz", + "integrity": "sha512-YUifsXXuknHlUsmlgyY0PKzgPOr7/FjCePfHNt0jxm83wHZi44VDMQ7/fGNkjY3/jV1MC+1CmZbaHzugyeRtpg==", + "dev": true, + "license": "MIT" + }, "node_modules/csstype": { "version": "3.2.3", "resolved": "https://registry.npmjs.org/csstype/-/csstype-3.2.3.tgz", @@ -1997,6 +2563,20 @@ "node": ">= 12" } }, + "node_modules/data-urls": { + "version": "7.0.0", + "resolved": "https://registry.npmjs.org/data-urls/-/data-urls-7.0.0.tgz", + "integrity": "sha512-23XHcCF+coGYevirZceTVD7NdJOqVn+49IHyxgszm+JIiHLoB2TkmPtsYkNWT1pvRSGkc35L6NHs0yHkN2SumA==", + "dev": true, + "license": "MIT", + "dependencies": { + "whatwg-mimetype": "^5.0.0", + "whatwg-url": "^16.0.0" + }, + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, "node_modules/debug": { "version": "4.4.3", "resolved": "https://registry.npmjs.org/debug/-/debug-4.4.3.tgz", @@ -2014,6 +2594,13 @@ } } }, + "node_modules/decimal.js": { + "version": "10.6.0", + "resolved": "https://registry.npmjs.org/decimal.js/-/decimal.js-10.6.0.tgz", + "integrity": "sha512-YpgQiITW3JXGntzdUmyUR1V812Hn8T1YVXhCu+wO3OpS4eU9l4YdD3qjyiKdV6mvV29zapkMeD390UVEf2lkUg==", + "dev": true, + "license": "MIT" + }, "node_modules/delayed-stream": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz", @@ -2032,6 +2619,16 @@ "node": ">= 0.8" } }, + "node_modules/dequal": { + "version": "2.0.3", + "resolved": "https://registry.npmjs.org/dequal/-/dequal-2.0.3.tgz", + "integrity": "sha512-0je+qPKHEMohvfRTCEo3CrPG6cAzAYgmzKyxRiYSSDkS6eGJdyVJm7WaYA5ECaAD9wLB2T4EEeymA5aFVcYXCA==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=6" + } + }, "node_modules/destroy": { "version": "1.2.0", "resolved": "https://registry.npmjs.org/destroy/-/destroy-1.2.0.tgz", @@ -2051,6 +2648,14 @@ "node": ">=8" } }, + "node_modules/dom-accessibility-api": { + "version": "0.5.16", + "resolved": "https://registry.npmjs.org/dom-accessibility-api/-/dom-accessibility-api-0.5.16.tgz", + "integrity": "sha512-X7BJ2yElsnOJ30pZF4uIIDfBEVgF4XEBxL9Bxhy6dnrm5hkzqmsWHGTiHqRiITNhMyFLyAiWndIJP7Z1NTteDg==", + "dev": true, + "license": "MIT", + "peer": true + }, "node_modules/dotenv": { "version": "17.4.2", "resolved": "https://registry.npmjs.org/dotenv/-/dotenv-17.4.2.tgz", @@ -2120,6 +2725,19 @@ "node": ">=10.13.0" } }, + "node_modules/entities": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/entities/-/entities-8.0.0.tgz", + "integrity": "sha512-zwfzJecQ/Uej6tusMqwAqU/6KL2XaB2VZ2Jg54Je6ahNBGNH6Ek6g3jjNCF0fG9EWQKGZNddNjU5F1ZQn/sBnA==", + "dev": true, + "license": "BSD-2-Clause", + "engines": { + "node": ">=20.19.0" + }, + "funding": { + "url": "https://github.com/fb55/entities?sponsor=1" + } + }, "node_modules/es-define-property": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz", @@ -2138,6 +2756,13 @@ "node": ">= 0.4" } }, + "node_modules/es-module-lexer": { + "version": "2.1.0", + "resolved": "https://registry.npmjs.org/es-module-lexer/-/es-module-lexer-2.1.0.tgz", + "integrity": "sha512-n27zTYMjYu1aj4MjCWzSP7G9r75utsaoc8m61weK+W8JMBGGQybd43GstCXZ3WNmSFtGT9wi59qQTW6mhTR5LQ==", + "dev": true, + "license": "MIT" + }, "node_modules/es-object-atoms": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/es-object-atoms/-/es-object-atoms-1.1.1.tgz", @@ -2222,6 +2847,16 @@ "integrity": "sha512-NiSupZ4OeuGwr68lGIeym/ksIZMJodUGOSCZ/FSnTxcrekbvqrgdUxlJOMpijaKZVjAJrWrGs/6Jy8OMuyj9ow==", "license": "MIT" }, + "node_modules/estree-walker": { + "version": "3.0.3", + "resolved": "https://registry.npmjs.org/estree-walker/-/estree-walker-3.0.3.tgz", + "integrity": "sha512-7RUKfXgSMMkzt6ZuXmqapOurLGPPfgj6l9uRZ7lRGolvk0y2yocc35LdcxKC5PQZdn2DMqioAQ2NoWcrTKmm6g==", + "dev": true, + "license": "MIT", + "dependencies": { + "@types/estree": "^1.0.0" + } + }, "node_modules/etag": { "version": "1.8.1", "resolved": "https://registry.npmjs.org/etag/-/etag-1.8.1.tgz", @@ -2231,6 +2866,16 @@ "node": ">= 0.6" } }, + "node_modules/expect-type": { + "version": "1.3.0", + "resolved": "https://registry.npmjs.org/expect-type/-/expect-type-1.3.0.tgz", + "integrity": "sha512-knvyeauYhqjOYvQ66MznSMs83wmHrCycNEN6Ao+2AeYEfxUIkuiVxdEa1qlGEPK+We3n0THiDciYSsCcgW/DoA==", + "dev": true, + "license": "Apache-2.0", + "engines": { + "node": ">=12.0.0" + } + }, "node_modules/express": { "version": "4.22.1", "resolved": "https://registry.npmjs.org/express/-/express-4.22.1.tgz", @@ -2671,6 +3316,19 @@ "node": ">= 0.4" } }, + "node_modules/html-encoding-sniffer": { + "version": "6.0.0", + "resolved": "https://registry.npmjs.org/html-encoding-sniffer/-/html-encoding-sniffer-6.0.0.tgz", + "integrity": "sha512-CV9TW3Y3f8/wT0BRFc1/KAVQ3TUHiXmaAb6VW9vtiMFf7SLoMd1PdAc4W3KFOFETBJUb90KatHqlsZMWV+R9Gg==", + "dev": true, + "license": "MIT", + "dependencies": { + "@exodus/bytes": "^1.6.0" + }, + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, "node_modules/http-errors": { "version": "2.0.1", "resolved": "https://registry.npmjs.org/http-errors/-/http-errors-2.0.1.tgz", @@ -2716,6 +3374,16 @@ "node": ">=0.10.0" } }, + "node_modules/indent-string": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/indent-string/-/indent-string-4.0.0.tgz", + "integrity": "sha512-EdDDZu4A2OyIK7Lr/2zG+w5jmbuk1DVBnEwREQvBzspBJkCEbRa8GxU1lghYcaGJCnRWibjDXlq779X1/y5xwg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/inherits": { "version": "2.0.4", "resolved": "https://registry.npmjs.org/inherits/-/inherits-2.0.4.tgz", @@ -2731,6 +3399,13 @@ "node": ">= 0.10" } }, + "node_modules/is-potential-custom-element-name": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/is-potential-custom-element-name/-/is-potential-custom-element-name-1.0.1.tgz", + "integrity": "sha512-bCYeRA2rVibKZd+s2625gGnGF/t7DSqDs4dP7CrLA1m7jKWz6pps0LpYLJN8Q64HtmPKJ1hrN3nzPNKFEKOUiQ==", + "dev": true, + "license": "MIT" + }, "node_modules/its-fine": { "version": "2.0.0", "resolved": "https://registry.npmjs.org/its-fine/-/its-fine-2.0.0.tgz", @@ -2767,6 +3442,57 @@ "integrity": "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==", "license": "MIT" }, + "node_modules/jsdom": { + "version": "29.1.1", + "resolved": "https://registry.npmjs.org/jsdom/-/jsdom-29.1.1.tgz", + "integrity": "sha512-ECi4Fi2f7BdJtUKTflYRTiaMxIB0O6zfR1fX0GXpUrf6flp8QIYn1UT20YQqdSOfk2dfkCwS8LAFoJDEppNK5Q==", + "dev": true, + "license": "MIT", + "dependencies": { + "@asamuzakjp/css-color": "^5.1.11", + "@asamuzakjp/dom-selector": "^7.1.1", + "@bramus/specificity": "^2.4.2", + "@csstools/css-syntax-patches-for-csstree": "^1.1.3", + "@exodus/bytes": "^1.15.0", + "css-tree": "^3.2.1", + "data-urls": "^7.0.0", + "decimal.js": "^10.6.0", + "html-encoding-sniffer": "^6.0.0", + "is-potential-custom-element-name": "^1.0.1", + "lru-cache": "^11.3.5", + "parse5": "^8.0.1", + "saxes": "^6.0.0", + "symbol-tree": "^3.2.4", + "tough-cookie": "^6.0.1", + "undici": "^7.25.0", + "w3c-xmlserializer": "^5.0.0", + "webidl-conversions": "^8.0.1", + "whatwg-mimetype": "^5.0.0", + "whatwg-url": "^16.0.1", + "xml-name-validator": "^5.0.0" + }, + "engines": { + "node": "^20.19.0 || ^22.13.0 || >=24.0.0" + }, + "peerDependencies": { + "canvas": "^3.0.0" + }, + "peerDependenciesMeta": { + "canvas": { + "optional": true + } + } + }, + "node_modules/jsdom/node_modules/lru-cache": { + "version": "11.3.5", + "resolved": "https://registry.npmjs.org/lru-cache/-/lru-cache-11.3.5.tgz", + "integrity": "sha512-NxVFwLAnrd9i7KUBxC4DrUhmgjzOs+1Qm50D3oF1/oL+r1NpZ4gA7xvG0/zJ8evR7zIKn4vLf7qTNduWFtCrRw==", + "dev": true, + "license": "BlueOak-1.0.0", + "engines": { + "node": "20 || >=22" + } + }, "node_modules/jsesc": { "version": "3.1.0", "resolved": "https://registry.npmjs.org/jsesc/-/jsesc-3.1.0.tgz", @@ -3114,6 +3840,17 @@ "react": "^16.5.1 || ^17.0.0 || ^18.0.0 || ^19.0.0" } }, + "node_modules/lz-string": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/lz-string/-/lz-string-1.5.0.tgz", + "integrity": "sha512-h5bgJWpxJNswbU7qCrV0tIKQCaS3blPDrqKWx+QxzuzL1zGUzij9XCWLrSLsJPu5t+eWA/ycetzYAO5IOMcWAQ==", + "dev": true, + "license": "MIT", + "peer": true, + "bin": { + "lz-string": "bin/bin.js" + } + }, "node_modules/magic-string": { "version": "0.30.21", "resolved": "https://registry.npmjs.org/magic-string/-/magic-string-0.30.21.tgz", @@ -3132,6 +3869,13 @@ "node": ">= 0.4" } }, + "node_modules/mdn-data": { + "version": "2.27.1", + "resolved": "https://registry.npmjs.org/mdn-data/-/mdn-data-2.27.1.tgz", + "integrity": "sha512-9Yubnt3e8A0OKwxYSXyhLymGW4sCufcLG6VdiDdUGVkPhpqLxlvP5vl1983gQjJl3tqbrM731mjaZaP68AgosQ==", + "dev": true, + "license": "CC0-1.0" + }, "node_modules/media-typer": { "version": "0.3.0", "resolved": "https://registry.npmjs.org/media-typer/-/media-typer-0.3.0.tgz", @@ -3192,6 +3936,16 @@ "node": ">= 0.6" } }, + "node_modules/min-indent": { + "version": "1.0.1", + "resolved": "https://registry.npmjs.org/min-indent/-/min-indent-1.0.1.tgz", + "integrity": "sha512-I9jwMn07Sy/IwOj3zVkVik2JTvgpaykDZEigL6Rx6N9LbMywwUSMtxET+7lVoDLLd3O3IXwJwvuuns8UB/HeAg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=4" + } + }, "node_modules/motion": { "version": "12.38.0", "resolved": "https://registry.npmjs.org/motion/-/motion-12.38.0.tgz", @@ -3322,6 +4076,17 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/obug": { + "version": "2.1.1", + "resolved": "https://registry.npmjs.org/obug/-/obug-2.1.1.tgz", + "integrity": "sha512-uTqF9MuPraAQ+IsnPf366RG4cP9RtUi7MLO1N3KEc+wb0a6yKpeL0lmk2IB1jY5KHPAlTc6T/JRdC/YqxHNwkQ==", + "dev": true, + "funding": [ + "https://github.com/sponsors/sxzz", + "https://opencollective.com/debug" + ], + "license": "MIT" + }, "node_modules/on-finished": { "version": "2.4.1", "resolved": "https://registry.npmjs.org/on-finished/-/on-finished-2.4.1.tgz", @@ -3347,6 +4112,19 @@ "node": ">=8" } }, + "node_modules/parse5": { + "version": "8.0.1", + "resolved": "https://registry.npmjs.org/parse5/-/parse5-8.0.1.tgz", + "integrity": "sha512-z1e/HMG90obSGeidlli3hj7cbocou0/wa5HacvI3ASx34PecNjNQeaHNo5WIZpWofN9kgkqV1q5YvXe3F0FoPw==", + "dev": true, + "license": "MIT", + "dependencies": { + "entities": "^8.0.0" + }, + "funding": { + "url": "https://github.com/inikulin/parse5?sponsor=1" + } + }, "node_modules/parseurl": { "version": "1.3.3", "resolved": "https://registry.npmjs.org/parseurl/-/parseurl-1.3.3.tgz", @@ -3362,6 +4140,13 @@ "integrity": "sha512-A/AGNMFN3c8bOlvV9RreMdrv7jsmF9XIfDeCd87+I8RNg6s78BhJxMu69NEMHBSJFxKidViTEdruRwEk/WIKqA==", "license": "MIT" }, + "node_modules/pathe": { + "version": "2.0.3", + "resolved": "https://registry.npmjs.org/pathe/-/pathe-2.0.3.tgz", + "integrity": "sha512-WUjGcAqP1gQacoQe+OBJsFA7Ld4DyXuUIjZ5cc75cLHvJ7dtNsTugphxIADwspS+AraAUePCKrSVtPLFj/F88w==", + "dev": true, + "license": "MIT" + }, "node_modules/picocolors": { "version": "1.1.1", "resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz", @@ -3415,6 +4200,22 @@ "dev": true, "license": "MIT" }, + "node_modules/pretty-format": { + "version": "27.5.1", + "resolved": "https://registry.npmjs.org/pretty-format/-/pretty-format-27.5.1.tgz", + "integrity": "sha512-Qb1gy5OrP5+zDf2Bvnzdl3jsTf1qXVMazbvCoKhtKqVs4/YK4ozX4gKQJJVyNe+cajNPn0KoC0MC3FUmaHWEmQ==", + "dev": true, + "license": "MIT", + "peer": true, + "dependencies": { + "ansi-regex": "^5.0.1", + "ansi-styles": "^5.0.0", + "react-is": "^17.0.1" + }, + "engines": { + "node": "^10.13.0 || ^12.13.0 || ^14.15.0 || >=15.0.0" + } + }, "node_modules/protobufjs": { "version": "7.5.6", "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.5.6.tgz", @@ -3461,6 +4262,16 @@ "node": ">=10" } }, + "node_modules/punycode": { + "version": "2.3.1", + "resolved": "https://registry.npmjs.org/punycode/-/punycode-2.3.1.tgz", + "integrity": "sha512-vYt7UD1U9Wg6138shLtLOvdAu+8DsC/ilFtEVHcH+wydcSpNE20AfSOduf6MkRFahL5FY7X1oU7nKVZFtfq8Fg==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=6" + } + }, "node_modules/qs": { "version": "6.14.2", "resolved": "https://registry.npmjs.org/qs/-/qs-6.14.2.tgz", @@ -3521,6 +4332,14 @@ "react": "^19.2.5" } }, + "node_modules/react-is": { + "version": "17.0.2", + "resolved": "https://registry.npmjs.org/react-is/-/react-is-17.0.2.tgz", + "integrity": "sha512-w2GsyukL62IJnlaff/nRegPQR94C/XXamvMWmSHRJ4y7Ts/4ocGRmTHvOs8PSE6pB3dWOrD/nueuU5sduBsQ4w==", + "dev": true, + "license": "MIT", + "peer": true + }, "node_modules/react-konva": { "version": "19.2.3", "resolved": "https://registry.npmjs.org/react-konva/-/react-konva-19.2.3.tgz", @@ -3576,6 +4395,30 @@ "node": ">=0.10.0" } }, + "node_modules/redent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/redent/-/redent-3.0.0.tgz", + "integrity": "sha512-6tDA8g98We0zd0GvVeMT9arEOnTw9qM03L9cJXaCjrip1OO764RDBLBfrB4cwzNGDj5OA5ioymC9GkizgWJDUg==", + "dev": true, + "license": "MIT", + "dependencies": { + "indent-string": "^4.0.0", + "strip-indent": "^3.0.0" + }, + "engines": { + "node": ">=8" + } + }, + "node_modules/require-from-string": { + "version": "2.0.2", + "resolved": "https://registry.npmjs.org/require-from-string/-/require-from-string-2.0.2.tgz", + "integrity": "sha512-Xf0nWe6RseziFMu+Ap9biiUbmplq6S9/p+7w7YXP/JBHhrUDDUhwa+vANyubuqfZWTveU//DYVGsDG7RKL/vEw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=0.10.0" + } + }, "node_modules/resolve-pkg-maps": { "version": "1.0.0", "resolved": "https://registry.npmjs.org/resolve-pkg-maps/-/resolve-pkg-maps-1.0.0.tgz", @@ -3665,6 +4508,19 @@ "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==", "license": "MIT" }, + "node_modules/saxes": { + "version": "6.0.0", + "resolved": "https://registry.npmjs.org/saxes/-/saxes-6.0.0.tgz", + "integrity": "sha512-xAg7SOnEhrm5zI3puOOKyy1OMcMlIJZYNJY7xLBwSze0UjhPLnWfj2GF2EpT0jmzaJKIWKHLsaSSajf35bcYnA==", + "dev": true, + "license": "ISC", + "dependencies": { + "xmlchars": "^2.2.0" + }, + "engines": { + "node": ">=v12.22.7" + } + }, "node_modules/scheduler": { "version": "0.27.0", "resolved": "https://registry.npmjs.org/scheduler/-/scheduler-0.27.0.tgz", @@ -3812,6 +4668,13 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/siginfo": { + "version": "2.0.0", + "resolved": "https://registry.npmjs.org/siginfo/-/siginfo-2.0.0.tgz", + "integrity": "sha512-ybx0WO1/8bSBLEWXZvEd7gMW3Sn3JFlW3TvX1nREbDLRNQNaeNN8WK0meBwPdAaOI7TtRRRJn/Es1zhrrCHu7g==", + "dev": true, + "license": "ISC" + }, "node_modules/source-map-js": { "version": "1.2.1", "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz", @@ -3821,6 +4684,13 @@ "node": ">=0.10.0" } }, + "node_modules/stackback": { + "version": "0.0.2", + "resolved": "https://registry.npmjs.org/stackback/-/stackback-0.0.2.tgz", + "integrity": "sha512-1XMJE5fQo1jGH6Y/7ebnwPOBEkIEnT4QF32d5R1+VXdXveM0IBMJt8zfaxX1P3QhVwrYe+576+jkANtSS2mBbw==", + "dev": true, + "license": "MIT" + }, "node_modules/statuses": { "version": "2.0.2", "resolved": "https://registry.npmjs.org/statuses/-/statuses-2.0.2.tgz", @@ -3830,6 +4700,33 @@ "node": ">= 0.8" } }, + "node_modules/std-env": { + "version": "4.1.0", + "resolved": "https://registry.npmjs.org/std-env/-/std-env-4.1.0.tgz", + "integrity": "sha512-Rq7ybcX2RuC55r9oaPVEW7/xu3tj8u4GeBYHBWCychFtzMIr86A7e3PPEBPT37sHStKX3+TiX/Fr/ACmJLVlLQ==", + "dev": true, + "license": "MIT" + }, + "node_modules/strip-indent": { + "version": "3.0.0", + "resolved": "https://registry.npmjs.org/strip-indent/-/strip-indent-3.0.0.tgz", + "integrity": "sha512-laJTa3Jb+VQpaC6DseHhF7dXVqHTfJPCRDaEbid/drOhgitgYku/letMUqOXFoWV0zIIUbjpdH2t+tYj4bQMRQ==", + "dev": true, + "license": "MIT", + "dependencies": { + "min-indent": "^1.0.0" + }, + "engines": { + "node": ">=8" + } + }, + "node_modules/symbol-tree": { + "version": "3.2.4", + "resolved": "https://registry.npmjs.org/symbol-tree/-/symbol-tree-3.2.4.tgz", + "integrity": "sha512-9QNk5KwDF+Bvz+PyObkmSYjI5ksVUYtjW7AU22r2NKcfLJcXp96hkDWU3+XndOsUb+AQ9QhfzfCT2O+CNWT5Tw==", + "dev": true, + "license": "MIT" + }, "node_modules/tailwind-merge": { "version": "3.5.0", "resolved": "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-3.5.0.tgz", @@ -3859,6 +4756,23 @@ "url": "https://opencollective.com/webpack" } }, + "node_modules/tinybench": { + "version": "2.9.0", + "resolved": "https://registry.npmjs.org/tinybench/-/tinybench-2.9.0.tgz", + "integrity": "sha512-0+DUvqWMValLmha6lr4kD8iAMK1HzV0/aKnCtWb9v9641TnP/MFb7Pc2bxoxQjTXAErryXVgUOfv2YqNllqGeg==", + "dev": true, + "license": "MIT" + }, + "node_modules/tinyexec": { + "version": "1.1.2", + "resolved": "https://registry.npmjs.org/tinyexec/-/tinyexec-1.1.2.tgz", + "integrity": "sha512-dAqSqE/RabpBKI8+h26GfLq6Vb3JVXs30XYQjdMjaj/c2tS8IYYMbIzP599KtRj7c57/wYApb3QjgRgXmrCukA==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=18" + } + }, "node_modules/tinyglobby": { "version": "0.2.16", "resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.16.tgz", @@ -3875,6 +4789,36 @@ "url": "https://github.com/sponsors/SuperchupuDev" } }, + "node_modules/tinyrainbow": { + "version": "3.1.0", + "resolved": "https://registry.npmjs.org/tinyrainbow/-/tinyrainbow-3.1.0.tgz", + "integrity": "sha512-Bf+ILmBgretUrdJxzXM0SgXLZ3XfiaUuOj/IKQHuTXip+05Xn+uyEYdVg0kYDipTBcLrCVyUzAPz7QmArb0mmw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=14.0.0" + } + }, + "node_modules/tldts": { + "version": "7.0.29", + "resolved": "https://registry.npmjs.org/tldts/-/tldts-7.0.29.tgz", + "integrity": "sha512-JIXCerhudr/N6OWLwLF1HVsTTUo7ry6qHa5eWZEkiMuxsIiAACL55tGLfqfHfoH7QaMQUW8fngD7u7TxWexYQg==", + "dev": true, + "license": "MIT", + "dependencies": { + "tldts-core": "^7.0.29" + }, + "bin": { + "tldts": "bin/cli.js" + } + }, + "node_modules/tldts-core": { + "version": "7.0.29", + "resolved": "https://registry.npmjs.org/tldts-core/-/tldts-core-7.0.29.tgz", + "integrity": "sha512-W99NuU7b1DcG3uJ3v9k9VztCH3WialNbBkBft5wCs8V8mexu0XQqaZEYb9l9RNNzK8+3EJ9PKWB0/RUtTQ/o+Q==", + "dev": true, + "license": "MIT" + }, "node_modules/toidentifier": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.1.tgz", @@ -3884,6 +4828,32 @@ "node": ">=0.6" } }, + "node_modules/tough-cookie": { + "version": "6.0.1", + "resolved": "https://registry.npmjs.org/tough-cookie/-/tough-cookie-6.0.1.tgz", + "integrity": "sha512-LktZQb3IeoUWB9lqR5EWTHgW/VTITCXg4D21M+lvybRVdylLrRMnqaIONLVb5mav8vM19m44HIcGq4qASeu2Qw==", + "dev": true, + "license": "BSD-3-Clause", + "dependencies": { + "tldts": "^7.0.5" + }, + "engines": { + "node": ">=16" + } + }, + "node_modules/tr46": { + "version": "6.0.0", + "resolved": "https://registry.npmjs.org/tr46/-/tr46-6.0.0.tgz", + "integrity": "sha512-bLVMLPtstlZ4iMQHpFHTR7GAGj2jxi8Dg0s2h2MafAE4uSWF98FC/3MomU51iQAMf8/qDUbKWf5GxuvvVcXEhw==", + "dev": true, + "license": "MIT", + "dependencies": { + "punycode": "^2.3.1" + }, + "engines": { + "node": ">=20" + } + }, "node_modules/tslib": { "version": "2.8.1", "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.1.tgz", @@ -3937,6 +4907,16 @@ "node": ">=14.17" } }, + "node_modules/undici": { + "version": "7.25.0", + "resolved": "https://registry.npmjs.org/undici/-/undici-7.25.0.tgz", + "integrity": "sha512-xXnp4kTyor2Zq+J1FfPI6Eq3ew5h6Vl0F/8d9XU5zZQf1tX9s2Su1/3PiMmUANFULpmksxkClamIZcaUqryHsQ==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=20.18.1" + } + }, "node_modules/undici-types": { "version": "6.21.0", "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz", @@ -4541,6 +5521,109 @@ "@esbuild/win32-x64": "0.25.12" } }, + "node_modules/vitest": { + "version": "4.1.5", + "resolved": "https://registry.npmjs.org/vitest/-/vitest-4.1.5.tgz", + "integrity": "sha512-9Xx1v3/ih3m9hN+SbfkUyy0JAs72ap3r7joc87XL6jwF0jGg6mFBvQ1SrwaX+h8BlkX6Hz9shdd1uo6AF+ZGpg==", + "dev": true, + "license": "MIT", + "dependencies": { + "@vitest/expect": "4.1.5", + "@vitest/mocker": "4.1.5", + "@vitest/pretty-format": "4.1.5", + "@vitest/runner": "4.1.5", + "@vitest/snapshot": "4.1.5", + "@vitest/spy": "4.1.5", + "@vitest/utils": "4.1.5", + "es-module-lexer": "^2.0.0", + "expect-type": "^1.3.0", + "magic-string": "^0.30.21", + "obug": "^2.1.1", + "pathe": "^2.0.3", + "picomatch": "^4.0.3", + "std-env": "^4.0.0-rc.1", + "tinybench": "^2.9.0", + "tinyexec": "^1.0.2", + "tinyglobby": "^0.2.15", + "tinyrainbow": "^3.1.0", + "vite": "^6.0.0 || ^7.0.0 || ^8.0.0", + "why-is-node-running": "^2.3.0" + }, + "bin": { + "vitest": "vitest.mjs" + }, + "engines": { + "node": "^20.0.0 || ^22.0.0 || >=24.0.0" + }, + "funding": { + "url": "https://opencollective.com/vitest" + }, + "peerDependencies": { + "@edge-runtime/vm": "*", + "@opentelemetry/api": "^1.9.0", + "@types/node": "^20.0.0 || ^22.0.0 || >=24.0.0", + "@vitest/browser-playwright": "4.1.5", + "@vitest/browser-preview": "4.1.5", + "@vitest/browser-webdriverio": "4.1.5", + "@vitest/coverage-istanbul": "4.1.5", + "@vitest/coverage-v8": "4.1.5", + "@vitest/ui": "4.1.5", + "happy-dom": "*", + "jsdom": "*", + "vite": "^6.0.0 || ^7.0.0 || ^8.0.0" + }, + "peerDependenciesMeta": { + "@edge-runtime/vm": { + "optional": true + }, + "@opentelemetry/api": { + "optional": true + }, + "@types/node": { + "optional": true + }, + "@vitest/browser-playwright": { + "optional": true + }, + "@vitest/browser-preview": { + "optional": true + }, + "@vitest/browser-webdriverio": { + "optional": true + }, + "@vitest/coverage-istanbul": { + "optional": true + }, + "@vitest/coverage-v8": { + "optional": true + }, + "@vitest/ui": { + "optional": true + }, + "happy-dom": { + "optional": true + }, + "jsdom": { + "optional": true + }, + "vite": { + "optional": false + } + } + }, + "node_modules/w3c-xmlserializer": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/w3c-xmlserializer/-/w3c-xmlserializer-5.0.0.tgz", + "integrity": "sha512-o8qghlI8NZHU1lLPrpi2+Uq7abh4GGPpYANlalzWxyWteJOCsr/P+oPBA49TOLu5FTZO4d3F9MnWJfiMo4BkmA==", + "dev": true, + "license": "MIT", + "dependencies": { + "xml-name-validator": "^5.0.0" + }, + "engines": { + "node": ">=18" + } + }, "node_modules/web-streams-polyfill": { "version": "3.3.3", "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz", @@ -4550,6 +5633,58 @@ "node": ">= 8" } }, + "node_modules/webidl-conversions": { + "version": "8.0.1", + "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-8.0.1.tgz", + "integrity": "sha512-BMhLD/Sw+GbJC21C/UgyaZX41nPt8bUTg+jWyDeg7e7YN4xOM05YPSIXceACnXVtqyEw/LMClUQMtMZ+PGGpqQ==", + "dev": true, + "license": "BSD-2-Clause", + "engines": { + "node": ">=20" + } + }, + "node_modules/whatwg-mimetype": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/whatwg-mimetype/-/whatwg-mimetype-5.0.0.tgz", + "integrity": "sha512-sXcNcHOC51uPGF0P/D4NVtrkjSU2fNsm9iog4ZvZJsL3rjoDAzXZhkm2MWt1y+PUdggKAYVoMAIYcs78wJ51Cw==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=20" + } + }, + "node_modules/whatwg-url": { + "version": "16.0.1", + "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-16.0.1.tgz", + "integrity": "sha512-1to4zXBxmXHV3IiSSEInrreIlu02vUOvrhxJJH5vcxYTBDAx51cqZiKdyTxlecdKNSjj8EcxGBxNf6Vg+945gw==", + "dev": true, + "license": "MIT", + "dependencies": { + "@exodus/bytes": "^1.11.0", + "tr46": "^6.0.0", + "webidl-conversions": "^8.0.1" + }, + "engines": { + "node": "^20.19.0 || ^22.12.0 || >=24.0.0" + } + }, + "node_modules/why-is-node-running": { + "version": "2.3.0", + "resolved": "https://registry.npmjs.org/why-is-node-running/-/why-is-node-running-2.3.0.tgz", + "integrity": "sha512-hUrmaWBdVDcxvYqnyh09zunKzROWjbZTiNy8dBEjkS7ehEDQibXJ7XvlmtbwuTclUiIyN+CyXQD4Vmko8fNm8w==", + "dev": true, + "license": "MIT", + "dependencies": { + "siginfo": "^2.0.0", + "stackback": "0.0.2" + }, + "bin": { + "why-is-node-running": "cli.js" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/ws": { "version": "8.20.0", "resolved": "https://registry.npmjs.org/ws/-/ws-8.20.0.tgz", @@ -4571,6 +5706,23 @@ } } }, + "node_modules/xml-name-validator": { + "version": "5.0.0", + "resolved": "https://registry.npmjs.org/xml-name-validator/-/xml-name-validator-5.0.0.tgz", + "integrity": "sha512-EvGK8EJ3DhaHfbRlETOWAS5pO9MZITeauHKJyb8wyajUfQUenkIg2MvLDTZ4T/TgIcm3HU0TFBgWWboAZ30UHg==", + "dev": true, + "license": "Apache-2.0", + "engines": { + "node": ">=18" + } + }, + "node_modules/xmlchars": { + "version": "2.2.0", + "resolved": "https://registry.npmjs.org/xmlchars/-/xmlchars-2.2.0.tgz", + "integrity": "sha512-JZnDKK8B0RCDw84FNdDAIpZK+JuJw+s7Lz8nksI7SIuU3UXJJslUthsi+uWBUYOwPFwW7W7PRLRfUKpxjtjFCw==", + "dev": true, + "license": "MIT" + }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", diff --git a/package.json b/package.json index 4b43b20..4f8d419 100644 --- a/package.json +++ b/package.json @@ -9,7 +9,9 @@ "preview": "vite preview", "start": "node server.ts", "clean": "rm -rf dist", - "lint": "tsc --noEmit" + "lint": "tsc --noEmit", + "test": "vitest", + "test:run": "vitest run" }, "dependencies": { "@google/genai": "^1.29.0", @@ -31,12 +33,17 @@ "zustand": "^5.0.12" }, "devDependencies": { + "@testing-library/jest-dom": "^6.9.1", + "@testing-library/react": "^16.3.2", + "@testing-library/user-event": "^14.6.1", "@types/express": "^4.17.21", "@types/node": "^22.14.0", "autoprefixer": "^10.4.21", + "jsdom": "^29.1.1", "tailwindcss": "^4.1.14", "tsx": "^4.21.0", "typescript": "~5.8.2", - "vite": "^6.2.0" + "vite": "^6.2.0", + "vitest": "^4.1.5" } } diff --git a/src/components/AISegmentation.test.tsx b/src/components/AISegmentation.test.tsx new file mode 100644 index 0000000..e0bb7ba --- /dev/null +++ b/src/components/AISegmentation.test.tsx @@ -0,0 +1,43 @@ +import { fireEvent, render, screen } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { AISegmentation } from './AISegmentation'; + +const apiMock = vi.hoisted(() => ({ + getAiModelStatus: vi.fn(), + predictMask: vi.fn(), +})); + +vi.mock('../lib/api', () => ({ + getAiModelStatus: apiMock.getAiModelStatus, + predictMask: apiMock.predictMask, +})); + +describe('AISegmentation', () => { + beforeEach(() => { + resetStore(); + vi.clearAllMocks(); + useStore.setState({ + frames: [{ id: 'frame-1', projectId: 'project-1', index: 0, url: '/frame.jpg', width: 640, height: 360 }], + }); + apiMock.getAiModelStatus.mockResolvedValue({ + selected_model: 'sam2', + gpu: { available: true, device: 'cuda', name: 'RTX 4090', torch_available: true }, + models: [ + { id: 'sam2', label: 'SAM 2', available: true, loaded: false, device: 'cuda', supports: ['point', 'box'], message: 'SAM 2 ready', package_available: true, checkpoint_exists: true, python_ok: true, torch_ok: true, cuda_required: false }, + { id: 'sam3', label: 'SAM 3', available: false, loaded: false, device: 'cuda', supports: ['semantic'], message: 'SAM 3 missing runtime', package_available: false, checkpoint_exists: false, python_ok: false, torch_ok: true, cuda_required: true }, + ], + }); + }); + + it('lets the user choose SAM3 for subsequent predictions', async () => { + render(); + + const sam3Button = (await screen.findByText('SAM3')).closest('button')!; + fireEvent.click(sam3Button); + + expect(useStore.getState().aiModel).toBe('sam3'); + expect(await screen.findByText('SAM 3 missing runtime')).toBeInTheDocument(); + }); +}); diff --git a/src/components/AISegmentation.tsx b/src/components/AISegmentation.tsx index 39600e3..b45f638 100644 --- a/src/components/AISegmentation.tsx +++ b/src/components/AISegmentation.tsx @@ -1,11 +1,11 @@ -import React, { useState, useCallback } from 'react'; +import React, { useState, useCallback, useEffect } from 'react'; import { Target, PlusCircle, MinusCircle, SquareDashed, Sparkles, SendToBack, Image as ImageIcon, Undo, Redo, Loader2 } from 'lucide-react'; import { cn } from '../lib/utils'; import { Stage, Layer, Image as KonvaImage, Circle, Path, Group } from 'react-konva'; import useImage from 'use-image'; import { OntologyInspector } from './OntologyInspector'; import { useStore } from '../store/useStore'; -import { predictMask } from '../lib/api'; +import { getAiModelStatus, predictMask, type AiRuntimeStatus } from '../lib/api'; interface AISegmentationProps { onSendToWorkspace: () => void; @@ -17,9 +17,15 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) { const masks = useStore((state) => state.masks); const addMask = useStore((state) => state.addMask); const clearMasks = useStore((state) => state.clearMasks); + const frames = useStore((state) => state.frames); + const currentFrameIndex = useStore((state) => state.currentFrameIndex); + const activeTemplateId = useStore((state) => state.activeTemplateId); + const activeClass = useStore((state) => state.activeClass); + const aiModel = useStore((state) => state.aiModel); + const setAiModel = useStore((state) => state.setAiModel); - const [modelSize, setModelSize] = useState('vit_l'); const [semanticText, setSemanticText] = useState(''); + const [modelStatus, setModelStatus] = useState(null); const [autoDeleteBg, setAutoDeleteBg] = useState(true); const [cropMode, setCropMode] = useState(false); const [isInferencing, setIsInferencing] = useState(false); @@ -29,10 +35,29 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) { const [position, setPosition] = useState({ x: 0, y: 0 }); const [points, setPoints] = useState<{ x: number, y: number, type: 'pos'|'neg' }[]>([]); const [cursorPos, setCursorPos] = useState({ x: 0, y: 0 }); - const [image] = useImage('https://images.unsplash.com/photo-1549317661-bd32c8ce0be2?q=80&w=2070&auto=format&fit=crop'); + const currentFrame = frames[currentFrameIndex] || null; + const previewUrl = currentFrame?.url || 'https://images.unsplash.com/photo-1549317661-bd32c8ce0be2?q=80&w=2070&auto=format&fit=crop'; + const [image] = useImage(previewUrl); + const frameMasks = currentFrame ? masks.filter((mask) => mask.frameId === currentFrame.id) : masks; + const selectedModelStatus = modelStatus?.models.find((model) => model.id === aiModel); + const modelCanInfer = selectedModelStatus?.available ?? true; const effectiveTool = storeActiveTool; + useEffect(() => { + let cancelled = false; + getAiModelStatus(aiModel) + .then((status) => { + if (!cancelled) setModelStatus(status); + }) + .catch(() => { + if (!cancelled) setModelStatus(null); + }); + return () => { + cancelled = true; + }; + }, [aiModel]); + const handleWheel = (e: any) => { e.evt.preventDefault(); const scaleBy = 1.1; @@ -63,22 +88,44 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) { const runInference = useCallback(async () => { if (points.length === 0 && !semanticText.trim()) return; + if (!currentFrame?.id) { + console.warn('AI inference skipped: no project frame is selected'); + return; + } + + const imageWidth = currentFrame.width || image?.naturalWidth || image?.width || 0; + const imageHeight = currentFrame.height || image?.naturalHeight || image?.height || 0; + if (imageWidth <= 0 || imageHeight <= 0) { + console.warn('AI inference skipped: active frame dimensions are unavailable'); + return; + } + setIsInferencing(true); try { const result = await predictMask({ - imageUrl: 'https://images.unsplash.com/photo-1549317661-bd32c8ce0be2?q=80&w=2070&auto=format&fit=crop', + imageId: currentFrame.id, + imageWidth, + imageHeight, + model: aiModel, points: points.map((p) => ({ x: p.x, y: p.y, type: p.type })), text: semanticText.trim() || undefined, - modelSize, }); result.masks.forEach((m) => { + const label = activeClass?.name || m.label; + const color = activeClass?.color || m.color; addMask({ id: m.id, - frameId: 'frame-ai-1', + frameId: currentFrame.id, + templateId: activeTemplateId || undefined, + classId: activeClass?.id, + className: activeClass?.name, + classZIndex: activeClass?.zIndex, + saveStatus: 'draft', + saved: false, pathData: m.pathData, - label: m.label, - color: m.color, + label, + color, segmentation: m.segmentation, bbox: m.bbox, area: m.area, @@ -89,7 +136,7 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) { } finally { setIsInferencing(false); } - }, [points, semanticText, modelSize, addMask]); + }, [activeClass, activeTemplateId, addMask, aiModel, currentFrame?.height, currentFrame?.id, currentFrame?.width, image?.height, image?.naturalHeight, image?.naturalWidth, image?.width, points, semanticText]); const handleStageClick = (e: any) => { if (effectiveTool === 'move') return; @@ -117,17 +164,26 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) { {/* Model Select */}

视觉基础模型选型

-
- {['vit_b', 'vit_l', 'vit_h'].map(m => ( +
+ {(modelStatus?.models || [ + { id: 'sam2' as const, label: 'SAM 2', available: true, message: '正在读取 SAM 2 状态' }, + { id: 'sam3' as const, label: 'SAM 3', available: false, message: '正在读取 SAM 3 状态' }, + ]).map((m) => ( ))}
+
+
{selectedModelStatus?.message || '正在读取模型状态...'}
+
GPU: {modelStatus?.gpu.available ? `${modelStatus.gpu.name || 'CUDA'} 可用` : '不可用或未检测到 CUDA'}
+
{/* Prompt Tools */} @@ -206,16 +262,16 @@ export function AISegmentation({ onSendToWorkspace }: AISegmentationProps) {
diff --git a/src/components/CanvasArea.test.tsx b/src/components/CanvasArea.test.tsx new file mode 100644 index 0000000..a462b7a --- /dev/null +++ b/src/components/CanvasArea.test.tsx @@ -0,0 +1,130 @@ +import { fireEvent, render, screen, waitFor } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { CanvasArea } from './CanvasArea'; + +const apiMock = vi.hoisted(() => ({ + predictMask: vi.fn(), +})); + +vi.mock('../lib/api', () => ({ + predictMask: apiMock.predictMask, +})); + +describe('CanvasArea', () => { + const frame = { id: 'frame-1', projectId: 'project-1', index: 0, url: '/frame.jpg', width: 640, height: 360 }; + + beforeEach(() => { + resetStore(); + vi.clearAllMocks(); + }); + + it('calls AI prediction with the active frame when a point prompt is placed', async () => { + useStore.setState({ + activeTemplateId: '2', + activeClass: { id: 'c1', name: '胆囊', color: '#ff0000', zIndex: 20 }, + activeClassId: 'c1', + }); + apiMock.predictMask.mockResolvedValueOnce({ + masks: [ + { + id: 'mask-1', + pathData: 'M 0 0 L 10 0 L 10 10 Z', + label: 'AI Mask', + color: '#06b6d4', + segmentation: [[0, 0, 10, 0, 10, 10]], + bbox: [0, 0, 10, 10], + area: 100, + }, + ], + }); + + render(); + fireEvent.click(screen.getByTestId('konva-stage')); + + await waitFor(() => expect(apiMock.predictMask).toHaveBeenCalledWith({ + imageId: 'frame-1', + imageWidth: 640, + imageHeight: 360, + model: 'sam2', + points: [{ x: 120, y: 80, type: 'pos' }], + box: undefined, + })); + expect(useStore.getState().masks[0]).toEqual(expect.objectContaining({ + id: 'mask-1', + frameId: 'frame-1', + pathData: 'M 0 0 L 10 0 L 10 10 Z', + templateId: '2', + classId: 'c1', + className: '胆囊', + classZIndex: 20, + label: '胆囊', + color: '#ff0000', + saveStatus: 'draft', + })); + }); + + it('renders only masks that belong to the current frame', () => { + useStore.setState({ + masks: [ + { id: 'm1', frameId: 'frame-1', pathData: 'M 0 0 Z', label: 'A', color: '#fff' }, + { id: 'm2', frameId: 'frame-2', pathData: 'M 1 1 Z', label: 'B', color: '#000' }, + ], + }); + + render(); + + expect(screen.getAllByTestId('konva-path')).toHaveLength(1); + expect(screen.getByText('遮罩数: 1')).toBeInTheDocument(); + }); + + it('applies the selected class to current-frame masks and marks saved masks dirty', () => { + useStore.setState({ + activeTemplateId: '2', + activeClass: { id: 'c1', name: '胆囊', color: '#ff0000', zIndex: 20 }, + activeClassId: 'c1', + masks: [ + { + id: 'm1', + frameId: 'frame-1', + annotationId: '99', + pathData: 'M 0 0 Z', + label: '旧标签', + color: '#06b6d4', + saved: true, + saveStatus: 'saved', + }, + ], + }); + + render(); + fireEvent.click(screen.getByRole('button', { name: '应用分类' })); + + expect(useStore.getState().masks[0]).toEqual(expect.objectContaining({ + templateId: '2', + classId: 'c1', + className: '胆囊', + classZIndex: 20, + label: '胆囊', + color: '#ff0000', + saveStatus: 'dirty', + saved: false, + })); + }); + + it('delegates clear to the workspace handler so saved annotations can be deleted', () => { + const onClearMasks = vi.fn(); + useStore.setState({ + masks: [ + { id: 'm1', frameId: 'frame-1', pathData: 'M 0 0 Z', label: 'A', color: '#fff' }, + ], + }); + + render(); + fireEvent.click(screen.getByRole('button', { name: '清空遮罩' })); + + expect(onClearMasks).toHaveBeenCalled(); + expect(useStore.getState().masks).toHaveLength(1); + }); +}); diff --git a/src/components/CanvasArea.tsx b/src/components/CanvasArea.tsx index 80c3035..de2ff59 100644 --- a/src/components/CanvasArea.tsx +++ b/src/components/CanvasArea.tsx @@ -3,14 +3,15 @@ import { Stage, Layer, Image as KonvaImage, Circle, Rect, Path, Group } from 're import useImage from 'use-image'; import { useStore } from '../store/useStore'; import { predictMask } from '../lib/api'; -import { cn } from '../lib/utils'; +import type { Frame } from '../store/useStore'; interface CanvasAreaProps { activeTool: string; - frameUrl: string; + frame: Frame | null; + onClearMasks?: () => void; } -export function CanvasArea({ activeTool, frameUrl }: CanvasAreaProps) { +export function CanvasArea({ activeTool, frame, onClearMasks }: CanvasAreaProps) { const containerRef = useRef(null); const [stageSize, setStageSize] = useState({ width: 800, height: 600 }); const [scale, setScale] = useState(1); @@ -24,13 +25,20 @@ export function CanvasArea({ activeTool, frameUrl }: CanvasAreaProps) { const masks = useStore((state) => state.masks); const addMask = useStore((state) => state.addMask); const clearMasks = useStore((state) => state.clearMasks); + const setMasks = useStore((state) => state.setMasks); const storeActiveTool = useStore((state) => state.activeTool); - const setActiveTool = useStore((state) => state.setActiveTool); + const aiModel = useStore((state) => state.aiModel); + const activeTemplateId = useStore((state) => state.activeTemplateId); + const activeClass = useStore((state) => state.activeClass); const effectiveTool = activeTool || storeActiveTool; // Load the actual frame image - const [image] = useImage(frameUrl || ''); + const [image] = useImage(frame?.url || ''); + const frameMasks = masks.filter((mask) => mask.frameId === frame?.id); + const savedMaskCount = frameMasks.filter((mask) => mask.saveStatus === 'saved' || mask.saved).length; + const draftMaskCount = frameMasks.filter((mask) => !mask.annotationId).length; + const dirtyMaskCount = frameMasks.filter((mask) => mask.saveStatus === 'dirty').length; useEffect(() => { const handleResize = () => { @@ -85,21 +93,44 @@ export function CanvasArea({ activeTool, frameUrl }: CanvasAreaProps) { }; const runInference = useCallback(async (promptPoints?: typeof points, promptBox?: { x1: number, y1: number, x2: number, y2: number }) => { + if (!frame?.id) { + console.warn('Inference skipped: no active frame'); + return; + } + + const imageWidth = frame.width || image?.naturalWidth || image?.width || 0; + const imageHeight = frame.height || image?.naturalHeight || image?.height || 0; + if (imageWidth <= 0 || imageHeight <= 0) { + console.warn('Inference skipped: active frame dimensions are unavailable'); + return; + } + setIsInferencing(true); try { const result = await predictMask({ - imageUrl: frameUrl || '', + imageId: frame.id, + imageWidth, + imageHeight, + model: aiModel, points: promptPoints?.map((p) => ({ x: p.x, y: p.y, type: p.type })), box: promptBox, }); result.masks.forEach((m) => { + const label = activeClass?.name || m.label; + const color = activeClass?.color || m.color; addMask({ id: m.id, - frameId: 'frame-1', + frameId: frame.id, + templateId: activeTemplateId || undefined, + classId: activeClass?.id, + className: activeClass?.name, + classZIndex: activeClass?.zIndex, + saveStatus: 'draft', + saved: false, pathData: m.pathData, - label: m.label, - color: m.color, + label, + color, segmentation: m.segmentation, bbox: m.bbox, area: m.area, @@ -110,7 +141,33 @@ export function CanvasArea({ activeTool, frameUrl }: CanvasAreaProps) { } finally { setIsInferencing(false); } - }, [addMask]); + }, [activeClass, activeTemplateId, addMask, aiModel, frame?.height, frame?.id, frame?.width, image?.height, image?.naturalHeight, image?.naturalWidth, image?.width]); + + const handleApplyActiveClass = () => { + if (!frame?.id || !activeClass) return; + setMasks(masks.map((mask) => { + if (mask.frameId !== frame.id) return mask; + return { + ...mask, + templateId: activeTemplateId || mask.templateId, + classId: activeClass.id, + className: activeClass.name, + classZIndex: activeClass.zIndex, + label: activeClass.name, + color: activeClass.color, + saveStatus: mask.annotationId ? 'dirty' : 'draft', + saved: Boolean(mask.annotationId) ? false : mask.saved, + }; + })); + }; + + const handleClearMasks = () => { + if (onClearMasks) { + onClearMasks(); + return; + } + clearMasks(); + }; const handleStageMouseDown = (e: any) => { if (effectiveTool === 'box_select') { @@ -199,7 +256,7 @@ export function CanvasArea({ activeTool, frameUrl }: CanvasAreaProps) { )} {/* AI Returned Masks */} - {masks.map((mask) => ( + {frameMasks.map((mask) => ( 光标: {cursorPos.x.toFixed(2)}, {cursorPos.y.toFixed(2)} 当前图层树: OBJECT_VEHICLE_01 缩放比: {(scale * 100).toFixed(0)}% - 遮罩数: {masks.length} + 遮罩数: {frameMasks.length} + 已保存: {savedMaskCount} + 未保存: {draftMaskCount} + 待更新: {dirtyMaskCount} - {masks.length > 0 && ( - + {frameMasks.length > 0 && ( +
+ {activeClass && ( + + )} + +
)} ); diff --git a/src/components/Dashboard.test.tsx b/src/components/Dashboard.test.tsx new file mode 100644 index 0000000..2adb60b --- /dev/null +++ b/src/components/Dashboard.test.tsx @@ -0,0 +1,115 @@ +import { act, render, screen, waitFor } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { Dashboard } from './Dashboard'; + +const apiMock = vi.hoisted(() => ({ + getDashboardOverview: vi.fn(), +})); + +const wsMock = vi.hoisted(() => { + const state = { + callback: undefined as undefined | ((data: any) => void), + connected: false, + }; + return { + state, + progressWS: { + connect: vi.fn(() => { state.connected = true; }), + disconnect: vi.fn(() => { state.connected = false; }), + isConnected: vi.fn(() => state.connected), + onProgress: vi.fn((cb: (data: any) => void) => { + state.callback = cb; + return vi.fn(); + }), + }, + }; +}); + +vi.mock('../lib/websocket', () => ({ + progressWS: wsMock.progressWS, +})); + +vi.mock('../lib/api', () => ({ + getDashboardOverview: apiMock.getDashboardOverview, +})); + +describe('Dashboard', () => { + beforeEach(() => { + vi.useRealTimers(); + vi.clearAllMocks(); + wsMock.state.connected = false; + wsMock.state.callback = undefined; + apiMock.getDashboardOverview.mockResolvedValue({ + summary: { + project_count: 2, + parsing_task_count: 1, + annotation_count: 5, + frame_count: 100, + template_count: 3, + system_load_percent: 12, + }, + tasks: [ + { + id: 'project-1', + project_id: 1, + name: '真实项目.mp4', + progress: 60, + status: 'pending', + frame_count: 10, + updated_at: '2026-05-01T00:00:00Z', + }, + ], + activity: [ + { + id: 'activity-1', + kind: 'project', + time: '2026-05-01T00:00:00Z', + message: '项目状态: pending', + project: '真实项目.mp4', + }, + ], + }); + }); + + it('loads dashboard stats, tasks, and activity from the backend overview endpoint', async () => { + render(); + + await waitFor(() => expect(apiMock.getDashboardOverview).toHaveBeenCalled()); + expect(screen.getByText('项目总数')).toBeInTheDocument(); + expect(screen.getByText('已存标注')).toBeInTheDocument(); + expect(screen.getByText('真实项目.mp4')).toBeInTheDocument(); + expect(screen.getByText('项目状态: pending')).toBeInTheDocument(); + expect(screen.queryByText('City_Driving_Dataset_004.mp4')).not.toBeInTheDocument(); + }); + + it('connects to the progress stream and updates progress tasks', async () => { + render(); + + await waitFor(() => expect(wsMock.progressWS.connect).toHaveBeenCalled()); + + act(() => { + wsMock.state.callback?.({ + type: 'progress', + taskId: 'task-1', + projectName: 'demo.mp4', + progress: 44, + status: '正在截取帧', + }); + }); + + expect(await screen.findByText('demo.mp4')).toBeInTheDocument(); + expect(screen.getByText('44%')).toBeInTheDocument(); + }); + + it('adds activity logs for complete and status messages', async () => { + render(); + + act(() => { + wsMock.state.callback?.({ type: 'status', message: 'Progress stream active' }); + wsMock.state.callback?.({ type: 'complete', taskId: '1', filename: 'done.mp4' }); + }); + + await waitFor(() => expect(screen.getByText('Progress stream active')).toBeInTheDocument()); + expect(screen.getByText('解析完成: done.mp4')).toBeInTheDocument(); + }); +}); diff --git a/src/components/Dashboard.tsx b/src/components/Dashboard.tsx index 3fe3ae0..ed4e68a 100644 --- a/src/components/Dashboard.tsx +++ b/src/components/Dashboard.tsx @@ -2,30 +2,68 @@ import React, { useState, useEffect } from 'react'; import { Activity, Clock, Folders, CheckCircle2, Loader2 } from 'lucide-react'; import { progressWS, type ProgressMessage } from '../lib/websocket'; import { cn } from '../lib/utils'; +import { getDashboardOverview, type DashboardActivity, type DashboardOverview, type DashboardTask } from '../lib/api'; -interface QueueTask { - id: string; - name: string; - progress: number; - status: string; -} +const emptySummary: DashboardOverview['summary'] = { + project_count: 0, + parsing_task_count: 0, + annotation_count: 0, + frame_count: 0, + template_count: 0, + system_load_percent: 0, +}; export function Dashboard() { - const [tasks, setTasks] = useState([ - { id: '1', name: 'City_Driving_Dataset_004.mp4', progress: 85, status: '正在截取帧 (30fps)' }, - { id: '2', name: 'Pedestrian_Night_Vision_02.mkv', progress: 32, status: '正在截取帧 (60fps)' }, - { id: '3', name: 'Drone_Mapping_Sector_7.avi', progress: 0, status: '队列排队等待中' }, - ]); + const [summary, setSummary] = useState(emptySummary); + const [tasks, setTasks] = useState([]); const [isConnected, setIsConnected] = useState(false); - const [activityLog, setActivityLog] = useState>([ - { time: '10 分钟前', message: '语义归档完成 54 帧', project: 'Highway_Data' }, - { time: '25 分钟前', message: '项目解析开始', project: 'City_Driving_Dataset_004' }, - { time: '1 小时前', message: '模板库更新: Cityscapes_v2', project: '系统' }, - { time: '2 小时前', message: 'AI 推理完成 12 个实例', project: 'Nav_Cam_Left' }, - ]); + const [activityLog, setActivityLog] = useState([]); + const [isLoading, setIsLoading] = useState(true); + const [loadError, setLoadError] = useState(''); + + useEffect(() => { + let cancelled = false; + + const loadOverview = () => { + getDashboardOverview() + .then((overview) => { + if (cancelled) return; + setSummary(overview.summary); + setTasks((prev) => { + if (prev.length === 0) return overview.tasks; + const overviewIds = new Set(overview.tasks.map((task) => task.id)); + const wsOnly = prev.filter((task) => !task.id.startsWith('task-') && !overviewIds.has(task.id) && task.progress < 100); + return [...overview.tasks, ...wsOnly]; + }); + setActivityLog((prev) => { + if (prev.length === 0) return overview.activity; + const byId = new Map(prev.map((item) => [item.id, item])); + overview.activity.forEach((item) => byId.set(item.id, item)); + return Array.from(byId.values()).slice(0, 10); + }); + setLoadError(''); + }) + .catch((err) => { + console.error('Failed to load dashboard overview:', err); + if (!cancelled) setLoadError('Dashboard 数据加载失败'); + }) + .finally(() => { + if (!cancelled) setIsLoading(false); + }); + }; + + loadOverview(); + const overviewInterval = setInterval(loadOverview, 5000); + + return () => { + cancelled = true; + clearInterval(overviewInterval); + }; + }, []); useEffect(() => { let mounted = true; + const taskTitle = (data: ProgressMessage) => data.filename || data.projectName || data.taskId || '后台任务'; const timer = setTimeout(() => { if (mounted) progressWS.connect(); }, 500); @@ -34,7 +72,7 @@ export function Dashboard() { if (!mounted) return; setIsConnected(progressWS.isConnected()); - if (data.type === 'progress' && data.taskId && data.filename) { + if (data.type === 'progress' && data.taskId) { setTasks((prev) => { const exists = prev.find((t) => t.id === data.taskId); if (exists) { @@ -48,9 +86,12 @@ export function Dashboard() { ...prev, { id: data.taskId!, - name: data.filename!, + project_id: data.project_id ?? Number(data.task_id || 0), + name: taskTitle(data), progress: data.progress ?? 0, status: data.status ?? '处理中', + frame_count: 0, + updated_at: new Date().toISOString(), }, ]; }); @@ -63,7 +104,7 @@ export function Dashboard() { ) ); setActivityLog((prev) => [ - { time: '刚刚', message: `解析完成: ${data.filename || data.taskId}`, project: '系统' }, + { id: `ws-complete-${Date.now()}`, kind: 'websocket', time: new Date().toISOString(), message: data.message || `解析完成: ${taskTitle(data)}`, project: data.projectName || '系统' }, ...prev.slice(0, 9), ]); } @@ -71,14 +112,18 @@ export function Dashboard() { if (data.type === 'error' && data.taskId) { setTasks((prev) => prev.map((t) => - t.id === data.taskId ? { ...t, status: `错误: ${data.message || '未知错误'}` } : t + t.id === data.taskId ? { ...t, progress: data.progress ?? t.progress, status: `错误: ${data.error || data.message || '未知错误'}` } : t ) ); + setActivityLog((prev) => [ + { id: `ws-error-${Date.now()}`, kind: 'websocket', time: new Date().toISOString(), message: data.message || `解析失败: ${taskTitle(data)}`, project: data.projectName || '系统' }, + ...prev.slice(0, 9), + ]); } if (data.type === 'status') { setActivityLog((prev) => [ - { time: '刚刚', message: data.message || '状态更新', project: '系统' }, + { id: `ws-status-${Date.now()}`, kind: 'websocket', time: new Date().toISOString(), message: data.message || '状态更新', project: '系统' }, ...prev.slice(0, 9), ]); } @@ -97,12 +142,24 @@ export function Dashboard() { }, []); const stats = [ - { label: '运行中项目', value: '14', icon: Folders, color: 'text-blue-400', bg: 'bg-blue-400/10' }, - { label: '排队处理任务', value: tasks.length.toString(), icon: Clock, color: 'text-orange-400', bg: 'bg-orange-400/10' }, - { label: '已归档批次', value: '128', icon: CheckCircle2, color: 'text-emerald-400', bg: 'bg-emerald-400/10' }, - { label: '系统负载', value: '78%', icon: Activity, color: 'text-cyan-400', bg: 'bg-cyan-400/10' }, + { label: '项目总数', value: summary.project_count.toString(), icon: Folders, color: 'text-blue-400', bg: 'bg-blue-400/10' }, + { label: '处理任务', value: summary.parsing_task_count.toString(), icon: Clock, color: 'text-orange-400', bg: 'bg-orange-400/10' }, + { label: '已存标注', value: summary.annotation_count.toString(), icon: CheckCircle2, color: 'text-emerald-400', bg: 'bg-emerald-400/10' }, + { label: '系统负载', value: `${summary.system_load_percent}%`, icon: Activity, color: 'text-cyan-400', bg: 'bg-cyan-400/10' }, ]; + function formatActivityTime(value: string | null): string { + if (!value) return '未知时间'; + const date = new Date(value); + if (Number.isNaN(date.getTime())) return value; + return date.toLocaleString('zh-CN', { + month: '2-digit', + day: '2-digit', + hour: '2-digit', + minute: '2-digit', + }); + } + return (
@@ -119,6 +176,7 @@ export function Dashboard() {

系统全局数据吞吐状态与所有接入项目进度实时洞察驾驶舱。

+ {loadError &&

{loadError}

}
@@ -140,8 +198,11 @@ export function Dashboard() {
-

解析队列 (FFmpeg 挂起任务)

+

解析队列 (后台任务)

+ {isLoading && ( +
正在读取后端 Dashboard 数据...
+ )} {tasks.map((task) => (
@@ -152,7 +213,7 @@ export function Dashboard() {
- {task.status === '已完成' ? ( + {task.status === '已完成' || task.progress >= 100 ? ( ) : task.status.includes('错误') ? ( ● @@ -160,10 +221,11 @@ export function Dashboard() { )} {task.status} + 帧: {task.frame_count}
))} - {tasks.length === 0 && ( + {!isLoading && tasks.length === 0 && (
当前无处理任务
)}
@@ -172,16 +234,22 @@ export function Dashboard() {

近期实时流转记录

- {activityLog.map((log, i) => ( -
+ {isLoading && ( +
正在读取近期流转记录...
+ )} + {activityLog.map((log) => ( +
-
{log.time}
+
{formatActivityTime(log.time)}
{log.message}
归属项目: {log.project}
))} + {!isLoading && activityLog.length === 0 && ( +
暂无近期流转记录
+ )}
diff --git a/src/components/FrameTimeline.test.tsx b/src/components/FrameTimeline.test.tsx new file mode 100644 index 0000000..f0b4553 --- /dev/null +++ b/src/components/FrameTimeline.test.tsx @@ -0,0 +1,62 @@ +import { act, fireEvent, render, screen } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { FrameTimeline } from './FrameTimeline'; + +describe('FrameTimeline', () => { + beforeEach(() => { + resetStore(); + vi.useRealTimers(); + }); + + it('renders empty state when no frames are loaded', () => { + render(); + + expect(screen.getByText('暂无帧数据')).toBeInTheDocument(); + expect(screen.getByText('0')).toBeInTheDocument(); + }); + + it('changes the current frame through thumbnails and range input', () => { + useStore.setState({ + frames: [ + { id: 'f1', projectId: 'p1', index: 0, url: '/1.jpg', width: 640, height: 360 }, + { id: 'f2', projectId: 'p1', index: 1, url: '/2.jpg', width: 640, height: 360 }, + { id: 'f3', projectId: 'p1', index: 2, url: '/3.jpg', width: 640, height: 360 }, + ], + }); + + render(); + fireEvent.click(screen.getByAltText('frame-1')); + expect(useStore.getState().currentFrameIndex).toBe(1); + + fireEvent.change(screen.getByRole('slider'), { target: { value: '3' } }); + expect(useStore.getState().currentFrameIndex).toBe(2); + }); + + it('plays forward using the project parse fps and stops at the end', () => { + vi.useFakeTimers(); + useStore.setState({ + currentProject: { id: 'p1', name: 'P', status: 'ready', parse_fps: 10 }, + frames: [ + { id: 'f1', projectId: 'p1', index: 0, url: '/1.jpg', width: 640, height: 360 }, + { id: 'f2', projectId: 'p1', index: 1, url: '/2.jpg', width: 640, height: 360 }, + ], + }); + + const { container } = render(); + fireEvent.click(container.querySelector('button')!); + + act(() => { + vi.advanceTimersByTime(100); + }); + + expect(useStore.getState().currentFrameIndex).toBe(1); + + act(() => { + vi.advanceTimersByTime(100); + }); + + expect(screen.getByText('播放序列 (F5)')).toBeInTheDocument(); + }); +}); diff --git a/src/components/FrameTimeline.tsx b/src/components/FrameTimeline.tsx index 67937b1..47f258c 100644 --- a/src/components/FrameTimeline.tsx +++ b/src/components/FrameTimeline.tsx @@ -1,16 +1,42 @@ -import React, { useState } from 'react'; +import React, { useEffect, useMemo, useState } from 'react'; import { Play, Pause } from 'lucide-react'; import { cn } from '../lib/utils'; import { useStore } from '../store/useStore'; export function FrameTimeline() { const frames = useStore((state) => state.frames); + const currentProject = useStore((state) => state.currentProject); const currentFrameIndex = useStore((state) => state.currentFrameIndex); const setCurrentFrame = useStore((state) => state.setCurrentFrame); const [isPlaying, setIsPlaying] = useState(false); const totalFrames = frames.length; const currentFrame = totalFrames > 0 ? currentFrameIndex + 1 : 0; + const playbackFps = useMemo(() => { + const fps = currentProject?.parse_fps || currentProject?.original_fps || 12; + return Math.min(Math.max(fps, 1), 30); + }, [currentProject?.original_fps, currentProject?.parse_fps]); + + useEffect(() => { + if (!isPlaying || totalFrames <= 1) return; + + const timer = window.setTimeout(() => { + if (currentFrameIndex >= totalFrames - 1) { + setIsPlaying(false); + return; + } + + setCurrentFrame(currentFrameIndex + 1); + }, 1000 / playbackFps); + + return () => window.clearTimeout(timer); + }, [currentFrameIndex, isPlaying, playbackFps, setCurrentFrame, totalFrames]); + + useEffect(() => { + if (totalFrames === 0) { + setIsPlaying(false); + } + }, [totalFrames]); // show frames around current frame const frameWindow = 20; @@ -45,8 +71,14 @@ export function FrameTimeline() {
diff --git a/src/components/Login.test.tsx b/src/components/Login.test.tsx new file mode 100644 index 0000000..1596430 --- /dev/null +++ b/src/components/Login.test.tsx @@ -0,0 +1,42 @@ +import { fireEvent, render, screen, waitFor } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { Login } from './Login'; + +const apiMock = vi.hoisted(() => ({ + login: vi.fn(), +})); + +vi.mock('../lib/api', () => ({ + login: apiMock.login, +})); + +describe('Login', () => { + beforeEach(() => { + resetStore(); + vi.clearAllMocks(); + }); + + it('logs in with the development credentials and stores the token', async () => { + apiMock.login.mockResolvedValueOnce({ token: 'fake-jwt-token-for-admin' }); + + render(); + fireEvent.click(screen.getByRole('button', { name: '安全登录' })); + + await waitFor(() => expect(apiMock.login).toHaveBeenCalledWith('admin', '123456')); + expect(useStore.getState().isAuthenticated).toBe(true); + expect(localStorage.getItem('token')).toBe('fake-jwt-token-for-admin'); + }); + + it('shows backend login errors', async () => { + apiMock.login.mockRejectedValueOnce({ response: { data: { detail: 'Invalid credentials' } } }); + + render(); + fireEvent.change(screen.getByDisplayValue('admin'), { target: { value: 'bad' } }); + fireEvent.click(screen.getByRole('button', { name: '安全登录' })); + + expect(await screen.findByText('Invalid credentials')).toBeInTheDocument(); + expect(useStore.getState().isAuthenticated).toBe(false); + }); +}); diff --git a/src/components/ModelStatusBadge.test.tsx b/src/components/ModelStatusBadge.test.tsx new file mode 100644 index 0000000..8fd5d14 --- /dev/null +++ b/src/components/ModelStatusBadge.test.tsx @@ -0,0 +1,45 @@ +import { render, screen, waitFor } from '@testing-library/react'; +import { beforeEach, describe, expect, it, vi } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { ModelStatusBadge } from './ModelStatusBadge'; + +const apiMock = vi.hoisted(() => ({ + getAiModelStatus: vi.fn(), +})); + +vi.mock('../lib/api', () => ({ + getAiModelStatus: apiMock.getAiModelStatus, +})); + +describe('ModelStatusBadge', () => { + beforeEach(() => { + resetStore(); + vi.clearAllMocks(); + apiMock.getAiModelStatus.mockResolvedValue({ + selected_model: 'sam2', + gpu: { available: true, device: 'cuda', name: 'RTX 4090', torch_available: true }, + models: [ + { id: 'sam2', label: 'SAM 2', available: true, loaded: false, device: 'cuda', supports: ['point', 'box'], message: 'SAM 2 ready', package_available: true, checkpoint_exists: true, python_ok: true, torch_ok: true, cuda_required: false }, + { id: 'sam3', label: 'SAM 3', available: false, loaded: false, device: 'cuda', supports: ['semantic'], message: 'SAM 3 missing runtime', package_available: false, checkpoint_exists: false, python_ok: false, torch_ok: true, cuda_required: true }, + ], + }); + }); + + it('loads real model status for the selected model', async () => { + render(); + + expect(await screen.findByText('SAM 2 可用')).toBeInTheDocument(); + expect(apiMock.getAiModelStatus).toHaveBeenCalledWith('sam2'); + }); + + it('shows unavailable state when SAM3 is selected but not runnable', async () => { + useStore.getState().setAiModel('sam3'); + + render(); + + await waitFor(() => expect(apiMock.getAiModelStatus).toHaveBeenCalledWith('sam3')); + expect(await screen.findByText('SAM 3 不可用')).toBeInTheDocument(); + expect(screen.getByTitle('SAM 3 missing runtime')).toBeInTheDocument(); + }); +}); diff --git a/src/components/ModelStatusBadge.tsx b/src/components/ModelStatusBadge.tsx new file mode 100644 index 0000000..979f06b --- /dev/null +++ b/src/components/ModelStatusBadge.tsx @@ -0,0 +1,56 @@ +import React, { useEffect, useState } from 'react'; +import { Cpu, Loader2 } from 'lucide-react'; +import { getAiModelStatus, type AiRuntimeStatus } from '../lib/api'; +import { cn } from '../lib/utils'; +import { useStore } from '../store/useStore'; + +interface ModelStatusBadgeProps { + compact?: boolean; +} + +export function ModelStatusBadge({ compact = false }: ModelStatusBadgeProps) { + const aiModel = useStore((state) => state.aiModel); + const [status, setStatus] = useState(null); + const [isLoading, setIsLoading] = useState(true); + + useEffect(() => { + let cancelled = false; + setIsLoading(true); + getAiModelStatus(aiModel) + .then((data) => { + if (!cancelled) setStatus(data); + }) + .catch(() => { + if (!cancelled) setStatus(null); + }) + .finally(() => { + if (!cancelled) setIsLoading(false); + }); + return () => { + cancelled = true; + }; + }, [aiModel]); + + const model = status?.models.find((item) => item.id === aiModel); + const ready = Boolean(model?.available); + const gpuReady = Boolean(status?.gpu.available); + const label = compact + ? (gpuReady ? 'GPU' : 'CPU') + : `${model?.label || aiModel.toUpperCase()} ${ready ? '可用' : '不可用'}`; + + return ( +
+ {isLoading ? : } + {label} +
+ ); +} diff --git a/src/components/OntologyInspector.test.tsx b/src/components/OntologyInspector.test.tsx new file mode 100644 index 0000000..73bbdc4 --- /dev/null +++ b/src/components/OntologyInspector.test.tsx @@ -0,0 +1,60 @@ +import { fireEvent, render, screen, within } from '@testing-library/react'; +import { beforeEach, describe, expect, it } from 'vitest'; +import { resetStore } from '../test/storeTestUtils'; +import { useStore } from '../store/useStore'; +import { OntologyInspector } from './OntologyInspector'; + +describe('OntologyInspector', () => { + beforeEach(() => { + resetStore(); + useStore.setState({ + templates: [ + { + id: 't1', + name: '腹腔镜模板', + classes: [ + { id: 'c1', name: '胆囊', color: '#ff0000', zIndex: 20, category: '器官' }, + { id: 'c2', name: '肝脏', color: '#00ff00', zIndex: 10, category: '器官' }, + ], + rules: [], + }, + ], + }); + }); + + it('shows template classes and changes the active template', () => { + render(); + + fireEvent.change(screen.getByRole('combobox'), { target: { value: 't1' } }); + + expect(useStore.getState().activeTemplateId).toBe('t1'); + expect(screen.getByText('胆囊')).toBeInTheDocument(); + expect(screen.getByText('肝脏')).toBeInTheDocument(); + }); + + it('selects a concrete class for subsequent masks', () => { + render(); + + fireEvent.click(screen.getByText('胆囊')); + + expect(useStore.getState().activeClassId).toBe('c1'); + expect(useStore.getState().activeClass).toEqual(expect.objectContaining({ + id: 'c1', + name: '胆囊', + zIndex: 20, + })); + }); + + it('adds custom classes locally without backend persistence', () => { + const { container } = render(); + const customSection = screen.getByText('自定义分类').parentElement!; + fireEvent.click(within(customSection).getByRole('button')); + fireEvent.change(screen.getByPlaceholderText('分类名称'), { target: { value: '新局部分类' } }); + fireEvent.keyDown(screen.getByPlaceholderText('分类名称'), { key: 'Enter' }); + + expect(screen.getAllByText('新局部分类')).toHaveLength(2); + expect(useStore.getState().activeClass).toEqual(expect.objectContaining({ name: '新局部分类' })); + expect(useStore.getState().templates[0].classes).toHaveLength(2); + expect(container).toHaveTextContent('2 个分类来自模板 + 1 个自定义'); + }); +}); diff --git a/src/components/OntologyInspector.tsx b/src/components/OntologyInspector.tsx index d68a0d2..b39c8fb 100644 --- a/src/components/OntologyInspector.tsx +++ b/src/components/OntologyInspector.tsx @@ -2,11 +2,16 @@ import React, { useState } from 'react'; import { Layers, ChevronDown, Tag, Eye, Plus, X } from 'lucide-react'; import { useStore } from '../store/useStore'; import type { TemplateClass } from '../store/useStore'; +import { cn } from '../lib/utils'; +import { getActiveTemplate } from '../lib/templateSelection'; export function OntologyInspector() { const templates = useStore((state) => state.templates); const activeTemplateId = useStore((state) => state.activeTemplateId); + const activeClassId = useStore((state) => state.activeClassId); + const activeClass = useStore((state) => state.activeClass); const setActiveTemplateId = useStore((state) => state.setActiveTemplateId); + const setActiveClass = useStore((state) => state.setActiveClass); // Project-level custom classes (in addition to template classes) const [customClasses, setCustomClasses] = useState([]); @@ -14,10 +19,17 @@ export function OntologyInspector() { const [newClassName, setNewClassName] = useState(''); const [newClassColor, setNewClassColor] = useState('#06b6d4'); - const activeTemplate = templates.find((t) => t.id === activeTemplateId) || templates[0] || null; + const activeTemplate = getActiveTemplate(templates, activeTemplateId); const templateClasses = activeTemplate?.classes || []; const allClasses = [...templateClasses, ...customClasses].sort((a, b) => b.zIndex - a.zIndex); + const handleSelectClass = (templateClass: TemplateClass) => { + if (activeTemplate && !activeTemplateId) { + setActiveTemplateId(activeTemplate.id); + } + setActiveClass(templateClass); + }; + const handleAddCustom = () => { if (!newClassName.trim()) return; const maxZ = allClasses.length > 0 ? Math.max(...allClasses.map((c) => c.zIndex)) : 0; @@ -29,6 +41,7 @@ export function OntologyInspector() { category: '自定义', }; setCustomClasses([...customClasses, newClass]); + handleSelectClass(newClass); setNewClassName(''); setShowAddForm(false); }; @@ -47,7 +60,10 @@ export function OntologyInspector() {