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5ab4602535
- 接入 SAM2 视频传播能力：新增 /api/ai/propagate，支持用当前帧 mask/polygon/bbox 作为 seed，通过 SAM2 video predictor 向前、向后或双向传播，并可保存为真实 annotation。 - 接入 SAM3 video tracker：通过独立 Python 3.12 external worker 调用 SAM3 video predictor/tracker，使用本地 checkpoint 与 bbox seed 执行视频级跟踪，并在模型状态中标记 video_track 能力。 - 完善 SAM 模型分发：sam_registry 按 model_id 明确区分 sam2 propagation 与 sam3 video_track，避免两个模型链路混用。 - 打通前端“传播片段”：VideoWorkspace 使用当前选中 mask 和当前 AI 模型调用后端传播接口，传播结果回写并刷新工作区已保存标注。 - 增强 SAM3 本地 checkpoint 配置：新增 sam3_checkpoint_path 配置和 .env.example 示例，状态检查改为基于本地 checkpoint/独立环境/模型包可用性。 - 完善视频拆帧参数：/api/media/parse 支持 parse_fps、max_frames、target_width，后端任务保存帧时间戳、源帧号和 frame_sequence 元数据。 - 增加运行时 schema 兼容处理：启动时为旧 frames 表补充 timestamp_ms 和 source_frame_number 列，避免旧库升级后缺字段。 - 强化 Canvas 标注编辑：补齐多边形闭合、点工具、顶点拖拽、边中点插入、Delete/Backspace 删除、区域合并和重叠去除等交互。 - 增强语义分类联动：选中 mask 后可通过右侧语义分类树更新标签、颜色和 class metadata，并同步到保存/导出链路。 - 增加关键帧时间轴体验：FrameTimeline 显示具体时间信息，并支持键盘左右方向键切换关键帧。 - 完善 AI 交互分割参数：前端保留正向点、反向点、框选和 interactive prompt 的调用状态，支持 SAM2 细化候选区域与 SAM3 bbox 入口。 - 扩展后端/前端 API 类型：新增 propagateMasks、传播请求/响应 schema，并补齐 annotation、导出、模型状态和任务接口的测试覆盖。 - 更新项目文档：同步 README、AGENTS、接口契约、需求冻结、设计冻结、前端元素审计、实施计划和测试计划，标明真实功能边界与剩余风险。 - 增加测试覆盖：补充 SAM2/SAM3 传播、SAM3 状态、媒体拆帧参数、Canvas 编辑、语义标签切换、时间轴、工作区传播和 API 合约测试。 - 加强仓库安全边界：将 sam3权重/ 加入 .gitignore，避免本地模型权重被误提交。 验证：npm run test:run；pytest backend/tests；npm run lint；npm run build；python -m py_compile；git diff --check。
514 lines
17 KiB
TypeScript
514 lines
17 KiB
TypeScript
import { beforeEach, describe, expect, it, vi } from 'vitest';
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const axiosMock = vi.hoisted(() => {
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const client = {
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get: vi.fn(),
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post: vi.fn(),
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patch: vi.fn(),
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delete: vi.fn(),
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interceptors: {
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request: { use: vi.fn() },
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response: { use: vi.fn() },
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},
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};
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return { client, create: vi.fn(() => client) };
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});
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vi.mock('axios', () => ({
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default: {
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create: axiosMock.create,
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},
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}));
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describe('api client contracts', () => {
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beforeEach(() => {
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vi.clearAllMocks();
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vi.setSystemTime(new Date('2026-05-01T00:00:00Z'));
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});
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it('maps backend project fields into frontend project fields', async () => {
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const { getProjects } = await import('./api');
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axiosMock.client.get.mockResolvedValueOnce({
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data: [
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{
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id: 7,
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name: 'Demo',
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description: 'desc',
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status: 'ready',
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frame_count: 12,
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original_fps: 29.97,
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parse_fps: 10,
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thumbnail_url: 'thumb',
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video_path: 'uploads/demo.mp4',
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source_type: 'video',
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created_at: 'created',
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updated_at: 'updated',
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},
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],
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});
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await expect(getProjects()).resolves.toEqual([
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expect.objectContaining({
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id: '7',
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name: 'Demo',
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status: 'ready',
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frames: 12,
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fps: '30FPS',
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thumbnail_url: 'thumb',
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video_path: 'uploads/demo.mp4',
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source_type: 'video',
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createdAt: 'created',
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updatedAt: 'updated',
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}),
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]);
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/projects');
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});
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it('updates projects with PATCH instead of the old PUT contract', async () => {
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const { updateProject } = await import('./api');
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axiosMock.client.patch.mockResolvedValueOnce({ data: { id: 3, name: 'Renamed', status: 'ready' } });
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await updateProject('3', { name: 'Renamed' } as any);
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expect(axiosMock.client.patch).toHaveBeenCalledWith('/api/projects/3', { name: 'Renamed' });
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});
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it('normalizes legacy project status values returned by existing databases', async () => {
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const { getProjects } = await import('./api');
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axiosMock.client.get.mockResolvedValueOnce({
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data: [
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{ id: 1, name: 'Old Ready', status: 'Ready' },
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{ id: 2, name: 'Old Parsing', status: 'Parsing' },
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{ id: 3, name: 'Old Error', status: 'Error' },
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],
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});
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await expect(getProjects()).resolves.toEqual([
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expect.objectContaining({ status: 'ready' }),
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expect.objectContaining({ status: 'parsing' }),
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expect.objectContaining({ status: 'error' }),
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]);
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});
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it('exports COCO from the backend route shape', async () => {
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const { exportCoco } = await import('./api');
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const blob = new Blob(['{}'], { type: 'application/json' });
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axiosMock.client.get.mockResolvedValueOnce({ data: blob });
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await expect(exportCoco('9')).resolves.toBe(blob);
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/export/9/coco', {
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responseType: 'blob',
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});
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});
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it('exports PNG masks from the backend route shape', async () => {
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const { exportMasks } = await import('./api');
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const blob = new Blob(['zip'], { type: 'application/zip' });
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axiosMock.client.get.mockResolvedValueOnce({ data: blob });
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await expect(exportMasks('9')).resolves.toBe(blob);
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/export/9/masks', {
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responseType: 'blob',
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});
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});
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it('loads dashboard overview from the backend summary endpoint', async () => {
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const { getDashboardOverview } = await import('./api');
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const overview = {
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summary: {
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project_count: 2,
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parsing_task_count: 1,
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annotation_count: 5,
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frame_count: 100,
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template_count: 3,
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system_load_percent: 12,
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},
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tasks: [
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{ id: 'project-1', project_id: 1, name: 'Demo', progress: 60, status: 'pending', frame_count: 10, updated_at: 'now' },
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],
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activity: [
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{ id: 'project-1', kind: 'project', time: 'now', message: '项目状态: pending', project: 'Demo' },
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],
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};
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axiosMock.client.get.mockResolvedValueOnce({ data: overview });
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await expect(getDashboardOverview()).resolves.toEqual(overview);
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/dashboard/overview');
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});
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it('queues media parsing and manages processing task lifecycle', async () => {
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const { cancelTask, getTask, parseMedia, retryTask } = await import('./api');
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const task = {
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id: 12,
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task_type: 'parse_video',
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status: 'queued',
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progress: 0,
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message: '解析任务已入队',
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project_id: 9,
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celery_task_id: 'celery-12',
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payload: { source_type: 'video' },
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result: null,
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error: null,
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created_at: 'created',
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started_at: null,
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finished_at: null,
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updated_at: 'updated',
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};
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axiosMock.client.post.mockResolvedValueOnce({ data: task });
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axiosMock.client.get.mockResolvedValueOnce({ data: { ...task, status: 'success', progress: 100 } });
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axiosMock.client.post.mockResolvedValueOnce({ data: { ...task, status: 'cancelled', progress: 100 } });
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axiosMock.client.post.mockResolvedValueOnce({ data: { ...task, id: 13, status: 'queued', progress: 0 } });
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await expect(parseMedia('9', { parseFps: 15, maxFrames: 120, targetWidth: 960 })).resolves.toEqual(task);
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/media/parse', null, {
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params: { project_id: '9', parse_fps: 15, max_frames: 120, target_width: 960 },
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});
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await expect(getTask(12)).resolves.toEqual(expect.objectContaining({ status: 'success', progress: 100 }));
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/tasks/12');
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await expect(cancelTask(12)).resolves.toEqual(expect.objectContaining({ status: 'cancelled' }));
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/tasks/12/cancel');
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await expect(retryTask(12)).resolves.toEqual(expect.objectContaining({ id: 13, status: 'queued' }));
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/tasks/12/retry');
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});
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it('lists, saves, updates, and deletes annotations with the backend annotation contract', async () => {
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const { deleteAnnotation, getProjectAnnotations, propagateMasks, saveAnnotation, updateAnnotation } = await import('./api');
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const saved = {
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id: 1,
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project_id: 9,
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frame_id: 5,
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template_id: 2,
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mask_data: { polygons: [[[0, 0], [1, 0], [1, 1]]] },
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points: null,
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bbox: null,
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created_at: 'created',
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updated_at: 'updated',
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};
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axiosMock.client.get.mockResolvedValueOnce({ data: [saved] });
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axiosMock.client.post.mockResolvedValueOnce({ data: saved });
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await expect(getProjectAnnotations('9', '5')).resolves.toEqual([saved]);
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expect(axiosMock.client.get).toHaveBeenCalledWith('/api/ai/annotations', {
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params: { project_id: 9, frame_id: 5 },
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});
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await expect(saveAnnotation({
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project_id: 9,
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frame_id: 5,
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template_id: 2,
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mask_data: { polygons: [[[0, 0], [1, 0], [1, 1]]], label: 'mask' },
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})).resolves.toEqual(saved);
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/annotate', {
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project_id: 9,
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frame_id: 5,
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template_id: 2,
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mask_data: { polygons: [[[0, 0], [1, 0], [1, 1]]], label: 'mask' },
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});
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axiosMock.client.patch.mockResolvedValueOnce({ data: { ...saved, mask_data: { ...saved.mask_data, label: 'updated' } } });
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await expect(updateAnnotation('1', {
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template_id: 2,
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mask_data: { polygons: [[[0, 0], [1, 0], [1, 1]]], label: 'updated' },
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})).resolves.toEqual(expect.objectContaining({ mask_data: expect.objectContaining({ label: 'updated' }) }));
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expect(axiosMock.client.patch).toHaveBeenCalledWith('/api/ai/annotations/1', {
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template_id: 2,
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mask_data: { polygons: [[[0, 0], [1, 0], [1, 1]]], label: 'updated' },
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});
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axiosMock.client.delete.mockResolvedValueOnce({ data: null });
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await expect(deleteAnnotation('1')).resolves.toBeUndefined();
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expect(axiosMock.client.delete).toHaveBeenCalledWith('/api/ai/annotations/1');
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axiosMock.client.post.mockResolvedValueOnce({
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data: {
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model: 'sam2',
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direction: 'forward',
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source_frame_id: 5,
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processed_frame_count: 3,
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created_annotation_count: 2,
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annotations: [saved],
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},
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});
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await expect(propagateMasks({
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project_id: 9,
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frame_id: 5,
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model: 'sam2',
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seed: {
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polygons: [[[0, 0], [1, 0], [1, 1]]],
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label: 'mask',
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color: '#06b6d4',
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},
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direction: 'forward',
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max_frames: 30,
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})).resolves.toEqual(expect.objectContaining({ created_annotation_count: 2 }));
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/propagate', {
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project_id: 9,
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frame_id: 5,
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model: 'sam2',
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seed: {
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polygons: [[[0, 0], [1, 0], [1, 1]]],
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label: 'mask',
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color: '#06b6d4',
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},
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direction: 'forward',
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max_frames: 30,
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}, {
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timeout: 600000,
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});
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});
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it('imports GT masks through multipart form data', async () => {
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const { importGtMask } = await import('./api');
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const file = new File(['mask'], 'mask.png', { type: 'image/png' });
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const saved = [{ id: 1, project_id: 9, frame_id: 5, template_id: null, mask_data: null, points: null, bbox: null }];
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axiosMock.client.post.mockResolvedValueOnce({ data: saved });
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await expect(importGtMask(file, '9', '5', '2')).resolves.toEqual(saved);
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expect(axiosMock.client.post).toHaveBeenCalledWith(
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'/api/ai/import-gt-mask',
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expect.any(FormData),
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{ headers: { 'Content-Type': 'multipart/form-data' } },
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);
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const form = axiosMock.client.post.mock.calls.at(-1)?.[1] as FormData;
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expect(form.get('file')).toBe(file);
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expect(form.get('project_id')).toBe('9');
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expect(form.get('frame_id')).toBe('5');
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expect(form.get('template_id')).toBe('2');
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});
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it('builds annotation payloads from frontend masks and restores saved annotations to masks', async () => {
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const { annotationToMask, buildAnnotationPayload } = await import('./api');
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const frame = { id: '5', projectId: '9', index: 0, url: '/frame.jpg', width: 100, height: 50 };
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const payload = buildAnnotationPayload('9', {
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id: 'm1',
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frameId: '5',
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pathData: 'M 10 10 L 90 10 L 90 40 Z',
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label: '胆囊',
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color: '#ff0000',
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classId: 'c1',
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className: '胆囊',
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classZIndex: 20,
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segmentation: [[10, 10, 90, 10, 90, 40]],
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bbox: [10, 10, 80, 30],
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}, frame, '2');
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expect(payload).toEqual({
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project_id: 9,
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frame_id: 5,
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template_id: 2,
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mask_data: {
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polygons: [[[0.1, 0.2], [0.9, 0.2], [0.9, 0.8]]],
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label: '胆囊',
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color: '#ff0000',
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class: { id: 'c1', name: '胆囊', color: '#ff0000', zIndex: 20 },
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},
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bbox: [0.1, 0.2, 0.8, 0.6],
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});
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expect(annotationToMask({
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id: 3,
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project_id: 9,
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frame_id: 5,
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template_id: 2,
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mask_data: {
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polygons: [[[0.1, 0.2], [0.9, 0.2], [0.9, 0.8]]],
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label: '旧标签',
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color: '#06b6d4',
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class: { id: 'c1', name: '胆囊', color: '#ff0000', zIndex: 20 },
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},
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points: [[0.5, 0.5]],
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bbox: null,
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created_at: 'created',
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updated_at: 'updated',
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}, frame)).toEqual(expect.objectContaining({
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id: 'annotation-3',
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annotationId: '3',
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frameId: '5',
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templateId: '2',
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classId: 'c1',
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className: '胆囊',
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classZIndex: 20,
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label: '胆囊',
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color: '#ff0000',
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saveStatus: 'saved',
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saved: true,
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pathData: 'M 10 10 L 90 10 L 90 40 Z',
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points: [[50, 25]],
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bbox: [10, 10, 80, 30],
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}));
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});
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it('preserves editable point regions in annotation payloads', async () => {
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const { buildAnnotationPayload } = await import('./api');
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const frame = { id: '5', projectId: '9', index: 0, url: '/frame.jpg', width: 100, height: 50 };
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expect(buildAnnotationPayload('9', {
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id: 'm1',
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frameId: '5',
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pathData: 'M 10 10 L 90 10 L 90 40 Z',
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label: 'GT Mask',
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color: '#22c55e',
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segmentation: [[10, 10, 90, 10, 90, 40]],
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points: [[50, 25]],
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}, frame)).toEqual(expect.objectContaining({
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points: [[0.5, 0.5]],
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}));
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});
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it('normalizes positive and negative point prompts for AI prediction', async () => {
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const { predictMask } = await import('./api');
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axiosMock.client.post.mockResolvedValueOnce({
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data: {
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polygons: [[[0.25, 0.25], [0.75, 0.25], [0.75, 0.75], [0.25, 0.75]]],
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scores: [0.9],
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},
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});
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const result = await predictMask({
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imageId: '42',
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imageWidth: 400,
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imageHeight: 200,
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points: [
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{ x: 200, y: 100, type: 'pos' },
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{ x: 40, y: 20, type: 'neg' },
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],
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});
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expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/predict', {
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image_id: 42,
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prompt_type: 'point',
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prompt_data: {
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points: [[0.5, 0.5], [0.1, 0.1]],
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labels: [1, 0],
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},
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model: 'sam2',
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});
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expect(result.masks[0]).toEqual(expect.objectContaining({
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pathData: 'M 100 50 L 300 50 L 300 150 L 100 150 Z',
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segmentation: [[100, 50, 300, 50, 300, 150, 100, 150]],
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bbox: [100, 50, 200, 100],
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area: 20000,
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confidence: 0.9,
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}));
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});
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it('normalizes box prompts for AI prediction', async () => {
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const { predictMask } = await import('./api');
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axiosMock.client.post.mockResolvedValueOnce({ data: { polygons: [], scores: [] } });
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await predictMask({
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imageId: '5',
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imageWidth: 640,
|
|
imageHeight: 320,
|
|
box: { x1: 64, y1: 32, x2: 320, y2: 160 },
|
|
});
|
|
|
|
expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/predict', {
|
|
image_id: 5,
|
|
prompt_type: 'box',
|
|
prompt_data: [0.1, 0.1, 0.5, 0.5],
|
|
model: 'sam2',
|
|
});
|
|
});
|
|
|
|
it('normalizes combined box and point prompts for interactive SAM2 refinement', async () => {
|
|
const { predictMask } = await import('./api');
|
|
axiosMock.client.post.mockResolvedValueOnce({ data: { polygons: [], scores: [] } });
|
|
|
|
await predictMask({
|
|
imageId: '5',
|
|
imageWidth: 640,
|
|
imageHeight: 320,
|
|
box: { x1: 64, y1: 32, x2: 320, y2: 160 },
|
|
points: [
|
|
{ x: 128, y: 64, type: 'pos' },
|
|
{ x: 256, y: 128, type: 'neg' },
|
|
],
|
|
});
|
|
|
|
expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/predict', {
|
|
image_id: 5,
|
|
prompt_type: 'interactive',
|
|
prompt_data: {
|
|
box: [0.1, 0.1, 0.5, 0.5],
|
|
points: [[0.2, 0.2], [0.4, 0.4]],
|
|
labels: [1, 0],
|
|
},
|
|
model: 'sam2',
|
|
});
|
|
});
|
|
|
|
it('uses semantic prompt type for text-only AI prediction', async () => {
|
|
const { predictMask } = await import('./api');
|
|
axiosMock.client.post.mockResolvedValueOnce({ data: { polygons: [], scores: [] } });
|
|
|
|
await predictMask({
|
|
imageId: '6',
|
|
imageWidth: 640,
|
|
imageHeight: 360,
|
|
model: 'sam3',
|
|
text: '分割胆囊',
|
|
});
|
|
|
|
expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/predict', {
|
|
image_id: 6,
|
|
prompt_type: 'semantic',
|
|
prompt_data: '分割胆囊',
|
|
model: 'sam3',
|
|
});
|
|
});
|
|
|
|
it('passes AI post-processing options to prediction endpoint', async () => {
|
|
const { predictMask } = await import('./api');
|
|
axiosMock.client.post.mockResolvedValueOnce({ data: { polygons: [], scores: [] } });
|
|
|
|
await predictMask({
|
|
imageId: '7',
|
|
imageWidth: 640,
|
|
imageHeight: 360,
|
|
points: [{ x: 320, y: 180, type: 'pos' }],
|
|
options: {
|
|
crop_to_prompt: true,
|
|
auto_filter_background: true,
|
|
min_score: 0.05,
|
|
},
|
|
});
|
|
|
|
expect(axiosMock.client.post).toHaveBeenCalledWith('/api/ai/predict', {
|
|
image_id: 7,
|
|
prompt_type: 'point',
|
|
prompt_data: {
|
|
points: [[0.5, 0.5]],
|
|
labels: [1],
|
|
},
|
|
model: 'sam2',
|
|
options: {
|
|
crop_to_prompt: true,
|
|
auto_filter_background: true,
|
|
min_score: 0.05,
|
|
},
|
|
});
|
|
});
|
|
|
|
it('loads AI model and GPU runtime status', async () => {
|
|
const { getAiModelStatus } = await import('./api');
|
|
const status = {
|
|
selected_model: 'sam2',
|
|
gpu: { available: false, device: 'cpu', name: null, torch_available: true, torch_version: '2.x', cuda_version: null },
|
|
models: [
|
|
{ id: 'sam2', label: 'SAM 2', available: true, loaded: false, device: 'cpu', supports: ['point'], 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 runtime', package_available: false, checkpoint_exists: false, checkpoint_path: null, python_ok: false, torch_ok: true, cuda_required: true },
|
|
],
|
|
};
|
|
axiosMock.client.get.mockResolvedValueOnce({ data: status });
|
|
|
|
await expect(getAiModelStatus('sam3')).resolves.toEqual(status);
|
|
expect(axiosMock.client.get).toHaveBeenCalledWith('/api/ai/models/status', {
|
|
params: { selected_model: 'sam3' },
|
|
});
|
|
});
|
|
});
|