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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。
249 lines
8.6 KiB
Python
249 lines
8.6 KiB
Python
import numpy as np
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def _create_project_and_frame(client):
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project = client.post("/api/projects", json={"name": "AI Project"}).json()
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": 0,
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"image_url": "frames/0.jpg",
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"width": 640,
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"height": 360,
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}).json()
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template = client.post("/api/templates", json={
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"name": "Template",
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"color": "#06b6d4",
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"z_index": 0,
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"classes": [],
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"rules": [],
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}).json()
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return project, frame, template
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def test_predict_accepts_point_object_with_labels(client, monkeypatch):
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_, frame, _ = _create_project_and_frame(client)
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calls = {}
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monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
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def fake_predict_points(image, points, labels):
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calls["args"] = (points, labels)
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return (
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[[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
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[0.95],
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)
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monkeypatch.setattr("routers.ai.sam_registry.predict_points", lambda model, image, points, labels: fake_predict_points(image, points, labels))
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response = client.post("/api/ai/predict", json={
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"image_id": frame["id"],
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"prompt_type": "point",
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"prompt_data": {"points": [[0.5, 0.5], [0.1, 0.1]], "labels": [1, 0]},
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})
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assert response.status_code == 200
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assert response.json()["scores"] == [0.95]
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assert calls["args"] == ([[0.5, 0.5], [0.1, 0.1]], [1, 0])
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def test_predict_box_and_semantic_fallback(client, monkeypatch):
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_, frame, _ = _create_project_and_frame(client)
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monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
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monkeypatch.setattr("routers.ai.sam_registry.predict_box", lambda model, image, box: (
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[[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
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[0.8],
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))
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monkeypatch.setattr("routers.ai.sam_registry.predict_semantic", lambda model, image, text: (
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[[[0.0, 0.0], [1.0, 0.0], [1.0, 1.0]]],
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[0.5],
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))
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box_response = client.post("/api/ai/predict", json={
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"image_id": frame["id"],
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"prompt_type": "box",
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"prompt_data": [0.2, 0.2, 0.8, 0.8],
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})
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semantic_response = client.post("/api/ai/predict", json={
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"image_id": frame["id"],
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"prompt_type": "semantic",
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"prompt_data": "胆囊",
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})
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assert box_response.status_code == 200
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assert box_response.json()["scores"] == [0.8]
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assert semantic_response.status_code == 200
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assert semantic_response.json()["scores"] == [0.5]
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def test_model_status_reports_runtime(client, monkeypatch):
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monkeypatch.setattr("routers.ai.sam_registry.runtime_status", lambda selected_model=None: {
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"selected_model": selected_model or "sam2",
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"gpu": {
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"available": False,
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"device": "cpu",
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"name": None,
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"torch_available": True,
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"torch_version": "2.x",
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"cuda_version": None,
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},
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"models": [
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{
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"id": "sam2",
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"label": "SAM 2",
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"available": True,
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"loaded": False,
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"device": "cpu",
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"supports": ["point", "box", "auto"],
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"message": "ready",
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"package_available": True,
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"checkpoint_exists": True,
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"checkpoint_path": "model.pt",
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"python_ok": True,
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"torch_ok": True,
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"cuda_required": False,
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},
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{
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"id": "sam3",
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"label": "SAM 3",
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"available": False,
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"loaded": False,
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"device": "unavailable",
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"supports": ["semantic"],
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"message": "missing Python 3.12+ runtime",
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"package_available": False,
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"checkpoint_exists": False,
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"checkpoint_path": None,
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"python_ok": False,
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"torch_ok": True,
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"cuda_required": True,
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},
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],
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})
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response = client.get("/api/ai/models/status?selected_model=sam3")
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assert response.status_code == 200
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body = response.json()
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assert body["selected_model"] == "sam3"
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assert body["models"][1]["id"] == "sam3"
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assert body["models"][1]["available"] is False
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def test_predict_validation_errors(client, monkeypatch):
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project, _, _ = _create_project_and_frame(client)
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assert client.post("/api/ai/predict", json={
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"image_id": 999,
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"prompt_type": "point",
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"prompt_data": [[0.5, 0.5]],
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}).status_code == 404
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": 1,
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"image_url": "frames/1.jpg",
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}).json()
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monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
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assert client.post("/api/ai/predict", json={
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"image_id": frame["id"],
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"prompt_type": "box",
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"prompt_data": [0.1, 0.2],
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}).status_code == 400
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def test_save_annotation_validates_project_and_frame(client):
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project, frame, template = _create_project_and_frame(client)
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saved = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"template_id": template["id"],
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"mask_data": {"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]]},
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"points": [[0.5, 0.5]],
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"bbox": [0.1, 0.1, 0.8, 0.8],
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})
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assert saved.status_code == 201
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assert saved.json()["project_id"] == project["id"]
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listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
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assert listing.status_code == 200
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assert listing.json()[0]["id"] == saved.json()["id"]
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frame_listing = client.get(f"/api/ai/annotations?project_id={project['id']}&frame_id={frame['id']}")
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assert frame_listing.status_code == 200
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assert len(frame_listing.json()) == 1
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missing_project = client.post("/api/ai/annotate", json={"project_id": 999})
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assert missing_project.status_code == 404
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missing_frame = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": 999,
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})
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assert missing_frame.status_code == 404
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missing_project_list = client.get("/api/ai/annotations?project_id=999")
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assert missing_project_list.status_code == 404
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def test_update_and_delete_annotation(client):
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project, frame, template = _create_project_and_frame(client)
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saved = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"template_id": template["id"],
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"mask_data": {
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"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
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"label": "AI Mask",
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"color": "#06b6d4",
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},
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"points": [[0.5, 0.5]],
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"bbox": [0.1, 0.1, 0.8, 0.8],
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}).json()
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updated = client.patch(f"/api/ai/annotations/{saved['id']}", json={
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"template_id": template["id"],
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"mask_data": {
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"polygons": [[[0.2, 0.2], [0.8, 0.2], [0.8, 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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"points": [[0.4, 0.4]],
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"bbox": [0.2, 0.2, 0.6, 0.6],
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})
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assert updated.status_code == 200
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body = updated.json()
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assert body["mask_data"]["label"] == "胆囊"
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assert body["mask_data"]["class"]["id"] == "c1"
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assert body["points"] == [[0.4, 0.4]]
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assert body["bbox"] == [0.2, 0.2, 0.6, 0.6]
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listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
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assert listing.status_code == 200
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assert listing.json()[0]["mask_data"]["class"]["name"] == "胆囊"
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deleted = client.delete(f"/api/ai/annotations/{saved['id']}")
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assert deleted.status_code == 204
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empty_listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
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assert empty_listing.status_code == 200
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assert empty_listing.json() == []
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def test_update_and_delete_annotation_validation(client):
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project, frame, template = _create_project_and_frame(client)
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saved = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"template_id": template["id"],
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}).json()
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assert client.patch("/api/ai/annotations/999", json={"bbox": [0, 0, 1, 1]}).status_code == 404
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assert client.delete("/api/ai/annotations/999").status_code == 404
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assert client.patch(
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f"/api/ai/annotations/{saved['id']}",
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json={"template_id": 999},
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).status_code == 404
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