- 打通工作区真实标注闭环:支持手工多边形、矩形、圆形、点区域和线段生成 mask,并可保存、回显、更新和删除后端 annotation。 - 增强 polygon 编辑器:支持顶点拖动、顶点删除、边中点插入、多 polygon 子区域选择编辑,以及区域合并和区域去除。 - 接入 GT mask 导入:后端支持二值/多类别 mask 拆分、contour 转 polygon、distance transform seed point,前端支持导入、回显和 seed point 拖动编辑。 - 完善导出能力:COCO JSON 导出对齐前端,PNG mask ZIP 同时包含单标注 mask、按 zIndex 融合的 semantic_frame 和 semantic_classes.json。 - 打通异步任务管理:新增任务取消、重试、失败详情接口与 Dashboard 控件,worker 支持取消状态检查并通过 Redis/WebSocket 推送 cancelled 事件。 - 对接 Dashboard 后端数据:概览统计、解析队列和实时流转记录从 FastAPI 聚合接口与 WebSocket 更新。 - 增强 AI 推理参数:前端发送 crop_to_prompt、auto_filter_background 和 min_score,后端支持点/框 prompt 局部裁剪推理、结果回映射和负向点/低分过滤。 - 接入 SAM3 基础设施:新增独立 Python 3.12 sam3 环境安装脚本、外部 worker helper、后端桥接和真实 Python/CUDA/包/HF checkpoint access 状态检测。 - 保留 SAM3 授权边界:当前官方 facebook/sam3 gated 权重未授权时状态接口会返回不可用,不伪装成可推理。 - 增强前端状态管理:新增 mask undo/redo 历史栈、AI 模型选择状态、保存状态 dirty/draft/saved 流转和项目状态归一化。 - 更新前端 API 封装:补充 annotation CRUD、GT mask import、mask ZIP export、task cancel/retry/detail、AI runtime status 和 prediction options。 - 更新 UI 控件:ToolsPalette、AISegmentation、VideoWorkspace 和 CanvasArea 接入真实操作、导入导出、撤销重做、任务控制和模型状态。 - 新增 polygon-clipping 依赖,用于前端区域 union/difference 几何运算。 - 完善后端 schemas/status/progress:补充 AI 模型外部状态字段、任务 cancelled 状态和进度事件 payload。 - 补充测试覆盖:新增后端任务控制、SAM3 桥接、GT mask、导出融合、AI options 测试;补充前端 Canvas、Dashboard、VideoWorkspace、ToolsPalette、API 和 store 测试。 - 更新 README、AGENTS 和 doc 文档:冻结当前需求/设计/测试计划,标注真实功能、剩余 Mock、SAM3 授权边界和后续实施顺序。
165 lines
6.3 KiB
Python
165 lines
6.3 KiB
Python
def test_upload_rejects_unsupported_file_type(client):
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response = client.post(
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"/api/media/upload",
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files={"file": ("notes.txt", b"text", "text/plain")},
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)
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assert response.status_code == 400
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assert "Unsupported file type" in response.json()["detail"]
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def test_upload_auto_creates_project(client, monkeypatch):
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uploaded = []
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monkeypatch.setattr("routers.media.upload_file", lambda object_name, data, content_type, length: uploaded.append(object_name))
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monkeypatch.setattr("routers.media.get_presigned_url", lambda object_name, expires=3600: f"http://storage/{object_name}")
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response = client.post(
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"/api/media/upload",
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files={"file": ("clip.mp4", b"video", "video/mp4")},
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)
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assert response.status_code == 201
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data = response.json()
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assert data["project_id"] is not None
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assert data["object_name"] == f"uploads/{data['project_id']}/clip.mp4"
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assert uploaded == ["uploads/general/clip.mp4", f"uploads/{data['project_id']}/clip.mp4"]
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def test_upload_links_existing_project(client, monkeypatch):
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project = client.post("/api/projects", json={"name": "Existing"}).json()
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monkeypatch.setattr("routers.media.upload_file", lambda *args, **kwargs: None)
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monkeypatch.setattr("routers.media.get_presigned_url", lambda object_name, expires=3600: f"http://storage/{object_name}")
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response = client.post(
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"/api/media/upload",
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data={"project_id": str(project["id"])},
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files={"file": ("clip.mp4", b"video", "video/mp4")},
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)
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assert response.status_code == 201
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detail = client.get(f"/api/projects/{project['id']}").json()
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assert detail["video_path"] == f"uploads/{project['id']}/clip.mp4"
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def test_upload_dicom_batch_filters_files_and_creates_project(client, monkeypatch):
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uploaded = []
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monkeypatch.setattr("routers.media.upload_file", lambda object_name, data, content_type, length: uploaded.append(object_name))
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response = client.post(
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"/api/media/upload/dicom",
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files=[
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("files", ("a.dcm", b"dcm", "application/dicom")),
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("files", ("skip.txt", b"text", "text/plain")),
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],
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)
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assert response.status_code == 201
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data = response.json()
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assert data["uploaded_count"] == 1
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assert uploaded == [f"uploads/{data['project_id']}/dicom/a.dcm"]
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def test_parse_media_queues_background_task(client, monkeypatch):
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project = client.post("/api/projects", json={
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"name": "Parse Me",
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"video_path": "uploads/1/clip.mp4",
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"source_type": "video",
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"parse_fps": 5,
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}).json()
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class FakeAsyncResult:
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id = "celery-1"
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queued = []
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monkeypatch.setattr("routers.media.parse_project_media.delay", lambda task_id: queued.append(task_id) or FakeAsyncResult())
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published = []
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monkeypatch.setattr("routers.media.publish_task_progress_event", lambda task: published.append(task.id))
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response = client.post(f"/api/media/parse?project_id={project['id']}")
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assert response.status_code == 202
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data = response.json()
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assert data["task_type"] == "parse_video"
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assert data["status"] == "queued"
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assert data["progress"] == 0
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assert data["project_id"] == project["id"]
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assert data["celery_task_id"] == "celery-1"
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assert queued == [data["id"]]
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assert published == [data["id"]]
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detail = client.get(f"/api/tasks/{data['id']}")
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assert detail.status_code == 200
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assert detail.json()["status"] == "queued"
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project_detail = client.get(f"/api/projects/{project['id']}").json()
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assert project_detail["status"] == "parsing"
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def test_parse_task_runner_registers_frames(client, db_session, monkeypatch, tmp_path):
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from models import ProcessingTask
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from services.media_task_runner import run_parse_media_task
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project = client.post("/api/projects", json={
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"name": "Parse Me",
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"video_path": "uploads/1/clip.mp4",
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"source_type": "video",
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"parse_fps": 5,
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}).json()
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task = ProcessingTask(
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task_type="parse_video",
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status="queued",
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progress=0,
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project_id=project["id"],
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payload={"source_type": "video"},
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)
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db_session.add(task)
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db_session.commit()
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db_session.refresh(task)
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frame_file = tmp_path / "frame_000001.jpg"
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frame_file.write_bytes(b"fake image")
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monkeypatch.setattr("services.media_task_runner.download_file", lambda object_name: b"video")
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monkeypatch.setattr("services.media_task_runner.parse_video", lambda local_path, output_dir, fps: ([str(frame_file)], 25.0))
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monkeypatch.setattr("services.media_task_runner.extract_thumbnail", lambda local_path, thumbnail_path: open(thumbnail_path, "wb").write(b"thumb"))
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monkeypatch.setattr("services.media_task_runner.upload_file", lambda *args, **kwargs: None)
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monkeypatch.setattr("services.media_task_runner.upload_frames_to_minio", lambda frame_files, project_id: [f"projects/{project_id}/frames/frame_000001.jpg"])
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published = []
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monkeypatch.setattr(
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"services.media_task_runner.publish_task_progress_event",
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lambda event_task: published.append((event_task.status, event_task.progress, event_task.message)),
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)
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result = run_parse_media_task(db_session, task.id)
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assert result["frames_extracted"] == 1
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db_session.refresh(task)
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assert task.status == "success"
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assert task.progress == 100
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assert ("running", 5, "后台解析已启动") in published
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assert ("success", 100, "解析完成") in published
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project_detail = client.get(f"/api/projects/{project['id']}").json()
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assert project_detail["status"] == "ready"
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frames = client.get(f"/api/projects/{project['id']}/frames").json()
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assert "frame_000001.jpg" in frames[0]["image_url"]
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def test_parse_task_runner_skips_already_cancelled_task(db_session):
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from models import ProcessingTask
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from services.media_task_runner import run_parse_media_task
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task = ProcessingTask(
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task_type="parse_video",
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status="cancelled",
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progress=100,
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message="任务已取消",
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project_id=1,
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payload={"source_type": "video"},
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)
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db_session.add(task)
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db_session.commit()
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db_session.refresh(task)
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result = run_parse_media_task(db_session, task.id)
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assert result["status"] == "cancelled"
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assert result["message"] == "任务已取消"
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