feat: 打通全栈标注闭环、异步拆帧与模型状态
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。
This commit is contained in:
@@ -1,10 +1,6 @@
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"""Media upload and parsing endpoints."""
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import logging
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import os
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import shutil
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import subprocess
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import tempfile
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from pathlib import Path
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from typing import List, Optional
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@@ -12,13 +8,12 @@ from fastapi import APIRouter, Depends, File, Form, HTTPException, UploadFile, s
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from sqlalchemy.orm import Session
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from database import get_db
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from minio_client import upload_file, get_presigned_url, download_file
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from models import Project, Frame
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from schemas import FrameOut
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from services.frame_parser import (
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parse_video, parse_dicom, upload_frames_to_minio,
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extract_thumbnail, get_video_fps,
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)
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from minio_client import upload_file, get_presigned_url
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from models import ProcessingTask, Project
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from progress_events import publish_task_progress_event
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from schemas import ProcessingTaskOut
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from statuses import PROJECT_STATUS_PARSING, PROJECT_STATUS_PENDING, TASK_STATUS_QUEUED
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from worker_tasks import parse_project_media
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/media", tags=["Media"])
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@@ -79,7 +74,7 @@ async def upload_media(
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project = Project(
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name=file.filename,
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description="Auto-created from upload",
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status="pending",
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status=PROJECT_STATUS_PENDING,
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video_path=object_name,
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source_type="video",
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)
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@@ -135,7 +130,7 @@ async def upload_dicom_batch(
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project = Project(
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name=first_name,
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description=f"DICOM series with {len(files)} files",
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status="pending",
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status=PROJECT_STATUS_PENDING,
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source_type="dicom",
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)
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db.add(project)
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@@ -168,19 +163,18 @@ async def upload_dicom_batch(
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@router.post(
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"/parse",
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status_code=status.HTTP_202_ACCEPTED,
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response_model=ProcessingTaskOut,
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summary="Trigger frame extraction",
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)
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def parse_media(
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project_id: int,
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source_type: Optional[str] = None,
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db: Session = Depends(get_db),
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) -> dict:
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"""Trigger frame extraction for a project's uploaded media.
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) -> ProcessingTask:
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"""Create a background task for media frame extraction.
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* video: uses FFmpeg or OpenCV fallback, extracts thumbnail.
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* dicom: uses pydicom to read DCM frames.
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Extracted frames are uploaded to MinIO and registered in the database.
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The Celery worker performs the heavy FFmpeg/OpenCV/pydicom work and
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updates the persisted task record as it progresses.
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"""
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project = db.query(Project).filter(Project.id == project_id).first()
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if not project:
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@@ -190,100 +184,24 @@ def parse_media(
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raise HTTPException(status_code=400, detail="Project has no media uploaded")
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effective_source = source_type or project.source_type or "video"
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parse_fps = project.parse_fps or 30.0
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tmp_dir = tempfile.mkdtemp(prefix=f"seg_parse_{project_id}_")
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output_dir = os.path.join(tmp_dir, "frames")
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os.makedirs(output_dir, exist_ok=True)
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try:
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if effective_source == "dicom":
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# Download all dicom files from MinIO
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dcm_dir = os.path.join(tmp_dir, "dcm")
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os.makedirs(dcm_dir, exist_ok=True)
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from minio_client import get_minio_client, BUCKET_NAME
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client = get_minio_client()
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prefix = project.video_path
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objects = list(client.list_objects(BUCKET_NAME, prefix=prefix, recursive=True))
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for obj in objects:
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if obj.object_name.lower().endswith(".dcm"):
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data = download_file(obj.object_name)
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local_dcm = os.path.join(dcm_dir, os.path.basename(obj.object_name))
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with open(local_dcm, "wb") as f:
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f.write(data)
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frame_files = parse_dicom(dcm_dir, output_dir)
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else:
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# Video: download and parse
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media_bytes = download_file(project.video_path)
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local_path = os.path.join(tmp_dir, Path(project.video_path).name)
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with open(local_path, "wb") as f:
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f.write(media_bytes)
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frame_files, original_fps = parse_video(local_path, output_dir, fps=int(parse_fps))
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project.original_fps = original_fps
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# Extract thumbnail from first frame
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thumbnail_path = os.path.join(tmp_dir, "thumbnail.jpg")
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try:
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extract_thumbnail(local_path, thumbnail_path)
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with open(thumbnail_path, "rb") as f:
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thumb_data = f.read()
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thumb_object = f"projects/{project_id}/thumbnail.jpg"
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upload_file(thumb_object, thumb_data, content_type="image/jpeg", length=len(thumb_data))
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project.thumbnail_url = thumb_object
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logger.info("Uploaded thumbnail for project_id=%s", project_id)
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except Exception as exc: # noqa: BLE001
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logger.warning("Thumbnail extraction failed: %s", exc)
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except Exception as exc: # noqa: BLE001
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logger.error("Frame extraction failed: %s", exc)
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shutil.rmtree(tmp_dir, ignore_errors=True)
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raise HTTPException(status_code=500, detail="Frame extraction failed") from exc
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# Upload frames to MinIO
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try:
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object_names = upload_frames_to_minio(frame_files, project_id)
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except Exception as exc: # noqa: BLE001
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logger.error("Frame upload failed: %s", exc)
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shutil.rmtree(tmp_dir, ignore_errors=True)
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raise HTTPException(status_code=500, detail="Frame upload to storage failed") from exc
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# Register frames in DB
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frames_out = []
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for idx, obj_name in enumerate(object_names):
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local_frame = frame_files[idx]
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try:
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import cv2
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img = cv2.imread(local_frame)
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h, w = img.shape[:2] if img is not None else (None, None)
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except Exception: # noqa: BLE001
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h, w = None, None
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frame = Frame(
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project_id=project_id,
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frame_index=idx,
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image_url=obj_name,
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width=w,
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height=h,
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)
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db.add(frame)
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frames_out.append(frame)
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task = ProcessingTask(
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task_type=f"parse_{effective_source}",
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status=TASK_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=project_id,
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payload={"source_type": effective_source},
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)
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project.status = PROJECT_STATUS_PARSING
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db.add(task)
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db.commit()
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for f in frames_out:
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db.refresh(f)
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db.refresh(task)
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publish_task_progress_event(task)
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# Cleanup temp files
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shutil.rmtree(tmp_dir, ignore_errors=True)
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project.status = "ready"
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async_result = parse_project_media.delay(task.id)
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task.celery_task_id = async_result.id
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db.commit()
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db.refresh(task)
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logger.info("Parsed %d frames for project_id=%s", len(frames_out), project_id)
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return {
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"project_id": project_id,
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"frames_extracted": len(frames_out),
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"status": "ready",
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"message": "Frame extraction completed successfully.",
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}
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logger.info("Queued parse task id=%s project_id=%s celery_id=%s", task.id, project_id, async_result.id)
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return task
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