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- 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。
287 lines
9.1 KiB
Python
287 lines
9.1 KiB
Python
"""Annotation export endpoints (COCO, PNG masks)."""
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import io
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import json
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import logging
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import os
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import zipfile
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from datetime import datetime
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from typing import Any, Dict, List
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import numpy as np
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from fastapi import APIRouter, Depends, HTTPException, status
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from fastapi.responses import StreamingResponse
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from sqlalchemy.orm import Session
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from database import get_db
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from models import Project, Annotation, Frame, Template
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/export", tags=["Export"])
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def _mask_from_polygon(
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polygon: List[List[float]],
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width: int,
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height: int,
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) -> np.ndarray:
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"""Render a normalized polygon to a binary mask."""
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import cv2
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pts = np.array(
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[[int(p[0] * width), int(p[1] * height)] for p in polygon],
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dtype=np.int32,
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)
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mask = np.zeros((height, width), dtype=np.uint8)
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cv2.fillPoly(mask, [pts], 255)
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return mask
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def _annotation_z_index(annotation: Annotation) -> int:
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class_meta = (annotation.mask_data or {}).get("class") or {}
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if isinstance(class_meta, dict) and class_meta.get("zIndex") is not None:
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try:
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return int(class_meta["zIndex"])
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except (TypeError, ValueError):
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pass
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if annotation.template and annotation.template.z_index is not None:
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return int(annotation.template.z_index)
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return 0
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def _annotation_class_key(annotation: Annotation) -> str:
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class_meta = (annotation.mask_data or {}).get("class") or {}
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if isinstance(class_meta, dict):
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if class_meta.get("id"):
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return f"class:{class_meta['id']}"
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if class_meta.get("name"):
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return f"name:{class_meta['name']}"
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if annotation.template_id:
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return f"template:{annotation.template_id}"
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return f"annotation:{annotation.id}"
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def _annotation_label(annotation: Annotation) -> str:
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mask_data = annotation.mask_data or {}
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class_meta = mask_data.get("class") or {}
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if isinstance(class_meta, dict) and class_meta.get("name"):
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return str(class_meta["name"])
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if mask_data.get("label"):
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return str(mask_data["label"])
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if annotation.template and annotation.template.name:
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return str(annotation.template.name)
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return f"Annotation {annotation.id}"
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def _annotation_color(annotation: Annotation) -> str:
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mask_data = annotation.mask_data or {}
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class_meta = mask_data.get("class") or {}
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if isinstance(class_meta, dict) and class_meta.get("color"):
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return str(class_meta["color"])
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if mask_data.get("color"):
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return str(mask_data["color"])
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if annotation.template and annotation.template.color:
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return str(annotation.template.color)
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return "#ffffff"
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@router.get(
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"/{project_id}/coco",
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summary="Export annotations in COCO format",
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)
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def export_coco(project_id: int, db: Session = Depends(get_db)) -> StreamingResponse:
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"""Export all annotations for a project as a COCO-format JSON file."""
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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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raise HTTPException(status_code=404, detail="Project not found")
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annotations = (
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db.query(Annotation)
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.filter(Annotation.project_id == project_id)
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.all()
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)
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frames = (
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db.query(Frame)
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.filter(Frame.project_id == project_id)
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.order_by(Frame.frame_index)
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.all()
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)
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templates = db.query(Template).all()
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# Build COCO structure
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images = []
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for idx, frame in enumerate(frames):
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images.append({
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"id": frame.id,
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"file_name": frame.image_url,
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"width": frame.width or 1920,
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"height": frame.height or 1080,
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"frame_index": idx,
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})
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categories = []
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template_id_to_cat_id: Dict[int, int] = {}
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for cat_idx, tmpl in enumerate(templates, start=1):
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categories.append({
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"id": cat_idx,
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"name": tmpl.name,
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"color": tmpl.color,
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})
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template_id_to_cat_id[tmpl.id] = cat_idx
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coco_annotations = []
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ann_id = 1
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for ann in annotations:
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if not ann.mask_data:
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continue
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polygons = ann.mask_data.get("polygons", [])
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if not polygons:
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continue
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# Use first polygon for bbox / area approximation
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first_poly = polygons[0]
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xs = [p[0] for p in first_poly]
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ys = [p[1] for p in first_poly]
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width = ann.frame.width if ann.frame else 1920
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height = ann.frame.height if ann.frame else 1080
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bbox = [
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min(xs) * width,
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min(ys) * height,
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(max(xs) - min(xs)) * width,
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(max(ys) - min(ys)) * height,
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]
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area = bbox[2] * bbox[3]
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segmentation = []
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for poly in polygons:
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flat = []
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for p in poly:
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flat.append(p[0] * width)
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flat.append(p[1] * height)
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segmentation.append(flat)
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coco_annotations.append({
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"id": ann_id,
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"image_id": ann.frame_id,
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"category_id": template_id_to_cat_id.get(ann.template_id, 0),
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"segmentation": segmentation,
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"area": area,
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"bbox": bbox,
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"iscrowd": 0,
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})
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ann_id += 1
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coco = {
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"info": {
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"description": f"Annotations for {project.name}",
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"version": "1.0",
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"year": datetime.now().year,
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"date_created": datetime.now().isoformat(),
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},
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"images": images,
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"annotations": coco_annotations,
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"categories": categories,
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}
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data = json.dumps(coco, ensure_ascii=False, indent=2).encode("utf-8")
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filename = f"project_{project_id}_coco.json"
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return StreamingResponse(
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io.BytesIO(data),
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media_type="application/json",
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headers={"Content-Disposition": f'attachment; filename="{filename}"'},
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)
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@router.get(
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"/{project_id}/masks",
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summary="Export PNG masks as a ZIP archive",
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)
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def export_masks(project_id: int, db: Session = Depends(get_db)) -> StreamingResponse:
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"""Export individual masks plus z-index fused semantic masks inside a ZIP."""
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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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raise HTTPException(status_code=404, detail="Project not found")
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import cv2
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annotations = (
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db.query(Annotation)
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.filter(Annotation.project_id == project_id)
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.all()
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)
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frames = (
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db.query(Frame)
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.filter(Frame.project_id == project_id)
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.order_by(Frame.frame_index)
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.all()
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)
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class_values: dict[str, int] = {}
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semantic_classes: list[dict[str, Any]] = []
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def class_value(annotation: Annotation) -> int:
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key = _annotation_class_key(annotation)
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if key not in class_values:
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value = len(class_values) + 1
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class_values[key] = value
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semantic_classes.append({
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"value": value,
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"key": key,
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"label": _annotation_label(annotation),
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"color": _annotation_color(annotation),
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"zIndex": _annotation_z_index(annotation),
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"template_id": annotation.template_id,
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})
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return class_values[key]
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zip_buffer = io.BytesIO()
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with zipfile.ZipFile(zip_buffer, "w", zipfile.ZIP_DEFLATED) as zf:
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frame_masks: dict[int, list[tuple[Annotation, np.ndarray]]] = {}
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for ann in annotations:
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if not ann.mask_data:
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continue
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polygons = ann.mask_data.get("polygons", [])
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if not polygons:
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continue
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width = ann.frame.width if ann.frame else 1920
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height = ann.frame.height if ann.frame else 1080
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combined = np.zeros((height, width), dtype=np.uint8)
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for poly in polygons:
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mask = _mask_from_polygon(poly, width, height)
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combined = np.maximum(combined, mask)
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_, encoded = cv2.imencode(".png", combined)
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fname = f"mask_{ann.id:06d}.png"
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zf.writestr(fname, encoded.tobytes())
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if ann.frame_id is not None:
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frame_masks.setdefault(ann.frame_id, []).append((ann, combined))
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for frame in frames:
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entries = frame_masks.get(frame.id, [])
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if not entries:
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continue
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width = frame.width or 1920
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height = frame.height or 1080
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semantic = np.zeros((height, width), dtype=np.uint8)
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for ann, mask in sorted(entries, key=lambda item: _annotation_z_index(item[0])):
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semantic[mask > 0] = class_value(ann)
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_, encoded = cv2.imencode(".png", semantic)
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zf.writestr(f"semantic_frame_{frame.frame_index:06d}.png", encoded.tobytes())
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zf.writestr(
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"semantic_classes.json",
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json.dumps({"classes": semantic_classes}, ensure_ascii=False, indent=2).encode("utf-8"),
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)
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zip_buffer.seek(0)
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filename = f"project_{project_id}_masks.zip"
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return StreamingResponse(
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zip_buffer,
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media_type="application/zip",
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headers={"Content-Disposition": f'attachment; filename="{filename}"'},
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)
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