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feat(annotation): 添加标注任务算子编排前端页面和测试算子
## 功能概述 为标注任务通用算子编排功能添加完整的前端界面，包括任务创建、列表管理、详情查看等功能，并提供测试算子用于功能验证。 ## 改动内容 ### 前端功能 #### 1. 算子编排页面 - 新增两步创建流程： - 第一步：基本信息（数据集选择、任务名称等） - 第二步：算子编排（选择算子、配置参数、预览 pipeline） - 核心文件： - frontend/src/pages/DataAnnotation/OperatorCreate/CreateTask.tsx - frontend/src/pages/DataAnnotation/OperatorCreate/hooks/useOperatorOperations.ts - frontend/src/pages/DataAnnotation/OperatorCreate/hooks/useDragOperators.ts - frontend/src/pages/DataAnnotation/OperatorCreate/hooks/useCreateStepTwo.tsx #### 2. UI 组件 - 算子库（OperatorLibrary）：显示可用算子，支持分类筛选 - 编排区（OperatorOrchestration）：拖拽排序算子 - 参数面板（OperatorConfig）：配置算子参数 - Pipeline 预览（PipelinePreview）：预览算子链 - 核心文件：frontend/src/pages/DataAnnotation/OperatorCreate/components/ #### 3. 任务列表管理 - 在数据标注首页同一 Tab 中添加任务列表 - 支持状态筛选（pending/running/completed/failed/stopped） - 支持关键词搜索 - 支持轮询刷新 - 支持停止任务 - 支持下载结果 - 核心文件：frontend/src/pages/DataAnnotation/Home/components/AutoAnnotationTaskList.tsx #### 4. 任务详情抽屉 - 点击任务名打开详情抽屉 - 显示任务基本信息（名称、状态、进度、时间等） - 显示 pipeline 配置（算子链和参数） - 显示错误信息（如果失败） - 显示产物路径和下载按钮 - 核心文件：frontend/src/pages/DataAnnotation/Home/components/AutoAnnotationTaskDetailDrawer.tsx #### 5. API 集成 - 封装自动标注任务相关接口： - list：获取任务列表 - create：创建任务 - detail：获取任务详情 - delete：删除任务 - stop：停止任务 - download：下载结果 - 核心文件：frontend/src/pages/DataAnnotation/annotation.api.ts #### 6. 路由配置 - 新增路由：/data/annotation/create-auto-task - 集成到数据标注首页 - 核心文件： - frontend/src/routes/routes.ts - frontend/src/pages/DataAnnotation/Home/DataAnnotation.tsx #### 7. 算子模型增强 - 新增 runtime 字段用于标注算子筛选 - 核心文件：frontend/src/pages/OperatorMarket/operator.model.ts ### 后端功能 #### 1. 测试算子（test_annotation_marker） - 功能：在图片上绘制测试标记并输出 JSON 标注 - 用途：测试标注功能是否正常工作 - 实现文件： - runtime/ops/annotation/test_annotation_marker/process.py - runtime/ops/annotation/test_annotation_marker/metadata.yml - runtime/ops/annotation/test_annotation_marker/__init__.py #### 2. 算子注册 - 将测试算子注册到 annotation ops 包 - 添加到运行时白名单 - 核心文件： - runtime/ops/annotation/__init__.py - runtime/python-executor/datamate/auto_annotation_worker.py #### 3. 数据库初始化 - 添加测试算子到数据库 - 添加算子分类关联 - 核心文件：scripts/db/data-operator-init.sql ### 问题修复 #### 1. outputDir 默认值覆盖问题 - 问题：前端设置空字符串默认值导致 worker 无法注入真实输出目录 - 解决：过滤掉空/null 的 outputDir，确保 worker 能注入真实输出目录 - 修改位置：frontend/src/pages/DataAnnotation/OperatorCreate/hooks/useOperatorOperations.ts #### 2. targetClasses 默认值类型问题 - 问题：YOLO 算子 metadata 中 targetClasses 默认值是字符串 '[]' 而不是列表 - 解决：改为列表 [] - 修改位置：runtime/ops/annotation/image_object_detection_bounding_box/metadata.yml ## 关键特性 ### 用户体验 - 统一的算子编排界面（与数据清洗保持一致） - 直观的拖拽操作 - 实时的 pipeline 预览 - 完整的任务管理功能 ### 功能完整性 - 任务创建：两步流程，清晰明了 - 任务管理：列表展示、状态筛选、搜索 - 任务操作：停止、下载 - 任务详情：完整的信息展示 ### 可测试性 - 提供测试算子用于功能验证 - 支持快速测试标注流程 ## 验证结果 - ESLint 检查：✅ 通过 - 前端构建：✅ 通过（10.91s） - 功能测试：✅ 所有功能正常 ## 部署说明 1. 执行数据库初始化脚本（如果是新环境） 2. 重启前端服务 3. 重启后端服务（如果修改了 worker 白名单） ## 使用说明 1. 进入数据标注页面 2. 点击创建自动标注任务 3. 选择数据集和文件 4. 从算子库拖拽算子到编排区 5. 配置算子参数 6. 预览 pipeline 7. 提交任务 8. 在任务列表中查看进度 9. 点击任务名查看详情 10. 下载标注结果 ## 相关文件 - 前端页面：frontend/src/pages/DataAnnotation/OperatorCreate/ - 任务管理：frontend/src/pages/DataAnnotation/Home/components/ - API 集成：frontend/src/pages/DataAnnotation/annotation.api.ts - 测试算子：runtime/ops/annotation/test_annotation_marker/ - 数据库脚本：scripts/db/data-operator-init.sql
This commit is contained in:
@@ -2,7 +2,9 @@
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"""Annotation-related operators (e.g. YOLO detection)."""
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from . import image_object_detection_bounding_box
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from . import test_annotation_marker
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__all__ = [
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"image_object_detection_bounding_box",
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"test_annotation_marker",
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]
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@@ -40,7 +40,7 @@ settings:
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name: '目标类别'
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description: 'COCO 类别 ID 列表；为空表示全部类别。'
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type: 'input'
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defaultVal: '[]'
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defaultVal: []
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outputDir:
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name: '输出目录'
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description: '算子输出目录（由运行时注入）。'
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12
runtime/ops/annotation/test_annotation_marker/__init__.py
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12
runtime/ops/annotation/test_annotation_marker/__init__.py
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@@ -0,0 +1,12 @@
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"""Test annotation marker operator package."""
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from datamate.core.base_op import OPERATORS
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from .process import test_annotation_marker
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OPERATORS.register_module(
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module_name="test_annotation_marker",
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module_path="ops.annotation.test_annotation_marker.process",
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)
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__all__ = ["test_annotation_marker"]
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37
runtime/ops/annotation/test_annotation_marker/metadata.yml
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37
runtime/ops/annotation/test_annotation_marker/metadata.yml
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@@ -0,0 +1,37 @@
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name: '测试标注标记'
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name_en: 'Test Annotation Marker'
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description: '系统测试算子：在图片上添加测试标记并输出对应标注文件。'
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description_en: 'System testing operator: draw test marker on images and emit annotation json.'
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language: 'python'
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vendor: 'datamate'
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raw_id: 'test_annotation_marker'
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version: '1.0.0'
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types:
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- 'annotation'
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modal: 'image'
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inputs: 'image'
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outputs: 'image'
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settings:
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markerText:
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name: '标记文本'
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description: '绘制在图片上的测试标记文本。'
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type: 'input'
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defaultVal: 'TEST_ANNOTATION'
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markerColor:
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name: '标记颜色'
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description: '标记颜色（OpenCV BGR，格式如 0,255,0）。'
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type: 'input'
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defaultVal: '0,255,0'
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markerThickness:
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name: '线宽'
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description: '标记框线宽。'
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type: 'inputNumber'
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defaultVal: 2
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min: 1
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max: 10
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step: 1
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outputDir:
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name: '输出目录'
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description: '算子输出目录（由运行时注入）。'
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type: 'input'
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defaultVal: ''
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122
runtime/ops/annotation/test_annotation_marker/process.py
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122
runtime/ops/annotation/test_annotation_marker/process.py
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@@ -0,0 +1,122 @@
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from __future__ import annotations
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import json
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import os
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from typing import Any, Dict, Tuple
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import cv2
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from loguru import logger
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from datamate.core.base_op import Mapper
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class test_annotation_marker(Mapper):
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def __init__(self, *args, **kwargs):
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super(test_annotation_marker, self).__init__(*args, **kwargs)
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self._marker_text = str(kwargs.get("markerText", "TEST_ANNOTATION"))
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self._marker_color = self._parse_color(kwargs.get("markerColor", "0,255,0"))
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self._marker_thickness = int(kwargs.get("markerThickness", 2) or 2)
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self._output_dir = kwargs.get("outputDir")
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@staticmethod
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def _parse_color(value: Any) -> Tuple[int, int, int]:
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if isinstance(value, (list, tuple)) and len(value) >= 3:
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try:
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bgr = tuple(int(max(min(float(item), 255), 0)) for item in value[:3])
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return bgr # type: ignore[return-value]
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except Exception:
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return 0, 255, 0
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if isinstance(value, str):
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parts = [part.strip() for part in value.split(",")]
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if len(parts) >= 3:
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try:
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bgr = tuple(int(max(min(float(item), 255), 0)) for item in parts[:3])
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return bgr # type: ignore[return-value]
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except Exception:
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return 0, 255, 0
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return 0, 255, 0
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def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:
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image_path = sample.get(self.image_key) or sample.get("image")
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if not image_path or not os.path.exists(image_path):
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logger.warning("test_annotation_marker: image not found: {}", image_path)
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return sample
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image = cv2.imread(image_path)
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if image is None:
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logger.warning("test_annotation_marker: failed to read image: {}", image_path)
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return sample
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image_height, image_width = image.shape[:2]
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margin = max(min(image_width, image_height) // 20, 10)
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x1, y1 = margin, margin
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x2, y2 = image_width - margin, image_height - margin
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cv2.rectangle(
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image,
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(x1, y1),
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(x2, y2),
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self._marker_color,
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self._marker_thickness,
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)
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cv2.putText(
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image,
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self._marker_text,
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(x1, max(y1 - 10, 20)),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.8,
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self._marker_color,
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max(self._marker_thickness, 1),
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cv2.LINE_AA,
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)
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if self._output_dir and os.path.exists(self._output_dir):
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output_dir = self._output_dir
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else:
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output_dir = os.path.dirname(image_path)
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images_dir = os.path.join(output_dir, "images")
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annotations_dir = os.path.join(output_dir, "annotations")
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os.makedirs(images_dir, exist_ok=True)
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os.makedirs(annotations_dir, exist_ok=True)
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base_name = os.path.basename(image_path)
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name_without_ext = os.path.splitext(base_name)[0]
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output_image_path = os.path.join(images_dir, base_name)
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output_json_path = os.path.join(annotations_dir, f"{name_without_ext}.json")
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cv2.imwrite(output_image_path, image)
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annotations = {
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"image": base_name,
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"width": image_width,
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"height": image_height,
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"marker": {
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"text": self._marker_text,
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"color_bgr": list(self._marker_color),
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"thickness": self._marker_thickness,
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},
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"detections": [
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{
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"label": self._marker_text,
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"class_id": -1,
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"confidence": 1.0,
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"bbox_xyxy": [x1, y1, x2, y2],
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"bbox_xywh": [x1, y1, x2 - x1, y2 - y1],
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}
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],
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}
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with open(output_json_path, "w", encoding="utf-8") as file:
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json.dump(annotations, file, indent=2, ensure_ascii=False)
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sample["output_image"] = output_image_path
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sample["annotations_file"] = output_json_path
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sample["annotations"] = annotations
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sample["detection_count"] = 1
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return sample
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@@ -122,7 +122,7 @@ DEFAULT_OUTPUT_ROOT = os.getenv(
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DEFAULT_OPERATOR_WHITELIST = os.getenv(
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"AUTO_ANNOTATION_OPERATOR_WHITELIST",
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"ImageObjectDetectionBoundingBox",
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"ImageObjectDetectionBoundingBox,test_annotation_marker",
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)
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