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Apache-2.0

PLabel

半自动标注系统是基于BS架构,纯Web页面操作,由鹏城实验室叶齐祥、曾炜、田永鸿教授团队自主研发,由工程师邹安平维护,集成视频抽帧,目标检测、视频跟踪、ReID分类、人脸检测等算法,实现了对图像,视频的自动标注,并可以对自动算法的结果进行人工标注,最终得到标注结果,同时也可以对视频、图片、医疗(包括dicom文件及病理图像)相关的数据进行人工标注,标注结果支持COCO及VOC格式。支持多人协同标注。 半自动标注系统主要功能有:用户管理,数据集管理,自动标注,人工标注,ReID标注,车流统计,视频标注,医疗CT标注,超大图像标注,模型管理与重训,报表管理。数据标注过程一个非常重要的因素是数据安全,在标注使用中防止数据泄露,采用基于web标注工具是有效避免数据泄露的措施之一。 半自动标注系统以保证性能的情况下最小化人工标注代价为目标,不断提升自动标注效率,减少人工标注和人工参与过程。

Install

参见doc目录下环境搭建word文档,本标注系统将会一直维护下去,有疑问、使用过程发现Bug、或者想要新增功能,都可以直接联系zouap@pcl.ac.cn,工作日一般会及时回复。

Usage

1、用户管理,只有管理员LabelSystem01帐号可以创建用户,分管理员、标注人员、审核人员三类。 在用户管理中可以对每个标注人员进行分表存储标注数据(适合大批量标注,100W级以上),也可以将所有用户的操作的标注数据都存储在同一张表中。

2、数据集管理 支持视频(需要ffmpeg能支持的格式)、CT影像(.ima,.dcm)、超大图像(.svs,.tiff)、通用图片(常见的图片格式都能支持) 4种类型的数据。视频支持抽帧,抽帧可以指定文件名方式,也支持视频合并,4种类型数据都可以支持预览。视频目前只有mp4格式可以在线预览,其它的视频需要抽帧之后才能预览。

(注:视频及超大图像需要docker镜像的支持)

3、自动标注 自动标注目前集成了人、车的几种开源算法,基于mmdetection平台,搜集所有开源数据集训练得到的目标检测模型,新建自动标注时,需要选择数据集,然后等待自动标注完成后,可以查看标注结果。 自动标注也可以集成开发者的自己的模型,需要开发者在数据库表中插入对应的模型位置及运行脚本,当然模型的输出结果需要与系统读取的数据格式一致。

4、人工标注 人工标注可以基于自动标注的结果,也可以对数据集中的数据进行直接标注,包括点、多边形、矩形三种标注。人工标注以任务的方式存在,可以看到当前标注的进度。 在人工标注页面的按钮中,还可以进行单目标跟踪、多目标跟踪、自动单个图像目标检测。 包括复制、粘贴、上下左右移动等功能按钮。 对于人工标注任务在标注人员标注完成后,还可以转给审核人员进行审核。

5、ReID标注 可以对多个镜头进行ReID标注,在标注页面可以看到多个镜头的ReID标注,大大提升效率,同时查看ReID结果页面会列出所有的ReID标注结果,还可以直接对结果进行修改,删除。也可以对结果进行导出,包括抠图导出,原图导出,标注导出。

6、车流统计 操作MP4格式的视频,可以在视频上对车辆行驶方向进行标注。

7、视频标注 操作MP4格式的视频,可以在视频上对目标进行标注。

8、超大图像标注 对tiff及svs格式的图像进行标注,集成了openseadragon组件,并进行了二次开发,以便可以进行标注。

9、报表管理 对标注人员的工作进行度量。

10、操作日志管理 用户登录、登出日志查看。

11、模型重训 可以基于mmdetection使用用户标注的数据重训人、车的检测模型。

标注请使用chrome浏览器,相关截图可参见doc目录下《半自动标注系统介绍_V1.4》,有疑问可以直接联系zouap@pcl.ac.cn,工作日一般会及时回复。

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简介

半自动标注系统是基于BS架构,由鹏城实验室叶齐翔教授团队自主研发,集成视频抽帧,目标检测、视频跟踪、ReID分类、人脸检测等算法,实现了对图像,视频的自动标注,并可以对自动算法的结果进行人工标注,最终得到标注结果,同时也可以对视频、图片、医疗(包括dicom文件及病理图像)相关的数据进行人工标注,标注结果支持COCO及VOC格式。支持多人协同标注。 半自动标注系统主要功能有:用户管理,数据集管理,自动标注,人工标注,ReID标注,车流统计,视频标注,医疗CT标注,超大图像标注,模型管理与重训,报表管理。数据标注过程一个非常重要的因素是数据安全,在标注使用中防止数据泄露,采用基于web标注工具是有效避免数据泄露的措施之一。 半自动标注系统以保证性能的情况下最小化人工标注代价为目标,不断提... 展开 收起
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