1.Labelme

Labelmeis a graphical image annotation tool inspired byhttp://labelme.csail.mit.edu.Itis written in Python and usesQtfor its graphical interface.

Convert to VOC dataset:RGB infos:

2.ImageTagger

This is a collaborative online tool for labeling image data.

If you are participating in RoboCup, you should not install your own instance but rather use the one provided by the Hamburg Bit-Bots (https://imagetagger.bit-bots.de). This enables you to use already labeled images from other teams and to share your own.

For a short overview of the functions please have a look at the following poster:https://robocup.informatik.uni-hamburg.de/wp-content/uploads/2017/11/imagetagger-poster.pdf

Used dependencies

The ImageTagger relies on the following plugins, libraries and frameworks:

• Bootstrap
• Django
• Django REST Framework
• django-registration
• django-widget-tweaks
• imgAreaSelect
• jCanvas
• jQuery
• jQuery-Autocomplete
• jQuery-File-Upload
• Pillow
• PostgreSQL

主要的缺点：

（1）导出格式只有自定义的txt格式，不支持Pascal Voc等主流格式。

（2）Image Segmentation方面的标注不是太灵活，而且不支持Mask image的导出。

3.Labelbox

The most versatile data labeling platform for training expert AI. https://www.labelbox.com

支持导出Mask image：

主要的缺点：

（1）代码发布到Cloud上，部署比较麻烦些

（2）数据整理也比较麻烦（segmentation mask image是通过的链接的方式放置在json/xml文件中）

4.Colabeler

主要的缺点：

（1）不支持segmentation mask的导出

（2）没有源代码，不易扩展