README.md

labelme: Image Annotation Tool with Python

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu.
It is written in Python and uses Qt for its graphical interface.

Requirements

• Ubuntu / macOS / Windows
• Python2 / Python3
• PyQt4 / PyQt5

Installation

There are options:

• Platform agonistic installation: Anaconda, Docker
• Platform specific installation: Ubuntu, macOS

Anaconda

You need install Anaconda, then run below:

conda create --name=labelme python=2.7
source activate labelme
conda install pyqt
pip install labelme

Docker

You need install docker, then run below:

wget https://raw.githubusercontent.com/wkentaro/labelme/master/scripts/labelme_on_docker
chmod u+x labelme_on_docker

# Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS
./labelme_on_docker static/apc2016_obj3.jpg -O static/apc2016_obj3.json

Ubuntu

sudo apt-get install python-qt4 pyqt4-dev-tools
sudo pip install labelme

macOS

brew install qt qt4 || brew install pyqt  # qt4 is deprecated
pip install labelme

Usage

Annotation

Run labelme --help for detail.

labelme  # Open GUI
labelme static/apc2016_obj3.jpg  # Specify file
labelme static/apc2016_obj3.jpg -O static/apc2016_obj3.json  # Close window after the save

The annotations are saved as a JSON file. The file includes the image itself.

Visualization

To view the json file quickly, you can use utility script:

labelme_draw_json static/apc2016_obj3.json

Convert to Dataset

To convert the json to set of image and label, you can run following:

labelme_json_to_dataset static/apc2016_obj3.json

Sample

• Original Image
• Screenshot
• Generated Json File
• Visualized Json File

Screencast

Acknowledgement

This repo is the fork of mpitid/pylabelme, whose development has already stopped.