labelme: Image Polygonal Annotation 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,
and supports annotations for semantic and instance segmentation.

Fig 1. Example of annotations for instance segmentation.

Requirements

Ubuntu / macOS / Windows
Python2 / Python3
PyQt4 / PyQt5 / PySide2

Installation

There are options:

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

Anaconda

You need install Anaconda, then run below:

# python2
conda create --name=labelme python=2.7
source activate labelme
# conda install -c conda-forge pyside2
conda install pyqt
pip install labelme
# if you'd like to use the latest version. run below:
# pip install git+https://github.com/wkentaro/labelme.git

# python3
conda create --name=labelme python=3.6
source activate labelme
# conda install -c conda-forge pyside2
# conda install pyqt
pip install pyqt5  # pyqt5 can be installed via pip on python3
pip install labelme

Docker

You need install docker, then run below:

wget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O 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 examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json
./labelme_on_docker examples/semantic_segmentation/data_annotated

Ubuntu

# Ubuntu 14.04 / Ubuntu 16.04
# Python2
# sudo apt-get install python-qt4  # PyQt4
sudo apt-get install python-pyqt5  # PyQt5
sudo pip install labelme
# Python3
sudo apt-get install python3-pyqt5  # PyQt5
sudo pip3 install labelme

macOS

# macOS Sierra
brew install pyqt  # maybe pyqt5
pip install labelme  # both python2/3 should work

# or install standalone executable / app
brew install wkentaro/labelme/labelme
brew cask install wkentaro/labelme/labelme

Usage

Run labelme --help for detail.
The annotations are saved as a JSON file.

labelme  # just open gui

# tutorial (single image example)
cd examples/tutorial
labelme apc2016_obj3.jpg  # specify image file
labelme apc2016_obj3.jpg -O apc2016_obj3.json  # close window after the save
labelme apc2016_obj3.jpg --nodata  # not include image data but relative image path in JSON file
labelme apc2016_obj3.jpg \
  --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball  # specify label list

# semantic segmentation example
cd examples/semantic_segmentation
labelme data_annotated/  # Open directory to annotate all images in it
labelme data_annotated/ --labels labels.txt  # specify label list with a file

For more advanced usage, please refer to the examples:

FAQ

How to convert JSON file to numpy array? See examples/tutorial.
How to load label PNG file? See examples/tutorial.
How to get annotations for semantic segmentation? See examples/semantic_segmentation.
How to get annotations for instance segmentation? See examples/instance_segmentation.

Screencast

Testing

pip install hacking pytest pytest-qt
flake8 .
pytest -v tests

How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.
Also, there are pre-built executables in the release section.

# Setup conda
conda create --name labelme python=3.6
conda activate labelme

# Build the standalone executable
pip install .
pip uninstall -y matplotlib
pip install pyinstaller
pyinstaller labelme.spec
dist/labelme --version

Acknowledgement

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

butchersix/labelme