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.
- Ubuntu / macOS / Windows
- Python2 / Python3
- PyQt4 / PyQt5
There are options:
- Platform agonistic installation: Anaconda, Docker
- Platform specific installation: Ubuntu, macOS
You need install Anaconda, then run below:
# python2
conda create --name=labelme python=2.7
source activate labelme
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 pyqt
pip install pyqt5 # pyqt5 can be installed via pip on python3
pip install labelmeYou 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 examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json
labelme_on_docker examples/semantic_segmentation/data_annotated# Ubuntu 14.04 / Ubuntu 16.04
# Python2
# sudo apt-get install python-qt4 pyqt4-dev-tools # PyQt4
sudo apt-get install python-pyqt5 pyqt5-dev-tools # PyQt5
sudo pip install labelme
# Python3
sudo apt-get install python3-pyqt5 pyqt5-dev-tools # PyQt5
sudo pip3 install labelme# 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/labelmeRun 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 fileFor more advanced usage, please refer to the examples:
- How to convert JSON file to numpy array? See examples/tutorial.
- How to load label.png generated by labelme_json_to_dataset? 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.
pip install hacking pytest pytest-qt
flake8 .
pytest -v testsBelow is an example on macOS, and there are pre-built apps in the release section.
git clone https://github.com/wkentaro/labelme.git
cd labelme
virtualenv venv --python /usr/local/bin/python3
. venv/bin/activate
pip install -e .
pip uninstall matplotlib
pip install pyinstaller
pyinstaller app.py \
--onefile \
--windowed \
--name labelme \
--icon labelme/icons/icon.icns \
--specpath $(mktemp -d) \
--noconfirm
open dist/labelme.appThis repo is the fork of mpitid/pylabelme, whose development has already stopped.