labelmeWithAI

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

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 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 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 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-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

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

# or install standalone app
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:

• Tutorial (Single Image Example)
• Semantic Segmentation Example
• Instance Segmentation Example

FAQ

• 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.

Screencast

Testing

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

How to build standalone app

Below 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.app

Acknowledgement

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