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wkentaro
5.8K Stars 1.8K Forks Other 1.2K Commits 91 Opened issues

Description

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

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labelme

Image Polygonal Annotation with Python


Description

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.


VOC dataset example of instance segmentation.


Other examples (semantic segmentation, bbox detection, and classification).


Various primitives (polygon, rectangle, circle, line, and point).

Features

  • [x] Image annotation for polygon, rectangle, circle, line and point. (tutorial)
  • [x] Image flag annotation for classification and cleaning. (#166)
  • [x] Video annotation. (video annotation)
  • [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). (#144)
  • [x] Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)
  • [x] Exporting COCO-format dataset for instance segmentation. (instance segmentation)

Requirements

Installation

There are options:

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

or you can install everything by conda command

conda install labelme -c conda-forge

Docker

You need install docker, then run below:

# on macOS
socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\"$DISPLAY\" &
docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=docker.for.mac.host.internal:0 -v $(pwd):/root/workdir wkentaro/labelme

on Linux

xhost + docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=:0 -v $(pwd):/root/workdir wkentaro/labelme

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

or install standalone executable from:

https://github.com/wkentaro/labelme/releases

Ubuntu 19.10+ / Debian (sid)

sudo apt-get install labelme

macOS

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

or install standalone executable/app from:

https://github.com/wkentaro/labelme/releases

Windows

Install Anaconda, then in an Anaconda Prompt run:

# python3
conda create --name=labelme python=3.6
conda activate labelme
pip install 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:

Command Line Arguments

  • --output
    specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on.
  • The first time you run labelme, it will create a config file in
    ~/.labelmerc
    . You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the
    --config
    flag.
  • Without the
    --nosortlabels
    flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided.
  • Flags are assigned to an entire image. Example
  • Labels are assigned to a single polygon. Example

FAQ

Testing

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

Developing

git clone https://github.com/wkentaro/labelme.git
cd labelme

Install anaconda3 and labelme

curl -L https://github.com/wkentaro/dotfiles/raw/master/local/bin/install_anaconda3.sh | bash -s . source .anaconda3/bin/activate pip install -e .

How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.

# Setup conda
conda create --name labelme python==3.6.0
conda activate labelme

Build the standalone executable

pip install . pip install pyinstaller pyinstaller labelme.spec dist/labelme --version

Acknowledgement

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

Cite This Project

If you use this project in your research or wish to refer to the baseline results published in the README, please use the following BibTeX entry.

@misc{labelme2016,
  author =       {Kentaro Wada},
  title =        {{labelme: Image Polygonal Annotation with Python}},
  howpublished = {\url{https://github.com/wkentaro/labelme}},
  year =         {2016}
}

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