README.md


    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

    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

    How to contribute

    Make sure below test passes on your environment.
    See .github/workflows/ci.yml for more detail.

    pip install black hacking pytest pytest-qt
    
    flake8 .
    black --line-length 79 --check labelme/
    MPLBACKEND='agg' pytest tests/ -m 'not gpu'

    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}
    }

    项目简介

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/wkentaro/labelme

    发行版本 45

    v4.5.11

    全部发行版

    贡献者 51

    全部贡献者

    开发语言

    • Python 99.7 %
    • Dockerfile 0.3 %