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<img src="https://github.com/wkentaro/labelme/blob/master/labelme/icons/icon.png?raw=true" align="right" />
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# labelme: Image Polygonal Annotation with Python
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[![PyPI Version](https://img.shields.io/pypi/v/labelme.svg)](https://pypi.python.org/pypi/labelme)
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[![Python Versions](https://img.shields.io/pypi/pyversions/labelme.svg)](https://pypi.org/project/labelme)
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[![Travis Build Status](https://travis-ci.org/wkentaro/labelme.svg?branch=master)](https://travis-ci.org/wkentaro/labelme)
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[![Docker Build Status](https://img.shields.io/docker/build/wkentaro/labelme.svg)](https://hub.docker.com/r/wkentaro/labelme)
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Labelme is a graphical image annotation tool inspired by <http://labelme.csail.mit.edu>.  
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It is written in Python and uses Qt for its graphical interface,  
and supports annotations for semantic and instance segmentation.
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<img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/.readme/annotation.jpg?raw=true" width="80%" />  
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Fig 1. Example of annotations for instance segmentation.
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## Requirements
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- Ubuntu / macOS / Windows
- Python2 / Python3
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- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted)
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## Installation
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There are options:

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

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### Anaconda
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You need install [Anaconda](https://www.continuum.io/downloads), then run below:

```bash
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# python2
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conda create --name=labelme python=2.7
source activate labelme
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# conda install -c conda-forge pyside2
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conda install pyqt
pip install labelme
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# if you'd like to use the latest version. run below:
# pip install git+https://github.com/wkentaro/labelme.git
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# python3
conda create --name=labelme python=3.6
source activate labelme
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# conda install -c conda-forge pyside2
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# conda install pyqt
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pip install pyqt5  # pyqt5 can be installed via pip on python3
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pip install labelme
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```

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### Docker
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You need install [docker](https://www.docker.com), then run below:
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```bash
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wget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O labelme_on_docker
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chmod u+x labelme_on_docker

# Maybe you need http://sourabhbajaj.com/blog/2017/02/07/gui-applications-docker-mac/ on macOS
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./labelme_on_docker examples/tutorial/apc2016_obj3.jpg -O examples/tutorial/apc2016_obj3.json
./labelme_on_docker examples/semantic_segmentation/data_annotated
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```
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### Ubuntu
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```bash
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# Ubuntu 14.04 / Ubuntu 16.04
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# Python2
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# sudo apt-get install python-qt4  # PyQt4
sudo apt-get install python-pyqt5  # PyQt5
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sudo pip install labelme
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# Python3
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sudo apt-get install python3-pyqt5  # PyQt5
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sudo pip3 install labelme
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```

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### macOS
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```bash
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# macOS Sierra
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brew install pyqt  # maybe pyqt5
pip install labelme  # both python2/3 should work
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# or install standalone executable / app
brew install wkentaro/labelme/labelme
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brew cask install wkentaro/labelme/labelme
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```

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## Usage
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Run `labelme --help` for detail.  
The annotations are saved as a [JSON](http://www.json.org/) file.
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```bash
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labelme  # just open gui

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# tutorial (single image example)
cd examples/tutorial
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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
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```

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For more advanced usage, please refer to the examples:
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* [Tutorial (Single Image Example)](https://github.com/wkentaro/labelme/blob/master/examples/tutorial)
* [Semantic Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation)
* [Instance Segmentation Example](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation)
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## FAQ

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- **How to convert JSON file to numpy array?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#convert-to-dataset).
- **How to load label PNG file?** See [examples/tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial#how-to-load-label-png-file).
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- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation).
- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation).
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## Screencast
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<img src="https://github.com/wkentaro/labelme/blob/master/.readme/screencast.gif?raw=true" width="70%"/>
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## Testing
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```bash
pip install hacking pytest pytest-qt
flake8 .
pytest -v tests
```


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## How to build standalone executable
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Below shows how to build the standalone executable on macOS, Linux and Windows.  
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Also, there are pre-built executables in
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[the release section](https://github.com/wkentaro/labelme/releases).
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```bash
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# Setup conda
conda create --name labelme python=3.6
conda activate labelme
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# Build the standalone executable
pip install .
pip uninstall -y matplotlib
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pip install pyinstaller
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pyinstaller labelme.spec
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dist/labelme --version
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```


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## Acknowledgement
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This repo is the fork of [mpitid/pylabelme](https://github.com/mpitid/pylabelme),
whose development has already stopped.