labelme 3.0.1

Image Polygonal Annotation with Python.

<img src="https://github.com/wkentaro/labelme/blob/master/labelme/icons/icon.png?raw=true" align="right" />

# labelme: Image Polygonal Annotation with Python

[](https://pypi.python.org/pypi/labelme)
[](https://pypi.org/project/labelme)
[](https://travis-ci.org/wkentaro/labelme)
[](https://hub.docker.com/r/wkentaro/labelme)


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.

<img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/.readme/annotation.jpg?raw=true" width="80%" />
Fig 1. Example of annotations for instance segmentation.


## Features

- [x] Polygonal annotation
- [x] Predefined labels ([#76](https://github.com/wkentaro/labelme/pull/76))
- [x] Auto save mode ([#138](https://github.com/wkentaro/labelme/pull/138))
- [x] Support custom json keys ([#140](https://github.com/wkentaro/labelme/pull/140))
- [x] Undo by Ctrl-Z ([#112](https://github.com/wkentaro/labelme/pull/112), [#143](https://github.com/wkentaro/labelme/pull/143))
- [x] Customize by config file ([#144](https://github.com/wkentaro/labelme/pull/144))
- [x] VOC dataset export ([semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation))
- [x] Image level flag annotation ([#166](https://github.com/wkentaro/labelme/pull/166))


## Requirements

- Ubuntu / macOS / Windows
- Python2 / Python3
- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro) / [PySide2](https://wiki.qt.io/PySide2_GettingStarted)


## Installation

There are options:

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

### Anaconda

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

```bash
# 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
```

### Docker

You need install [docker](https://www.docker.com), then run below:

```bash
wget https://raw.githubusercontent.com/wkentaro/labelme/master/labelme/cli/on_docker.py -O 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

```bash
# 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
```

### macOS

```bash
# 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/labelme
```


## Usage

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

```bash
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)](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)


## FAQ

- **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).
- **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).


## Screencast

<img src="https://github.com/wkentaro/labelme/blob/master/.readme/screencast.gif?raw=true" width="70%"/>


## Testing

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


## How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.
Also, there are pre-built executables in
[the release section](https://github.com/wkentaro/labelme/releases).

```bash
# Setup conda
conda create --name labelme python=3.6
conda activate labelme

# Build the standalone executable
pip install .
pip uninstall -y matplotlib
pip install pyinstaller
pyinstaller labelme.spec
dist/labelme --version
```


## Acknowledgement

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