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# labelme: Image Polygonal Annotation with Python

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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%" />
<i>Fig 1. Annotation example of instance segmentation.</i>

<img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg?raw=true" width="19%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationClassPNG/2011_000006.png?raw=true" width="19%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg?raw=true" width="19%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationObjectPNG/2011_000006.png?raw=true" width="19%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg?raw=true" width="19%" />  
<i>Fig 2. VOC dataset example of instance segmentation.</i>

<img src="https://github.com/wkentaro/labelme/raw/master/examples/semantic_segmentation/.readme/annotation.jpg?raw=true" width="30%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/bbox_detection/.readme/annotation.jpg?raw=true" width="32%" /> <img src="https://github.com/wkentaro/labelme/blob/master/examples/classification/.readme/annotation_cat.jpg?raw=true" width="33%" />  
<i>Fig 3. Other examples (semantic segmentation, bbox detection, and classification).</i>

<img src="https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png" width="32%" />  
<i>Fig 4. Various primitives (polygon, rectangle, circle, line, and point).</i>


## Features

- [x] Image annotation for polygon, rectangle, circle, line and point. ([tutorial](https://github.com/wkentaro/labelme/blob/master/examples/tutorial))
- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))
- [x] Video annotation. ([video annotation](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation))
- [x] GUI customization (predefined labels / flags, auto-saving, label validation, etc). ([#144](https://github.com/wkentaro/labelme/pull/144))
- [x] Exporting VOC-like dataset for semantic/instance segmentation. ([semantic segmentation](https://github.com/wkentaro/labelme/blob/master/examples/semantic_segmentation), [instance segmentation](https://github.com/wkentaro/labelme/blob/master/examples/instance_segmentation))



## 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](#anaconda), [Docker](#docker)
- Platform specific installation: [Ubuntu](#ubuntu), [macOS](#macos), [Windows](#windows)

### 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
```

### Windows

Firstly, follow instruction in [Anaconda](#anaconda).

```bash
# Pillow 5 causes dll load error on Windows.
# https://github.com/wkentaro/labelme/pull/174
conda install pillow=4.0.0
```


## 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)
* [Video Annotation Example](https://github.com/wkentaro/labelme/blob/master/examples/video_annotation)


## 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
```


## Developing

```bash
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.  
Also, there are pre-built executables in
[the release section](https://github.com/wkentaro/labelme/releases).

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

# Build the standalone executable
conda install pyqt
pip install .
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.
