labelme
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](examples/tutorial))
- [x] Image flag annotation for classification and cleaning. ([#166](https://github.com/wkentaro/labelme/pull/166))
- [x] Video annotation. ([video annotation](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-format dataset for semantic/instance segmentation. ([semantic segmentation](examples/semantic_segmentation), [instance segmentation](examples/instance_segmentation))
- [x] Exporting COCO-format dataset for instance segmentation. ([instance segmentation](examples/instance_segmentation))
## Requirements
- Ubuntu / macOS / Windows
- Python2 / Python3
- [PyQt4 / PyQt5](http://www.riverbankcomputing.co.uk/software/pyqt/intro)
## Installation
There are options:
- Platform agnostic installation: [Anaconda](#anaconda), [Docker](#docker)
- Platform specific installation: [Ubuntu](#ubuntu), [macOS](#macos), [Windows](#windows)
- Pre-build binaries from [the release section](https://github.com/wkentaro/labelme/releases)
### 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
# or you can install everything by conda command
# conda install labelme -c conda-forge
```
### Docker
You need install [docker](https://www.docker.com), then run below:
```bash
# 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
```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
# or install standalone executable from:
# https://github.com/wkentaro/labelme/releases
```
### Ubuntu 19.10+ / Debian (sid)
```bash
sudo apt-get install labelme
```
### macOS
```bash
# 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](https://www.continuum.io/downloads), then in an Anaconda Prompt run:
```bash
# 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](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)](examples/tutorial)
* [Semantic Segmentation Example](examples/semantic_segmentation)
* [Instance Segmentation Example](examples/instance_segmentation)
* [Video Annotation Example](examples/video_annotation)
### 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](examples/classification)
- Labels are assigned to a single polygon. [Example](examples/bbox_detection)
## FAQ
- **How to convert JSON file to numpy array?** See [examples/tutorial](examples/tutorial#convert-to-dataset).
- **How to load label PNG file?** See [examples/tutorial](examples/tutorial#how-to-load-label-png-file).
- **How to get annotations for semantic segmentation?** See [examples/semantic_segmentation](examples/semantic_segmentation).
- **How to get annotations for instance segmentation?** See [examples/instance_segmentation](examples/instance_segmentation).
## 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.
```bash
# 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.
```bash
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](https://github.com/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.
```bash
@misc{labelme2016,
author = {Kentaro Wada},
title = {{labelme: Image Polygonal Annotation with Python}},
howpublished = {\url{https://github.com/wkentaro/labelme}},
year = {2016}
}
```