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
Starter Bundle
If you're new to Labelme, you can get started with Labelme Starter Bundle (FREE), which contains:
- Installation guides for all platforms: Windows, macOS, and Linux 💻
- Step-by-step tutorials: first annotation to editing, exporting, and integrating with other programs 📕
- A compilation of valuable resources for further exploration 🔗.
Installation
There are options:
Anaconda
You need install Anaconda, then run below:
conda create --name=labelme python=3
source activate labelme
pip install labelme
Ubuntu
sudo apt-get install labelme
sudo pip3 install labelme
macOS
brew install pyqt
pip install labelme
brew install wkentaro/labelme/labelme
Windows
Install Anaconda, then in an Anaconda Prompt run:
conda create --name=labelme python=3
conda activate labelme
pip install labelme
Usage
Run labelme --help
for detail.
The annotations are saved as a JSON file.
labelme
cd examples/tutorial
labelme apc2016_obj3.jpg
labelme apc2016_obj3.jpg -O apc2016_obj3.json
labelme apc2016_obj3.jpg --nodata
labelme apc2016_obj3.jpg \
--labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball
cd examples/semantic_segmentation
labelme data_annotated/
labelme data_annotated/ --labels labels.txt
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
Examples
How to develop
git clone https://github.com/wkentaro/labelme.git
cd labelme
curl -L https://github.com/wkentaro/dotfiles/raw/main/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.
conda create --name labelme python=3.9
conda activate labelme
pip install .
pip install 'matplotlib<3.3'
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 -r requirements-dev.txt
ruff format --check
ruff check
MPLBACKEND='agg' pytest -vsx tests/
Acknowledgement
This repo is the fork of mpitid/pylabelme.