LabelImg is a graphical image annotation tool.
It is written in Python and uses Qt for its graphical interface.
Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet.
- Windows & Linux
- OS X. Binaries for OS X are not yet available. Help would be appreciated. At present, it must be built from source.
Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.
Python 2 + Qt4
sudo apt-get install pyqt4-dev-tools sudo pip install lxml make qt4py2 python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Python 3 + Qt5
sudo apt-get install pyqt5-dev-tools sudo pip3 install lxml make qt5py3 python3 labelImg.py python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Python 2 + Qt4
brew install qt qt4 brew install libxml2 make qt4py2 python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Download and setup Python 2.6 or later, PyQt4 and install lxml.
Open cmd and go to labelImg directory
pyrcc4 -o resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
pip install labelImg labelImg labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
I tested pip on Ubuntu14.04 and 16.04. However, I didn't test pip on MacOS and Windows
docker run -it \ --user $(id -u) \ -e DISPLAY=unix$DISPLAY \ --workdir=$(pwd) \ --volume="/home/$USER:/home/$USER" \ --volume="/etc/group:/etc/group:ro" \ --volume="/etc/passwd:/etc/passwd:ro" \ --volume="/etc/shadow:/etc/shadow:ro" \ --volume="/etc/sudoers.d:/etc/sudoers.d:ro" \ -v /tmp/.X11-unix:/tmp/.X11-unix \ tzutalin/py2qt4 make qt4py2;./labelImg.py
You can pull the image which has all of the installed and required dependencies. Watch a demo video
- Build and launch using the instructions above.
- Click 'Change default saved annotation folder' in Menu/File
- Click 'Open Dir'
- Click 'Create RectBox'
- Click and release left mouse to select a region to annotate the rect box
- You can use right mouse to drag the rect box to copy or move it
The annotation will be saved to the folder you specify.
You can refer to the below hotkeys to speed up your workflow.
You can edit the data/predefined_classes.txt to load pre-defined classes
| Ctrl + u | Load all of the images from a directory |
| Ctrl + r | Change the default annotation target dir |
| Ctrl + s | Save |
| Ctrl + d | Copy the current label and rect box |
| Space | Flag the current image as verified |
| w | Create a rect box |
| d | Next image |
| a | Previous image |
| del | Delete the selected rect box |
| Ctrl++ | Zoom in |
| Ctrl-- | Zoom out |
| ↑→↓← | Keyboard arrows to move selected rect box |
Send a pull request
- ImageNet Utils to download image, create a label text for machine learning, etc
- Docker hub to run it