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.
Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.
Ubuntu Linux
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]
macOS
Python 2 + Qt4
brew install qt qt4
brew install libxml2
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Python 3 + Qt5 (Works on macOS High Sierra)
brew install qt # will install qt-5.x.x
brew install libxml2
make qt5py3
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
NEW Python 3 Virtualenv + Binary This avoids a lot of the QT / Python version issues, and gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider this script: build-tools/build-for-macos.sh
brew install python3
pip install pipenv
pipenv --three
pipenv shell
pip install py2app
pip install PyQt5 lxml
make qt5py3
rm -rf build dist
python setup.py py2app -A
mv "dist/labelImg.app" /Applications
Windows
Download and setup Python 2.6 or later, PyQt4 and install lxml.
Open cmd and go to the labelImg directory
pyrcc4 -o resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Windows + Anaconda
Download and install Anaconda (Python 3+)
Open the Anaconda Prompt and go to the labelImg directory
conda install pyqt=5
pyrcc5 -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 Ubuntu 14.04 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
The annotation will be saved to the folder you specify.
You can refer to the below hotkeys to speed up your workflow.
data/predefined_classes.txt
define the list of classes that will be used for your training.A txt file of yolo format will be saved in the same folder as your image with same name. A file named "classes.txt" is saved to that folder too. "classes.txt" defines the list of class names that your yolo label refers to.
Note:
You can edit the data/predefined_classes.txt to load pre-defined classes