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.
Watch a demo video
Installation
Download prebuilt binaries
- Windows & Linux
- macOS. Binaries for macOS are not yet available. Help would be appreciated. At present, it must be built from source.
Build from source
Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.
Ubuntu Linux
Python 2 Qt4
代码语言：javascript复制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
代码语言：javascript复制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
代码语言：javascript复制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)
代码语言：javascript复制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
代码语言：javascript复制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" /ApplicationsWindows
Download and setup Python 2.6 or later, PyQt4 and install lxml.
Open cmd and go to the labelImg directory
代码语言：javascript复制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
代码语言：javascript复制conda install pyqt=5
pyrcc5 -o resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]Get from PyPI
代码语言：javascript复制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
Use Docker
代码语言：javascript复制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.pyYou can pull the image which has all of the installed and required dependencies. Watch a demo video
Usage
Steps (PascalVOC)
- 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.
Steps (YOLO)
- In
data/predefined_classes.txtdefine the list of classes that will be used for your training. - Build and launch using the instructions above.
- Right below "Save" button in toolbar, click "PascalVOC" button to switch to YOLO format.
- You may use Open/OpenDIR to process single or multiple images. When finished with single image, click save.
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:
- Your label list shall not change in the middle of processing a list of images. When you save a image, classes.txt will also get updated, while previous annotations will not be updated.
- You shouldn't use "default class" function when saving to YOLO format, it will not be referred.
- When saving as YOLO format, "difficult" flag is discarded.
Create pre-defined classes
You can edit the data/predefined_classes.txt to load pre-defined classes
