labelImg
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. Besides, it also supports YOLO format
Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8. However, Python 3 or above and PyQt5 are strongly recommended.
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 (Recommended)
sudo apt-get install pyqt5-dev-tools sudo pip3 install -r requirements/requirements-linux-python3.txt 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]
Python 3 + Qt5 (Recommended)
brew install qt # Install qt-5.x.x by Homebrewbrew install libxml2 or using pip pip3 install pyqt5 lxml # Install qt and lxml by pipmake qt5py3 python3 labelImg.py python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Python 3 Virtualenv (Recommended)
Virtualenv can avoid a lot of the QT / Python version issues
brew install python3 pip3 install pipenv pipenv run pip install pyqt5==5.13.2 lxml pipenv run make qt5py3 python3 labelImg.py [Optional] rm -rf build dist; python setup.py py2app -A;mv "dist/labelImg.app" /Applications
Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh
Install Python,PyQt5and install lxml.
Open cmd and go to the labelImg directory
pyrcc4 -o line/resources.py resources.qrc For pyqt5, pyrcc5 -o libs/resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Download and install Anaconda (Python 3+)
Open the Anaconda Prompt and go to the labelImg directory
conda install pyqt=5 pyrcc5 -o libs/resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
pip3 install labelImg labelImg labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
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.
In data/predefined_classes.txt
define the list of classes that will be used for your training.
Build and launch using the instructions above.
Right below "Save" button in the toolbar, click "PascalVOC" button to switch to YOLO format.
You may use Open/OpenDIR to process single or multiple images. When finished with a 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 an 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.
You can edit thedata/predefined_classes.txtto 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 |
Verify Image:
When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.
Difficult:
The difficult field is set to 1 indicates that the object has been annotated as "difficult", for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training.
Send a pull request
Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
ImageNet Utils to download image, create a label text for machine learning, etc
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