labelImg: 数据标注工具 |
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labelImg: 数据标注工具
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 
Watch a demo video Installation Build from sourceLinux/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. Ubuntu LinuxPython 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] macOSPython 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 Homebrew brew install libxml2 or using pip pip3 install pyqt5 lxml # Install qt and lxml by pip make 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" /ApplicationsNote: 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 WindowsInstall Python, PyQt5 and install lxml. Open cmd and go to the labelImg directory pyrcc4 -o lib/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] Windows + AnacondaDownload and install Anaconda (Python 3+) Open the Anaconda Prompt and go to the labelImg directory conda install pyqt=5 conda install -c anaconda lxml pyrcc5 -o libs/resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Get from PyPI but only python3.0 or aboveThis is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora. pip3 install labelImg labelImg labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Use Docker 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 itThe 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.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. Create pre-defined classesYou can edit the data/predefined_classes.txt to load pre-defined classes Hotkeys 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 boxVerify 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. How to reset the settingsIn case there are issues with loading the classes, you can either: From the top menu of the labelimg click on Menu/File/Reset All Remove the .labelImgSettings.pkl from your home directory. In Linux and Mac you can do: rm ~/.labelImgSettings.pkl How to contributeSend a pull request LicenseFree software: MIT license Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg Related ImageNet Utils to download image, create a label text for machine learning, etc Use Docker to run labelImg Generating the PASCAL VOC TFRecord files App Icon based on Icon by Nick Roach (GPL) Setup python development in vscode The link of this project on iHub platform Stargazers over time
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