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开源软件名称:labelImg开源软件地址:https://gitee.com/monkeycc/labelImg开源软件介绍:LabelImgLabelImg 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 usedby ImageNet. Besides, it also supports YOLO and CreateML formats. InstallationGet 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 labelImglabelImglabelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Build from sourceLinux/Ubuntu/Mac requires at least Python2.6 and has been tested with PyQt4.8. However, Python3 or above and PyQt5 are strongly recommended. Ubuntu LinuxPython 3 + Qt5 sudo apt-get install pyqt5-dev-toolssudo pip3 install -r requirements/requirements-linux-python3.txtmake qt5py3python3 labelImg.pypython3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] macOSPython 3 + Qt5 brew install qt # Install qt-5.x.x by Homebrewbrew install libxml2or using pippip3 install pyqt5 lxml # Install qt and lxml by pipmake qt5py3python3 labelImg.pypython3 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 python3pip3 install pipenvpipenv run pip install pyqt5==5.15.2 lxmlpipenv run make qt5py3pipenv run 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 WindowsInstall Python,PyQt5and install lxml. Open cmd and go to the labelImg directory pyrcc4 -o libs/resources.py resources.qrcFor pyqt5, pyrcc5 -o libs/resources.py resources.qrcpython labelImg.pypython labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] If you want to package it into a separate EXE file Install pyinstaller and execute:pip install pyinstallerpyinstaller --hidden-import=pyqt5 --hidden-import=lxml -F -n "labelImg" -c labelImg.py -p ./libs -p ./ Windows + AnacondaDownload and install Anaconda (Python 3+) Open the Anaconda Prompt and go to the labelImg directory conda install pyqt=5conda install -c anaconda lxmlpyrcc5 -o libs/resources.py resources.qrcpython labelImg.pypython labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Use Dockerdocker 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/py2qt4make qt4py2;./labelImg.py You can pull the image which has all of the installed and required dependencies. Watch a demo video UsageSteps (PascalVOC)
The annotation will be saved to the folder you specify. You can refer to the below hotkeys to speed up your workflow. Steps (YOLO)
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:
Create pre-defined classesYou can edit thedata/predefined_classes.txtto load pre-defined classes Annotation visualization
(Choose Display Labels mode in View to show/hide lablels) Hotkeys
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. How to reset the settingsIn case there are issues with loading the classes, you can either:
How to contributeSend a pull request LicenseCitation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg Related and additional tools
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