一言难尽的数据标注
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 used by?ImageNet. Besides,it also supports YOLO format Watch a demo video InstallationBuild from sourceLinux/Ubuntu/Mac requires at least?Python 2.6?and has been tested with?PyQt 4.8. However,?Python 3 or above?and?PyQt5are 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 --three # or pipenv install pyqt5 lxml pipenv run pip install pyqt5 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 WindowsInstall?Python,?PyQt5?and?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]
Windows + AnacondaDownload 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]
Get from PyPI but only python3.0 or abovepip3 install labelImg
labelImg
labelImg [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/py2qt4 make 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 the?data/predefined_classes.txt?to load pre-defined classes 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 contributeSend a pull request LicenseFree software: MIT license Citation: Tzutalin. LabelImg. Git code (2015).?https://github.com/tzutalin/labelImg Related
ref:https://github.com/tzutalin/labelImg (编辑:李大同) 【声明】本站内容均来自网络,其相关言论仅代表作者个人观点,不代表本站立场。若无意侵犯到您的权利,请及时与联系站长删除相关内容! |