LabelImg使用方法---于小勇

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

InstallationDownload prebuilt binariesWindows & LinuxmacOS. 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

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

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

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)

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

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" /Applications

Windows

Download and setup Python 2.6 or later, PyQt4 and install lxml.

Open cmd and go to the labelImg directory

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

conda install pyqt=5 pyrcc5 -o resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]Get from PyPIpip 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 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)Build and launch using the instructions above.Click 'Change default saved annotation folder' in Menu/FileClick 'Open Dir'Click 'Create RectBox'Click and release left mouse to select a region to annotate the rect boxYou 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.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 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

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 box

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