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 and CreateML formats.

Watch a demo video

change log

remove predifine class, add auto check save dir classes.txt file and load label
change auto save model is true
remove change save dir and set auto save label to open dir
env python3.11 +pyside6

Installation

Get from PyPI but only support python3.0 or above

This 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]

Build from source

Ubuntu Linux

Python 3 + PySide6

pip install -r requirements/requirements-linux-python3.txt
make pyside6
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

macOS

Python 3 + PySide6

pip3 install pyside6 lxml
make pyside6
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 pyside6 lxml
pipenv run make pyside6
pipenv 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

Windows

Open cmd and go to the labelImg directory

pip install pyside6 lxml
pyside6-rcc -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Windows + Anaconda

Open the Anaconda Prompt and go to the labelImg directory

conda install pyside6
conda install -c anaconda lxml
pyside6-rcc -o libs/resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

You 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 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 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.

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
Ctrl + Shift + d	Delete the current image
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.

How to reset the settings

In 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

build

pyinstaller --hidden-import=lxml --hidden-import=PySide6 -F -n "labelImg" -c labelImg.py -p ./libs -p

jackson2213 / labelimg