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 source

Linux/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 Linux

Python 3 + Qt5

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]

macOS

Python 3 + Qt5

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==5.12.1 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

Windows

Install 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 + Anaconda

Download 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 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]

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.py

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.

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

Video Frame Mode

If you have frames of a video, you might encounter a lot of similar pictures. This would be the mode of your choice then. Just go to View and activate Video Frame Mode, or simply hit Ctrl + Alt + v. You are now provided with extra buttons in the left Panel. You will find "Save as base" - which will allow you, to memorize the currently set labels as base "Apply base" - which will insert the last memorized labels into the current frame "Next Frame" - which will combine Next Image and Apply base. So if you want to go through the frames rather quick, this will be your choice.

Additionally you will find a Randomization input in the right Panel. This allows you to set a randomization maximum in x and y direction, so if you apply a base to the current frame, each label will be slightly moved from its original position in the base. This will help in many Machine Learning Applications.

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
Ctrl + Alt + v	Activate Video Frame Mode
Ctrl + Alt + s	Save Base of current frame [Video Frame Mode]
Ctrl + Alt + a	Apply Base to current frame [Video Frame Mode]
Ctrl + Alt + n	Save, open next frame and apply Base [Video Frame Mode]
f	Set Base, save, next and apply Base [Video Frame Mode]
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

How to contribute

Send a pull request

License

Free software: MIT license

Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg

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

scholz/labelImg