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. Besdies, it also supports YOLO format

Watch a demo video

Installation

Download prebuilt binaries

Windows
macOS. 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 -r requirements/requirements-linux-python3.txt
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
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

As a side note, if mssing pyrcc5 or lxml, try
pip3 install pyqt5 lxml

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 PyPI

pip 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 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 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

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 being 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 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) <https://www.elegantthemes.com/> <https://www.iconfinder.com/icons/1054978/shop_tag_icon> __

HarryZhang0415 / labelImg