• Stars
    star
    20,885
  • Rank 1,192 (Top 0.03 %)
  • Language
    Python
  • License
    MIT License
  • Created about 9 years ago
  • Updated over 1 year ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.
/readme/images/labelimg.png

Label Studio is a modern, multi-modal data annotation tool

LabelImg, the popular image annotation tool created by Tzutalin with the help of dozens contributors, is no longer actively being developed and has become part of the Label Studio community. Check out Label Studio, the most flexible open source data labeling tool for images, text, hypertext, audio, video and time-series data. Install Label Studio and join the slack community to get started.

/readme/images/label-studio-1-6-player-screenshot.png

About LabelImg

GitHub Workflow Status

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.

Demo Image

Demo Image

Watch a demo video

Installation

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]

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==5.15.2 lxml
pipenv run make qt5py3
pipenv run python3 labelImg.py
[Optional] rm -rf build dist; pipenv run 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 libs/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]

If you want to package it into a separate EXE file

Install pyinstaller and execute:

pip install pyinstaller
pyinstaller --hidden-import=pyqt5 --hidden-import=lxml -F -n "labelImg" -c labelImg.py -p ./libs -p ./
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]

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)

  1. Build and launch using the instructions above.
  2. Click 'Change default saved annotation folder' in Menu/File
  3. Click 'Open Dir'
  4. Click 'Create RectBox'
  5. Click and release left mouse to select a region to annotate the rect box
  6. 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)

  1. In data/predefined_classes.txt define the list of classes that will be used for your training.
  2. Build and launch using the instructions above.
  3. Right below "Save" button in the toolbar, click "PascalVOC" button to switch to YOLO format.
  4. 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

Annotation visualization

  1. Copy the existing lables file to same folder with the images. The labels file name must be same with image file name.
  2. Click File and choose 'Open Dir' then Open the image folder.
  3. Select image in File List, it will appear the bounding box and label for all objects in that image.

(Choose Display Labels mode in View to show/hide lablels)

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:

  1. From the top menu of the labelimg click on Menu/File/Reset All
  2. 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

Related and additional tools

  1. Label Studio to label images, text, audio, video and time-series data for machine learning and AI
  2. ImageNet Utils to download image, create a label text for machine learning, etc
  3. Use Docker to run labelImg
  4. Generating the PASCAL VOC TFRecord files
  5. App Icon based on Icon by Nick Roach (GPL)
  6. Setup python development in vscode
  7. The link of this project on iHub platform
  8. Convert annotation files to CSV format or format for Google Cloud AutoML

Stargazers over time

More Repositories

1

label-studio

Label Studio is a multi-type data labeling and annotation tool with standardized output format
JavaScript
16,524
star
2

awesome-data-labeling

A curated list of awesome data labeling tools
3,470
star
3

label-studio-frontend

Data labeling react app that is backend agnostic and can be embedded into your applications — distributed as an NPM package
JavaScript
318
star
4

label-studio-ml-backend

Configs and boilerplates for Label Studio's Machine Learning backend
Python
263
star
5

label-studio-converter

Tools for converting Label Studio annotations into common dataset formats
Python
253
star
6

label-studio-transformers

Label data using HuggingFace's transformers and automatically get a prediction service
Python
176
star
7

RLHF

Collection of links, tutorials and best practices of how to collect the data and build end-to-end RLHF system to finetune Generative AI models
Jupyter Notebook
62
star
8

label-studio-sdk

Label Studio SDK
Python
51
star
9

dm2

Full-fledged Data Exploration Tool for Label Studio
JavaScript
35
star
10

pyheartex

Heartex Python SDK - Connect your own models to Heartex Data Labeling
Python
28
star
11

brand-sentiment-analysis

Scripts utilizing Heartex platform to build brand sentiment analysis from the news
CSS
22
star
12

label-studio-evalme

Evaluation metrics package
Python
7
star
13

label-studio-terraform

HCL
5
star
14

label-studio-examples

Example Code to Supplement the Label Studio Blog
Python
5
star
15

label-studio-tools

Python
4
star
16

text-classifier

Tensorflow-based text classifier that could be integrated with Heartex/Label Studio
Python
4
star
17

awesome-human-in-the-loop

Awesome List of Human in the Loop resources and references for retraining models.
4
star
18

smartfew

SmartFew is your swiss knife for semi-supervised structuring of unlabeled data using Few Shot Learning.
Python
4
star
19

charts

3
star
20

heartexlabs.github.io

Label Studio website with the documentation
HTML
2
star
21

awesome-active-learning

A curated list of awesome active learning related topics
2
star
22

label-studio-addon-dicom

DICOM format annotation and labeling support for Label Studio
2
star
23

articles

Materials we publish on Medium and other resources about labeling, machine learning, active learning, etc
1
star