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<h1 align="center"> <img src="labelme/icons/icon.png"><br/>labelme </h1> <h4 align="center"> Image Polygonal Annotation with Python </h4> <div align="center"> <a href="https://pypi.python.org/pypi/labelme"><img src="https://img.shields.io/pypi/v/labelme.svg"></a> <a href="https://pypi.org/project/labelme"><img src="https://img.shields.io/pypi/pyversions/labelme.svg"></a> <a href="https://github.com/labelmeai/labelme/actions"><img src="https://github.com/labelmeai/labelme/workflows/ci/badge.svg?branch=main&event=push"></a> </div> <div align="center"> <a href="#starter-guide"><b>Starter Guide</b></a> | <a href="#installation"><b>Installation</b></a> | <a href="#usage"><b>Usage</b></a> | <a href="#examples"><b>Examples</b></a> <!-- | <a href="https://github.com/labelmeai/labelme/discussions"><b>Community</b></a> --> <!-- | <a href="https://www.youtube.com/playlist?list=PLI6LvFw0iflh3o33YYnVIfOpaO0hc5Dzw"><b>Youtube FAQ</b></a> --> </div> <br/> <div align="center"> <img src="examples/instance_segmentation/.readme/annotation.jpg" width="70%"> </div>

Description

Labelme is a graphical image annotation tool inspired by http://labelme.csail.mit.edu.
It is written in Python and uses Qt for its graphical interface.

<img src="examples/instance_segmentation/data_dataset_voc/JPEGImages/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClass/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationClassVisualization/2011_000006.jpg" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObject/2011_000006.png" width="19%" /> <img src="examples/instance_segmentation/data_dataset_voc/SegmentationObjectVisualization/2011_000006.jpg" width="19%" />
<i>VOC dataset example of instance segmentation.</i>

<img src="examples/semantic_segmentation/.readme/annotation.jpg" width="30%" /> <img src="examples/bbox_detection/.readme/annotation.jpg" width="30%" /> <img src="examples/classification/.readme/annotation_cat.jpg" width="35%" />
<i>Other examples (semantic segmentation, bbox detection, and classification).</i>

<img src="https://user-images.githubusercontent.com/4310419/47907116-85667800-de82-11e8-83d0-b9f4eb33268f.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/4310419/47922172-57972880-deae-11e8-84f8-e4324a7c856a.gif" width="30%" /> <img src="https://user-images.githubusercontent.com/14256482/46932075-92145f00-d080-11e8-8d09-2162070ae57c.png" width="32%" />
<i>Various primitives (polygon, rectangle, circle, line, and point).</i>

Features

Starter Guide

If you're new to Labelme, you can get started with Labelme Starter (FREE), which contains:

Installation

There are options:

Anaconda

You need install Anaconda, then run below:

# python3
conda create --name=labelme python=3
source activate labelme
# conda install -c conda-forge pyside2
# conda install pyqt
# pip install pyqt5  # pyqt5 can be installed via pip on python3
pip install labelme
# or you can install everything by conda command
# conda install labelme -c conda-forge

Ubuntu

sudo apt-get install labelme

# or
sudo pip3 install labelme

# or install standalone executable from:
# https://github.com/labelmeai/labelme/releases

# or install from source
pip3 install git+https://github.com/labelmeai/labelme

macOS

brew install pyqt  # maybe pyqt5
pip install labelme

# or install standalone executable/app from:
# https://github.com/labelmeai/labelme/releases

# or install from source
pip3 install git+https://github.com/labelmeai/labelme

Windows

Install Anaconda, then in an Anaconda Prompt run:

conda create --name=labelme python=3
conda activate labelme
pip install labelme

# or install standalone executable/app from:
# https://github.com/labelmeai/labelme/releases

# or install from source
pip3 install git+https://github.com/labelmeai/labelme

Usage

Run labelme --help for detail.
The annotations are saved as a JSON file.

labelme  # just open gui

# tutorial (single image example)
cd examples/tutorial
labelme apc2016_obj3.jpg  # specify image file
labelme apc2016_obj3.jpg -O apc2016_obj3.json  # close window after the save
labelme apc2016_obj3.jpg --nodata  # not include image data but relative image path in JSON file
labelme apc2016_obj3.jpg \
  --labels highland_6539_self_stick_notes,mead_index_cards,kong_air_dog_squeakair_tennis_ball  # specify label list

# semantic segmentation example
cd examples/semantic_segmentation
labelme data_annotated/  # Open directory to annotate all images in it
labelme data_annotated/ --labels labels.txt  # specify label list with a file

Command Line Arguments

FAQ

Examples

How to develop

git clone https://github.com/labelmeai/labelme.git
cd labelme

# Install anaconda3 and labelme
curl -L https://github.com/wkentaro/dotfiles/raw/main/local/bin/install_anaconda3.sh | bash -s .
source .anaconda3/bin/activate
pip install -e .

How to build standalone executable

Below shows how to build the standalone executable on macOS, Linux and Windows.

# Setup conda
conda create --name labelme python=3.9
conda activate labelme

# Build the standalone executable
pip install .
pip install 'matplotlib<3.3'
pip install pyinstaller
pyinstaller labelme.spec
dist/labelme --version

How to contribute

Make sure below test passes on your environment.
See .github/workflows/ci.yml for more detail.

pip install -r requirements-dev.txt

ruff format --check  # `ruff format` to auto-fix
ruff check  # `ruff check --fix` to auto-fix
MPLBACKEND='agg' pytest -vsx tests/

Acknowledgement

This repo is the fork of mpitid/pylabelme.