Awesome

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

• Image annotation for polygon, rectangle, circle, line and point. (tutorial)
• Image flag annotation for classification and cleaning. (#166)
• Video annotation. (video annotation)
• GUI customization (predefined labels / flags, auto-saving, label validation, etc). (#144)
• Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation)
• Exporting COCO-format dataset for instance segmentation. (instance segmentation)

Starter Guide

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

• Installation guides for all platforms: Windows, macOS, and Linux 💻
• Step-by-step tutorials: first annotation to editing, exporting, and integrating with other programs 📕
• A compilation of valuable resources for further exploration 🔗.

Installation

There are options:

• Platform agnostic installation: Anaconda
• Platform specific installation: Ubuntu, macOS, Windows
• Pre-build binaries from the release section

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

• --output specifies the location that annotations will be written to. If the location ends with .json, a single annotation will be written to this file. Only one image can be annotated if a location is specified with .json. If the location does not end with .json, the program will assume it is a directory. Annotations will be stored in this directory with a name that corresponds to the image that the annotation was made on.
• The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the --config flag.
• Without the --nosortlabels flag, the program will list labels in alphabetical order. When the program is run with this flag, it will display labels in the order that they are provided.
• Flags are assigned to an entire image. Example
• Labels are assigned to a single polygon. Example

FAQ

• How to convert JSON file to numpy array? See examples/tutorial.
• How to load label PNG file? See examples/tutorial.
• How to get annotations for semantic segmentation? See examples/semantic_segmentation.
• How to get annotations for instance segmentation? See examples/instance_segmentation.

Examples

• Image Classification
• Bounding Box Detection
• Semantic Segmentation
• Instance Segmentation
• Video Annotation

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.