labelme

Image Polygonal Annotation with Python

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.

VOC dataset example of instance segmentation.

Other examples (semantic segmentation, bbox detection, and classification).

Various primitives (polygon, rectangle, circle, line, and point).

Features

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

Requirements

• Ubuntu / macOS / Windows
• Python3
• PyQt5 / PySide2

Installation

There are options:

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

Anaconda

You need install Anaconda, then run below:

```bash

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

```

Docker

You need install docker, then run below:

```bash

on macOS

socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\"$DISPLAY\" & docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=docker.for.mac.host.internal:0 -v $(pwd):/root/workdir wkentaro/labelme

on Linux

xhost + docker run -it -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=:0 -v $(pwd):/root/workdir wkentaro/labelme ```

Ubuntu

```bash sudo apt-get install labelme

or

sudo pip3 install labelme

or install standalone executable from:

https://github.com/wkentaro/labelme/releases

```

macOS

```bash brew install pyqt # maybe pyqt5 pip install labelme

or

brew install wkentaro/labelme/labelme # command line interface

brew install --cask wkentaro/labelme/labelme # app

or install standalone executable/app from:

https://github.com/wkentaro/labelme/releases

```

Windows

Install Anaconda, then in an Anaconda Prompt run:

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

or install standalone executable/app from:

https://github.com/wkentaro/labelme/releases

```

Usage

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

```bash labelme # just open gui

tutorial (single image example)

cd examples/tutorial labelme apc2016obj3.jpg # specify image file labelme apc2016obj3.jpg -O apc2016obj3.json # close window after the save labelme apc2016obj3.jpg --nodata # not include image data but relative image path in JSON file labelme apc2016obj3.jpg \ --labels highland6539selfsticknotes,meadindexcards,kongairdogsqueakairtennisball # specify label list

semantic segmentation example

cd examples/semanticsegmentation labelme dataannotated/ # Open directory to annotate all images in it labelme data_annotated/ --labels labels.txt # specify label list with a file ```

For more advanced usage, please refer to the examples:

• Tutorial (Single Image Example)
• Semantic Segmentation Example
• Instance Segmentation Example
• Video Annotation Example

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.

Developing

```bash git clone https://github.com/wkentaro/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.

```bash

Setup conda

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

Build the standalone executable

pip install . 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.

```bash pip install -r requirements-dev.txt

flake8 . black --line-length 79 --check labelme/ MPLBACKEND='agg' pytest -vsx tests/ ```

Acknowledgement

This repo is the fork of mpitid/pylabelme.