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)

Installation

There are 2 options to install labelme:

Option 1: Using pip

For more detail, check "Install Labelme using Pip".

bash pip install labelme

Option 2: Using standalone executable (Easiest)

If you're willing to invest in the convenience of simple installation without any dependencies (Python, Qt), you can download the standalone executable from "Install Labelme as App".

It's a one-time payment for lifetime access, and it helps us to maintain this project.

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 ```

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

```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 '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.

```bash 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.