# Labelme — Image Annotation Tool with Polygon Support

> Labelme is a graphical image annotation tool written in Python that supports polygon, rectangle, circle, line, and point annotations for creating labeled datasets for computer vision tasks.

## Install

Save in your project root:

# Labelme — Image Annotation Tool with Polygon Support

## Quick Use
```bash
pip install labelme
labelme  # opens the GUI
# or annotate a specific image directory:
labelme /path/to/images --output /path/to/annotations
```

## Introduction
Labelme is an open-source image and video annotation tool inspired by the MIT LabelMe project. It provides a Qt-based graphical interface for drawing polygons, rectangles, circles, lines, and points on images, saving annotations in a JSON format that integrates easily with training pipelines for detection, segmentation, and classification models.

## What Labelme Does
- Draws polygon, rectangle, circle, line, and point annotations on images
- Supports semantic segmentation, instance segmentation, and object detection labeling
- Exports annotations in JSON format convertible to COCO, VOC, and YOLO formats
- Provides AI-assisted annotation using built-in SAM integration for auto-polygon generation
- Handles video annotation frame-by-frame with label propagation

## Architecture Overview
Labelme is a desktop application built with Python and Qt (via PyQt/PySide). The GUI renders images in a canvas widget where users draw shapes interactively. Annotations are stored as JSON files alongside images, each containing a list of shapes with coordinates, labels, and optional flags. Community-contributed conversion scripts transform these JSON files into standard dataset formats. The AI-assist feature integrates segment-anything models for semi-automatic polygon tracing.

## Self-Hosting & Configuration
- Install via `pip install labelme` on Python 3.8+
- No server required; runs as a standalone desktop application
- Configure label lists by passing a `--labels` flag or providing a labels.txt file
- Customize annotation output directory with `--output`
- Enable AI-assisted annotation with `--ai` flag (downloads model on first use)

## Key Features
- Multi-shape annotation: polygons, rectangles, circles, lines, points, and line strips
- AI-assisted polygon generation reduces manual tracing time
- Group and flag annotations for complex labeling taxonomies
- Cross-platform: works on Windows, macOS, and Linux
- Conversion scripts for COCO, Pascal VOC, and custom formats included

## Comparison with Similar Tools
- **Label Studio** — web-based multi-modal annotation platform with team features; heavier setup than Labelme's single-command install
- **CVAT** — enterprise-grade annotation tool by Intel with video tracking; more complex but offers collaboration features
- **VGG Image Annotator (VIA)** — browser-based lightweight annotator; no AI assist, limited shape types
- **Roboflow** — cloud annotation platform with auto-labeling and dataset management; commercial, Labelme is fully open source
- **LabelImg** — focused on bounding-box annotation only; Labelme supports polygons and more shape types

## FAQ
**Q: Can Labelme output COCO-format JSON directly?**
A: Labelme saves in its own JSON format, but ships with a `labelme2coco` conversion script. Third-party tools also support the conversion.

**Q: Does Labelme support team annotation workflows?**
A: Labelme is a single-user desktop tool. For team workflows, consider Label Studio or CVAT and import/export via shared annotation files.

**Q: How do I use the AI-assisted annotation feature?**
A: Launch with `labelme --ai`, click on an object, and Labelme will generate a polygon outline automatically using a segment-anything model.

**Q: Can I annotate video files?**
A: Yes. Open a video file and Labelme will extract frames, allowing frame-by-frame annotation with label carry-forward.

## Sources
- https://github.com/wkentaro/labelme
- https://labelme.io

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Author: AI Open Source