Introduction to Labelme
Labelme is a powerful tool designed for image annotation, enabling users to mark up images with various shapes. Utilizing Python and Qt, Labelme's graphical interface provides an intuitive and accessible environment for multiple image annotation tasks. This project draws its inspiration from the original Labelme at MIT CSAIL.
Features of Labelme
Labelme is versatile and can be used for different kinds of annotations:
- Image Annotation: Supports annotation through polygons, rectangles, circles, lines, and points. This feature is useful for detailed image segmentation and annotation.
- Image Flag Annotation: Aids in image classification and cleaning.
- Video Annotation: Extends the annotation capabilities to video frames, supporting dynamic dataset creation.
- GUI Customization: Offers customization options for labels, automatic saving, and label validation for a better user experience.
- Dataset Exporting: Can export datasets in VOC or COCO formats, which are widely used in semantic and instance segmentation tasks.
Getting Started
Labelme provides a comprehensive Starter Guide, suitable for new users to get acquainted with the tool. This guide includes:
- Detailed installation steps for various operating systems including Windows, macOS, and Linux.
- Step-by-step tutorials for annotation, including editing and exporting.
- A curated list of additional resources for deeper understanding and integration with other tools.
Installation
Setting up Labelme can be done using multiple methods:
- Anaconda: A platform-independent method for installing Labelme.
- Ubuntu: Installable through system package managers or pip.
- macOS: Available via Homebrew or pip.
- Windows: Installable using Anaconda or direct executable from the Labelme GitHub releases page.
Each method comes with specific instructions to suit different user preferences and system configurations.
Usage
Labelme's functionality can be accessed through a simple command line interface:
- Open the GUI with the
labelmecommand. - Annotations are saved as JSON files, which store data in a structured, easy-to-read format.
- Supports detailed customization using command line arguments, such as specifying output directories or sorting label lists.
The tool is flexible to fit the needs of individual annotation projects, offering straightforward ways to manage and execute detailed annotation tasks.
Examples
Labelme supports several use cases, demonstrated through various examples:
- Image Classification
- Bounding Box Detection
- Semantic Segmentation
- Instance Segmentation
- Video Annotation
These examples provide users with templates to get started on their projects immediately.
Development and Contribution
Interested in contributing to Labelme? The project is hosted on GitHub, where you can clone the repository and build the tool for development purposes. The project includes detailed instructions for setting up a development environment on different operating systems and for creating standalone executables.
Acknowledgement
The current repository is a fork of the original mpitid/pylabelme, building upon its foundation with additional features and improvements.
By offering a robust set of tools and supporting a wide array of annotation tasks, Labelme stands out as a vital resource for developers and researchers interested in image data analysis and machine learning projects.