omero-annotate-ai 0.2.3

OMERO integration for AI-powered image annotation and segmentation workflows

OMERO Annotate.AI

Package to support reproducible image annotation workflows for AI training using OMERO data repositories. This Python package provides Jupyter widgets and tools for reproducible annotation, training, and inference using micro-SAM, Cellpose, and other AI models directly with OMERO datasets.

Key Features

• Interactive Jupyter widgets for OMERO connection and workflow configuration
• AI-assisted annotation using micro-SAM integration
• Reproducible workflows with YAML configuration tracking
• Training data preparation for BiaPy and DL4MicEverywhere
• Direct OMERO integration with automatic result storage

Quick Start

Installation

# Recommended: Using pixi
pixi init myproject && cd myproject
pixi add micro-sam
pixi add --pypi omero-annotate-ai
pixi shell

# Alternative: Conda + pip
conda install -c conda-forge micro-sam
pip install omero-annotate-ai

📖 See Installation Guide for detailed instructions and troubleshooting.

Basic Usage

OMERO Connection Widget

Annotation Pipeline Widget

from omero_annotate_ai import create_omero_connection_widget, create_workflow_widget, create_pipeline

# Connect to OMERO
conn_widget = create_omero_connection_widget()
conn_widget.display()
conn = conn_widget.get_connection()

# Configure annotation workflow  
workflow_widget = create_workflow_widget(connection=conn)
workflow_widget.display()
config = workflow_widget.get_config()

# Run annotation pipeline
pipeline = create_pipeline(config, conn)
table_id, processed_images = pipeline.run_full_workflow()

Example Notebooks

Try these example notebooks to get started:

• Widget-based annotation workflow
• YAML-based configuration
• Training with BiaPy

Alternative: YAML Configuration

For batch processing and reproducible workflows, you can also use YAML configuration files:

from omero_annotate_ai.core.annotation_config import load_config
from omero_annotate_ai.core.annotation_pipeline import create_pipeline

# Load configuration from YAML
config = load_config("annotation_config.yaml")
conn = create_connection(host="omero.server.com", user="username")

# Run annotation pipeline
pipeline = create_pipeline(config, conn)
results = pipeline.run_full_workflow()

See the YAML Configuration Guide for complete documentation.

Documentation

📚 Complete Documentation

• Installation Guide - Detailed installation instructions and troubleshooting
• micro-SAM Tutorial - Step-by-step annotation workflow tutorial
• YAML Configuration Guide - Complete YAML configuration reference and examples
• API Reference - Complete API documentation
• Examples - Jupyter notebook tutorials

Links

• GitHub Repository
• PyPI Package
• Issues & Support

Contributing

We welcome contributions! For development setup:

1. Fork the repository
2. Clone and set up development environment:

   git clone https://github.com/YOUR_USERNAME/omero_annotate_ai.git
   cd omero_annotate_ai
   pixi install
   

3. Make changes and run tests: pixi run pytest
4. Submit a pull request

See Installation Guide - Development Setup for detailed instructions.

Contact

Maarten Paul - [e-mail]

Acknowledgments: Developed within the NL-BioImaging infrastructure, funded by NWO.