ezstitcher 1.1.0

An easy-to-use microscopy image stitching and processing tool

EZStitcher

High-Performance Microscopy Image Stitching

EZStitcher is a Python library that simplifies complex microscopy image processing workflows. Built on the robust Ashlar stitching engine, it provides an intuitive pipeline architecture for processing large microscopy datasets.

🚀 Key Features

• Simplified Interface for Non-Coders

  • One-liner function for common workflows
  • Auto-detection of Z-stacks and channels

• Intelligent Z-Stack Processing

  • Advanced focus detection with quality metrics
  • Multiple projection methods (max, mean, best-focus)
  • Per-plane 3D stitching capability

• Multi-Channel Support

  • Independent fluorescence channel processing
  • Channel-specific processing pipelines
  • Custom weighted composite generation

• Flexible Pipeline Architecture

  • AutoPipelineFactory for quick workflow creation
  • Customizable processing steps
  • Modular design for easy extension

🎯 Supported Microscopes

• ImageXpress
• Opera Phenix
• Extensible architecture for additional microscope types

⚡ Quick Start

# Install with pyenv (recommended)
pyenv install 3.11
pyenv global 3.11

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate

# Install EZStitcher
pip install ezstitcher

📊 Basic Usage

Simplified Interface (Recommended for Beginners)

from ezstitcher import stitch_plate

# Stitch a plate with a single function call
stitch_plate("path/to/microscopy/data")

Using AutoPipelineFactory

from ezstitcher.core import AutoPipelineFactory
from ezstitcher.core.pipeline_orchestrator import PipelineOrchestrator

orchestrator = PipelineOrchestrator(plate_path="path/to/images")
factory = AutoPipelineFactory(input_dir=orchestrator.workspace_path)
pipelines = factory.create_pipelines()
orchestrator.run(pipelines=pipelines)

🔧 Advanced Usage Example

from ezstitcher.core.pipeline import Pipeline
from ezstitcher.core.steps import Step, PositionGenerationStep, ImageStitchingStep
from ezstitcher.core.specialized_steps import ZFlatStep, CompositeStep
from n2v.models import N2V
from basicpy import BaSiC
import numpy as np

def n2v_process(images, model_path):
    model = N2V(None, model_path, 'N2V')
    return [model.predict(img, 'N2V') for img in images]

def basic_process(images):
    basic = BaSiC()
    basic.fit(np.stack(images))
    return list(basic.transform(np.stack(images)))

# Position generation pipeline
position_pipeline = Pipeline(
    input_dir=orchestrator.workspace_path,
    steps=[
        ZFlatStep(),
        CompositeStep(),
        PositionGenerationStep()
    ]
)

# Assembly pipeline with advanced processing
assembly_pipeline = Pipeline(
    input_dir=orchestrator.workspace_path,
    steps=[
        # N2V denoising
        Step(func=(n2v_process, {'model_path': 'path/to/model.h5'})),

        # BaSiC flatfield correction
        Step(func=basic_process),

        # Normalize
        Step(func=IP.stack_percentile_normalize),

        # Stitch
        ImageStitchingStep()
    ]
)

orchestrator.run(pipelines=[position_pipeline, assembly_pipeline])

📚 Documentation

Our comprehensive documentation is organized into several sections:

• Quick Start Guide: Get up and running in minutes
• Basic Usage: Simple one-liner interface for common tasks
• Intermediate Usage: Custom pipelines with pre-built steps
• Advanced Usage: Create custom steps and extend functionality
• Core Concepts: Understanding EZStitcher's architecture
• API Reference: Detailed API documentation

🤝 Contributing

We welcome contributions! See our Contributing Guide to get started.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.