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.
1 maintainer
Metadata
1 maintainer
Release files for ezstitcher 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ezstitcher-1.1.0.tar.gz | 70.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ezstitcher-1.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 149.7 kB
Release files / ezstitcher-1.1.0.tar.gz
| Download URL | ezstitcher-1.1.0.tar.gz |
|---|---|
| Size | 70.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9353e234ea64f118ed4aac6fa7698500cd4a4a5d99906078e9b90f1e809de230
|
|
BLAKE2b-256 checksum How to use checksums |
d299b1d1f80eed2e5618d53314f1e6b6d340c635deda196241a48b21784ab74b
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.12
|
Release files / ezstitcher-1.1.0-py3-none-any.whl
| Download URL | ezstitcher-1.1.0-py3-none-any.whl |
|---|---|
| Size | 78.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
29e2f7e388ea7ea89bf80f2fe07f2e9d4660a79798fa80a946dbc4207c5ad450
|
|
BLAKE2b-256 checksum How to use checksums |
513956c364f87e177607ed39a8d73c6fdef57b003f2660c0240529fe4fa6481c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.12
|
