shinier 0.1.9

Python port of the SHINE toolbox with added options (color management, dithering, EHS), optimized for large image sets.

🌟 SHINIER

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Spectrum, Histogram, and Intensity Normalization, Equalization, and Refinement.

🎯 Overview

SHINIER is a modern Python implementation of SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization), originally developed in MATLAB by Willenbockel et al., 2010. It provides precise control over luminance, contrast, histograms, and spectral content across large image sets for well-calibrated visual experiments.

Key Features and Improvements

• 🎨 Color Processing — New modes for color image control with modern color-space standards (Rec.601 / Rec.709 / Rec.2020).
• 🖼️ Dithering Support — Reduces quantization artifacts and enhances output image quality.
• ⚡ Optimized Performance — Efficient memory management and faster processing for large image sets (optional Cython/C++ convolution core).
• 🕰 Legacy Mode — Ensures full backward compatibility with MATLAB’s original SHINE toolbox.
• 🔢 High-Precision Arithmetic — Computations in floating-point precision rather than 8-bit integer space, minimizing rounding errors in multi-stage processing.
• 📦 Object-Oriented Design — Modular, extensible architecture with a clean Python API.
• 😀 User-Friendly CLI — Guided, prompt-based interface for users who prefer not to write code.

For detailed technical documentation (algorithms, numerical choices, and MATLAB vs Python behavior), see
documentation/documentation.md.

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🚀 Quick Start

Installation

Pip Install (recommended):

pip install shinier

Note: SHINIER includes a Cython-compiled C++ extension (_cconvolve) for faster convolution. If a C/C++ compiler is available, it will build automatically during installation, otherwise, it will fall back to a slower NumPy-based implementation.

Install compilers:

macOS: xcode-select --install 

Linux: sudo apt install build-essential 

Windows: Visual Studio C++ Build Tools

Install from source (development version):

git clone https://github.com/Charestlab/shinier.git
cd shinier
pip install -e ".[dev]"

Verify the install:

import shinier, sys
print("shinier version:", getattr(shinier, "__version__", "unknown"))

😀 User-friendly Interface

Call the following bash command to quickly start using the interactive CLI.

shinier --show_results --image_index=1

🧩 Example in Python

Run the following python code to make sure the package is running properly.

from shinier import Options, ImageDataset, ImageProcessor, utils

opt = Options(mode=3)  # Spatial frequency matching
dataset = ImageDataset(options=opt)
results = ImageProcessor(dataset=dataset, options=opt, verbose=1)
_ = utils.show_processing_overview(processor=results, img_idx=0)

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Processing modes

Change the mode number (e.g. opt = Options(mode=3)) to change image processing. See details below:

Mode	Operations	Description
1	lum_match	Luminance (mean/std) matching
2	hist_match	Histogram matching
3	sf_match	Rotational spatial frequency matching
4	spec_match	Full 2D Fourier spectrum matching
5	hist_match → sf_match	Histogram, then spatial frequency
6	hist_match → spec_match	Histogram, then spectrum
7	sf_match → hist_match	Spatial frequency, then histogram
8	spec_match → hist_match (default)	Spectrum, then histogram (recommended)
9	dithering	Dithering only

Below is an example of results obtained using mode 5 with joint histogram equalization and spatial frequency normalization.

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🏛️ Technical information

See the accompanying the paper: The SHINIER the Better: An Adaptation of the SHINE Toolbox on Python

And documentation:

1. Package Overview
2. Package Architecture
3. MATLAB vs Python Differences
4. Detailed Processing Modes
5. Package Main Classes
6. Visualization Functions
7. Implemented Algorithms
8. Memory Management and Performance
9. Testing and Validation
10. Usage Demonstrations
11. Additional Resources

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📚 Citing

If you use SHINIER, please cite both of these articles:

References

• Salvas-Hébert, M., Dupuis-Roy, N., Landry, C., Charest, I., & Gosselin, F. (2025). The SHINIER the Better: An Adaptation of the SHINE Toolbox on Python
• Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W. (2010). Controlling low-level image properties: The SHINE toolbox. Behavior Research Methods, 42(3), 671–684. https://doi.org/10.3758/BRM.42.3.671

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🤝 Contributing

See CONTRIBUTING.md for guidelines (coding standards, tests, docs, and PR flow).

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📄 License

See LICENSE for more information.

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🛠️ Troubleshooting

• No compiler available: install a C/C++ toolchain or proceed with the NumPy fallback (slower).
• Import errors after upgrade: try pip install --upgrade pip setuptools wheel and reinstall.
• Windows build issues: ensure MSVC Build Tools are installed and on PATH.

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Code developed by Nicolas Dupuis-Roy and Mathias Salvas-Hébert
Version 0.1.9 - Complete technical documentation