SHINIER Documentation

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.

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

CLI demo

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)

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	`ie_methods` or `dithering`	Standalone per-image transform (histogram-derived enhancement or dithering)

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

CLI demo

Technical information

Documentation:

Package Overview
Package Architecture
MATLAB vs Python Differences
Detailed Processing Modes
Border Artifacts and FFT Padding
Package Main Classes
StimulusMasker
Visualization Functions
Implemented Algorithms
Memory Management and Performance
Testing and Validation
Usage Demonstrations
Additional Resources

Citing

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

References

Salvas-Hébert, M., Dupuis-Roy, N., Landry, C., Charest, I., & Gosselin, F. (2026). SHINIER: An open-source Python package for controlling low-level image properties. SoftwareX, 35, Article 102884. https://doi.org/10.1016/j.softx.2026.102884
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

Contributing

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

License

See LICENSE for more information.

Troubleshooting

No compiler available: install a C/C++ toolchain or proceed with the NumPy fallback (slower).
Compiled extension fails to load (e.g., NumPy too old / ABI mismatch): SHINIER warns and uses the NumPy fallback; upgrade NumPy (and pip/setuptools/wheel) and reinstall to restore the compiled core.
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.

Code developed by Nicolas Dupuis-Roy and Mathias Salvas-Hébert
Version 0.2.2 - Complete technical documentation