SHINIER Documentation๏ƒ

๐ŸŒŸ SHINIER๏ƒ

   โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—
   โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ•— โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ•โ•โ•โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—
   โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—  โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•”โ•
   โ•šโ•โ•โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ•šโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•”โ•โ•โ•  โ–ˆโ–ˆโ•”โ•โ•โ–ˆโ–ˆโ•—
   โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘ โ•šโ–ˆโ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ•‘โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ•—โ–ˆโ–ˆโ•‘  โ–ˆโ–ˆโ•‘
   โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ•  โ•šโ•โ•โ•šโ•โ•โ•šโ•โ•  โ•šโ•โ•โ•โ•šโ•โ•โ•šโ•โ•โ•โ•โ•โ•โ•โ•šโ•โ•  โ•šโ•โ•

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

License: BSD-3-Clause Python versions PyPI version Tests๏ƒ

๐ŸŽฏ Overview๏ƒ

SHINIER is a modern Python implementation of SHINE (Spectrum, Histogram, and Intensity Normalization and Equalization), originally developed in MATLAB by Willenbockel etย 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๏ƒ

  • ๐ŸŽจ Color Processing โ€” New modes for color image control with modern color-space standards (Rec.601 / Rec.709 / Rec.2020).

  • ๐Ÿ–ผ๏ธ Dithering Support โ€” Reduces quantization artifacts and enhances output image quality.

  • โšก Optimized Performance โ€” Efficient memory management and faster processing for large image sets (optional Cython/C++ convolution core).

  • ๐Ÿ•ฐ Legacy Mode โ€” Ensures full backward compatibility with MATLABโ€™s original SHINE toolbox.

  • ๐Ÿ”ข High-Precision Arithmetic โ€” Computations in floating-point precision rather than 8-bit integer space, minimizing rounding errors in multi-stage processing.

  • ๐Ÿ“ฆ Object-Oriented Design โ€” Modular, extensible architecture with a clean Python API.

  • ๐Ÿ˜€ User-Friendly CLI โ€” 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.

๐Ÿš€ Quick Start๏ƒ

Installation๏ƒ

Install from source (development version):๏ƒ

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

Verify the install:๏ƒ

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

๐Ÿ˜€ User-friendly Interface๏ƒ

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

shinier --show_results --image_index=1

CLI demo

๐Ÿงฉ Example in Python๏ƒ

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๏ƒ

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 โ†’ sf_match

Histogram, then spatial frequency

6

hist_match โ†’ spec_match

Histogram, then spectrum

7

sf_match โ†’ hist_match

Spatial frequency, then histogram

8

spec_match โ†’ 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.

CLI demo

๐Ÿ›๏ธ Technical information๏ƒ

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. Border Artifacts and FFT Padding

  6. Package Main Classes

  7. StimulusMasker

  8. Visualization Functions

  9. Implemented Algorithms

  10. Memory Management and Performance

  11. Testing and Validation

  12. Usage Demonstrations

  13. Additional Resources

๐Ÿ“š Citing๏ƒ

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

References๏ƒ

  • Salvas-Hรฉbert, 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), 671โ€“684. https://doi.org/10.3758/BRM.42.3.671

๐Ÿค Contributing๏ƒ

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

๐Ÿ“„ License๏ƒ

See LICENSE for more information.

๐Ÿ› ๏ธ Troubleshooting๏ƒ

  • No compiler available: install a C/C++ toolchain or proceed with the NumPy fallback (slower).

  • Import errors after upgrade: try pip install โ€“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-Hรฉbert
Version 0.2.0 - Complete technical documentation