sbl-optimizer 0.1.5

Thermal swell pattern optimizer for Swell by Light

sbl-optimizer

Print pattern optimizer for Swell by Light (SbL).
SbL is an approachable technique for freeform raised textures on paper and other materials. SbL-Optimizer improves print patterns considering heat diffusion, making the resulting swell patterns better match the original images.

Optimization of the printed pattern results in a uniform temperature pattern closely matching the original pattern.

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Table of Contents

• sbl-optimizer
  • Table of Contents
  • Features
  • Installation
    • 1. From PyPI
    • 2. From Source
    • Requirements
  • Quick Start
  • Configuration
  • Command Line Interface
    • Usage
    • Arguments
    • Options
  • Examples
  • Citation
  • Contributing
  • License

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Features

• Adaptive heat‐pattern optimization
  Compensates for heat diffusion to produce raised textures that match your design.
• Simple CLI
  One‐command execution, with JSON‐based configuration and image input.
• Built on Python
  Uses NumPy, Pillow, and Matplotlib for computations and visualizations.

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Installation

Install sbl-optimizer in two easy ways:

1. From PyPI

The simplest method—just pip:

pip install sbl-optimizer

2. From Source

Clone the repository and install locally:

git clone https://github.com/sosucat/sbl-optimizer.git
cd sbl-optimizer
pip install .

Requirements

Ensure you have Python and dependencies:

Libraries	Versions
Python	3.9 – 3.11
NumPy	1.24 - 2.2
Pillow	9.5 - 11.x
Matplotlib	3.7 - 3.10

You can also install dependencies manually:

pip install numpy pillow matplotlib

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Quick Start

1. If you are NOT in the project's root directory,
   change the directory to the project's root directory:

   cd sbl_optimizer
   

   Note: make sure you are at sbl_optimizer not sbl_optimizer/sbl_optimizer

2. Run the optimizer to generate an optimized pattern that accounts for heat diffusion:

   python -m sbl_optimizer.main sample.jpg
   

3. Locate the generated optimized pattern sample_opt.pdf in the project's root directory.

4. Check the simulated heat distribution sample_temperature.png and swell pattern sample_swell.png.

5. Print the sample_opt.pdf on paper, apply paste, and expose to a strong LED spotlight. Darker printed regions absorb more heat and swell.

6. Try optimizing your own pattern.

   python -m sbl_optimizer.main path/to/your_image
   

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Configuration

Some parameters can be customized via a JSON file (default: config.json shipped with the package).

config.json

{
  "swell_temperature": 145.0,
  "light_power": 100.0,
  "light_diameter": 0.06,
  "alpha": 5e-07
}

Key	Type	Description
swell_temperature	float	Target swelling temperature (°C).
light_power	float	Light source power (W).
light_diameter	float	Diameter of the light circle on paper (m).
alpha	float	Thermal diffusivity of paper (m²/s).

To override defaults:

python -m sbl_optimizer.main <IMAGE> --config path/to/your_config.json

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Command Line Interface

Usage

python -m sbl_optimizer.main [OPTIONS] <IMAGE>

Arguments

<IMAGE>
Path to the input image (JPG, PNG, etc.)

Options

Option	Description
-c, --config <FILE>	Path to JSON config file
-h, --help	Show help message and exit

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Examples

1. Use default settings on sample.jpg in the project's root directory.

   python -m sbl_optimizer.main sample.jpg
   

2. Use custom settings

   python -m sbl_optimizer.main sample.jpg --config my_config.json
   

3. Save only error logs (disable plotting)
   Edit main.py:

   • comment out save_plots, uncomment save_errors

Output files (in same folder as input image sample.jpg):

File	Description
sample_opt.pdf	Optimized pattern to be printed.
sample_temperature.png	Simulated temperature distribution.
sample_swell.png	Simulated swell pattern.

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Citation

If you use sbl-optimizer in your research or projects, please cite:\

@inproceedings{10.1145/3689050.3704420,
author = {Ichihashi, Sosuke and Howell, Noura and Oh, HyunJoo},
title = {Swell by Light: An Approachable Technique for Freeform Raised Textures},
year = {2025},
isbn = {9798400711978},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3689050.3704420},
doi = {10.1145/3689050.3704420},
booktitle = {Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction},
articleno = {45},
numpages = {16},
keywords = {2.5D fabrication, Personal fabrication, tactile rendering},
location = {Bordeaux / Talence, France},
series = {TEI '25}
}

See the paper and project page for more details.

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Contributing

Contributions, issues, and feature requests are welcome!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/foo)
3. Commit your changes (git commit -am 'Add foo')
4. Push to the branch (git push origin feature/foo)
5. Open a Pull Request

Please follow the existing code style and write tests for new functionality.

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License

This project is licensed under the MIT License.