<!-- Language Selector -->
[🔤 English](#sbl-optimizer) | [🇯🇵 日本語](#sbl-optimizer-日本語)
# sbl-optimizer
[](https://github.com/sosucat/sbl-optimizer/blob/main/LICENSE)
[](https://badge.fury.io/py/sbl-optimizer)
[](https://github.com/sosucat/sbl-optimizer)
[](https://colab.research.google.com/drive/1Kpvq15wZrzsnQI28_JfkDSqCwT1ouyxj?usp=sharing)
[](https://colab.research.google.com/drive/1KX5W0MG34zS_qX7RqeMLizrkhlWp8ojh?usp=sharing)\
[](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/)
[](https://sites.gatech.edu/futurefeelings/2025/08/06/make-puffy-patterns-with-light-advanced/)
[](https://sosuke-ichihashi.com/)
[](https://doi.org/10.1145/3689050.3704420)
[](https://youtu.be/LomVS_jHxl0?feature=shared)
Print pattern optimizer for ``Swell by Light``.\
[Swell by Light (SbL)](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) 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.
---
## Table of Contents
- [sbl-optimizer](#sbl-optimizer)
- [Table of Contents](#table-of-contents)
- [Features](#features)
- [Installation](#installation)
- [1. (Recommended) From PyPI](#1-recommended-from-pypi)
- [2. (Alternative) From Source](#2-alternative-from-source)
- [Requirements](#requirements)
- [Quick Start](#quick-start)
- [Command Line Interface](#command-line-interface)
- [Usage](#usage)
- [Arguments](#arguments)
- [Options](#options)
- [Configuration](#configuration)
- [Examples](#examples)
- [Citation](#citation)
- [License](#license)
---
## 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.
---
## Installation
Install `sbl-optimizer` in two easy ways:
### 1. (Recommended) From PyPI
```bash
pip install sbl-optimizer
```
### 2. (Alternative) From Source
Clone the repository and install locally:
```bash
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 if they were not installed automatically:
```bash
pip install numpy pillow matplotlib
```
---
## Quick Start
1. Run the optimizer with a sample image:
```bash
sbl-optimizer
```
2. Locate the generated optimized pattern `sample_opt.pdf` in the current directory.
3. Check the simulated temperature distribution `sample_temperature.png` and swell pattern `sample_swell.png`.
4. Print the `sample_opt.pdf` on paper, apply paste, and expose to a strong LED spotlight. Darker printed regions absorb more heat and swell.
5. Try optimizing your own pattern.
```bash
sbl-optimizer path/to/your_image.png
```
---
## Command Line Interface
### Usage
```bash
sbl-optimizer [OPTIONS] <IMAGE>
```
### Arguments
`<IMAGE>`\
Path to the input image (JPG, PNG)
### Options
| Option | Description |
| ----------------------- | -------------------------- |
| `-c`, `--config <FILE>` | Path to JSON config file |
| `-h`, `--help` | Show help message and exit |
---
## Configuration
Some parameters can be customized via a JSON file (default: `config.json` shipped with the package).
`config.json`
```json
{
"swell_temperature": 145.0,
"light_power": 100.0,
"light_diameter": 0.06,
"alpha": 5e-07,
"verbose": 1,
"resolution": 120000
}
```
| Key | Type | Description |
|---------------------|--------|---------------------------------------------------------------|
| `swell_temperature` | float | Target swelling temperature (°C). Adjust this based on the swell temperature of the paste you have. |
| `light_power` | float | Light source power (W). Adjust this according to your light's power. |
| `light_diameter` | float | Diameter of the light circle on paper (m). Adjust based on the light circle diameter during heating. |
| `alpha` | float | Thermal diffusivity of paper (m²/s). |
| `verbose` | int | Bool enabling logging. 0: turned off; 1: turned on. |
| `resolution` | int | Number of cells paper is divided into in thermal simulations. Reduce this for faster optimization. Increase for a finer result.|
To override defaults:
```bash
sbl-optimizer --config path/to/your_config.json <IMAGE>
```
---
## Examples
1. Use default settings on a sample image.
```bash
sbl-optimizer
```
2. Use default settings on your image.
```bash
sbl-optimizer flower.jpg
```
3. Use custom settings
```bash
sbl-optimizer --config my_config.json
```
, where ``my_config.json`` looks like this:
```json
{
"swell_temperature": 135.0,
"light_power": 120.0,
"light_diameter": 0.07,
"alpha": 5e-07,
"verbose": 0,
"resolution": 50000
}
```
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. |
---
## Citation
If you use **sbl-optimizer** in your research or projects, please cite:
Sosuke Ichihashi, Noura Howell, and HyunJoo Oh. 2025.\
Swell by Light: An Approachable Technique for Freeform Raised Textures. \
In Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '25). Association for Computing Machinery, New York, NY, USA, Article 45, 1–16. https://doi.org/10.1145/3689050.3704420
```bibtex
@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](https://doi.org/10.1145/3689050.3704420) and [project page](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) for more details.
---
## License
This project is licensed under the [MIT License](LICENSE).\
---
# sbl-optimizer 日本語
[](https://github.com/sosucat/sbl-optimizer/blob/main/LICENSE)
[](https://badge.fury.io/py/sbl-optimizer)
[](https://github.com/sosucat/sbl-optimizer?tab=readme-ov-file#sbl-optimizer-%E6%97%A5%E6%9C%AC%E8%AA%9E)
[](https://colab.research.google.com/drive/15zYmaNvh88jztUcqpzwLXtT4YMRk2i1G?usp=sharing)
[](https://colab.research.google.com/drive/1GLLGjPD7EhUV6evPHUeh6qHe3aNMV47u?usp=sharing)\
[](https://sites.gatech.edu/futurefeelings/2025/07/03/swell-by-light-tei-25-2/)
[](https://sites.gatech.edu/futurefeelings/2025/08/07/%e5%85%89%e3%81%a7%e3%83%87%e3%82%b3%e3%83%9c%e3%82%b3%e6%a8%a1%e6%a7%98%e3%82%92%e4%bd%9c%e3%82%8b%ef%bc%88%e4%b8%8a%e7%b4%9a%e7%b7%a8%ef%bc%89/)
[](https://sosuke-ichihashi.com/)
[](https://doi.org/10.1145/3689050.3704420)
[](https://youtu.be/LomVS_jHxl0?feature=shared)
``Swell by Light (熱光学式2.5次元印刷)`` 用の印刷パターン最適化ツールです。\
[Swell by Light](https://sites.gatech.edu/futurefeelings/2025/07/03/swell-by-light-tei-25-2/) は、紙やその他の素材に自由な形のデコボコ模様を作るための手軽な技術です。sbL-optimizer は熱伝導などを考慮して模様の濃淡を調節し、できあがるデコボコ模様を元画像の模様により近づけます。
模様の濃淡の最適化により、元画像の模様に近い均一な温度分布が得られます。
---
## 目次
- [sbl-optimizer 日本語](#sbl-optimizer-日本語)
- [目次](#目次)
- [特徴](#特徴)
- [インストール](#インストール)
- [1. （推奨）PyPI から](#1-推奨pypi-から)
- [2. ソースから](#2-ソースから)
- [必要条件](#必要条件)
- [クイックスタート](#クイックスタート)
- [コマンドラインインターフェース](#コマンドラインインターフェース)
- [使用方法](#使用方法)
- [引数](#引数)
- [オプション](#オプション)
- [設定](#設定)
- [使用例](#使用例)
- [引用](#引用)
- [ライセンス](#ライセンス)
---
## 特徴
- **熱伝導を考慮した模様最適化**
熱伝導・放射などを考慮し、元画像通りのデコボコ模様ができるように模様の濃淡を調整。
- **シンプルなコマンドラインインターフェース (CLI)**
JSON ベースの設定と画像入力でワンコマンド実行。
- **Python ベース**
NumPy、Pillow、Matplotlib を使用した計算・可視化。
---
## インストール
`sbl-optimizer` は以下の 2 つの方法でインストールできます。
### 1. （推奨）PyPI から
```bash
pip install sbl-optimizer
```
### 2. ソースから
リポジトリをクローンしてローカルにインストールします。
```bash
git clone https://github.com/sosucat/sbl-optimizer.git
cd sbl-optimizer
pip install .
```
### 必要条件
Python と以下のライブラリが必要です：
| ライブラリ | バージョン |
| ------------ | ------------- |
| Python | 3.9 – 3.11 |
| NumPy | 1.24 - 2.2 |
| Pillow | 9.5 - 11.x |
| Matplotlib | 3.7 - 3.10 |
自動でインストールされなかった場合は手動でインストールしてください。
```bash
pip install numpy pillow matplotlib
```
---
## クイックスタート
1. サンプル画像で最適化を実行します：
```bash
sbl-optimizer
```
2. 現在のディレクトリに生成された最適化された模様 `sample_opt.pdf` をチェック。
3. シミュレートされた温度分布 `sample_temperature.png` とデコボコ模様 `sample_swell.png` をチェック。
4. `sample_opt.pdf` を印刷し、熱膨張ペーストを塗り、強力な LED スポットライトを照射。模様通りのデコボコが出てきます。
5. 自分の模様画像を最適化してみましょう。
```bash
sbl-optimizer path/to/your_image.png
```
---
## コマンドラインインターフェース
### 使用方法
```bash
sbl-optimizer [OPTIONS] <IMAGE>
```
### 引数
`<IMAGE>`\
入力画像のパス（JPG, PNG）
### オプション
| オプション | 説明 |
| ------------------------ | -------------------------------------- |
| `-c`, `--config <FILE>` | JSON 設定ファイルのパス |
| `-h`, `--help` | ヘルプメッセージを表示して終了 |
---
## 設定
一部のパラメータは JSON ファイル（デフォルト：パッケージ付属の `config.json`）でカスタマイズできます。
`config.json`
```json
{
"swell_temperature": 145.0,
"light_power": 100.0,
"light_diameter": 0.06,
"alpha": 5e-07,
"verbose": 1,
"resolution": 120000
}
```
| キー | 型 | 説明 |
|-----------------------|--------|--------------------------------------------------------------------------------------------------|
| `swell_temperature` | float | 膨張温度 [C°]。使用する膨張ペーストに記載されている、ペーストが膨らみ始める温度に応じて調整してください。 |
| `light_power` | float | 光出力 [W]。使用するライトの出力に応じて調節してください。 |
| `light_diameter` | float | 紙面に当たる光の直径 [m]。紙に光を当てて加熱する際に、光が当たる範囲に応じて調整してください。 |
| `alpha` | float | 紙の熱流量 [m²/s]。厚紙や極端に薄い紙などを使用する際に調節してください。 |
| `verbose` | int | ログ出力の有無。 0：ログなし。 1: ログあり。 |
| `resolution` | int | 熱シミュレーションの解像度。解像度を下げれば、最適化速度が上がる。解像度を上げれば、より画像に忠実なデコボコ模様になる。 |
カスタム設定で実行するには：
```bash
sbl-optimizer --config path/to/your_config.json <IMAGE>
```
---
## 使用例
1. サンプル画像をデフォルト設定で最適化
```bash
sbl-optimizer
```
2. 自前の画像をデフォルト設定で最適化
``` bash
sbl-optimizer flower.jpg
```
3. サンプル画像をカスタム設定で最適化
```bash
sbl-optimizer --config my_config.json
```
``my_config.json`` の例：
```json
{
"swell_temperature": 135.0,
"light_power": 120.0,
"light_diameter": 0.07,
"alpha": 5e-07,
"verbose": 0,
"resolution": 50000
}
```
出力ファイル（入力画像 `sample.jpg` と同じフォルダ）：
| ファイル名 | 説明 |
| ---------------------------- | ---------------------------------- |
| `sample_opt.pdf` | 印刷用の最適化された模様。 |
| `sample_temperature.png` | シミュレートされた温度分布。 |
| `sample_swell.png` | シミュレートされたデコボコ模様。 |
---
## 引用
研究やプロジェクトで **sbl-optimizer** を使用する場合は、以下を引用してください。
Sosuke Ichihashi, Noura Howell, and HyunJoo Oh. 2025.\
Swell by Light: An Approachable Technique for Freeform Raised Textures. \
In Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '25). Association for Computing Machinery, New York, NY, USA, Article 45, 1–16. https://doi.org/10.1145/3689050.3704420
```bibtex
@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}
}
```
詳細は [論文](https://doi.org/10.1145/3689050.3704420) および [プロジェクトページ](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) を参照してください。
---
## ライセンス
このプロジェクトは [MIT ライセンス](LICENSE) の下で公開されています。
Raw data
{
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"maintainer_email": "Sosuke Ichihashi <pengu1n.i843@gmail.com>",
"keywords": "hci, fabrication, diy, swell by light, heat transfer",
"author": null,
"author_email": "Sosuke Ichihashi <pengu1n.i843@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/8f/7c/f2e8bb25e405a091585dbf7966872ef970e33575ecf437ae60088feb719f/sbl_optimizer-0.2.2.tar.gz",
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"description": "<!-- Language Selector -->\r\n[\ud83d\udd24 English](#sbl-optimizer) | [\ud83c\uddef\ud83c\uddf5 \u65e5\u672c\u8a9e](#sbl-optimizer-\u65e5\u672c\u8a9e)\r\n\r\n# sbl-optimizer\r\n\r\n[](https://github.com/sosucat/sbl-optimizer/blob/main/LICENSE)\r\n[](https://badge.fury.io/py/sbl-optimizer)\r\n[](https://github.com/sosucat/sbl-optimizer)\r\n[](https://colab.research.google.com/drive/1Kpvq15wZrzsnQI28_JfkDSqCwT1ouyxj?usp=sharing)\r\n[](https://colab.research.google.com/drive/1KX5W0MG34zS_qX7RqeMLizrkhlWp8ojh?usp=sharing)\\\r\n[](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/)\r\n[](https://sites.gatech.edu/futurefeelings/2025/08/06/make-puffy-patterns-with-light-advanced/)\r\n[](https://sosuke-ichihashi.com/)\r\n[](https://doi.org/10.1145/3689050.3704420)\r\n[](https://youtu.be/LomVS_jHxl0?feature=shared)\r\n\r\nPrint pattern optimizer for ``Swell by Light``.\\\r\n[Swell by Light (SbL)](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) 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.\r\n\r\n\r\n\r\n\r\n\r\nOptimization of the printed pattern results in a uniform temperature pattern closely matching the original pattern.\r\n\r\n---\r\n\r\n## Table of Contents\r\n\r\n- [sbl-optimizer](#sbl-optimizer)\r\n - [Table of Contents](#table-of-contents)\r\n - [Features](#features)\r\n - [Installation](#installation)\r\n - [1. (Recommended) From PyPI](#1-recommended-from-pypi)\r\n - [2. (Alternative) From Source](#2-alternative-from-source)\r\n - [Requirements](#requirements)\r\n - [Quick Start](#quick-start)\r\n - [Command Line Interface](#command-line-interface)\r\n - [Usage](#usage)\r\n - [Arguments](#arguments)\r\n - [Options](#options)\r\n - [Configuration](#configuration)\r\n - [Examples](#examples)\r\n - [Citation](#citation)\r\n - [License](#license)\r\n\r\n---\r\n\r\n## Features\r\n\r\n- **Adaptive heat\u2010pattern optimization** \r\n Compensates for heat diffusion to produce raised textures that match your design.\r\n- **Simple CLI** \r\n One\u2010command execution, with JSON\u2010based configuration and image input.\r\n- **Built on Python** \r\n Uses NumPy, Pillow, and Matplotlib for computations and visualizations.\r\n\r\n---\r\n\r\n## Installation\r\nInstall `sbl-optimizer` in two easy ways:\r\n\r\n### 1. (Recommended) From PyPI\r\n\r\n```bash\r\npip install sbl-optimizer\r\n```\r\n\r\n### 2. (Alternative) From Source\r\nClone the repository and install locally:\r\n\r\n```bash\r\ngit clone https://github.com/sosucat/sbl-optimizer.git\r\ncd sbl-optimizer\r\npip install .\r\n```\r\n\r\n### Requirements\r\n\r\nEnsure you have Python and dependencies:\r\n| Libraries | Versions |\r\n| ---------- | ---------- |\r\n| Python | 3.9 \u2013 3.11 |\r\n| NumPy | 1.24 - 2.2 |\r\n| Pillow | 9.5 - 11.x |\r\n| Matplotlib | 3.7 - 3.10 |\r\n\r\nYou can also install dependencies manually if they were not installed automatically:\r\n\r\n```bash\r\npip install numpy pillow matplotlib\r\n```\r\n---\r\n\r\n## Quick Start\r\n\r\n1. Run the optimizer with a sample image:\r\n\r\n ```bash\r\n sbl-optimizer\r\n ```\r\n\r\n2. Locate the generated optimized pattern `sample_opt.pdf` in the current directory.\r\n\r\n3. Check the simulated temperature distribution `sample_temperature.png` and swell pattern `sample_swell.png`.\r\n\r\n4. Print the `sample_opt.pdf` on paper, apply paste, and expose to a strong LED spotlight. Darker printed regions absorb more heat and swell.\r\n\r\n5. Try optimizing your own pattern.\r\n \r\n ```bash\r\n sbl-optimizer path/to/your_image.png\r\n ```\r\n\r\n---\r\n\r\n## Command Line Interface\r\n\r\n### Usage\r\n\r\n```bash\r\nsbl-optimizer [OPTIONS] <IMAGE>\r\n```\r\n\r\n### Arguments\r\n\r\n `<IMAGE>`\\\r\n Path to the input image (JPG, PNG)\r\n\r\n### Options\r\n\r\n| Option | Description |\r\n| ----------------------- | -------------------------- |\r\n| `-c`, `--config <FILE>` | Path to JSON config file |\r\n| `-h`, `--help` | Show help message and exit |\r\n\r\n---\r\n\r\n## Configuration\r\n\r\nSome parameters can be customized via a JSON file (default: `config.json` shipped with the package).\r\n\r\n`config.json`\r\n```json\r\n{\r\n \"swell_temperature\": 145.0,\r\n \"light_power\": 100.0,\r\n \"light_diameter\": 0.06,\r\n \"alpha\": 5e-07,\r\n \"verbose\": 1,\r\n \"resolution\": 120000\r\n}\r\n```\r\n\r\n| Key | Type | Description |\r\n|---------------------|--------|---------------------------------------------------------------|\r\n| `swell_temperature` | float | Target swelling temperature (\u00b0C). Adjust this based on the swell temperature of the paste you have. |\r\n| `light_power` | float | Light source power (W). Adjust this according to your light's power. |\r\n| `light_diameter` | float | Diameter of the light circle on paper (m). Adjust based on the light circle diameter during heating. |\r\n| `alpha` | float | Thermal diffusivity of paper (m\u00b2/s). |\r\n| `verbose` | int | Bool enabling logging. 0: turned off; 1: turned on. |\r\n| `resolution` | int | Number of cells paper is divided into in thermal simulations. Reduce this for faster optimization. Increase for a finer result.|\r\n\r\nTo override defaults:\r\n\r\n```bash\r\nsbl-optimizer --config path/to/your_config.json <IMAGE>\r\n```\r\n\r\n---\r\n\r\n## Examples\r\n\r\n1. Use default settings on a sample image.\r\n ```bash\r\n sbl-optimizer\r\n ```\r\n\r\n2. Use default settings on your image.\r\n ```bash\r\n sbl-optimizer flower.jpg\r\n ```\r\n\r\n3. Use custom settings\r\n ```bash\r\n sbl-optimizer --config my_config.json\r\n ```\r\n , where ``my_config.json`` looks like this:\r\n ```json\r\n {\r\n \"swell_temperature\": 135.0,\r\n \"light_power\": 120.0,\r\n \"light_diameter\": 0.07,\r\n \"alpha\": 5e-07,\r\n \"verbose\": 0,\r\n \"resolution\": 50000\r\n }\r\n ```\r\n\r\nOutput files (in same folder as input image `sample.jpg`):\r\n\r\n| File | Description |\r\n| ------------------------- | ----------------------------------- |\r\n| `sample_opt.pdf` | Optimized pattern to be printed. |\r\n| `sample_temperature.png` | Simulated temperature distribution. |\r\n| `sample_swell.png` | Simulated swell pattern. |\r\n\r\n---\r\n\r\n## Citation\r\n\r\nIf you use **sbl-optimizer** in your research or projects, please cite:\r\n\r\nSosuke Ichihashi, Noura Howell, and HyunJoo Oh. 2025.\\\r\nSwell by Light: An Approachable Technique for Freeform Raised Textures. \\\r\nIn Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '25). Association for Computing Machinery, New York, NY, USA, Article 45, 1\u201316. https://doi.org/10.1145/3689050.3704420\r\n\r\n```bibtex\r\n@inproceedings{10.1145/3689050.3704420,\r\nauthor = {Ichihashi, Sosuke and Howell, Noura and Oh, HyunJoo},\r\ntitle = {Swell by Light: An Approachable Technique for Freeform Raised Textures},\r\nyear = {2025},\r\nisbn = {9798400711978},\r\npublisher = {Association for Computing Machinery},\r\naddress = {New York, NY, USA},\r\nurl = {https://doi.org/10.1145/3689050.3704420},\r\ndoi = {10.1145/3689050.3704420},\r\nbooktitle = {Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction},\r\narticleno = {45},\r\nnumpages = {16},\r\nkeywords = {2.5D fabrication, Personal fabrication, tactile rendering},\r\nlocation = {Bordeaux / Talence, France},\r\nseries = {TEI '25}\r\n}\r\n```\r\n\r\nSee the [paper](https://doi.org/10.1145/3689050.3704420) and [project page](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) for more details.\r\n\r\n---\r\n\r\n## License\r\n\r\nThis project is licensed under the [MIT License](LICENSE).\\\r\n\r\n\r\n\r\n---\r\n\r\n# sbl-optimizer \u65e5\u672c\u8a9e\r\n\r\n[](https://github.com/sosucat/sbl-optimizer/blob/main/LICENSE)\r\n[](https://badge.fury.io/py/sbl-optimizer)\r\n[](https://github.com/sosucat/sbl-optimizer?tab=readme-ov-file#sbl-optimizer-%E6%97%A5%E6%9C%AC%E8%AA%9E)\r\n[](https://colab.research.google.com/drive/15zYmaNvh88jztUcqpzwLXtT4YMRk2i1G?usp=sharing)\r\n[](https://colab.research.google.com/drive/1GLLGjPD7EhUV6evPHUeh6qHe3aNMV47u?usp=sharing)\\\r\n[](https://sites.gatech.edu/futurefeelings/2025/07/03/swell-by-light-tei-25-2/)\r\n[](https://sites.gatech.edu/futurefeelings/2025/08/07/%e5%85%89%e3%81%a7%e3%83%87%e3%82%b3%e3%83%9c%e3%82%b3%e6%a8%a1%e6%a7%98%e3%82%92%e4%bd%9c%e3%82%8b%ef%bc%88%e4%b8%8a%e7%b4%9a%e7%b7%a8%ef%bc%89/)\r\n[](https://sosuke-ichihashi.com/)\r\n[](https://doi.org/10.1145/3689050.3704420)\r\n[](https://youtu.be/LomVS_jHxl0?feature=shared)\r\n\r\n``Swell by Light (\u71b1\u5149\u5b66\u5f0f2.5\u6b21\u5143\u5370\u5237)`` \u7528\u306e\u5370\u5237\u30d1\u30bf\u30fc\u30f3\u6700\u9069\u5316\u30c4\u30fc\u30eb\u3067\u3059\u3002\\\r\n[Swell by Light](https://sites.gatech.edu/futurefeelings/2025/07/03/swell-by-light-tei-25-2/) \u306f\u3001\u7d19\u3084\u305d\u306e\u4ed6\u306e\u7d20\u6750\u306b\u81ea\u7531\u306a\u5f62\u306e\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8\u3092\u4f5c\u308b\u305f\u3081\u306e\u624b\u8efd\u306a\u6280\u8853\u3067\u3059\u3002sbL-optimizer \u306f\u71b1\u4f1d\u5c0e\u306a\u3069\u3092\u8003\u616e\u3057\u3066\u6a21\u69d8\u306e\u6fc3\u6de1\u3092\u8abf\u7bc0\u3057\u3001\u3067\u304d\u3042\u304c\u308b\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8\u3092\u5143\u753b\u50cf\u306e\u6a21\u69d8\u306b\u3088\u308a\u8fd1\u3065\u3051\u307e\u3059\u3002\r\n\r\n\r\n\r\n\r\n\r\n\u6a21\u69d8\u306e\u6fc3\u6de1\u306e\u6700\u9069\u5316\u306b\u3088\u308a\u3001\u5143\u753b\u50cf\u306e\u6a21\u69d8\u306b\u8fd1\u3044\u5747\u4e00\u306a\u6e29\u5ea6\u5206\u5e03\u304c\u5f97\u3089\u308c\u307e\u3059\u3002\r\n\r\n---\r\n\r\n## \u76ee\u6b21\r\n\r\n- [sbl-optimizer \u65e5\u672c\u8a9e](#sbl-optimizer-\u65e5\u672c\u8a9e)\r\n - [\u76ee\u6b21](#\u76ee\u6b21)\r\n - [\u7279\u5fb4](#\u7279\u5fb4)\r\n - [\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb](#\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb)\r\n - [1. \uff08\u63a8\u5968\uff09PyPI \u304b\u3089](#1-\u63a8\u5968pypi-\u304b\u3089)\r\n - [2. \u30bd\u30fc\u30b9\u304b\u3089](#2-\u30bd\u30fc\u30b9\u304b\u3089)\r\n - [\u5fc5\u8981\u6761\u4ef6](#\u5fc5\u8981\u6761\u4ef6)\r\n - [\u30af\u30a4\u30c3\u30af\u30b9\u30bf\u30fc\u30c8](#\u30af\u30a4\u30c3\u30af\u30b9\u30bf\u30fc\u30c8)\r\n - [\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9](#\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9)\r\n - [\u4f7f\u7528\u65b9\u6cd5](#\u4f7f\u7528\u65b9\u6cd5)\r\n - [\u5f15\u6570](#\u5f15\u6570)\r\n - [\u30aa\u30d7\u30b7\u30e7\u30f3](#\u30aa\u30d7\u30b7\u30e7\u30f3)\r\n - [\u8a2d\u5b9a](#\u8a2d\u5b9a)\r\n - [\u4f7f\u7528\u4f8b](#\u4f7f\u7528\u4f8b)\r\n - [\u5f15\u7528](#\u5f15\u7528)\r\n - [\u30e9\u30a4\u30bb\u30f3\u30b9](#\u30e9\u30a4\u30bb\u30f3\u30b9)\r\n\r\n---\r\n\r\n## \u7279\u5fb4\r\n\r\n- **\u71b1\u4f1d\u5c0e\u3092\u8003\u616e\u3057\u305f\u6a21\u69d8\u6700\u9069\u5316** \r\n \u71b1\u4f1d\u5c0e\u30fb\u653e\u5c04\u306a\u3069\u3092\u8003\u616e\u3057\u3001\u5143\u753b\u50cf\u901a\u308a\u306e\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u6a21\u69d8\u306e\u6fc3\u6de1\u3092\u8abf\u6574\u3002\r\n- **\u30b7\u30f3\u30d7\u30eb\u306a\u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9 (CLI)** \r\n JSON \u30d9\u30fc\u30b9\u306e\u8a2d\u5b9a\u3068\u753b\u50cf\u5165\u529b\u3067\u30ef\u30f3\u30b3\u30de\u30f3\u30c9\u5b9f\u884c\u3002\r\n- **Python \u30d9\u30fc\u30b9** \r\n NumPy\u3001Pillow\u3001Matplotlib \u3092\u4f7f\u7528\u3057\u305f\u8a08\u7b97\u30fb\u53ef\u8996\u5316\u3002\r\n\r\n---\r\n\r\n## \u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\r\n`sbl-optimizer` \u306f\u4ee5\u4e0b\u306e 2 \u3064\u306e\u65b9\u6cd5\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3067\u304d\u307e\u3059\u3002\r\n\r\n### 1. \uff08\u63a8\u5968\uff09PyPI \u304b\u3089\r\n\r\n```bash\r\npip install sbl-optimizer\r\n```\r\n\r\n### 2. \u30bd\u30fc\u30b9\u304b\u3089\r\n\u30ea\u30dd\u30b8\u30c8\u30ea\u3092\u30af\u30ed\u30fc\u30f3\u3057\u3066\u30ed\u30fc\u30ab\u30eb\u306b\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002\r\n\r\n```bash\r\ngit clone https://github.com/sosucat/sbl-optimizer.git\r\ncd sbl-optimizer\r\npip install .\r\n```\r\n\r\n### \u5fc5\u8981\u6761\u4ef6\r\n\r\nPython \u3068\u4ee5\u4e0b\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u304c\u5fc5\u8981\u3067\u3059\uff1a\r\n| \u30e9\u30a4\u30d6\u30e9\u30ea | \u30d0\u30fc\u30b8\u30e7\u30f3 |\r\n| ------------ | ------------- |\r\n| Python | 3.9 \u2013 3.11 |\r\n| NumPy | 1.24 - 2.2 |\r\n| Pillow | 9.5 - 11.x |\r\n| Matplotlib | 3.7 - 3.10 |\r\n\r\n\u81ea\u52d5\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3055\u308c\u306a\u304b\u3063\u305f\u5834\u5408\u306f\u624b\u52d5\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\n```bash\r\npip install numpy pillow matplotlib\r\n```\r\n---\r\n\r\n## \u30af\u30a4\u30c3\u30af\u30b9\u30bf\u30fc\u30c8\r\n\r\n1. \u30b5\u30f3\u30d7\u30eb\u753b\u50cf\u3067\u6700\u9069\u5316\u3092\u5b9f\u884c\u3057\u307e\u3059\uff1a\r\n\r\n ```bash\r\n sbl-optimizer\r\n ```\r\n\r\n2. \u73fe\u5728\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u306b\u751f\u6210\u3055\u308c\u305f\u6700\u9069\u5316\u3055\u308c\u305f\u6a21\u69d8 `sample_opt.pdf` \u3092\u30c1\u30a7\u30c3\u30af\u3002\r\n\r\n3. \u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u6e29\u5ea6\u5206\u5e03 `sample_temperature.png` \u3068\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8 `sample_swell.png` \u3092\u30c1\u30a7\u30c3\u30af\u3002\r\n\r\n4. `sample_opt.pdf` \u3092\u5370\u5237\u3057\u3001\u71b1\u81a8\u5f35\u30da\u30fc\u30b9\u30c8\u3092\u5857\u308a\u3001\u5f37\u529b\u306a LED \u30b9\u30dd\u30c3\u30c8\u30e9\u30a4\u30c8\u3092\u7167\u5c04\u3002\u6a21\u69d8\u901a\u308a\u306e\u30c7\u30b3\u30dc\u30b3\u304c\u51fa\u3066\u304d\u307e\u3059\u3002\r\n\r\n5. \u81ea\u5206\u306e\u6a21\u69d8\u753b\u50cf\u3092\u6700\u9069\u5316\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002\r\n \r\n ```bash\r\n sbl-optimizer path/to/your_image.png\r\n ```\r\n\r\n---\r\n\r\n## \u30b3\u30de\u30f3\u30c9\u30e9\u30a4\u30f3\u30a4\u30f3\u30bf\u30fc\u30d5\u30a7\u30fc\u30b9\r\n\r\n### \u4f7f\u7528\u65b9\u6cd5\r\n\r\n```bash\r\nsbl-optimizer [OPTIONS] <IMAGE>\r\n```\r\n\r\n### \u5f15\u6570\r\n\r\n `<IMAGE>`\\\r\n \u5165\u529b\u753b\u50cf\u306e\u30d1\u30b9\uff08JPG, PNG\uff09\r\n\r\n### \u30aa\u30d7\u30b7\u30e7\u30f3\r\n\r\n| \u30aa\u30d7\u30b7\u30e7\u30f3 | \u8aac\u660e |\r\n| ------------------------ | -------------------------------------- |\r\n| `-c`, `--config <FILE>` | JSON \u8a2d\u5b9a\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9 |\r\n| `-h`, `--help` | \u30d8\u30eb\u30d7\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u8868\u793a\u3057\u3066\u7d42\u4e86 |\r\n\r\n---\r\n\r\n## \u8a2d\u5b9a\r\n\r\n\u4e00\u90e8\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f JSON \u30d5\u30a1\u30a4\u30eb\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\uff1a\u30d1\u30c3\u30b1\u30fc\u30b8\u4ed8\u5c5e\u306e `config.json`\uff09\u3067\u30ab\u30b9\u30bf\u30de\u30a4\u30ba\u3067\u304d\u307e\u3059\u3002\r\n\r\n`config.json`\r\n```json\r\n{\r\n \"swell_temperature\": 145.0,\r\n \"light_power\": 100.0,\r\n \"light_diameter\": 0.06,\r\n \"alpha\": 5e-07,\r\n \"verbose\": 1,\r\n \"resolution\": 120000\r\n}\r\n```\r\n\r\n| \u30ad\u30fc | \u578b | \u8aac\u660e |\r\n|-----------------------|--------|--------------------------------------------------------------------------------------------------|\r\n| `swell_temperature` | float | \u81a8\u5f35\u6e29\u5ea6 [C\u00b0]\u3002\u4f7f\u7528\u3059\u308b\u81a8\u5f35\u30da\u30fc\u30b9\u30c8\u306b\u8a18\u8f09\u3055\u308c\u3066\u3044\u308b\u3001\u30da\u30fc\u30b9\u30c8\u304c\u81a8\u3089\u307f\u59cb\u3081\u308b\u6e29\u5ea6\u306b\u5fdc\u3058\u3066\u8abf\u6574\u3057\u3066\u304f\u3060\u3055\u3044\u3002 |\r\n| `light_power` | float | \u5149\u51fa\u529b [W]\u3002\u4f7f\u7528\u3059\u308b\u30e9\u30a4\u30c8\u306e\u51fa\u529b\u306b\u5fdc\u3058\u3066\u8abf\u7bc0\u3057\u3066\u304f\u3060\u3055\u3044\u3002 |\r\n| `light_diameter` | float | \u7d19\u9762\u306b\u5f53\u305f\u308b\u5149\u306e\u76f4\u5f84 [m]\u3002\u7d19\u306b\u5149\u3092\u5f53\u3066\u3066\u52a0\u71b1\u3059\u308b\u969b\u306b\u3001\u5149\u304c\u5f53\u305f\u308b\u7bc4\u56f2\u306b\u5fdc\u3058\u3066\u8abf\u6574\u3057\u3066\u304f\u3060\u3055\u3044\u3002 |\r\n| `alpha` | float | \u7d19\u306e\u71b1\u6d41\u91cf [m\u00b2/s]\u3002\u539a\u7d19\u3084\u6975\u7aef\u306b\u8584\u3044\u7d19\u306a\u3069\u3092\u4f7f\u7528\u3059\u308b\u969b\u306b\u8abf\u7bc0\u3057\u3066\u304f\u3060\u3055\u3044\u3002 |\r\n| `verbose` | int | \u30ed\u30b0\u51fa\u529b\u306e\u6709\u7121\u3002 0\uff1a\u30ed\u30b0\u306a\u3057\u3002 1: \u30ed\u30b0\u3042\u308a\u3002 |\r\n| `resolution` | int | \u71b1\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306e\u89e3\u50cf\u5ea6\u3002\u89e3\u50cf\u5ea6\u3092\u4e0b\u3052\u308c\u3070\u3001\u6700\u9069\u5316\u901f\u5ea6\u304c\u4e0a\u304c\u308b\u3002\u89e3\u50cf\u5ea6\u3092\u4e0a\u3052\u308c\u3070\u3001\u3088\u308a\u753b\u50cf\u306b\u5fe0\u5b9f\u306a\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8\u306b\u306a\u308b\u3002 |\r\n\r\n\u30ab\u30b9\u30bf\u30e0\u8a2d\u5b9a\u3067\u5b9f\u884c\u3059\u308b\u306b\u306f\uff1a\r\n\r\n```bash\r\nsbl-optimizer --config path/to/your_config.json <IMAGE>\r\n```\r\n\r\n---\r\n\r\n## \u4f7f\u7528\u4f8b\r\n\r\n1. \u30b5\u30f3\u30d7\u30eb\u753b\u50cf\u3092\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u3067\u6700\u9069\u5316\r\n ```bash\r\n sbl-optimizer\r\n ```\r\n\r\n2. \u81ea\u524d\u306e\u753b\u50cf\u3092\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u3067\u6700\u9069\u5316\r\n ``` bash\r\n sbl-optimizer flower.jpg\r\n ```\r\n\r\n3. \u30b5\u30f3\u30d7\u30eb\u753b\u50cf\u3092\u30ab\u30b9\u30bf\u30e0\u8a2d\u5b9a\u3067\u6700\u9069\u5316\r\n ```bash\r\n sbl-optimizer --config my_config.json\r\n ```\r\n ``my_config.json`` \u306e\u4f8b\uff1a\r\n ```json\r\n {\r\n \"swell_temperature\": 135.0,\r\n \"light_power\": 120.0,\r\n \"light_diameter\": 0.07,\r\n \"alpha\": 5e-07,\r\n \"verbose\": 0,\r\n \"resolution\": 50000\r\n }\r\n ```\r\n\r\n\u51fa\u529b\u30d5\u30a1\u30a4\u30eb\uff08\u5165\u529b\u753b\u50cf `sample.jpg` \u3068\u540c\u3058\u30d5\u30a9\u30eb\u30c0\uff09\uff1a\r\n\r\n| \u30d5\u30a1\u30a4\u30eb\u540d | \u8aac\u660e |\r\n| ---------------------------- | ---------------------------------- |\r\n| `sample_opt.pdf` | \u5370\u5237\u7528\u306e\u6700\u9069\u5316\u3055\u308c\u305f\u6a21\u69d8\u3002 |\r\n| `sample_temperature.png` | \u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u6e29\u5ea6\u5206\u5e03\u3002 |\r\n| `sample_swell.png` | \u30b7\u30df\u30e5\u30ec\u30fc\u30c8\u3055\u308c\u305f\u30c7\u30b3\u30dc\u30b3\u6a21\u69d8\u3002 |\r\n\r\n---\r\n\r\n## \u5f15\u7528\r\n\r\n\u7814\u7a76\u3084\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u3067 **sbl-optimizer** \u3092\u4f7f\u7528\u3059\u308b\u5834\u5408\u306f\u3001\u4ee5\u4e0b\u3092\u5f15\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\nSosuke Ichihashi, Noura Howell, and HyunJoo Oh. 2025.\\\r\nSwell by Light: An Approachable Technique for Freeform Raised Textures. \\\r\nIn Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '25). Association for Computing Machinery, New York, NY, USA, Article 45, 1\u201316. https://doi.org/10.1145/3689050.3704420\r\n\r\n```bibtex\r\n@inproceedings{10.1145/3689050.3704420,\r\nauthor = {Ichihashi, Sosuke and Howell, Noura and Oh, HyunJoo},\r\ntitle = {Swell by Light: An Approachable Technique for Freeform Raised Textures},\r\nyear = {2025},\r\nisbn = {9798400711978},\r\npublisher = {Association for Computing Machinery},\r\naddress = {New York, NY, USA},\r\nurl = {https://doi.org/10.1145/3689050.3704420},\r\ndoi = {10.1145/3689050.3704420},\r\nbooktitle = {Proceedings of the Nineteenth International Conference on Tangible, Embedded, and Embodied Interaction},\r\narticleno = {45},\r\nnumpages = {16},\r\nkeywords = {2.5D fabrication, Personal fabrication, tactile rendering},\r\nlocation = {Bordeaux / Talence, France},\r\nseries = {TEI '25}\r\n}\r\n```\r\n\r\n\u8a73\u7d30\u306f [\u8ad6\u6587](https://doi.org/10.1145/3689050.3704420) \u304a\u3088\u3073 [\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u30da\u30fc\u30b8](https://sites.gatech.edu/futurefeelings/2025/03/07/swell-by-light-tei-25/) \u3092\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002\r\n\r\n---\r\n\r\n\r\n## \u30e9\u30a4\u30bb\u30f3\u30b9\r\n\r\n\u3053\u306e\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306f [MIT \u30e9\u30a4\u30bb\u30f3\u30b9](LICENSE) \u306e\u4e0b\u3067\u516c\u958b\u3055\u308c\u3066\u3044\u307e\u3059\u3002\r\n",
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