https://github.com/tdh8316/triangler

:triangular_ruler: Convert images to Low-Poly art using Delaunay triangulation.
https://github.com/tdh8316/triangler

delaunay delaunay-triangulation image-processing numba numpy python triangulation

Last synced: about 1 month ago
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:triangular_ruler: Convert images to Low-Poly art using Delaunay triangulation.

• Host: GitHub
• URL: https://github.com/tdh8316/triangler
• Owner: tdh8316
• License: mit
• Created: 2020-02-15T14:26:35.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-07-05T13:45:49.000Z (over 1 year ago)
• Last Synced: 2025-09-08T05:02:52.366Z (about 1 month ago)
• Topics: delaunay, delaunay-triangulation, image-processing, numba, numpy, python, triangulation
• Language: Python
• Homepage:
• Size: 25.5 MB
• Stars: 97
• Watchers: 2
• Forks: 14
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Overview

📐 Convert images to Low-Poly art using [Delaunay triangulation](https://en.wikipedia.org/wiki/Delaunay_triangulation).

![sample](images/triangler-monroe.jpg)

## Table of contents

1. [Installation](#installation)

2. [Usage](#usage)

3. [Use as a library](#api)

4. [Sample](#sample)

5. [License](#license)

## Installation

You need [Python](https://www.python.org/) 3.10 or higher.

I strongly recommend to use virtual environment such as Anaconda.

You can [download Anaconda here](https://www.anaconda.com/distribution/#download-section).

Follow manual below to create conda virtual environment for Triangler with the Anaconda.

```cmd

$ conda create -n triangler python=3.12

$ activate triangler

(triangler)$ python -m pip install git+https://github.com/tdh8316/triangler/

```

## Usage

```

(triangler)$ python -m triangler -h

positional arguments:

  input                 Input image

options:

  -h, --help            show this help message and exit

  -o OUTPUT, --output OUTPUT

                        Output image name

  -p POINTS, --points POINTS

                        Number of sample points to use

  -e {canny,entropy,sobel}, --edge-detector {canny,entropy,sobel}

                        Edge detection algorithm

  -s {poisson_disk,threshold}, --sampler {poisson_disk,threshold}

                        Point sampling algorithm

  -r {centroid,mean}, --renderer {centroid,mean}

                        Color polygon rendering algorithm

  -l, --reduce          Reduce the result image size to match the input image

  -V, --version         show program's version number and exit

  -d, --debug           Enable debug mode

```

## API

You can also use Triangler as a library.

```python

import triangler

triangler.convert(

    img="INPUT_IMAGE.jpg",

    save_path="OUTPUT_IMAGE.jpg",

)

```

## Sample

### Effect of the number of points

|                Original Image                |                 5000 Points                  |

|:--------------------------------------------:|:--------------------------------------------:|

|         ![sample](images/monroe.jpg)         | ![sample](images/triangler-monroe-5000p.jpg) |

|               **1000 Points**                |                **500 Points**                |

| ![sample](images/triangler-monroe-1000p.jpg) | ![sample](images/triangler-monroe-500p.jpg)  |

### More samples

| Original                    | Triangler                             |

|-----------------------------|---------------------------------------|

| ![sample](images/birds.jpg) | ![sample](images/triangler-birds.jpg) |

## License

Licensed under the MIT License.

Copyright 2024 `Donghyeok Tak`

## Credits

Some algorithms, including the Poisson disk sampling, are based

on [pmaldonado/PyTri](https://github.com/pmaldonado/PyTri).