https://github.com/takana671/noisetexture

Generating noise texture images.
https://github.com/takana671/noisetexture

cellular-noise cython fbm noise-textures numpy periodic-noise perlin-noise python3 voronoi

Last synced: 5 days ago
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Generating noise texture images.

• Host: GitHub
• URL: https://github.com/takana671/noisetexture
• Owner: taKana671
• Created: 2024-10-13T03:40:53.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-11-10T04:47:26.000Z (3 months ago)
• Last Synced: 2024-11-10T05:23:42.697Z (3 months ago)
• Topics: cellular-noise, cython, fbm, noise-textures, numpy, periodic-noise, perlin-noise, python3, voronoi
• Language: Cython
• Homepage:
• Size: 75.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NoiseTexture

This repository contains python and cython codes that can generate noise images, which can be used for texture and heightmap to visualize the terrain in 3D. 

In the python modules, numpy, and in the Cython modules, C array is mainly used. Those modules have the same functions, which return the array to be converted to an image.

Their difference is speed. See [speed comparison](#speed-comparison) result below.

# Requirements

* Cython 3.0.11

* numpy 2.1.2

* opencv-contrib-python 4.10.0.84

* opencv-python 4.10.0.84

# Environment

* Python 3.11

* Windows11

# Building Cython code

```

python setup.py build_ext --inplace

```

# Example

```

from cynoise.perlin import Perlin

# from pynoise.perlin import Perlin

from create_image import create_image_8bit, create_image_16bit

maker = Perlin()

arr = maker.pnoise3()

create_image_8bit(arr)

create_image_16bit(arr)

# change the number of lattices and the image size. The grid default is 4, size default is 256. 

maker = Perlin(grid=8, size=257)

```

A noise image will be output as png file.   

For more details of methods and parameters, please see source codes.

![sample](https://github.com/user-attachments/assets/d8c7a581-de6b-4af6-90ad-a4d095d6a854)

# Speed ​​comparison

The execution time of each methods were measured like this.

```

maker = Voroni()

reslt = %timeit -o maker.noise2()

print(reslt.best, reslt.loops, reslt.repeat)

```

    

      

      python

      cython

    

    

      method

      best(s)

      loops

      repeat

      best(s)

      loops

      repeat

    

    

      Perlin.noise2

      1.210008

      1

      7

      0.017233

      100

      7

    

    

      Perlin.noise3

      2.081957

      1

      7

      0.023179

      10

      7

    

    

      Perlin.wrap

      4.889988

      1

      7

      0.043762

      10

      7

    

    

      FBM.noise2

      3.849672

      1

      7

      0.041291

      10

      7

    

    

      FBM.wrap

      15.43603

      1

      7

      0.139114

      10

      7

    

    

      Cellular.noise2

      1.420607

      1

      7

      0.036839

      10

      7

    

    

      Cellular.noise3

      3.434327

      1

      7

      0.090029

      10

      7

    

    

      Cellular.noise24

      4.833801

      1

      7

      0.099891

      10

      7

    

    

      Cellular.cnoise2

      4.860955

      1

      7

      0.153122

      10

      7

    

    

      Cellular.cnoise3

      13.82344

      1

      7

      0.332647

      1

      7

    

    

      Periodic.noise2

      1.494618

      1

      7

      0.021754

      10

      7

    

    

      Periodic.noise3

      2.582619

      1

      7

      0.031351

      10

      7

    

    

      Voronoi.noise2

      1.464140

      1

      7

      0.097766

      10

      7

    

    

      Voronoi.noise3

      3.533389

      1

      7

      0.158923

      10

      7