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https://github.com/maciejczyzewski/fast_gpu_voronoi

GPU-Accelerated Jump Flooding Algorithm for Voronoi Diagram in log*(n)
https://github.com/maciejczyzewski/fast_gpu_voronoi

algorithm gpgpu gpu opencl research voronoi

Last synced: 7 days ago
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GPU-Accelerated Jump Flooding Algorithm for Voronoi Diagram in log*(n)

Host: GitHub
URL: https://github.com/maciejczyzewski/fast_gpu_voronoi
Owner: maciejczyzewski
License: mit
Created: 2019-05-25T00:52:56.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2022-12-27T15:35:11.000Z (almost 2 years ago)
Last Synced: 2024-10-31T13:33:42.619Z (14 days ago)
Topics: algorithm, gpgpu, gpu, opencl, research, voronoi
Language: Python
Homepage: https://git.io/jfa_star_slides
Size: 68.4 MB
Stars: 25
Watchers: 3
Forks: 4
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        [**Demo (Google Colab)**](https://colab.research.google.com/drive/1feoho8M_1a4tOpYFDPkqDVJeWHwdhJox?usp=sharing)



  







|     | Research | Authors |

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

| \[[slides](https://maciejczyzewski.github.io/fast_gpu_voronoi/slides_small.pdf)\] | __GPU-Accelerated Jump Flooding Algorithm for Voronoi Diagram in log*(n)__ [this] | Maciej A. Czyzewski |

| \[[article](https://vgl.csa.iisc.ac.in/pdf/pub/ICVGIP14_Talha.pdf)\] | Facet-JFA: Faster computation of discrete Voronoi diagrams [2014] | Talha Bin Masood, Hari Krishna Malladi, Vijay Natarajan |

| \[[article](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.101.8568&rep=rep1&type=pdf)\] | Jump Flooding in GPU with Applications to Voronoi Diagram and Distance Transform [2006] | Guodong Rong, Tiow-Seng Tan |

# Implemented Algorithms

JFA*

JFA+

JFA

used improvement

noise+selection

noise

--



num. of needed steps

log*(n)

log4(p)

log2(p)

step size

p/(3^i)

p/(2^i)

p/(2^i)

research

(our)

(our)

[Guodong 2006]

# Installation & Example

Project can be installed using `pip`:

```bash

$ pip3 install fast_gpu_voronoi

```

Here is a small example to whet your appetite:

```python3

from fast_gpu_voronoi       import Instance

from fast_gpu_voronoi.jfa   import JFA_star

from fast_gpu_voronoi.debug import save

I = Instance(alg=JFA_star, x=50, y=50, \

        pts=[[ 7,14], [33,34], [27,10],

             [35,10], [23,42], [34,39]])

I.run()

print(I.M.shape)                 # (50, 50, 1)

save(I.M, I.x, I.y, force=True)  # __1_debug.png

```

# Development

If you want to contribute, first clone git repository and then run tests:

```bash

$ git clone [email protected]:maciejczyzewski/fast_gpu_voronoi.git

$ pip3 install -r requirements.txt

$ pytest

```

# Results

| Our method                      | Current best          |

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

| JFA\*                           | JFA                   |

| ![JFA_star](docs/jfa_star2.gif) | ![JFA](docs/jfa2.gif) |

| steps = log\*(2000) = 4          | steps = log(720) ~= 10 |

_...for x = 720; y = 720; seeds = 2000 (read as n = 2000; p = 720)._

# Thanks