https://github.com/sunsided/convchain-rs

Bitmap generation from a single example with convolutions and MCMC - a Rust port of ConvChain.
https://github.com/sunsided/convchain-rs

game-development mcmc procedural-generation rust texture-synthesis

Last synced: about 1 month ago
JSON representation

Bitmap generation from a single example with convolutions and MCMC - a Rust port of ConvChain.

• Host: GitHub
• URL: https://github.com/sunsided/convchain-rs
• Owner: sunsided
• License: mit
• Created: 2021-08-10T22:27:29.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-02-28T09:49:33.000Z (11 months ago)
• Last Synced: 2024-12-03T08:12:02.012Z (about 2 months ago)
• Topics: game-development, mcmc, procedural-generation, rust, texture-synthesis
• Language: Rust
• Homepage: https://crates.io/crates/convchain
• Size: 34.2 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

Awesome Lists containing this project

README

        
# ConvChain (Rust Port)

A port of Maxim Gumin's [ConvChain] to Rust:

 

> ConvChain is a Markov chain of images that converges to input-like images.

> That is, the distribution of NxN patterns in the outputs converges to the distribution

> of NxN patterns in the input as the process goes on.

Please have a look on the [original repo](https://github.com/mxgmn/ConvChain) for a

more thorough description of the application of the [Metropolis algorithm] to the problem. 

```rust

fn main() {

    // Generate a sample (this is the SimpleMaze example):

    let pattern = [

        true,  true,  true,  true,

        true, false, false, false,

        true, false,  true, false,

        true, false, false, false,

    ];

    let sample = ConvChainSample::new(&pattern, 4, 4);

    // Initialize the chain using given sample and a

    // - 32x32 output size

    // - receptor size of 2

    // - temperature of 1.0

    let mut chain = ConvChain::new(&sample, 32, 2, 1.0);

    

    // Generate the 32x32 field using 10 iterations.

    let field: &[bool] = chain.process(10);

}

```

## Example run

Here are a couple of outputs generated with the code, using different values

for the receptor size and number of iterations:

| Input | r=3, it=10 | r=5, it=10 | r=5, it=20 | r=5, it=100 |  

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

| ![](resources/Smile.png) | ![](docs/smile-10-3.png) | ![](docs/smile-10-5.png) | ![](docs/smile-10-5.png) | ![](docs/smile-100-5.png) |

This repository provides ports of both original "slow" and "fast" implementations. 

To run the example, execute the following from the repository root:

```console

$ cargo run --release --example fast

```

This will process the jobs defined in [resources/samples.xml] and produce output images

in the current directory.

To run the benchmarks, execute

```console

$ cargo bench

```

[ConvChain]: https://github.com/mxgmn/ConvChain

[resources/samples.xml]: resources/samples.xml

[Metropolis algorithm]: https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm