#texture #synthesis #procedural

texture-synthesis

Multiresolution Stochastic Texture Synthesis, a non-parametric example-based algorithm for image generation

7 releases (4 breaking)

✓ Uses Rust 2018 edition

new 0.5.0 Sep 13, 2019
0.4.2 Sep 5, 2019
0.3.0 Sep 3, 2019
0.2.3 Sep 3, 2019
0.1.0 Aug 27, 2019

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🎨 texture-synthesis

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A light Rust API for Multiresolution Stochastic Texture Synthesis [1], a non-parametric example-based algorithm for image generation.

The repo also includes multiple code examples to get you started (along with test images), and you can find a compiled binary with a command line interface under the release tab.

Also see our talk More Like This, Please! Texture Synthesis and Remixing from a Single Example which explains this technique and the background more in-depth:

Video thumbnail

Features and examples

1. Single example generation

Imgur

Generate similar-looking images from a single example.

API - 01_single_example_synthesis

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    //create a new session
    let texsynth = ts::Session::builder()
        //load a single example image
        .add_example(&"imgs/1.jpg")
        .build()?;

    //generate an image
    let generated = texsynth.run(None);

    //save the image to the disk
    generated.save("out/01.jpg")
}

CLI

cargo run --release -- --out out/01.jpg generate imgs/1.jpg

You should get the following result with the images provided in this repo:

2. Multi example generation

Imgur

We can also provide multiple example images and the algorithm will "remix" them into a new image.

API - 02_multi_example_synthesis

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    // create a new session
    let texsynth = ts::Session::builder()
        // load multiple example image
        .add_examples(&[
            &"imgs/multiexample/1.jpg",
            &"imgs/multiexample/2.jpg",
            &"imgs/multiexample/3.jpg",
            &"imgs/multiexample/4.jpg",
        ])
        // we can ensure all of them come with same size
        // that is however optional, the generator doesnt care whether all images are same sizes
        // however, if you have guides or other additional maps, those have to be same size(s) as corresponding example(s)
        .resize_input(300, 300)
        // randomly initialize first 10 pixels
        .random_init(10)
        .seed(211)
        .build()?;

    // generate an image
    let generated = texsynth.run(None);

    // save the image to the disk
    generated.save("out/02.jpg")?;

    //save debug information to see "remixing" borders of different examples in map_id.jpg
    //different colors represent information coming from different maps
    generated.save_debug("out/")
}

CLI

cargo run --release -- --rand-init 10 --seed 211 --in-size 300 -o out/02.png --debug-out-dir out generate imgs/multiexample/1.jpg imgs/multiexample/2.jpg imgs/multiexample/3.jpg imgs/multiexample/4.jpg

You should get the following result with the images provided in this repo:

3. Guided Synthesis

Imgur

We can also guide the generation by providing a transformation "FROM"-"TO" in a form of guide maps

API - 03_guided_synthesis

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    let texsynth = ts::Session::builder()
        // NOTE: it is important that example(s) and their corresponding guides have same size(s)
        // you can ensure that by overwriting the input images sizes with .resize_input()
        .add_example(ts::Example::builder(&"imgs/2.jpg").with_guide(&"imgs/masks/2_example.jpg"))
        // load target "heart" shape that we would like the generated image to look like
        // now the generator will take our target guide into account during synthesis
        .load_target_guide(&"imgs/masks/2_target.jpg")
        .build()?;

    let generated = texsynth.run(None);

    // save the image to the disk
    generated.save("out/03.jpg")
}

CLI

cargo run --release -- -o out/03.png generate --target-guide imgs/masks/2_target.jpg --guides imgs/masks/2_example.jpg -- imgs/2.jpg

NOTE: Note the use of -- to delimit the path to the example imgs/2.jpg, if you don't specify --, the path to the example will be used as another guide path and there won't be any examples.

You should get the following result with the images provided in this repo:

4. Style Transfer

Imgur

Texture synthesis API supports auto-generation of example guide maps, which produces a style transfer-like effect.

API - 04_style_transfer

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    let texsynth = ts::Session::builder()
        // load example which will serve as our style, note you can have more than 1!
        .add_examples(&[&"imgs/multiexample/4.jpg"])
        // load target which will be the content
        // with style transfer, we do not need to provide example guides
        // they will be auto-generated if none were provided
        .load_target_guide(&"imgs/tom.jpg")
        .alpha(0.8)
        .build()?;

    // generate an image that applies 'style' to "tom.jpg"
    let generated = texsynth.run(None);

    // save the result to the disk
    generated.save("out/04.jpg")
}

CLI

cargo run --release -- --alpha 0.8 -o out/04.png transfer-style --style imgs/multiexample/4.jpg --guide imgs/tom.jpg

You should get the following result with the images provided in this repo:

5. Inpaint

Imgur

We can also fill-in missing information with inpaint. By changing the seed, we will get different version of the 'fillment'.

API - 05_inpaint

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    let texsynth = ts::Session::builder()
        // let the generator know which part we would like to fill in
        // if we had more examples, they would be additional information
        // the generator could use to inpaint
        .inpaint_example(
            &"imgs/masks/3_inpaint.jpg",
            // load a "corrupted" example with missing red information we would like to fill in
            ts::Example::builder(&"imgs/3.jpg")
                // we would also like to prevent sampling from "corrupted" red areas
                // otherwise, generator will treat that those as valid areas it can copy from in the example,
                // we could also use SampleMethod::Ignore to ignore the example altogether, but we
                // would then need at least 1 other example image to actually source from
                // example.set_sample_method(ts::SampleMethod::Ignore);
                .set_sample_method(&"imgs/masks/3_inpaint.jpg"),
        )
        // during inpaint, it is important to ensure both input and output are the same size
        .resize_input(400, 400)
        .output_size(400, 400)
        .build()?;

    let generated = texsynth.run(None);

    //save the result to the disk
    generated.save("out/05.jpg")
}

CLI

cargo run --release -- --in-size 400 --out-size 400 --inpaint imgs/masks/3_inpaint.jpg -o out/05.png generate imgs/3.jpg

You should get the following result with the images provided in this repo:

6. Tiling texture

We can make the generated image tile (meaning it will not have seams if you put multiple images together side-by-side). By invoking inpaint mode together with tiling, we can make an existing image tile.

API - 06_tiling_texture

use texture_synthesis as ts;

fn main() -> Result<(), ts::Error> {
    // Let's start layering some of the "verbs" of texture synthesis
    // if we just run tiling_mode(true) we will generate a completely new image from scratch (try it!)
    // but what if we want to tile an existing image?
    // we can use inpaint!

    let texsynth = ts::Session::builder()
        // load a mask that specifies borders of the image we can modify to make it tiling
        .inpaint_example(&"imgs/masks/1_tile.jpg", ts::Example::new(&"imgs/1.jpg"))
        //ensure correct sizes
        .resize_input(400, 400)
        .output_size(400, 400)
        //turn on tiling mode!
        .tiling_mode(true)
        .build()?;

    let generated = texsynth.run(None);

    generated.save("out/06.jpg")
}

CLI

cargo run --release -- --inpaint imgs/masks/1_tile.jpg --in-size 400 --out-size 400 --tiling -o out/06.bmp generate imgs/1.jpg

You should get the following result with the images provided in this repo:

7. Combining texture synthesis 'verbs'

We can also combine multiple modes together. For example, multi-example guided synthesis:

Or chaining multiple stages of generation together:

For more use cases and examples, please refer to the presentation "More Like This, Please! Texture Synthesis and Remixing from a Single Example"

Command line binary

  • Download the binary for your OS, or install it from source, cargo install --path=.. Note: if you want to show a progress window you will need to enable the progress feature, eg. cargo install --path=. --features="progress"
  • Open a terminal
  • Navigate to the directory where you downloaded the binary, if you didn't just cargo install it
  • Run texture_synthesis --help to get a list of all of the options and commands you can run
  • Refer to the examples section in this readme for examples of running the binary

Limitations

  • Struggles with complex semantics beyond pixel color (unless you guide it)
  • Not great with regular textures (seams can become obvious)
  • Cannot infer new information from existing information (only operates on what’s already there)
  • Designed for single exemplars or very small datasets (unlike Deep Learning based approaches)

Links/references

[1] [Opara & Stachowiak] "More Like This, Please! Texture Synthesis and Remixing from a Single Example"

[2] [Harrison] Image Texture Tools

[3] [Ashikhmin] Synthesizing Natural Textures

[4] [Efros & Leung] Texture Synthesis by Non-parametric Sampling

[5] [Wey & Levoy] Fast Texture Synthesis using Tree-structured Vector Quantization

[6] [De Bonet] Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images

[7] All the test images in this repo are from Unsplash

Contributing

We welcome community contributions to this project.

Please read our Contributor Guide for more information on how to get started.

License

Licensed under either of

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Dependencies

~2.5MB
~45K SLoC