Crate kmeans_colors

Calculate the k-means of a set of data.

Overview

This crate provides traits for calculating and implementing a k-means clustering algorithm. The original implementation of this library was created for finding k-means colors in image buffers. Applications of crate functionality can be seen on the README page.

When using the library, set default-features = false in the Cargo.toml to avoid bringing in the binary dependencies. If working with colors, implementations have been provided for the palette Lab and Srgb color types behind the palette_color feature.

The binary located in src/bin/kmeans_colors shows examples of crate functionality.

The Calculate trait

k-means calculations can be provided for other data types by implementing the Calculate trait. See the Lab and Srgb implementations in kmeans.rs for examples.

Calculating k-means with palette_color

The palette_color feature provides implementations of the Calculate trait for the Lab color space and Srgb color space. Each space has advantages and drawbacks due to the characteristics of the color space.

The Lab calculation produces more perceptually accurate results at a slightly slower runtime. Srgb calculation will converge faster than Lab but the results may not visually correlate as well to the original image. Overall, properly converged results should not differ that drastically except at lower k counts. At k=1, the average color of an image, results should match almost exactly.

Note: If k-means calculation is taking too long, try scaling down the image size. A full-size image is not required for calculating the color palette or dominant color.

Calculating k-means

A basic workflow consists of reading a pixel buffer in, converting it into a flat array, then using that array with the k-means functions. The following example converts an array of u8 into Lab colors then finds the k-means.

use palette::{Lab, Pixel, Srgb};
use kmeans_colors::{get_kmeans, Calculate, Kmeans, MapColor, Sort};

// An image buffer of one black pixel and one white pixel
let img_vec = [0u8, 0, 0, 255, 255, 255];

// Convert RGB [u8] buffer to Lab for k-means
let lab: Vec<Lab> = Srgb::from_raw_slice(&img_vec)
    .iter()
    .map(|x| x.into_format().into())
    .collect();

// Iterate over the runs, keep the best results
let mut result = Kmeans::new();
(0..runs).for_each(|i| {
    let run_result = get_kmeans(
        k,
        max_iter,
        converge,
        verbose,
        &lab,
        seed + i as u64,
    );
    if run_result.score < result.score {
        result = run_result;
    }
});

// Convert indexed colors back to Srgb<u8> for output
let rgb = &result.centroids
    .iter()
    .map(|x| Srgb::from(*x).into_format())
    .collect::<Vec<Srgb<u8>>>();
let buffer = Srgb::map_indices_to_centroids(&rgb, &result.indices);

Because the initial seeds are random, the k-means calculation should be run multiple times in order to assure that the best result has been found. The algorithm may find itself in local minima that is not the optimal result. This is especially true for Lab but Srgb may only need one run.

The binary uses 8 as the default k. The iteration limit is set to 20, RGB usually converges in under 10 iterations depending on the k. The convergence factor defaults to 10.0 for Lab and 0.0025 for Srgb. The number of runs defaults to 3 for one of the binary subcommands. Through testing, these numbers were found to be an adequate trade-off between performance and accuracy. If the results do not appear correct, raise the iteration limit as convergence was probably not met.

Getting the dominant color

After k-means calculation, the dominant color can be found by sorting the results and taking the centroid of the first item. The sort_indexed_colors function sorts the colors from darkest to lightest and returns an array of CentroidData.

use kmeans_colors::Sort;

// Using the results from the previous example, process the centroid data
let mut res = Lab::sort_indexed_colors(&result.centroids, &result.indices);

// We can find the dominant color directly
let dominant_color = Lab::get_dominant_color(&res);

// Or we can manually sort the vec by percentage, and the most appearing
// color will be the first element
res.sort_unstable_by(|a, b| (b.percentage).partial_cmp(&a.percentage).unwrap());
let dominant_color = res.first().unwrap().centroid;

Structs

CentroidData	A struct containing a centroid, its percentage within a buffer, and the centroid's index.
Kmeans	Result of k-means calculation with convergence score, centroids, and indexed buffer.

Traits

Calculate	A trait for enabling k-means calculation of a data type.
MapColor	A trait for mapping colors to their corresponding centroids.
Sort	A trait for sorting indexed k-means colors.

Functions

get_kmeans

Find the k-means centroids of a buffer.