commonMain.quantize.QuantizerCelebi.kt Maven / Gradle / Ivy
/*
* Copyright (c) 2024, Google LLC, OpenSavvy and contributors.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package opensavvy.material3.colors.quantize
/**
* An image quantizer that improves on the quality of a standard K-Means algorithm by setting the
* K-Means initial state to the output of a Wu quantizer, instead of random centroids. Improves on
* speed by several optimizations, as implemented in Wsmeans, or Weighted Square Means, K-Means with
* those optimizations.
*
*
* This algorithm was designed by M. Emre Celebi, and was found in their 2011 paper, Improving
* the Performance of K-Means for Color Quantization. https://arxiv.org/abs/1101.0395
*/
object QuantizerCelebi {
/**
* Reduce the number of colors needed to represented the input, minimizing the difference between
* the original image and the recolored image.
*
* @param pixels Colors in ARGB format.
* @param maxColors The number of colors to divide the image into. A lower number of colors may be
* returned.
* @return Map with keys of colors in ARGB format, and values of number of pixels in the original
* image that correspond to the color in the quantized image.
*/
fun quantize(pixels: IntArray, maxColors: Int): Map {
val wu = QuantizerWu()
val wuResult: QuantizerResult = wu.quantize(pixels, maxColors)
val wuClustersAsObjects: Set = wuResult.colorToCount.keys
var index = 0
val wuClusters = IntArray(wuClustersAsObjects.size)
for (argb in wuClustersAsObjects) {
wuClusters[index++] = argb
}
return QuantizerWsmeans.quantize(pixels, wuClusters, maxColors)
}
}