android-34/com/android/systemui/monet/quantize/QuantizerCelebi.java

/*
 * Copyright (C) 2023 The Android Open Source Project
 *
 * 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 com.android.systemui.monet.quantize;

import java.util.Map;
import java.util.Set;

/**
 * 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.
 *
 * <p>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
 */
public final class QuantizerCelebi {
    private 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.
     */
    public static Map<Integer, Integer> quantize(int[] pixels, int maxColors) {
        QuantizerWu wu = new QuantizerWu();
        QuantizerResult wuResult = wu.quantize(pixels, maxColors);

        Set<Integer> wuClustersAsObjects = wuResult.colorToCount.keySet();
        int index = 0;
        int[] wuClusters = new int[wuClustersAsObjects.size()];
        for (Integer argb : wuClustersAsObjects) {
            wuClusters[index++] = argb;
        }

        return QuantizerWsmeans.quantize(pixels, wuClusters, maxColors);
    }
}