xref: /aosp_15_r20/external/libmonet/quantize/QuantizerCelebi.java (revision 970e10460f970939fd510dd6ad3e0d65908272e3)

/*
 * Copyright 2021 Google LLC
 *
 * 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.google.ux.material.libmonet.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);
  }
}