content: /*
   * 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.material.color.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);
    }
  }
  