| /* |
| * Copyright (C) 2021 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.internal.graphics.palette; |
| |
| import java.util.List; |
| |
| /** |
| * An implementation of Celebi's quantization method. |
| * See Celebi 2011, “Improving the Performance of K-Means for Color Quantization” |
| * |
| * First, Wu's quantizer runs. The results are used as starting points for a subsequent Kmeans |
| * run. Using Wu's quantizer ensures 100% reproducible quantization results, because the starting |
| * centroids are always the same. It also ensures high quality results, Wu is a box-cutting |
| * quantization algorithm, much like medican color cut. It minimizes variance, much like Kmeans. |
| * Wu is shown to be the highest quality box-cutting quantization algorithm. |
| * |
| * Second, a Kmeans quantizer tweaked for performance is run. Celebi calls this a weighted |
| * square means quantizer, or WSMeans. Optimizations include operating on a map of image pixels |
| * rather than all image pixels, and avoiding excess color distance calculations by using a |
| * matrix and geometrical properties to know when there won't be any cluster closer to a pixel. |
| */ |
| public class CelebiQuantizer implements Quantizer { |
| private List<Palette.Swatch> mSwatches; |
| |
| public CelebiQuantizer() { |
| } |
| |
| @Override |
| public void quantize(int[] pixels, int maxColors) { |
| WuQuantizer wu = new WuQuantizer(); |
| wu.quantize(pixels, maxColors); |
| WSMeansQuantizer kmeans = new WSMeansQuantizer(wu.getColors(), new LABPointProvider(), |
| wu.inputPixelToCount()); |
| kmeans.quantize(pixels, maxColors); |
| mSwatches = kmeans.getQuantizedColors(); |
| } |
| |
| @Override |
| public List<Palette.Swatch> getQuantizedColors() { |
| return mSwatches; |
| } |
| } |