Merge changes from topics "quantizer_improved_logic", "reintroduce_scaling_fixes" into sc-dev

* changes:
  Quantize image to 128 colors instead of 5
  Quantizer improvements
  Sort colors from high count => low count
  Don't interpolate image input to quantizer
  Cache set wallpaper as PNG instead of JPEG
  Only rescale wallpaper if its > display height

core/java/android/app/WallpaperColors.java

@@ -30,6 +30,7 @@ import android.util.Log;
 import android.util.Size;
 
 import com.android.internal.graphics.ColorUtils;
+import com.android.internal.graphics.cam.Cam;
 import com.android.internal.graphics.palette.CelebiQuantizer;
 import com.android.internal.graphics.palette.Palette;
 import com.android.internal.graphics.palette.VariationalKMeansQuantizer;
@@ -43,7 +44,7 @@ import java.util.Collections;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
-import java.util.stream.Collectors;
+import java.util.Set;
 
 /**
  * Provides information about the colors of a wallpaper.
@@ -176,7 +177,7 @@ public final class WallpaperColors implements Parcelable {
             shouldRecycle = true;
             Size optimalSize = calculateOptimalSize(bitmap.getWidth(), bitmap.getHeight());
             bitmap = Bitmap.createScaledBitmap(bitmap, optimalSize.getWidth(),
-                    optimalSize.getHeight(), true /* filter */);
+                    optimalSize.getHeight(), false /* filter */);
         }
 
         final Palette palette;
@@ -189,7 +190,7 @@ public final class WallpaperColors implements Parcelable {
         } else {
             palette = Palette
                     .from(bitmap, new CelebiQuantizer())
-                    .maximumColorCount(5)
+                    .maximumColorCount(128)
                     .resizeBitmapArea(MAX_WALLPAPER_EXTRACTION_AREA)
                     .generate();
         }
@@ -278,7 +279,7 @@ public final class WallpaperColors implements Parcelable {
     /**
      * Constructs a new object from a set of colors, where hints can be specified.
      *
-     * @param populationByColor Map with keys of colors, and value representing the number of
+     * @param colorToPopulation Map with keys of colors, and value representing the number of
      *                          occurrences of color in the wallpaper.
      * @param colorHints        A combination of color hints.
      * @hide
@@ -286,20 +287,105 @@ public final class WallpaperColors implements Parcelable {
      * @see WallpaperColors#fromBitmap(Bitmap)
      * @see WallpaperColors#fromDrawable(Drawable)
      */
-    public WallpaperColors(@NonNull Map<Integer, Integer> populationByColor,
+    public WallpaperColors(@NonNull Map<Integer, Integer> colorToPopulation,
             @ColorsHints int colorHints) {
-        mAllColors = populationByColor;
+        mAllColors = colorToPopulation;
 
-        ArrayList<Map.Entry<Integer, Integer>> mapEntries = new ArrayList(
+        final Map<Integer, Cam> colorToCam = new HashMap<>();
-                populationByColor.entrySet());
+        for (int color : colorToPopulation.keySet()) {
-        mapEntries.sort((a, b) ->
+            colorToCam.put(color, Cam.fromInt(color));
-                a.getValue().compareTo(b.getValue())
+        }
-        );
+        final double[] hueProportions = hueProportions(colorToCam, colorToPopulation);
-        mMainColors = mapEntries.stream().map(entry -> Color.valueOf(entry.getKey())).collect(
+        final Map<Integer, Double> colorToHueProportion = colorToHueProportion(
-                Collectors.toList());
+                colorToPopulation.keySet(), colorToCam, hueProportions);
+
+        final Map<Integer, Double> colorToScore = new HashMap<>();
+        for (Map.Entry<Integer, Double> mapEntry : colorToHueProportion.entrySet()) {
+            int color = mapEntry.getKey();
+            double proportion = mapEntry.getValue();
+            double score = score(colorToCam.get(color), proportion);
+            colorToScore.put(color, score);
+        }
+        ArrayList<Map.Entry<Integer, Double>> mapEntries = new ArrayList(colorToScore.entrySet());
+        mapEntries.sort((a, b) -> b.getValue().compareTo(a.getValue()));
+
+        List<Integer> colorsByScoreDescending = new ArrayList<>();
+        for (Map.Entry<Integer, Double> colorToScoreEntry : mapEntries) {
+            colorsByScoreDescending.add(colorToScoreEntry.getKey());
+        }
+
+        List<Integer> mainColorInts = new ArrayList<>();
+        findSeedColorLoop:
+        for (int color : colorsByScoreDescending) {
+            Cam cam = colorToCam.get(color);
+            for (int otherColor : mainColorInts) {
+                Cam otherCam = colorToCam.get(otherColor);
+                if (hueDiff(cam, otherCam) < 15) {
+                    continue findSeedColorLoop;
+                }
+            }
+            mainColorInts.add(color);
+        }
+        List<Color> mainColors = new ArrayList<>();
+        for (int colorInt : mainColorInts) {
+            mainColors.add(Color.valueOf(colorInt));
+        }
+        mMainColors = mainColors;
         mColorHints = colorHints;
     }
 
+    private static double hueDiff(Cam a, Cam b) {
+        return (180f - Math.abs(Math.abs(a.getHue() - b.getHue()) - 180f));
+    }
+
+    private static double score(Cam cam, double proportion) {
+        return cam.getChroma() + (proportion * 100);
+    }
+
+    private static Map<Integer, Double> colorToHueProportion(Set<Integer> colors,
+            Map<Integer, Cam> colorToCam, double[] hueProportions) {
+        Map<Integer, Double> colorToHueProportion = new HashMap<>();
+        for (int color : colors) {
+            final int hue = wrapDegrees(Math.round(colorToCam.get(color).getHue()));
+            double proportion = 0.0;
+            for (int i = hue - 15; i < hue + 15; i++) {
+                proportion += hueProportions[wrapDegrees(i)];
+            }
+            colorToHueProportion.put(color, proportion);
+        }
+        return colorToHueProportion;
+    }
+
+    private static int wrapDegrees(int degrees) {
+        if (degrees < 0) {
+            return (degrees % 360) + 360;
+        } else if (degrees >= 360) {
+            return degrees % 360;
+        } else {
+            return degrees;
+        }
+    }
+
+    private static double[] hueProportions(@NonNull Map<Integer, Cam> colorToCam,
+            Map<Integer, Integer> colorToPopulation) {
+        final double[] proportions = new double[360];
+
+        double totalPopulation = 0;
+        for (Map.Entry<Integer, Integer> entry : colorToPopulation.entrySet()) {
+            totalPopulation += entry.getValue();
+        }
+
+        for (Map.Entry<Integer, Integer> entry : colorToPopulation.entrySet()) {
+            final int color = (int) entry.getKey();
+            final int population = colorToPopulation.get(color);
+            final Cam cam = colorToCam.get(color);
+            final int hue = wrapDegrees(Math.round(cam.getHue()));
+            proportions[hue] = proportions[hue] + ((double) population / totalPopulation);
+        }
+
+        return proportions;
+    }
+
     public static final @android.annotation.NonNull Creator<WallpaperColors> CREATOR = new Creator<WallpaperColors>() {
         @Override
         public WallpaperColors createFromParcel(Parcel in) {

core/java/com/android/internal/graphics/palette/CelebiQuantizer.java

@@ -19,26 +19,32 @@ package com.android.internal.graphics.palette;
 import java.util.List;
 
 /**
- * An implementation of Celebi's WSM quantizer, or, a Kmeans quantizer that starts with centroids
+ * An implementation of Celebi's quantization method.
- * from a Wu quantizer to ensure 100% reproducible and quality results, and has some optimizations
- * to the Kmeans algorithm.
- *
  * 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() { }
+    public CelebiQuantizer() {
+    }
 
     @Override
     public void quantize(int[] pixels, int maxColors) {
-        WuQuantizer wu = new WuQuantizer(pixels, maxColors);
+        WuQuantizer wu = new WuQuantizer();
         wu.quantize(pixels, maxColors);
-        List<Palette.Swatch> wuSwatches = wu.getQuantizedColors();
+        WSMeansQuantizer kmeans = new WSMeansQuantizer(wu.getColors(), new LABPointProvider(),
-        LABCentroid labCentroidProvider = new LABCentroid();
+                wu.inputPixelToCount());
-        WSMeansQuantizer kmeans =
-                new WSMeansQuantizer(WSMeansQuantizer.createStartingCentroids(labCentroidProvider,
-                        wuSwatches), labCentroidProvider, pixels, maxColors);
         kmeans.quantize(pixels, maxColors);
         mSwatches = kmeans.getQuantizedColors();
     }

core/java/com/android/internal/graphics/palette/LABPointProvider.java

@@ -26,11 +26,11 @@ import android.graphics.ColorSpace;
  *  in L*a*b* space, also known as deltaE, is a universally accepted standard across industries
  *  and worldwide.
  */
-public class LABCentroid implements CentroidProvider {
+public class LABPointProvider implements PointProvider {
     final ColorSpace.Connector mRgbToLab;
     final ColorSpace.Connector mLabToRgb;
 
-    public LABCentroid() {
+    public LABPointProvider() {
         mRgbToLab = ColorSpace.connect(
                 ColorSpace.get(ColorSpace.Named.SRGB),
                 ColorSpace.get(ColorSpace.Named.CIE_LAB));
@@ -39,7 +39,7 @@ public class LABCentroid implements CentroidProvider {
     }
 
     @Override
-    public float[] getCentroid(int color) {
+    public float[] fromInt(int color) {
         float r = Color.red(color) / 255.f;
         float g =  Color.green(color) / 255.f;
         float b = Color.blue(color) / 255.f;
@@ -49,7 +49,7 @@ public class LABCentroid implements CentroidProvider {
     }
 
     @Override
-    public int getColor(float[] centroid) {
+    public int toInt(float[] centroid) {
         float[] rgb = mLabToRgb.transform(centroid);
         int color = Color.rgb(rgb[0], rgb[1], rgb[2]);
         return color;

core/java/com/android/internal/graphics/palette/Mean.java

@@ -1,44 +0,0 @@
-/*
- * 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.Random;
-
-/**
- * Represents a centroid in Kmeans algorithms.
- */
-public class Mean {
-    public float[] center;
-
-    /**
-     * Constructor.
-     *
-     * @param upperBound maximum value of a dimension in the space Kmeans is optimizing in
-     * @param random used to generate a random center
-     */
-    Mean(int upperBound, Random random) {
-        center =
-                new float[]{
-                        random.nextInt(upperBound + 1), random.nextInt(upperBound + 1),
-                        random.nextInt(upperBound + 1)
-                };
-    }
-
-    Mean(float[] center) {
-        this.center = center;
-    }
-}

core/java/com/android/internal/graphics/palette/MeanBucket.java

@@ -1,42 +0,0 @@
-/*
- * 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.HashSet;
-import java.util.Set;
-
-class MeanBucket {
-    float[] mTotal = {0.f, 0.f, 0.f};
-    int mCount = 0;
-    Set<Integer> mColors = new HashSet<>();
-
-    void add(float[] colorAsDoubles, int color, int colorCount) {
-        assert (colorAsDoubles.length == 3);
-        mColors.add(color);
-        mTotal[0] += (colorAsDoubles[0] * colorCount);
-        mTotal[1] += (colorAsDoubles[1] * colorCount);
-        mTotal[2] += (colorAsDoubles[2] * colorCount);
-        mCount += colorCount;
-    }
-
-    float[] getCentroid() {
-        if (mCount == 0) {
-            return null;
-        }
-        return new float[]{mTotal[0] / mCount, mTotal[1] / mCount, mTotal[2] / mCount};
-    }
-}

core/java/com/android/internal/graphics/palette/PointProvider.java

@@ -18,21 +18,18 @@ package com.android.internal.graphics.palette;
 
 import android.annotation.ColorInt;
 
-interface CentroidProvider {
+/**
-    /**
+ * Interface that allows quantizers to have a plug-and-play interface for experimenting with
-     * @return 3 dimensions representing the color
+ * quantization in different color spaces.
-     */
+ */
-    float[] getCentroid(@ColorInt int color);
+public interface PointProvider {
+    /** Convert a color to 3 coordinates representing the color in a color space. */
+    float[] fromInt(@ColorInt int argb);
 
-    /**
+    /** Convert 3 coordinates in the color space into a color */
-     * @param centroid 3 dimensions representing the color
-     * @return 32-bit ARGB representation
-     */
     @ColorInt
-    int getColor(float[] centroid);
+    int toInt(float[] point);
 
-    /**
+    /** Find the distance between two colosrin the color space */
-     * Distance between two centroids.
-     */
     float distance(float[] a, float[] b);
 }

core/java/com/android/internal/graphics/palette/QuantizerMap.java

@@ -0,0 +1,62 @@
+/*
+ * 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 android.annotation.NonNull;
+import android.annotation.Nullable;
+
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * Converts a set of pixels/colors into a map with keys of unique colors, and values of the count
+ * of the unique color in the original set of pixels.
+ *
+ * This allows other quantizers to get a significant speed boost by simply running this quantizer,
+ * and then performing operations using the map, rather than for each pixel.
+ */
+public final class QuantizerMap implements Quantizer {
+    private HashMap<Integer, Integer> mColorToCount;
+    private Palette mPalette;
+
+    @Override
+    public void quantize(@NonNull int[] pixels, int colorCount) {
+        final HashMap<Integer, Integer> colorToCount = new HashMap<>();
+        for (int pixel : pixels) {
+            colorToCount.merge(pixel, 1, Integer::sum);
+        }
+        mColorToCount = colorToCount;
+
+        List<Palette.Swatch> swatches = new ArrayList<>();
+        for (Map.Entry<Integer, Integer> entry : colorToCount.entrySet()) {
+            swatches.add(new Palette.Swatch(entry.getKey(), entry.getValue()));
+        }
+        mPalette = Palette.from(swatches);
+    }
+
+    @Override
+    public List<Palette.Swatch> getQuantizedColors() {
+        return mPalette.getSwatches();
+    }
+
+    @Nullable
+    public Map<Integer, Integer> getColorToCount() {
+        return mColorToCount;
+    }
+}

core/java/com/android/internal/graphics/palette/WSMeansQuantizer.java

@@ -16,18 +16,20 @@
 
 package com.android.internal.graphics.palette;
 
+import android.annotation.NonNull;
+import android.annotation.Nullable;
+import android.util.Log;
+
 import java.util.ArrayList;
 import java.util.Arrays;
-import java.util.HashMap;
 import java.util.HashSet;
 import java.util.List;
 import java.util.Map;
 import java.util.Random;
 import java.util.Set;
 
-
 /**
- * A color quantizer based on the Kmeans algorithm.
+ * A color quantizer based on the Kmeans algorithm. Prefer using QuantizerCelebi.
  *
  * This is an implementation of Kmeans based on Celebi's 2011 paper,
  * "Improving the Performance of K-Means for Color Quantization". In the paper, this algorithm is
@@ -36,253 +38,237 @@ import java.util.Set;
  * well as indexing colors by their count, thus minimizing the number of points to move around.
  *
  * Celebi's paper also stabilizes results and guarantees high quality by using starting centroids
- * from Wu's quantization algorithm. See CelebiQuantizer for more info.
+ * from Wu's quantization algorithm. See QuantizerCelebi for more info.
  */
-public class WSMeansQuantizer implements Quantizer {
+public final class WSMeansQuantizer implements Quantizer {
-    Mean[] mMeans;
+    private static final String TAG = "QuantizerWsmeans";
-    private final Map<Integer, Integer> mCountByColor = new HashMap<>();
+    private static final boolean DEBUG = false;
-    private final Map<Integer, Integer> mMeanIndexByColor = new HashMap<>();
+    private static final int MAX_ITERATIONS = 10;
-    private final Set<Integer> mUniqueColors = new HashSet<>();
+    // Points won't be moved to a closer cluster, if the closer cluster is within
-    private final List<Palette.Swatch> mSwatches = new ArrayList<>();
+    // this distance. 3.0 used because L*a*b* delta E < 3 is considered imperceptible.
-    private final CentroidProvider mCentroidProvider;
+    private static final float MIN_MOVEMENT_DISTANCE = 3.0f;
 
-    public WSMeansQuantizer(
+    private final PointProvider mPointProvider;
-            float[][] means, CentroidProvider centroidProvider, int[] pixels, int maxColors) {
+    private @Nullable Map<Integer, Integer> mInputPixelToCount;
-        if (pixels == null) {
+    private float[][] mClusters;
-            pixels = new int[]{};
+    private int[] mClusterPopulations;
-        }
+    private float[][] mPoints;
-        mCentroidProvider = centroidProvider;
+    private int[] mPixels;
-        mMeans = new Mean[maxColors];
+    private int[] mClusterIndices;
-        for (int i = 0; i < means.length; i++) {
+    private int[][] mIndexMatrix = {};
-            mMeans[i] = new Mean(means[i]);
+    private float[][] mDistanceMatrix = {};
+
+    private Palette mPalette;
+
+    public WSMeansQuantizer(int[] inClusters, PointProvider pointProvider,
+            @Nullable Map<Integer, Integer> inputPixelToCount) {
+        mPointProvider = pointProvider;
+
+        mClusters = new float[inClusters.length][3];
+        int index = 0;
+        for (int cluster : inClusters) {
+            float[] point = pointProvider.fromInt(cluster);
+            mClusters[index++] = point;
         }
 
-        if (maxColors > means.length) {
+        mInputPixelToCount = inputPixelToCount;
-            // Always initialize Random with the same seed. Ensures the results of quantization
-            // are consistent, even when random centroids are required.
-            Random random = new Random(0x42688);
-            int randomMeansToCreate = maxColors - means.length;
-            for (int i = 0; i < randomMeansToCreate; i++) {
-                mMeans[means.length + i] = new Mean(100, random);
-            }
-        }
-
-        for (int pixel : pixels) {
-            // These are pixels from the bitmap that is being quantized.
-            // Depending on the bitmap & downscaling, it may have pixels that are less than opaque
-            // Ignore those pixels.
-            ///
-            // Note: they don't _have_ to be ignored, for example, we could instead turn them
-            // opaque. Traditionally, including outside Android, quantizers ignore transparent
-            // pixels, so that strategy was chosen.
-            int alpha = (pixel >> 24) & 0xff;
-            if (alpha < 255) {
-                continue;
-            }
-            Integer currentCount = mCountByColor.get(pixel);
-            if (currentCount == null) {
-                currentCount = 0;
-                mUniqueColors.add(pixel);
-            }
-            mCountByColor.put(pixel, currentCount + 1);
-        }
-        for (int color : mUniqueColors) {
-            int closestMeanIndex = -1;
-            double closestMeanDistance = -1;
-            float[] centroid = mCentroidProvider.getCentroid(color);
-            for (int i = 0; i < mMeans.length; i++) {
-                double distance = mCentroidProvider.distance(centroid, mMeans[i].center);
-                if (closestMeanIndex == -1 || distance < closestMeanDistance) {
-                    closestMeanIndex = i;
-                    closestMeanDistance = distance;
-                }
-            }
-            mMeanIndexByColor.put(color, closestMeanIndex);
-        }
-
-        if (pixels.length == 0) {
-            return;
-        }
-
-        predict(maxColors, 0);
-    }
-
-    /** Create starting centroids for K-means from a set of colors. */
-    public static float[][] createStartingCentroids(CentroidProvider centroidProvider,
-            List<Palette.Swatch> swatches) {
-        float[][] startingCentroids = new float[swatches.size()][];
-        for (int i = 0; i < swatches.size(); i++) {
-            startingCentroids[i] = centroidProvider.getCentroid(swatches.get(i).getInt());
-        }
-        return startingCentroids;
-    }
-
-    /** Create random starting centroids for K-means. */
-    public static float[][] randomMeans(int maxColors, int upperBound) {
-        float[][] means = new float[maxColors][];
-
-        // Always initialize Random with the same seed. Ensures the results of quantization
-        // are consistent, even when random centroids are required.
-        Random random = new Random(0x42688);
-        for (int i = 0; i < maxColors; i++) {
-            means[i] = new Mean(upperBound, random).center;
-        }
-        return means;
-    }
-
-
-    @Override
-    public void quantize(int[] pixels, int maxColors) {
-
     }
 
     @Override
     public List<Palette.Swatch> getQuantizedColors() {
-        return mSwatches;
+        return mPalette.getSwatches();
     }
 
-    private void predict(int maxColors, int iterationsCompleted) {
+    @Override
-        double[][] centroidDistance = new double[maxColors][maxColors];
+    public void quantize(@NonNull int[] pixels, int maxColors) {
-        for (int i = 0; i <= maxColors; i++) {
+        assert (pixels.length > 0);
-            for (int j = i + 1; j < maxColors; j++) {
+
-                float[] meanI = mMeans[i].center;
+        if (mInputPixelToCount == null) {
-                float[] meanJ = mMeans[j].center;
+            QuantizerMap mapQuantizer = new QuantizerMap();
-                double distance = mCentroidProvider.distance(meanI, meanJ);
+            mapQuantizer.quantize(pixels, maxColors);
-                centroidDistance[i][j] = distance;
+            mInputPixelToCount = mapQuantizer.getColorToCount();
-                centroidDistance[j][i] = distance;
+        }
+
+        mPoints = new float[mInputPixelToCount.size()][3];
+        mPixels = new int[mInputPixelToCount.size()];
+        int index = 0;
+        for (int pixel : mInputPixelToCount.keySet()) {
+            mPixels[index] = pixel;
+            mPoints[index] = mPointProvider.fromInt(pixel);
+            index++;
+        }
+        if (mClusters.length > 0) {
+            // This implies that the constructor was provided starting clusters. If that was the
+            // case, we limit the number of clusters to the number of starting clusters and don't
+            // initialize random clusters.
+            maxColors = Math.min(maxColors, mClusters.length);
+        }
+        maxColors = Math.min(maxColors, mPoints.length);
+
+        initializeClusters(maxColors);
+        for (int i = 0; i < MAX_ITERATIONS; i++) {
+            calculateClusterDistances(maxColors);
+            if (!reassignPoints(maxColors)) {
+                break;
             }
-        }
+            recalculateClusterCenters(maxColors);
-
-        // Construct a K×K matrix M in which row i is a permutation of
-        // 1,2,…,K that represents the clusters in increasing order of
-        // distance of their centers from ci;
-        int[][] distanceMatrix = new int[maxColors][maxColors];
-        for (int i = 0; i < maxColors; i++) {
-            double[] distancesFromIToAnotherMean = centroidDistance[i];
-            double[] sortedByDistanceAscending = distancesFromIToAnotherMean.clone();
-            Arrays.sort(sortedByDistanceAscending);
-            int[] outputRow = new int[maxColors];
-            for (int j = 0; j < maxColors; j++) {
-                outputRow[j] = findIndex(distancesFromIToAnotherMean, sortedByDistanceAscending[j]);
-            }
-            distanceMatrix[i] = outputRow;
-        }
-
-        //   for (i=1;i≤N′;i=i+ 1) do
-        //   Let Sp be the cluster that xi was assigned to in the previous
-        //   iteration;
-        //   p=m[i];
-        //   min_dist=prev_dist=jjxi−cpjj2;
-        boolean anyColorMoved = false;
-        for (int intColor : mUniqueColors) {
-            float[] color = mCentroidProvider.getCentroid(intColor);
-            int indexOfCurrentMean = mMeanIndexByColor.get(intColor);
-            Mean currentMean = mMeans[indexOfCurrentMean];
-            double minDistance = mCentroidProvider.distance(color, currentMean.center);
-            for (int j = 1; j < maxColors; j++) {
-                int indexOfClusterFromCurrentToJ = distanceMatrix[indexOfCurrentMean][j];
-                double distanceBetweenJAndCurrent =
-                        centroidDistance[indexOfCurrentMean][indexOfClusterFromCurrentToJ];
-                if (distanceBetweenJAndCurrent >= (4 * minDistance)) {
-                    break;
-                }
-                double distanceBetweenJAndColor = mCentroidProvider.distance(mMeans[j].center,
-                        color);
-                if (distanceBetweenJAndColor < minDistance) {
-                    minDistance = distanceBetweenJAndColor;
-                    mMeanIndexByColor.remove(intColor);
-                    mMeanIndexByColor.put(intColor, j);
-                    anyColorMoved = true;
-                }
-            }
-        }
-
-        List<MeanBucket> buckets = new ArrayList<>();
-        for (int i = 0; i < maxColors; i++) {
-            buckets.add(new MeanBucket());
-        }
-
-        for (int intColor : mUniqueColors) {
-            int meanIndex = mMeanIndexByColor.get(intColor);
-            MeanBucket meanBucket = buckets.get(meanIndex);
-            meanBucket.add(mCentroidProvider.getCentroid(intColor), intColor,
-                    mCountByColor.get(intColor));
         }
 
         List<Palette.Swatch> swatches = new ArrayList<>();
-        boolean done = !anyColorMoved && iterationsCompleted > 0 || iterationsCompleted >= 100;
+        for (int i = 0; i < maxColors; i++) {
-        if (done) {
+            float[] cluster = mClusters[i];
-            for (int i = 0; i < buckets.size(); i++) {
+            int colorInt = mPointProvider.toInt(cluster);
-                MeanBucket a = buckets.get(i);
+            swatches.add(new Palette.Swatch(colorInt, mClusterPopulations[i]));
-                if (a.mCount <= 0) {
-                    continue;
-                }
-                List<MeanBucket> bucketsToMerge = new ArrayList<>();
-                for (int j = i + 1; j < buckets.size(); j++) {
-                    MeanBucket b = buckets.get(j);
-                    if (b.mCount == 0) {
-                        continue;
-                    }
-                    float[] bCentroid = b.getCentroid();
-                    assert (a.mCount > 0);
-                    assert (a.getCentroid() != null);
-
-                    assert (bCentroid != null);
-                    if (mCentroidProvider.distance(a.getCentroid(), b.getCentroid()) < 5) {
-                        bucketsToMerge.add(b);
-                    }
-                }
-
-                for (MeanBucket bucketToMerge : bucketsToMerge) {
-                    float[] centroid = bucketToMerge.getCentroid();
-                    a.add(centroid, mCentroidProvider.getColor(centroid), bucketToMerge.mCount);
-                    buckets.remove(bucketToMerge);
-                }
-            }
-
-            for (MeanBucket bucket : buckets) {
-                float[] centroid = bucket.getCentroid();
-                if (centroid == null) {
-                    continue;
-                }
-
-                int rgb = mCentroidProvider.getColor(centroid);
-                swatches.add(new Palette.Swatch(rgb, bucket.mCount));
-                mSwatches.clear();
-                mSwatches.addAll(swatches);
-            }
-        } else {
-            List<MeanBucket> emptyBuckets = new ArrayList<>();
-            for (int i = 0; i < buckets.size(); i++) {
-                MeanBucket bucket = buckets.get(i);
-                if ((bucket.getCentroid() == null) || (bucket.mCount == 0)) {
-                    emptyBuckets.add(bucket);
-                    for (Integer color : mUniqueColors) {
-                        int meanIndex = mMeanIndexByColor.get(color);
-                        if (meanIndex > i) {
-                            mMeanIndexByColor.put(color, meanIndex--);
-                        }
-                    }
-                }
-            }
-
-            Mean[] newMeans = new Mean[buckets.size()];
-            for (int i = 0; i < buckets.size(); i++) {
-                float[] centroid = buckets.get(i).getCentroid();
-                newMeans[i] = new Mean(centroid);
-            }
-
-            predict(buckets.size(), iterationsCompleted + 1);
         }
-
+        mPalette = Palette.from(swatches);
     }
 
-    private static int findIndex(double[] list, double element) {
+
-        for (int i = 0; i < list.length; i++) {
+    private void initializeClusters(int maxColors) {
-            if (list[i] == element) {
+        boolean hadInputClusters = mClusters.length > 0;
-                return i;
+        if (!hadInputClusters) {
+            int additionalClustersNeeded = maxColors - mClusters.length;
+            if (DEBUG) {
+                Log.d(TAG, "have " + mClusters.length + " clusters, want " + maxColors
+                        + " results, so need " + additionalClustersNeeded + " additional clusters");
+            }
+
+            Random random = new Random(0x42688);
+            List<float[]> additionalClusters = new ArrayList<>(additionalClustersNeeded);
+            Set<Integer> clusterIndicesUsed = new HashSet<>();
+            for (int i = 0; i < additionalClustersNeeded; i++) {
+                int index = random.nextInt(mPoints.length);
+                while (clusterIndicesUsed.contains(index)
+                        && clusterIndicesUsed.size() < mPoints.length) {
+                    index = random.nextInt(mPoints.length);
+                }
+                clusterIndicesUsed.add(index);
+                additionalClusters.add(mPoints[index]);
+            }
+
+            float[][] newClusters = (float[][]) additionalClusters.toArray();
+            float[][] clusters = Arrays.copyOf(mClusters, maxColors);
+            System.arraycopy(newClusters, 0, clusters, clusters.length, newClusters.length);
+            mClusters = clusters;
+        }
+
+        mClusterIndices = new int[mPixels.length];
+        mClusterPopulations = new int[mPixels.length];
+        Random random = new Random(0x42688);
+        for (int i = 0; i < mPixels.length; i++) {
+            int clusterIndex = random.nextInt(maxColors);
+            mClusterIndices[i] = clusterIndex;
+            mClusterPopulations[i] = mInputPixelToCount.get(mPixels[i]);
+        }
+    }
+
+    void calculateClusterDistances(int maxColors) {
+        if (mDistanceMatrix.length != maxColors) {
+            mDistanceMatrix = new float[maxColors][maxColors];
+        }
+
+        for (int i = 0; i <= maxColors; i++) {
+            for (int j = i + 1; j < maxColors; j++) {
+                float distance = mPointProvider.distance(mClusters[i], mClusters[j]);
+                mDistanceMatrix[j][i] = distance;
+                mDistanceMatrix[i][j] = distance;
             }
         }
-        throw new IllegalArgumentException("Element not in list");
+
+        if (mIndexMatrix.length != maxColors) {
+            mIndexMatrix = new int[maxColors][maxColors];
+        }
+
+        for (int i = 0; i < maxColors; i++) {
+            ArrayList<Distance> distances = new ArrayList<>(maxColors);
+            for (int index = 0; index < maxColors; index++) {
+                distances.add(new Distance(index, mDistanceMatrix[i][index]));
+            }
+            distances.sort(
+                    (a, b) -> Float.compare(a.getDistance(), b.getDistance()));
+
+            for (int j = 0; j < maxColors; j++) {
+                mIndexMatrix[i][j] = distances.get(j).getIndex();
+            }
+        }
+    }
+
+    boolean reassignPoints(int maxColors) {
+        boolean colorMoved = false;
+        for (int i = 0; i < mPoints.length; i++) {
+            float[] point = mPoints[i];
+            int previousClusterIndex = mClusterIndices[i];
+            float[] previousCluster = mClusters[previousClusterIndex];
+            float previousDistance = mPointProvider.distance(point, previousCluster);
+
+            float minimumDistance = previousDistance;
+            int newClusterIndex = -1;
+            for (int j = 1; j < maxColors; j++) {
+                int t = mIndexMatrix[previousClusterIndex][j];
+                if (mDistanceMatrix[previousClusterIndex][t] >= 4 * previousDistance) {
+                    // Triangle inequality proves there's can be no closer center.
+                    break;
+                }
+                float distance = mPointProvider.distance(point, mClusters[t]);
+                if (distance < minimumDistance) {
+                    minimumDistance = distance;
+                    newClusterIndex = t;
+                }
+            }
+            if (newClusterIndex != -1) {
+                float distanceChange = (float)
+                        Math.abs((Math.sqrt(minimumDistance) - Math.sqrt(previousDistance)));
+                if (distanceChange > MIN_MOVEMENT_DISTANCE) {
+                    colorMoved = true;
+                    mClusterIndices[i] = newClusterIndex;
+                }
+            }
+        }
+        return colorMoved;
+    }
+
+    void recalculateClusterCenters(int maxColors) {
+        mClusterPopulations = new int[maxColors];
+        float[] aSums = new float[maxColors];
+        float[] bSums = new float[maxColors];
+        float[] cSums = new float[maxColors];
+        for (int i = 0; i < mPoints.length; i++) {
+            int clusterIndex = mClusterIndices[i];
+            float[] point = mPoints[i];
+            int pixel = mPixels[i];
+            int count =  mInputPixelToCount.get(pixel);
+            mClusterPopulations[clusterIndex] += count;
+            aSums[clusterIndex] += point[0] * count;
+            bSums[clusterIndex] += point[1] * count;
+            cSums[clusterIndex] += point[2] * count;
+
+        }
+        for (int i = 0; i < maxColors; i++) {
+            int count = mClusterPopulations[i];
+            float aSum = aSums[i];
+            float bSum = bSums[i];
+            float cSum = cSums[i];
+            mClusters[i][0] = aSum / count;
+            mClusters[i][1] = bSum / count;
+            mClusters[i][2] = cSum / count;
+        }
+    }
+
+    private static class Distance {
+        private final int mIndex;
+        private final float mDistance;
+
+        int getIndex() {
+            return mIndex;
+        }
+
+        float getDistance() {
+            return mDistance;
+        }
+
+        Distance(int index, float distance) {
+            mIndex = index;
+            mDistance = distance;
+        }
     }
 }

core/java/com/android/internal/graphics/palette/WuQuantizer.java

@@ -16,431 +16,447 @@
 
 package com.android.internal.graphics.palette;
 
+import static java.lang.System.arraycopy;
+
+import android.annotation.NonNull;
+import android.annotation.Nullable;
+import android.graphics.Color;
+
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Map;
+import java.util.Set;
 
-// All reference Wu implementations are based on the original C code by Wu.
-// Comments on methods are the same as in the original implementation, and the comment below
-// is the original class header.
 
 /**
- * Wu's Color Quantizer (v. 2) (see Graphics Gems vol. II, pp. 126-133) Author: Xiaolin Wu
+ * Wu's quantization algorithm is a box-cut quantizer that minimizes variance. It takes longer to
+ * run than, say, median color cut, but provides the highest quality results currently known.
  *
- * <p>Algorithm: Greedy orthogonal bipartition of RGB space for variance minimization aided by
+ * Prefer `QuantizerCelebi`: coupled with Kmeans, this provides the best-known results for image
- * inclusion-exclusion tricks. For speed no nearest neighbor search is done. Slightly better
+ * quantization.
- * performance can be expected by more sophisticated but more expensive versions.
+ *
+ * Seemingly all Wu implementations are based off of one C code snippet that cites a book from 1992
+ * Graphics Gems vol. II, pp. 126-133. As a result, it is very hard to understand the mechanics of
+ * the algorithm, beyond the commentary provided in the C code. Comments on the methods of this
+ * class are avoided in favor of finding another implementation and reading the commentary there,
+ * avoiding perpetuating the same incomplete and somewhat confusing commentary here.
  */
-public class WuQuantizer implements Quantizer {
+public final class WuQuantizer implements Quantizer {
-    private static final int MAX_COLORS = 256;
+    // A histogram of all the input colors is constructed. It has the shape of a
-    private static final int RED = 2;
+    // cube. The cube would be too large if it contained all 16 million colors:
-    private static final int GREEN = 1;
+    // historical best practice is to use 5 bits  of the 8 in each channel,
-    private static final int BLUE = 0;
+    // reducing the histogram to a volume of ~32,000.
+    private static final int BITS = 5;
+    private static final int MAX_INDEX = 32;
+    private static final int SIDE_LENGTH = 33;
+    private static final int TOTAL_SIZE = 35937;
 
-    private static final int QUANT_SIZE = 33;
+    private int[] mWeights;
-    private final List<Palette.Swatch> mSwatches = new ArrayList<>();
+    private int[] mMomentsR;
+    private int[] mMomentsG;
+    private int[] mMomentsB;
+    private double[] mMoments;
+    private Box[] mCubes;
+    private Palette mPalette;
+    private int[] mColors;
+    private Map<Integer, Integer> mInputPixelToCount;
 
     @Override
     public List<Palette.Swatch> getQuantizedColors() {
-        return mSwatches;
+        return mPalette.getSwatches();
-    }
-
-    private static final class Box {
-        int mR0; /* min value, exclusive */
-        int mR1; /* max value, inclusive */
-        int mG0;
-        int mG1;
-        int mB0;
-        int mB1;
-        int mVol;
-    }
-
-    private final int mSize; /* image size, in bytes. */
-    private int mMaxColors;
-    private int[] mQadd;
-    private final int[] mPixels;
-
-    private final double[][][] mM2 = new double[QUANT_SIZE][QUANT_SIZE][QUANT_SIZE];
-    private final long[][][] mWt = new long[QUANT_SIZE][QUANT_SIZE][QUANT_SIZE];
-    private final long[][][] mMr = new long[QUANT_SIZE][QUANT_SIZE][QUANT_SIZE];
-    private final long[][][] mMg = new long[QUANT_SIZE][QUANT_SIZE][QUANT_SIZE];
-    private final long[][][] mMb = new long[QUANT_SIZE][QUANT_SIZE][QUANT_SIZE];
-
-    public WuQuantizer(int[] pixels, int maxColorCount) {
-        if (pixels == null) {
-            pixels = new int[]{};
-        }
-        this.mPixels = pixels;
-        this.mSize = pixels.length;
     }
 
     @Override
-    public void quantize(int[] colors, int maxColorCount) {
+    public void quantize(@NonNull int[] pixels, int colorCount) {
-        // All of the sample Wu implementations are reimplementations of a snippet of C code from
+        assert (pixels.length > 0);
-        // the early 90s. They all cap the maximum # of colors at 256, and it is impossible to tell
-        // if this is a requirement, a consequence of QUANT_SIZE, or arbitrary.
-        //
-        // Also, the number of maximum colors should be capped at the number of pixels - otherwise,
-        // If extraction is run on a set of pixels whose count is less than max colors,
-        // then colors.length < max colors, and accesses to colors[index] throw an
-        // ArrayOutOfBoundsException.
-        this.mMaxColors = Math.min(Math.min(MAX_COLORS, maxColorCount), colors.length);
-        Box[] cube = new Box[mMaxColors];
-        int red, green, blue;
 
-        int next, i, k;
+        QuantizerMap quantizerMap = new QuantizerMap();
-        long weight;
+        quantizerMap.quantize(pixels, colorCount);
-        double[] vv = new double[mMaxColors];
+        mInputPixelToCount = quantizerMap.getColorToCount();
-        double temp;
+        // Extraction should not be run on using a color count higher than the number of colors
-
+        // in the pixels. The algorithm doesn't expect that to be the case, unexpected results and
-        compute3DHistogram(mWt, mMr, mMg, mMb, mM2);
+        // exceptions may occur.
-        computeMoments(mWt, mMr, mMg, mMb, mM2);
+        Set<Integer> uniqueColors = mInputPixelToCount.keySet();
-
+        if (uniqueColors.size() <= colorCount) {
-        for (i = 0; i < mMaxColors; i++) {
+            mColors = new int[mInputPixelToCount.keySet().size()];
-            cube[i] = new Box();
+            int index = 0;
-        }
+            for (int color : uniqueColors) {
-
+                mColors[index++] = color;
-        cube[0].mR0 = cube[0].mG0 = cube[0].mB0 = 0;
-        cube[0].mR1 = cube[0].mG1 = cube[0].mB1 = QUANT_SIZE - 1;
-        next = 0;
-
-        for (i = 1; i < mMaxColors; ++i) {
-            if (cut(cube[next], cube[i])) {
-                vv[next] = (cube[next].mVol > 1) ? getVariance(cube[next]) : 0.0f;
-                vv[i] = (cube[i].mVol > 1) ? getVariance(cube[i]) : 0.0f;
-            } else {
-                vv[next] = 0.0f;
-                i--;
             }
-            next = 0;
+        } else {
-            temp = vv[0];
+            constructHistogram(mInputPixelToCount);
-            for (k = 1; k <= i; ++k) {
+            createMoments();
-                if (vv[k] > temp) {
+            CreateBoxesResult createBoxesResult = createBoxes(colorCount);
-                    temp = vv[k];
+            mColors = createResult(createBoxesResult.mResultCount);
-                    next = k;
-                }
-            }
-            if (temp <= 0.0f) {
-                break;
-            }
-        }
-
-        for (k = 0; k < mMaxColors; ++k) {
-            weight = getVolume(cube[k], mWt);
-            if (weight > 0) {
-                red = (int) (getVolume(cube[k], mMr) / weight);
-                green = (int) (getVolume(cube[k], mMg) / weight);
-                blue = (int) (getVolume(cube[k], mMb) / weight);
-                colors[k] = (255 << 24) | (red << 16) | (green << 8) | blue;
-            } else {
-                colors[k] = 0;
-            }
-        }
-
-        int bitsPerPixel = 0;
-        while ((1 << bitsPerPixel) < mMaxColors) {
-            bitsPerPixel++;
         }
 
         List<Palette.Swatch> swatches = new ArrayList<>();
-        for (int l = 0; l < k; l++) {
+        for (int color : mColors) {
-            int pixel = colors[l];
+            swatches.add(new Palette.Swatch(color, 0));
-            if (pixel == 0) {
-                continue;
-            }
-            swatches.add(new Palette.Swatch(pixel, 0));
         }
-        mSwatches.clear();
+        mPalette = Palette.from(swatches);
-        mSwatches.addAll(swatches);
     }
 
-    /* Histogram is in elements 1..HISTSIZE along each axis,
+    @Nullable
-     * element 0 is for base or marginal value
+    public int[] getColors() {
-     * NB: these must start out 0!
+        return mColors;
-     */
+    }
-    private void compute3DHistogram(
-            long[][][] vwt, long[][][] vmr, long[][][] vmg, long[][][] vmb, double[][][] m2) {
-        // build 3-D color histogram of counts, r/g/b, and c^2
-        int r, g, b;
-        int i;
-        int inr;
-        int ing;
-        int inb;
-        int[] table = new int[256];
 
-        for (i = 0; i < 256; i++) {
+    /** Keys are color ints, values are the number of pixels in the image matching that color int */
-            table[i] = i * i;
+    @Nullable
-        }
+    public Map<Integer, Integer> inputPixelToCount() {
+        return mInputPixelToCount;
+    }
 
-        mQadd = new int[mSize];
+    private static int getIndex(int r, int g, int b) {
+        return (r << 10) + (r << 6) + (g << 5) + r + g + b;
+    }
 
-        for (i = 0; i < mSize; ++i) {
+    private void constructHistogram(Map<Integer, Integer> pixels) {
-            int rgb = mPixels[i];
+        mWeights = new int[TOTAL_SIZE];
-            // Skip less than opaque pixels. They're not meaningful in the context of palette
+        mMomentsR = new int[TOTAL_SIZE];
-            // generation for UI schemes.
+        mMomentsG = new int[TOTAL_SIZE];
-            if ((rgb >>> 24) < 0xff) {
+        mMomentsB = new int[TOTAL_SIZE];
-                continue;
+        mMoments = new double[TOTAL_SIZE];
-            }
+
-            r = ((rgb >> 16) & 0xff);
+        for (Map.Entry<Integer, Integer> pair : pixels.entrySet()) {
-            g = ((rgb >> 8) & 0xff);
+            int pixel = pair.getKey();
-            b = (rgb & 0xff);
+            int count = pair.getValue();
-            inr = (r >> 3) + 1;
+            int red = Color.red(pixel);
-            ing = (g >> 3) + 1;
+            int green = Color.green(pixel);
-            inb = (b >> 3) + 1;
+            int blue = Color.blue(pixel);
-            mQadd[i] = (inr << 10) + (inr << 6) + inr + (ing << 5) + ing + inb;
+            int bitsToRemove = 8 - BITS;
-            /*[inr][ing][inb]*/
+            int iR = (red >> bitsToRemove) + 1;
-            ++vwt[inr][ing][inb];
+            int iG = (green >> bitsToRemove) + 1;
-            vmr[inr][ing][inb] += r;
+            int iB = (blue >> bitsToRemove) + 1;
-            vmg[inr][ing][inb] += g;
+            int index = getIndex(iR, iG, iB);
-            vmb[inr][ing][inb] += b;
+            mWeights[index] += count;
-            m2[inr][ing][inb] += table[r] + table[g] + table[b];
+            mMomentsR[index] += (red * count);
+            mMomentsG[index] += (green * count);
+            mMomentsB[index] += (blue * count);
+            mMoments[index] += (count * ((red * red) + (green * green) + (blue * blue)));
         }
     }
 
-    /* At conclusion of the histogram step, we can interpret
+    private void createMoments() {
-     *   wt[r][g][b] = sum over voxel of P(c)
+        for (int r = 1; r < SIDE_LENGTH; ++r) {
-     *   mr[r][g][b] = sum over voxel of r*P(c)  ,  similarly for mg, mb
+            int[] area = new int[SIDE_LENGTH];
-     *   m2[r][g][b] = sum over voxel of c^2*P(c)
+            int[] areaR = new int[SIDE_LENGTH];
-     * Actually each of these should be divided by 'size' to give the usual
+            int[] areaG = new int[SIDE_LENGTH];
-     * interpretation of P() as ranging from 0 to 1, but we needn't do that here.
+            int[] areaB = new int[SIDE_LENGTH];
-     *
+            double[] area2 = new double[SIDE_LENGTH];
-     * We now convert histogram into moments so that we can rapidly calculate
-     * the sums of the above quantities over any desired box.
-     */
-    private void computeMoments(
-            long[][][] vwt, long[][][] vmr, long[][][] vmg, long[][][] vmb, double[][][] m2) {
-        /* compute cumulative moments. */
-        int i, r, g, b;
-        int line, line_r, line_g, line_b;
-        int[] area = new int[QUANT_SIZE];
-        int[] area_r = new int[QUANT_SIZE];
-        int[] area_g = new int[QUANT_SIZE];
-        int[] area_b = new int[QUANT_SIZE];
-        double line2;
-        double[] area2 = new double[QUANT_SIZE];
 
-        for (r = 1; r < QUANT_SIZE; ++r) {
+            for (int g = 1; g < SIDE_LENGTH; ++g) {
-            for (i = 0; i < QUANT_SIZE; ++i) {
+                int line = 0;
-                area2[i] = area[i] = area_r[i] = area_g[i] = area_b[i] = 0;
+                int lineR = 0;
-            }
+                int lineG = 0;
-            for (g = 1; g < QUANT_SIZE; ++g) {
+                int lineB = 0;
-                line2 = line = line_r = line_g = line_b = 0;
+
-                for (b = 1; b < QUANT_SIZE; ++b) {
+                double line2 = 0.0;
-                    line += vwt[r][g][b];
+                for (int b = 1; b < SIDE_LENGTH; ++b) {
-                    line_r += vmr[r][g][b];
+                    int index = getIndex(r, g, b);
-                    line_g += vmg[r][g][b];
+                    line += mWeights[index];
-                    line_b += vmb[r][g][b];
+                    lineR += mMomentsR[index];
-                    line2 += m2[r][g][b];
+                    lineG += mMomentsG[index];
+                    lineB += mMomentsB[index];
+                    line2 += mMoments[index];
 
                     area[b] += line;
-                    area_r[b] += line_r;
+                    areaR[b] += lineR;
-                    area_g[b] += line_g;
+                    areaG[b] += lineG;
-                    area_b[b] += line_b;
+                    areaB[b] += lineB;
                     area2[b] += line2;
 
-                    vwt[r][g][b] = vwt[r - 1][g][b] + area[b];
+                    int previousIndex = getIndex(r - 1, g, b);
-                    vmr[r][g][b] = vmr[r - 1][g][b] + area_r[b];
+                    mWeights[index] = mWeights[previousIndex] + area[b];
-                    vmg[r][g][b] = vmg[r - 1][g][b] + area_g[b];
+                    mMomentsR[index] = mMomentsR[previousIndex] + areaR[b];
-                    vmb[r][g][b] = vmb[r - 1][g][b] + area_b[b];
+                    mMomentsG[index] = mMomentsG[previousIndex] + areaG[b];
-                    m2[r][g][b] = m2[r - 1][g][b] + area2[b];
+                    mMomentsB[index] = mMomentsB[previousIndex] + areaB[b];
+                    mMoments[index] = mMoments[previousIndex] + area2[b];
                 }
             }
         }
     }
 
-    private long getVolume(Box cube, long[][][] mmt) {
+    private CreateBoxesResult createBoxes(int maxColorCount) {
-        /* Compute sum over a box of any given statistic */
+        mCubes = new Box[maxColorCount];
-        return (mmt[cube.mR1][cube.mG1][cube.mB1]
+        for (int i = 0; i < maxColorCount; i++) {
-                - mmt[cube.mR1][cube.mG1][cube.mB0]
+            mCubes[i] = new Box();
-                - mmt[cube.mR1][cube.mG0][cube.mB1]
-                + mmt[cube.mR1][cube.mG0][cube.mB0]
-                - mmt[cube.mR0][cube.mG1][cube.mB1]
-                + mmt[cube.mR0][cube.mG1][cube.mB0]
-                + mmt[cube.mR0][cube.mG0][cube.mB1]
-                - mmt[cube.mR0][cube.mG0][cube.mB0]);
-    }
-
-    /* The next two routines allow a slightly more efficient calculation
-     * of Vol() for a proposed subbox of a given box.  The sum of Top()
-     * and Bottom() is the Vol() of a subbox split in the given direction
-     * and with the specified new upper bound.
-     */
-    private long getBottom(Box cube, int dir, long[][][] mmt) {
-        /* Compute part of Vol(cube, mmt) that doesn't depend on r1, g1, or b1 */
-        /* (depending on dir) */
-        switch (dir) {
-            case RED:
-                return (-mmt[cube.mR0][cube.mG1][cube.mB1]
-                        + mmt[cube.mR0][cube.mG1][cube.mB0]
-                        + mmt[cube.mR0][cube.mG0][cube.mB1]
-                        - mmt[cube.mR0][cube.mG0][cube.mB0]);
-            case GREEN:
-                return (-mmt[cube.mR1][cube.mG0][cube.mB1]
-                        + mmt[cube.mR1][cube.mG0][cube.mB0]
-                        + mmt[cube.mR0][cube.mG0][cube.mB1]
-                        - mmt[cube.mR0][cube.mG0][cube.mB0]);
-            case BLUE:
-                return (-mmt[cube.mR1][cube.mG1][cube.mB0]
-                        + mmt[cube.mR1][cube.mG0][cube.mB0]
-                        + mmt[cube.mR0][cube.mG1][cube.mB0]
-                        - mmt[cube.mR0][cube.mG0][cube.mB0]);
-            default:
-                return 0;
         }
-    }
+        double[] volumeVariance = new double[maxColorCount];
+        Box firstBox = mCubes[0];
+        firstBox.r1 = MAX_INDEX;
+        firstBox.g1 = MAX_INDEX;
+        firstBox.b1 = MAX_INDEX;
 
-    private long getTop(Box cube, int dir, int pos, long[][][] mmt) {
+        int generatedColorCount = 0;
-        /* Compute remainder of Vol(cube, mmt), substituting pos for */
+        int next = 0;
-        /* r1, g1, or b1 (depending on dir) */
-        switch (dir) {
-            case RED:
-                return (mmt[pos][cube.mG1][cube.mB1]
-                        - mmt[pos][cube.mG1][cube.mB0]
-                        - mmt[pos][cube.mG0][cube.mB1]
-                        + mmt[pos][cube.mG0][cube.mB0]);
-            case GREEN:
-                return (mmt[cube.mR1][pos][cube.mB1]
-                        - mmt[cube.mR1][pos][cube.mB0]
-                        - mmt[cube.mR0][pos][cube.mB1]
-                        + mmt[cube.mR0][pos][cube.mB0]);
-            case BLUE:
-                return (mmt[cube.mR1][cube.mG1][pos]
-                        - mmt[cube.mR1][cube.mG0][pos]
-                        - mmt[cube.mR0][cube.mG1][pos]
-                        + mmt[cube.mR0][cube.mG0][pos]);
-            default:
-                return 0;
-        }
-    }
 
-    private double getVariance(Box cube) {
+        for (int i = 1; i < maxColorCount; i++) {
-        /* Compute the weighted variance of a box */
+            if (cut(mCubes[next], mCubes[i])) {
-        /* NB: as with the raw statistics, this is really the variance * size */
+                volumeVariance[next] = (mCubes[next].vol > 1) ? variance(mCubes[next]) : 0.0;
-        double dr, dg, db, xx;
+                volumeVariance[i] = (mCubes[i].vol > 1) ? variance(mCubes[i]) : 0.0;
-        dr = getVolume(cube, mMr);
+            } else {
-        dg = getVolume(cube, mMg);
+                volumeVariance[next] = 0.0;
-        db = getVolume(cube, mMb);
+                i--;
-        xx =
-                mM2[cube.mR1][cube.mG1][cube.mB1]
-                        - mM2[cube.mR1][cube.mG1][cube.mB0]
-                        - mM2[cube.mR1][cube.mG0][cube.mB1]
-                        + mM2[cube.mR1][cube.mG0][cube.mB0]
-                        - mM2[cube.mR0][cube.mG1][cube.mB1]
-                        + mM2[cube.mR0][cube.mG1][cube.mB0]
-                        + mM2[cube.mR0][cube.mG0][cube.mB1]
-                        - mM2[cube.mR0][cube.mG0][cube.mB0];
-        return xx - (dr * dr + dg * dg + db * db) / getVolume(cube, mWt);
-    }
-
-    /* We want to minimize the sum of the variances of two subboxes.
-     * The sum(c^2) terms can be ignored since their sum over both subboxes
-     * is the same (the sum for the whole box) no matter where we split.
-     * The remaining terms have a minus sign in the variance formula,
-     * so we drop the minus sign and MAXIMIZE the sum of the two terms.
-     */
-    private double maximize(
-            Box cube,
-            int dir,
-            int first,
-            int last,
-            int[] cut,
-            long wholeR,
-            long wholeG,
-            long wholeB,
-            long wholeW) {
-        long half_r, half_g, half_b, half_w;
-        long base_r, base_g, base_b, base_w;
-        int i;
-        double temp, max;
-
-        base_r = getBottom(cube, dir, mMr);
-        base_g = getBottom(cube, dir, mMg);
-        base_b = getBottom(cube, dir, mMb);
-        base_w = getBottom(cube, dir, mWt);
-
-        max = 0.0f;
-        cut[0] = -1;
-
-        for (i = first; i < last; ++i) {
-            half_r = base_r + getTop(cube, dir, i, mMr);
-            half_g = base_g + getTop(cube, dir, i, mMg);
-            half_b = base_b + getTop(cube, dir, i, mMb);
-            half_w = base_w + getTop(cube, dir, i, mWt);
-            /* now half_x is sum over lower half of box, if split at i */
-            if (half_w == 0) /* subbox could be empty of pixels! */ {
-                continue; /* never split into an empty box */
             }
-            temp = (half_r * half_r + half_g * half_g + half_b * half_b) / (double) half_w;
-            half_r = wholeR - half_r;
-            half_g = wholeG - half_g;
-            half_b = wholeB - half_b;
-            half_w = wholeW - half_w;
-            if (half_w == 0) /* subbox could be empty of pixels! */ {
-                continue; /* never split into an empty box */
-            }
-            temp += (half_r * half_r + half_g * half_g + half_b * half_b) / (double) half_w;
 
-            if (temp > max) {
+            next = 0;
-                max = temp;
+
-                cut[0] = i;
+            double temp = volumeVariance[0];
+            for (int k = 1; k <= i; k++) {
+                if (volumeVariance[k] > temp) {
+                    temp = volumeVariance[k];
+                    next = k;
+                }
+            }
+            generatedColorCount = i + 1;
+            if (temp <= 0.0) {
+                break;
             }
         }
 
-        return max;
+        return new CreateBoxesResult(maxColorCount, generatedColorCount);
     }
 
-    private boolean cut(Box set1, Box set2) {
+    private int[] createResult(int colorCount) {
-        int dir;
+        int[] colors = new int[colorCount];
-        int[] cutr = new int[1];
+        int nextAvailableIndex = 0;
-        int[] cutg = new int[1];
+        for (int i = 0; i < colorCount; ++i) {
-        int[] cutb = new int[1];
+            Box cube = mCubes[i];
-        double maxr, maxg, maxb;
+            int weight = volume(cube, mWeights);
-        long whole_r, whole_g, whole_b, whole_w;
+            if (weight > 0) {
+                int r = (volume(cube, mMomentsR) / weight);
+                int g = (volume(cube, mMomentsG) / weight);
+                int b = (volume(cube, mMomentsB) / weight);
+                int color = Color.rgb(r, g, b);
+                colors[nextAvailableIndex++] = color;
+            }
+        }
+        int[] resultArray = new int[nextAvailableIndex];
+        arraycopy(colors, 0, resultArray, 0, nextAvailableIndex);
+        return resultArray;
+    }
 
-        whole_r = getVolume(set1, mMr);
+    private double variance(Box cube) {
-        whole_g = getVolume(set1, mMg);
+        int dr = volume(cube, mMomentsR);
-        whole_b = getVolume(set1, mMb);
+        int dg = volume(cube, mMomentsG);
-        whole_w = getVolume(set1, mWt);
+        int db = volume(cube, mMomentsB);
+        double xx =
+                mMoments[getIndex(cube.r1, cube.g1, cube.b1)]
+                        - mMoments[getIndex(cube.r1, cube.g1, cube.b0)]
+                        - mMoments[getIndex(cube.r1, cube.g0, cube.b1)]
+                        + mMoments[getIndex(cube.r1, cube.g0, cube.b0)]
+                        - mMoments[getIndex(cube.r0, cube.g1, cube.b1)]
+                        + mMoments[getIndex(cube.r0, cube.g1, cube.b0)]
+                        + mMoments[getIndex(cube.r0, cube.g0, cube.b1)]
+                        - mMoments[getIndex(cube.r0, cube.g0, cube.b0)];
 
-        maxr = maximize(set1, RED, set1.mR0 + 1, set1.mR1, cutr, whole_r, whole_g, whole_b,
+        int hypotenuse = (dr * dr + dg * dg + db * db);
-                whole_w);
+        int volume2 = volume(cube, mWeights);
-        maxg = maximize(set1, GREEN, set1.mG0 + 1, set1.mG1, cutg, whole_r, whole_g, whole_b,
+        double variance2 = xx - ((double) hypotenuse / (double) volume2);
-                whole_w);
+        return variance2;
-        maxb = maximize(set1, BLUE, set1.mB0 + 1, set1.mB1, cutb, whole_r, whole_g, whole_b,
+    }
-                whole_w);
 
-        if (maxr >= maxg && maxr >= maxb) {
+    private boolean cut(Box one, Box two) {
-            dir = RED;
+        int wholeR = volume(one, mMomentsR);
-            if (cutr[0] < 0) return false; /* can't split the box */
+        int wholeG = volume(one, mMomentsG);
-        } else if (maxg >= maxr && maxg >= maxb) {
+        int wholeB = volume(one, mMomentsB);
-            dir = GREEN;
+        int wholeW = volume(one, mWeights);
+
+        MaximizeResult maxRResult =
+                maximize(one, Direction.RED, one.r0 + 1, one.r1, wholeR, wholeG, wholeB, wholeW);
+        MaximizeResult maxGResult =
+                maximize(one, Direction.GREEN, one.g0 + 1, one.g1, wholeR, wholeG, wholeB, wholeW);
+        MaximizeResult maxBResult =
+                maximize(one, Direction.BLUE, one.b0 + 1, one.b1, wholeR, wholeG, wholeB, wholeW);
+        Direction cutDirection;
+        double maxR = maxRResult.mMaximum;
+        double maxG = maxGResult.mMaximum;
+        double maxB = maxBResult.mMaximum;
+        if (maxR >= maxG && maxR >= maxB) {
+            if (maxRResult.mCutLocation < 0) {
+                return false;
+            }
+            cutDirection = Direction.RED;
+        } else if (maxG >= maxR && maxG >= maxB) {
+            cutDirection = Direction.GREEN;
         } else {
-            dir = BLUE;
+            cutDirection = Direction.BLUE;
         }
 
-        set2.mR1 = set1.mR1;
+        two.r1 = one.r1;
-        set2.mG1 = set1.mG1;
+        two.g1 = one.g1;
-        set2.mB1 = set1.mB1;
+        two.b1 = one.b1;
 
-        switch (dir) {
+        switch (cutDirection) {
             case RED:
-                set2.mR0 = set1.mR1 = cutr[0];
+                one.r1 = maxRResult.mCutLocation;
-                set2.mG0 = set1.mG0;
+                two.r0 = one.r1;
-                set2.mB0 = set1.mB0;
+                two.g0 = one.g0;
+                two.b0 = one.b0;
                 break;
             case GREEN:
-                set2.mG0 = set1.mG1 = cutg[0];
+                one.g1 = maxGResult.mCutLocation;
-                set2.mR0 = set1.mR0;
+                two.r0 = one.r0;
-                set2.mB0 = set1.mB0;
+                two.g0 = one.g1;
+                two.b0 = one.b0;
                 break;
             case BLUE:
-                set2.mB0 = set1.mB1 = cutb[0];
+                one.b1 = maxBResult.mCutLocation;
-                set2.mR0 = set1.mR0;
+                two.r0 = one.r0;
-                set2.mG0 = set1.mG0;
+                two.g0 = one.g0;
+                two.b0 = one.b1;
                 break;
+            default:
+                throw new IllegalArgumentException("unexpected direction " + cutDirection);
         }
-        set1.mVol = (set1.mR1 - set1.mR0) * (set1.mG1 - set1.mG0) * (set1.mB1 - set1.mB0);
+
-        set2.mVol = (set2.mR1 - set2.mR0) * (set2.mG1 - set2.mG0) * (set2.mB1 - set2.mB0);
+        one.vol = (one.r1 - one.r0) * (one.g1 - one.g0) * (one.b1 - one.b0);
+        two.vol = (two.r1 - two.r0) * (two.g1 - two.g0) * (two.b1 - two.b0);
 
         return true;
     }
+
+    private MaximizeResult maximize(
+            Box cube,
+            Direction direction,
+            int first,
+            int last,
+            int wholeR,
+            int wholeG,
+            int wholeB,
+            int wholeW) {
+        int baseR = bottom(cube, direction, mMomentsR);
+        int baseG = bottom(cube, direction, mMomentsG);
+        int baseB = bottom(cube, direction, mMomentsB);
+        int baseW = bottom(cube, direction, mWeights);
+
+        double max = 0.0;
+        int cut = -1;
+        for (int i = first; i < last; i++) {
+            int halfR = baseR + top(cube, direction, i, mMomentsR);
+            int halfG = baseG + top(cube, direction, i, mMomentsG);
+            int halfB = baseB + top(cube, direction, i, mMomentsB);
+            int halfW = baseW + top(cube, direction, i, mWeights);
+
+            if (halfW == 0) {
+                continue;
+            }
+            double tempNumerator = halfR * halfR + halfG * halfG + halfB * halfB;
+            double tempDenominator = halfW;
+            double temp = tempNumerator / tempDenominator;
+
+            halfR = wholeR - halfR;
+            halfG = wholeG - halfG;
+            halfB = wholeB - halfB;
+            halfW = wholeW - halfW;
+            if (halfW == 0) {
+                continue;
+            }
+
+            tempNumerator = halfR * halfR + halfG * halfG + halfB * halfB;
+            tempDenominator = halfW;
+            temp += (tempNumerator / tempDenominator);
+            if (temp > max) {
+                max = temp;
+                cut = i;
+            }
+        }
+        return new MaximizeResult(cut, max);
+    }
+
+    private static int volume(Box cube, int[] moment) {
+        return (moment[getIndex(cube.r1, cube.g1, cube.b1)]
+                - moment[getIndex(cube.r1, cube.g1, cube.b0)]
+                - moment[getIndex(cube.r1, cube.g0, cube.b1)]
+                + moment[getIndex(cube.r1, cube.g0, cube.b0)]
+                - moment[getIndex(cube.r0, cube.g1, cube.b1)]
+                + moment[getIndex(cube.r0, cube.g1, cube.b0)]
+                + moment[getIndex(cube.r0, cube.g0, cube.b1)]
+                - moment[getIndex(cube.r0, cube.g0, cube.b0)]);
+    }
+
+    private static int bottom(Box cube, Direction direction, int[] moment) {
+        switch (direction) {
+            case RED:
+                return -moment[getIndex(cube.r0, cube.g1, cube.b1)]
+                        + moment[getIndex(cube.r0, cube.g1, cube.b0)]
+                        + moment[getIndex(cube.r0, cube.g0, cube.b1)]
+                        - moment[getIndex(cube.r0, cube.g0, cube.b0)];
+            case GREEN:
+                return -moment[getIndex(cube.r1, cube.g0, cube.b1)]
+                        + moment[getIndex(cube.r1, cube.g0, cube.b0)]
+                        + moment[getIndex(cube.r0, cube.g0, cube.b1)]
+                        - moment[getIndex(cube.r0, cube.g0, cube.b0)];
+            case BLUE:
+                return -moment[getIndex(cube.r1, cube.g1, cube.b0)]
+                        + moment[getIndex(cube.r1, cube.g0, cube.b0)]
+                        + moment[getIndex(cube.r0, cube.g1, cube.b0)]
+                        - moment[getIndex(cube.r0, cube.g0, cube.b0)];
+            default:
+                throw new IllegalArgumentException("unexpected direction " + direction);
+        }
+    }
+
+    private static int top(Box cube, Direction direction, int position, int[] moment) {
+        switch (direction) {
+            case RED:
+                return (moment[getIndex(position, cube.g1, cube.b1)]
+                        - moment[getIndex(position, cube.g1, cube.b0)]
+                        - moment[getIndex(position, cube.g0, cube.b1)]
+                        + moment[getIndex(position, cube.g0, cube.b0)]);
+            case GREEN:
+                return (moment[getIndex(cube.r1, position, cube.b1)]
+                        - moment[getIndex(cube.r1, position, cube.b0)]
+                        - moment[getIndex(cube.r0, position, cube.b1)]
+                        + moment[getIndex(cube.r0, position, cube.b0)]);
+            case BLUE:
+                return (moment[getIndex(cube.r1, cube.g1, position)]
+                        - moment[getIndex(cube.r1, cube.g0, position)]
+                        - moment[getIndex(cube.r0, cube.g1, position)]
+                        + moment[getIndex(cube.r0, cube.g0, position)]);
+            default:
+                throw new IllegalArgumentException("unexpected direction " + direction);
+        }
+    }
+
+    private enum Direction {
+        RED,
+        GREEN,
+        BLUE
+    }
+
+    private static class MaximizeResult {
+        // < 0 if cut impossible
+        final int mCutLocation;
+        final double mMaximum;
+
+        MaximizeResult(int cut, double max) {
+            mCutLocation = cut;
+            mMaximum = max;
+        }
+    }
+
+    private static class CreateBoxesResult {
+        final int mRequestedCount;
+        final int mResultCount;
+
+        CreateBoxesResult(int requestedCount, int resultCount) {
+            mRequestedCount = requestedCount;
+            mResultCount = resultCount;
+        }
+    }
+
+    private static class Box {
+        public int r0 = 0;
+        public int r1 = 0;
+        public int g0 = 0;
+        public int g1 = 0;
+        public int b0 = 0;
+        public int b1 = 0;
+        public int vol = 0;
+    }
 }
+
+

services/core/java/com/android/server/wallpaper/WallpaperManagerService.java

@@ -628,7 +628,7 @@ public class WallpaperManagerService extends IWallpaperManager.Stub
             }
 
             // scale if the crop height winds up not matching the recommended metrics
-            needScale = wpData.mHeight != cropHint.height()
+            needScale = cropHint.height() > wpData.mHeight
                     || cropHint.height() > GLHelper.getMaxTextureSize()
                     || cropHint.width() > GLHelper.getMaxTextureSize();
 
@@ -752,7 +752,7 @@ public class WallpaperManagerService extends IWallpaperManager.Stub
 
                         f = new FileOutputStream(wallpaper.cropFile);
                         bos = new BufferedOutputStream(f, 32*1024);
-                        finalCrop.compress(Bitmap.CompressFormat.JPEG, 100, bos);
+                        finalCrop.compress(Bitmap.CompressFormat.PNG, 100, bos);
                         bos.flush();  // don't rely on the implicit flush-at-close when noting success
                         success = true;
                     }