android palette

所在包目录

android-sdk-windows\extras\android\compatibility\v7\palette\libs

 private void extract(Bitmap bitmap) {        // 提取颜色        // Palette palette = Palette.generate(bitmap);         Palette.generateAsync(bitmap, new Palette.PaletteAsyncListener() {            @Override            public void onGenerated(Palette palette) {                // 提取                // 有活力的颜色                Palette.Swatch vibrant = palette.getVibrantSwatch();                 // 有活力的暗色                Palette.Swatch darkVibrant = palette.getDarkVibrantSwatch();                 // 有活力的亮色                Palette.Swatch lightVibrant = palette.getLightVibrantSwatch();                 // 柔和的颜色                Palette.Swatch muted = palette.getMutedSwatch();                 // 柔和的暗色                Palette.Swatch darkMuted = palette.getDarkMutedSwatch();                 // 柔和的亮色                Palette.Swatch lightMuted = palette.getLightMutedSwatch();                 mTextView.setText("有活力的颜色");                if (vibrant != null) {                    ll.setBackgroundColor(vibrant.getRgb());                    mTextView.setBackgroundColor(vibrant.getRgb());                    mTextView.setTextColor(vibrant.getTitleTextColor());                }    }

以下的分析转载了，再慢慢看有时间
http://blog.csdn.net/yebo0505/article/details/43234113

第一步，将图片缩小，再整个过程中，可以降低计算量和减少内存的使用，跟不缩小也能达到一样的效果
[java] view plaincopy在CODE上查看代码片派生到我的代码片
/**
* Scale the bitmap down so that it’s smallest dimension is
* {@value #CALCULATE_BITMAP_MIN_DIMENSION}px. If {@code bitmap} is smaller than this, than it
* is returned.
*/
private static Bitmap scaleBitmapDown(Bitmap bitmap) {
final int minDimension = Math.min(bitmap.getWidth(), bitmap.getHeight());

    if (minDimension <= CALCULATE_BITMAP_MIN_DIMENSION) {          // If the bitmap is small enough already, just return it          return bitmap;      }      final float scaleRatio = CALCULATE_BITMAP_MIN_DIMENSION / (float) minDimension;      return Bitmap.createScaledBitmap(bitmap,              Math.round(bitmap.getWidth() * scaleRatio),              Math.round(bitmap.getHeight() * scaleRatio),              false);  }

第二步，将缩小后的图片数据，放在一个int 数组里
[java] view plaincopy在CODE上查看代码片派生到我的代码片
/**
* Factory-method to generate a {@link ColorCutQuantizer} from a {@link Bitmap} object.
*
* @param bitmap Bitmap to extract the pixel data from
* @param maxColors The maximum number of colors that should be in the result palette.
*/
static ColorCutQuantizer fromBitmap(Bitmap bitmap, int maxColors) {
final int width = bitmap.getWidth();
final int height = bitmap.getHeight();

   final int[] pixels = new int[width * height];     bitmap.getPixels(pixels, 0, width, 0, 0, width, height);     return new ColorCutQuantizer(new ColorHistogram(pixels), maxColors);

}

第三步，将这个int 数组由小到大排序，就相当于，将一张图片一样的颜色堆在一起，然后计算共有多少种颜色，每种颜色它是多大，这些是在一个叫ColorHistogram（颜色直方图）类里面计算的，用颜色直方图来说，就是共有多少柱颜色，每柱颜色有多高

[java] view plaincopy在CODE上查看代码片派生到我的代码片
/**
* Class which provides a histogram for RGB values.
*/
final class ColorHistogram {

private final int[] mColors;  private final int[] mColorCounts;  private final int mNumberColors;  /**  * A new {@link ColorHistogram} instance.  *  * @param pixels array of image contents  */  ColorHistogram(final int[] pixels) {      // Sort the pixels to enable counting below      Arrays.sort(pixels);      // Count number of distinct colors      mNumberColors = countDistinctColors(pixels);      // Create arrays      mColors = new int[mNumberColors];      mColorCounts = new int[mNumberColors];      // Finally count the frequency of each color      countFrequencies(pixels);  }  /**  * @return 获取共用多少柱不同颜色 number of distinct colors in the image.  */  int getNumberOfColors() {      return mNumberColors;  }  /**  * @return 获取排好序后的不同颜色的数组  an array containing all of the distinct colors in the image.  */  int[] getColors() {      return mColors;  }  /**  * @return 获取保存每一柱有多高的数组 an array containing the frequency of a distinct colors within the image.  */  int[] getColorCounts() {      return mColorCounts;  }  //计算共用多少柱不同颜色  private static int countDistinctColors(final int[] pixels) {      if (pixels.length < 2) {          // If we have less than 2 pixels we can stop here          return pixels.length;      }      // If we have at least 2 pixels, we have a minimum of 1 color...      int colorCount = 1;      int currentColor = pixels[0];      // Now iterate from the second pixel to the end, counting distinct colors      for (int i = 1; i < pixels.length; i++) {          // If we encounter a new color, increase the population          if (pixels[i] != currentColor) {              currentColor = pixels[i];              colorCount++;          }      }      return colorCount;  }  //计算每一柱有多高  private void countFrequencies(final int[] pixels) {      if (pixels.length == 0) {          return;      }      int currentColorIndex = 0;      int currentColor = pixels[0];      mColors[currentColorIndex] = currentColor;      mColorCounts[currentColorIndex] = 1;      Log.i("pixels.length",""+ pixels.length);      if (pixels.length == 1) {          // If we only have one pixel, we can stop here          return;      }      // Now iterate from the second pixel to the end, population distinct colors      for (int i = 1; i < pixels.length; i++) {          if (pixels[i] == currentColor) {              // We've hit the same color as before, increase population              mColorCounts[currentColorIndex]++;          } else {              // We've hit a new color, increase index              currentColor = pixels[i];              currentColorIndex++;              mColors[currentColorIndex] = currentColor;              mColorCounts[currentColorIndex] = 1;          }      }  }

}
第四步，将各种颜色，根据RGB转HSL算法，得出对应的HSL（H: Hue 色相，S：Saturation 饱和度L Lightness 明度）,根据特定的条件，比如是明度L是否接近白色，黑色，还有一个判断叫isNearRedILine，解释是@return true if the color lies close to the red side of the I line（接近红色私密区域附近？）.，然后根据这三个条件，过滤掉这些颜色，什么是HSL和RGB转HSL算法可以查看下百科，比较有详细说明

[java] view plaincopy在CODE上查看代码片派生到我的代码片
/**
* Private constructor.
*
* @param colorHistogram histogram representing an image’s pixel data
* @param maxColors The maximum number of colors that should be in the result palette.
*/
private ColorCutQuantizer(ColorHistogram colorHistogram, int maxColors) {
final int rawColorCount = colorHistogram.getNumberOfColors();
final int[] rawColors = colorHistogram.getColors();//颜色数组
final int[] rawColorCounts = colorHistogram.getColorCounts();//对应rawColors每一个颜色数组的大小

   // First, lets pack the populations into a SparseIntArray so that they can be easily     // retrieved without knowing a color's index     mColorPopulations = new SparseIntArray(rawColorCount);     for (int i = 0; i < rawColors.length; i++) {         mColorPopulations.append(rawColors[i], rawColorCounts[i]);     }     // Now go through all of the colors and keep those which we do not want to ignore     mColors = new int[rawColorCount];     int validColorCount = 0;     for (int color : rawColors) {         if (!shouldIgnoreColor(color)) {             mColors[validColorCount++] = color;         }     }     Log.d("mColors length", ""+mColors.length);     if (validColorCount <= maxColors) {         // The image has fewer colors than the maximum requested, so just return the colors         mQuantizedColors = new ArrayList<Swatch>();         for (final int color : mColors) {             mQuantizedColors.add(new Swatch(color, mColorPopulations.get(color)));         }     } else {         // We need use quantization to reduce the number of colors         mQuantizedColors = quantizePixels(validColorCount - 1, maxColors);     }

}
[java] view plaincopy在CODE上查看代码片派生到我的代码片

[java] view plaincopy在CODE上查看代码片派生到我的代码片
这里截了张图看看
[java] view plaincopy在CODE上查看代码片派生到我的代码片

第五步，根据是各种亮度，饱和度的取值范围，比如有活力的暗色，有活力的亮色，柔和的颜色，柔和的暗色，柔和的亮色，找到对应的颜色
[java] view plaincopy在CODE上查看代码片派生到我的代码片
private Swatch findColor(float targetLuma, float minLuma, float maxLuma,
float targetSaturation, float minSaturation, float maxSaturation) {
Swatch max = null;
float maxValue = 0f;

   for (Swatch swatch : mSwatches) {         final float sat = swatch.getHsl()[1];         final float luma = swatch.getHsl()[2];         if (sat >= minSaturation && sat <= maxSaturation &&                 luma >= minLuma && luma <= maxLuma &&                 !isAlreadySelected(swatch)) {             float thisValue = createComparisonValue(sat, targetSaturation, luma, targetLuma,                     swatch.getPopulation(), mHighestPopulation);             if (max == null || thisValue > maxValue) {                 max = swatch;                 maxValue = thisValue;             }         }     }     return max;

}

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