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;
}