以下為將bitmap圖像處理為毛玻璃效果的圖像的工具類：

public class FastBlurUtil {public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {// Stack Blur v1.0 from// http://www.quasimondo.com/StackBlurForCanvas/StackBlurDemo.html//// Java Author: Mario Klingemann <mario at quasimondo.com>// http://incubator.quasimondo.com// created Feburary 29, 2004// Android port : Yahel Bouaziz <yahel at kayenko.com>// http://www.kayenko.com// ported april 5th, 2012// This is a compromise between Gaussian Blur and Box blur// It creates much better looking blurs than Box Blur, but is// 7x faster than my Gaussian Blur implementation.//// I called it Stack Blur because this describes best how this// filter works internally: it creates a kind of moving stack// of colors whilst scanning through the image. Thereby it// just has to add one new block of color to the right side// of the stack and remove the leftmost color. The remaining// colors on the topmost layer of the stack are either added on// or reduced by one, depending on if they are on the right or// on the left side of the stack.//// If you are using this algorithm in your code please add// the following line://// Stack Blur Algorithm by Mario Klingemann <mario@quasimondo.com>Bitmap bitmap;if (canReuseInBitmap) {bitmap = sentBitmap;} else {bitmap = sentBitmap.copy(sentBitmap.getConfig(), true);}if (radius < 1) {return (null);}int w = bitmap.getWidth();int h = bitmap.getHeight();int[] pix = new int[w * h];bitmap.getPixels(pix, 0, w, 0, 0, w, h);int wm = w - 1;int hm = h - 1;int wh = w * h;int div = radius + radius + 1;int r[] = new int[wh];int g[] = new int[wh];int b[] = new int[wh];int rsum, gsum, bsum, x, y, i, p, yp, yi, yw;int vmin[] = new int[Math.max(w, h)];int divsum = (div + 1) >> 1;divsum *= divsum;int dv[] = new int[256 * divsum];for (i = 0; i < 256 * divsum; i++) {dv[i] = (i / divsum);}yw = yi = 0;int[][] stack = new int[div][3];int stackpointer;int stackstart;int[] sir;int rbs;int r1 = radius + 1;int routsum, goutsum, boutsum;int rinsum, ginsum, binsum;for (y = 0; y < h; y++) {rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;for (i = -radius; i <= radius; i++) {p = pix[yi + Math.min(wm, Math.max(i, 0))];sir = stack[i + radius];sir[0] = (p & 0xff0000) >> 16;sir[1] = (p & 0x00ff00) >> 8;sir[2] = (p & 0x0000ff);rbs = r1 - Math.abs(i);rsum += sir[0] * rbs;gsum += sir[1] * rbs;bsum += sir[2] * rbs;if (i > 0) {rinsum += sir[0];ginsum += sir[1];binsum += sir[2];} else {routsum += sir[0];goutsum += sir[1];boutsum += sir[2];}}stackpointer = radius;for (x = 0; x < w; x++) {r[yi] = dv[rsum];g[yi] = dv[gsum];b[yi] = dv[bsum];rsum -= routsum;gsum -= goutsum;bsum -= boutsum;stackstart = stackpointer - radius + div;sir = stack[stackstart % div];routsum -= sir[0];goutsum -= sir[1];boutsum -= sir[2];if (y == 0) {vmin[x] = Math.min(x + radius + 1, wm);}p = pix[yw + vmin[x]];sir[0] = (p & 0xff0000) >> 16;sir[1] = (p & 0x00ff00) >> 8;sir[2] = (p & 0x0000ff);rinsum += sir[0];ginsum += sir[1];binsum += sir[2];rsum += rinsum;gsum += ginsum;bsum += binsum;stackpointer = (stackpointer + 1) % div;sir = stack[(stackpointer) % div];routsum += sir[0];goutsum += sir[1];boutsum += sir[2];rinsum -= sir[0];ginsum -= sir[1];binsum -= sir[2];yi++;}yw += w;}for (x = 0; x < w; x++) {rinsum = ginsum = binsum = routsum = goutsum = boutsum = rsum = gsum = bsum = 0;yp = -radius * w;for (i = -radius; i <= radius; i++) {yi = Math.max(0, yp) + x;sir = stack[i + radius];sir[0] = r[yi];sir[1] = g[yi];sir[2] = b[yi];rbs = r1 - Math.abs(i);rsum += r[yi] * rbs;gsum += g[yi] * rbs;bsum += b[yi] * rbs;if (i > 0) {rinsum += sir[0];ginsum += sir[1];binsum += sir[2];} else {routsum += sir[0];goutsum += sir[1];boutsum += sir[2];}if (i < hm) {yp += w;}}yi = x;stackpointer = radius;for (y = 0; y < h; y++) {// Preserve alpha channel: ( 0xff000000 & pix[yi] )pix[yi] = (0xff000000 & pix[yi]) | (dv[rsum] << 16) | (dv[gsum] << 8) | dv[bsum];rsum -= routsum;gsum -= goutsum;bsum -= boutsum;stackstart = stackpointer - radius + div;sir = stack[stackstart % div];routsum -= sir[0];goutsum -= sir[1];boutsum -= sir[2];if (x == 0) {vmin[y] = Math.min(y + r1, hm) * w;}p = x + vmin[y];sir[0] = r[p];sir[1] = g[p];sir[2] = b[p];rinsum += sir[0];ginsum += sir[1];binsum += sir[2];rsum += rinsum;gsum += ginsum;bsum += binsum;stackpointer = (stackpointer + 1) % div;sir = stack[stackpointer];routsum += sir[0];goutsum += sir[1];boutsum += sir[2];rinsum -= sir[0];ginsum -= sir[1];binsum -= sir[2];yi += w;}}bitmap.setPixels(pix, 0, w, 0, 0, w, h);return (bitmap);}}

　　可以看出，使用方法非常簡單，傳入待續話的bitmap、虛化程序（一般為8），和是否重用flag。

　　然后，對于大圖，往往會出現OOM異常的報錯，那是因為當虛化開始時，虛擬機開始不斷進行內存回收，包括所有軟引用的回收。然后，仍然出現了內存溢出。那就意味著我們只能對小圖進行虛化，這樣才能防止內存溢出。但我并不想換其他圖，那么，我們應該把這張圖先進性縮放。

縮放方法：

public static Bitmap createScaledBitmap(Bitmap src, int dstWidth, int dstHeight, boolean filter)

　　第四個輸入輸入的參數filter，是指縮放邊緣效果，filter為true則會得到一個邊緣平滑的bitmap，反之，則會得到一個邊緣鋸齒、pixelrelated的bitmap。這里，我們對圖片進行虛化，無所謂邊緣效果，所以filter=false。

　　那么，怎么組合運行虛化代碼實現大圖虛化呢？以下為具體實現代碼：

int scaleRatio = 10; int blurRadius = 8; Bitmap scaledBitmap = Bitmap.createScaledBitmap(originBitmap,originBitmap.getWidth() / scaleRatio,originBitmap.getHeight() / scaleRatio,false); Bitmap blurBitmap = FastBlur.doBlur(scaledBitmap, blurRadius, true); imageView.setScaleType(ImageView.ScaleType.CENTER_CROP); imageView.setImageBitmap(blurBitmap);

　　如果圖片的虛化效果較弱或者并不是很明顯，提供增強虛化效果的方法：

1、增大scaleRatio的數值（增大縮放比），使用更小的bitmap去虛化，這樣可以得到更好的虛化效果，而且有利于減小內存消耗。

2、增大blurRadius的數值，這樣可以提高續話層度，不過會導致cpu更加intensive。

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參考資料：實現虛化、bitmap代碼地址

轉載于:https://www.cnblogs.com/swalka/p/5275090.html

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