編輯：關於Android編程
簡述：
做直播類app的時候點擊進入直播間接通的過程中首先顯示一張模糊的毛玻璃效果的圖片，那麼此時就要考慮使用高斯模糊的時候了。Android中提供了RenderScript來操作圖片，但是這個的使用版本要求是在API17以上，所以我們還可以考慮使用第三方可FastBlur。
使用RenderScript方案：
Renderscript是android平台上進行高性能計算的框架。Renderscript主要面向並行計算，雖然它對計算密集型工作也是有益的。Renderscript在運行時將在設備上可用的處理器間平衡負載，比如多核CPU，GPU或者DSP，它讓你專注於算法而不是平衡負載。RenderScript對圖像處理，計算攝影學，計算機視覺方面的應用非常有用。
為了提高模糊的效果以及減少模糊過程中性能的消耗，首先對待模糊的圖片進行壓縮。廢話少說，上代碼：
/**
* 類描述：利用RenderScript進行圖片的高斯模糊（該方案兼容最低版本API17）
* Created by lizhenya on 16/5/28.
*/
public class RenderScriptBlur {
private static final float BITMAP_SCALE = 0.4f;
private static final int BLUR_RADIUS = 7;
public static Bitmap blur(Context context, Bitmap bitmap) {
return blur(context, bitmap, BITMAP_SCALE, BLUR_RADIUS);
}
public static Bitmap blur(Context context, Bitmap bitmap, float bitmap_scale) {
return blur(context, bitmap, bitmap_scale, BLUR_RADIUS);
}
public static Bitmap blur(Context context, Bitmap bitmap, int blur_radius) {
return blur(context, bitmap, BITMAP_SCALE, blur_radius);
}
@TargetApi(Build.VERSION_CODES.JELLY_BEAN_MR1)
public static Bitmap blur(Context context, Bitmap bitmap, float bitmap_scale, int blur_radius) {
//先對圖片進行壓縮然後再blur
Bitmap inputBitmap = Bitmap.createScaledBitmap(bitmap, Math.round(bitmap.getWidth() * bitmap_scale),
Math.round(bitmap.getHeight() * bitmap_scale), false);
//創建空的Bitmap用於輸出
Bitmap outputBitmap = Bitmap.createBitmap(inputBitmap);
//①、初始化Renderscript
RenderScript rs = RenderScript.create(context);
//②、Create an Intrinsic Blur Script using the Renderscript
ScriptIntrinsicBlur theIntrinsic = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));
//③、native層分配內存空間
Allocation tmpIn = Allocation.createFromBitmap(rs, inputBitmap);
Allocation tmpOut = Allocation.createFromBitmap(rs, outputBitmap);
//④、設置blur的半徑然後進行blur
theIntrinsic.setRadius(blur_radius);
theIntrinsic.setInput(tmpIn);
theIntrinsic.forEach(tmpOut);
//⑤、拷貝blur後的數據到java緩沖區中
tmpOut.copyTo(outputBitmap);
//⑥、銷毀Renderscript
rs.destroy();
bitmap.recycle();
return outputBitmap;
}
}
實際效果如何呢？下面我把多次測試打印出來的日志貼出來：
07-28 17:16:32.394 20734-20734/? E/TAg: 耗時時間：30ms 07-28 17:16:33.450 20734-20734/? E/TAg: 耗時時間：13ms 07-28 17:16:34.207 20734-20734/? E/TAg: 耗時時間：7ms 07-28 17:16:35.045 20734-20734/? E/TAg: 耗時時間：9ms 07-28 17:16:35.909 20734-20734/? E/TAg: 耗時時間：6ms 07-28 17:16:36.806 20734-20734/? E/TAg: 耗時時間：9ms 07-28 17:16:39.312 20734-20734/? E/TAg: 耗時時間：7ms 07-28 17:16:44.045 20734-20734/? E/TAg: 耗時時間：8ms 07-28 17:16:44.877 20734-20734/? E/TAg: 耗時時間：8ms 07-28 17:16:45.555 20734-20734/? E/TAg: 耗時時間：13ms 07-28 17:16:46.202 20734-20734/? E/TAg: 耗時時間：8ms 07-28 17:16:46.901 20734-20734/? E/TAg: 耗時時間：9ms 07-28 17:16:47.952 20734-20734/? E/TAg: 耗時時間：7ms上面的日志可以一眼看得出平均時間是小於16ms的，時間性能槓槓的。但是這個方案只能適用於API17以上的版本，對於低版本怎麼辦呢？上網搜了一圈，看到一個第三方的框架FastBlur。
使用FastBlur兼容低版本：
/**
* Created by paveld on 3/6/14.
*/
public class StackBlur {
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
// http://incubator.quasimondo.com
// created Feburary 29, 2004
// Android port : Yahel Bouaziz
// 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
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);
}
}
這個方案的時間性能慘不忍睹呀，遠遠大於16ms，
07-28 18:33:14.751 11928-11928/? E/TAg: 耗時時間：483ms 07-28 18:33:17.602 11928-11928/? E/TAg: 耗時時間：431ms 07-28 18:33:19.176 11928-11928/? E/TAg: 耗時時間：369ms 07-28 18:33:20.452 11928-11928/? E/TAg: 耗時時間：405ms 07-28 18:33:21.599 11928-11928/? E/TAg: 耗時時間：412ms 07-28 18:33:22.791 11928-11928/? E/TAg: 耗時時間：368ms 07-28 18:33:23.970 11928-11928/? E/TAg: 耗時時間：314ms 07-28 18:33:25.059 11928-11928/? E/TAg: 耗時時間：335ms 07-28 18:33:25.886 11928-11928/? E/TAg: 耗時時間：384ms 07-28 18:33:26.806 11928-11928/? E/TAg: 耗時時間：404ms 07-28 18:33:27.423 11928-11928/? E/TAg: 耗時時間：473ms 07-28 18:33:27.959 11928-11928/? E/TAg: 耗時時間：373ms
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