模糊(Blur)的学习与体会（一）
前段时间看见有同学做了个模糊背景，感觉很好，就想着做这么些个效果，于是就诞生了这几篇算是学习笔记和学习体会吧。
文章大致分为三节。一，模糊的初体验；二，模糊与Canvas的结合；三，Fast blur与Box blur的原理。如果以后在Blur的章节中有补充的时候，我将直接添加在后续博客中，在此就不特别说明了。
一，高斯模糊的初体验
这一节主要学习如何实现图片的模糊，其实非常简单，调用Fast blur这个方法即可。
Fast blur是实现模糊效果的一种算法，和后面我们要学习的Box blur类似。这是Fast blur的具体实现代码：
public class FastBlur {
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);
}
}
我们先暂时不去深究其算法，仅仅先了解其输入和输出，知其然就可以了，第三节我们再一起学习。
如方法所见，传入一个Bitmap对象(sentBitmap)和模糊半径(radius)，结果则返回一张经过处理后的Bitmap，这张Bitmap即是我们所求。
这是Activity的布局内容：
这里面放了两个ImageView，第一个用来显示原图片，第二个用来显示处理后的图片，而TextView用来显示图片处理的耗时。
再看Java代码：
public class MainActivity extends AppCompatActivity{
private ImageView image, image_blur = null;
private TextView timeText = null;
private Bitmap bmp = null;
@Override
protected void onCreate(Bundle savedInstanceState) {
super.onCreate(savedInstanceState);
setContentView(R.layout.activity_main);
image_blur = (ImageView) findViewById(R.id.ivBlur);
timeText = (TextView) findViewById(R.id.time);
image = (ImageView) findViewById(R.id.picture);
applyBlur();
}
private void applyBlur() {
image.getViewTreeObserver().addOnPreDrawListener(new ViewTreeObserver.OnPreDrawListener() {
@Override
public boolean onPreDraw() {
image.getViewTreeObserver().removeOnPreDrawListener(this);
image.buildDrawingCache();
bmp = image.getDrawingCache();
blur(bmp, image_blur);
return true;
}
});
}
private void blur(Bitmap bkg, View view) {
long startMs = System.currentTimeMillis();
float radius = 20;
bkg = FastBlur.doBlur(bkg, (int) radius, true);
view.setBackground(new BitmapDrawable(getResources(), bkg));
timeText.setText(System.currentTimeMillis() - startMs + "ms");
}
}
修改模糊半径radius可变化模糊效果，半径越大越模糊。
效果还不错，不过您有没有注意到一个细节，在图片左上角有处理的耗时，居然达到了900+ms，这是我们不能接受的。因为在Android显示中，每一秒至少需要40帧，即模糊耗时不能超过17ms，于是就有了先压缩再模糊的处理。
我们先来看一下效果：
模糊处理这是就只会占有很少的时间，仅10+ms。
如代码所示：
private void blur(Bitmap bkg, View view) {
long startMs = System.currentTimeMillis();
float radius = 2;
float scaleFactor = 10;
Bitmap overlay = Bitmap.createBitmap((int) (view.getMeasuredWidth() / scaleFactor),
(int) (view.getMeasuredHeight() / scaleFactor), Bitmap.Config.ARGB_8888);
Canvas canvas = new Canvas(overlay);
canvas.scale((float) 1 / scaleFactor, (float) 1 / scaleFactor);
canvas.drawBitmap(bkg, 0, 0, null);
overlay = FastBlur.doBlur(overlay, (int) radius, true);
view.setBackground(new BitmapDrawable(getResources(), overlay));
timeText.setText(System.currentTimeMillis() - startMs + "ms");
}其处理原理是先将图片的大小压缩N倍（我们这里是10倍，你可以试试不同的压缩比，以达到自己的要求），然后将压缩后的图片进行Fast blur。
因为模糊处理的过程就是降低图片精度，而压缩图片尺寸（大小）后再放大，也能达到降低图片精度的效果，而且图片被压缩后，Fast blur所处理的数据会成指数倍的减小，所以整个处理耗时就会变得很少。
好了，到这里我们已经对高斯模糊有了初步的了解。在下一节中我们将结合Canvas来实现圆形、圆角矩形、空心圆的模糊效果。
参考资料：http://blog.jobbole.com/63894/