android 高斯模糊实现

高斯模糊

高斯模糊就是将指定像素变换为其与周边像素加权平均后的值，权重就是高斯分布函数计算出来的值。

一种实现

点击打开链接<-这里是一片关于高斯模糊算法的介绍，我们需要首先根据高斯分布函数计算权重值，为了提高效率我们采用一维高斯分布函数，然后处理图像的时候在横向和纵向进行两次计算得到结果。下面是一种实现

[java] view
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public static void gaussBlur(int[] data, int width, int height, int radius,

float sigma) {

float pa = (float) (1 / (Math.sqrt(2 * Math.PI) * sigma));

float pb = -1.0f / (2 * sigma * sigma);

// generate the Gauss Matrix

float[] gaussMatrix = new float[radius * 2 + 1];

float gaussSum = 0f;

for (int i = 0, x = -radius; x <= radius; ++x, ++i) {

float g = (float) (pa * Math.exp(pb * x * x));

gaussMatrix[i] = g;

gaussSum += g;

}

for (int i = 0, length = gaussMatrix.length; i < length; ++i) {

gaussMatrix[i] /= gaussSum;

}

// x direction

for (int y = 0; y < height; ++y) {

for (int x = 0; x < width; ++x) {

float r = 0, g = 0, b = 0;

gaussSum = 0;

for (int j = -radius; j <= radius; ++j) {

int k = x + j;

if (k >= 0 && k < width) {

int index = y * width + k;

int color = data[index];

int cr = (color & 0x00ff0000) >> 16;

int cg = (color & 0x0000ff00) >> 8;

int cb = (color & 0x000000ff);

r += cr * gaussMatrix[j + radius];

g += cg * gaussMatrix[j + radius];

b += cb * gaussMatrix[j + radius];

gaussSum += gaussMatrix[j + radius];

}

}

int index = y * width + x;

int cr = (int) (r / gaussSum);

int cg = (int) (g / gaussSum);

int cb = (int) (b / gaussSum);

data[index] = cr << 16 | cg << 8 | cb | 0xff000000;

}

}

// y direction

for (int x = 0; x < width; ++x) {

for (int y = 0; y < height; ++y) {

float r = 0, g = 0, b = 0;

gaussSum = 0;

for (int j = -radius; j <= radius; ++j) {

int k = y + j;

if (k >= 0 && k < height) {

int index = k * width + x;

int color = data[index];

int cr = (color & 0x00ff0000) >> 16;

int cg = (color & 0x0000ff00) >> 8;

int cb = (color & 0x000000ff);

r += cr * gaussMatrix[j + radius];

g += cg * gaussMatrix[j + radius];

b += cb * gaussMatrix[j + radius];

gaussSum += gaussMatrix[j + radius];

}

}

int index = y * width + x;

int cr = (int) (r / gaussSum);

int cg = (int) (g / gaussSum);

int cb = (int) (b / gaussSum);

data[index] = cr << 16 | cg << 8 | cb | 0xff000000;

}

}

}

实际测试会发现这种计算方式是很耗时间的，而且模糊半径越大，从原理也可以看到计算量是平方增长的，所以计算时间也越长。

RenderScript

RenderScript是Android在API 11之后加入的，用于高效的图片处理，包括模糊、混合、矩阵卷积计算等，代码示例如下

[java] view
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public Bitmap blurBitmap(Bitmap bitmap){

//Let's create an empty bitmap with the same size of the bitmap we want to blur

Bitmap outBitmap = Bitmap.createBitmap(bitmap.getWidth(), bitmap.getHeight(), Config.ARGB_8888);

//Instantiate a new Renderscript

RenderScript rs = RenderScript.create(getApplicationContext());

//Create an Intrinsic Blur Script using the Renderscript

ScriptIntrinsicBlur blurScript = ScriptIntrinsicBlur.create(rs, Element.U8_4(rs));

//Create the Allocations (in/out) with the Renderscript and the in/out bitmaps

Allocation allIn = Allocation.createFromBitmap(rs, bitmap);

Allocation allOut = Allocation.createFromBitmap(rs, outBitmap);

//Set the radius of the blur

blurScript.setRadius(25.f);

//Perform the Renderscript

blurScript.setInput(allIn);

blurScript.forEach(allOut);

//Copy the final bitmap created by the out Allocation to the outBitmap

allOut.copyTo(outBitmap);

//recycle the original bitmap

bitmap.recycle();

//After finishing everything, we destroy the Renderscript.

rs.destroy();

return outBitmap;

}

（示例来源 https://gist.github.com/Mariuxtheone/903c35b4927c0df18cf8）

FastBlur

[java] view
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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 <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);

}

这里的方法也可以实现高斯模糊的效果，但使用了特殊的算法，比第一种可以快很多，但比起RenderScript还是慢一些

（示例来源 Android高级模糊技术）

实现YAHOO天气的动态模糊效果

　　YAHOO天气中的背景会随着手指上滑模糊程度加深，实际使用中发现怎么都达不到那样流畅的效果，因为手势刷新的速度很快，每一帧都去重新模糊计算一遍，还是会有延迟，造成页面卡顿。后来在一次偶然的开发中发现其实不需要每一帧都重新去模糊一遍，而是将图片最大程度模糊一次，之后和原图叠加，通过改变叠加的模糊图片的alpha值来达到不同程度的模糊效果。下面是一个例子，可以看到随着模糊图片alpha值的变化，叠加后产生不同程度的模糊效果。



随滑动变换alpha值的代码如下

[java] view
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mBlurImage.setOnTouchListener(new OnTouchListener() {

private float mLastY;

@Override

public boolean onTouch(View v, MotionEvent event) {

switch (event.getAction()) {

case MotionEvent.ACTION_DOWN:

mLastY = event.getY();

break;

case MotionEvent.ACTION_MOVE:

float y = event.getY();

float alphaDelt = (y - mLastY) / 1000;

float alpha = mBlurImage.getAlpha() + alphaDelt;

if (alpha > 1.0) {

alpha = 1.0f;

} else if (alpha < 0.0) {

alpha = 0.0f;

}

mTextView.setText(String.valueOf(alpha));

mBlurImage.setAlpha(alpha);

break;

case MotionEvent.ACTION_UP:

break;

}

return true;

}

});

示例代码下载 http://download.csdn.net/detail/xu_fu/7628139