Android软件开发之高斯模糊问题
之前看到Android软件中用到和IOS系统类似的模糊效果，自己琢磨着也想做一个，于是在网上搜索了很多的相关资料，发现这篇博客《Android高级模糊技术》写得特别好，所以就开始好好地研究。
等到我把这个功能做到软件上，问题出现了，什么问题呢？
本来是准备用模糊图片来作为软件全屏界面的背景，可是布局显示的模糊图片在右下边缘一直出现黑色的边，不能铺满整个屏幕。一开始以为是模糊的参数需要调整，模糊后的图片变小了，但是把模糊后的图片的height和width打印出来，发现没有问题。
后来我想到，实际使用的图片比屏幕的尺寸小一点，而模糊处理的过程之前并没有对图片大小进行调整，导致输出的模糊图片虽然和视图（屏幕）大小一致，但是图片的模糊区域却和原图片相同大小，从而留下了空余的部分——黑色的边缘。于是又写了一个缩放图片的工具类，在模糊处理之前来同步图片和视图（屏幕）的大小，发现问题解决！
FastBlur.java
该文件是图片模糊的像素处理类，直接放入工程中
package com.kuk.tools;
import android.graphics.Bitmap;
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 <[email protected]>
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);
}
}
PictureZoom.java
此文件是缩放图片类
import android.content.Context;
import android.graphics.Bitmap;
import android.graphics.Matrix;
/**
* 此类的功能是用来缩放图片
* <p>
* 防止图片大小和屏幕大小不一致，造成模糊处理后会出现图片显示异常
* </p>
* @author Macneil.Gu
*/
public class PictureZoom {
private Context context;
public PictureZoom(Context context) {
this.context = context;
}
/**
* 缩放图片至指定的大小
*
* @param bitmap Bitmap格式的图片
* @param x 指定的宽
* @param y 指定的长
* @return 缩放后的指定大小图片
*/
public Bitmap Zoom(Bitmap bitmap, float x, float y) {
//图片的宽和高
int width = bitmap.getWidth();
int height = bitmap.getHeight();
//原图片和指定图片宽高比（缩放率），指定/原
float sx = x / width;
float sy = y / height;
//缩放图片动作
Matrix mtr = new Matrix();
mtr.postScale(sx, sy);
//创建新的图片
Bitmap bm = Bitmap.createBitmap(bitmap, 0, 0, width, height, mtr, true);
return bm;
}
}
MainActivity.java
高斯模糊处理函数，这里对原来的函数修改了一点点
/**
* 高斯模糊处理
* <p>
* <code>将图片剪裁成1/8后进行模糊处理，可以大大减少模糊处理的时间，提高代码执行效率</code>
* </p>
* @param bitmap 需要模糊处理的图片
* @param view 显示图片的视图
*/
private void blur(Bitmap bitmap, View view) {
float scaleFactor = 8;
float radius = 2;
Bitmap overlay = Bitmap.createBitmap(
(int) (view.getMeasuredWidth() / scaleFactor),
(int) (view.getMeasuredHeight() / scaleFactor),
Bitmap.Config.ARGB_8888);
Canvas canvas = new Canvas(overlay);
canvas.translate(-view.getLeft() / scaleFactor, -view.getTop()
/ scaleFactor);
canvas.scale(1 / scaleFactor, 1 / scaleFactor);
Paint paint = new Paint();
paint.setFlags(Paint.FILTER_BITMAP_FLAG); // 双缓冲机制
canvas.drawBitmap(bitmap, 0, 0, paint);
overlay = FastBlur.doBlur(overlay, (int) radius, true);
view.setBackgroundDrawable(new BitmapDrawable(getResources(), overlay));
}
>>原博文的注解：
● scaleFactor提供了需要缩小的等级，在代码中我把bitmap的尺寸缩小到原图的1/8。因为这个bitmap在模糊处理时会先被缩小然后再放大，所以在我的模糊算法中就不用radius这个参数了，所以把它设成2。
● 接着需要创建bitmap，这个bitmap比最后需要的小八倍。
● 请注意我给Paint提供了FILTER_BITMAP_FLAG标示，这样的话在处理bitmap缩放的时候，就可以达到双缓冲的效果，模糊处理的过程就更加顺畅了。
● 接下来和之前一样进行模糊处理操作，这次的图片小了很多，幅度也降低了很多，所以模糊过程非常快。
● 把模糊处理后的图片作为背景，它会自动进行放大操作的。
调用上述的模糊处理函数，对指定图片模糊处理，并显示到布局的ImageView上。
// 获取窗口服务 WindowManager wm = (WindowManager) getSystemService(Context.WINDOW_SERVICE); // 缩放图片 PictureZoom pz = new PictureZoom(this); // 把图片缩放到窗口的长宽 Bitmap mybm = pz.Zoom(bm, wm.getDefaultDisplay().getWidth(), wm.getDefaultDisplay().getHeight()); // 模糊处理，blurImage是ImageView控件，这里是作为背景显示的 blur(mybm, blurImage);
>>将上述的三个文件放在同一个包下使用，否则需要导入文件使用。如果大家喜欢剖根究底，可以仔细阅读原博文。