高斯模糊处理头像作为背景图的两种高效便捷方法

第一种是最简单方便的结合强大的Glide图片加载框架的使用（推荐）

先来看看效果图吧！

这种是使用头像作为背景模糊背景使用的：





Glide框架结合使用

第一步添加下面依赖并同步

compile 'com.github.bumptech.glide:glide:3.7.0'
compile 'jp.wasabeef:glide-transformations:2.0.1'

第二步glide代码的使用如下

//头像
final String photo = MapUtil.getValueStr(data, "fileUrl");
Glide.with(mContext)
.load(photo)
.dontAnimate()
//加载过程中的图片显示
.placeholder(R.mipmap.bg4)
//加载失败中的图片显示
//如果重试3次（下载源代码可以根据需要修改）还是无法成功加载图片，则用错误占位符图片显示。
.error(R.mipmap.bg4)
//第二个参数是圆角半径，第三个是模糊程度，2-5之间个人感觉比较好。
.bitmapTransform(new BlurTransformation(PersonalActivity.this, 14, 1))
.into(iv_person_bg);

这种方法是不是很简单一行代码解决问题

.bitmapTransform(new BlurTransformation(PersonalActivity.this, 14, 1))

第二种方法稍微麻烦点，效果都一样

方法中会用到这个类

package teacherlove.zontonec.com.ztteacherlove.helper;

import android.graphics.Bitmap;
import android.graphics.BitmapFactory;

import java.io.BufferedInputStream;
import java.io.BufferedOutputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.net.URL;

/**
*
*   @data 创建时间: 2017/8/24
*
*   @author 创建人: kris_liutao
*
*   @description  功能描述: 高斯模糊处理背景图
*
*/

public class BlurImageview {

/**
* 根据imagepath获取bitmap
*/
/**
* 得到本地或者网络上的bitmap url - 网络或者本地图片的绝对路径,比如:
* A.网络路径: url="http://blog.foreverlove.us/girl2.png" ;
* B.本地路径:url="file://mnt/sdcard/photo/image.png";
* C.支持的图片格式 ,png, jpg,bmp,gif等等
* @param url
* @return
*/
public static int IO_BUFFER_SIZE = 2 * 1024;

public static Bitmap GetUrlBitmap(String url, int scaleRatio) {

int blurRadius = 8;//通常设置为8就行。
if (scaleRatio <= 0) {
scaleRatio = 10;
}

Bitmap originBitmap = null;
InputStream in = null;
BufferedOutputStream out = null;
try {
in = new BufferedInputStream(new URL(url).openStream(), IO_BUFFER_SIZE);
final ByteArrayOutputStream dataStream = new ByteArrayOutputStream();
out = new BufferedOutputStream(dataStream, IO_BUFFER_SIZE);
copy(in, out);
out.flush();
byte[] data = dataStream.toByteArray();
originBitmap = BitmapFactory.decodeByteArray(data, 0, data.length);

Bitmap scaledBitmap = Bitmap.createScaledBitmap(originBitmap,
originBitmap.getWidth() / scaleRatio,
originBitmap.getHeight() / scaleRatio,
false);
Bitmap blurBitmap = doBlur(scaledBitmap, blurRadius, true);
return blurBitmap;
} catch (IOException e) {
e.printStackTrace();
return null;
}
}

private static void copy(InputStream in, OutputStream out)
throws IOException {
byte[] b = new byte[IO_BUFFER_SIZE];
int read;
while ((read = in.read(b)) != -1) {
out.write(b, 0, read);
}
}

//    把本地图片毛玻璃化
public static Bitmap toBlur(Bitmap originBitmap, int scaleRatio) {
//        int scaleRatio = 10;
// 增大scaleRatio缩放比，使用一样更小的bitmap去虚化可以到更好的得模糊效果，而且有利于占用内存的减小；
int blurRadius = 8;//通常设置为8就行。
//增大blurRadius，可以得到更高程度的虚化，不过会导致CPU更加intensive

/* 其中前三个参数很明显，其中宽高我们可以选择为原图尺寸的1/10；
第四个filter是指缩放的效果，filter为true则会得到一个边缘平滑的bitmap，
反之，则会得到边缘锯齿、pixelrelated的bitmap。
这里我们要对缩放的图片进行虚化，所以无所谓边缘效果，filter=false。*/
if (scaleRatio <= 0) {
scaleRatio = 10;
}
Bitmap scaledBitmap = Bitmap.createScaledBitmap(originBitmap,
originBitmap.getWidth() / scaleRatio,
originBitmap.getHeight() / scaleRatio,
false);
Bitmap blurBitmap = doBlur(scaledBitmap, blurRadius, true);
return blurBitmap;
}

public static Bitmap doBlur(Bitmap sentBitmap, int radius, boolean canReuseInBitmap) {

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);
}

}

然后在需要加载高斯模糊图的地方使用下面这个方法

final String pattern = "2";//此处参数可以随意设置根据个人需求而言
new Thread(new Runnable() {
@Override
public void run() {
int scaleRatio = 0;
if (TextUtils.isEmpty(pattern)) {
scaleRatio = 0;
} else if (scaleRatio < 0) {
scaleRatio = 10;
} else {
scaleRatio = Integer.parseInt(pattern);
}
//下面的这个方法必须在子线程中执行
final Bitmap blurBitmap = BlurImageview.GetUrlBitmap(photo, scaleRatio);

//刷新ui必须在主线程中执行
App.runOnUIThread(new Runnable() {
@Override
public void run() {                                           iv_person_bg.setScaleType(ImageView.ScaleType.CENTER_CROP);                                               iv_person_bg.setImageBitmap(blurBitmap);
}
});
}
}).start();

这个刷新UI我给写在Application类的子类App下面了

/**
* 在主线程中刷新UI的方法
*
* @param r
*/
public static void runOnUIThread(Runnable r) {
App.getMainHandler().post(r);
}

//用来在主线程中刷新ui
private static Handler mHandler;

public static Handler getMainHandler() {
return mHandler;
}

到此，就是所有使用到的代码，简简单单完成高斯模糊0.0