本文实例为大家分享了本地图片或者网络图片高斯模糊效果(毛玻璃效果)，具体内容如下

首先看效果图

1.本地图片高斯模糊

2.网络图片高斯模糊

下面是使用步骤

一、实现本地图片或者网络图片的毛玻璃效果特别方便，只需要把下面的FastBlurUtil类复制到你的项目中就行

package com.testdemo.blur_image_lib10;

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;

/**

* Created by qcl on 14/7/15.

*/

public class FastBlurUtil {

/**

* 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);

}

}

二、使用实例

package com.testdemo;

import android.app.Activity;

import android.content.res.Resources;

import android.graphics.Bitmap;

import android.graphics.BitmapFactory;

import android.os.Bundle;

import android.text.TextUtils;

import android.view.View;

import android.widget.EditText;

import android.widget.ImageView;

import com.testdemo.blur_image_lib10.FastBlurUtil;

public class MainActivity10_BlurImage extends Activity {

ImageView image;

EditText edit;

@Override

protected void onCreate(Bundle savedInstanceState) {

super.onCreate(savedInstanceState);

setContentView(R.layout.activity_main10_blur_image);

image = (ImageView) findViewById(R.id.image);

edit = (EditText) findViewById(R.id.edit);

findViewById(R.id.button2).setOnClickListener(new View.OnClickListener() {

@Override

public void onClick(View v) {

String pattern = edit.getText().toString();

int scaleRatio = 0;

if (TextUtils.isEmpty(pattern)) {

scaleRatio = 0;

} else if (scaleRatio < 0) {

scaleRatio = 10;

} else {

scaleRatio = Integer.parseInt(pattern);

}

// 获取需要被模糊的原图bitmap

Resources res = getResources();

Bitmap scaledBitmap = BitmapFactory.decodeResource(res, R.drawable.filter);

// scaledBitmap为目标图像，10是缩放的倍数(越大模糊效果越高)

Bitmap blurBitmap = FastBlurUtil.toBlur(scaledBitmap, scaleRatio);

image.setScaleType(ImageView.ScaleType.CENTER_CROP);

image.setImageBitmap(blurBitmap);

}

});

findViewById(R.id.button).setOnClickListener(new View.OnClickListener() {

@Override

public void onClick(View v) {

//url为网络图片的url，10 是缩放的倍数(越大模糊效果越高)

final String pattern = edit.getText().toString();

final String url =

// "http://imgs.duwu.me/duwu/doc/cover/201601/18/173040803962.jpg";

"http://b.hiphotos.baidu.com/album/pic/item/caef76094b36acafe72d0e667cd98d1000e99c5f.jpg?psign=e72d0e667cd98d1001e93901213fb80e7aec54e737d1b867";

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 blurBitmap2 = FastBlurUtil.GetUrlBitmap(url, scaleRatio);

// 刷新ui必须在主线程中执行

APP.runOnUIThread(new Runnable() {//这个是我自己封装的在主线程中刷新ui的方法。

@Override

public void run() {

image.setScaleType(ImageView.ScaleType.CENTER_CROP);

image.setImageBitmap(blurBitmap2);

}

});

}

}).start();

}

});

}

}

下面是上面的布局文件

xmlns:tools="http://schemas.android.com/tools"

android:layout_width="match_parent"

android:layout_height="match_parent"

android:orientation="vertical">

android:id="@+id/image2"

android:layout_width="match_parent"

android:layout_height="220dp"

android:background="@drawable/filter"/>

android:layout_width="match_parent"

android:layout_height="wrap_content"

android:orientation="horizontal">

android:id="@+id/edit"

android:layout_width="wrap_content"

android:layout_height="wrap_content"

android:layout_marginTop="15dp"

android:hint="输入模糊度"

/>

android:id="@+id/button2"

android:layout_width="wrap_content"

android:layout_height="wrap_content"

android:text="转化毛玻璃"/>

android:id="@+id/button"

android:layout_width="wrap_content"

android:layout_height="wrap_content"

android:layout_marginLeft="4dp"

android:text="转化网络图片毛玻璃"/>

android:id="@+id/image"

android:layout_width="match_parent"

android:layout_height="220dp"

android:layout_below="@+id/image2"

/>

三、注意事项

1.一定不要忘记intent权限

2.加载网络图片时一定要在子线程中执行。

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。