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
github网址:https://github.com/qiushi123/blurimageqcl
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
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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:
* <p>
* a.网络路径: url="http://blog.foreverlove.us/girl2.png" ;
* <p>
* b.本地路径:url="file://mnt/sdcard/photo/image.png";
* <p>
* 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);
}
}
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二、使用实例
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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();
}
});
}
}
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下面是上面的布局文件
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<linearlayout xmlns:android= " http://schemas.android.com/apk/res/android "
xmlns:tools= "http://schemas.android.com/tools"
android:layout_width= "match_parent"
android:layout_height= "match_parent"
android:orientation= "vertical" >
<imageview
android:id= "@+id/image2"
android:layout_width= "match_parent"
android:layout_height= "220dp"
android:background= "@drawable/filter" />
<linearlayout
android:layout_width= "match_parent"
android:layout_height= "wrap_content"
android:orientation= "horizontal" >
<edittext
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"
/>
<button
android:id= "@+id/button2"
android:layout_width= "wrap_content"
android:layout_height= "wrap_content"
android:text= "转化毛玻璃" />
<button
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" />
</linearlayout>
<imageview
android:id= "@+id/image"
android:layout_width= "match_parent"
android:layout_height= "220dp"
android:layout_below= "@+id/image2"
/>
</linearlayout>
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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。
github网址:https://github.com/qiushi123/blurimageqcl
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服务器之家。