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一行java代码实现高斯模糊效果

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

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首先看效果图

1.本地图片高斯模糊

一行java代码实现高斯模糊效果

2.网络图片高斯模糊

一行java代码实现高斯模糊效果

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

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

 
 
 
 
 
 
 
 
 
 
 
 

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创新互联。


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