1、http://jhlabs.com/ip/index.html

public static byte[] blur(byte[] data) throws IOException {
ByteArrayInputStream bais = new ByteArrayInputStream(data);
BufferedImage img = ImageIO.read(bais);
GaussianFilter gaussianFilter = new GaussianFilter();
gaussianFilter.filter(img, img);
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ImageIO.write(img, "jpg", baos);
return baos.toByteArray();

Maven地址:http://mvnrepository.com/artifact/com.jhlabs/filters

除了高斯模糊,这人还写了好多图片处理的filter,参见:http://jhlabs.com/ip/filters/index.html

2、别人写的一个类,也很赞

package com.extr.util;

import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.awt.image.PixelGrabber;
import java.io.ByteArrayInputStream;
import java.io.ByteArrayOutputStream;
import java.io.IOException; public class GaussianBlurUtil { public static byte[] blur(byte[] data) throws IOException {
ByteArrayInputStream bais = new ByteArrayInputStream(data);
BufferedImage img = ImageIO.read(bais);
img = blur(img, 50);
ByteArrayOutputStream baos = new ByteArrayOutputStream();
ImageIO.write(img, "jpg", baos);
return baos.toByteArray();
} /**
* 模糊执行方法
*
* @param img 原图片
* @param radius 模糊权重
* @return 模糊后图片
*/
public static BufferedImage blur(BufferedImage img, int radius) throws IOException {
int height = img.getHeight();
int width = img.getWidth();
int[] values = getPixArray(img, width, height);
values = doBlur(values, width, height, radius);
img.setRGB(0, 0, width, height, values, 0, width);
return img;
} /**
* 获取图像像素矩阵
*
* @param im
* @param w
* @param h
* @return
*/
private static int[] getPixArray(Image im, int w, int h) {
int[] pix = new int[w * h];
PixelGrabber pg = null;
try {
pg = new PixelGrabber(im, 0, 0, w, h, pix, 0, w);
if (pg.grabPixels() != true)
try {
throw new java.awt.AWTException("pg error" + pg.status());
} catch (Exception eq) {
eq.printStackTrace();
}
} catch (Exception ex) {
ex.printStackTrace();
}
return pix;
} /**
* 高斯模糊算法。
*
* @param pix
* @param w
* @param h
* @param radius
* @return
*/
public static int[] doBlur(int[] pix, int w, int h, int radius) throws IOException {
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++) {
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;
}
}
return pix;
}
}

  

高斯模糊的Java实现的更多相关文章

  1. Spark案例分析

    一.需求:计算网页访问量前三名 import org.apache.spark.rdd.RDD import org.apache.spark.{SparkConf, SparkContext} /* ...

  2. 简单的java高斯模糊算法

    import java.awt.Color; import java.awt.image.BufferedImage; import java.io.File; import java.io.IOEx ...

  3. [Android]-图片JNI(C++\Java)高斯模糊的实现与比較

    版权声明:本文作者:Qiujuer https://github.com/qiujuer; 转载请注明出处,盗版必究! !! https://blog.csdn.net/qiujuer/article ...

  4. Android开发学习之路-动态高斯模糊怎么做

    什么是高斯模糊? 高斯模糊(英语:Gaussian Blur),也叫高斯平滑,是在Adobe Photoshop.GIMP以及Paint.NET等图像处理软件中广泛使用的处理效果,通常用它来减少图像噪 ...

  5. Android 图片滤镜工具——高斯模糊

    ===================高斯模糊========================= 创建一个 ImageFilter 类(滤镜工具),代码如下: import android.graph ...

  6. Atitit Gaussian Blur 高斯模糊 的原理and实现and 用途

    Atitit Gaussian Blur 高斯模糊 的原理and实现and 用途 1.1. 高斯模糊 的原理(周边像素的平均值+正态分布的权重1 1.2. 高斯模糊 的用途(磨皮,毛玻璃效果,背景虚化 ...

  7. Android高斯模糊

    传送门 github地址:http://developer.android.com/guide/topics/renderscript/compute.html: https://github.com ...

  8. 一步步实现滑动验证码,Java图片处理关键代码

    最近滑动验证码在很多网站逐步流行起来,一方面对用户体验来说,比较新颖,操作简单,另一方面相对图形验证码来说,安全性并没有很大的降低.当然到目前为止,没有绝对的安全验证,只是不断增加攻击者的绕过成本. ...

  9. Android图像处理 - 高斯模糊的原理及实现

    欢迎大家前往云+社区,获取更多腾讯海量技术实践干货哦~ 由 天天P图攻城狮 发布在云+社区 作者简介:damonxia(夏正冬),天天P图Android工程师 前言 高斯模糊是图像处理中几乎每个程序员 ...

随机推荐

  1. css3实现旋转表

    如图所示: css部分: <style> #clock{width:100px; height:100px; border-radius:50%; border:4px solid bla ...

  2. js,JQ获取短信验证码倒计时

    按钮 <a href="javasript:void(0);"onclick="settime(this);">获取手机验证码</a> ...

  3. Codeforces 1017F The Neutral Zone 数论

    原文链接https://www.cnblogs.com/zhouzhendong/p/CF1017F.html 题目传送门 - CF1017F 题意 假设一个数 $x$ 分解质因数后得到结果 $x=p ...

  4. Machine Learning 算法可视化实现1 - 线性回归

    一.原理和概念 1.回归 回归最简单的定义是,给出一个点集D,用一个函数去拟合这个点集.而且使得点集与拟合函数间的误差最小,假设这个函数曲线是一条直线,那就被称为线性回归:假设曲线是一条二次曲线,就被 ...

  5. Shiro笔记(四)Shiro的realm认证

    认证流程: 1.获取当前Subject.调用SecurityUtils.getSubject(); 2.测试当前用户是否已经被认证,即是否已经登录,调用Subject的isAurhenticated( ...

  6. vue-cli@3.x之使用vue ui创建项目-来自一个战五渣的体验

    1. 全局安装vue-cli yarn global add @vue/cli // 检查安装是否成功 vue -V // 3.2.2 2. 初始化 vue ui 执行命令 vue ui 2.1 该命 ...

  7. asp.net core自定义模型验证——前端验证

    转载请注明出处:http://www.cnblogs.com/zhiyong-ITNote/ 官方网站:https://docs.microsoft.com/zh-cn/aspnet/core/mvc ...

  8. 845. Greatest Common Divisor

    描述 Given two numbers, number a and number b. Find the greatest common divisor of the given two numbe ...

  9. 服务端spark gbdt模型计算性能优化

    服务端使用训练出来的模型,spark模型计算第一步是实现spark模型加载. 线上服务对用户体验影响极大,故需要对模型使用进行优化. 1.多线程并发进行计算,线上两个服务.优化cpu 2.在扩召回集, ...

  10. Centos6.5部署Rsyslog-日志的存储方式及监测服务状态

    1.以IP地址命名 在/etc/rsyslog.conf中加入如下配置,并做好备注.添加这三行配置之后,远程日志会被单独输出到一个以IP命名的日志文件中. #IP format by zhz at x ...