javascript - js高斯模糊算法问题
迷茫
迷茫 2017-04-10 15:01:02
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<!DOCTYPE html>
<html lang="en">
<html>
<head>
  <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
  <meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=0" />
  <title>高斯模糊</title>
 </head>
 <body>
    <p id="wrap" class="wrap">
        <img src="img/12338563.jpg" alt="" id="imageSource" height="500" width="800"/>
        <canvas id="myCanvas"></canvas>
    </p>
 <script type="text/javascript">
    //-------------------------------做一个基于canvas的【图片上传,剪切】的插件
    //---------------------------------测试需在服务器环境下
        window.onload = function() { 
            var canvas = document.getElementById("myCanvas"); 
            var image = document.getElementById("imageSource"); 
            // re-size the canvas deminsion 
            console.log(image.width)
            canvas.width = image.width; 
            canvas.height = image.height; 
            // get 2D render object 
            var context = canvas.getContext("2d"); 
            context.drawImage(image, 0, 0,canvas.width,canvas.height); 
            var canvasData = context.getImageData(0, 0, canvas.width, canvas.height); //获取图片信息


            //对于一个数组来说,它的每一个元素并不拥有坐标,因此需要写一个方法,也就是告诉【x,y,radius】,就会返还一个数组,当然首先要确定的是【width,height】因为只有这样才能够给数组一个坐标
            //对于canvas来说,确定【width,height】就确定了【canvasData.data】的长度为【width*height*4】
            //但是坐标范围用width,height来固定

            //-------------------------------------------------高斯模糊开始------------------------------------------------------
            var radius = 20 ; //定义模糊半径
            var quan = getQuan(radius);
            var quanSub = ArrSub(quan);
            quan = jiaQuan(quan,quanSub); //获取权值

            var canvasDataCtrl = canvasData ;
            var width = canvas.width ,
                height = canvas.height ;
            console.log(new Date().getSeconds()) //高斯模糊开始时间
            for (var i=0;i<width ;i++ )
            {
                for (var j=0;j<height ;j++ )
                {
                    var index= (width*4)*j+i*4;
                    canvasDataCtrl.data[index] = getValue(i,j,0,quan); //r
                    canvasDataCtrl.data[index+1] = getValue(i,j,1,quan); //g
                    canvasDataCtrl.data[index+2] = getValue(i,j,2,quan); //b
                    canvasDataCtrl.data[index+3] = getValue(i,j,3,quan); //a
                }
            }
            context.putImageData(canvasDataCtrl, 0, 0);

            console.log(new Date().getSeconds()) //高斯模糊结束时间
            //console.log(value)
            //在chrome上运行时间大概有30s,运行这么长时间,肯定是算法问题,网上的StackBlur运行时间就很短
            //在计算量上,如果图片是像素是500×800,那这张图片所包含的rgba信息就有160,0000 之多
            //再加上要对每个像素的rgba值进行高斯转换,计算量就相当之大了,怎么解决呢
            console.log('模糊完成')


            function gaosi(x,y,a){ //根据高斯二维公式,获取点的高斯值
                var e = Math.E ;
                var pi = Math.PI ;
                return 1/(2*pi*a*a)*Math.pow(e,-((x*x+y*y)/(2*a*a)))
            }
            //假设radius=x,那么会获得一个(2*x+1)×(2*x+1)的数组矩阵
            //数组第一个元素的坐标是【-x,x】

            function getQuan(radius){ //获取每个点的高斯值,返回数组
                var quan = []
                for (var i=-radius;i<=radius ;i++ )
                {
                    for (var j=radius;j>=-radius ;j-- )
                    {
                        quan.push(gaosi(i,j,20));
                    }
                }
                return quan ;
            }
            function ArrSub(arr){ //返回高斯数组所有元素的值的和
                var sub = 0 ;
                for (var i=0,len=quan.length;i<len ;i++ )
                {
                    sub += quan[i] ;
                }
                return sub ;
            }
            function jiaQuan(arr,quanSub){ //获取权的数组
                for (var i=0,len=arr.length;i<len ;i++ )
                {
                    arr[i] = arr[i]/quanSub;
                }
                return arr ;
            }
            function getValue(x,y,index,quan){ //根据坐标以及rgba[index]来算出高斯模糊的最终值
                var imgdata = getInfoArr(x,y,index)
                var value = 0 ;
                for (var i=0,len=imgdata.length;i<len ;i++ )
                {
                    value += imgdata[i]*quan[i] ;
                }
                return Math.round(value) ;//返回整数
            }

            function getInfo(x,y,index){ //返回点的【rgba】值
                if (x<0)
                {
                    x=-x
                }else if (x>width-1)
                {
                    x=2*(width-1)-x
                }
                if (y<0)
                {
                    y=-y
                }else if (y>height-1)
                {
                    y=2*(height-1)-y
                }
                var i = (width*4)*y+x*4+index;
                return canvasData.data[i]
            }
            function getInfoArr(x,y,index){ //根据坐标和radius【模糊半径】,返回要和权数组相乘的数组
                var arr = [];
                for (var i=x-radius;i<=x+radius ;i++ )
                {
                    for (var j=y+radius;j>=y-radius ;j-- )
                    {
                        arr.push(getInfo(i,j,index))
                    }
                }
                return arr ;
            }
        }; 

 </script>
 </body>
</html>

最近看了阮一峰老师关于实现高斯模糊效果的博客,自己就用js和canvas写了这么一个效果
无奈执行时间太长,看stackblur的源码,又看不太懂
希望大家指教一下

迷茫
迷茫

业精于勤,荒于嬉;行成于思,毁于随。

모든 응답(3)
迷茫

高斯模糊有两种方案做:

  • 直接用二维公式进行二重循环,复杂度为O(xy(2r)^2)
  • 用一维公式分别对x、y循环,复杂度为O(2xy(2r))

测试结果:

  • 用二重循环:(500*800,20) 4566ms
  • 分别循环:(500*800,20) 237ms

可以发现刚好差20倍左右,也就是radius模糊半径的值

结果图:



实现代码如下:

代码较长,建议移步到我的博客看代码

html:

html<!DOCTYPE html>
<html>
<head lang="en">
    <meta charset="UTF-8">
    <title>test</title>
    <script src="GaussianBlur.js"></script>
</head>
<body>
    <img src="images/test3.jpg" alt="img source" id="imgSource">
    <canvas id="canvas"></canvas>
</body>
</html>

javascript:

  • gaussBlur : 二重循环
  • gaussBlur1 : 分别循环
javascript/**
 * Created by zhaofengmiao on 15/3/22.
 */
window.onload = function(){
    var img = document.getElementById("imgSource"),
        canvas = document.getElementById('canvas'),
        width = img.width,
        height = img.height;

    // console.log(width);

    canvas.width = width;
    canvas.height = height;

    var context = canvas.getContext("2d");
    context.drawImage(img, 0, 0);

    var canvasData = context.getImageData(0, 0, canvas.width, canvas.height);

    //console.log(canvasData);

    // 开始
    var startTime = +new Date();

    var tempData = gaussBlur(canvasData, 20);


    context.putImageData(tempData,0,0);

    var endTime = +new Date();
    console.log(" 一共经历时间:" + (endTime - startTime) + "ms");
}

/**
 * 此函数为二重循环
 */
function gaussBlur(imgData, radius, sigma) {
    var pixes = imgData.data,
        width = imgData.width,
        height = imgData.height;

    radius = radius || 5;
    sigma = sigma || radius / 3;

    var gaussEdge = radius * 2 + 1;    // 高斯矩阵的边长

    var gaussMatrix = [],
        gaussSum = 0,
        a = 1 / (2 * sigma * sigma * Math.PI),
        b = -a * Math.PI;

    for (var i=-radius; i<=radius; i++) {
        for (var j=-radius; j<=radius; j++) {
            var gxy = a * Math.exp((i * i + j * j) * b);
            gaussMatrix.push(gxy);
            gaussSum += gxy;    // 得到高斯矩阵的和,用来归一化
        }
    }
    var gaussNum = (radius + 1) * (radius + 1);
    for (var i=0; i<gaussNum; i++) {
        gaussMatrix[i] = gaussMatrix[i] / gaussSum;    // 除gaussSum是归一化
    }

    //console.log(gaussMatrix);

    // 循环计算整个图像每个像素高斯处理之后的值
    for (var x=0; x<width;x++) {
        for (var y=0; y<height; y++) {
            var r = 0,
                g = 0,
                b = 0;

            //console.log(1);

            // 计算每个点的高斯处理之后的值
            for (var i=-radius; i<=radius; i++) {
                // 处理边缘
                var m = handleEdge(i, x, width);
                for (var j=-radius; j<=radius; j++) {
                    // 处理边缘
                    var mm = handleEdge(j, y, height);

                    var currentPixId = (mm * width + m) * 4;

                    var jj = j + radius;
                    var ii = i + radius;
                    r += pixes[currentPixId] * gaussMatrix[jj * gaussEdge + ii];
                    g += pixes[currentPixId + 1] * gaussMatrix[jj * gaussEdge + ii];
                    b += pixes[currentPixId + 2] * gaussMatrix[jj * gaussEdge + ii];

                }
            }
            var pixId = (y * width + x) * 4;

            pixes[pixId] = ~~r;
            pixes[pixId + 1] = ~~g;
            pixes[pixId + 2] = ~~b;
        }
    }
    imgData.data = pixes;
    return imgData;
}

function handleEdge(i, x, w) {
    var  m = x + i;
    if (m < 0) {
        m = -m;
    } else if (m >= w) {
        m = w + i - x;
    }
    return m;
}

/**
 * 此函数为分别循环
 */
function gaussBlur1(imgData,radius, sigma) {
    var pixes = imgData.data;
    var width = imgData.width;
    var height = imgData.height;
    var gaussMatrix = [],
        gaussSum = 0,
        x, y,
        r, g, b, a,
        i, j, k, len;


    radius = Math.floor(radius) || 3;
    sigma = sigma || radius / 3;

    a = 1 / (Math.sqrt(2 * Math.PI) * sigma);
    b = -1 / (2 * sigma * sigma);
    //生成高斯矩阵
    for (i = 0, x = -radius; x <= radius; x++, i++){
        g = a * Math.exp(b * x * x);
        gaussMatrix[i] = g;
        gaussSum += g;

    }
    //归一化, 保证高斯矩阵的值在[0,1]之间
    for (i = 0, len = gaussMatrix.length; i < len; i++) {
        gaussMatrix[i] /= gaussSum;
    }
    //x 方向一维高斯运算
    for (y = 0; y < height; y++) {
        for (x = 0; x < width; x++) {
            r = g = b = a = 0;
            gaussSum = 0;
            for(j = -radius; j <= radius; j++){
                k = x + j;
                if(k >= 0 && k < width){//确保 k 没超出 x 的范围
                    //r,g,b,a 四个一组
                    i = (y * width + k) * 4;
                    r += pixes[i] * gaussMatrix[j + radius];
                    g += pixes[i + 1] * gaussMatrix[j + radius];
                    b += pixes[i + 2] * gaussMatrix[j + radius];
                    // a += pixes[i + 3] * gaussMatrix[j];
                    gaussSum += gaussMatrix[j + radius];
                }
            }
            i = (y * width + x) * 4;
            // 除以 gaussSum 是为了消除处于边缘的像素, 高斯运算不足的问题
            // console.log(gaussSum)
            pixes[i] = r / gaussSum;
            pixes[i + 1] = g / gaussSum;
            pixes[i + 2] = b / gaussSum;
            // pixes[i + 3] = a ;
        }
    }
    //y 方向一维高斯运算
    for (x = 0; x < width; x++) {
        for (y = 0; y < height; y++) {
            r = g = b = a = 0;
            gaussSum = 0;
            for(j = -radius; j <= radius; j++){
                k = y + j;
                if(k >= 0 && k < height){//确保 k 没超出 y 的范围
                    i = (k * width + x) * 4;
                    r += pixes[i] * gaussMatrix[j + radius];
                    g += pixes[i + 1] * gaussMatrix[j + radius];
                    b += pixes[i + 2] * gaussMatrix[j + radius];
                    // a += pixes[i + 3] * gaussMatrix[j];
                    gaussSum += gaussMatrix[j + radius];
                }
            }
            i = (y * width + x) * 4;
            pixes[i] = r / gaussSum;
            pixes[i + 1] = g / gaussSum;
            pixes[i + 2] = b / gaussSum;
            // pixes[i] = r ;
            // pixes[i + 1] = g ;
            // pixes[i + 2] = b ;
            // pixes[i + 3] = a ;
        }
    }
    //end
    imgData.data = pixes;
    return imgData;
}
刘奇

有人写过高斯模糊的jQuery插件 https://github.com/finom/jQuery-Gaussian-Blur 里面用的是svg

PHPzhong

你好,我最近也正在纠结高斯模糊这个问题,之前一直在用stackblur,但是貌似对透明的处理不是很理想,如果启用模糊alpha通道,就会把透明处理成白色,如果不模糊,就会是黑色,然后圆角图标模糊出来的边缘就很不好看。最近也在思考是否自己写一个方法实现。
大致看了你的方法,可否考虑把宽度和高度分开模糊,而不是双重的循环。望交流。

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