原文:js canvas画布实现高斯模糊效果
image.png
<!doctype html>
<html lang="zh">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, user-scalable=no, initial-scale=1.0, maximum-scale=1.0, minimum-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>canvas画布的高斯模糊效果</title>
</head>
<body>
<canvas id="canvas"></canvas>
</body>
<script>
var canvas = document.getElementById('canvas');
var ctx = canvas.getContext('2d');
let img = new Image();
//这里直接修改图片的路径
img.src = "636753681750720000/Block/preview.jpg";
img.onload = function () {
//设置canvas的宽高
canvas.height = img.height;
canvas.width = img.width;
//将图像绘制到canvas上面
ctx.drawImage(img, 0, 0, img.width, img.height);
//从画布获取一半图像
var data = ctx.getImageData(0, 0, img.width/2, img.height);
//将图像数据进行高斯模糊 data.data是一个数组，每四个值代表一个像素点的rgba的值，data.width data.height 分别代表图像数据的宽高
var emptyData = gaussBlur(data);
//将模糊的图像数据再渲染到画布上面
ctx.putImageData(emptyData, 0, 0);
};
function gaussBlur(imgData) {
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;
var radius = 10;
var sigma = 5;
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;
}
}
return imgData;
}
</script>
</html>
我们先不分析这个源码的具体内容,
我们先简单改一下接口.
function gaussBlur(domImg,src) {
var canvas = document.createElement('canvas');
var ctx = canvas.getContext('2d');
let img = new Image();
//这里直接修改图片的路径
img.src = src;
img.onload = function () {
//设置canvas的宽高
console.log(img.height,img.width);
canvas.height = img.height;
canvas.width = img.width;
//将图像绘制到canvas上面
ctx.drawImage(img, 0, 0, img.width, img.height);
//从画布获取一半图像
var data = ctx.getImageData(0, 0, img.width, img.height);
//将图像数据进行高斯模糊 data.data是一个数组，每四个值代表一个像素点的rgba的值，data.width data.height 分别代表图像数据的宽高
var emptyData = gaussB(data);
//将模糊的图像数据再渲染到画布上面
ctx.putImageData(emptyData, 0, 0);
domImg.src = canvas.toDataURL();
};
function gaussB(imgData) {
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;
var radius = 10;
var sigma = 5;
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;
}
}
return imgData;
}
}
发现模糊效果似乎还差一点点, 怎么弄?
image.png
虽然没弄懂 高斯矩阵到底是个什么原理.
但我稍微改了下基础变量的值,
var radius = 40;
var sigma = 80;
结果
image.png
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