概述
1. 利用h5的canvas画布实现图片绘制
2. 灰度化和二值化算法参考自
https://www.cnblogs.com/rushoooooo/articles/2366154.html
3. 前置知识点: js, canvas
效果概览
完整代码
<!DOCTYPE HTML>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
</head>
<body>
<p>
<input type="button" value="灰度化" onclick="javascript:ProcessToGrayImage();">
<input type="button" value="二值化" onclick="javascript:OTSUAlgorithm();">
</p>
<canvas id="myCanvas"></canvas>
</body>
<!-- demo1. 创建画布 -->
<script type="text/javascript">
var img=new Image();
//解决跨域问题
img.crossOrigin = "Anonymous";
img.src="http://img2.imgtn.bdimg.com/it/u=626341007,68367128&fm=214&gp=0.jpg";
var canvas;
var ctx;
//图片加载完成触发
img.onload = function(){
canvas=document.getElementById("myCanvas");
canvas.width = img.width;
canvas.height = img.height;
ctx=canvas.getContext("2d");
ctx.drawImage(img,0,0,img.width,img.height);
}
</script>
<!-- demo2: 彩色图像灰度化 -->
<script type="text/javascript">
//彩色图像灰度化
function ProcessToGrayImage(){
//取得图像数据
var imgData=ctx.getImageData(10,10,50,50);
var canvasData = ctx.getImageData(0, 0, canvas.width, canvas.height);
//这个循环是取得图像的每一个点,在计算灰度后将灰度设置给原图像
for (var x = 0; x < canvasData.width; x++) {
//alert("x="+x);
for (var y = 0; y < canvasData.height; y++) {
//alert("y="+y);
// Index of the pixel in the array
var idx = (x + y * canvas.width) * 4;
// The RGB values
var r = canvasData.data[idx + 0];
var g = canvasData.data[idx + 1];
var b = canvasData.data[idx + 2];
//更新图像数据
var gray = CalculateGrayValue(r , g , b);
canvasData.data[idx + 0] = gray;
canvasData.data[idx + 1] = gray;
canvasData.data[idx + 2] = gray;
}
}
ctx.putImageData(canvasData, 0, 0);
}
//计算图像的灰度值,公式为:Gray = R*0.299 + G*0.587 + B*0.114
function CalculateGrayValue(rValue,gValue,bValue){
return parseInt(rValue * 0.299 + gValue * 0.587 + bValue * 0.114);
}
</script>
<!-- demo3: 二值化-->
<script type="text/javascript">
//一维OTSU图像处理算法
function OTSUAlgorithm(){
var m_pFstdHistogram = new Array();//表示灰度值的分布点概率
var m_pFGrayAccu = new Array();//其中每一个值等于m_pFstdHistogram中从0到当前下标值的和
var m_pFGrayAve = new Array();//其中每一值等于m_pFstdHistogram中从0到当前指定下标值*对应的下标之和
var m_pAverage=0;//值为m_pFstdHistogram【256】中每一点的分布概率*当前下标之和
var m_pHistogram = new Array();//灰度直方图
var i,j;
var temp=0,fMax=0;//定义一个临时变量和一个最大类间方差的值
var nThresh = 0;//最优阀值
//获取灰度图像的信息
var imageInfo = GetGrayImageInfo();
if(imageInfo == null){
window.alert("图像还没有转化为灰度图像!");
return;
}
//初始化各项参数
for(i=0; i<256; i++){
m_pFstdHistogram[i] = 0;
m_pFGrayAccu[i] = 0;
m_pFGrayAve[i] = 0;
m_pHistogram[i] = 0;
}
//获取图像信息
var canvasData = imageInfo[0];
//获取图像的像素
var pixels = canvasData.data;
//下面统计图像的灰度分布信息
for(i=0; i<pixels.length; i+=4){
//获取r的像素值,因为灰度图像,r=g=b,所以取第一个即可
var r = pixels[i];
m_pHistogram[r]++;
}
//下面计算每一个灰度点在图像中出现的概率
var size = canvasData.width * canvasData.height;
for(i=0; i<256; i++){
m_pFstdHistogram[i] = m_pHistogram[i] / size;
}
//下面开始计算m_pFGrayAccu和m_pFGrayAve和m_pAverage的值
for(i=0; i<256; i++){
for(j=0; j<=i; j++){
//计算m_pFGaryAccu[256]
m_pFGrayAccu[i] += m_pFstdHistogram[j];
//计算m_pFGrayAve[256]
m_pFGrayAve[i] += j * m_pFstdHistogram[j];
}
//计算平均值
m_pAverage += i * m_pFstdHistogram[i];
}
//下面开始就算OSTU的值,从0-255个值中分别计算ostu并寻找出最大值作为分割阀值
for (i = 0 ; i < 256 ; i++){
temp = (m_pAverage * m_pFGrayAccu[i] - m_pFGrayAve[i])
* (m_pAverage * m_pFGrayAccu[i] - m_pFGrayAve[i])
/ (m_pFGrayAccu[i] * (1 - m_pFGrayAccu[i]));
if (temp > fMax)
{
fMax = temp;
nThresh = i;
}
}
//下面执行二值化过程
for(i=0; i<canvasData.width; i++){
for(j=0; j<canvasData.height; j++){
//取得每一点的位置
var ids = (i + j*canvasData.width)*4;
//取得像素的R分量的值
var r = canvasData.data[ids];
//与阀值进行比较,如果小于阀值,那么将改点置为0,否则置为255
var gray = r>nThresh?255:0;
canvasData.data[ids+0] = gray;
canvasData.data[ids+1] = gray;
canvasData.data[ids+2] = gray;
}
}
//显示二值化图像
var newImage = document.getElementById('myCanvas').getContext('2d');
newImage.putImageData(canvasData,0,0);
}
//获取图像的灰度图像的信息
function GetGrayImageInfo(){
var canvas = document.getElementById('myCanvas');
var ctx = canvas.getContext('2d');
var canvasData = ctx.getImageData(0, 0, canvas.width, canvas.height);
if(canvasData.data.length==0){
return null;
}
return [canvasData,ctx];
}
//下面对灰度图像进行处理,将目标信息分割出来
function DividedTarget(){
//读取二值化图像信息
var imageInfo = document.getElementById('myCanvasThreshold');
if(imageInfo == null){
window.alert("没有发现二值化图像信息!");
return;
}
//取得上下文
var ctx = imageInfo.getContext('2d');
//获取图像数据
var canvasData = imageInfo.getImageData(0, 0, ctx.width, ctx.height);
var newVanvasData = canvasData;
//取得图像的宽和高
var width = canvasData.width;
var height = canvasData.height;
//算法开始
var cursor = 2;
for(var x=0; x<width; x++){
for(var y=0; y<height; y++){
//取得每一点的位置
var ids = (x + y*canvasData.width)*4;
//取得像素的R分量的值
var r = canvasData.data[ids];
//如果是目标点
if(r==0){
}
}
}
}
</script>
</html>