基于H5实现图像处理：灰度化和二值化

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>