我想设计一个程序,可以帮助我在5种预定义的颜色中评估哪一种更类似于可变颜色,以及与可变颜色的百分比。问题是我不知道如何手动一步一步地做到这一点。所以想一个程序就更难了。

更多细节:颜色来自不同颜色的管子和凝胶的照片。我有5个不同颜色的管子,每个代表5个等级中的1个。我想拍摄其他样本的照片,然后在电脑上通过比较颜色来评估样本属于哪个级别,我也想知道一个近似的百分比。我想要一个这样做的程序:http://www.colortools.net/color_matcher.html

如果你能告诉我该采取什么步骤,即使它们需要我手动思考和执行。那会很有帮助的。


当前回答

颜色值有不止一个维度,所以没有内在的方法来比较两种颜色。您必须为您的用例确定颜色的含义,从而确定如何最好地比较它们。

很可能你想比较颜色的色相、饱和度和/或明度属性,而不是红/绿/蓝组件。如果你不知道如何比较它们,那就拿几对样品颜色,在心里比较一下,然后试着向自己证明/解释为什么它们相似/不同。

一旦你知道了你想要比较的颜色的哪些属性/成分,那么你就需要弄清楚如何从颜色中提取这些信息。

最有可能的是,你只需要将颜色从常见的RedGreenBlue表示转换为HueSaturationLightness,然后计算类似的东西

avghue = (color1.hue + color2.hue)/2
distance = abs(color1.hue-avghue)

这个例子会给你一个简单的标量值,指示颜色的渐变/色相彼此之间的距离。

参见维基百科上的HSL和HSV。

其他回答

请参阅维基百科关于色差的文章以获得正确的线索。 基本上,你想要在多维颜色空间中计算一个距离度量。

但是RGB并不是“感知上一致的”,所以Vadim建议的欧几里得RGB距离度量将与人类感知的颜色之间的距离不匹配。首先,L*a*b*是一个感知上均匀的颜色空间,delta度量是常用的。但有更精致的色彩空间和更精致的delta公式,更接近人类的感知。

你需要学习更多关于颜色空间和光源的知识来进行转换。但如果想要一个比欧几里得RGB度量更好的快速公式,只需这样做:

假设你的RGB值在sRGB颜色空间中 找到sRGB到L*a*b*的转换公式 将sRGB颜色转换为L*a*b* 计算两个L*a*b*值之间的delta

计算成本不高,只是一些非线性公式和一些乘法和加法。

如果你有两个颜色对象c1和c2,你可以比较c1和c2的每个RGB值。

int diffRed   = Math.abs(c1.getRed()   - c2.getRed());
int diffGreen = Math.abs(c1.getGreen() - c2.getGreen());
int diffBlue  = Math.abs(c1.getBlue()  - c2.getBlue());

你可以将这些值除以饱和度的差异(255),你就会得到两者之间的差异。

float pctDiffRed   = (float)diffRed   / 255;
float pctDiffGreen = (float)diffGreen / 255;
float pctDiffBlue   = (float)diffBlue  / 255;

之后你就可以找到平均色差的百分比。

(pctDiffRed + pctDiffGreen + pctDiffBlue) / 3 * 100

这就得到了c和c之间的百分比差。

Actually I walked the same path a couple of months ago. There is no perfect answer to the question (that was asked here a couple of times) but there is one, more sophisticated than the sqrt(r-r) etc. answer and more easy to implement directly with RGB without moving to all kinds of alternate color spaces. I found this formula here which is a low cost approximation of the quite complicated real formula (by the CIE which is the W3C of colors, since this is a not finished quest, you can find older and simpler color difference equations there). Good Luck.

编辑:为了子孙后代,这里是相关的C代码:

typedef struct {
     unsigned char r, g, b;
} RGB;

double ColourDistance(RGB e1, RGB e2)
{
    long rmean = ( (long)e1.r + (long)e2.r ) / 2;
    long r = (long)e1.r - (long)e2.r;
    long g = (long)e1.g - (long)e2.g;
    long b = (long)e1.b - (long)e2.b;
    return sqrt((((512+rmean)*r*r)>>8) + 4*g*g + (((767-rmean)*b*b)>>8));
}

这只是我第一次想到的一个想法(如果愚蠢的话,对不起)。 颜色的三个分量可以假定为点的三维坐标,然后可以计算点之间的距离。

外汇期货

Point1 has R1 G1 B1
Point2 has R2 G2 B2

颜色之间的距离为

d=sqrt((r2-r1)^2+(g2-g1)^2+(b2-b1)^2)

比例是

p=d/sqrt((255)^2+(255)^2+(255)^2)

The best way is deltaE. DeltaE is a number that shows the difference of the colors. If deltae < 1 then the difference can't recognize by human eyes. I wrote a code in canvas and js for converting rgb to lab and then calculating delta e. On this example the code is recognising pixels which have different color with a base color that I saved as LAB1. and then if it is different makes those pixels red. You can increase or reduce the sensitivity of the color difference with increae or decrease the acceptable range of delta e. In this example I assigned 10 for deltaE in the line that I wrote (deltae <= 10):

<script>   
  var constants = {
    canvasWidth: 700, // In pixels.
    canvasHeight: 600, // In pixels.
    colorMap: new Array() 
          };



  // -----------------------------------------------------------------------------------------------------

  function fillcolormap(imageObj1) {


    function rgbtoxyz(red1,green1,blue1){ // a converter for converting rgb model to xyz model
 var red2 = red1/255;
 var green2 = green1/255;
 var blue2 = blue1/255;
 if(red2>0.04045){
      red2 = (red2+0.055)/1.055;
      red2 = Math.pow(red2,2.4);
 }
 else{
      red2 = red2/12.92;
 }
 if(green2>0.04045){
      green2 = (green2+0.055)/1.055;
      green2 = Math.pow(green2,2.4);    
 }
 else{
      green2 = green2/12.92;
 }
 if(blue2>0.04045){
      blue2 = (blue2+0.055)/1.055;
      blue2 = Math.pow(blue2,2.4);    
 }
 else{
      blue2 = blue2/12.92;
 }
 red2 = (red2*100);
 green2 = (green2*100);
 blue2 = (blue2*100);
 var x = (red2 * 0.4124) + (green2 * 0.3576) + (blue2 * 0.1805);
 var y = (red2 * 0.2126) + (green2 * 0.7152) + (blue2 * 0.0722);
 var z = (red2 * 0.0193) + (green2 * 0.1192) + (blue2 * 0.9505);
 var xyzresult = new Array();
 xyzresult[0] = x;
 xyzresult[1] = y;
 xyzresult[2] = z;
 return(xyzresult);
} //end of rgb_to_xyz function
function xyztolab(xyz){ //a convertor from xyz to lab model
 var x = xyz[0];
 var y = xyz[1];
 var z = xyz[2];
 var x2 = x/95.047;
 var y2 = y/100;
 var z2 = z/108.883;
 if(x2>0.008856){
      x2 = Math.pow(x2,1/3);
 }
 else{
      x2 = (7.787*x2) + (16/116);
 }
 if(y2>0.008856){
      y2 = Math.pow(y2,1/3);
 }
 else{
      y2 = (7.787*y2) + (16/116);
 }
 if(z2>0.008856){
      z2 = Math.pow(z2,1/3);
 }
 else{
      z2 = (7.787*z2) + (16/116);
 }
 var l= 116*y2 - 16;
 var a= 500*(x2-y2);
 var b= 200*(y2-z2);
 var labresult = new Array();
 labresult[0] = l;
 labresult[1] = a;
 labresult[2] = b;
 return(labresult);

}

    var canvas = document.getElementById('myCanvas');
    var context = canvas.getContext('2d');
    var imageX = 0;
    var imageY = 0;

    context.drawImage(imageObj1, imageX, imageY, 240, 140);
    var imageData = context.getImageData(0, 0, 240, 140);
    var data = imageData.data;
    var n = data.length;
   // iterate over all pixels

    var m = 0;
    for (var i = 0; i < n; i += 4) {
      var red = data[i];
      var green = data[i + 1];
      var blue = data[i + 2];
    var xyzcolor = new Array();
    xyzcolor = rgbtoxyz(red,green,blue);
    var lab = new Array();
    lab = xyztolab(xyzcolor);
    constants.colorMap.push(lab); //fill up the colormap array with lab colors.         
      } 

  }

// -----------------------------------------------------------------------------------------------------

    function colorize(pixqty) {

         function deltae94(lab1,lab2){    //calculating Delta E 1994

         var c1 = Math.sqrt((lab1[1]*lab1[1])+(lab1[2]*lab1[2]));
         var c2 =  Math.sqrt((lab2[1]*lab2[1])+(lab2[2]*lab2[2]));
         var dc = c1-c2;
         var dl = lab1[0]-lab2[0];
         var da = lab1[1]-lab2[1];
         var db = lab1[2]-lab2[2];
         var dh = Math.sqrt((da*da)+(db*db)-(dc*dc));
         var first = dl;
         var second = dc/(1+(0.045*c1));
         var third = dh/(1+(0.015*c1));
         var deresult = Math.sqrt((first*first)+(second*second)+(third*third));
         return(deresult);
          } // end of deltae94 function
    var lab11 =  new Array("80","-4","21");
    var lab12 = new Array();
    var k2=0;
    var canvas = document.getElementById('myCanvas');
                                        var context = canvas.getContext('2d');
                                        var imageData = context.getImageData(0, 0, 240, 140);
                                        var data = imageData.data;

    for (var i=0; i<pixqty; i++) {

    lab12 = constants.colorMap[i];

    var deltae = deltae94(lab11,lab12);     
                                        if (deltae <= 10) {

                                        data[i*4] = 255;
                                        data[(i*4)+1] = 0;
                                        data[(i*4)+2] = 0;  
                                        k2++;
                                        } // end of if 
                                } //end of for loop
    context.clearRect(0,0,240,140);
    alert(k2);
    context.putImageData(imageData,0,0);
} 
// -----------------------------------------------------------------------------------------------------

$(window).load(function () {    
  var imageObj = new Image();
  imageObj.onload = function() {
  fillcolormap(imageObj);    
  }
  imageObj.src = './mixcolor.png';
});

// ---------------------------------------------------------------------------------------------------
 var pixno2 = 240*140; 
 </script>