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
快速回答
我找到这个帖子是因为我需要这个问题的Swift版本。由于还没有人给出答案，我的答案是:
extension UIColor {
var rgba: (red: CGFloat, green: CGFloat, blue: CGFloat, alpha: CGFloat) {
var red: CGFloat = 0
var green: CGFloat = 0
var blue: CGFloat = 0
var alpha: CGFloat = 0
getRed(&red, green: &green, blue: &blue, alpha: &alpha)
return (red, green, blue, alpha)
}
func isSimilar(to colorB: UIColor) -> Bool {
let rgbA = self.rgba
let rgbB = colorB.rgba
let diffRed = abs(CGFloat(rgbA.red) - CGFloat(rgbB.red))
let diffGreen = abs(rgbA.green - rgbB.green)
let diffBlue = abs(rgbA.blue - rgbB.blue)
let pctRed = diffRed
let pctGreen = diffGreen
let pctBlue = diffBlue
let pct = (pctRed + pctGreen + pctBlue) / 3 * 100
return pct < 10 ? true : false
}
}
用法:
let black: UIColor = UIColor.black
let white: UIColor = UIColor.white
let similar: Bool = black.isSimilar(to: white)
我设置小于10%的差异返回相似的颜色，但你可以自定义这自己。
其他回答
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));
}
快速回答
我找到这个帖子是因为我需要这个问题的Swift版本。由于还没有人给出答案，我的答案是:
extension UIColor {
var rgba: (red: CGFloat, green: CGFloat, blue: CGFloat, alpha: CGFloat) {
var red: CGFloat = 0
var green: CGFloat = 0
var blue: CGFloat = 0
var alpha: CGFloat = 0
getRed(&red, green: &green, blue: &blue, alpha: &alpha)
return (red, green, blue, alpha)
}
func isSimilar(to colorB: UIColor) -> Bool {
let rgbA = self.rgba
let rgbB = colorB.rgba
let diffRed = abs(CGFloat(rgbA.red) - CGFloat(rgbB.red))
let diffGreen = abs(rgbA.green - rgbB.green)
let diffBlue = abs(rgbA.blue - rgbB.blue)
let pctRed = diffRed
let pctGreen = diffGreen
let pctBlue = diffBlue
let pct = (pctRed + pctGreen + pctBlue) / 3 * 100
return pct < 10 ? true : false
}
}
用法:
let black: UIColor = UIColor.black
let white: UIColor = UIColor.white
let similar: Bool = black.isSimilar(to: white)
我设置小于10%的差异返回相似的颜色，但你可以自定义这自己。
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。
您需要将任何RGB颜色转换为Lab颜色空间，以便能够以人类看到它们的方式进行比较。否则你会得到RGB颜色“匹配”在一些非常奇怪的方式。
关于颜色差异的维基百科链接向您介绍了多年来定义的各种Lab颜色空间差异算法。最简单的方法是检查两种实验室颜色的欧几里得距离，可以工作，但有一些缺陷。
在OpenIMAJ项目中有一个更复杂的CIEDE2000算法的Java实现。提供你的两组Lab颜色，它会给你一个距离值。
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>
