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
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)
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));
}
我在我的android up中使用了这个，它似乎令人满意，尽管不建议使用RGB空间:
public double colourDistance(int red1,int green1, int blue1, int red2, int green2, int blue2)
{
double rmean = ( red1 + red2 )/2;
int r = red1 - red2;
int g = green1 - green2;
int b = blue1 - blue2;
double weightR = 2 + rmean/256;
double weightG = 4.0;
double weightB = 2 + (255-rmean)/256;
return Math.sqrt(weightR*r*r + weightG*g*g + weightB*b*b);
}
然后我用下面的方法得到相似度的百分比:
double maxColDist = 764.8339663572415;
double d1 = colourDistance(red1,green1,blue1,red2,green2,blue2);
String s1 = (int) Math.round(((maxColDist-d1)/maxColDist)*100) + "% match";
它工作得很好。
以下所有方法的结果都是0-100。
internal static class ColorDifference
{
internal enum Method
{
Binary, // true or false, 0 is false
Square,
Dimensional,
CIE76
}
public static double Calculate(Method method, int argb1, int argb2)
{
int[] c1 = ColorConversion.ArgbToArray(argb1);
int[] c2 = ColorConversion.ArgbToArray(argb2);
return Calculate(method, c1[1], c2[1], c1[2], c2[2], c1[3], c2[3], c1[0], c2[0]);
}
public static double Calculate(Method method, int r1, int r2, int g1, int g2, int b1, int b2, int a1 = -1, int a2 = -1)
{
switch (method)
{
case Method.Binary:
return (r1 == r2 && g1 == g2 && b1 == b2 && a1 == a2) ? 0 : 100;
case Method.CIE76:
return CalculateCIE76(r1, r2, g1, g2, b1, b2);
case Method.Dimensional:
if (a1 == -1 || a2 == -1) return Calculate3D(r1, r2, g1, g2, b1, b2);
else return Calculate4D(r1, r2, g1, g2, b1, b2, a1, a2);
case Method.Square:
return CalculateSquare(r1, r2, g1, g2, b1, b2, a1, a2);
default:
throw new InvalidOperationException();
}
}
public static double Calculate(Method method, Color c1, Color c2, bool alpha)
{
switch (method)
{
case Method.Binary:
return (c1.R == c2.R && c1.G == c2.G && c1.B == c2.B && (!alpha || c1.A == c2.A)) ? 0 : 100;
case Method.CIE76:
if (alpha) throw new InvalidOperationException();
return CalculateCIE76(c1, c2);
case Method.Dimensional:
if (alpha) return Calculate4D(c1, c2);
else return Calculate3D(c1, c2);
case Method.Square:
if (alpha) return CalculateSquareAlpha(c1, c2);
else return CalculateSquare(c1, c2);
default:
throw new InvalidOperationException();
}
}
// A simple idea, based on on a Square
public static double CalculateSquare(int argb1, int argb2)
{
int[] c1 = ColorConversion.ArgbToArray(argb1);
int[] c2 = ColorConversion.ArgbToArray(argb2);
return CalculateSquare(c1[1], c2[1], c1[2], c2[2], c1[3], c2[3]);
}
public static double CalculateSquare(Color c1, Color c2)
{
return CalculateSquare(c1.R, c2.R, c1.G, c2.G, c1.B, c2.B);
}
public static double CalculateSquareAlpha(int argb1, int argb2)
{
int[] c1 = ColorConversion.ArgbToArray(argb1);
int[] c2 = ColorConversion.ArgbToArray(argb2);
return CalculateSquare(c1[1], c2[1], c1[2], c2[2], c1[3], c2[3], c1[0], c2[0]);
}
public static double CalculateSquareAlpha(Color c1, Color c2)
{
return CalculateSquare(c1.R, c2.R, c1.G, c2.G, c1.B, c2.B, c1.A, c2.A);
}
public static double CalculateSquare(int r1, int r2, int g1, int g2, int b1, int b2, int a1 = -1, int a2 = -1)
{
if (a1 == -1 || a2 == -1) return (Math.Abs(r1 - r2) + Math.Abs(g1 - g2) + Math.Abs(b1 - b2)) / 7.65;
else return (Math.Abs(r1 - r2) + Math.Abs(g1 - g2) + Math.Abs(b1 - b2) + Math.Abs(a1 - a2)) / 10.2;
}
// from:http://stackoverflow.com/questions/9018016/how-to-compare-two-colors
public static double Calculate3D(int argb1, int argb2)
{
int[] c1 = ColorConversion.ArgbToArray(argb1);
int[] c2 = ColorConversion.ArgbToArray(argb2);
return Calculate3D(c1[1], c2[1], c1[2], c2[2], c1[3], c2[3]);
}
public static double Calculate3D(Color c1, Color c2)
{
return Calculate3D(c1.R, c2.R, c1.G, c2.G, c1.B, c2.B);
}
public static double Calculate3D(int r1, int r2, int g1, int g2, int b1, int b2)
{
return Math.Sqrt(Math.Pow(Math.Abs(r1 - r2), 2) + Math.Pow(Math.Abs(g1 - g2), 2) + Math.Pow(Math.Abs(b1 - b2), 2)) / 4.41672955930063709849498817084;
}
// Same as above, but made 4D to include alpha channel
public static double Calculate4D(int argb1, int argb2)
{
int[] c1 = ColorConversion.ArgbToArray(argb1);
int[] c2 = ColorConversion.ArgbToArray(argb2);
return Calculate4D(c1[1], c2[1], c1[2], c2[2], c1[3], c2[3], c1[0], c2[0]);
}
public static double Calculate4D(Color c1, Color c2)
{
return Calculate4D(c1.R, c2.R, c1.G, c2.G, c1.B, c2.B, c1.A, c2.A);
}
public static double Calculate4D(int r1, int r2, int g1, int g2, int b1, int b2, int a1, int a2)
{
return Math.Sqrt(Math.Pow(Math.Abs(r1 - r2), 2) + Math.Pow(Math.Abs(g1 - g2), 2) + Math.Pow(Math.Abs(b1 - b2), 2) + Math.Pow(Math.Abs(a1 - a2), 2)) / 5.1;
}
/**
* Computes the difference between two RGB colors by converting them to the L*a*b scale and
* comparing them using the CIE76 algorithm { http://en.wikipedia.org/wiki/Color_difference#CIE76}
*/
public static double CalculateCIE76(int argb1, int argb2)
{
return CalculateCIE76(Color.FromArgb(argb1), Color.FromArgb(argb2));
}
public static double CalculateCIE76(Color c1, Color c2)
{
return CalculateCIE76(c1.R, c2.R, c1.G, c2.G, c1.B, c2.B);
}
public static double CalculateCIE76(int r1, int r2, int g1, int g2, int b1, int b2)
{
int[] lab1 = ColorConversion.ColorToLab(r1, g1, b1);
int[] lab2 = ColorConversion.ColorToLab(r2, g2, b2);
return Math.Sqrt(Math.Pow(lab2[0] - lab1[0], 2) + Math.Pow(lab2[1] - lab1[1], 2) + Math.Pow(lab2[2] - lab1[2], 2)) / 2.55;
}
}
internal static class ColorConversion
{
public static int[] ArgbToArray(int argb)
{
return new int[] { (argb >> 24), (argb >> 16) & 0xFF, (argb >> 8) & 0xFF, argb & 0xFF };
}
public static int[] ColorToLab(int R, int G, int B)
{
// http://www.brucelindbloom.com
double r, g, b, X, Y, Z, fx, fy, fz, xr, yr, zr;
double Ls, fas, fbs;
double eps = 216.0f / 24389.0f;
double k = 24389.0f / 27.0f;
double Xr = 0.964221f; // reference white D50
double Yr = 1.0f;
double Zr = 0.825211f;
// RGB to XYZ
r = R / 255.0f; //R 0..1
g = G / 255.0f; //G 0..1
b = B / 255.0f; //B 0..1
// assuming sRGB (D65)
if (r <= 0.04045) r = r / 12;
else r = (float)Math.Pow((r + 0.055) / 1.055, 2.4);
if (g <= 0.04045) g = g / 12;
else g = (float)Math.Pow((g + 0.055) / 1.055, 2.4);
if (b <= 0.04045) b = b / 12;
else b = (float)Math.Pow((b + 0.055) / 1.055, 2.4);
X = 0.436052025f * r + 0.385081593f * g + 0.143087414f * b;
Y = 0.222491598f * r + 0.71688606f * g + 0.060621486f * b;
Z = 0.013929122f * r + 0.097097002f * g + 0.71418547f * b;
// XYZ to Lab
xr = X / Xr;
yr = Y / Yr;
zr = Z / Zr;
if (xr > eps) fx = (float)Math.Pow(xr, 1 / 3.0);
else fx = (float)((k * xr + 16.0) / 116.0);
if (yr > eps) fy = (float)Math.Pow(yr, 1 / 3.0);
else fy = (float)((k * yr + 16.0) / 116.0);
if (zr > eps) fz = (float)Math.Pow(zr, 1 / 3.0);
else fz = (float)((k * zr + 16.0) / 116);
Ls = (116 * fy) - 16;
fas = 500 * (fx - fy);
fbs = 200 * (fy - fz);
int[] lab = new int[3];
lab[0] = (int)(2.55 * Ls + 0.5);
lab[1] = (int)(fas + 0.5);
lab[2] = (int)(fbs + 0.5);
return lab;
}
}
比较颜色的唯一“正确”方法是在CIELab或CIELuv中使用delta。
但对于很多应用，我认为这是一个足够好的近似:
子弹会= 3 * | diana | + 4个数2 + 3 * * |人物dG |专题| dB专题|
我认为在比较颜色时，加权曼哈顿距离更有意义。记住，颜色原色只存在于我们的大脑中。它们没有任何物理意义。CIELab和CIELuv是根据我们对颜色的感知建立的统计模型。
我尝试了各种方法，如LAB颜色空间，HSV比较，我发现光度在这个目的上非常有效。
这是Python版本
def lum(c):
def factor(component):
component = component / 255;
if (component <= 0.03928):
component = component / 12.92;
else:
component = math.pow(((component + 0.055) / 1.055), 2.4);
return component
components = [factor(ci) for ci in c]
return (components[0] * 0.2126 + components[1] * 0.7152 + components[2] * 0.0722) + 0.05;
def color_distance(c1, c2):
l1 = lum(c1)
l2 = lum(c2)
higher = max(l1, l2)
lower = min(l1, l2)
return (higher - lower) / higher
c1 = ImageColor.getrgb('white')
c2 = ImageColor.getrgb('yellow')
print(color_distance(c1, c2))
会给你
0.0687619047619048
