HSV颜色空间

色调H
        用角度度量，取值范围为0°～360°，从红色开始按逆时针方向计算，红色为0°，绿色为120°,蓝色为240°。它们的补色是：黄色为60°，青色为180°,品红为300°；

饱和度S
        饱和度S表示颜色接近光谱色的程度。一种颜色，可以看成是某种光谱色与白色混合的结果。其中光谱色所占的比例愈大，颜色接近光谱色的程度就愈高，颜色的饱和度也就愈高。饱和度高，颜色则深而艳。光谱色的白光成分为0，饱和度达到最高。通常取值范围为0%～100%，值越大，颜色越饱和。

明度V
        明度表示颜色明亮的程度，对于光源色，明度值与发光体的光亮度有关；对于物体色，此值和物体的透射比或反射比有关。通常取值范围为0%（黑）到100%（白）。
RGB和CMY颜色模型都是面向硬件的，而HSV（Hue Saturation Value）颜色模型是面向用户的。

        HSV模型的三维表示从RGB立方体演化而来。设想从RGB沿立方体对角线的白色顶点向黑色顶点观察，就可以看到立方体的六边形外形。六边形边界表示色彩，水平轴表示纯度，明度沿垂直轴测量。

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

using namespace std;
using namespace cv;

class CalcHistogram
{
private:
int histSize[3];         //直方图项的数量
float hranges[2];        //h通道像素的最小和最大值
float sranges[2];
float vranges[2];
const float *ranges[3];  //各通道的范围
int channels[3];         //三个通道
int dims;


public:
CalcHistogram(int hbins = 30, int sbins = 32, int vbins = 32)
{
histSize[0] = hbins;
histSize[1] = sbins;
histSize[2] = vbins;
hranges[0] = 0; hranges[1] = 180;
sranges[0] = 0; sranges[1] = 256;
vranges[0] = 0; vranges[1] = 256;
ranges[0] = hranges;
ranges[1] = sranges;
ranges[2] = vranges;
channels[0] = 0;
channels[1] = 1;
channels[2] = 2;
dims = 3;
}


Mat getHistogram(const Mat &image);
void getHistogramImage(const Mat &image);
};


Mat CalcHistogram::getHistogram(const Mat &image)
{
Mat hist;
calcHist(&image,
1,
channels,
Mat(),
hist,
dims,
histSize,
ranges,
true,      //直方图每一维的histSize是均匀的
false
);


return hist;
}


void CalcHistogram::getHistogramImage(const Mat &image)
{
Mat hist = getHistogram(image);
int scale = 4;
int hbins = histSize[0];
int sbins = histSize[1];
int vbins = histSize[2];
float *hist_sta = new float[sbins];
float *hist_val = new float[vbins];
float *hist_hue = new float[hbins];
memset(hist_val, 0, vbins*sizeof(float));
memset(hist_sta, 0, sbins*sizeof(float));
memset(hist_hue, 0, hbins*sizeof(float));


for (int s = 0; s < sbins; s++)
{
for (int v = 0; v < vbins; v++)
{
for (int h = 0; h<hbins; h++)
{
float binVal = hist.at<float>(h, s, v);
hist_hue[h] += binVal;
hist_val[v] += binVal;
hist_sta[s] += binVal;
}
}
}


double max_sta = 0, max_val = 0, max_hue = 0;
for (int i = 0; i<sbins; ++i)
{
if (hist_sta[i]>max_sta)
max_sta = hist_sta[i];
}
for (int i = 0; i<vbins; ++i)
{
if (hist_val[i]>max_val)
max_val = hist_val[i];
}
for (int i = 0; i<hbins; ++i)
{
if (hist_hue[i]>max_hue)
max_hue = hist_hue[i];
}


Mat sta_img = Mat::zeros(240, sbins*scale + 20, CV_8UC3);
Mat val_img = Mat::zeros(240, vbins*scale + 20, CV_8UC3);
Mat hue_img = Mat::zeros(240, hbins*scale + 20, CV_8UC3);


for (int i = 0; i<sbins; ++i)
{
int intensity = cvRound(hist_sta[i] * (sta_img.rows - 10) / max_sta);
rectangle(sta_img, Point(i*scale + 10, sta_img.rows - intensity), Point((i + 1)*scale - 1 + 10, sta_img.rows - 1), Scalar(0, 255, 0), 1);
}
for (int i = 0; i<vbins; ++i)
{
int intensity = cvRound(hist_val[i] * (val_img.rows - 10) / max_val);
rectangle(val_img, Point(i*scale + 10, val_img.rows - intensity), Point((i + 1)*scale - 1 + 10, val_img.rows - 1), Scalar(0, 0, 255), 1);
}
for (int i = 0; i<hbins; ++i)
{
int intensity = cvRound(hist_hue[i] * (hue_img.rows - 10) / max_hue);
rectangle(hue_img, Point(i*scale + 10, hue_img.rows - intensity), Point((i + 1)*scale - 1 + 10, hue_img.rows - 1), Scalar(255, 0, 0), 1);
}


imshow("Shist", sta_img);
imshow("Vhist", val_img);
imshow("Hhist", hue_img);


delete[] hist_sta;
delete[] hist_val;
delete[] hist_hue;
}