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Apparently it does not, however if you follow the equations to convert RGB/BGR values to HSI values, it should not be that hard. If we suppose R, G, and B are the red, green, and blue values of a color and H,S, and I the hue, saturation and intensity respectively then:

which can be translated into:

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{

  Mat src = imread("baboon.jpg", 1);

  if(src.empty())
    cerr << "Error: Loading image" << endl;
  Mat hsi(src.rows, src.cols, src.type());

  float r, g, b, h, s, in;

  for(int i = 0; i < src.rows; i++)
    {
      for(int j = 0; j < src.cols; j++)
        {
          b = src.at<Vec3b>(i, j)[0];
          g = src.at<Vec3b>(i, j)[1];
          r = src.at<Vec3b>(i, j)[2];

          in = (b + g + r) / 3;

          int min_val = 0;
          min_val = std::min(r, std::min(b,g));

          if(r + g + b == 765)
            {
              s = 0;
              h = 0;
            }else{
              s = 1 - 3*(min_val/(b + g + r));
              if(s < 0.00001)
                {
                  s = 0;
                }else if(s > 0.99999)
                {
                  s = 1;
                }
            }

          if(s != 0)
            {
              h = 0.5 * ((r - g) + (r - b)) / sqrt(((r - g)*(r - g)) + ((r - b)*(g - b)));
              h = acos(h);

              if(b <= g)
                {
                  h = h;
                } else{
                  h = ((360 * 3.14159265) / 180.0) - h;
                }
            }

          hsi.at<Vec3b>(i, j)[0] = (h * 180) / 3.14159265;
          hsi.at<Vec3b>(i, j)[1] = s*100;
          hsi.at<Vec3b>(i, j)[2] = in;
        }
    }

  namedWindow("RGB image", CV_WINDOW_AUTOSIZE);
  namedWindow("HSI image", CV_WINDOW_AUTOSIZE);

  imshow("RGB image", src);
  imshow("HSI image", hsi);

  waitKey(0);
  return 0;
}

Apparently it does not, however if you follow the equations to convert RGB/BGR values to HSI values, it should not be that hard. If we suppose R, G, and B are the red, green, and blue values of a color and H,S, and I the hue, saturation and intensity respectively then:

which can be translated into:

#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main()
{

  Mat src = imread("baboon.jpg", 1);

  if(src.empty())
    cerr << "Error: Loading image" << endl;
  Mat hsi(src.rows, src.cols, src.type());

  float r, g, b, h, s, in;

  for(int i = 0; i < src.rows; i++)
    {
      for(int j = 0; j < src.cols; j++)
        {
          b = src.at<Vec3b>(i, j)[0];
          g = src.at<Vec3b>(i, j)[1];
          r = src.at<Vec3b>(i, j)[2];

          in = (b + g + r) / 3;

          int min_val = 0;
          min_val = std::min(r, std::min(b,g));

          if(r + g + b == 765)
            {
              s = 0;
              h = 0;
            }else{
              s = 1 - 3*(min_val/(b + g + r));
           if(s < 0.00001)
             {
                  s = 0;
             }else if(s > 0.99999)
                {
0.99999){
                  s = 1;
                }
            }

          if(s != 0)
            {
              h = 0.5 * ((r - g) + (r - b)) / sqrt(((r - g)*(r - g)) + ((r - b)*(g - b)));
              h = acos(h);

              if(b <= g)
                {
                  h = h;
                } else{
                  h = ((360 * 3.14159265) / 180.0) - h;
                }
            }

          hsi.at<Vec3b>(i, j)[0] = (h * 180) / 3.14159265;
          hsi.at<Vec3b>(i, j)[1] = s*100;
          hsi.at<Vec3b>(i, j)[2] = in;
        }
    }

  namedWindow("RGB image", CV_WINDOW_AUTOSIZE);
  namedWindow("HSI image", CV_WINDOW_AUTOSIZE);

  imshow("RGB image", src);
  imshow("HSI image", hsi);

  waitKey(0);
  return 0;
}