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Something like this should do the trick:

Mat img = imread("/path/to/actual/image.png");
Mat imgHSV;
cvtColor(img, imgHSV, COLOR_BGR2HSV);

vector<Mat> channels, channelsHSV;
split(img, channels);
split(ingHSV, channelsHSV);

double minR, maxR, minG, maxG, minB, maxB;
minMaxLoc(channels[0], &minB, &maxB);
minMaxLoc(channels[1], &minG, &maxG);
minMaxLoc(channels[2], &minR, &maxR);

double minH, maxH, minS, maxS, minV, maxV;
minMaxLoc(channelsHSV[0], &minH, &maxH);
minMaxLoc(channelsHSV[1], &minS, &maxS);
minMaxLoc(channelsHSV[2], &minV, &maxV);

That code will give you all values except the median. Calculating the median needs some extra block of code, but you can base your code on top of the following snippet

``` namespace cv { // calculates the median value of a single channel // based on https://github.com/arnaudgelas/OpenCVExamples/blob/master/cvMat/Statistics/Median/Median.cpp double median( cv::Mat channel ) { double m = (channel.rows*channel.cols) / 2; int bin = 0; double med = -1.0;

    int histSize = 256;
    float range[] = { 0, 256 };
    const float* histRange = { range };
    bool uniform = true;
    bool accumulate = false;
    cv::Mat hist;
    cv::calcHist( &channel, 1, 0, cv::Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );

    for ( int i = 0; i < histSize && med < 0.0; ++i )
    {
        bin += cvRound( hist.at< float >( i ) );
        if ( bin > m && med < 0.0 )
            med = i;
    }

    return med;
}

} ```

which was originally posted here: https://gist.github.com/heisters/9cd68181397fbd35031b

Something like this should do the trick:

Mat img = imread("/path/to/actual/image.png");
Mat imgHSV;
cvtColor(img, imgHSV, COLOR_BGR2HSV);

vector<Mat> channels, channelsHSV;
split(img, channels);
split(ingHSV, channelsHSV);

double minR, maxR, minG, maxG, minB, maxB;
minMaxLoc(channels[0], &minB, &maxB);
minMaxLoc(channels[1], &minG, &maxG);
minMaxLoc(channels[2], &minR, &maxR);

double minH, maxH, minS, maxS, minV, maxV;
minMaxLoc(channelsHSV[0], &minH, &maxH);
minMaxLoc(channelsHSV[1], &minS, &maxS);
minMaxLoc(channelsHSV[2], &minV, &maxV);

That code will give you all values except the median. Calculating the median needs some extra block of code, but you can base your code on top of the following snippet

namespace cv {
// calculates the median value of a single channel
// based on https://github.com/arnaudgelas/OpenCVExamples/blob/master/cvMat/Statistics/Median/Median.cpp
double median( cv::Mat channel )
{
double m = (channel.rows*channel.cols) / 2;
int bin = 0;
double med = -1.0;
 
-1.0;
 int histSize = 256;
  float range[] = { 0, 256 };
 const float* histRange = { range };
 bool uniform = true;
 bool accumulate = false;
 cv::Mat hist;
  cv::calcHist( &channel, 1, 0, cv::Mat(), hist, 1, &histSize, &histRange, uniform, accumulate );
 for ( int i = 0; i < histSize && med < 0.0; ++i )
 {
  bin += cvRound( hist.at< float >( i ) );
  if ( bin > m && med < 0.0 )
 med = i;
 }
  return med;
 }
}
 
}
``` 
which was originally posted here: https://gist.github.com/heisters/9cd68181397fbd35031b