opencv with windows form application (Visual Studio 13 GUI) c++

edit

how can I write the following code in windows form application in Visual studio, not QT? I want to make use of GUI and make an interactive application that executes the function on click. I searched few links but didn't get what exactly they were trying to say.I tried adding code in header files but it is giving me an error.can someone help me and tell me steps to do GUI? here is the code.

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

using namespace cv;
using namespace std;

//
// if you squint hard, you'll see the close similarity to opencv's face recognition code ;)
//
void lbp_hist(const Mat &I, Mat &histogram)
{
Mat_<uchar> img(I);
Mat_<float> hist(1, 256, 0.0f);
const int m=1;
for (int r=m; r<img.rows-m; r++)
{
    for (int c=m; c<img.cols-m; c++)
    {
        uchar v = 0;
        uchar cen = img(r,c);
        v |= (img(r-1,c  ) > cen) << 0;
        v |= (img(r-1,c+1) > cen) << 1;
        v |= (img(r  ,c+1) > cen) << 2;
        v |= (img(r+1,c+1) > cen) << 3;
        v |= (img(r+1,c  ) > cen) << 4;
        v |= (img(r+1,c-1) > cen) << 5;
        v |= (img(r  ,c-1) > cen) << 6;
        v |= (img(r-1,c-1) > cen) << 7;
        hist(v) ++;
    }
}
histogram = hist;
}

struct Classifier
{
    vector<Mat> histograms;
vector<int> labels;

Classifier( vector<Mat> &histograms, vector<int> &labels)
    : histograms(histograms)
    , labels(labels)
{} // nothing else to do

// nearest neighbour distance:
int predict(const Mat &histogram)
{
    double minDist=DBL_MAX;
    int minLabel = -1;
    for (size_t i=0; i<histograms.size(); i++)
    {
        // i tried a few, and CHISQR_ALT seemed best.
        double d = compareHist(histogram, histograms[i], HISTCMP_CHISQR_ALT);
        if (d < minDist)
        {
            minDist = d;
            minLabel = labels[i];
        }
    }
    return minLabel;
}
};

int main() 
{
    Mat ref = imread("agref.jpg",1);
     Mat a1 = imread("ag1.jpg",1);
Mat a2 = imread("ag2.jpg",1);
// make histograms
Mat h1,h2,h3;
lbp_hist(ref,h1);
lbp_hist(a1,h2);
lbp_hist(a2,h3);
// since we only got a few images(you need **lots* more!!)
// augment our train data by simply flipping the existing images:
Mat f1;  flip(ref,f1,0); // x-axis
Mat f2;  flip(a1,f2,0);
Mat f3;  flip(a2,f3,0);
Mat h4,h5,h6;
lbp_hist(f1,h4);
lbp_hist(f2,h5);
lbp_hist(f3,h6);

// setup data for classifying:
// (a histogram, and a label for each img)
vector<Mat> hists;
vector<int> labels; // 1==agglutinated, 0==not.
// ref
hists.push_back(h1); labels.push_back(0);
hists.push_back(h4); labels.push_back(0);
// agglutinated:
hists.push_back(h2); labels.push_back(1);
hists.push_back(h3); labels.push_back(1);
hists.push_back(h5); labels.push_back(1);
hists.push_back(h6); labels.push_back(1);

// "train" the classifier:
Classifier cls(hists, labels);

// flip images for testing, so they again look a bit different:
// (again, this is only for the demo)
Mat f7;  flip(ref,f7,1); // y-axis
Mat f8;  flip(a1,f8,1);
Mat f9;  flip(a2,f9,1);
Mat h7,h8,h9;
lbp_hist(f7,h7);
lbp_hist(f8,h8);
lbp_hist(f9,h9);

int p1 = cls.predict(h7); // ref
int p2 = cls.predict(h8); // agg.
int p3 = cls ...