Revision history [back]

hey, no idea under which rock you found that sample, but it's using the 1.0 c-api, and you should not.

don't even bother trying to get it to run, it's outdated. just as an idea, how it would look in c++ :

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/ml/ml.hpp"
#include <time.h>

using namespace std;
using namespace cv;

void cross( Mat & img, float x, float y, const Scalar & color ) {
    line (img, Point(x - 2, y - 2), Point(x + 2, y + 2), color);
    line (img, Point(x + 2, y - 2), Point(x - 2, y + 2), color);
}

int main(int argc, char **argv)
{
    const int s = 1000;
    int size = 400;

    RNG rng(time (NULL));

    Mat_<float> pts(s,2); // our training data, s points with 2 dimensions (x,y)
    Mat_<int>   res(s,1); // our training labels, one label(id) for each point

    Mat img = Mat::zeros(size, size,CV_8UC3);  // data visualization

    for (int i = 0; i < s; i++) {
        pts(i,0) = rng.uniform(0.0f,float(size));
        pts(i,1) = rng.uniform(0.0f,float(size));
        if (pts(i,1) > 50 * cos (pts(i,0) * CV_PI / 100) + 200) {
            cross( img, pts(i,0), pts(i,1), Scalar(0,0,255) );
            res(i) = 1;
        } else {
            if (pts(i,0) > 200) {
                cross( img, pts(i,0), pts(i,1), Scalar(0,255,0) );
                res(i) = 2;
            } else {
                cross( img, pts(i,0), pts(i,1), Scalar(255,0,0) );
                res(i) = 3;
            }
        }
    }

    namedWindow("SVM", CV_WINDOW_AUTOSIZE);
    imshow("SVM", img);
    waitKey(0);

    pts /= size; // downscale to [0..1]

    // setup the svm    
    TermCriteria criteria( CV_TERMCRIT_EPS, 1000, FLT_EPSILON );
    SVMParams param( SVM::C_SVC, SVM::RBF, 10.0, 8.0, 1.0, 10.0, 0.5, 0.1, NULL, criteria );

    SVM svm;
    svm.train(pts, res, Mat(), Mat(), param);

    // make a prediction for each point on the grid:
    for (int i = 0; i < size; i++) {
        for (int j = 0; j < size; j++) {
            float a[] = { float(j)/size, float(i)/size };
            Mat m(1,2,CV_32F,a);
            float ret = svm.predict(m);
            switch ((int) ret) {
                case 1:  circle( img, Point(j,i), 1, Scalar(0,0,90) );  break;
                case 2:  circle( img, Point(j,i), 1, Scalar(0,90,0) );  break;
                case 3:  circle( img, Point(j,i), 1, Scalar(90,0,0) );  break;
            }
        }
    }

    // overlay the training points again
    pts *= size; // upscale again for drawing
    for (int i = 0; i < s; i++) {
        switch (res(i)) {
            case 1: cross( img, pts(i,0), pts(i,1), Scalar(0,0,255) );  break;
            case 2: cross( img, pts(i,0), pts(i,1), Scalar(0,255,0) );  break;
            case 3: cross( img, pts(i,0), pts(i,1), Scalar(255,0,0) );  break;
        }
    }

    // overlay the support vecs
    int sv_num = svm.get_support_vector_count();
    for (int i = 0; i < sv_num; i++) {
        const float * support = svm.get_support_vector(i);
        circle( img, Point( int(support[0]*size), int(support[1]*size) ), 5, Scalar(200,200,200) );
    }

    imshow("SVM", img);
    waitKey(0);

    return 0;
}