Simple demo program showing how to use boosting in openCV

opencv_2class_boosting_example.cpp

/* 
 * Simple demo program showing how to use boosted training in openCV
 * File:   opencv_2class_boosting_example.cpp
 * Author: Jan Hendriks, [email protected], http://www.janhendriks.de
 *
 * Created on 18. August 2011, 13:55
 */

#include <cstdlib>
#include "opencv/cv.h"
#include "opencv/ml.h"

using namespace std;
using namespace cv;


int main(int argc, char** argv) {
    const int ntestsamples = 11; // ntestsamples > 10, otherwise boosting does not work?
    CvMat* featureVectorSamples = cvCreateMat(ntestsamples, 2, CV_32F);
    {
        CvMat mat;
        cvGetRows(featureVectorSamples, &mat, 0, 1);
        cvSet(&mat, cvRealScalar(1));
    }
    {
        CvMat mat;
        cvGetRows(featureVectorSamples, &mat, 1, ntestsamples);
        cvSet(&mat, cvRealScalar(0));
    }

    int var_count = featureVectorSamples->cols; // number of single features=variables
    int nsamples_all = featureVectorSamples->rows; // number of samples=feature vectors

    CvMat* classLabelResponses = cvCreateMat(nsamples_all, 1, CV_32S);
    {
        CvMat mat;
        cvGetRows(classLabelResponses, &mat, 0, 1);
        cvSet(&mat, cvRealScalar(1));
    }
    {
        CvMat mat;
        cvGetRows(classLabelResponses, &mat, 1, nsamples_all);
        cvSet(&mat, cvRealScalar(-1));
    }

    printf("data dim: %d rows, %d columns - responses dim: %d rows, %d columns\n", featureVectorSamples->rows, featureVectorSamples->cols, classLabelResponses->rows, classLabelResponses->cols);

    CvMat* var_type = cvCreateMat(var_count + 1, 1, CV_8U);
    var_type->data.ptr[var_count] = CV_VAR_CATEGORICAL; // = cvSetReal1D( var_type, var_count, CV_VAR_CATEGORICAL );

    printf("var_type dim: %d rows, %d columns\n", var_type->rows, var_type->cols);

/*
    // create sample_idx
    CvMat* sample_idx = cvCreateMat(nsamples_all, 1, CV_32SC1); // CV_8UC1 ); // CV_32SC1
    {
        CvMat mat;
        cvGetRows(sample_idx, &mat, 0, nsamples_all);
        cvSet(&mat, cvRealScalar(1));
    }

    CvMat* var_idx = cvCreateMat(1, var_count, CV_8UC1); // CV_32SC1); // CV_8UC1);
    {
        CvMat mat;
        cvGetCols(var_idx, &mat, 0, var_count);
        cvSet(&mat, cvRealScalar(1));
    }

    for (int i = 0; i < sample_idx->rows; ++i) {
        sample_idx->data.ptr[i] = 1; // consider every sample
    }
*/
    CvBoost boost; // (featureVectorSamples, CV_ROW_SAMPLE, classLabelResponses, var_idx, sample_idx, var_type, NULL, CvBoostParams(CvBoost::DISCRETE, 100, 0.95, 5, false, 0)); // *this->parameters);;

    printf("Training ... ");

    boost.train(featureVectorSamples, CV_ROW_SAMPLE, classLabelResponses, 0, 0, var_type, 0, CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0));
//    boost.train(featureVectorSamples, CV_ROW_SAMPLE, classLabelResponses, var_idx, sample_idx, var_type, NULL, CvBoostParams(CvBoost::REAL, 100, 0.95, 5, false, 0)); // *this->parameters);

    printf("done!\n");
//    boost.save("./boosttest.xml", "boost");

    return EXIT_SUCCESS;
}