Using LIBSVM with OpenCV Mat

  LIBSVM is the most popular machine learning tool developed by C. J. Lin at National Taiwan University (http://www.csie.ntu.edu.tw/~cjlin/libsvm/). This code demonstrates how to load a data matrix in CSV format using OpenCV, and allocates LIBSVM data structure to do SVM predict. 

#include "svm.h"

#include <iostream>

#include "opencv2/opencv.hpp"

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/ml/ml.hpp"

#include <iostream>

using namespace cv;

using namespace std;

const char *CSV_FILE = "/Users/kuanting/libsvm-3.17/heart_scale.csv";

const char *MODEL_FILE = "/Users/kuanting/libsvm-3.17/heart_scale.model";

int main(int argc, char * argv[])

{

    CvMLData dataFile;

    

    // Load matrix data in csv format

    if (dataFile.read_csv(CSV_FILE) != 0)

    {

        fprintf(stderr, "Can't read csv file %s\n", CSV_FILE);

        return -1;

    }

    

    Mat dataMat(dataFile.get_values()); // Default data type is float

    

    struct svm_model *SVMModel;

    if ((SVMModel = svm_load_model(MODEL_FILE)) == 0) {

        fprintf(stderr, "Can't load SVM model %s", MODEL_FILE);

        return -2;

    }

    

    struct svm_node *svmVec;

    svmVec = (struct svm_node *)malloc((dataMat.cols+1)*sizeof(struct svm_node));

    double *predictions = new double[dataMat.rows];

    float *dataPtr = dataMat.ptr<float>(); // Get data from OpenCV Mat

    double prob_est[2];  // Probability estimation

    int r, c;
    for (r=0; r<dataMat.rows; r++)
    {
        for (c=0; c<dataMat.cols; c++)
        {

            svmVec[c].index = c+1;  // Index starts from 1; Pre-computed kernel starts from 0

            svmVec[c].value = dataPtr[r*dataMat.cols + c];
        }
        svmVec[c].index = -1;   // End of line

        

        if(svm_check_probability_model(SVMModel))

        {
            predictions[r] = svm_predict_probability(SVMModel, svmVec, prob_est);
            printf("%f\t%f\t%f\n", predictions[r], prob_est[0], prob_est[1]);
        }
        else
        {
            predictions[r] = svm_predict(SVMModel, svmVec);
            printf("%f\n", predictions[r]);
        }
    }

    

    return 0;

}