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;
}
