Hi, I used some sort of SVM:
include "opencv2/core/core.hpp"
include "opencv2/features2d/features2d.hpp"
include "opencv2/highgui/highgui.hpp"
include "opencv2/nonfree/features2d.hpp"
include "opencv2/nonfree/nonfree.hpp"
include <iostream>
include <stdio.h>
include <stdlib.h>
using namespace std;
include <opencv2 core="" core.hpp="">
include <opencv2 highgui="" highgui.hpp="">
include <opencv2 ml="" ml.hpp="">
using namespace cv;
int main() { // Data for visual representation int width = 512, height = 512; Mat image = Mat::zeros(height, width, CV_8UC3);
//Load Data and Labels from csv files
CvMLData mlData;
mlData.read_csv("/home/rahim/workspace/svm/DataTrain1-svm.csv");
const CvMat* tmpDataTr = mlData.get_values();
cv::Mat DataTrt(tmpDataTr, true);
tmpDataTr->CvMat::~CvMat();
cout << "M = "<< DataTrt << " " << DataTrt << endl << endl;
Mat DataTr = DataTrt.t();
mlData.read_csv("/home/rahim/workspace/svm/LabelTrain1-svm.csv");
const CvMat* tmpLabelTr = mlData.get_values();
cv::Mat LabelTrt(tmpLabelTr, true);
tmpLabelTr->CvMat::~CvMat();
cout << "M = "<< LabelTrt << " " << LabelTrt << endl << endl;
Mat LabelTr = LabelTrt.t();
cout << "Data size = " << DataTr.size() << endl;
cout << "labels size = " << LabelTr.size() << endl;
// Set up SVM's parameters
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
// Train the SVM
CvSVM SVM;
//SVM.train(trainingData, traininglabels, Mat(), Mat(), params);
SVM.train(DataTr, LabelTr, Mat(), Mat(), params);
Vec3b green(0,255,0), blue (255,0,0);
// Show the decision regions given by the SVM
for (int i = 0; i < image.rows; ++i)
for (int j = 0; j < image.cols; ++j)
{
Mat sampleMat = (Mat_<float>(1,2) << j,i);
float response = SVM.predict(sampleMat);
if (response == 1)
image.at<Vec3b>(i,j) = green;
else if (response == -1)
image.at<Vec3b>(i,j) = blue;
}
// Show the training data
int thickness = -1;
int lineType = 8;
circle( image, Point(501, 10), 5, Scalar( 0, 0, 0), thickness, lineType);
circle( image, Point(255, 10), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point(501, 255), 5, Scalar(255, 255, 255), thickness, lineType);
circle( image, Point( 10, 501), 5, Scalar(255, 255, 255), thickness, lineType);
// Show support vectors
thickness = 2;
lineType = 8;
int c = SVM.get_support_vector_count();
for (int i = 0; i < c; ++i)
{
const float* v = SVM.get_support_vector(i);
circle( image, Point( (int) v[0], (int) v[1]), 6, Scalar(128, 128, 128), thickness, lineType);
}
imwrite("result.png", image); // save the image
imshow("SVM Simple Example", image); // show it to the user
waitKey(0);
}
but I got the following error : guments do not match (Response array must contain as many elements as the total number of samples) in cvPreprocessCategoricalResponses
the output for DataTr.size() and LabelTr.size() are [18009] and [18001] respectively
