Error:Bad argument (in the case of classification problem the responses must be categorical; either specify varType when creating TrainData, or pass integer responses) in cv::ml::SVMImpl::train, file C:\build\master_winpack-build-win64-vc15\opencv\modules\ml\src\svm.cpp, line 1624
//Programme:
include "SVM.h"
include < stdio.h>
include < opencv2\opencv.hpp>
//#include < opencv2\gpu\gpu.hpp>
using namespace cv; using namespace std; using namespace cv::ml;
void svm1() {
//Read Hog feature from XML file
///////////////////////////////////////////////////////////////////////////
printf("1. Feature data xml load\n");
//create xml to read
FileStorage read_PositiveXml("Positive.xml", FileStorage::READ);
FileStorage read_NegativeXml("Negative.xml", FileStorage::READ);
//Positive Mat
Mat pMat;
read_PositiveXml["Descriptor_of_images"] >> pMat;
//Read Row, Cols
int pRow, pCol;
pRow = pMat.rows; pCol = pMat.cols;
//Negative Mat
Mat nMat;
read_NegativeXml["Descriptor_of_images"] >> nMat;
//Read Row, Cols
int nRow, nCol;
nRow = nMat.rows; nCol = nMat.cols;
//Rows, Cols printf
printf(" pRow=%d pCol=%d, nRow=%d nCol=%d\n", pRow, pCol, nRow, nCol);
//release
read_PositiveXml.release();
//release
read_NegativeXml.release();
/////////////////////////////////////////////////////////////////////////////////
//Make training data for SVM
///////////////////////////////////////////////////////////////////////////////// printf("2. Make training data for SVM\n"); //descriptor data set Mat PN_Descriptor_mtx(pRow + nRow, pCol, CV_32FC1); //in here pCol and nCol is descriptor number, so two value must be same; memcpy(PN_Descriptor_mtx.data, pMat.data, sizeof(float) * pMat.cols * pMat.rows); int startP = sizeof(float) * pMat.cols * pMat.rows; memcpy(&(PN_Descriptor_mtx.data[startP]), nMat.data, sizeof(float) * nMat.cols * nMat.rows); //data labeling Mat labels(pRow + nRow, 1, CV_32FC1, Scalar(-1.0)); labels.rowRange(0, pRow) = Scalar(1.0); /////////////////////////////////////////////////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////////
//Set svm parameter
/////////////////////////////////////////////////////////////////////////////////
printf("4. SVM training\n");
Ptr<ml::SVM> svm = ml::SVM::create();
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM:: RBF);
svm->setGamma(0.50625);
svm->setC(100);
//cout << "params set" << endl;
printf("4. params set\n");
Ptr<ml::TrainData> tData = ml::TrainData::create(PN_Descriptor_mtx, ml::SampleTypes::ROW_SAMPLE, labels);
svm->train(tData);
// ...
Mat query; // input, 1channel, 1 row (apply reshape(1,1) if nessecary)
Mat res; // output
svm->predict(query, res);
//Trained data save
/////////////////////////////////////////////////////////////////////////////////
printf("5. SVM xml save\n");
svm->save("trainedSVM.xml");
// FileStorage hogXml("testXML.xml", FileStorage::WRITE); //FileStorage::READ
// write(hogXml, "Data", PN_Descriptor_mtx);
// write(hogXml, "Label", labels);
// hogXml.release();
}
