Opencv 3 SVM training

Question

As you may know, many things changed in OpenCV 3 (in comparision to the openCV2 or the old first version).

In the old days, to train SVM one would use:

CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::POLY;
params.gamma = 3;

CvSVM svm;
svm.train(training_mat, labels, Mat(), Mat(), params);

In the third version of API, there is no CvSVMParams nor CvSVM. Surprisingly, there is a documentation page about SVM, but it tells everything, but not how to really use it (at least I cannot make it out). Moreover, it looks like no one in the Internet uses SVM from OpenCV's 3.0.

Currently, I only managed to get the following:

ml::SVM.Params params;
params.svmType = ml::SVM::C_SVC;
params.kernelType = ml::SVM::POLY;
params.gamma = 3;

Can you please provide me with information, how to rewrite the actual training to openCV 3?

Answer 1

with opencv3.0, it's definitely different , but not difficult:

Ptr<ml::SVM> svm = ml::SVM::create();
// edit: the params struct got removed,
// we use setter/getter now:
svm->setType(ml::SVM::C_SVC);
svm->setKernel(ml::SVM::POLY);
svm->setGamma(3); 

Mat trainData; // one row per feature
Mat labels;    
svm->train( trainData , ml::ROW_SAMPLE , labels );
// ...
Mat query; // input, 1channel, 1 row (apply reshape(1,1) if nessecary)
Mat res;   // output
svm->predict(query, res);