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Hi.

I'm using HOG with custom svm. I've used https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp

but my feature vector has 2268 features and get_svm_detector() give me 2269 features, this don't fail but it is very slow ( ~ 6 s), why?

With other application that it use 2268 features it take about ~64 ms.

Hi.

I'm using HOG with custom svm. I've used https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp

but my feature vector has 2268 features and get_svm_detector() give me 2269 features, this don't fail but it is very slow ( ~ 6 s), why?

With other application that it use 2268 features it take takes about ~64 ms.

I'm using HOG with custom svm. I've used https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp

https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp but my feature vector has 2268 features and get_svm_detector() give me 2269 features, support vectors, this don't fail give me error but it hog.detectMultiScale is very slow ( ~ 6 s), why? why?

With other application that it use svmLight I get 2268 features it support vectors and hog.detectMultiScale takes about ~64 ms.

I'm using HOG with custom svm. I've used https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp but my feature vector has 2268 features and get_svm_detector() give me 2269 support vectors, this don't give me error but hog.detectMultiScale is very slow ( ~ 6 s), why?

With svmLight I get 2268 support vectors and hog.detectMultiScale takes about ~64 ms.

Ptr<SVM> svm;
Ptr<TrainData> trainData = TrainData::loadFromCSV("./trainData.csv");    

svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, (int)1000, 1e-3));

svm->trainAuto(ejemplosEntrenamiento,10,SVM::getDefaultGrid(SVM::C),SVM::getDefaultGrid(SVM::GAMMA),SVM::getDefaultGrid(SVM::P),SVM::getDefaultGrid(SVM::NU),SVM::getDefaultGrid(SVM::COEF),SVM::getDefaultGrid(SVM::DEGREE),true );

svm->save("model.yml");

HOGDescriptor hog;

Ptr<SVM> svm = StatModel::load<SVM>( "model.yml" );
Ptr<SVM> svm = StatModel::load<SVM>( "clasificador.yml" );

vector< float > hog_detector;

// get the support vectors
Mat sv = svm->getSupportVectors();
const int sv_total = sv.rows;

// get the decision function
Mat alpha, svidx;
double rho = svm->getDecisionFunction(0, alpha, svidx);

CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
           (alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );
CV_Assert( sv.type() == CV_32F );

hog_detector.clear();
hog_detector.resize(sv.cols + 1);
memcpy(&hog_detector[0], sv.ptr(), sv.cols*sizeof(hog_detector[0]));
hog_detector[sv.cols] = (float)-rho;

hog.setSVMDetector( hog_detector );

// Then, it take me about 6 s!!
hog.detectMultiScale(img, detections, thresholdDetection, winStride, padding);

I'm using HOG with custom svm. I've used https://github.com/opencv/opencv/blob/master/samples/cpp/train_HOG.cpp but my feature vector has 2268 features and get_svm_detector() give me 2269 support vectors, this don't give me error but hog.detectMultiScale is very slow ( ~ 6 s), why?

With svmLight I get 2268 support vectors and hog.detectMultiScale takes about ~64 ms.

I Add the code:

Ptr<SVM> svm;
Ptr<TrainData> trainData = TrainData::loadFromCSV("./trainData.csv");    

svm = SVM::create();
svm->setType(SVM::C_SVC);
svm->setKernel(SVM::LINEAR);
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, (int)1000, 1e-3));

svm->trainAuto(ejemplosEntrenamiento,10,SVM::getDefaultGrid(SVM::C),SVM::getDefaultGrid(SVM::GAMMA),SVM::getDefaultGrid(SVM::P),SVM::getDefaultGrid(SVM::NU),SVM::getDefaultGrid(SVM::COEF),SVM::getDefaultGrid(SVM::DEGREE),true svm->trainAuto(trainData,10,SVM::getDefaultGrid(SVM::C),SVM::getDefaultGrid(SVM::GAMMA),SVM::getDefaultGrid(SVM::P),SVM::getDefaultGrid(SVM::NU),SVM::getDefaultGrid(SVM::COEF),SVM::getDefaultGrid(SVM::DEGREE),true );

svm->save("model.yml");

In other class:

HOGDescriptor hog;

Ptr<SVM> svm = StatModel::load<SVM>( "model.yml" );
Ptr<SVM> svm = StatModel::load<SVM>( "clasificador.yml" );

vector< float > hog_detector;

// get the support vectors
Mat sv = svm->getSupportVectors();
const int sv_total = sv.rows;

// get the decision function
Mat alpha, svidx;
double rho = svm->getDecisionFunction(0, alpha, svidx);

CV_Assert( alpha.total() == 1 && svidx.total() == 1 && sv_total == 1 );
CV_Assert( (alpha.type() == CV_64F && alpha.at<double>(0) == 1.) ||
           (alpha.type() == CV_32F && alpha.at<float>(0) == 1.f) );
CV_Assert( sv.type() == CV_32F );

hog_detector.clear();
hog_detector.resize(sv.cols + 1);
memcpy(&hog_detector[0], sv.ptr(), sv.cols*sizeof(hog_detector[0]));
hog_detector[sv.cols] = (float)-rho;

hog.setSVMDetector( hog_detector );

// Then, it take me about 6 s!!
hog.detectMultiScale(img, detections, thresholdDetection, winStride, padding);