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Updated ml module interfaces and documentation

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This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
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26
samples/cpp/train_HOG.cpp
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@@ -141,7 +141,7 @@ Mat get_hogdescriptor_visu(const Mat& color_origImg, vector<float>& descriptorVa
int cellSize = 8;
int gradientBinSize = 9;
float radRangeForOneBin = (float)(CV_PI/(float)gradientBinSize); // dividing 180<EFBFBD> into 9 bins, how large (in rad) is one bin?
float radRangeForOneBin = (float)(CV_PI/(float)gradientBinSize); // dividing 180 into 9 bins, how large (in rad) is one bin?
// prepare data structure: 9 orientation / gradient strenghts for each cell
int cells_in_x_dir = DIMX / cellSize;
@@ -313,23 +313,23 @@ void compute_hog( const vector< Mat > & img_lst, vector< Mat > & gradient_lst, c
void train_svm( const vector< Mat > & gradient_lst, const vector< int > & labels )
{
/* Default values to train SVM */
SVM::Params params;
params.coef0 = 0.0;
params.degree = 3;
params.termCrit.epsilon = 1e-3;
params.gamma = 0;
params.kernelType = SVM::LINEAR;
params.nu = 0.5;
params.p = 0.1; // for EPSILON_SVR, epsilon in loss function?
params.C = 0.01; // From paper, soft classifier
params.svmType = SVM::EPS_SVR; // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
Mat train_data;
convert_to_ml( gradient_lst, train_data );
clog << "Start training...";
Ptr<SVM> svm = StatModel::train<SVM>(train_data, ROW_SAMPLE, Mat(labels), params);
Ptr<SVM> svm = SVM::create();
/* Default values to train SVM */
svm->setCoef0(0.0);
svm->setDegree(3);
svm->setTermCriteria(TermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 1000, 1e-3 ));
svm->setGamma(0);
svm->setKernel(SVM::LINEAR);
svm->setNu(0.5);
svm->setP(0.1); // for EPSILON_SVR, epsilon in loss function?
svm->setC(0.01); // From paper, soft classifier
svm->setType(SVM::EPS_SVR); // C_SVC; // EPSILON_SVR; // may be also NU_SVR; // do regression task
svm->train(train_data, ROW_SAMPLE, Mat(labels));
clog << "...[done]" << endl;
svm->save( "my_people_detector.yml" );