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made everything compile and even run somehow

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This commit is contained in:
Vadim Pisarevsky
2014-08-03 01:41:09 +04:00
parent 10b60f8d16
commit c20ff6ce19
31 changed files with 11910 additions and 9061 deletions
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66
samples/cpp/bagofwords_classification.cpp
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@@ -2326,14 +2326,14 @@ static void removeBowImageDescriptorsByCount( vector<ObdImage>& images, vector<M
CV_Assert( bowImageDescriptors.size() == objectPresent.size() );
}
static void setSVMParams( const SVM::Params& svmParams, Mat& class_wts_cv, const Mat& responses, bool balanceClasses )
static void setSVMParams( SVM::Params& svmParams, Mat& class_wts_cv, const Mat& responses, bool balanceClasses )
{
int pos_ex = countNonZero(responses == 1);
int neg_ex = countNonZero(responses == -1);
cout << pos_ex << " positive training samples; " << neg_ex << " negative training samples" << endl;
svmParams.svm_type = CvSVM::C_SVC;
svmParams.kernel_type = CvSVM::RBF;
svmParams.svmType = SVM::C_SVC;
svmParams.kernelType = SVM::RBF;
if( balanceClasses )
{
Mat class_wts( 2, 1, CV_32FC1 );
@@ -2351,43 +2351,44 @@ static void setSVMParams( const SVM::Params& svmParams, Mat& class_wts_cv, const
class_wts.at<float>(1) = static_cast<float>(pos_ex)/static_cast<float>(pos_ex+neg_ex);
}
class_wts_cv = class_wts;
svmParams.class_weights = &class_wts_cv;
svmParams.classWeights = class_wts_cv;
}
}
static void setSVMTrainAutoParams( CvParamGrid& c_grid, CvParamGrid& gamma_grid,
CvParamGrid& p_grid, CvParamGrid& nu_grid,
CvParamGrid& coef_grid, CvParamGrid& degree_grid )
static void setSVMTrainAutoParams( ParamGrid& c_grid, ParamGrid& gamma_grid,
ParamGrid& p_grid, ParamGrid& nu_grid,
ParamGrid& coef_grid, ParamGrid& degree_grid )
{
c_grid = CvSVM::get_default_grid(CvSVM::C);
c_grid = SVM::getDefaultGrid(SVM::C);
gamma_grid = CvSVM::get_default_grid(CvSVM::GAMMA);
gamma_grid = SVM::getDefaultGrid(SVM::GAMMA);
p_grid = CvSVM::get_default_grid(CvSVM::P);
p_grid.step = 0;
p_grid = SVM::getDefaultGrid(SVM::P);
p_grid.logStep = 0;
nu_grid = CvSVM::get_default_grid(CvSVM::NU);
nu_grid.step = 0;
nu_grid = SVM::getDefaultGrid(SVM::NU);
nu_grid.logStep = 0;
coef_grid = CvSVM::get_default_grid(CvSVM::COEF);
coef_grid.step = 0;
coef_grid = SVM::getDefaultGrid(SVM::COEF);
coef_grid.logStep = 0;
degree_grid = CvSVM::get_default_grid(CvSVM::DEGREE);
degree_grid.step = 0;
degree_grid = SVM::getDefaultGrid(SVM::DEGREE);
degree_grid.logStep = 0;
}
static void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsExt, const string& objClassName, VocData& vocData,
static Ptr<SVM> trainSVMClassifier( const SVMTrainParamsExt& svmParamsExt, const string& objClassName, VocData& vocData,
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
const string& resPath )
{
/* first check if a previously trained svm for the current class has been saved to file */
string svmFilename = resPath + svmsDir + "/" + objClassName + ".xml.gz";
Ptr<SVM> svm;
FileStorage fs( svmFilename, FileStorage::READ);
if( fs.isOpened() )
{
cout << "*** LOADING SVM CLASSIFIER FOR CLASS " << objClassName << " ***" << endl;
svm.load( svmFilename.c_str() );
svm = StatModel::load<SVM>( svmFilename );
}
else
{
@@ -2438,20 +2439,24 @@ static void trainSVMClassifier( CvSVM& svm, const SVMTrainParamsExt& svmParamsEx
}
cout << "TRAINING SVM FOR CLASS ..." << objClassName << "..." << endl;
CvSVMParams svmParams;
CvMat class_wts_cv;
SVM::Params svmParams;
Mat class_wts_cv;
setSVMParams( svmParams, class_wts_cv, responses, svmParamsExt.balanceClasses );
CvParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid;
svm = SVM::create(svmParams);
ParamGrid c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid;
setSVMTrainAutoParams( c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
svm.train_auto( trainData, responses, Mat(), Mat(), svmParams, 10, c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid );
svm->trainAuto(TrainData::create(trainData, ROW_SAMPLE, responses), 10,
c_grid, gamma_grid, p_grid, nu_grid, coef_grid, degree_grid);
cout << "SVM TRAINING FOR CLASS " << objClassName << " COMPLETED" << endl;
svm.save( svmFilename.c_str() );
svm->save( svmFilename );
cout << "SAVED CLASSIFIER TO FILE" << endl;
}
return svm;
}
static void computeConfidences( CvSVM& svm, const string& objClassName, VocData& vocData,
static void computeConfidences( const Ptr<SVM>& svm, const string& objClassName, VocData& vocData,
Ptr<BOWImgDescriptorExtractor>& bowExtractor, const Ptr<FeatureDetector>& fdetector,
const string& resPath )
{
@@ -2477,12 +2482,12 @@ static void computeConfidences( CvSVM& svm, const string& objClassName, VocData&
if( imageIdx == 0 )
{
// In the first iteration, determine the sign of the positive class
float classVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], false );
float scoreVal = confidences[imageIdx] = svm.predict( bowImageDescriptors[imageIdx], true );
float classVal = confidences[imageIdx] = svm->predict( bowImageDescriptors[imageIdx], noArray(), 0 );
float scoreVal = confidences[imageIdx] = svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
signMul = (classVal < 0) == (scoreVal < 0) ? 1.f : -1.f;
}
// svm output of decision function
confidences[imageIdx] = signMul * svm.predict( bowImageDescriptors[imageIdx], true );
confidences[imageIdx] = signMul * svm->predict( bowImageDescriptors[imageIdx], noArray(), StatModel::RAW_OUTPUT );
}
cout << "WRITING QUERY RESULTS TO VOC RESULTS FILE FOR CLASS " << objClassName << "..." << endl;
@@ -2592,9 +2597,8 @@ int main(int argc, char** argv)
for( size_t classIdx = 0; classIdx < objClasses.size(); ++classIdx )
{
// Train a classifier on train dataset
CvSVM svm;
trainSVMClassifier( svm, svmTrainParamsExt, objClasses[classIdx], vocData,
bowExtractor, featureDetector, resPath );
Ptr<SVM> svm = trainSVMClassifier( svmTrainParamsExt, objClasses[classIdx], vocData,
bowExtractor, featureDetector, resPath );
// Now use the classifier over all images on the test dataset and rank according to score order
// also calculating precision-recall etc.