/** Train SVM classifier using data from 'data_file' and save trained model to 'model_file'
*/
void TrainClassifier(const string& data_file, const string& model_file, bool sse_on) {
// List of image file names and its labels
TFileList file_list;
// Structure of images and its labels
TDataSet data_set;
// Structure of features of images and its labels
TFeatures features;
// Model which would be trained
TModel model;
// Parameters of classifier
TClassifierParams params;
// Load list of image file names and its labels
LoadFileList(data_file, &file_list);
// Load images
LoadImages(file_list, &data_set);
// Extract features from images
ExtractFeatures(data_set, &features, sse_on);
// PLACE YOUR CODE HERE
// You can change parameters of classifier here
params.C = 0.01;
TClassifier classifier(params);
// Train classifier
classifier.Train(features, &model);
// Save model to file
model.Save(model_file);
// Clear dataset structure
ClearDataset(&data_set);
}
HOGFeatureClassifier::TModel OptimizeThresholdsInModel(const string &images_list, const string& model_file, RecognitionStatistics &stat)
{
TModel model;
model.Load(model_file);
float model_threshold = ImageRecognition::FindOptimalThresholdForModel(images_list, model, stat);
model.setModelThreshold(model_threshold);
model.Save(model_file);
return model;
}
