/**Predict data from 'data_file' using model from 'model_file' and save predictions to 'prediction_file'
*/
void PredictData(const string& data_file,
const string& model_file,
const string& prediction_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;
// List of image labels
TLabels labels;
// 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);
// Classifier
TClassifier classifier = TClassifier(TClassifierParams());
// Trained model
TModel model;
// Load model from file
model.Load(model_file);
// Predict images by its features using 'model' and store predictions
// to 'labels'
classifier.Predict(features, model, &labels);
// Save predictions
SavePredictions(file_list, labels, prediction_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;
}
void ResponseImage(vector<ImageRecognition::SlidingRect> &rects, const Mat &image, const string &model_filename, RecognitionStatistics &stat)
{
TModel model;
model.Load(model_filename);
ResponseImage(rects, image, model, stat);
}
