Model Model::create(vector<Alumno *> students){
ostringstream s, s2;
QString sDir = QFileDialog::getSaveFileName(0, "Nombre Modelo", "/home");
s << "INSERT INTO Model("
"directory) VALUES('" << sDir.toStdString().c_str() << "');";
QSqlQuery query(QString(s.str().c_str()));
cout << s.str() << endl;
int idModel = query.lastInsertId().toInt();
s2 << "INSERT INTO ModelStudent("
"id_student,"
"id_model) VALUES";
system("echo '' > FaceRecognition/modelos.csv");
for(int i = 0; i < students.size(); i++){
string com ="bash createCSV.sh " + students[i]->getDirectory() + " " +
to_string(students[i]->getId()) + " >> FaceRecognition/modelos.csv";
cout << com << endl;
system(com.c_str());
s2 << "(" << students[i]->getId() << ", " << idModel << ")";
if(i == students.size() - 1){
s2 << ";";
} else s2 << ", \n";
}
cout << s2.str() << endl;
QSqlQuery query2(QString(s2.str().c_str()));
vector<Mat> images;
vector<int> labels;
try{
read_csv("FaceRecognition/modelos.csv", images, labels);
}catch (cv::Exception& e) {
cerr << "ERROR OPENING CSV FILE" << endl;
exit(1);
}
for(int i = 0; i < images.size(); i++){
equalizeHist(images[i], images[i]);
cv::resize(images[i], images[i], Size(48,48));
}
Model model = Model(createEigenFaceRecognizer(0, 3000));
model->train(images, labels);
model->save(sDir.toStdString());
model.setId(idModel);
model.setDirectory(sDir.toStdString());
model.setStudents(students);
cout << "END" << endl;
return model;
}
Ptr<FaceRecognizer> gender_detection(string fn_csv)
{
string output_folder;
//string fn_csv = string("/Users/xueqianjiang/Desktop/male.txt");
// These vectors hold the images and corresponding labels.
vector<Mat> images;
vector<int> labels;
// Read in the data. This can fail if no valid
// input filename is given.
try {
read_csv(fn_csv, images, labels);
} catch (Exception& e) {
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// this part will add in the EigenFaceRecognizer in order to in reduce the dimension further
// int height = images[0].rows;
//PCA model
//Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
//train the PCA model and without loss of information
//model->train(images, labels);
//save the results of the train
// model->save("eigenface.yml");
//take out the feature values from the eigenfaces and then rank them from largest to smallest
//Mat eigenvalues = model->getMat("eigenvalues");
//take out the feature values from the model，eigenvectures and the eigenvalues are in the same order
//Mat W = model->getMat("eigenvectors");
//打算保留前121个特征向量，代码中没有体现原因，但选择121是经过斟酌的，首先，在我的实验中，"前121个特征值之和/所有特征值总和>0.97"；其次，121=11^2，可以将结果表示成一个11*11的2维图像方阵，交给fisherface去计算
// int xth = 121;
//after PCA
//vector<Mat> reduceDemensionimages;
//choose the first xth eigenvalues and get rid of the rest
// Mat evs = Mat(W, Range::all(), Range(0, xth));
//for(int i=0;i<images.size();i++)
//{
// Mat mean= model->getMat("mean");
//subspaceProjection
//Mat projection = subspaceProject(W, mean, images[i].reshape(1,1));
//reduced dimensionality and save them
//reduceDemensionimages.push_back(projection.reshape(1,sqrt(xth)));
// }
// Quit if there are not enough images for this demo.
// Ptr<FaceRecognizer> fishermodel = createFisherFaceRecognizer();
//fishermodel->train(reduceDemensionimages,labels);
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
return model;
}
haar_cascade::haar_cascade(const string& haar_path, const string & csv_path):haar_path(haar_path),csv_path(csv_path)
{
read_csv(this->csv_path,this->images,this->labels);
}
