Model Model::create(string path){
ostringstream s4, s5;
s4 << "INSERT INTO Model(directory) VALUES('" + path + ".txt');";
QSqlQuery query4(QString(s4.str().c_str()));
int idModel = query4.lastInsertId().toInt();
cout << s4.str() << endl;
string command = "bash createCSV.sh " + path + " > FaceRecognition/modelos.csv";
cout << command << endl;
system(command.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);
}
s5 << "INSERT INTO ModelStudent("
"id_model,"
"id_student) VALUES";
set<int> ids(labels.begin(), labels.end());
map<int, int> pairs;
for(auto it = ids.begin(); it != ids.end(); ++it){
Alumno* a = Alumno::create("", string(path + "/" + to_string(*it)));
pairs[(*it)] = a->getId();
s5 << "(" << idModel << ", " << a->getId() << ")";
if(next(it) == ids.end()) s5 << ";";
else s5 << ", \n";
}
cout << s5.str() << endl;
vector<int> newLabels;
for(int i = 0; i < labels.size(); i++){
newLabels.push_back(pairs[labels[i]]);
}
for(int i = 0; i < images.size(); i++){
equalizeHist(images[i], images[i]);
cv::resize(images[i], images[i], Size(48,48));
}
QSqlQuery query5(QString(s5.str().c_str()));
Model model = Model(createEigenFaceRecognizer(0, 3000));
model->train(images, newLabels);
model->save(string(path + ".txt"));
cout << "END" << endl;
return model;
}
void ofxFaceRecognizer::setup(int method_used, int _maxFaces, bool bAlreadySavedModel, string folderName) {
//eigen take much longer to load and longer to generate. also makes much larger yml file
string method_names[3] = {"eigen","fisher","lbph"};
// Create a FaceRecognizer and train it on the given images:
methodId = method_used;
methodName = method_names[method_used];
if(methodId == 0){
model = createEigenFaceRecognizer();
}
if(methodId == 1){
model = createFisherFaceRecognizer();
}
if(methodId == 2){
model = createLBPHFaceRecognizer();
}
//if(_maxFaces > 0){
maxFaces = _maxFaces;
//by default my training images should be 150x150 pixels
//will reset if you use loadTrainingImages()
image_width = 150;
image_height = 150;
//load in training images
loadTrainingImages(folderName,maxFaces);
string compiledDatabasePath = ofToDataPath("model_"+methodName+"_"+ofToString(maxFaces)+"_"+folderName+".yml");
if(bAlreadySavedModel){
cout<<"model .yml supposedly existing"<<endl;
model->load(compiledDatabasePath);
cout<<"loaded "<<maxFaces<<" faces with model "<<methodName<<endl;
}else{
cout<<"start training new model. this might take a very long time"<<endl;
cout<<"compiledDatabasePath "<<compiledDatabasePath<<endl;
cout<<"more so for fisher than eigen"<<endl;
model->train(allTrainingMats, allTrainingLabels);
model->save(ofToDataPath(compiledDatabasePath));
cout<<"trained and saved .yml with "<<maxFaces<<" faces with model "<<methodName<<endl;
}
// Quit if there are not enough images for this demo.
if(allTrainingMats.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);
}
}
int main( int argc, char** argv )
{
// Medida de tiempo inicial
clock_t t0, t_load, t_loop, t_loop0;
double dtime;
t0 = clock();
// Declaration of variables
CascadeClassifier face_cascade; // Cascade Classifier
Mat captureFrame, grayscaleFrame; // Captured and converted to gray Frames
double x_face_pos, y_face_pos, area_face; // Coordinates of the detected face
vector< Rect_<int> > faces; // Vector of faces
#ifdef RASPBERRY
RaspiCamCvCapture * captureDevice; // Video input
#else
CvCapture * captureDevice; // Video input
#endif
char sTmp[255];
#ifdef TRACE
sprintf(sTmp,"\n Directorio de ejecucion del programa: ");
trace (sTmp);
cout << get_ProgramDirectory();
#endif
#ifdef RECOGNITION
// Declaration of variables Face recognition
string people[MAX_PEOPLE]; // Each person of the model of face recognition
int im_width, im_height; // heigh, witdh of 1st images of the model of face recognition
int prediction_seuil; // Prediction limit
Ptr<FaceRecognizer> model; // Model of face recognitio
// Prediction limit depending on the device
#ifdef EIGENFACES
prediction_seuil = 10;
#else
prediction_seuil = 1000;
#endif
// Model of face recognition
#ifdef EIGENFACES
model = createEigenFaceRecognizer();
#else
model = createFisherFaceRecognizer();
#endif
// Read measures file
read_measures_file(im_width, im_height, '=');
// Read people file
read_people_file(people, '(', ')', '=');
// Load model
load_model_recognition(model);
t_load = clock();
#ifdef DEBUG
dtime = difftime(t_load,t0);
sprintf(sTmp,"\n (Face Tracking) tiempo de carga del modelo = ");
debug(sTmp);
cout << print_time(dtime);
#endif
#endif
// Video input depending on the device
#ifdef RASPBERRY
captureDevice = raspiCamCvCreateCameraCapture(0); // Index doesn't really matter
#else
captureDevice = cvCreateCameraCapture(0);
#endif
// Load of Haar Cascade
if (!load_haar_cascade(face_cascade)) { return -1;}
// Create new window
SHOW cvNamedWindow("Face tracking", 1);
do {
t_loop0 = clock();
#ifdef RASPBERRY
IplImage* image = raspiCamCvQueryFrame(captureDevice); // Get images from the video input
#else
IplImage* image = cvQueryFrame(captureDevice); // Get images from the video input
#endif
captureFrame = cvarrToMat(image); // Convert images to Mat
cvtColor(captureFrame, grayscaleFrame, CV_RGB2GRAY); // Convert the image to gray scale
// Detection and Face Recognition
face_detection(face_cascade, grayscaleFrame, captureFrame, &faces, x_face_pos, y_face_pos, area_face);
#ifdef RECOGNITION
// Detection and Face Recognition
face_recognition(people, grayscaleFrame, captureFrame, &faces, im_width, im_height, model,
prediction_seuil, x_face_pos, y_face_pos, area_face);
#endif
// Display results
#ifdef SHOW
imshow("Face tracking", captureFrame);
#endif
t_loop = clock();
#ifdef DEBUG
dtime = difftime(t_loop,t_loop0);
sprintf(sTmp,"\n (Face Tracking) tiempo del bucle del reconocimiento de cara = ");
debug(sTmp);
cout << print_time(dtime);
#endif
} while(cvWaitKey(10) < 0);
// Destroy window
#ifdef SHOW
cvDestroyWindow("Face tracking");
#endif
#ifdef TRACE
trace ("\n");
#endif
#ifdef RASPBERRY
raspiCamCvReleaseCapture(&captureDevice);
#else
cvReleaseCapture(&captureDevice);
#endif
return 0;
}
//////////////////// FaceRecognizer ////////////////////
ICLASS_API FaceRecognizer* __stdcall Create_EigenFaceRecognizer(int num_components, double threshold)
{
Ptr<FaceRecognizer> r = createEigenFaceRecognizer(num_components, threshold);
r.addref();
return r;
}
void PCAFaceRecognition() {
#define NUMBER_OF_FACES 10
#define NUMBER_OF_IMAGES_PER_FACE 3
vector<Mat> known_face_images;
vector<int> known_labels;
vector<Mat> unknown_face_images;
vector<int> unknown_labels;
// Load greyscale face images (which are from http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html)
char file_name[40];
Mat original_images,row_so_far,image_so_far,temp_image1;
int face_number = 1;
for (; face_number<=NUMBER_OF_FACES; face_number++)
{
for (int image_number = 1; image_number<=NUMBER_OF_IMAGES_PER_FACE; image_number++)
{
sprintf(file_name,"Media/att_faces/s%d/%d.pgm",face_number,image_number);
Mat current_image = imread(file_name,0);
if (image_number>1)
{
known_face_images.push_back(current_image);
known_labels.push_back(face_number);
}
else
{
// Keep the last image of each face as a test case.
unknown_face_images.push_back(current_image);
unknown_labels.push_back(face_number);
}
cvtColor(current_image, current_image, CV_GRAY2BGR);
if (image_number == 2)
{
if (face_number%10 == 1)
{
if (face_number > 1)
if (face_number == 11)
original_images = row_so_far.clone();
else original_images = JoinImagesVertically( original_images, "", row_so_far, "", 1 );
row_so_far = current_image.clone();
}
else
{
char image_number_string[10],previous_image_number_string[10];
sprintf(previous_image_number_string,"%d",face_number-1);
sprintf(image_number_string,"%d",face_number);
row_so_far = JoinImagesHorizontally( row_so_far, (face_number%10==2)?previous_image_number_string:"", current_image, image_number_string, 1 );
}
}
}
}
if (face_number <= 11)
original_images = row_so_far.clone();
else original_images = JoinImagesVertically( original_images, "", row_so_far, "", 1 );
imshow("Known face images", original_images);
imwrite("pca_unknown_faces.bmp",original_images);
Ptr<FaceRecognizer> face_recogniser = createEigenFaceRecognizer();
face_recogniser->train(known_face_images, known_labels);
char previous_face_number_string[100]="";
char face_number_string[100]="";
int correct_count = 0;
for (face_number = 0; face_number < (int)unknown_face_images.size(); face_number++)
{
int predicted_face_number = 0;
double recognition_confidence = 0.0;
face_recogniser->predict(unknown_face_images[face_number],predicted_face_number,recognition_confidence);
if (unknown_labels[face_number]==predicted_face_number)
correct_count++;
strcpy(previous_face_number_string,face_number_string);
cvtColor(unknown_face_images[face_number], temp_image1, CV_GRAY2BGR);
sprintf(face_number_string,"%d (%.0f)",predicted_face_number,recognition_confidence);
Point location(2,15);
putText( temp_image1, face_number_string, location, FONT_HERSHEY_SIMPLEX, 0.4, unknown_labels[face_number]==predicted_face_number?Scalar( 0,255,0 ):Scalar( 0,0,255 ) );
if (face_number%10 == 0)
{
if (face_number > 10)
image_so_far = JoinImagesVertically( image_so_far, "", row_so_far, "", 1 );
else image_so_far = row_so_far.clone();
row_so_far = temp_image1.clone();
}
else
{
row_so_far = JoinImagesHorizontally( row_so_far, "", temp_image1, "", 1 );
}
}
if (face_number > 10)
image_so_far = JoinImagesVertically( image_so_far, "", row_so_far, "", 1 );
else image_so_far = row_so_far.clone();
char output[300];
sprintf(output,"OVERALL Recognised %d/%d (with %d training image%s of %d subjects)",correct_count,unknown_face_images.size(),NUMBER_OF_IMAGES_PER_FACE-1,(NUMBER_OF_IMAGES_PER_FACE-1==1)?"":"s",NUMBER_OF_FACES);
Point location(10,image_so_far.rows-10);
putText( image_so_far, output, location, FONT_HERSHEY_SIMPLEX, 0.4, Scalar( 255,0,0 ) );
imshow("Recognised faces using PCA (Eigenfaces)", image_so_far);
}
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;
}
