Ptr<FaceRecognizer> Trainer::load(const char* fname,int type)
{
Ptr<FaceRecognizer> model;
if(type==0)
model=createFisherFaceRecognizer();
else
model=createLBPHFaceRecognizer();
model->load(fname);
char lable_fname[MAX_PATH];
sprintf(lable_fname,"%s.labels",fname);
FILE* fp=fopen(lable_fname,"rt");
if(fp==0)
{
printf("Fail to load .labels file, please re-train the model\n");
return NULL;
}
int class_count=0;
fscanf(fp,"%d",&class_count);fgetc(fp);
names.clear();
char buf[256];
for(int i=0;i<class_count;++i)
{
fscanf(fp,"%s",buf);
names.push_back(buf);
}
fclose(fp);
return model;
}
ICLASS_API FaceRecognizer* __stdcall Create_LBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y, double threshold)
{
Ptr<FaceRecognizer> r = createLBPHFaceRecognizer(radius, neighbors, grid_x, grid_y, threshold);
r.addref();
return r;
}
PersonRecognizer::PersonRecognizer(const vector<Mat> &imgs, int radius, int neighbors, int grid_x, int grid_y, double threshold)
{
//all images are faces of the same person, so initialize the same label for all.
vector<int> labels(imgs.size());
for (vector<int>::iterator it = labels.begin() ; it != labels.end() ; *(it++) = PERSON_LABEL);
_faceSize = Size(imgs[0].size().width, imgs[0].size().height);
//build recognizer model:
_model = createLBPHFaceRecognizer(radius, neighbors, grid_x, grid_y, threshold);
_model->train(imgs, labels);
}
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);
}
}
void haar_cascade::train() {
// Create a FaceRecognizer and train it on the given images:
// model = createFisherFaceRecognizer();
model = createLBPHFaceRecognizer();
model->train(this->images, this->labels);
// That's it for learning the Face Recognition model. You now
// need to create the classifier for the task of Face Detection.
// We are going to use the haar cascade you have specified in the
// command line arguments:
//
this->cascade.load(this->haar_path);
}
Ptr<FaceRecognizer> new_person_detection(string filename){
Ptr<FaceRecognizer> LBPH_model = createLBPHFaceRecognizer();
vector<Mat> images;
vector<int> labels;
try {
read_csv(filename, images, labels);
} catch (Exception& e) {
cerr << "Error opening file \"" << filename << "\". Reason: " << e.msg << endl;
// nothing more we can do
exit(1);
}
LBPH_model -> train(images, labels);
return LBPH_model;
}
void Trainer::train(const char* saveto,int type)
{
Ptr<FaceRecognizer> model;
if(type==0)
model=createFisherFaceRecognizer();
else
model=createLBPHFaceRecognizer();
printf("\nbegin training...\n");
model->train(images, labels);
model->save(saveto);
char lable_fname[MAX_PATH];
sprintf(lable_fname,"%s.labels",saveto);
FILE* fp=fopen(lable_fname,"wt");
fprintf(fp,"%d\n",names.size());
for(int i=0,iLen=names.size();i<iLen;++i)
{
fprintf(fp,"%s\n",names[i].c_str());
}
fclose(fp);
printf("Trained model saved to \"%s\"\n",saveto);
}
//Globals #include "global.hpp" #ifndef GLOBAL #define GLOBAL string file_name = "lbph_results_400_with_filter.csv"; int conf = 400; string face_cascade_name = "lbpcascade_frontalface.xml"; //string face_cascade_name = "haarcascade_frontalface_alt.xml"; CascadeClassifier face_cascade; Ptr<FaceRecognizer> model_lbph = createLBPHFaceRecognizer(1,8,8,8, conf); #endif
