std::vector< std::vector< std::vector<double> > > NeuralNetwork::readDataFromFile(std::string fpath){
std::vector< std::vector<double> > inputMatrix;
std::vector< std::vector<double> > outputMatrix;
std::ifstream trainingFile(fpath);
std::string line;
while(getline(trainingFile, line)){
//structure is x_1,...,x_n y_1,y_2,...,y_m (comma and space separated)
int spacePosition = (int)line.find(" ");
int lastCommaPosition = -1;
int nextCommaPosition = (int)line.find(",",lastCommaPosition+1);
//get inputs
std::vector<double> input;
while(nextCommaPosition != std::string::npos && nextCommaPosition < spacePosition){
input.push_back(stod(line.substr(lastCommaPosition+1,nextCommaPosition)));
lastCommaPosition = nextCommaPosition;
nextCommaPosition = (int)line.find(",",nextCommaPosition+1);
}
input.push_back(stod(line.substr(lastCommaPosition+1,spacePosition)));
//get outputs
std::vector<double> output;
lastCommaPosition = spacePosition;
nextCommaPosition = (int)line.find(",",lastCommaPosition+1);
while(nextCommaPosition != std::string::npos && nextCommaPosition < line.length()){
output.push_back(stod(line.substr(lastCommaPosition+1,nextCommaPosition)));
lastCommaPosition = nextCommaPosition;
nextCommaPosition = (int)line.find(",",lastCommaPosition+1);
}
output.push_back(stod(line.substr(lastCommaPosition+1)));
//add these training data points to the training matrices
inputMatrix.push_back(input);
outputMatrix.push_back(output);
}
trainingFile.close();
std::vector< std::vector< std::vector<double> > > dataFromFile;
dataFromFile.push_back(inputMatrix);
dataFromFile.push_back(outputMatrix);
return dataFromFile;
}
void LRMachine::loadTrainingSet(std::string filename) {
// std::cout << "I'm loading training set with the LRMachine from " << filename << std::endl;
std::string line;
std::ifstream trainingFile(filename.c_str());
if(trainingFile.is_open()){
while(std::getline(trainingFile,line)) {
Sample tmp;
tmp.setInput(Utils::vStovD(Utils::split(line,';')));
std::getline(trainingFile,line);
std::vector<int> res;
res.push_back(atoi(line.c_str()));
tmp.setResult(res);
C_trainingSet.push_back(tmp);
}
trainingFile.close();
} else{
std::cout << "Unable to open file" << std::endl;
}
}
void FacialFeatureRecognizerTest::compareSmileFrownSimilarFacesTest()
{
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
FacialFeatureRecognizer recognizer(model, 0, NULL, NULL, NULL);
QString trainingFile("/home/zane/Documents/COS301/training.xml");
recognizer.loadTrainingFromXML(trainingFile);
Mat face1 = imread("../../testFiles/FaceRec/barack_smile.jpg", CV_LOAD_IMAGE_UNCHANGED);
Mat face2 = imread("../../testFiles/FaceRec/barack_frown.jpg", CV_LOAD_IMAGE_UNCHANGED);
string faceCascade = "/home/zane/Documents/COS301/MainProject/testFiles/haarcascade_frontalface_alt2.xml";
Filter* faceDetect = new FaceDetectFilter(faceCascade);
Filter* preProc = new PreProcessingFilter(140, 150);
ImageData* data1 = new ImageData(face1, 0);
data1 = faceDetect->filter(data1);
data1 = preProc->filter(data1);
ImageData* data2 = new ImageData(face2, 0);
data2 = faceDetect->filter(data2);
data2 = preProc->filter(data2);
double expected = 5800;
double actual = recognizer.compareFaces(data1->faces[0], data2->faces[0]);
QVERIFY(actual <= expected);
}
