void GazeTracker::updateLeft(const cv::Mat *image, const cv::Mat *eyeGrey) {
if (isActive()) {
output.gazePointLeft = Point(_gaussianProcessXLeft->getmean(Utils::SharedImage(image, &ignore)), _gaussianProcessYLeft->getmean(Utils::SharedImage(image, &ignore)));
// Neural network
// Resize image to 16x8
cv::resize(*eyeGrey, *_nnEye, _nnEye->size());
cv::equalizeHist(*_nnEye, *_nnEye);
fann_type inputs[_nnEyeWidth * _nnEyeHeight];
for (int i = 0; i < _nnEyeWidth * _nnEyeHeight; ++i) {
inputs[i] = (float)(_nnEye->data[i] + 129) / 257.0f;
}
fann_type *outputs = fann_run(_ANNLeft, inputs);
Utils::mapFromNeuralNetworkToScreenCoordinates(Point(outputs[0], outputs[1]), output.nnGazePointLeft);
if (_gammaX != 0) {
// Overwrite the NN output with the GP output with calibration errors removed
output.nnGazePoint.x = (output.gazePoint.x + output.gazePointLeft.x) / 2;
output.nnGazePoint.y = (output.gazePoint.y + output.gazePointLeft.y) / 2;
removeCalibrationError(output.nnGazePoint);
output.nnGazePointLeft.x = output.nnGazePoint.x;
output.nnGazePointLeft.y = output.nnGazePoint.y;
}
}
}
void GazeTracker::update_left(const IplImage *image, const IplImage *eyegrey, vector<vector<int> > vector_h_v_combined) {
if (isActive()) {
// TODO ARCADI 23/12
//output.gazepoint_left = Point(gpx_left->getmean(SharedImage(image, &ignore)),
// gpy_left->getmean(SharedImage(image, &ignore)));
// TODO ONUR COMBINED H AND V
//vector<vector<int> > vector_h_v_combined;
//vector_h_v_combined.insert(vector_h_v_combined.end(), vector_horizontal_left.begin(), vector_horizontal_left.end());
//vector_h_v_combined.insert(vector_h_v_combined.end(), vector_vertical_left.begin(), vector_vertical_left.end());
//cout << "UPDATE LEFT! Vertical Vector size: " << vector_h_v_combined.size() << endl;
//cout.flush();
//output.gazepoint_left = Point(histx_left->getmean(vector_horizontal_left),
// histy_left->getmean(vector_vertical_left));
// Use combined only for Y
//output.gazepoint_left = Point(histx_left->getmean(vector_horizontal_left),
// histy_left->getmean(vector_h_v_combined));
// Use combined for both directions
output.gazepoint_left = Point(histx_left->getmean(vector_h_v_combined),
histy_left->getmean(vector_h_v_combined));
/*
vector<int> vector_horizontal_and_vertical_left;
copy( vector_horizontal_left.begin(), vector_horizontal_left.end(), back_inserter(vector_horizontal_and_vertical_left));
copy( vector_vertical_left.begin(), vector_vertical_left.end(), back_inserter(vector_horizontal_and_vertical_left));
output.gazepoint_left = Point(histx_left->getmean(vector_horizontal_and_vertical_left),
histy_left->getmean(vector_horizontal_and_vertical_left));
*/
// Neural network
// Resize image to 16x8
cvResize(eyegrey, nn_eye);
cvEqualizeHist(nn_eye, nn_eye);
fann_type inputs[nn_eyewidth * nn_eyeheight];
for (int i = 0; i < nn_eyewidth * nn_eyeheight; ++i)
{
inputs[i] = (float)(nn_eye->imageData[i] + 129) / 257.0f;
}
fann_type* outputs = fann_run(ANN_left, inputs);
mapFromNeuralNetworkToScreenCoordinates(Point(outputs[0], outputs[1]), output.nn_gazepoint_left);
if(gamma_x != 0) {
// Overwrite the NN output with the GP output with calibration errors removed
output.nn_gazepoint.x = (output.gazepoint.x + output.gazepoint_left.x) / 2;
output.nn_gazepoint.y = (output.gazepoint.y + output.gazepoint_left.y) / 2;
removeCalibrationError(output.nn_gazepoint);
output.nn_gazepoint_left.x = output.nn_gazepoint.x;
output.nn_gazepoint_left.y = output.nn_gazepoint.y;
}
}
}
