cv::Point findEyeCenter(cv::Mat face, cv::Rect eye, std::string debugWindow) {
cv::Mat eyeROIUnscaled = face(eye);
cv::Mat eyeROI;
scaleToFastSize(eyeROIUnscaled, eyeROI);
// draw eye region
rectangle(face,eye,1234);
//-- Find the gradient
cv::Mat gradientX = computeMatXGradient(eyeROI);
cv::Mat gradientY = computeMatXGradient(eyeROI.t()).t();
//-- Normalize and threshold the gradient
// compute all the magnitudes
cv::Mat mags = matrixMagnitude(gradientX, gradientY);
//compute the threshold
double gradientThresh = computeDynamicThreshold(mags, kGradientThreshold);
//double gradientThresh = kGradientThreshold;
//double gradientThresh = 0;
//normalize
for (int y = 0; y < eyeROI.rows; ++y) {
double *Xr = gradientX.ptr<double>(y), *Yr = gradientY.ptr<double>(y);
const double *Mr = mags.ptr<double>(y);
for (int x = 0; x < eyeROI.cols; ++x) {
double gX = Xr[x], gY = Yr[x];
double magnitude = Mr[x];
if (magnitude > gradientThresh) {
Xr[x] = gX/magnitude;
Yr[x] = gY/magnitude;
} else {
Xr[x] = 0.0;
Yr[x] = 0.0;
}
}
}
// imshow(debugWindow,gradientX);
//-- Create a blurred and inverted image for weighting
cv::Mat weight;
GaussianBlur( eyeROI, weight, cv::Size( kWeightBlurSize, kWeightBlurSize ), 0, 0 );
for (int y = 0; y < weight.rows; ++y) {
unsigned char *row = weight.ptr<unsigned char>(y);
for (int x = 0; x < weight.cols; ++x) {
row[x] = (255 - row[x]);
}
}
//imshow(debugWindow,weight);
//-- Run the algorithm!
cv::Mat outSum = cv::Mat::zeros(eyeROI.rows,eyeROI.cols,CV_64F);
// for each possible center
// printf("Eye Size: %ix%i\n",outSum.cols,outSum.rows);
for (int y = 0; y < weight.rows; ++y) {
const unsigned char *Wr = weight.ptr<unsigned char>(y);
const double *Xr = gradientX.ptr<double>(y), *Yr = gradientY.ptr<double>(y);
for (int x = 0; x < weight.cols; ++x) {
double gX = Xr[x], gY = Yr[x];
if (gX == 0.0 && gY == 0.0) {
continue;
}
testPossibleCentersFormula(x, y, Wr[x], gX, gY, outSum);
}
}
// scale all the values down, basically averaging them
double numGradients = (weight.rows*weight.cols);
cv::Mat out;
outSum.convertTo(out, CV_32F,1.0/numGradients);
//imshow(debugWindow,out);
//-- Find the maximum point
cv::Point maxP;
double maxVal;
cv::minMaxLoc(out, NULL,&maxVal,NULL,&maxP);
//-- Flood fill the edges
if(kEnablePostProcess) {
cv::Mat floodClone;
//double floodThresh = computeDynamicThreshold(out, 1.5);
double floodThresh = maxVal * kPostProcessThreshold;
cv::threshold(out, floodClone, floodThresh, 0.0f, cv::THRESH_TOZERO);
if(kPlotVectorField) {
//plotVecField(gradientX, gradientY, floodClone);
imwrite("eyeFrame.png",eyeROIUnscaled);
}
cv::Mat mask = floodKillEdges(floodClone);
//imshow(debugWindow + " Mask",mask);
//imshow(debugWindow,out);
// redo max
cv::minMaxLoc(out, NULL,&maxVal,NULL,&maxP,mask);
}
return unscalePoint(maxP,eye);
}
cv::Point findEyeCenter(cv::Mat face, cv::Rect eye, std::string debugWindow)
{
cv::Mat eyeROIUnscaled = face(eye);
cv::Mat eyeROI;
if(GBStatus)
{
cv::GaussianBlur( face, face, cv::Size(3,3), 0, 0, cv::BORDER_DEFAULT );
}
if(debugL1 && GBStatus && (debugWindow == costado))
{
std::string gaussianBlur_window = "GaussianBlur";
cv::namedWindow(gaussianBlur_window,CV_WINDOW_NORMAL);
cv::moveWindow(gaussianBlur_window, gaussBlurx, gaussBlury);
imshow(gaussianBlur_window,face);
}
scaleToFastSize(eyeROIUnscaled, eyeROI);
// draw eye region
rectangle(face,eye,1234);
//-- Find the gradient
cv::Mat gradientX;
cv::Mat gradientY;
if(sobel)
{
cv::Sobel( eyeROIUnscaled, gradientX, ddepth, 1, 0, 3, scale, delta, cv::BORDER_DEFAULT );
// cv::Sobel( (eyeROI.t()).t(), gradientY, ddepth, 0, 1, 3, scale, delta, cv::BORDER_DEFAULT );
cv::Sobel (eyeROIUnscaled, gradientY, ddepth, 0, 1, 3, scale, delta, cv::BORDER_DEFAULT );
}
if(maxgradient)
{
gradientX = computeMatXGradient(eyeROI);
gradientY = computeMatXGradient(eyeROI.t()).t();
}
calculos("Gradient Y",gradientY);
cv::Mat abs_grad_x, abs_grad_y;
cv::convertScaleAbs( gradientX, abs_grad_x );
cv::convertScaleAbs( gradientY, abs_grad_y );
calculos("Valor absoluto Gradient Y",abs_grad_y);
// cv::Mat gradientY = computeMatXGradient(eyeROI);
//-- Normalize and threshold the gradient
// compute all the magnitudes
cv::Mat mags;
if(debugL6)
{
mags = matrixMagnitude(gradientX, gradientY);
}
else
{
addWeighted( abs_grad_x, 0.5, abs_grad_y, 0.5, 0, mags );
}
calculos("Sobel",mags);
//compute the threshold
double gradientThresh = computeDynamicThreshold(mags, kGradientThreshold);
//double gradientThresh = kGradientThreshold;
//double gradientThresh = 0;
//normalize
if(debugL1 && (debugWindow == costado))
{
/////////// std::string eyeROI_window = debugWindow;
/////////// cv::namedWindow(eyeROI_window,CV_WINDOW_NORMAL);
/////////// cv::moveWindow(eyeROI_window, 640, 350);
/////////// imshow(eyeROI_window,eyeROI);
std::string Sobel_window = "Salida del Filtro";
cv::namedWindow(Sobel_window,CV_WINDOW_NORMAL);
cv::moveWindow(Sobel_window, sobelx, sobely);
imshow(Sobel_window,mags);
}
for (int y = 0; y < eyeROI.rows; ++y)
{
double *Xr = gradientX.ptr<double>(y), *Yr = gradientY.ptr<double>(y);
const double *Mr = mags.ptr<double>(y);
for (int x = 0; x < eyeROI.cols; ++x)
{
double gX = Xr[x], gY = Yr[x];
double magnitude = Mr[x];
if (magnitude > gradientThresh)
{
Xr[x] = gX/magnitude;
Yr[x] = gY/magnitude;
}
else
{
Xr[x] = 0.0;
Yr[x] = 0.0;
}
}
}
if(debugL4)
{
// imshow(debugWindow,gradientX);
}
//-- Create a blurred and inverted image for weighting
cv::Mat weight;
GaussianBlur( eyeROI, weight, cv::Size( kWeightBlurSize, kWeightBlurSize ), 0, 0 );
for (int y = 0; y < weight.rows; ++y)
{
unsigned char *row = weight.ptr<unsigned char>(y);
for (int x = 0; x < weight.cols; ++x)
{
row[x] = (255 - row[x]);
}
}
if(debugL4)
{
imshow(debugWindow,weight);
}
//-- Run the algorithm!
cv::Mat outSum = cv::Mat::zeros(eyeROI.rows,eyeROI.cols,CV_64F);
// for each possible center
// printf("Eye Size: %ix%i\n",outSum.cols,outSum.rows);
if(debugL4)
{
imshow("outSum",outSum);
}
for (int y = 0; y < weight.rows; ++y)
{
const unsigned char *Wr = weight.ptr<unsigned char>(y);
const double *Xr = gradientX.ptr<double>(y), *Yr = gradientY.ptr<double>(y);
for (int x = 0; x < weight.cols; ++x)
{
double gX = Xr[x], gY = Yr[x];
if (gX == 0.0 && gY == 0.0)
{
continue;
}
testPossibleCentersFormula(x, y, Wr[x], gX, gY, outSum);
}
}
// scale all the values down, basically averaging them
double numGradients = (weight.rows*weight.cols);
cv::Mat out;
outSum.convertTo(out, CV_32F,1.0/numGradients);
if(debugL4)
{
imshow(debugWindow,outSum);
}
//-- Find the maximum point
cv::Point maxP;
double maxVal;
cv::minMaxLoc(out, NULL,&maxVal,NULL,&maxP);
//-- Flood fill the edges
if(kEnablePostProcess)
{
cv::Mat floodClone;
//double floodThresh = computeDynamicThreshold(out, 1.5);
double floodThresh = maxVal * kPostProcessThreshold;
cv::threshold(out, floodClone, floodThresh, 0.0f, cv::THRESH_TOZERO);
if(kPlotVectorField)
{
//plotVecField(gradientX, gradientY, floodClone);
imwrite("eyeFrame.png",eyeROIUnscaled);
}
cv::Mat mask = floodKillEdges(floodClone);
if(debugL4)
{
imshow(debugWindow + " Mask",mask);
// imshow(debugWindow,out);
//redo max;
}
cv::minMaxLoc(out, NULL,&maxVal,NULL,&maxP,mask);
}
cv::circle(eyeROI, maxP, 3,cv::Scalar(0,0,255));
if(debugL1 && (debugWindow == costado))
{
calculos("EyeROI", eyeROI);
std::string eyeROI_window = debugWindow;
cv::namedWindow(eyeROI_window,CV_WINDOW_NORMAL);
cv::moveWindow(eyeROI_window, 640, 350);
imshow(eyeROI_window,eyeROI);
int c = cv::waitKey(10);
if( (char)c == 'g' )
{
imwrite("ojo.png",eyeROIUnscaled);
}
}
return unscalePoint(maxP,eye);
}
