cv::Mat FacePreprocessor::normalizeImage(cv::Mat face){
cv::Mat image32f;
face.convertTo(image32f,CV_32F);
cv::Mat mu;
blur(image32f, mu,cv::Size(registeredFaceWidth,registeredFaceWidth));
cv::Mat mu2;
blur(image32f.mul(image32f), mu2,cv::Size(registeredFaceWidth,registeredFaceWidth));
cv::Mat sigma;
cv::sqrt(mu2 - mu.mul(mu), sigma);
cv::Mat out;
cv::Mat filtered;
cv::subtract(image32f,mu,out);
//cv::imshow("Subtract mean", mat2gray(out));
cv::Mat out2;
cv::divide(out,sigma,out2);
//imshow("divide", mat2gray(out2));
//cout<<out2<<endl;
//cv::imshow("initial image", mat2gray(image32f));
//cv::imshow("mu", mat2gray(mu));
//cv::imshow("sigma",mat2gray(sigma));
//waitKey();
//cout<<mat2gray(out2)<<endl;
return mat2gray(out2);
}
//
// Calculates the TanTriggs Preprocessing as described in:
//
// Tan, X., and Triggs, B. "Enhanced local texture feature sets for face
// recognition under difficult lighting conditions.". IEEE Transactions
// on Image Processing 19 (2010), 1635–650.
//
// Default parameters are taken from the paper.
//
cv::Mat FacePreprocessor::tan_triggs_preprocessing(InputArray src,
float alpha, float tau , float gamma, int sigma0,
int sigma1) {
// Convert to floating point:
Mat X = src.getMat();
X.convertTo(X, CV_32FC1);
// Start preprocessing:
Mat I;
pow(X, gamma, I);
// Calculate the DOG Image:
{
Mat gaussian0, gaussian1;
// Kernel Size:
int kernel_sz0 = (3*sigma0);
int kernel_sz1 = (3*sigma1);
// Make them odd for OpenCV:
kernel_sz0 += ((kernel_sz0 % 2) == 0) ? 1 : 0;
kernel_sz1 += ((kernel_sz1 % 2) == 0) ? 1 : 0;
GaussianBlur(I, gaussian0, Size(kernel_sz0,kernel_sz0), sigma0, sigma0);
GaussianBlur(I, gaussian1, Size(kernel_sz1,kernel_sz1), sigma1, sigma1);
subtract(gaussian0, gaussian1, I);
}
{
double meanI = 0.0;
{
Mat tmp;
pow(abs(I), alpha, tmp);
meanI = mean(tmp).val[0];
}
I = I / pow(meanI, 1.0/alpha);
}
{
double meanI = 0.0;
{
Mat tmp;
pow(min(abs(I), tau), alpha, tmp);
meanI = mean(tmp).val[0];
}
I = I / pow(meanI, 1.0/alpha);
}
// Squash into the tanh:
{
for(int r = 0; r < I.rows; r++) {
for(int c = 0; c < I.cols; c++) {
I.at<float>(r,c) = tanh(I.at<float>(r,c) / tau);
}
}
I = tau * I;
}
return mat2gray(I);
}
cv::Mat FacePreprocessor::meanNormalizeImage(cv::Mat face){
cv::Mat image32f;
face.convertTo(image32f,CV_32F);
cv::Mat mu;
blur(image32f, mu,cv::Size(registeredFaceWidth,registeredFaceWidth));
cv::Mat out;
cv::subtract(image32f,mu,out);
return mat2gray(out);
}
void findEyeCenterByColorSegmentation(const Mat& image, Point2f & eyeCoord, float coordinateWeight, int kmeansIterations, int kmeansRepeats, int blurSize) {
Mat img, gray_img;
Mat colorpoints, kmeansPoints;
img = equalizeImage(image);
medianBlur(img, img, blurSize);
cvtColor(image, gray_img, CV_BGR2GRAY);
gray_img = imcomplement(gray_img);
vector<Mat> layers(3);
split(img, layers);
for (int i = 0 ; i < layers.size(); i++) {
layers[i] = layers[i].reshape(1,1).t();
}
hconcat(layers, colorpoints);
// add coordinates
colorpoints.convertTo(colorpoints, CV_32FC1);
Mat coordinates = matrixPointCoordinates(img.rows,img.cols,false) *coordinateWeight;
hconcat(colorpoints, coordinates, kmeansPoints);
Mat locIndex(img.size().area(),kmeansIterations,CV_32FC1,Scalar::all(-1));
linspace(0, img.size().area(), 1).copyTo(locIndex.col(0));
Mat index_img(img.rows,img.cols,CV_32FC1,Scalar::all(0));
Mat bestLabels, centers, clustered , colorsum , minColorPtIndex;
for(int it = 1 ; it < kmeansIterations ; it++) {
if (kmeansPoints.rows < 2) {
break;
}
kmeans(kmeansPoints,2,bestLabels,TermCriteria(CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, kmeansRepeats, 0.001),kmeansRepeats,KMEANS_PP_CENTERS,centers);
reduce(centers.colRange(0, 3), colorsum, 1, CV_REDUCE_SUM);
if (colorsum.at<float>(0) < colorsum.at<float>(1)) {
findNonZero(bestLabels==0, minColorPtIndex);
}
else {
findNonZero(bestLabels==1, minColorPtIndex);
}
minColorPtIndex = minColorPtIndex.reshape(1).col(1);
for (int i = 0; i <minColorPtIndex.rows ; i ++) {
locIndex.at<float>(i,it) = locIndex.at<float>(minColorPtIndex.at<int>(i),it-1);
}
Mat temp;
for (int i = 0; i <minColorPtIndex.rows ; i ++) {
temp.push_back(kmeansPoints.row(minColorPtIndex.at<int>(i)));
}
temp.copyTo(kmeansPoints);
temp.release();
for (int i = 0 ; i < minColorPtIndex.rows ; i ++) {
int r, c;
ind2sub(locIndex.at<float>(i,it), index_img.cols, index_img.rows, r, c);
index_img.at<float>(r,c) +=1;
}
}
// imagesc("layered",mat2gray(index_img));
Mat layerweighted_img = index_img.mul(index_img);
layerweighted_img = mat2gray(layerweighted_img);
gray_img.convertTo(gray_img, CV_32FC1,1/255.0);
Mat composed = gray_img.mul(layerweighted_img);
float zoomRatio = 5.0f;
Mat zoomed;
imresize(composed, zoomRatio, zoomed);
Mat score = calculateImageSymmetryScore(zoomed);
// imagesc("score", score);
Mat scoresum;
reduce(score.rowRange(0, zoomed.cols/6), scoresum, 0, CV_REDUCE_SUM,CV_32FC1);
// plotVectors("scoresum", scoresum.t());
double minVal , maxVal;
Point minLoc, maxLoc;
minMaxLoc(scoresum,&minVal,&maxVal,&minLoc,&maxLoc);
float initialHC = (float)maxLoc.x/zoomRatio;
line(zoomed, Point(maxLoc.x,0), Point(maxLoc.x,zoomed.rows-1), Scalar::all(255));
// imshow("zoomed", zoomed);
int bestx = 0,bestlayer = 0;
Mat bestIndex_img = index_img >=1;
minMaxLoc(index_img,&minVal,&maxVal,&minLoc,&maxLoc);
for (int i = 1 ; i<=maxVal; i++) {
Mat indexlayer_img = index_img >=i;
medianBlur(indexlayer_img, indexlayer_img, 5);
erode(indexlayer_img, indexlayer_img, blurSize);
erode(indexlayer_img, indexlayer_img, blurSize);
indexlayer_img = removeSmallBlobs(indexlayer_img);
indexlayer_img = fillHoleInBinary(indexlayer_img);
indexlayer_img = fillConvexHulls(indexlayer_img);
Mat score = calculateImageSymmetryScore(indexlayer_img);
Mat scoresum;
reduce(score.rowRange(0, indexlayer_img.cols/6), scoresum, 0, CV_REDUCE_SUM,CV_32FC1);
minMaxLoc(scoresum,&minVal,&maxVal,&minLoc,&maxLoc);
if (abs(maxLoc.x - initialHC) < abs(bestx - initialHC)) {
if (sum(indexlayer_img)[0]/255 < indexlayer_img.size().area()/5*2 &&
sum(indexlayer_img)[0]/255 > indexlayer_img.size().area()/6) {
bestx = maxLoc.x;
bestlayer = i;
bestIndex_img = indexlayer_img.clone();
}
}
}
Point2f massCenter = findMassCenter_BinaryBiggestBlob(bestIndex_img);
eyeCoord = Point2f(initialHC,massCenter.y);
}
