Fields FieldAlgorithms::fieldsByLaplasian(MultiArray<2, float> & image)
{
MultiArray<2, float> valley(image.shape());
Pyramid pyramid(image);
for (int i = 3; i > 0; i--)
{
MultiArray<2, float> resized(pyramid.get(i));
MultiArray<2, float> tmpArr(resized.shape());
laplacianOfGaussianMultiArray(resized, tmpArr, 1.0);
valley += pyramid.toOriginalSize(tmpArr);
}
MultiArray<2, float> peak = valley * -1;
//remove DC component
float thrhld = valley[argMax(valley)] * 0.3;
threshold(valley, valley, thrhld);
thrhld = peak[argMax(peak)] * 0.3;
threshold(peak, peak, thrhld);
//Edge as Gradients
MultiArray<2, float> edgeField(image.shape());
gaussianGradientMagnitude(image, edgeField, 1.0);
//Localize
std::vector<Shape2> valleyLocals(localizePOI(image));
//Result as Field Object
Fields fields(valley, valleyLocals, peak, edgeField, image);
//A heuristic initialization of the localization, as a priori known position
Shape2 nextToIris = Shape2(image.width() / 2, image.height() / 2);
fields.specializedIrisValley = localizeByFollowingLocalMaxima(image, nextToIris);
return fields;
}
void mark(
MultiArray& m,const Sequence& pts,
const point& p,int dx,int dy)
{
for(int k=p.x<0||p.y<0||p.x>=m.shape()[0]||p.y>=m.shape()[1]?1:0,
k_end=distZ2inf(p,pts.begin(),pts.end()); k<k_end; ++k) {
int x=p.x-k*dx;
int y=p.y-k*dy;
if(x>=0&&y>=0&&x<m.shape()[0]&&y<m.shape()[1])m[x][y]=true;
}
}
void test() {
using namespace vigra;
typedef Thresholding<3, float>::V V;
MultiArray<3, float> data(V(24,33,40));
FillRandom<float, float*>::fillRandom(data.data(), data.data()+data.size());
{
HDF5File f("test.h5", HDF5File::Open);
f.write("test", data);
}
SourceHDF5<3, float> source("test.h5", "test");
SinkHDF5<3, vigra::UInt8> sink("thresh.h5", "thresh");
sink.setBlockShape(V(10,10,10));
Thresholding<3, float> bs(&source, V(6,4,7));
bs.run(0.5, 0, 1, &sink);
HDF5File f("thresh.h5", HDF5File::Open);
MultiArray<3, UInt8> r;
f.readAndResize("thresh", r);
MultiArray<3, UInt8> t(data.shape());
transformMultiArray(srcMultiArrayRange(data), destMultiArray(t), Threshold<float, UInt8>(-std::numeric_limits<float>::max(), 0.5, 1, 0));
shouldEqual(t.shape(), r.shape());
shouldEqualSequence(r.begin(), r.end(), t.begin());
}
MultiArray<2, float > FieldAlgorithms::matchGradients(MultiArray<2, TinyVector<float, 2> > &imageGradients, MultiArray<2, TinyVector<float, 2> > &mask)
{
MultiArray<2, float > dest(imageGradients.shape());
dest = 0;
//to make sure, that the mask is always fully within the image, consider the mask shape
int diff = (mask.width() -1) / 2;
for (int x = diff; x + diff < imageGradients.width(); x ++)
{
for (int y = diff; y + diff < imageGradients.height(); y++)
{
//The masks center is at point x,y now
//Every vector of the image currently 'covered' by the mask, is compared to its corresponding vector in the mask.
float vals = 0;
for (int xM = 0; xM < mask.width(); xM++)
{
for (int yM = 0; yM < mask.height(); yM++)
{
TinyVector<float, 2> imageVal = imageGradients((x - diff) + xM, (y - diff) + yM);
TinyVector<float, 2> maskVal = mask(xM, yM);
vals += compareVectors(imageVal, maskVal);
}
}
int res = vals / (mask.size());
dest(x,y) = res;
}
}
return dest;
};
Fields FieldAlgorithms::fieldsByErosionDilation(MultiArray<2, float> & image)
{
Shape2 shape = image.shape();
MultiArray<2, float> source(image);
MultiArray<2, float> t1(shape);
MultiArray<2, float> t2(shape);
double radius = 9; //Sinnvoll?
multiGrayscaleErosion(source, t1, radius);
multiGrayscaleDilation(source, t2, radius);
MultiArray<2, float> psi_e = (t1- t2) * -1;
//Opening of source
multiGrayscaleErosion(source, t1, radius);
multiGrayscaleDilation(t1, t2, radius);
MultiArray<2, float> psi_p = source - t2;
MultiArray<2, float> psi_pSmooth(psi_p.shape());
gaussianSmoothMultiArray(psi_p, psi_pSmooth, 8.0);
//Opening of source
MultiArray<2, float> inverse = source * -1;
multiGrayscaleErosion(inverse, t1, 9);
multiGrayscaleDilation(t1, t2, 9);
MultiArray<2, float> psi_v = t2 - inverse;
MultiArray<2, float> psi_vSmooth(psi_v.shape());
gaussianSmoothMultiArray(psi_v, psi_vSmooth, 3.0);
std::vector<Shape2> valleyLocals(0);
Fields fields(psi_vSmooth, valleyLocals, psi_pSmooth, psi_e, image);
return fields;
};
MultiArray<2, float > FieldAlgorithms::morphologyByGradientPattern(MultiArray<2, float> & image, MultiArray<2,float> &mask) {
Shape2 shape(image.shape());
MultiArray<2, TinyVector<float, 2>> imageGradients(shape);
MultiArray<2, TinyVector<float, 2>> maskGradients(mask.shape());
gaussianGradientMultiArray(image, imageGradients, 2.0);
gaussianGradientMultiArray(mask, maskGradients, 2.0);
//Threshold gradients with small magnitude,
//because we only consider directions from now on.
MultiArray<2, float> magnitudes(shape);
gaussianGradientMagnitude(image, magnitudes, 3.0);
float thrhld = magnitudes[argMax(magnitudes)] * 0.3;
thresholdGrad(magnitudes, imageGradients, thrhld);
//The actual machting:
return matchGradients(imageGradients, maskGradients);
};
Fields FieldAlgorithms::fieldsByGradientPattern(MultiArray<2, float> & image)
{
//Get Masks for Valley and Peak
//not efficient, but can be scaled easly
vigra::ImageImportInfo valleyInfo("../images/valleyMask.png");
MultiArray<2, float> valleyArray(valleyInfo.shape());
importImage(valleyInfo, valleyArray);
MultiArray<2, float > valleyMask(9,9);
resizeImageNoInterpolation(valleyArray, valleyMask);
vigra::ImageImportInfo peakInfo("../images/peakMask.png");
MultiArray<2, float> peakArray(peakInfo.shape());
importImage(peakInfo, peakArray);
MultiArray<2, float > peakMask(9,9);
resizeImageNoInterpolation(peakArray, peakMask);
Pyramid pyramid(image);
MultiArray<2, float> valleyField(image.shape());
MultiArray<2, float> peakField(image.shape());
//go 3 octaves
for (int i = 3; i > 0; i--)
{
MultiArray<2, float> resized(pyramid.get(i));
MultiArray<2, float> tmpArr = morphologyByGradientPattern(resized, valleyMask);
valleyField += pyramid.toOriginalSize(tmpArr);
tmpArr = morphologyByGradientPattern(resized, peakMask);
peakField += pyramid.toOriginalSize(tmpArr);
}
//Edge as Gradients
MultiArray<2, float> edgeField(image.shape());
gaussianGradientMagnitude(image, edgeField, 1.0);
//Localize, smooth for increased range of interaction (following local maxima)
MultiArray<2, float> smoothed(valleyField.shape());
gaussianSmoothMultiArray(valleyField, smoothed, 6.0);
std::vector<Shape2> valleyLocals(localizePOI(smoothed));
//Result as Field Object
Fields fields(valleyField, valleyLocals, peakField, edgeField, image);
//A heuristic initialization of the localization, as a priori known position
Shape2 nextToIris = Shape2(image.width() / 2 + 10, image.height() / 2);
fields.specializedIrisValley = Shape2(image.width() / 2 + 10, image.height() / 2 + 15);//localizeByFollowingLocalMaxima(valleyField, nextToIris);
return fields;
};
void Deformation::drawFunctions(MultiArray<2, int> &distanceToCenter)
{
std::vector<MultiArray<2,float>> functions = getFit(distanceToCenter);
MultiArray<2,float> resEdge(distanceToCenter.shape());
MultiArray<2,float> resValley(distanceToCenter.shape());
MultiArray<2,float> resBoth(distanceToCenter.shape());
for (int i = 0; i<distanceToCenter.width() / 2;i++)
{
int yEdge = functions[0][i] > distanceToCenter.height() ? distanceToCenter.height() -1 : (distanceToCenter.height() - (functions[0][i] -1));
int yValley = functions[1][i] > distanceToCenter.height() ? distanceToCenter.height() -1 : (distanceToCenter.height() - (functions[1][i] -1));
int yBoth = functions[2][i] > distanceToCenter.height() ? distanceToCenter.height() -1 : (distanceToCenter.height() - (functions[2][i] -1));
resEdge(i, yEdge) = 1;
resValley(i, yValley) = 1;
resBoth(i, yBoth) = 1;
}
exportImage(resEdge,"./../images/results/edgeFunction.png");
exportImage(resValley,"./../images/results/valleyFunction.png");
exportImage(resBoth ,"./../images/results/bothFunction.png");
std::cout << "\n expected Radius: ";
int rad = argMax(functions[2]);
std::cout << rad;
functions[2][rad] = 0;
rad = argMax(functions[2]);
std::cout << "\n next best Radius: ";
std::cout << rad;
functions[2][rad] = 0;
rad = argMax(functions[2]);
std::cout << "\n next best Radius: ";
std::cout << rad;
functions[2][rad] = 0;
rad = argMax(functions[2]);
std::cout << "\n next best Radius: ";
std::cout << rad;
functions[2][rad] = 0;
rad = argMax(functions[2]);
std::cout << "\n";
}
void fitPSF(DataParams ¶ms, MultiArray<2, double> &ps) {
double minval=9999999, maxval = 0;
int size = 2*ps.shape(0)*ps.shape(1);
std::vector<double> data2(2*ps.shape(0)*ps.shape(1));
for(int i=0, counter = 0;i<ps.shape(0);++i) {
for(int j=0;j<ps.shape(1);++j,++counter) {
//std::cout<<i<<" "<<j<<" "<<counter<<std::endl;
data2[2*counter] = std::sqrt(std::abs(std::pow(i-ps.shape(0)/2,2)+std::pow(j-ps.shape(1)/2,2)));
data2[2*counter+1] = ps(i,j);
if (ps(i,j)< minval){minval = ps(i,j);}
if (ps(i,j)> maxval){maxval = ps(i,j);}
}
}
double sigma = 2.0, scale = maxval - minval, offset = minval;
//std::cout<<sigma<<" "<<scale<<" "<<offset<<std::endl;
fitGaussian(&data2[0], size/2, sigma, scale, offset);
params.setSigma(sigma);
}
double fitPSF2D(MultiArray<2, double> &ps, double &sigma) {
double minval=9999999, maxval = 0;
int w = ps.shape(0), h = ps.shape(1);
int size = ps.shape(0)*ps.shape(1);
std::vector<double> data2(3*ps.shape(0)*ps.shape(1));
//std::ofstream outputRoi;
//outputRoi.open("c:\\tmp\\outputRoi.txt");
for(int i=0, counter = 0;i<ps.shape(0);++i) {
for(int j=0;j<ps.shape(1);++j,++counter) {
//std::cout<<i<<" "<<j<<" "<<counter<<std::endl;
// outputRoi <<i<<" "<<j<<" "<<ps(i,j)<<std::endl;
data2[3*counter] = i-ps.shape(0)/2;
data2[3*counter+1] = j-ps.shape(1)/2;
data2[3*counter+2] = ps(i,j);
if (ps(i,j)< minval){minval = ps(i,j);}
if (ps(i,j)> maxval){maxval = ps(i,j);}
}
}
//outputRoi.close();
double scale = maxval - minval, offset = minval, x0=0, y0=0;
sigma = 2.0;
//std::cout<<"sigma: "<<sigma<<" scale: "<<scale<<" offset:"<<offset<<std::endl;
fitGaussian2D(&data2[0], size, sigma, scale, offset, x0, y0);
double SSE = 0, SSD = 0, sumY = 0, sumY2 = 0;
for(int i= w/2-2, counter = 0;i<w/2+2;++i) {
for(int j=h/2-2;j<h/2+2;++j,++counter) {
double xs = sq((i-ps.shape(0)/2 - x0) / sigma)+ sq((j-ps.shape(1)/2 - y0) / sigma);
double e = std::exp(-0.5 * xs);
double r = ps(i,j) - (scale * e + offset);
SSE += r*r;
sumY+= ps(i,j);
sumY2+= sq(ps(i,j));
}
}
SSD = sumY2 - sq(sumY) / size;
double error = 1-SSE/SSD;
sigma = std::abs(sigma);
//std::cout<<"sigma: "<<sigma<<" scale: "<<scale<<" offset:"<<offset<< " x0: " << x0<<" y0: "<<y0<<" Error: "<<error<<std::endl;
//std::cin.get();
return error;
}
