/* Do inference for class ------------------------------------------------- */
int EnsembleNaiveBayes::doInferenceForClass(int classNumber, const Matrix1D<double> &newFeatures, double &cost,
Matrix1D<double> &classesProbs, Matrix1D<double> &allCosts)
{
int nmax=ensemble.size();
double minCost=1, maxCost=1;
int votes=0;
MultidimArray<double> newFeaturesn;
for (int n=0; n<nmax; n++)
{
double costn;
const MultidimArray<int> &ensembleFeatures_n=ensembleFeatures[n];
newFeaturesn.resizeNoCopy(XSIZE(ensembleFeatures_n));
FOR_ALL_ELEMENTS_IN_ARRAY1D(newFeaturesn)
{
int idx=A1D_ELEM(ensembleFeatures_n,i);
A1D_ELEM(newFeaturesn,i)=VEC_ELEM(newFeatures,idx);
}
int k=ensemble[n]->doInference(newFeaturesn, costn, classesProbs, allCosts);
if (k==classNumber)
{
votes++;
if (minCost>0 || minCost>costn)
minCost=costn;
if (maxCost>0 || maxCost<costn)
maxCost=costn;
}
}
if (judgeCombination[classNumber]=='m')
cost=minCost;
else if (judgeCombination[classNumber]=='M')
cost=maxCost;
else
cost=minCost;
return votes;
}
//majorAxis and minorAxis is the estimated particle size in px
void ProgSortByStatistics::processInprocessInputPrepareSPTH(MetaData &SF, bool trained)
{
//#define DEBUG
PCAMahalanobisAnalyzer tempPcaAnalyzer0;
PCAMahalanobisAnalyzer tempPcaAnalyzer1;
PCAMahalanobisAnalyzer tempPcaAnalyzer2;
PCAMahalanobisAnalyzer tempPcaAnalyzer3;
PCAMahalanobisAnalyzer tempPcaAnalyzer4;
//Morphology
tempPcaAnalyzer0.clear();
//Signal to noise ratio
tempPcaAnalyzer1.clear();
tempPcaAnalyzer2.clear();
tempPcaAnalyzer3.clear();
//Histogram analysis, to detect black points and saturated parts
tempPcaAnalyzer4.clear();
double sign = 1;//;-1;
int numNorm = 3;
int numDescriptors0=numNorm;
int numDescriptors2=4;
int numDescriptors3=11;
int numDescriptors4 = 10;
MultidimArray<float> v0(numDescriptors0);
MultidimArray<float> v2(numDescriptors2);
MultidimArray<float> v3(numDescriptors3);
MultidimArray<float> v4(numDescriptors4);
if (verbose>0)
{
std::cout << " Sorting particle set by new xmipp method..." << std::endl;
}
int nr_imgs = SF.size();
if (verbose>0)
init_progress_bar(nr_imgs);
int c = XMIPP_MAX(1, nr_imgs / 60);
int imgno = 0, imgnoPCA=0;
bool thereIsEnable=SF.containsLabel(MDL_ENABLED);
bool first=true;
// We assume that at least there is one particle
size_t Xdim, Ydim, Zdim, Ndim;
getImageSize(SF,Xdim,Ydim,Zdim,Ndim);
//Initialization:
MultidimArray<double> nI, modI, tempI, tempM, ROI;
MultidimArray<bool> mask;
nI.resizeNoCopy(Ydim,Xdim);
modI.resizeNoCopy(Ydim,Xdim);
tempI.resizeNoCopy(Ydim,Xdim);
tempM.resizeNoCopy(Ydim,Xdim);
mask.resizeNoCopy(Ydim,Xdim);
mask.initConstant(true);
MultidimArray<double> autoCorr(2*Ydim,2*Xdim);
MultidimArray<double> smallAutoCorr;
Histogram1D hist;
Matrix2D<double> U,V,temp;
Matrix1D<double> D;
MultidimArray<int> radial_count;
MultidimArray<double> radial_avg;
Matrix1D<int> center(2);
MultidimArray<int> distance;
int dim;
center.initZeros();
v0.initZeros(numDescriptors0);
v2.initZeros(numDescriptors2);
v3.initZeros(numDescriptors3);
v4.initZeros(numDescriptors4);
ROI.resizeNoCopy(Ydim,Xdim);
ROI.setXmippOrigin();
FOR_ALL_ELEMENTS_IN_ARRAY2D(ROI)
{
double temp = std::sqrt(i*i+j*j);
if ( temp < (Xdim/2))
A2D_ELEM(ROI,i,j)= 1;
else
A2D_ELEM(ROI,i,j)= 0;
}
Image<double> img;
FourierTransformer transformer(FFTW_BACKWARD);
FOR_ALL_OBJECTS_IN_METADATA(SF)
{
if (thereIsEnable)
{
int enabled;
SF.getValue(MDL_ENABLED,enabled,__iter.objId);
if ( (enabled==-1) )
{
imgno++;
continue;
}
}
img.readApplyGeo(SF,__iter.objId);
if (targetXdim!=-1 && targetXdim!=XSIZE(img()))
selfScaleToSize(LINEAR,img(),targetXdim,targetXdim,1);
MultidimArray<double> &mI=img();
mI.setXmippOrigin();
mI.statisticsAdjust(0,1);
mask.setXmippOrigin();
//The size of v1 depends on the image size and must be declared here
int numDescriptors1 = XSIZE(mI)/2; //=100;
MultidimArray<float> v1(numDescriptors1);
v1.initZeros(numDescriptors1);
double var = 1;
normalize(transformer,mI,tempI,modI,0,var,mask);
modI.setXmippOrigin();
tempI.setXmippOrigin();
nI = sign*tempI*(modI*modI);
tempM = (modI*modI);
A1D_ELEM(v0,0) = (tempM*ROI).sum();
int index = 1;
var+=2;
while (index < numNorm)
{
normalize(transformer,mI,tempI,modI,0,var,mask);
modI.setXmippOrigin();
tempI.setXmippOrigin();
nI += sign*tempI*(modI*modI);
tempM += (modI*modI);
A1D_ELEM(v0,index) = (tempM*ROI).sum();
index++;
var+=2;
}
nI /= tempM;
tempPcaAnalyzer0.addVector(v0);
nI=(nI*ROI);
auto_correlation_matrix(mI,autoCorr);
if (first)
{
radialAveragePrecomputeDistance(autoCorr, center, distance, dim);
first=false;
}
fastRadialAverage(autoCorr, distance, dim, radial_avg, radial_count);
for (int n = 0; n < numDescriptors1; ++n)
A1D_ELEM(v1,n)=(float)DIRECT_A1D_ELEM(radial_avg,n);
tempPcaAnalyzer1.addVector(v1);
#ifdef DEBUG
//String name = "000005@Images/Extracted/run_002/extra/BPV_1386.stk";
String name = "000010@Images/Extracted/run_001/extra/KLH_Dataset_I_Training_0028.stk";
//String name = "001160@Images/Extracted/run_001/DefaultFamily5";
std::cout << img.name() << std::endl;
if (img.name()==name2)
{
FileName fpName = "test_1.txt";
mI.write(fpName);
fpName = "test_2.txt";
nI.write(fpName);
fpName = "test_3.txt";
tempM.write(fpName);
fpName = "test_4.txt";
ROI.write(fpName);
//exit(1);
}
#endif
nI.binarize(0);
int im = labelImage2D(nI,nI,8);
compute_hist(nI, hist, 0, im, im+1);
size_t l;
int k,i,j;
hist.maxIndex(l,k,i,j);
A1D_ELEM(hist,j)=0;
hist.maxIndex(l,k,i,j);
nI.binarizeRange(j-1,j+1);
double x0=0,y0=0,majorAxis=0,minorAxis=0,ellipAng=0;
size_t area=0;
fitEllipse(nI,x0,y0,majorAxis,minorAxis,ellipAng,area);
A1D_ELEM(v2,0)=majorAxis/((img().xdim) );
A1D_ELEM(v2,1)=minorAxis/((img().xdim) );
A1D_ELEM(v2,2)= (fabs((img().xdim)/2-x0)+fabs((img().ydim)/2-y0))/((img().xdim)/2);
A1D_ELEM(v2,3)=area/( (double)((img().xdim)/2)*((img().ydim)/2) );
for (int n=0 ; n < numDescriptors2 ; n++)
{
if ( std::isnan(std::abs(A1D_ELEM(v2,n))))
A1D_ELEM(v2,n)=0;
}
tempPcaAnalyzer2.addVector(v2);
//mI.setXmippOrigin();
//auto_correlation_matrix(mI*ROI,autoCorr);
//auto_correlation_matrix(nI,autoCorr);
autoCorr.window(smallAutoCorr,-5,-5, 5, 5);
smallAutoCorr.copy(temp);
svdcmp(temp,U,D,V);
for (int n = 0; n < numDescriptors3; ++n)
A1D_ELEM(v3,n)=(float)VEC_ELEM(D,n); //A1D_ELEM(v3,n)=(float)VEC_ELEM(D,n)/VEC_ELEM(D,0);
tempPcaAnalyzer3.addVector(v3);
double minVal=0.;
double maxVal=0.;
mI.computeDoubleMinMax(minVal,maxVal);
compute_hist(mI, hist, minVal, maxVal, 100);
for (int n=0 ; n <= numDescriptors4-1 ; n++)
{
A1D_ELEM(v4,n)= (hist.percentil((n+1)*10));
}
tempPcaAnalyzer4.addVector(v4);
#ifdef DEBUG
if (img.name()==name1)
{
FileName fpName = "test.txt";
mI.write(fpName);
fpName = "test3.txt";
nI.write(fpName);
}
#endif
imgno++;
imgnoPCA++;
if (imgno % c == 0 && verbose>0)
progress_bar(imgno);
}
tempPcaAnalyzer0.evaluateZScore(2,20,trained);
tempPcaAnalyzer1.evaluateZScore(2,20,trained);
tempPcaAnalyzer2.evaluateZScore(2,20,trained);
tempPcaAnalyzer3.evaluateZScore(2,20,trained);
tempPcaAnalyzer4.evaluateZScore(2,20,trained);
pcaAnalyzer.push_back(tempPcaAnalyzer0);
pcaAnalyzer.push_back(tempPcaAnalyzer1);
pcaAnalyzer.push_back(tempPcaAnalyzer1);
pcaAnalyzer.push_back(tempPcaAnalyzer3);
pcaAnalyzer.push_back(tempPcaAnalyzer4);
}
void runThread(ThreadArgument &thArg)
{
int thread_id = thArg.thread_id;
ProgXrayImport * ptrProg= (ProgXrayImport *)thArg.workClass;
MetaData localMD;
Image<double> Iaux;
FileName fnImgIn, fnImgOut;
size_t first = 0, last = 0;
MultidimArray<char> mask;
while (ptrProg->td->getTasks(first, last))
{
for (size_t i=first; i<=last; i++)
{
ptrProg->inMD.getValue(MDL_IMAGE, fnImgIn, ptrProg->objIds[i]);
MDRow rowGeo;
ptrProg->readGeoInfo(fnImgIn, rowGeo);
// ptrProg->readAndCrop(fnImgIn, Iaux, ptrProg->cropSizeX, ptrProg->cropSizeY);
Iaux.read(fnImgIn);
Iaux().selfWindow(ptrProg->cropSizeYi,ptrProg->cropSizeXi,
(int)(YSIZE(Iaux())-ptrProg->cropSizeYe-1),(int)(XSIZE(Iaux())-ptrProg->cropSizeXe-1));
Iaux().resetOrigin();
if (XSIZE(ptrProg->IavgDark())!=0)
{
Iaux()-=ptrProg->IavgDark();
forcePositive(Iaux());
}
double currentBeam = 1;
double expTime = 1;
double slitWidth = 1;
if ( ptrProg->dSource == ptrProg->MISTRAL )
{
size_t idx = fnImgIn.getPrefixNumber();
currentBeam = dMi(ptrProg->cBeamArray, idx-1);
expTime = dMi(ptrProg->expTimeArray, idx-1);
slitWidth = dMi(ptrProg->slitWidthArray, idx-1);
}
else
ptrProg->readCorrectionInfo(fnImgIn, currentBeam, expTime, slitWidth);
Iaux() *= 1.0/(currentBeam*expTime*slitWidth);
if (XSIZE(ptrProg->IavgFlat())!=0)
Iaux()/=ptrProg->IavgFlat();
// Assign median filter to zero valued pixels to avoid -inf when applying log10
Iaux().equal(0,mask);
mask.resizeNoCopy(Iaux());
if (XSIZE(ptrProg->bpMask()) != 0)
mask += ptrProg->bpMask();
boundMedianFilter(Iaux(), mask);
if (ptrProg->logFix)
{
Iaux().selfLog();
if (ptrProg->selfAttFix)
Iaux() *= -1.;
}
fnImgOut.compose(i+1, ptrProg->fnOut);
size_t objId = localMD.addObject();
localMD.setValue(MDL_IMAGE,fnImgOut,objId);
localMD.setRow(rowGeo, objId); //
// localMD.setValue(MDL_ANGLE_TILT,Iaux.tilt(),objId);
Iaux.write(fnImgOut);
if (thread_id==0)
progress_bar(i);
}
}
//Lock for update the total counter
ptrProg->mutex.lock();
ptrProg->outMD.unionAll(localMD);
ptrProg->mutex.unlock();
}
void PolyZernikes::fit(const Matrix1D<int> & coef, MultidimArray<double> & im, MultidimArray<double> &weight,
MultidimArray<bool> & ROI, int verbose)
{
this->create(coef);
size_t xdim = XSIZE(im);
size_t ydim = YSIZE(im);
//int numZer = (size_t)coef.sum();
int numZer = (size_t)coef.sum();
//Actually polOrder corresponds to the polynomial order +1
int polOrder=(int)ZERNIKE_ORDER(coef.size());
im.setXmippOrigin();
Matrix2D<double> polValue(polOrder,polOrder);
//First argument means number of images
//Second argument means number of pixels
WeightedLeastSquaresHelper weightedLeastSquaresHelper;
Matrix2D<double>& zerMat=weightedLeastSquaresHelper.A;
zerMat.resizeNoCopy((size_t)ROI.sum(), numZer);
double iMaxDim2 = 2./std::max(xdim,ydim);
size_t pixel_idx=0;
weightedLeastSquaresHelper.b.resizeNoCopy((size_t)ROI.sum());
weightedLeastSquaresHelper.w.resizeNoCopy(weightedLeastSquaresHelper.b);
FOR_ALL_ELEMENTS_IN_ARRAY2D(im)
{
if ( (A2D_ELEM(ROI,i,j)))
{
//For one i we swap the different j
double y=i*iMaxDim2;
double x=j*iMaxDim2;
//polValue = [ 0 y y2 y3 ...
// x xy xy2 xy3 ...
// x2 x2y x2y2 x2y3 ]
//dMij(polValue,py,px) py es fila, px es columna
for (int py = 0; py < polOrder; ++py)
{
double ypy=std::pow(y,py);
for (int px = 0; px < polOrder; ++px)
dMij(polValue,px,py) = ypy*std::pow(x,px);
}
Matrix2D<int> *fMat;
//We generate the representation of the Zernike polynomials
for (int k=0; k < numZer; ++k)
{
fMat = &fMatV[k];
if (fMat == NULL)
continue;
double temp = 0;
for (size_t px = 0; px < (*fMat).Xdim(); ++px)
for (size_t py = 0; py < (*fMat).Ydim(); ++py)
temp += dMij(*fMat,py,px)*dMij(polValue,py,px);
dMij(zerMat,pixel_idx,k) = temp;
}
VEC_ELEM(weightedLeastSquaresHelper.b,pixel_idx)=A2D_ELEM(im,i,j);
VEC_ELEM(weightedLeastSquaresHelper.w,pixel_idx)=std::abs(A2D_ELEM(weight,i,j));
++pixel_idx;
}
}
Matrix1D<double> zernikeCoefficients;
weightedLeastSquares(weightedLeastSquaresHelper, zernikeCoefficients);
fittedCoeffs = zernikeCoefficients;
// Pointer to the image to be fitted
MultidimArray<double> reconstructed;
reconstructed.resizeNoCopy(im);
pixel_idx=0;
FOR_ALL_ELEMENTS_IN_ARRAY2D(im)
if (A2D_ELEM(ROI,i,j))
{
double temp=0;
for (int k=0; k < numZer; ++k)
temp+=dMij(zerMat,pixel_idx,k)*VEC_ELEM(fittedCoeffs,k);
A2D_ELEM(reconstructed,i,j)=temp;
if ( fabs(A2D_ELEM(reconstructed,i,j)-A2D_ELEM(im,i,j)) > PI)
A2D_ELEM(ROI,i,j) = false;
++pixel_idx;
}
pixel_idx=0;
if (verbose > 0)
{
Image<double> save;
save()=reconstructed;
save.write("reconstructedZernikes.xmp");
ROI.write("ROI.txt");
}
}
