void symmetriseMap(MultidimArray<DOUBLE> &img, FileName &fn_sym, bool do_wrap)
{
if (img.getDim() != 3)
REPORT_ERROR("symmetriseMap ERROR: symmetriseMap can only be run on 3D maps!");
img.setXmippOrigin();
SymList SL;
SL.read_sym_file(fn_sym);
Matrix2D<DOUBLE> L(4, 4), R(4, 4); // A matrix from the list
MultidimArray<DOUBLE> sum, aux;
sum = img;
aux.resize(img);
for (int isym = 0; isym < SL.SymsNo(); isym++)
{
SL.get_matrices(isym, L, R);
applyGeometry(img, aux, R, IS_INV, do_wrap);
sum += aux;
}
// Overwrite the input
img = sum / (SL.SymsNo() + 1);
}
/* Constructor */
PyObject *
FourierProjector_new(PyTypeObject *type, PyObject *args, PyObject *kwargs)
{
FourierProjectorObject *self = (FourierProjectorObject*) type->tp_alloc(type, 0);
XMIPP_TRY
if (self != NULL)
{
PyObject *image = NULL;
double padding_factor, max_freq, spline_degree;
if (PyArg_ParseTuple(args, "O|ddd", &image, &padding_factor, &max_freq, &spline_degree))
{
Image_Value(image).getDimensions(self->dims);
MultidimArray<double> *pdata;
Image_Value(image).data->getMultidimArrayPointer(pdata);
pdata->setXmippOrigin();
self->fourier_projector = new FourierProjector(*(pdata), padding_factor, max_freq, spline_degree);
}
}
XMIPP_CATCH
return (PyObject *) self;
}
TEST_F( FftwTest, directFourierTransformComplex)
{
MultidimArray< std::complex< double > > FFT1, complxDouble;
FourierTransformer transformer1;
typeCast(mulDouble, complxDouble);
transformer1.FourierTransform(complxDouble, FFT1, false);
transformer1.inverseFourierTransform();
transformer1.inverseFourierTransform();
MultidimArray<std::complex<double> > auxFFT;
auxFFT.resize(3,3);
DIRECT_A2D_ELEM(auxFFT,0,0) = std::complex<double>(2.77778,0);
DIRECT_A2D_ELEM(auxFFT,0,1) = std::complex<double>(-0.0555556,0.096225);
DIRECT_A2D_ELEM(auxFFT,0,2) = std::complex<double>(-0.0555556,-0.096225);
DIRECT_A2D_ELEM(auxFFT,1,0) = std::complex<double>(-0.388889,0.673575) ;
DIRECT_A2D_ELEM(auxFFT,1,1) = std::complex<double>(-0.388889,-0.096225);
DIRECT_A2D_ELEM(auxFFT,1,2) = std::complex<double>(-0.0555556,-0.288675);
DIRECT_A2D_ELEM(auxFFT,2,0) = std::complex<double>(-0.388889,-0.673575) ;
DIRECT_A2D_ELEM(auxFFT,2,1) = std::complex<double>(-0.0555556,0.288675) ;
DIRECT_A2D_ELEM(auxFFT,2,2) = std::complex<double>(-0.388889,0.096225) ;
EXPECT_EQ(FFT1,auxFFT);
transformer1.cleanup();
}
void FourierProjector::produceSideInfo()
{
// Zero padding
MultidimArray<double> Vpadded;
int paddedDim=(int)(paddingFactor*volumeSize);
// JMRT: TODO: I think it is a very poor design to modify the volume passed
// in the construct, it will be padded anyway, so new memory should be allocated
volume->window(Vpadded,FIRST_XMIPP_INDEX(paddedDim),FIRST_XMIPP_INDEX(paddedDim),FIRST_XMIPP_INDEX(paddedDim),
LAST_XMIPP_INDEX(paddedDim),LAST_XMIPP_INDEX(paddedDim),LAST_XMIPP_INDEX(paddedDim));
volume->clear();
// Make Fourier transform, shift the volume origin to the volume center and center it
MultidimArray< std::complex<double> > Vfourier;
transformer3D.completeFourierTransform(Vpadded,Vfourier);
ShiftFFT(Vfourier, FIRST_XMIPP_INDEX(XSIZE(Vpadded)), FIRST_XMIPP_INDEX(YSIZE(Vpadded)), FIRST_XMIPP_INDEX(ZSIZE(Vpadded)));
CenterFFT(Vfourier,true);
Vfourier.setXmippOrigin();
// Compensate for the Fourier normalization factor
double K=(double)(XSIZE(Vpadded)*XSIZE(Vpadded)*XSIZE(Vpadded))/(double)(volumeSize*volumeSize);
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(Vfourier)
DIRECT_MULTIDIM_ELEM(Vfourier,n)*=K;
Vpadded.clear();
// Compute Bspline coefficients
if (BSplineDeg==3)
{
MultidimArray< double > VfourierRealAux, VfourierImagAux;
Complex2RealImag(Vfourier, VfourierRealAux, VfourierImagAux);
Vfourier.clear();
produceSplineCoefficients(BSPLINE3,VfourierRealCoefs,VfourierRealAux);
produceSplineCoefficients(BSPLINE3,VfourierImagCoefs,VfourierImagAux);
//VfourierRealAux.clear();
//VfourierImagAux.clear();
}
else
Complex2RealImag(Vfourier, VfourierRealCoefs, VfourierImagCoefs);
// Allocate memory for the 2D Fourier transform
projection().initZeros(volumeSize,volumeSize);
projection().setXmippOrigin();
transformer2D.FourierTransform(projection(),projectionFourier,false);
// Calculate phase shift terms
phaseShiftImgA.initZeros(projectionFourier);
phaseShiftImgB.initZeros(projectionFourier);
double shift=-FIRST_XMIPP_INDEX(volumeSize);
double xxshift = -2 * PI * shift / volumeSize;
for (size_t i=0; i<YSIZE(projectionFourier); ++i)
{
double phasey=(double)(i) * xxshift;
for (size_t j=0; j<XSIZE(projectionFourier); ++j)
{
// Phase shift to move the origin of the image to the corner
double dotp = (double)(j) * xxshift + phasey;
sincos(dotp,&DIRECT_A2D_ELEM(phaseShiftImgB,i,j),&DIRECT_A2D_ELEM(phaseShiftImgA,i,j));
}
}
}
void Steerable::generate3DFilter(MultidimArray<double>& h3D,
std::vector< MultidimArray<double> > &hx1,
std::vector< MultidimArray<double> > &hy1,
std::vector< MultidimArray<double> > &hz1)
{
h3D.initZeros(XSIZE(hz1[0]),XSIZE(hy1[0]),XSIZE(hx1[0]));
h3D.setXmippOrigin();
FOR_ALL_ELEMENTS_IN_ARRAY3D(h3D)
for (int n=0; n<6; n++)
h3D(k,i,j)+=(hz1[n](k)*hy1[n](i)*hx1[n](j));
}
// MORE INFO HERE: http://code.google.com/p/googletest/wiki/AdvancedGuide
// Modify this test so it uses Fixures as test_image and test_metadata
TEST( MultidimTest, Size)
{
MultidimArray<int> md;
md.resize(2,3);
size_t x,y,z, n;
md.getDimensions(x,y,z,n);
EXPECT_EQ((size_t)1, n) << "MultidimArray: wrong n size";
EXPECT_EQ((size_t)1, z) << "MultidimArray: wrong y size";
EXPECT_EQ((size_t)2, y) << "MultidimArray: wrong z size";
EXPECT_EQ((size_t)3, x) << "MultidimArray: wrong x size";
}
void getSpectrum(MultidimArray<double>& Min,
MultidimArray<double>& spectrum,
int spectrum_type)
{
MultidimArray<Complex > Faux;
int xsize = XSIZE(Min);
Matrix1D<double> f(3);
MultidimArray<double> count(xsize);
FourierTransformer transformer;
spectrum.initZeros(xsize);
count.initZeros();
transformer.FourierTransform(Min, Faux, false);
FOR_ALL_ELEMENTS_IN_FFTW_TRANSFORM(Faux)
{
long int idx = ROUND(sqrt(kp * kp + ip * ip + jp * jp));
if (spectrum_type == AMPLITUDE_SPECTRUM)
{
spectrum(idx) += abs(dAkij(Faux, k, i, j));
}
else
{
spectrum(idx) += norm(dAkij(Faux, k, i, j));
}
count(idx) += 1.;
}
for (long int i = 0; i < xsize; i++)
if (count(i) > 0.)
{
spectrum(i) /= count(i);
}
}
// Computes the average of a number of frames in movies
void computeAvg(const FileName &movieFile, int begin, int end, MultidimArray<double> &avgImg)
{
ImageGeneric movieStack;
MultidimArray<double> frameImage, shiftedFrame;
Matrix1D<double> shiftMatrix(2);
int N=end-begin+1;
for (size_t i=begin;i<=end;i++)
{
movieStack.readMapped(movieFile,i);
movieStack().getImage(frameImage);
if (i==begin)
avgImg.initZeros(YSIZE(frameImage), XSIZE(frameImage));
if (darkImageCorr)
frameImage-=darkImage;
if (gainImageCorr)
frameImage/=gainImage;
if (globalShiftCorr)
{
XX(shiftMatrix)=XX(shiftVector[i-1]);
YY(shiftMatrix)=YY(shiftVector[i-1]);
translate(LINEAR, shiftedFrame, frameImage, shiftMatrix, WRAP);
avgImg+=shiftedFrame;
}
else
avgImg+=frameImage;
}
avgImg/=double(N);
frameImage.clear();
movieStack.clear();
}
void Projector::griddingCorrect(MultidimArray<DOUBLE> &vol_in)
{
// Correct real-space map by dividing it by the Fourier transform of the interpolator(s)
vol_in.setXmippOrigin();
FOR_ALL_ELEMENTS_IN_ARRAY3D(vol_in)
{
DOUBLE r = sqrt((DOUBLE)(k*k+i*i+j*j));
// if r==0: do nothing (i.e. divide by 1)
if (r > 0.)
{
DOUBLE rval = r / (ori_size * padding_factor);
DOUBLE sinc = sin(PI * rval) / ( PI * rval);
//DOUBLE ftblob = blob_Fourier_val(rval, blob) / blob_Fourier_val(0., blob);
// Interpolation (goes with "interpolator") to go from arbitrary to fine grid
if (interpolator==NEAREST_NEIGHBOUR && r_min_nn == 0)
{
// NN interpolation is convolution with a rectangular pulse, which FT is a sinc function
A3D_ELEM(vol_in, k, i, j) /= sinc;
}
else if (interpolator==TRILINEAR || (interpolator==NEAREST_NEIGHBOUR && r_min_nn > 0) )
{
// trilinear interpolation is convolution with a triangular pulse, which FT is a sinc^2 function
A3D_ELEM(vol_in, k, i, j) /= sinc * sinc;
}
else
REPORT_ERROR("BUG Projector::griddingCorrect: unrecognised interpolator scheme.");
//#define DEBUG_GRIDDING_CORRECT
#ifdef DEBUG_GRIDDING_CORRECT
if (k==0 && i==0 && j > 0)
std::cerr << " j= " << j << " sinc= " << sinc << std::endl;
#endif
}
}
}
// Converts an OpenCV matrix to XMIPP MultidimArray
void opencv2Xmipp(const cv::Mat &opencvMat, MultidimArray<double> &xmippArray)
{
int h = opencvMat.rows;
int w = opencvMat.cols;
xmippArray.initZeros(h, w);
FOR_ALL_DIRECT_ELEMENTS_IN_ARRAY2D(xmippArray)
DIRECT_A2D_ELEM(xmippArray,i,j) = opencvMat.at<float>(i,j);
}
/* Normalizations ---------------------------------------------------------- */
void normalize_OldXmipp(MultidimArray<double> &I)
{
double mean,std;
I.computeAvgStdev(mean,std);
double istd=1.0/std;
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(I)
DIRECT_MULTIDIM_ELEM(I,n)=(DIRECT_MULTIDIM_ELEM(I,n)-mean)*istd;
}
void resizeMap(MultidimArray<double >& img, int newsize)
{
FourierTransformer transformer;
MultidimArray<Complex > FT, FT2;
transformer.FourierTransform(img, FT, false);
windowFourierTransform(FT, FT2, newsize);
if (img.getDim() == 2)
{
img.resize(newsize, newsize);
}
else if (img.getDim() == 3)
{
img.resize(newsize, newsize, newsize);
}
transformer.inverseFourierTransform(FT2, img);
}
void normalize_Near_OldXmipp(MultidimArray<double> &I, const MultidimArray<int> &bg_mask)
{
double avg=0., stddev, min, max;
double avgbg, stddevbg, minbg, maxbg;
I.computeStats(avg, stddev, min, max);
computeStats_within_binary_mask(bg_mask, I, minbg, maxbg, avgbg,
stddevbg);
I -= avg;
I /= stddevbg;
}
/* Filter generation ------------------------------------------------------- */
void Steerable::generate1DFilters(double sigma,
const MultidimArray<double> &Vtomograph,
std::vector< MultidimArray<double> > &hx1,
std::vector< MultidimArray<double> > &hy1,
std::vector< MultidimArray<double> > &hz1){
// Initialization
MultidimArray<double> aux;
aux.initZeros(XSIZE(Vtomograph));
aux.setXmippOrigin();
for (int i=0; i<6; i++) hx1.push_back(aux);
aux.initZeros(YSIZE(Vtomograph));
aux.setXmippOrigin();
for (int i=0; i<6; i++) hy1.push_back(aux);
aux.initZeros(ZSIZE(Vtomograph));
aux.setXmippOrigin();
for (int i=0; i<6; i++) hz1.push_back(aux);
double sigma2=sigma*sigma;
double k1 = 1.0/pow((2.0*PI*sigma),(3.0/2.0));
double k2 = -1.0/(sigma2);
FOR_ALL_ELEMENTS_IN_ARRAY1D(hx1[0])
{
double i2=i*i;
double g = -exp(-i2/(2.0*sigma2));
hx1[0](i) = k1*k2*g*(1.0-(i2/sigma2));
hx1[1](i) = k1*k2*g;
hx1[2](i) = k1*k2*g;
hx1[3](i) = k1*k2*k2*g*i;
hx1[4](i) = k1*k2*k2*g*i;
hx1[5](i) = k1*k2*k2*g;
}
FOR_ALL_ELEMENTS_IN_ARRAY1D(hy1[0])
{
double i2=i*i;
double g = -exp(-i2/(2.0*sigma2));
hy1[0](i) = g;
hy1[1](i) = g*(1.0-(i2/sigma2));
hy1[2](i) = g;
hy1[3](i) = g*i;
hy1[4](i) = g;
hy1[5](i) = g*i;
}
FOR_ALL_ELEMENTS_IN_ARRAY1D(hz1[0])
{
double i2=i*i;
double g = -exp(-i2/(2.0*sigma2));
hz1[0](i) = g;
hz1[1](i) = g;
hz1[2](i) = g*(1.0-(i2/sigma2));
hz1[3](i) = g;
hz1[4](i) = g*i;
hz1[5](i) = g*i;
}
}
/* Do inference ------------------------------------------------------------ */
int EnsembleNaiveBayes::doInference(const Matrix1D<double> &newFeatures,
double &cost, MultidimArray<int> &votes,
Matrix1D<double> &classesProbs, Matrix1D<double> &allCosts)
{
int nmax=ensemble.size();
MultidimArray<double> minCost, maxCost;
votes.initZeros(K);
minCost.initZeros(K);
minCost.initConstant(1);
maxCost.initZeros(K);
maxCost.initConstant(1);
double bestMinCost=0;
int bestClass=0;
MultidimArray<double> newFeaturesn;
for (int n=0; n<nmax; n++)
{
double costn;
newFeaturesn.initZeros(XSIZE(ensembleFeatures[n]));
FOR_ALL_ELEMENTS_IN_ARRAY1D(newFeaturesn)
newFeaturesn(i)=newFeatures(ensembleFeatures[n](i));
int k=ensemble[n]->doInference(newFeaturesn, costn, classesProbs, allCosts);
votes(k)++;
if (minCost(k)>0 || minCost(k)>costn)
minCost(k)=costn;
if (maxCost(k)>0 || maxCost(k)<costn)
maxCost(k)=costn;
if (minCost(k)<bestMinCost)
{
bestMinCost=minCost(k);
bestClass=k;
}
}
if (judgeCombination[bestClass]=='m')
cost=minCost(bestClass);
else if (judgeCombination[bestClass]=='M')
cost=maxCost(bestClass);
else
cost=minCost(bestClass);
return bestClass;
}
void FourierProjector::produceSideInfo()
{
// Zero padding
MultidimArray<double> Vpadded;
int paddedDim=(int)(paddingFactor*volumeSize);
volume->window(Vpadded,FIRST_XMIPP_INDEX(paddedDim),FIRST_XMIPP_INDEX(paddedDim),FIRST_XMIPP_INDEX(paddedDim),
LAST_XMIPP_INDEX(paddedDim),LAST_XMIPP_INDEX(paddedDim),LAST_XMIPP_INDEX(paddedDim));
volume->clear();
// Make Fourier transform, shift the volume origin to the volume center and center it
MultidimArray< std::complex<double> > Vfourier;
transformer3D.completeFourierTransform(Vpadded,Vfourier);
ShiftFFT(Vfourier, FIRST_XMIPP_INDEX(XSIZE(Vpadded)), FIRST_XMIPP_INDEX(YSIZE(Vpadded)), FIRST_XMIPP_INDEX(ZSIZE(Vpadded)));
CenterFFT(Vfourier,true);
Vfourier.setXmippOrigin();
// Compensate for the Fourier normalization factor
double K=(double)(XSIZE(Vpadded)*XSIZE(Vpadded)*XSIZE(Vpadded))/(double)(volumeSize*volumeSize);
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(Vfourier)
DIRECT_MULTIDIM_ELEM(Vfourier,n)*=K;
Vpadded.clear();
// Compute Bspline coefficients
if (BSplineDeg==3)
{
MultidimArray< double > VfourierRealAux, VfourierImagAux;
Complex2RealImag(Vfourier, VfourierRealAux, VfourierImagAux);
Vfourier.clear();
produceSplineCoefficients(BSPLINE3,VfourierRealCoefs,VfourierRealAux);
produceSplineCoefficients(BSPLINE3,VfourierImagCoefs,VfourierImagAux);
//VfourierRealAux.clear();
//VfourierImagAux.clear();
}
else
Complex2RealImag(Vfourier, VfourierRealCoefs, VfourierImagCoefs);
// Allocate memory for the 2D Fourier transform
projection().initZeros(volumeSize,volumeSize);
projection().setXmippOrigin();
transformer2D.FourierTransform(projection(),projectionFourier,false);
}
// Fourier ring correlation -----------------------------------------------
// from precalculated Fourier Transforms, and without sampling rate etc.
void getFSC(MultidimArray< Complex >& FT1,
MultidimArray< Complex >& FT2,
MultidimArray< double >& fsc)
{
if (!FT1.sameShape(FT2))
{
REPORT_ERROR("fourierShellCorrelation ERROR: MultidimArrays have different shapes!");
}
MultidimArray< int > radial_count(XSIZE(FT1));
MultidimArray<double> num, den1, den2;
Matrix1D<double> f(3);
num.initZeros(radial_count);
den1.initZeros(radial_count);
den2.initZeros(radial_count);
fsc.initZeros(radial_count);
FOR_ALL_ELEMENTS_IN_FFTW_TRANSFORM(FT1)
{
int idx = ROUND(sqrt(kp * kp + ip * ip + jp * jp));
if (idx >= XSIZE(FT1))
{
continue;
}
Complex z1 = DIRECT_A3D_ELEM(FT1, k, i, j);
Complex z2 = DIRECT_A3D_ELEM(FT2, k, i, j);
double absz1 = abs(z1);
double absz2 = abs(z2);
num(idx) += (conj(z1) * z2).real;
den1(idx) += absz1 * absz1;
den2(idx) += absz2 * absz2;
radial_count(idx)++;
}
FOR_ALL_ELEMENTS_IN_ARRAY1D(fsc)
{
fsc(i) = num(i) / sqrt(den1(i) * den2(i));
}
}
void MlModel::initialise()
{
// Auxiliary vector with relevant size in Fourier space
MultidimArray<double > aux;
aux.initZeros(ori_size / 2 + 1);
// Now resize all relevant vectors
Iref.resize(nr_classes);
pdf_class.resize(nr_classes, 1. / (double)nr_classes);
pdf_direction.resize(nr_classes);
group_names.resize(nr_groups, "");
sigma2_noise.resize(nr_groups, aux);
nr_particles_group.resize(nr_groups);
tau2_class.resize(nr_classes, aux);
fsc_halves_class.resize(nr_classes, aux);
sigma2_class.resize(nr_classes, aux);
data_vs_prior_class.resize(nr_classes, aux);
// TODO handle these two correctly.
bfactor_correction.resize(nr_groups, 0.);
scale_correction.resize(nr_groups, 1.);
acc_rot.resize(nr_classes, 0);
acc_trans.resize(nr_classes, 0);
if (ref_dim == 2)
{
Matrix1D<double> empty(2);
prior_offset_class.resize(nr_classes, empty);
}
// These arrays will be resized when they are filled
orientability_contrib.resize(nr_classes);
Projector ref(ori_size, interpolator, padding_factor, r_min_nn);
PPref.clear();
// Now fill the entire vector with instances of "ref"
PPref.resize(nr_classes, ref);
}
void CTF::getCenteredImage(MultidimArray<DOUBLE> &result, DOUBLE Tm,
bool do_abs, bool do_only_flip_phases, bool do_intact_until_first_peak, bool do_damping)
{
result.setXmippOrigin();
DOUBLE xs = (DOUBLE)XSIZE(result) * Tm;
DOUBLE ys = (DOUBLE)YSIZE(result) * Tm;
FOR_ALL_ELEMENTS_IN_ARRAY2D(result)
{
DOUBLE x = (DOUBLE)j / xs;
DOUBLE y = (DOUBLE)i / ys;
A2D_ELEM(result, i, j) = getCTF(x, y, do_abs, do_only_flip_phases, do_intact_until_first_peak, do_damping);
}
}
void run()
{
// Get angles ==============================================================
MetaData angles;
angles.read(fnIn);
size_t AngleNo = angles.size();
if (AngleNo == 0 || !angles.containsLabel(MDL_ANGLE_ROT))
REPORT_ERROR(ERR_MD_BADLABEL, "Input file doesn't contain angular information");
double maxWeight = -99.e99;
MultidimArray<double> weight;
weight.initZeros(AngleNo);
if (angles.containsLabel(MDL_WEIGHT))
{
// Find maximum weight
int i=0;
FOR_ALL_OBJECTS_IN_METADATA(angles)
{
double w;
angles.getValue(MDL_WEIGHT,w,__iter.objId);
DIRECT_A1D_ELEM(weight,i++)=w;
maxWeight=XMIPP_MAX(w,maxWeight);
}
}
/* 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;
}
/** Kullback-Leibner divergence */
double getKullbackLeibnerDivergence(MultidimArray<Complex >& Fimg,
MultidimArray<Complex >& Fref, MultidimArray<double>& sigma2,
MultidimArray<double>& p_i, MultidimArray<double>& q_i, int highshell, int lowshell)
{
// First check dimensions are OK
if (!Fimg.sameShape(Fref))
{
REPORT_ERROR("getKullbackLeibnerDivergence ERROR: Fimg and Fref are not of the same shape.");
}
if (highshell < 0)
{
highshell = XSIZE(Fimg) - 1;
}
if (lowshell < 0)
{
lowshell = 0;
}
if (highshell > XSIZE(sigma2))
{
REPORT_ERROR("getKullbackLeibnerDivergence ERROR: highshell is larger than size of sigma2 array.");
}
if (highshell < lowshell)
{
REPORT_ERROR("getKullbackLeibnerDivergence ERROR: highshell is smaller than lowshell.");
}
// Initialize the histogram
MultidimArray<int> histogram;
int histogram_size = 101;
int histogram_origin = histogram_size / 2;
double sigma_max = 10.;
double histogram_factor = histogram_origin / sigma_max;
histogram.initZeros(histogram_size);
// This way this will work in both 2D and 3D
FOR_ALL_ELEMENTS_IN_FFTW_TRANSFORM(Fimg)
{
int ires = ROUND(sqrt(kp * kp + ip * ip + jp * jp));
if (ires >= lowshell && ires <= highshell)
{
// Use FT of masked image for noise estimation!
double diff_real = (DIRECT_A3D_ELEM(Fref, k, i, j)).real - (DIRECT_A3D_ELEM(Fimg, k, i, j)).real;
double diff_imag = (DIRECT_A3D_ELEM(Fref, k, i, j)).imag - (DIRECT_A3D_ELEM(Fimg, k, i, j)).imag;
double sigma = sqrt(DIRECT_A1D_ELEM(sigma2, ires));
// Divide by standard deviation to normalise all the difference
diff_real /= sigma;
diff_imag /= sigma;
// Histogram runs from -10 sigma to +10 sigma
diff_real += sigma_max;
diff_imag += sigma_max;
// Make histogram on-the-fly;
// Real part
int ihis = ROUND(diff_real * histogram_factor);
if (ihis < 0)
{
ihis = 0;
}
else if (ihis >= histogram_size)
{
ihis = histogram_size - 1;
}
histogram(ihis)++;
// Imaginary part
ihis = ROUND(diff_imag * histogram_factor);
if (ihis < 0)
{
ihis = 0;
}
else if (ihis > histogram_size)
{
ihis = histogram_size;
}
histogram(ihis)++;
}
}
// Normalise the histogram and the discretised analytical Gaussian
double norm = (double)histogram.sum();
double gaussnorm = 0.;
for (int i = 0; i < histogram_size; i++)
{
double x = (double)i / histogram_factor;
gaussnorm += gaussian1D(x - sigma_max, 1. , 0.);
}
// Now calculate the actual Kullback-Leibner divergence
double kl_divergence = 0.;
p_i.resize(histogram_size);
q_i.resize(histogram_size);
for (int i = 0; i < histogram_size; i++)
{
// Data distribution
p_i(i) = (double)histogram(i) / norm;
// Theoretical distribution
double x = (double)i / histogram_factor;
q_i(i) = gaussian1D(x - sigma_max, 1. , 0.) / gaussnorm;
if (p_i(i) > 0.)
{
kl_divergence += p_i(i) * log(p_i(i) / q_i(i));
}
}
kl_divergence /= (double)histogram_size;
return kl_divergence;
}
//#define DEBUG
void ProgResolutionIBW::run()
{
V.read(fnVol);
//Mask generation
Image<double> aux;
double bg_mean;
MultidimArray<double> Vmask;
detectBackground(V(),aux(),0.1,bg_mean);
#ifdef DEBUG
aux.write("PPPmask_no_ero_03.vol");
#endif
//Mask volume erosion to expand the mask boundaries
Vmask.initZeros(V());
erode3D(aux(),Vmask, 18,0,2);
//Correction of some flaws produced in the edges of the mask volume
FOR_ALL_DIRECT_ELEMENTS_IN_ARRAY3D(Vmask)
if (k<=4 || i<=4 || j<=4 ||
k>=ZSIZE(Vmask)-4 || i>=YSIZE(Vmask)-4 || j>=XSIZE(Vmask)-4)
DIRECT_A3D_ELEM(Vmask,k,i,j)=1;
aux()=Vmask;
#ifdef DEBUG
aux.write("PPPmask_ero_03.vol");
#endif
//Sobel edge detection applied to original volume
Image<double> Vedge;
computeEdges(V(),Vedge());
#ifdef DEBUG
Vedge.write("PPPvolume_sobel_unmask_03.vol");
#endif
//Masked volume generation
const MultidimArray<double> &mVedge=Vedge();
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(mVedge)
if (DIRECT_MULTIDIM_ELEM(Vmask,n)==1)
DIRECT_MULTIDIM_ELEM(mVedge,n)=0;
#ifdef DEBUG
Vedge.write("volume_sobel_mask_03.vol");
#endif
double minval, maxval, avg, stddev;
//Invert the mask to meet computeStats_within_binary_mask requirements
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(Vmask)
if (DIRECT_MULTIDIM_ELEM(Vmask,n)==1)
DIRECT_MULTIDIM_ELEM(Vmask,n)=0;
else
DIRECT_MULTIDIM_ELEM(Vmask,n)=1;
//Threshold is 3 times the standard deviation of unmasked pixel values
double thresh;
computeStats_within_binary_mask(Vmask,mVedge,minval, maxval, avg, stddev);
thresh=3*stddev;
//Final edge volume generated by setting to 1 positions with values > threshold
Image<double> Vaux;
Vaux().initZeros(mVedge);
FOR_ALL_DIRECT_ELEMENTS_IN_MULTIDIMARRAY(mVedge)
if (DIRECT_MULTIDIM_ELEM(mVedge,n)>=thresh)
DIRECT_MULTIDIM_ELEM(Vaux(),n)=1;
#ifdef DEBUG
Vaux.write("volumen_bordes_definitivo_03.vol");
#endif
const MultidimArray<double> &mVaux=Vaux();
//Spline coefficient volume from original volume, to allow <1 step sizes
MultidimArray<double> Volcoeffs;
Volcoeffs.initZeros(V());
produceSplineCoefficients(3,Volcoeffs,V());
//Width parameter volume initialization
Image<double> widths;
widths().resizeNoCopy(V());
widths().initConstant(1e5);
double step=0.25;
Matrix1D<double> direction(3);
//Calculation of edge width for 10 different directions, if a smaller value is found for a different
//direction on a given position the former value is overwritten
//Direction (1,0,0)
VECTOR_R3(direction,1,0,0);
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (0,1,0)
VECTOR_R3(direction,0,1,0);
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (0,0,1)
VECTOR_R3(direction,0,0,1);
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (1,1,0)
VECTOR_R3(direction,(1/sqrt(2)),(1/sqrt(2)),0);
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (1,0,1)
VECTOR_R3(direction,(1/sqrt(2)),0,(1/sqrt(2)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (0,1,1)
VECTOR_R3(direction,0,(1/sqrt(2)),(1/sqrt(2)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (1,1,1)
VECTOR_R3(direction,(1/sqrt(3)),(1/sqrt(3)),(1/sqrt(3)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (-1,1,1)
VECTOR_R3(direction,-(1/sqrt(3)),(1/sqrt(3)),(1/sqrt(3)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (1,1,-1)
VECTOR_R3(direction,(1/sqrt(3)),(1/sqrt(3)),-(1/sqrt(3)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
//Direction (1,-1,1)
VECTOR_R3(direction,(1/sqrt(3)),-(1/sqrt(3)),(1/sqrt(3)));
edgeWidth(Volcoeffs, mVaux, widths(), direction, step);
#ifdef DEBUG
std::cout << "width stats: ";
widths().printStats();
std::cout << std::endl;
widths.write("PPPwidths.vol");
#endif
double ibw=calculateIBW(widths());
std::cout << "Resolution ibw= " << ibw << std::endl;
if (fnOut!="")
widths.write(fnOut);
}
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();
}
EnsembleNaiveBayes::EnsembleNaiveBayes(
const std::vector < MultidimArray<double> > &features,
const Matrix1D<double> &priorProbs,
int discreteLevels, int numberOfClassifiers,
double samplingFeatures, double samplingIndividuals,
const std::string &newJudgeCombination)
{
int NFeatures=XSIZE(features[0]);
int NsubFeatures=CEIL(NFeatures*samplingFeatures);
K=features.size();
judgeCombination=newJudgeCombination;
#ifdef WEIGHTED_SAMPLING
// Measure the classification power of each variable
NaiveBayes *nb_weights=new NaiveBayes(features, priorProbs, discreteLevels);
MultidimArray<double> weights=nb_weights->__weights;
delete nb_weights;
double sumWeights=weights.sum();
#endif
for (int n=0; n<numberOfClassifiers; n++)
{
// Produce the set of features for this subclassifier
MultidimArray<int> subFeatures(NsubFeatures);
FOR_ALL_ELEMENTS_IN_ARRAY1D(subFeatures)
{
#ifdef WEIGHTED_SAMPLING
double random_sum_weight=rnd_unif(0,sumWeights);
int j=0;
do
{
double wj=DIRECT_A1D_ELEM(weights,j);
if (wj<random_sum_weight)
{
random_sum_weight-=wj;
j++;
if (j==NFeatures)
{
j=NFeatures-1;
break;
}
}
else
break;
}
while (true);
DIRECT_A1D_ELEM(subFeatures,i)=j;
#else
DIRECT_A1D_ELEM(subFeatures,i)=round(rnd_unif(0,NFeatures-1));
#endif
}
// Container for the new training sample
std::vector< MultidimArray<double> > newFeatures;
// Produce the data set for each class
for (int k=0; k<K; k++)
{
int NIndividuals=YSIZE(features[k]);
int NsubIndividuals=CEIL(NIndividuals*samplingIndividuals);
MultidimArray<int> subIndividuals(NsubIndividuals);
FOR_ALL_ELEMENTS_IN_ARRAY1D(subIndividuals)
subIndividuals(i)=ROUND(rnd_unif(0,NsubIndividuals-1));
MultidimArray<double> newFeaturesK;
newFeaturesK.initZeros(NsubIndividuals,NsubFeatures);
const MultidimArray<double>& features_k=features[k];
FOR_ALL_ELEMENTS_IN_ARRAY2D(newFeaturesK)
DIRECT_A2D_ELEM(newFeaturesK,i,j)=DIRECT_A2D_ELEM(features_k,
DIRECT_A1D_ELEM(subIndividuals,i),
DIRECT_A1D_ELEM(subFeatures,j));
newFeatures.push_back(newFeaturesK);
}
// Create a Naive Bayes classifier with this data
NaiveBayes *nb=new NaiveBayes(newFeatures, priorProbs, discreteLevels);
ensemble.push_back(nb);
ensembleFeatures.push_back(subFeatures);
}
}
//#define DEBUG
void normalize_tomography(MultidimArray<double> &I, double tilt, double &mui,
double &sigmai, bool tiltMask, bool tomography0,
double mu0, double sigma0)
{
// Tilt series are always 2D
I.checkDimension(2);
const int L=2;
// Build a mask using the tilt angle
I.setXmippOrigin();
MultidimArray<int> mask;
mask.initZeros(I);
int Xdimtilt=(int)XMIPP_MIN(FLOOR(0.5*(XSIZE(I)*cos(DEG2RAD(tilt)))),
0.5*(XSIZE(I)-(2*L+1)));
int N=0;
for (int i=STARTINGY(I); i<=FINISHINGY(I); i++)
for (int j=-Xdimtilt; j<=Xdimtilt;j++)
{
A2D_ELEM(mask,i,j)=1;
N++;
}
// Estimate the local variance
MultidimArray<double> localVariance;
localVariance.initZeros(I);
double meanVariance=0;
FOR_ALL_ELEMENTS_IN_ARRAY2D(I)
{
// Center a mask of size 5x5 and estimate the variance within the mask
double meanPiece=0, variancePiece=0;
double Npiece=0;
for (int ii=i-L; ii<=i+L; ii++)
{
if (INSIDEY(I,ii))
for (int jj=j-L; jj<=j+L; jj++)
{
if (INSIDEX(I,jj))
{
double pixval=A2D_ELEM(I,ii,jj);
meanPiece+=pixval;
variancePiece+=pixval*pixval;
++Npiece;
}
}
}
meanPiece/=Npiece;
variancePiece=variancePiece/(Npiece-1)-
Npiece/(Npiece-1)*meanPiece*meanPiece;
A2D_ELEM(localVariance,i,j)=variancePiece;
meanVariance+=variancePiece;
}
meanVariance*=1.0/N;
// Test the hypothesis that the variance in this piece is
// the same as the variance in the whole image
double iFu=1/icdf_FSnedecor(4*L*L+4*L,N-1,0.975);
double iFl=1/icdf_FSnedecor(4*L*L+4*L,N-1,0.025);
double iMeanVariance=1.0/meanVariance;
FOR_ALL_ELEMENTS_IN_ARRAY2D(localVariance)
{
if (A2D_ELEM(localVariance,i,j)==0)
A2D_ELEM(mask,i,j)=-2;
if (A2D_ELEM(mask,i,j)==1)
{
double ratio=A2D_ELEM(localVariance,i,j)*iMeanVariance;
double thl=ratio*iFu;
double thu=ratio*iFl;
if (thl>1 || thu<1)
A2D_ELEM(mask,i,j)=-1;
}
}
#ifdef DEBUG
Image<double> save;
save()=I;
save.write("PPP.xmp");
save()=localVariance;
save.write("PPPLocalVariance.xmp");
Image<int> savemask;
savemask()=mask;
savemask.write("PPPmask.xmp");
#endif
// Compute the statistics again in the reduced mask
double avg, stddev, min, max;
computeStats_within_binary_mask(mask, I, min, max, avg, stddev);
double cosTilt=cos(DEG2RAD(tilt));
double iCosTilt=1.0/cosTilt;
if (tomography0)
{
double iadjustedStddev=1.0/(sigma0*cosTilt);
FOR_ALL_ELEMENTS_IN_ARRAY2D(I)
switch (A2D_ELEM(mask,i,j))
{
case -2:
A2D_ELEM(I,i,j)=0;
break;
case 0:
if (tiltMask)
A2D_ELEM(I,i,j)=0;
else
A2D_ELEM(I,i,j)=(A2D_ELEM(I,i,j)*iCosTilt-mu0)*iadjustedStddev;
break;
case -1:
case 1:
A2D_ELEM(I,i,j)=(A2D_ELEM(I,i,j)*iCosTilt-mu0)*iadjustedStddev;
break;
}
}
else
{
// Fill data array with oversampled Fourier transform, and calculate its power spectrum
void Projector::computeFourierTransformMap(MultidimArray<DOUBLE> &vol_in, MultidimArray<DOUBLE> &power_spectrum, int current_size, int nr_threads, bool do_gridding)
{
MultidimArray<DOUBLE> Mpad;
MultidimArray<Complex > Faux;
FourierTransformer transformer;
// DEBUGGING: multi-threaded FFTWs are giving me a headache?
// For a long while: switch them off!
//transformer.setThreadsNumber(nr_threads);
DOUBLE normfft;
// Size of padded real-space volume
int padoridim = padding_factor * ori_size;
// Initialize data array of the oversampled transform
ref_dim = vol_in.getDim();
// Make Mpad
switch (ref_dim)
{
case 2:
Mpad.initZeros(padoridim, padoridim);
normfft = (DOUBLE)(padding_factor * padding_factor);
break;
case 3:
Mpad.initZeros(padoridim, padoridim, padoridim);
if (data_dim ==3)
normfft = (DOUBLE)(padding_factor * padding_factor * padding_factor);
else
normfft = (DOUBLE)(padding_factor * padding_factor * padding_factor * ori_size);
break;
default:
REPORT_ERROR("Projector::computeFourierTransformMap%%ERROR: Dimension of the data array should be 2 or 3");
}
// First do a gridding pre-correction on the real-space map:
// Divide by the inverse Fourier transform of the interpolator in Fourier-space
// 10feb11: at least in 2D case, this seems to be the wrong thing to do!!!
// TODO: check what is best for subtomo!
if (do_gridding)// && data_dim != 3)
griddingCorrect(vol_in);
// Pad translated map with zeros
vol_in.setXmippOrigin();
Mpad.setXmippOrigin();
FOR_ALL_ELEMENTS_IN_ARRAY3D(vol_in) // This will also work for 2D
A3D_ELEM(Mpad, k, i, j) = A3D_ELEM(vol_in, k, i, j);
// Translate padded map to put origin of FT in the center
CenterFFT(Mpad, true);
// Calculate the oversampled Fourier transform
transformer.FourierTransform(Mpad, Faux, false);
// Free memory: Mpad no longer needed
Mpad.clear();
// Resize data array to the right size and initialise to zero
initZeros(current_size);
// Fill data only for those points with distance to origin less than max_r
// (other points will be zero because of initZeros() call above
// Also calculate radial power spectrum
power_spectrum.initZeros(ori_size / 2 + 1);
MultidimArray<DOUBLE> counter(power_spectrum);
counter.initZeros();
int max_r2 = r_max * r_max * padding_factor * padding_factor;
FOR_ALL_ELEMENTS_IN_FFTW_TRANSFORM(Faux) // This will also work for 2D
{
int r2 = kp*kp + ip*ip + jp*jp;
// The Fourier Transforms are all "normalised" for 2D transforms of size = ori_size x ori_size
if (r2 <= max_r2)
{
// Set data array
A3D_ELEM(data, kp, ip, jp) = DIRECT_A3D_ELEM(Faux, k, i, j) * normfft;
// Calculate power spectrum
int ires = ROUND( sqrt((DOUBLE)r2) / padding_factor );
// Factor two because of two-dimensionality of the complex plane
DIRECT_A1D_ELEM(power_spectrum, ires) += norm(A3D_ELEM(data, kp, ip, jp)) / 2.;
DIRECT_A1D_ELEM(counter, ires) += 1.;
}
}
// Calculate radial average of power spectrum
FOR_ALL_DIRECT_ELEMENTS_IN_ARRAY1D(power_spectrum)
{
if (DIRECT_A1D_ELEM(counter, i) < 1.)
DIRECT_A1D_ELEM(power_spectrum, i) = 0.;
else
DIRECT_A1D_ELEM(power_spectrum, i) /= DIRECT_A1D_ELEM(counter, i);
}
transformer.cleanup();
}
bool checkImageCorners(const FileName &name)
{
const int windowSize=11;
const int windowSize_2=windowSize/2;
const double Flower=0.4871; // MATLAB: N=11*11-1; finv(0.00005,N,N)
// const double Fupper=2.0530; // MATLAB: N=11*11-1; finv(0.99995,N,N)
ImageGeneric I;
I.readMapped(name);
size_t Xdim, Ydim, Zdim;
I.getDimensions(Xdim,Ydim,Zdim);
if (Zdim>1)
return true;
if (Xdim<=2*windowSize || Ydim<=2*windowSize)
return true;
MultidimArray<double> window;
size_t i=Ydim/2;
size_t j=Xdim/2;
I().window(window,0,0,i-windowSize_2,j-windowSize_2,0,0,i+windowSize_2,j+windowSize_2);
double stddev0=window.computeStddev();
double var0=stddev0*stddev0;
i=windowSize_2;
j=windowSize_2;
I().window(window,0,0,i-windowSize_2,j-windowSize_2,0,0,i+windowSize_2,j+windowSize_2);
double stddev1=window.computeStddev();
double var1=stddev1*stddev1;
double F=var1/var0;
if (F<Flower)//|| F>Fupper)
return false;
i=Ydim-1-windowSize_2;
j=windowSize_2;
I().window(window,0,0,i-windowSize_2,j-windowSize_2,0,0,i+windowSize_2,j+windowSize_2);
stddev1=window.computeStddev();
var1=stddev1*stddev1;
F=var1/var0;
if (F<Flower)//|| F>Fupper)
return false;
i=windowSize_2;
j=Xdim-1-windowSize_2;
I().window(window,0,0,i-windowSize_2,j-windowSize_2,0,0,i+windowSize_2,j+windowSize_2);
stddev1=window.computeStddev();
var1=stddev1*stddev1;
F=var1/var0;
if (F<Flower)//|| F>Fupper)
return false;
i=Ydim-1-windowSize_2;
j=Xdim-1-windowSize_2;
I().window(window,0,0,i-windowSize_2,j-windowSize_2,0,0,i+windowSize_2,j+windowSize_2);
stddev1=window.computeStddev();
var1=stddev1*stddev1;
F=var1/var0;
if (F<Flower)//|| F>Fupper)
return false;
return true;
}
// Shift an image through phase-shifts in its Fourier Transform (without pretabulated sine and cosine)
void shiftImageInFourierTransform(MultidimArray<Complex >& in,
MultidimArray<Complex >& out,
double oridim, Matrix1D<double> shift)
{
out.resize(in);
shift /= -oridim;
double dotp, a, b, c, d, ac, bd, ab_cd, x, y, z, xshift, yshift, zshift;
switch (in.getDim())
{
case 1:
xshift = XX(shift);
if (ABS(xshift) < XMIPP_EQUAL_ACCURACY)
{
out = in;
return;
}
for (long int j = 0; j < XSIZE(in); j++)
{
x = j;
dotp = 2 * PI * (x * xshift);
a = cos(dotp);
b = sin(dotp);
c = DIRECT_A1D_ELEM(in, j).real;
d = DIRECT_A1D_ELEM(in, j).imag;
ac = a * c;
bd = b * d;
ab_cd = (a + b) * (c + d); // (ab_cd-ac-bd = ad+bc : but needs 4 multiplications)
DIRECT_A1D_ELEM(out, j) = Complex(ac - bd, ab_cd - ac - bd);
}
break;
case 2:
xshift = XX(shift);
yshift = YY(shift);
if (ABS(xshift) < XMIPP_EQUAL_ACCURACY && ABS(yshift) < XMIPP_EQUAL_ACCURACY)
{
out = in;
return;
}
for (long int i = 0; i < XSIZE(in); i++)
for (long int j = 0; j < XSIZE(in); j++)
{
x = j;
y = i;
dotp = 2 * PI * (x * xshift + y * yshift);
a = cos(dotp);
b = sin(dotp);
c = DIRECT_A2D_ELEM(in, i, j).real;
d = DIRECT_A2D_ELEM(in, i, j).imag;
ac = a * c;
bd = b * d;
ab_cd = (a + b) * (c + d);
DIRECT_A2D_ELEM(out, i, j) = Complex(ac - bd, ab_cd - ac - bd);
}
for (long int i = YSIZE(in) - 1; i >= XSIZE(in); i--)
{
y = i - YSIZE(in);
for (long int j = 0; j < XSIZE(in); j++)
{
x = j;
dotp = 2 * PI * (x * xshift + y * yshift);
a = cos(dotp);
b = sin(dotp);
c = DIRECT_A2D_ELEM(in, i, j).real;
d = DIRECT_A2D_ELEM(in, i, j).imag;
ac = a * c;
bd = b * d;
ab_cd = (a + b) * (c + d);
DIRECT_A2D_ELEM(out, i, j) = Complex(ac - bd, ab_cd - ac - bd);
}
}
break;
case 3:
xshift = XX(shift);
yshift = YY(shift);
zshift = ZZ(shift);
if (ABS(xshift) < XMIPP_EQUAL_ACCURACY && ABS(yshift) < XMIPP_EQUAL_ACCURACY && ABS(zshift) < XMIPP_EQUAL_ACCURACY)
{
out = in;
return;
}
for (long int k = 0; k < ZSIZE(in); k++)
{
z = (k < XSIZE(in)) ? k : k - ZSIZE(in);
for (long int i = 0; i < YSIZE(in); i++)
{
y = (i < XSIZE(in)) ? i : i - YSIZE(in);
for (long int j = 0; j < XSIZE(in); j++)
{
x = j;
dotp = 2 * PI * (x * xshift + y * yshift + z * zshift);
a = cos(dotp);
b = sin(dotp);
c = DIRECT_A3D_ELEM(in, k, i, j).real;
d = DIRECT_A3D_ELEM(in, k, i, j).imag;
ac = a * c;
bd = b * d;
ab_cd = (a + b) * (c + d);
DIRECT_A3D_ELEM(out, k, i, j) = Complex(ac - bd, ab_cd - ac - bd);
}
}
}
break;
default:
REPORT_ERROR("shiftImageInFourierTransform ERROR: dimension should be 1, 2 or 3!");
}
}
void Steerable::singleFilter(const MultidimArray<double>& Vin,
MultidimArray<double> &hx1, MultidimArray<double> &hy1, MultidimArray<double> &hz1,
MultidimArray<double> &Vout){
MultidimArray< std::complex<double> > H, Aux;
Vout.initZeros(Vin);
// Filter in X
#define MINUS_ONE_POWER(n) (((n)%2==0)? 1:-1)
FourierTransformer transformer;
transformer.FourierTransform(hx1,H);
FOR_ALL_ELEMENTS_IN_ARRAY1D(H)
H(i)*= MINUS_ONE_POWER(i);
FourierTransformer transformer2;
MultidimArray<double> aux(XSIZE(Vin));
transformer2.setReal(aux);
for (size_t k=0; k<ZSIZE(Vin); k++)
for (size_t i=0; i<YSIZE(Vin); i++)
{
for (size_t j=0; j<XSIZE(Vin); j++)
DIRECT_A1D_ELEM(aux,j)=DIRECT_A3D_ELEM(Vin,k,i,j);
transformer2.FourierTransform( );
transformer2.getFourierAlias( Aux );
Aux*=H;
transformer2.inverseFourierTransform( );
for (size_t j=0; j<XSIZE(Vin); j++)
DIRECT_A3D_ELEM(Vout,k,i,j)=XSIZE(aux)*DIRECT_A1D_ELEM(aux,j);
}
// Filter in Y
transformer.FourierTransform(hy1,H);
FOR_ALL_ELEMENTS_IN_ARRAY1D(H)
H(i)*= MINUS_ONE_POWER(i);
aux.initZeros(YSIZE(Vin));
transformer2.setReal(aux);
for (size_t k=0; k<ZSIZE(Vin); k++)
for (size_t j=0; j<XSIZE(Vin); j++)
{
for (size_t i=0; i<YSIZE(Vin); i++)
DIRECT_A1D_ELEM(aux,i)=DIRECT_A3D_ELEM(Vout,k,i,j);
transformer2.FourierTransform( );
transformer2.getFourierAlias( Aux );
Aux*=H;
transformer2.inverseFourierTransform( );
for (size_t i=0; i<YSIZE(Vin); i++)
DIRECT_A3D_ELEM(Vout,k,i,j)=XSIZE(aux)*DIRECT_A1D_ELEM(aux,i);
}
// Filter in Z
transformer.FourierTransform(hz1,H);
FOR_ALL_ELEMENTS_IN_ARRAY1D(H)
H(i)*= MINUS_ONE_POWER(i);
aux.initZeros(ZSIZE(Vin));
transformer2.setReal(aux);
for (size_t i=0; i<YSIZE(Vin); i++)
for (size_t j=0; j<XSIZE(Vin); j++)
{
for (size_t k=0; k<ZSIZE(Vin); k++)
DIRECT_A1D_ELEM(aux,k)=DIRECT_A3D_ELEM(Vout,k,i,j);
transformer2.FourierTransform( );
transformer2.getFourierAlias( Aux );
Aux*=H;
transformer2.inverseFourierTransform( );
for (size_t k=0; k<ZSIZE(Vin); k++)
DIRECT_A3D_ELEM(Vout,k,i,j)=XSIZE(aux)*DIRECT_A1D_ELEM(aux,k);
}
// If Missing wedge
if (MW!=NULL)
MW->removeWedge(Vout);
}
