void processImage(const FileName &fnImg, const FileName &fnImgOut, const MDRow &rowIn, MDRow &rowOut)
{
// Read input volume
Image<double> volume;
volume.read(fnImg);
volume().setXmippOrigin();
mask_prm.generate_mask(volume());
// Compute center of mass
Matrix1D<double> centerOfMass;
volume().centerOfMass(centerOfMass, &mask_prm.get_binary_mask());
// Move origin to that center of mass
selfTranslate(BSPLINE3,volume(),-centerOfMass, DONT_WRAP);
volume.write(fnImgOut);
}
void processImage(const FileName &fnImg, const FileName &fnImgOut, const MDRow &rowIn, MDRow &rowOut)
{
double amin = rnd_gaus(0, sigma);
double amax = rnd_gaus(0, sigma);
double minval = min_val + amin;
double maxval = max_val + amax;
Im.read(fnImg);
MultidimArray<double> &ImP = Im();
ImP.setXmippOrigin();
if (mask_prm.type == NO_MASK)
ImP.rangeAdjust(minval, maxval);
else
{
mask_prm.generate_mask(ImP);
ImP.rangeAdjust(minval, maxval,mask_prm.get_binary_mask());
}
Im.write(fnImgOut);
}
void run ()
{
mask.allowed_data_types = INT_MASK;
// Main program =========================================================
params.V1.read(fn1);
params.V1().setXmippOrigin();
params.V2.read(fn2);
params.V2().setXmippOrigin();
// Initialize best_fit
double best_fit = 1e38;
Matrix1D<double> best_align(8);
bool first = true;
// Generate mask
if (mask_enabled)
{
mask.generate_mask(params.V1());
params.mask_ptr = &(mask.get_binary_mask());
}
else
params.mask_ptr = NULL;
// Exhaustive search
if (!usePowell && !useFRM)
{
// Count number of iterations
int times = 1;
if (!tell)
{
if (grey_scale0 != grey_scaleF)
times *= FLOOR(1 + (grey_scaleF - grey_scale0) / step_grey);
if (grey_shift0 != grey_shiftF)
times *= FLOOR(1 + (grey_shiftF - grey_shift0) / step_grey_shift);
if (rot0 != rotF)
times *= FLOOR(1 + (rotF - rot0) / step_rot);
if (tilt0 != tiltF)
times *= FLOOR(1 + (tiltF - tilt0) / step_tilt);
if (psi0 != psiF)
times *= FLOOR(1 + (psiF - psi0) / step_psi);
if (scale0 != scaleF)
times *= FLOOR(1 + (scaleF - scale0) / step_scale);
if (z0 != zF)
times *= FLOOR(1 + (zF - z0) / step_z);
if (y0 != yF)
times *= FLOOR(1 + (yF - y0) / step_y);
if (x0 != xF)
times *= FLOOR(1 + (xF - x0) / step_x);
init_progress_bar(times);
}
else
std::cout << "#grey_factor rot tilt psi scale z y x fitness\n";
// Iterate
int itime = 0;
int step_time = CEIL((double)times / 60.0);
Matrix1D<double> r(3);
Matrix1D<double> trial(9);
for (double grey_scale = grey_scale0; grey_scale <= grey_scaleF ; grey_scale += step_grey)
for (double grey_shift = grey_shift0; grey_shift <= grey_shiftF ; grey_shift += step_grey_shift)
for (double rot = rot0; rot <= rotF ; rot += step_rot)
for (double tilt = tilt0; tilt <= tiltF ; tilt += step_tilt)
for (double psi = psi0; psi <= psiF ; psi += step_psi)
for (double scale = scale0; scale <= scaleF ; scale += step_scale)
for (ZZ(r) = z0; ZZ(r) <= zF ; ZZ(r) += step_z)
for (YY(r) = y0; YY(r) <= yF ; YY(r) += step_y)
for (XX(r) = x0; XX(r) <= xF ; XX(r) += step_x)
{
// Form trial vector
trial(0) = grey_scale;
trial(1) = grey_shift;
trial(2) = rot;
trial(3) = tilt;
trial(4) = psi;
trial(5) = scale;
trial(6) = ZZ(r);
trial(7) = YY(r);
trial(8) = XX(r);
// Evaluate
double fit = fitness(MATRIX1D_ARRAY(trial));
// The best?
if (fit < best_fit || first)
{
best_fit = fit;
best_align = trial;
first = false;
if (tell)
std::cout << "Best so far\n";
}
// Show fit
if (tell)
std::cout << trial << " " << fit << std::endl;
else
if (++itime % step_time == 0)
progress_bar(itime);
}
if (!tell)
progress_bar(times);
}
else if (usePowell)
{
// Use Powell optimization
Matrix1D<double> x(9), steps(9);
double fitness;
int iter;
steps.initConstant(1);
if (onlyShift)
steps(0)=steps(1)=steps(2)=steps(3)=steps(4)=steps(5)=0;
if (params.alignment_method == COVARIANCE)
steps(0)=steps(1)=0;
x(0)=grey_scale0;
x(1)=grey_shift0;
x(2)=rot0;
x(3)=tilt0;
x(4)=psi0;
x(5)=scale0;
x(6)=z0;
x(7)=y0;
x(8)=x0;
powellOptimizer(x,1,9,&wrapperFitness,NULL,0.01,fitness,iter,steps,true);
best_align=x;
best_fit=fitness;
first=false;
}
else if (useFRM)
{
String scipionPython;
initializeScipionPython(scipionPython);
PyObject * pFunc = getPointerToPythonFRMFunction();
double rot,tilt,psi,x,y,z,score;
Matrix2D<double> A;
alignVolumesFRM(pFunc, params.V1(), params.V2(), Py_None, rot,tilt,psi,x,y,z,score,A,maxShift,maxFreq,params.mask_ptr);
best_align.initZeros(9);
best_align(0)=1; // Gray scale
best_align(1)=0; // Gray shift
best_align(2)=rot;
best_align(3)=tilt;
best_align(4)=psi;
best_align(5)=1; // Scale
best_align(6)=z;
best_align(7)=y;
best_align(8)=x;
best_fit=-score;
}
if (!first)
std::cout << "The best correlation is for\n"
<< "Scale : " << best_align(5) << std::endl
<< "Translation (X,Y,Z) : " << best_align(8)
<< " " << best_align(7) << " " << best_align(6)
<< std::endl
<< "Rotation (rot,tilt,psi): "
<< best_align(2) << " " << best_align(3) << " "
<< best_align(4) << std::endl
<< "Best grey scale : " << best_align(0) << std::endl
<< "Best grey shift : " << best_align(1) << std::endl
<< "Fitness value : " << best_fit << std::endl;
Matrix1D<double> r(3);
XX(r) = best_align(8);
YY(r) = best_align(7);
ZZ(r) = best_align(6);
Matrix2D<double> A,Aaux;
Euler_angles2matrix(best_align(2), best_align(3), best_align(4),
A, true);
translation3DMatrix(r,Aaux);
A = A * Aaux;
scale3DMatrix(vectorR3(best_align(5), best_align(5), best_align(5)),Aaux);
A = A * Aaux;
if (verbose!=0)
std::cout << "xmipp_transform_geometry will require the following values"
<< "\n Angles: " << best_align(2) << " "
<< best_align(3) << " " << best_align(4)
<< "\n Shifts: " << A(0,3) << " " << A(1,3) << " " << A(2,3)
<< std::endl;
if (apply)
{
applyTransformation(params.V2(),params.Vaux(),MATRIX1D_ARRAY(best_align));
params.V2()=params.Vaux();
params.V2.write(fnOut);
}
}
