def back_projection(image, angle, interpolation=None):
'''
back_projection: Returns 3D volume with values projected under an angle
@param image: 2d projection image
@type image: 2d numpy/cupy array of floats
@param angle: Tilt angle of projection
@type angle: float32
@param interpolation: interpolation type using in rotation. filtered bspline is recommended.
@return:
@author Gijs van der Schot
'''
dimx, dimy = image.shape
dims = [dimx, dimy, dimx]
ndim = (dimx * 3) // 2
out = xp.dstack([image] * (ndim))
bp = xp.zeros_like(out)
transform(out,
output=bp,
rotation=(0, angle, 0),
rotation_order='sxyz',
interpolation=interpolation,
center=numpy.array([dims[0] // 2, dims[1] // 2, ndim // 2]),
device=device)
return bp[:, :, ndim // 2 - dimx // 2:ndim // 2 + dimx // 2].copy()
def subPixelShifts(volume, scale=[1, 1, 1], cutout=20, shifts=True):
from pytom.voltools import transform
import numpy as np
volC = xp.array(
[volume.shape[0] // 2, volume.shape[1] // 2, volume.shape[2] // 2])
cx, cy, cz = center = find_coords_max_ccmap(volume)
x, y, z = volume.shape
if cx - cutout // 2 < 0 or cy - cutout // 2 < 0 or cz - cutout // 2 < 0 or cx + cutout // 2 >= x or cy + cutout // 2 >= y or cz + cutout // 2 >= z:
return [
volume.max(),
xp.array([cx - x // 2, cy - y // 2, cz - z // 2])
]
cutvol = volume[cx - cutout // 2:cx + cutout // 2,
cy - cutout // 2:cy + cutout // 2,
cz - cutout // 2:cz + cutout // 2]
nx, ny, nz = [int(np.ceil(cutout / s)) for s in scale]
nC = xp.array([nx // 2, ny // 2, nz // 2])
centered = xp.zeros((nx, ny, nz), dtype=xp.float32)
centered[nx // 2 - cutout // 2:nx // 2 + cutout // 2,
ny // 2 - cutout // 2:ny // 2 + cutout // 2,
nz // 2 - cutout // 2:nz // 2 + cutout // 2] = cutvol
interpolated_peak = xp.zeros_like(centered)
transform(centered,
scale=scale,
device=device,
interpolation='filt_bspline',
output=interpolated_peak)
interpol_center = find_coords_max_ccmap(interpolated_peak)
shifts = xp.array(center) - volC + (xp.array(interpol_center) -
xp.array(nC)) * xp.array(scale)
return [interpolated_peak.max(), shifts]
def subPixelPeak(inp, k=1, ignore_border=1):
from pytom.voltools import transform
is2d = len(inp.shape.squeeze()) == 2
if is2d:
inp2 = inp.squeeze()[ignore_border:-ignore_border,
ignore_border:-ignore_border]
scale = (k, k, 1)
out = xp.array(inp.squeeze(), dtype=xp.float32)
x, y = xp.array(xp.unravel_index(inp2.argmax(),
inp2.shape)) + ignore_border
out = xp.expand_dims(out, 2)
translation = [inp.shape[0] // 2 - x, inp.shape[1] // 2 - y, 0]
else:
inp2 = inp[ignore_border:-ignore_border, ignore_border:-ignore_border,
ignore_border:-ignore_border]
scale = (k, k, k)
out = xp.array(inp, dtype=xp.float32)
x, y, z = xp.array(xp.unravel_index(inp2.argmax(),
inp2.shape)) + ignore_border
translation = [
inp.shape[0] // 2 - x, inp.shape[1] // 2 - y, inp.shape[2] // 2 - z
]
zoomed = xp.zeros_like(out, dtype=xp.float32)
transform(out,
output=zoomed,
scale=scale,
translation=translation,
device='gpu:0',
interpolation='filt_bspline')
shifts = [x2, y2, z2] = xp.unravel_index(zoomed.argmax(), zoomed.shape)
return [zoomed[x2, y2, z2], shifts[3 - is2d]]
def rotateWeighting(weighting, rotation, mask=None, binarize=False):
"""
rotateWeighting: Rotates a frequency weighting volume around the center. If the volume provided is reduced complex, it will be rescaled to full size, ftshifted, rotated, iftshifted and scaled back to reduced size.
@param weighting: A weighting volume in reduced complex convention
@type weighting: cupy or numpy array
@param rotation: rotation angles in zxz order
@type rotation: list
@param mask:=None is there a rotation mask? A mask with all = 1 will be generated otherwise. Such mask should be \
provided anyway.
@type mask: cupy or numpy ndarray
@return: weight as reduced complex volume
@rtype: L{pytom_volume.vol_comp}
"""
from pytom_volume import vol, limit, vol_comp
from pytom_volume import rotate
from pytom.voltools import transform
assert type(weighting) == vol or type(
weighting
) == vol_comp, "rotateWeighting: input neither vol nor vol_comp"
from pytom.tompy.transform import fourier_reduced2full, fourier_full2reduced
weighting = fourier_reduced2full(weighting,
isodd=weighting.shape[0] % 2 == 1)
weighting = xp.fft.fftshift(weighting)
weightingRotated = xp.zeros_like(weighting)
transform(weighting,
output=weightingRotated,
rotation=rotation,
rotation_order='rzxz',
device=device,
interpolation='filt_bspline')
if not mask is None:
weightingRotated *= mask
weightingRotated = xp.fft.fftshift(weightingRotated)
returnVolume = fourier_full2reduced(weightingRotated)
if binarize:
returnVolume[returnVolume < 0.5] = 0
returnVolume[returnVolume >= 0.5] = 1
return returnVolume
def cut_patch(projection,
ang,
pick_position,
vol_size=200,
binning=8,
dimz=None,
offset=[0, 0, 0],
projection_name=None):
from pytom.tompy.tools import taper_edges
#from pytom.gpu.initialize import xp
from pytom.voltools import transform
from pytom.tompy.transform import rotate3d, rotate_axis
from pytom.tompy.transform import cut_from_projection
from pytom.tompy.io import read
# Get the size of the original projection
dim_x = projection.shape[0]
dim_z = dim_x if dimz is None else dimz
x, y, z = pick_position
x = (x + offset[0]) * binning
y = (y + offset[1]) * binning
z = (z + offset[2]) * binning
# Get coordinates of the paricle adjusted for the tilt angle
yy = y # assume the rotation axis is around y
xx = (xp.cos(ang * xp.pi / 180) *
(x - dim_x / 2) - xp.sin(ang * xp.pi / 180) *
(z - dim_z / 2)) + dim_x / 2
# Cut the small patch out
patch = projection[max(0,
int(xp.floor(xx)) -
vol_size // 2):int(xp.floor(xx)) + vol_size // 2,
int(yy) - vol_size // 2:int(yy) + vol_size // 2, :]
shiftedPatch = xp.zeros_like(patch)
transform(patch,
output=shiftedPatch,
translation=[xx % 1, yy % 1, 0],
device=device,
interpolation='filt_bspline')
return shiftedPatch.squeeze()
def recenterVolume(volume, densityNegative=False):
from scipy.ndimage import center_of_mass
from pytom.tompy.io import read, write
from pytom.tompy.tools import paste_in_center
from pytom.gpu.initialize import xp
from pytom_numpy import vol2npy
import os
try:
a = vol2npy(volume).copy()
vol = True
except:
a = volume
vol = False
if densityNegative:
a *= -1
x, y, z = list(map(int, center_of_mass(a)))
cx, cy, cz = a.shape[0] // 2, a.shape[1] // 2, a.shape[2] // 2
sx = min(x, a.shape[0] - x)
sy = min(y, a.shape[0] - y)
sz = min(z, a.shape[0] - z)
ac = a[x - sx:x + sx, y - sy:y + sy, z - sz:z + sz]
b = xp.zeros_like(a)
b = paste_in_center(ac, b)
if densityNegative: b *= -1
if vol:
write('recenteredDBV21.em', b)
from pytom.basic.files import read
vol = read('recenteredDBV21.em')
os.system('rm recenteredDBV21.em')
return vol
else:
return b
def profile2FourierVol(profile, dim=None, reduced=False):
"""
create Volume from 1d radial profile, e.g., to modulate signal with \
specific function such as CTF or FSC. Simple linear interpolation is used\
for sampling.
@param profile: profile
@type profile: 1-d L{pytom_volume.vol} or 1-d python array
@param dim: dimension of (cubic) output
@type dim: L{int}
@param reduced: If true reduced Fourier representation (N/2+1, N, N) is generated.
@type reduced: L{bool}
@return: 3-dim complex volume with spherically symmetrical profile
@rtype: L{pytom_volume.vol}
@author: FF
"""
if dim is None:
try:
dim = [
2 * profile.shape[0],
] * 3
except:
dim = [
2 * len(profile),
] * 3
is3D = (len(dim) == 3)
nx, ny = dim[:2]
if reduced:
if is3D:
nz = int(dim[2] // 2) + 1
else:
ny = int(ny // 2) + 1
else:
if is3D:
nz = dim[2]
try:
r_max = profile.shape[0] - 1
except:
r_max = len(profile) - 1
if len(dim) == 3:
if reduced:
X, Y, Z = xp.meshgrid(xp.arange(-nx // 2, nx // 2 + nx % 2),
xp.arange(-ny // 2, ny // 2 + ny % 2),
xp.arange(0, nz))
else:
X, Y, Z = xp.meshgrid(xp.arange(-nx // 2, nx // 2 + nx % 2),
xp.arange(-ny // 2, ny // 2 + ny % 2),
xp.arange(-nz // 2, nz // 2 + nz % 2))
R = xp.sqrt(X**2 + Y**2 + Z**2)
else:
if reduced:
X, Y = xp.meshgrid(xp.arange(-nx // 2, ny // 2 + ny % 2),
xp.arange(0, ny))
else:
X, Y = xp.meshgrid(xp.arange(-nx // 2, nx // 2 + nx % 2),
xp.arange(-ny // 2, ny // 2 + ny % 2))
R = xp.sqrt(X**2 + Y**2)
IR = xp.floor(R).astype(xp.int64)
valIR_l1 = IR.copy()
valIR_l2 = valIR_l1 + 1
val_l1, val_l2 = xp.zeros_like(X, dtype=xp.float64), xp.zeros_like(
X, dtype=xp.float64)
l1 = R - IR.astype(xp.float32)
l2 = 1 - l1
try:
profile = xp.array(profile)
except:
import numpy
profile = xp.array(numpy.array(profile))
for n in xp.arange(r_max):
val_l1[valIR_l1 == n] = profile[n]
val_l2[valIR_l2 == n + 1] = profile[n + 1]
val_l1[IR == r_max] = profile[n + 1]
val_l2[IR == r_max] = profile[n + 1]
val_l1[R > r_max] = 0
val_l2[R > r_max] = 0
fkernel = l2 * val_l1 + l1 * val_l2
if reduced:
fkernel = xp.fft.fftshift(fkernel, axes=(0, 1))
else:
fkernel = xp.fft.fftshift(fkernel)
return fkernel
def weightedXCF(volume, reference, numberOfBands, wedgeAngle=-1, gpu=False):
"""
weightedXCF: Determines the weighted correlation function for volume and reference
@param volume: A volume
@param reference: A reference
@param numberOfBands:Number of bands
@param wedgeAngle: A optional wedge angle
@return: The weighted correlation function
@rtype: L{pytom_volume.vol}
@author: Thomas Hrabe
@todo: does not work yet -> test is disabled
"""
if gpu:
import cupy as xp
else:
import numpy as xp
from pytom.tompy.correlation import bandCF
from pytom.tompy.transforms import fourier_reduced2full
from math import sqrt
import pytom_freqweight
result = xp.zeros_like(volume)
q = 0
if wedgeAngle >= 0:
wedgeFilter = pytom_freqweight.weight(wedgeAngle, 0, volume.sizeX(),
volume.sizeY(), volume.sizeZ())
wedgeVolume = wedgeFilter.getWeightVolume(True)
else:
wedgeVolume = xp.ones_like(volume)
w = xp.sqrt(1 / float(volume.size))
numberVoxels = 0
for i in range(numberOfBands):
"""
notation according Steward/Grigorieff paper
"""
band = [0, 0]
band[0] = i * volume.shape[0] / numberOfBands
band[1] = (i + 1) * volume.shape[0] / numberOfBands
r = bandCF(volume, reference, band, gpu=gpu)
cc = r[0]
filter = r[1]
#get bandVolume
bandVolume = filter.getWeightVolume(True)
filterVolumeReduced = bandVolume * wedgeVolume
filterVolume = fourier_reduced2full(filterVolumeReduced)
#determine number of voxels != 0
N = filterVolume[abs(filterVolume) < 1].sum()
#add to number of total voxels
numberVoxels = numberVoxels + N
cc2 = r[0].copy()
cc2 = cc2**2
cc = shiftscale(cc, w, 1)
ccdiv = cc2 / (cc)
ccdiv = ccdiv**3
#abs(ccdiv); as suggested by grigorief
ccdiv = shiftscale(ccdiv, 0, N)
result = result + ccdiv
result = shiftscale(result, 0, 1 / float(numberVoxels))
return result
def averageGPU(particleList,
averageName,
showProgressBar=False,
verbose=False,
createInfoVolumes=False,
weighting=False,
norm=False,
gpuId=None,
profile=True):
"""
average : Creates new average from a particleList
@param particleList: The particles
@param averageName: Filename of new average
@param verbose: Prints particle information. Disabled by default.
@param createInfoVolumes: Create info data (wedge sum, inverted density) too? False by default.
@param weighting: apply weighting to each average according to its correlation score
@param norm: apply normalization for each particle
@return: A new Reference object
@rtype: L{pytom.basic.structures.Reference}
@author: Thomas Hrabe
@change: limit for wedgeSum set to 1% or particles to avoid division by small numbers - FF
"""
import time
from pytom.tompy.io import read, write, read_size
from pytom.tompy.filter import bandpass as lowpassFilter, rotateWeighting, applyFourierFilter, applyFourierFilterFull, create_wedge
from pytom.voltools import transform, StaticVolume
from pytom.basic.structures import Reference
from pytom.tompy.normalise import mean0std1
from pytom.tompy.tools import volumesSameSize, invert_WedgeSum, create_sphere
from pytom.tompy.transform import fourier_full2reduced, fourier_reduced2full
from cupyx.scipy.fftpack.fft import fftn as fftnP
from cupyx.scipy.fftpack.fft import ifftn as ifftnP
from cupyx.scipy.fftpack.fft import get_fft_plan
from pytom.tools.ProgressBar import FixedProgBar
from multiprocessing import RawArray
import numpy as np
import cupy as xp
if not gpuId is None:
device = f'gpu:{gpuId}'
xp.cuda.Device(gpuId).use()
else:
print(gpuId)
raise Exception('Running gpu code on non-gpu device')
print(device)
cstream = xp.cuda.Stream()
if profile:
stream = xp.cuda.Stream.null
t_start = stream.record()
# from pytom.tools.ProgressBar import FixedProgBar
from math import exp
import os
if len(particleList) == 0:
raise RuntimeError('The particle list is empty. Aborting!')
if showProgressBar:
progressBar = FixedProgBar(0, len(particleList), 'Particles averaged ')
progressBar.update(0)
numberAlignedParticles = 0
# pre-check that scores != 0
if weighting:
wsum = 0.
for particleObject in particleList:
wsum += particleObject.getScore().getValue()
if wsum < 0.00001:
weighting = False
print("Warning: all scores have been zero - weighting not applied")
import time
sx, sy, sz = read_size(particleList[0].getFilename())
wedgeInfo = particleList[0].getWedge().convert2numpy()
print('angle: ', wedgeInfo.getWedgeAngle())
wedgeZero = xp.fft.fftshift(
xp.array(wedgeInfo.returnWedgeVolume(sx, sy, sz, True).get(),
dtype=xp.float32))
# wedgeZeroReduced = fourier_full2reduced(wedgeZero)
wedge = xp.zeros_like(wedgeZero, dtype=xp.float32)
wedgeSum = xp.zeros_like(wedge, dtype=xp.float32)
print('init texture')
wedgeText = StaticVolume(xp.fft.fftshift(wedgeZero),
device=device,
interpolation='filt_bspline')
newParticle = xp.zeros((sx, sy, sz), dtype=xp.float32)
centerX = sx // 2
centerY = sy // 2
centerZ = sz // 2
result = xp.zeros((sx, sy, sz), dtype=xp.float32)
fftplan = get_fft_plan(wedge.astype(xp.complex64))
n = 0
total = len(particleList)
# total = int(np.floor((11*1024**3 - mempool.total_bytes())/(sx*sy*sz*4)))
# total = 128
#
#
# particlesNP = np.zeros((total, sx, sy, sz),dtype=np.float32)
# particles = []
# mask = create_sphere([sx,sy,sz], sx//2-6, 2)
# raw = RawArray('f', int(particlesNP.size))
# shared_array = np.ctypeslib.as_array(raw)
# shared_array[:] = particlesNP.flatten()
# procs = allocateProcess(particleList, shared_array, n, total, wedgeZero.size)
# del particlesNP
if profile:
t_end = stream.record()
t_end.synchronize()
time_took = xp.cuda.get_elapsed_time(t_start, t_end)
print(f'startup time {n:5d}: \t{time_took:.3f}ms')
t_start = stream.record()
for particleObject in particleList:
rotation = particleObject.getRotation()
rotinvert = rotation.invert()
shiftV = particleObject.getShift()
# if n % total == 0:
# while len(procs):
# procs =[proc for proc in procs if proc.is_alive()]
# time.sleep(0.1)
# print(0.1)
# # del particles
# # xp._default_memory_pool.free_all_blocks()
# # pinned_mempool.free_all_blocks()
# particles = xp.array(shared_array.reshape(total, sx, sy, sz), dtype=xp.float32)
# procs = allocateProcess(particleList, shared_array, n, total, size=wedgeZero.size)
# #pinned_mempool.free_all_blocks()
# #print(mempool.total_bytes()/1024**3)
particle = read(particleObject.getFilename(), deviceID=device)
#particle = particles[n%total]
if norm: # normalize the particle
mean0std1(particle) # happen inplace
# apply its wedge to
#particle = applyFourierFilter(particle, wedgeZeroReduced)
#particle = (xp.fft.ifftn( xp.fft.fftn(particle) * wedgeZero)).real
particle = (ifftnP(fftnP(particle, plan=fftplan) * wedgeZero,
plan=fftplan)).real
### create spectral wedge weighting
wedge *= 0
wedgeText.transform(
rotation=[rotinvert[0], rotinvert[2], rotinvert[1]],
rotation_order='rzxz',
output=wedge)
#wedge = xp.fft.fftshift(fourier_reduced2full(create_wedge(30, 30, 21, 42, 42, 42, rotation=[rotinvert[0],rotinvert[2], rotinvert[1]])))
# if analytWedge:
# # > analytical buggy version
# wedge = wedgeInfo.returnWedgeVolume(sx, sy, sz, True, rotinvert)
# else:
# # > FF: interpol bugfix
# wedge = rotateWeighting(weighting=wedgeInfo.returnWedgeVolume(sx, sy, sz, True), rotation=[rotinvert[0], rotinvert[2], rotinvert[1]])
# # < FF
# # > TH bugfix
# # wedgeVolume = wedgeInfo.returnWedgeVolume(wedgeSizeX=sizeX, wedgeSizeY=sizeY, wedgeSizeZ=sizeZ,
# # humanUnderstandable=True, rotation=rotinvert)
# # wedge = rotate(volume=wedgeVolume, rotation=rotinvert, imethod='linear')
# # < TH
### shift and rotate particle
newParticle *= 0
transform(particle,
output=newParticle,
rotation=[-rotation[1], -rotation[2], -rotation[0]],
center=[centerX, centerY, centerZ],
translation=[-shiftV[0], -shiftV[1], -shiftV[2]],
device=device,
interpolation='filt_bspline',
rotation_order='rzxz')
#write(f'trash/GPU_{n}.em', newParticle)
# print(rotation.toVector())
# break
result += newParticle
wedgeSum += xp.fft.fftshift(wedge)
# if showProgressBar:
# numberAlignedParticles = numberAlignedParticles + 1
# progressBar.update(numberAlignedParticles)
if n % total == 0:
if profile:
t_end = stream.record()
t_end.synchronize()
time_took = xp.cuda.get_elapsed_time(t_start, t_end)
print(f'total time {n:5d}: \t{time_took:.3f}ms')
t_start = stream.record()
cstream.synchronize()
n += 1
print('averaged particles')
###apply spectral weighting to sum
result = lowpassFilter(result, high=sx / 2 - 1, sigma=0)
# if createInfoVolumes:
write(averageName[:len(averageName) - 3] + '-PreWedge.em', result)
write(averageName[:len(averageName) - 3] + '-WedgeSumUnscaled.em',
fourier_full2reduced(wedgeSum))
wedgeSumINV = invert_WedgeSum(wedgeSum,
r_max=sx // 2 - 2.,
lowlimit=.05 * len(particleList),
lowval=.05 * len(particleList))
wedgeSumINV = wedgeSumINV
#print(wedgeSum.mean(), wedgeSum.std())
if createInfoVolumes:
write(averageName[:len(averageName) - 3] + '-WedgeSumInverted.em',
xp.fft.fftshift(wedgeSumINV))
result = applyFourierFilterFull(result, xp.fft.fftshift(wedgeSumINV))
# do a low pass filter
result = lowpassFilter(result, sx / 2 - 2, (sx / 2 - 1) / 10.)[0]
write(averageName, result)
if createInfoVolumes:
resultINV = result * -1
# write sign inverted result to disk (good for chimera viewing ... )
write(averageName[:len(averageName) - 3] + '-INV.em', resultINV)
newReference = Reference(averageName, particleList)
return newReference
def alignImagesUsingAlignmentResultFile(alignmentResultsFile,
weighting=None,
lowpassFilter=0.9,
binning=1,
circleFilter=False):
import os
from pytom.basic.files import read as readCVol
from pytom_numpy import vol2npy, npy2vol
from pytom.gui.guiFunctions import fmtAR, headerAlignmentResults, datatype, datatypeAR, loadstar
from pytom.reconstruction.reconstructionStructures import Projection, ProjectionList
from pytom.tompy.io import read, write, read_size
from pytom.tompy.tools import taper_edges, create_circle
from pytom.tompy.filter import circle_filter, ramp_filter, exact_filter
import pytom.voltools as vt
from pytom.gpu.initialize import xp, device
print("Create aligned images from alignResults.txt")
alignmentResults = loadstar(alignmentResultsFile, dtype=datatypeAR)
imageList = alignmentResults['FileName']
tilt_angles = alignmentResults['TiltAngle']
imdim = read_size(imageList[0], 'x')
if binning > 1:
imdim = int(float(imdim) / float(binning) + .5)
else:
imdim = imdim
sliceWidth = imdim
if (weighting != None) and (float(weighting) < -0.001):
weightSlice = xp.fft.fftshift(ramp_filter(imdim, imdim))
if circleFilter:
circleFilterRadius = imdim // 2
circleSlice = xp.fft.fftshift(
circle_filter(imdim, imdim, circleFilterRadius))
else:
circleSlice = xp.ones((imdim, imdim))
# design lowpass filter
if lowpassFilter:
if lowpassFilter > 1.:
lowpassFilter = 1.
print("Warning: lowpassFilter > 1 - set to 1 (=Nyquist)")
# weighting filter: arguments: (()dimx, dimy), cutoff radius, sigma
lpf = xp.fft.fftshift(
create_circle((imdim, imdim),
lowpassFilter * (imdim // 2),
sigma=0.4 * lowpassFilter * (imdim // 2)))
projectionList = ProjectionList()
for n, image in enumerate(imageList):
atx = alignmentResults['AlignmentTransX'][n] / binning
aty = alignmentResults['AlignmentTransY'][n] / binning
rot = alignmentResults['InPlaneRotation'][n]
mag = 1 / alignmentResults['Magnification'][n]
projection = Projection(imageList[n],
tiltAngle=tilt_angles[n],
alignmentTransX=atx,
alignmentTransY=aty,
alignmentRotation=rot,
alignmentMagnification=mag)
projectionList.append(projection)
stack = xp.zeros((imdim, imdim, len(imageList)), dtype=xp.float32)
phiStack = xp.zeros((1, 1, len(imageList)), dtype=xp.float32)
thetaStack = xp.zeros((1, 1, len(imageList)), dtype=xp.float32)
offsetStack = xp.zeros((1, 2, len(imageList)), dtype=xp.float32)
for (ii, projection) in enumerate(projectionList):
print(f'Align {projection._filename}')
image = read(str(projection._filename))[::binning, ::binning].squeeze()
if lowpassFilter:
image = xp.abs((xp.fft.ifftn(xp.fft.fftn(image) * lpf)))
tiltAngle = projection._tiltAngle
# normalize to contrast - subtract mean and norm to mean
immean = image.mean()
image = (image - immean) / immean
# smoothen borders to prevent high contrast oscillations
image = taper_edges(image, imdim // 30)[0]
# transform projection according to tilt alignment
transX = projection._alignmentTransX / binning
transY = projection._alignmentTransY / binning
rot = float(projection._alignmentRotation)
mag = float(projection._alignmentMagnification)
inputImage = xp.expand_dims(image, 2).copy()
outputImage = xp.zeros_like(inputImage, dtype=xp.float32)
vt.transform(inputImage.astype(xp.float32),
rotation=[0, 0, rot],
rotation_order='rxyz',
output=outputImage,
device=device,
translation=[transX, transY, 0],
scale=[mag, mag, 1],
interpolation='filt_bspline')
image = outputImage.squeeze()
# smoothen once more to avoid edges
image = taper_edges(image, imdim // 30)[0]
# analytical weighting
if (weighting != None) and (weighting < 0):
# image = (ifft(complexRealMult(fft(image), w_func)) / (image.sizeX() * image.sizeY() * image.sizeZ()))
image = xp.fft.ifftn(
xp.fft.fftn(image) * weightSlice * circleSlice)
elif (weighting != None) and (weighting > 0):
weightSlice = xp.fft.fftshift(
exact_filter(tilt_angles, tiltAngle, imdim, imdim, sliceWidth))
image = xp.fft.ifftn(
xp.fft.fftn(image) * weightSlice * circleSlice)
thetaStack[0, 0, ii] = int(round(projection.getTiltAngle()))
offsetStack[0, :, ii] = xp.array([
int(round(projection.getOffsetX())),
int(round(projection.getOffsetY()))
])
stack[:, :, ii] = image
arrays = []
for fname, arr in (('stack.mrc', stack), ('offsetStack.mrc', offsetStack),
('thetaStack.mrc', thetaStack), ('phiStack.mrc',
phiStack)):
if 'gpu' in device:
arr = arr.get()
import numpy as np
res = npy2vol(np.array(arr, dtype='float32', order='F'), 3)
arrays.append(res)
#
# write('stack.mrc', stack)
# stack = readCVol('stack.mrc')
# write('offsetstack.mrc', offsetStack)
# offsetStack = readCVol('offsetstack.mrc')
# write('thetastack.mrc', thetaStack)
# thetaStack = readCVol('thetastack.mrc')
# write('phistack.mrc', phiStack)
# phiStack = readCVol('phistack.mrc')
#
# os.remove('stack.mrc')
# os.remove('offsetstack.mrc')
# os.remove('thetastack.mrc')
# os.remove('psistack.mrc')
return arrays
def alignImageUsingAlignmentResultFile(alignmentResultsFile,
indexImage,
weighting=None,
lowpassFilter=0.9,
binning=1,
circleFilter=False):
import pytom_freqweight
from pytom_numpy import vol2npy
from pytom.gui.guiFunctions import fmtAR, headerAlignmentResults, datatype, datatypeAR, loadstar
from pytom.reconstruction.reconstructionStructures import Projection, ProjectionList
from pytom.tompy.io import read, write, read_size
from pytom.tompy.tools import taper_edges, create_circle
from pytom.tompy.filter import circle_filter, ramp_filter, exact_filter, ellipse_filter
import pytom.voltools as vt
from pytom.gpu.initialize import xp, device
# print("Create aligned images from alignResults.txt")
alignmentResults = loadstar(alignmentResultsFile, dtype=datatypeAR)
imageList = alignmentResults['FileName']
tilt_angles = alignmentResults['TiltAngle']
imdimX = read_size(imageList[0], 'x')
imdimY = read_size(imageList[0], 'y')
if binning > 1:
imdimX = int(float(imdimX) / float(binning) + .5)
imdimY = int(float(imdimY) / float(binning) + .5)
sliceWidth = imdimX
if (weighting != None) and (float(weighting) < -0.001):
weightSlice = xp.fft.fftshift(ramp_filter(imdimY, imdimX))
if circleFilter:
circleFilterRadiusX = imdimX // 2
circleFilterRadiusY = imdimY // 2
circleSlice = xp.fft.fftshift(
ellipse_filter(imdimX, imdimY, circleFilterRadiusX,
circleFilterRadiusY))
else:
circleSlice = xp.ones((imdimX, imdimY))
# design lowpass filter
if lowpassFilter:
if lowpassFilter > 1.:
lowpassFilter = 1.
print("Warning: lowpassFilter > 1 - set to 1 (=Nyquist)")
# weighting filter: arguments: (()dimx, dimy), cutoff radius, sigma
# lpf = xp.fft.fftshift(create_circle((imdimX,imdimY),lowpassFilter*(imdim//2), sigma=0.4*lowpassFilter*(imdim//2)))
projectionList = ProjectionList()
for n, image in enumerate(imageList):
atx = alignmentResults['AlignmentTransX'][n]
aty = alignmentResults['AlignmentTransY'][n]
rot = alignmentResults['InPlaneRotation'][n]
mag = 1 / (alignmentResults['Magnification'][n])
projection = Projection(imageList[n],
tiltAngle=tilt_angles[n],
alignmentTransX=atx,
alignmentTransY=aty,
alignmentRotation=rot,
alignmentMagnification=mag)
projectionList.append(projection)
imdim = min(imdimY, imdimX)
for (ii, projection) in enumerate(projectionList):
if not ii == indexImage:
continue
from pytom.tompy.transform import resize
# print(f'read {projection._filename}')
image = read(str(projection._filename)).squeeze()
if binning > 1:
image = resize(image, 1 / binning)
#write(f'test/image_{ii}.mrc', image, tilt_angle=tilt_angles[ii])
tiltAngle = projection._tiltAngle
# 1 -- normalize to contrast - subtract mean and norm to mean
immean = image.mean()
image = (image - immean) / immean
# 2 -- smoothen borders to prevent high contrast oscillations
image = taper_edges(image, imdim // 30)[0]
# 3 -- square if needed
if 0 and imdimY != imdimX:
newImage = xp.zeros((imdim, imdim, 1), dtype=xp.float32)
pasteCenter(image, newImage)
image = newImage
# 4 -- transform projection according to tilt alignment
transX = projection._alignmentTransX / binning
transY = projection._alignmentTransY / binning
rot = float(projection._alignmentRotation)
mag = float(projection._alignmentMagnification)
inputImage = xp.expand_dims(image, 2).copy()
outputImage = xp.zeros_like(inputImage, dtype=xp.float32)
vt.transform(
inputImage.astype(xp.float32),
rotation=[0, 0, rot],
rotation_order='rxyz',
output=outputImage,
center=[inputImage.shape[0] // 2, inputImage.shape[1] // 2, 0],
device=device,
translation=[transX, transY, 0],
scale=[mag, mag, 1],
interpolation='filt_bspline')
del image
image = outputImage.squeeze()
# 5 -- Optional Low Pass Filter
if lowpassFilter:
from pytom.tompy.filter import bandpass_circle
image = bandpass_circle(
image,
high=lowpassFilter * (min(imdimX, imdimY) // 2),
sigma=0.4 * lowpassFilter * (min(imdimX, imdimY) // 2))
# image = xp.abs((xp.fft.ifftn(xp.fft.fftn(image) * lpf)))
# 6 -- smoothen once more to avoid edges
image = taper_edges(image, imdim // 30)[0]
# 7 -- analytical weighting
if (weighting != None) and (weighting < 0):
# image = (ifft(complexRealMult(fft(image), w_func)) / (image.sizeX() * image.sizeY() * image.sizeZ()))
image = xp.fft.ifftn(
xp.fft.fftn(image) * weightSlice.T * circleSlice).real
elif (weighting != None) and (weighting > 0):
weightSlice = xp.fft.fftshift(
exact_filter(tilt_angles, tiltAngle, imdim, imdim, sliceWidth))
image = xp.fft.ifftn(
xp.fft.fftn(image) * weightSlice * circleSlice).real
del inputImage, outputImage, circleSlice
write(f'inputImage_{ii}.mrc', image)
return image.astype(xp.float32)
def scale(volume, factor, interpolation='Spline'):
"""
scale: Scale a volume by a factor in REAL SPACE - see also resize function for more accurate operation in Fourier \
space
@param volume: input volume
@type volume: L{pytom_volume.vol}
@param factor: a factor > 0. Factors < 1 will de-magnify the volume, factors > 1 will magnify.
@type factor: L{float}
@param interpolation: Can be Spline (default), Cubic or Linear
@type interpolation: L{str}
@return: The scaled volume
@author: Thomas Hrabe
"""
from pytom.voltools import transform
from pytom.tompy.tools import paste_in_center
if factor <= 0:
raise RuntimeError('Scaling factor must be > 0!')
interpolation_dict = {
'Spline': 'filt_bspline',
'Linear': 'linear',
'Cubic': 'filt_bspline',
'filt_bspline': 'filt_bspline',
'linear': 'linear'
}
interpolation = interpolation_dict[interpolation]
volume = volume.squeeze()
sizeX = volume.shape[0]
sizeY = volume.shape[1]
sizeZ = 1
newSizeX = int(xp.floor(sizeX * float(factor) + 0.5))
newSizeY = int(xp.floor(sizeY * float(factor) + 0.5))
newSizeZ = 1
if len(volume.shape) == 3:
sizeZ = volume.shape[2]
newSizeZ = int(xp.floor(sizeZ * factor + 0.5))
scaleF = [1 / factor, 1 / factor, 1 / factor]
else:
scaleF = [1 / factor, 1 / factor, 1]
volume = xp.expand_dims(volume, 2)
if factor == 1:
rescaledVolume = volume
elif factor > 1:
newVolume = xp.zeros((newSizeX, newSizeY, newSizeZ),
dtype=volume.dtype)
newVolume = paste_in_center(volume, newVolume)
rescaledVolume = xp.zeros_like(newVolume)
transform(newVolume,
scale=scaleF,
output=rescaledVolume,
device=device,
interpolation=interpolation)
else:
rescaledVolumeFull = xp.zeros_like(volume)
transform(volume,
scale=scaleF,
output=rescaledVolumeFull,
device=device,
interpolation=interpolation)
rescaledVolume = xp.zeros((newSizeX, newSizeY, newSizeZ),
dtype=volume.dtype)
rescaledVolume = paste_in_center(rescaledVolumeFull, rescaledVolume)
return rescaledVolume