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 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 fourierReconstructGPU(vol_img,
vol_bp,
vol_phi,
vol_the,
recPosVol=[0, 0, 0],
vol_offsetProjections=[0, 0, 0]):
from pytom_numpy import vol2npy
import cupy as cp
from pytom.voltools import transform
from cupy.fft import fftshift, ifftn, fftn
import numpy
from cupyx.scipy.ndimage import rotate
interpolation = 'filt_bspline'
projections = vol2npy(vol_img)
projections = cp.array(projections)
proj_angles = vol2npy(vol_the)[0, 0, :]
dims = (projections.shape[0], projections.shape[0], projections.shape[0])
tot = cp.zeros(dims, cp.complex64)
for n in range(projections.shape[2]):
org = cp.zeros(dims, dtype=cp.complex64)
rot = cp.zeros(dims, dtype=cp.complex64)
ff = fftshift(fftn(fftshift(projections[:, :, n])))
org[:, :, dims[0] // 2] = ff
#print(n,ff.shape, org.shape)
if 0:
rot.real = rotate(org.real,
proj_angles[n],
axes=(2, 0),
reshape=False)
rot.imag = rotate(org.imag,
proj_angles[n],
axes=(2, 0),
reshape=False)
else:
rot.real = transform(org.real,
rotation=(0, proj_angles[n], 0),
rotation_order='sxyz',
interpolation=interpolation)
rot.imag = transform(org.imag,
rotation=(0, proj_angles[n], 0),
rotation_order='sxyz',
interpolation=interpolation)
tot += rot
del org, rot
if dims[0] % 2:
t = cp.zeros_like(tot)
t[1:, 1:, 1:] = tot[:-1, :-1, :-1]
else:
t = tot
return fftshift((ifftn(fftshift(t)).real)).get()
def applyRotatedFilter(particle, filter, rotation, rotation_order='rzxz'):
return applyFourierFilter(
particle,
transform(filter,
rotation=rotation,
rotation_order=rotation_order,
device=device))
def subPixelShiftsImage(volume, scale=[1, 1, 1], cutout=20, shifts=True):
from pytom.voltools import transform
import numpy as np
if shifts:
volC = xp.array(
[vol.shape[0] // 2, volume.shape[1] // 2, vol.shape[2] // 2])
else:
volC = xp.array([0, 0, 0], dtype=xp.int32)
cx, cy, cz = center = find_coords_max_ccmap(volume)
cutvol = volume[cx - cutout // 2:cx + cutout // 2,
cy - cutout // 2:cy + cutout // 2, :]
nx, ny, nz = [int(np.ceil(cutout / s)) for s in scale]
nC = xp.array([nx // 2, ny // 2, 0])
centered = xp.zeros((nx, ny, 1), dtype=xp.float32)
centered[nx // 2 - cutout // 2:nx // 2 + cutout // 2,
ny // 2 - cutout // 2:ny // 2 + cutout // 2, :] = cutvol
interpolated_peak = transform(centered,
scale=scale,
device=device,
interpolation='filt_bspline')
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 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 find_sub_pixel_max_value_voltools(inp, k=.1, ignore_border=50):
"""
To find the highest point in a 2D array, with subpixel accuracy based on 1D spline interpolation.
The algorithm is based on a matlab script "tom_peak.m"
@param inp: A 2D numpy array containing the data points.
@type inp: numpy array 2D
@param k: The smoothing factor used in the spline interpolation, must be 1 <= k <= 5.
@type k: int
@return: A list of all points of maximal value in the structure of tuples with the x position, the y position and
the value.
@returntype: list
"""
# assert len(ixp.shape) == 2
# assert isinstance(k, int) and 1 <= k <= 5
import cupy
import numpy as xp
from scipy.interpolate import InterpolatedUnivariateSpline
from pytom.voltools import transform
inp2 = inp[ignore_border:-ignore_border, ignore_border:-ignore_border]
out = cupy.array(inp, dtype=cupy.float32)
x, y = xp.array(xp.unravel_index(inp2.argmax(),
inp2.shape)) + ignore_border
out = cupy.expand_dims(out, 2)
zoomed = cupy.zeros_like(out, dtype=cupy.float32)
transform(out,
output=zoomed,
scale=(k, k, 1),
translation=[inp.shape[0] // 2 - x, inp.shape[1] // 2 - y, 0],
device='gpu:0',
interpolation='filt_bspline')
z = zoomed.get().squeeze()
x2, y2 = xp.unravel_index(z.argmax(), z.shape)
return [
x + (x2 - z.shape[0] // 2) * k - z.shape[0] // 2,
y + (y2 - z.shape[1] // 2) * k - z.shape[0] // 2, z, x, y, x2, y2
]
def find_sub_pixel_voltools(inp, k=0.1, border=75, max_shift=15):
import pytom.voltools as vt
inp = xp.ascontiguousarray(inp)
# find the position of the initial maximum
dimx, dimy = inp.squeeze().shape
initial_max = xp.unravel_index(
inp[border:-border, border:-border].argmax(),
(dimx - border * 2, dimy - border * 2))
ix, iy = [v + border for v in initial_max]
# Create an array with specific size so the zoom fits in (size = max_shift * 2 /k)
# Center of array is initial max
out = int(xp.around(max_shift * 2 / k))
model = xp.zeros((out, out), dtype=xp.float32)
model[out // 2 - max_shift:out // 2 + max_shift, out // 2 -
max_shift:out // 2 + max_shift] = inp[ix - max_shift:ix + max_shift,
iy - max_shift:iy + max_shift]
zoomedVol = xp.expand_dims(model, 2)
# Scale image
vt.transform(zoomedVol,
scale=(k, k, 1),
interpolation='filt_bspline',
device=device,
output=zoomedVol)
# fig, ax = subplots(1, 2, figsize=(10, 5))
# ax[0].imshow(inp.get().squeeze())
# ax[1].imshow(zoomedVol.squeeze().get())
# show()
# Find new max and update the initial max according to the shift
transformed_max = xp.unravel_index(zoomedVol.argmax(), zoomedVol.shape)
interpolX, interpolY = ix + (transformed_max[0] - out // 2) * k, iy + (
transformed_max[1] - out // 2) * k
return interpolX, interpolY, zoomedVol.squeeze()
def add_transformed_particle_to_sum(particle,
sum,
rotation=None,
rotation_order='rzxz',
translation=None,
scale=None):
from pytom.voltools import transform
rot = transform(particle,
rotation=rotation,
translation=translation,
scale=scale,
rotation_order=rotation_order,
device=device,
interpolation='filt_bspline')
sum += xp.array(rot)
return sum
def subPixelMax3D(volume,
k=.01,
ignore_border=50,
interpolation='filt_bspline',
plan=None,
profile=True,
num_threads=1024,
zoomed=None,
fast_sum=None,
max_id=None):
"""
Function to find the highest point in a 3D array, with subpixel accuracy using cubic spline interpolation.
@param inp: A 3D numpy/cupy array containing the data points.
@type inp: numpy/cupy array 3D
@param k: The interpolation factor used in the spline interpolation, k < 1 is zoomed in, k>1 zoom out.
@type k: float
@return: A list of maximal value in the interpolated volume and a list of x position, the y position and
the value.
@returntype: list
"""
from pytom.voltools import transform
from pytom.tompy.io import write
ox, oy, oz = volume.shape
ib = ignore_border
cropped_volume = volume[ib:ox - ib, ib:oy - ib,
ib:oz - ib].astype(xp.float32)
if profile:
stream = xp.cuda.Stream.null
t_start = stream.record()
# x,y,z = xp.array(maxIndex(cropped_volume)) + ignore_border
x, y, z = xp.array(
xp.unravel_index(cropped_volume.argmax(),
cropped_volume.shape)) + ignore_border
dx, dy, dz = volume.shape
translation = [dx // 2 - x, dy // 2 - y, dz // 2 - z]
if profile:
t_end = stream.record()
t_end.synchronize()
time_took = xp.cuda.get_elapsed_time(t_start, t_end)
print(f'initial find max time: \t{time_took:.3f}ms')
t_start = stream.record()
b = border = max(0, int(volume.shape[0] // 2 - 4 / k))
zx, zy, zz = volume.shape
out = volume[b:zx - b, b:zy - b, b:zz - b]
transform(out,
output=zoomed,
scale=(k, k, k),
device=device,
translation=translation,
interpolation=interpolation)
if profile:
t_end = stream.record()
t_end.synchronize()
time_took = xp.cuda.get_elapsed_time(t_start, t_end)
print(f'transform finished in \t{time_took:.3f}ms')
t_start = stream.record()
nblocks = int(xp.ceil(zoomed.size / num_threads / 2))
argmax((
nblocks,
1,
), (num_threads, 1, 1), (zoomed, fast_sum, max_id, zoomed.size),
shared_mem=8 * num_threads)
x2, y2, z2 = xp.unravel_index(max_id[fast_sum.argmax()], zoomed.shape)
peakValue = zoomed[x2][y2][z2]
peakShift = [
x + (x2 - zoomed.shape[0] // 2) * k - volume.shape[0] // 2,
y + (y2 - zoomed.shape[1] // 2) * k - volume.shape[1] // 2,
z + (z2 - zoomed.shape[2] // 2) * k - volume.shape[2] // 2
]
if profile:
t_end = stream.record()
t_end.synchronize()
time_took = xp.cuda.get_elapsed_time(t_start, t_end)
print(f'argmax finished in \t{time_took:.3f}ms')
t_start = stream.record()
print()
return [peakValue, peakShift]
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
def alignVolumesAndFilterByFSC(vol1,
vol2,
mask=None,
nband=None,
iniRot=None,
iniTrans=None,
interpolation='linear',
fsc_criterion=0.143,
verbose=0):
"""
align two volumes, compute their FSC, and filter by FSC
@param vol1: volume 1
@param vol2: volume 2
@mask: mask volume
@type mask: L{pytom_volume.vol}
@param nband: Number of bands
@type nband: L{int}
@param iniRot: initial guess for rotation
@param iniTrans: initial guess for translation
@param interpolation: interpolation type - 'linear' (default) or 'spline'
@param fsc_criterion: filter -> 0 according to resolution criterion
@type fsc_criterion: float
@param verbose: verbose level (0=mute, 1 some output, 2=talkative)
@type verbose: int
@type interpolation: str
@return: (filvol1, filvol2, fsc, fsc_fil, optiRot, optiTrans) i.e., filtered volumes, their FSC, the corresponding\
filter that was applied to the volumes, and the optimal rotation and translation of vol2 with respect to vol1\
note: filvol2 is NOT rotated and translated!
@author: FF
"""
from pytom_volume import transformSpline, vol
from pytom.tompy.correlation import FSC
from pytom.tompy.filter import filter_volume_by_profile
from pytom.tompy.structures import Alignment
from pytom.tompy.correlation import nxcc
from pytom.voltools import transform
assert isinstance(object=vol1, class_or_type_or_tuple=vol
), "alignVolumesAndFilterByFSC: vol1 must be of type vol"
assert isinstance(object=vol2, class_or_type_or_tuple=vol
), "alignVolumesAndFilterByFSC: vol2 must be of type vol"
# filter volumes prior to alignment according to SNR
fsc = FSC(volume1=vol1, volume2=vol2, numberBands=nband)
fil = design_fsc_filter(fsc=fsc, fildim=int(vol2.shape[2] // 2))
#filter only one volume so that resulting CCC is weighted by SNR only once
filvol2 = filter_volume_by_profile(volume=vol2, profile=fil)
# align vol2 to vol1
if verbose == 2:
alignment = Alignment(vol1=vol1,
vol2=filvol2,
score=nxcc,
mask=mask,
iniRot=iniRot,
iniTrans=iniTrans,
opti='fmin_powell',
interpolation=interpolation,
verbose=verbose)
else:
alignment = Alignment(vol1=vol1,
vol2=filvol2,
score=nxcc,
mask=mask,
iniRot=iniRot,
iniTrans=iniTrans,
opti='fmin_powell',
interpolation=interpolation,
verbose=False)
optiScore, optiRot, optiTrans = alignment.localOpti(iniRot=iniRot,
iniTrans=iniTrans)
if verbose:
from pytom.angles.angleFnc import differenceAngleOfTwoRotations
from pytom.basic.structures import Rotation
diffAng = differenceAngleOfTwoRotations(rotation1=Rotation(0, 0, 0),
rotation2=optiRot)
print(
"Alignment densities: Rotations: %2.3f, %2.3f, %2.3f; Translations: %2.3f, %2.3f, %2.3f "
% (optiRot[0], optiRot[1], optiRot[2], optiTrans[0], optiTrans[1],
optiTrans[2]))
print("Orientation difference: %2.3f deg" % diffAng)
vol2_alig = xp.zeros(vol2.shape, dtype=xp.float32)
transform(vol2,
output=vol2_alig,
rotation=[optiRot[0], optiRot[2], optiRot[1]],
rotation_order='rzxz',
center=[
int(vol2.shape[0] // 2),
int(vol2.shape[1] // 2),
int(vol2.shape[2] // 2)
],
interpolation='filt_bspline',
translation=[optiTrans[0], optiTrans[1], optiTrans[2]],
device=device)
# finally compute FSC and filter of both volumes
if not nband:
nband = int(vol2.sizeX() / 2)
fsc = FSC(volume1=vol1, volume2=vol2_alig, numberBands=nband)
fil = design_fsc_filter(fsc=fsc,
fildim=int(vol2.shape[0] // 2),
fsc_criterion=fsc_criterion)
filvol1 = filter_volume_by_profile(volume=vol1, profile=fil)
#filvol2 = filter_volume_by_profile( volume=vol2_alig, profile=fil)
filvol2 = filter_volume_by_profile(volume=vol2, profile=fil)
return (filvol1, filvol2, fsc, fil, optiRot, optiTrans)
