def evaluate(self):
self.tomo.value = tomopy.normalize_nf(
self.tomo.value,
self.flats.value,
self.darks.value,
self.flat_loc.value,
cutoff=self.cutoff.value,
ncore=self.ncore.value,
out=self.out.value)
def reconstruct(filename,inputPath="", outputPath="", COR=COR, doOutliers=doOutliers, outlier_diff=outlier_diff, outlier_size=outlier_size, doFWringremoval=doFWringremoval, ringSigma=ringSigma,ringLevel=ringLevel, ringWavelet=ringWavelet,pad_sino=pad_sino, doPhaseRetrieval=doPhaseRetrieval, propagation_dist=propagation_dist, kev=kev,alphaReg=alphaReg, butterworthpars=butterworthpars, doPolarRing=doPolarRing,Rarc=Rarc, Rmaxwidth=Rmaxwidth, Rtmax=Rtmax, Rthr=Rthr, Rtmin=Rtmin, useAutoCOR=useAutoCOR, use360to180=use360to180, num_substacks=num_substacks,recon_slice=recon_slice):
# Convert filename to list type if only one file name is given
if type(filename) != list:
filename=[filename]
# If useAutoCor == true, a list of COR will be automatically calculated for all files
# If a list of COR is given, only entries with boolean False will use automatic COR calculation
if useAutoCOR==True or (len(COR) != len(filename)):
logging.info('using auto COR for all input files')
COR = [False]*len(filename)
for x in range(len(filename)):
logging.info('opening data set, checking metadata')
fdata, gdata = read_als_832h5_metadata(inputPath[x]+filename[x]+'.h5')
pxsize = float(gdata['pxsize'])/10.0 # convert from metadata (mm) to this script (cm)
numslices = int(gdata['nslices'])
# recon_slice == True, only center slice will be reconstructed
# if integer is given, a specific
if recon_slice != False:
if (type(recon_slice) == int) and (recon_slice <= numslices):
sinorange [recon_slice-1, recon_slice]
else:
sinorange = [numslices//2-1, numslices//2]
else:
sinorange = [0, numslices]
# Calculate number of substacks (chunks)
substacks = num_substacks #(sinorange[1]-sinorange[0]-1)//num_sino_per_substack+1
if (sinorange[1]-sinorange[0]) >= substacks:
num_sino_per_substack = (sinorange[1]-sinorange[0])//num_substacks
else:
num_sino_per_substack = 1
firstcor, lastcor = 0, int(gdata['nangles'])-1
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
autocor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
if (type(COR[x]) == bool) or (COR[x]<0) or (COR[x]=='auto'):
firstcor, lastcor = 0, int(gdata['nangles'])-1
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
cor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
else:
cor = COR[x]
logging.info('Dataset %s, has %d total slices, reconstructing slices %d through %d in %d substack(s), using COR: %f',filename[x], int(gdata['nslices']), sinorange[0], sinorange[1]-1, substacks, cor)
for y in range(0, substacks):
logging.info('Starting dataset %s (%d of %d), substack %d of %d',filename[x], x+1, len(filename), y+1, substacks)
logging.info('Reading sinograms...')
projs, flat, dark, floc = dxchange.read_als_832h5(inputPath[x]+filename[x]+'.h5', sino=(sinorange[0]+y*num_sino_per_substack, sinorange[0]+(y+1)*num_sino_per_substack, 1))
logging.info('Doing remove outliers, norm (nearest flats), and -log...')
if doOutliers:
projs = tomopy.remove_outlier(projs, outlier_diff, size=outlier_size, axis=0)
flat = tomopy.remove_outlier(flat, outlier_diff, size=outlier_size, axis=0)
tomo = tomopy.normalize_nf(projs, flat, dark, floc)
tomo = tomopy.minus_log(tomo, out=tomo) # in place logarithm
# Use padding to remove halo in reconstruction if present
if pad_sino:
npad = int(np.ceil(tomo.shape[2] * np.sqrt(2)) - tomo.shape[2])//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
cor_rec = cor + npad # account for padding
else:
cor_rec = cor
if doFWringremoval:
logging.info('Doing ring (Fourier-wavelet) function...')
tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet)
if doPhaseRetrieval:
logging.info('Doing Phase retrieval...')
#tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
logging.info('Doing recon (gridrec) function and scaling/masking, with cor %f...',cor_rec)
rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 270, 90), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
#rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 180+angularrange/2, 180-angularrange/2), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
rec /= pxsize # intensity values in cm^-1
if pad_sino:
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
else:
rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0)
if doPolarRing:
logging.info('Doing ring (polar mean filter) function...')
rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin)
logging.info('Writing reconstruction slices to %s', filename[x])
#dxchange.write_tiff_stack(rec, fname=outputPath+'alpha'+str(alphaReg)+'/rec'+filename[x]+'/rec'+filename[x], start=sinorange[0]+y*num_sino_per_substack)
dxchange.write_tiff_stack(rec, fname=outputPath + 'recon_'+filename[x]+'/recon_'+filename[x], start=sinorange[0]+y*num_sino_per_substack)
logging.info('Reconstruction Complete: '+ filename[x])
def recon(
filename,
inputPath='./',
outputPath=None,
outputFilename=None,
doOutliers1D=False, # outlier removal in 1d (along sinogram columns)
outlier_diff1D=750, # difference between good data and outlier data (outlier removal)
outlier_size1D=3, # radius around each pixel to look for outliers (outlier removal)
doOutliers2D=False, # outlier removal, standard 2d on each projection
outlier_diff2D=750, # difference between good data and outlier data (outlier removal)
outlier_size2D=3, # radius around each pixel to look for outliers (outlier removal)
doFWringremoval=True, # Fourier-wavelet ring removal
doTIringremoval=False, # Titarenko ring removal
doSFringremoval=False, # Smoothing filter ring removal
ringSigma=3, # damping parameter in Fourier space (Fourier-wavelet ring removal)
ringLevel=8, # number of wavelet transform levels (Fourier-wavelet ring removal)
ringWavelet='db5', # type of wavelet filter (Fourier-wavelet ring removal)
ringNBlock=0, # used in Titarenko ring removal (doTIringremoval)
ringAlpha=1.5, # used in Titarenko ring removal (doTIringremoval)
ringSize=5, # used in smoothing filter ring removal (doSFringremoval)
doPhaseRetrieval=False, # phase retrieval
alphaReg=0.0002, # smaller = smoother (used for phase retrieval)
propagation_dist=75, # sample-to-scintillator distance (phase retrieval)
kev=24, # energy level (phase retrieval)
butterworth_cutoff=0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction)
butterworth_order=2, # for reconstruction
doPolarRing=False, # ring removal
Rarc=30, # min angle needed to be considered ring artifact (ring removal)
Rmaxwidth=100, # max width of rings to be filtered (ring removal)
Rtmax=3000.0, # max portion of image to filter (ring removal)
Rthr=3000.0, # max value of offset due to ring artifact (ring removal)
Rtmin=-3000.0, # min value of image to filter (ring removal)
cor=None, # center of rotation (float). If not used then cor will be detected automatically
corFunction='pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm'
voInd=None, # index of slice to use for cor search (vo)
voSMin=-40, # min radius for searching in sinogram (vo)
voSMax=40, # max radius for searching in sinogram (vo)
voSRad=10, # search radius (vo)
voStep=0.5, # search step (vo)
voRatio=2.0, # ratio of field-of-view and object size (vo)
voDrop=20, # drop lines around vertical center of mask (vo)
nmInd=None, # index of slice to use for cor search (nm)
nmInit=None, # initial guess for center (nm)
nmTol=0.5, # desired sub-pixel accuracy (nm)
nmMask=True, # if True, limits analysis to circular region (nm)
nmRatio=1.0, # ratio of radius of circular mask to edge of reconstructed image (nm)
nmSinoOrder=False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space
use360to180=False, # use 360 to 180 conversion
doBilateralFilter=False, # if True, uses bilateral filter on image just before write step # NOTE: image will be converted to 8bit if it is not already
bilateral_srad=3, # spatial radius for bilateral filter (image will be converted to 8bit if not already)
bilateral_rrad=30, # range radius for bilateral filter (image will be converted to 8bit if not already)
castTo8bit=False, # convert data to 8bit before writing
cast8bit_min=-10, # min value if converting to 8bit
cast8bit_max=30, # max value if converting to 8bit
useNormalize_nf=False, # normalize based on background intensity (nf)
chunk_proj=100, # chunk size in projection direction
chunk_sino=100, # chunk size in sinogram direction
npad=None, # amount to pad data before reconstruction
projused=None, #should be slicing in projection dimension (start,end,step)
sinoused=None, #should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack.
correcttilt=0, #tilt dataset
tiltcenter_slice=None, # tilt center (x direction)
tiltcenter_det=None, # tilt center (y direction)
angle_offset=0, #this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus)
anglelist=None, #if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed.
doBeamHardening=False, #turn on beam hardening correction, based on "Correction for beam hardening in computed tomography", Gabor Herman, 1979 Phys. Med. Biol. 24 81
BeamHardeningCoefficients=None, #6 values, tomo = a0 + a1*tomo + a2*tomo^2 + a3*tomo^3 + a4*tomo^4 + a5*tomo^5
projIgnoreList=None, #projections to be ignored in the reconstruction (for simplicity in the code, they will not be removed and will be processed as all other projections but will be set to zero absorption right before reconstruction.
*args,
**kwargs):
start_time = time.time()
print("Start {} at:".format(filename) +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
outputPath = inputPath if outputPath is None else outputPath
outputFilename = filename if outputFilename is None else outputFilename
tempfilenames = [outputPath + 'tmp0.h5', outputPath + 'tmp1.h5']
filenametowrite = outputPath + '/rec' + filename.strip(
".h5") + '/' + outputFilename
#filenametowrite = outputPath+'/rec'+filename+'/'+outputFilename
print("cleaning up previous temp files", end="")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print(", reading metadata")
datafile = h5py.File(inputPath + filename, 'r')
gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs)
pxsize = float(gdata['pxsize']) / 10 # /10 to convert unites from mm to cm
numslices = int(gdata['nslices'])
numangles = int(gdata['nangles'])
angularrange = float(gdata['arange'])
numrays = int(gdata['nrays'])
npad = int(np.ceil(numrays * np.sqrt(2)) -
numrays) // 2 if npad is None else npad
projused = (0, numangles - 1, 1) if projused is None else projused
# ndark = int(gdata['num_dark_fields'])
# ind_dark = list(range(0, ndark))
# group_dark = [numangles - 1]
inter_bright = int(gdata['i0cycle'])
nflat = int(gdata['num_bright_field'])
ind_flat = list(range(0, nflat))
if inter_bright > 0:
group_flat = list(range(0, numangles, inter_bright))
if group_flat[-1] != numangles - 1:
group_flat.append(numangles - 1)
elif inter_bright == 0:
group_flat = [0, numangles - 1]
else:
group_flat = None
ind_tomo = list(range(0, numangles))
floc_independent = dxchange.reader._map_loc(ind_tomo, group_flat)
#figure out the angle list (a list of angles, one per projection image)
dtemp = datafile[list(datafile.keys())[0]]
fltemp = list(dtemp.keys())
firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle', 0))
if anglelist is None:
#the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be.
#we add the 270 to be inte same orientation as previous software used at bl832
angle_offset = 270 + angle_offset - firstangle
anglelist = tomopy.angles(numangles, angle_offset,
angle_offset - angularrange)
elif anglelist == -1:
anglelist = np.zeros(shape=numangles)
for icount in range(0, numangles):
anglelist[icount] = np.pi / 180 * (270 + angle_offset - float(
dtemp[fltemp[icount]].attrs['rot_angle']))
#if projused is different than default, need to chnage numangles and angularrange
#can't do useNormalize_nf and doOutliers2D at the same time, or doOutliers2D and doOutliers1D at the same time, b/c of the way we chunk, for now just disable that
if useNormalize_nf == True and doOutliers2D == True:
useNormalize_nf = False
print(
"we cannot currently do useNormalize_nf and doOutliers2D at the same time, turning off useNormalize_nf"
)
if doOutliers2D == True and doOutliers1D == True:
doOutliers1D = False
print(
"we cannot currently do doOutliers1D and doOutliers2D at the same time, turning off doOutliers1D"
)
#figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices)
if sinoused is None:
sinoused = (0, numslices, 1)
elif sinoused[0] < 0:
sinoused = (
int(np.floor(numslices / 2.0) - np.ceil(sinoused[1] / 2.0)),
int(np.floor(numslices / 2.0) + np.floor(sinoused[1] / 2.0)), 1)
num_proj_per_chunk = np.minimum(chunk_proj, projused[1] - projused[0])
numprojchunks = (projused[1] - projused[0] - 1) // num_proj_per_chunk + 1
num_sino_per_chunk = np.minimum(chunk_sino, sinoused[1] - sinoused[0])
numsinochunks = (sinoused[1] - sinoused[0] - 1) // num_sino_per_chunk + 1
numprojused = (projused[1] - projused[0]) // projused[2]
numsinoused = (sinoused[1] - sinoused[0]) // sinoused[2]
BeamHardeningCoefficients = (
0, 1, 0, 0, 0,
.1) if BeamHardeningCoefficients is None else BeamHardeningCoefficients
if cor is None:
print("Detecting center of rotation", end="")
if angularrange > 300:
lastcor = int(np.floor(numangles / 2) - 1)
else:
lastcor = numangles - 1
#I don't want to see the warnings about the reader using a deprecated variable in dxchange
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename, ind_tomo=(0, lastcor))
tomo = tomo.astype(np.float32)
if useNormalize_nf:
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
else:
tomopy.normalize(tomo, flat, dark, out=tomo)
if corFunction == 'vo':
# same reason for catching warnings as above
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cor = tomopy.find_center_vo(tomo,
ind=voInd,
smin=voSMin,
smax=voSMax,
srad=voSRad,
step=voStep,
ratio=voRatio,
drop=voDrop)
elif corFunction == 'nm':
cor = tomopy.find_center(
tomo,
tomopy.angles(numangles, angle_offset,
angle_offset - angularrange),
ind=nmInd,
init=nmInit,
tol=nmTol,
mask=nmMask,
ratio=nmRatio,
sinogram_order=nmSinoOrder)
elif corFunction == 'pc':
cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25)
else:
raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].")
print(", {}".format(cor))
else:
print("using user input center of {}".format(cor))
function_list = []
if doOutliers1D:
function_list.append('remove_outlier1d')
if doOutliers2D:
function_list.append('remove_outlier2d')
if useNormalize_nf:
function_list.append('normalize_nf')
else:
function_list.append('normalize')
function_list.append('minus_log')
if doBeamHardening:
function_list.append('beam_hardening')
if doFWringremoval:
function_list.append('remove_stripe_fw')
if doTIringremoval:
function_list.append('remove_stripe_ti')
if doSFringremoval:
function_list.append('remove_stripe_sf')
if correcttilt:
function_list.append('correcttilt')
if use360to180:
function_list.append('do_360_to_180')
if doPhaseRetrieval:
function_list.append('phase_retrieval')
function_list.append('recon_mask')
if doPolarRing:
function_list.append('polar_ring')
if castTo8bit:
function_list.append('castTo8bit')
if doBilateralFilter:
function_list.append('bilateral_filter')
function_list.append('write_output')
# Figure out first direction to slice
for func in function_list:
if slice_dir[func] != 'both':
axis = slice_dir[func]
break
done = False
curfunc = 0
curtemp = 0
while True: # Loop over reading data in certain chunking direction
if axis == 'proj':
niter = numprojchunks
else:
niter = numsinochunks
for y in range(niter): # Loop over chunks
print("{} chunk {} of {}".format(axis, y + 1, niter))
if curfunc == 0:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if axis == 'proj':
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename,
ind_tomo=range(
y * num_proj_per_chunk + projused[0],
np.minimum(
(y + 1) * num_proj_per_chunk + projused[0],
numangles)),
sino=(sinoused[0], sinoused[1], sinoused[2]))
else:
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename,
ind_tomo=range(projused[0], projused[1],
projused[2]),
sino=(y * num_sino_per_chunk + sinoused[0],
np.minimum((y + 1) * num_sino_per_chunk +
sinoused[0], numslices), 1))
else:
if axis == 'proj':
start, end = y * num_proj_per_chunk, np.minimum(
(y + 1) * num_proj_per_chunk, numprojused)
tomo = dxchange.reader.read_hdf5(
tempfilenames[curtemp],
'/tmp/tmp',
slc=((start, end, 1), (0, numslices, 1),
(0, numrays, 1))) #read in intermediate file
else:
start, end = y * num_sino_per_chunk, np.minimum(
(y + 1) * num_sino_per_chunk, numsinoused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],
'/tmp/tmp',
slc=((0, numangles,
1), (start, end, 1),
(0, numrays, 1)))
dofunc = curfunc
keepvalues = None
while True: # Loop over operations to do in current chunking direction
func_name = function_list[dofunc]
newaxis = slice_dir[func_name]
if newaxis != 'both' and newaxis != axis:
# We have to switch axis, so flush to disk
if y == 0:
try:
os.remove(tempfilenames[1 - curtemp])
except OSError:
pass
appendaxis = 1 if axis == 'sino' else 0
dxchange.writer.write_hdf5(
tomo,
fname=tempfilenames[1 - curtemp],
gname='tmp',
dname='tmp',
overwrite=False,
appendaxis=appendaxis) #writing intermediate file...
break
print(func_name, end=" ")
curtime = time.time()
if func_name == 'remove_outlier1d':
tomo = tomo.astype(np.float32, copy=False)
remove_outlier1d(tomo,
outlier_diff1D,
size=outlier_size1D,
out=tomo)
if func_name == 'remove_outlier2d':
tomo = tomo.astype(np.float32, copy=False)
tomopy.remove_outlier(tomo,
outlier_diff2D,
size=outlier_size2D,
axis=0,
out=tomo)
elif func_name == 'normalize_nf':
tomo = tomo.astype(np.float32, copy=False)
tomopy.normalize_nf(
tomo, flat, dark, floc_independent, out=tomo
) #use floc_independent b/c when you read file in proj chunks, you don't get the correct floc returned right now to use here.
elif func_name == 'normalize':
tomo = tomo.astype(np.float32, copy=False)
tomopy.normalize(tomo, flat, dark, out=tomo)
elif func_name == 'minus_log':
mx = np.float32(0.00000000000000000001)
ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo)
tomopy.minus_log(tomo, out=tomo)
elif func_name == 'beam_hardening':
loc_dict = {
'a{}'.format(i): np.float32(val)
for i, val in enumerate(BeamHardeningCoefficients)
}
tomo = ne.evaluate(
'a0 + a1*tomo + a2*tomo**2 + a3*tomo**3 + a4*tomo**4 + a5*tomo**5',
local_dict=loc_dict,
out=tomo)
elif func_name == 'remove_stripe_fw':
tomo = tomopy.remove_stripe_fw(tomo,
sigma=ringSigma,
level=ringLevel,
pad=True,
wname=ringWavelet)
elif func_name == 'remove_stripe_ti':
tomo = tomopy.remove_stripe_ti(tomo,
nblock=ringNBlock,
alpha=ringAlpha)
elif func_name == 'remove_stripe_sf':
tomo = tomopy.remove_stripe_sf(tomo, size=ringSize)
elif func_name == 'correcttilt':
if tiltcenter_slice is None:
tiltcenter_slice = numslices / 2.
if tiltcenter_det is None:
tiltcenter_det = tomo.shape[2] / 2
new_center = tiltcenter_slice - 0.5 - sinoused[0]
center_det = tiltcenter_det - 0.5
#add padding of 10 pixels, to be unpadded right after tilt correction. This makes the tilted image not have zeros at certain edges, which matters in cases where sample is bigger than the field of view. For the small amounts we are generally tilting the images, 10 pixels is sufficient.
# tomo = tomopy.pad(tomo, 2, npad=10, mode='edge')
# center_det = center_det + 10
cntr = (center_det, new_center)
for b in range(tomo.shape[0]):
tomo[b] = st.rotate(
tomo[b],
correcttilt,
center=cntr,
preserve_range=True,
order=1,
mode='edge',
clip=True
) #center=None means image is rotated around its center; order=1 is default, order of spline interpolation
# tomo = tomo[:, :, 10:-10]
elif func_name == 'do_360_to_180':
# Keep values around for processing the next chunk in the list
keepvalues = [
angularrange, numangles, projused, num_proj_per_chunk,
numprojchunks, numprojused, numrays, anglelist
]
#why -.5 on one and not on the other?
if tomo.shape[0] % 2 > 0:
tomo = sino_360_to_180(
tomo[0:-1, :, :],
overlap=int(
np.round((tomo.shape[2] - cor - .5)) * 2),
rotation='right')
angularrange = angularrange / 2 - angularrange / (
tomo.shape[0] - 1)
else:
tomo = sino_360_to_180(
tomo[:, :, :],
overlap=int(np.round((tomo.shape[2] - cor)) * 2),
rotation='right')
angularrange = angularrange / 2
numangles = int(numangles / 2)
projused = (0, numangles - 1, 1)
num_proj_per_chunk = np.minimum(chunk_proj,
projused[1] - projused[0])
numprojchunks = (projused[1] - projused[0] -
1) // num_proj_per_chunk + 1
numprojused = (projused[1] - projused[0]) // projused[2]
numrays = tomo.shape[2]
anglelist = anglelist[:numangles]
elif func_name == 'phase_retrieval':
tomo = tomopy.retrieve_phase(tomo,
pixel_size=pxsize,
dist=propagation_dist,
energy=kev,
alpha=alphaReg,
pad=True)
elif func_name == 'recon_mask':
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
if projIgnoreList is not None:
for badproj in projIgnoreList:
tomo[badproj] = 0
rec = tomopy.recon(
tomo,
anglelist,
center=cor + npad,
algorithm='gridrec',
filter_name='butterworth',
filter_par=[butterworth_cutoff, butterworth_order])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
elif func_name == 'polar_ring':
rec = np.ascontiguousarray(rec, dtype=np.float32)
rec = tomopy.remove_ring(rec,
theta_min=Rarc,
rwidth=Rmaxwidth,
thresh_max=Rtmax,
thresh=Rthr,
thresh_min=Rtmin,
out=rec)
elif func_name == 'castTo8bit':
rec = convert8bit(rec, cast8bit_min, cast8bit_max)
elif func_name == 'bilateral_filter':
rec = pyF3D.run_BilateralFilter(
rec,
spatialRadius=bilateral_srad,
rangeRadius=bilateral_rrad)
elif func_name == 'write_output':
dxchange.write_tiff_stack(rec,
fname=filenametowrite,
start=y * num_sino_per_chunk +
sinoused[0])
print('(took {:.2f} seconds)'.format(time.time() - curtime))
dofunc += 1
if dofunc == len(function_list):
break
if y < niter - 1 and keepvalues: # Reset original values for next chunk
angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist = keepvalues
curtemp = 1 - curtemp
curfunc = dofunc
if curfunc == len(function_list):
break
axis = slice_dir[function_list[curfunc]]
print("cleaning up temp files")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print("End Time: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
print('It took {:.3f} s to process {}'.format(time.time() - start_time,
inputPath + filename))
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 3 16:20:30 2015
@author: lbluque
"""
import tomopy
first, flats, darks, floc = tomopy.read_als_832h5('../../TestDatasets/20151021_105637_ALS10_RT_210lbs_10x.h5', ind_tomo=(0,))
first = tomopy.normalize_nf(first, flats, darks, floc)
base = '20151021_105637_ALS10_RT_210lbs_10x_tomopy_phase_'
dist = (0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 13.2284, 100.0, 500.0)
alpha = (1.0e-3, 1.0e-2, 1.0e-1)
for d in dist:
for a in alpha:
print a
phase = tomopy.retrieve_phase(first,
pixel_size=0.000129,
dist=d,
alpha=a,
energy=40)
name = base + 'dist{0:.1f}_alpha{1:.1e}'.format(d*10.0, a)
print(name)
tomopy.write_tiff(phase, fname='filters/'+name)
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, help='path to input raw '
'dataset', required=True)
parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output '
'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices '
'to reconstruct', default=5)
parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction'
' algorithm', default='gridrec',
choices=['art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'])
parser.add_argument('-c', '--center', type=float, help='Center of rotation',
default=None)
parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter'
' used for reconstruction',
choices=['none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'],
default='butterworth')
parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal '
'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-T')
parser.add_argument('-lf', '--log-file', type=str, help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist', args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize'])/10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step*(args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input,
ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :], tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(tomo, theta, center=args.center, emission=False,
algorithm=args.algorithm, filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec/px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh,
thresh_max=thresh_max, thresh_min=thresh_min,
theta_min=theta_min, rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# Generate a new uuid based on host ID and current time
fdata['uuid'] = str(uuid.uuid1())
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger("fast_tomopy.fast_tomo_recon")
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, help="path to input raw " "dataset", required=True)
parser.add_argument(
"-o",
"--output-file",
type=str,
help="full path to h5 output " "file",
default=os.path.join(os.getcwd(), "fast-tomopy.h5"),
)
parser.add_argument("-sn", "--sino-num", type=int, help="Number of slices " "to reconstruct", default=5)
parser.add_argument(
"-a",
"--algorithm",
type=str,
help="Reconstruction" " algorithm",
default="gridrec",
choices=[
"art",
"bart",
"fbp",
"gridrec",
"mlem",
"ospml_hybrid",
"ospml_quad",
"pml_hybrid",
"pml_quad",
"sirt",
],
)
parser.add_argument("-c", "--center", type=float, help="Center of rotation", default=None)
parser.add_argument(
"-fn",
"--filter-name",
type=str,
help="Name of filter" " used for reconstruction",
choices=["none", "shepp", "cosine", "hann", "hamming", "ramlak", "parzen", "butterworth"],
default="butterworth",
)
parser.add_argument(
"-rr",
"--ring-remove",
type=str,
help="Ring removal " "method",
choices=["Octopus", "Tomopy-FW", "Tomopy-T"],
default="Tomopy-T",
)
parser.add_argument("-lf", "--log-file", type=str, help="log file name", default="fast-tomopy.log")
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, "Directory of output file does not exist", args.output_file)
# Read file metadata
logger.info("Reading input file metadata")
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata["nangles"])
last = proj_total - 1
sino_total = int(gdata["nslices"])
ray_total = int(gdata["nrays"])
px_size = float(gdata["pxsize"]) / 10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step * (args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info("Reading full first and last projection for COR")
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1)
logger.info("Detected center: %f", args.center)
# Read and normalize raw sinograms
logger.info("Reading raw data")
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info("Normalizing raw data")
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info("Preprocessing normalized data")
if args.ring_remove == "Tomopy-FW":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == "Tomopy-T":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode="edge")
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info("Reconstructing normalized data")
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(
tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name
)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec / px_size
# Remove rings from reconstruction
if args.ring_remove == "Octopus":
logger.info("Removing rings from reconstructions with %s", args.ring_remove)
thresh = float(gdata["ring_threshold"])
thresh_max = float(gdata["upp_ring_value"])
thresh_min = float(gdata["low_ring_value"])
theta_min = int(gdata["max_arc_length"])
rwidth = int(gdata["max_ring_size"])
rec = tomopy.remove_rings(
rec,
center_x=args.center,
thresh=thresh,
thresh_max=thresh_max,
thresh_min=thresh_min,
theta_min=theta_min,
rwidth=rwidth,
)
# Write reconstruction data to new hdf5 file
fdata["stage"] = "fast-tomopy"
fdata["stage_flow"] = "/raw/" + fdata["stage"]
fdata["stage_version"] = "fast-tomopy-0.1"
# Generate a new uuid based on host ID and current time
fdata["uuid"] = str(uuid.uuid1())
gdata["Reconstruction_Type"] = "tomopy-gridrec"
gdata["ring_removal_method"] = args.ring_remove
gdata["rfilter"] = args.filter_name
logger.info("Writing reconstructed data to h5 file")
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
def cleaning(
filename,
bffilename=None,
inputPath='/', #input path, location of the data set to reconstruct
outputPath=None, # define an output path (default is inputPath), a sub-folder will be created based on file name
outputFilename=None, #file name for output tif files (a number and .tiff will be added). default is based on input filename
fulloutputPath=None, # definte the full output path, no automatic sub-folder will be created
doFWringremoval=True, # Fourier-wavelet ring removal
ringSigma=3, # damping parameter in Fourier space (Fourier-wavelet ring removal)
ringLevel=8, # number of wavelet transform levels (Fourier-wavelet ring removal)
ringWavelet='db5', # type of wavelet filter (Fourier-wavelet ring removal)
ringNBlock=0, # used in Titarenko ring removal (doTIringremoval)
ringAlpha=1.5, # used in Titarenko ring removal (doTIringremoval)
ringSize=5, # used in smoothing filter ring removal (doSFringremoval)
butterworth_cutoff=0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction)
butterworth_order=2, # for reconstruction
npad=None, # amount to pad data before reconstruction
projused=None, # should be slicing in projection dimension (start,end,step) Be sure to add one to the end as stop in python means the last value is omitted
sinoused=None, # should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack.
angle_offset=0, # this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus)
anglelist=None, # if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed.
cor=None, # center of rotation (float). If not used then cor will be detected automatically
corFunction='pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm'
voInd=None, # index of slice to use for cor search (vo)
voSMin=-40, # min radius for searching in sinogram (vo)
voSMax=40, # max radius for searching in sinogram (vo)
voSRad=10, # search radius (vo)
voStep=0.5, # search step (vo)
voRatio=2.0, # ratio of field-of-view and object size (vo)
voDrop=20, # drop lines around vertical center of mask (vo)
nmInd=None, # index of slice to use for cor search (nm)
nmInit=None, # initial guess for center (nm)
nmTol=0.5, # desired sub-pixel accuracy (nm)
nmMask=True, # if True, limits analysis to circular region (nm)
nmRatio=1.0, # ratio of radius of circular mask to edge of reconstructed image (nm)
nmSinoOrder=False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space
useNormalize_nf=False, # normalize based on background intensity (nf)
bfexposureratio=1 #ratio of exposure time of bf to exposure time of sample
):
start_time = time.time()
print("Start {} at:".format(filename) +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
outputFilename = os.path.splitext(
filename)[0] if outputFilename is None else outputFilename
outputPath = inputPath + 'rec' + os.path.splitext(
filename
)[0] + '/' if outputPath is None else outputPath + 'rec' + os.path.splitext(
filename)[0] + '/'
fulloutputPath = outputPath if fulloutputPath is None else fulloutputPath
tempfilenames = [fulloutputPath + 'tmp0.h5', fulloutputPath + 'tmp1.h5']
filenametowrite = fulloutputPath + outputFilename
print(filenametowrite)
print("cleaning up previous temp files", end="")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print(", reading metadata")
datafile = h5py.File(inputPath + filename, 'r')
gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs)
numslices = int(gdata['nslices'])
numangles = int(gdata['nangles'])
print('There are ' + str(numslices) + ' sinograms and ' + str(numangles) +
' projections')
angularrange = float(gdata['arange'])
numrays = int(gdata['nrays'])
npad = int(np.ceil(numrays * np.sqrt(2)) -
numrays) // 2 if npad is None else npad
if projused is not None and (
projused[1] > numangles - 1 or projused[0] < 0
): #allows program to deal with out of range projection values
if projused[1] > numangles:
print(
"End Projection value greater than number of angles. Value has been lowered to the number of angles "
+ str(numangles))
projused = (projused[0], numangles, projused[2])
if projused[0] < 0:
print("Start Projection value less than zero. Value raised to 0")
projused = (0, projused[1], projused[2])
if projused is None:
projused = (0, numangles, 1)
else:
#if projused is different than default, need to chnage numangles and angularrange
#dula attempting to do this with these two lines, we'll see if it works! 11/16/17
testrange = range(projused[0], projused[1], projused[2])
#+1 because we need to compensate for the range functions last value always being one less than the second arg
angularrange = (angularrange /
(numangles - 1)) * (projused[1] - projused[0])
# want angular range to stay constant if we keep the end values consistent
numangles = len(testrange)
# ndark = int(gdata['num_dark_fields'])
# ind_dark = list(range(0, ndark))
# group_dark = [numangles - 1]
inter_bright = int(gdata['i0cycle'])
if inter_bright > 0:
group_flat = list(range(0, numangles, inter_bright))
if group_flat[-1] != numangles - 1:
group_flat.append(numangles - 1)
elif inter_bright == 0:
group_flat = [0, numangles - 1]
else:
group_flat = None
# figure out the angle list (a list of angles, one per projection image)
dtemp = datafile[list(datafile.keys())[0]]
fltemp = list(dtemp.keys())
firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle', 0))
anglegap = angularrange / (numangles - 1)
firstangle += anglegap * projused[0] #accounting for projused argument
if anglelist is None:
#the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be.
#we add the 270 to be inte same orientation as previous software used at bl832
angle_offset = 270 + angle_offset - firstangle
anglelist = tomopy.angles(numangles, angle_offset,
angle_offset - angularrange)
elif anglelist == -1:
anglelist = np.zeros(shape=numangles)
for icount in range(0, numangles):
anglelist[icount] = np.pi / 180 * (270 + angle_offset - float(
dtemp[fltemp[icount]].attrs['rot_angle']))
#figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices)
if sinoused is None:
sinoused = (0, numslices, 1)
elif sinoused[0] < 0:
sinoused = (
int(np.floor(numslices / 2.0) - np.ceil(sinoused[1] / 2.0)),
int(np.floor(numslices / 2.0) + np.floor(sinoused[1] / 2.0)), 1)
if cor is None:
print("Detecting center of rotation", end="")
if angularrange > 300:
lastcor = int(np.floor(numangles / 2) - 1)
else:
lastcor = numangles - 1
# I don't want to see the warnings about the reader using a deprecated variable in dxchange
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename, ind_tomo=(0, lastcor))
if bffilename is not None:
tomobf, flatbf, darkbf, flocbf = dxchange.read_als_832h5(
inputPath + bffilename)
flat = tomobf
tomo = tomo.astype(np.float32)
if useNormalize_nf:
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
if bfexposureratio != 1:
tomo = tomo * bfexposureratio
else:
tomopy.normalize(tomo, flat, dark, out=tomo)
if bfexposureratio != 1:
tomo = tomo * bfexposureratio
if corFunction == 'vo':
# same reason for catching warnings as above
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cor = tomopy.find_center_vo(tomo,
ind=voInd,
smin=voSMin,
smax=voSMax,
srad=voSRad,
step=voStep,
ratio=voRatio,
drop=voDrop)
elif corFunction == 'nm':
cor = tomopy.find_center(
tomo,
tomopy.angles(numangles, angle_offset,
angle_offset - angularrange),
ind=nmInd,
init=nmInit,
tol=nmTol,
mask=nmMask,
ratio=nmRatio,
sinogram_order=nmSinoOrder)
elif corFunction == 'pc':
cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25)
else:
raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].")
print(", {}".format(cor))
else:
print("using user input center of {}".format(cor))
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename,
ind_tomo=range(projused[0], projused[1], projused[2]),
sino=(sinoused[0], sinoused[1], sinoused[2]))
tomo = tomo.astype(np.float32, copy=False)
tomopy.normalize(tomo, flat, dark, out=tomo)
mx = np.float32(0.01)
ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo)
tomopy.minus_log(tomo, out=tomo)
tomo = tomopy.remove_stripe_fw(tomo,
sigma=ringSigma,
level=ringLevel,
pad=True,
wname=ringWavelet)
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
tomo = np.swapaxes(tomo, 0, 1)
theta = anglelist
print('It took {:.3f} s to process {}'.format(time.time() - start_time,
inputPath + filename))
return tomo, theta, cor
if __name__ == '__main__':
# Set path to the micro-CT data to reconstruct.
fname = 'data_dir/sample.h5'
# Select the sinogram range to reconstruct.
start = 0
end = 16
# Read the ALS raw data.
proj, flat, dark, grp_flat = dxchange.read_als_832h5(fname, sino=(start, end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], 0, 180)
# Flat-field correction of raw data.
proj = tomopy.normalize_nf(proj, flat, dark, grp_flat)
# Find rotation center.
rot_center = tomopy.find_center(proj, theta, init=1024, ind=0, tol=0.5)
print("Center of rotation:", rot_center)
proj = tomopy.minus_log(proj)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/als_h5')
if float(gdata['arange'])>300:
lastcor = int(np.floor(int(gdata['nangles'])/2)-1)
else:
lastcor = int(gdata['nangles'])-1
#projs, flat, dark, floc = dxchange.read_als_832h5(idirectory+iname[x]+'.h5', ind_tomo=(firstcor, lastcor))
projs, flat, dark, flocOld = dxchange.read_als_832h5(idirectory+iname[x]+'.h5', ind_tomo=(firstcor, lastcor))
if specialBakCrop[x]>0:
num_per_flat = flat.shape[0]//original_floc_length
flat = flat[0:-specialBakCrop[x]*num_per_flat]
#logging.info('length flats: %d',flat.shape[0])
if useNormalize_nf:
projs = tomopy.normalize_nf(projs, flat, dark, floc)
else:
projs = tomopy.normalize(projs, flat, dark)
cor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
else:
cor = fixedcor[x]
logging.info('Center of rotation: %f', cor)
logging.info('recon slices %d through %d in %d chunk(s)', sinorange[0], sinorange[1]-1, chunks)
# for alphaReg in thealphavalues:
for y in range(0, chunks):
logging.info('dataset %d/%d, chunk %d/%d (%d-%d)', x+1,len(iname), y+1, chunks,sinorange[0]+y*num_sino_per_chunk,sinorange[0]+(y+1)*num_sino_per_chunk-1)
def recon(
filename,
inputPath = './',
outputPath = None,
outputFilename = None,
doOutliers1D = False, # outlier removal in 1d (along sinogram columns)
outlier_diff1D = 750, # difference between good data and outlier data (outlier removal)
outlier_size1D = 3, # radius around each pixel to look for outliers (outlier removal)
doOutliers2D = False, # outlier removal, standard 2d on each projection
outlier_diff2D = 750, # difference between good data and outlier data (outlier removal)
outlier_size2D = 3, # radius around each pixel to look for outliers (outlier removal)
doFWringremoval = True, # Fourier-wavelet ring removal
doTIringremoval = False, # Titarenko ring removal
doSFringremoval = False, # Smoothing filter ring removal
ringSigma = 3, # damping parameter in Fourier space (Fourier-wavelet ring removal)
ringLevel = 8, # number of wavelet transform levels (Fourier-wavelet ring removal)
ringWavelet = 'db5', # type of wavelet filter (Fourier-wavelet ring removal)
ringNBlock = 0, # used in Titarenko ring removal (doTIringremoval)
ringAlpha = 1.5, # used in Titarenko ring removal (doTIringremoval)
ringSize = 5, # used in smoothing filter ring removal (doSFringremoval)
doPhaseRetrieval = False, # phase retrieval
alphaReg = 0.0002, # smaller = smoother (used for phase retrieval)
propagation_dist = 75, # sample-to-scintillator distance (phase retrieval)
kev = 24, # energy level (phase retrieval)
butterworth_cutoff = 0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction)
butterworth_order = 2, # for reconstruction
doTranslationCorrection = False, # correct for linear drift during scan
xshift = 0, # undesired dx transation correction (from 0 degree to 180 degree proj)
yshift = 0, # undesired dy transation correction (from 0 degree to 180 degree proj)
doPolarRing = False, # ring removal
Rarc=30, # min angle needed to be considered ring artifact (ring removal)
Rmaxwidth=100, # max width of rings to be filtered (ring removal)
Rtmax=3000.0, # max portion of image to filter (ring removal)
Rthr=3000.0, # max value of offset due to ring artifact (ring removal)
Rtmin=-3000.0, # min value of image to filter (ring removal)
cor=None, # center of rotation (float). If not used then cor will be detected automatically
corFunction = 'pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm'
voInd = None, # index of slice to use for cor search (vo)
voSMin = -40, # min radius for searching in sinogram (vo)
voSMax = 40, # max radius for searching in sinogram (vo)
voSRad = 10, # search radius (vo)
voStep = 0.5, # search step (vo)
voRatio = 2.0, # ratio of field-of-view and object size (vo)
voDrop = 20, # drop lines around vertical center of mask (vo)
nmInd = None, # index of slice to use for cor search (nm)
nmInit = None, # initial guess for center (nm)
nmTol = 0.5, # desired sub-pixel accuracy (nm)
nmMask = True, # if True, limits analysis to circular region (nm)
nmRatio = 1.0, # ratio of radius of circular mask to edge of reconstructed image (nm)
nmSinoOrder = False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space
use360to180 = False, # use 360 to 180 conversion
doBilateralFilter = False, # if True, uses bilateral filter on image just before write step # NOTE: image will be converted to 8bit if it is not already
bilateral_srad = 3, # spatial radius for bilateral filter (image will be converted to 8bit if not already)
bilateral_rrad = 30, # range radius for bilateral filter (image will be converted to 8bit if not already)
castTo8bit = False, # convert data to 8bit before writing
cast8bit_min=-10, # min value if converting to 8bit
cast8bit_max=30, # max value if converting to 8bit
useNormalize_nf = False, # normalize based on background intensity (nf)
chunk_proj = 100, # chunk size in projection direction
chunk_sino = 100, # chunk size in sinogram direction
npad = None, # amount to pad data before reconstruction
projused = None, #should be slicing in projection dimension (start,end,step)
sinoused = None, #should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack.
correcttilt = 0, #tilt dataset
tiltcenter_slice = None, # tilt center (x direction)
tiltcenter_det = None, # tilt center (y direction)
angle_offset = 0, #this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus)
anglelist = None, #if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed.
doBeamHardening = False, #turn on beam hardening correction, based on "Correction for beam hardening in computed tomography", Gabor Herman, 1979 Phys. Med. Biol. 24 81
BeamHardeningCoefficients = None, #6 values, tomo = a0 + a1*tomo + a2*tomo^2 + a3*tomo^3 + a4*tomo^4 + a5*tomo^5
projIgnoreList = None, #projections to be ignored in the reconstruction (for simplicity in the code, they will not be removed and will be processed as all other projections but will be set to zero absorption right before reconstruction.
*args, **kwargs):
start_time = time.time()
print("Start {} at:".format(filename)+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
outputPath = inputPath if outputPath is None else outputPath
outputFilename = filename if outputFilename is None else outputFilename
outputFilename = outputFilename.replace('.h5','')
tempfilenames = [outputPath+'tmp0.h5',outputPath+'tmp1.h5']
filenametowrite = outputPath+'/rec'+filename.strip(".h5")+'/'+outputFilename
#filenametowrite = outputPath+'/rec'+filename+'/'+outputFilename
print("cleaning up previous temp files", end="")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print(", reading metadata")
datafile = h5py.File(inputPath+filename, 'r')
gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs)
pxsize = float(gdata['pxsize'])/10 # /10 to convert units from mm to cm
numslices = int(gdata['nslices'])
numangles = int(gdata['nangles'])
angularrange = float(gdata['arange'])
numrays = int(gdata['nrays'])
npad = int(np.ceil(numrays * np.sqrt(2)) - numrays)//2 if npad is None else npad
projused = (0,numangles-1,1) if projused is None else projused
# ndark = int(gdata['num_dark_fields'])
# ind_dark = list(range(0, ndark))
# group_dark = [numangles - 1]
inter_bright = int(gdata['i0cycle'])
nflat = int(gdata['num_bright_field'])
ind_flat = list(range(0, nflat))
if inter_bright > 0:
group_flat = list(range(0, numangles, inter_bright))
if group_flat[-1] != numangles - 1:
group_flat.append(numangles - 1)
elif inter_bright == 0:
group_flat = [0, numangles - 1]
else:
group_flat = None
ind_tomo = list(range(0, numangles))
floc_independent = dxchange.reader._map_loc(ind_tomo, group_flat)
#figure out the angle list (a list of angles, one per projection image)
dtemp = datafile[list(datafile.keys())[0]]
fltemp = list(dtemp.keys())
firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle',0))
if anglelist is None:
#the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be.
#we add the 270 to be inte same orientation as previous software used at bl832
angle_offset = 270 + angle_offset - firstangle
anglelist = tomopy.angles(numangles, angle_offset, angle_offset-angularrange)
elif anglelist==-1:
anglelist = np.zeros(shape=numangles)
for icount in range(0,numangles):
anglelist[icount] = np.pi/180*(270 + angle_offset - float(dtemp[fltemp[icount]].attrs['rot_angle']))
#if projused is different than default, need to chnage numangles and angularrange
#can't do useNormalize_nf and doOutliers2D at the same time, or doOutliers2D and doOutliers1D at the same time, b/c of the way we chunk, for now just disable that
if useNormalize_nf==True and doOutliers2D==True:
useNormalize_nf = False
print("we cannot currently do useNormalize_nf and doOutliers2D at the same time, turning off useNormalize_nf")
if doOutliers2D==True and doOutliers1D==True:
doOutliers1D = False
print("we cannot currently do doOutliers1D and doOutliers2D at the same time, turning off doOutliers1D")
#figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices)
if sinoused is None:
sinoused = (0,numslices,1)
elif sinoused[0]<0:
sinoused=(int(np.floor(numslices/2.0)-np.ceil(sinoused[1]/2.0)),int(np.floor(numslices/2.0)+np.floor(sinoused[1]/2.0)),1)
num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0])
numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1
num_sino_per_chunk = np.minimum(chunk_sino,sinoused[1]-sinoused[0])
numsinochunks = (sinoused[1]-sinoused[0]-1)//num_sino_per_chunk+1
numprojused = (projused[1]-projused[0])//projused[2]
numsinoused = (sinoused[1]-sinoused[0])//sinoused[2]
BeamHardeningCoefficients = (0, 1, 0, 0, 0, .1) if BeamHardeningCoefficients is None else BeamHardeningCoefficients
if cor is None:
print("Detecting center of rotation", end="")
if angularrange>300:
lastcor = int(np.floor(numangles/2)-1)
else:
lastcor = numangles-1
#I don't want to see the warnings about the reader using a deprecated variable in dxchange
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=(0,lastcor))
tomo = tomo.astype(np.float32)
if useNormalize_nf:
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
else:
tomopy.normalize(tomo, flat, dark, out=tomo)
if corFunction == 'vo':
# same reason for catching warnings as above
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cor = tomopy.find_center_vo(tomo, ind=voInd, smin=voSMin, smax=voSMax, srad=voSRad, step=voStep,
ratio=voRatio, drop=voDrop)
elif corFunction == 'nm':
cor = tomopy.find_center(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange),
ind=nmInd, init=nmInit, tol=nmTol, mask=nmMask, ratio=nmRatio,
sinogram_order=nmSinoOrder)
elif corFunction == 'pc':
cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25)
else:
raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].")
print(", {}".format(cor))
else:
print("using user input center of {}".format(cor))
function_list = []
if doOutliers1D:
function_list.append('remove_outlier1d')
if doOutliers2D:
function_list.append('remove_outlier2d')
if useNormalize_nf:
function_list.append('normalize_nf')
else:
function_list.append('normalize')
function_list.append('minus_log')
if doBeamHardening:
function_list.append('beam_hardening')
if doFWringremoval:
function_list.append('remove_stripe_fw')
if doTIringremoval:
function_list.append('remove_stripe_ti')
if doSFringremoval:
function_list.append('remove_stripe_sf')
if correcttilt:
function_list.append('correcttilt')
if use360to180:
function_list.append('do_360_to_180')
if doPhaseRetrieval:
function_list.append('phase_retrieval')
function_list.append('recon_mask')
if doPolarRing:
function_list.append('polar_ring')
if castTo8bit:
function_list.append('castTo8bit')
if doBilateralFilter:
function_list.append('bilateral_filter')
function_list.append('write_output')
# Figure out first direction to slice
for func in function_list:
if slice_dir[func] != 'both':
axis = slice_dir[func]
break
done = False
curfunc = 0
curtemp = 0
while True: # Loop over reading data in certain chunking direction
if axis=='proj':
niter = numprojchunks
else:
niter = numsinochunks
for y in range(niter): # Loop over chunks
print("{} chunk {} of {}".format(axis, y+1, niter))
if curfunc==0:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if axis=='proj':
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(y*num_proj_per_chunk+projused[0],np.minimum((y + 1)*num_proj_per_chunk+projused[0],numangles)),sino=(sinoused[0],sinoused[1], sinoused[2]) )
else:
tomo, flat, dark, floc = dxchange.read_als_832h5(inputPath+filename,ind_tomo=range(projused[0],projused[1],projused[2]),sino=(y*num_sino_per_chunk+sinoused[0],np.minimum((y + 1)*num_sino_per_chunk+sinoused[0],numslices),1) )
else:
if axis=='proj':
start, end = y * num_proj_per_chunk, np.minimum((y + 1) * num_proj_per_chunk,numprojused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((start,end,1),(0,numslices,1),(0,numrays,1))) #read in intermediate file
else:
start, end = y * num_sino_per_chunk, np.minimum((y + 1) * num_sino_per_chunk,numsinoused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],'/tmp/tmp',slc=((0,numangles,1),(start,end,1),(0,numrays,1)))
dofunc = curfunc
keepvalues = None
while True: # Loop over operations to do in current chunking direction
func_name = function_list[dofunc]
newaxis = slice_dir[func_name]
if newaxis != 'both' and newaxis != axis:
# We have to switch axis, so flush to disk
if y==0:
try:
os.remove(tempfilenames[1-curtemp])
except OSError:
pass
appendaxis = 1 if axis=='sino' else 0
dxchange.writer.write_hdf5(tomo,fname=tempfilenames[1-curtemp],gname='tmp',dname='tmp',overwrite=False,appendaxis=appendaxis) #writing intermediate file...
break
print(func_name, end=" ")
curtime = time.time()
if func_name == 'remove_outlier1d':
tomo = tomo.astype(np.float32,copy=False)
remove_outlier1d(tomo, outlier_diff1D, size=outlier_size1D, out=tomo)
if func_name == 'remove_outlier2d':
tomo = tomo.astype(np.float32,copy=False)
tomopy.remove_outlier(tomo, outlier_diff2D, size=outlier_size2D, axis=0, out=tomo)
elif func_name == 'normalize_nf':
tomo = tomo.astype(np.float32,copy=False)
tomopy.normalize_nf(tomo, flat, dark, floc_independent, out=tomo) #use floc_independent b/c when you read file in proj chunks, you don't get the correct floc returned right now to use here.
elif func_name == 'normalize':
tomo = tomo.astype(np.float32,copy=False)
tomopy.normalize(tomo, flat, dark, out=tomo)
elif func_name == 'minus_log':
mx = np.float32(0.00000000000000000001)
ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo)
tomopy.minus_log(tomo, out=tomo)
elif func_name == 'beam_hardening':
loc_dict = {'a{}'.format(i):np.float32(val) for i,val in enumerate(BeamHardeningCoefficients)}
tomo = ne.evaluate('a0 + a1*tomo + a2*tomo**2 + a3*tomo**3 + a4*tomo**4 + a5*tomo**5', local_dict=loc_dict, out=tomo)
elif func_name == 'remove_stripe_fw':
tomo = tomopy.remove_stripe_fw(tomo, sigma=ringSigma, level=ringLevel, pad=True, wname=ringWavelet)
elif func_name == 'remove_stripe_ti':
tomo = tomopy.remove_stripe_ti(tomo, nblock=ringNBlock, alpha=ringAlpha)
elif func_name == 'remove_stripe_sf':
tomo = tomopy.remove_stripe_sf(tomo, size=ringSize)
elif func_name == 'correcttilt':
if tiltcenter_slice is None:
tiltcenter_slice = numslices/2.
if tiltcenter_det is None:
tiltcenter_det = tomo.shape[2]/2
new_center = tiltcenter_slice - 0.5 - sinoused[0]
center_det = tiltcenter_det - 0.5
#add padding of 10 pixels, to be unpadded right after tilt correction. This makes the tilted image not have zeros at certain edges, which matters in cases where sample is bigger than the field of view. For the small amounts we are generally tilting the images, 10 pixels is sufficient.
# tomo = tomopy.pad(tomo, 2, npad=10, mode='edge')
# center_det = center_det + 10
cntr = (center_det, new_center)
for b in range(tomo.shape[0]):
tomo[b] = st.rotate(tomo[b], correcttilt, center=cntr, preserve_range=True, order=1, mode='edge', clip=True) #center=None means image is rotated around its center; order=1 is default, order of spline interpolation
# tomo = tomo[:, :, 10:-10]
elif func_name == 'do_360_to_180':
# Keep values around for processing the next chunk in the list
keepvalues = [angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist]
#why -.5 on one and not on the other?
if tomo.shape[0]%2>0:
tomo = sino_360_to_180(tomo[0:-1,:,:], overlap=int(np.round((tomo.shape[2]-cor-.5))*2), rotation='right')
angularrange = angularrange/2 - angularrange/(tomo.shape[0]-1)
else:
tomo = sino_360_to_180(tomo[:,:,:], overlap=int(np.round((tomo.shape[2]-cor))*2), rotation='right')
angularrange = angularrange/2
numangles = int(numangles/2)
projused = (0,numangles-1,1)
num_proj_per_chunk = np.minimum(chunk_proj,projused[1]-projused[0])
numprojchunks = (projused[1]-projused[0]-1)//num_proj_per_chunk+1
numprojused = (projused[1]-projused[0])//projused[2]
numrays = tomo.shape[2]
anglelist = anglelist[:numangles]
elif func_name == 'phase_retrieval':
tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
elif func_name == 'translation_correction':
tomo = linear_translation_correction(tomo,dx=xshift,dy=yshift,interpolation=False):
elif func_name == 'recon_mask':
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
if projIgnoreList is not None:
for badproj in projIgnoreList:
tomo[badproj] = 0
rec = tomopy.recon(tomo, anglelist, center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[butterworth_cutoff, butterworth_order])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
elif func_name == 'polar_ring':
rec = np.ascontiguousarray(rec, dtype=np.float32)
rec = tomopy.remove_ring(rec, theta_min=Rarc, rwidth=Rmaxwidth, thresh_max=Rtmax, thresh=Rthr, thresh_min=Rtmin,out=rec)
elif func_name == 'castTo8bit':
rec = convert8bit(rec, cast8bit_min, cast8bit_max)
elif func_name == 'bilateral_filter':
rec = pyF3D.run_BilateralFilter(rec, spatialRadius=bilateral_srad, rangeRadius=bilateral_rrad)
elif func_name == 'write_output':
dxchange.write_tiff_stack(rec, fname=filenametowrite, start=y*num_sino_per_chunk + sinoused[0])
print('(took {:.2f} seconds)'.format(time.time()-curtime))
dofunc+=1
if dofunc==len(function_list):
break
if y<niter-1 and keepvalues: # Reset original values for next chunk
angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist = keepvalues
curtemp = 1 - curtemp
curfunc = dofunc
if curfunc==len(function_list):
break
axis = slice_dir[function_list[curfunc]]
print("cleaning up temp files")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print("End Time: "+time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
print('It took {:.3f} s to process {}'.format(time.time()-start_time,inputPath+filename))
inputPath = '{}_{:d}{}'.format(fn,y,fileextension)
tomo[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('loading flat images')
for y in range(0,len(floc)):
inputPath = '{}{}_{:d}{}'.format(fn,flatextension,floc[y],fileextension)
flat[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('loading dark images')
for y in range(0,numdrk):
inputPath = '{}{}_{:d}{}'.format(fn,darkextension,y,fileextension)
dark[y] = dxchange.reader.read_tiff(inputPath,slc = (sinoused, raysused))
print('normalizing')
tomo = tomo.astype(np.float32)
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
tomopy.minus_log(tomo, out=tomo)
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
rec = tomopy.recon(tomo, tomopy.angles(numangles, angle_offset, angle_offset-angularrange), center=cor+npad, algorithm='gridrec', filter_name='butterworth', filter_par=[.25, 2])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
print('writing recon')
dxchange.write_tiff_stack(rec, fname='rec/'+fn, start=sinoused[0])
def reconstruct(filename,
inputPath="",
outputPath="",
COR=COR,
doOutliers=doOutliers,
outlier_diff=outlier_diff,
outlier_size=outlier_size,
doFWringremoval=doFWringremoval,
ringSigma=ringSigma,
ringLevel=ringLevel,
ringWavelet=ringWavelet,
pad_sino=pad_sino,
doPhaseRetrieval=doPhaseRetrieval,
propagation_dist=propagation_dist,
kev=kev,
alphaReg=alphaReg,
butterworthpars=butterworthpars,
doPolarRing=doPolarRing,
Rarc=Rarc,
Rmaxwidth=Rmaxwidth,
Rtmax=Rtmax,
Rthr=Rthr,
Rtmin=Rtmin,
useAutoCOR=useAutoCOR,
use360to180=use360to180,
num_substacks=num_substacks,
recon_slice=recon_slice):
# Convert filename to list type if only one file name is given
if type(filename) != list:
filename = [filename]
# If useAutoCor == true, a list of COR will be automatically calculated for all files
# If a list of COR is given, only entries with boolean False will use automatic COR calculation
if useAutoCOR == True or (len(COR) != len(filename)):
logging.info('using auto COR for all input files')
COR = [False] * len(filename)
for x in range(len(filename)):
logging.info('opening data set, checking metadata')
fdata, gdata = read_als_832h5_metadata(inputPath[x] + filename[x] +
'.h5')
pxsize = float(
gdata['pxsize']
) / 10.0 # convert from metadata (mm) to this script (cm)
numslices = int(gdata['nslices'])
# recon_slice == True, only center slice will be reconstructed
# if integer is given, a specific
if recon_slice != False:
if (type(recon_slice) == int) and (recon_slice <= numslices):
sinorange[recon_slice - 1, recon_slice]
else:
sinorange = [numslices // 2 - 1, numslices // 2]
else:
sinorange = [0, numslices]
# Calculate number of substacks (chunks)
substacks = num_substacks #(sinorange[1]-sinorange[0]-1)//num_sino_per_substack+1
if (sinorange[1] - sinorange[0]) >= substacks:
num_sino_per_substack = (sinorange[1] -
sinorange[0]) // num_substacks
else:
num_sino_per_substack = 1
firstcor, lastcor = 0, int(gdata['nangles']) - 1
projs, flat, dark, floc = dxchange.read_als_832h5(
inputPath[x] + filename[x] + '.h5', ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
autocor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
if (type(COR[x]) == bool) or (COR[x] < 0) or (COR[x] == 'auto'):
firstcor, lastcor = 0, int(gdata['nangles']) - 1
projs, flat, dark, floc = dxchange.read_als_832h5(
inputPath[x] + filename[x] + '.h5',
ind_tomo=(firstcor, lastcor))
projs = tomopy.normalize_nf(projs, flat, dark, floc)
cor = tomopy.find_center_pc(projs[0], projs[1], tol=0.25)
else:
cor = COR[x]
logging.info(
'Dataset %s, has %d total slices, reconstructing slices %d through %d in %d substack(s), using COR: %f',
filename[x], int(gdata['nslices']), sinorange[0], sinorange[1] - 1,
substacks, cor)
for y in range(0, substacks):
logging.info('Starting dataset %s (%d of %d), substack %d of %d',
filename[x], x + 1, len(filename), y + 1, substacks)
logging.info('Reading sinograms...')
projs, flat, dark, floc = dxchange.read_als_832h5(
inputPath[x] + filename[x] + '.h5',
sino=(sinorange[0] + y * num_sino_per_substack,
sinorange[0] + (y + 1) * num_sino_per_substack, 1))
logging.info(
'Doing remove outliers, norm (nearest flats), and -log...')
if doOutliers:
projs = tomopy.remove_outlier(projs,
outlier_diff,
size=outlier_size,
axis=0)
flat = tomopy.remove_outlier(flat,
outlier_diff,
size=outlier_size,
axis=0)
tomo = tomopy.normalize_nf(projs, flat, dark, floc)
tomo = tomopy.minus_log(tomo, out=tomo) # in place logarithm
# Use padding to remove halo in reconstruction if present
if pad_sino:
npad = int(
np.ceil(tomo.shape[2] * np.sqrt(2)) - tomo.shape[2]) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
cor_rec = cor + npad # account for padding
else:
cor_rec = cor
if doFWringremoval:
logging.info('Doing ring (Fourier-wavelet) function...')
tomo = tomopy.remove_stripe_fw(tomo,
sigma=ringSigma,
level=ringLevel,
pad=True,
wname=ringWavelet)
if doPhaseRetrieval:
logging.info('Doing Phase retrieval...')
#tomo = tomopy.retrieve_phase(tomo, pixel_size=pxsize, dist=propagation_dist, energy=kev, alpha=alphaReg, pad=True)
tomo = tomopy.retrieve_phase(tomo,
pixel_size=pxsize,
dist=propagation_dist,
energy=kev,
alpha=alphaReg,
pad=True)
logging.info(
'Doing recon (gridrec) function and scaling/masking, with cor %f...',
cor_rec)
rec = tomopy.recon(tomo,
tomopy.angles(tomo.shape[0], 270, 90),
center=cor_rec,
algorithm='gridrec',
filter_name='butterworth',
filter_par=butterworthpars)
#rec = tomopy.recon(tomo, tomopy.angles(tomo.shape[0], 180+angularrange/2, 180-angularrange/2), center=cor_rec, algorithm='gridrec', filter_name='butterworth', filter_par=butterworthpars)
rec /= pxsize # intensity values in cm^-1
if pad_sino:
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
else:
rec = tomopy.circ_mask(rec, 0, ratio=1.0, val=0.0)
if doPolarRing:
logging.info('Doing ring (polar mean filter) function...')
rec = tomopy.remove_ring(rec,
theta_min=Rarc,
rwidth=Rmaxwidth,
thresh_max=Rtmax,
thresh=Rthr,
thresh_min=Rtmin)
logging.info('Writing reconstruction slices to %s', filename[x])
#dxchange.write_tiff_stack(rec, fname=outputPath+'alpha'+str(alphaReg)+'/rec'+filename[x]+'/rec'+filename[x], start=sinorange[0]+y*num_sino_per_substack)
dxchange.write_tiff_stack(rec,
fname=outputPath + 'recon_' +
filename[x] + '/recon_' + filename[x],
start=sinorange[0] +
y * num_sino_per_substack)
logging.info('Reconstruction Complete: ' + filename[x])
def fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', type=str, help='path to input raw '
'dataset', required=True)
parser.add_argument('-o', '--output-file', type=str, help='full path to h5 output '
'file', default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn', '--sino-num', type=int, help='Number of slices '
'to reconstruct', default=5)
parser.add_argument('-a', '--algorithm', type=str, help='Reconstruction'
' algorithm', default='gridrec',
choices=['art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'])
parser.add_argument('-c', '--center', type=float, help='Center of rotation',
default=None)
parser.add_argument('-fn', '--filter-name', type=str, help='Name of filter'
' used for reconstruction',
choices=['none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'],
default='butterworth')
parser.add_argument('-rr', '--ring-remove', type=str, help='Ring removal '
'method', choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-FW')
parser.add_argument('-lf', '--log-file', type=str, help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist', args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = tomopy.read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize'])/10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step*(args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input,
ind_tomo=(0, last))
first_last = tomopy.normalize_nf(first_last, flats, darks, floc)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :], tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total*np.sqrt(2)) - ray_total)//2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(tomo.shape[0], 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
rec = tomopy.recon(tomo, theta, center=args.center, emission=False,
algorithm=args.algorithm, filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec/px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec, center_x=args.center, thresh=thresh,
thresh_max=thresh_max, thresh_min=thresh_min,
theta_min=theta_min, rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# WHAT ABOUT uuid ????? Who asigns this???
del fdata['uuid'] # I'll get rid of it altogether then...
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
# Set path to the micro-CT data to reconstruct.
fname = 'data_dir/sample.h5'
# Select the sinogram range to reconstruct.
start = 0
end = 16
# Read the ALS raw data.
proj, flat, dark, grp_flat = dxchange.read_als_832h5(fname,
sino=(start, end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], 0, 180)
# Flat-field correction of raw data.
proj = tomopy.normalize_nf(proj, flat, dark, grp_flat)
# Find rotation center.
rot_center = tomopy.find_center(proj, theta, init=1024, ind=0, tol=0.5)
print("Center of rotation:", rot_center)
proj = tomopy.minus_log(proj)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/als_h5')
inputPath = '{}_{:d}{}'.format(fn, y, fileextension)
tomo[y] = dxchange.reader.read_tiff(inputPath, slc=(sinoused, raysused))
print('loading flat images')
for y in range(0, len(floc)):
inputPath = '{}{}_{:d}{}'.format(fn, flatextension, floc[y], fileextension)
flat[y] = dxchange.reader.read_tiff(inputPath, slc=(sinoused, raysused))
print('loading dark images')
for y in range(0, numdrk):
inputPath = '{}{}_{:d}{}'.format(fn, darkextension, y, fileextension)
dark[y] = dxchange.reader.read_tiff(inputPath, slc=(sinoused, raysused))
print('normalizing')
tomo = tomo.astype(np.float32)
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
tomopy.minus_log(tomo, out=tomo)
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
rec = tomopy.recon(tomo,
tomopy.angles(numangles, angle_offset,
angle_offset - angularrange),
center=cor + npad,
algorithm='gridrec',
filter_name='butterworth',
filter_par=[.25, 2])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)