def remove_stripe(data, params):
log.info(' *** remove stripe:')
if (params.remove_stripe_method == 'fw'):
log.info(' *** *** fourier wavelet')
data = tomopy.remove_stripe_fw(data,
level=params.fw_level,
wname=params.fw_filter,
sigma=params.fw_sigma,
pad=params.fw_pad)
log.info(' *** *** *** fw level %d ' % params.fw_level)
log.info(' *** *** *** fw wname %s ' % params.fw_filter)
log.info(' *** *** *** fw sigma %f ' % params.fw_sigma)
log.info(' *** *** *** fw pad %r ' % params.fw_pad)
elif (params.remove_stripe_method == 'ti'):
log.info(' *** *** titarenko')
data = tomopy.remove_stripe_ti(data,
nblock=params.ti_nblock,
alpha=params.ti_alpha)
log.info(' *** *** *** ti nblock %d ' % params.ti_nblock)
log.info(' *** *** *** ti alpha %f ' % params.ti_alpha)
elif (params.remove_stripe_method == 'sf'):
log.info(' *** *** smoothing filter')
data = tomopy.remove_stripe_sf(data, size == params.sf_size)
log.info(' *** *** *** sf size %d ' % params.sf_size)
elif (params.remove_stripe_method == 'none'):
log.warning(' *** *** OFF')
return data
def recon_slice(row_sino,
theta,
center_pos,
sinogram_order=False,
algorithm=None,
init_recon=None,
ncore=None,
nchunk=None,
**kwargs):
t = time.time()
internal_print(row_sino.shape)
row_sino = row_sino.astype('float32')
# row_sino = tomopy.normalize_bg(row_sino) # WARNING: normalize_bg can unpredicatably give bad results for some slices
row_sino = tomopy.remove_stripe_ti(row_sino, alpha=4)
rec = tomopy.recon(row_sino,
theta,
center=center_pos,
sinogram_order=sinogram_order,
algorithm=algorithm,
init_recon=init_recon,
ncore=ncore,
nchunk=nchunk,
filter_name='parzen',
**kwargs)
internal_print('recon: ' + str(time.time() - t))
return rec
def test_ti(self):
# Prepare some test data
prj = self.phantom_prj()
# Prepare parameters (different from tomopy_cli or tomopy defaults)
params = make_params()
params.remove_stripe_method = 'ti'
params.ti_alpha = 1.2
params.ti_nblock = 2
result = remove_stripe(np.copy(prj), params)
# Compare results
expected = tomopy.remove_stripe_ti(np.copy(prj), alpha=1.2, nblock=2)
np.testing.assert_array_equal(result, expected)
def recon_slice(row_sino, center_pos, sinogram_order=False, algorithm=None,
init_recon=None, ncore=None, nchunk=None, **kwargs):
t = time.time()
ang = tomopy.angles(row_sino.shape[0])
print(row_sino.shape)
row_sino = row_sino.astype('float32')
# row_sino = tomopy.normalize_bg(row_sino) # WARNING: normalize_bg can unpredicatably give bad results for some slices
row_sino = tomopy.remove_stripe_ti(row_sino, alpha=4)
rec = tomopy.recon(row_sino, ang, center=center_pos, sinogram_order=sinogram_order, algorithm=algorithm,
init_recon=init_recon, ncore=ncore, nchunk=nchunk, **kwargs)
print('recon: ' + str(time.time() - t))
return rec
def srxfftomo_recon(corrected_proj_tiff=None,
proj=None,
rot_center=None,
recon_algorithm='art',
recon_section=None,
outpath=None,
recon_outpath='recon',
samplename=None,
starting_angle=0,
last_angle=180):
'''
input needs to be either
1) corrected_proj_tiff, provided as a file path and name,
e.g. '/home/xf05id1/localdata/TomoCommissioning/testsample_01/testsample_01_corrected.tiff'
it is a stack of tiff generated by srxfftomo_correction, where background and stage round out have been corrected
2) projection numpy array, returned from srxfftomo_correction
The 'proj' array assignment has priority. If it is None, proj was be read from corrected_proj_tiff
recon_section is a list of upper and lower bounds for y range, e.g. [380, 420]
'''
if proj is None:
print('proj is not asigned, load corrected projection from:',
corrected_proj_tiff)
proj = tifffile.imread(corrected_proj_tiff)
else:
print('proj array is not None, use it as is')
print('running reconstruction')
if recon_section is not None:
proj = proj[:, recon_section[0]:recon_section[1], :]
theta = tomopy.angles(proj.shape[0], ang1=starting_angle, ang2=last_angle)
print(proj.shape, theta.shape)
proj = tomopy.remove_stripe_ti(proj)
rec = tomopy.recon(proj,
theta,
center=rot_center,
algorithm=recon_algorithm)
print('saving reconstruction into')
recon_outfile = outpath + '/' + samplename + '/' + recon_outpath + '/' + samplename + '_recon'
tomopy.write_tiff_stack(rec, fname=recon_outfile)
return rec
def reconstruct(self,
center=None,
mask_ratio=1,
poisson_maxcount=None,
remove_ring=True):
if center is None:
center = self.center
if self.padded:
ind = int((self.sinogram.shape[1] - self.shape[1]) / 2)
center += ind
nang = self.sinogram.shape[0]
theta = tomopy.angles(nang, ang1=0, ang2=self.fin_angle)
data = self.sinogram[:, np.newaxis, :]
data = tomopy.remove_stripe_ti(data)
# dxchange.write_tiff(np.squeeze(data), '/raid/home/mingdu/data/shirley/local_tomo/temp/raw', dtype='float32')
if poisson_maxcount is not None:
if self.is_mlogged:
data = np.exp(-data)
data = self.add_poisson_noise(data, max_count=poisson_maxcount)
if self.is_mlogged:
data = -np.log(data)
m_value = np.mean(data[np.isfinite(data)])
data[np.isinf(data)] = m_value
# dxchange.write_tiff(np.squeeze(data), '/raid/home/mingdu/data/shirley/local_tomo/temp/noise_sino', dtype='float32')
rec = tomopy.recon(data,
theta,
center=center,
algorithm='gridrec',
filter_name='parzen')
if self.padded:
rec = rec[:, ind:ind + self.shape[1], ind:ind + self.shape[1]]
if remove_ring:
rec = tomopy.remove_ring(rec)
rec = np.squeeze(rec)
# if self.normalized_bg:
# rec = rec * self.scaler
self.recon_mask = tomopy.misc.corr._get_mask(rec.shape[0],
rec.shape[1], mask_ratio)
self.recon = rec
def preprocess_data(prj,
flat,
dark,
FF_norm=flat_field_norm,
remove_rings=remove_rings,
FF_drift_corr=flat_field_drift_corr,
downsapling=binning):
if FF_norm: # dark-flat field correction
prj = tomopy.normalize(prj, flat, dark)
if FF_drift_corr: # flat field drift correction
prj = tomopy.normalize_bg(prj, air=50)
prj[prj <= 0] = 1 # check dark<data
prj = tomopy.minus_log(prj) # -logarithm
if remove_rings: # remove rings
# prj = tomopy.remove_stripe_fw(
# prj, level=7, wname='sym16', sigma=1, pad=True)
prj = tomopy.remove_stripe_ti(prj, 2)
if downsapling > 0: # binning
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
return prj
# Read APS 2-BM raw data.
if (int(key) > 6):
proj, flat, dark, theta = read_aps_2bm_custom(fname, sino=sino)
else:
proj, flat, dark, theta = dxchange.read_aps_2bm(fname, sino=sino)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
#proj = tomopy.remove_stripe_fw(proj,level=5,wname='sym16',sigma=1,pad=True)
proj = tomopy.remove_stripe_ti(proj,2)
proj = tomopy.remove_stripe_sf(proj,10)
# phase retrieval
##data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True)
# Find rotation center
#rot_center = tomopy.find_center(proj, theta, init=rot_center, ind=start, tol=0.5)
print(index, 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.
# Select the sinogram range to reconstruct.
start = 600
end = 900
# Read the APS 1-ID raw data.
proj, flat, dark = dxchange.read_aps_1id(fname, sino=(start, end))
print(proj.shape, flat.shape, dark.shape)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=360.0)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
ndata = tomopy.remove_stripe_ti(ndata)
ndata = tomopy.remove_stripe_sf(ndata)
# phase retrieval
# ndata = tomopy.prep.phase.retrieve_phase(ndata, pixel_size=detector_pixel_size_x, dist=sample_detector_distance, energy=monochromator_energy, alpha=8e-3, pad=True)
# Find rotation center.
#rot_center = tomopy.find_center(ndata, theta, init=1024, ind=0, tol=0.5)
rot_center = 576
binning = 0
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center/np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
#d.median_filter(size=medfilt_size, axis=0) # Apply a median filter in the projection plane
data = tomopy.median_filter(data, size=medfilt_size, axis=0)
#if remove_stripe1: d.stripe_removal(level=stripe_lvl, sigma=sig, wname=Wname)
if remove_stripe1: data = tomopy.remove_stripe_fw(data, level=stripe_lvl, wname=Wname, sigma=sig)
# z = 3
# eng = 31
# pxl = 0.325e-4
# rat = 5e-03
# rat = 1e-03
#d.phase_retrieval(dist=z, energy=eng, pixel_size=pxl, alpha=rat,padding=True)
#data = tomopy.retrieve_phase(data, dist=z, energy=eng, pixel_size=pxl, alpha=rat,pad=True)
#if remove_stripe2: d.stripe_removal2()
if remove_stripe2: data = tomopy.remove_stripe_ti(data)
#d.downsample2d(level=level) # apply binning on the data
data = tomopy.downsample(data, level=level) # apply binning on the data
theta = tomopy.angles(data.shape[0])
if 1:
#if not best_center: d.optimize_center()
if not best_center: calc_center = tomopy.find_center(data, theta, emission=False, ind=0, tol=0.3)
else:
#d.center=best_center/pow(2,level) # Manage the rotation center
calc_center = best_center/pow(2,level) # Manage the rotation center
#d.gridrec(ringWidth=RingW) # Run the reconstruction
rec = tomopy.recon(data, theta, center=calc_center, algorithm='gridrec', emission=False)
#d.apply_mask(ratio=1)
rec = tomopy.circ_mask(rec, axis=0)
if sino_chunk_end > sino_end:
break
sino = (sino_chunk_start, sino_chunk_end)
# Read APS 32-ID raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(fname,
sino=sino)
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
# remove stripes
#proj = tomopy.remove_stripe_fw(proj,level=5,wname='sym16',sigma=1,pad=True)
proj = tomopy.remove_stripe_ti(proj, 2)
proj = tomopy.remove_stripe_sf(proj, 10)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True)
# Find rotation center
#rot_center = tomopy.find_center(proj, theta, init=rot_center, ind=start, tol=0.5)
print(index, rot_center)
proj = tomopy.minus_log(proj)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj,
theta,
center=rot_center,
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))
def recon_hdf5(src_fanme, dest_folder, sino_range, sino_step, shift_grid, center_vec=None, center_eq=None, dtype='float32',
algorithm='gridrec', tolerance=1, chunk_size=20, save_sino=False, sino_blur=None, flattened_radius=120,
mode='180', test_mode=False, phase_retrieval=None, ring_removal=True, **kwargs):
"""
center_eq: a and b parameters in fitted center position equation center = a*slice + b.
"""
if not os.path.exists(dest_folder):
try:
os.mkdir(dest_folder)
except:
pass
sino_ini = int(sino_range[0])
sino_end = int(sino_range[1])
sino_ls_all = np.arange(sino_ini, sino_end, sino_step, dtype='int')
alloc_set = allocate_mpi_subsets(sino_ls_all.size, size, task_list=sino_ls_all)
sino_ls = alloc_set[rank]
# prepare metadata
f = h5py.File(src_fanme)
dset = f['exchange/data']
full_shape = dset.shape
theta = tomopy.angles(full_shape[0])
if center_eq is not None:
a, b = center_eq
center_ls = sino_ls * a + b
center_ls = np.round(center_ls)
for iblock in range(int(sino_ls.size/chunk_size)+1):
print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
istart = iblock*chunk_size
iend = np.min([(iblock+1)*chunk_size, sino_ls.size])
fstart = sino_ls[istart]
fend = sino_ls[iend]
center = center_ls[istart:iend]
data = dset[:, fstart:fend:sino_step, :]
data[np.isnan(data)] = 0
data = data.astype('float32')
data = tomopy.remove_stripe_ti(data, alpha=4)
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_ring(rec)
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
for i in range(rec.shape[0]):
slice = fstart + i*sino_step
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:05d}.tiff').format(slice, sino_ini))
if save_sino:
dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, int(center[i])))
iblock += 1
print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0))
else:
# divide chunks
grid_bins = np.append(np.ceil(shift_grid[:, 0, 0]), full_shape[1])
chunks = []
center_ls = []
istart = 0
counter = 0
# irow should be 0 for slice 0
irow = np.searchsorted(grid_bins, sino_ls[0], side='right')-1
for i in range(sino_ls.size):
counter += 1
sino_next = i+1 if i != sino_ls.size-1 else i
if counter >= chunk_size or sino_ls[sino_next] >= grid_bins[irow+1] or sino_next == i:
iend = i+1
chunks.append((istart, iend))
istart = iend
center_ls.append(center_vec[irow])
if sino_ls[sino_next] >= grid_bins[irow+1]:
irow += 1
counter = 0
# reconstruct chunks
iblock = 1
for (istart, iend), center in izip(chunks, center_ls):
print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
fstart = sino_ls[istart]
fend = sino_ls[iend-1]
print('Reading data...')
data = dset[:, fstart:fend+1:sino_step, :]
if mode == '360':
overlap = 2 * (dset.shape[2] - center)
data = tomosaic.morph.sino_360_to_180(data, overlap=overlap, rotation='right')
theta = tomopy.angles(data.shape[0])
data[np.isnan(data)] = 0
data = data.astype('float32')
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
if ring_removal:
data = tomopy.remove_stripe_ti(data, alpha=4)
if phase_retrieval:
data = tomopy.retrieve_phase(data, kwargs['pixel_size'], kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
rec0 = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1]-1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]), np.arange(rec.shape[1]))
mask0 = ((xx-cent)**2+(yy-cent)**2 <= flattened_radius**2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask])/2
else:
rec = tomopy.recon(data, theta, center=center, algorithm=algorithm, **kwargs)
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
for i in range(rec.shape[0]):
slice = fstart + i*sino_step
if test_mode:
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype)
else:
dxchange.write_tiff(rec[i, :, :], fname=os.path.join(dest_folder, 'recon/recon_{:05d}.tiff').format(slice), dtype=dtype)
if save_sino:
dxchange.write_tiff(data[:, i, :], fname=os.path.join(dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(slice, center), dtype=dtype)
print('Block {:d} finished in {:.2f} s.'.format(iblock, time.time()-t0))
iblock += 1
return
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 30 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 25.74 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 1000 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
miss_angles = [500, 1050]
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
print(theta)
# Manage the missing angles:
#proj_size = np.shape(proj)
#theta = np.linspace(0,180,proj_size[0])
print(proj.shape, theta.shape)
proj = np.concatenate(
(proj[0:miss_angles[0], :, :], proj[miss_angles[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:miss_angles[0]], theta[miss_angles[1] + 1:-1]))
print(proj.shape, theta.shape)
# zinger_removal
#proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
#flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
# remove stripes
# data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
data = tomopy.remove_stripe_ti(data, 2)
# phase retrieval
# data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center / np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm=algorithm,
filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
##rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
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
def recon_single(fname,
center,
dest_folder,
sino_range=None,
chunk_size=50,
read_theta=True,
pad_length=0,
phase_retrieval=False,
ring_removal=True,
algorithm='gridrec',
flattened_radius=40,
crop=None,
remove_padding=True,
**kwargs):
prj_shape = read_data_adaptive(fname, shape_only=True)
if read_theta:
theta = read_data_adaptive(fname, proj=(0, 1), return_theta=True)
else:
theta = tomopy.angles(prj_shape[0])
if sino_range is None:
sino_st = 0
sino_end = prj_shape[1]
sino_step = 1
else:
sino_st, sino_end = sino_range[:2]
if len(sino_range) == 3:
sino_step = sino_range[-1]
else:
sino_step = 1
chunks = np.arange(0, sino_end, chunk_size * sino_step, dtype='int')
for chunk_st in chunks:
t0 = time.time()
chunk_end = min(chunk_st + chunk_size * sino_step, prj_shape[1])
data, flt, drk = read_data_adaptive(fname,
sino=(chunk_st, chunk_end,
sino_step),
return_theta=False)
data = tomopy.normalize(data, flt, drk)
data = data.astype('float32')
data = tomopy.remove_stripe_ti(data, alpha=4)
if phase_retrieval:
data = tomopy.retrieve_phase(data, kwargs['pixel_size'],
kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
if pad_length != 0:
data = pad_sinogram(data, pad_length)
if ring_removal:
data = tomopy.remove_stripe_ti(data, alpha=4)
rec0 = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1] - 1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]),
np.arange(rec.shape[1]))
mask0 = ((xx - cent)**2 + (yy - cent)**2 <= flattened_radius**2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask]) / 2
else:
rec = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
if pad_length != 0 and remove_padding:
rec = rec[:, pad_length:pad_length + prj_shape[2],
pad_length:pad_length + prj_shape[2]]
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
if crop is not None:
crop = np.asarray(crop)
rec = rec[:, crop[0, 0]:crop[1, 0], crop[0, 1]:crop[1, 1]]
for i in range(rec.shape[0]):
slice = chunk_st + sino_step * i
internal_print('Saving slice {}'.format(slice))
dxchange.write_tiff(
rec[i, :, :],
fname=os.path.join(dest_folder,
'recon_{:05d}.tiff').format(slice),
dtype='float32')
internal_print('Block finished in {:.2f} s.'.format(time.time() - t0))
# Select the sinogram range to reconstruct.
start = 600
end = 900
# Read the APS 1-ID raw data.
proj, flat, dark = dxchange.read_aps_1id(fname, sino=(start, end))
print(proj.shape, flat.shape, dark.shape)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=360.0)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
ndata = tomopy.remove_stripe_ti(ndata)
ndata = tomopy.remove_stripe_sf(ndata)
# phase retrieval
# ndata = tomopy.prep.phase.retrieve_phase(ndata, pixel_size=detector_pixel_size_x, dist=sample_detector_distance, energy=monochromator_energy, alpha=8e-3, pad=True)
# Find rotation center.
#rot_center = tomopy.find_center(ndata, theta, init=1024, ind=0, tol=0.5)
rot_center = 576
binning = 0
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center / np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
prj = tomopy.minus_log(prj)
print('\n** minus log applied!')
if debug:
print('## Debug: after minus log:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' % (np.min(prj), np.max(prj)))
prj = tomopy.misc.corr.remove_neg(prj, val=0.001)
prj = tomopy.misc.corr.remove_nan(prj, val=0.001)
prj[np.where(prj == np.inf)] = 0.001
if debug:
print('## Debug: after cleaning bad values:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' % (np.min(prj), np.max(prj)))
prj = tomopy.remove_stripe_ti(prj,4)
print('\n** Stripe removal done!')
if debug:
print('## Debug: after remove_stripe:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' % (np.min(prj), np.max(prj)))
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(prj,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
prj = tomopy.median_filter(prj,size=medfilt_size)
print('\n** Median filter done!')
if debug:
print('## Debug: after nedian filter:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' % (np.min(prj), np.max(prj)))
def write_center_360(file_name,
output_path,
slice_no,
center_st,
center_end,
medfilt_size=1,
level=0,
debug=1):
try:
prj0, flat, dark, theta = dxchange.read_aps_32id(file_name,
sino=(slice_no,
slice_no + 1))
except:
prj0, flat, dark = dxchange.read_aps_32id(file_name,
sino=(slice_no,
slice_no + 1))
f = h5py.File(file_name, "r")
theta = tomopy.angles(f['exchange/data'].shape[0], ang1=0, ang2=360)
if debug:
print('## Debug: after reading data:')
print('\n** Shape of the data:' + str(np.shape(prj0)))
print('** Shape of theta:' + str(np.shape(theta)))
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj0), np.max(prj0)))
prj0 = tomopy.normalize(prj0, flat, dark)
print('\n** Flat field correction done!')
for center in range(center_st, center_end):
overlap = 2 * (f['exchange/data'].shape[2] - center)
prj = np.copy(prj0)
prj = sino_360_to_180(prj, overlap=overlap, rotation='right')
print('\n** Sinogram converted!')
if debug:
print('## Debug: after normalization:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
prj = tomopy.minus_log(prj)
print('\n** minus log applied!')
if debug:
print('## Debug: after minus log:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
prj = tomopy.misc.corr.remove_neg(prj, val=0.001)
prj = tomopy.misc.corr.remove_nan(prj, val=0.001)
prj[np.where(prj == np.inf)] = 0.001
if debug:
print('## Debug: after cleaning bad values:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
prj = tomopy.remove_stripe_ti(prj, 4)
print('\n** Stripe removal done!')
if debug:
print('## Debug: after remove_stripe:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
prj = tomopy.median_filter(prj, size=medfilt_size)
print('\n** Median filter done!')
if debug:
print('## Debug: after nedian filter:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
if level > 0:
prj = tomopy.downsample(prj, level=level)
print('\n** Down sampling done!\n')
if debug:
print('## Debug: after down sampling:')
print('\n** Min and max val in prj before recon: %0.5f, %0.3f' %
(np.min(prj), np.max(prj)))
rec = tomopy.recon(prj, theta, center=center, algorithm='gridrec')
print('\nReconstruction done!\n')
dxchange.write_tiff(rec,
fname=os.path.join(output_path,
'{0:.2f}'.format(center)),
dtype='float32')
def preprocess_data(prj,
flat,
dark,
FF_norm=flat_field_norm,
remove_rings=remove_rings,
medfilt_size=medfilt_size,
FF_drift_corr=flat_field_drift_corr,
downspling=binning):
if FF_norm:
# normalize the prj
print('\n*** Applying flat field correction:')
start_norm_time = time.time()
prj = tomopy.normalize(prj, flat, dark)
print(' done in %0.3f min' % ((time.time() - start_norm_time) / 60))
if FF_drift_corr:
print('\n*** Applying flat field drift correction:')
start_norm_bg_time = time.time()
prj = tomopy.normalize_bg(prj, air=100)
print(' done in %0.3f min' %
((time.time() - start_norm_bg_time) / 60))
# Applying -log
print('\n*** Applying -log:')
start_log_time = time.time()
prj = tomopy.minus_log(prj)
print(' done in %0.3f min' % ((time.time() - start_log_time) / 60))
prj = tomopy.misc.corr.remove_neg(prj, val=0.000)
prj = tomopy.misc.corr.remove_nan(prj, val=0.000)
prj[np.where(prj == np.inf)] = 0.000
# prj[np.where(prj == 0)] = 0.000
print('\n*** Min and max val in prj before recon: %0.3f, %0.3f' %
(np.min(prj), np.max(prj)))
if remove_rings:
# remove ring artefacts
tmp = prj[-1, :, :] # use to fixe the bug of remove_stripe_ti
print('\n*** Applying ring removal algo:')
start_ring_time = time.time()
prj = tomopy.remove_stripe_ti(prj, 2)
# prj = tomopy.remove_stripe_sf(prj,10); prj = tomopy.misc.corr.remove_neg(prj, val=0.000) # remove the neg values coming from remove_stripe_sf
print(' done in %0.3f min' % ((time.time() - start_ring_time) / 60))
prj[-1, :, :] = tmp # fixe the bug of remove_stripe_ti
if phase_retrieval:
# phase retrieval
prj = tomopy.prep.phase.retrieve_phase(
prj,
pixel_size=detector_pixel_size_x,
dist=sample_detector_distance,
energy=monochromator_energy,
alpha=alpha,
pad=True)
# Filtering data with 2D median filter before downsampling and recon
if medfilt_size > 1:
start_filter_time = time.time()
print('\n*** Applying median filter')
#prj = tomopy.median_filter(prj,size=1)
prj = ndimage.median_filter(prj,
footprint=np.ones(
(1, medfilt_size, medfilt_size)))
print(' done in %0.3f min' %
((time.time() - start_filter_time) / 60))
# Downsampling data:
if downspling > 0:
print('\n** Applying downsampling')
start_down_time = time.time()
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
print(' done in %0.3f min' % ((time.time() - start_down_time) / 60))
print('\n*** Shape of the data:' + str(np.shape(prj)))
print(' Dimension of theta:' + str(np.shape(theta)))
return prj
def find_center_discrete(source_folder,
file_grid,
shift_grid,
row_range,
search_range,
search_step=1,
slice=600,
method='entropy',
data_format='aps_32id',
output_fname='center_pos.txt',
read_theta=True):
t00 = time.time()
log = open(output_fname, 'a')
row_st, row_end = row_range
center_st, center_end = search_range
row_list = range(row_st, row_end)
sets = allocate_mpi_subsets(len(row_list), size, task_list=row_list)
full_shape = read_data_adaptive(os.path.join(source_folder, file_grid[0,
0]),
shape_only=True)
if read_theta:
_, _, _, theta = read_data_adaptive(os.path.join(
source_folder, file_grid[0, 0]),
proj=(0, 1))
else:
theta = tomopy.angles(full_shape[0])
for row in sets[rank]:
internal_print('Row {}'.format(row))
t0 = time.time()
slice = int(shift_grid[row, 0, 0] + slice)
# create sinogram
try:
sino = dxchange.read_tiff(
os.path.join('center_temp', 'sino',
'sino_{:05d}.tiff'.format(slice)))
sino = sino.reshape([sino.shape[0], 1, sino.shape[1]])
except:
center_vec = [center_st] * file_grid.shape[0]
center_vec = np.array(center_vec)
sino, _ = create_row_sinogram(file_grid,
shift_grid,
source_folder,
slice,
center_vec,
0,
blend_method='pyramid',
data_format=data_format)
dxchange.write_tiff(
sino,
os.path.join('center_temp', 'sino',
'sino_{:05d}.tiff'.format(slice)))
sino = tomopy.remove_stripe_ti(sino, alpha=4)
if method == 'manual' or 'entropy':
write_center(sino,
theta,
dpath='center/{}'.format(row),
cen_range=(center_st, center_end, search_step))
if method == 'entropy':
center = minimum_entropy(os.path.join('center', str(row)),
reliability_screening=True)
if center is None:
internal_print(
'Entropy result did not pass reliability screening. Switching to CNN...'
)
rad = int(sino.shape[-1] * 0.3)
rec_mid = int(sino.shape[-1] / 2)
center = search_in_folder_dnn(
os.path.join('center', str(row)),
window=((rec_mid - rad, rec_mid - rad),
(rec_mid + rad, rec_mid + rad)))
internal_print('For {} center is {}. ({} s)'.format(
row, center,
time.time() - t0))
log.write('{} {}\n'.format(row, center))
elif method == 'vo':
mid = sino.shape[2] / 2
smin = (center_st - mid) * 2
smax = (center_end - mid) * 2
center = find_center_vo(sino,
smin=smin,
smax=smax,
step=search_step)
internal_print('For {} center is {}. ({} s)'.format(
row, center,
time.time() - t0))
log.write('{} {}\n'.format(row, center))
log.close()
internal_print('Total time: {} s.'.format(time.time() - t00))
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))
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 30 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 1.17e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 25.74 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 1000 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
miss_angles = [500,1050]
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
print (theta)
# Manage the missing angles:
#proj_size = np.shape(proj)
#theta = np.linspace(0,180,proj_size[0])
print(proj.shape, theta.shape)
proj = np.concatenate((proj[0:miss_angles[0],:,:], proj[miss_angles[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
print(proj.shape, theta.shape)
# zinger_removal
#proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
#flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
# remove stripes
# data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
data = tomopy.remove_stripe_ti(data, 2)
# phase retrieval
# data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
else:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
##rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
print("Size of projection is", proj.shape)
# Flat-field correction of raw data.
tic = time.time()
print("Normalization....")
proj = tomopy.normalize(proj, white2, dark)
print("Done. %.4f sec" % (time.time() - tic))
# Set data collection angles as equally spaced between theta_start ~ theta_end (in degrees.)
theta = tomopy.angles(proj.shape[0], ang1=th_start, ang2=th_end)
# Ring removal.
tic = time.time()
print('Apply Ring removal filter...')
#proj = tomopy.remove_stripe_fw(proj) # pretty effective, bg distortion
proj = tomopy.remove_stripe_ti(proj) # pretty effective, bg distortion
#proj = tomopy.remove_stripe_sf(proj) # less useful, but won't distort background
print("Done. %.4f sec" % (time.time() - tic))
tic = time.time()
print('Calculate minus_log of projection...')
proj = tomopy.minus_log(proj)
print("Done. %.4f sec" % (time.time() - tic))
print(proj.shape, proj.dtype, proj.max(), proj.min())
print('Create & Cleanup destination folder...')
# create output folder if not exist
if not os.path.exists(outpath):
os.makedirs(outpath)
else:
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, foobar = dx.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, foobar = dx.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize(tomo, flats, darks)
tomo = tomopy.minus_log(tomo)
# 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,
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 evaluate(self):
self.tomo.value = tomopy.remove_stripe_ti(self.tomo.value,
nblock=self.nblock.value,
alpha=self.alpha.value,
ncore=self.ncore.value,
nchunk=self.nchunk.value)
def recon_hdf5(src_fanme,
dest_folder,
sino_range,
sino_step,
shift_grid,
center_vec=None,
center_eq=None,
dtype='float32',
algorithm='gridrec',
tolerance=1,
chunk_size=20,
save_sino=False,
sino_blur=None,
flattened_radius=120,
mode='180',
test_mode=False,
phase_retrieval=None,
ring_removal=True,
crop=None,
num_iter=None,
pad_length=0,
read_theta=True,
**kwargs):
"""
center_eq: a and b parameters in fitted center position equation center = a*slice + b.
"""
if not os.path.exists(dest_folder):
try:
os.mkdir(dest_folder)
except:
pass
sino_ini = int(sino_range[0])
sino_end = int(sino_range[1])
sino_ls_all = np.arange(sino_ini, sino_end, sino_step, dtype='int')
alloc_set = allocate_mpi_subsets(sino_ls_all.size,
size,
task_list=sino_ls_all)
sino_ls = alloc_set[rank]
# prepare metadata
f = h5py.File(src_fanme)
dset = f['exchange/data']
full_shape = dset.shape
if read_theta:
_, _, _, theta = read_data_adaptive(src_fanme, proj=(0, 1))
else:
theta = tomopy.angles(full_shape[0])
if center_eq is not None:
a, b = center_eq
center_ls = sino_ls_all * a + b
center_ls = np.round(center_ls)
for iblock in range(int(sino_ls.size / chunk_size) + 1):
internal_print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
istart = iblock * chunk_size
iend = np.min([(iblock + 1) * chunk_size, sino_ls.size])
sub_sino_ls = sino_ls[istart:iend - 1]
center = np.take(center_ls, sub_sino_ls)
data = np.zeros([dset.shape[0], len(sub_sino_ls), dset.shape[2]])
for ind, i in enumerate(sub_sino_ls):
data[:, ind, :] = dset[:, i, :]
data[np.isnan(data)] = 0
data = data.astype('float32')
data = tomopy.remove_stripe_ti(data, alpha=4)
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
if phase_retrieval:
data = tomopy.retrieve_phase(data, kwargs['pixel_size'],
kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
if pad_length != 0:
data = pad_sinogram(data, pad_length)
data = tomopy.remove_stripe_ti(data, alpha=4)
if ring_removal:
rec0 = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1] - 1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]),
np.arange(rec.shape[1]))
mask0 = ((xx - cent)**2 +
(yy - cent)**2 <= flattened_radius**2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask]) / 2
else:
rec = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
if pad_length != 0:
rec = rec[:, pad_length:pad_length + full_shape[2],
pad_length:pad_length + full_shape[2]]
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
if crop is not None:
crop = np.asarray(crop)
rec = rec[:, crop[0, 0]:crop[1, 0], crop[0, 1]:crop[1, 1]]
for i in range(rec.shape[0]):
slice = sub_sino_ls[i]
dxchange.write_tiff(
rec[i, :, :],
fname=os.path.join(
dest_folder, 'recon/recon_{:05d}.tiff').format(slice))
if save_sino:
dxchange.write_tiff(
data[:, i, :],
fname=os.path.join(
dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(
slice, int(center[i])))
iblock += 1
internal_print('Block {:d} finished in {:.2f} s.'.format(
iblock,
time.time() - t0))
else:
# divide chunks
grid_bins = np.append(np.ceil(shift_grid[:, 0, 0]), full_shape[1])
chunks = []
center_ls = []
istart = 0
counter = 0
# irow should be 0 for slice 0
irow = np.searchsorted(grid_bins, sino_ls[0], side='right') - 1
for i in range(sino_ls.size):
counter += 1
sino_next = i + 1 if i != sino_ls.size - 1 else i
if counter >= chunk_size or sino_ls[sino_next] >= grid_bins[
irow + 1] or sino_next == i:
iend = i + 1
chunks.append((istart, iend))
istart = iend
center_ls.append(center_vec[irow])
if sino_ls[sino_next] >= grid_bins[irow + 1]:
irow += 1
counter = 0
# reconstruct chunks
iblock = 1
for (istart, iend), center in zip(chunks, center_ls):
internal_print('Beginning block {:d}.'.format(iblock))
t0 = time.time()
internal_print('Reading data...')
sub_sino_ls = sino_ls[istart:iend]
data = np.zeros([dset.shape[0], len(sub_sino_ls), dset.shape[2]])
for ind, i in enumerate(sub_sino_ls):
data[:, ind, :] = dset[:, i, :]
if mode == '360':
overlap = 2 * (dset.shape[2] - center)
data = tomosaic.sino_360_to_180(data,
overlap=overlap,
rotation='right')
theta = tomopy.angles(data.shape[0])
data[np.isnan(data)] = 0
data = data.astype('float32')
if sino_blur is not None:
for i in range(data.shape[1]):
data[:, i, :] = gaussian_filter(data[:, i, :], sino_blur)
if phase_retrieval:
data = tomopy.retrieve_phase(data, kwargs['pixel_size'],
kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
if pad_length != 0:
data = pad_sinogram(data, pad_length)
data = tomopy.remove_stripe_ti(data, alpha=4)
if ring_removal:
rec0 = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
rec = tomopy.remove_ring(np.copy(rec0))
cent = int((rec.shape[1] - 1) / 2)
xx, yy = np.meshgrid(np.arange(rec.shape[2]),
np.arange(rec.shape[1]))
mask0 = ((xx - cent)**2 +
(yy - cent)**2 <= flattened_radius**2)
mask = np.zeros(rec.shape, dtype='bool')
for i in range(mask.shape[0]):
mask[i, :, :] = mask0
rec[mask] = (rec[mask] + rec0[mask]) / 2
else:
rec = tomopy.recon(data,
theta,
center=center + pad_length,
algorithm=algorithm,
**kwargs)
if pad_length != 0:
rec = rec[:, pad_length:pad_length + full_shape[2],
pad_length:pad_length + full_shape[2]]
rec = tomopy.remove_outlier(rec, tolerance)
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
if crop is not None:
crop = np.asarray(crop)
rec = rec[:, crop[0, 0]:crop[1, 0], crop[0, 1]:crop[1, 1]]
for i in range(rec.shape[0]):
slice = sub_sino_ls[i]
if test_mode:
dxchange.write_tiff(
rec[i, :, :],
fname=os.path.join(
dest_folder,
'recon/recon_{:05d}_{:d}.tiff').format(
slice, center),
dtype=dtype)
else:
dxchange.write_tiff(
rec[i, :, :],
fname=os.path.join(
dest_folder,
'recon/recon_{:05d}.tiff').format(slice),
dtype=dtype)
if save_sino:
dxchange.write_tiff(
data[:, i, :],
fname=os.path.join(
dest_folder, 'sino/recon_{:05d}_{:d}.tiff').format(
slice, center),
dtype=dtype)
internal_print('Block {:d} finished in {:.2f} s.'.format(
iblock,
time.time() - t0))
iblock += 1
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 preprocess_data(prj, flat, dark, FF_norm=flat_field_norm, remove_rings = remove_rings, medfilt_size=medfilt_size, FF_drift_corr=flat_field_drift_corr, downspling=binning):
if FF_norm:
# normalize the prj
print('\n*** Applying flat field correction:')
start_norm_time = time.time()
prj = tomopy.normalize(prj, flat, dark)
print(' done in %0.3f min' % ((time.time() - start_norm_time)/60))
if FF_drift_corr:
print('\n*** Applying flat field drift correction:')
start_norm_bg_time = time.time()
prj = tomopy.normalize_bg(prj, air=100)
print(' done in %0.3f min' % ((time.time() - start_norm_bg_time)/60))
# Applying -log
print('\n*** Applying -log:')
start_log_time = time.time()
prj = tomopy.minus_log(prj)
print(' done in %0.3f min' % ((time.time() - start_log_time)/60))
prj = tomopy.misc.corr.remove_neg(prj, val=0.000)
prj = tomopy.misc.corr.remove_nan(prj, val=0.000)
prj[np.where(prj == np.inf)] = 0.000
# prj[np.where(prj == 0)] = 0.000
print('\n*** Min and max val in prj before recon: %0.3f, %0.3f' % (np.min(prj), np.max(prj)))
if remove_rings:
# remove ring artefacts
tmp = prj[-1,:,:] # use to fixe the bug of remove_stripe_ti
print('\n*** Applying ring removal algo:')
start_ring_time = time.time()
prj = tomopy.remove_stripe_ti(prj,2)
# prj = tomopy.remove_stripe_sf(prj,10); prj = tomopy.misc.corr.remove_neg(prj, val=0.000) # remove the neg values coming from remove_stripe_sf
print(' done in %0.3f min' % ((time.time() - start_ring_time)/60))
prj[-1,:,:] = tmp # fixe the bug of remove_stripe_ti
if phase_retrieval:
# phase retrieval
prj = tomopy.prep.phase.retrieve_phase(prj,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
# Filtering data with 2D median filter before downsampling and recon
if medfilt_size>1:
start_filter_time = time.time()
print('\n*** Applying median filter')
#prj = tomopy.median_filter(prj,size=1)
prj = ndimage.median_filter(prj,footprint=np.ones((1, medfilt_size, medfilt_size)))
print(' done in %0.3f min' % ((time.time() - start_filter_time)/60))
# Downsampling data:
if downspling>0:
print('\n** Applying downsampling')
start_down_time = time.time()
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
print(' done in %0.3f min' % ((time.time() - start_down_time)/60))
print('\n*** Shape of the data:'+str(np.shape(prj)))
print(' Dimension of theta:'+str(np.shape(theta)))
return prj
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec', options=None, num_iter=100, dark_file=None):
sample_detector_distance = 10 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.247e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
monochromator_energy = 35 # Energy of incident wave in keV
alpha = 1e-01 # Phase retrieval coeff.
zinger_level = 500 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
if dark_file is not None:
proj_, flat, dark, theta_ = dxchange.read_aps_32id(dark_file, sino=sino)
del proj_, theta_
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
# flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
##data = tomopy.normalize(proj, flat, dark, cutoff=0.8)
data = tomopy.normalize(proj, flat, dark)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# phase retrieval
#data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
print("Raw data: ", h5fname)
print("Center: ", rot_center)
data = tomopy.minus_log(data)
data = tomopy.remove_nan(data, val=0.0)
data = tomopy.remove_neg(data, val=0.00)
data[np.where(data == np.inf)] = 0.00
rot_center = rot_center/np.power(2, float(binning))
data = tomopy.downsample(data, level=binning)
data = tomopy.downsample(data, level=binning, axis=1)
print(algorithm)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
elif algorithm == "astra_fbp":
if options == 'linear':
options = {'proj_type':'linear', 'method':'FBP'}
else:
options = {'proj_type':'cuda', 'method':'FBP_CUDA'}
rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options, ncore=1)
elif algorithm == "astra_sirt":
extra_options = {'MinConstraint':0}
options = {'proj_type':'cuda', 'method':'SIRT_CUDA', 'num_iter':num_iter, 'extra_options':extra_options}
rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options)
elif algorithm == tomopy.astra:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options)
else:
try:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm, filter_name='parzen')
except:
rec = tomopy.recon(data, theta, center=rot_center, algorithm=algorithm)
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
