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)
# prj = tomopy.remove_all_stripe(prj)
if downsapling > 0: # binning
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
return prj
def reconstruct_sirt(fname, sino_range, theta_st=0, theta_end=PI, n_epochs=200,
output_folder=None, downsample=None, center=None):
if output_folder is None:
output_folder = 'sirt_niter_{}_ds_{}_{}_{}'.format(n_epochs, *downsample)
t0 = time.time()
print('Reading data...')
prj, flt, drk, _ = dxchange.read_aps_32id(fname, sino=sino_range)
print('Data reading: {} s'.format(time.time() - t0))
print('Data shape: {}'.format(prj.shape))
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj)
# scale up to prevent precision issue
prj *= 1.e2
if downsample is not None:
prj = tomopy.downsample(prj, level=downsample[0], axis=0)
prj = tomopy.downsample(prj, level=downsample[1], axis=1)
prj = tomopy.downsample(prj, level=downsample[2], axis=2)
print('Downsampled shape: {}'.format(prj.shape))
n_theta = prj.shape[0]
theta = np.linspace(theta_st, theta_end, n_theta)
print('Starting reconstruction...')
t0 = time.time()
extra_options = {'MinConstraint': 0}
options = {'proj_type': 'cuda', 'method': 'SIRT_CUDA', 'num_iter': n_epochs, 'extra_options': extra_options}
res = tomopy.recon(prj, theta, center=center, algorithm=tomopy.astra, options=options)
dxchange.write_tiff_stack(res, fname=os.path.join(output_folder, 'recon'), dtype='float32',
overwrite=True)
print('Reconstruction time: {} s'.format(time.time() - t0))
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 = [141,226]
# 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])
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]))
# 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)
# 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 reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 3 # 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 = 22.7 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # 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)
# 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)
# 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 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)] = 0.000,2
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, nblock=0, alpha=2)
prj = tomopy.remove_stripe_fw(prj)
# 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
# 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'):
sample_detector_distance = 8 # 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 = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # 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)
# 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)
# Reconstruct object.
if algorithm == 'sirtfbp':
rec = rec_sirtfbp(data, theta, rot_center)
elif algorithm == 'astrasirt':
extra_options ={'MinConstraint':0}
options = {'proj_type':'cuda', 'method':'SIRT_CUDA', 'num_iter':200, 'extra_options':extra_options}
rec = tomopy.recon(data, theta, center=rot_center, algorithm=tomopy.astra, options=options)
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 reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 8 # 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 = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# h5fname_norm = '/local/data/2019-02/Burke/C47M_0015.h5'
h5fname_norm = '/local/data/2019-02/Burke/kc78_Menardii_0003.h5'
proj1, flat, dark, theta1 = dxchange.read_aps_32id(h5fname_norm, sino=sino)
proj, dummy, dummy1, theta = dxchange.read_aps_32id(h5fname, 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=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=20)
# 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 downsample_img(img, ds, axis=0):
if isinstance(ds, int):
if img.ndim == 3:
res = tomopy.downsample(img, level=int(np.log2(ds)), axis=0)
else:
res = tomopy.downsample(img[:, np.newaxis, :],
level=int(np.log2(ds)),
axis=0)
else:
zm = np.ones(img.ndim)
zm[axis] = 1. / ds
res = zoom(img, zm)
return res
def find_center_vo(tomo,
ind=None,
smin=-50,
smax=50,
srad=6,
step=0.5,
ratio=0.5,
drop=20):
"""
Transplanted from TomoPy with minor fixes.
Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`.
Parameters
----------
tomo : ndarray
3D tomographic data.
ind : int, optional
Index of the slice to be used for reconstruction.
smin, smax : int, optional
Coarse search radius. Reference to the horizontal center of the sinogram.
srad : float, optional
Fine search radius.
step : float, optional
Step of fine searching.
ratio : float, optional
The ratio between the FOV of the camera and the size of object.
It's used to generate the mask.
drop : int, optional
Drop lines around vertical center of the mask.
Returns
-------
float
Rotation axis location.
"""
tomo = dtype.as_float32(tomo)
if ind is None:
ind = tomo.shape[1] // 2
_tomo = tomo[:, ind, :]
# Enable cache for FFTW.
pyfftw.interfaces.cache.enable()
# Reduce noise by smooth filters. Use different filters for coarse and fine search
_tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1))
_tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2))
# Coarse and fine searches for finding the rotation center.
if _tomo.shape[0] * _tomo.shape[1] > 4e6: # If data is large (>2kx2k)
_tomo_coarse = downsample(np.expand_dims(_tomo_cs, 1), level=2)[:,
0, :]
init_cen = _search_coarse(_tomo_coarse, smin / 4, smax / 4, ratio,
drop)
fine_cen = _search_fine(_tomo_fs, srad, step, init_cen * 4, ratio,
drop)
else:
init_cen = _search_coarse(_tomo_cs, smin, smax, ratio, drop)
fine_cen = _search_fine(_tomo_fs, srad, step, init_cen, ratio, drop)
logger.debug('Rotation center search finished: %i', fine_cen)
return fine_cen
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(sino.shape[1], ang1=0.0, ang2=180.0)
print(sino.shape, sdark.shape, sflat.shape, theta.shape)
# Quick normalization just to see something ....
ndata = sino / float(np.amax(sino))
slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center/np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, sinogram_order=True, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start", nargs='?', const=1, type=int, default=1, help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print (nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 0
end = 512
sino=(start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
# Normalize to 1 using the air counts
ndata = tomopy.normalize_bg(ndata, air=5)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
ndata = tomopy.minus_log(ndata)
# Set binning and number of iterations
binning = 8
iters = 21
print("Original", ndata.shape)
ndata = tomopy.downsample(ndata, level=binning, axis=1)
# ndata = tomopy.downsample(ndata, level=binning, axis=2)
print("Processing:", ndata.shape)
fdir = 'aligned' + '/noblur_iter_' + str(iters) + '_bin_' + str(binning)
print(fdir)
cprj, sx, sy, conv = alignment.align_seq(ndata, theta, fdir=fdir, iters=iters, pad=(10, 10), blur=False, save=True, debug=True)
np.save(fdir + '/shift_x', sx)
np.save(fdir + '/shift_y', sy)
# Write aligned projections as stack of TIFs.
dxchange.write_tiff_stack(cprj, fname=fdir + '/radios/image')
def file_io():
##########################################################################################################
## fdir = '/local/dataraid/'
fdir = '/local/data/2018-04/Dubacq/'
file_name_Im1 = 'tomo_manip7G-sc_7124eV_1200prj_354.h5'
file_name_Im2 = 'tomo_manip7G-sc_7200eV_1200prj_352.h5'
prj = 0
binning = 1
medfilt_size = 3
crop = [200, 300]
##########################################################################################################
# Reading Images to align:
Im1, flat1, dark1, theta = dxchange.read_aps_32id(fdir + file_name_Im1,
proj=(prj, prj + 1, 1))
Im2, flat2, dark2, theta = dxchange.read_aps_32id(fdir + file_name_Im2,
proj=(prj, prj + 1, 1))
Im1 = Im1 / np.mean(flat1, axis=0)
Im2 = Im2 / np.mean(flat2, axis=0)
if medfilt_size > 0:
Im1 = ndimage.median_filter(Im1,
footprint=np.ones(
(1, medfilt_size, medfilt_size)))
Im2 = ndimage.median_filter(Im2,
footprint=np.ones(
(1, medfilt_size, medfilt_size)))
if binning > 0:
Im1 = tomopy.downsample(Im1, level=binning)
Im1 = tomopy.downsample(Im1, level=binning, axis=1)
Im2 = tomopy.downsample(Im2, level=binning)
Im2 = tomopy.downsample(Im2, level=binning, axis=1)
# if 1:
# plt.figure(),
# plt.subplot(1,2,1), plt.imshow(np.squeeze(Im1), cmap='gray', aspect="auto", interpolation='none'), plt.colorbar()
# plt.subplot(1,2,2), plt.imshow(np.squeeze(Im2), cmap='gray', aspect="auto", interpolation='none'), plt.colorbar()
# plt.show()
return Im1, Im2
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start", nargs='?', const=1, type=int, default=1, help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print (nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 0
end = 512
sino=(start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
print(ndata.shape)
binning = 8
ndata = tomopy.downsample(ndata, level=binning, axis=1)
print(ndata.shape)
# Normalize to 1 using the air counts
ndata = tomopy.normalize_bg(ndata, air=5)
## slider(ndata)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
rot_center = 960
print("Center of rotation: ", rot_center)
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
def find_center_vo(tomo, ind=None, smin=-50, smax=50, srad=6, step=0.5,
ratio=0.5, drop=20):
"""
Transplanted from TomoPy with minor fixes.
Find rotation axis location using Nghia Vo's method. :cite:`Vo:14`.
Parameters
----------
tomo : ndarray
3D tomographic data.
ind : int, optional
Index of the slice to be used for reconstruction.
smin, smax : int, optional
Coarse search radius. Reference to the horizontal center of the sinogram.
srad : float, optional
Fine search radius.
step : float, optional
Step of fine searching.
ratio : float, optional
The ratio between the FOV of the camera and the size of object.
It's used to generate the mask.
drop : int, optional
Drop lines around vertical center of the mask.
Returns
-------
float
Rotation axis location.
"""
tomo = dtype.as_float32(tomo)
if ind is None:
ind = tomo.shape[1] // 2
_tomo = tomo[:, ind, :]
# Enable cache for FFTW.
pyfftw.interfaces.cache.enable()
# Reduce noise by smooth filters. Use different filters for coarse and fine search
_tomo_cs = ndimage.filters.gaussian_filter(_tomo, (3, 1))
_tomo_fs = ndimage.filters.median_filter(_tomo, (2, 2))
# Coarse and fine searches for finding the rotation center.
if _tomo.shape[0] * _tomo.shape[1] > 4e6: # If data is large (>2kx2k)
_tomo_coarse = downsample(np.expand_dims(_tomo_cs,1), level=2)[:, 0, :]
init_cen = _search_coarse(_tomo_coarse, smin/4, smax/4, ratio, drop)
fine_cen = _search_fine(_tomo_fs, srad, step, init_cen*4, ratio, drop)
else:
init_cen = _search_coarse(_tomo_cs, smin, smax, ratio, drop)
fine_cen = _search_fine(_tomo_fs, srad, step, init_cen, ratio, drop)
# logger.debug('Rotation center search finished: %i', fine_cen)
return fine_cen
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
proj = np.swapaxes(sino,0,1)
flat = np.swapaxes(sflat,0,1)
dark = np.swapaxes(sdark,0,1)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0)
print(proj.shape, dark.shape, flat.shape, theta.shape)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
#slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center/np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def main(arg):
fname = '/local/dataraid/elettra/Oak_16bit_slice343_all_repack.h5'
# Read the hdf raw data.
sino, sflat, sdark, th = dxchange.read_aps_32id(fname)
slider(sino)
proj = np.swapaxes(sino, 0, 1)
flat = np.swapaxes(sflat, 0, 1)
dark = np.swapaxes(sdark, 0, 1)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], ang1=0.0, ang2=180.0)
print(proj.shape, dark.shape, flat.shape, theta.shape)
# Flat-field correction of raw data.
ndata = tomopy.normalize(proj, flat, dark)
#slider(ndata)
# Find rotation center.
rot_center = 962
binning = 1
ndata = tomopy.downsample(ndata, level=int(binning))
rot_center = rot_center / np.power(2, float(binning))
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
def prepare_slice(grid, shift_grid, grid_lines, slice_in_tile, ds_level=0, method='max', blend_options=None, pad=None,
rot_center=None, assert_width=None, sino_blur=None, color_correction=False, normalize=True,
mode='180', phase_retrieval=None, **kwargs):
sinos = [None] * grid.shape[1]
for col in range(grid.shape[1]):
try:
sinos[col] = load_sino(grid[grid_lines[col], col], slice_in_tile[col], normalize=normalize)
except:
pass
t = time.time()
row_sino = register_recon(grid, grid_lines, shift_grid, sinos, method=method, blend_options=blend_options,
color_correction=color_correction, assert_width=assert_width)
if not pad is None:
row_sino, rot_center = pad_sino(row_sino, pad, rot_center)
print('stitch: ' + str(time.time() - t))
print('final size: ' + str(row_sino.shape))
t = time.time()
row_sino = tomopy.downsample(row_sino, level=ds_level)
print('downsample: ' + str(time.time() - t))
print('new shape : ' + str(row_sino.shape))
# t = time.time()
# row_sino = tomopy.remove_stripe_fw(row_sino, 2)
# print('strip removal: ' + str(time.time() - t))
# Minus Log
row_sino = tomosaic.util.preprocess(row_sino)
if sino_blur is not None:
row_sino[:, 0, :] = gaussian_filter(row_sino[:, 0, :], sino_blur)
if mode == '360':
overlap = 2 * (row_sino.shape[2] - rot_center)
row_sino = tomosaic.morph.sino_360_to_180(row_sino, overlap=overlap, rotation='right')
if phase_retrieval:
row_sino = tomopy.retrieve_phase(row_sino, kwargs['pixel_size'], kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
return row_sino, rot_center
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 8 # 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 = 24.9 # Energy of incident wave in keV
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # 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)
# 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)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32")
data_pad[:,:,N//4:5*N//4] = data
data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data = data_pad
rot_center = rot_center+N//4
# 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')
rec = rec[:,N//4:5*N//4,N//4:5*N//4]
print("Algorithm: ", algorithm)
# Mask each reconstructed slice with a circle.
# rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
#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)
# Write data as stack of TIFs.
#tomopy.xtomo_writer(d.data_recon, output_name,
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 _binning(data, params):
data = tomopy.downsample(data, level=int(params.binning), axis=2)
data = tomopy.downsample(data, level=int(params.binning), axis=1)
return data
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 25 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.143e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
#monochromator_energy = 24.9 # Energy of incident wave in keV
# used pink beam
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum) + 1))
fnum_dark = str("%4.4d" % (int(fnum) + 2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
fnflat = '/local/data/2018-11/Chawla/1G_A/1G_A_0002.hdf'
fndark = '/local/data/2018-11/Chawla/1G_A/1G_A_0003.hdf'
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=7,
wname='sym16',
sigma=1,
pad=True)
# 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)
# 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)
#rec = np.swapaxes(rec,0,2)
return rec
def tomo(params):
fname = str(params.input_file_path)
start = params.slice_start
end = params.slice_end
# Read raw data.
if (params.full_reconstruction == False):
end = start + 1
# LOG.info('Slice start/end: %s', end)
proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end))
LOG.info('Slice start/end: %s, %s', start, end)
LOG.info('Data successfully imported: %s', fname)
LOG.info('Projections: %s', proj.shape)
LOG.info('Flat: %s', flat.shape)
LOG.info('Dark: %s', dark.shape)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark)
LOG.info('Normalization completed')
data = tomopy.downsample(data, level=int(params.binning))
LOG.info('Binning: %s', params.binning)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=5,
wname='sym16',
sigma=1,
pad=True)
LOG.info('Ring removal completed')
# 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=290, ind=0, tol=0.5)
# Set rotation center.
rot_center = params.center / np.power(2, float(params.binning))
LOG.info('Rotation center: %s', rot_center)
data = tomopy.minus_log(data)
LOG.info('Minus log compled')
# Reconstruct object using Gridrec algorithm.
LOG.info('Reconstruction started using %s',
params.reconstruction_algorithm)
if (str(params.reconstruction_algorithm) == 'sirt'):
LOG.info('Iteration: %s', params.iteration_count)
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm='sirt',
num_iter=params.iteration_count)
else:
LOG.info('Filter: %s', params.filter)
rec = tomopy.recon(data,
theta,
center=rot_center,
algorithm='gridrec',
filter_name=params.filter)
LOG.info('Reconstrion of %s completed', rec.shape)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
if (params.dry_run == False):
# Write data as stack of TIFs.
fname = str(params.output_path) + 'reco'
dxchange.write_tiff_stack(rec, fname=fname, overwrite=True)
LOG.info('Reconstrcution saved: %s', fname)
if (params.full_reconstruction == False):
return rec
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 31 # 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 = 65 # Energy of incident wave in keV
# used pink beam
alpha = 1e-5 * 2**4 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum) + 1))
fnum_dark = str("%4.4d" % (int(fnum) + 2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
#flat, dum3, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=7,
wname='sym16',
sigma=1,
pad=True)
# 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)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0], data.shape[1], 3 * N // 2],
dtype="float32")
data_pad[:, :, N // 4:5 * N // 4] = data
data_pad[:, :, 0:N // 4] = np.tile(
np.reshape(data[:, :, 0], [data.shape[0], data.shape[1], 1]),
(1, 1, N // 4))
data_pad[:, :, 5 * N // 4:] = np.tile(
np.reshape(data[:, :, -1], [data.shape[0], data.shape[1], 1]),
(1, 1, N // 4))
data = data_pad
rot_center = rot_center + N // 4
nframes = 1
nproj = 1500
theta = np.linspace(0, np.pi * nframes, nproj * nframes, endpoint=False)
rec = np.zeros((nframes, data.shape[1], data.shape[2], data.shape[2]),
dtype='float32')
for time_frame in range(0, nframes):
rec0 = tomopy.recon(data[time_frame * nproj:(time_frame + 1) * nproj],
theta[time_frame * nproj:(time_frame + 1) * nproj],
center=rot_center,
algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95)
rec = rec[:, :, N // 4:5 * N // 4, N // 4:5 * N // 4]
print("Algorithm: ", algorithm)
return rec
file_name = sys.argv[1]
ntheta = 900
sino_start = 0
sino_end = 800
ptheta = 100 # chunk size for reading
binning = 0
for k in range(int(np.ceil(ntheta/ptheta))):
print(k)
prj, flat, dark, theta = dxchange.read_aps_32id(
file_name, sino=(sino_start, sino_end), proj=(ptheta*k,min(ntheta,ptheta*(k+1))))
prj = tomopy.normalize(prj, flat, dark)
prj[prj <= 0] = 1
prj = tomopy.minus_log(prj)
# prj = tomopy.remove_stripe_fw(
# prj, level=7, wname='sym16', sigma=1, pad=True)
# prj = tomopy.remove_stripe_ti(prj,2)
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
# save data
dxchange.write_tiff_stack(prj, f'{file_name[:-3]}/data/d', start=ptheta*k, overwrite=True)
# save theta
np.save(file_name[:-3]+'/data/theta',theta)
print(theta)
def prepare_slice(grid,
shift_grid,
grid_lines,
slice_in_tile,
ds_level=0,
method='max',
blend_options=None,
pad=None,
rot_center=None,
assert_width=None,
sino_blur=None,
color_correction=False,
normalize=True,
mode='180',
phase_retrieval=None,
data_format='aps_32id',
**kwargs):
sinos = [None] * grid.shape[1]
t = time.time()
for col in range(grid.shape[1]):
if os.path.exists(grid[grid_lines[col], col]):
sinos[col] = load_sino(grid[grid_lines[col], col],
slice_in_tile[col],
normalize=normalize,
data_format=data_format)
else:
pass
internal_print('reading: ' + str(time.time() - t))
t = time.time()
row_sino = register_recon(grid,
grid_lines,
shift_grid,
sinos,
method=method,
blend_options=blend_options,
color_correction=color_correction,
assert_width=assert_width)
if not pad is None:
row_sino, rot_center = pad_sino(row_sino, pad, rot_center)
internal_print('stitch: ' + str(time.time() - t))
internal_print('final size: ' + str(row_sino.shape))
t = time.time()
row_sino = tomopy.downsample(row_sino, level=ds_level)
internal_print('downsample: ' + str(time.time() - t))
internal_print('new shape : ' + str(row_sino.shape))
# t = time.time()
# row_sino = tomopy.remove_stripe_fw(row_sino, 2)
# print('strip removal: ' + str(time.time() - t))
# Minus Log
row_sino = tomosaic.util.preprocess(row_sino)
if sino_blur is not None:
row_sino[:, 0, :] = gaussian_filter(row_sino[:, 0, :], sino_blur)
if mode == '360':
overlap = 2 * (row_sino.shape[2] - rot_center)
row_sino = tomosaic.sino_360_to_180(row_sino,
overlap=overlap,
rotation='right')
if phase_retrieval:
row_sino = tomopy.retrieve_phase(row_sino, kwargs['pixel_size'],
kwargs['dist'], kwargs['energy'],
kwargs['alpha'])
return row_sino, rot_center
binning = 1
medfilt_size = 1
nProj = 4841
####################################################################################################################
if 0:
#prj, flat, dark, theta = dxchange.read_aps_32id(file_name, proj=(nProj, nProj+1))
prj, flat, dark, theta = dxchange.read_aps_32id(file_name, proj=(0, nProj+1, 1210)) # open proj from 0 to 720 deg with 180deg step --> 5 proj
prj = tomopy.normalize(prj, flat, dark)
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 > 1.0)] = 1
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
prj = ndimage.median_filter(prj,footprint=np.ones((1, medfilt_size, medfilt_size)))
#prj = np.squeeze(prj); avg = np.mean(prj); std = np.std(prj)
#plt.imshow(prj, cmap='gray', aspect="auto", interpolation='none', vmin=avg-3*std, vmax=avg+3*std), plt.colorbar(), plt.show()
##plt.imshow(prj, cmap='gray', aspect="auto", interpolation='none', vmin=0, vmax=0.1), plt.colorbar(), plt.show()
dxchange.write_tiff(prj, fname='/local/dataraid/2018-06/DeAndrade/2018-06-19/brain_petrapoxy/tmp/data', dtype='float32', overwrite=False)
sino_st = 750
sino_end = 1250
if 0:
prj, flat, dark, theta = dxchange.read_aps_32id(file_name, sino=(sino_st,sino_end,1), proj=(0, 1210, 1)) # open proj from 0 to 720 deg with 180deg step --> 5 proj
prj = tomopy.normalize(prj, flat, dark)
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 31 # 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 = 65 # Energy of incident wave in keV
# used pink beam
alpha = 4*1e-4 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum)+1))
fnum_dark = str("%4.4d" % (int(fnum)+2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
#flat, dum3, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
# 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)
# padding
N = data.shape[2]
data_pad = np.zeros([data.shape[0],data.shape[1],3*N//2],dtype = "float32")
data_pad[:,:,N//4:5*N//4] = data
data_pad[:,:,0:N//4] = np.tile(np.reshape(data[:,:,0],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data_pad[:,:,5*N//4:] = np.tile(np.reshape(data[:,:,-1],[data.shape[0],data.shape[1],1]),(1,1,N//4))
data = data_pad
rot_center = rot_center+N//4
nframes = 8
nproj = 1500
theta = np.linspace(0, np.pi*nframes, nproj*nframes, endpoint=False)
rec = np.zeros(
(nframes, data.shape[1], data.shape[2], data.shape[2]), dtype='float32')
for time_frame in range(0, nframes):
rec0 = tomopy.recon(data[time_frame*nproj:(time_frame+1)*nproj], theta[time_frame*nproj:(
time_frame+1)*nproj], center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95)
rec = rec[:,:,N//4:5*N//4,N//4:5*N//4]
print("Algorithm: ", algorithm)
return rec
def image_downsample(img, ds):
temp = downsample(downsample(img, level=int(np.log2(ds)), axis=1),
level=int(np.log2(ds)),
axis=2)
return temp
# 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)
rec_method = None
#rec_method = 'sirf-fbp'
if rec_method == 'sirf-fbp':
# Reconstruct object using sirt-fbp algorithm.
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
test_sirtfbp_iter = True
if test_sirtfbp_iter:
nCol = ndata.shape[2]
output_name = './test_iter/'
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
def image_downsample(img, ds):
temp = downsample(downsample(img, level=ds-1, axis=1), level=ds-1, axis=2)
return temp
def evaluate(self):
self.tomo.value = tomopy.downsample(
self.tomo.value, level=int(self.level.value), axis=int(self.axis.value))
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument(
"top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start",
nargs='?',
const=1,
type=int,
default=1,
help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print(nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 0
end = 512
sino = (start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
print(ndata.shape)
binning = 8
ndata = tomopy.downsample(ndata, level=binning, axis=1)
print(ndata.shape)
# Normalize to 1 using the air counts
ndata = tomopy.normalize_bg(ndata, air=5)
## slider(ndata)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
rot_center = 960
print("Center of rotation: ", rot_center)
ndata = tomopy.minus_log(ndata)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(ndata, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='/local/dataraid/mark/rec/recon')
# 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)
rec_method = None
#rec_method = 'sirf-fbp'
if rec_method == 'sirf-fbp':
# Reconstruct object using sirt-fbp algorithm.
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
test_sirtfbp_iter = True
if test_sirtfbp_iter:
nCol = ndata.shape[2]
output_name = './test_iter/'
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument(
"top", help="top directory where the tiff images are located: /data/")
parser.add_argument("start",
nargs='?',
const=1,
type=int,
default=1,
help="index of the first image: 1000 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[0]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
print(nfile, index_start, index_end, fname)
# Select the sinogram range to reconstruct.
start = 0
end = 512
sino = (start, end)
# Read the tiff raw data.
ndata = dxchange.read_tiff_stack(fname, ind=ind_tomo, slc=(sino, None))
# Normalize to 1 using the air counts
ndata = tomopy.normalize_bg(ndata, air=5)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(ndata.shape[0])
ndata = tomopy.minus_log(ndata)
# Set binning and number of iterations
binning = 8
iters = 21
print("Original", ndata.shape)
ndata = tomopy.downsample(ndata, level=binning, axis=1)
# ndata = tomopy.downsample(ndata, level=binning, axis=2)
print("Processing:", ndata.shape)
fdir = 'aligned' + '/noblur_iter_' + str(iters) + '_bin_' + str(binning)
print(fdir)
cprj, sx, sy, conv = alignment.align_seq(ndata,
theta,
fdir=fdir,
iters=iters,
pad=(10, 10),
blur=False,
save=True,
debug=True)
np.save(fdir + '/shift_x', sx)
np.save(fdir + '/shift_y', sy)
# Write aligned projections as stack of TIFs.
dxchange.write_tiff_stack(cprj, fname=fdir + '/radios/image')
h5fname = "/home/beams/VNIKITIN/tomobank_rec/dk_MCFG_1_p_s1_.h5"
sino = (1300, 1316) # slices for reconstructions
nframes = 8 # time frames for reconstruction
frame = 94 # middle time frame for reconstruction
nproj = 300 # number of angles for 180 degrees interval
binning = 2
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
proj = proj[(frame-nframes//2)*nproj:(frame+nframes//2)*nproj, :, :]
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(
data, level=7, wname='sym16', sigma=1, pad=True)
# log fitler
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
# Binning
data = tomopy.downsample(data, level=binning, axis=2)
if data.shape[1] > 1:
data = tomopy.downsample(data, level=binning, axis=1)
# reshape for 4d
data = np.reshape(data,[nframes,nproj,data.shape[1],data.shape[2]])
np.save('data.npy',data)
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 reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
sample_detector_distance = 25 # Propagation distance of the wavefront in cm
detector_pixel_size_x = 2.143e-4 # Detector pixel size in cm (5x: 1.17e-4, 2X: 2.93e-4)
#monochromator_energy = 24.9 # Energy of incident wave in keV
# used pink beam
alpha = 1e-02 # Phase retrieval coeff.
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
# Read APS 2-BM raw data.
# DIMAX saves 3 files: proj, flat, dark
# when loading the data set select the proj file (larger size)
fname = os.path.splitext(h5fname)[0]
fbase = fname.rsplit('_', 1)[0]
fnum = fname.rsplit('_', 1)[1]
fext = os.path.splitext(h5fname)[1]
fnum_flat = str("%4.4d" % (int(fnum)+1))
fnum_dark = str("%4.4d" % (int(fnum)+2))
fnproj = fbase + '_' + fnum + fext
fnflat = fbase + '_' + fnum_flat + fext
fndark = fbase + '_' + fnum_dark + fext
fnflat = '/local/data/2018-11/Chawla/1G_A/1G_A_0002.hdf'
fndark = '/local/data/2018-11/Chawla/1G_A/1G_A_0003.hdf'
print('proj', fnproj)
print('flat', fnflat)
print('dark', fndark)
# Read APS 2-BM DIMAX raw data.
proj, dum, dum2, theta = dxchange.read_aps_32id(fnproj, sino=sino)
dum3, flat, dum4, dum5 = dxchange.read_aps_32id(fnflat, sino=sino)
dum6, dum7, dark, dum8 = dxchange.read_aps_32id(fndark, sino=sino)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,pad=True)
# 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)
# 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)
#rec = np.swapaxes(rec,0,2)
return rec
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)))
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)))
if auto_center == False:
tomopy.write_center(prj,theta,dpath=output_path,cen_range=[Center_st/pow(2,level),Center_end/pow(2,level),((Center_end - Center_st)/float(N_recon))/pow(2,level)])
else:
rot_axis = tomopy.find_center_vo(prj)
rot_axis = rot_axis * pow(2,level)
print('*** Rotation center: %0.2f' % rot_axis)
rec = tomopy.recon(prj, theta, center=rot_axis/pow(2,level), algorithm='gridrec', filter_name='parzen')
rec=np.squeeze(rec)
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, nframes, frame, nproj, binning, tv):
# Read APS 32-BM raw data.
print("Read data")
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
print("Processing")
proj = proj[(frame - nframes / 2) * nproj:(frame + nframes / 2) *
nproj, :, :]
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=7,
wname='sym16',
sigma=1,
pad=True)
print("Raw data: ", h5fname)
print("Frames for reconstruction:", (frame - nframes / 2), "..",
(frame + nframes / 2))
# Phase retrieval for tomobank id 00080
# sample_detector_distance = 25
# detector_pixel_size_x = 3.0e-4
# monochromator_energy = 16
# phase retrieval
# data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-03,pad=True)
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
# Binning
data = tomopy.downsample(data, level=binning, axis=2)
if data.shape[1] > 1:
data = tomopy.downsample(data, level=binning, axis=1)
rot_center = data.shape[2] / 2
theta = np.linspace(0, np.pi * nframes, nproj * nframes, endpoint=False)
if tv:
import rectv
# Reconstruct. Iterative TV.
[Ntheta, Nz, N] = data.shape
Nzp = 4 # number of slices to process simultaniously by gpus
M = nframes # number of basis functions, must be a multiple of nframes
lambda0 = pow(2, -9) # regularization parameter 1
lambda1 = pow(2, 2) # regularization parameter 2
niters = 1024 # number of iterations
ngpus = 1 # number of gpus
# reorder input data for compatibility
data = np.ndarray.flatten(data.swapaxes(0, 1))
rec = np.zeros([N * N * Nz * M], dtype='float32') # memory for result
# Make a class for tv
cl = rectv.rectv(N, Ntheta, M, nframes, Nz, Nzp, ngpus, lambda0,
lambda1)
# Run iterations
cl.itertvR_wrap(rec, data, niters)
rec = np.rot90(
np.reshape(rec, [Nz, M, N, N]).swapaxes(0, 1), axes=(2, 3)
) / Ntheta * nframes * 2 # reorder result for compatibility
rec = rec[::M / nframes]
else:
# Reconstruct object. FBP.
rec = np.zeros((nframes, data.shape[1], data.shape[2], data.shape[2]),
dtype='float32')
for time_frame in range(0, nframes):
rec0 = tomopy.recon(
data[time_frame * nproj:(time_frame + 1) * nproj],
theta[time_frame * nproj:(time_frame + 1) * nproj],
center=rot_center - np.mod(time_frame, 2),
algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec[time_frame] = tomopy.circ_mask(rec0, axis=0, ratio=0.95)
return rec
