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 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 clean_sino(h5fname, chunks=None):
import tomopy
import dxchange
# Read APS 32-BM raw data.
#print('reading data')
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=chunks)
#proj, flat, dark, theta = dxchange.read_aps_32id(h5fname)
# # Manage the missing angles:
# if blocked_views is not None:
# print("Blocked Views: ", blocked_views)
# proj = np.concatenate((proj[0:blocked_views[0],:,:], proj[blocked_views[1]+1:-1,:,:]), axis=0)
# theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1:-1]))
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
#data = tomopy.normalize(proj, flat, dark, cutoff=(1,2))
# remove stripes
#print('removing stripes, may take a while',flush=True)
data = tomopy.remove_stripe_fw(data,level=7,wname='sym16',sigma=1,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
data=np.swapaxes(data,0,1)
return data, theta
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, 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)
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
stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2]))
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' + os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
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 rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
data_shape = get_dx_dims(h5fname, 'data')
print(data_shape)
ssino = int(data_shape[1] * nsino)
center_range = (rot_center-center_search_width, rot_center+center_search_width, 0.5)
#print(sino,ssino, center_range)
#print(center_range[0], center_range[1], center_range[2])
# Select sinogram range to reconstruct
sino = None
start = ssino
end = start + 1
sino = (start, end)
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, 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)
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
stack = np.empty((len(np.arange(*center_range)), data_shape[0], data_shape[2]))
index = 0
for axis in np.arange(*center_range):
stack[index] = data[:, 0, :]
index = index + 1
# Reconstruct the same slice with a range of centers.
rec = tomopy.recon(stack, theta, center=np.arange(*center_range), sinogram_order=True, algorithm='gridrec', filter_name='parzen', nchunk=1)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
index = 0
# Save images to a temporary folder.
fname = os.path.dirname(h5fname) + os.sep + 'try_rec/' + path_base_name(h5fname) + os.sep + 'recon_' ##+ os.path.splitext(os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + str('{0:.2f}'.format(axis) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:blocked_views[0]], theta[blocked_views[1] + 1:-1]))
# 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("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
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
rec = tomopy.recon(proj, theta, **_kwargs)
# 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 remove_nan_neg_inf(data, params):
log.info(' *** remove nan, neg and inf')
if(params.fix_nan_and_inf == True):
log.info(' *** *** ON')
log.info(' *** *** replacement value %f ' % params.fix_nan_and_inf_value)
data = tomopy.remove_nan(data, val=params.fix_nan_and_inf_value)
data = tomopy.remove_neg(data, val= 0.0)
data[np.isinf(data)] = params.fix_nan_and_inf_value
else:
log.warning(' *** *** OFF')
return data
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1]+1:-1, :, :]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]],
theta[blocked_views[1]+1: -1]))
# 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("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
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
rec = tomopy.recon(proj, theta, **_kwargs)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
return rec
def prj_pro(prj):
prj = prj + np.abs(prj.min())
prj = prj + prj.mean()
print prj.max(), prj.min()
prj = prj / prj.max()
print prj.max(), prj.min()
prj = tomopy.minus_log(prj)
prj = tomopy.remove_nan(prj)
print prj.max(), prj.min()
prj = tomopy.remove_neg(prj)
print prj.max(), prj.min()
prj = prj * 500
print prj.max(), prj.min()
return prj
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("dname", help="directory containing multiple datasets: /data/")
parser.add_argument("--iters", nargs='?', type=int, default=10, help="number of iteration for alignment (default 7)")
args = parser.parse_args()
dname = args.dname
iters = args.iters
if os.path.isdir(dname):
# Add a trailing slash if missing
top = os.path.join(dname, '')
print("DNAME:", dname)
h5_file_list = list(filter(lambda x: x.endswith(('.h5', '.hdf')), os.listdir(top)))
prj = np.zeros((61, 101, 151), dtype='float32')
for m in range(len(h5_file_list)):
print(h5_file_list[m])
# Read the XRF raw data.
# prj += dx.read_hdf5(os.path.join(top, h5_file_list[m]), dataset='/exchange/data').astype('float32').copy()
# ang = dx.read_hdf5(os.path.join(top, h5_file_list[4]), dataset='/exchange/theta').astype('float32').copy()
# ang *= np.pi / 180.
proj, flat, dark, theta = dx.read_aps_32id(top+h5_file_list[m])
prj += proj
proj, flat, dark, theta = dx.read_aps_32id(top+h5_file_list[0])
ang = theta
# Clean folder.
try:
shutil.rmtree('tmp/iters')
except:
pass
prj = tomopy.remove_nan(prj, val=0.0)
prj = tomopy.remove_neg(prj, val=0.0)
prj[np.where(prj == np.inf)] = 0.0
print (prj.min(), prj.max())
prj, sx, sy, conv = tomopy.align_joint(prj, ang, iters=100, pad=(0, 0),
blur=True, rin=0.8, rout=0.95, center=None,
algorithm='pml_hybrid',
upsample_factor=100,
save=True, debug=True)
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("fname",
help="file name of xrf dxchange file: ./data.h5")
parser.add_argument("--iters",
nargs='?',
type=int,
default=10,
help="number of iteration for alignment (default 10)")
args = parser.parse_args()
fname = args.fname
iters = args.iters
if os.path.isfile(fname):
# Add a trailing slash if missing
top = os.path.join(fname, '')
data, theta, elements = dxr.read_dx_xrf(fname)
print("Elements: ", elements)
prj = np.sum(data, axis=0)
# Clean folder.
try:
shutil.rmtree('tmp/iters')
except:
pass
prj = tomopy.remove_nan(prj, val=0.0)
prj = tomopy.remove_neg(prj, val=0.0)
prj[np.where(prj == np.inf)] = 0.0
prj, sx, sy, conv = tomopy.align_joint(prj,
theta,
iters=iters,
pad=(0, 0),
blur=True,
rin=0.8,
rout=0.95,
center=None,
algorithm='pml_hybrid',
upsample_factor=100,
save=True,
debug=True)
def reconstruct(h5fname, sino, rot_center, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0],:,:], proj[blocked_views[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]], theta[blocked_views[1]+1:-1]))
# 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("Center: ", rot_center)
# # 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)
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
# Reconstruct object.
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec')
# 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
# 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
def reconstruct(h5fname, sino, rot_center, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:blocked_views[0]], theta[blocked_views[1] + 1:-1]))
# 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("Center: ", rot_center)
# Phase retrieval for tomobank id from 00032 to 00056
# sample_detector_distance = 6.0
# detector_pixel_size_x = 0.65e-4
# monochromator_energy = 27.4
# Phase retrieval for tomobank id 00058 and tomobank id 00059
# sample_detector_distance = 6.0
# detector_pixel_size_x = 0.65e-4
# monochromator_energy = 27.4
# Phase retrieval for tomobank id 00060 and tomobank id 00063
# sample_detector_distance = 2.5
# detector_pixel_size_x = 0.65e-4
# monochromator_energy = 27.4
# Phase retrieval for tomobank id 00064
# sample_detector_distance = 0.8
# detector_pixel_size_x = 1.4e-4
# monochromator_energy = 55.0
# Phase retrieval for tomobank id 00065
# sample_detector_distance = 5.8
# detector_pixel_size_x = 1.4e-4
# monochromator_energy = 55.0
# Phase retrieval for tomobank id 00066
# sample_detector_distance = 15.8
# detector_pixel_size_x = 1.4e-4
# monochromator_energy = 55.0
# Phase retrieval for tomobank id 00067
# sample_detector_distance = 30.8
# detector_pixel_size_x = 1.4e-4
# monochromator_energy = 55.0
# Phase retrieval for tomobank id 00068
# sample_detector_distance = 15.0
# detector_pixel_size_x = 4.1e-4
# monochromator_energy = 14.0
# Phase retrieval for tomobank id 00069
# sample_detector_distance = 0.4
# detector_pixel_size_x = 3.7e-4
# monochromator_energy = 36.085
# Phase retrieval for tomobank id 00070
# sample_detector_distance = 5.0
# detector_pixel_size_x = 0.65e-4
# monochromator_energy = 24.999
# Phase retrieval for tomobank id 00071
# sample_detector_distance = 1.5
# detector_pixel_size_x = 0.65e-4
# monochromator_energy = 24.999
# Phase retrieval for tomobank id 00072
# sample_detector_distance = 1.5
# detector_pixel_size_x = 1.43e-4
# monochromator_energy = 20.0
# Phase retrieval for tomobank id 00073
# sample_detector_distance = 1.0
# detector_pixel_size_x = 0.74e-4
# monochromator_energy = 25.0
# Phase retrieval for tomobank id 00074
# sample_detector_distance = 1.0
# detector_pixel_size_x = 0.74e-4
# monochromator_energy = 25.0
# Phase retrieval for tomobank id 00075
# sample_detector_distance = 11.0
# detector_pixel_size_x = 1.43e-4
# monochromator_energy = 60
# Phase retrieval for tomobank id 00076
# sample_detector_distance = 9.0
# detector_pixel_size_x = 2.2e-4
# monochromator_energy = 65
# # 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)
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
# Reconstruct object.
rec = tomopy.recon(data, theta, center=rot_center, algorithm='gridrec')
# 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, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:blocked_views[0]], theta[blocked_views[1] + 1:-1]))
# 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("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
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# use the accelerated version
if algorithm in ["mlem", "sirt"]:
_kwargs["accelerated"] = True
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
sname = os.path.join(args.output_dir, 'proj_{}'.format(args.algorithm))
print(proj.shape)
tmp = np.zeros((proj.shape[0], proj.shape[2]))
tmp[:, :] = proj[:, 0, :]
output_image(tmp, sname + "." + args.format)
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
print("Starting reconstruction with kwargs={}...".format(_kwargs))
rec = tomopy.recon(data, theta, **_kwargs)
print("Completed reconstruction...")
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
obj = np.zeros(rec.shape, dtype=rec.dtype)
label = "{} @ {}".format(algorithm.upper(), h5fname)
quantify_difference(label, obj, rec)
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
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', 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 transform_scalars(dataset, rot_center=0, tune_rot_center=True):
"""Reconstruct sinograms using the tomopy gridrec algorithm
Typically, a data exchange file would be loaded for this
reconstruction. This operation will attempt to perform
flat-field correction of the raw data using the dark and
white background data found in the data exchange file.
This operator also requires either the tomviz/tomopy-pipeline
docker image, or a python environment with tomopy installed.
"""
from tomviz import utils
import numpy as np
import tomopy
# Get the current volume as a numpy array.
array = utils.get_array(dataset)
dark = dataset.dark
white = dataset.white
angles = utils.get_tilt_angles(dataset)
tilt_axis = dataset.tilt_axis
# TomoPy wants the tilt axis to be zero, so ensure that is true
if tilt_axis == 2:
order = [2, 1, 0]
array = np.transpose(array, order)
if dark is not None and white is not None:
dark = np.transpose(dark, order)
white = np.transpose(white, order)
if angles is not None:
# tomopy wants radians
theta = np.radians(angles)
else:
# Assume it is equally spaced between 0 and 180 degrees
theta = tomopy.angles(array.shape[0])
# Perform flat-field correction of raw data
if white is not None and dark is not None:
array = tomopy.normalize(array, white, dark, cutoff=1.4)
if rot_center == 0:
# Try to find it automatically
init = array.shape[2] / 2.0
rot_center = tomopy.find_center(array,
theta,
init=init,
ind=0,
tol=0.5)
elif tune_rot_center:
# Tune the center
rot_center = tomopy.find_center(array,
theta,
init=rot_center,
ind=0,
tol=0.5)
# Calculate -log(array)
array = tomopy.minus_log(array)
# Remove nan, neg, and inf values
array = tomopy.remove_nan(array, val=0.0)
array = tomopy.remove_neg(array, val=0.00)
array[np.where(array == np.inf)] = 0.00
# Perform the reconstruction
array = tomopy.recon(array, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
array = tomopy.circ_mask(array, axis=0, ratio=0.95)
# Set the transformed array
child = utils.make_child_dataset(dataset)
utils.mark_as_volume(child)
utils.set_array(child, array)
return_values = {}
return_values['reconstruction'] = child
return return_values
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("fname", help="Full file name: /data/fname.raw")
parser.add_argument("--start", nargs='?', type=int, default=0, help="First image to read")
parser.add_argument("--nimg", nargs='?', type=int, default=1, help="Number of images to read")
parser.add_argument("--ndark", nargs='?', type=int, default=10, help="Number of dark images")
parser.add_argument("--nflat", nargs='?', type=int, default=10, help="Number of white images")
args = parser.parse_args()
fname = args.fname
start = args.start
end = args.start + args.nimg
nflat, ndark, nimg, height, width = read_adimec_header(fname)
print("Image Size:", width, height)
print("Dataset metadata (nflat, ndark, nimg:", nflat, ndark, nimg)
# override nflat and ndark from header with the passed parameter
# comment the two lines below if the meta data in the binary
# file for nflat and ndark is correct
nflat = args.nflat
ndark = args.ndark
proj = read_adimec_stack(fname, img=(start, end))
print("Projection:", proj.shape)
# slider(proj)
flat = read_adimec_stack(fname, img=(nimg-ndark-nflat, nimg-ndark))
print("Flat:", flat.shape)
# slider(flat)
dark = read_adimec_stack(fname, img=(nimg-ndark, nimg))
print("Dark:", dark.shape)
# slider(dark)
nproj = tomopy.normalize(proj, flat, dark)
print("Normalized projection:", nproj.shape)
# slider(proj)
proj = nproj[:,100:110, :]
print("Sino chunk:", proj.shape)
slider(proj)
theta = tomopy.angles(proj.shape[0])
print(theta.shape)
proj = tomopy.minus_log(proj)
proj = tomopy.remove_nan(proj, val=0.0)
proj = tomopy.remove_neg(proj, val=0.00)
proj[np.where(proj == np.inf)] = 0.00
rot_center = 1280
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
# Write data as stack of TIFs.
dxchange.write_tiff_stack(rec, fname='recon_dir/recon')
for m in range(len(fname)):
print(fname[m])
prj += dx.read_hdf5(os.path.join(mydir, fname[m]),
dataset='/exchange/data').astype('float32').copy()
ang = dx.read_hdf5(os.path.join(mydir, f),
dataset='/exchange/theta').astype('float32').copy()
ang *= np.pi / 180.
# Clean folder.
try:
shutil.rmtree('tmp/iters')
except:
pass
prj = tomopy.remove_nan(prj, val=0.0)
prj = tomopy.remove_neg(prj, val=0.0)
prj[np.where(prj == np.inf)] = 0.0
# prj = tomopy.median_filter(prj, size=3)
print(prj.min(), prj.max())
prj, sx, sy, conv = tomopy.align_joint(prj,
ang,
iters=100,
pad=(0, 0),
blur=True,
rin=0.8,
rout=0.95,
center=None,
algorithm='pml_hybrid',
upsample_factor=100,
def main():
args = parse_arguments()
context = zmq.Context()
# TQM setup
if args.my_distributor_addr is not None:
addr_split = re.split("://|:", args.my_distributor_addr)
tmq.init_tmq()
# Handshake w. remote processes
print(addr_split)
tmq.handshake(addr_split[1], int(addr_split[2]), args.num_sinograms,
args.num_columns)
else:
print("No distributor..")
# Subscriber setup
print("Subscribing to: {}".format(args.data_source_addr))
subscriber_socket = context.socket(zmq.SUB)
subscriber_socket.set_hwm(args.data_source_hwm)
subscriber_socket.connect(args.data_source_addr)
subscriber_socket.setsockopt(zmq.SUBSCRIBE, b'')
# Local publisher socket
if args.my_publisher_addr is not None:
publisher_socket = context.socket(zmq.PUB)
publisher_socket.set_hwm(200) # XXX
publisher_socket.bind(args.my_publisher_addr)
if args.data_source_synch_addr is not None:
synchronize_subs(context, args.data_source_synch_addr)
# Setup flatbuffer builder and serializer
builder = flatbuffers.Builder(0)
serializer = TraceSerializer.ImageSerializer(builder)
# White/dark fields
white_imgs = []
tot_white_imgs = 0
dark_imgs = []
tot_dark_imgs = 0
# Receive images
total_received = 0
total_size = 0
seq = 0
time0 = time.time()
while True:
msg = subscriber_socket.recv()
total_received += 1
total_size += len(msg)
if msg == b"end_data": break # End of data acquisition
# This is mostly for data rate tests
if args.skip_serialize:
print("Skipping rest. Received msg: {}".format(total_received))
continue
# Deserialize msg to image
read_image = serializer.deserialize(serialized_image=msg)
serializer.info(read_image) # print image information
# Local checks
if args.check_seq:
if seq != read_image.Seq():
print("Wrong sequence number: {} != {}".format(
seq, read_image.Seq()))
seq += 1
# Push image to workers (REQ/REP)
my_image_np = read_image.TdataAsNumpy()
if args.uint8_to_float32:
my_image_np.dtype = np.uint8
sub = np.array(my_image_np, dtype="float32")
elif args.uint16_to_float32:
my_image_np.dtype = np.uint16
sub = np.array(my_image_np, dtype="float32")
else:
sub = my_image_np
sub = sub.reshape((1, read_image.Dims().Y(), read_image.Dims().X()))
# If incoming data is projection
if read_image.Itype() is serializer.ITypes.Projection:
rotation = read_image.Rotation()
if args.degree_to_radian: rotation = rotation * math.pi / 180.
# Tomopy operations expect 3D data, reshape incoming projections.
if args.normalize:
# flat/dark fields' corresponding rows
if tot_white_imgs > 0 and tot_dark_imgs > 0:
print(
"normalizing: white_imgs.shape={}; dark_imgs.shape={}".
format(
np.array(white_imgs).shape,
np.array(dark_imgs).shape))
sub = tp.normalize(sub, flat=white_imgs, dark=dark_imgs)
if args.remove_stripes:
print("removing stripes")
sub = tp.remove_stripe_fw(sub,
level=7,
wname='sym16',
sigma=1,
pad=True)
if args.mlog:
print("applying -log")
sub = -np.log(sub)
if args.remove_invalids:
print("removing invalids")
sub = tp.remove_nan(sub, val=0.0)
sub = tp.remove_neg(sub, val=0.00)
sub[np.where(sub == np.inf)] = 0.00
# Publish to the world
if (args.my_publisher_addr
is not None) and (total_received % args.my_publisher_freq
== 0):
builder.Reset()
serializer = TraceSerializer.ImageSerializer(builder)
mub = np.reshape(
sub, (read_image.Dims().Y(), read_image.Dims().X()))
serialized_data = serializer.serialize(
image=mub,
uniqueId=0,
rotation=0,
itype=serializer.ITypes.Projection)
print("Publishing:{}".format(read_image.UniqueId()))
publisher_socket.send(serialized_data)
# Send to workers
if args.num_sinograms is not None:
sub = sub[:, args.beg_sinogram:args.beg_sinogram +
args.num_sinograms, :]
ncols = sub.shape[2]
sub = sub.reshape(sub.shape[0] * sub.shape[1] * sub.shape[2])
if args.my_distributor_addr is not None:
tmq.push_image(sub, args.num_sinograms, ncols, rotation,
read_image.UniqueId(), read_image.Center())
# If incoming data is white field
if read_image.Itype() is serializer.ITypes.White:
#print("White field data is received: {}".format(read_image.UniqueId()))
white_imgs.extend(sub)
tot_white_imgs += 1
# If incoming data is white-reset
if read_image.Itype() is serializer.ITypes.WhiteReset:
#print("White-reset data is received: {}".format(read_image.UniqueId()))
white_imgs = []
white_imgs.extend(sub)
tot_white_imgs += 1
# If incoming data is dark field
if read_image.Itype() is serializer.ITypes.Dark:
#print("Dark data is received: {}".format(read_image.UniqueId()))
dark_imgs.extend(sub)
tot_dark_imgs += 1
# If incoming data is dark-reset
if read_image.Itype() is serializer.ITypes.DarkReset:
#print("Dark-reset data is received: {}".format(read_image.UniqueId()))
dark_imgs = []
dark_imgs.extend(sub)
tot_dark_imgs += 1
time1 = time.time()
# Profile information
elapsed_time = time1 - time0
tot_MiBs = (total_size * 1.) / 2**20
print(
"Received number of projections: {}; Total size (MiB): {:.2f}; Elapsed time (s): {:.2f}"
.format(total_received, tot_MiBs, elapsed_time))
print("Rate (MiB/s): {:.2f}; (msg/s): {:.2f}".format(
tot_MiBs / elapsed_time, total_received / elapsed_time))
# Finalize TMQ
if not args.my_distributor_addr is not None:
tmq.done_image()
tmq.finalize_tmq()
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("dname",
help="directory containing multiple datasets: /data/")
parser.add_argument("--iters",
nargs='?',
type=int,
default=10,
help="number of iteration for alignment (default 7)")
args = parser.parse_args()
dname = args.dname
iters = args.iters
if os.path.isdir(dname):
# Add a trailing slash if missing
top = os.path.join(dname, '')
print("DNAME:", dname)
h5_file_list = list(
filter(lambda x: x.endswith(('.h5', '.hdf')), os.listdir(top)))
prj = np.zeros((61, 101, 151), dtype='float32')
for m in range(len(h5_file_list)):
print(h5_file_list[m])
# Read the XRF raw data.
# prj += dx.read_hdf5(os.path.join(top, h5_file_list[m]), dataset='/exchange/data').astype('float32').copy()
# ang = dx.read_hdf5(os.path.join(top, h5_file_list[4]), dataset='/exchange/theta').astype('float32').copy()
# ang *= np.pi / 180.
proj, flat, dark, theta = dx.read_aps_32id(top + h5_file_list[m])
prj += proj
proj, flat, dark, theta = dx.read_aps_32id(top + h5_file_list[0])
ang = theta
# Clean folder.
try:
shutil.rmtree('tmp/iters')
except:
pass
prj = tomopy.remove_nan(prj, val=0.0)
prj = tomopy.remove_neg(prj, val=0.0)
prj[np.where(prj == np.inf)] = 0.0
print(prj.min(), prj.max())
prj, sx, sy, conv = tomopy.align_joint(prj,
ang,
iters=100,
pad=(0, 0),
blur=True,
rin=0.8,
rout=0.95,
center=None,
algorithm='pml_hybrid',
upsample_factor=100,
save=True,
debug=True)
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
prj = np.zeros((61, 101, 151), dtype='float32')
for m in range(len(fname)):
print(fname[m])
prj += dx.read_hdf5(os.path.join(mydir, fname[m]), dataset='/exchange/data').astype('float32').copy()
ang = dx.read_hdf5(os.path.join(mydir, f), dataset='/exchange/theta').astype('float32').copy()
ang *= np.pi / 180.
# Clean folder.
try:
shutil.rmtree('tmp/iters')
except:
pass
prj = tomopy.remove_nan(prj, val=0.0)
prj = tomopy.remove_neg(prj, val=0.0)
prj[np.where(prj == np.inf)] = 0.0
# prj = tomopy.median_filter(prj, size=3)
print (prj.min(), prj.max())
prj, sx, sy, conv = tomopy.align_joint(prj, ang, iters=100, pad=(0, 0),
blur=True, rin=0.8, rout=0.95, center=None,
algorithm='pml_hybrid',
upsample_factor=100,
save=True, debug=True)
# np.save('tmp/sx.npy', sx)
# np.save('tmp/sy.npy', sy)
def reconstruct(h5fname, sino, rot_center, args, blocked_views=None):
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(h5fname, sino=sino)
# Manage the missing angles:
if blocked_views is not None:
print("Blocked Views: ", blocked_views)
proj = np.concatenate((proj[0:blocked_views[0], :, :],
proj[blocked_views[1]+1:-1, :, :]), axis=0)
theta = np.concatenate((theta[0:blocked_views[0]],
theta[blocked_views[1]+1: -1]))
# 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("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
algorithm = args.algorithm
ncores = args.ncores
nitr = args.num_iter
# always add algorithm
_kwargs = {"algorithm": algorithm}
# assign number of cores
_kwargs["ncore"] = ncores
# use the accelerated version
if algorithm in ["mlem", "sirt"]:
_kwargs["accelerated"] = True
# don't assign "num_iter" if gridrec or fbp
if algorithm not in ["fbp", "gridrec"]:
_kwargs["num_iter"] = nitr
sname = os.path.join(args.output_dir, 'proj_{}'.format(args.algorithm))
print(proj.shape)
tmp = np.zeros((proj.shape[0], proj.shape[2]))
tmp[:,:] = proj[:,0,:]
output_image(tmp, sname + "." + args.format)
# Reconstruct object.
with timemory.util.auto_timer(
"[tomopy.recon(algorithm='{}')]".format(algorithm)):
print("Starting reconstruction with kwargs={}...".format(_kwargs))
rec = tomopy.recon(data, theta, **_kwargs)
print("Completed reconstruction...")
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
obj = np.zeros(rec.shape, dtype=rec.dtype)
label = "{} @ {}".format(algorithm.upper(), h5fname)
quantify_difference(label, obj, rec)
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
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
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, 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