def build_panorama(src_folder, file_grid, shift_grid, frame=0, method='max', method2=None, blend_options={}, blend_options2={},
blur=None, color_correction=False, margin=100):
t00 = time.time()
root = os.getcwd()
os.chdir(src_folder)
cam_size = g_shapes(file_grid[0, 0])
cam_size = cam_size[1:3]
img_size = shift_grid[-1, -1] + cam_size
buff = np.zeros([1, 1])
last_none = False
if method2 is None:
for (y, x), value in np.ndenumerate(file_grid):
if (value != None and frame < g_shapes(value)[0]):
prj, flt, drk = read_aps_32id_adaptive(value, proj=(frame, frame + 1))
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj, blur=blur)
t0 = time.time()
buff = blend(buff, np.squeeze(prj), shift_grid[y, x, :], method=method, color_correction=color_correction, **blend_options)
print('Rank: {:d}; Frame: {:d}; Pos: ({:d}, {:d}); Method: {:s}; Color Corr.:{:b}; Tile stitched in '
'{:.2f} s.'.format(rank, frame, y, x, method, color_correction, time.time()-t0))
if last_none:
buff[margin:, margin:-margin][np.isnan(buff[margin:, margin:-margin])] = 0
last_none = False
else:
last_none = True
else:
for y in range(file_grid.shape[0]):
temp_grid = file_grid[y:y+1, :]
temp_shift = np.copy(shift_grid[y:y+1, :, :])
offset = np.min(temp_shift[:, :, 0])
temp_shift[:, :, 0] = temp_shift[:, :, 0] - offset
row_buff = np.zeros([1, 1])
prj, flt, drk = read_aps_32id_adaptive(temp_grid[0, 0], proj=(frame, frame + 1))
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj, blur=blur)
row_buff, _ = arrange_image(row_buff, np.squeeze(prj), temp_shift[0, 0, :], order=1)
for x in range(1, temp_grid.shape[1]):
value = temp_grid[0, x]
if (value != None and frame < g_shapes(value)[0]):
prj, flt, drk = read_aps_32id_adaptive(value, proj=(frame, frame + 1))
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj, blur=blur)
t0 = time.time()
row_buff = blend(row_buff, np.squeeze(prj), temp_shift[0, x, :], method=method, color_correction=color_correction, **blend_options)
print('Rank: {:d}; Frame: {:d}; Pos: ({:d}, {:d}); Method: {:s}; Color Corr.:{:b}; Tile stitched in '
'{:.2f} s.'.format(rank, frame, y, x, method, color_correction, time.time() - t0))
if last_none:
row_buff[margin:, margin:-margin][np.isnan(row_buff[margin:, margin:-margin])] = 0
last_none = False
else:
last_none = True
t0 = time.time()
buff = blend(buff, row_buff, [offset, 0], method=method2, color_correction=False, **blend_options2)
print('Rank: {:d}; Frame: {:d}; Row: {:d}; Row stitched in {:.2f} s.'.format(rank, frame, y, time.time()-t0))
print('Rank: {:d}; Frame: {:d}; Panorama built in {:.2f} s.'.format(rank, frame, time.time()-t00))
os.chdir(root)
return buff
def rec_test(file_name, sino_start, sino_end):
print "\n#### Processing " + file_name
sino_start = sino_start + 200
sino_end = sino_start + 2
print "Test reconstruction of slice [%d]" % sino_start
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_start, sino_end))
# Manage the missing angles:
theta = tomopy.angles(prj.shape[0])
prj = np.concatenate((prj[0 : miss_angles[0], :, :], prj[miss_angles[1] + 1 : -1, :, :]), axis=0)
theta = np.concatenate((theta[0 : miss_angles[0]], theta[miss_angles[1] + 1 : -1]))
# normalize the prj
prj = tomopy.normalize(prj, flat, dark)
# reconstruct
rec = tomopy.recon(prj, theta, center=best_center, algorithm="gridrec", emission=False)
# Write data as stack of TIFs.
tomopy.io.writer.write_tiff_stack(rec, fname=output_name)
print "Slice saved as [%s_00000.tiff]" % output_name
# show the reconstructed slice
pl.gray()
pl.axis("off")
pl.imshow(rec[0])
def main(argv):
try:
opts, args = getopt.getopt(argv,"hc:s:",["core=","sino="])
except getopt.GetoptError:
print 'test.py -c <ncore> -s <nsino>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'test.py -c <ncore> -s <nsino>'
sys.exit()
elif opt in ("-c", "--core"):
ncore = int(arg)
elif opt in ("-s", "--sino"):
nsino = int(arg)
file_name = '/local/decarlo/data/proj_10.hdf'
output_name = './recon/proj10_rec'
sino_start = 200
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_start, sino_start+nsino))
# Fix flats because sample did not move
flat = np.full((flat.shape[0], flat.shape[1], flat.shape[2]), 1000)
# Set angles
theta = tomopy.angles(prj.shape[0])
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=0, type=int, default=0, help="index of the first image: 10001 (default 0)")
args = parser.parse_args()
top = args.top
# Select the sinogram range to reconstruct.
start = 290
end = 294
print(top)
# Read the Australian Synchrotron Facility data
proj, flat, dark = dxchange.read_aps_5bm(top)
# proj, flat, dark = dxchange.read_aps_5bm(fname, sino=(start, end))
slider(proj)
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0])
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
slider(proj)
def rec_test(file_name, sino_start, sino_end, astra_method, extra_options, num_iter=1):
print '\n#### Processing '+ file_name
sino_start = sino_start + 200
sino_end = sino_start + 2
print "Test reconstruction of slice [%d]" % sino_start
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_start, sino_end))
# Manage the missing angles:
theta = tomopy.angles(prj.shape[0])
prj = np.concatenate((prj[0:miss_angles[0],:,:], prj[miss_angles[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
# normalize the prj
prj = tomopy.normalize(prj, flat, dark)
# remove ring artefacts
prjn = tomopy.remove_stripe_fw(prj)
# reconstruct
rec = tomopy.recon(prj[:,::reduce_amount,::reduce_amount], theta, center=float(best_center)/reduce_amount, algorithm=tomopy.astra, options={'proj_type':proj_type,'method':astra_method,'extra_options':extra_options,'num_iter':num_iter}, emission=False)
# Write data as stack of TIFs.
tomopy.io.writer.write_tiff_stack(rec, fname=output_name)
print "Slice saved as [%s_00000.tiff]" % output_name
def rec_try(h5fname, nsino, rot_center, center_search_width, algorithm, binning):
zinger_level = 800 # Zinger level for projections
zinger_level_w = 1000 # Zinger level for white
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)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
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)
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) + '/' + 'try_rec/' + '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 find_rotation_axis(h5fname, nsino):
data_size = get_dx_dims(h5fname, 'data')
ssino = int(data_size[1] * nsino)
# 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=5,wname='sym16',sigma=1,pad=True)
# find rotation center
rot_center = tomopy.find_center_vo(data)
return rot_center
def main():
#****************************************************************************
file_name = '/local/dataraid/databank/dataExchange/tmp/Australian_rank3.h5'
output_name = '/local/dataraid/databank/dataExchange/tmp/rec/Australian_rank3'
sino_start = 290
sino_end = 294
# Read HDF5 file.
exchange_rank = 3;
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, exchange_rank, sino=(sino_start, sino_end))
theta = tomopy.angles(prj.shape[0])
# normalize the data
prj = tomopy.normalize(prj, flat, dark)
best_center=1184
print "Best Center: ", best_center
calc_center = best_center
#calc_center = tomopy.find_center(prj, theta, emission=False, ind=0, init=best_center, tol=0.8)
print "Calculated Center:", calc_center
# reconstruct
rec = tomopy.recon(prj, theta, center=calc_center, algorithm='gridrec', emission=False)
#rec = tomopy.circ_mask(rec, axis=0)
# Write data as stack of TIFs.
tomopy.io.writer.write_tiff_stack(rec, fname=output_name)
plt.gray()
plt.axis('off')
plt.imshow(rec[0])
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 = 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 save_partial_frames(file_grid, save_folder, prefix, frame=0):
for (y, x), value in np.ndenumerate(file_grid):
print(value)
if (value != None):
prj, flt, drk = read_aps_32id_adaptive(value, proj=(frame, frame + 1))
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj)
fname = prefix + 'Y' + str(y).zfill(2) + '_X' + str(x).zfill(2)
dxchange.write_tiff(np.squeeze(prj), fname=os.path.join(save_folder, 'partial_frames', 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
# 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 load_sino(filename, sino_n, normalize=True):
print('Loading {:s}, slice {:d}'.format(filename, sino_n))
sino_n = int(sino_n)
sino, flt, drk = read_aps_32id_adaptive(filename, sino=(sino_n, sino_n + 1))
if not normalize:
flt[:, :, :] = flt.max()
drk[:, :, :] = 0
sino = tomopy.normalize(sino, flt, drk)
# 1st slice of each tile of some samples contains mostly abnormally large values which should be removed.
if sino.max() > 1e2:
sino[np.abs(sino) > 1] = 1
return np.squeeze(sino)
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 write_first_frames(ui):
root = os.getcwd()
os.chdir(ui.raw_folder)
try:
os.mkdir('first_frames')
except:
pass
for i in ui.filelist:
ui.boxMetaOut.insert(END, i + '\n')
prj, flt, drk = dxchange.read_aps_32id(i, proj=(0, 1))
prj = tomopy.normalize(prj, flt, drk)
dxchange.write_tiff(prj, os.path.join('first_frames', os.path.splitext(i)[0]))
def main(argv):
try:
opts, args = getopt.getopt(argv,"hc:s:",["core=","sino="])
except getopt.GetoptError:
print 'test.py -c <ncore> -s <nsino>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'test.py -c <ncore> -s <nsino>'
sys.exit()
elif opt in ("-c", "--core"):
ncore = int(arg)
elif opt in ("-s", "--sino"):
nsino = int(arg)
# **********************************************
#file_name = '/local/decarlo/data/proj_10.hdf'
#output_name = './recon/proj10_rec'
#sino_start = 0
#sino_end = 2048
# **********************************************
file_name = '/local/decarlo/data/Hornby_APS_2011.h5'
output_name = './recon/Hornby_APS_2011_'
best_center=1024
sino_start = 0
sino_end = 1792
# **********************************************
step_00 = time.time()
step_02_delta_total = 0
count = 0
while (sino_start <= (sino_end - nsino)):
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_start, sino_start+nsino))
# Fix flats because sample did not move
flat = np.full((flat.shape[0], flat.shape[1], flat.shape[2]), 1000)
# Set angles
theta = tomopy.angles(prj.shape[0])
# normalize the prj
prj = tomopy.normalize(prj, flat, dark)
best_center = 1298
step_01 = time.time()
# reconstruct
rec = tomopy.recon(prj, theta, center=best_center, algorithm='gridrec', emission=False, ncore = ncore)
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: 10001 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
template = os.listdir(top)[1]
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
index_end = index_start + nfile
ind_tomo = range(index_start, index_end)
fname = top + template
# Read the tiff raw data.
rdata = dxchange.read_tiff_stack(fname, ind=ind_tomo)
particle_bed_reference = particle_bed_location(rdata[0], plot=False)
print("Particle bed location: ", particle_bed_reference)
# Cut the images to remove the particle bed
cdata = rdata[:, 0:particle_bed_reference, :]
# Find the image when the shutter starts to close
dark_index = shutter_off(rdata)
print("shutter closes on image: ", dark_index)
# Set the [start, end] index of the blocked images, flat and dark.
flat_range = [0, 1]
data_range = [48, dark_index]
dark_range = [dark_index, nfile]
# # for fast testing
# data_range = [48, dark_index]
flat = cdata[flat_range[0]:flat_range[1], :, :]
proj = cdata[data_range[0]:data_range[1], :, :]
dark = np.zeros((dark_range[1]-dark_range[0], proj.shape[1], proj.shape[2]))
# if you want to use the shutter closed images as dark uncomment this:
#dark = cdata[dark_range[0]:dark_range[1], :, :]
ndata = tomopy.normalize(proj, flat, dark)
ndata = tomopy.normalize_bg(ndata, air=ndata.shape[2]/2.5)
ndata = tomopy.minus_log(ndata)
sharpening(ndata)
slider(ndata)
def main(arg):
parser = argparse.ArgumentParser()
parser.add_argument("fname", help="file name of a single dataset to normalize: /data/sample.h5")
args = parser.parse_args()
fname = args.fname
if os.path.isfile(fname):
data_shape = get_dx_dims(fname, 'data')
# Select projgram range to reconstruct.
proj_start = 0
proj_end = data_shape[0]
chunks = 6 # number of projgram chunks to reconstruct
# only one chunk at the time is converted
# allowing for limited RAM machines to complete a full reconstruction
nProj_per_chunk = (proj_end - proj_start)/chunks
print("Normalizing [%d] slices from slice [%d] to [%d] in [%d] chunks of [%d] slices each" % ((proj_end - proj_start), proj_start, proj_end, chunks, nProj_per_chunk))
strt = 0
for iChunk in range(0,chunks):
print('\n -- chunk # %i' % (iChunk+1))
proj_chunk_start = np.int(proj_start + nProj_per_chunk*iChunk)
proj_chunk_end = np.int(proj_start + nProj_per_chunk*(iChunk+1))
print('\n --------> [%i, %i]' % (proj_chunk_start, proj_chunk_end))
if proj_chunk_end > proj_end:
break
nproj = (int(proj_chunk_start), int(proj_chunk_end))
# Reconstruct.
proj, flat, dark, dummy = dxchange.read_aps_32id(fname, proj=nproj)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=0.9)
# Write data as stack of TIFs.
tifffname = os.path.dirname(fname) + os.sep + os.path.splitext(os.path.basename(fname))[0]+ '_tiff' + os.sep + os.path.splitext(os.path.basename(fname))[0]
print("Converted files: ", tifffname)
dxchange.write_tiff_stack(data, fname=tifffname, start=strt)
strt += nproj[1] - nproj[0]
else:
print("File Name does not exist: ", fname)
def flat_correction(proj, flat, dark, params):
log.info(' *** normalization')
if (params.flat_correction_method == 'standard'):
data = tomopy.normalize(proj,
flat,
dark,
cutoff=params.normalization_cutoff)
log.info(' *** *** ON %f cut-off' % params.normalization_cutoff)
elif (params.flat_correction_method == 'air'):
data = tomopy.normalize_bg(proj, air=params.air)
log.info(' *** *** air %d pixels' % params.air)
elif (params.flat_correction_method == 'none'):
data = proj
log.warning(' *** *** normalization is turned off')
return data
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 save_partial_frames(file_grid,
save_folder,
prefix,
frame=0,
data_format='aps_32id'):
for (y, x), value in np.ndenumerate(file_grid):
print(value)
if (value != None):
prj, flt, drk, _ = read_data_adaptive(value,
proj=(frame, frame + 1),
data_format=data_format)
prj = tomopy.normalize(prj, flt, drk)
prj = preprocess(prj)
fname = prefix + 'Y' + str(y).zfill(2) + '_X' + str(x).zfill(2)
dxchange.write_tiff(np.squeeze(prj),
fname=os.path.join(save_folder,
'partial_frames', fname))
def rec_full(file_name, sino_start, sino_end):
print "\n#### Processing " + file_name
chunks = 10 # number of data chunks for the reconstruction
nSino_per_chunk = (sino_end - sino_start) / chunks
print "Reconstructing [%d] slices from slice [%d] to [%d] in [%d] chunks of [%d] slices each" % (
(sino_end - sino_start),
sino_start,
sino_end,
chunks,
nSino_per_chunk,
)
for iChunk in range(0, chunks):
print "\n -- chunk # %i" % (iChunk + 1)
sino_chunk_start = sino_start + nSino_per_chunk * iChunk
sino_chunk_end = sino_start + nSino_per_chunk * (iChunk + 1)
print "\n --------> [%i, %i]" % (sino_chunk_start, sino_chunk_end)
if sino_chunk_end > sino_end:
break
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_chunk_start, sino_chunk_end))
# Manage the missing angles:
theta = tomopy.angles(prj.shape[0])
prj = np.concatenate((prj[0 : miss_angles[0], :, :], prj[miss_angles[1] + 1 : -1, :, :]), axis=0)
theta = np.concatenate((theta[0 : miss_angles[0]], theta[miss_angles[1] + 1 : -1]))
# normalize the prj
prj = tomopy.normalize(prj, flat, dark)
# reconstruct
rec = tomopy.recon(prj, theta, center=best_center, algorithm="gridrec", emission=False)
print output_name
# Write data as stack of TIFs.
tomopy.io.writer.write_tiff_stack(rec, fname=output_name, start=sino_chunk_start)
def rec_full(file_name, sino_start, sino_end, astra_method, extra_options, num_iter=1):
print '\n#### Processing '+ file_name
chunks = 10 # number of data chunks for the reconstruction
nSino_per_chunk = (sino_end - sino_start)/chunks
print "Reconstructing [%d] slices from slice [%d] to [%d] in [%d] chunks of [%d] slices each" % ((sino_end - sino_start), sino_start, sino_end, chunks, nSino_per_chunk)
strt = 0
for iChunk in range(0,chunks):
print '\n -- chunk # %i' % (iChunk+1)
sino_chunk_start = sino_start + nSino_per_chunk*iChunk
sino_chunk_end = sino_start + nSino_per_chunk*(iChunk+1)
print '\n --------> [%i, %i]' % (sino_chunk_start, sino_chunk_end)
if sino_chunk_end > sino_end:
break
# Read HDF5 file.
prj, flat, dark = tomopy.io.exchange.read_aps_32id(file_name, sino=(sino_chunk_start, sino_chunk_end))
# Manage the missing angles:
theta = tomopy.angles(prj.shape[0])
prj = np.concatenate((prj[0:miss_angles[0],:,:], prj[miss_angles[1]+1:-1,:,:]), axis=0)
theta = np.concatenate((theta[0:miss_angles[0]], theta[miss_angles[1]+1:-1]))
# normalize the prj
prj = tomopy.normalize(prj, flat, dark)
# remove ring artefacts
prj = tomopy.remove_stripe_fw(prj)
# reconstruct
rec = tomopy.recon(prj[:,::reduce_amount,::reduce_amount], theta, center=float(best_center)/reduce_amount, algorithm=tomopy.astra, options={'proj_type':proj_type,'method':astra_method,'extra_options':extra_options,'num_iter':num_iter}, emission=False)
print output_name
# Write data as stack of TIFs.
tomopy.io.writer.write_tiff_stack(rec, fname=output_name, start=strt)
strt += prj[:,::reduce_amount,:].shape[1]
def preprocess_13bm(fname,
dark_value=None,
zinger_threshold=0.2,
zinger_filter_size=3):
logging.basicConfig(
#filename=fname + '_preprocess.log',
#filemode='w',
format='%(asctime)s %(levelname)-8s %(message)s',
level=logging.INFO,
datefmt='%Y-%m-%d %H:%M:%S')
logging.info('Reading data')
proj, flat, dark, theta = dxchange.read_aps_13bm(fname + '.h5', 'hdf5')
logging.info('proj.shape: %s', proj.shape)
logging.info('flat.shape: %s', flat.shape)
logging.info('dark.shape: %s', dark.shape)
if (dark_value != None):
dark = dark * 0 + dark_value
logging.info('dark[0]: %s', dark.item(0))
logging.info('last theta: %s', theta[-1])
logging.info('Normalizing')
proj = tomopy.normalize(proj, flat, dark)
logging.info("After normalizing, min=%f, max=%f", proj.min(), proj.max())
logging.info('Removing zingers. threshold=%f, size=%d', zinger_threshold,
zinger_filter_size)
proj = tomopy.misc.corr.remove_outlier(proj,
zinger_threshold,
size=zinger_filter_size)
logging.info("After remove_outlier, min=%f, max=%f", proj.min(),
proj.max())
logging.info('Converting to integer')
proj = (10000. * proj).astype(np.int16)
logging.info('Writing volume file')
dxchange.write_netcdf4(proj, fname=fname + '.volume')
logging.info('Volume file written')
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 flat_correction(proj, flat, dark, params):
log.info(' *** normalization')
if(params.flat_correction_method == 'standard'):
try:
data = tomopy.normalize(proj, flat, dark,
cutoff=params.normalization_cutoff / params.bright_exp_ratio)
data *= params.bright_exp_ratio
except AttributeError:
log.warning(' *** *** No bright_exp_ratio found. Ignore')
log.info(' *** *** ON %f cut-off' % params.normalization_cutoff)
elif(params.flat_correction_method == 'air'):
data = tomopy.normalize_bg(proj, air=params.air)
log.info(' *** *** air %d pixels' % params.air)
elif(params.flat_correction_method == 'none'):
data = proj
log.warning(' *** *** normalization is turned off')
else:
raise ValueError("Unknown value for *flat_correction_method*: {}. "
"Valid options are {}"
"".format(params.flat_correction_method,
config.SECTIONS['flat-correction']['flat-correction-method']['choices']))
return data
def main():
data_top = '/local/data/2020-02/Stock/'
file_name = '099_B949_81_84_B2'
# data_top = '/local/data/'
# file_name = 'tomo_00001'
top = '/local/data/2020-02/Stock/'
# log.info(os.listdir(top))
h5_file_list = list(filter(lambda x: x.endswith(('.h5', '.hdf')), os.listdir(top)))
h5_file_list.sort()
print("Found: %s" % h5_file_list)
for fname in h5_file_list:
full_file_name = data_top + fname
data_size = get_dx_dims(full_file_name)
print(data_size)
ssino = int(data_size[1] * 0.5)
detector_center = int(data_size[2] * 0.5)
# Select sinogram range to reconstruct
sino_start = ssino
sino_end = sino_start + 10
sino = (int(sino_start), int(sino_end))
# Read APS 2-BM raw data
proj, flat, dark, theta = dxchange.read_aps_32id(full_file_name, sino=sino)
tomo_ind = tomopy.normalize(proj, flat, dark)
print(os.path.splitext(full_file_name)[0]+'_sino')
dxchange.write_tiff_stack(tomo_ind,fname=os.path.splitext(full_file_name)[0]+'_sino', axis=1)
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 fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger('fast_tomopy.fast_tomo_recon')
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument('-i',
'--input',
type=str,
help='path to input raw '
'dataset',
required=True)
parser.add_argument('-o',
'--output-file',
type=str,
help='full path to h5 output '
'file',
default=os.path.join(os.getcwd(), "fast-tomopy.h5"))
parser.add_argument('-sn',
'--sino-num',
type=int,
help='Number of slices '
'to reconstruct',
default=5)
parser.add_argument('-a',
'--algorithm',
type=str,
help='Reconstruction'
' algorithm',
default='gridrec',
choices=[
'art', 'bart', 'fbp', 'gridrec', 'mlem',
'ospml_hybrid', 'ospml_quad', 'pml_hybrid',
'pml_quad', 'sirt'
])
parser.add_argument('-c',
'--center',
type=float,
help='Center of rotation',
default=None)
parser.add_argument('-fn',
'--filter-name',
type=str,
help='Name of filter'
' used for reconstruction',
choices=[
'none', 'shepp', 'cosine', 'hann', 'hamming',
'ramlak', 'parzen', 'butterworth'
],
default='butterworth')
parser.add_argument('-rr',
'--ring-remove',
type=str,
help='Ring removal '
'method',
choices=['Octopus', 'Tomopy-FW', 'Tomopy-T'],
default='Tomopy-T')
parser.add_argument('-lf',
'--log-file',
type=str,
help='log file name',
default='fast-tomopy.log')
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, 'Directory of output file does not exist',
args.output_file)
# Read file metadata
logger.info('Reading input file metadata')
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata['nangles'])
last = proj_total - 1
sino_total = int(gdata['nslices'])
ray_total = int(gdata['nrays'])
px_size = float(gdata['pxsize']) / 10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step * (args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info('Reading full first and last projection for COR')
first_last, flats, darks, foobar = dx.read_als_832h5(args.input,
ind_tomo=(0,
last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :],
first_last[1, :, :],
tol=0.1)
logger.info('Detected center: %f', args.center)
# Read and normalize raw sinograms
logger.info('Reading raw data')
tomo, flats, darks, foobar = dx.read_als_832h5(args.input, sino=sino)
logger.info('Normalizing raw data')
tomo = tomopy.normalize(tomo, flats, darks)
tomo = tomopy.minus_log(tomo)
# Remove stripes from sinograms (remove rings)
logger.info('Preprocessing normalized data')
if args.ring_remove == 'Tomopy-FW':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == 'Tomopy-T':
logger.info('Removing stripes from sinograms with %s',
args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info('Reconstructing normalized data')
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(tomo,
theta,
center=args.center,
algorithm=args.algorithm,
filter_name=filter_name)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec / px_size
# Remove rings from reconstruction
if args.ring_remove == 'Octopus':
logger.info('Removing rings from reconstructions with %s',
args.ring_remove)
thresh = float(gdata['ring_threshold'])
thresh_max = float(gdata['upp_ring_value'])
thresh_min = float(gdata['low_ring_value'])
theta_min = int(gdata['max_arc_length'])
rwidth = int(gdata['max_ring_size'])
rec = tomopy.remove_rings(rec,
center_x=args.center,
thresh=thresh,
thresh_max=thresh_max,
thresh_min=thresh_min,
theta_min=theta_min,
rwidth=rwidth)
# Write reconstruction data to new hdf5 file
fdata['stage'] = 'fast-tomopy'
fdata['stage_flow'] = '/raw/' + fdata['stage']
fdata['stage_version'] = 'fast-tomopy-0.1'
# Generate a new uuid based on host ID and current time
fdata['uuid'] = str(uuid.uuid1())
gdata['Reconstruction_Type'] = 'tomopy-gridrec'
gdata['ring_removal_method'] = args.ring_remove
gdata['rfilter'] = args.filter_name
logger.info('Writing reconstructed data to h5 file')
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
def 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 fast_tomo_recon(argv):
"""
Reconstruct subset slices (sinograms) equally spaced within tomographic
dataset
"""
logger = logging.getLogger("fast_tomopy.fast_tomo_recon")
# Parse arguments passed to function
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input", type=str, help="path to input raw " "dataset", required=True)
parser.add_argument(
"-o",
"--output-file",
type=str,
help="full path to h5 output " "file",
default=os.path.join(os.getcwd(), "fast-tomopy.h5"),
)
parser.add_argument("-sn", "--sino-num", type=int, help="Number of slices " "to reconstruct", default=5)
parser.add_argument(
"-a",
"--algorithm",
type=str,
help="Reconstruction" " algorithm",
default="gridrec",
choices=[
"art",
"bart",
"fbp",
"gridrec",
"mlem",
"ospml_hybrid",
"ospml_quad",
"pml_hybrid",
"pml_quad",
"sirt",
],
)
parser.add_argument("-c", "--center", type=float, help="Center of rotation", default=None)
parser.add_argument(
"-fn",
"--filter-name",
type=str,
help="Name of filter" " used for reconstruction",
choices=["none", "shepp", "cosine", "hann", "hamming", "ramlak", "parzen", "butterworth"],
default="butterworth",
)
parser.add_argument(
"-rr",
"--ring-remove",
type=str,
help="Ring removal " "method",
choices=["Octopus", "Tomopy-FW", "Tomopy-T"],
default="Tomopy-T",
)
parser.add_argument("-lf", "--log-file", type=str, help="log file name", default="fast-tomopy.log")
args = parser.parse_args()
fh = logging.FileHandler(args.log_file)
fh.setLevel(logging.INFO)
fh.setFormatter(formatter)
logger.addHandler(fh)
if os.path.isdir(os.path.dirname(args.output_file)) is False:
raise IOError(2, "Directory of output file does not exist", args.output_file)
# Read file metadata
logger.info("Reading input file metadata")
fdata, gdata = read_als_832h5_metadata(args.input)
proj_total = int(gdata["nangles"])
last = proj_total - 1
sino_total = int(gdata["nslices"])
ray_total = int(gdata["nrays"])
px_size = float(gdata["pxsize"]) / 10 # cm
# Set parameters for sinograms to read
step = sino_total // (args.sino_num + 2)
start = step
end = step * (args.sino_num + 1)
sino = (start, end, step)
# Read full first and last projection to determine center of rotation
if args.center is None:
logger.info("Reading full first and last projection for COR")
first_last, flats, darks, floc = tomopy.read_als_832h5(args.input, ind_tomo=(0, last))
first_last = tomopy.normalize(first_last, flats, darks)
args.center = tomopy.find_center_pc(first_last[0, :, :], first_last[1, :, :], tol=0.1)
logger.info("Detected center: %f", args.center)
# Read and normalize raw sinograms
logger.info("Reading raw data")
tomo, flats, darks, floc = tomopy.read_als_832h5(args.input, sino=sino)
logger.info("Normalizing raw data")
tomo = tomopy.normalize_nf(tomo, flats, darks, floc)
# Remove stripes from sinograms (remove rings)
logger.info("Preprocessing normalized data")
if args.ring_remove == "Tomopy-FW":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_fw(tomo)
elif args.ring_remove == "Tomopy-T":
logger.info("Removing stripes from sinograms with %s", args.ring_remove)
tomo = tomopy.remove_stripe_ti(tomo)
# Pad sinograms with edge values
npad = int(np.ceil(ray_total * np.sqrt(2)) - ray_total) // 2
tomo = tomopy.pad(tomo, 2, npad=npad, mode="edge")
args.center += npad # account for padding
filter_name = np.array(args.filter_name, dtype=(str, 16))
theta = tomopy.angles(proj_total, 270, 90)
logger.info("Reconstructing normalized data")
# Reconstruct sinograms
# rec = tomopy.minus_log(tomo, out=tomo)
rec = tomopy.recon(
tomo, theta, center=args.center, emission=False, algorithm=args.algorithm, filter_name=filter_name
)
rec = tomopy.circ_mask(rec[:, npad:-npad, npad:-npad], 0)
rec = rec / px_size
# Remove rings from reconstruction
if args.ring_remove == "Octopus":
logger.info("Removing rings from reconstructions with %s", args.ring_remove)
thresh = float(gdata["ring_threshold"])
thresh_max = float(gdata["upp_ring_value"])
thresh_min = float(gdata["low_ring_value"])
theta_min = int(gdata["max_arc_length"])
rwidth = int(gdata["max_ring_size"])
rec = tomopy.remove_rings(
rec,
center_x=args.center,
thresh=thresh,
thresh_max=thresh_max,
thresh_min=thresh_min,
theta_min=theta_min,
rwidth=rwidth,
)
# Write reconstruction data to new hdf5 file
fdata["stage"] = "fast-tomopy"
fdata["stage_flow"] = "/raw/" + fdata["stage"]
fdata["stage_version"] = "fast-tomopy-0.1"
# Generate a new uuid based on host ID and current time
fdata["uuid"] = str(uuid.uuid1())
gdata["Reconstruction_Type"] = "tomopy-gridrec"
gdata["ring_removal_method"] = args.ring_remove
gdata["rfilter"] = args.filter_name
logger.info("Writing reconstructed data to h5 file")
write_als_832h5(rec, args.input, fdata, gdata, args.output_file, step)
return
# exchange_rank = ExchangeRank,
# slices_start=slice_first,
# slices_end=slice_first+1)
exchange_rank = ExchangeRank
data, white, dark = tomopy.io.exchange.read_aps_32id(file_name,
exchange_rank,
sino=(slice_first, slice_first+4))
theta = tomopy.angles(data.shape[0])
# Xtomo object creation and pipeline of methods.
##d = tomopy.xtomo_dataset(log='debug')
##d.dataset(data, white, dark, theta)
#if perform_norm: d.normalize() # flat & dark field correction
if perform_norm: data = tomopy.normalize(data, white, dark)
##if drift_correct: d.correct_drift()
if drift_correct: data = tomopy.normalize_bg(data)
#d.median_filter(size=medfilt_size, axis=0) # Apply a median filter in the projection plane
data = tomopy.median_filter(data, size=medfilt_size, axis=0)
#if remove_stripe1: d.stripe_removal(level=stripe_lvl, sigma=sig, wname=Wname)
if remove_stripe1: data = tomopy.remove_stripe_fw(data, level=stripe_lvl, wname=Wname, sigma=sig)
# z = 3
# eng = 31
# pxl = 0.325e-4
# rat = 5e-03
# rat = 1e-03
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 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 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)
rot_center += data_shape[2] // 4
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)
data = tomopy.remove_stripe_fw(data,
level=7,
wname='sym16',
sigma=2,
pad=True)
#data = tomopy.remove_stripe_ti(data, alpha=1.5)
data = tomopy.remove_stripe_sf(data, size=150)
# 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)
# 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
stack = np.empty(
(len(np.arange(*center_range)), data.shape[0], data.shape[2]))
print(stack.shape)
print(data.shape)
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)
rec = rec[:, N // 4:5 * N // 4, N // 4:5 * N // 4]
# 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) + '/' + 'try_rec/' + 'recon_' + os.path.splitext(
os.path.basename(h5fname))[0]
for axis in np.arange(*center_range):
rfname = fname + '_' + str('{0:.2f}'.format(axis - N // 4) + '.tiff')
dxchange.write_tiff(rec[index], fname=rfname, overwrite=True)
index = index + 1
print("Reconstructions: ", fname)
sino_chunk_end = sino_start + nSino_per_chunk * (iChunk + 1)
print('\n --------> [%i, %i]' % (sino_chunk_start, sino_chunk_end))
if sino_chunk_end > sino_end:
break
sino = (int(sino_chunk_start), int(sino_chunk_end))
# Read APS 32-BM raw data.
proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=sino)
# zinger_removal
#proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
#flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
data = tomopy.remove_stripe_fw(data,
level=5,
wname='sym16',
sigma=1,
pad=True)
#data = tomopy.prep.stripe.remove_stripe_ti(data,alpha=7)
#data = tomopy.prep.stripe.remove_stripe_sf(data,size=51)
# phase retrieval
data = tomopy.prep.phase.retrieve_phase(
data,
pixel_size=detector_pixel_size_x,
dist=sample_detector_distance,
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 center(io_paras, data_paras, center_start, center_end, center_step, diag_cycle=0,
mode='diag', normalize=True, stripe_removal=10, phase_retrieval=False):
# Input and output
datafile = io_paras.get('datafile')
path2white = io_paras.get('path2white', datafile)
path2dark = io_paras.get('path2dark', path2white)
out_dir = io_paras.get('out_dir')
diag_cent_dir = io_paras.get('diag_cent_dir', out_dir+"/center_diagnose/")
recon_dir = io_paras.get('recon_dir', out_dir+"/recon/")
out_prefix = io_paras.get('out_prefix', "recon_")
# Parameters of dataset
NumCycles = data_paras.get('NumCycles', 1) # Number of cycles used for recon
ProjPerCycle = data_paras.get('ProjPerCycle') # Number of projections per cycle, N_theta
cycle_offset = data_paras.get('cycle_offset', 0) # Offset in output cycle number
proj_start = data_paras.get('proj_start', 0) # Starting projection of reconstruction
proj_step = data_paras.get('proj_step')
z_start = data_paras.get('z_start', 0)
z_end = data_paras.get('z_end', z_start+1)
z_step = data_paras.get('z_step')
x_start = data_paras.get('x_start')
x_end = data_paras.get('x_end', x_start+1)
x_step = data_paras.get('x_step')
white_start = data_paras.get('white_start')
white_end = data_paras.get('white_end')
dark_start = data_paras.get('dark_start')
dark_end = data_paras.get('dark_end')
# Set start and end of each subcycle
projections_start = diag_cycle * ProjPerCycle + proj_start
projections_end = projections_start + ProjPerCycle
slice1 = slice(projections_start, projections_end, proj_step)
slice2 = slice(z_start, z_end, z_step)
slice3 = slice(x_start, x_end, x_step)
slices = (slice1, slice2, slice3)
white_slices = (slice(white_start, white_end), slice2, slice3)
dark_slices = (slice(dark_start, dark_end), slice2, slice3)
print("Running center diagnosis (projs %s to %s)"
% (projections_start, projections_end))
# Read HDF5 file.
print("Reading datafile %s..." % datafile, end="")
sys.stdout.flush()
data, white, dark = reader.read_aps_2bm(datafile, slices, white_slices, dark_slices,
path2white=path2white, path2dark=path2dark)
theta = gen_theta(data.shape[0])
print("Done!")
print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s."
% (data.shape, white.shape, dark.shape))
## Normalize dataset using data_white and data_dark
if normalize:
data = tomopy.normalize(data, white, dark, cutoff=None, ncore=_ncore, nchunk=None)
## Remove stripes caused by dead pixels in the detector
if stripe_removal:
data = tomopy.remove_stripe_fw(data, level=stripe_removal, wname='db5',
sigma=2, pad=True, ncore=None, nchunk=None)
# data = tomopy.remove_stripe_ti(data, nblock=0, alpha=1.5,
# ncore=None, nchunk=None)
# # Show preprocessed projection
# plt.figure("%s-prep" % projections_start)
# plt.imshow(d.data[0,:,:], cmap=cm.Greys_r)
# plt.savefig(out_dir+"/preprocess/%s-prep.jpg"
# % projections_start)
# # plt.show()
# continue
## Phase retrieval
if phase_retrieval:
data = tomopy.retrieve_phase(data,
pixel_size=6.5e-5, dist=33, energy=30,
alpha=1e-3, pad=True, ncore=_ncore, nchunk=None)
## Determine and set the center of rotation
### Using optimization method to automatically find the center
# d.optimize_center()
if 'opti' in mode:
print("Optimizing center ...", end="")
sys.stdout.flush()
rot_center = tomopy.find_center(data, theta, ind=None, emission=True, init=None,
tol=0.5, mask=True, ratio=1.)
print("Done!")
print("center = %s" % rot_center)
### Output the reconstruction results using a range of centers,
### and then manually find the optimal center.
if 'diag' in mode:
if not os.path.exists(diag_cent_dir):
os.makedirs(diag_cent_dir)
print("Testing centers ...", end="")
sys.stdout.flush()
tomopy.write_center(data, theta, dpath=diag_cent_dir,
cen_range=[center_start, center_end, center_step],
ind=None, emission=False, mask=False, ratio=1.)
print("Done!")
for m in range(chunks):
sino_start = start + num_sino * m
sino_end = start + num_sino * (m + 1)
# Read APS 32-ID raw data.
proj, flat, dark = tomopy.read_aps_32id(fname,
sino=(sino_start, sino_end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0])
# Remove the missing angles from data.
proj = np.concatenate(
(proj[0:miss_projs[0], :, :], proj[miss_projs[1] + 1:-1, :, :]),
axis=0)
theta = np.concatenate(
(theta[0:miss_projs[0]], theta[miss_projs[1] + 1:-1]))
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj,
theta,
center=1024,
algorithm='gridrec',
emission=False)
# Write data as stack of TIFs.
tomopy.write_tiff_stack(rec, fname='recon_dir/recon', start=sino_start)
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()
cores = (1,) #range(mp.cpu_count(), 0, -4)
f = open('benchmark_results.txt', 'a')
for dataset in datasets:
f.write('*****************************************************************************************************\n')
f.write(dataset + '\n\n')
for algorithm in algorithms:
for sino in sinos:
for core in cores:
start_time = time.time()
tomo, flats, darks, floc = tomopy.read_als_832h5(dataset,
sino=(0, sino, 1))
end_time = time.time() - start_time
f.write('Function: {0}, Number of sinos: {1}, Runtime (s): {2}\n'.format('read', sino, end_time))
theta = tomopy.angles(tomo.shape[0])
tomo = tomopy.normalize(tomo, flats, darks, ncore=core)
end_time = time.time() - start_time - end_time
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Runtime (s): {3}\n'.format('normalize', sino, core, end_time))
tomo = tomopy.remove_stripe_fw(tomo, ncore=core)
end_time = time.time() - start_time - end_time
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Runtime (s): {3}\n'.format('stripe_fw', sino, core, end_time))
rec = tomopy.recon(tomo, theta, center=datasets[dataset],
algorithm=algorithm, emission=False,
ncore=core)
end_time = time.time() - start_time - end_time
rec = tomopy.circ_mask(rec, 0)
f.write('Function: {0}, Number of sinos: {1}, Number of cores: {2}, Algorithm: {3}, Runtime (s): {4}\n'.format('recon', sino, core, algorithm, end_time))
outname = os.path.join('.', '{0}_{1}_slices_{2}_cores_{3}'.format(dataset.split('.')[0], str(algorithm), str(sino), str(core)), dataset.split('.')[0])
tomopy.write_tiff_stack(rec, fname=outname)
end_time = time.time() - start_time - end_time
f.write('Function: {0}, Number of images: {1}, Runtime (s): {2}\n\n'.format('write', rec.shape[0], end_time))
break
sino = (int(sino_chunk_start), int(sino_chunk_end))
# Read APS 2-BM raw data.
if (int(key) > 6):
proj, flat, dark, theta = read_aps_2bm_custom(fname, sino=sino)
else:
proj, flat, dark, theta = dxchange.read_aps_2bm(fname, sino=sino)
# zinger_removal
proj = tomopy.misc.corr.remove_outlier(proj, zinger_level, size=15, axis=0)
flat = tomopy.misc.corr.remove_outlier(flat, zinger_level_w, size=15, axis=0)
# Flat-field correction of raw data.
data = tomopy.normalize(proj, flat, dark, cutoff=1.4)
# remove stripes
#proj = tomopy.remove_stripe_fw(proj,level=5,wname='sym16',sigma=1,pad=True)
proj = tomopy.remove_stripe_ti(proj,2)
proj = tomopy.remove_stripe_sf(proj,10)
# phase retrieval
##data = tomopy.prep.phase.retrieve_phase(data,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=8e-3,pad=True)
# Find rotation center
#rot_center = tomopy.find_center(proj, theta, init=rot_center, ind=start, tol=0.5)
print(index, rot_center)
proj = tomopy.minus_log(proj)
proj, flat, dark, theta = None, None, None, None
# create MpiArray from Proj data
proj = MpiArray.fromglobalarray(proj)
proj.scatter(0)
proj.arr = None # remove full array to save memory
# share flats, darks, and theta to all MPI nodes
flat = comm.bcast(flat, root=0)
dark = comm.bcast(dark, root=0)
theta = comm.bcast(theta, root=0)
# Flat field correct data
logger.info("Flat field correcting data")
proj.scatter(0)
tomopy.normalize(proj.local_arr, flat, dark, ncore=1, out=proj.local_arr)
np.clip(proj.local_arr, 1e-6, 1.0, proj.local_arr)
del flat, dark
# Remove Stripe
# NOTE: we need to change remove_strip_fw to take sinogram order data, since it internally rotates the data
#proj.scatter(1)
#proj.local_arr = tomopy.remove_stripe_fw(proj.local_arr, ncore=1)
# Take the minus log to prepare for reconstruction
#NOTE: no scatter required since minus_log doesn't care about order
tomopy.minus_log(proj.local_arr, ncore=1, out=proj.local_arr)
# Find rotation center per set of sinograms
logger.info("Finding center of rotation")
proj.scatter(1)
def recon(
filename,
inputPath='./',
outputPath=None,
outputFilename=None,
doOutliers1D=False, # outlier removal in 1d (along sinogram columns)
outlier_diff1D=750, # difference between good data and outlier data (outlier removal)
outlier_size1D=3, # radius around each pixel to look for outliers (outlier removal)
doOutliers2D=False, # outlier removal, standard 2d on each projection
outlier_diff2D=750, # difference between good data and outlier data (outlier removal)
outlier_size2D=3, # radius around each pixel to look for outliers (outlier removal)
doFWringremoval=True, # Fourier-wavelet ring removal
doTIringremoval=False, # Titarenko ring removal
doSFringremoval=False, # Smoothing filter ring removal
ringSigma=3, # damping parameter in Fourier space (Fourier-wavelet ring removal)
ringLevel=8, # number of wavelet transform levels (Fourier-wavelet ring removal)
ringWavelet='db5', # type of wavelet filter (Fourier-wavelet ring removal)
ringNBlock=0, # used in Titarenko ring removal (doTIringremoval)
ringAlpha=1.5, # used in Titarenko ring removal (doTIringremoval)
ringSize=5, # used in smoothing filter ring removal (doSFringremoval)
doPhaseRetrieval=False, # phase retrieval
alphaReg=0.0002, # smaller = smoother (used for phase retrieval)
propagation_dist=75, # sample-to-scintillator distance (phase retrieval)
kev=24, # energy level (phase retrieval)
butterworth_cutoff=0.25, #0.1 would be very smooth, 0.4 would be very grainy (reconstruction)
butterworth_order=2, # for reconstruction
doPolarRing=False, # ring removal
Rarc=30, # min angle needed to be considered ring artifact (ring removal)
Rmaxwidth=100, # max width of rings to be filtered (ring removal)
Rtmax=3000.0, # max portion of image to filter (ring removal)
Rthr=3000.0, # max value of offset due to ring artifact (ring removal)
Rtmin=-3000.0, # min value of image to filter (ring removal)
cor=None, # center of rotation (float). If not used then cor will be detected automatically
corFunction='pc', # center of rotation function to use - can be 'pc', 'vo', or 'nm'
voInd=None, # index of slice to use for cor search (vo)
voSMin=-40, # min radius for searching in sinogram (vo)
voSMax=40, # max radius for searching in sinogram (vo)
voSRad=10, # search radius (vo)
voStep=0.5, # search step (vo)
voRatio=2.0, # ratio of field-of-view and object size (vo)
voDrop=20, # drop lines around vertical center of mask (vo)
nmInd=None, # index of slice to use for cor search (nm)
nmInit=None, # initial guess for center (nm)
nmTol=0.5, # desired sub-pixel accuracy (nm)
nmMask=True, # if True, limits analysis to circular region (nm)
nmRatio=1.0, # ratio of radius of circular mask to edge of reconstructed image (nm)
nmSinoOrder=False, # if True, analyzes in sinogram space. If False, analyzes in radiograph space
use360to180=False, # use 360 to 180 conversion
doBilateralFilter=False, # if True, uses bilateral filter on image just before write step # NOTE: image will be converted to 8bit if it is not already
bilateral_srad=3, # spatial radius for bilateral filter (image will be converted to 8bit if not already)
bilateral_rrad=30, # range radius for bilateral filter (image will be converted to 8bit if not already)
castTo8bit=False, # convert data to 8bit before writing
cast8bit_min=-10, # min value if converting to 8bit
cast8bit_max=30, # max value if converting to 8bit
useNormalize_nf=False, # normalize based on background intensity (nf)
chunk_proj=100, # chunk size in projection direction
chunk_sino=100, # chunk size in sinogram direction
npad=None, # amount to pad data before reconstruction
projused=None, #should be slicing in projection dimension (start,end,step)
sinoused=None, #should be sliceing in sinogram dimension (start,end,step). If first value is negative, it takes the number of slices from the second value in the middle of the stack.
correcttilt=0, #tilt dataset
tiltcenter_slice=None, # tilt center (x direction)
tiltcenter_det=None, # tilt center (y direction)
angle_offset=0, #this is the angle offset from our default (270) so that tomopy yields output in the same orientation as previous software (Octopus)
anglelist=None, #if not set, will assume evenly spaced angles which will be calculated by the angular range and number of angles found in the file. if set to -1, will read individual angles from each image. alternatively, a list of angles can be passed.
doBeamHardening=False, #turn on beam hardening correction, based on "Correction for beam hardening in computed tomography", Gabor Herman, 1979 Phys. Med. Biol. 24 81
BeamHardeningCoefficients=None, #6 values, tomo = a0 + a1*tomo + a2*tomo^2 + a3*tomo^3 + a4*tomo^4 + a5*tomo^5
projIgnoreList=None, #projections to be ignored in the reconstruction (for simplicity in the code, they will not be removed and will be processed as all other projections but will be set to zero absorption right before reconstruction.
*args,
**kwargs):
start_time = time.time()
print("Start {} at:".format(filename) +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
outputPath = inputPath if outputPath is None else outputPath
outputFilename = filename if outputFilename is None else outputFilename
tempfilenames = [outputPath + 'tmp0.h5', outputPath + 'tmp1.h5']
filenametowrite = outputPath + '/rec' + filename.strip(
".h5") + '/' + outputFilename
#filenametowrite = outputPath+'/rec'+filename+'/'+outputFilename
print("cleaning up previous temp files", end="")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print(", reading metadata")
datafile = h5py.File(inputPath + filename, 'r')
gdata = dict(dxchange.reader._find_dataset_group(datafile).attrs)
pxsize = float(gdata['pxsize']) / 10 # /10 to convert unites from mm to cm
numslices = int(gdata['nslices'])
numangles = int(gdata['nangles'])
angularrange = float(gdata['arange'])
numrays = int(gdata['nrays'])
npad = int(np.ceil(numrays * np.sqrt(2)) -
numrays) // 2 if npad is None else npad
projused = (0, numangles - 1, 1) if projused is None else projused
# ndark = int(gdata['num_dark_fields'])
# ind_dark = list(range(0, ndark))
# group_dark = [numangles - 1]
inter_bright = int(gdata['i0cycle'])
nflat = int(gdata['num_bright_field'])
ind_flat = list(range(0, nflat))
if inter_bright > 0:
group_flat = list(range(0, numangles, inter_bright))
if group_flat[-1] != numangles - 1:
group_flat.append(numangles - 1)
elif inter_bright == 0:
group_flat = [0, numangles - 1]
else:
group_flat = None
ind_tomo = list(range(0, numangles))
floc_independent = dxchange.reader._map_loc(ind_tomo, group_flat)
#figure out the angle list (a list of angles, one per projection image)
dtemp = datafile[list(datafile.keys())[0]]
fltemp = list(dtemp.keys())
firstangle = float(dtemp[fltemp[0]].attrs.get('rot_angle', 0))
if anglelist is None:
#the offset angle should offset from the angle of the first image, which is usually 0, but in the case of timbir data may not be.
#we add the 270 to be inte same orientation as previous software used at bl832
angle_offset = 270 + angle_offset - firstangle
anglelist = tomopy.angles(numangles, angle_offset,
angle_offset - angularrange)
elif anglelist == -1:
anglelist = np.zeros(shape=numangles)
for icount in range(0, numangles):
anglelist[icount] = np.pi / 180 * (270 + angle_offset - float(
dtemp[fltemp[icount]].attrs['rot_angle']))
#if projused is different than default, need to chnage numangles and angularrange
#can't do useNormalize_nf and doOutliers2D at the same time, or doOutliers2D and doOutliers1D at the same time, b/c of the way we chunk, for now just disable that
if useNormalize_nf == True and doOutliers2D == True:
useNormalize_nf = False
print(
"we cannot currently do useNormalize_nf and doOutliers2D at the same time, turning off useNormalize_nf"
)
if doOutliers2D == True and doOutliers1D == True:
doOutliers1D = False
print(
"we cannot currently do doOutliers1D and doOutliers2D at the same time, turning off doOutliers1D"
)
#figure out how user can pass to do central x number of slices, or set of slices dispersed throughout (without knowing a priori the value of numslices)
if sinoused is None:
sinoused = (0, numslices, 1)
elif sinoused[0] < 0:
sinoused = (
int(np.floor(numslices / 2.0) - np.ceil(sinoused[1] / 2.0)),
int(np.floor(numslices / 2.0) + np.floor(sinoused[1] / 2.0)), 1)
num_proj_per_chunk = np.minimum(chunk_proj, projused[1] - projused[0])
numprojchunks = (projused[1] - projused[0] - 1) // num_proj_per_chunk + 1
num_sino_per_chunk = np.minimum(chunk_sino, sinoused[1] - sinoused[0])
numsinochunks = (sinoused[1] - sinoused[0] - 1) // num_sino_per_chunk + 1
numprojused = (projused[1] - projused[0]) // projused[2]
numsinoused = (sinoused[1] - sinoused[0]) // sinoused[2]
BeamHardeningCoefficients = (
0, 1, 0, 0, 0,
.1) if BeamHardeningCoefficients is None else BeamHardeningCoefficients
if cor is None:
print("Detecting center of rotation", end="")
if angularrange > 300:
lastcor = int(np.floor(numangles / 2) - 1)
else:
lastcor = numangles - 1
#I don't want to see the warnings about the reader using a deprecated variable in dxchange
with warnings.catch_warnings():
warnings.simplefilter("ignore")
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename, ind_tomo=(0, lastcor))
tomo = tomo.astype(np.float32)
if useNormalize_nf:
tomopy.normalize_nf(tomo, flat, dark, floc, out=tomo)
else:
tomopy.normalize(tomo, flat, dark, out=tomo)
if corFunction == 'vo':
# same reason for catching warnings as above
with warnings.catch_warnings():
warnings.simplefilter("ignore")
cor = tomopy.find_center_vo(tomo,
ind=voInd,
smin=voSMin,
smax=voSMax,
srad=voSRad,
step=voStep,
ratio=voRatio,
drop=voDrop)
elif corFunction == 'nm':
cor = tomopy.find_center(
tomo,
tomopy.angles(numangles, angle_offset,
angle_offset - angularrange),
ind=nmInd,
init=nmInit,
tol=nmTol,
mask=nmMask,
ratio=nmRatio,
sinogram_order=nmSinoOrder)
elif corFunction == 'pc':
cor = tomopy.find_center_pc(tomo[0], tomo[1], tol=0.25)
else:
raise ValueError("\'corFunction\' must be one of: [ pc, vo, nm ].")
print(", {}".format(cor))
else:
print("using user input center of {}".format(cor))
function_list = []
if doOutliers1D:
function_list.append('remove_outlier1d')
if doOutliers2D:
function_list.append('remove_outlier2d')
if useNormalize_nf:
function_list.append('normalize_nf')
else:
function_list.append('normalize')
function_list.append('minus_log')
if doBeamHardening:
function_list.append('beam_hardening')
if doFWringremoval:
function_list.append('remove_stripe_fw')
if doTIringremoval:
function_list.append('remove_stripe_ti')
if doSFringremoval:
function_list.append('remove_stripe_sf')
if correcttilt:
function_list.append('correcttilt')
if use360to180:
function_list.append('do_360_to_180')
if doPhaseRetrieval:
function_list.append('phase_retrieval')
function_list.append('recon_mask')
if doPolarRing:
function_list.append('polar_ring')
if castTo8bit:
function_list.append('castTo8bit')
if doBilateralFilter:
function_list.append('bilateral_filter')
function_list.append('write_output')
# Figure out first direction to slice
for func in function_list:
if slice_dir[func] != 'both':
axis = slice_dir[func]
break
done = False
curfunc = 0
curtemp = 0
while True: # Loop over reading data in certain chunking direction
if axis == 'proj':
niter = numprojchunks
else:
niter = numsinochunks
for y in range(niter): # Loop over chunks
print("{} chunk {} of {}".format(axis, y + 1, niter))
if curfunc == 0:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if axis == 'proj':
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename,
ind_tomo=range(
y * num_proj_per_chunk + projused[0],
np.minimum(
(y + 1) * num_proj_per_chunk + projused[0],
numangles)),
sino=(sinoused[0], sinoused[1], sinoused[2]))
else:
tomo, flat, dark, floc = dxchange.read_als_832h5(
inputPath + filename,
ind_tomo=range(projused[0], projused[1],
projused[2]),
sino=(y * num_sino_per_chunk + sinoused[0],
np.minimum((y + 1) * num_sino_per_chunk +
sinoused[0], numslices), 1))
else:
if axis == 'proj':
start, end = y * num_proj_per_chunk, np.minimum(
(y + 1) * num_proj_per_chunk, numprojused)
tomo = dxchange.reader.read_hdf5(
tempfilenames[curtemp],
'/tmp/tmp',
slc=((start, end, 1), (0, numslices, 1),
(0, numrays, 1))) #read in intermediate file
else:
start, end = y * num_sino_per_chunk, np.minimum(
(y + 1) * num_sino_per_chunk, numsinoused)
tomo = dxchange.reader.read_hdf5(tempfilenames[curtemp],
'/tmp/tmp',
slc=((0, numangles,
1), (start, end, 1),
(0, numrays, 1)))
dofunc = curfunc
keepvalues = None
while True: # Loop over operations to do in current chunking direction
func_name = function_list[dofunc]
newaxis = slice_dir[func_name]
if newaxis != 'both' and newaxis != axis:
# We have to switch axis, so flush to disk
if y == 0:
try:
os.remove(tempfilenames[1 - curtemp])
except OSError:
pass
appendaxis = 1 if axis == 'sino' else 0
dxchange.writer.write_hdf5(
tomo,
fname=tempfilenames[1 - curtemp],
gname='tmp',
dname='tmp',
overwrite=False,
appendaxis=appendaxis) #writing intermediate file...
break
print(func_name, end=" ")
curtime = time.time()
if func_name == 'remove_outlier1d':
tomo = tomo.astype(np.float32, copy=False)
remove_outlier1d(tomo,
outlier_diff1D,
size=outlier_size1D,
out=tomo)
if func_name == 'remove_outlier2d':
tomo = tomo.astype(np.float32, copy=False)
tomopy.remove_outlier(tomo,
outlier_diff2D,
size=outlier_size2D,
axis=0,
out=tomo)
elif func_name == 'normalize_nf':
tomo = tomo.astype(np.float32, copy=False)
tomopy.normalize_nf(
tomo, flat, dark, floc_independent, out=tomo
) #use floc_independent b/c when you read file in proj chunks, you don't get the correct floc returned right now to use here.
elif func_name == 'normalize':
tomo = tomo.astype(np.float32, copy=False)
tomopy.normalize(tomo, flat, dark, out=tomo)
elif func_name == 'minus_log':
mx = np.float32(0.00000000000000000001)
ne.evaluate('where(tomo>mx, tomo, mx)', out=tomo)
tomopy.minus_log(tomo, out=tomo)
elif func_name == 'beam_hardening':
loc_dict = {
'a{}'.format(i): np.float32(val)
for i, val in enumerate(BeamHardeningCoefficients)
}
tomo = ne.evaluate(
'a0 + a1*tomo + a2*tomo**2 + a3*tomo**3 + a4*tomo**4 + a5*tomo**5',
local_dict=loc_dict,
out=tomo)
elif func_name == 'remove_stripe_fw':
tomo = tomopy.remove_stripe_fw(tomo,
sigma=ringSigma,
level=ringLevel,
pad=True,
wname=ringWavelet)
elif func_name == 'remove_stripe_ti':
tomo = tomopy.remove_stripe_ti(tomo,
nblock=ringNBlock,
alpha=ringAlpha)
elif func_name == 'remove_stripe_sf':
tomo = tomopy.remove_stripe_sf(tomo, size=ringSize)
elif func_name == 'correcttilt':
if tiltcenter_slice is None:
tiltcenter_slice = numslices / 2.
if tiltcenter_det is None:
tiltcenter_det = tomo.shape[2] / 2
new_center = tiltcenter_slice - 0.5 - sinoused[0]
center_det = tiltcenter_det - 0.5
#add padding of 10 pixels, to be unpadded right after tilt correction. This makes the tilted image not have zeros at certain edges, which matters in cases where sample is bigger than the field of view. For the small amounts we are generally tilting the images, 10 pixels is sufficient.
# tomo = tomopy.pad(tomo, 2, npad=10, mode='edge')
# center_det = center_det + 10
cntr = (center_det, new_center)
for b in range(tomo.shape[0]):
tomo[b] = st.rotate(
tomo[b],
correcttilt,
center=cntr,
preserve_range=True,
order=1,
mode='edge',
clip=True
) #center=None means image is rotated around its center; order=1 is default, order of spline interpolation
# tomo = tomo[:, :, 10:-10]
elif func_name == 'do_360_to_180':
# Keep values around for processing the next chunk in the list
keepvalues = [
angularrange, numangles, projused, num_proj_per_chunk,
numprojchunks, numprojused, numrays, anglelist
]
#why -.5 on one and not on the other?
if tomo.shape[0] % 2 > 0:
tomo = sino_360_to_180(
tomo[0:-1, :, :],
overlap=int(
np.round((tomo.shape[2] - cor - .5)) * 2),
rotation='right')
angularrange = angularrange / 2 - angularrange / (
tomo.shape[0] - 1)
else:
tomo = sino_360_to_180(
tomo[:, :, :],
overlap=int(np.round((tomo.shape[2] - cor)) * 2),
rotation='right')
angularrange = angularrange / 2
numangles = int(numangles / 2)
projused = (0, numangles - 1, 1)
num_proj_per_chunk = np.minimum(chunk_proj,
projused[1] - projused[0])
numprojchunks = (projused[1] - projused[0] -
1) // num_proj_per_chunk + 1
numprojused = (projused[1] - projused[0]) // projused[2]
numrays = tomo.shape[2]
anglelist = anglelist[:numangles]
elif func_name == 'phase_retrieval':
tomo = tomopy.retrieve_phase(tomo,
pixel_size=pxsize,
dist=propagation_dist,
energy=kev,
alpha=alphaReg,
pad=True)
elif func_name == 'recon_mask':
tomo = tomopy.pad(tomo, 2, npad=npad, mode='edge')
if projIgnoreList is not None:
for badproj in projIgnoreList:
tomo[badproj] = 0
rec = tomopy.recon(
tomo,
anglelist,
center=cor + npad,
algorithm='gridrec',
filter_name='butterworth',
filter_par=[butterworth_cutoff, butterworth_order])
rec = rec[:, npad:-npad, npad:-npad]
rec /= pxsize # convert reconstructed voxel values from 1/pixel to 1/cm
rec = tomopy.circ_mask(rec, 0)
elif func_name == 'polar_ring':
rec = np.ascontiguousarray(rec, dtype=np.float32)
rec = tomopy.remove_ring(rec,
theta_min=Rarc,
rwidth=Rmaxwidth,
thresh_max=Rtmax,
thresh=Rthr,
thresh_min=Rtmin,
out=rec)
elif func_name == 'castTo8bit':
rec = convert8bit(rec, cast8bit_min, cast8bit_max)
elif func_name == 'bilateral_filter':
rec = pyF3D.run_BilateralFilter(
rec,
spatialRadius=bilateral_srad,
rangeRadius=bilateral_rrad)
elif func_name == 'write_output':
dxchange.write_tiff_stack(rec,
fname=filenametowrite,
start=y * num_sino_per_chunk +
sinoused[0])
print('(took {:.2f} seconds)'.format(time.time() - curtime))
dofunc += 1
if dofunc == len(function_list):
break
if y < niter - 1 and keepvalues: # Reset original values for next chunk
angularrange, numangles, projused, num_proj_per_chunk, numprojchunks, numprojused, numrays, anglelist = keepvalues
curtemp = 1 - curtemp
curfunc = dofunc
if curfunc == len(function_list):
break
axis = slice_dir[function_list[curfunc]]
print("cleaning up temp files")
for tmpfile in tempfilenames:
try:
os.remove(tmpfile)
except OSError:
pass
print("End Time: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()))
print('It took {:.3f} s to process {}'.format(time.time() - start_time,
inputPath + filename))
def hdf5_retrieve_phase(src_folder,
src_fname,
dest_folder,
dest_fname,
method='paganin',
corr_flat=False,
dtype='float16',
sino_range=None,
**kwargs):
src_fname = check_fname_ext(src_fname, 'h5')
dest_fname = check_fname_ext(dest_fname, 'h5')
o = h5py.File('{:s}/{:s}'.format(src_folder, src_fname))
dset_src = o['exchange/data']
n_frames = dset_src.shape[0]
if rank == 0:
if not os.path.exists(dest_folder):
os.mkdir(dest_folder)
if os.path.exists(dest_folder + '/' + dest_fname):
print('Warning: File already exists. Continue anyway? (y/n) ')
cont = six.moves.input()
if cont in ['n', 'N']:
exit()
else:
print('Old file will be overwritten.')
os.remove(dest_folder + '/' + dest_fname)
#f = make_empty_hdf5(dest_folder, dest_fname, dset_src.shape, dtype=dtype)
f = h5py.File(dest_folder + '/' + dest_fname)
comm.Barrier()
if rank != 0:
f = h5py.File(dest_folder + '/' + dest_fname)
full_shape = dset_src.shape
grp = f.create_group('exchange')
if sino_range is None:
dset_dest = grp.create_dataset('data', full_shape, dtype=dtype)
dset_flat = grp.create_dataset('data_white',
(1, full_shape[1], full_shape[2]),
dtype=dtype)
dset_dark = grp.create_dataset('data_dark',
(1, full_shape[1], full_shape[2]),
dtype=dtype)
else:
sino_start = sino_range[0]
sino_end = sino_range[1]
dset_dest = grp.create_dataset(
'data', (full_shape[0], (sino_end - sino_start), full_shape[2]),
dtype=dtype)
dset_flat = grp.create_dataset(
'data_white', (1, (sino_end - sino_start), full_shape[2]),
dtype=dtype)
dset_dark = grp.create_dataset(
'data_dark', (1, (sino_end - sino_start), full_shape[2]),
dtype=dtype)
dset_flat[:, :, :] = np.ones(dset_flat.shape, dtype=dtype)
dset_dark[:, :, :] = np.zeros(dset_dark.shape, dtype=dtype)
comm.Barrier()
flt = o['exchange/data_white'].value
drk = o['exchange/data_dark'].value
print('Method: {:s}'.format(method), kwargs)
alloc_set = allocate_mpi_subsets(n_frames, size)
for frame in alloc_set[rank]:
t0 = time.time()
print(' Rank: {:d}; current frame: {:d}.'.format(rank, frame))
if sino_range is None:
temp = dset_src[frame, :, :]
else:
sino_start = sino_range[0]
sino_end = sino_range[1]
temp = dset_src[frame, sino_start:sino_end, :]
if corr_flat:
temp = temp.reshape([1, temp.shape[0], temp.shape[1]])
temp = tomopy.normalize(temp, flt, drk)
temp = preprocess(temp)
temp = np.squeeze(temp)
temp = retrieve_phase(temp, method=method, **kwargs)
dset_dest[frame, :, :] = temp.astype(dtype)
print(' Done in {:.2f}s. '.format(time.time() - t0))
f.close()
comm.Barrier()
return
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 recon(io_paras, data_paras, rot_center=None, normalize=True, stripe_removal=10, phase_retrieval=False,
opt_center=False, diag_center=False, output="tiff"):
# Input and output
datafile = io_paras.get('datafile')
path2white = io_paras.get('path2white', datafile)
path2dark = io_paras.get('path2dark', path2white)
out_dir = io_paras.get('out_dir')
diag_cent_dir = io_paras.get('diag_cent_dir', out_dir+"/center_diagnose/")
recon_dir = io_paras.get('recon_dir', out_dir+"/recon/")
out_prefix = io_paras.get('out_prefix', "recon_")
# Parameters of dataset
NumCycles = data_paras.get('NumCycles', 1) # Number of cycles used for recon
ProjPerCycle = data_paras.get('ProjPerCycle') # Number of projections per cycle, N_theta
cycle_offset = data_paras.get('cycle_offset', 0) # Offset in output cycle number
proj_start = data_paras.get('proj_start', 0) # Starting projection of reconstruction
proj_step = data_paras.get('proj_step')
z_start = data_paras.get('z_start', 0)
z_end = data_paras.get('z_end', z_start+1)
z_step = data_paras.get('z_step')
x_start = data_paras.get('x_start')
x_end = data_paras.get('x_end', x_start+1)
x_step = data_paras.get('x_step')
white_start = data_paras.get('white_start')
white_end = data_paras.get('white_end')
dark_start = data_paras.get('dark_start')
dark_end = data_paras.get('dark_end')
rot_center_copy = rot_center
for cycle in xrange(NumCycles):
# Set start and end of each cycle
projections_start = cycle * ProjPerCycle + proj_start
projections_end = projections_start + ProjPerCycle
slice1 = slice(projections_start, projections_end, proj_step)
slice2 = slice(z_start, z_end, z_step)
slice3 = slice(x_start, x_end, x_step)
slices = (slice1, slice2, slice3)
white_slices = (slice(white_start, white_end), slice2, slice3)
dark_slices = (slice(dark_start, dark_end), slice2, slice3)
print("Running cycle #%s (projs %s to %s)"
% (cycle, projections_start, projections_end))
# Read HDF5 file.
print("Reading datafile %s..." % datafile, end="")
sys.stdout.flush()
data, white, dark = reader.read_aps_2bm(datafile, slices, white_slices, dark_slices,
path2white=path2white, path2dark=path2dark)
theta = gen_theta(data.shape[0])
print("Done!")
print("Data shape = %s;\nwhite shape = %s;\ndark shape = %s."
% (data.shape, white.shape, dark.shape))
## Normalize dataset using data_white and data_dark
if normalize:
print("Normalizing data ...")
# white = white.mean(axis=0).reshape(-1, *data.shape[1:])
# dark = dark.mean(axis=0).reshape(-1, *data.shape[1:])
# data = (data - dark) / (white - dark)
data = tomopy.normalize(data, white, dark, cutoff=None, ncore=_ncore, nchunk=None)[...]
## Remove stripes caused by dead pixels in the detector
if stripe_removal:
print("Removing stripes ...")
data = tomopy.remove_stripe_fw(data, level=stripe_removal, wname='db5', sigma=2,
pad=True, ncore=_ncore, nchunk=None)
# data = tomopy.remove_stripe_ti(data, nblock=0, alpha=1.5,
# ncore=None, nchunk=None)
# # Show preprocessed projection
# plt.figure("%s-prep" % projections_start)
# plt.imshow(d.data[0,:,:], cmap=cm.Greys_r)
# plt.savefig(out_dir+"/preprocess/%s-prep.jpg"
# % projections_start)
# # plt.show()
# continue
## Phase retrieval
if phase_retrieval:
print("Retrieving phase ...")
data = tomopy.retrieve_phase(data,
pixel_size=1e-4, dist=50, energy=20,
alpha=1e-3, pad=True, ncore=_ncore, nchunk=None)
## Determine and set the center of rotation
if opt_center or (rot_center == None):
### Using optimization method to automatically find the center
# d.optimize_center()
print("Optimizing center ...", end="")
sys.stdout.flush()
rot_center = tomopy.find_center(data, theta, ind=None, emission=True, init=None,
tol=0.5, mask=True, ratio=1.)
print("Done!")
print("center = %s" % rot_center)
if diag_center:
### Output the reconstruction results using a range of centers,
### and then manually find the optimal center.
# d.diagnose_center()
if not os.path.exists(diag_cent_dir):
os.makedirs(diag_cent_dir)
print("Testing centers ...", end="")
sys.stdout.flush()
tomopy.write_center(data, theta, dpath=diag_cent_dir,
cen_range=[center_start, center_end, center_step],
ind=None, emission=False, mask=False, ratio=1.)
print("Done!")
## Flip odd frames
if (cycle % 2):
data[...] = data[...,::-1]
rot_center = data.shape[-1] - rot_center_copy
else:
rot_center = rot_center_copy
## Reconstruction using FBP
print("Running gridrec ...", end="")
sys.stdout.flush()
recon = tomopy.recon(data, theta, center=rot_center, emission=False, algorithm='gridrec',
# num_gridx=None, num_gridy=None, filter_name='shepp',
ncore=_ncore, nchunk=_nchunk)
print("Done!")
## Collect background
# if cycle == 0:
# bg = recon
# elif cycle < 4:
# bg += recon
# else:
# recon -= bg/4.
# Write to stack of TIFFs.
if not os.path.exists(recon_dir):
os.makedirs(recon_dir)
out_fname = recon_dir+"/"+out_prefix+"t_%d" % (cycle + cycle_offset)
if "hdf" in output:
hdf_fname = out_fname + ".hdf5"
print("Writing reconstruction output file %s..."
% hdf_fname, end="")
sys.stdout.flush()
tomopy.write_hdf5(recon, fname=hdf_fname, gname='exchange', overwrite=False)
print("Done!")
if "tif" in output:
tiff_fname = out_fname + ".tiff"
print("Writing reconstruction tiff files %s ..."
% tiff_fname, end="")
sys.stdout.flush()
tomopy.write_tiff_stack(recon, fname=tiff_fname, axis=0, digit=5, start=0, overwrite=False)
print("Done!")
if "bin" in output:
bin_fname = out_fname + ".bin"
print("Writing reconstruction to binary files %s..."
% bin_fname, end="")
sys.stdout.flush()
recon.tofile(bin_fname)
def preprocess_data(prj, flat, dark, FF_norm=flat_field_norm, remove_rings = remove_rings, medfilt_size=medfilt_size, FF_drift_corr=flat_field_drift_corr, downspling=binning):
if FF_norm:
# normalize the prj
print('\n*** Applying flat field correction:')
start_norm_time = time.time()
prj = tomopy.normalize(prj, flat, dark)
print(' done in %0.3f min' % ((time.time() - start_norm_time)/60))
if FF_drift_corr:
print('\n*** Applying flat field drift correction:')
start_norm_bg_time = time.time()
prj = tomopy.normalize_bg(prj, air=100)
print(' done in %0.3f min' % ((time.time() - start_norm_bg_time)/60))
# Applying -log
print('\n*** Applying -log:')
start_log_time = time.time()
prj = tomopy.minus_log(prj)
print(' done in %0.3f min' % ((time.time() - start_log_time)/60))
prj = tomopy.misc.corr.remove_neg(prj, val=0.000)
prj = tomopy.misc.corr.remove_nan(prj, val=0.000)
prj[np.where(prj == np.inf)] = 0.000
# prj[np.where(prj == 0)] = 0.000
print('\n*** Min and max val in prj before recon: %0.3f, %0.3f' % (np.min(prj), np.max(prj)))
if remove_rings:
# remove ring artefacts
tmp = prj[-1,:,:] # use to fixe the bug of remove_stripe_ti
print('\n*** Applying ring removal algo:')
start_ring_time = time.time()
prj = tomopy.remove_stripe_ti(prj,2)
# prj = tomopy.remove_stripe_sf(prj,10); prj = tomopy.misc.corr.remove_neg(prj, val=0.000) # remove the neg values coming from remove_stripe_sf
print(' done in %0.3f min' % ((time.time() - start_ring_time)/60))
prj[-1,:,:] = tmp # fixe the bug of remove_stripe_ti
if phase_retrieval:
# phase retrieval
prj = tomopy.prep.phase.retrieve_phase(prj,pixel_size=detector_pixel_size_x,dist=sample_detector_distance,energy=monochromator_energy,alpha=alpha,pad=True)
# Filtering data with 2D median filter before downsampling and recon
if medfilt_size>1:
start_filter_time = time.time()
print('\n*** Applying median filter')
#prj = tomopy.median_filter(prj,size=1)
prj = ndimage.median_filter(prj,footprint=np.ones((1, medfilt_size, medfilt_size)))
print(' done in %0.3f min' % ((time.time() - start_filter_time)/60))
# Downsampling data:
if downspling>0:
print('\n** Applying downsampling')
start_down_time = time.time()
prj = tomopy.downsample(prj, level=binning)
prj = tomopy.downsample(prj, level=binning, axis=1)
print(' done in %0.3f min' % ((time.time() - start_down_time)/60))
print('\n*** Shape of the data:'+str(np.shape(prj)))
print(' Dimension of theta:'+str(np.shape(theta)))
return prj
def reconstruct(h5fname, sino, rot_center, binning, algorithm='gridrec'):
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 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: 10001 (default 1)")
args = parser.parse_args()
top = args.top
index_start = int(args.start)
# Total number of images to read
nfile = len(fnmatch.filter(os.listdir(top), '*.tif'))
# Read the raw data
rdata = hspeed.load_raw(top, index_start)
particle_bed_reference = hspeed.particle_bed_location(rdata[0], plot=False)
print("Particle bed location: ", particle_bed_reference)
# Cut the images to remove the particle bed
cdata = rdata[:, 0:particle_bed_reference, :]
# Find the image when the shutter starts to close
dark_index = hspeed.shutter_off(rdata)
print("Shutter CLOSED on image: ", dark_index)
# Find the images when the laser is on
laser_on_index = hspeed.laser_on(rdata, particle_bed_reference, alpha=1.00)
print("Laser ON on image: ", laser_on_index)
# Set the [start, end] index of the blocked images, flat and dark.
laser_on_index = 47
flat_range = [0, 1]
data_range = [laser_on_index, dark_index]
dark_range = [dark_index, nfile]
flat = cdata[flat_range[0]:flat_range[1], :, :]
proj = cdata[data_range[0]:data_range[1], :, :]
dark = np.zeros(
(dark_range[1] - dark_range[0], proj.shape[1], proj.shape[2]))
# if you want to use the shutter closed images as dark uncomment this:
#dark = cdata[dark_range[0]:dark_range[1], :, :]
# ndata = tomopy.normalize(proj, flat, dark)
# ndata = tomopy.normalize_bg(ndata, air=ndata.shape[2]/2.5)
# ndata = tomopy.minus_log(ndata)
# hspeed.slider(ndata)
# ndata = hspeed.scale_to_one(ndata)
# ndata = hspeed.sobel_stack(ndata)
# hspeed.slider(ndata)
ndata = tomopy.normalize(proj, flat, dark)
ndata = tomopy.normalize_bg(ndata, air=ndata.shape[2] / 2.5)
ndata = tomopy.minus_log(ndata)
blur_radius = 3.0
threshold = .04
nddata = hspeed.label(ndata, blur_radius, threshold)
f = tp.locate(ndata[100, :, :], 41, invert=True)
print(f.head)
plt.figure() # make a new figure
tp.annotate(f, ndata[100, :, :])
# Select the sinogram range to reconstruct.
start = 800
end = 804
# Read the APS 1-ID raw data.
proj, flat, dark = tomopy.io.exchange.read_anka_topotomo(fname, ind_tomo, ind_flat, ind_dark, sino=(start, end))
# Set data collection angles as equally spaced between 0-180 degrees.
theta = tomopy.angles(proj.shape[0], 0, 180)
print proj.shape
print flat.shape
print dark.shape
# Flat-field correction of raw data.
proj = tomopy.normalize(proj, flat, dark)
# Set rotation axis location manually.
best_center = 993.825;
rot_center = best_center
# Find rotation center.
#rot_center = tomopy.find_center(proj, theta, emission=False, init=best_center, ind=0, tol=0.3)
print "Center of rotation:", rot_center
# Reconstruct object using Gridrec algorithm.
rec = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec', emission=False)
# Mask each reconstructed slice with a circle.
rec = tomopy.circ_mask(rec, axis=0, ratio=0.95)
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
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
# test
angle = 0
print getFilename(data_folder + "/2012*_%.3f_*.fits", angle)
#
step = 0.85
angles = np.arange(0.0, 181.9 + step, step)
list_data_files = map(
lambda x: getFilename(data_folder + "/2012*_%.3f_*.fits", x), angles)
data = []
for _file in list_data_files:
data.append(pyfits.open(_file)[0].data)
# now start processing
# normalize
print "* normalizing ..."
proj = tomopy.normalize(data, ob_data, df_data)
print " done."
# checking
print proj.shape
theta = np.arange(0.0, 181.9 + step, step)
print(theta)
theta *= np.pi / 180.
# calculate rotation center
print "* finding center ..."
rot_center = tomopy.find_center(proj,
theta,
emission=False,
init=1024,
tol=0.5)
print " done."
print("Center of rotation: ", rot_center)
print(scanlog_content)
# Read raw data.
prj, flat, dark, theta = p05.reco.get_rawdata(scanlog_content, raw_dir, verbose=True)
print(prj.shape, flat.shape, dark.shape, theta.shape)
# Select the sinogram range to reconstruct.
start = 1024
end = 1024
prj = prj[:, [start,end], :]
flat = flat[:, [start,end], :]
dark = dark[:, [start,end], :]
# Flat-field correction of raw data.
data = tomopy.normalize(prj, flat, dark)
# remove stripes
data = tomopy.prep.stripe.remove_stripe_fw(data,level=5,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=8e-3,pad=True)
# Set rotation center.
rot_center = 1552
print(rot_center)
data = tomopy.minus_log(data)
# Use test_sirtfbp_iter = True to test which number of iterations is suitable for your dataset
# Filters are saved in .mat files in "./¨
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
