def main(argv):
"""To do...
Usage
-----
Parameters
---------
Example
--------------------------
"""
# Get the from and to number of files to process:
sino_idx = int(argv[0])
# Get paths:
infile = argv[1]
outfile = argv[2]
# Essential reconstruction parameters:
angles = float(argv[3])
offset = float(argv[4])
recpar = argv[5]
scale = int(float(argv[6]))
overpad = True if argv[7] == "True" else False
logtrsf = True if argv[8] == "True" else False
circle = True if argv[9] == "True" else False
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[10] == "True" else False
flat_end = True if argv[11] == "True" else False
half_half = True if argv[12] == "True" else False
half_half_line = int(argv[13])
ext_fov = True if argv[14] == "True" else False
norm_sx = int(argv[19])
norm_dx = int(argv[20])
ext_fov_rot_right = argv[15]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[16])
ext_fov_normalize = True if argv[17] == "True" else False
ext_fov_average = True if argv[18] == "True" else False
skip_ringrem = True if argv[21] == "True" else False
ringrem = argv[22]
# Extra reconstruction parameters:
zerone_mode = True if argv[23] == "True" else False
corr_offset = float(argv[24])
reconmethod = argv[25]
# Force overpadding in case of GRIDREC for unknown reasons:
if reconmethod == "GRIDREC":
overpad = True
decim_factor = int(argv[26])
downsc_factor = int(argv[27])
# Parameters for postprocessing:
postprocess_required = True if argv[28] == "True" else False
polarfilt_opt = argv[29]
convert_opt = argv[30]
crop_opt = argv[31]
# Parameters for on-the-fly phase retrieval:
phaseretrieval_required = True if argv[32] == "True" else False
phrtmethod = int(argv[33])
phrt_param1 = double(argv[34]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[35]) # param2( e.g. thresh or delta)
energy = double(argv[36])
distance = double(argv[37])
pixsize = double(argv[38]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[39] == "True" else False
approx_win = int(argv[40])
angles_projfrom = int(argv[41])
angles_projto = int(argv[42])
rolling = True if argv[43] == "True" else False
roll_shift = int(int(argv[44]) / decim_factor)
preprocessingplan_fromcache = True if argv[45] == "True" else False
dynamic_ff = True if argv[46] == "True" else False
nr_threads = int(argv[47])
tmppath = argv[48]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[49]
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
dset_min = -1
dset_max = -1
if (zerone_mode):
if ('min' in dset.attrs):
dset_min = float(dset.attrs['min'])
else:
zerone_mode = False
if ('max' in dset.attrs):
dset_max = float(dset.attrs['max'])
else:
zerone_mode = False
num_sinos = tdf.get_nr_sinos(dset) # Pay attention to the downscale factor
if (num_sinos == 0):
exit()
# Check extrema:
if (sino_idx >= num_sinos / downsc_factor):
sino_idx = num_sinos / downsc_factor - 1
# Get correction plan and phase retrieval plan (if required):
skipflat = False
corrplan = 0
im_dark = 0
EFF = 0
filtEFF = 0
if (preprocessing_required):
if not dynamic_ff:
# Load flat fielding plan either from cache (if required) or from TDF file
# and cache it for faster re-use:
if (preprocessingplan_fromcache):
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat']) and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
else:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat']) and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
# Dowscale flat and dark images if necessary:
if isinstance(corrplan['im_flat'], ndarray):
corrplan['im_flat'] = corrplan['im_flat'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark'], ndarray):
corrplan['im_dark'] = corrplan['im_dark'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_flat_after'], ndarray):
corrplan['im_flat_after'] = corrplan['im_flat_after'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark_after'], ndarray):
corrplan['im_dark_after'] = corrplan['im_dark_after'][::downsc_factor,::downsc_factor]
else:
# Dynamic flat fielding:
if "/tomo" in f_in:
if "/flat" in f_in:
flat_dset = f_in['flat']
if "/dark" in f_in:
im_dark = _medianize(f_in['dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in:
flat_dset = f_in['/exchange/data_white']
if "/exchange/data_dark" in f_in:
im_dark = _medianize(f_in['/exchange/data_dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat:
EFF, filtEFF = dff_prepare_plan(flat_dset, 16, im_dark)
# Downscale images if necessary:
im_dark = im_dark[::downsc_factor,::downsc_factor]
EFF = EFF[::downsc_factor,::downsc_factor,:]
filtEFF = filtEFF[::downsc_factor,::downsc_factor,:]
f_in.close()
# Run computation:
process(sino_idx, num_sinos, infile, outfile, preprocessing_required, corrplan, skipflat, norm_sx,
norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average, ringrem, phaseretrieval_required, phrtmethod, phrt_param1,
phrt_param2, energy, distance, pixsize, phrtpad, approx_win, angles, angles_projfrom,
angles_projto, offset, logtrsf, recpar, circle, scale, overpad, reconmethod, rolling,
roll_shift, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset,
postprocess_required, polarfilt_opt, convert_opt, crop_opt, dynamic_ff, EFF, filtEFF,
im_dark, nr_threads, logfilename, tmppath)
def main(argv):
"""To do...
"""
lock = Lock()
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
sino_idx = int(argv[0])
# Get paths:
infile = argv[1]
outpath = argv[2]
# Essential reconstruction parameters::
angles = float(argv[3])
off_step = float(argv[4])
param1 = argv[5]
scale = int(float(argv[6]))
overpad = True if argv[7] == "True" else False
logtrsf = True if argv[8] == "True" else False
circle = True if argv[9] == "True" else False
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[10] == "True" else False
flat_end = True if argv[11] == "True" else False
half_half = True if argv[12] == "True" else False
half_half_line = int(argv[13])
ext_fov = True if argv[14] == "True" else False
norm_sx = int(argv[19])
norm_dx = int(argv[20])
ext_fov_rot_right = argv[15]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[16])
ext_fov_normalize = True if argv[17] == "True" else False
ext_fov_average = True if argv[18] == "True" else False
skip_ringrem = True if argv[21] == "True" else False
ringrem = argv[22]
# Extra reconstruction parameters:
zerone_mode = True if argv[23] == "True" else False
corr_offset = float(argv[24])
reconmethod = argv[25]
decim_factor = int(argv[26])
downsc_factor = int(argv[27])
# Parameters for postprocessing:
postprocess_required = True if argv[28] == "True" else False
convert_opt = argv[29]
crop_opt = argv[30]
# Parameters for on-the-fly phase retrieval:
phaseretrieval_required = True if argv[31] == "True" else False
phrtmethod = int(argv[32])
phrt_param1 = double(argv[33]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[34]) # param2( e.g. thresh or delta)
energy = double(argv[35])
distance = double(argv[36])
pixsize = double(argv[37]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[38] == "True" else False
approx_win = int(argv[39])
angles_projfrom = int(argv[40])
angles_projto = int(argv[41])
preprocessingplan_fromcache = True if argv[42] == "True" else False
tmppath = argv[43]
if not tmppath.endswith(sep): tmppath += sep
nr_threads = int(argv[44])
off_from = float(argv[45])
off_to = float(argv[46])
slice_prefix = argv[47]
logfilename = argv[48]
if not exists(outpath):
makedirs(outpath)
if not outpath.endswith(sep): outpath += sep
# Log info:
log = open(logfilename, "w")
log.write(linesep + "\tInput dataset: %s" % (infile))
log.write(linesep + "\tOutput path: %s" % (outpath))
log.write(linesep + "\t--------------")
log.write(linesep + "\tLoading flat and dark images...")
log.close()
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
dset_min = -1
dset_max = -1
if (zerone_mode):
if ('min' in dset.attrs):
dset_min = float(dset.attrs['min'])
else:
zerone_mode = False
if ('max' in dset.attrs):
dset_max = float(dset.attrs['max'])
else:
zerone_mode = False
num_sinos = tdf.get_nr_sinos(dset) # Pay attention to the downscale factor
if (num_sinos == 0):
exit()
# Check extrema:
if (sino_idx >= num_sinos / downsc_factor):
sino_idx = num_sinos / downsc_factor - 1
# Get correction plan and phase retrieval plan (if required):
corrplan = 0
if (preprocessing_required):
# Load flat fielding plan either from cache (if required) or from TDF file and cache it for faster re-use:
if (preprocessingplan_fromcache):
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
else:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
# Dowscale flat and dark images if necessary:
if isinstance(corrplan['im_flat'], ndarray):
corrplan['im_flat'] = corrplan[
'im_flat'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_dark'], ndarray):
corrplan['im_dark'] = corrplan[
'im_dark'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_flat_after'], ndarray):
corrplan['im_flat_after'] = corrplan[
'im_flat_after'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_dark_after'], ndarray):
corrplan['im_dark_after'] = corrplan[
'im_dark_after'][::downsc_factor, ::downsc_factor]
f_in.close()
# Log infos:
log = open(logfilename, "a")
log.write(linesep + "\tPerforming preprocessing...")
log.close()
# Run computation:
process(sino_idx, num_sinos, infile, outpath, preprocessing_required,
corrplan, norm_sx, norm_dx, flat_end, half_half, half_half_line,
ext_fov, ext_fov_rot_right, ext_fov_overlap, ext_fov_normalize,
ext_fov_average, ringrem, phaseretrieval_required, phrtmethod,
phrt_param1, phrt_param2, energy, distance, pixsize, phrtpad,
approx_win, angles, angles_projfrom, angles_projto, off_step,
logtrsf, param1, circle, scale, overpad, reconmethod, zerone_mode,
dset_min, dset_max, decim_factor, downsc_factor, corr_offset,
postprocess_required, convert_opt, crop_opt, nr_threads, off_from,
off_to, logfilename, lock, slice_prefix)
def main(argv):
"""Try to guess the center of rotation of the input CT dataset.
Parameters
----------
infile : array_like
HDF5 input dataset
outfile : string
Full path where the identified center of rotation will be written as output
scale : int
If sub-pixel precision is interesting, use e.g. 2.0 to get a center of rotation
of .5 value. Use 1.0 if sub-pixel precision is not required
angles : int
Total number of angles of the input dataset
method : string
One of the following options: "registration"
tmppath : string
Temporary path where look for cached flat/dark files
"""
# Get path:
infile = argv[0] # The HDF5 file on the
outfile = argv[1] # The txt file with the proposed center
scale = float(argv[2])
angles = float(argv[3])
method = argv[4]
tmppath = argv[5]
if not tmppath.endswith(sep): tmppath += sep
pyfftw_cache_disable()
pyfftw_cache_enable()
pyfftw_set_keepalive_time(1800)
# Create a silly temporary log:
tmplog = tmppath + basename(infile) + str(time.time())
# Open the HDF5 file (take into account also older TDF versions):
f_in = getHDF5( infile, 'r' )
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
# Get flats and darks from cache or from file:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, True, tmplog)
remove(tmplog)
plan2cache(corrplan, infile, tmppath)
# Get first and the 180 deg projections:
im1 = tdf.read_tomo(dset,0).astype(float32)
idx = int(round(num_proj/angles * pi)) - 1
im2 = tdf.read_tomo(dset,idx).astype(float32)
# Apply simple flat fielding (if applicable):
if (isinstance(corrplan['im_flat_after'], ndarray) and isinstance(corrplan['im_flat'], ndarray) and
isinstance(corrplan['im_dark'], ndarray) and isinstance(corrplan['im_dark_after'], ndarray)) :
im1 = ((abs(im1 - corrplan['im_dark'])) / (abs(corrplan['im_flat'] - corrplan['im_dark'])
+ finfo(float32).eps)).astype(float32)
im2 = ((abs(im2 - corrplan['im_dark_after'])) / (abs(corrplan['im_flat_after'] - corrplan['im_dark_after'])
+ finfo(float32).eps)).astype(float32)
# Scale projections (if required) to get subpixel estimation:
if ( abs(scale - 1.0) > finfo(float32).eps ):
im1 = imresize(im1, (int(round(scale*im1.shape[0])), int(round(scale*im1.shape[1]))), interp='bicubic', mode='F');
im2 = imresize(im2, (int(round(scale*im2.shape[0])), int(round(scale*im2.shape[1]))), interp='bicubic', mode='F');
# Find the center (flipping left-right im2):
cen = findcenter.usecorrelation(im1, im2[ :,::-1])
cen = cen / scale
# Print center to output file:
text_file = open(outfile, "w")
text_file.write(str(int(cen)))
text_file.close()
# Close input HDF5:
f_in.close()
def main(argv): """To do... """ lock = Lock() skip_flat = True first_done = False pyfftw_cache_disable() pyfftw_cache_enable() pyfftw_set_keepalive_time(1800) # Get the from and to number of files to process: idx = int(argv[0]) # Get full paths of input TDF and output TDF: infile = argv[1] outfile = argv[2] # Get the phase retrieval parameters: beta = double(argv[3]) # param1( e.g. regParam, or beta) delta = double(argv[4]) # param2( e.g. thresh or delta) energy = double(argv[5]) distance = double(argv[6]) pixsize = double(argv[7]) / 1000.0 # pixsixe from micron to mm: pad = True if argv[8] == "True" else False # Tmp path and log file: tmppath = argv[9] if not tmppath.endswith(sep): tmppath += sep logfilename = argv[10] # Open the HDF5 file: f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] num_proj = tdf.get_nr_projs(dset) num_sinos = tdf.get_nr_sinos(dset) # Check if the HDF5 makes sense: if (num_proj == 0): log = open(logfilename,"a") log.write(linesep + "\tNo projections found. Process will end.") log.close() exit() # Get flats and darks from cache or from file: try: corrplan = cache2plan(infile, tmppath) except Exception as e: #print "Error(s) when reading from cache" corrplan = extract_flatdark(f_in, True, logfilename) remove(logfilename) plan2cache(corrplan, infile, tmppath) # Read projection: im = tdf.read_tomo(dset,idx).astype(float32) f_in.close() # Apply simple flat fielding (if applicable): if (isinstance(corrplan['im_flat_after'], ndarray) and isinstance(corrplan['im_flat'], ndarray) and isinstance(corrplan['im_dark'], ndarray) and isinstance(corrplan['im_dark_after'], ndarray)) : if (idx < num_proj/2): im = (im - corrplan['im_dark']) / (abs(corrplan['im_flat'] - corrplan['im_dark']) + finfo(float32).eps) else: im = (im - corrplan['im_dark_after']) / (abs(corrplan['im_flat_after'] - corrplan['im_dark_after']) + finfo(float32).eps) # Prepare plan: im = im.astype(float32) plan = prepare_plan (im, beta, delta, energy, distance, pixsize, padding=pad) # Perform phase retrieval (first time also PyFFTW prepares a plan): im = phase_retrieval(im, plan) # Write down reconstructed preview file (file name modified with metadata): im = im.astype(float32) outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str( nanmin(im)) + '$' + str( nanmax(im) ) im.tofile(outfile)
def main(argv):
"""Try to guess the amount of overlap in the case of extended FOV CT.
Parameters
----------
infile : array_like
HDF5 input dataset
outfile : string
Full path where the identified overlap will be written as output
scale : int
If sub-pixel precision is interesting, use e.g. 2.0 to get an overlap
of .5 value. Use 1.0 if sub-pixel precision is not required
tmppath : int
Temporary path where look for cached flat/dark files
"""
# Get path:
infile = argv[0] # The HDF5 file on the SSD
outfile = argv[1] # The txt file with the proposed center
scale = float(argv[2])
tmppath = argv[3]
if not tmppath.endswith(sep): tmppath += sep
# Create a silly temporary log:
tmplog = tmppath + basename(infile) + str(time.time())
# Open the HDF5 file:
f_in = getHDF5( infile, 'r' )
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
num_proj = tdf.get_nr_projs(dset)
# Get first and 180 deg projections:
im1 = tdf.read_tomo(dset,0).astype(float32)
im2 = tdf.read_tomo(dset,num_proj/2).astype(float32)
# Get flats and darks from cache or from file:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, True, tmplog)
remove(tmplog)
plan2cache(corrplan, infile, tmppath)
# Apply simple flat fielding (if applicable):
if (isinstance(corrplan['im_flat_after'], ndarray) and isinstance(corrplan['im_flat'], ndarray) and
isinstance(corrplan['im_dark'], ndarray) and isinstance(corrplan['im_dark_after'], ndarray)) :
im1 = ((abs(im1 - corrplan['im_dark'])) / (abs(corrplan['im_flat'] - corrplan['im_dark']) + finfo(float32).eps)).astype(float32)
im2 = ((abs(im2 - corrplan['im_dark_after'])) / (abs(corrplan['im_flat_after'] - corrplan['im_dark_after']) + finfo(float32).eps)).astype(float32)
# Scale projections (if required) to get subpixel estimation:
if ( abs(scale - 1.0) > finfo(float32).eps ):
im1 = imresize(im1, (int(round(scale*im1.shape[0])), int(round(scale*im1.shape[1]))), interp='bicubic', mode='F');
im2 = imresize(im2, (int(round(scale*im2.shape[0])), int(round(scale*im2.shape[1]))), interp='bicubic', mode='F');
# Find the center (flipping left-right im2): DISTINGUISH BETWEEN AIR ON THE RIGHT AND ON THE LEFT??????
cen = findcenter.usecorrelation(im1, im2[ :,::-1])
cen = (cen / scale)*2.0
# Print center to output file:
text_file = open(outfile, "w")
text_file.write(str(int(abs(cen))))
text_file.close()
# Close input HDF5:
f_in.close()
def main(argv):
"""To do...
"""
lock = Lock()
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
sino_idx = int(argv[0])
# Get paths:
infile = argv[1]
outpath = argv[2]
# Essential reconstruction parameters::
angles = float(argv[3])
offset = float(argv[4])
param1 = argv[5]
scale = int(float(argv[6]))
overpad = True if argv[7] == "True" else False
logtrsf = True if argv[8] == "True" else False
circle = True if argv[9] == "True" else False
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[10] == "True" else False
flat_end = True if argv[11] == "True" else False
half_half = True if argv[12] == "True" else False
half_half_line = int(argv[13])
ext_fov = True if argv[14] == "True" else False
norm_sx = int(argv[19])
norm_dx = int(argv[20])
ext_fov_rot_right = argv[15]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[16])
ext_fov_normalize = True if argv[17] == "True" else False
ext_fov_average = True if argv[18] == "True" else False
skip_ringrem = True if argv[21] == "True" else False
ringrem = argv[22]
# Extra reconstruction parameters:
zerone_mode = True if argv[23] == "True" else False
corr_offset = float(argv[24])
reconmethod = argv[25]
decim_factor = int(argv[26])
downsc_factor = int(argv[27])
# Parameters for postprocessing:
postprocess_required = True if argv[28] == "True" else False
convert_opt = argv[29]
crop_opt = argv[30]
# Parameters for on-the-fly phase retrieval:
phaseretrieval_required = True if argv[31] == "True" else False
phrtmethod = int(argv[32])
phrt_param1 = double(argv[33]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[34]) # param2( e.g. thresh or delta)
energy = double(argv[35])
distance = double(argv[36])
pixsize = double(argv[37]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[38] == "True" else False
approx_win = int(argv[39])
preprocessingplan_fromcache = True if argv[40] == "True" else False
tmppath = argv[41]
if not tmppath.endswith(sep): tmppath += sep
nr_threads = int(argv[42])
angles_from = float(argv[43])
angles_to = float(argv[44])
slice_prefix = argv[45]
logfilename = argv[46]
if not exists(outpath):
makedirs(outpath)
if not outpath.endswith(sep): outpath += sep
# Log info:
log = open(logfilename,"w")
log.write(linesep + "\tInput dataset: %s" % (infile))
log.write(linesep + "\tOutput path: %s" % (outpath))
log.write(linesep + "\t--------------")
log.write(linesep + "\tLoading flat and dark images...")
log.close()
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
dset_min = -1
dset_max = -1
if (zerone_mode):
if ('min' in dset.attrs):
dset_min = float(dset.attrs['min'])
else:
zerone_mode = False
if ('max' in dset.attrs):
dset_max = float(dset.attrs['max'])
else:
zerone_mode = False
num_sinos = tdf.get_nr_sinos(dset) # Pay attention to the downscale factor
if (num_sinos == 0):
exit()
# Check extrema:
if (sino_idx >= num_sinos / downsc_factor):
sino_idx = num_sinos / downsc_factor - 1
# Get correction plan and phase retrieval plan (if required):
corrplan = 0
if (preprocessing_required):
# Load flat fielding plan either from cache (if required) or from TDF file and cache it for faster re-use:
if (preprocessingplan_fromcache):
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
else:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
# Dowscale flat and dark images if necessary:
if isinstance(corrplan['im_flat'], ndarray):
corrplan['im_flat'] = corrplan['im_flat'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark'], ndarray):
corrplan['im_dark'] = corrplan['im_dark'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_flat_after'], ndarray):
corrplan['im_flat_after'] = corrplan['im_flat_after'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark_after'], ndarray):
corrplan['im_dark_after'] = corrplan['im_dark_after'][::downsc_factor,::downsc_factor]
f_in.close()
# Log infos:
log = open(logfilename,"a")
log.write(linesep + "\tPerforming preprocessing...")
log.close()
# Run computation:
process( sino_idx, num_sinos, infile, outpath, preprocessing_required, corrplan, norm_sx,
norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average, ringrem,
phaseretrieval_required, phrtmethod, phrt_param1, phrt_param2, energy, distance, pixsize, phrtpad,
approx_win, angles, offset, logtrsf, param1, circle, scale, overpad, reconmethod, zerone_mode,
dset_min, dset_max, decim_factor, downsc_factor, corr_offset, postprocess_required, convert_opt,
crop_opt, nr_threads, angles_from, angles_to, logfilename, lock, slice_prefix )
def main(argv):
"""Try to guess the amount of overlap in the case of extended FOV CT.
Parameters
----------
infile : array_like
HDF5 input dataset
outfile : string
Full path where the identified overlap will be written as output
scale : int
If sub-pixel precision is interesting, use e.g. 2.0 to get an overlap
of .5 value. Use 1.0 if sub-pixel precision is not required
tmppath : int
Temporary path where look for cached flat/dark files
"""
# Get path:
infile = argv[0] # The HDF5 file on the SSD
outfile = argv[1] # The txt file with the proposed center
scale = float(argv[2])
tmppath = argv[3]
if not tmppath.endswith(sep): tmppath += sep
# Create a silly temporary log:
tmplog = tmppath + basename(infile) + str(time.time())
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
num_proj = tdf.get_nr_projs(dset)
# Get first and 180 deg projections:
im1 = tdf.read_tomo(dset, 0).astype(float32)
im2 = tdf.read_tomo(dset, num_proj / 2).astype(float32)
# Get flats and darks from cache or from file:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, True, tmplog)
remove(tmplog)
plan2cache(corrplan, infile, tmppath)
# Apply simple flat fielding (if applicable):
if (isinstance(corrplan['im_flat_after'], ndarray)
and isinstance(corrplan['im_flat'], ndarray)
and isinstance(corrplan['im_dark'], ndarray)
and isinstance(corrplan['im_dark_after'], ndarray)):
im1 = ((abs(im1 - corrplan['im_dark'])) /
(abs(corrplan['im_flat'] - corrplan['im_dark']) +
finfo(float32).eps)).astype(float32)
im2 = ((abs(im2 - corrplan['im_dark_after'])) /
(abs(corrplan['im_flat_after'] - corrplan['im_dark_after']) +
finfo(float32).eps)).astype(float32)
# Scale projections (if required) to get subpixel estimation:
if (abs(scale - 1.0) > finfo(float32).eps):
im1 = imresize(im1, (int(round(
scale * im1.shape[0])), int(round(scale * im1.shape[1]))),
interp='bicubic',
mode='F')
im2 = imresize(im2, (int(round(
scale * im2.shape[0])), int(round(scale * im2.shape[1]))),
interp='bicubic',
mode='F')
# Find the center (flipping left-right im2): DISTINGUISH BETWEEN AIR ON THE RIGHT AND ON THE LEFT??????
cen = findcenter.usecorrelation(im1, im2[:, ::-1])
cen = (cen / scale) * 2.0
# Print center to output file:
text_file = open(outfile, "w")
text_file.write(str(int(abs(cen))))
text_file.close()
# Close input HDF5:
f_in.close()
def main(argv):
"""To do...
"""
lock = Lock()
skip_flat = True
first_done = False
pyfftw_cache_disable()
pyfftw_cache_enable()
pyfftw_set_keepalive_time(1800)
# Get the from and to number of files to process:
idx = int(argv[0])
# Get full paths of input TDF and output TDF:
infile = argv[1]
outfile = argv[2]
# Get the phase retrieval parameters:
method = int(argv[3])
param1 = double(argv[4]) # param1( e.g. regParam, or beta)
param2 = double(argv[5]) # param2( e.g. thresh or delta)
energy = double(argv[6])
distance = double(argv[7])
pixsize = double(argv[8]) / 1000.0 # pixsixe from micron to mm:
pad = True if argv[9] == "True" else False
# Tmp path and log file:
tmppath = argv[10]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[11]
# Open the HDF5 file and check it contains flat files:
skipflat = False
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
if not "/flat" in f_in:
skipflat = True
else:
dset = f_in['exchange/data']
if not "/exchange/data_white" in f_in:
skipflat = True
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
# Check if the HDF5 makes sense:
if (num_proj == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Get flats and darks from cache or from file:
if not skipflat:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, True, logfilename)
remove(logfilename)
plan2cache(corrplan, infile, tmppath)
# Read projection:
im = tdf.read_tomo(dset, idx).astype(float32)
f_in.close()
# Apply simple flat fielding (if applicable):
if not skipflat:
if (isinstance(corrplan['im_flat_after'], ndarray)
and isinstance(corrplan['im_flat'], ndarray)
and isinstance(corrplan['im_dark'], ndarray)
and isinstance(corrplan['im_dark_after'], ndarray)):
if (idx < num_proj / 2):
im = (im - corrplan['im_dark']) / (
abs(corrplan['im_flat'] - corrplan['im_dark']) +
finfo(float32).eps)
else:
im = (im - corrplan['im_dark_after']) / (
abs(corrplan['im_flat_after'] - corrplan['im_dark_after'])
+ finfo(float32).eps)
# Prepare plan:
im = im.astype(float32)
if (method == 0):
# Paganin 2002:
plan = tiehom_plan(im, param1, param2, energy, distance, pixsize, pad)
im = tiehom(im, plan).astype(float32)
elif (method == 1):
# Paganin 2020:
plan = tiehom_plan2020(im, param1, param2, energy, distance, pixsize,
pad)
im = tiehom2020(im, plan).astype(float32)
else:
plan = phrt_plan(im, energy, distance, pixsize, param2, param1, method,
pad)
im = phrt(im, plan, method).astype(float32)
# Write down reconstructed preview file (file name modified with metadata):
im = im.astype(float32)
outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(
im.shape[0]) + '_' + str(nanmin(im)) + '$' + str(nanmax(im))
im.tofile(outfile)
def main(argv):
"""To do...
Usage
-----
Parameters
---------
Example
--------------------------
The following line processes the first ten TIFF files of input path
"/home/in" and saves the processed files to "/home/out" with the
application of the Boin and Haibel filter with smoothing via a Butterworth
filter of order 4 and cutoff frequency 0.01:
reconstruct 0 4 C:\Temp\Dullin_Aug_2012\sino_noflat C:\Temp\Dullin_Aug_2012\sino_noflat\output
9.0 10.0 0.0 0.0 0.0 true sino slice C:\Temp\Dullin_Aug_2012\sino_noflat\tomo_conv flat dark
"""
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
sino_idx = int(argv[0])
# Get paths:
infile = argv[1]
outfile = argv[2]
# Essential reconstruction parameters::
angles = float(argv[3])
offset = float(argv[4])
param1 = argv[5]
scale = int(float(argv[6]))
overpad = True if argv[7] == "True" else False
logtrsf = True if argv[8] == "True" else False
circle = True if argv[9] == "True" else False
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[10] == "True" else False
flat_end = True if argv[11] == "True" else False
half_half = True if argv[12] == "True" else False
half_half_line = int(argv[13])
ext_fov = True if argv[14] == "True" else False
norm_sx = int(argv[17])
norm_dx = int(argv[18])
ext_fov_rot_right = argv[15]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[16])
skip_ringrem = True if argv[19] == "True" else False
ringrem = argv[20]
# Extra reconstruction parameters:
zerone_mode = True if argv[21] == "True" else False
corr_offset = float(argv[22])
reconmethod = argv[23]
decim_factor = int(argv[24])
downsc_factor = int(argv[25])
# Parameters for postprocessing:
postprocess_required = True if argv[26] == "True" else False
convert_opt = argv[27]
crop_opt = argv[28]
# Parameters for on-the-fly phase retrieval:
phaseretrieval_required = True if argv[29] == "True" else False
beta = double(argv[30]) # param1( e.g. regParam, or beta)
delta = double(argv[31]) # param2( e.g. thresh or delta)
energy = double(argv[32])
distance = double(argv[33])
pixsize = double(argv[34]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[35] == "True" else False
approx_win = int(argv[36])
preprocessingplan_fromcache = True if argv[37] == "True" else False
nr_threads = int(argv[38])
tmppath = argv[39]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[40]
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
dset_min = -1
dset_max = -1
if (zerone_mode):
if ('min' in dset.attrs):
dset_min = float(dset.attrs['min'])
else:
zerone_mode = False
if ('max' in dset.attrs):
dset_max = float(dset.attrs['max'])
else:
zerone_mode = False
num_sinos = tdf.get_nr_sinos(dset) # Pay attention to the downscale factor
if (num_sinos == 0):
exit()
# Check extrema:
if (sino_idx >= num_sinos):
sino_idx = num_sinos - 1
# Get correction plan and phase retrieval plan (if required):
corrplan = 0
if (preprocessing_required):
# Load flat fielding plan either from cache (if required) or from TDF file and cache it for faster re-use:
if (preprocessingplan_fromcache):
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
else:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
plan2cache(corrplan, infile, tmppath)
# Dowscale flat and dark images if necessary:
if isinstance(corrplan['im_flat'], ndarray):
corrplan['im_flat'] = corrplan['im_flat'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark'], ndarray):
corrplan['im_dark'] = corrplan['im_dark'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_flat_after'], ndarray):
corrplan['im_flat_after'] = corrplan['im_flat_after'][::downsc_factor,::downsc_factor]
if isinstance(corrplan['im_dark_after'], ndarray):
corrplan['im_dark_after'] = corrplan['im_dark_after'][::downsc_factor,::downsc_factor]
f_in.close()
# Run computation:
process( sino_idx, num_sinos, infile, outfile, preprocessing_required, corrplan, norm_sx,
norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, ringrem,
phaseretrieval_required, beta, delta, energy, distance, pixsize, phrtpad, approx_win, angles, offset,
logtrsf, param1, circle, scale, overpad, reconmethod, zerone_mode, dset_min, dset_max, decim_factor,
downsc_factor, corr_offset, postprocess_required, convert_opt, crop_opt, nr_threads, logfilename )
def main(argv): """To do... """ # Get the zero-order index of the sinogram to pre-process: idx = int(argv[0]) # Get paths: infile = argv[1] outfile = argv[2] # Normalization parameters: norm_sx = int(argv[3]) norm_dx = int(argv[4]) # Params for flat fielding with post flats/darks: flat_end = True if argv[5] == "True" else False half_half = True if argv[6] == "True" else False half_half_line = int(argv[7]) # Params for extended FOV: ext_fov = True if argv[8] == "True" else False ext_fov_rot_right = argv[9] if ext_fov_rot_right == "True": ext_fov_rot_right = True if (ext_fov): norm_sx = 0 else: ext_fov_rot_right = False if (ext_fov): norm_dx = 0 ext_fov_overlap = int(argv[10]) ext_fov_normalize = True if argv[11] == "True" else False ext_fov_average = True if argv[12] == "True" else False # Method and parameters coded into a string: ringrem = argv[13] # Flat fielding method (conventional or dynamic): dynamic_ff = True if argv[14] == "True" else False # Tmp path and log file: tmppath = argv[15] if not tmppath.endswith(sep): tmppath += sep logfilename = argv[16] # Open the HDF5 file: f_in = getHDF5(infile, 'r') try: if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] except: log = open(logfilename,"a") log.write(linesep + "\tError reading input dataset. Process will end.") log.close() exit() num_proj = tdf.get_nr_projs(dset) num_sinos = tdf.get_nr_sinos(dset) # Check if the HDF5 makes sense: if (num_sinos == 0): log = open(logfilename,"a") log.write(linesep + "\tNo projections found. Process will end.") log.close() exit() # Get flat and darks from cache or from file: skipflat = False skipdark = False if not dynamic_ff: try: corrplan = cache2plan(infile, tmppath) except Exception as e: #print "Error(s) when reading from cache" corrplan = extract_flatdark(f_in, flat_end, logfilename) if (isscalar(corrplan['im_flat']) and isscalar(corrplan['im_flat_after']) ): skipflat = True else: plan2cache(corrplan, infile, tmppath) else: # Dynamic flat fielding: if "/tomo" in f_in: if "/flat" in f_in: flat_dset = f_in['flat'] if "/dark" in f_in: im_dark = _medianize(f_in['dark']) else: skipdark = True else: skipflat = True # Nothing to do in this case else: if "/exchange/data_white" in f_in: flat_dset = f_in['/exchange/data_white'] if "/exchange/data_dark" in f_in: im_dark = _medianize(f_in['/exchange/data_dark']) else: skipdark = True else: skipflat = True # Nothing to do in this case # Prepare plan for dynamic flat fielding with 16 repetitions: if not skipflat: EFF, filtEFF = dff_prepare_plan(flat_dset, 16, im_dark) # Read input image: im = tdf.read_sino(dset,idx).astype(float32) f_in.close() # Perform pre-processing (flat fielding, extended FOV, ring removal): if not skipflat: if dynamic_ff: # Dynamic flat fielding with downsampling = 2: im = dynamic_flat_fielding(im, idx, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx) else: im = flat_fielding(im, idx, corrplan, flat_end, half_half, half_half_line, norm_sx, norm_dx) if ext_fov: im = extfov_correction(im, ext_fov_rot_right, ext_fov_overlap, ext_fov_normalize, ext_fov_average) if not skipflat and not dynamic_ff: im = ring_correction (im, ringrem, flat_end, corrplan['skip_flat_after'], half_half, half_half_line, ext_fov) else: im = ring_correction (im, ringrem, False, False, half_half, half_half_line, ext_fov) # Write down reconstructed preview file (file name modified with metadata): im = im.astype(float32) outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str( nanmin(im)) + '$' + str( nanmax(im) ) im.tofile(outfile)
def main(argv):
"""To do...
"""
# Get the zero-order index of the sinogram to pre-process:
idx = int(argv[0])
# Get paths:
infile = argv[1]
outfile = argv[2]
# Normalization parameters:
norm_sx = int(argv[3])
norm_dx = int(argv[4])
# Params for flat fielding with post flats/darks:
flat_end = True if argv[5] == "True" else False
half_half = True if argv[6] == "True" else False
half_half_line = int(argv[7])
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[8] == "True" else False
# Parameters for rotation:
rotation = float(argv[9])
interp = argv[10]
border = argv[11]
# Tmp path and log file:
tmppath = argv[12]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[13]
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
try:
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
except:
log = open(logfilename, "a")
log.write(linesep + "\tError reading input dataset. Process will end.")
log.close()
exit()
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
# Check if the HDF5 makes sense:
if (num_sinos == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Get flat and darks from cache or from file:
skipflat = False
skipdark = False
if not dynamic_ff:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat'])
and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
else:
# Dynamic flat fielding:
if "/tomo" in f_in:
if "/flat" in f_in:
flat_dset = f_in['flat']
if "/dark" in f_in:
im_dark = _medianize(f_in['dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in:
flat_dset = f_in['/exchange/data_white']
if "/exchange/data_dark" in f_in:
im_dark = _medianize(f_in['/exchange/data_dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat:
EFF, filtEFF = dff_prepare_plan(flat_dset, 16, im_dark)
# Read input image:
im = tdf.read_tomo(dset, idx).astype(float32)
f_in.close()
# Perform pre-processing (flat fielding, extended FOV, ring removal):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im = dynamic_flat_fielding(im, EFF, filtEFF, 2, im_dark)
else:
im = flat_fielding(im, idx, corrplan, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
# Rotate:
rows, cols = im.shape
M = cv2.getRotationMatrix2D((cols / 2, rows / 2), rotation, 1)
if interp == 'nearest':
interpflag = cv2.INTER_NEAREST
elif interp == 'cubic':
interpflag = cv2.INTER_CUBIC
elif interp == 'lanczos':
interpflag = cv2.INTER_LANCZOS4
else:
interpflag = cv2.INTER_LINEAR
if border == 'constant':
borderflag = cv2.BORDER_CONSTANT
else:
borderflag = cv2.BORDER_REPLICATE
im = cv2.warpAffine(im,
M, (cols, rows),
flags=interpflag,
borderMode=borderflag)
# Write down reconstructed preview file (file name modified with metadata):
im = im.astype(float32)
outfile2 = outfile + '_' + str(im.shape[1]) + 'x' + str(
im.shape[0]) + '_' + str(nanmin(im)) + '$' + str(
nanmax(im)) + '_after.raw'
im.tofile(outfile2)
def main(argv):
"""To do...
"""
# Get the zero-order index of the sinogram to pre-process:
idx = int(argv[0])
# Get paths:
infile = argv[1]
outfile = argv[2]
# Normalization parameters:
norm_sx = int(argv[3])
norm_dx = int(argv[4])
# Params for flat fielding with post flats/darks:
flat_end = True if argv[5] == "True" else False
half_half = True if argv[6] == "True" else False
half_half_line = int(argv[7])
# Params for extended FOV:
ext_fov = True if argv[8] == "True" else False
ext_fov_rot_right = argv[9]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[10])
ext_fov_normalize = True if argv[11] == "True" else False
ext_fov_average = True if argv[12] == "True" else False
# Method and parameters coded into a string:
ringrem = argv[13]
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[14] == "True" else False
# Tmp path and log file:
tmppath = argv[15]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[16]
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
try:
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
except:
log = open(logfilename, "a")
log.write(linesep + "\tError reading input dataset. Process will end.")
log.close()
exit()
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
# Check if the HDF5 makes sense:
if (num_sinos == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Get flat and darks from cache or from file:
skipflat = False
skipdark = False
if not dynamic_ff:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat'])
and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
else:
# Dynamic flat fielding:
if "/tomo" in f_in:
if "/flat" in f_in:
flat_dset = f_in['flat']
if "/dark" in f_in:
im_dark = _medianize(f_in['dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in:
flat_dset = f_in['/exchange/data_white']
if "/exchange/data_dark" in f_in:
im_dark = _medianize(f_in['/exchange/data_dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat:
EFF, filtEFF = dff_prepare_plan(flat_dset, 16, im_dark)
# Read input image:
im = tdf.read_sino(dset, idx).astype(float32)
f_in.close()
# Perform pre-processing (flat fielding, extended FOV, ring removal):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im = dynamic_flat_fielding(im, idx, EFF, filtEFF, 2, im_dark,
norm_sx, norm_dx)
else:
im = flat_fielding(im, idx, corrplan, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
if ext_fov:
im = extfov_correction(im, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average)
if not skipflat and not dynamic_ff:
im = ring_correction(im, ringrem, flat_end,
corrplan['skip_flat_after'], half_half,
half_half_line, ext_fov)
else:
im = ring_correction(im, ringrem, False, False, half_half,
half_half_line, ext_fov)
# Write down reconstructed preview file (file name modified with metadata):
im = im.astype(float32)
outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(
im.shape[0]) + '_' + str(nanmin(im)) + '$' + str(nanmax(im))
im.tofile(outfile)
def main(argv):
"""To do...
Usage
-----
Parameters
---------
Example
--------------------------
"""
# Get the from and to number of files to process:
sino_idx = int(argv[0])
# Get paths:
infile = argv[1]
outfile = argv[2]
# Essential reconstruction parameters:
angles = float(argv[3])
offset = float(argv[4])
recpar = argv[5]
scale = int(float(argv[6]))
overpad = True if argv[7] == "True" else False
logtrsf = True if argv[8] == "True" else False
circle = True if argv[9] == "True" else False
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[10] == "True" else False
flat_end = True if argv[11] == "True" else False
half_half = True if argv[12] == "True" else False
half_half_line = int(argv[13])
ext_fov = True if argv[14] == "True" else False
norm_sx = int(argv[19])
norm_dx = int(argv[20])
ext_fov_rot_right = argv[15]
if ext_fov_rot_right == "True":
ext_fov_rot_right = True
if (ext_fov):
norm_sx = 0
else:
ext_fov_rot_right = False
if (ext_fov):
norm_dx = 0
ext_fov_overlap = int(argv[16])
ext_fov_normalize = True if argv[17] == "True" else False
ext_fov_average = True if argv[18] == "True" else False
skip_ringrem = True if argv[21] == "True" else False
ringrem = argv[22]
# Extra reconstruction parameters:
zerone_mode = True if argv[23] == "True" else False
corr_offset = float(argv[24])
reconmethod = argv[25]
# Force overpadding in case of GRIDREC for unknown reasons:
if reconmethod == "GRIDREC":
overpad = True
decim_factor = int(argv[26])
downsc_factor = int(argv[27])
# Parameters for postprocessing:
postprocess_required = True if argv[28] == "True" else False
polarfilt_opt = argv[29]
convert_opt = argv[30]
crop_opt = argv[31]
# Parameters for on-the-fly phase retrieval:
phaseretrieval_required = True if argv[32] == "True" else False
phrtmethod = int(argv[33])
phrt_param1 = double(argv[34]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[35]) # param2( e.g. thresh or delta)
energy = double(argv[36])
distance = double(argv[37])
pixsize = double(argv[38]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[39] == "True" else False
approx_win = int(argv[40])
angles_projfrom = int(argv[41])
angles_projto = int(argv[42])
rolling = True if argv[43] == "True" else False
roll_shift = int(int(argv[44]) / decim_factor)
preprocessingplan_fromcache = True if argv[45] == "True" else False
dynamic_ff = True if argv[46] == "True" else False
nr_threads = int(argv[47])
tmppath = argv[48]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[49]
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
dset_min = -1
dset_max = -1
if (zerone_mode):
if ('min' in dset.attrs):
dset_min = float(dset.attrs['min'])
else:
zerone_mode = False
if ('max' in dset.attrs):
dset_max = float(dset.attrs['max'])
else:
zerone_mode = False
num_sinos = tdf.get_nr_sinos(dset) # Pay attention to the downscale factor
if (num_sinos == 0):
exit()
# Check extrema:
if (sino_idx >= num_sinos / downsc_factor):
sino_idx = num_sinos / downsc_factor - 1
# Get correction plan and phase retrieval plan (if required):
skipflat = False
corrplan = 0
im_dark = 0
EFF = 0
filtEFF = 0
if (preprocessing_required):
if not dynamic_ff:
# Load flat fielding plan either from cache (if required) or from TDF file
# and cache it for faster re-use:
if (preprocessingplan_fromcache):
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat'])
and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
else:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat'])
and isscalar(corrplan['im_flat_after'])):
skipflat = True
else:
plan2cache(corrplan, infile, tmppath)
# Dowscale flat and dark images if necessary:
if isinstance(corrplan['im_flat'], ndarray):
corrplan['im_flat'] = corrplan[
'im_flat'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_dark'], ndarray):
corrplan['im_dark'] = corrplan[
'im_dark'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_flat_after'], ndarray):
corrplan['im_flat_after'] = corrplan[
'im_flat_after'][::downsc_factor, ::downsc_factor]
if isinstance(corrplan['im_dark_after'], ndarray):
corrplan['im_dark_after'] = corrplan[
'im_dark_after'][::downsc_factor, ::downsc_factor]
else:
# Dynamic flat fielding:
if "/tomo" in f_in:
if "/flat" in f_in:
flat_dset = f_in['flat']
if "/dark" in f_in:
im_dark = _medianize(f_in['dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in:
flat_dset = f_in['/exchange/data_white']
if "/exchange/data_dark" in f_in:
im_dark = _medianize(f_in['/exchange/data_dark'])
else:
skipdark = True
else:
skipflat = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat:
EFF, filtEFF = dff_prepare_plan(flat_dset, 16, im_dark)
# Downscale images if necessary:
im_dark = im_dark[::downsc_factor, ::downsc_factor]
EFF = EFF[::downsc_factor, ::downsc_factor, :]
filtEFF = filtEFF[::downsc_factor, ::downsc_factor, :]
f_in.close()
# Run computation:
process(sino_idx, num_sinos, infile, outfile, preprocessing_required,
corrplan, skipflat, norm_sx, norm_dx, flat_end, half_half,
half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average, ringrem,
phaseretrieval_required, phrtmethod, phrt_param1, phrt_param2,
energy, distance, pixsize, phrtpad, approx_win, angles,
angles_projfrom, angles_projto, offset, logtrsf, recpar, circle,
scale, overpad, reconmethod, rolling, roll_shift, zerone_mode,
dset_min, dset_max, decim_factor, downsc_factor, corr_offset,
postprocess_required, polarfilt_opt, convert_opt, crop_opt,
dynamic_ff, EFF, filtEFF, im_dark, nr_threads, logfilename,
tmppath)
def main(argv): """To do... Usage ----- Parameters --------- Example -------------------------- The following line processes the first ten TIFF files of input path "/home/in" and saves the processed files to "/home/out" with the application of the Boin and Haibel filter with smoothing via a Butterworth filter of order 4 and cutoff frequency 0.01: destripe /home/in /home/out 1 10 1 0.01 4 """ lock = Lock() skip_ringrem = False skip_flat = False skip_flat_after = True first_done = False # Get the number of sino to pre-process: idx = int(argv[0]) # Get paths: infile = argv[1] outfile = argv[2] # Normalization parameters: norm_sx = int(argv[3]) norm_dx = int(argv[4]) # Params for flat fielding with post flats/darks: flat_end = True if argv[5] == "True" else False half_half = True if argv[6] == "True" else False half_half_line = int(argv[7]) # Params for extended FOV: ext_fov = True if argv[8] == "True" else False ext_fov_rot_right = argv[9] if ext_fov_rot_right == "True": ext_fov_rot_right = True if (ext_fov): norm_sx = 0 else: ext_fov_rot_right = False if (ext_fov): norm_dx = 0 ext_fov_overlap = int(argv[10]) # Method and parameters coded into a string: ringrem = argv[11] # Tmp path and log file: tmppath = argv[12] if not tmppath.endswith(sep): tmppath += sep logfilename = argv[13] # Open the HDF5 file: f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] prov_dset = f_in['provenance/detector_output'] num_proj = tdf.get_nr_projs(dset) num_sinos = tdf.get_nr_sinos(dset) # Check if the HDF5 makes sense: if (num_sinos == 0): log = open(logfilename,"a") log.write(linesep + "\tNo projections found. Process will end.") log.close() exit() # Get flat and darks from cache or from file: try: corrplan = cache2plan(infile, tmppath) except Exception as e: #print "Error(s) when reading from cache" corrplan = extract_flatdark(f_in, flat_end, logfilename) plan2cache(corrplan, infile, tmppath) # Read input image: im = tdf.read_sino(dset,idx).astype(float32) f_in.close() # Perform pre-processing (flat fielding, extended FOV, ring removal): im = flat_fielding(im, idx, corrplan, flat_end, half_half, half_half_line, norm_sx, norm_dx) im = extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap) im = ring_correction (im, ringrem, flat_end, corrplan['skip_flat_after'], half_half, half_half_line, ext_fov) # Write down reconstructed preview file (file name modified with metadata): im = im.astype(float32) outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str( nanmin(im)) + '$' + str( nanmax(im) ) im.tofile(outfile)
def main(argv):
"""Try to guess the center of rotation of the input CT dataset.
Parameters
----------
infile : array_like
HDF5 input dataset
outfile : string
Full path where the identified center of rotation will be written as output
scale : int
If sub-pixel precision is interesting, use e.g. 2.0 to get a center of rotation
of .5 value. Use 1.0 if sub-pixel precision is not required
angles : int
Total number of angles of the input dataset
proj_from : int
Initial projections to consider for the assumed angles
proj_to : int
Final projections to consider for the assumed angles
method : string
(not implemented yet)
tmppath : string
Temporary path where look for cached flat/dark files
"""
# Get path:
infile = argv[0] # The HDF5 file on the
outfile = argv[1] # The txt file with the proposed center
scale = float(argv[2])
angles = float(argv[3])
proj_from = int(argv[4])
proj_to = int(argv[5])
method = argv[6]
tmppath = argv[7]
if not tmppath.endswith(sep): tmppath += sep
pyfftw_cache_disable()
pyfftw_cache_enable()
pyfftw_set_keepalive_time(1800)
# Create a silly temporary log:
tmplog = tmppath + basename(infile) + str(time.time())
# Open the HDF5 file (take into account also older TDF versions):
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
# Get flats and darks from cache or from file:
try:
corrplan = cache2plan(infile, tmppath)
except Exception as e:
#print "Error(s) when reading from cache"
corrplan = extract_flatdark(f_in, True, tmplog)
remove(tmplog)
plan2cache(corrplan, infile, tmppath)
# Get first and the 180 deg projections:
im1 = tdf.read_tomo(dset, proj_from).astype(float32)
idx = int(round((proj_to - proj_from) / angles * pi)) + proj_from
im2 = tdf.read_tomo(dset, idx).astype(float32)
# Apply simple flat fielding (if applicable):
if (isinstance(corrplan['im_flat_after'], ndarray)
and isinstance(corrplan['im_flat'], ndarray)
and isinstance(corrplan['im_dark'], ndarray)
and isinstance(corrplan['im_dark_after'], ndarray)):
im1 = ((abs(im1 - corrplan['im_dark'])) /
(abs(corrplan['im_flat'] - corrplan['im_dark']) +
finfo(float32).eps)).astype(float32)
im2 = ((abs(im2 - corrplan['im_dark_after'])) /
(abs(corrplan['im_flat_after'] - corrplan['im_dark_after']) +
finfo(float32).eps)).astype(float32)
# Scale projections (if required) to get subpixel estimation:
if (abs(scale - 1.0) > finfo(float32).eps):
im1 = imresize(im1, (int(round(
scale * im1.shape[0])), int(round(scale * im1.shape[1]))),
interp='bicubic',
mode='F')
im2 = imresize(im2, (int(round(
scale * im2.shape[0])), int(round(scale * im2.shape[1]))),
interp='bicubic',
mode='F')
# Find the center (flipping left-right im2):
cen = findcenter.usecorrelation(im1, im2[:, ::-1])
cen = cen / scale
# Print center to output file:
text_file = open(outfile, "w")
text_file.write(str(int(cen)))
text_file.close()
# Close input HDF5:
f_in.close()