def _process(lock, int_from, int_to, infile, outfile, outshape, outtype, method, plan, logfilename): # Process the required subset of images: for i in range(int_from, int_to + 1): # Read input image: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im = tdf.read_tomo(dset,i).astype(float32) f_in.close() t1 = time() # Perform phase retrieval (first time also PyFFTW prepares a plan): if (method == 0): im = tiehom(im, plan).astype(float32) else: im = phrt(im, plan, method).astype(float32) t2 = time() # Save processed image to HDF5 file (atomic procedure - lock used): _write_data(lock, im, i, outfile, outshape, outtype, logfilename, t2 - t1, t1 - t0)
def _process (lock, int_from, int_to, infile, outfile, outshape, outtype, skipflat, plan, 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, dynamic_ff, EFF, filtEFF, im_dark, logfilename): # Process the required subset of images: for i in range(int_from, int_to + 1): # Read input image: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im = tdf.read_sino(dset,i).astype(float32) f_in.close() t1 = time() # 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, i, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx) else: im = flat_fielding(im, i, plan, 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, plan['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) t2 = time() # Save processed image to HDF5 file (atomic procedure - lock used): _write_data(lock, im, i, outfile, outshape, outtype, logfilename, t2 - t1, t1 - t0)
def _process(lock, int_from, int_to, infile, outfile, outshape, outtype,
method, plan, logfilename):
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image:
t0 = time()
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_tomo(dset, i).astype(float32)
f_in.close()
t1 = time()
# Perform phase retrieval (first time also PyFFTW prepares a plan):
if (method == 0):
im = tiehom(im, plan).astype(float32)
elif (method == 1):
im = tiehom2020(im, plan).astype(float32)
else:
im = phrt(im, plan, method).astype(float32)
t2 = time()
# Save processed image to HDF5 file (atomic procedure - lock used):
_write_data(lock, im, i, outfile, outshape, outtype, logfilename,
t2 - t1, t1 - t0)
def _write_data(lock, im, index, outfile, outshape, outtype, rescale_factor,
logfilename, cputime, itime):
lock.acquire()
try:
t0 = time()
f_out = getHDF5(outfile, 'a')
f_out_dset = f_out.require_dataset('exchange/data',
outshape,
outtype,
chunks=tdf.get_dset_chunks(
outshape[0]))
im = im * rescale_factor
tdf.write_tomo(f_out_dset, index, im.astype(outtype))
# Set minimum and maximum:
if (amin(im[:]) < float(f_out_dset.attrs['min'])):
f_out_dset.attrs['min'] = str(amin(im[:]))
if (amax(im[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out.close()
t1 = time()
# Print out execution time:
log = open(logfilename, "a")
log.write(linesep +
"\ttomo_%s processed (CPU: %0.3f sec - I/O: %0.3f sec)." %
(str(index).zfill(4), cputime, t1 - t0 + itime))
log.close()
finally:
lock.release()
def _write_data(lock, im, index, outfile, outshape, outtype, rescale_factor, logfilename, cputime, itime):
lock.acquire()
try:
t0 = time()
f_out = getHDF5(outfile, 'a')
f_out_dset = f_out.require_dataset('exchange/data', outshape, outtype, chunks=tdf.get_dset_chunks(outshape[0]))
im = im * rescale_factor
tdf.write_tomo(f_out_dset,index,im.astype(outtype))
# Set minimum and maximum:
if (amin(im[:]) < float(f_out_dset.attrs['min'])):
f_out_dset.attrs['min'] = str(amin(im[:]))
if (amax(im[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out.close()
t1 = time()
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\ttomo_%s processed (CPU: %0.3f sec - I/O: %0.3f sec)." % (str(index).zfill(4), cputime, t1 - t0 + itime))
log.close()
finally:
lock.release()
def _process(lock, int_from, int_to, infile, outfile, outshape, outtype, skipflat, plan, flat_end, half_half, half_half_line, dynamic_ff, EFF, filtEFF, im_dark, rotation, interp, border, rescale_factor, logfilename): # Process the required subset of images: for i in range(int_from, int_to + 1): # Read input image: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im = tdf.read_tomo(dset,i).astype(float32) f_in.close() t1 = time() # 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, i, plan, flat_end, half_half, half_half_line, norm_sx, norm_dx) t2 = time() # Rotate: rows, cols = im.shape #if (cols > rows): # angle = - arctan(2.0 / cols) * (rows / 2.0) / 2.0 #else: # angle = - arctan(2.0 / rows) * (cols / 2.0) / 2.0 #print(angle) 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) # Save processed image to HDF5 file (atomic procedure - lock used): _write_data(lock, im, i, outfile, outshape, outtype, rescale_factor, logfilename, t2 - t1, t1 - t0)
def _write_data(lock, im, index, offset, abs_offset, imfilename, timestamp, projorder, tot_files,
provenance_dt, outfile, dsetname, outshape, outtype, logfilename, itime, n_images):
"""To do...
"""
lock.acquire()
try:
# Open the HDF5 file to be populated with projections (or sinograms):
t0 = time.time()
f_out = getHDF5(outfile, 'a')
f_out_dset = f_out.require_dataset(dsetname, outshape, outtype, chunks=tdf.get_dset_chunks(outshape[0]))
# Write the projection file or sinogram file:
if projorder:
tdf.write_tomo(f_out_dset, index - abs_offset, im)
else:
tdf.write_sino(f_out_dset, index - abs_offset, im)
# Set minimum and maximum (without Infs and NaNs):
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
if (numpy.amin(tmp[:]) < float(f_out_dset.attrs['min'])):
f_out_dset.attrs['min'] = str(numpy.amin(tmp[:]))
if (numpy.amax(tmp[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(numpy.amax(tmp[:]))
f_out_dset.attrs['avg'] = str(float(f_out_dset.attrs['avg']) + numpy.mean(tmp[:])/(1.0*n_images) )
# Save provenance metadata:
provenance_dset = f_out.require_dataset('provenance/detector_output', (tot_files,), dtype=provenance_dt)
provenance_dset["filename", offset - abs_offset + index] = numpy.string_(os.path.basename(imfilename))
provenance_dset["timestamp", offset - abs_offset + index] = numpy.string_(datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])
# Close the HDF5:
f_out.close()
t1 = time.time()
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s processed (I: %0.3f sec - O: %0.3f sec)." % (os.path.basename(imfilename), itime, t1 - t0))
log.close()
except:
io_warning = True
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\tError when writing to TDF %s. File skipped." % (os.path.basename(imfilename)))
log.close()
pass
finally:
lock.release()
def _write_data(lock, im, index, offset, abs_offset, imfilename, newfilename,
timestamp, projorder, tot_files, provenance_dt, outfile,
dsetname, outshape, outtype, logfilename, itime):
"""To do...
"""
lock.acquire()
try:
# Open the HDF5 file to be populated with projections (or sinograms):
t0 = time.time()
f_out = getHDF5(outfile, 'a')
f_out_dset = f_out.require_dataset(dsetname,
outshape,
outtype,
chunks=tdf.get_dset_chunks(
outshape[0]))
# Write the projection file or sinogram file:
if projorder:
tdf.write_tomo(f_out_dset, index - abs_offset, im)
else:
tdf.write_sino(f_out_dset, index - abs_offset, im)
# Set minimum and maximum:
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
if (numpy.amin(tmp[:]) < float(f_out_dset.attrs['min'])):
f_out_dset.attrs['min'] = str(numpy.amin(tmp[:]))
if (numpy.amax(tmp[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(numpy.amax(tmp[:]))
# Save provenance metadata:
provenance_dset = f_out.require_dataset('provenance/detector_output',
(tot_files, ),
dtype=provenance_dt)
provenance_dset["filename", offset - abs_offset + index] = newfilename
provenance_dset["timestamp",
offset - abs_offset + index] = numpy.string_(
datetime.datetime.fromtimestamp(timestamp).
strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])
# Close the HDF5:
f_out.close()
t1 = time.time()
# Print out execution time:
log = open(logfilename, "a")
log.write(os.linesep +
"\t%s processed (I: %0.3f sec - O: %0.3f sec)." %
(os.path.basename(imfilename), itime, t1 - t0))
log.close()
finally:
lock.release()
def _write_data(im, index, outfile, outshape, outtype):
f_out = getHDF5(outfile, 'a')
f_out_dset = f_out.require_dataset('exchange/data',
outshape,
outtype,
chunks=tdf.get_dset_chunks(outshape[0]))
tdf.write_sino(f_out_dset, index, im.astype(outtype))
# Set minimum and maximum:
if (amin(im[:]) < float(f_out_dset.attrs['min'])):
f_out_dset.attrs['min'] = str(amin(im[:]))
if (amax(im[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out.close()
def _process(lock, int_from, int_to, infile, outfile, outshape, outtype,
skipflat, plan, 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, dynamic_ff, EFF,
filtEFF, im_dark, logfilename):
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image:
t0 = time()
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset, i).astype(float32)
f_in.close()
t1 = time()
# 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, i, EFF, filtEFF, 2, im_dark,
norm_sx, norm_dx)
else:
im = flat_fielding(im, i, plan, 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,
plan['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)
t2 = time()
# Save processed image to HDF5 file (atomic procedure - lock used):
_write_data(lock, im, i, outfile, outshape, outtype, logfilename,
t2 - t1, t1 - t0)
def _process(lock, int_from, int_to, infile, dset_str, TIFFFormat, projorder, outpath, outprefix, logfilename): """To do... """ try: f = getHDF5(infile, 'r') dset = f[dset_str] # Process the required subset of images: for i in range(int_from, int_to + 1): # Read input image: t0 = time.time() if projorder: im = tdf.read_tomo(dset, i) else: im = tdf.read_sino(dset, i) # Cast type (if required but it should never occur): if (((im.dtype).type is float64) or ((im.dtype).type is float16)): im = im.astype(float32, copy=False) if (TIFFFormat): fname = outpath + outprefix + '_' + str(i).zfill(4) + '.tif' imsave(fname, im) else: fname = outpath + outprefix + '_' + str(i).zfill(4) + '_' + str(im.shape[1]) + \ 'x' + str(im.shape[0]) + '_' + str(im.dtype) + '.raw' im.tofile(fname) t1 = time.time() # Print out execution time: _write_log(lock, fname, logfilename, t1 - t0) f.close() except Exception: pass
def _write_data(lock, im, index, offset, abs_offset, imfilename, timestamp, projorder, tot_files,
provenance_dt, outfile, dsetname, outshape, outtype, logfilename, itime):
"""To do...
"""
lock.acquire()
try:
# Open the HDF5 file to be populated with projections (or sinograms):
t0 = time.time()
f_out = getHDF5( outfile, 'a' )
f_out_dset = f_out.require_dataset(dsetname, outshape, outtype, chunks=tdf.get_dset_chunks(outshape[0]))
# Write the projection file or sinogram file:
if projorder:
tdf.write_tomo(f_out_dset, index - abs_offset, im)
else:
tdf.write_sino(f_out_dset, index - abs_offset, im)
# Set minimum and maximum:
if ( numpy.amin(im[:]) < float(f_out_dset.attrs['min']) ):
f_out_dset.attrs['min'] = str(numpy.amin(im[:]))
if ( numpy.amax(im[:]) > float(f_out_dset.attrs['max'])):
f_out_dset.attrs['max'] = str(numpy.amax(im[:]))
# Save provenance metadata:
provenance_dset = f_out.require_dataset('provenance/detector_output', (tot_files,), dtype=provenance_dt)
provenance_dset["filename", offset - abs_offset + index] = numpy.string_(os.path.basename(imfilename))
provenance_dset["timestamp", offset - abs_offset + index] = numpy.string_(
datetime.datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])
# Close the HDF5:
f_out.close()
t1 = time.time()
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s processed (I: %0.3f sec - O: %0.3f sec)." % (os.path.basename(imfilename), itime, t1 - t0))
log.close()
finally:
lock.release()
def main(argv):
"""
Print dimensions of HDF5 file
"""
#
# Get the parameters:
#
infile = argv[0]
f = getHDF5(infile, 'r')
if "/tomo" in f:
dset = f['tomo']
else:
dset = f['exchange/data']
print("Projections: " + tdf.get_nr_projs(dset))
print("Slices: " + tdf.get_nr_sinos(dset))
print("DetectorSize: " + tdf.get_det_size(dset))
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()
rescale_factor = 10000.0 # For 16-bit floating point
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile = argv[2]
outfile = argv[3]
# Params for flat fielding with post flats/darks:
flat_end = True if argv[4] == "True" else False
half_half = True if argv[5] == "True" else False
half_half_line = int(argv[6])
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[7] == "True" else False
# Parameters for rotation:
rotation = float(argv[8])
interp = argv[9]
border = argv[10]
# Nr of threads and log file:
nr_threads = int(argv[11])
logfilename = argv[12]
# Log input parameters:
log = open(logfilename,"w")
log.write(linesep + "\tInput TDF file: %s" % (infile))
log.write(linesep + "\tOutput TDF file: %s" % (outfile))
log.write(linesep + "\t--------------")
log.write(linesep + "\tOpening input dataset...")
log.close()
# Remove a previous copy of output:
if exists(outfile):
remove(outfile)
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
tomoprefix = 'tomo'
flatprefix = 'flat'
darkprefix = 'dark'
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
tomoprefix = prov_dset.attrs['tomo_prefix']
flatprefix = prov_dset.attrs['flat_prefix']
darkprefix = prov_dset.attrs['dark_prefix']
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
if (num_sinos == 0):
log = open(logfilename,"a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_proj) or (int_to == -1)):
int_to = num_proj - 1
# Prepare the work plan for flat and dark images:
log = open(logfilename,"a")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the work plan...")
log.close()
# Extract flat and darks:
skipflat = False
skipdark = False
# Following variables make sense only for dynamic flat fielding:
EFF = -1
filtEFF = -1
im_dark = -1
# Following variable makes sense only for conventional flat fielding:
plan = -1
if not dynamic_ff:
plan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(plan['im_flat']) and isscalar(plan['im_flat_after'])):
skipflat = True
else:
skipflat = False
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)
# Get the corrected outshape (in this case it's easy):
im = tdf.read_tomo(dset,0).astype(float32)
outshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_proj)
# Create the output HDF5 file:
f_out = getHDF5(outfile, 'w')
#f_out_dset = f_out.create_dataset('exchange/data', outshape, im.dtype)
f_out_dset = f_out.create_dataset('exchange/data', outshape, float16)
f_out_dset.attrs['min'] = str(amin(im[:]))
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out_dset.attrs['version'] = '1.0'
f_out_dset.attrs['axes'] = "y:theta:x"
f_out_dset.attrs['rescale_factor'] = str(rescale_factor)
f_out.close()
f_in.close()
# Log infos:
log = open(logfilename,"a")
log.write(linesep + "\tWork plan prepared correctly.")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPerforming pre processing...")
log.close()
# Run several threads for independent computation without waiting for threads
# completion:
for num in range(nr_threads):
start = (num_proj / nr_threads) * num
if (num == nr_threads - 1):
end = num_proj - 1
else:
end = (num_proj / nr_threads) * (num + 1) - 1
Process(target=_process, args=(lock, start, end, infile, outfile, outshape, float16, skipflat, plan,
flat_end, half_half, half_half_line, dynamic_ff, EFF, filtEFF, im_dark, rotation, interp, border,
rescale_factor, logfilename)).start()
def main(argv):
"""Extract a 2D image (projection or sinogram) from the input TDF file (DataExchange HDF5) and
creates a 32-bit RAW file to disk.
Parameters
----------
argv[0] : string
The absolute path of the input TDF.
argv[1] : int
The relative position of the image within the dataset.
argv[2] : string
One of the following options: 'tomo', 'sino', 'flat', 'dark'.
argv[3] : string
The absolute path of the output 32-bit RAW image file. Filename will be modified by adding
image width, image height, minimum and maximum value of the input TDF dataset.
Example
-------
tools_extractdata "S:\\dataset.tdf" 128 tomo "R:\\proj"
"""
try:
#
# Get input parameters:
#
infile = argv[0]
index = int(argv[1])
imtype = argv[2]
outfile = argv[3]
#
# Body
#
# Check if file exists:
if not os.path.exists(infile):
#log = open(logfilename,"a")
#log.write(os.linesep + "\tError: input TDF file not found. Process will end.")
#log.close()
exit()
# Open the HDF5 file:
f = getHDF5( infile, 'r' )
if (imtype == 'sino'):
if "/tomo" in f:
dset = f['tomo']
else:
dset = f['exchange/data']
im = tdf.read_sino( dset, index )
elif (imtype == 'dark'):
if "/dark" in f:
dset = f['dark']
else:
dset = f['exchange/data_dark']
im = tdf.read_tomo( dset, index )
elif (imtype == 'flat'):
if "/flat" in f:
dset = f['flat']
else:
dset = f['exchange/data_white']
im = tdf.read_tomo( dset, index )
else:
if "/tomo" in f:
dset = f['tomo']
else:
dset = f['exchange/data']
im = tdf.read_tomo( dset, index )
min = float(numpy.nanmin(im[:]))
max = float(numpy.nanmax(im[:]))
# Get global attributes (if any):
try:
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
min = float(dset_tomo.attrs['min'])
max = float(dset_tomo.attrs['max'])
except:
pass
f.close()
# Cast type:
im = im.astype(float32)
# Modify file name:
outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str(min) + '$' + str(max)
# Write RAW data to disk:
im.tofile(outfile)
except:
exit()
def main(argv):
"""
Converts a TDF file (HDF5 Tomo Data Format) into a sequence of TIFF (uncompressed) files.
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) data, a subset of the volume can
be specified, ranging from the parameter "from" to the parameter "to"
(see next). In most cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) data, a subset of the volume can
be specified, ranging from the parameter "from" (see previous parameter) to
the parameter "to". If the value -1 is specified, all the projection (or sinogram)
data will be considered.
in_file : string
path with filename of the TDF to read from (e.g. "Z:\\sample1.tdf").
out_path : string
path that will contain the sequence of TIFF files (e.g. "Z:\\sample1\\tomo\\"). WARNING:
the program does NOT automatically create non-existing folders and subfolders specified
in the path. Moreover, if files with the same name already exist they will be automatically
overwritten.
file_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the projection (or
sinogram) data. E.g. "tomo" will create files having name "tomo_0001.tif", "tomo_0002.tif".
flat_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the flat (white field)
data. E.g. "flat" will create files having name "flat_1.tif", "flat_2.tif". If dark or flat data have
to be skipped the string "-" can be specified.
dark_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the dark (dark field)
data. E.g. "dark" will create files having name "dark_1.tif", "dark_2.tif". If dark or flat data have
to be skipped the string "-" can be specified.
projection_order : boolean string
specify the string "True" to create TIFF files for projections (the most common case), "False"
for sinograms.
TIFF_format : boolean string
specify the string "True" to create TIFF files, "False" for RAW files.
nr_threads : int
number of multiple threads (actually processes) to consider to speed up the whole conversion process.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the projections data to a sequence of tomo*.tif files:
python tdf2tiff.py 0 -1 "C:\Temp\wet12T4part2.tdf" "C:\Temp\tomo" tomo flat dark True True 3 "C:\Temp\log.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- TIFFFile from C. Gohlke
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
lock = Lock()
# To be used without flat fielding (just conversion):
first_done = False
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
infile = argv[2]
outpath = argv[3]
fileprefix = argv[4]
flatprefix = argv[5]
darkprefix = argv[6]
if (flatprefix == "-"):
skipflat = True
else:
skipflat = False
if (darkprefix == "-"):
skipdark = True
else:
skipdark = False
if (fileprefix == "-"):
skiptomo = True
else:
skiptomo = False
projorder = argv[7]
if projorder == "True":
projorder = True
else:
projorder = False
TIFFFormat = argv[8]
if TIFFFormat == "True":
TIFFFormat = True
else:
TIFFFormat = False
nr_threads = int(argv[9])
logfilename = argv[10]
# Check prefixes and path:
if not outpath.endswith(os.path.sep): outpath += os.path.sep
# Init variables:
num_files = 0
num_flats = 0
num_darks = 0
# Get the files in infile:
log = open(logfilename,"w")
log.write(os.linesep + "\tInput TDF: %s" % (infile))
if (TIFFFormat):
log.write(os.linesep + "\tOutput path where TIFF files will be created: %s" % (outpath))
else:
log.write(os.linesep + "\tOutput path where RAW files will be created: %s" % (outpath))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tFile output prefix: %s" % (fileprefix))
log.write(os.linesep + "\tFlat images output prefix: %s" % (flatprefix))
log.write(os.linesep + "\tDark images output prefix: %s" % (darkprefix))
log.write(os.linesep + "\t--------------")
if (not (skiptomo)):
if (int_to != -1):
log.write(os.linesep + "\tThe subset [%d,%d] of the data will be considered." % (int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
log.write(os.linesep + "\t--------------")
log.close()
if not os.path.exists(infile):
log = open(logfilename,"a")
log.write(os.linesep + "\tError: input TDF file not found. Process will end.")
log.close()
exit()
# Open the HDF5 file:
f = getHDF5(infile, 'r')
oldTDF = False
try:
dset = f['tomo']
oldTDF = True
except Exception:
pass
if not oldTDF:
#try:
dset = f['exchange/data']
#except Exception:
# log = open(logfilename,"a")
# log.write(os.linesep + "\tError: invalid TDF format. Process will end.")
# log.close()
# exit()
if projorder:
num_files = tdf.get_nr_projs(dset)
else:
num_files = tdf.get_nr_sinos(dset)
f.close()
# Get attributes:
try:
f = getHDF5(infile, 'r')
if ('version' in f.attrs) and (f.attrs['version'] == 'TDF 1.0'):
log = open(logfilename,"a")
log.write(os.linesep + "\tTDF version 1.0 found.")
log.write(os.linesep + "\t--------------")
log.close()
f.close()
except:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: TDF version unknown. Some features will not be available.")
log.write(os.linesep + "\t--------------")
log.close()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_files) or (int_to == -1)):
int_to = num_files - 1
# Spawn the process for the conversion of flat images:
if not skipflat:
f = getHDF5(infile, 'r')
if oldTDF:
dset_str = 'flat'
else:
dset_str = 'exchange/data_white'
num_flats = tdf.get_nr_projs(f[dset_str])
f.close()
if (num_flats > 0):
Process(target=_process, args=(lock, 0, num_flats - 1, infile, dset_str, TIFFFormat,
True, outpath, flatprefix, logfilename)).start()
#_process(lock, 0, num_flats - 1, infile, dset_str, TIFFFormat, projorder,
#outpath, flatprefix, logfilename)
# Spawn the process for the conversion of dark images:
if not skipdark:
f = getHDF5(infile, 'r')
if oldTDF:
dset_str = 'dark'
else:
dset_str = 'exchange/data_dark'
num_darks = tdf.get_nr_projs(f[dset_str])
f.close()
if (num_darks > 0):
Process(target=_process, args=(lock, 0, num_darks - 1, infile, dset_str, TIFFFormat,
True, outpath, darkprefix, logfilename)).start()
#_process(lock, 0, num_darks - 1, infile, dset_str, TIFFFormat, projorder,
#outpath, darkprefix, logfilename)
# Spawn the processes for the conversion of projection or sinogram images:
if not skiptomo:
if oldTDF:
dset_str = 'tomo'
else:
dset_str = 'exchange/data'
# Start the process for the conversion of the projections (or sinograms) in a
# multi-threaded way:
for num in range(nr_threads):
start = ((int_to - int_from + 1) / nr_threads) * num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ((int_to - int_from + 1) / nr_threads) * (num + 1) + int_from - 1
Process(target=_process, args=(lock, start, end, infile, dset_str, TIFFFormat,
projorder, outpath, fileprefix, logfilename)).start()
def main(argv):
"""Extract a 2D image (projection or sinogram) from the input TDF file (DataExchange HDF5) and
creates a 32-bit RAW file to disk.
Parameters
----------
argv[0] : string
The absolute path of the input TDF.
argv[1] : int
The relative position of the image within the dataset.
argv[2] : string
One of the following options: 'tomo', 'sino', 'flat', 'dark'.
argv[3] : string
The absolute path of the output 32-bit RAW image file. Filename will be modified by adding
image width, image height, minimum and maximum value of the input TDF dataset.
Example
-------
tools_extractdata "S:\\dataset.tdf" 128 tomo "R:\\proj"
"""
try:
#
# Get input parameters:
#
infile = argv[0]
index = int(argv[1])
imtype = argv[2]
outfile = argv[3]
#
# Body
#
# Check if file exists:
if not os.path.exists(infile):
#log = open(logfilename,"a")
#log.write(os.linesep + "\tError: input TDF file not found. Process will end.")
#log.close()
exit()
# Open the HDF5 file:
f = getHDF5(infile, 'r')
if (imtype == 'sino'):
if "/tomo" in f:
dset = f['tomo']
else:
dset = f['exchange/data']
im = tdf.read_sino(dset, index)
elif (imtype == 'dark'):
if "/dark" in f:
dset = f['dark']
else:
dset = f['exchange/data_dark']
im = tdf.read_tomo(dset, index)
elif (imtype == 'flat'):
if "/flat" in f:
dset = f['flat']
else:
dset = f['exchange/data_white']
im = tdf.read_tomo(dset, index)
else:
if "/tomo" in f:
dset = f['tomo']
else:
dset = f['exchange/data']
im = tdf.read_tomo(dset, index)
# Remove Infs e NaNs
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
# Sort the gray levels:
tmp = numpy.sort(tmp)
# Return as minimum the value the skip 0.30% of "black" tail and 0.05% of "white" tail:
low_idx = int(tmp.shape[0] * 0.0030)
high_idx = int(tmp.shape[0] * 0.9995)
min = tmp[low_idx]
max = tmp[high_idx]
# Modify file name:
outfile = outfile + '_' + str(im.shape[1]) + 'x' + str(
im.shape[0]) + '_' + str(min) + '$' + str(max)
# Cast type:
im = im.astype(float32)
# Write RAW data to disk:
im.tofile(outfile)
except:
exit()
def process(sino_idx, num_sinos, infile, outpath, preprocessing_required, corr_plan, 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, logtransform, param1, circle, scale, pad, method, 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): """To do... """ slice_nr = sino_idx # Perform reconstruction (on-the-fly preprocessing and phase retrieval, if required): if (phaseretrieval_required): # In this case a bunch of sinograms is loaded into memory: # # Load the temporary data structure reading the input TDF file. # To know the right dimension the first sinogram is pre-processed. # # Open the TDF file and get the dataset: f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] # Downscaling and decimation factors considered when determining the approximation window: zrange = arange(sino_idx - approx_win*downsc_factor/2, sino_idx + approx_win*downsc_factor/2, downsc_factor) zrange = zrange[ (zrange >= 0) ] zrange = zrange[ (zrange < num_sinos) ] approx_win = zrange.shape[0] # Approximation window cannot be odd: if (approx_win % 2 == 1): approx_win = approx_win-1 zrange = zrange[0:approx_win] # Read one sinogram to get the proper dimensions: test_im = tdf.read_sino(dset, zrange[0]*downsc_factor).astype(float32) # Apply decimation and downscaling (if required): test_im = test_im[::decim_factor, ::downsc_factor] # Perform the pre-processing of the first sinogram to get the right dimension: if (preprocessing_required): test_im = flat_fielding (test_im, zrange[0], corr_plan, flat_end, half_half, half_half_line/decim_factor, norm_sx, norm_dx).astype(float32) if ext_fov: test_im = extfov_correction (test_im, ext_fov_rot_right, ext_fov_overlap/downsc_factor, ext_fov_normalize, ext_fov_average).astype(float32) test_im = ring_correction (test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line/decim_factor, ext_fov).astype(float32) # Now we can allocate memory for the bunch of slices: tmp_im = empty((approx_win, test_im.shape[0], test_im.shape[1]), dtype=float32) tmp_im[0,:,:] = test_im # Reading all the the sinos from TDF file and close: for ct in range(1, approx_win): test_im = tdf.read_sino(dset, zrange[ct]*downsc_factor).astype(float32) test_im = test_im[::decim_factor, ::downsc_factor] # Perform the pre-processing for each sinogram of the bunch: if (preprocessing_required): test_im = flat_fielding (test_im, zrange[ct], corr_plan, flat_end, half_half, half_half_line/decim_factor, norm_sx, norm_dx).astype(float32) if ext_fov: test_im = extfov_correction (test_im, ext_fov_rot_right, ext_fov_overlap/downsc_factor, ext_fov_normalize, ext_fov_average).astype(float32) test_im = ring_correction (test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line/decim_factor, ext_fov).astype(float32) tmp_im[ct,:,:] = test_im f_in.close() # Now everything has to refer to a downscaled dataset: sino_idx = ((zrange == sino_idx).nonzero()) # # Perform phase retrieval: # # Prepare the plan: if (phrtmethod == 0): # Paganin's: phrtplan = tiehom_plan (tmp_im[:,0,:], phrt_param1, phrt_param2, energy, distance, pixsize*downsc_factor, padding=phrtpad) else: phrtplan = phrt_plan (tmp_im[:,0,:], energy, distance, pixsize*downsc_factor, phrt_param2, phrt_param1, phrtmethod, padding=phrtpad) # Process each projection (whose height depends on the size of the bunch): for ct in range(0, tmp_im.shape[1]): if (phrtmethod == 0): tmp_im[:,ct,:] = tiehom(tmp_im[:,ct,:], phrtplan).astype(float32) else: tmp_im[:,ct,:] = phrt(tmp_im[:,ct,:], phrtplan, phrtmethod).astype(float32) # Extract the requested sinogram: im = tmp_im[sino_idx[0],:,:].squeeze() else: # Read only one sinogram: f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im = tdf.read_sino(dset,sino_idx * downsc_factor).astype(float32) f_in.close() # Downscale and decimate the sinogram: im = im[::decim_factor,::downsc_factor] #sino_idx = sino_idx/downsc_factor # Perform the preprocessing of the sinogram (if required): if (preprocessing_required): im = flat_fielding (im, sino_idx, corr_plan, flat_end, half_half, half_half_line/decim_factor, norm_sx, norm_dx).astype(float32) if ext_fov: test_im = extfov_correction (test_im, ext_fov_rot_right, ext_fov_overlap/downsc_factor, ext_fov_normalize, ext_fov_average).astype(float32) im = ring_correction (im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line/decim_factor, ext_fov) # Log infos: log = open(logfilename,"a") log.write(linesep + "\tPerforming reconstruction with multiple centers of rotation...") log.write(linesep + "\t--------------") log.close() # Split the computation into multiple processes: for num in range(nr_threads): start = ( (angles_to - angles_from + 1) / nr_threads)*num + angles_from if (num == nr_threads - 1): end = angles_to else: end = ( (angles_to - angles_from + 1) / nr_threads)*(num + 1) + angles_from - 1 Process(target=reconstruct, args=(im, angles, offset/downsc_factor, logtransform, param1, circle, scale, pad, method, zerone_mode, dset_min, dset_max, corr_offset, postprocess_required, convert_opt, crop_opt, start, end, outpath, slice_nr, downsc_factor, decim_factor, logfilename, lock, slice_prefix)).start()
def main(argv):
"""
Converts a sequence of TIFF files into a TDF file (HDF5 Tomo Data Format).
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" to the parameter "to" (see next). In most
cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" (see previous parameter) to the parameter
"to". If the value -1 is specified, all the projection files will be considered.
in_path : string
path containing the sequence of TIFF files to consider (e.g. "Z:\\sample1\\tomo\\").
out_file : string
path with filename of the TDF to create (e.g. "Z:\\sample1.tdf"). WARNING: the program
does NOT automatically create non-existing folders and subfolders specified in the path.
Moreover, if a file with the same name already exists it will be automatically deleted and
overwritten.
crop_top : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the top of the image. Leave 0 for no cropping.
crop_bottom : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the bottom of the image. Leave 0 for no cropping.
crop_left : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the left of the image. Leave 0 for no cropping.
crop_right : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the right of the image. Leave 0 for no cropping.
file_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the projection (or
sinogram) files. E.g. "tomo" will consider files having name "tomo_0001.tif", "tomo_0002.tif".
flat_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the flat (white field)
files. E.g. "flat" will consider files having name "flat_1.tif", "flat_2.tif". If dark or flat files have
to be skipped the string "-" can be specified.
dark_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the dark (dark field)
files. E.g. "dark" will consider files having name "dark_1.tif", "dark_2.tif". If dark or flat files have
to be skipped the string "-" can be specified.
projection_order : boolean string
specify the string "True" if the TIFF files represent projections (the most common case), "False"
for sinograms.
privilege_sino : boolean string
specify the string "True" if the TDF will privilege a fast read/write of sinograms (the most common
case), "False" for fast read/write of projections.
compression : scalar, integer
an integer value in the range of [1,9] to be used as GZIP compression factor in the HDF5 file, where
1 is the minimum compression (and maximum speed) and 9 is the maximum (and slow) compression.
The value 0 can be specified with the meaning of no compression.
nr_threads : int
number of multiple threads (actually processes) to consider to speed up the whole conversion process.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the tomo*.tif* projections to a TDF with no cropping and minimum compression:
python tiff2tdf.py 0 -1 "Z:\\rawdata\\c_1\\tomo\\" "Z:\\work\\c1_compr9.tdf" 0 0 0 0 tomo flat
dark True True 1 "S:\\conversion.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- TIFFFile from C. Gohlke's website http://www.lfd.uci.edu/~gohlke/
(consider also to install TIFFFile.c for performances).
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
lock = Lock()
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
inpath = argv[2]
outfile = argv[3]
crop_top = int(argv[4]) # 0 for all means "no cropping"
crop_bottom = int(argv[5])
crop_left = int(argv[6])
crop_right = int(argv[7])
tomoprefix = argv[8]
flatprefix = argv[9] # - means "do not consider flat or darks"
darkprefix = argv[10] # - means "do not consider flat or darks"
if (flatprefix == "-") or (darkprefix == "-"):
skipflat = True
else:
skipflat = False
projorder = True if argv[11] == "True" else False
privilege_sino = True if argv[12] == "True" else False
# Get compression factor:
compr_opts = int(argv[13])
compressionFlag = True;
if (compr_opts <= 0):
compressionFlag = False;
elif (compr_opts > 9):
compr_opts = 9
nr_threads = int(argv[14])
logfilename = argv[15]
# Check prefixes and path:
if not inpath.endswith(os.path.sep): inpath += os.path.sep
# Get the files in inpath:
log = open(logfilename,"w")
log.write(os.linesep + "\tInput path: %s" % (inpath))
log.write(os.linesep + "\tOutput TDF file: %s" % (outfile))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tProjection file prefix: %s" % (tomoprefix))
log.write(os.linesep + "\tDark file prefix: %s" % (darkprefix))
log.write(os.linesep + "\tFlat file prefix: %s" % (flatprefix))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tCropping:")
log.write(os.linesep + "\t\tTop: %d pixels" % (crop_top))
log.write(os.linesep + "\t\tBottom: %d pixels" % (crop_bottom))
log.write(os.linesep + "\t\tLeft: %d pixels" % (crop_left))
log.write(os.linesep + "\t\tRight: %d pixels" % (crop_right))
if (int_to != -1):
log.write(os.linesep + "\tThe subset [%d,%d] of the input files will be considered." % (int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
if (privilege_sino):
log.write(os.linesep + "\tFast I/O for sinograms privileged.")
else:
log.write(os.linesep + "\tFast I/O for projections privileged.")
if (compressionFlag):
log.write(os.linesep + "\tTDF compression factor: %d" % (compr_opts))
else:
log.write(os.linesep + "\tTDF compression: none.")
if (skipflat):
log.write(os.linesep + "\tWarning: flat/dark images (if any) will not be considered.")
log.write(os.linesep + "\t--------------")
log.close()
# Remove a previous copy of output:
if os.path.exists(outfile):
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: an output file with the same name was overwritten.")
os.remove(outfile)
log.close()
log = open(logfilename,"a")
log.write(os.linesep + "\tBrowsing input files...")
log.close()
# Pythonic way to get file list:
if os.path.exists(inpath):
tomo_files = sorted(glob(inpath + tomoprefix + '*.tif*'))
num_files = len(tomo_files)
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tError: input path does not exist. Process will end.")
log.close()
exit()
if (num_files == 0):
log = open(logfilename,"a")
log.write(os.linesep + "\tError: no projection files found. Check input path and file prefixes.")
log.close()
exit()
log = open(logfilename,"a")
log.write(os.linesep + "\tInput files browsed correctly.")
log.close()
# Check extrema (int_to == -1 means all files):
if ( (int_to >= num_files) or (int_to == -1) ):
int_from = 0
int_to = num_files - 1
# In case of subset specified:
num_files = int_to - int_from + 1
# Prepare output HDF5 output (should this be atomic?):
im = imread(tomo_files[0])
# Crop:
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
log = open(logfilename,"a")
log.write(os.linesep + "\tPreparing the working plan...")
log.close()
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_files)
datashape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_files)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files, im.shape[0])
datashape = tdf.get_dset_shape(im.shape[1], num_files, im.shape[0])
if not os.path.isfile(outfile):
f = getHDF5( outfile, 'w' )
f.attrs['version'] = '1.0'
f.attrs['implements'] = "exchange:provenance"
echange_group = f.create_group( 'exchange' )
if (compressionFlag):
dset = f.create_dataset('exchange/data', datashape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]),
compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
dset = f.create_dataset('exchange/data', datashape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]))
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
dset.attrs['min'] = str(numpy.amin(im[:]))
dset.attrs['max'] = str(numpy.amax(im[:]))
# Get the total number of files to consider:
tot_files = num_files
if not skipflat:
num_flats = len(sorted(glob(inpath + flatprefix + '*.tif*')))
num_darks = len(sorted(glob(inpath + darkprefix + '*.tif*')))
tot_files = tot_files + num_flats + num_darks
# Create provenance dataset:
provenance_dt = numpy.dtype([("filename", numpy.dtype("S255")), ("timestamp", numpy.dtype("S255"))])
metadata_group = f.create_group( 'provenance' )
provenance_dset = metadata_group.create_dataset('detector_output', (tot_files,), dtype=provenance_dt)
provenance_dset.attrs['tomo_prefix'] = tomoprefix;
provenance_dset.attrs['dark_prefix'] = darkprefix;
provenance_dset.attrs['flat_prefix'] = flatprefix;
provenance_dset.attrs['first_index'] = int(tomo_files[0][-8:-4]);
# Handle the metadata:
if (os.path.isfile(inpath + 'logfile.xml')):
with open (inpath + 'logfile.xml', "r") as file:
xml_command = file.read()
tdf.parse_metadata(f, xml_command)
f.close()
# Print out about plan preparation:
log = open(logfilename,"a")
log.write(os.linesep + "\tWorking plan prepared succesfully.")
log.close()
# Get the files in inpath:
if not skipflat:
#
# Flat part
#
flat_files = sorted(glob(inpath + flatprefix + '*.tif*'))
num_flats = len(flat_files)
if ( num_flats > 0):
# Create acquisition group:
im = imread(flat_files[0])
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
#flatshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_flats)
flatshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_flats)
f = getHDF5( outfile, 'a' )
if (compressionFlag):
dset = f.create_dataset('exchange/data_white', flatshape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]),
compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
dset = f.create_dataset('exchange/data_white', flatshape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]))
dset.attrs['min'] = str(numpy.amin(im[:]))
dset.attrs['max'] = str(numpy.amax(im[:]))
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
f.close()
#process(lock, 0, num_flats - 1, 0, flat_files, True, outfile, 'exchange/data_white', dsetshape, im.dtype,
# crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: flat files not found.")
log.close()
#
# Dark part
#
dark_files = sorted(glob(inpath + darkprefix + '*.tif*'))
num_darks = len(dark_files)
if ( num_darks > 0):
im = imread(dark_files[0])
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
#darkshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_flats)
darkshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_darks)
f = getHDF5( outfile, 'a' )
if (compressionFlag):
dset = f.create_dataset('exchange/data_dark', darkshape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]),
compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
dset = f.create_dataset('exchange/data_dark', darkshape, im.dtype, chunks=tdf.get_dset_chunks(im.shape[1]))
dset.attrs['min'] = str(numpy.amin(im))
dset.attrs['max'] = str(numpy.amax(im))
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
f.close()
#process(lock, 0, num_darks - 1, num_flats, dark_files, True, outfile, 'exchange/data_dark', dsetshape, im.dtype,
# crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: dark files not found.")
log.close()
# Process the required subset of images:
if not skipflat:
flatdark_offset = num_flats + num_darks
else:
flatdark_offset = 0
# Spawn the process for the conversion of flat images:
Process(target=_process, args=(lock, 0, num_flats - 1, 0, 0, flat_files, True, outfile, 'exchange/data_white',
flatshape, im.dtype, crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )).start()
# Spawn the process for the conversion of dark images:
Process(target=_process, args=(lock, 0, num_darks - 1, num_flats, 0, dark_files, True, outfile, 'exchange/data_dark',
darkshape, im.dtype, crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )).start()
# Start the process for the conversion of the projections (or sinograms) in a multi-threaded way:
for num in range(nr_threads):
start = ( (int_to - int_from + 1) / nr_threads)*num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ( (int_to - int_from + 1) / nr_threads)*(num + 1) + int_from - 1
Process(target=_process, args=(lock, start, end, flatdark_offset, int_from, tomo_files, projorder, outfile, 'exchange/data',
datashape, im.dtype, crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )).start()
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):
"""
Converts a set of HIS files into a TDF file (HDF5 Tomo Data Format).
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" to the parameter "to" (see next). In most
cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" (see previous parameter) to the parameter
"to". If the value -1 is specified, all the projection files will be considered.
data_in_path : string
path of the HIS file of the projections (e.g. "Z:\\sample1.his").
dark_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_dark.his").
flat_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_flat.his").
postdark_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_postdark.his").
postflat_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_postflat.his").
out_file : string
path with filename of the TDF to create (e.g. "Z:\\sample1.tdf"). WARNING: the program
does NOT automatically create non-existing folders and subfolders specified in the path.
Moreover, if a file with the same name already exists it will be automatically deleted and
overwritten.
crop_top : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the top of the image. Leave 0 for no cropping.
crop_bottom : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the bottom of the image. Leave 0 for no cropping.
crop_left : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the left of the image. Leave 0 for no cropping.
crop_right : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the right of the image. Leave 0 for no cropping.
privilege_sino : boolean string
specify the string "True" if the TDF will privilege a fast read/write of sinograms (the most common
case), "False" for fast read/write of projections.
compression : scalar, integer
an integer value in the range of [1,9] to be used as GZIP compression factor in the HDF5 file, where
1 is the minimum compression (and maximum speed) and 9 is the maximum (and slow) compression.
The value 0 can be specified with the meaning of no compression.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the tomo*.tif* projections to a TDF with no cropping and minimum compression:
python his2tdf.py 0 -1 "tomo.his" "dark.his" "flat.his" "postdark.his" "postflat.his" "dataset.tdf" 0 0 0 0
True True 1 "S:\\conversion.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
tomo_file = argv[2]
dark_file = argv[3]
flat_file = argv[4]
darkpost_file = argv[5]
flatpost_file = argv[6]
outfile = argv[7]
crop_top = int(argv[8]) # 0 for all means "no cropping"
crop_bottom = int(argv[9])
crop_left = int(argv[10])
crop_right = int(argv[11])
projorder = argv[12]
if projorder == "True":
projorder = True
else:
projorder = False
privilege_sino = argv[13]
if privilege_sino == "True":
privilege_sino = True
else:
privilege_sino = False
# Get compression factor:
compr_opts = int(argv[14])
compressionFlag = True;
if (compr_opts <= 0):
compressionFlag = False;
elif (compr_opts > 9):
compr_opts = 9
logfilename = argv[15]
# Get the files in inpath:
log = open(logfilename,"w")
log.write(os.linesep + "\tInput HIS files:")
log.write(os.linesep + "\t\tProjections: %s" % (tomo_file))
log.write(os.linesep + "\t\tDark: %s" % (dark_file))
log.write(os.linesep + "\t\tFlat: %s" % (flat_file))
log.write(os.linesep + "\t\tPost dark: %s" % (darkpost_file))
log.write(os.linesep + "\t\tPost flat: %s" % (flatpost_file))
log.write(os.linesep + "\tOutput TDF file: %s" % (outfile))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tCropping:")
log.write(os.linesep + "\t\tTop: %d pixels" % (crop_top))
log.write(os.linesep + "\t\tBottom: %d pixels" % (crop_bottom))
log.write(os.linesep + "\t\tLeft: %d pixels" % (crop_left))
log.write(os.linesep + "\t\tRight: %d pixels" % (crop_right))
if (int_to != -1):
log.write(os.linesep + "\tThe subset [%d,%d] of the input files will be considered." % (int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
if (privilege_sino):
log.write(os.linesep + "\tFast I/O for sinograms privileged.")
else:
log.write(os.linesep + "\tFast I/O for projections privileged.")
if (compressionFlag):
log.write(os.linesep + "\tTDF compression factor: %d" % (compr_opts))
else:
log.write(os.linesep + "\tTDF compression: none.")
log.write(os.linesep + "\t--------------")
log.close()
# Remove a previous copy of output:
if os.path.exists(outfile):
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: an output file with the same name was overwritten.")
os.remove(outfile)
log.close()
# Check input file:
if not os.path.exists(tomo_file):
log = open(logfilename,"a")
log.write(os.linesep + "\tError: input HIS file for projections does not exist. Process will end.")
log.close()
exit()
# First time get the plan:
log = open(logfilename,"a")
log.write(os.linesep + "\tPreparing the working plan...")
log.close()
# Get info from projection file:
dim1, dim2, dimz, dtype = _getHISdim ( tomo_file )
if ( ((int_to - int_from + 1) > 0) and ((int_to - int_from + 1) < dimz) ):
dimz = int_to - int_from + 1
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_files)
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, dimz)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files, im.shape[0])
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, dimz)
f = getHDF5( outfile, 'w' )
print dsetshape
f.attrs['version'] = '1.0'
f.attrs['implements'] = "exchange:provenance"
echange_group = f.create_group( 'exchange' )
if (compressionFlag):
dset = f.create_dataset('exchange/data', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right), compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
dset = f.create_dataset('exchange/data', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right))
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
dset.attrs['min'] = str(numpy.iinfo(dtype).max)
dset.attrs['max'] = str(numpy.iinfo(dtype).min)
# Get the total number of files to consider:
num_darks = 0
num_flats = 0
num_postdarks = 0
num_postflats = 0
if os.path.exists(dark_file):
dim1, dim2, num_darks, dtype = _getHISdim ( dark_file )
if os.path.exists(flat_file):
dim1, dim2, num_flats, dtype = _getHISdim ( flat_file )
if os.path.exists(darkpost_file):
dim1, dim2, num_postdarks, dtype = _getHISdim ( darkpost_file )
if os.path.exists(flatpost_file):
dim1, dim2, num_postflats, dtype = _getHISdim ( flatpost_file )
tot_files = dimz + num_darks + num_flats + num_postdarks + num_postflats
# Create provenance dataset:
provenance_dt = numpy.dtype([("filename", numpy.dtype("S255")), ("timestamp", numpy.dtype("S255"))])
metadata_group = f.create_group( 'provenance' )
provenance_dset = metadata_group.create_dataset('detector_output', (tot_files,), dtype=provenance_dt)
provenance_dset.attrs['tomo_prefix'] = 'tomo';
provenance_dset.attrs['dark_prefix'] = 'dark';
provenance_dset.attrs['flat_prefix'] = 'flat';
provenance_dset.attrs['first_index'] = 1;
# Handle the metadata:
if (os.path.isfile(os.path.dirname(tomo_file) + os.sep + 'logfile.xml')):
with open (os.path.dirname(tomo_file) + os.sep + 'logfile.xml', "r") as file:
xml_command = file.read()
tdf.parse_metadata(f, xml_command)
# Print out about plan preparation:
first_done = True
log = open(logfilename,"a")
log.write(os.linesep + "\tWorking plan prepared succesfully.")
log.close()
# Get the data from HIS:
if (num_darks > 0) or (num_postdarks > 0):
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_files)
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_darks + num_postdarks)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files, im.shape[0])
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_darks + num_postdarks)
if (compressionFlag):
darkdset = f.create_dataset('exchange/data_dark', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right), compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
darkdset = f.create_dataset('exchange/data_dark', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right))
if privilege_sino:
darkdset.attrs['axes'] = "y:theta:x"
else:
darkdset.attrs['axes'] = "theta:y:x"
darkdset.attrs['min'] = str(numpy.iinfo(dtype).max)
darkdset.attrs['max'] = str(numpy.iinfo(dtype).min)
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: dark images (if any) not considered.")
log.close()
if (num_flats > 0) or (num_postflats > 0):
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0], num_files)
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_flats + num_postflats)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files, im.shape[0])
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_flats + num_postflats)
if (compressionFlag):
flatdset = f.create_dataset('exchange/data_white', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right), compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
flatdset = f.create_dataset('exchange/data_white', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right))
if privilege_sino:
flatdset.attrs['axes'] = "y:theta:x"
else:
flatdset.attrs['axes'] = "theta:y:x"
flatdset.attrs['min'] = str(numpy.iinfo(dtype).max)
flatdset.attrs['max'] = str(numpy.iinfo(dtype).min)
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: flat images (if any) not considered.")
log.close()
# Process the HIS:
provenance_offset = 0
if num_flats > 0:
provenance_offset = _processHIS( flat_file, flatdset, 0, provenance_dset, provenance_offset,
0, 'flat', crop_top, crop_bottom, crop_left, crop_right, logfilename )
if num_postflats > 0:
provenance_offset = _processHIS( flatpost_file, flatdset, num_flats, provenance_dset, provenance_offset,
7, 'flat', crop_top, crop_bottom, crop_left, crop_right, logfilename )
if num_darks > 0:
provenance_offset = _processHIS( dark_file, darkdset, 0, provenance_dset, provenance_offset,
0, 'dark', crop_top, crop_bottom, crop_left, crop_right, logfilename )
if num_postdarks > 0:
provenance_offset = _processHIS( darkpost_file, darkdset, num_darks, provenance_dset, provenance_offset,
7, 'dark', crop_top, crop_bottom, crop_left, crop_right, logfilename )
provenance_offset = _processHIS( tomo_file, dset, 0, provenance_dset, provenance_offset,
0, 'tomo', crop_top, crop_bottom, crop_left, crop_right, logfilename, int_from, int_to )
# Close TDF:
f.close()
def process(sino_idx, num_sinos, infile, outfile, preprocessing_required, corr_plan, 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, logtransform, param1, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset, postprocess_required, convert_opt,
crop_opt, nr_threads, logfilename):
"""To do...
"""
# Perform reconstruction (on-the-fly preprocessing and phase retrieval, if required):
if (phaseretrieval_required):
# In this case a bunch of sinograms is loaded into memory:
#
# Load the temporary data structure reading the input TDF file.
# To know the right dimension the first sinogram is pre-processed.
#
# Open the TDF file and get the dataset:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
# Downscaling and decimation factors considered when determining the approximation window:
zrange = arange(sino_idx - approx_win*downsc_factor/2, sino_idx + approx_win*downsc_factor/2, downsc_factor)
zrange = zrange[ (zrange >= 0) ]
zrange = zrange[ (zrange < num_sinos) ]
approx_win = zrange.shape[0]
# Approximation window cannot be odd:
if (approx_win % 2 == 1):
approx_win = approx_win-1
zrange = zrange[0:approx_win]
# Read one sinogram to get the proper dimensions:
test_im = tdf.read_sino(dset, zrange[0]).astype(float32)
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing of the first sinogram to get the right dimension:
if (preprocessing_required):
test_im = flat_fielding (test_im, zrange[0]/downsc_factor, corr_plan, flat_end, half_half,
half_half_line/decim_factor, norm_sx, norm_dx).astype(float32)
test_im = extfov_correction (test_im, ext_fov, ext_fov_rot_right, ext_fov_overlap/downsc_factor).astype(float32)
test_im = ring_correction (test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line/decim_factor, ext_fov).astype(float32)
# Now we can allocate memory for the bunch of slices:
tmp_im = empty((approx_win, test_im.shape[0], test_im.shape[1]), dtype=float32)
tmp_im[0,:,:] = test_im
# Reading all the the sinos from TDF file and close:
for ct in range(1, approx_win):
test_im = tdf.read_sino(dset, zrange[ct]).astype(float32)
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing for each sinogram of the bunch:
if (preprocessing_required):
test_im = flat_fielding (test_im, zrange[ct]/downsc_factor, corr_plan, flat_end, half_half,
half_half_line/decim_factor, norm_sx, norm_dx).astype(float32)
test_im = extfov_correction (test_im, ext_fov, ext_fov_rot_right, ext_fov_overlap/downsc_factor).astype(float32)
test_im = ring_correction (test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line/decim_factor, ext_fov).astype(float32)
tmp_im[ct,:,:] = test_im
f_in.close()
# Now everything has to refer to a downscaled dataset:
sino_idx = ((zrange == sino_idx).nonzero())
#
# Perform phase retrieval:
#
# Prepare the plan:
phrtplan = prepare_plan (tmp_im[:,0,:], beta, delta, energy, distance, pixsize*downsc_factor, padding=phrtpad)
# Process each projection (whose height depends on the size of the bunch):
for ct in range(0, tmp_im.shape[1]):
tmp_im[:,ct,:] = phase_retrieval(tmp_im[:,ct,:], phrtplan).astype(float32)
# Extract the requested sinogram:
im = tmp_im[sino_idx[0],:,:].squeeze()
else:
# Read only one sinogram:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset,sino_idx).astype(float32)
f_in.close()
# Downscale and decimate the sinogram:
im = im[::decim_factor,::downsc_factor]
sino_idx = sino_idx/downsc_factor
# Perform the preprocessing of the sinogram (if required):
if (preprocessing_required):
im = flat_fielding (im, sino_idx, corr_plan, flat_end, half_half, half_half_line/decim_factor,
norm_sx, norm_dx).astype(float32)
im = extfov_correction (im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
#imsave('C:\\Temp\\StupidFolder\\sinoprimaringrem.tif', im.astype(float32))
im = ring_correction (im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line/decim_factor, ext_fov)
#imsave('C:\\Temp\\StupidFolder\\sinodoporingrem.tif', im.astype(float32))
# Actual reconstruction:
im = reconstruct(im, angles, offset/downsc_factor, logtransform, param1, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, corr_offset).astype(float32)
# Apply post-processing (if required):
if postprocess_required:
im = postprocess(im, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im.shape[1]
if siz % 2:
rang = arange(-siz / 2 + 1, siz / 2 + 1)
else:
rang = arange(-siz / 2,siz / 2)
x,y = meshgrid(rang,rang)
z = x ** 2 + y ** 2
a = (z < (siz / 2 - int(round(abs(offset)/downsc_factor)) ) ** 2)
im = im * a
# 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( amin(im)) + '$' + str( amax(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):
"""
Converts a set of HIS files into a TDF file (HDF5 Tomo Data Format).
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" to the parameter "to" (see next). In most
cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" (see previous parameter) to the parameter
"to". If the value -1 is specified, all the projection files will be considered.
data_in_path : string
path of the HIS file of the projections (e.g. "Z:\\sample1.his").
dark_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_dark.his").
flat_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_flat.his").
postdark_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_postdark.his").
postflat_in_path : string
path of the HIS file of the flat (e.g. "Z:\\sample1_postflat.his").
out_file : string
path with filename of the TDF to create (e.g. "Z:\\sample1.tdf"). WARNING: the program
does NOT automatically create non-existing folders and subfolders specified in the path.
Moreover, if a file with the same name already exists it will be automatically deleted and
overwritten.
crop_top : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the top of the image. Leave 0 for no cropping.
crop_bottom : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the bottom of the image. Leave 0 for no cropping.
crop_left : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the left of the image. Leave 0 for no cropping.
crop_right : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the right of the image. Leave 0 for no cropping.
privilege_sino : boolean string
specify the string "True" if the TDF will privilege a fast read/write of sinograms (the most common
case), "False" for fast read/write of projections.
compression : scalar, integer
an integer value in the range of [1,9] to be used as GZIP compression factor in the HDF5 file, where
1 is the minimum compression (and maximum speed) and 9 is the maximum (and slow) compression.
The value 0 can be specified with the meaning of no compression.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the tomo*.tif* projections to a TDF with no cropping and minimum compression:
python his2tdf.py 0 -1 "tomo.his" "dark.his" "flat.his" "postdark.his" "postflat.his" "dataset.tdf" 0 0 0 0
True True 1 "S:\\conversion.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
tomo_file = argv[2]
flat_file = argv[3]
outfile = argv[4]
crop_top = int(argv[5]) # 0 for all means "no cropping"
crop_bottom = int(argv[6])
crop_left = int(argv[7])
crop_right = int(argv[8])
projorder = argv[9]
if projorder == "True":
projorder = True
else:
projorder = False
privilege_sino = argv[10]
if privilege_sino == "True":
privilege_sino = True
else:
privilege_sino = False
# Get compression factor:
compr_opts = int(argv[11])
compressionFlag = True
if (compr_opts <= 0):
compressionFlag = False
elif (compr_opts > 9):
compr_opts = 9
logfilename = argv[12]
# Get the files in inpath:
log = open(logfilename,"w")
log.write(os.linesep + "\tInput PiXirad files:")
log.write(os.linesep + "\t\tProjections: %s" % (tomo_file))
log.write(os.linesep + "\t\tFlat: %s" % (flat_file))
log.write(os.linesep + "\tOutput TDF file: %s" % (outfile))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tCropping:")
log.write(os.linesep + "\t\tTop: %d pixels" % (crop_top))
log.write(os.linesep + "\t\tBottom: %d pixels" % (crop_bottom))
log.write(os.linesep + "\t\tLeft: %d pixels" % (crop_left))
log.write(os.linesep + "\t\tRight: %d pixels" % (crop_right))
if (int_to != -1):
log.write(os.linesep + "\tThe subset [%d,%d] of the input files will be considered." % (int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
if (privilege_sino):
log.write(os.linesep + "\tFast I/O for sinograms privileged.")
else:
log.write(os.linesep + "\tFast I/O for projections privileged.")
if (compressionFlag):
log.write(os.linesep + "\tTDF compression factor: %d" % (compr_opts))
else:
log.write(os.linesep + "\tTDF compression: none.")
log.write(os.linesep + "\t--------------")
log.close()
# Remove a previous copy of output:
if os.path.exists(outfile):
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: an output file with the same name was overwritten.")
os.remove(outfile)
log.close()
# Check input file:
if not os.path.exists(tomo_file):
log = open(logfilename,"a")
log.write(os.linesep + "\tError: input PiXirad file for projections does not exist. Process will end.")
log.close()
exit()
# First time get the plan:
log = open(logfilename,"a")
log.write(os.linesep + "\tPreparing the work plan...")
log.close()
# Get info from projection file:
data = _read_pixirad_data(tomo_file)
dim1 = data.shape[0]
dim2 = data.shape[1]
dimz = data.shape[2]
dtype = data.dtype
if (((int_to - int_from + 1) > 0) and ((int_to - int_from + 1) < dimz)):
dimz = int_to - int_from + 1
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0],
#num_files)
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, dimz)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files,
#im.shape[0])
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, dimz)
f = getHDF5(outfile, 'w')
f.attrs['version'] = '1.0'
f.attrs['implements'] = "exchange:provenance"
echange_group = f.create_group('exchange')
if (compressionFlag):
dset = f.create_dataset('exchange/data', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right), \
compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
dset = f.create_dataset('exchange/data', dsetshape, dtype)
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
dset.attrs['min'] = str(numpy.iinfo(dtype).max)
dset.attrs['max'] = str(numpy.iinfo(dtype).min)
# Get the total number of files to consider:
num_flats = 0
if os.path.exists(flat_file):
data = _read_pixirad_data(flat_file)
dim1 = data.shape[0]
dim2 = data.shape[1]
num_flats = data.shape[2]
dtype = data.dtype
tot_files = dimz + num_flats
# Create provenance dataset:
provenance_dt = numpy.dtype([("filename", numpy.dtype("S255")), ("timestamp", numpy.dtype("S255"))])
metadata_group = f.create_group('provenance')
provenance_dset = metadata_group.create_dataset('detector_output', (tot_files,), dtype=provenance_dt)
provenance_dset.attrs['tomo_prefix'] = 'tomo'
provenance_dset.attrs['flat_prefix'] = 'flat'
provenance_dset.attrs['first_index'] = 1
# Handle the metadata:
if (os.path.isfile(os.path.dirname(tomo_file) + os.sep + 'logfile.xml')):
with open(os.path.dirname(tomo_file) + os.sep + 'logfile.xml', "r") as file:
xml_command = file.read()
tdf.parse_metadata(f, xml_command)
# Print out about plan preparation:
first_done = True
log = open(logfilename,"a")
log.write(os.linesep + "\tWork plan prepared succesfully.")
log.close()
# Get the data from Pixirad:
if (num_flats > 0) or (num_postflats > 0):
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0],
#num_files)
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_flats)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files,
#im.shape[0])
dsetshape = tdf.get_dset_shape(dim1 - crop_left - crop_right, dim2 - crop_top - crop_bottom, num_flats)
if (compressionFlag):
flatdset = f.create_dataset('exchange/data_white', dsetshape, dtype, chunks=tdf.get_dset_chunks(dim1 - crop_left - crop_right), compression="gzip", compression_opts=compr_opts, shuffle=True, fletcher32=True)
else:
flatdset = f.create_dataset('exchange/data_white', dsetshape, dtype)
if privilege_sino:
flatdset.attrs['axes'] = "y:theta:x"
else:
flatdset.attrs['axes'] = "theta:y:x"
flatdset.attrs['min'] = str(numpy.iinfo(dtype).max)
flatdset.attrs['max'] = str(numpy.iinfo(dtype).min)
else:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: flat images (if any) not considered.")
log.close()
# Process the Pixirad data:
provenance_offset = 0
# (Stupid idea for time offset):
if num_flats > 0:
provenance_offset = _process_dset(flat_file, flatdset, 0, provenance_dset, provenance_offset,
0, 'flat', crop_top, crop_bottom, crop_left, crop_right, logfilename)
provenance_offset = _process_dset(tomo_file, dset, 0, provenance_dset, provenance_offset,
0, 'tomo', crop_top, crop_bottom, crop_left, crop_right, logfilename, int_from, int_to)
# Close TDF:
f.close()
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 _process(lock, int_from, int_to, num_sinos, infile_1, infile_2, infile_3,
outfile_abs, outfile_ref, outfile_sca, r1, r2, r3, d1, d2, d3,
dd1, dd2, dd3, shiftVert_1, shiftHoriz_1, shiftVert_2,
shiftHoriz_2, shiftVert_3, shiftHoriz_3, outshape, outtype,
skipflat_1, skipflat_2, skipflat_3, plan_1, plan_2, plan_3,
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, dynamic_ff, EFF_1, EFF_2, EFF_3,
filtEFF_1, filtEFF_2, filtEFF_3, im_dark_1, im_dark_2, im_dark_3,
logfilename):
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image for top, left and right:
t0 = time()
f_in = getHDF5(infile_1, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
flat_avg1 = float(f_in['flat'].attrs['avg'])
dark_avg1 = float(f_in['dark'].attrs['avg'])
else:
dset = f_in['exchange/data']
flat_avg1 = float(f_in['exchange/data_white'].attrs['avg'])
dark_avg1 = float(f_in['exchange/data_dark'].attrs['avg'])
# Processing in the sinogram domain so a vertical shift of the
# projection requires loading a different sinogram:
idx1 = min(max(0, i - shiftVert_1), num_sinos - 1)
im_1 = tdf.read_sino(dset, idx1).astype(float32)
f_in.close()
f_in = getHDF5(infile_2, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
flat_avg2 = float(f_in['flat'].attrs['avg'])
dark_avg2 = float(f_in['dark'].attrs['avg'])
else:
dset = f_in['exchange/data']
flat_avg2 = float(f_in['exchange/data_white'].attrs['avg'])
dark_avg2 = float(f_in['exchange/data_dark'].attrs['avg'])
# Processing in the sinogram domain so a vertical shift of the
# projection requires loading a different sinogram:
idx2 = min(max(0, i - shiftVert_2), num_sinos - 1)
im_2 = tdf.read_sino(dset, idx2).astype(float32)
f_in.close()
f_in = getHDF5(infile_3, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
flat_avg3 = float(f_in['flat'].attrs['avg'])
dark_avg3 = float(f_in['dark'].attrs['avg'])
else:
dset = f_in['exchange/data']
flat_avg3 = float(f_in['exchange/data_white'].attrs['avg'])
dark_avg3 = float(f_in['exchange/data_dark'].attrs['avg'])
# Processing in the sinogram domain so a vertical shift of the projection
# requires loading a different sinogram:
idx3 = min(max(0, i - shiftVert_3), num_sinos - 1)
im_3 = tdf.read_sino(dset, idx3).astype(float32)
f_in.close()
t1 = time()
# Perform pre-processing (flat fielding, extended FOV, ring removal):
if not skipflat_1:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im_1 = dynamic_flat_fielding(im_1, idx1, EFF_1, filtEFF_1, 2,
im_dark_1, norm_sx, norm_dx)
else:
im_1 = flat_fielding(im_1, idx1, plan_1, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
if ext_fov:
im_1 = extfov_correction(im_1, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average)
if not skipflat_1 and not dynamic_ff:
im_1 = ring_correction(im_1, ringrem, flat_end,
plan_1['skip_flat_after'], half_half,
half_half_line, ext_fov)
else:
im_1 = ring_correction(im_1, ringrem, False, False, half_half,
half_half_line, ext_fov)
# Perform pre-processing (flat fielding, extended FOV, ring removal):
if not skipflat_2:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im_2 = dynamic_flat_fielding(im_2, idx2, EFF_2, filtEFF_2, 2,
im_dark_2, norm_sx, norm_dx)
else:
im_2 = flat_fielding(im_2, idx2, plan_2, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
if ext_fov:
im_2 = extfov_correction(im_2, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average)
if not skipflat_2 and not dynamic_ff:
im_2 = ring_correction(im_2, ringrem, flat_end,
plan_2['skip_flat_after'], half_half,
half_half_line, ext_fov)
else:
im_2 = ring_correction(im_2, ringrem, False, False, half_half,
half_half_line, ext_fov)
# Perform pre-processing (flat fielding, extended FOV, ring removal):
if not skipflat_3:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im_3 = dynamic_flat_fielding(im_3, idx3, EFF_3, filtEFF_3, 2,
im_dark_3, norm_sx, norm_dx)
else:
im_3 = flat_fielding(im_3, idx3, plan_3, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
if ext_fov:
im_3 = extfov_correction(im_3, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average)
if not skipflat_3 and not dynamic_ff:
im_3 = ring_correction(im_3, ringrem, flat_end,
plan_3['skip_flat_after'], half_half,
half_half_line, ext_fov)
else:
im_3 = ring_correction(im_3, ringrem, False, False, half_half,
half_half_line, ext_fov)
t2 = time()
# Processing in the sinogram domain so a vertical shift of the
# projection requires loading a different sinogram:
# Only horizontal shift can be considered at a sinogram level:
if (shiftHoriz_1 != 0):
im_1 = _shift_horiz(im_1, shiftHoriz_1)
if (shiftHoriz_2 != 0):
im_2 = _shift_horiz(im_2, shiftHoriz_2)
if (shiftHoriz_3 != 0):
im_3 = _shift_horiz(im_3, shiftHoriz_3)
# Re-normalize with average of the flat-field images:
max_val = amax(
array([
mean(flat_avg1 - dark_avg1),
mean(flat_avg2 - dark_avg2),
mean(flat_avg3 - dark_avg3)
]))
im_1 = im_1 * (flat_avg1 - dark_avg1) / max_val
im_2 = im_2 * (flat_avg2 - dark_avg2) / max_val
im_3 = im_3 * (flat_avg3 - dark_avg3) / max_val
# Apply GDEI:
(im_abs, im_ref, im_sca) = gdei(im_1, im_2, im_3, r1, r2, r3, d1, d2,
d3, dd1, dd2, dd3)
# Save processed image to HDF5 file (atomic procedure - lock used):
lock.acquire()
try:
t3 = time()
_write_data(im_abs, i, outfile_abs, outshape, outtype)
_write_data(im_ref, i, outfile_ref, outshape, outtype)
_write_data(im_sca, i, outfile_sca, outshape, outtype)
t4 = time()
# Print out execution time:
log = open(logfilename, "a")
log.write(
linesep +
"\tsino_%s processed (CPU: %0.3f sec - I/O: %0.3f sec)." %
(str(i).zfill(4), t2 - t1, (t1 - t0) + (t4 - t3)))
log.close()
finally:
lock.release()
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 = 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...
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])
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):
"""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:
int_from = int(argv[0])
int_to = int(argv[1])
# Get full paths of input TDF and output TDF:
infile = argv[2]
outfile = argv[3]
# Get the phase retrieval parameters:
method = int(argv[4])
param1 = double(argv[5]) # e.g. regParam, or beta
param2 = double(argv[6]) # e.g. thresh or delta
energy = double(argv[7])
distance = double(argv[8])
pixsize = double(argv[9]) / 1000.0 # pixsixe from micron to mm:
pad = True if argv[10] == "True" else False
# Number of threads (actually processes) to use and logfile:
nr_threads = int(argv[11])
logfilename = argv[12]
# Log infos:
log = open(logfilename,"w")
log.write(linesep + "\tInput TDF file: %s" % (infile))
log.write(linesep + "\tOutput TDF file: %s" % (outfile))
log.write(linesep + "\t--------------")
if (method == 0):
log.write(linesep + "\tMethod: TIE-Hom (Paganin et al., 2002)")
log.write(linesep + "\t--------------")
log.write(linesep + "\tDelta/Beta: %0.1f" % ((param2/param1)) )
#else:
# log.write(linesep + "\tMethod: Projected CTF (Moosmann et al., 2011)")
# log.write(linesep + "\t--------------")
# log.write(linesep + "\tDelta/Beta: %0.1f" % ((param2/param1)) )
log.write(linesep + "\tEnergy: %0.1f keV" % (energy))
log.write(linesep + "\tDistance: %0.1f mm" % (distance))
log.write(linesep + "\tPixel size: %0.3f micron" % (pixsize*1000))
log.write(linesep + "\t--------------")
log.write(linesep + "\tBrowsing input files...")
log.close()
# Remove a previous copy of output:
if exists(outfile):
remove(outfile)
# 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)
if (num_proj == 0):
log = open(logfilename,"a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
log = open(logfilename,"a")
log.write(linesep + "\tInput files browsed correctly.")
log.close()
# Check extrema (int_to == -1 means all files):
if ( (int_to >= num_proj) or (int_to == -1) ):
int_to = num_proj - 1
if ( (int_from < 0) ):
int_from = 0
# Prepare the plan:
log = open(logfilename,"a")
log.write(linesep + "\tPreparing the work plan...")
log.close()
im = tdf.read_tomo(dset,0).astype(float32)
outshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_proj)
f_out = getHDF5(outfile, 'w')
f_out_dset = f_out.create_dataset('exchange/data', outshape, float32)
f_out_dset.attrs['min'] = str(amin(im[:]))
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out_dset.attrs['version'] = '1.0'
f_out_dset.attrs['axes'] = "y:theta:x"
f_in.close()
f_out.close()
if (method == 0):
# Paganin's:
plan = tiehom_plan (im, param1, param2, energy, distance, pixsize, pad)
else:
plan = phrt_plan (im, energy, distance, pixsize, param2, param1, method, pad)
# Run several threads for independent computation without waiting for threads completion:
for num in range(nr_threads):
start = (num_proj / nr_threads)*num
if (num == nr_threads - 1):
end = num_proj - 1
else:
end = (num_proj / nr_threads)*(num + 1) - 1
Process(target=_process, args=(lock, start, end, infile, outfile, outshape, float32, method, plan, logfilename)).start()
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
--------------------------
"""
lock = Lock()
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile_1 = argv[2]
infile_2 = argv[3]
infile_3 = argv[4]
outfile_abs = argv[5]
outfile_ref = argv[6]
outfile_sca = argv[7]
# Normalization parameters:
norm_sx = int(argv[8])
norm_dx = int(argv[9])
# Params for flat fielding with post flats/darks:
flat_end = True if argv[10] == "True" else False
half_half = True if argv[11] == "True" else False
half_half_line = int(argv[12])
# Params for extended FOV:
ext_fov = True if argv[13] == "True" else False
ext_fov_rot_right = argv[14]
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[15])
ext_fov_normalize = True if argv[16] == "True" else False
ext_fov_average = True if argv[17] == "True" else False
# Method and parameters coded into a string:
ringrem = argv[18]
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[19] == "True" else False
# Shift parameters:
shiftVert_1 = int(argv[20])
shiftHoriz_1 = int(argv[21])
shiftVert_2 = int(argv[22])
shiftHoriz_2 = int(argv[23])
shiftVert_3 = int(argv[24])
shiftHoriz_3 = int(argv[25])
# DEI coefficients:
r1 = float(argv[26])
r2 = float(argv[27])
r3 = float(argv[28])
d1 = float(argv[29])
d2 = float(argv[30])
d3 = float(argv[31])
dd1 = float(argv[32])
dd2 = float(argv[33])
dd3 = float(argv[34])
# Nr of threads and log file:
nr_threads = int(argv[35])
logfilename = argv[36]
# Log input parameters:
log = open(logfilename, "w")
log.write(linesep + "\tInput TDF file #1: %s" % (infile_1))
log.write(linesep + "\tInput TDF file #2: %s" % (infile_2))
log.write(linesep + "\tInput TDF file #3: %s" % (infile_3))
log.write(linesep + "\tOutput TDF file for Absorption: %s" % (outfile_abs))
log.write(linesep + "\tOutput TDF file for Refraction: %s" % (outfile_ref))
log.write(linesep + "\tOutput TDF file for Scattering: %s" % (outfile_sca))
log.write(linesep + "\t--------------")
log.write(linesep + "\tOpening input dataset...")
log.close()
# Remove a previous copy of output:
#if exists(outfile):
# remove(outfile)
# Open the HDF5 files:
f_in_1 = getHDF5(infile_1, 'r')
f_in_2 = getHDF5(infile_2, 'r')
f_in_3 = getHDF5(infile_3, 'r')
if "/tomo" in f_in_1:
dset_1 = f_in_1['tomo']
tomoprefix_1 = 'tomo'
flatprefix_1 = 'flat'
darkprefix_1 = 'dark'
else:
dset_1 = f_in_1['exchange/data']
if "/provenance/detector_output" in f_in_1:
prov_dset_1 = f_in_1['provenance/detector_output']
tomoprefix_1 = prov_dset_1.attrs['tomo_prefix']
flatprefix_1 = prov_dset_1.attrs['flat_prefix']
darkprefix_1 = prov_dset_1.attrs['dark_prefix']
if "/tomo" in f_in_2:
dset_2 = f_in_2['tomo']
tomoprefix_2 = 'tomo'
flatprefix_2 = 'flat'
darkprefix_2 = 'dark'
else:
dset_2 = f_in_2['exchange/data']
if "/provenance/detector_output" in f_in_2:
prov_dset_2 = f_in_2['provenance/detector_output']
tomoprefix_2 = prov_dset_2.attrs['tomo_prefix']
flatprefix_2 = prov_dset_2.attrs['flat_prefix']
darkprefix_2 = prov_dset_2.attrs['dark_prefix']
if "/tomo" in f_in_3:
dset_3 = f_in_3['tomo']
tomoprefix_3 = 'tomo'
flatprefix_3 = 'flat'
darkprefix_3 = 'dark'
else:
dset_3 = f_in_3['exchange/data']
if "/provenance/detector_output" in f_in_3:
prov_dset_3 = f_in_1['provenance/detector_output']
tomoprefix_3 = prov_dset_3.attrs['tomo_prefix']
flatprefix_3 = prov_dset_3.attrs['flat_prefix']
darkprefix_3 = prov_dset_3.attrs['dark_prefix']
# Assuming that what works for the dataset #1 works for the other two:
num_proj = tdf.get_nr_projs(dset_1)
num_sinos = tdf.get_nr_sinos(dset_1)
if (num_sinos == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_sinos) or (int_to == -1)):
int_to = num_sinos - 1
# Prepare the work plan for flat and dark images:
log = open(logfilename, "a")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the work plan...")
log.close()
# Extract flat and darks:
skipflat_1 = False
skipdark_1 = False
skipflat_2 = False
skipdark_2 = False
skipflat_3 = False
skipdark_3 = False
# Following variables make sense only for dynamic flat fielding:
EFF_1 = -1
filtEFF_1 = -1
im_dark_1 = -1
EFF_2 = -1
filtEFF_2 = -1
im_dark_2 = -1
EFF_3 = -1
filtEFF_3 = -1
im_dark_3 = -1
# Following variable makes sense only for conventional flat fielding:
plan_1 = -1
plan_2 = -1
plan_3 = -1
if not dynamic_ff:
plan_1 = extract_flatdark(f_in_1, flat_end, logfilename)
if (isscalar(plan_1['im_flat']) and isscalar(plan_1['im_flat_after'])):
skipflat_1 = True
else:
skipflat_1 = False
plan_2 = extract_flatdark(f_in_2, flat_end, logfilename)
if (isscalar(plan_2['im_flat']) and isscalar(plan_2['im_flat_after'])):
skipflat_2 = True
else:
skipflat_2 = False
plan_3 = extract_flatdark(f_in_3, flat_end, logfilename)
if (isscalar(plan_3['im_flat']) and isscalar(plan_3['im_flat_after'])):
skipflat_3 = True
else:
skipflat_3 = False
else:
# Dynamic flat fielding:
if "/tomo" in f_in_1:
if "/flat" in f_in_1:
flat_dset_1 = f_in_1['flat']
if "/dark" in f_in_1:
im_dark_1 = _medianize(f_in_1['dark'])
else:
skipdark_1 = True
else:
skipflat_1 = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in_1:
flat_dset_1 = f_in_1['/exchange/data_white']
if "/exchange/data_dark" in f_in_1:
im_dark_1 = _medianize(f_in_1['/exchange/data_dark'])
else:
skipdark_1 = True
else:
skipflat_1 = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat_1:
EFF_1, filtEFF_1 = dff_prepare_plan(flat_dset_1, 16, im_dark_1)
# Dynamic flat fielding:
if "/tomo" in f_in_2:
if "/flat" in f_in_2:
flat_dset_2 = f_in_2['flat']
if "/dark" in f_in_2:
im_dark_2 = _medianize(f_in_2['dark'])
else:
skipdark_2 = True
else:
skipflat_2 = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in_2:
flat_dset_2 = f_in_2['/exchange/data_white']
if "/exchange/data_dark" in f_in_2:
im_dark_2 = _medianize(f_in_2['/exchange/data_dark'])
else:
skipdark_2 = True
else:
skipflat_2 = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat_2:
EFF_2, filtEFF_2 = dff_prepare_plan(flat_dset_2, 16, im_dark_2)
# Dynamic flat fielding:
if "/tomo" in f_in_3:
if "/flat" in f_in_3:
flat_dset_3 = f_in_3['flat']
if "/dark" in f_in_3:
im_dark_3 = _medianize(f_in_3['dark'])
else:
skipdark_3 = True
else:
skipflat_3 = True # Nothing to do in this case
else:
if "/exchange/data_white" in f_in_3:
flat_dset_3 = f_in_3['/exchange/data_white']
if "/exchange/data_dark" in f_in_3:
im_dark_3 = _medianize(f_in_3['/exchange/data_dark'])
else:
skipdark_3 = True
else:
skipflat_3 = True # Nothing to do in this case
# Prepare plan for dynamic flat fielding with 16 repetitions:
if not skipflat_3:
EFF_3, filtEFF_3 = dff_prepare_plan(flat_dset_3, 16, im_dark_3)
# Outfile shape can be determined only after first processing in ext FOV mode:
if (ext_fov):
# Read input sino:
idx = num_sinos / 2
im = tdf.read_sino(dset_1, idx).astype(float32)
im = extfov_correction(im, ext_fov_rot_right, ext_fov_overlap,
ext_fov_normalize, ext_fov_average)
# Get the corrected outshape:
outshape = tdf.get_dset_shape(im.shape[1], num_sinos, im.shape[0])
else:
# Get the corrected outshape (in this case it's easy):
im = tdf.read_tomo(dset_1, 0).astype(float32)
outshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_proj)
f_in_1.close()
f_in_2.close()
f_in_3.close()
# Create the output HDF5 files:
f_out_abs = getHDF5(outfile_abs, 'w')
f_out_dset_abs = f_out_abs.create_dataset('exchange/data', outshape,
float32)
f_out_dset_abs.attrs['min'] = str(finfo(float32).max)
f_out_dset_abs.attrs['max'] = str(finfo(float32).min)
f_out_dset_abs.attrs['version'] = '1.0'
f_out_dset_abs.attrs['axes'] = "y:theta:x"
f_out_abs.close()
f_out_ref = getHDF5(outfile_ref, 'w')
f_out_dset_ref = f_out_ref.create_dataset('exchange/data', outshape,
float32)
f_out_dset_ref.attrs['min'] = str(finfo(float32).max)
f_out_dset_ref.attrs['max'] = str(finfo(float32).min)
f_out_dset_ref.attrs['version'] = '1.0'
f_out_dset_ref.attrs['axes'] = "y:theta:x"
f_out_ref.close()
f_out_sca = getHDF5(outfile_sca, 'w')
f_out_dset_sca = f_out_sca.create_dataset('exchange/data', outshape,
float32)
f_out_dset_sca.attrs['min'] = str(finfo(float32).max)
f_out_dset_sca.attrs['max'] = str(finfo(float32).min)
f_out_dset_sca.attrs['version'] = '1.0'
f_out_dset_sca.attrs['axes'] = "y:theta:x"
f_out_sca.close()
# Log infos:
log = open(logfilename, "a")
log.write(linesep + "\tWork plan prepared correctly.")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPerforming GDEI...")
log.close()
# Run several threads for independent computation without waiting for threads
# completion:
for num in range(nr_threads):
start = (num_sinos / nr_threads) * num
if (num == nr_threads - 1):
end = num_sinos - 1
else:
end = (num_sinos / nr_threads) * (num + 1) - 1
Process(
target=_process,
args=(lock, start, end, num_sinos, infile_1, infile_2, infile_3,
outfile_abs, outfile_ref, outfile_sca, r1, r2, r3, d1, d2,
d3, dd1, dd2, dd3, shiftVert_1, shiftHoriz_1, shiftVert_2,
shiftHoriz_2, shiftVert_3, shiftHoriz_3, outshape, float32,
skipflat_1, skipflat_2, skipflat_3, plan_1, plan_2, plan_3,
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, dynamic_ff,
EFF_1, EFF_2, EFF_3, filtEFF_1, filtEFF_2, filtEFF_3,
im_dark_1, im_dark_2, im_dark_3, logfilename)).start()
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):
"""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 process(sino_idx, num_sinos, infile, outfile, preprocessing_required, corr_plan, 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, logtransform, recpar, circle, scale, pad, method, 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):
"""To do...
"""
# Perform reconstruction (on-the-fly preprocessing and phase retrieval, if
# required):
if (phaseretrieval_required):
# In this case a bunch of sinograms is loaded into memory:
#
# Load the temporary data structure reading the input TDF file.
# To know the right dimension the first sinogram is pre-processed.
#
# Open the TDF file and get the dataset:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
# Downscaling and decimation factors considered when determining the
# approximation window:
zrange = arange(sino_idx - approx_win * downsc_factor / 2, sino_idx + approx_win * downsc_factor / 2, downsc_factor)
zrange = zrange[(zrange >= 0)]
zrange = zrange[(zrange < num_sinos)]
approx_win = zrange.shape[0]
# Approximation window cannot be odd:
if (approx_win % 2 == 1):
approx_win = approx_win - 1
zrange = zrange[0:approx_win]
# Read one sinogram to get the proper dimensions:
test_im = tdf.read_sino(dset, zrange[0]*downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing of the first sinogram to get the right
# dimension:
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
#test_im = dynamic_flat_fielding(test_im, zrange[0] / downsc_factor, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
test_im = dynamic_flat_fielding(test_im, zrange[0] , EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
else:
#test_im = flat_fielding(test_im, zrange[0] / downsc_factor, corr_plan, flat_end, half_half,
# half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
test_im = flat_fielding(test_im, zrange[0], corr_plan, flat_end, half_half,
half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(test_im, ext_fov_rot_right, ext_fov_overlap / downsc_factor, ext_fov_normalize, ext_fov_average).astype(float32)
if not skipflat and not dynamic_ff:
test_im = ring_correction(test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov).astype(float32)
else:
test_im = ring_correction(test_im, ringrem, False, False, half_half,
half_half_line / decim_factor, ext_fov).astype(float32)
# Now we can allocate memory for the bunch of slices:
tmp_im = empty((approx_win, test_im.shape[0], test_im.shape[1]), dtype=float32)
tmp_im[0,:,:] = test_im
# Reading all the the sinos from TDF file and close:
for ct in range(1, approx_win):
# Read the sinogram:
test_im = tdf.read_sino(dset, zrange[ct]*downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing for each sinogram of the bunch:
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
#test_im = dynamic_flat_fielding(test_im, zrange[ct] / downsc_factor, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
test_im = dynamic_flat_fielding(test_im, zrange[ct], EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
else:
#test_im = flat_fielding(test_im, zrange[ct] / downsc_factor, corr_plan, flat_end, half_half,
# half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
test_im = flat_fielding(test_im, zrange[ct], corr_plan, flat_end, half_half,
half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(test_im, ext_fov_rot_right, ext_fov_overlap / downsc_factor, ext_fov_normalize, ext_fov_average).astype(float32)
if not skipflat and not dynamic_ff:
test_im = ring_correction(test_im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov).astype(float32)
else:
test_im = ring_correction(test_im, ringrem, False, False, half_half,
half_half_line / decim_factor, ext_fov).astype(float32)
tmp_im[ct,:,:] = test_im
f_in.close()
# Now everything has to refer to a downscaled dataset:
sino_idx = ((zrange == sino_idx).nonzero())
#
# Perform phase retrieval:
#
# Prepare the plan:
if (phrtmethod == 0):
# Paganin's:
phrtplan = tiehom_plan(tmp_im[:,0,:], phrt_param1, phrt_param2, energy, distance, pixsize * downsc_factor, phrtpad)
else:
phrtplan = phrt_plan(tmp_im[:,0,:], energy, distance, pixsize * downsc_factor, phrt_param2, phrt_param1, phrtmethod, phrtpad)
#phrtplan = prepare_plan (tmp_im[:,0,:], beta, delta, energy, distance,
#pixsize*downsc_factor, padding=phrtpad)
# Process each projection (whose height depends on the size of the bunch):
for ct in range(0, tmp_im.shape[1]):
#tmp_im[:,ct,:] = phase_retrieval(tmp_im[:,ct,:], phrtplan).astype(float32)
if (phrtmethod == 0):
tmp_im[:,ct,:] = tiehom(tmp_im[:,ct,:], phrtplan).astype(float32)
else:
tmp_im[:,ct,:] = phrt(tmp_im[:,ct,:], phrtplan, phrtmethod).astype(float32)
# Extract the requested sinogram:
im = tmp_im[sino_idx[0],:,:].squeeze()
else:
# Read only one sinogram:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset,sino_idx * downsc_factor).astype(float32)
f_in.close()
# Apply projection removal (if required):
im = im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
im = im[::decim_factor,::downsc_factor]
#sino_idx = sino_idx / downsc_factor # Downscaling for the index already applied
# Perform the preprocessing of the sinogram (if required):
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im = dynamic_flat_fielding(im, sino_idx, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
else:
im = flat_fielding(im, sino_idx, corr_plan, flat_end, half_half, half_half_line / decim_factor,
norm_sx, norm_dx).astype(float32)
if ext_fov:
im = extfov_correction(im, ext_fov_rot_right, ext_fov_overlap / downsc_factor, ext_fov_normalize, ext_fov_average)
if not skipflat and not dynamic_ff:
im = ring_correction(im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
else:
im = ring_correction(im, ringrem, False, False, half_half,
half_half_line / decim_factor, ext_fov)
# Additional ring removal before reconstruction:
#im = boinhaibel(im, '11;')
#im = munchetal(im, '5;1.8')
#im = rivers(im, '13;')
#im = raven(im, '11;0.8')
#im = oimoen(im, '51;51')
# Actual reconstruction:
im = reconstruct(im, angles, offset / downsc_factor, logtransform, recpar, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, corr_offset, rolling, roll_shift, tmppath).astype(float32)
# Apply post-processing (if required):
if postprocess_required:
im = polarfilter(im, polarfilt_opt)
im = croprescale(im, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im.shape[1]
if siz % 2:
rang = arange(-siz / 2 + 1, siz / 2 + 1)
else:
rang = arange(-siz / 2,siz / 2)
x,y = meshgrid(rang,rang)
z = x ** 2 + y ** 2
a = (z < (siz / 2 - int(round(abs(offset) / downsc_factor))) ** 2)
im = im * a
# 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(amin(im)) + '$' + str(amax(im))
im.tofile(outfile)
def process_gridrec(lock, int_from, int_to, num_sinos, infile, outpath,
preprocessing_required, skipflat, corr_plan, 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, angles, angles_projfrom,
angles_projto, offset, logtransform, param1, circle, scale,
pad, 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, outprefix, logfilename):
"""To do...
"""
# Process the required subset of images:
for i in range(int_from, int_to + 1, 2):
# Read two sinograms:
t0 = time()
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im1 = tdf.read_sino(dset, i * downsc_factor).astype(float32)
if ((i + downsc_factor) <= (int_to + 1)):
im2 = tdf.read_sino(dset, i * downsc_factor +
downsc_factor).astype(float32)
else:
im2 = im1
f_in.close()
t1 = time()
# Apply projection removal (if required):
im1 = im1[angles_projfrom:angles_projto, :]
im2 = im2[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
im1 = im1[::decim_factor, ::downsc_factor]
im2 = im2[::decim_factor, ::downsc_factor]
#i = i / downsc_factor
# Perform the preprocessing of the sinograms (if required):
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im1 = dynamic_flat_fielding(im1, i, EFF, filtEFF, 2,
im_dark, norm_sx, norm_dx)
else:
im1 = flat_fielding(im1, i, corr_plan, flat_end, half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
im1 = extfov_correction(im1, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize, ext_fov_average)
if not skipflat:
im1 = ring_correction(im1, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
else:
im1 = ring_correction(im1, ringrem, False, False, half_half,
half_half_line / decim_factor, ext_fov)
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im2 = dynamic_flat_fielding(im2, i + 1, EFF, filtEFF, 2,
im_dark, norm_sx, norm_dx)
else:
im2 = flat_fielding(im2, i + 1, corr_plan, flat_end,
half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
im2 = extfov_correction(im2, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize, ext_fov_average)
if not skipflat and not dynamic_ff:
im2 = ring_correction(im2, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
else:
im2 = ring_correction(im2, ringrem, False, False, half_half,
half_half_line, ext_fov)
# Actual reconstruction:
[im1,
im2] = reconstruct_gridrec(im1, im2, angles, offset / downsc_factor,
logtransform, param1, circle, scale, pad,
rolling, roll_shift, zerone_mode, dset_min,
dset_max, decim_factor, downsc_factor,
corr_offset)
# Appy post-processing (if required):
if postprocess_required:
# Filter (if required):
im1 = polarfilter(im1, polarfilt_opt)
im2 = polarfilter(im2, polarfilt_opt)
im1 = croprescale(im1, convert_opt, crop_opt, circle)
im2 = croprescale(im2, convert_opt, crop_opt, circle)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im1.shape[1]
if siz % 2:
rang = arange(-siz / 2 + 1, siz / 2 + 1)
else:
rang = arange(-siz / 2, siz / 2)
x, y = meshgrid(rang, rang)
z = x**2 + y**2
a = (z <
(siz / 2 - int(round(abs(offset) / downsc_factor)))**2)
im1 = im1 * a
im2 = im2 * a
# Write down reconstructed slices:
t2 = time()
fname1 = outpath + outprefix + '_' + str(i).zfill(4) + '.tif'
imsave(fname1, im1)
fname2 = outpath + outprefix + '_' + str(i + 1).zfill(4) + '.tif'
imsave(fname2, im2)
t3 = time()
# Write log (atomic procedure - lock used):
write_log_gridrec(lock, fname1, fname2, logfilename, t2 - t1,
(t3 - t2) + (t1 - t0))
def process_gridrec(lock, int_from, int_to, num_sinos, infile, outpath, preprocessing_required, skipflat, corr_plan, 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, angles, angles_projfrom, angles_projto, offset, logtransform, param1, circle, scale, pad, 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, outprefix, logfilename): """To do... """ # Process the required subset of images: for i in range(int_from, int_to + 1, 2): # Read two sinograms: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im1 = tdf.read_sino(dset,i*downsc_factor).astype(float32) if ( (i + downsc_factor) <= (int_to + 1) ): im2 = tdf.read_sino(dset,i*downsc_factor + downsc_factor).astype(float32) else: im2 = im1 f_in.close() t1 = time() # Apply projection removal (if required): im1 = im1[angles_projfrom:angles_projto, :] im2 = im2[angles_projfrom:angles_projto, :] # Apply decimation and downscaling (if required): im1 = im1[::decim_factor,::downsc_factor] im2 = im2[::decim_factor,::downsc_factor] #i = i / downsc_factor # Perform the preprocessing of the sinograms (if required): if (preprocessing_required): if not skipflat: if dynamic_ff: # Dynamic flat fielding with downsampling = 2: im1 = dynamic_flat_fielding(im1, i, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx) else: im1 = flat_fielding (im1, i, corr_plan, flat_end, half_half, half_half_line / decim_factor, norm_sx, norm_dx).astype(float32) if ext_fov: im1 = extfov_correction (im1, ext_fov_rot_right, ext_fov_overlap / downsc_factor, ext_fov_normalize, ext_fov_average) if not skipflat: im1 = ring_correction (im1, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line / decim_factor, ext_fov) else: im1 = ring_correction (im1, ringrem, False, False, half_half, half_half_line / decim_factor, ext_fov) if not skipflat: if dynamic_ff: # Dynamic flat fielding with downsampling = 2: im2 = dynamic_flat_fielding(im2, i + 1, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx) else: im2 = flat_fielding (im2, i + 1, corr_plan, flat_end, half_half, half_half_line / decim_factor, norm_sx, norm_dx).astype(float32) if ext_fov: im2 = extfov_correction (im2, ext_fov_rot_right, ext_fov_overlap / downsc_factor, ext_fov_normalize, ext_fov_average) if not skipflat and not dynamic_ff: im2 = ring_correction (im2, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line / decim_factor, ext_fov) else: im2 = ring_correction (im2, ringrem, False, False, half_half, half_half_line, ext_fov) # Actual reconstruction: [im1, im2] = reconstruct_gridrec(im1, im2, angles, offset / downsc_factor, logtransform, param1, circle, scale, pad, rolling, roll_shift, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset) # Appy post-processing (if required): if postprocess_required: # Filter (if required): im1 = polarfilter(im1, polarfilt_opt) im2 = polarfilter(im2, polarfilt_opt) im1 = croprescale(im1, convert_opt, crop_opt, circle) im2 = croprescale(im2, convert_opt, crop_opt, circle) else: # Create the circle mask for fancy output: if (circle == True): siz = im1.shape[1] if siz % 2: rang = arange(-siz / 2 + 1, siz / 2 + 1) else: rang = arange(-siz / 2,siz / 2) x,y = meshgrid(rang,rang) z = x ** 2 + y ** 2 a = (z < (siz / 2 - int(round(abs(offset)/downsc_factor)) ) ** 2) im1 = im1 * a im2 = im2 * a # Write down reconstructed slices: t2 = time() fname1 = outpath + outprefix + '_' + str(i).zfill(4) + '.tif' imsave(fname1, im1) fname2 = outpath + outprefix + '_' + str(i + 1).zfill(4) + '.tif' imsave(fname2, im2) t3 = time() # Write log (atomic procedure - lock used): write_log_gridrec(lock, fname1, fname2, logfilename, t2 - t1, (t3 - t2) + (t1 - t0) )
def process(lock, int_from, int_to, num_sinos, infile, outpath,
preprocessing_required, skipflat, corr_plan, 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,
angles, angles_projfrom, angles_projto, offset, logtransform,
param1, circle, scale, pad, method, 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, outprefix,
logfilename):
"""To do...
"""
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Perform reconstruction (on-the-fly preprocessing and phase retrieval, if
# required):
#if (phaseretrieval_required):
# # Load into memory a bunch of sinograms:
# t0 = time()
# # Open the TDF file for reading:
# f_in = getHDF5(infile, 'r')
# if "/tomo" in f_in:
# dset = f_in['tomo']
# else:
# dset = f_in['exchange/data']
# # Prepare the data structure according to the approximation window:
# tmp_im = numpy.empty((tdf.get_nr_projs(dset),tdf.get_det_size(dset),
# approx_win), dtype=float32)
# # Load the temporary data structure reading the input TDF file:
# # (It can be parallelized Open-MP style)
# ct = 0
# for j in range(i - approx_win/2, i + approx_win/2 + 1):
# if (j < 0):
# j = 0
# if (j >= num_sinos):
# j = num_sinos - 1
# a = tdf.read_sino(dset,j).astype(float32)
# tmp_im[:,:,ct] = a
# ct = ct + 1
# # Close the TDF file:
# f_in.close()
# t1 = time()
# # Perform the processing:
# if (preprocessing_required):
# ct = 0
# # (It can be parallelized Open-MP style)
# for j in range(i - approx_win/2, i + approx_win/2 + 1):
# if (j < 0):
# j = 0
# if (j >= num_sinos):
# j = num_sinos - 1
# tmp_im[:,:,ct] = flat_fielding (tmp_im[:,:,ct], j, corr_plan, flat_end,
# half_half, half_half_line, norm_sx, norm_dx).astype(float32)
# tmp_im[:,:,ct] = extfov_correction (tmp_im[:,:,ct], ext_fov,
# ext_fov_rot_right, ext_fov_overlap).astype(float32)
# tmp_im[:,:,ct] = ring_correction (tmp_im[:,:,ct], ringrem, flat_end,
# corr_plan['skip_flat_after'], half_half, half_half_line,
# ext_fov).astype(float32)
# ct = ct + 1
# # Perform phase retrieval:
# # (It can be parallelized Open-MP style)
# for ct in range(0, tmp_im.shape[0]):
# tmp_im[ct,:,:] = phase_retrieval(tmp_im[ct,:,:].T,
# phrt_plan).astype(float32).T
# ct = ct + 1
# # Extract the central processed sinogram:
# im = tmp_im[:,:,approx_win/2]
#else:
# Read only one sinogram:
t0 = time()
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset, i * downsc_factor).astype(float32)
f_in.close()
t1 = time()
# Apply projection removal (if required):
im = im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
im = im[::decim_factor, ::downsc_factor]
#i = i / downsc_factor
# Perform the preprocessing of the sinogram (if required):
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im = dynamic_flat_fielding(im, i, EFF, filtEFF, 2, im_dark,
norm_sx,
norm_dx).astype(float32)
else:
im = flat_fielding(im, i, corr_plan, flat_end, half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
im = extfov_correction(im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize, ext_fov_average)
if not skipflat and not dynamic_ff:
im = ring_correction(im, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
else:
im = ring_correction(im, ringrem, False, False, half_half,
half_half_line, ext_fov)
# Actual reconstruction:
im = reconstruct(im, angles, offset / downsc_factor, logtransform,
param1, circle, scale, pad, method, rolling,
roll_shift, zerone_mode, dset_min, dset_max,
decim_factor, downsc_factor,
corr_offset).astype(float32)
# Apply post-processing (if required):
if postprocess_required:
im = polarfilter(im, polarfilt_opt)
im = croprescale(im, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im.shape[1]
if siz % 2:
rang = arange(-siz / 2 + 1, siz / 2 + 1)
else:
rang = arange(-siz / 2, siz / 2)
x, y = meshgrid(rang, rang)
z = x**2 + y**2
a = (z < (siz / 2 - abs(offset))**2)
im = im * a
# Write down reconstructed slice:
t2 = time()
fname = outpath + outprefix + '_' + str(i).zfill(4) + '.tif'
imsave(fname, im)
t3 = time()
# Write log (atomic procedure - lock used):
write_log(lock, fname, logfilename, t2 - t1, (t3 - t2) + (t1 - t0))
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:
int_from = int(argv[0])
int_to = int(argv[1])
# Get full paths of input TDF and output TDF:
infile = argv[2]
outfile = argv[3]
# Get the phase retrieval parameters:
method = int(argv[4])
param1 = double(argv[5]) # e.g. regParam, or beta
param2 = double(argv[6]) # e.g. thresh or delta
energy = double(argv[7])
distance = double(argv[8])
pixsize = double(argv[9]) / 1000.0 # pixsixe from micron to mm:
pad = True if argv[10] == "True" else False
# Number of threads (actually processes) to use and logfile:
nr_threads = int(argv[11])
logfilename = argv[12]
# Log infos:
log = open(logfilename, "w")
log.write(linesep + "\tInput TDF file: %s" % (infile))
log.write(linesep + "\tOutput TDF file: %s" % (outfile))
log.write(linesep + "\t--------------")
if (method == 0):
log.write(linesep + "\tMethod: TIE-Hom (Paganin et al., 2002)")
log.write(linesep + "\t--------------")
log.write(linesep + "\tDelta/Beta: %0.1f" % ((param2 / param1)))
elif (method == 1):
log.write(linesep +
"\tMethod: Generalized TIE-Hom (Paganin et al., 2020)")
log.write(linesep + "\t--------------")
log.write(linesep + "\tDelta/Beta: %0.1f" % ((param2 / param1)))
else:
log.write(linesep + "\tMethod: Projected CTF (Moosmann et al., 2011)")
log.write(linesep + "\t--------------")
log.write(linesep + "\tRegularization: %0.3f" % (param2))
log.write(linesep + "\tThreshold: %0.3f" % (param1))
log.write(linesep + "\tEnergy: %0.1f keV" % (energy))
log.write(linesep + "\tDistance: %0.1f mm" % (distance))
log.write(linesep + "\tPixel size: %0.3f micron" % (pixsize * 1000))
log.write(linesep + "\t--------------")
log.write(linesep + "\tBrowsing input files...")
log.close()
# Remove a previous copy of output:
if exists(outfile):
remove(outfile)
# 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)
if (num_proj == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
log = open(logfilename, "a")
log.write(linesep + "\tInput files browsed correctly.")
log.close()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_proj) or (int_to == -1)):
int_to = num_proj - 1
if ((int_from < 0)):
int_from = 0
# Prepare the plan:
log = open(logfilename, "a")
log.write(linesep + "\tPreparing the work plan...")
log.close()
im = tdf.read_tomo(dset, 0).astype(float32)
outshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_proj)
f_out = getHDF5(outfile, 'w')
f_out_dset = f_out.create_dataset('exchange/data', outshape, float32)
f_out_dset.attrs['min'] = str(amin(im[:]))
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out_dset.attrs['version'] = '1.0'
f_out_dset.attrs['axes'] = "y:theta:x"
f_in.close()
f_out.close()
if (method == 0):
# Paganin 2020:
plan = tiehom_plan(im, param1, param2, energy, distance, pixsize, pad)
elif (method == 1):
# Paganin 2020:
plan = tiehom_plan2020(im, param1, param2, energy, distance, pixsize,
pad)
else:
plan = phrt_plan(im, energy, distance, pixsize, param2, param1, method,
pad)
# Run several threads for independent computation without waiting for threads completion:
for num in range(nr_threads):
start = (num_proj / nr_threads) * num
if (num == nr_threads - 1):
end = num_proj - 1
else:
end = (num_proj / nr_threads) * (num + 1) - 1
Process(target=_process,
args=(lock, start, end, infile, outfile, outshape, float32,
method, plan, logfilename)).start()
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
"""
lock = Lock()
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile = argv[2]
outpath = argv[3]
# Essential reconstruction parameters:
angles = float(argv[4])
offset = float(argv[5])
param1 = argv[6]
scale = int(float(argv[7]))
overpad = True if argv[8] == "True" else False
logtrsf = True if argv[9] == "True" else False
circle = True if argv[10] == "True" else False
outprefix = argv[11]
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[12] == "True" else False
flat_end = True if argv[13] == "True" else False
half_half = True if argv[14] == "True" else False
half_half_line = int(argv[15])
ext_fov = True if argv[16] == "True" else False
norm_sx = int(argv[21])
norm_dx = int(argv[22])
ext_fov_rot_right = argv[17]
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[18])
ext_fov_normalize = True if argv[19] == "True" else False
ext_fov_average = True if argv[20] == "True" else False
skip_ringrem = True if argv[23] == "True" else False
ringrem = argv[24]
# Extra reconstruction parameters:
zerone_mode = True if argv[25] == "True" else False
corr_offset = float(argv[26])
reconmethod = argv[27]
# Force overpadding in case of GRIDREC for unknown reasons:
if reconmethod == "GRIDREC":
overpad = True
decim_factor = int(argv[28])
downsc_factor = int(argv[29])
# Parameters for postprocessing:
postprocess_required = True if argv[30] == "True" else False
polarfilt_opt = argv[31]
convert_opt = argv[32]
crop_opt = argv[33]
angles_projfrom = int(argv[34])
angles_projto = int(argv[35])
rolling = True if argv[36] == "True" else False
roll_shift = int(int(argv[37]) / decim_factor)
dynamic_ff = True if argv[38] == "True" else False
nr_threads = int(argv[39])
logfilename = argv[40]
process_id = int(logfilename[-6:-4])
# Check prefixes and path:
#if not infile.endswith(sep): infile += sep
if not exists(outpath):
makedirs(outpath)
if not outpath.endswith(sep): outpath += sep
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
tomoprefix = 'tomo'
flatprefix = 'flat'
darkprefix = 'dark'
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
tomoprefix = prov_dset.attrs['tomo_prefix']
flatprefix = prov_dset.attrs['flat_prefix']
darkprefix = prov_dset.attrs['dark_prefix']
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):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_sinos / downsc_factor) or (int_to == -1)):
int_to = num_sinos / downsc_factor - 1
# Log info:
log = open(logfilename, "w")
log.write(linesep + "\tInput file: %s" % (infile))
log.write(linesep + "\tOutput path: %s" % (outpath))
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the work plan...")
log.close()
# Get correction plan and phase retrieval plan (if required):
corrplan = -1
phrtplan = -1
skipflat = False
im_dark = -1
EFF = -1
filtEFF = -1
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:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat'])
and isscalar(corrplan['im_flat_after'])):
skipflat = True
# 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()
# Log infos:
log = open(logfilename, "a")
log.write(linesep + "\tWork plan prepared correctly.")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPerforming reconstruction...")
log.close()
# Run several threads for independent computation without waiting for threads
# completion:
for num in range(nr_threads):
start = ((int_to - int_from + 1) / nr_threads) * num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ((int_to - int_from + 1) / nr_threads) * (num +
1) + int_from - 1
if (reconmethod == 'GRIDREC'):
Process(
target=process_gridrec,
args=(lock, start, end, num_sinos, infile, outpath,
preprocessing_required, skipflat, 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, angles, angles_projfrom,
angles_projto, offset, logtrsf, param1, circle, scale,
overpad, 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, outprefix,
logfilename)).start()
else:
Process(
target=process,
args=(lock, start, end, num_sinos, infile, outpath,
preprocessing_required, skipflat, 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, angles, angles_projfrom,
angles_projto, offset, logtrsf, param1, 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,
outprefix, logfilename)).start()
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()
rescale_factor = 10000.0 # For 16-bit floating point
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile = argv[2]
outfile = argv[3]
# Params for flat fielding with post flats/darks:
flat_end = True if argv[4] == "True" else False
half_half = True if argv[5] == "True" else False
half_half_line = int(argv[6])
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[7] == "True" else False
# Parameters for rotation:
rotation = float(argv[8])
interp = argv[9]
border = argv[10]
# Nr of threads and log file:
nr_threads = int(argv[11])
logfilename = argv[12]
# Log input parameters:
log = open(logfilename, "w")
log.write(linesep + "\tInput TDF file: %s" % (infile))
log.write(linesep + "\tOutput TDF file: %s" % (outfile))
log.write(linesep + "\t--------------")
log.write(linesep + "\tOpening input dataset...")
log.close()
# Remove a previous copy of output:
if exists(outfile):
remove(outfile)
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
tomoprefix = 'tomo'
flatprefix = 'flat'
darkprefix = 'dark'
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
tomoprefix = prov_dset.attrs['tomo_prefix']
flatprefix = prov_dset.attrs['flat_prefix']
darkprefix = prov_dset.attrs['dark_prefix']
num_proj = tdf.get_nr_projs(dset)
num_sinos = tdf.get_nr_sinos(dset)
if (num_sinos == 0):
log = open(logfilename, "a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_proj) or (int_to == -1)):
int_to = num_proj - 1
# Prepare the work plan for flat and dark images:
log = open(logfilename, "a")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the work plan...")
log.close()
# Extract flat and darks:
skipflat = False
skipdark = False
# Following variables make sense only for dynamic flat fielding:
EFF = -1
filtEFF = -1
im_dark = -1
# Following variable makes sense only for conventional flat fielding:
plan = -1
if not dynamic_ff:
plan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(plan['im_flat']) and isscalar(plan['im_flat_after'])):
skipflat = True
else:
skipflat = False
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)
# Get the corrected outshape (in this case it's easy):
im = tdf.read_tomo(dset, 0).astype(float32)
outshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_proj)
# Create the output HDF5 file:
f_out = getHDF5(outfile, 'w')
#f_out_dset = f_out.create_dataset('exchange/data', outshape, im.dtype)
f_out_dset = f_out.create_dataset('exchange/data', outshape, float16)
f_out_dset.attrs['min'] = str(amin(im[:]))
f_out_dset.attrs['max'] = str(amax(im[:]))
f_out_dset.attrs['version'] = '1.0'
f_out_dset.attrs['axes'] = "y:theta:x"
f_out_dset.attrs['rescale_factor'] = str(rescale_factor)
f_out.close()
f_in.close()
# Log infos:
log = open(logfilename, "a")
log.write(linesep + "\tWork plan prepared correctly.")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPerforming pre processing...")
log.close()
# Run several threads for independent computation without waiting for threads
# completion:
for num in range(nr_threads):
start = (num_proj / nr_threads) * num
if (num == nr_threads - 1):
end = num_proj - 1
else:
end = (num_proj / nr_threads) * (num + 1) - 1
Process(target=_process,
args=(lock, start, end, infile, outfile, outshape, float16,
skipflat, plan, flat_end, half_half, half_half_line,
dynamic_ff, EFF, filtEFF, im_dark, rotation, interp,
border, rescale_factor, logfilename)).start()
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
"""
lock = Lock()
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile = argv[2]
outpath = argv[3]
# Essential reconstruction parameters:
angles = float(argv[4])
offset = float(argv[5])
param1 = argv[6]
scale = int(float(argv[7]))
overpad = True if argv[8] == "True" else False
logtrsf = True if argv[9] == "True" else False
circle = True if argv[10] == "True" else False
outprefix = argv[11]
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[12] == "True" else False
flat_end = True if argv[13] == "True" else False
half_half = True if argv[14] == "True" else False
half_half_line = int(argv[15])
ext_fov = True if argv[16] == "True" else False
norm_sx = int(argv[21])
norm_dx = int(argv[22])
ext_fov_rot_right = argv[17]
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[18])
ext_fov_normalize = True if argv[19] == "True" else False
ext_fov_average = True if argv[20] == "True" else False
skip_ringrem = True if argv[23] == "True" else False
ringrem = argv[24]
# Extra reconstruction parameters:
zerone_mode = True if argv[25] == "True" else False
corr_offset = float(argv[26])
reconmethod = argv[27]
# Force overpadding in case of GRIDREC for unknown reasons:
if reconmethod == "GRIDREC":
overpad = True
decim_factor = int(argv[28])
downsc_factor = int(argv[29])
# Parameters for postprocessing:
postprocess_required = True if argv[30] == "True" else False
polarfilt_opt = argv[31]
convert_opt = argv[32]
crop_opt = argv[33]
angles_projfrom = int(argv[34])
angles_projto = int(argv[35])
rolling = True if argv[36] == "True" else False
roll_shift = int(int(argv[37]) / decim_factor)
dynamic_ff = True if argv[38] == "True" else False
nr_threads = int(argv[39])
logfilename = argv[40]
process_id = int(logfilename[-6:-4])
# Check prefixes and path:
#if not infile.endswith(sep): infile += sep
if not exists(outpath):
makedirs(outpath)
if not outpath.endswith(sep): outpath += sep
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
tomoprefix = 'tomo'
flatprefix = 'flat'
darkprefix = 'dark'
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
tomoprefix = prov_dset.attrs['tomo_prefix']
flatprefix = prov_dset.attrs['flat_prefix']
darkprefix = prov_dset.attrs['dark_prefix']
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):
log = open(logfilename,"a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_sinos / downsc_factor) or (int_to == -1)):
int_to = num_sinos / downsc_factor - 1
# Log info:
log = open(logfilename,"w")
log.write(linesep + "\tInput file: %s" % (infile))
log.write(linesep + "\tOutput path: %s" % (outpath))
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the work plan...")
log.close()
# Get correction plan and phase retrieval plan (if required):
corrplan = -1
phrtplan = -1
skipflat = False
im_dark = -1
EFF = -1
filtEFF = -1
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:
corrplan = extract_flatdark(f_in, flat_end, logfilename)
if (isscalar(corrplan['im_flat']) and isscalar(corrplan['im_flat_after']) ):
skipflat = True
# 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()
# Log infos:
log = open(logfilename,"a")
log.write(linesep + "\tWork plan prepared correctly.")
log.write(linesep + "\t--------------")
log.write(linesep + "\tPerforming reconstruction...")
log.close()
# Run several threads for independent computation without waiting for threads completion:
for num in range(nr_threads):
start = ( (int_to - int_from + 1) / nr_threads)*num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ( (int_to - int_from + 1) / nr_threads)*(num + 1) + int_from - 1
if (reconmethod == 'GRIDREC'):
Process(target=process_gridrec, args=(lock, start, end, num_sinos, infile, outpath, preprocessing_required, skipflat,
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, angles, angles_projfrom, angles_projto,
offset, logtrsf, param1, circle, scale, overpad, 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, outprefix,
logfilename )).start()
else:
Process(target=process, args=(lock, start, end, num_sinos, infile, outpath, preprocessing_required, skipflat,
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, angles, angles_projfrom, angles_projto,
offset, logtrsf, param1, 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, outprefix,
logfilename )).start()
def _process(lock, int_from, int_to, infile, outfile, outshape, outtype,
skipflat, plan, flat_end, half_half, half_half_line, dynamic_ff,
EFF, filtEFF, im_dark, rotation, interp, border, rescale_factor,
logfilename):
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image:
t0 = time()
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_tomo(dset, i).astype(float32)
f_in.close()
t1 = time()
# 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, i, plan, flat_end, half_half,
half_half_line, norm_sx, norm_dx)
t2 = time()
# Rotate:
rows, cols = im.shape
#if (cols > rows):
# angle = - arctan(2.0 / cols) * (rows / 2.0) / 2.0
#else:
# angle = - arctan(2.0 / rows) * (cols / 2.0) / 2.0
#print(angle)
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)
# Save processed image to HDF5 file (atomic procedure - lock used):
_write_data(lock, im, i, outfile, outshape, outtype, rescale_factor,
logfilename, t2 - t1, t1 - t0)
def process(lock, int_from, int_to, num_sinos, infile, outpath, preprocessing_required, corr_plan, norm_sx, norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, ringrem, angles, offset, logtransform, param1, circle, scale, pad, method, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset, postprocess_required, convert_opt, crop_opt, outprefix, logfilename): """To do... """ # Process the required subset of images: for i in range(int_from, int_to + 1): # Perform reconstruction (on-the-fly preprocessing and phase retrieval, if required): #if (phaseretrieval_required): # # Load into memory a bunch of sinograms: # t0 = time() # # Open the TDF file for reading: # f_in = getHDF5(infile, 'r') # if "/tomo" in f_in: # dset = f_in['tomo'] # else: # dset = f_in['exchange/data'] # # Prepare the data structure according to the approximation window: # tmp_im = numpy.empty((tdf.get_nr_projs(dset),tdf.get_det_size(dset), approx_win), dtype=float32) # # Load the temporary data structure reading the input TDF file: # # (It can be parallelized Open-MP style) # ct = 0 # for j in range(i - approx_win/2, i + approx_win/2 + 1): # if (j < 0): # j = 0 # if (j >= num_sinos): # j = num_sinos - 1 # a = tdf.read_sino(dset,j).astype(float32) # tmp_im[:,:,ct] = a # ct = ct + 1 # # Close the TDF file: # f_in.close() # t1 = time() # # Perform the processing: # if (preprocessing_required): # ct = 0 # # (It can be parallelized Open-MP style) # for j in range(i - approx_win/2, i + approx_win/2 + 1): # if (j < 0): # j = 0 # if (j >= num_sinos): # j = num_sinos - 1 # tmp_im[:,:,ct] = flat_fielding (tmp_im[:,:,ct], j, corr_plan, flat_end, half_half, half_half_line, norm_sx, norm_dx).astype(float32) # tmp_im[:,:,ct] = extfov_correction (tmp_im[:,:,ct], ext_fov, ext_fov_rot_right, ext_fov_overlap).astype(float32) # tmp_im[:,:,ct] = ring_correction (tmp_im[:,:,ct], ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, ext_fov).astype(float32) # ct = ct + 1 # # Perform phase retrieval: # # (It can be parallelized Open-MP style) # for ct in range(0, tmp_im.shape[0]): # tmp_im[ct,:,:] = phase_retrieval(tmp_im[ct,:,:].T, phrt_plan).astype(float32).T # ct = ct + 1 # # Extract the central processed sinogram: # im = tmp_im[:,:,approx_win/2] #else: # Read only one sinogram: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im = tdf.read_sino(dset,i).astype(float32) f_in.close() t1 = time() # Perform the preprocessing of the sinogram (if required): if (preprocessing_required): im = flat_fielding (im, i, corr_plan, flat_end, half_half, half_half_line, norm_sx, norm_dx).astype(float32) im = extfov_correction (im, ext_fov, ext_fov_rot_right, ext_fov_overlap) im = ring_correction (im, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, ext_fov) # Actual reconstruction: im = reconstruct(im, angles, offset, logtransform, param1, circle, scale, pad, method, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset).astype(float32) # Appy post-processing (if required): if postprocess_required: im = postprocess(im, convert_opt, crop_opt) else: # Create the circle mask for fancy output: if (circle == True): siz = im.shape[1] if siz % 2: rang = arange(-siz / 2 + 1, siz / 2 + 1) else: rang = arange(-siz / 2,siz / 2) x,y = meshgrid(rang,rang) z = x ** 2 + y ** 2 a = (z < (siz / 2 - abs(offset) ) ** 2) im = im * a # Write down reconstructed slice: t2 = time() fname = outpath + outprefix + '_' + str(i).zfill(4) + '.tif' imsave(fname, im) t3 = time() # Write log (atomic procedure - lock used): write_log(lock, fname, logfilename, t2 - t1, (t3 - t2) + (t1 - t0) )
def main(argv):
"""
Converts a TDF file (HDF5 Tomo Data Format) into a sequence of TIFF (uncompressed) files.
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) data, a subset of the volume can
be specified, ranging from the parameter "from" to the parameter "to"
(see next). In most cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) data, a subset of the volume can
be specified, ranging from the parameter "from" (see previous parameter) to
the parameter "to". If the value -1 is specified, all the projection (or sinogram)
data will be considered.
in_file : string
path with filename of the TDF to read from (e.g. "Z:\\sample1.tdf").
out_path : string
path that will contain the sequence of TIFF files (e.g. "Z:\\sample1\\tomo\\"). WARNING:
the program does NOT automatically create non-existing folders and subfolders specified
in the path. Moreover, if files with the same name already exist they will be automatically
overwritten.
file_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the projection (or
sinogram) data. E.g. "tomo" will create files having name "tomo_0001.tif", "tomo_0002.tif".
flat_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the flat (white field)
data. E.g. "flat" will create files having name "flat_1.tif", "flat_2.tif". If dark or flat data have
to be skipped the string "-" can be specified.
dark_prefix : string
string to be assumed as the filename prefix of the TIFF files to create for the dark (dark field)
data. E.g. "dark" will create files having name "dark_1.tif", "dark_2.tif". If dark or flat data have
to be skipped the string "-" can be specified.
projection_order : boolean string
specify the string "True" to create TIFF files for projections (the most common case), "False"
for sinograms.
TIFF_format : boolean string
specify the string "True" to create TIFF files, "False" for RAW files.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the projections data to a sequence of tomo*.tif files:
python tdf2tiff.py 0 -1 "C:\Temp\wet12T4part2.tdf" "C:\Temp\tomo" tomo flat dark True True "C:\Temp\log.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- TIFFFile from C. Gohlke's website http://www.lfd.uci.edu/~gohlke/
(consider also to install TIFFFile.c for performances).
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
# To be used without flat fielding (just conversion):
first_done = False
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
infile = argv[2]
outpath = argv[3]
fileprefix = argv[4]
flatprefix = argv[5]
darkprefix = argv[6]
if (flatprefix == "-") or (darkprefix == "-"):
skipflat = True
else:
skipflat = False
projorder = argv[7]
if projorder == "True":
projorder = True
else:
projorder = False
TIFFFormat = argv[8]
if TIFFFormat == "True":
TIFFFormat = True
else:
TIFFFormat = False
logfilename = argv[9]
# Check prefixes and path:
if not outpath.endswith(os.path.sep): outpath += os.path.sep
# Init variables:
num_files = 0
num_flats = 0
num_darks = 0
# Get the files in infile:
log = open(logfilename,"w")
log.write(os.linesep + "\tInput TDF: %s" % (infile))
if ( TIFFFormat ):
log.write(os.linesep + "\tOutput path where TIFF files will be created: %s" % (outpath))
else:
log.write(os.linesep + "\tOutput path where RAW files will be created: %s" % (outpath))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tFile output prefix: %s" % (fileprefix))
log.write(os.linesep + "\tDark images output prefix: %s" % (flatprefix))
log.write(os.linesep + "\tFlat images output prefix: %s" % (darkprefix))
log.write(os.linesep + "\t--------------")
if (int_to != -1):
log.write(os.linesep + "\tThe subset [%d,%d] of the data will be considered." % (int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
if (skipflat):
log.write(os.linesep + "\tWarning: flat/dark images (if any) will not be considered.")
log.write(os.linesep + "\t--------------")
log.close()
if not os.path.exists(infile):
log = open(logfilename,"a")
log.write(os.linesep + "\tError: input TDF file not found. Process will end.")
log.close()
exit()
# Open the HDF5 file:
f = getHDF5( infile, 'r' )
oldTDF = False;
try:
dset = f['tomo']
oldTDF = True
except Exception:
pass
if not oldTDF:
#try:
dset = f['exchange/data']
#except Exception:
# log = open(logfilename,"a")
# log.write(os.linesep + "\tError: invalid TDF format. Process will end.")
# log.close()
# exit()
if projorder:
num_files = tdf.get_nr_projs(dset)
else:
num_files = tdf.get_nr_sinos(dset)
f.close()
# Get attributes:
try:
f = getHDF5( infile, 'r' )
if ('version' in f.attrs) and (f.attrs['version'] == 'TDF 1.0'):
log = open(logfilename,"a")
log.write(os.linesep + "\tTDF version 1.0 found.")
log.write(os.linesep + "\t--------------")
log.close()
f.close()
except:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: TDF version unknown. Some features will not be available.")
log.write(os.linesep + "\t--------------")
log.close()
# Check extrema (int_to == -1 means all files):
if ( (int_to >= num_files) or (int_to == -1) ):
int_to = num_files - 1
# Get the files in infile:
if not skipflat:
#
# Flat part
#
try:
t0 = time.time()
f = getHDF5( infile, 'r' )
if oldTDF:
dset = f['flat']
else:
dset = f['exchange/data_white']
num_flats = tdf.get_nr_projs(dset)
#if ('version' in f.attrs) and (f.attrs['version'] == "TDF 1.0"):
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
provenance_dset = f['provenance/detector_output']
# Process the required subset of images:
for i in range(0, num_flats):
# Read input image:
t1 = time.time()
im = tdf.read_tomo( dset, i )
if ( TIFFFormat ):
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
if (os.path.splitext(provenance_dset["filename", i])[0] == provenance_dset["filename", i]):
fname = outpath + provenance_dset["filename", i] + '.tif'
else:
fname = outpath + provenance_dset["filename", i]
else:
fname = outpath + flatprefix + '_' + str(i + 1).zfill(4) + '.tif'
else:
fname = outpath + flatprefix + '_' + str(i + 1).zfill(4) + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str(im.dtype) + '.raw'
# Cast type (if required but it should never occur):
if ((im.dtype).type is float64):
im = im.astype(float32, copy=False)
if ( TIFFFormat ):
imsave(fname, im)
else:
im.tofile(fname)
try:
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
t = int(time.mktime(datetime.datetime.strptime(provenance_dset["timestamp", i], "%Y-%m-%d %H:%M:%S.%f").timetuple()))
os.utime(fname, (t,t) )
except:
pass
t2 = time.time()
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s created in %0.3f sec." % (os.path.basename(fname), t2 - t1))
log.close()
f.close()
except Exception:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: no dataset named \"flat\" found.")
log.close()
pass
#
# Dark part
#
try:
t0 = time.time()
f = getHDF5( infile, 'r' )
if oldTDF:
dset = f['dark']
else:
dset = f['exchange/data_dark']
num_darks = tdf.get_nr_projs(dset)
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
provenance_dset = f['provenance/detector_output']
# Process the required subset of images:
for i in range(0, num_darks):
# Read input image:
t1 = time.time()
im = tdf.read_tomo( dset, i )
if ( TIFFFormat ):
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
if (os.path.splitext(provenance_dset["filename", num_flats + i])[0] == provenance_dset["filename", num_flats + i]):
fname = outpath + provenance_dset["filename", num_flats + i] + '.tif'
else:
fname = outpath + provenance_dset["filename", num_flats + i]
else:
fname = outpath + darkprefix + '_' + str(i + 1).zfill(4) + '.tif'
else:
fname = outpath + darkprefix + '_' + str(i + 1).zfill(4) + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str(im.dtype) + '.raw'
# Cast type (if required but it should never occur):
if ((im.dtype).type is float64):
im = im.astype(float32, copy=False)
if ( TIFFFormat ):
imsave(fname, im)
else:
im.tofile(fname)
try:
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
t = int(time.mktime(datetime.datetime.strptime(provenance_dset["timestamp", num_flats + i], "%Y-%m-%d %H:%M:%S.%f").timetuple()))
os.utime(fname, (t,t) )
except:
pass
t2 = time.time()
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s created in %0.3f sec." % (os.path.basename(fname), t2 - t1))
log.close()
f.close()
except Exception:
log = open(logfilename,"a")
log.write(os.linesep + "\tWarning: no dataset named \"dark\" found.")
log.close()
pass
else:
num_flats = 0
num_darks = 0
#
# Tomo part
#
# Read i-th image from input folder:
t0 = time.time()
f = getHDF5( infile, 'r' )
if oldTDF:
dset = f['tomo']
else:
dset = f['exchange/data']
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
provenance_dset = f['provenance/detector_output']
if not skipflat:
offset = num_flats + num_darks
else:
offset = 0
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image:
t1 = time.time()
if projorder:
im = tdf.read_tomo( dset, i )
else:
im = tdf.read_sino( dset, i )
# Cast type (if required but it should never occur):
if ((im.dtype).type is float64):
im = im.astype(float32, copy=False)
# Save file:
if ( TIFFFormat ):
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
if (os.path.splitext(provenance_dset["filename", offset + i])[0] == provenance_dset["filename", offset + i]):
fname = outpath + provenance_dset["filename", offset + i] + '.tif'
else:
fname = outpath + provenance_dset["filename", offset + i]
else:
fname = outpath + fileprefix + '_' + str(i + 1).zfill(4) + '.tif'
imsave(fname, im)
else:
fname = outpath + fileprefix + '_' + str(i + 1).zfill(4) + '_' + str(im.shape[1]) + 'x' + str(im.shape[0]) + '_' + str(im.dtype) + '.raw'
im.tofile(fname)
# Change modified date:
try:
if ('version' in f.attrs):
if (f.attrs['version'] == '1.0'):
t = int(time.mktime(datetime.datetime.strptime(provenance_dset["timestamp", offset + i], "%Y-%m-%d %H:%M:%S.%f").timetuple()))
os.utime(fname, (t,t) )
except:
pass
t2 = time.time()
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s created in %0.3f sec." % (os.path.basename(fname), t2 - t1))
log.close()
f.close()
def process(sino_idx, num_sinos, infile, outpath, preprocessing_required,
corr_plan, 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,
logtransform, param1, circle, scale, pad, method, 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):
"""To do...
"""
slice_nr = sino_idx
# Perform reconstruction (on-the-fly preprocessing and phase retrieval, if required):
if (phaseretrieval_required):
# In this case a bunch of sinograms is loaded into memory:
#
# Load the temporary data structure reading the input TDF file.
# To know the right dimension the first sinogram is pre-processed.
#
# Open the TDF file and get the dataset:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
# Downscaling and decimation factors considered when determining the approximation window:
zrange = arange(sino_idx - approx_win * downsc_factor / 2,
sino_idx + approx_win * downsc_factor / 2,
downsc_factor)
zrange = zrange[(zrange >= 0)]
zrange = zrange[(zrange < num_sinos)]
approx_win = zrange.shape[0]
# Approximation window cannot be odd:
if (approx_win % 2 == 1):
approx_win = approx_win - 1
zrange = zrange[0:approx_win]
# Read one sinogram to get the proper dimensions:
test_im = tdf.read_sino(dset,
zrange[0] * downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing of the first sinogram to get the right dimension:
if (preprocessing_required):
test_im = flat_fielding(test_im, zrange[0], corr_plan, flat_end,
half_half, half_half_line / decim_factor,
norm_sx, norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(test_im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize,
ext_fov_average).astype(float32)
test_im = ring_correction(test_im, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
# Now we can allocate memory for the bunch of slices:
tmp_im = empty((approx_win, test_im.shape[0], test_im.shape[1]),
dtype=float32)
tmp_im[0, :, :] = test_im
# Reading all the the sinos from TDF file and close:
for ct in range(1, approx_win):
test_im = tdf.read_sino(dset,
zrange[ct] * downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing for each sinogram of the bunch:
if (preprocessing_required):
test_im = flat_fielding(test_im, zrange[ct], corr_plan,
flat_end, half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(
test_im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor, ext_fov_normalize,
ext_fov_average).astype(float32)
test_im = ring_correction(test_im, ringrem, flat_end,
corr_plan['skip_flat_after'],
half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
tmp_im[ct, :, :] = test_im
f_in.close()
# Now everything has to refer to a downscaled dataset:
sino_idx = ((zrange == sino_idx).nonzero())
#
# Perform phase retrieval:
#
# Prepare the plan:
if (phrtmethod == 0):
# Paganin's:
phrtplan = tiehom_plan(tmp_im[:, 0, :],
phrt_param1,
phrt_param2,
energy,
distance,
pixsize * downsc_factor,
padding=phrtpad)
else:
phrtplan = phrt_plan(tmp_im[:, 0, :],
energy,
distance,
pixsize * downsc_factor,
phrt_param2,
phrt_param1,
phrtmethod,
padding=phrtpad)
# Process each projection (whose height depends on the size of the bunch):
for ct in range(0, tmp_im.shape[1]):
if (phrtmethod == 0):
tmp_im[:, ct, :] = tiehom(tmp_im[:, ct, :],
phrtplan).astype(float32)
else:
tmp_im[:, ct, :] = phrt(tmp_im[:, ct, :], phrtplan,
phrtmethod).astype(float32)
# Extract the requested sinogram:
im = tmp_im[sino_idx[0], :, :].squeeze()
else:
# Read only one sinogram:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset, sino_idx * downsc_factor).astype(float32)
f_in.close()
# Apply projection removal (if required):
im = im[angles_projfrom:angles_projto, :]
# Downscale and decimate the sinogram:
im = im[::decim_factor, ::downsc_factor]
#sino_idx = sino_idx/downsc_factor
# Perform the preprocessing of the sinogram (if required):
if (preprocessing_required):
im = flat_fielding(im, sino_idx, corr_plan, flat_end, half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
im = extfov_correction(im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize, ext_fov_average)
im = ring_correction(im, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
# Log infos:
log = open(logfilename, "a")
log.write(
linesep +
"\tPerforming reconstruction with multiple centers of rotation...")
log.write(linesep + "\t--------------")
log.close()
# Split the computation into multiple processes:
for num in range(nr_threads):
start = ((off_to - off_from + 1) / nr_threads) * num + off_from
if (num == nr_threads - 1):
end = off_to
else:
end = ((off_to - off_from + 1) / nr_threads) * (num +
1) + off_from - 1
Process(target=reconstruct,
args=(im, angles, angles_projfrom, angles_projto,
offset / downsc_factor, logtransform, param1, circle,
scale, pad, method, zerone_mode, dset_min, dset_max,
corr_offset, postprocess_required, convert_opt, crop_opt,
start, end, outpath, slice_nr, downsc_factor,
logfilename, lock, slice_prefix)).start()
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 process(sino_idx, num_sinos, infile, outfile, preprocessing_required,
corr_plan, 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, logtransform, recpar,
circle, scale, pad, method, 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):
"""To do...
"""
# Perform reconstruction (on-the-fly preprocessing and phase retrieval, if
# required):
if (phaseretrieval_required):
# In this case a bunch of sinograms is loaded into memory:
#
# Load the temporary data structure reading the input TDF file.
# To know the right dimension the first sinogram is pre-processed.
#
# Open the TDF file and get the dataset:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
# Downscaling and decimation factors considered when determining the
# approximation window:
zrange = arange(sino_idx - approx_win * downsc_factor / 2,
sino_idx + approx_win * downsc_factor / 2,
downsc_factor)
zrange = zrange[(zrange >= 0)]
zrange = zrange[(zrange < num_sinos)]
approx_win = zrange.shape[0]
# Approximation window cannot be odd:
if (approx_win % 2 == 1):
approx_win = approx_win - 1
zrange = zrange[0:approx_win]
# Read one sinogram to get the proper dimensions:
test_im = tdf.read_sino(dset,
zrange[0] * downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing of the first sinogram to get the right
# dimension:
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
#test_im = dynamic_flat_fielding(test_im, zrange[0] / downsc_factor, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
test_im = dynamic_flat_fielding(test_im, zrange[0], EFF,
filtEFF, 2, im_dark,
norm_sx, norm_dx)
else:
#test_im = flat_fielding(test_im, zrange[0] / downsc_factor, corr_plan, flat_end, half_half,
# half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
test_im = flat_fielding(test_im, zrange[0], corr_plan,
flat_end, half_half,
half_half_line / decim_factor,
norm_sx, norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(test_im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize,
ext_fov_average).astype(float32)
if not skipflat and not dynamic_ff:
test_im = ring_correction(test_im, ringrem, flat_end,
corr_plan['skip_flat_after'],
half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
else:
test_im = ring_correction(test_im, ringrem, False, False,
half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
# Now we can allocate memory for the bunch of slices:
tmp_im = empty((approx_win, test_im.shape[0], test_im.shape[1]),
dtype=float32)
tmp_im[0, :, :] = test_im
# Reading all the the sinos from TDF file and close:
for ct in range(1, approx_win):
# Read the sinogram:
test_im = tdf.read_sino(dset,
zrange[ct] * downsc_factor).astype(float32)
# Apply projection removal (if required):
test_im = test_im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
test_im = test_im[::decim_factor, ::downsc_factor]
# Perform the pre-processing for each sinogram of the bunch:
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
#test_im = dynamic_flat_fielding(test_im, zrange[ct] / downsc_factor, EFF, filtEFF, 2, im_dark, norm_sx, norm_dx)
test_im = dynamic_flat_fielding(
test_im, zrange[ct], EFF, filtEFF, 2, im_dark,
norm_sx, norm_dx)
else:
#test_im = flat_fielding(test_im, zrange[ct] / downsc_factor, corr_plan, flat_end, half_half,
# half_half_line / decim_factor, norm_sx, norm_dx).astype(float32)
test_im = flat_fielding(test_im, zrange[ct], corr_plan,
flat_end, half_half,
half_half_line / decim_factor,
norm_sx,
norm_dx).astype(float32)
if ext_fov:
test_im = extfov_correction(
test_im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor, ext_fov_normalize,
ext_fov_average).astype(float32)
if not skipflat and not dynamic_ff:
test_im = ring_correction(test_im, ringrem, flat_end,
corr_plan['skip_flat_after'],
half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
else:
test_im = ring_correction(test_im, ringrem, False, False,
half_half,
half_half_line / decim_factor,
ext_fov).astype(float32)
tmp_im[ct, :, :] = test_im
f_in.close()
# Now everything has to refer to a downscaled dataset:
sino_idx = ((zrange == sino_idx).nonzero())
#
# Perform phase retrieval:
#
# Prepare the plan:
if (phrtmethod == 0):
# Paganin 2002:
phrtplan = tiehom_plan(tmp_im[:, 0, :], phrt_param1, phrt_param2,
energy, distance, pixsize * downsc_factor,
phrtpad)
elif (phrtmethod == 1):
# Paganin 2020:
phrtplan = tiehom_plan2020(tmp_im[:, 0, :], phrt_param1,
phrt_param2, energy, distance,
pixsize * downsc_factor, phrtpad)
else:
phrtplan = phrt_plan(tmp_im[:, 0, :], energy, distance,
pixsize * downsc_factor, phrt_param2,
phrt_param1, phrtmethod, phrtpad)
#phrtplan = prepare_plan (tmp_im[:,0,:], beta, delta, energy, distance,
#pixsize*downsc_factor, padding=phrtpad)
# Process each projection (whose height depends on the size of the bunch):
for ct in range(0, tmp_im.shape[1]):
#tmp_im[:,ct,:] = phase_retrieval(tmp_im[:,ct,:], phrtplan).astype(float32)
if (phrtmethod == 0):
tmp_im[:, ct, :] = tiehom(tmp_im[:, ct, :],
phrtplan).astype(float32)
elif (phrtmethod == 1):
tmp_im[:, ct, :] = tiehom2020(tmp_im[:, ct, :],
phrtplan).astype(float32)
else:
tmp_im[:, ct, :] = phrt(tmp_im[:, ct, :], phrtplan,
phrtmethod).astype(float32)
# Extract the requested sinogram:
im = tmp_im[sino_idx[0], :, :].squeeze()
else:
# Read only one sinogram:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
else:
dset = f_in['exchange/data']
im = tdf.read_sino(dset, sino_idx * downsc_factor).astype(float32)
f_in.close()
# Apply projection removal (if required):
im = im[angles_projfrom:angles_projto, :]
# Apply decimation and downscaling (if required):
im = im[::decim_factor, ::downsc_factor]
#sino_idx = sino_idx / downsc_factor # Downscaling for the index already applied
# Perform the preprocessing of the sinogram (if required):
if (preprocessing_required):
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im = dynamic_flat_fielding(im, sino_idx, EFF, filtEFF, 2,
im_dark, norm_sx, norm_dx)
else:
im = flat_fielding(im, sino_idx, corr_plan, flat_end,
half_half,
half_half_line / decim_factor, norm_sx,
norm_dx).astype(float32)
if ext_fov:
im = extfov_correction(im, ext_fov_rot_right,
ext_fov_overlap / downsc_factor,
ext_fov_normalize, ext_fov_average)
if not skipflat and not dynamic_ff:
im = ring_correction(im, ringrem, flat_end,
corr_plan['skip_flat_after'], half_half,
half_half_line / decim_factor, ext_fov)
else:
im = ring_correction(im, ringrem, False, False, half_half,
half_half_line / decim_factor, ext_fov)
# Additional ring removal before reconstruction:
#im = boinhaibel(im, '11;')
#im = munchetal(im, '5;1.8')
#im = rivers(im, '13;')
#im = raven(im, '11;0.8')
#im = oimoen(im, '51;51')
# Actual reconstruction:
im = reconstruct(im, angles, offset / downsc_factor, logtransform, recpar,
circle, scale, pad, method, zerone_mode, dset_min,
dset_max, corr_offset, rolling, roll_shift,
tmppath).astype(float32)
# Apply post-processing (if required):
if postprocess_required:
im = polarfilter(im, polarfilt_opt)
im = croprescale(im, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im.shape[1]
if siz % 2:
rang = arange(-siz / 2 + 1, siz / 2 + 1)
else:
rang = arange(-siz / 2, siz / 2)
x, y = meshgrid(rang, rang)
z = x**2 + y**2
a = (z < (siz / 2 - int(round(abs(offset) / downsc_factor)))**2)
im = im * a
# 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(amin(im)) + '$' + str(amax(im))
im.tofile(outfile)
def process_gridrec(lock, int_from, int_to, num_sinos, infile, outpath, preprocessing_required, corr_plan, norm_sx, norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, ringrem, angles, offset, logtransform, param1, circle, scale, pad, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset, postprocess_required, convert_opt, crop_opt, outprefix, logfilename): """To do... """ # Process the required subset of images: for i in range(int_from, int_to + 1, 2): # Read two sinograms: t0 = time() f_in = getHDF5(infile, 'r') if "/tomo" in f_in: dset = f_in['tomo'] else: dset = f_in['exchange/data'] im1 = tdf.read_sino(dset,i).astype(float32) if ( (i + 1) <= (int_to + 1) ): im2 = tdf.read_sino(dset,i + 1).astype(float32) else: im2 = im1 f_in.close() t1 = time() # Perform the preprocessing of the sinograms (if required): if (preprocessing_required): im1 = flat_fielding (im1, i, corr_plan, flat_end, half_half, half_half_line, norm_sx, norm_dx).astype(float32) im1 = extfov_correction (im1, ext_fov, ext_fov_rot_right, ext_fov_overlap) im1 = ring_correction (im1, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, ext_fov) im2 = flat_fielding (im2, i + 1, corr_plan, flat_end, half_half, half_half_line, norm_sx, norm_dx).astype(float32) im2 = extfov_correction (im2, ext_fov, ext_fov_rot_right, ext_fov_overlap) im2 = ring_correction (im2, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, ext_fov) # Actual reconstruction: [im1, im2] = reconstruct_gridrec(im1, im2, angles, offset, logtransform, param1, circle, scale, pad, zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset) # Appy post-processing (if required): if postprocess_required: im1 = postprocess(im1, convert_opt, crop_opt, circle) im2 = postprocess(im2, convert_opt, crop_opt, circle) else: # Create the circle mask for fancy output: if (circle == True): siz = im1.shape[1] if siz % 2: rang = arange(-siz / 2 + 1, siz / 2 + 1) else: rang = arange(-siz / 2,siz / 2) x,y = meshgrid(rang,rang) z = x ** 2 + y ** 2 a = (z < (siz / 2 - int(round(abs(offset)/downsc_factor)) ) ** 2) im1 = im1 * a im2 = im2 * a # Write down reconstructed slices: t2 = time() fname1 = outpath + outprefix + '_' + str(i).zfill(4) + '.tif' imsave(fname1, im1) fname2 = outpath + outprefix + '_' + str(i + 1).zfill(4) + '.tif' imsave(fname2, im2) t3 = time() # Write log (atomic procedure - lock used): write_log_gridrec(lock, fname1, fname2, logfilename, t2 - t1, (t3 - t2) + (t1 - t0) )
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
"""
lock = Lock()
skip_flat = False
skip_flat_after = True
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get paths:
infile = argv[2]
outpath = argv[3]
# Essential reconstruction parameters::
angles = float(argv[4])
offset = float(argv[5])
param1 = argv[6]
scale = int(float(argv[7]))
overpad = True if argv[8] == "True" else False
logtrsf = True if argv[9] == "True" else False
circle = True if argv[10] == "True" else False
outprefix = argv[11]
# Parameters for on-the-fly pre-processing:
preprocessing_required = True if argv[12] == "True" else False
flat_end = True if argv[13] == "True" else False
half_half = True if argv[14] == "True" else False
half_half_line = int(argv[15])
ext_fov = True if argv[16] == "True" else False
norm_sx = int(argv[19])
norm_dx = int(argv[20])
ext_fov_rot_right = argv[17]
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[18])
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]
nr_threads = int(argv[31])
logfilename = argv[32]
process_id = int(logfilename[-6:-4])
# Check prefixes and path:
#if not infile.endswith(sep): infile += sep
if not exists(outpath):
makedirs(outpath)
if not outpath.endswith(sep): outpath += sep
# Open the HDF5 file:
f_in = getHDF5(infile, 'r')
if "/tomo" in f_in:
dset = f_in['tomo']
tomoprefix = 'tomo'
flatprefix = 'flat'
darkprefix = 'dark'
else:
dset = f_in['exchange/data']
if "/provenance/detector_output" in f_in:
prov_dset = f_in['provenance/detector_output']
tomoprefix = prov_dset.attrs['tomo_prefix']
flatprefix = prov_dset.attrs['flat_prefix']
darkprefix = prov_dset.attrs['dark_prefix']
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):
log = open(logfilename,"a")
log.write(linesep + "\tNo projections found. Process will end.")
log.close()
exit()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_sinos) or (int_to == -1)):
int_to = num_sinos - 1
# Log info:
log = open(logfilename,"w")
log.write(linesep + "\tInput file: %s" % (infile))
log.write(linesep + "\tOutput path: %s" % (outpath))
log.write(linesep + "\t--------------")
log.write(linesep + "\tPreparing the working plan...")
log.close()
# Get correction plan and phase retrieval plan (if required):
corrplan = -1
phrtplan = -1
if (preprocessing_required):
corrplan = extract_flatdark(f_in, flat_end, logfilename)
f_in.close()
# Log infos:
log = open(logfilename,"a")
log.write(linesep + "\tWorking plan prepared correctly.")
log.write(linesep + "\t-------")
log.write(linesep + "\tPerforming reconstruction...")
log.close()
# Run several threads for independent computation without waiting for threads completion:
for num in range(nr_threads):
start = ( (int_to - int_from + 1) / nr_threads)*num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ( (int_to - int_from + 1) / nr_threads)*(num + 1) + int_from - 1
if (reconmethod == 'GRIDREC'):
Process(target=process_gridrec, args=(lock, start, end, 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, ringrem,
angles, offset, logtrsf, param1, circle, scale, overpad,
zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset,
postprocess_required, convert_opt, crop_opt, outprefix, logfilename )).start()
else:
Process(target=process, args=(lock, start, end, 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, ringrem,
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, outprefix, logfilename )).start()
def main(argv):
"""
Converts a sequence of TIFF files into a TDF file (HDF5 Tomo Data Format).
Parameters
----------
from : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" to the parameter "to" (see next). In most
cases, this parameter is 0.
to : scalar, integer
among all the projections (or sinogram) files, a subset of files can be specified,
ranging from the parameter "from" (see previous parameter) to the parameter
"to". If the value -1 is specified, all the projection files will be considered.
in_path : string
path containing the sequence of TIFF files to consider (e.g. "Z:\\sample1\\tomo\\").
out_file : string
path with filename of the TDF to create (e.g. "Z:\\sample1.tdf"). WARNING: the program
does NOT automatically create non-existing folders and subfolders specified in the path.
Moreover, if a file with the same name already exists it will be automatically deleted and
overwritten.
crop_top : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the top of the image. Leave 0 for no cropping.
crop_bottom : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the bottom of the image. Leave 0 for no cropping.
crop_left : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the left of the image. Leave 0 for no cropping.
crop_right : scalar, integer
during the conversion, images can be cropped if required. This parameter specifies the number
of pixels to crop from the right of the image. Leave 0 for no cropping.
file_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the projection (or
sinogram) files. E.g. "tomo" will consider files having name "tomo_0001.tif", "tomo_0002.tif".
flat_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the flat (white field)
files. E.g. "flat" will consider files having name "flat_1.tif", "flat_2.tif". If dark or flat files have
to be skipped the string "-" can be specified.
dark_prefix : string
string to be assumed as the filename prefix of the TIFF files to consider for the dark (dark field)
files. E.g. "dark" will consider files having name "dark_1.tif", "dark_2.tif". If dark or flat files have
to be skipped the string "-" can be specified.
projection_order : boolean string
specify the string "True" if the TIFF files represent projections (the most common case), "False"
for sinograms.
privilege_sino : boolean string
specify the string "True" if the TDF will privilege a fast read/write of sinograms (the most common
case), "False" for fast read/write of projections.
compression : scalar, integer
an integer value in the range of [1,9] to be used as GZIP compression factor in the HDF5 file, where
1 is the minimum compression (and maximum speed) and 9 is the maximum (and slow) compression.
The value 0 can be specified with the meaning of no compression.
nr_threads : int
number of multiple threads (actually processes) to consider to speed up the whole conversion process.
log_file : string
path with filename of a log file (e.g. "R:\\log.txt") where info about the conversion is reported.
Returns
-------
no return value
Example
-------
Example call to convert all the tomo*.tif* projections to a TDF with no cropping and minimum compression:
python tiff2tdf.py 0 -1 "Z:\\rawdata\\c_1\\tomo\\" "Z:\\work\\c1_compr9.tdf" 0 0 0 0 tomo flat
dark True True 1 "S:\\conversion.txt"
Requirements
-------
- Python 2.7 with the latest NumPy, SciPy, H5Py.
- TIFFFile from C. Gohlke's website http://www.lfd.uci.edu/~gohlke/
(consider also to install TIFFFile.c for performances).
- tdf.py
Tests
-------
Tested with WinPython-64bit-2.7.6.3 (Windows) and Anaconda 2.1.0 (Linux 64-bit).
"""
lock = Lock()
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1]) # -1 means "all files"
# Get paths:
inpath = argv[2]
outfile = argv[3]
crop_top = int(argv[4]) # 0 for all means "no cropping"
crop_bottom = int(argv[5])
crop_left = int(argv[6])
crop_right = int(argv[7])
tomoprefix = argv[8]
flatprefix = argv[9] # - means "do not consider flat or darks"
darkprefix = argv[10] # - means "do not consider flat or darks"
if (flatprefix == "-") or (darkprefix == "-"):
skipflat = True
else:
skipflat = False
projorder = True if argv[11] == "True" else False
privilege_sino = True if argv[12] == "True" else False
# Get compression factor:
compr_opts = int(argv[13])
compressionFlag = True
if (compr_opts <= 0):
compressionFlag = False
elif (compr_opts > 9):
compr_opts = 9
nr_threads = int(argv[14])
logfilename = argv[15]
# Check prefixes and path:
if not inpath.endswith(os.path.sep): inpath += os.path.sep
# Get the files in inpath:
log = open(logfilename, "w")
log.write(os.linesep + "\tInput path: %s" % (inpath))
log.write(os.linesep + "\tOutput TDF file: %s" % (outfile))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tProjection file prefix: %s" % (tomoprefix))
log.write(os.linesep + "\tDark file prefix: %s" % (darkprefix))
log.write(os.linesep + "\tFlat file prefix: %s" % (flatprefix))
log.write(os.linesep + "\t--------------")
log.write(os.linesep + "\tCropping:")
log.write(os.linesep + "\t\tTop: %d pixels" % (crop_top))
log.write(os.linesep + "\t\tBottom: %d pixels" % (crop_bottom))
log.write(os.linesep + "\t\tLeft: %d pixels" % (crop_left))
log.write(os.linesep + "\t\tRight: %d pixels" % (crop_right))
if (int_to != -1):
log.write(
os.linesep +
"\tThe subset [%d,%d] of the input files will be considered." %
(int_from, int_to))
if (projorder):
log.write(os.linesep + "\tProjection order assumed.")
else:
log.write(os.linesep + "\tSinogram order assumed.")
if (privilege_sino):
log.write(os.linesep + "\tFast I/O for sinograms privileged.")
else:
log.write(os.linesep + "\tFast I/O for projections privileged.")
if (compressionFlag):
log.write(os.linesep + "\tTDF compression factor: %d" % (compr_opts))
else:
log.write(os.linesep + "\tTDF compression: none.")
if (skipflat):
log.write(
os.linesep +
"\tWarning: flat/dark images (if any) will not be considered.")
log.write(os.linesep + "\t--------------")
log.close()
# Remove a previous copy of output:
if os.path.exists(outfile):
log = open(logfilename, "a")
log.write(
os.linesep +
"\tWarning: an output file with the same name was overwritten.")
os.remove(outfile)
log.close()
log = open(logfilename, "a")
log.write(os.linesep + "\tBrowsing input files...")
log.close()
# Pythonic way to get file list:
if os.path.exists(inpath):
tomo_files = sorted(glob(inpath + tomoprefix + '*.tif*'))
num_files = len(tomo_files)
else:
log = open(logfilename, "a")
log.write(os.linesep +
"\tError: input path does not exist. Process will end.")
log.close()
exit()
if (num_files == 0):
log = open(logfilename, "a")
log.write(
os.linesep +
"\tError: no projection files found. Check input path and file prefixes."
)
log.close()
exit()
log = open(logfilename, "a")
log.write(os.linesep + "\tInput files browsed correctly.")
log.close()
# Check extrema (int_to == -1 means all files):
if ((int_to >= num_files) or (int_to == -1)):
int_from = 0
int_to = num_files - 1
# In case of subset specified:
num_files = int_to - int_from + 1
# Prepare output HDF5 output (should this be atomic?):
im = imread(tomo_files[0])
# Crop:
im = im[crop_top:im.shape[0] - crop_bottom,
crop_left:im.shape[1] - crop_right]
log = open(logfilename, "a")
log.write(os.linesep + "\tPreparing the work plan...")
log.close()
#dsetshape = (num_files,) + im.shape
if projorder:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0],
#num_files)
datashape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_files)
else:
#dsetshape = tdf.get_dset_shape(privilege_sino, im.shape[1], num_files,
#im.shape[0])
datashape = tdf.get_dset_shape(im.shape[1], num_files, im.shape[0])
if not os.path.isfile(outfile):
f = getHDF5(outfile, 'w')
f.attrs['version'] = '1.0'
f.attrs['implements'] = "exchange:provenance"
echange_group = f.create_group('exchange')
if (compressionFlag):
dset = f.create_dataset('exchange/data',
datashape,
im.dtype,
chunks=tdf.get_dset_chunks(im.shape[1]),
compression="gzip",
compression_opts=compr_opts,
shuffle=True,
fletcher32=True)
else:
dset = f.create_dataset('exchange/data', datashape, im.dtype)
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
dset.attrs['min'] = str(numpy.amin(tmp[:]))
dset.attrs['max'] = str(numpy.amax(tmp[:]))
dset.attrs['avg'] = str(0.0)
# Get the total number of files to consider:
tot_files = num_files
if not skipflat:
num_flats = len(sorted(glob(inpath + flatprefix + '*.tif*')))
num_darks = len(sorted(glob(inpath + darkprefix + '*.tif*')))
tot_files = tot_files + num_flats + num_darks
# Create provenance dataset:
provenance_dt = numpy.dtype([("filename", numpy.dtype("S255")),
("timestamp", numpy.dtype("S255"))])
metadata_group = f.create_group('provenance')
provenance_dset = metadata_group.create_dataset('detector_output',
(tot_files, ),
dtype=provenance_dt)
provenance_dset.attrs['tomo_prefix'] = tomoprefix
provenance_dset.attrs['dark_prefix'] = darkprefix
provenance_dset.attrs['flat_prefix'] = flatprefix
provenance_dset.attrs['first_index'] = int(tomo_files[0][-8:-4])
# Handle the metadata:
if (os.path.isfile(inpath + 'logfile.xml')):
with open(inpath + 'logfile.xml', "r") as file:
xml_command = file.read()
tdf.parse_metadata(f, xml_command)
f.close()
# Print out about plan preparation:
log = open(logfilename, "a")
log.write(os.linesep + "\tWork plan prepared succesfully.")
log.close()
# Get the files in inpath:
if not skipflat:
#
# Flat part
#
flat_files = sorted(glob(inpath + flatprefix + '*.tif*'))
num_flats = len(flat_files)
if (num_flats > 0):
# Create acquisition group:
im = imread(flat_files[0])
im = im[crop_top:im.shape[0] - crop_bottom,
crop_left:im.shape[1] - crop_right]
#flatshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0],
#num_flats)
flatshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_flats)
f = getHDF5(outfile, 'a')
if (compressionFlag):
dset = f.create_dataset('exchange/data_white',
flatshape,
im.dtype,
chunks=tdf.get_dset_chunks(
im.shape[1]),
compression="gzip",
compression_opts=compr_opts,
shuffle=True,
fletcher32=True)
else:
dset = f.create_dataset('exchange/data_white', flatshape,
im.dtype)
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
dset.attrs['min'] = str(numpy.amin(tmp[:]))
dset.attrs['max'] = str(numpy.amax(tmp[:]))
dset.attrs['avg'] = str(0.0)
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
f.close()
#process(lock, 0, num_flats - 1, 0, flat_files, True, outfile,
#'exchange/data_white', dsetshape, im.dtype,
# crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt,
# logfilename )
else:
log = open(logfilename, "a")
log.write(os.linesep + "\tWarning: flat files not found.")
log.close()
#
# Dark part
#
dark_files = sorted(glob(inpath + darkprefix + '*.tif*'))
num_darks = len(dark_files)
if (num_darks > 0):
im = imread(dark_files[0])
im = im[crop_top:im.shape[0] - crop_bottom,
crop_left:im.shape[1] - crop_right]
#darkshape = tdf.get_dset_shape(privilege_sino, im.shape[1], im.shape[0],
#num_flats)
darkshape = tdf.get_dset_shape(im.shape[1], im.shape[0], num_darks)
f = getHDF5(outfile, 'a')
if (compressionFlag):
dset = f.create_dataset('exchange/data_dark',
darkshape,
im.dtype,
chunks=tdf.get_dset_chunks(
im.shape[1]),
compression="gzip",
compression_opts=compr_opts,
shuffle=True,
fletcher32=True)
else:
dset = f.create_dataset('exchange/data_dark', darkshape,
im.dtype)
tmp = im[:].astype(numpy.float32)
tmp = tmp[numpy.nonzero(numpy.isfinite(tmp))]
dset.attrs['min'] = str(numpy.amin(tmp))
dset.attrs['max'] = str(numpy.amax(tmp))
dset.attrs['avg'] = str(0.0)
if privilege_sino:
dset.attrs['axes'] = "y:theta:x"
else:
dset.attrs['axes'] = "theta:y:x"
f.close()
#process(lock, 0, num_darks - 1, num_flats, dark_files, True, outfile,
#'exchange/data_dark', dsetshape, im.dtype,
# crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt,
# logfilename )
else:
log = open(logfilename, "a")
log.write(os.linesep + "\tWarning: dark files not found.")
log.close()
# Process the required subset of images:
if not skipflat:
flatdark_offset = num_flats + num_darks
else:
flatdark_offset = 0
# Spawn the process for the conversion of flat images:
if (num_flats > 0):
Process(target=_process,
args=(lock, 0, num_flats - 1, 0, 0, flat_files, True, outfile,
'exchange/data_white', flatshape, im.dtype, crop_top,
crop_bottom, crop_left, crop_right, tot_files,
provenance_dt, logfilename)).start()
# Spawn the process for the conversion of dark images:
if (num_darks > 0):
Process(target=_process,
args=(lock, 0, num_darks - 1, num_flats, 0, dark_files, True,
outfile, 'exchange/data_dark', darkshape, im.dtype,
crop_top, crop_bottom, crop_left, crop_right, tot_files,
provenance_dt, logfilename)).start()
# Start the process for the conversion of the projections (or sinograms) in a multi-threaded way:
for num in range(nr_threads):
start = ((int_to - int_from + 1) / nr_threads) * num + int_from
if (num == nr_threads - 1):
end = int_to
else:
end = ((int_to - int_from + 1) / nr_threads) * (num +
1) + int_from - 1
Process(target=_process,
args=(lock, start, end, flatdark_offset, int_from, tomo_files,
projorder, outfile, 'exchange/data', datashape, im.dtype,
crop_top, crop_bottom, crop_left, crop_right, tot_files,
provenance_dt, logfilename)).start()
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
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()
