STP Core¶
STP Core provides .....
Here is how to add a link to your documentation Docs and here is how to add a reference [A1]
Features¶
- Example of how to write documentation
Contribute¶
- Documentation: https://github.com/decarlof/pore3d/tree/master/doc
- Issue Tracker: https://github.com/decarlof/pore3d/docs/issues
- Source Code: https://github.com/decarlof/pore3d/
Content¶
Install¶
This section covers the basics of how to download and install STP Core
Contents:
Installing from source¶
Clone the STP Core from GitHub repository:
git clone https://github.com/ElettraSciComp/STP-Core.git STP-Core
then:
cd STP-Core
python setup.py install
API reference¶
project Modules:
io.tdf¶
Functions:
parse_metadata(f, xml_command) |
Fill the specified HDF5 file with metadata according to the DataExchange initiative. |
read_tomo(dataset, index) |
Extract the tomographic projection at the specified relative index from the HDF5 dataset. |
parse_metadata(f, xml_command) |
Fill the specified HDF5 file with metadata according to the DataExchange initiative. |
read_sino(dataset, index) |
Extract the sinogram at the specified relative index from the HDF5 dataset. |
write_tomo(dataset, index, im) |
Modify the tomographic projection at the specified relative index from the HDF5 dataset with the image passed as input. |
write_sino(dataset, index, im) |
Modify the sinogram at the specified relative index from the HDF5 dataset with the image passed as input. |
get_nr_projs(dataset) |
Get the number of projections of the input dataset. |
get_nr_sinos(dataset) |
Get the number of sinograms (or slices) of the input dataset. |
get_det_size(dataset) |
Get the width of the detector (nr of pixels) of the input dataset. |
get_dset_shape(det_size, fov_height, nr_proj) |
Get the shape of the dataset by arranging the input parameters. |
get_dset_chunks(det_size) |
Get a good chunk combination. |
-
stp_core.io.tdf.get_det_size(dataset)[source]¶ Get the width of the detector (nr of pixels) of the input dataset.
Parameters: dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
-
stp_core.io.tdf.get_dset_chunks(det_size)[source]¶ Get a good chunk combination. This function needs improvement...
Parameters: det_size (int) – Width of the detector.
-
stp_core.io.tdf.get_dset_shape(det_size, fov_height, nr_proj)[source]¶ Get the shape of the dataset by arranging the input parameters.
Parameters: - det_size (int) – Width of the detector.
- fov_height (int) – Height of the FOV, i.e. the number of sinograms (or slices) of the dataset.
- nr_proj (int) – Number of collected projections.
-
stp_core.io.tdf.get_nr_projs(dataset)[source]¶ Get the number of projections of the input dataset.
Parameters: dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
-
stp_core.io.tdf.get_nr_sinos(dataset)[source]¶ Get the number of sinograms (or slices) of the input dataset.
Parameters: dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
-
stp_core.io.tdf.parse_metadata(f, xml_command)[source]¶ Fill the specified HDF5 file with metadata according to the DataExchange initiative. The metadata in input are described in a XML format.
Parameters: - f (HDF5 file) – HDF5 file open with h5py API
- xml_command (string) – Immaginary part of the complex X-ray refraction index.
-
stp_core.io.tdf.read_sino(dataset, index)[source]¶ Extract the sinogram at the specified relative index from the HDF5 dataset.
Parameters: - dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
- index (int) – Relative position of the sinogram within the dataset.
-
stp_core.io.tdf.read_tomo(dataset, index)[source]¶ Extract the tomographic projection at the specified relative index from the HDF5 dataset.
Parameters: - dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
- index (int) – Relative position of the tomographic projection within the dataset.
-
stp_core.io.tdf.write_sino(dataset, index, im)[source]¶ Modify the sinogram at the specified relative index from the HDF5 dataset with the image passed as input.
Parameters: - dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
- index (int) – Relative position of the sinogram within the dataset.
- im (array_like) – Image data as numpy array.
-
stp_core.io.tdf.write_tomo(dataset, index, im)[source]¶ Modify the tomographic projection at the specified relative index from the HDF5 dataset with the image passed as input.
Parameters: - dataset (HDF5 dataset) – HDF5 dataset as returned by the h5py API.
- index (int) – Relative position of the tomographic projection within the dataset.
- im (array_like) – Image data as numpy array.
phaseretrieval.tiehom¶
Functions:
tiehom_plan(im, beta, delta, energy, ...) |
Pre-compute data to save time in further execution of phase_retrieval with TIE-HOM (Paganin’s) algorithm. |
tiehom(im, plan[, nr_threads]) |
Process a tomographic projection image with the TIE-HOM (Paganin’s) phase retrieval algorithm. |
-
stp_core.phaseretrieval.tiehom.tiehom(im, plan, nr_threads=2)[source]¶ Process a tomographic projection image with the TIE-HOM (Paganin’s) phase retrieval algorithm.
Parameters: - im (array_like) – Flat corrected image data as numpy array.
- plan (structure) – Structure with pre-computed data (see tiehom_plan function).
- nr_threads (int) – Number of threads to be used in the computation of FFT by PyFFTW (default = 2).
-
stp_core.phaseretrieval.tiehom.tiehom_plan(im, beta, delta, energy, distance, pixsize, padding)[source]¶ Pre-compute data to save time in further execution of phase_retrieval with TIE-HOM (Paganin’s) algorithm.
Parameters: - im (array_like) – Image data as numpy array. Only image size (shape) is actually used.
- beta (double) – Immaginary part of the complex X-ray refraction index.
- delta (double) – Decrement from unity of the complex X-ray refraction index.
- energy [KeV] (double) – Energy in KeV of the incident X-ray beam.
- distance [mm] (double) – Sample-to-detector distance in mm.
- pixsize [mm] (double) – Size in mm of the detector element.
- padding (bool) – Apply image padding to better process the boundary of the image
phaseretrieval.phrt¶
Functions:
phrt_plan(im, energy, distance, pixsize, ...) |
Pre-compute data to save time in further execution of phase_retrieval. |
phrt(im, plan[, method, nr_threads]) |
Process a tomographic projection image with the selected phase retrieval algorithm. |
-
stp_core.phaseretrieval.phrt.phrt(im, plan, method=4, nr_threads=2)[source]¶ Process a tomographic projection image with the selected phase retrieval algorithm.
Parameters: - im (array_like) – Flat corrected image data as numpy array.
- plan (structure) – Structure with pre-computed data (see prepare_plan function)
- method (int) – Phase retrieval filter {1 = TIE (default), 2 = CTF, 3 = CTF first-half sine, 4 = Quasiparticle, 5 = Quasiparticle first half sine}.
- nr_threads (int) – Number of threads to be used in the computation of FFT by PyFFTW
- Credits
- ——-
- Julian Moosmann, KIT (Germany) is acknowledged for this code
-
stp_core.phaseretrieval.phrt.phrt_plan(im, energy, distance, pixsize, regpar, thresh, method, padding)[source]¶ Pre-compute data to save time in further execution of phase_retrieval.
Parameters: - im (array_like) – Image data as numpy array. Only image size (shape) is actually used.
- energy [KeV] (double) – Energy in KeV of the incident X-ray beam.
- distance [mm] (double) – Sample-to-detector distance in mm.
- pixsize [mm] (double) – Size in mm of the detector element.
- regpar (double) – Regularization parameter: RegPar is - log10 of the constant to be added to the denominator to regularize the singularity at zero frequency, i.e. 1/sin(x) -> 1/(sin(x)+10^-RegPar). Typical values in the range [2.0, 3.0]. (Suggestion for default: 2.5).
- thresh (double) – Parameter for Quasiparticle phase retrieval which defines the width of the rings to be cropped around the zero crossing of the CTF denominator in Fourier space. Typical values in the range [0.01, 0.1]. (Suggestion for default: 0.1).
- method (int) – Phase retrieval algorithm {1 = TIE (default), 2 = CTF, 3 = CTF first-half sine, 4 = Quasiparticle, 5 = Quasiparticle first half sine}.
- padding (bool) – Apply image padding to better process the boundary of the image.
References
Notes
Credits to Julian Moosmann, KIT (Germany) is acknowledged for this code
postprocess¶
Functions:
postprocess(im, convert_opt, crop_opt) |
Post-process a reconstructed image. |
-
stp_core.postprocess.postprocess.postprocess(im, convert_opt, crop_opt)[source]¶ Post-process a reconstructed image.
Parameters: im (array_like) – Image data as numpy array.
- convert_opt : string
String containing degradation method (8-bit or 16-bit) and min/max rescaling value (e.g. “linear8:-0.01;0.01”). In current version only “linear” for 16-bit and “linear8” are implemented.
- crop_opt : double
String containing the parameters to crop an image separated by : with order top, bottom, left, right. (e.g. “100:100:100:100”)
preprocess.extfov_correction¶
Functions:
extfov_correction(im, ext_fov, ...) |
Apply sinogram correction for extended FOV acquisition mode |
-
stp_core.preprocess.extfov_correction.extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap)[source]¶ Apply sinogram correction for extended FOV acquisition mode
Parameters: im (array_like) – Image data (sinogram) as numpy array.
ext (bool) – True if the extended FOV mode has been performed.
ext_fov_rot_right (bool) –
- True if the extended FOV mode has been performed with rotation center
shifted to the right, left otherwise.
- ext_fov_overlap : int
Number of overlapping pixels.
preprocess.extract_flatdark¶
Functions:
extract_flatdark(f_in, flat_end, logfilename) |
Extract the flat and dark reference images to be used during the pre-processing step. |
-
stp_core.preprocess.extract_flatdark.extract_flatdark(f_in, flat_end, logfilename)[source]¶ Extract the flat and dark reference images to be used during the pre-processing step.
Parameters: - f_in (HDF5 data structure) – The data structure containing the flat and dark acquired images.
- flat_end (bool) – Consider the flat/dark images acquired after the projections (if any).
- logilename (string) – Absolute file of a log text file where infos are appended.
preprocess.flat_fielding¶
Functions:
flat_fielding(im, i, plan, flat_end, ...) |
Process a sinogram with conventional flat fielding plus reference normalization. |
-
stp_core.preprocess.flat_fielding.flat_fielding(im, i, plan, flat_end, half_half, half_half_line, norm_sx, norm_dx)[source]¶ Process a sinogram with conventional flat fielding plus reference normalization.
Parameters: - im (array_like) – Image data as numpy array
- i (int) – Index of the sinogram with reference to the height of a projection
- plan (structure) – Structure created by the extract_flatdark function (see extract_flatdark.py). This structure contains the flat/dark images acquired before the acquisition of the projections and the flat/dark images acquired after the acquisition of the projections as well as a few flags.
- flat_end (bool) – True if the process considers the flat/dark images (if any) acquired after the acquisition of the projections.
- half_half (bool) – True if the process has to be separated by processing the first part of the sinogram with the flat/dark images acquired before the acquisition of the projections and the second part with the flat/dark images acquired after the acquisition of the projections.
- half_half_line (int) – Usually this value is equal to the height of the projection FOV / 2 but the two parts of the sinogram to process can have a different size.
- norm_sx (int) – Width in pixels of the left window to be consider for the normalization of the sinogram. This value has to be zero in the case of ROI-CT.
- norm_dx (int) – Width in pixels of the right window to be consider for the normalization of the sinogram. This value has to be zero in the case of ROI-CT.
- Example (using h5py, tdf.py, tifffile.py)
- ————————–
- >>> sino_idx = 512
- >>> f = getHDF5(‘dataset.h5’, ‘r’)
- >>> im = tdf.read_sino(f[‘exchange/data’], sino_idx)
- >>> plan = extract_flatdark(f_in, True, False, False, ‘tomo’, ‘dark’, ‘flat’, ‘logfile.txt’)
- >>> im = flat_fielding(im, sino_idx, plan, True, True, 900, 0, 0)
- >>> imsave(‘sino_corr.tif’, im)
preprocess.ring_correction¶
Functions:
ring_correction(im, ringrem, flat_end, ...) |
Apply ring artifacts compensation by de-striping the input sinogram. |
-
stp_core.preprocess.ring_correction.ring_correction(im, ringrem, flat_end, skip_flat_after, half_half, half_half_line, ext_fov)[source]¶ Apply ring artifacts compensation by de-striping the input sinogram.
Parameters: im (array_like) – Image data (sinogram) as numpy array.
- ringrem : string
String containing ring removal method and parameters
- half_half : bool
True to separately process the sinogram in two parts
- half_half_line : int
Line number considered to identify the two parts to be processed separately. (This parameter is ignored if half_half is False)
skip_flat_after e ext_fov SERVE???
reconstruct.rec_astra¶
Functions:
recon_astra_fbp(im, angles, method, filter_type) |
Reconstruct the input sinogram by using the FBP implemented in ASTRA toolbox. |
recon_astra_iterative(im, angles, method, ...) |
Reconstruct the input sinogram by using one of the iterative algorithms implemented in ASTRA toolbox. |
-
stp_core.reconstruct.rec_astra.recon_astra_fbp(im, angles, method, filter_type)[source]¶ Reconstruct the input sinogram by using the FBP implemented in ASTRA toolbox.
Parameters: im (array_like) – Image data (sinogram) as numpy array.
- angles : double
Value in radians representing the number of angles of the sinogram.
- method : string
A string with either “FBP” or “FBP_CUDA”.
- filter_type : string
The available options are “ram-lak”, “shepp-logan”, “cosine”, “hamming”, “hann”, “tukey”, “lanczos”, “triangular”, “gaussian”, “barlett-hann”, “blackman”, “nuttall”, “blackman-harris”, “blackman-nuttall”, “flat-top”, “kaiser”, “parzen”.
-
stp_core.reconstruct.rec_astra.recon_astra_iterative(im, angles, method, iterations, zerone_mode)[source]¶ Reconstruct the input sinogram by using one of the iterative algorithms implemented in ASTRA toolbox.
Parameters: im (array_like) – Image data (sinogram) as numpy array.
- angles : double
Value in radians representing the number of angles of the sinogram.
- method : string
A string with e.g “SIRT” or “SIRT_CUDA” (see ASTRA documentation)
- iterations : int
Number of iterations for the algebraic technique
- zerone_mode : bool
True if the input sinogram has been rescaled to the [0,1] range (therefore positivity constraints are applied)
reconstruct.rec_fista_tv¶
Functions:
recon_fista_tv(im, angles, lam, fista_iter, iter) |
Reconstruct the input sinogram by using the FISTA-TV algorithm |
-
stp_core.reconstruct.rec_fista_tv.recon_fista_tv(im, angles, lam, fista_iter, iter)[source]¶ Reconstruct the input sinogram by using the FISTA-TV algorithm
Parameters: im (array_like) – Image data (sinogram) as numpy array.
- angles : double
Value in radians representing the number of angles of the input sinogram.
- lam : double
Regularization parameter of the FISTA algorithm.
- fista_iter : int
Number of iterations of the FISTA algorihtm.
- iter : int
Number of iterations of the TV minimization.
reconstruct.rec_gridrec¶
Functions:
recon_gridrec(im1, im2, angles, oversampling) |
Reconstruct two sinograms (of the same CT scan) with direct Fourier algorithm. |
-
stp_core.reconstruct.rec_gridrec.recon_gridrec(im1, im2, angles, oversampling)[source]¶ Reconstruct two sinograms (of the same CT scan) with direct Fourier algorithm.
Parameters: - im1 (array_like) – Sinogram image data as numpy array.
- im2 (array_like) – Sinogram image data as numpy array.
- angles (double) – Value in radians representing the number of angles of the input sinogram.
- oversampling (double) – Input sinogram is rescaled to increase the sampling of the Fourier space and avoid artifacts. Suggested value in the range [1.2,1.6].
reconstruct.rec_mr_fbp¶
Functions:
recon_mr_fbp(im, angles) |
Reconstruct a sinogram with the Minimum Residual FBP algorithm (Pelt, 2013). |
-
stp_core.reconstruct.rec_mr_fbp.recon_mr_fbp(im, angles)[source]¶ Reconstruct a sinogram with the Minimum Residual FBP algorithm (Pelt, 2013).
Parameters: - im (array_like) – Sinogram image data as numpy array.
- angles (double) – Value in radians representing the number of angles of the input sinogram.
utils.caching¶
Functions:
cache2plan(infile, cachepath) |
Read from cache the flat/dark images of the input TDF file. |
plan2cache(corr_plan, infile, cachepath) |
Write to cache the flat/dark images of the input TDF file. |
utils.findcenter¶
Functions:
usecorrelation(im1, im2) |
Assess the offset (to be used for e.g. |
-
stp_core.utils.findcenter.usecorrelation(im1, im2)[source]¶ - Assess the offset (to be used for e.g. the assessment of the center of rotation or the
- ovarlap) by computation the peak of the correlation between the two input images.
Parameters: im1 (array_like) – Image data as numpy array.
- im2 : array_like
Image data as numpy array.
returns: An integer value of the location of the maximum peak correlation.
utils.padding¶
Functions:
upperPowerOfTwo(v) |
Return the upper power of two of input value |
replicatePadImage(im, marg0, marg1) |
Pad the input image by replicating first and last column as well as first and last row the specified number of times. |
zeroPadImage(im, marg0, marg1) |
Pad the input image by adding zeros. |
padImage(im, n_pad0, n_pad1) |
Replicate pad the input image to the specified new dimensions. |
padSmoothWidth(im, n_pad) |
Pad the input image to the specified new width by replicate padding with Hanning smoothing to zero. |
-
stp_core.utils.padding.padImage(im, n_pad0, n_pad1)[source]¶ Replicate pad the input image to the specified new dimensions.
Parameters: - im (array_like) – Image data as numpy array
- n_pad0 (int) – The new height of the image
- n_pad1 (int) – The new width of the image
- Return value
- ———-
- A padded image
-
stp_core.utils.padding.padSmoothWidth(im, n_pad)[source]¶ Pad the input image to the specified new width by replicate padding with Hanning smoothing to zero.
Parameters: - im (array_like) – Image data as numpy array.
- n_pad (int) – The new width of the image.
- Return value
- ———-
- A padded image
-
stp_core.utils.padding.replicatePadImage(im, marg0, marg1)[source]¶ - Pad the input image by replicating first and last column as well as first and last row
- the specified number of times.
Parameters: - im (array_like) – Image data as numpy array.
- marg0 (int) – The number of times first and last row have to be replicated.
- marg1 (int) – The number of times first and last column have to be replicated.
- Return value
- ———-
- A replicated-padded image.
-
stp_core.utils.padding.upperPowerOfTwo(v)[source]¶ Return the upper power of two of input value
Parameters: - v (int) – A positive integer value
- Return value
- ———-
- An integer value
-
stp_core.utils.padding.zeroPadImage(im, marg0, marg1)[source]¶ Pad the input image by adding zeros.
Parameters: - im (array_like) – Image data as numpy array.
- marg0 (int) – The number of zero rows to add before first and after last row.
- marg1 (int) – The number of zero rows to add before first and after last column.
- Return value
- ———-
- A zero-padded image.
Examples¶
Here we describe what the examples are doing. You can cite with [B1].
exec_his2tdf¶
This section contains the exec_his2tdf script.
Download file: exec_his2tdf.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import datetime
import os
import os.path
import numpy
import time
from time import strftime
from sys import argv, exit
from glob import glob
from h5py import File as getHDF5
import stp_core.io.tdf as tdf
def _getHISdim ( HISfilename ):
dim1 = 0
dim2 = 0
dimz = 0
bytecode = numpy.uint16
# Open file:
try:
infile = open(HISfilename, "rb")
# Get file infos:
tot_bytes = os.path.getsize(HISfilename)
# Read header:
Image_tag = infile.read(2)
Comment_len = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim1 = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim2 = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim1_offset = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim2_offset = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
HeaderType = numpy.fromstring(infile.read(2), numpy.uint16)[0]
Dump = infile.read(50)
Comment = infile.read(Comment_len)
# Set total number of bytes read so far:
bytes_read = 64 + Comment_len
# Set image type:
bpp = len(numpy.array(0, bytecode).tostring())
# Define chunk size:
chunksize = dim1 * dim2 * bpp
# Determine number of expected projections:
dimz = (tot_bytes - bytes_read) / (chunksize + 64) + 1
finally:
# Close file:
infile.close()
return (dim1, dim2, dimz, bytecode)
def _processHIS( HISfilename, dset, dset_offset, provenance_dset, provenance_offset, time_offset, prefix, crop_top, crop_bottom, crop_left, crop_right, logfilename, int_from=0, int_to=-1):
# Open file:
infile = open(HISfilename, "rb")
# Get file infos:
tot_bytes = os.path.getsize(HISfilename)
# Read header:
Image_tag = infile.read(2)
Comment_len = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim1 = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim2 = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim1_offset = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
dim2_offset = numpy.fromstring(infile.read(2), numpy.uint16)[0].astype(numpy.int_)
HeaderType = numpy.fromstring(infile.read(2), numpy.uint16)[0]
Dump = infile.read(50)
Comment = infile.read(Comment_len)
# Set total number of bytes read so far:
bytes_read = 64 + Comment_len
# Set image type:
bytecode = numpy.uint16
bpp = len(numpy.array(0, bytecode).tostring())
# Define chunk size:
chunksize = dim1 * dim2 * bpp
# Determine number of expected projections:
num_proj = (tot_bytes - bytes_read) / (chunksize + 64) + 1
# Read first projection:
t1 = time.time()
block = infile.read(chunksize)
# Convert as numpy array:
data = numpy.fromstring(block, bytecode)
im = numpy.reshape( data, [dim2, dim1])
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
print numpy.amax(im[:])
print dset.attrs['max']
# Set minimum and maximum:
if ( numpy.amin(im[:]) < float(dset.attrs['min']) ):
dset.attrs['min'] = str(numpy.amin(im[:]))
if ( numpy.amax(im[:]) > float(dset.attrs['max'])):
dset.attrs['max'] = str(numpy.amax(im[:]))
print numpy.amax(im[:])
print dset.attrs['max']
# Check extrema (int_to == -1 means all files) for the projections:
if ( (int_to >= num_proj) or (int_to <= 0) ):
int_to = num_proj - 1
if ( (int_from >= num_proj) or (int_from < 0) ):
int_from = 0
# Process first projection (fill HDF5):
i = 0
first_index = int(provenance_dset.attrs['first_index'])
# Save processed image to HDF5 file:
#tifffile.imsave('tomo_' + str(i).zfill(4) + '.tif', data)
if (i >= int_from) and (i <= int_to):
tdf.write_tomo(dset, i + dset_offset - int_from,im)
# Save provenance metadata:
t = time.time() + time_offset*3600
provenance_dset["filename", provenance_offset + i - int_from] = prefix + '_' + str(i + dset_offset + first_index).zfill(4)
provenance_dset["timestamp", provenance_offset + i - int_from] = numpy.string_(datetime.datetime.fromtimestamp(t).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])
# Print out execution time:
t2 = time.time()
log = open(logfilename,"a")
log.write(os.linesep + "\t%s converted in %0.3f sec." % (provenance_dset["filename", provenance_offset + i - int_from], t2 - t1))
log.close()
# Read all the other projections:
try:
while block:
# Skip a few bytes:
t1 = time.time()
dump = infile.read(64)
# Read the meaningful data:
block = infile.read(chunksize)
# Convert as numpy array:
data = numpy.fromstring(block, bytecode)
im = numpy.reshape( data, [dim2, dim1])
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
# Set minimum and maximum:
if ( float(numpy.amin(im[:])) < float(dset.attrs['min']) ):
dset.attrs['min'] = str(numpy.amin(im[:]))
if ( float(numpy.amax(im[:])) > float(dset.attrs['max'])):
dset.attrs['max'] = str(numpy.amax(im[:]))
# Process first projection (fill HDF5):
i = i + 1
# Save processed image to HDF5 file:
#tifffile.imsave('tomo_' + str(i).zfill(4) + '.tif', data)
if (i >= int_from) and (i <= int_to):
tdf.write_tomo( dset, i + dset_offset - int_from,im )
# Save provenance metadata:
t = time.time() + time_offset*3600
provenance_dset["filename", provenance_offset + i - int_from] = prefix + '_' + str(i + dset_offset + first_index - int_from).zfill(4)
provenance_dset["timestamp", provenance_offset + i - int_from] = numpy.string_(datetime.datetime.fromtimestamp(t).strftime('%Y-%m-%d %H:%M:%S.%f')[:-3])
# Print out execution time:
t2 = time.time()
log = open(logfilename,"a")
log.write(os.linesep + "\t%s converted in %0.3f sec." % (provenance_dset["filename", provenance_offset + i - int_from], t2 - t1))
log.close()
except Exception, e:
#log = open(logfilename,"a")
#log.write(str(e))
#log.close()
pass
finally:
# Close file:
infile.close()
return provenance_offset + i + 1
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 work 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)
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 + "\tWork 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)
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)
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()
if __name__ == "__main__":
main(argv[1:])
|
exec_preprocessing¶
This section contains the exec_preprocessing script.
Download file: exec_preprocessing.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: August, 8th 2016
#
from sys import argv, exit
from os import remove, sep, linesep
from os.path import exists
from numpy import float32, amin, amax, isscalar
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.dynamic_flatfielding import dff_prepare_plan, dynamic_flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark, _medianize
from h5py import File as getHDF5
# pystp-specific:
import stp_core.io.tdf as tdf
def _write_data(lock, im, index, outfile, outshape, outtype, 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]))
tdf.write_sino(f_out_dset,index,im.astype(float32))
# 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 + "\tsino_%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, norm_sx, norm_dx, flat_end,
half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, 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)
im = extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
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 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()
# 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]
# Normalization parameters:
norm_sx = int(argv[4])
norm_dx = int(argv[5])
# Params for flat fielding with post flats/darks:
flat_end = True if argv[6] == "True" else False
half_half = True if argv[7] == "True" else False
half_half_line = int(argv[8])
# Params for extended FOV:
ext_fov = True if argv[9] == "True" else False
ext_fov_rot_right = argv[10]
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[11])
# Method and parameters coded into a string:
ringrem = argv[12]
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[13] == "True" else False
# Nr of threads and log file:
nr_threads = int(argv[14])
logfilename = argv[15]
# 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_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 = 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)
# 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,idx).astype(float32)
im = extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
# 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,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.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.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_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, infile, outfile, outshape, im.dtype, skipflat, plan, norm_sx,
norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, ringrem,
dynamic_ff, EFF, filtEFF, im_dark, logfilename )).start()
#start = int_from # 0
#end = int_to # num_sinos - 1
#_process(lock, start, end, infile, outfile, outshape, im.dtype, skipflat, plan, norm_sx,
# norm_dx, flat_end, half_half, half_half_line, ext_fov, ext_fov_rot_right, ext_fov_overlap, ringrem,
# dynamic_ff, EFF, filtEFF, im_dark, logfilename)
#255 256 C:\Temp\BrunGeorgos.tdf C:\Temp\BrunGeorgos_corr.tdf 0 0 True True 900 False False 0 rivers:11;0 False 1 C:\Temp\log_00.txt
if __name__ == "__main__":
main(argv[1:])
|
exec_reconstruct¶
This section contains the exec_reconstruct script.
Download file: exec_reconstruct.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
# python:
from sys import argv, exit
from os import remove, sep, makedirs, linesep
from os.path import basename, exists
from numpy import finfo, copy, float32, double, amin, amax, tile, concatenate, log as nplog
from numpy import arange, meshgrid, isscalar, ndarray, pi, roll
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark, _medianize
from stp_core.preprocess.dynamic_flatfielding import dff_prepare_plan, dynamic_flat_fielding
from stp_core.reconstruct.rec_astra import recon_astra_fbp, recon_astra_iterative
from stp_core.reconstruct.rec_fista_tv import recon_fista_tv
from stp_core.reconstruct.rec_mr_fbp import recon_mr_fbp
from stp_core.reconstruct.rec_gridrec import recon_gridrec
from stp_core.postprocess.postprocess import postprocess
from stp_core.utils.padding import upperPowerOfTwo, padImage, padSmoothWidth
from tifffile import imread, imsave
from h5py import File as getHDF5
# pystp-specific:
import stp_core.io.tdf as tdf
def reconstruct(im, angles, offset, logtransform, param1, circle, scale, pad, method, rolling, roll_shift,
zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset):
"""Reconstruct a sinogram with FBP algorithm (from ASTRA toolbox).
Parameters
----------
im1 : array_like
Sinogram image data as numpy array.
center : float
Offset of the center of rotation to use for the tomographic
reconstruction with respect to the half of sinogram width
(default=0, i.e. half width).
logtransform : boolean
Apply logarithmic transformation before reconstruction (default=True).
filter : string
Filter to apply before the application of the reconstruction algorithm. Filter
types are: ram-lak, shepp-logan, cosine, hamming, hann, tukey, lanczos, triangular,
gaussian, barlett-hann, blackman, nuttall, blackman-harris, blackman-nuttall,
flat-top, kaiser, parzen.
circle : boolean
Create a circle in the reconstructed image and set to zero pixels outside the
circle (default=False).
Example (using tiffile.py)
--------------------------
>>> # Read input (uncorrected) sinogram
>>> sino_im1 = imread('sino_0050.tif')
>>>
>>> # Get flat and dark correction images:
>>> im_dark = medianize("\project\tomo", "dark*.tif")
>>> im_flat = medianize("\project\tomo", "flat*.tif")
>>>
>>> # Perform flat fielding and normalization:
>>> sino_im = normalize(sino_im1, (10,10), (0,0), im_dark, im_flat, 50)
>>>
>>> # Actual reconstruction:
>>> out = reconstruct_fbp(sino_im, -3.0)
>>>
>>> # Save output slice:
>>> imsave('slice_0050.tif', out)
"""
# Copy images and ensure they are of type float32:
#im_f = copy(im.astype(float32))
im_f = im.astype(float32)
# Decimate projections if required:
if decim_factor > 1:
im_f = im_f[::decim_factor,:]
# Upscale projections (if required):
if (abs(scale - 1.0) > finfo(float32).eps):
siz_orig1 = im_f.shape[1]
im_f = imresize(im_f, (im_f.shape[0], int(round(scale * im_f.shape[1]))), interp='bicubic', mode='F')
offset = int(offset * scale)
# Apply transformation for changes in the center of rotation:
if (offset != 0):
if (offset >= 0):
im_f = im_f[:,:-offset]
tmp = im_f[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im_f = concatenate((tmp.T,im_f), axis=1) # Concatenate tmp before the image
else:
im_f = im_f[:,abs(offset):]
tmp = im_f[:,im_f.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im_f = concatenate((im_f,tmp.T), axis=1) # Concatenate tmp after the image
# Downscale projections (without pixel averaging):
if downsc_factor > 1:
im_f = im_f[:,::downsc_factor]
# Sinogram rolling (if required). It doesn't make sense in limited angle tomography, so check if 180 or 360:
if ((rolling == True) and (roll_shift > 0)):
if ( (angles - pi) < finfo(float32).eps ):
# Flip the last rows:
im_f[-roll_shift:,:] = im_f[-roll_shift:,::-1]
# Now roll the sinogram:
im_f = roll(im_f, roll_shift, axis=0)
elif ((angles - pi*2.0) < finfo(float32).eps):
# Only roll the sinogram:
im_f = roll(im_f, roll_shift, axis=0)
# Scale image to [0,1] range (if required):
if (zerone_mode):
#print dset_min
#print dset_max
#print numpy.amin(im_f[:])
#print numpy.amax(im_f[:])
#im_f = (im_f - dset_min) / (dset_max - dset_min)
# Cheating the whole process:
im_f = (im_f - numpy.amin(im_f[:])) / (numpy.amax(im_f[:]) - numpy.amin(im_f[:]))
# Apply log transform:
if (logtransform == True):
im_f[im_f <= finfo(float32).eps] = finfo(float32).eps
im_f = -nplog(im_f + corr_offset)
# Replicate pad image to double the width:
if (pad):
dim_o = im_f.shape[1]
n_pad = im_f.shape[1] + im_f.shape[1] / 2
marg = (n_pad - dim_o) / 2
# Pad image:
im_f = padSmoothWidth(im_f, n_pad)
# Perform the actual reconstruction:
if (method.startswith('FBP')):
im_f = recon_astra_fbp(im_f, angles, method, param1)
elif (method == 'MR-FBP_CUDA'):
im_f = recon_mr_fbp(im_f, angles)
elif (method == 'FISTA-TV_CUDA'):
im_f = recon_fista_tv(im_f, angles, param1, param1)
else:
im_f = recon_astra_iterative(im_f, angles, method, param1, zerone_mode)
# Crop:
if (pad):
im_f = im_f[marg:dim_o + marg, marg:dim_o + marg]
# Resize (if necessary):
if (abs(scale - 1.0) > finfo(float32).eps):
im_f = imresize(im_f, (siz_orig1, siz_orig1), interp='nearest', mode='F')
# Return output:
return im_f.astype(float32)
def reconstruct_gridrec(im1, im2, angles, offset, logtransform, param1, circle, scale, pad, rolling, roll_shift,
zerone_mode, dset_min, dset_max, decim_factor, downsc_factor, corr_offset):
"""Reconstruct a sinogram with FBP algorithm (from ASTRA toolbox).
Parameters
----------
im1 : array_like
Sinogram image data as numpy array.
center : float
Offset of the center of rotation to use for the tomographic
reconstruction with respect to the half of sinogram width
(default=0, i.e. half width).
logtransform : boolean
Apply logarithmic transformation before reconstruction (default=True).
filter : string
Filter to apply before the application of the reconstruction algorithm. Filter
types are: ram-lak, shepp-logan, cosine, hamming, hann, tukey, lanczos, triangular,
gaussian, barlett-hann, blackman, nuttall, blackman-harris, blackman-nuttall,
flat-top, kaiser, parzen.
circle : boolean
Create a circle in the reconstructed image and set to zero pixels outside the
circle (default=False).
Example (using tiffile.py)
--------------------------
>>> # Read input (uncorrected) sinogram
>>> sino_im1 = imread('sino_0050.tif')
>>>
>>> # Get flat and dark correction images:
>>> im_dark = medianize("\project\tomo", "dark*.tif")
>>> im_flat = medianize("\project\tomo", "flat*.tif")
>>>
>>> # Perform flat fielding and normalization:
>>> sino_im = normalize(sino_im1, (10,10), (0,0), im_dark, im_flat, 50)
>>>
>>> # Actual reconstruction:
>>> out = reconstruct_fbp(sino_im, -3.0)
>>>
>>> # Save output slice:
>>> imsave('slice_0050.tif', out)
"""
# Ensure images are of type float32:
im_f1 = im1.astype(float32)
im_f2 = im2.astype(float32)
# Decimate projections if required:
if decim_factor > 1:
im_f1 = im_f1[::decim_factor,:]
im_f2 = im_f2[::decim_factor,:]
# Upscale projections (if required):
if (abs(scale - 1.0) > finfo(float32).eps):
siz_orig1 = im_f.shape[1]
im_f1 = imresize(im_f1, (im_f1.shape[0], int(round(scale * im_f1.shape[1]))), interp='bicubic', mode='F')
im_f2 = imresize(im_f2, (im_f2.shape[0], int(round(scale * im_f2.shape[1]))), interp='bicubic', mode='F')
offset = int(offset * scale)
# Apply transformation for changes in the center of rotation:
if (offset != 0):
if (offset >= 0):
im_f1 = im_f1[:,:-offset]
tmp = im_f1[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im_f1 = concatenate((tmp.T,im_f1), axis=1) # Concatenate tmp before the image
im_f2 = im_f2[:,:-offset]
tmp = im_f2[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im_f2 = concatenate((tmp.T,im_f2), axis=1) # Concatenate tmp before the image
else:
im_f1 = im_f1[:,abs(offset):]
tmp = im_f1[:,im_f1.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im_f1 = concatenate((im_f1,tmp.T), axis=1) # Concatenate tmp after the image
im_f2 = im_f2[:,abs(offset):]
tmp = im_f2[:,im_f2.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im_f2 = concatenate((im_f2,tmp.T), axis=1) # Concatenate tmp after the image
# Downscale projections (without pixel averaging):
if downsc_factor > 1:
im_f1 = im_f1[:,::downsc_factor]
im_f2 = im_f2[:,::downsc_factor]
# Sinogram rolling (if required). It doesn't make sense in limited angle tomography, so check if 180 or 360:
if ((rolling == True) and (roll_shift > 0)):
if ( (angles - pi) < finfo(float32).eps ):
# Flip the last rows:
im_f1[-roll_shift:,:] = im_f1[-roll_shift:,::-1]
im_f2[-roll_shift:,:] = im_f2[-roll_shift:,::-1]
# Now roll the sinogram:
im_f1 = roll(im_f1, roll_shift, axis=0)
im_f2 = roll(im_f2, roll_shift, axis=0)
elif ((angles - pi*2.0) < finfo(float32).eps):
# Only roll the sinogram:
im_f1 = roll(im_f1, roll_shift, axis=0)
im_f2 = roll(im_f2, roll_shift, axis=0)
# Scale image to [0,1] range (if required):
if (zerone_mode):
#print dset_min
#print dset_max
#print numpy.amin(im_f[:])
#print numpy.amax(im_f[:])
#im_f = (im_f - dset_min) / (dset_max - dset_min)
# Cheating the whole process:
im_f1 = (im_f1 - numpy.amin(im_f1[:])) / (numpy.amax(im_f1[:]) - numpy.amin(im_f1[:]))
im_f2 = (im_f2 - numpy.amin(im_f2[:])) / (numpy.amax(im_f2[:]) - numpy.amin(im_f2[:]))
# Apply log transform:
if (logtransform == True):
im_f1[im_f1 <= finfo(float32).eps] = finfo(float32).eps
im_f1 = -nplog(im_f1 + corr_offset)
im_f2[im_f2 <= finfo(float32).eps] = finfo(float32).eps
im_f2 = -nplog(im_f2 + corr_offset)
# Replicate pad image to double the width:
if (pad):
dim_o = im_f1.shape[1]
n_pad = im_f1.shape[1] + im_f1.shape[1] / 2
marg = (n_pad - dim_o) / 2
# Pad image:
im_f1 = padSmoothWidth(im_f1, n_pad)
im_f2 = padSmoothWidth(im_f2, n_pad)
# Perform the actual reconstruction:
[im_f1, im_f2] = recon_gridrec(im_f1, im_f2, angles, param1)
# Crop:
if (pad):
im_f1 = im_f1[marg:dim_o + marg, marg:dim_o + marg]
im_f2 = im_f2[marg:dim_o + marg, marg:dim_o + marg]
# Resize (if necessary):
if (abs(scale - 1.0) > finfo(float32).eps):
im_f1 = imresize(im_f1, (siz_orig1, siz_orig1), interp='nearest', mode='F')
im_f2 = imresize(im_f2, (siz_orig1, siz_orig1), interp='nearest', mode='F')
# Return output:
return [im_f1.astype(float32), im_f2.astype(float32)]
def write_log(lock, fname, logfilename, cputime, iotime):
"""To do...
"""
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\t%s reconstructed (CPU: %0.3f sec - I/O: %0.3f sec)." % (basename(fname), cputime, iotime))
log.close()
finally:
lock.release()
def write_log_gridrec(lock, fname1, fname2, logfilename, cputime, iotime):
"""To do...
"""
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\t%s reconstructed (CPU: %0.3f sec - I/O: %0.3f sec)." % (basename(fname1), cputime/2, iotime/2))
log.write(linesep + "\t%s reconstructed (CPU: %0.3f sec - I/O: %0.3f sec)." % (basename(fname2), cputime/2, iotime/2))
log.close()
finally:
lock.release()
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, 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, 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).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()
# Apply projection removal (if required):
im1 = im1[angles_projfrom:angles_projto, :]
im2 = im2[angles_projfrom:angles_projto, :]
# 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, norm_sx, norm_dx).astype(float32)
im1 = extfov_correction (im1, ext_fov, ext_fov_rot_right, ext_fov_overlap)
if not skipflat:
im1 = ring_correction (im1, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, ext_fov)
else:
im1 = ring_correction (im1, ringrem, False, False, half_half, half_half_line, ext_fov)
if not skipflat:
if dynamic_ff:
# Dynamic flat fielding with downsampling = 2:
im2 = dynamic_flat_fielding(im2, i, 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, norm_sx, norm_dx).astype(float32)
im2 = extfov_correction (im2, ext_fov, ext_fov_rot_right, ext_fov_overlap)
if not skipflat and not dynamic_ff:
im2 = ring_correction (im2, ringrem, flat_end, corr_plan['skip_flat_after'], half_half, half_half_line, 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, 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:
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 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, 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, 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).astype(float32)
f_in.close()
t1 = time()
# Apply projection removal (if required):
im = im[angles_projfrom:angles_projto, :]
# 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, norm_sx, norm_dx).astype(float32)
im = extfov_correction (im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
if not skipflat and not dynamic_ff:
im = ring_correction (im, ringrem, flat_end, corr_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)
# Actual reconstruction:
im = reconstruct(im, angles, offset, 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 = 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):
"""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]
angles_projfrom = int(argv[31])
angles_projto = int(argv[32])
rolling = True if argv[33] == "True" else False
roll_shift = int(argv[34])
dynamic_ff = True if argv[35] == "True" else False
nr_threads = int(argv[36])
logfilename = argv[37]
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 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, 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, 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, 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, convert_opt, crop_opt, dynamic_ff, EFF, filtEFF, im_dark, outprefix,
logfilename )).start()
#start = int_from
#end = int_to
#if (reconmethod == 'GRIDREC'):
# process_gridrec(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, 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, convert_opt, crop_opt, dynamic_ff, EFF, filtEFF, im_dark, outprefix, logfilename)
#else:
# process(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, 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, convert_opt, crop_opt, dynamic_ff, EFF, filtEFF, im_dark, outprefix, logfilename)
# Example:
# 255 255 C:\Temp\BrunGeorgos.tdf C:\Temp\BrunGeorgos 3.1416 -31.0 shepp-logan 1.0 False False True slice True True True 5 False False 100 0 0 False rivers:11;0 False 0.0 FBP_CUDA 1 1 False - - 0 1799 False 2 C:\Temp\log_00.txt
if __name__ == "__main__":
main(argv[1:])
|
exec_postprocessing¶
This section contains the exec_postprocessing script.
Download file: exec_postprocessing.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
from sys import argv, exit
from glob import glob
from os import linesep
from os.path import sep, basename, exists
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.postprocess.postprocess import postprocess
from tifffile import imread, imsave
def _write_log(lock, fname, logfilename, cputime, iotime):
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\t%s processed (CPU: %0.3f sec - I/O: %0.3f sec)." % (basename(fname), cputime, iotime))
log.close()
finally:
lock.release()
def _process(lock, int_from, int_to, files, outpath, convert_opt, crop_opt, outprefix, logfilename):
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read i-th slice:
t0 = time()
im = imread(files[i])
t1 = time()
# Post process the image:
im = postprocess(im, convert_opt, crop_opt)
# Write down post-processed 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...
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()
# Get the from and to number of files to process:
int_from = int(argv[0])
int_to = int(argv[1])
# Get input and output paths:
inpath = argv[2]
outpath = argv[3]
if not inpath.endswith(sep): inpath += sep
if not outpath.endswith(sep): outpath += sep
# Get parameters:
convert_opt = argv[4]
crop_opt = argv[5]
outprefix = argv[6]
# Number of threads to use and logfile:
nr_threads = int(argv[7])
logfilename = argv[8]
# Get the files in infile:
log = open(logfilename,"w")
log.write(linesep + "\tInput TIFF folder: %s" % (inpath))
log.write(linesep + "\tOutput TIFF folder: %s" % (outpath))
log.write(linesep + "\t--------------")
if (int_to != -1):
log.write(linesep + "\tThe subset [%d,%d] of the input files will be considered." % (int_from, int_to))
log.write(linesep + "\tCropping:")
crop_opt_num = crop_opt.split(":")
log.write(linesep + "\t\tTop: %s pixels" % (crop_opt_num[0]))
log.write(linesep + "\t\tBottom: %s pixels" % (crop_opt_num[1]))
log.write(linesep + "\t\tLeft: %s pixels" % (crop_opt_num[2]))
log.write(linesep + "\t\tRight: %s pixels" % (crop_opt_num[3]))
conv_method, conv_args = convert_opt.split(":", 1)
if (conv_method == "linear8"):
min, max = conv_args.split(";")
log.write(linesep + "\tConversion to 8-bit by remapping range [%s,%s] to [0,255]." % (min, max))
elif (conv_method == "linear"):
min, max = conv_args.split(";")
log.write(linesep + "\tConversion to 16-bit by remapping range [%s,%s] to [0,65535]." % (min, max))
log.write(linesep + "\t--------------")
log.write(linesep + "\tBrowsing input folder...")
log.close()
files = sorted(glob(inpath + '*.tif*'))
num_files = len(files)
if ((int_to >= num_files) or (int_to == -1)):
int_to = num_files - 1
# Log infos:
log = open(logfilename,"a")
log.write(linesep + "\tInput folder browsed correctly.")
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
Process(target=_process, args=(lock, start, end, files, outpath, convert_opt, crop_opt, outprefix, logfilename )).start()
#start = 0
#end = num_files - 1
#process(lock, start, end, files, outpath, convert_opt, crop_opt, outprefix, logfilename )
#0 -1 C:\Temp\BrunGeorgos C:\Temp\BrunGeorgos\slice_8 linear8:-0.01;0.01 10:10:10:20 slice C:\Temp\log_00_conv.txt
if __name__ == "__main__":
main(argv[1:])
|
exec_phaseretrieval¶
This section contains the exec_phaseretrieval script.
Download file: exec_phaseretrieval.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
from sys import argv, exit
from os import remove, sep, linesep
from os.path import exists
from numpy import float32, double, amin, amax
from time import time
from multiprocessing import Process, Lock
from pyfftw.interfaces.cache import enable as pyfftw_cache_enable, disable as pyfftw_cache_disable
from pyfftw.interfaces.cache import set_keepalive_time as pyfftw_set_keepalive_time
# pystp-specific:
from stp_core.phaseretrieval.tiehom import tiehom, tiehom_plan
from stp_core.phaseretrieval.phrt import phrt, phrt_plan
from h5py import File as getHDF5
# pystp-specific:
import stp_core.io.tdf as tdf
def _write_data(lock, im, index, outfile, outshape, outtype, 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]))
tdf.write_tomo(f_out_dset,index,im.astype(float32))
# 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, 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 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, im.dtype)
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, im.dtype, method, plan, logfilename)).start()
#start = 0
#end = num_proj - 1
#_process(lock, start, end, infile, outfile, outshape, im.dtype, method, plan, logfilename)
#255 256 C:\Temp\BrunGeorgos_corr.tdf C:\Temp\BrunGeorgos_corr_phrt.tdf 0 1.0 2000.0 22.0 300.0 2.2 False 1 C:\Temp\log_00.txt
#255 256 C:\Temp\BrunGeorgos_corr.tdf C:\Temp\BrunGeorgos_corr_phrt.tdf 4 2.5 1.0 22.0 300.0 2.2 False 1 C:\Temp\log_00.txt
if __name__ == "__main__":
main(argv[1:])
|
exec_tdf2tiff¶
This section contains the exec_tdf2tiff script.
Download file: exec_tdf2tiff.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import datetime
import os
import os.path
import time
from sys import argv, exit
from numpy import float32, float64
from tifffile import imread, imsave
from h5py import File as getHDF5
# pystp-specific:
import stp_core.io.tdf as tdf
from multiprocessing import Process, Lock
def _write_log(lock, fname, logfilename, iotime):
"""To do...
"""
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(os.linesep + "\t%s converted in %0.3f sec." % (os.path.basename(fname), iotime))
log.close()
finally:
lock.release()
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 )
if ( TIFFFormat ):
fname = outpath + outprefix + '_' + str(i).zfill(4) + '.tif'
else:
fname = outpath + outprefix + '_' + str(i).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)
t1 = time.time()
# Print out execution time:
_write_log(lock, fname, logfilename, t1 - t0)
f.close()
except Exception:
pass
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()
#_process(lock, start, end, infile, dset_str, TIFFFormat, projorder, outpath, fileprefix, logfilename)
if __name__ == "__main__":
main(argv[1:])
|
exec_tiff2tdf¶
This section contains the exec_tiff2tdf script.
Download file: exec_tiff2tdf.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import datetime
import os
import os.path
import numpy
import time
from time import strftime
from sys import argv, exit
from glob import glob
from tifffile import imread, imsave
from h5py import File as getHDF5
# pystp-specific:
import stp_core.io.tdf as tdf
from multiprocessing import Process, Lock
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 _process(lock, int_from, int_to, offset, abs_offset, files, projorder, outfile, dsetname, outshape, outtype,
crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename):
"""To do...
"""
# Process the required subset of images:
for i in range(int_from, int_to + 1):
# Read input image:
t0 = time.time()
im = imread(files[i])
# Crop:
im = im[crop_top:im.shape[0]-crop_bottom,crop_left:im.shape[1]-crop_right]
# Get the timestamp:
t = os.path.getmtime(files[i])
t1 = time.time()
# Save processed image to HDF5 file (atomic procedure - lock used):
_write_data(lock, im, i, offset, abs_offset, files[i], t, projorder, tot_files, provenance_dt,
outfile, dsetname, outshape, outtype, logfilename, t1 - t0)
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"
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 + "\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)
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)
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:
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()
#process(lock, start, end, offset, tomo_files, projorder, outfile, 'exchange/data',
# datashape, im.dtype, crop_top, crop_bottom, crop_left, crop_right, tot_files, provenance_dt, logfilename )
if __name__ == "__main__":
main(argv[1:])
|
tools_autolimit¶
This section contains the tools_autolimit script.
Download file: tools_autolimit.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import os
import os.path
import numpy
import time
from glob import glob
from sys import argv, exit
from h5py import File as getHDF5
from numpy import float32
from tifffile import imread, imsave
# pystp-specific:
import stp_core.io.tdf as tdf
def main(argv):
"""Computes min/max limits to be used in image degradation to 8-bit or 16-bit.
Parameters
----------
argv[0] : string
The absolute path of the input folder containing reconstructed TIFF files.
argv[1] : string
The absolute path of the output txt file with the proposed limits as string "min:max".
Example
-------
tools_autolimit "S:\\SampleA\\slices" "R:\\Temp\\autolimit.txt"
"""
try:
# Get input and output paths:
inpath = argv[0]
outfile = argv[1] # The txt file with the proposed center
if not inpath.endswith(os.path.sep): inpath += os.path.sep
# Get the number of files in folder:
files = sorted(glob(inpath + '*.tif*'))
num_files = len(files)
# Read the median slice from disk:
im = imread(files[num_files/2])
# Flat the image and sort it:
im_flat = im.flatten()
im_flat = numpy.sort(im_flat)
# Return as minimum the value the skip 0.30% of "black" tail and 0.005% of "white" tail:
low_idx = int(im_flat.shape[0] * 0.0030)
high_idx = int(im_flat.shape[0] * 0.9995)
min = im_flat[low_idx]
max = im_flat[high_idx]
# Print center to output file:
text_file = open(outfile, "w")
text_file.write( str(min) + ":" + str(max) )
text_file.close()
except:
exit()
if __name__ == "__main__":
main(argv[1:])
|
tools_multiangle¶
This section contains the tools_multiangle script.
Download file: tools_multiangle.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: Sept, 28th 2016
#
# python:
from sys import argv, exit
from os import remove, sep, linesep, listdir, makedirs
from os.path import exists, dirname, basename, splitext
from numpy import array, finfo, copy, float32, double, amin, amax, tile, concatenate, asarray
from numpy import empty, reshape, log as nplog, arange, squeeze, fromfile, ndarray, where, meshgrid
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark
from stp_core.phaseretrieval.tiehom import tiehom, tiehom_plan
from stp_core.phaseretrieval.phrt import phrt, phrt_plan
from stp_core.reconstruct.rec_astra import recon_astra_fbp, recon_astra_iterative
from stp_core.reconstruct.rec_fista_tv import recon_fista_tv
from stp_core.reconstruct.rec_mr_fbp import recon_mr_fbp
from stp_core.reconstruct.rec_gridrec import recon_gridrec
from stp_core.postprocess.postprocess import postprocess
from stp_core.utils.padding import upperPowerOfTwo, padImage, padSmoothWidth
from stp_core.utils.caching import cache2plan, plan2cache
from tifffile import imread, imsave
from h5py import File as getHDF5
import stp_core.io.tdf as tdf
def write_log(lock, fname, logfilename):
"""To do...
"""
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\t%s reconstructed." % basename(fname))
log.close()
finally:
lock.release()
def reconstruct(im, angles, offset, logtransform, param1, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, corr_offset, postprocess_required, convert_opt,
crop_opt, start, end, outpath, sino_idx, downsc_factor, decim_factor, logfilename, lock, slice_prefix):
"""Reconstruct a sinogram with FBP algorithm (from ASTRA toolbox).
Parameters
----------
im1 : array_like
Sinogram image data as numpy array.
center : float
Offset of the center of rotation to use for the tomographic
reconstruction with respect to the half of sinogram width
(default=0, i.e. half width).
logtransform : boolean
Apply logarithmic transformation before reconstruction (default=True).
filter : string
Filter to apply before the application of the reconstruction algorithm. Filter
types are: ram-lak, shepp-logan, cosine, hamming, hann, tukey, lanczos, triangular,
gaussian, barlett-hann, blackman, nuttall, blackman-harris, blackman-nuttall,
flat-top, kaiser, parzen.
circle : boolean
Create a circle in the reconstructed image and set to zero pixels outside the
circle (default=False).
"""
# Copy required due to multithreading:
im_f = im
# Decimate projections if required:
#if decim_factor > 1:
# im = im[::decim_factor,:]
# Upscale projections (if required):
if (abs(scale - 1.0) > finfo(float32).eps):
siz_orig1 = im_f.shape[1]
im_f = imresize(im_f, (im_f.shape[0], int(round(scale * im_f.shape[1]))), interp='bicubic', mode='F')
offset = int(offset * scale)
offset = int(round(offset))
# Apply transformation for changes in the center of rotation:
if (offset != 0):
if (offset >= 0):
im_f = im_f[:,:-offset]
tmp = im_f[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im_f = concatenate((tmp.T,im_f), axis=1) # Concatenate tmp before the image
else:
im_f = im_f[:,abs(offset):]
tmp = im_f[:,im_f.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im_f = concatenate((im_f,tmp.T), axis=1) # Concatenate tmp after the image
# Downscale projections (without pixel averaging):
#if downsc_factor > 1:
# im = im[:,::downsc_factor]
# Scale image to [0,1] range (if required):
if (zerone_mode):
#print dset_min
#print dset_max
#print numpy.amin(im_f[:])
#print numpy.amax(im_f[:])
#im_f = (im_f - dset_min) / (dset_max - dset_min)
# Cheating the whole process:
im_f = (im_f - numpy.amin(im_f[:])) / (numpy.amax(im_f[:]) - numpy.amin(im_f[:]))
# Apply log transform:
if (logtransform == True):
im_f[im_f <= finfo(float32).eps] = finfo(float32).eps
im_f = -nplog(im_f + corr_offset)
# Replicate pad image to double the width:
if (pad):
dim_o = im_f.shape[1]
n_pad = im_f.shape[1] + im_f.shape[1] / 2
marg = (n_pad - dim_o) / 2
# Pad image:
im_f = padSmoothWidth(im_f, n_pad)
# Loop for all the required angles:
for i in range(int(round(start)), int(round(end)) + 1):
# Save image for next step:
im = im_f
# Apply projection removal (if required):
im_f = im_f[0:int(round(i/decim_factor)), :]
# Perform the actual reconstruction:
if (method.startswith('FBP')):
im_f = recon_astra_fbp(im_f, angles, method, param1)
elif (method == 'MR-FBP_CUDA'):
im_f = recon_mr_fbp(im_f, angles)
elif (method == 'FISTA-TV_CUDA'):
im_f = recon_fista_tv(im_f, angles, param1, param1)
elif (method == 'MLEM'):
im_f = recon_tomopy_iterative(im_f, angles, method, param1)
elif (method == 'GRIDREC'):
[im_f, im_f] = recon_gridrec(im_f, im_f, angles, param1)
else:
im_f = recon_astra_iterative(im_f, angles, method, param1, zerone_mode)
# Crop:
if (pad):
im_f = im_f[marg:dim_o + marg, marg:dim_o + marg]
# Resize (if necessary):
if (abs(scale - 1.0) > finfo(float32).eps):
im_f = imresize(im_f, (siz_orig1, siz_orig1), interp='nearest', mode='F')
# Apply post-processing (if required):
if postprocess_required:
im_f = postprocess(im_f, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im_f.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_f = im_f * a
# Write down reconstructed image (file name modified with metadata):
fname = outpath + slice_prefix + '_' + str(sino_idx).zfill(4) + '_off=' + str(offset*downsc_factor).zfill(4) + '_proj=' + str(i).zfill(4) + '.tif'
imsave(fname, im_f)
# Restore original image for next step:
im_f = im
# Write log (atomic procedure - lock used):
write_log(lock, fname, logfilename )
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, 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]).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:
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).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)
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()
# Actual reconstruction:
reconstruct(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)
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[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
phrtmethod = int(argv[30])
phrt_param1 = double(argv[31]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[32]) # param2( e.g. thresh or delta)
energy = double(argv[33])
distance = double(argv[34])
pixsize = double(argv[35]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[36] == "True" else False
approx_win = int(argv[37])
preprocessingplan_fromcache = True if argv[38] == "True" else False
tmppath = argv[39]
if not tmppath.endswith(sep): tmppath += sep
nr_threads = int(argv[40])
angles_from = float(argv[41])
angles_to = float(argv[42])
slice_prefix = argv[43]
logfilename = argv[44]
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):
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()
# 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, 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 )
# Sample:
# 26 C:\Temp\dataset42.tdf C:\Temp\test_angles 3.1416 -72.0 shepp-logan 1.0 False True True True True False 5 False False 100 0 0 False rivers:11;0 False 0.0 FBP_CUDA 1 4 False - - False 0 1.0 1000.0 22 150 2.2 True 16 False C:\Temp 1 1148 1248 slice C:\Temp\log_angles_00.txt
if __name__ == "__main__":
main(argv[1:])
|
tools_extractdata¶
This section contains the tools_extractdata script.
Download file: tools_extractdata.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import os
import os.path
import numpy
import time
from sys import argv, exit
from h5py import File as getHDF5
from numpy import float32
# pystp-specific:
import stp_core.io.tdf as tdf
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()
if __name__ == "__main__":
main(argv[1:])
|
tools_guesscenter¶
This section contains the tools_guesscenter script.
Download file: tools_guesscenter.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: Sept, 28th 2016
#
from math import pi
from numpy import float32, double, finfo, ndarray
from scipy.misc import imresize #scipy 0.12
from os import sep, remove
from os.path import basename
from sys import argv
from h5py import File as getHDF5
from pyfftw.interfaces.cache import enable as pyfftw_cache_enable, disable as pyfftw_cache_disable
from pyfftw.interfaces.cache import set_keepalive_time as pyfftw_set_keepalive_time
import time
# pystp-specific:
import stp_core.io.tdf as tdf
import stp_core.utils.findcenter as findcenter
from stp_core.utils.caching import cache2plan, plan2cache
from stp_core.preprocess.extract_flatdark import extract_flatdark
from stp_core.preprocess.flat_fielding import flat_fielding
from tifffile import imread, imsave # only for debug
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()
if __name__ == "__main__":
main(argv[1:])
|
tools_raw2tiff32¶
This section contains the tools_raw2tiff32 script.
Download file: tools_raw2tiff32.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
import os
import os.path
from sys import argv, exit
from tifffile import *
from numpy import zeros, fromfile, float32
def main(argv):
"""Convert an input 32-bit RAW image to TIFF format
Parameters
----------
argv[0] : string
The absolute path of input 32-bit RAW image file.
argv[1] : string
The absolute path of output 32-bit TIFF image file.
argv[2] : int
Width of the input RAW image.
argv[3] : int
Height of the input RAW image.
Example
-------
tools_raw2tiff32 "R:\\slice.raw" "R:\\slice.tiff" 2048 2048
"""
#
# Get the parameters:
#
infile = argv[0]
outfile = argv[1]
width = int(argv[2])
height = int(argv[3])
#
# Body
#
# Check if file exists:
if not os.path.exists(infile):
exit()
try:
# Prepare RAW matrix:
im = zeros((width,height), dtype=float32)
# Read RAW file:
im = fromfile(infile, float32).reshape((height,width))
# Save TIFF 32:
imsave(outfile, im)
except:
exit()
if __name__ == "__main__":
main(argv[1:])
|
tools_multioffset¶
This section contains the tools_multioffset script.
Download file: tools_multioffset.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: Sept, 28th 2016
#
# python:
from sys import argv, exit
from os import remove, sep, linesep, listdir, makedirs
from os.path import exists, dirname, basename, splitext
from numpy import array, finfo, copy, float32, double, amin, amax, tile, concatenate, asarray
from numpy import empty, reshape, log as nplog, arange, squeeze, fromfile, ndarray, where, meshgrid
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark
from stp_core.phaseretrieval.tiehom import tiehom, tiehom_plan
from stp_core.phaseretrieval.phrt import phrt, phrt_plan
from stp_core.reconstruct.rec_astra import recon_astra_fbp, recon_astra_iterative
from stp_core.reconstruct.rec_fista_tv import recon_fista_tv
from stp_core.reconstruct.rec_mr_fbp import recon_mr_fbp
from stp_core.reconstruct.rec_gridrec import recon_gridrec
from stp_core.postprocess.postprocess import postprocess
from stp_core.utils.padding import upperPowerOfTwo, padImage, padSmoothWidth
from stp_core.utils.caching import cache2plan, plan2cache
from tifffile import imread, imsave
from h5py import File as getHDF5
import stp_core.io.tdf as tdf
def write_log(lock, fname, logfilename):
"""To do...
"""
lock.acquire()
try:
# Print out execution time:
log = open(logfilename,"a")
log.write(linesep + "\t%s reconstructed." % basename(fname))
log.close()
finally:
lock.release()
def reconstruct(im, angles, angles_projfrom, angles_projto, offset, logtransform, param1, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, corr_offset, postprocess_required, convert_opt,
crop_opt, start, end, outpath, sino_idx, downsc_factor, logfilename, lock, slice_prefix):
"""Reconstruct a sinogram with FBP algorithm (from ASTRA toolbox).
Parameters
----------
im1 : array_like
Sinogram image data as numpy array.
center : float
Offset of the center of rotation to use for the tomographic
reconstruction with respect to the half of sinogram width
(default=0, i.e. half width).
logtransform : boolean
Apply logarithmic transformation before reconstruction (default=True).
filter : string
Filter to apply before the application of the reconstruction algorithm. Filter
types are: ram-lak, shepp-logan, cosine, hamming, hann, tukey, lanczos, triangular,
gaussian, barlett-hann, blackman, nuttall, blackman-harris, blackman-nuttall,
flat-top, kaiser, parzen.
circle : boolean
Create a circle in the reconstructed image and set to zero pixels outside the
circle (default=False).
"""
# Copy required due to multithreading:
im_f = im
# Decimate projections if required:
#if decim_factor > 1:
# im = im[::decim_factor,:]
# Upscale projections (if required):
if (abs(scale - 1.0) > finfo(float32).eps):
siz_orig1 = im_f.shape[1]
im_f = imresize(im_f, (im_f.shape[0], int(round(scale * im_f.shape[1]))), interp='bicubic', mode='F')
offset = int(offset * scale)
# Loop for all the required offsets for the center of rotation:
for i in range(int(round(start)), int(round(end)) + 1, downsc_factor):
offset = int(round(i/downsc_factor))
# Apply transformation for changes in the center of rotation:
if (offset != 0):
if (offset >= 0):
im_f = im_f[:,:-offset]
tmp = im_f[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im_f = concatenate((tmp.T,im_f), axis=1) # Concatenate tmp before the image
else:
im_f = im_f[:,abs(offset):]
tmp = im_f[:,im_f.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im_f = concatenate((im_f,tmp.T), axis=1) # Concatenate tmp after the image
# Downscale projections (without pixel averaging):
#if downsc_factor > 1:
# im = im[:,::downsc_factor]
# Scale image to [0,1] range (if required):
if (zerone_mode):
#print dset_min
#print dset_max
#print numpy.amin(im_f[:])
#print numpy.amax(im_f[:])
#im_f = (im_f - dset_min) / (dset_max - dset_min)
# Cheating the whole process:
im_f = (im_f - numpy.amin(im_f[:])) / (numpy.amax(im_f[:]) - numpy.amin(im_f[:]))
# Apply log transform:
if (logtransform == True):
im_f[im_f <= finfo(float32).eps] = finfo(float32).eps
im_f = -nplog(im_f + corr_offset)
# Replicate pad image to double the width:
if (pad):
dim_o = im_f.shape[1]
n_pad = im_f.shape[1] + im_f.shape[1] / 2
marg = (n_pad - dim_o) / 2
# Pad image:
im_f = padSmoothWidth(im_f, n_pad)
# Perform the actual reconstruction:
if (method.startswith('FBP')):
im_f = recon_astra_fbp(im_f, angles, method, param1)
elif (method == 'MR-FBP_CUDA'):
im_f = recon_mr_fbp(im_f, angles)
elif (method == 'FISTA-TV_CUDA'):
im_f = recon_fista_tv(im_f, angles, param1, param1)
elif (method == 'MLEM'):
im_f = recon_tomopy_iterative(im_f, angles, method, param1)
elif (method == 'GRIDREC'):
[im_f, im_f] = recon_gridrec(im_f, im_f, angles, param1)
else:
im_f = recon_astra_iterative(im_f, angles, method, param1, zerone_mode)
# Crop:
if (pad):
im_f = im_f[marg:dim_o + marg, marg:dim_o + marg]
# Resize (if necessary):
if (abs(scale - 1.0) > finfo(float32).eps):
im_f = imresize(im_f, (siz_orig1, siz_orig1), interp='nearest', mode='F')
# Apply post-processing (if required):
if postprocess_required:
im_f = postprocess(im_f, convert_opt, crop_opt)
else:
# Create the circle mask for fancy output:
if (circle == True):
siz = im_f.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_f = im_f * a
# Write down reconstructed image (file name modified with metadata):
if ( i >= 0 ):
fname = outpath + slice_prefix + '_' + str(sino_idx).zfill(4) + '_proj=' + str(angles_projto - angles_projfrom) + '_col=' + str((im_f.shape[1] + offset)*downsc_factor).zfill(4) + '_off=+' + str(abs(offset*downsc_factor)).zfill(4) + '.tif'
else:
fname = outpath + slice_prefix + '_' + str(sino_idx).zfill(4) + '_proj=' + str(angles_projto - angles_projfrom) + '_col=' + str((im_f.shape[1] + offset)*downsc_factor).zfill(4) + '_off=-' + str(abs(offset*downsc_factor)).zfill(4) + '.tif'
imsave(fname, im_f)
# Restore original image for next step:
im_f = im
# Write log (atomic procedure - lock used):
write_log(lock, fname, logfilename )
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, 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]).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)
# 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]/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:
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).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)
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/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()
# Actual reconstruction:
reconstruct(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)
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[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
phrtmethod = int(argv[30])
phrt_param1 = double(argv[31]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[32]) # param2( e.g. thresh or delta)
energy = double(argv[33])
distance = double(argv[34])
pixsize = double(argv[35]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[36] == "True" else False
approx_win = int(argv[37])
angles_projfrom = int(argv[38])
angles_projto = 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])
off_from = float(argv[43])
off_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):
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()
# 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, 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 )
# Sample:
# 26 C:\Temp\dataset42.tdf C:\Temp\test_offset 3.1416 1.0 shepp-logan 1.0 False True True True True False 5 False False 100 0 0 False rivers:11;0 False 0.0 FBP_CUDA 1 4 False - - False 0 1.0 1000.0 22 150 2.2 True 16 0 1163 False C:\Temp 1 -78 -70 slice C:\Temp\log_angles_00.txt
if __name__ == "__main__":
main(argv[1:])
|
tools_guessoverlap¶
This section contains the tools_guessoverlap script.
Download file: tools_guessoverlap.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
from math import pi
from numpy import float32, double, finfo, ndarray
from scipy.misc import imresize #scipy 0.12
from os import sep, remove
from os.path import basename
from sys import argv
from h5py import File as getHDF5
import time
# pystp-specific:
import stp_core.io.tdf as tdf
import stp_core.utils.findcenter as findcenter
from stp_core.utils.caching import cache2plan, plan2cache
from stp_core.preprocess.extract_flatdark import extract_flatdark
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()
if __name__ == "__main__":
main(argv[1:])
|
preview_preprocessing¶
This section contains the preview_preprocessing script.
Download file: preview_preprocessing.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: Sept, 28th 2016
#
from sys import argv, exit
from os import remove, sep, linesep
from os.path import exists
from numpy import float32, nanmin, nanmax, isscalar
from time import time
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.dynamic_flatfielding import dff_prepare_plan, dynamic_flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark, _medianize
from h5py import File as getHDF5
from stp_core.utils.caching import cache2plan, plan2cache
import stp_core.io.tdf as tdf
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])
# Method and parameters coded into a string:
ringrem = argv[11]
# Flat fielding method (conventional or dynamic):
dynamic_ff = True if argv[12] == "True" else False
# Tmp path and log file:
tmppath = argv[13]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[14]
# 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)
im = extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
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)
# 255 C:\Temp\Pippo.tdf C:\Temp\pippo 0 0 True True 900 False False 0 rivers:3;0 False C:\Temp C:\Temp\log_00.txt
if __name__ == "__main__":
main(argv[1:])
|
preview_postprocessing¶
This section contains the preview_postprocessing script.
Download file: preview_postprocessing.py
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | ###########################################################################
# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
from sys import argv, exit
from glob import glob
from os import linesep
from os.path import sep, basename, exists
from time import time
from numpy import float32, nanmin, nanmax
from multiprocessing import Process, Lock
# pystp-specific:
from stp_core.postprocess.postprocess import postprocess
from tifffile import imread, imsave
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()
# Get the from and to number of files to process:
idx = int(argv[0])
# Get input and output paths:
inpath = argv[1]
outfile = argv[2]
if not inpath.endswith(sep): inpath += sep
# Get parameters:
convert_opt = argv[3]
crop_opt = argv[4]
crop_opt = '0:0:0:0'
outprefix = argv[5]
logfilename = argv[6]
# Get the files in infile:
files = sorted(glob(inpath + '*.tif*'))
num_files = len(files)
if ((idx >= num_files) or (idx == -1)):
idx = num_files - 1
# Read the image:
im = imread(files[idx])
# Process the image:
im = postprocess(im, convert_opt, crop_opt)
# 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)
if __name__ == "__main__":
main(argv[1:])
|
preview_reconstruct¶
This section contains the preview_reconstruct script.
Download file: preview_reconstruct.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: Sept, 28th 2016
#
# python:
from sys import argv, exit
from os import remove, sep, linesep, listdir
from os.path import exists, dirname, basename, splitext
from numpy import array, finfo, copy, float32, double, amin, amax, tile, concatenate, asarray, isscalar, pi
from numpy import empty, reshape, log as nplog, arange, squeeze, fromfile, ndarray, where, meshgrid, roll
from time import time
from multiprocessing import Process, Array
# pystp-specific:
from stp_core.preprocess.extfov_correction import extfov_correction
from stp_core.preprocess.flat_fielding import flat_fielding
from stp_core.preprocess.dynamic_flatfielding import dff_prepare_plan, dynamic_flat_fielding
from stp_core.preprocess.ring_correction import ring_correction
from stp_core.preprocess.extract_flatdark import extract_flatdark, _medianize
from stp_core.phaseretrieval.tiehom import tiehom, tiehom_plan
from stp_core.phaseretrieval.phrt import phrt, phrt_plan
from stp_core.reconstruct.rec_astra import recon_astra_fbp, recon_astra_iterative
from stp_core.reconstruct.rec_fista_tv import recon_fista_tv
from stp_core.reconstruct.rec_mr_fbp import recon_mr_fbp
from stp_core.reconstruct.rec_gridrec import recon_gridrec
from stp_core.postprocess.postprocess import postprocess
from stp_core.utils.padding import upperPowerOfTwo, padImage, padSmoothWidth
from stp_core.utils.caching import cache2plan, plan2cache
from tifffile import imread, imsave
from h5py import File as getHDF5
import stp_core.io.tdf as tdf
def reconstruct(im, angles, offset, logtransform, recpar, circle, scale, pad, method,
zerone_mode, dset_min, dset_max, corr_offset, rolling, roll_shift):
"""Reconstruct a sinogram with FBP algorithm (from ASTRA toolbox).
Parameters
----------
im1 : array_like
Sinogram image data as numpy array.
center : float
Offset of the center of rotation to use for the tomographic
reconstruction with respect to the half of sinogram width
(default=0, i.e. half width).
logtransform : boolean
Apply logarithmic transformation before reconstruction (default=True).
filter : string
Filter to apply before the application of the reconstruction algorithm. Filter
types are: ram-lak, shepp-logan, cosine, hamming, hann, tukey, lanczos, triangular,
gaussian, barlett-hann, blackman, nuttall, blackman-harris, blackman-nuttall,
flat-top, kaiser, parzen.
circle : boolean
Create a circle in the reconstructed image and set to zero pixels outside the
circle (default=False).
"""
offset = int(round(offset))
# Upscale projections (if required):
if (abs(scale - 1.0) > finfo(float32).eps):
siz_orig1 = im.shape[1]
im = imresize(im, (im.shape[0], int(round(scale * im.shape[1]))), interp='bicubic', mode='F')
offset = int(offset * scale)
# Apply transformation for changes in the center of rotation:
if (offset != 0):
if (offset >= 0):
im = im[:,:-offset]
tmp = im[:,0] # Get first column
tmp = tile(tmp, (offset,1)) # Replicate the first column the right number of times
im = concatenate((tmp.T,im), axis=1) # Concatenate tmp before the image
else:
im = im[:,abs(offset):]
tmp = im[:,im.shape[1] - 1] # Get last column
tmp = tile(tmp, (abs(offset),1)) # Replicate the last column the right number of times
im = concatenate((im,tmp.T), axis=1) # Concatenate tmp after the image
# Sinogram rolling (if required). It doesn't make sense in limited angle tomography, so check if 180 or 360:
if ((rolling == True) and (roll_shift > 0)):
if ( (angles - pi) < finfo(float32).eps ):
# Flip the last rows:
im[-roll_shift:,:] = im[-roll_shift:,::-1]
# Now roll the sinogram:
im = roll(im, roll_shift, axis=0)
elif ((angles - pi*2.0) < finfo(float32).eps):
# Only roll the sinogram:
im = roll(im, roll_shift, axis=0)
# Scale image to [0,1] range (if required):
if (zerone_mode):
#print dset_min
#print dset_max
#print numpy.amin(im_f[:])
#print numpy.amax(im_f[:])
#im_f = (im_f - dset_min) / (dset_max - dset_min)
# Cheating the whole process:
im = (im - numpy.amin(im[:])) / (numpy.amax(im[:]) - numpy.amin(im[:]))
# Apply log transform:
if (logtransform == True):
im[im <= finfo(float32).eps] = finfo(float32).eps
im = -nplog(im + corr_offset)
# Replicate pad image to double the width:
if (pad):
dim_o = im.shape[1]
n_pad = im.shape[1] + im.shape[1] / 2
marg = (n_pad - dim_o) / 2
# Pad image:
im = padSmoothWidth(im, n_pad)
# Perform the actual reconstruction:
if (method.startswith('FBP')):
im = recon_astra_fbp(im, angles, method, recpar)
elif (method == 'MR-FBP_CUDA'):
im = recon_mr_fbp(im, angles)
elif (method == 'FISTA-TV_CUDA'):
im = recon_fista_tv(im, angles, recpar, recpar)
elif (method == 'GRIDREC'):
[im, im] = recon_gridrec(im, im, angles, recpar)
else:
im = recon_astra_iterative(im, angles, method, recpar, zerone_mode)
# Crop:
if (pad):
im = im[marg:dim_o + marg, marg:dim_o + marg]
# Resize (if necessary):
if (abs(scale - 1.0) > finfo(float32).eps):
im = imresize(im, (siz_orig1, siz_orig1), interp='nearest', mode='F')
# Return output:
return im.astype(float32)
#def _testwritedownsino(tmp_im):
# for ct in range(0, tmp_im.shape[0]):
# a = tmp_im[ct,:,:].squeeze()
# fname = 'C:\\Temp\\StupidFolder\\sino_' + str(ct).zfill(4) + '.tif'
# imsave(fname, a.astype(float32))
#def _testwritedownproj(tmp_im):
# for ct in range(0, tmp_im.shape[1]):
# a = tmp_im[:,ct,:].squeeze()
# fname = 'C:\\Temp\\StupidFolder\\proj_' + str(ct).zfill(4) + '.tif'
# imsave(fname, a.astype(float32))
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, 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, convert_opt,
crop_opt, dynamic_ff, EFF, filtEFF, im_dark, 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)
# 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)
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 = extfov_correction(test_im, ext_fov, ext_fov_rot_right, ext_fov_overlap / downsc_factor).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]).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)
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 = extfov_correction(test_im, ext_fov, ext_fov_rot_right, ext_fov_overlap / downsc_factor).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).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
# 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)
im = extfov_correction(im, ext_fov, ext_fov_rot_right, ext_fov_overlap)
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).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)
#print "With %d thread(s): [%0.3f sec, %0.3f sec, %0.3f sec]." % (nr_threads,
#t1-t0, t2-t1, t3-t2)
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[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
phrtmethod = int(argv[30])
phrt_param1 = double(argv[31]) # param1( e.g. regParam, or beta)
phrt_param2 = double(argv[32]) # param2( e.g. thresh or delta)
energy = double(argv[33])
distance = double(argv[34])
pixsize = double(argv[35]) / 1000.0 # pixsixe from micron to mm:
phrtpad = True if argv[36] == "True" else False
approx_win = int(argv[37])
angles_projfrom = int(argv[38])
angles_projto = int(argv[39])
rolling = True if argv[40] == "True" else False
roll_shift = int(argv[41])
preprocessingplan_fromcache = True if argv[42] == "True" else False
dynamic_ff = True if argv[43] == "True" else False
nr_threads = int(argv[44])
tmppath = argv[45]
if not tmppath.endswith(sep): tmppath += sep
logfilename = argv[46]
# 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):
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, 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, convert_opt, crop_opt, dynamic_ff, EFF, filtEFF, im_dark, nr_threads, logfilename)
# Sample:
# 311 C:\Temp\BrunGeorgos.tdf C:\Temp\BrunGeorgos.raw 3.1416 -31.0 shepp-logan
# 1.0 False False True True True True 5 False False 100 0 0 False rivers:11;0
# False 0.0 FBP_CUDA 1 1 False - - True 5 1.0 1000.0 22 150 2.2 True 16 0 1799
# True True 2 C:\Temp\StupidFolder C:\Temp\log_00.txt
if __name__ == "__main__":
main(argv[1:])
|
preview_phaseretrieval¶
This section contains the preview_phaseretrieval script.
Download file: preview_phaseretrieval.py
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# (C) 2016 Elettra - Sincrotrone Trieste S.C.p.A.. All rights reserved. #
# #
# #
# This file is part of STP-Core, the Python core of SYRMEP Tomo Project, #
# a software tool for the reconstruction of experimental CT datasets. #
# #
# STP-Core is free software: you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the #
# Free Software Foundation, either version 3 of the License, or (at your #
# option) any later version. #
# #
# STP-Core is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License #
# for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with STP-Core. If not, see <http://www.gnu.org/licenses/>. #
# #
###########################################################################
#
# Author: Francesco Brun
# Last modified: July, 8th 2016
#
from sys import argv, exit
from os import remove, sep, linesep
from os.path import exists
from numpy import float32, double, nanmin, nanmax, finfo, ndarray
from time import time
from multiprocessing import Process, Lock
from pyfftw.interfaces.cache import enable as pyfftw_cache_enable, disable as pyfftw_cache_disable
from pyfftw.interfaces.cache import set_keepalive_time as pyfftw_set_keepalive_time
# pystp-specific:
from stp_core.phaseretrieval.tiehom import tiehom, tiehom_plan
from stp_core.phaseretrieval.phrt import phrt, phrt_plan
from h5py import File as getHDF5
from stp_core.utils.caching import cache2plan, plan2cache
from stp_core.preprocess.extract_flatdark import extract_flatdark
import stp_core.io.tdf as tdf
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:
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)
if (method == 0):
# Paganin's:
plan = tiehom_plan (im, param1, param2, energy, distance, pixsize, pad)
im = tiehom(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)
if __name__ == "__main__":
main(argv[1:])
|
Credits¶
Citations¶
We kindly request that you cite the following article [A1] if you use project.
| [A1] | Francesco Brun, Serena Pacile, Agostino Accardo, George Kourousias, Diego Dreossi, Lucia Mancini, Giuliana Tromba, and Roberto Pugliese. Enhanced and flexible software tools for x-ray computed tomography at the italian synchrotron radiation facility elettra. Fundamenta Informaticae, 141(2-3):233–243, Oct 2015. URL: http://doi.org/10.3233/FI-2015-1273, doi:10.3233/FI-2015-1273. |
References¶
| [B1] | Francesco Brun, Serena Pacile, Agostino Accardo, George Kourousias, Diego Dreossi, Lucia Mancini, Giuliana Tromba, and Roberto Pugliese. Enhanced and flexible software tools for x-ray computed tomography at the italian synchrotron radiation facility elettra. Fundamenta Informaticae, 141(2-3):233–243, Oct 2015. URL: http://doi.org/10.3233/FI-2015-1273, doi:10.3233/FI-2015-1273. |