tools_guessoverlapΒΆ
This section contains the tools_guessoverlap script.
Download file: tools_guessoverlap.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 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:])
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