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)