datadict = scipy.io.loadmat('../../../data/DendrRawData.mat')
# extract data (print(datadict.keys()))
dataRaw = datadict['data_raw3D']
angles = datadict['angles']
flats = datadict['flats_ar']
darks = datadict['darks_ar']
flats2 = np.zeros((1, np.size(flats, 0), np.size(flats, 1)), dtype='float32')
flats2[0, :, :] = flats[:]
darks2 = np.zeros((1, np.size(darks, 0), np.size(darks, 1)), dtype='float32')
darks2[0, :, :] = darks[:]
dataRaw = np.swapaxes(dataRaw, 0, 1)
# normalise the data, required format is [Projections, detectorsHoriz, Slices]
data_norm = normaliser(dataRaw, flats2, darks2, log='log')
dataRaw = np.float32(np.divide(dataRaw, np.max(dataRaw).astype(float)))
detectorHoriz = np.size(data_norm, 1)
N_size = 1000
slice_to_recon = 19 # select which slice to reconstruct
angles_rad = angles * (np.pi / 180.0)
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=
detectorHoriz, # DetectorsDimH # detector dimension (horizontal)
jitter = 5.0,\
sigmasmooth = 3, flatsnum=flatsnum)
plt.figure()
plt.subplot(121)
plt.imshow(projData3D_noisy[:, 0, :])
plt.title('2D Projection (before normalisation)')
plt.subplot(122)
plt.imshow(flatsSIM[:, 0, :])
plt.title('A selected simulated flat-field')
plt.show()
#%%
print("Normalise projections using ToMoBAR software")
# normalise the data, the required data format is [detectorsX, Projections, detectorsY]
projData3D_norm = normaliser(projData3D_noisy,
flatsSIM,
darks=None,
log='true',
method='mean')
intens_max = 0.15 * np.max(projData3D_norm)
sliceSel = 150
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_norm[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('Normalised 2D Projection (erroneous)')
plt.subplot(132)
plt.imshow(projData3D_norm[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_norm[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.figure()
plt.subplot(121)
plt.imshow(projData3D_noisy[:, 0, :])
plt.title('2D Projection (before normalisation)')
plt.subplot(122)
plt.imshow(flatsSIM[:, 0, :])
plt.title('A selected simulated flat-field')
plt.show()
#%%
print("Normalise projections using ToMoBAR software")
from tomobar.supp.suppTools import normaliser
# normalise the data, the required format is [detectorsX, Projections, detectorsY]
projData3D_norm = normaliser(projData3D_noisy,
flatsSIM,
darks=None,
log='true',
method='mean')
del projData3D_noisy
intens_max = np.max(projData3D_norm) * 0.5
sliceSel = 150
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_norm[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (erroneous)')
plt.subplot(132)
plt.imshow(projData3D_norm[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_norm[:, :, sliceSel], vmin=0, vmax=intens_max)
# load dendritic data
datadict = scipy.io.loadmat('../../data/DendrRawData.mat')
# extract data (print(datadict.keys()))
dataRaw = datadict['data_raw3D']
angles = datadict['angles']
flats = datadict['flats_ar']
darks= datadict['darks_ar']
flats2 = np.zeros((np.size(flats,0),1, np.size(flats,1)), dtype='float32')
flats2[:,0,:] = flats[:]
darks2 = np.zeros((np.size(darks,0),1, np.size(darks,1)), dtype='float32')
darks2[:,0,:] = darks[:]
# normalise the data, required format is [detectorsHoriz, Projections, Slices]
data_norm = normaliser(dataRaw, flats2, darks2, log='log', method = 'mean')
dataRaw = np.float32(np.divide(dataRaw, np.max(dataRaw).astype(float)))
data_norm = np.swapaxes(data_norm,0,2)
dataRaw = np.swapaxes(dataRaw,0,2)
detectorHoriz = np.size(data_norm,2)
N_size = 1000
slice_to_recon = 19 # select which slice to reconstruct
angles_rad = angles[:,0]*(np.pi/180.0)
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(DetectorsDimH = detectorHoriz, # Horizontal detector dimension
darks = np.array(h5f["darks"], dtype=np.float64)
phantom_tm = np.array(h5f["phantom"], dtype=np.float64)
h5f.close()
# Projection geometry related parameters:
N_size = 256 # Define phantom dimensions using a scalar value (cubic phantom)
Horiz_det = int(np.sqrt(2)*N_size) # detector column count (horizontal)
Vert_det = N_size # detector row count (vertical) (no reason for it to be > N)
angles_num = int(0.5*np.pi*N_size); # angles number
angles = np.linspace(0.0,179.9,angles_num,dtype='float32') # in degrees
angles_rad = angles*(np.pi/180.0)
intens_max_clean = projData3D_analyt.max()
#%%
from tomobar.supp.suppTools import normaliser
projData3D_norm = normaliser(projData3D_noisy, flats, darks, log='True', method='mean')
intens_max = np.max(projData3D_norm)
sliceSel = 150
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
plt.title('2D Projection (erroneous)')
plt.subplot(132)
plt.imshow(projData3D_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
h5f = h5py.File("C:/Users/lqg38422/Desktop/PSI_Phantom/flats_corrected.h5",
"r")
flats_full = np.array(h5f["/flats"], dtype=np.float64)
flats_full = np.swapaxes(flats_full, 1, 0)
flats = flats_full[:N_cut, :, :]
del flats_full
plt.figure()
plt.imshow(flats[:, 5, :], vmin=0, vmax=15000, cmap="gray")
print("Done!")
#%%
# normalising projections:
print("Mean normalisation...")
projections_norm = normaliser(projections,
flats,
darks,
log='true',
method='mean')
plt.figure()
plt.axis('off')
plt.imshow(projections_norm[:, 5, :], vmin=-0.1, vmax=0.4, cmap="gray")
print("Done!")
#%%
import numpy as np
import scipy
from skimage.transform import downscale_local_mean
import bm3d
def DFFC(data, flats, darks, downsample=20):