def test_tomobarDIR(self):
model = 4 # select a model
N_size = 64 # set dimension of the phantom
# create sinogram analytically
angles_num = int(0.5 * np.pi * N_size)
# angles number
angles = np.linspace(0.0, 179.9, angles_num, dtype='float32')
angles_rad = angles * (np.pi / 180.0)
P = int(np.sqrt(2) * N_size) # detectors
sino_num = np.ones((P, angles_num))
RectoolsDirect = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=0.0, # Center of Rotation (CoR) scalar
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device_projector='cpu')
"""
RectoolsIterative = RecToolsIR(DetectorsDimH = P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV = None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset = 0.0, # Center of Rotation (CoR) scalar
AnglesVec = angles_rad, # array of angles in radians
ObjSize = N_size, # a scalar to define reconstructed object dimensions
datafidelity = 'LS',
device_projector = 'cpu')
"""
RecFourier = RectoolsDirect.FOURIER(sino_num, 'linear')
h5f.close()
data_norm = np.swapaxes(data_norm[:, :, 0], 0, 1)
data_raw = np.swapaxes(data_raw[:, :, 0], 0, 1)
N_size = 2000
angles_number, detectorHoriz = np.shape(data_norm)
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=detectorHoriz, # Horizontal detector dimension
DetectorsDimV=None, # Vertical detector dimension (3D case)
CenterRotOffset=92, # Center of Rotation scalar
AnglesVec=angles_rad, # A vector of projection angles in radians
ObjSize=N_size, # Reconstructed object dimensions (scalar)
device_projector='gpu')
FBPrec = RectoolsDIR.FBP(data_norm)
plt.figure()
#plt.imshow(FBPrec[500:1500,500:1500], vmin=0, vmax=1, cmap="gray")
plt.imshow(FBPrec, vmin=0, vmax=1, cmap="gray")
plt.title('FBP reconstruction')
#%%
from tomobar.methodsIR import RecToolsIR
# set parameters and initiate a class object
Rectools = RecToolsIR(
DetectorsDimH=detectorHoriz, # Horizontal detector dimension
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)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=None, # Center of Rotation (CoR) scalar (for 3D case only)
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N_size, # a scalar to define reconstructed object dimensions
device='gpu')
FBPrec = RectoolsDIR.FBP(data_norm[:, :, slice_to_recon])
plt.figure()
plt.imshow(FBPrec[150:550, 150:550], vmin=0, vmax=0.005, cmap="gray")
plt.title('FBP reconstruction')
from tomobar.methodsIR import RecToolsIR
# set parameters and initiate a class object
Rectools = RecToolsIR(
DetectorsDimH=
plt.imshow(ProjData3D[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (analytical)')
plt.subplot(132)
plt.imshow(ProjData3D[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(ProjData3D[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
#%%
# initialise tomobar DIRECT reconstruction class ONCE
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=Horiz_det, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
AnglesVec=angles_rad, # array of angles in radians
ObjSize=N3D_size, # a scalar to define reconstructed object dimensions
device='gpu')
#%%
"""
print ("Adding noise to projection data")
from tomophantom.supp.artifacts import ArtifactsClass
artifacts_add = ArtifactsClass(projData3D_analyt)
projData3D_analyt_noisy = artifacts_add.noise(sigma=15000,noisetype='Poisson')
intens_max = 70
sliceSel = 150
plt.figure()
plt.subplot(131)
plt.figure()
plt.axis('off')
plt.imshow(projections_dff[:, 5, :], vmin=-0.1, vmax=0.4, cmap="gray")
#%%
# do reconstruction
angles_rad = np.zeros(N_size)
angles_step_rad = (360.0 / float(N_size)) * np.pi / 180.0
for i in range(0, N_size):
angles_rad[i] = i * angles_step_rad
RectoolsDIR = RecToolsDIR(
DetectorsDimH=N_cut, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
None, # DetectorsDimV # detector dimension (vertical) for 3D case only
CenterRotOffset=-118.0, # Center of Rotation (CoR) scalar
AnglesVec=np.float32(angles_rad), # array of angles in radians
ObjSize=1600, # a scalar to define reconstructed object dimensions
device_projector='cpu')
print("Reconstruction using FBP from tomobar")
recFBP = RectoolsDIR.FBP(projections_norm[:, 5, :]) # FBP reconstruction
plt.figure()
plt.imshow(recFBP, vmin=0, vmax=0.00095, cmap="gray")
#%%
# do reconstruction
angles_rad = np.zeros(N_cut)
angles_step_rad = (360.0 / float(N_cut)) * np.pi / 180.0
for i in range(0, N_cut):
plt.colorbar(ticks=[0, 0.5, 1], orientation='vertical')
plt.title('{}'.format('Distorted Phantom'))
#%%
# Generate projection data of distorted phantom
angles_num = int(np.pi * N_size)
# angles number
angles = np.linspace(0.0, 179.9, angles_num, dtype='float32')
angles_rad = angles * (np.pi / 180.0)
P = N_size #detectors
Rectools = RecToolsDIR(
DetectorsDimH=P, # Horizontal detector dimension
DetectorsDimV=None, # Vertical detector dimension (3D case)
CenterRotOffset=0.0, # Center of Rotation scalar
AnglesVec=angles_rad, # A vector of projection angles in radians
ObjSize=N_size, # Reconstructed object dimensions (scalar)
device_projector='gpu')
sino_num = Rectools.FORWPROJ(Object)
_noise_ = {}
_zingers_ = {}
_sinoshifts_ = {}
_stripes_ = {
'percentage': 0.75,
'maxthickness': 2.0,
'intensity': 0.15,
'type': 'mix',
