sino_an = TomoP2D.ModelSino(model, N_size, P, angles, path_library2D)
plt.figure(2)
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_an, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
#%%
# Adding artifacts and noise
from tomophantom.supp.artifacts import _Artifacts_
# adding noise and data misalignment
noisy_sino_misalign = _Artifacts_(sinogram = sino_an, \
noise_type='Poisson', noise_sigma=10000, noise_seed = 0, \
sinoshifts_maxamplitude = 10)
# adding zingers, stripes and noise
noisy_zing_stripe = _Artifacts_(sinogram = sino_an, \
noise_type='Poisson', noise_sigma=10000, noise_seed = 0, \
zingers_percentage=0.25, zingers_modulus = 10,
stripes_percentage = 1.0, stripes_maxthickness = 1.0)
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(noisy_zing_stripe, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical noisy sinogram with artifacts.', model))
#%%
# initialise tomobar DIRECT reconstruction class ONCE
plt.show()
# Projection geometry related parameters:
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(0.25 * 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)
print("Generate 3D analytical projection data with TomoPhantom")
projData3D_analyt = TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det,
angles, path_library3D)
# adding noise
projData3D_analyt_noise = _Artifacts_(sinogram = projData3D_analyt, \
noise_type='Poisson', noise_sigma=8000, noise_seed = 0)
intens_max = 45
sliceSel = int(0.5 * N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noise[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (analytical)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noise[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noise[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
}
# adding zingers and stripes
_zingers_ = {'percentage': 0.25, 'modulus': 10}
_stripes_ = {
'percentage': 1.0,
'maxthickness': 3.0,
'intensity': 0.3,
'type': 'full',
'variability': 0.005
}
projData3D_analyt_noisy = _Artifacts_(projData3D_analyt,
_noise_,
_zingers_,
_stripes_,
_sinoshifts_={})
intens_max = 70
sliceSel = int(0.5 * N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noisy[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (erroneous)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noisy[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noisy[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
indicesROI = phantom_2D > 0
#%%
# Adding noise
from tomophantom.supp.artifacts import _Artifacts_
# forming dictionaries with artifact types
_noise_ = {
'noise_type': 'Poisson',
'noise_sigma': 10000, # noise amplitude
'noise_seed': 0
}
noisy_sino = _Artifacts_(sino_an, **_noise_)
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(noisy_sino, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical noisy sinogram', model))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # Horizontal detector dimension
DetectorsDimV=None, # Vertical detector dimension (3D case)
CenterRotOffset=0.001, # Center of Rotation scalar
# forming dictionaries with artifact types
_noise_ = {
'type': 'Poisson',
'sigma': 10000, # noise amplitude
'seed': 0
}
_stripes_ = {
'percentage': 1.5,
'maxthickness': 2.0,
'intensity': 0.02,
'type': 'full',
'variability': 0.01
}
projData3D_analyt_noisy = _Artifacts_(projData3D_analyt, _noise_, {},
_stripes_, {})
intens_max = 70
sliceSel = int(0.5 * N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noisy[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D noisy Projection (analytical)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noisy[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Noisy sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noisy[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Noisy tangentogram view')
plt.show()
#%%
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(0.25*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)
print ("Generate 3D analytical projection data with TomoPhantom")
projData3D_analyt= TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det, angles, path_library3D)
# adding noise
_noise_ = {'noise_type' : 'Poisson',
'noise_sigma' : 8000, # noise amplitude
'noise_seed' : 0}
projData3D_analyt_noise = _Artifacts_(projData3D_analyt, **_noise_)
intens_max = 45
sliceSel = int(0.5*N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noise[:,sliceSel,:],vmin=0, vmax=intens_max)
plt.title('2D Projection (analytical)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noise[sliceSel,:,:],vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noise[:,:,sliceSel],vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
#%%
# Adding artifacts and noise
from tomophantom.supp.artifacts import _Artifacts_
plt.close('all')
# forming dictionaries with artifact types
_noise_ = {
'noise_type': 'Poisson',
'noise_sigma': 10000, # noise amplitude
'noise_seed': 0
}
# misalignment dictionary
_sinoshifts_ = {'sinoshifts_maxamplitude': 10}
[noisy_sino_misalign, shifts] = _Artifacts_(sino_an, **_noise_, **_sinoshifts_)
# adding zingers and stripes
_zingers_ = {'zingers_percentage': 2, 'zingers_modulus': 10}
_stripes_ = {
'stripes_percentage': 0.8,
'stripes_maxthickness': 2.0,
'stripes_intensity': 0.25,
'stripes_type': 'full',
'stripes_variability': 0.002
}
noisy_zing_stripe = _Artifacts_(sino_an, **_noise_, **_zingers_, **_stripes_)
plt.figure()
# forming dictionaries with artifact types
_noise_ = {
'noise_type': 'Poisson',
'noise_sigma': 10000, # noise amplitude
'noise_seed': 0
}
_stripes_ = {
'stripes_percentage': 1.2,
'stripes_maxthickness': 3.0,
'stripes_intensity': 0.25,
'stripes_type': 'mix',
'stripes_variability': 0.005
}
projData3D_analyt_noisy = _Artifacts_(projData3D_analyt, **_noise_,
**_stripes_)
intens_max = 70
sliceSel = int(0.5 * N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noisy[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D noisy Projection (analytical)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noisy[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Noisy sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noisy[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Noisy tangentogram view')
plt.show()
#%%
#%%
# Adding artifacts and noise
from tomophantom.supp.artifacts import _Artifacts_
# forming dictionaries with artifact types
_noise_ = {
'noise_type': 'Poisson',
'noise_sigma': 5000, # noise amplitude
'noise_seed': 0,
'noise_prelog': True
}
# misalignment dictionary
_sinoshifts_ = {'sinoshifts_maxamplitude': 10}
[[sino_misalign, sino_misalign_raw],
shifts] = _Artifacts_(sino_an, **_noise_, **_sinoshifts_)
# adding zingers and stripes
_zingers_ = {'zingers_percentage': 0.25, 'zingers_modulus': 10}
_stripes_ = {
'stripes_percentage': 1.2,
'stripes_maxthickness': 3.0,
'stripes_intensity': 0.3,
'stripes_type': 'full'
}
[sino_artifacts, sino_artifacts_raw] = _Artifacts_(sino_an, **_noise_,
**_zingers_, **_stripes_)
plt.figure()
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt[:,:,sliceSel],vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
# Adding artifacts and noise
# forming dictionaries with artifact types
_noise_ = {'noise_type' : 'Poisson',
'noise_sigma' : 10000, # noise amplitude
'noise_seed' : 0,
'noise_prelog': True}
# misalignment dictionary
_datashifts_ = {'datashifts_maxamplitude_pixel' : 10}
[[projData3D_analyt_misalign, projData3D_analyt_misalign_raw], shifts2D] = _Artifacts_(projData3D_analyt, **_noise_, **_datashifts_)
# adding zingers and stripes
_zingers_ = {'zingers_percentage' : 0.25,
'zingers_modulus' : 10}
_stripes_ = {'stripes_percentage' : 1.2,
'stripes_maxthickness' : 3.0,
'stripes_intensity' : 0.3,
'stripes_type' : 'full',
'stripes_variability' : 0.005}
[projData3D_analyt_noisy, projData3D_raw] = _Artifacts_(projData3D_analyt, **_noise_, **_zingers_, **_stripes_)
intens_max = 70
sliceSel = int(0.5*N_size)
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_an, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
#%%
# Adding noise
from tomophantom.supp.artifacts import _Artifacts_
# forming dictionaries with artifact types
_noise_ = {
'type': 'Poisson',
'sigma': 8000, # noise amplitude
'seed': 0
}
noisy_sino = _Artifacts_(sino_an, _noise_, {}, {}, {})
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(noisy_sino, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical noisy sinogram', model))
#%%
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%")
print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(
DetectorsDimH=P, # DetectorsDimH # detector dimension (horizontal)
DetectorsDimV=
angles = np.linspace(0.0, 179.9, angles_num, dtype='float32') # in degrees
angles_rad = angles * (np.pi / 180.0)
print("Generate 3D analytical projection data with TomoPhantom")
projData3D_analyt = TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det,
angles, path_library3D)
# adding noise
# forming dictionaries with artifact types
_noise_ = {
'type': 'Poisson',
'sigma': 8000, # noise amplitude
'seed': 0
}
projData3D_analyt_noise = _Artifacts_(projData3D_analyt, _noise_, {}, {}, {})
intens_max = 45
sliceSel = int(0.5 * N_size)
plt.figure()
plt.subplot(131)
plt.imshow(projData3D_analyt_noise[:, sliceSel, :], vmin=0, vmax=intens_max)
plt.title('2D Projection (analytical)')
plt.subplot(132)
plt.imshow(projData3D_analyt_noise[sliceSel, :, :], vmin=0, vmax=intens_max)
plt.title('Sinogram view')
plt.subplot(133)
plt.imshow(projData3D_analyt_noise[:, :, sliceSel], vmin=0, vmax=intens_max)
plt.title('Tangentogram view')
plt.show()
#%%
def process_frames(self, data):
# print "The input data shape is", data[0].shape
if (self.out_shape_sino[1] == 1):
# create a 2D phantom
model = TomoP2D.Model(self.model, self.dims, self.path_library2D)
# create a 2D sinogram
projdata_clean = TomoP2D.ModelSino(self.model, self.dims,
self.detectors_num, self.angles,
self.path_library2D)
# Adding artifacts and noise
# forming dictionaries with artifact types
_noise_ = {
'type': self.parameters['artifacts_noise_type'],
'sigma': self.parameters['artifacts_noise_sigma'],
'seed': 0,
'prelog': False
}
# misalignment dictionary
_sinoshifts_ = {}
if self.parameters[
'artifacts_misalignment_maxamplitude'] is not None:
_sinoshifts_ = {
'maxamplitude':
self.parameters['artifacts_misalignment_maxamplitude']
}
# adding zingers and stripes
_zingers_ = {}
if self.parameters['artifacts_zingers_percentage'] is not None:
_zingers_ = {
'percentage':
self.parameters['artifacts_zingers_percentage'],
'modulus': 10
}
_stripes_ = {}
if self.parameters['artifacts_stripes_percentage'] is not None:
_stripes_ = {
'percentage':
self.parameters['artifacts_stripes_percentage'],
'maxthickness':
self.parameters['artifacts_stripes_maxthickness'],
'intensity':
self.parameters['artifacts_stripes_intensity'],
'type':
self.parameters['artifacts_stripes_type'],
'variability':
self.parameters['artifacts_stripes_variability']
}
if self.parameters[
'artifacts_misalignment_maxamplitude'] is not None:
[projdata,
shifts] = _Artifacts_(projdata_clean, _noise_, _zingers_,
_stripes_, _sinoshifts_)
else:
projdata = _Artifacts_(projdata_clean, _noise_, _zingers_,
_stripes_, _sinoshifts_)
else:
# create a 3D phantom
frame_idx = self.out_pData[0].get_current_frame_idx()[0]
model = TomoP3D.ModelSub(self.model, self.dims,
(frame_idx, frame_idx + 1),
self.path_library3D)
model = np.swapaxes(model, 0, 1)
model = np.flipud(model[:, 0, :])
# create a 3D projection data
projdata_clean = TomoP3D.ModelSinoSub(
self.model, self.dims, self.detectors_num, self.dims,
(frame_idx, frame_idx + 1), self.angles, self.path_library3D)
# Adding artifacts and noise
# forming dictionaries with artifact types
_noise_ = {
'type': self.parameters['artifacts_noise_type'],
'sigma': self.parameters['artifacts_noise_sigma'],
'seed': 0,
'prelog': False
}
# misalignment dictionary
_sinoshifts_ = {}
if self.parameters[
'artifacts_misalignment_maxamplitude'] is not None:
_sinoshifts_ = {
'maxamplitude':
self.parameters['artifacts_misalignment_maxamplitude']
}
# adding zingers and stripes
_zingers_ = {}
if self.parameters['artifacts_zingers_percentage'] is not None:
_zingers_ = {
'percentage':
self.parameters['artifacts_zingers_percentage'],
'modulus': 10
}
_stripes_ = {}
if self.parameters['artifacts_stripes_percentage'] is not None:
_stripes_ = {
'percentage':
self.parameters['artifacts_stripes_percentage'],
'maxthickness':
self.parameters['artifacts_stripes_maxthickness'],
'intensity':
self.parameters['artifacts_stripes_intensity'],
'type':
self.parameters['artifacts_stripes_type'],
'variability':
self.parameters['artifacts_stripes_variability']
}
if self.parameters[
'artifacts_misalignment_maxamplitude'] is not None:
[projdata,
shifts] = _Artifacts_(projdata_clean, _noise_, _zingers_,
_stripes_, _sinoshifts_)
else:
projdata = _Artifacts_(projdata_clean, _noise_, _zingers_,
_stripes_, _sinoshifts_)
projdata = np.swapaxes(projdata, 0, 1)
return [projdata, model]
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_an = TomoP2D.ModelSino(model, N_size, P, angles, path_library2D)
# forming dictionaries with artifact types
_noise_ = {'type': 'Gaussian', 'sigma': 3.5, 'seed': 1}
# adding zingers
_zingers_ = {'percentage': 0.3, 'modulus': 15}
sino_an_noisy = _Artifacts_(sino_an,
_noise_,
_zingers_,
_stripes_={},
_sinoshifts_={})
plt.figure(1)
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_an_noisy, cmap="BuPu")
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
print("Applying Median Filter in 2D...")
pars = {
'input_data': sino_an_noisy, # input grayscale image
'kernel_size': 5
}
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}' '{}'.format('Analytical sinogram of model no.', model))
#%%
# Adding artifacts and noise
from tomophantom.supp.artifacts import _Artifacts_
# forming dictionaries with artifact types
_noise_ = {
'type': 'Poisson',
'sigma': 10000, # noise amplitude
'seed': 0
}
# misalignment dictionary
_sinoshifts_ = {'maxamplitude': 10}
noisy_sino_misalign = _Artifacts_(sino_an, _noise_, {}, {}, _sinoshifts_)
# adding zingers and stripes
_zingers_ = {'percentage': 0.25, 'modulus': 10}
_stripes_ = {
'percentage': 0.8,
'maxthickness': 2.0,
'intensity': 0.25,
'type': 'full',
'variability': 0.002
}
noisy_zing_stripe = _Artifacts_(sino_an,
_noise_,
_zingers_,
angles_rad = angles*(np.pi/180.0)
P = int(np.sqrt(2)*N_size) #detectors
sino_an = TomoP2D.ModelSino(model, N_size, P, angles, path_library2D)
plt.figure(2)
plt.rcParams.update({'font.size': 21})
plt.imshow(sino_an, cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}''{}'.format('Analytical sinogram of model no.',model))
#%%
# Adding artifacts and noise
from tomophantom.supp.artifacts import _Artifacts_
# adding noise
noisy_sino = _Artifacts_(sinogram = sino_an, \
noise_type='Poisson', noise_sigma=10000, noise_seed = 0)
plt.figure()
plt.rcParams.update({'font.size': 21})
plt.imshow(noisy_sino,cmap="gray")
plt.colorbar(ticks=[0, 150, 250], orientation='vertical')
plt.title('{}''{}'.format('Analytical noisy sinogram',model))
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
from tomobar.methodsDIR import RecToolsDIR
RectoolsDIR = RecToolsDIR(DetectorsDimH = P, # 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)
'sigma': 0.1, # noise amplitude
'seed': None
}
# adding zingers and stripes
_zingers_ = {'percentage': 0.5, 'modulus': 50}
_stripes_ = {
'percentage': 1.2,
'maxthickness': 3.0,
'intensity': 0.25,
'type': 'partial',
'variability': 0.005
}
Object = _Artifacts_(Object, _noise_, _zingers_, _stripes_, _sinoshifts_={})
Object = gaussian_filter(Object, sigma=3)
plt.figure(2)
plt.rcParams.update({'font.size': 21})
plt.imshow(Object, vmin=0, vmax=1, cmap="BuPu")
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')
