""" Compare the accuracy of reconstructions made from projections taken over 180 degrees with ones taken over 90 degrees. """ import astra1 import matplotlib.pyplot as plt import numpy as np import phantoms import offset90_v2 import graphs #create phantom jet = phantoms.shockjet(129, 800, 4, 2000, 6) s1 = astra1.sinogram(jet, np.pi, 180) s2 = astra1.sinogram(jet, np.pi / 2, 90) s3 = offset90_v2.mirror_sinogram(s2) graphs.colourmap(s1) graphs.colourmap(s2) graphs.colourmap(s3) graphs.colourmap(s1 - s3) # reconstruct for different amount of angles and plot the average discrepancy.. # .. from the phantom jet #ang_discrepancy = [] #angles = range(2,50,10) # range of different number of angles to take projections at #for a in angles: # s1 = astra1.sinogram(jet, np.pi, a) # r1 = astra1.reconstruct(s1, 'SIRT', 100)
def resize(r, d):
# resizes the reconstruction to the desired size
# r is the reconstruction
# d is the desired size
resized = np.zeros((d, d))
start = int((r.shape[0] - d) / 2)
resized = r[start + 2:start + d + 2, start:start + d]
return resized
if __name__ == "__main__":
j = phantoms.offset_jet(0, 0)
sino = astra1.sinogram(j, np.pi / 2, 180)
# make offset jet
ofsj = phantoms.offset_jet(48, 55)
graphs.colourmap(ofsj)
plt.title('Original phantom')
# take projections and make sinogram
offsino = astra1.sinogram(ofsj, np.pi / 2, 180)
graphs.colourmap(offsino)
plt.title('90 degrees sinogram')
# straighten
offsino = centre_sino(offsino) #, np.pi/2, 48, 55)
graphs.colourmap(offsino)
plt.title('Line of symmetry through axis of rotation')
def supergaussianjet(n, s, p):
# n is dimenstions of array (128)
# s is the standard deviation of the gaussian
# p is the power in the exponent of the super-gaussian
s2 = s * s
x, y = np.meshgrid(range(int(-n / 2), int(n / 2), 1),
range(int(-n / 2), int(n / 2), 1))
rp = (x * x + y * y)**(p / 2)
return np.array(np.exp(-rp / (2 * s2)))
if __name__ == "__main__":
jet = shockjet(129, 800, 4, 2000, 6)
graphs.colourmap(jet)
sino = astra1.sinogram(jet, np.pi, 180)
graphs.colourmap(sino)
reconstruction = astra1.reconstruct(sino, 'SIRT', 40, np.pi)
graphs.colourmap(reconstruction)
"""
offset = offset_bar(10,20)
graphs.colourmap(offset)
sino = astra1.sinogram(offset, np.pi, 180)
#graphs.colourmap(sino)
"""
#gaussian = gaussianjet(128,800)
#graphs.colourmap(gaussian)
#sino = astra1.sinogram(gaussian, np.pi, 180)
#graphs.colourmap(sino)
#graphs.plot3d(gaussian)
ang_discrepancy_no_noise = []
ang_discrepancy_poisson_noise = []
ang_discrepancy_sandp_noise = []
ang_discrepancy_gaussian_noise = []
angles = range(1,90)#range of different number of angles to take projections at
num_iterations = 15
alg ='FBP'
poi = False
sandp = False
gauss = True
for a in angles:
# a is number of angles used to get sinogram
#print(a, 'angles')
sinogram_ = astra1.sinogram(jet, np.pi, a, poi, sandp, gauss)
sinogram_correct_units = real_units.num_density(sinogram_)
reconstruction = astra1.reconstruct(sinogram_correct_units, alg, num_iterations)
d = (np.mean(abs(jet_correct_units - reconstruction)) / np.mean(jet_correct_units)) *100
if poi == True:
ang_discrepancy_poisson_noise.append(d)
if sandp == True:
ang_discrepancy_sandp_noise.append(d)
if gauss == True:
ang_discrepancy_gaussian_noise.append(d)
if [poi,gauss,sandp] == [False,False,False]:
To investigate the accuracy of the reconstruction with different numbers of
iterations.
"""
import astra1
import matplotlib.pyplot as plt
import numpy as np
import phantoms
import pylab
#create phantom
jet = phantoms.shockjet(129, 800, 4, 2000, 6)
# reconstruct for different number of iterations and plot the average discrepancy..
# .. from the phantom jet
iterations = range(
1, 40) # range of different number of iterations to reconstruct with
iter_discrepancy = []
alg = 'SIRT'
for i in iterations:
# i is number of iterations used to get reconstruction
print(i, 'iterations')
s = astra1.sinogram(jet, np.pi, 90)
r = astra1.reconstruct(s, alg, i)
e = np.mean(abs(jet - r)) / np.mean(jet) * 100
iter_discrepancy.append(e)
plt.figure()
plt.plot(iterations, iter_discrepancy)
plt.xlabel('Number of iterations for reconstruction')
plt.ylabel('Average discrepancy relative to average phantom value (%)')
#pylab.ylim(0,50)
plt.figure(10)
diff_phant_clean = (phant_centr - reconstruction_clean)
diff_phant_noise = (phant_centr - reconstruction_noise)
plt.title('Lineout of the difference between the phantom and the reconstruction.')
graphs.lineout(diff_phant_clean)
graphs.lineout(diff_phant_noise)
"""
for n in n_iterations:
print(n,'iterations')
# find the different reconstructions of the phantom
print('Noise')
sino_noise = astra1.sinogram(phant_centr, np.pi, n_projections, gaussian_noise=True, s_and_p_noise=True)
fbp_noise = astra1.reconstruct(sino_noise, 'FBP', n)
sirt_noise = astra1.reconstruct(sino_noise, 'SIRT', n)
print('Clean')
sino_clean = astra1.sinogram(phant_centr, np.pi, n_projections)
fbp_clean = astra1.reconstruct(sino_clean, 'FBP', n)
sirt_clean = astra1.reconstruct(sino_clean, 'SIRT', n)
print('90 Clean')
sino_90 = astra1.sinogram(phant_centr, np.pi/2, n_projections)
#sino_90 = offset90_v2_29_11.centre_sino(sino_90)
sino_90 = offset90_v2_29_11.mirror_sinogram(sino_90)
fbp_90 = astra1.reconstruct(sino_90, 'FBP', n, np.pi)
#fbp_90 = offset90_v2_29_11.resize(fbp_90, 129)
sirt_90 = astra1.reconstruct(sino_90, 'SIRT', n, np.pi)
sino_mirr = np.zeros((2*num_angles,s.shape[1]))
sino_mirr[0:num_angles, 0:s.shape[1]] += s
sinogram2 = np.flip(s,0)
sino_mirr[num_angles:2*num_angles, 0:s.shape[1]] += sinogram2[0:]
return sino_mirr
if __name__ == '__main__':
#create phantom
jet = phantoms.shockjet(129,800,4,2000,6)
# create sinogram from phantom
num_angles = 90
sinogram90 = astra1.sinogram(jet, np.pi/2, num_angles)
sino_mirr = mirror_sinogram(sinogram90)
# image of sinogram
imageio.imwrite('sino_mirr.png', sino_mirr)
graphs.colourmap(sino_mirr)
sinogram180 = astra1.sinogram(jet, np.pi, 2*num_angles)
## image of the full proper sinogram
imageio.imwrite('sinogram180.png', sinogram180)
# difference of sinogram from 90 and the normal 180
sino_diff = sinogram180 - sino_mirr
import imageio
import numpy as np
import real_units
import phantoms
#create phantom
jet = phantoms.shockjet(129, 800, 4, 2000, 6)
#create phantom with correct units
jet_correct_units = real_units.num_density(jet)
# above is commented out as messes with reconstruction scaling issues - FIXED
#above is commented out as messes with addition of noise
# create sinogram from phantom
sinogram_ = astra1.sinogram(jet, np.pi, 180, False, False, True)
#sinogram__= astra1.sinogram(jet_correct_units,np.pi,180,True)
sinogram_correct_units = real_units.num_density(sinogram_)
"""
# create sinogram with noise from phantom
sinogram_poisson_noise = astra1.sinogram_poisson(,sinogram,jet, np.pi, 180)
sinogram_poisson_s_and_p = astra1.add_salt_pepper(jet_correct_units,np.pi,180,0.01)
"""
# create reconstruction
reconstruction = astra1.reconstruct(sinogram_correct_units, 'FBP', 100)
# birds eye view of phantom
imageio.imwrite("shockjet_phantom.png", jet_correct_units)
# d is the desired size
resized = np.zeros((d, d))
start = int((r.shape[0] - d) / 2)
resized = r[start + 2:start + d + 2, start:start + d]
return resized
if __name__ == "__main__":
# make offset jet
ofsj = phantoms.offset_jet(48, 55)
graphs.colourmap(ofsj)
plt.title('Original phantom')
# take projections and make sinogram
offsino = astra1.sinogram(ofsj, np.pi / 2, 90)
graphs.colourmap(offsino)
plt.title('90 degrees sinogram')
# straighten
offsino = centre_sino(offsino) #, np.pi/2, 48, 55)
graphs.colourmap(offsino)
plt.title('Line of symmetry through axis of rotation')
# mirror
offsino = mirror_sinogram(offsino)
graphs.colourmap(offsino)
plt.title('Mirrored to 180 degrees')
# reconstruction
off_recon = astra1.reconstruct(offsino, 'FBP', 100, np.pi)
fbp_noise_err = []
sirt_noise_err = []
fbp_clean_err = []
sirt_clean_err = []
fbp_90_err = []
sirt_90_err = []
fbp_90_noise_err = []
sirt_90_noise_err = []
for n in n_projections:
print(n,'projections')
# find the different reconstructions of the phantom
print('Noise')
sino_noise = astra1.sinogram(phant, np.pi, n, gaussian_noise=True)
fbp_noise = astra1.reconstruct(sino_noise, 'FBP', n_iterations)
sirt_noise = astra1.reconstruct(sino_noise, 'SIRT', n_iterations)
print('Clean')
sino_clean = astra1.sinogram(phant, np.pi, n)
fbp_clean = astra1.reconstruct(sino_clean, 'FBP', n_iterations)
sirt_clean = astra1.reconstruct(sino_clean, 'SIRT', n_iterations)
print('90 Clean')
sino_90 = astra1.sinogram(phant, np.pi/2, n)
sino_90 = offset90_v2.centre_sino(sino_90)
#graphs.colourmap(sino_90)
sino_90 = offset90_v2.mirror_sinogram(sino_90)
fbp_90 = astra1.reconstruct(sino_90, 'FBP', n_iterations, np.pi)
sirt_90 = astra1.reconstruct(sino_90, 'SIRT', n_iterations, np.pi)
