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')
graphs.colourmap(sino - offsino)
# mirror
offsino = mirror_sinogram(offsino)
graphs.colourmap(offsino)
plt.title('Mirrored to 180 degrees')
# reconstruction
off_recon = astra1.reconstruct(offsino, 'FBP', 100, np.pi)
graphs.colourmap(off_recon)
plt.title('Reconstruction')
"""
# testing resizing works
testoff = phantoms.offset_jet(0, 0)
graphs.colourmap(testoff)
resized = resize(testoff, 129)
test = phantoms.shockjet(129, 800, 4, 2000, 6)
graphs.colourmap(resized-test)
plt.title('Difference')
"""
# 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_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]:
ang_discrepancy_no_noise.append(d)
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)
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)
#sirt_90 = offset90_v2_29_11.resize(sirt_90, 129)
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
graphs.colourmap(sino_diff)
plt.title('Difference in sinograms.')
# do reconstructions
reconstruction90 = astra1.reconstruct(sino_mirr,'FBP', 100)
reconstruction180 = astra1.reconstruct(sinogram180,'FBP', 100)
# difference between reconstruction from 90 degrees and 180 degrees
reconstruct_diff = reconstruction180 - reconstruction90
graphs.colourmap(reconstruct_diff)
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)
# image of sinogram
imageio.imwrite('sinogram.png', sinogram_correct_units)
# reconstruction
imageio.imwrite('reconstruction.png', reconstruction)
for x in range(0,np.shape(stack)[0]-10):
#graphs.colourmap(corrected_stack[x,0:,0:], equal=False)
graphs.colourmap(stack[x,0:,0:], equal=False)
#plt.clim(-4,5)
#plt.savefig(str(x))
#graphs.colourmap(corrected_stack[0,0:,0:], equal = False)
#graphs.colourmap(stack[0,0:,0:], equal = False)
"""
####### does reconstructions at different heights and plots them & sinos#######
for x in np.linspace(5, 125, 8, dtype=int):
#graphs.colourmap(corrected_stack[0:,x,0:], equal=False)
recon = astra1.reconstruct(corrected_stack[0:, x, 0:], 'SIRT', 100,
np.pi)
graphs.colourmap(recon)
plt.title(str(x))
####### plots a graph showing how the sinograms are corrected #######
mean = np.zeros(np.shape(stack[0, 0, 0:]))
for row in stack[0, 0:, 0:]:
mean = mean + row
print(row)
mean = mean / np.shape(stack[0, 0:, 0:])[1]
plt.figure()
x1 = list(range(lower_lim))
y1 = mean[0:lower_lim]
#n_stack = real_units.num_density(stack)
#smooth_n_stack = improve_data.smooth_sinos(n_stack)
####### does reconstructions at different heights #######
number_of_slices = np.shape(n_stack)[1]
reconstructions = []
for x in np.linspace(0, np.shape(n_stack)[1] - 1, number_of_slices, dtype=int):
print(x)
#sino_90 = offset90_v2_29_11.centre_sino(smooth_n_stack[0:90,x,0:])
#sino_180 = offset90_v2_29_11.mirror_sinogram(sino_90)
#sino_180 = offset90_v2_29_11.centre_sino(smooth_n_stack[0:180,x,0:])
sino_180 = offset90_v2_29_11.centre_sino(n_stack[0:180, x, 0:])
reconstructions.append(astra1.reconstruct(sino_180, 'SIRT', 30, np.pi))
for n in range(len(reconstructions)):
graphs.colourmap(reconstructions[n])
#row_num = int((n+1)*np.shape(n_stack)[1]/number_of_slices)
row_num = int(
(np.shape(n_stack)[1] - n) * np.shape(n_stack)[1] / number_of_slices)
plt.title('Row %i' % row_num)
#plt.clim(0,8E18) # set constant colourmap
plt.savefig(str(row_num))
#plt.close()
################ show improvement process: ##############################
############## as improvement in the sinogram #############################
# choose a height:
height = 120
