""" 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) # print(int(a/2)) # s2 = astra1.sinogram(jet, np.pi/2, int(a/2)) # #graphs.colourmap(s2)
# 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')
graphs.colourmap(sino - offsino)
# mirror
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)
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
graphs.colourmap(sino_diff)
plt.title('Difference in sinograms.')
# do reconstructions
reconstruction90 = astra1.reconstruct(sino_mirr,'FBP', 100)
reconstruction180 = astra1.reconstruct(sinogram180,'FBP', 100)
# calculate relative error as a percentage (relative to phantom)
rel_err = diff / p * 100
return np.mean(rel_err) # return the mean so that it can be plotted
# create the original phantom
#phant_centr = phantoms.shockjet(129, 800, 4, 2000, 6)
phant_centr = phantoms.shockjet(129, 800, 4, 2000, 6)
#phant_centr2 = phantoms.shockjet(128, 800, 4, 1000, 12)
#sino_noise = astra1.sinogram(phant_centr, np.pi, 180, gaussian_noise=True, s_and_p_noise=True)
#reconstruction_noise = astra1.reconstruct(sino_noise,'SIRT', 100)
"""
sino_clean = astra1.sinogram(phant_centr, np.pi, 180)
sino_noise = astra1.sinogram(phant_centr, np.pi, 180, gaussian_noise=True, s_and_p_noise=True)
graphs.colourmap(phant_centr)
graphs.colourmap(sino_clean)
graphs.colourmap(sino_noise)
# do reconstruction
reconstruction_clean = astra1.reconstruct(sino_clean,'SIRT', 100)
reconstruction_noise = astra1.reconstruct(sino_noise,'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__":
# 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)
for n in range(len(targets)):
print(target_names[n])
hybrid_ref = np.zeros(np.shape(targets[n]))
g = 0
for sect in range(len(targetdata[n])):
lhp = targetdata[n][sect][1]
rhp = targetdata[n][sect][2]
sect_height = int(1500 / n_fringes)
best = 1E99
for m in range(len(refdata)):
ref = refdata[m]
dif = abs(ref[sect][1] - lhp) + abs(ref[sect][2] - rhp)
if dif < best:
best = dif
best_sect = m
hybrid_ref[g:g + sect_height, :] = refs[best_sect][g:g +
sect_height, :]
g += sect_height
hybrid_refs.append(hybrid_ref)
return hybrid_refs
hybr = batch_best_reference(
r'C:\Users\Elliot Prestidge\Documents\4TH YEAR\Project\Data\6-3-20\1.3mm_refs',
r'C:\Users\Elliot Prestidge\Documents\4TH YEAR\Project\Data\6-3-20\1.3mm_targs'
)
for r in hybr:
graphs.colourmap(r)
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]
plt.plot(x1, y1, label='Data used for fit', color='b')
####### 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
sino = stack[0:, height, 0:]
grad_subsino = grad_sub_stack[0:, height, 0:]
for x in range(np.shape(smooth_n_stack)[2]):
flipped_stack[:, :, -x] = temp_stack[:, :, x]
smooth_n_stack = np.copy(flipped_stack)
lineouts = []
for n in range(len(smooth_n_stack)):
p = smooth_n_stack[n]
if sum(p[100, :]) < 0:
p = -p
smooth_n_stack[n] = p
lineouts.append(p[100, :])
frame = 1
for p in smooth_n_stack:
graphs.colourmap(p)
#plt.clim(0,9) # set colourmap range
plt.title('Frame %i' % frame)
#plt.clim(0,2E18)
plt.savefig(str(frame))
frame += 1
#for n in range(len(lineouts)):
plt.figure()
for n in range(6):
colour = str((len(lineouts) + 0.2) / (n + 0.2))
plt.plot(range(np.shape(smooth_n_stack)[2]),
lineouts[n],
label='Frame:' + str(n))