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read_recon_temp.py
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read_recon_temp.py
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# read files in Output folder
#%%
import os
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
Nel = 32 # number of electrodes
from skimage.segmentation import chan_vese
from EITLib.segmentation import scoring_function
import KTCScoring
#%%
dir_output = 'Output7/'
#%%
for i in range(1,5):
recon_file = sp.io.loadmat(dir_output + str(i) +'.mat')
plt.figure()
im = plt.imshow(recon_file['reconstruction'])
plt.title('reconstruction '+str(i))
plt.colorbar(im)
# plot the true conductivity
plt.figure()
phantom_file = sp.io.loadmat('GroundTruths/true'+str(i)+'.mat')
im = plt.imshow(phantom_file['truth'])
plt.title('true conductivity '+str(i))
plt.colorbar(im)
# load original reconstruction
plt.figure()
orig_recon = np.load(dir_output + str(i) +'.npz')['deltareco_pixgrid']
im = plt.imshow(np.log(orig_recon))
plt.title('log orig recon conductivity '+str(i))
plt.colorbar(im)
# load KTC challange recon
# segment with chan-vese
#plt.figure()
#seg = KTCScoring.cv_NLOpt(orig_recon, log_par=1.5, linear_par=1, exp_par=0)
#im = plt.imshow(seg)
#plt.colorbar(im)
#plt.title('chan-vese segmentation '+str(i))
print(i)
print(scoring_function(phantom_file['truth'], recon_file['reconstruction']))
# %%