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load_data.py
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load_data.py
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import numpy as np
import nibabel as nib
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import gradient_table
"""
0 : no denoising
1 : denoising using AONLM
2 : NLMeans with rician averaging denoising
3 : NLMeans with gaussian averaging denoising
"""
dname = 'data/'
def read_data(fimg, fbvals, fbvecs):
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
return data, affine, gtab
def get_train_dti(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = ''
elif denoised == 1:
den = '_AONLM_1.00_rician'
dname2 = 'training_denoised/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'training_denoised/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'training_denoised/NLM_Gaussian/'
fimg = dname + dname2 + 'training-data_DWIS_dti-scheme_SNR-' + str(snr) + den + '.nii.gz'
fbvals = dname + 'dti-scheme.bval'
fbvecs = dname + 'dti-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_test_dti(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = 'elef_testing/'
elif denoised == 1:
den = '_AONLM_1.00_rician'
dname2 = 'elef_testing/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'elef_testing/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'elef_testing/NLM_Gaussian/'
fimg = dname + dname2 + 'DWIS_dti-scheme_SNR-' + str(snr) + den + '.nii.gz'
fbvals = dname + 'dti-scheme.bval'
fbvecs = dname + 'dti-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_train_hardi(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = ''
elif denoised == 1:
den = '_AONLM_1.00_rician'
dname2 = 'training_denoised/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'training_denoised/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'training_denoised/NLM_Gaussian/'
fimg = dname + dname2 + 'training-data_DWIS_hardi-scheme_SNR-' + str(snr) + den + '.nii.gz'
fbvals = dname + 'hardi-scheme.bval'
fbvecs = dname + 'hardi-scheme.bvec'
print fimg
return read_data(fimg, fbvals, fbvecs)
def get_test_hardi(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = 'elef_testing/'
elif denoised == 1:
den = '_AONLM_1.00_rician'
dname2 = 'elef_testing/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'elef_testing/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'elef_testing/NLM_Gaussian/'
if snr != 0 :
fimg = dname + dname2 + 'DWIS_hardi-scheme_SNR-' + str(snr) + den + '.nii.gz'
else :
fimg = dname + 'DWIS_hardi-scheme_no-noise.nii.gz'
fbvals = dname + 'hardi-scheme.bval'
fbvecs = dname + 'hardi-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_jc_hardi(snr=20):
if snr == 0 :
fimg = dname + 'DWIS_jc-hardi-scheme_no-noise.nii.gz'
else :
#print('Only SNR 20 supported for now')
fimg = dname + 'DWIS_jc-hardi-scheme_SNR-20.nii.gz'
fbvals = dname + 'jc-hardi-scheme.bval'
fbvecs = dname + 'jc-hardi-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_test_wm_mask(nb):
if nb < 10 :
fimg = dname + 'jc_bundles/0' + str(nb) + '.nii'
else :
fimg = dname + 'jc_bundles/' + str(nb) + '.nii'
print(fimg)
img = nib.load(fimg)
data = img.get_data()
return data
def get_test_mask():
fimg = dname + 'wm.nii'
img = nib.load(fimg)
data = img.get_data()
return data
def get_train_dsi(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = ''
elif denoised == 1:
den = '_AONLM_1.00_gauss'
dname2 = 'training_denoised/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'training_denoised/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'training_denoised/NLM_Gaussian/'
fimg = dname + dname2 + 'training-data_DWIS_dsi-scheme_SNR-' + str(snr) + den + '.nii.gz'
fbvals = dname + 'dsi-scheme.bval'
fbvecs = dname + 'dsi-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_test_dsi(snr=30, denoised=0):
if denoised == 0:
den = ''
dname2 = 'elef_testing/'
elif denoised == 1:
den = '_AONLM_1.00_gauss'
dname2 = 'elef_testing/Coupe/'
elif denoised == 2:
den = '_denoised_nlmeans_rician'
dname2 = 'elef_testing/NLM_Rician/'
elif denoised == 3:
den = '_denoised_nlmeans'
dname2 = 'elef_testing/NLM_Gaussian/'
fimg = dname + dname2 + 'DWIS_dsi-scheme_SNR-' + str(snr) + den + '.nii.gz'
fbvals = dname + 'dsi-scheme.bval'
fbvecs = dname + 'dsi-scheme.bvec'
return read_data(fimg, fbvals, fbvecs)
def get_specific_data(training, category, snr, denoise):
if denoise == False:
denoise = 0
if training == True:
if category == 'dti':
return get_train_dti(snr, denoise)
if category == 'hardi':
return get_train_hardi(snr, denoise)
if category == 'dsi':
return get_train_dsi(snr, denoise)
if training == False:
if category == 'dti':
return get_test_dti(snr, denoise)
if category == 'hardi':
return get_test_hardi(snr, denoise)
if category == 'dsi':
return get_test_dsi(snr, denoise)
def get_train_mask():
fimg = dname + 'training-data_mask.nii.gz'
img = nib.load(fimg)
return img.get_data(), img.get_affine()
def get_train_rois():
fimg = dname + 'training-data_rois.nii.gz'
img = nib.load(fimg)
return img.get_data(), img.get_affine()
def get_train_gt_fibers():
streamlines = []
for i in range(1, 21):
ffib = dname + '/ground-truth-fibers/fiber-'
ffib += str(i).zfill(2) + '.txt'
streamlines.append(np.loadtxt(ffib))
fradii = dname + '/ground-truth-fibers/fibers-radii.txt'
return streamlines, np.loadtxt(fradii)