def test_save_sample(self):
dataset = ImagesDataset(self.subjects_list, transform=lambda x: x)
_ = len(dataset) # for coverage
sample = dataset[0]
output_path = self.dir / 'test.nii.gz'
paths_dict = {'t1': output_path}
dataset.save_sample(sample, paths_dict)
nii = nib.load(str(output_path))
ndims_output = len(nii.shape)
ndims_sample = len(sample['t1'][DATA].shape)
assert ndims_sample == ndims_output + 1
fitpars = np.zeros((6, nT))
fitpars[1, 55:] = -15
#fitpars[dim_modif, :45] = -7.5
#fitpars[dim_modif, 45:] = 7.5
#ov(sample["T1"]["data"][0], sample["T1"]["affine"])
transform = RandomMotionTimeCourseAffines(fitpars=fitpars,
time_points=time_points,
pct_oversampling=0.30,
verbose=True,
combine_axis=0)
transformed = transform(sample)
dataset.save_sample(
transformed,
dict(T1='/home/romain.valabregue/tmp/mot/t1_motion_axis0.nii.gz'))
sample = dataset[0]
transform = RandomMotionTimeCourseAffines(fitpars=fitpars,
time_points=time_points,
pct_oversampling=0.30,
verbose=True,
combine_axis=1)
transformed = transform(sample)
dataset.save_sample(
transformed,
dict(T1='/home/romain.valabregue/tmp/mot/t1_motion_axis1.nii.gz'))
sample = dataset[0]
transform = RandomMotionTimeCourseAffines(fitpars=fitpars,
ddno = dataset_not[0] #
dd= next(iter(dataload))
ddno = next(iter(dataloadnot))
ii = np.squeeze( dd['image']['data'][0,0,:],axis=1)
iio = np.squeeze( ddno['image']['data'][0,0,:],axis=1)
ov(ii)
ov(iio)
#save
sample = dataset[0]
output = dict(image=Path('/tmp/test_im.nii.gz'),label1=Path('/tmp/test_p1.nii.gz'),label2=Path('/tmp/test_p2.nii.gz'))
dataset.save_sample(sample, output)
#explore
out_dir = '/data/romain/data_exemple/augment/'
suj = [[
Image('T1','/data/romain/data_exemple/nifti_proc/PRISMA_MBB_DB/2017_03_07_DEV_236_MBB_DB_Pilote02/anat_S02_t1mpr_SAG_NSel_S176/cat12/s_S02_t1mpr_SAG_NSel_S176.nii.gz',INTENSITY),
Image('mask','/data/romain/data_exemple/nifti_proc/PRISMA_MBB_DB/2017_03_07_DEV_236_MBB_DB_Pilote02/anat_S02_t1mpr_SAG_NSel_S176/cat12/mask_brain_erode_dilate.nii.gz',LABEL)
]]
transforms = Compose((RandomBiasField(coefficients_range=(-0.5, 0.5),order=3, verbose=True), ))
landmarks_file = '/data/romain/data_exemple/landmarks_hcp300_res100.txt'
transforms = Compose((HistogramStandardization(landmarks_file, verbose=True, masking_method='mask'), Rescale(masking_method='mask',verbose=True)))
transforms = HistogramEqualize(verbose=True,masking_method='mask')
transforms = Rescale(verbose=True,masking_method='mask')
torch.manual_seed(12)
$ torchio-transform ~/Dropbox/MRI/t1.nii.gz RandomMotion /tmp/t1_motion.nii.gz --seed 42 --kwargs "degrees=10 translation=10 num_transforms=3 proportion_to_augment=1"
"""
from pprint import pprint
from torchio import Image, ImagesDataset, transforms, INTENSITY, LABEL, Subject
subject = Subject(
Image('label', '~/Dropbox/MRI/t1_brain_seg.nii.gz', LABEL),
Image('t1', '~/Dropbox/MRI/t1.nii.gz', INTENSITY),
)
subjects_list = [subject]
dataset = ImagesDataset(subjects_list)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=42,
degrees=10,
translation=10,
num_transforms=3,
)
transformed = transform(sample)
pprint(transformed['t1']['random_motion_times'])
pprint(transformed['t1']['random_motion_degrees'])
pprint(transformed['t1']['random_motion_translation'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(label='/tmp/t1_brain_seg_motion.nii.gz'))
Image('resection_gray_matter_left', images_dir / gp('gray_matter_left_seg'), None),
Image('resection_resectable_left', images_dir / gp('resectable_left_seg'), None),
Image('resection_gray_matter_right', images_dir / gp('gray_matter_right_seg'), None),
Image('resection_resectable_right', images_dir / gp('resectable_right_seg'), None),
)
df_volumes = pd.read_csv(Path('~/episurg/volumes.csv').expanduser())
volumes = df_volumes.Volume.values
transform = RandomResection(
volumes=volumes,
# sigmas_range=(0.75, 0.75),
keep_original=True,
verbose=True,
# seed=42,
)
dataset = ImagesDataset([subject])
transformed = dataset[0]
for i in range(10):
transformed = transform(dataset[0])
dataset.save_sample(
transformed,
dict(
image=f'/tmp/resected_{i}.nii.gz',
# image_original='/tmp/resected_original.nii.gz',
label=f'/tmp/resected_label_{i}.nii.gz',
),
)
suj = [[
Image(
'T1',
'/home/romain/QCcnn/motion_cati_brain_ms/brain_s_S02_Sag_MPRAGE.nii.gz',
'intensity'),
]]
dataset = ImagesDataset(suj, transform=transforms)
s = dataset[0]
ov(s['T1']['data'][0])
tt = dataset.get_transform()
plt.figure()
plt.plot(tt.fitpars.T)
dataset.save_sample(
s, dict(T1='/home/romain/QCcnn/motion_cati_brain_ms/toto10.nii'))
#look at distribution of metric on simulate motion
dir_cache = '/network/lustre/dtlake01/opendata/data/ds000030/rrr/CNN_cache'
dd = gdir(dir_cache, 'mask_mv')
fr = gfile(dd, 'resul')
name_res = get_parent_path(dd)[1]
res = [pd.read_csv(ff) for ff in fr]
sell_col = res[0].keys()
sell_col = [
'L1', 'MSE', 'corr', 'mean_DispP', 'rmse_Disp', 'rmse_DispTF', 'ssim',
'ssim_all', 'ssim_brain', 'ssim_p1', 'ssim_p2'
]
sell_col = [
'L1', 'MSE', 'corr', 'ssim', 'ssim_all', 'ssim_brain', 'ssim_p1', 'ssim_p2'
from pprint import pprint
from torchio import ImagesDataset, transforms, INTENSITY
paths = [{
't1': dict(path='~/Dropbox/MRI/t1.nii.gz', type=INTENSITY),
'colin': dict(path='/tmp/colin27_t1_tal_lin.nii.gz', type=INTENSITY),
}]
dataset = ImagesDataset(paths)
sample = dataset[0]
transform = transforms.RandomMotion(
seed=42,
degrees=20,
translation=15,
num_transforms=3,
verbose=True,
)
transformed = transform(sample)
pprint(transformed['t1']['random_motion_times'])
dataset.save_sample(transformed, dict(t1='/tmp/t1_motion.nii.gz'))
dataset.save_sample(transformed, dict(colin='/tmp/colin_motion.nii.gz'))
from torchio.transforms import Lambda
from torchio import Image, ImagesDataset, INTENSITY, LABEL, Subject
subject = Subject(
Image('label', '~/Dropbox/MRI/t1_brain_seg.nii.gz', LABEL),
Image('t1', '~/Dropbox/MRI/t1.nii.gz', INTENSITY),
)
subjects_list = [subject]
dataset = ImagesDataset(subjects_list)
sample = dataset[0]
transform = Lambda(lambda x: -1.5 * x, types_to_apply=INTENSITY)
transformed = transform(sample)
dataset.save_sample(transformed, {'t1': '/tmp/t1_lambda.nii'})
fp = corrupt_data(80, sigma=1, method='step', amplitude=20)
fp = np.zeros([6, 200])
fp[3, :] = 20
dico_params['fitpars'] = fp
for zs in z_slice:
t = RandomMotionFromTimeCourse(**dico_params)
dataset = ImagesDataset(suj,
transform=Compose(
(CenterCropOrPad(target_shape=(182, 218, zs)),
t)))
sample = dataset[0]
fout = dirpath[0] + '/mot_step_rot_xN{}'.format(zs)
dataset.save_sample(sample, dict(image=fout + '.nii'))
if do_plot:
#check the fitpars
fit_pars = t.fitpars
fig = plt.figure('fitpars_{}'.format(zs))
plt.plot(fit_pars.T)
fit_pars = t.fitpars_interp
fig = plt.figure('fit_interp_{}'.format(zs))
plt.plot(fit_pars.reshape(6, -1).T)
sys.exit(1)
#3D case
#def rotate_coordinates():
import math
Image('mask', '/data/romain/HCPdata/suj_100307/brain_mT1w_1mm.nii', LABEL)
]]
t = MotionSimTransform(std_rotation_angle=3,
std_translation=2,
nufft=True,
proc_scale=0,
verbose=True,
freq_encoding_dim=(0, ),
mvt_param=[0, 0, 0, 1, 10, 0])
transforms = Compose([t])
dataset = ImagesDataset(suj, transform=transforms)
sample = dataset[0]
dataset.save_sample(sample, dict(T1='/tmp/toto_no_center.nii'))
sample_orig = dataset[0]
for i in range(0, 1):
sample = deepcopy(sample_orig)
transformed = transforms(sample)
name = 'mot'
path = out_dir + f'{i}_{name}.nii.gz'
dataset.save_sample(transformed, dict(T1=path))
########################################################### T E S T S with txt
import pandas as pd
import nibabel as nb
