def get_motion_transform(type='motion1'):
if 'motion1' in type:
dico_params_mot = {
"maxDisp": (1, 6),
"maxRot": (1, 6),
"noiseBasePars": (5, 20, 0.8),
"swallowFrequency": (2, 6, 0.5),
"swallowMagnitude": (3, 6),
"suddenFrequency": (2, 6, 0.5),
"suddenMagnitude": (3, 6),
"verbose": False,
"keep_original": True,
"proba_to_augment": 1,
"preserve_center_pct": 0.1,
"keep_original": True,
"compare_to_original": True,
"oversampling_pct": 0,
"correct_motion": False
}
dico_params_mot = {
"maxDisp": (1, 4),
"maxRot": (1, 4),
"noiseBasePars": (5, 20, 0.8),
"swallowFrequency": (2, 6, 0.5),
"swallowMagnitude": (3, 4),
"suddenFrequency": (2, 6, 0.5),
"suddenMagnitude": (3, 4),
"verbose": False,
"keep_original": True,
"proba_to_augment": 1,
"preserve_center_pct": 0.1,
"keep_original": True,
"compare_to_original": True,
"oversampling_pct": 0,
"correct_motion": False
}
if 'elastic1' in type:
dico_elast = {
'num_control_points': 6,
'max_displacement': (30, 30, 30),
'proportion_to_augment': 1,
'image_interpolation': Interpolation.LINEAR
}
if type == 'motion1':
transforms = Compose([
RandomMotionFromTimeCourse(**dico_params_mot),
])
elif type == 'elastic1_and_motion1':
transforms = Compose([
RandomElasticDeformation(**dico_elast),
RandomMotionFromTimeCourse(**dico_params_mot)
])
if type == 'random_noise_1':
transforms = Compose([RandomNoise(std=(0.020, 0.2))])
return transforms
MapMetricWrapper("L1_map",
lambda x, y: torch.abs(x - y),
average_method="mean",
mask_keys=['mask2']),
"L2":
MetricWrapper("L2", MSELoss()),
#"SSIM": SSIM3D(average_method="mean"),
"SSIM_mask":
SSIM3D(average_method="mean", mask_keys=["mask", "mask2"]),
"SSIM_Wrapped":
MetricWrapper("SSIM_wrapped",
lambda x, y: functional_ssim(x, y, return_map=False),
use_mask=True,
mask_key="mask"),
"ssim_base":
MetricWrapper('SSIM_base', ssim3D)
}
motion_trsfm = RandomMotionFromTimeCourse(verbose=True,
compare_to_original=True,
metrics=metrics,
oversampling_pct=0.0)
dataset.set_transform(motion_trsfm)
tf = dataset[0]
computed_metrics = tf["T1"]["metrics"]
print("Computed metrics: {}".format(computed_metrics))
ov(tf['T1']['data'].squeeze().numpy())
"suddenMagnitude": (3, 4),
"verbose": False,
"keep_original": True,
"proba_to_augment": 1,
"preserve_center_pct": 0.1,
"keep_original": True,
"compare_to_original": True,
"oversampling_pct": 0,
"correct_motion": True
}
fipar = pd.read_csv(
'/home/romain/QCcnn/mask_mvt_val_cati_T1/ssim_0.6956839561462402_sample00220_suj_cat12_s_S07_3DT1_mvt.csv',
header=None)
dico_params['fitpars'] = fipar.values
t = RandomMotionFromTimeCourse(**dico_params)
dataset = ImagesDataset(suj,
transform=Compose(
(CenterCropOrPad(target_shape=(182, 218, 182)),
t)))
dataset = ImagesDataset(suj,
transform=Compose(
(CenterCropOrPad(target_shape=(176, 240, 256)),
t)))
dataset = ImagesDataset(suj,
transform=Compose(
(CenterCropOrPad(target_shape=(182, 218, 256)),
t)))
dataset = ImagesDataset(suj, transform=Compose((t, )))
s = dataset[0]
def get_motion_transform(type='motion1'):
if 'motion1' in type:
from torchio.metrics import SSIM3D, MetricWrapper, MapMetricWrapper
from torchio.metrics.ssim import functional_ssim
from torchio.metrics.old_metrics import th_pearsonr, NCC
from torch.nn import MSELoss, L1Loss
#from torch_similarity.modules import NormalizedCrossCorrelation
metrics = {
# "L1": MetricWrapper("L1", L1Loss()), #same as L1_map
#"NCC_c": MetricWrapper("L1", NCC()),
"L1_map":
MapMetricWrapper("L1_map",
lambda x, y: torch.abs(x - y),
average_method="mean",
mask_keys=['brain']),
# "L2": MapMetricWrapper("L2", MSELoss(), mask_keys=['brain']),
# "SSIM": SSIM3D(average_method="mean"),
"SSIM_mask":
SSIM3D(average_method="mean", mask_keys=["brain"]),
"NCC":
MetricWrapper("NCC_th_brain",
lambda x, y: th_pearsonr(x, y),
use_mask=True,
mask_key='brain'),
"NCC2":
MetricWrapper("NCC_th",
lambda x, y: th_pearsonr(x, y),
use_mask=False),
# "SSIM": MetricWrapper("SSIM", lambda x, y: functional_ssim(x, y, return_map=False),
# use_mask=True, mask_key="brain"),
"ssim_base":
MapMetricWrapper('SSIM_base',
lambda x, y: ssim3D(x, y, size_average=True),
average_method="mean",
mask_keys=['brain'])
}
# metrics = {"L1_map": MapMetricWrapper("L1_map", lambda x, y: torch.abs(x - y), average_method="mean",
# mask_keys=['brain'])}
dico_params_mot = {
"maxDisp": (1, 6),
"maxRot": (1, 6),
"noiseBasePars": (5, 20, 0.8),
"swallowFrequency": (2, 6, 0.5),
"swallowMagnitude": (3, 6),
"suddenFrequency": (2, 6, 0.5),
"suddenMagnitude": (3, 6),
"verbose": False,
"proba_to_augment": 1,
"preserve_center_pct": 0.1,
"compare_to_original": True,
"oversampling_pct": 0,
"correct_motion": False
}
dico_params_mot = {
"maxDisp": (1, 4),
"maxRot": (1, 4),
"noiseBasePars": (5, 20, 0.8),
"swallowFrequency": (2, 6, 0.5),
"swallowMagnitude": (3, 4),
"suddenFrequency": (2, 6, 0.5),
"suddenMagnitude": (3, 4),
"verbose": False,
"proba_to_augment": 1,
"preserve_center_pct": 0.1,
"compare_to_original": True,
"metrics": metrics,
"oversampling_pct": 0,
"correct_motion": False
}
if 'elastic1' in type:
dico_elast = {
'num_control_points': 6,
'max_displacement': (30, 30, 30),
'p': 1,
'image_interpolation': Interpolation.LINEAR
}
if type == 'motion1':
transforms = Compose([
RandomMotionFromTimeCourse(**dico_params_mot),
])
if type == 'elastic1':
transforms = Compose([
RandomElasticDeformation(**dico_elast),
])
elif type == 'elastic1_and_motion1':
transforms = Compose([
RandomElasticDeformation(**dico_elast),
RandomMotionFromTimeCourse(**dico_params_mot)
])
if type == 'random_noise_1':
transforms = Compose([RandomNoise(std=(0.020, 0.2))])
if type == 'AffFFT_random_noise':
transforms = Compose([
RandomAffineFFT(scales=(0.8, 1.2),
degrees=10,
oversampling_pct=0.2,
p=0.75),
RandomNoise(std=(0.020, 0.2))
])
if type == 'AffFFT_random_noise':
transforms = Compose([
RandomAffine(scales=(0.8, 1.2),
degrees=10,
p=0.75,
image_interpolation=Interpolation.NEAREST),
RandomNoise(std=(0.020, 0.2))
])
return transforms
out_path = '/data/romain/data_exemple/test2/'
if not os.path.exists(out_path): os.mkdir(out_path)
#plt.ioff()
data_ref, aff = read_image('/data/romain/data_exemple/suj_150423/mT1w_1mm.nii')
res, res_fitpar, extra_info = pd.DataFrame(), pd.DataFrame(), dict()
disp_str = disp_str_list[0]; s = 2; xx = 100
for disp_str in disp_str_list:
for s in [2, 20]: #[1, 2, 3, 5, 7, 10, 12 , 15, 20 ] : # [2,4,6] : #[1, 3 , 5 , 8, 10 , 12, 15, 20 , 25 ]:
for xx in x0:
dico_params['displacement_shift_strategy'] = disp_str
fp = corrupt_data(xx, sigma=s, method=mvt_type, amplitude=10, mvt_axes=mvt_axes)
dico_params['fitpars'] = fp
dico_params['nT'] = fp.shape[1]
t = RandomMotionFromTimeCourse(**dico_params)
if 'synth' in suj_type:
dataset = SubjectsDataset(suj, transform= torchio.Compose([tlab, t ]))
else:
dataset = SubjectsDataset(suj, transform= t )
sample = dataset[0]
fout = out_path + '/{}_{}_{}_s{}_freq{}_{}'.format(suj_type, mvt_axe_str, mvt_type, s, xx, disp_str)
fit_pars = t.fitpars - np.tile(t.to_substract[..., np.newaxis],(1,200))
# fig = plt.figure();plt.plot(fit_pars.T);plt.savefig(fout+'.png');plt.close(fig)
#sample['image'].save(fout+'.nii')
extra_info['x0'], extra_info['mvt_type'], extra_info['mvt_axe']= xx, mvt_type, mvt_axe_str
extra_info['shift_type'], extra_info['sigma'], extra_info['amp'] = disp_str, s, 10
extra_info['disp'] = np.sum(t.to_substract)