def apply_transform(self, sample: Subject) -> dict:
random_parameters_images_dict = {}
std = self.get_params(self.std_range)
if not self.is_tensor:
sample.check_consistent_shape()
for image_name, image_dict in sample.get_images_dict().items():
random_parameters_dict = {'std': std}
random_parameters_images_dict[image_name] = random_parameters_dict
image_dict[DATA] = add_noise(image_dict[DATA], std)
sample.add_transform(self, random_parameters_images_dict)
else:
sample = add_noise(sample, std)
return sample
def plot_subject(subject: Subject, save_plot_path: str):
if save_plot_path:
os.makedirs(save_plot_path, exist_ok=True)
data_dict = {}
sx, sy, sz = subject.spatial_shape
sx, sy, sz = min(sx, sy, sz) / sx, min(sx, sy, sz) / sy, min(sx, sy,
sz) / sz
for name, image in subject.get_images_dict(intensity_only=False).items():
if isinstance(image, LabelMap):
data_dict[name] = LabelMap(
tensor=squeeze_segmentation(image),
affine=np.eye(4) * np.array([sx, sy, sz, 1]),
)
else:
data_dict[name] = ScalarImage(tensor=image.data,
affine=np.eye(4) *
np.array([sx, sy, sz, 1]))
out_subject = Subject(data_dict)
out_subject.plot(reorient=False, show=True, figsize=(10, 10))
mpl, plt = import_mpl_plt()
backend_ = mpl.get_backend()
plt.ioff()
mpl.use("agg")
for x in range(max(out_subject.spatial_shape)):
out_subject.plot(
reorient=False,
indices=(
min(x, out_subject.spatial_shape[0] - 1),
min(x, out_subject.spatial_shape[1] - 1),
min(x, out_subject.spatial_shape[2] - 1),
),
output_path=f"{save_plot_path}/{x:03d}.png",
show=False,
figsize=(10, 10),
)
plt.close("all")
plt.ion()
mpl.use(backend_)
create_gifs(save_plot_path,
f"{save_plot_path}/{os.path.basename(save_plot_path)}.gif")
def apply_normalization(
self,
subject: Subject,
image_name: str,
mask: torch.Tensor,
) -> None:
image = subject[image_name]
mask = image.data != 0
standardized = self.znorm(
image.data,
mask,
)
if standardized is None:
message = ("Standard deviation is 0 for masked values"
f' in image "{image_name}" ({image.path})')
raise RuntimeError(message)
subject.get_images_dict(
intensity_only=True)[image_name]["data"] = standardized