def __init__(
self,
keys: KeysCollection,
sigma1: Union[Sequence[float], float] = 3.0,
sigma2: Union[Sequence[float], float] = 1.0,
alpha: float = 30.0,
) -> None:
super().__init__(keys)
self.converter = GaussianSharpen(sigma1, sigma2, alpha)
def __call__(self, data):
d = dict(data)
self.randomize()
if not self._do_transform:
return d
for key in self.keys:
sigma1 = ensure_tuple_size(tup=(self.x1, self.y1, self.z1), dim=d[key].ndim - 1)
sigma2 = ensure_tuple_size(tup=(self.x2, self.y2, self.z2), dim=d[key].ndim - 1)
d[key] = GaussianSharpen(sigma1=sigma1, sigma2=sigma2, alpha=self.a, approx=self.approx)(d[key])
return d
def __init__(
self,
keys: KeysCollection,
sigma1: Union[Sequence[float], float] = 3.0,
sigma2: Union[Sequence[float], float] = 1.0,
alpha: float = 30.0,
approx: str = "erf",
allow_missing_keys: bool = False,
) -> None:
super().__init__(keys, allow_missing_keys)
self.converter = GaussianSharpen(sigma1, sigma2, alpha, approx=approx)