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Blur

Bases: IntensityTransform

Blur an image using a Gaussian filter.

The standard deviations \((\sigma_1, \sigma_2, \sigma_3)\) of the Gaussian kernel along each spatial axis are independently sampled from the given range. Sigmas are specified in mm and internally converted to voxels using the image spacing.

Parameters:

Name Type Description Default
std float | tuple[float, float]

Standard deviation of the Gaussian kernel in mm. A scalar \(x\) means \(\sigma_i = x\) for every axis (deterministic). A 2-tuple \((a, b)\) means \(\sigma_i \sim \mathcal{U}(a, b)\). A 6-tuple \((a_1, b_1, a_2, b_2, a_3, b_3)\) means \(\sigma_i \sim \mathcal{U}(a_i, b_i)\) independently. A Choice or Distribution may also be passed. The default std=0 is a no-op (and warns).

 0.0
**kwargs Any

See Transform.

 {}

Examples:

>>> import torchio as tio
>>> transform = tio.Blur(std=2.0)
>>> transform = tio.Blur(std=(0, 4))
Source code in src/torchio/transforms/intensity/blur.py 
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class Blur(IntensityTransform):
    r"""Blur an image using a Gaussian filter.

    The standard deviations $(\sigma_1, \sigma_2, \sigma_3)$ of the
    Gaussian kernel along each spatial axis are independently sampled
    from the given range.  Sigmas are specified in mm and internally
    converted to voxels using the image spacing.

    Args:
        std: Standard deviation of the Gaussian kernel in mm.
            A scalar $x$ means $\sigma_i = x$ for every axis
            (deterministic).
            A 2-tuple $(a, b)$ means
            $\sigma_i \sim \mathcal{U}(a, b)$.
            A 6-tuple $(a_1, b_1, a_2, b_2, a_3, b_3)$ means
            $\sigma_i \sim \mathcal{U}(a_i, b_i)$ independently.
            A `Choice` or `Distribution` may also be passed.
            The default `std=0` is a no-op (and warns).
        **kwargs: See [`Transform`][torchio.Transform].

    Examples:
        >>> import torchio as tio
        >>> transform = tio.Blur(std=2.0)
        >>> transform = tio.Blur(std=(0, 4))
    """

    def __init__(
        self,
        *,
        std: float | tuple[float, float] = 0.0,
        **kwargs: Any,
    ) -> None:
        super().__init__(**kwargs)
        self.std = to_nonneg_range(std)
        self._warn_if_noop(is_noop=self.std.is_constant(0.0), hint="std=(0, 2)")

    def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
        """Sample per-axis standard deviations."""
        return {"std": self.std.sample()}

    def apply_transform(
        self,
        batch: SubjectsBatch,
        params: dict[str, Any],
    ) -> SubjectsBatch:
        """Apply Gaussian smoothing to each selected image."""
        sigmas_mm = params["std"]
        for _name, img_batch in self._get_images(batch).items():
            spacing = np.asarray(img_batch.affines[0].spacing, dtype=np.float64)
            # Convert mm sigmas to voxel sigmas.
            sigmas_vox = [
                s / sp if sp > 0 else 0.0
                for s, sp in zip(sigmas_mm, spacing, strict=True)
            ]
            img_batch.data = _gaussian_smooth(img_batch.data, sigmas_vox)
        return batch

invertible property

Whether this transform can be inverted.

forward(data) 

forward(data: Subject) -> Subject

forward(data: Image) -> Image

forward(data: Tensor) -> Tensor

forward(data: np.ndarray) -> np.ndarray

forward(data: sitk.Image) -> sitk.Image

forward(data: nib.Nifti1Image) -> nib.Nifti1Image

forward(data: dict) -> dict

forward(data: ImagesBatch) -> ImagesBatch

forward(data: SubjectsBatch) -> SubjectsBatch

Apply the transform.

The output type always matches the input type.

Parameters:

Name Type Description Default
data Any

Input data to transform.

 required
Source code in src/torchio/transforms/transform.py 
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def forward(self, data: Any) -> Any:
    """Apply the transform.

    The output type always matches the input type.

    Args:
        data: Input data to transform.
    """
    if self.copy:
        data = _copy.deepcopy(data)
    batch, unwrap = self._wrap(data)
    if torch.rand(1).item() > self.p:
        return unwrap(batch)
    params = self.make_params(batch)
    batch = self.apply_transform(batch, params)
    # Record history on the batch
    trace = AppliedTransform(name=type(self).__name__, params=params)
    if not hasattr(batch, "applied_transforms"):
        batch.applied_transforms = []
    batch.applied_transforms.append(trace)
    result = unwrap(batch)
    # Propagate history to outputs that can carry it
    if (
        hasattr(batch, "applied_transforms")
        and not isinstance(result, (SubjectsBatch, Tensor, np.ndarray))
        and not isinstance(result, dict)
    ):
        with contextlib.suppress(AttributeError):
            result.applied_transforms = list(batch.applied_transforms)
    return result

inverse(params)

Return a transform that undoes this one.

Override in invertible subclasses. The returned transform, when applied, reverses the effect of the forward pass with the given parameters.

Parameters:

Name Type Description Default
params dict[str, Any]

The parameters recorded in the forward pass.

 required

Returns:

Type Description
Transform

A new Transform instance that inverts this one.
Source code in src/torchio/transforms/transform.py 
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def inverse(self, params: dict[str, Any]) -> Transform:
    """Return a transform that undoes this one.

    Override in invertible subclasses. The returned transform,
    when applied, reverses the effect of the forward pass with
    the given parameters.

    Args:
        params: The parameters recorded in the forward pass.

    Returns:
        A new `Transform` instance that inverts this one.
    """
    msg = f"{type(self).__name__} is not invertible"
    raise NotImplementedError(msg)

to_hydra()

Export as a Hydra-compatible config dict.

Returns a dict with _target_ set to the fully qualified class name and only non-default field values included.

Returns:

Type Description
dict[str, Any]

Dict suitable for hydra.utils.instantiate().
Source code in src/torchio/transforms/transform.py 
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def to_hydra(self) -> dict[str, Any]:
    """Export as a Hydra-compatible config dict.

    Returns a dict with `_target_` set to the fully qualified
    class name and only non-default field values included.

    Returns:
        Dict suitable for `hydra.utils.instantiate()`.
    """
    from .parameter_range import _ParameterRange

    cls = type(self)
    target = f"torchio.{cls.__qualname__}"
    cfg: dict[str, Any] = {"_target_": target}

    for name, default in _collect_init_params(cls).items():
        value = getattr(self, name, default)
        if isinstance(value, _ParameterRange):
            if value._original == default:
                continue
            value = _hydra_value(value._original)
        elif value == default:
            continue
        else:
            value = _hydra_value(value)
        cfg[name] = value
    return cfg

make_params(batch)

Sample per-axis standard deviations.

Source code in src/torchio/transforms/intensity/blur.py 
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def make_params(self, batch: SubjectsBatch) -> dict[str, Any]:
    """Sample per-axis standard deviations."""
    return {"std": self.std.sample()}

apply_transform(batch, params)

Apply Gaussian smoothing to each selected image.

Source code in src/torchio/transforms/intensity/blur.py 
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def apply_transform(
    self,
    batch: SubjectsBatch,
    params: dict[str, Any],
) -> SubjectsBatch:
    """Apply Gaussian smoothing to each selected image."""
    sigmas_mm = params["std"]
    for _name, img_batch in self._get_images(batch).items():
        spacing = np.asarray(img_batch.affines[0].spacing, dtype=np.float64)
        # Convert mm sigmas to voxel sigmas.
        sigmas_vox = [
            s / sp if sp > 0 else 0.0
            for s, sp in zip(sigmas_mm, spacing, strict=True)
        ]
        img_batch.data = _gaussian_smooth(img_batch.data, sigmas_vox)
    return batch