Unary Operators

Affine Transform Filter

class AffineTransform(target_landmarks=None, source_landmarks=None, affine=None, rigid=False, interp_mode='bilinear', device='cpu', dtype=torch.float32)

Bases: camp.StructuredGridOperators.UnaryOperators._UnaryFilter.Filter

static Create(target_landmarks=None, source_landmarks=None, affine=None, rigid=False, interp_mode='bilinear', device='cpu', dtype=torch.float32)

Returns an Affine Transform Filter that can be applied to type StructuredGrid. This can be initiated using a pair of landmarks (target and source) or with a pre-defined affine transformation (affine). Either both target and source landmarks must be provided OR a pre-defined affine.

Parameters

• target_landmarks (tensor, optional) – Target or unmoving landmarks selected in the target space. This tensor should be of size Nxdim where N is the number of landmarks and dim is the dimensionality of the StructuredGrid the affine will be applied to.

• source_landmarks (tensor, optional) – Source or moving landmarks selected in the source space. This tensor should be of size Nxdim where N is the number of landmarks and dim is the dimensionality of the StructuredGrid the affine will be applied to.

• affine (tensor, optional) – Pre-defined affine. This should be of shape (dim + 1)x(dim + 1) where the added dimension stores the translation.

• rigid (bool) – If the affine should be reduced to rigid transform only. Default is False.

• interp_mode (str) – Resampling interpolation mode to be used when applying the defromation - one of ‘bilinear’ or ‘nearest’. Default: ‘bilinear’

• device (str) – Memory location for the created filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Note

When mode=’bilinear’ and the input is 5-D, the interpolation mode used internally will actually be trilinear. However, when the input is 4-D, the interpolation mode will legitimately be bilinear.

Returns

Affine transform filter object with the specified parameters.

forward(x, out_grid=None, xyz_affine=False)

Resamples the Core.StructuredGrid through the affine attribute onto the same grid or the out_grid if out_grid is provided.

Parameters

• x (Core.StructuredGrid) – StructuredGrid to be transformed by the affine attribute.

• out_grid (Core.StructuredGrid, optional) – An optional additional grid that specifies the output grid. If not specified, the output grid will be the same as the input grid (x).

• xyz_affine (bool, optional) – Is affine xyz ordered instead of zyx?

Returns

Affine transformed StructredGrid

Apply Grid Filter

class ApplyGrid(grid, interp_mode='bilinear', pad_mode='zeros', device='cpu', dtype=torch.float32)

Bases: camp.StructuredGridOperators.UnaryOperators._UnaryFilter.Filter

static Create(grid, interp_mode='bilinear', pad_mode='zeros', device='cpu', dtype=torch.float32)

Returns an Apply Grid Filter that contained a deformation field that can be applied to type StructuredGrid and adds all attributes to the appropriate memory device.

Parameters

• grid (StructuredGrid) – The deformation field to be applied by the Apply Grid Filter. This is assumed to be in real-world coordinates relative to the spacing and origin of the grid.

• interp_mode (str) – Resampling interpolation mode to be used when applying the defromation - one of ‘bilinear’ or ‘nearest’. Default: ‘bilinear’

• pad_mode (str) – padding mode for outside grid values - one of ‘zeros’, ‘border’, or ‘reflection’. Default: ‘zeros’

• device (str) – Memory location for the created Apply Grid Filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the Apply Grid Filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Note

When mode=’bilinear’ and the input is 5-D, the interpolation mode used internally will actually be trilinear. However, when the input is 4-D, the interpolation mode will legitimately be bilinear.

Returns

Apply Grid Filter with the specified parameters.

forward(in_grid, out_grid=None)

Apply the grid attribute to in_grid.

Parameters

• in_grid (StructuredGrid) – The :class:’StructuredGrid’ to apply the grid attribute to.

• out_grid (StructuredGrid, optional) – An optional additional grid that specifies the output grid. If not specified, the output grid will be the same as the input grid.

Returns

Returns in_grid resampled through the grid attribute onto the out_grid.

Divergence Filter

class Divergence(dim=2, device='cpu', dtype=torch.float32)

static Create(dim=2, device='cpu', dtype=torch.float32)

Create a object to calculate the divergence of a look-up table field StructuredGrid.

Parameters

• dim (int) – Dimension of the StructuredGrid the filter will be applied to (not including channels).

• device (str) – Memory location for the created filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Returns

Divergence filter object with the specified parameters.

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Fluid Kernel Filter

class FluidKernel(grid, alpha=1.0, beta=0.0, gamma=0.001, device='cpu', dtype=torch.float32)

static Create(grid, alpha=1.0, beta=0.0, gamma=0.001, device='cpu', dtype=torch.float32)

apply_forward(x)

apply_inverse(x)

forward(x, inverse)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

project_incompressible(x)

set_size(grid)

Gaussian Filter

class Gaussian(channels, kernel_size, sigma, dim=2, device='cpu', dtype=torch.float32)

Bases: camp.StructuredGridOperators.UnaryOperators._UnaryFilter.Filter

static Create(channels, kernel_size, sigma, dim=2, device='cpu', dtype=torch.float32)

Create a filter to gaussian blur a StructuredGrid with the specified number of channels.

Parameters

• channels (int) – Number of channels in the StructuredGrid to be blurred.

• kernel_size (int) – Size of the kernel to use.

• sigma (int) – Sigma of the gaussian kernel.

• dim (int) – Number of dimensions in the StructuredGrid the filter will be applied to (not including channels).

• device (str) – Memory location for the created filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Returns

Gaussian filter object with the specified parameters.

forward(x)

Apply the gaussian filter to the input StructuredGrid x.

Parameters

x (StructuredGrid) – StructuredGrid to apply the gaussian to.

Returns

Gaussian filtered StructuredGrid.

Gradient Filter

class Gradient(dim=2, device='cpu', dtype=torch.float32)

Bases: camp.StructuredGridOperators.UnaryOperators._UnaryFilter.Filter

static Create(dim=2, device='cpu', dtype=torch.float32)

Create a filter to calculate the central difference of a StructuredGrid.

Parameters

• dim (int) – Number of dimensions in the StructuredGrid the filter will be applied to (not including channels).

• device (str) – Memory location for the created filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Returns

Gaussian filter object with the specified parameters.

forward(x)

Calculate the gradient of the input StructuredGrid x.

Parameters

x (StructuredGrid) – StructuredGrid to calculate the gradients of.

Returns

Gradients of the input StructuredGrid.

Gradient Regularizer

class NormGradient(weight, dim=2, device='cpu', dtype=torch.float32)

static Create(weight, dim=2, device='cpu', dtype=torch.float32)

forward(vector_field)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Jacobian Determinant Filter

class JacobianDeterminant(dim=2, device='cpu', dtype=torch.float32)

static Create(dim=2, device='cpu', dtype=torch.float32)

Create a filter to calculate the Jacobian determinant of a look-up table StructuredGrid.

Parameters

• dim (int) – Number of dimensions in the StructuredGrid the filter will be applied to (not including channels).

• device (str) – Memory location for the created filter - one of ‘cpu’, ‘cuda’, or ‘cuda:X’ where X specifies the device identifier. Default: ‘cpu’

• dtype (str) – Data type for the filter attributes. Specified from torch memory types. Default: ‘torch.float32’

Returns

Jacobian determinant filter object.

forward(x)

Calculate the Jacobian determinant of the input StructuredGrid x.

Parameters

x (StructuredGrid) – StructuredGrid to calculate the Jacobian determinant of.

Returns

Jacobian determinant of the input StructuredGrid.

Radial Basis Filter

Resample World Filter

class ResampleWorld(grid, interp_mode='bilinear', pad_mode='zeros', device='cpu', dtype=torch.float32)

static Create(grid, interp_mode='bilinear', pad_mode='zeros', device='cpu', dtype=torch.float32)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Variance Equalize Filter

class VarianceEqualize(kernel_size=11, sigma=2.0, eps=0.001, device='cpu', dtype=torch.float32)

Takes an Image and gives the variance equalized version.

I_out, Im: PyCA Image3Ds sigma: (scalar) gaussian filter parameter eps: (scalar) division regularizer

sigma is the width (in voxels) of the gaussian kernel eps is the regularizer

for a gaussian kernel k, we have

I_ve = I’/sqrt(k * I’^2) where I’ = I - k*I

static Create(kernel_size=11, sigma=2.0, eps=0.001, device='cpu', dtype=torch.float32)

forward(x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.