cornucopia.intensity

OpConstTransform

OpConstTransform(value, op=None, value_name='value', **kwargs)

Bases: FinalTransform

Base class for arithmetic operations with a constant value

Parameters:

Name	Type	Description	Default
value	number or tensor	right-hand side of the operation	required
op	(add, mul)	Arithmetic operation	torch.add
value_name	str	Name used when returning the rhs value	'value'

AddValueTransform

AddValueTransform(value, op=None, value_name='value', **kwargs)

Bases: OpConstTransform

Add a constant value

MulValueTransform

MulValueTransform(value, op=None, value_name='value', **kwargs)

Bases: OpConstTransform

Multiply with a constant value

FillValueTransform

FillValueTransform(mask, value, mask_name='mask', value_name='value', **kwargs)

Bases: FinalTransform

Fills the tensor with a value inside a mask

Parameters:

Name	Type	Description	Default
mask	tensor	Mask of voxels in which to set the value	required
value	number or tensor	right-hand side of the operation	required
mask_name	str	Name used when returning the mask	'mask'
value_name	str	Name used when returning the rhs value	'value'

AddMulTransform

AddMulTransform(slope=1, offset=0, **kwargs)

Bases: FinalTransform

Constant intensity affine transform: y = x * slope + offset

Parameters:

Name	Type	Description	Default
slope	number or tensor	Affine slope	1
offset	number or tensor	Affine offset	0

ClipTransform

ClipTransform(vmin=None, vmax=None, **kwargs)

Bases: FinalTransform

Clip extremum values

Parameters:

Name	Type	Description	Default
vmin	number or tensor	Min value	None
vmax	number or tensor	Max valur	None

RandomMulTransform

RandomMulTransform(value=(0.5, 2), *, shared=True, **kwargs)

Bases: RandomizedTransform

Random multiplicative transform.

Parameters:

Name	Type	Description	Default
value	Sampler or [pair of] float	Bound for multiplicative value	(0.5, 2)
shared	(channels, tensors, channels + tensors, '')	Apply same transform to all images/channels	'channels'

RandomAddTransform

RandomAddTransform(value=1, *, shared=True, **kwargs)

Bases: RandomizedTransform

Random additive transform.

Parameters:

Name	Type	Description	Default
value	Sampler or [pair of] float	Bound for additive value	1
shared	(channels, tensors, channels + tensors, '')	Apply same transform to all images/channels	'channels'

RandomAddMulTransform

RandomAddMulTransform(slope=1, offset=0.5, *, shared=True, **kwargs)

Bases: RandomizedTransform

Random intensity affine transform.

Parameters:

Name	Type	Description	Default
slope	Sampler or [pair of] float	Bound for slope	1
offset	Sampler or [pair of] float	Bound for offset	0.5
shared	(channels, tensors, channels + tensors, None)	Apply same transform to all images/channels	'channels'

SplineUpsampleTransform

SplineUpsampleTransform(order=3, prefilter=False, **kwargs)

Bases: FinalTransform

Upsample a field using spline interpolation

Parameters:

Name	Type	Description	Default
order	int	Spline interpolation order	3
prefilter	bool	Splie prefiltering (True for interpolation, False for spline evaluation)	False

BaseFieldTransform

BaseFieldTransform(shape=5, vmin=0, vmax=1, order=3, slice=None, thickness=None, *, shared=False, **kwargs)

Bases: NonFinalTransform

Base class for transforms that sample a smooth field

Parameters:

Name	Type	Description	Default
shape	[list of] int	Number of spline control points	5
vmin	float	Minimum value	0
vmax	float	Maximum value	1
order	int	Spline order	3
slice	int	Slice direction, if slicewise.	None
thickness	int	Slice thickness, if slicewise. Note that shape will be scaled along the slice direction so that the number of nodes is approximately preserved.	None
returns	[list or dict of] {'input', 'output', 'field'}	Which tensor(s) to return	required
shared	(channels, tensors, channels + tensors, '')	Apply the same field to all channels	'channels'

make_field

make_field(batch, smallshape, fullshape=None, **backend)

Generate the random coefficients

upsample_field

upsample_field(coeff, fullshape)

Resize spline coefficients to full size

MulFieldTransform

MulFieldTransform(shape=5, vmin=0, vmax=1, order=3, slice=None, thickness=None, *, shared=False, **kwargs)

Bases: BaseFieldTransform

Smooth multiplicative (bias) field

RandomMulFieldTransform

RandomMulFieldTransform(shape=8, vmax=1, order=3, symmetric=False, *, shared=False, shared_field=None, **kwargs)

Bases: NonFinalTransform

Random multiplicative bias field transform

Parameters:

Name	Type	Description	Default
shape	Sampler or int	Sampler or Upper bound for number of control points	8
vmax	Sampler or float	Sampler or Upper bound for maximum value	1
order	int	Spline order	3
symmetric	bool or float	If a float, the bias field will take values in (symmetric-vmax, symmetric+vmax). If False, it will take values in (0, vmax). If True, it will take values in (1-vmax, 1+vmax).	False

Other Parameters:

Name	Type	Description
returns	[list or dict of] {'input', 'output', 'field'}	Which tensor(s) to return
shared	(channels, tensors, channels + tensors, '')	Whether to share random parameters across tensors and/or channels
shared_field	(channels, tensors, channels + tensors, '', None)	Whether to share random field across tensors and/or channels. By default: same as shared

RandomSlicewiseMulFieldTransform

RandomSlicewiseMulFieldTransform(shape=8, vmax=1, order=3, slice=None, thickness=32, shape_through=None, *, shared=False, shared_field=None, **kwargs)

Bases: NonFinalTransform

Random multiplicative bias field transform, per slice or slab

Parameters:

Name	Type	Description	Default
shape	Sampler or int	Sampler or Upper bound for number of control points	8
vmax	Sampler or float	Sampler or Upper bound for maximum value	1
order	int	Spline order	3
slice	int	Slice axis. If None, sample one randomly	None
thickness		Sampler or Upper bound for slice thickness	32
shape_through	Sampler or int	Sampler or Upper bound for number of control points along the slice direction. If None, same as shape.	None

Other Parameters:

Name	Type	Description
shared	(channels, tensors, channels + tensors, '')	Whether to share random parameters across tensors and/or channels
shared_field	(channels, tensors, channels + tensors, '', None)	Whether to share random field across tensors and/or channels. By default: same as shared

AddFieldTransform

AddFieldTransform(shape=5, vmin=0, vmax=1, order=3, slice=None, thickness=None, *, shared=False, **kwargs)

Bases: BaseFieldTransform

Smooth additive (bias) field

RandomAddFieldTransform

RandomAddFieldTransform(shape=8, vmin=-1, vmax=1, order=3, *, shared=False, shared_field=None, **kwargs)

Bases: NonFinalTransform

Random additive bias field transform

Parameters:

Name	Type	Description	Default
shape	Sampler or int	Sampler or Upper bound for number of control points	8
vmin	Sampler or float	Sampler or Lower bound for minimum value	-1
vmax	Sampler or float	Sampler or Upper bound for maximum value	1
order	int	Spline order	3

Other Parameters:

Name	Type	Description
shared	(channels, tensors, channels + tensors, '')	Whether to share random parameters across tensors and/or channels
shared_field	(channels, tensors, channels + tensors, '', None)	Whether to share random field across tensors and/or channels. By default: same as shared

GammaTransform

GammaTransform(gamma=1, vmin=None, vmax=None, *, shared=False, **kwargs)

Bases: NonFinalTransform

Gamma correction

References

1. https://en.wikipedia.org/wiki/Gamma_correction

Parameters:

Name	Type	Description	Default
gamma	float	Exponent of the Gamma transform	1
vmin	float	Value to use as the minimum (default: x.min())	None
vmax	float	Value to use as the maximum (default: x.max())	None
returns	[list or dict] {'input', 'output', 'vmin', 'vmax', 'gamma'}	Which tensors to return	required

Other Parameters:

Name	Type	Description
shared	(channels, tensors, channels + tensors, '')	Use the same vmin/vmax for all channels

RandomGammaTransform

RandomGammaTransform(gamma=(0.5, 2), *, shared=False, shared_minmax=None, **kwargs)

Bases: NonFinalTransform

Random Gamma transform.

Parameters:

Name	Type	Description	Default
gamma	Sampler or [pair of] float	Sampler or range for the exponent value	(0.5, 2)

Other Parameters:

Name	Type	Description
shared	(channels, tensors, channels + tensors, '')	Apply same gamma for all images/channels
shared_minmax	(channels, tensors, channels + tensors, '', None)	Use the same vmin/vmax for all channels. Default: same as shared.

ZTransform

ZTransform(mu=0, sigma=1, *, shared=False, **kwargs)

Bases: NonFinalTransform

Z-transform the data -> zero mean, unit standard deviation

Parameters:

Name	Type	Description	Default
mu	float	Target mean. If None, keep the input mean.	0
sigma	float	Target standard deviation. If None, keep the input sd.	1

Other Parameters:

Name	Type	Description
shared	(channels, tensors, channels + tensors, '')	Use the same mean/sigma for all images/channels

QuantileTransform

QuantileTransform(pmin=0.01, pmax=0.99, vmin=0, vmax=1, clip=False, max_samples=10000, **kwargs)

Bases: NonFinalTransform

Match lower and upper quantiles to (0, 1)

Parameters:

Name	Type	Description	Default
pmin	(0..1)	Lower quantile	0.01
pmax	(0..1)	Upper quantile	0.99
vmin	float	Lower target value	0
vmax	float	Upper target value	1
clip	bool	Clip values outside (vmin, vmax)	False

MinMaxTransform

MinMaxTransform(vmin=0, vmax=1, **kwargs)

Bases: NonFinalTransform

Match min and max values to (0, 1)

Parameters:

Name	Type	Description	Default
vmin	float	Lower target value	0
vmax	float	Upper target value	1
clip	bool	Clip values outside (vmin, vmax)	required