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RandomGamma

RandomGamma

Bases: RandomTransform, IntensityTransform

Randomly change contrast of an image by raising its values to the power \(\gamma\).

Parameters:

Name Type Description Default
log_gamma TypeRangeFloat

Tuple \((a, b)\) to compute the exponent \(\gamma = e ^ \beta\), where \(\beta \sim \mathcal{U}(a, b)\). If a single value \(d\) is provided, then \(\beta \sim \mathcal{U}(-d, d)\). Negative and positive values for this argument perform gamma compression and expansion, respectively. See the Gamma correction Wikipedia entry for more information.

 (-0.3, 0.3)
**kwargs

See Transform for additional keyword arguments.

 {}
Note

Fractional exponentiation of negative values is generally not well-defined for non-complex numbers. If negative values are found in the input image \(I\), the applied transform is \(\text{sign}(I) |I|^\gamma\), instead of the usual \(I^\gamma\). The RescaleIntensity transform may be used to ensure that all values are positive. This is generally not problematic, but it is recommended to visualize results on images with negative values. More information can be found on this StackExchange question.

Examples:

>>> import torchio as tio
>>> subject = tio.datasets.FPG()
>>> transform = tio.RandomGamma(log_gamma=(-0.3, 0.3))  # gamma between 0.74 and 1.34
>>> transformed = transform(subject)

__call__(data)

Transform data and return a result of the same type.

Parameters:

Name Type Description Default
data InputType

Instance of torchio.Subject, 4D torch.Tensor or numpy.ndarray with dimensions \((C, W, H, D)\), where \(C\) is the number of channels and \(W, H, D\) are the spatial dimensions. If the input is a tensor, the affine matrix will be set to identity. Other valid input types are a SimpleITK image, a torchio.Image, a NiBabel Nifti1 image or a dict. The output type is the same as the input type.

 required

get_base_args()

Provides easy access to the arguments used to instantiate the base class (Transform) of any transform.

This method is particularly useful when a new transform can be represented as a variant of an existing transform (e.g. all random transforms), allowing for seamless instantiation of the existing transform with the same arguments as the new transform during apply_transform.

Note

The p argument (probability of applying the transform) is excluded to avoid multiplying the probability of both existing and new transform.

add_base_args(arguments, overwrite_on_existing=False)

Add the init args to existing arguments

validate_keys_sequence(keys, name) staticmethod

Ensure that the input is not a string but a sequence of strings.

to_hydra_config()

Return a dictionary representation of the transform for Hydra instantiation.

arguments_are_dict()

Check if main arguments are dict.

Return True if the type of all attributes specified in the args_names have dict type.

plot

Source code 
import torchio as tio
subject = tio.datasets.FPG()
subject.remove_image('seg')
transform = tio.RandomGamma(log_gamma=(-0.3, -0.3))
transformed = transform(subject)
subject.add_image(transformed.t1, 'log -0.3')
transform = tio.RandomGamma(log_gamma=(0.3, 0.3))
transformed = transform(subject)
subject.add_image(transformed.t1, 'log 0.3')
subject.plot()