GaussianBlur

class torchvision.transforms.v2.GaussianBlur(kernel_size: Union[int, Sequence[int]], sigma: Union[int, float, Sequence[float]] = (0.1, 2.0))

[BETA] Blurs image with randomly chosen Gaussian blur.

Note

The GausssianBlur transform is in Beta stage, and while we do not expect disruptive breaking changes, some APIs may slightly change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753.

If the input is a Tensor, it is expected to have […, C, H, W] shape, where … means an arbitrary number of leading dimensions.

Parameters:

• kernel_size (int or sequence) – Size of the Gaussian kernel.

• sigma (float or tuple of python:float (min, max)) – Standard deviation to be used for creating kernel to perform blurring. If float, sigma is fixed. If it is tuple of float (min, max), sigma is chosen uniformly at random to lie in the given range.

Examples using GaussianBlur:

Illustration of transforms

Illustration of transforms

static get_params(sigma_min: float, sigma_max: float) → float

Choose sigma for random gaussian blurring.

Parameters:

• sigma_min (float) – Minimum standard deviation that can be chosen for blurring kernel.

• sigma_max (float) – Maximum standard deviation that can be chosen for blurring kernel.

Returns:

Standard deviation to be passed to calculate kernel for gaussian blurring.

Return type:

float