GaussianBlur — Torchvision 0.22 documentation (original) (raw)
class torchvision.transforms.GaussianBlur(kernel_size, sigma=(0.1, 2.0))[source]¶
Blurs image with randomly chosen Gaussian blur. If the image is torch Tensor, it is expected to have […, C, H, W] shape, where … means at most one leading dimension.
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.
Returns:
Gaussian blurred version of the input image.
Return type:
PIL Image or Tensor
Examples using GaussianBlur:
forward(img: Tensor) → Tensor[source]¶
Parameters:
img (PIL Image or Tensor) – image to be blurred.
Returns:
Gaussian blurred image
Return type:
PIL Image or Tensor
static get_params(sigma_min: float, sigma_max: float) → float[source]¶
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: