RandomPosterize

class torchvision.transforms.v2.RandomPosterize(bits: int, p: float = 0.5)

Posterize the image or video with a given probability by reducing the number of bits for each color channel.

If the input is a torch.Tensor, it should be of type torch.uint8, and it is expected to have […, 1 or 3, H, W] shape, where … means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode “L” or “RGB”.

Parameters:

• bits (int) – number of bits to keep for each channel (0-8)

• p (float) – probability of the image being posterized. Default value is 0.5

Examples using RandomPosterize:

Illustration of transforms

Illustration of transforms

transform(inpt: Any, params: dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms