There is a set of Pixel Level transforms that is used in the work Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

The authors also share the code => we can absorb some transforms that they have into the library.

https://github.com/hendrycks/robustness/blob/master/ImageNet-C/create_c/make_imagenet_c.py

• ShotNoise
• Defocus
• GlassBlur

A follow up on SixLabors/ImageSharp#1378 (comment).

Currently 32 bit test execution is only done for .NET Framework, with dotnet xunit which is an obsolete tool today, we need to adapt dotnet test, and add 32 bit CI targets for both net5.0 and netcoreapp3.1. Opening an issue to remember and track this debt.

See:
https://twitter.com/tannergooding/s

Description

The shape parameter in disk documentation (and maybe others using the same machinery) seems misleading to me. Based on the following phrasing, I would expect shape=None and the shape=dimensions_of_my_disk to lead to equal results. (but it doesn't)

Image shape which is used to determine the maximum extent