Image processing

Enhancement

A discussion in #614 revealed a good place for improvement - we should ensure that input image is continuous upon start of the augmentation pipeline. This could be implemented by adding image = np.ascontiguousarray(image) to image and mask targets.

A proposed place to add this call - somewhere at the beginning of A.Compose.__call__.

🚀 Feature

As reported by deepsource in here we abuse from using built-in input function in our functionality.

Motivation

We target to have a clean and healthy source code free of risk.

Pitch

Replace variable names whether it makes sense e.g. for image based functionality input -> image ; in l

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

Perhaps I missed something but it took me a while to realise that there was an __call__ on this object. Perhaps extending the example would help prevent people from having this issue in future.

https://github.com/scikit-image/scikit-image/blob/a655681010caff396a2f584686c524246ae359d3/skimage/transform/_geometric.py#L602

🚨🚨 Feature Request

If your feature will improve HUB

To explore the structure of a dataset it is convenient to have nicer and more informative prints of dataset objects and samples

Description of the possible solution

1) show ds

now

> ds
Dataset(path='hub://activeloop/abalone_full_dataset', tensors=['length', 'diameter', 'height', 'weight'])