Porting/Refactoring TFA dense_image_warp

[Luvideria]

This ticket refers to already opened ticket #163 and tensorflow/addons#2733

Currently tensorflow_addons.image.dense_image_warp(...) only supports bilinear interpolation, which is good enough for most, not enough for some. I implemented a higher order interpolation scheme: catmull rom (=Lanczos2) which gave better results (see: https://github.com/Luvideria/tensorflow-dense-warp-catmull-rom). It works fine but may need to be adjusted. It currently has a high maximum memory use (because of the buffer duplication for vectorization), I also managed to make another version using less memory but the code is too hacky (and slower).

I am not very familiar with building very fast or optimized tensorflow code, my implementation mostly relies on (and modifies) the previous implementation (bilinear).

The issue #163 relates to elastic transform, which can be improved by a higher order interpolation scheme.

[github-actions]

This issue is stale because it has been open for 180 days with no activity. It will be closed if no further activity occurs. Thank you.

[sachinprasadhs]

Thanks for reporting the issue! We have consolidated the development of KerasCV into the new KerasHub package, which supports image, text, and multi-modal models. Please read keras-team/keras-hub#1831. KerasHub will support all the core functionality of KerasCV.

KerasHub can be installed with !pip install -U keras-hub. Documentation and guides are available at keras.io/keras_hub.

With our focus shifted to KerasHub, we are not planning any further development or releases in KerasCV. If you encounter a KerasCV feature that is missing from KerasHub, or would like to propose an addition to the library, please file an issue with KerasHub.