elliesch / flipnslide Goto Github PK

Add several visualization functionalities to viz.py.

• show ingested image after preprocessing
• show cropped image compared to ingested image
• show randomly selected tiles

This is going to be a multi-step process:

• Create the Jupyter Book
• Populate the intro page
• Make an "easy" example page
• #13
• Make a references page
• Make a Code of Conduct page
• #14
• Push to Read the Docs

Checklist for publishing package to PyPI:

• Make setup.py file
• Generate build, dist, test_package.egg-info files
• Use twine to push to PyPI

Move tiling code into tiling.py

This file needs to offer users three tools:

• Tiling without overlap
• Tiling with 50% overlap, no augmentation
• Flip-n-Slide method (simultaneous overlap + permutation)

Outputs should be available in three data formats:

• numpy arrays (--> .npy)
• pytorch tensors (--> .pt)
• #8

Add Two Examples:

• One for use with already downloaded data
• One for use with coordinates (use SF Bay Area example from @elliesch's CryoCloud tutorial)

Add pytorch functionality to dataset.py.

• Turn tiles into a custom pytorch dataset
• Create a custom data loader from this dataset that loads augmentations at random
  • To do this, preserve the indexing function within the sliding transform function

In Release 1, we've added capability to output a tensorflow eager tensor. This is not GPU enabled, but will take more thought on how to properly enable GPU ready tensors. It additionally brings up the question as to whether we want to make our code more GPU ready for both tensorflow and PyTorch outputs in Release 2.

https://arxiv.org/abs/2404.10927

Create a file flipnslide.py to run everything from scratch as the main class.

Write now, tiling runs with a choice of tiler. Create flipnslide class to run tiling only using Flip-n-Slide method, without requiring user to choose which type of tiling.

Add unit tests through GitHub workflows.

Test ideas can be added in the comments below this. Enable GitHub Actions, and create unit tests.

Goal: >80% coverage

Tiling breaks down when an input image has no channels and is of shape (x px, y px).

This can easily be fixed by adding a dimension expansion:
np.expand_dims(instance_masks_20141128, axis=0)

The visualizations also need to be fixed.

The readme is currently unpopulated. Add documentation to this, including pip instructions once the repo is packaged and indexed.