oflibpytorch 1.0.0

Optical flow library using a custom flow class based on PyTorch tensors

Oflibpytorch: a handy python optical flow library, based on PyTorch tensors, that enables the manipulation and combination of flow fields while keeping track of valid areas (see “Usage”). It is mostly code written from scratch, but also contains useful wrappers for specific functions from libraries such as PyTorch’s grid_sample, to integrate them with the custom flow field class introduced by oflibpytorch. Features:

• Provides a custom flow field class for both backwards and forwards (‘source’ / ‘target’ based) flow fields

• Provides a number of class methods to create flow fields from lists of affine transforms, or a transformation matrix

• Provides a number of functions to resize the flow field, visualise it, warp images, find necessary image padding

• Allows for three different types of flow field combination operations

• Keeps track of valid flow field areas through said operations

Oflibpytorch is based on oflibnumpy (code on GitHub, documentation on ReadTheDocs) and is aimed at allowing the same operations to be performed with torch tensors instead of numpy arrays as far as currently feasible, and on the GPU if required.

Usage & Documentation

A user’s guide as well as full documentation of the library is available at ReadTheDocs. Some quick examples:

import oflibpytorch as of
# Make a flow field and display it
shape = (300, 400)
flow = of.Flow.from_transforms([['rotation', 200, 150, -30]], shape)
flow.show()

# Combine sequentially with another flow field, display the result
flow_2 = of.Flow.from_transforms([['translation', 40, 0]], shape)
result = of.combine_flows(flow, flow_2, mode=3)
result.show(show_mask=True, show_mask_borders=True)

result.show_arrows(show_mask=True, show_mask_borders=True)

Installation

In order for oflibpytorch to work, the python environment needs to contain a PyTorch installation. To enable GPU usage, the CUDA Toolkit is required as well. As it is difficult to guarantee an automatic installation via pip will use the correct versions and work on all operating systems, it is left to the user to install PyTorch and Cudatoolkit independently. The easiest route is a virtual conda environment and the recommended install command from the PyTorch website, configured for the user’s specific system. To install oflibpytorch itself, use the following command:

pip install oflibpytorch

Contribution & Support

• Source Code: https://github.com/RViMLab/oflibpytorch

• Issue Tracker: https://github.com/RViMLab/oflibpytorch/issues

License

Copyright (c) 2021 Claudio S. Ravasio, PhD student at University College London (UCL), research assistant at King’s College London (KCL), supervised by:

• Dr Christos Bergeles, PI of the Robotics and Vision in Medicine (RViM) lab in the School of Biomedical Engineering & Imaging Sciences (BMEIS) at King’s College London (KCL)

• Prof Lyndon Da Cruz, consultant ophthalmic surgeon, Moorfields Eye Hospital, London UK

This code is licensed under the MIT License.