Kornia is a differentiable computer vision library for PyTorch.

It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.

Overview

Inspired by OpenCV, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors.

At a granular level, Kornia is a library that consists of the following components:

Installation

From pip:

pip install kornia

From source:

python setup.py install

From source using pip:

pip install git+https://github.com/arraiyopensource/kornia

Quick Usage

import torchimport kornia

x_rad = kornia.pi * torch.rand(1, 3, 3)
x_deg = kornia.rad2deg(x_rad)

torch.allclose(x_rad, kornia.deg2rad(x_deg))  # True

Examples

Run our Jupyter notebooks examples to learn to use the library.

Cite

If you are using kornia in your research-related documents, it is recommended that you cite the paper.

@inproceedings{eriba2019kornia,
  author    = {E. Riba, D. Mishkin, D. Ponsa, E. Rublee and G. Bradski}
  title     = {Kornia: an Open Source Differentiable Computer Vision Library for PyTorch},
  booktitle = {Winter Conference on Applications of Computer Vision},
  year      = {2019},
  url       = {https://arxiv.org/pdf/1910.02190.pdf}
}


@misc{Arraiy2018,
  author    = {E. Riba, M. Fathollahi, W. Chaney, E. Rublee and G. Bradski}
  title     = {torchgeometry: when PyTorch meets geometry},
  booktitle = {PyTorch Developer Conference},
  year      = {2018},
  url       = {https://drive.google.com/file/d/1xiao1Xj9WzjJ08YY_nYwsthE-wxfyfhG/view?usp=sharing}
}

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider reading the CONTRIBUTING notes. The participation in this open source project is subject to Code of Conduct.