GitHub - kornia/kornia: Open Source Differentiable Computer Vision Library for P...

README.rst

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:

Component Description kornia a Differentiable Computer Vision library like OpenCV, with strong GPU support kornia.color a set of routines to perform color space conversions kornia.contrib a compilation of user contrib and experimental operators kornia.feature a module to perform feature detection kornia.filters a module to perform image filtering and edge detection kornia.geometry a geometric computer vision library to perform image transformations, 3D linear algebra and conversions using different camera models kornia.losses a stack of loss functions to solve different vision tasks kornia.utils image to tensor utilities and metrics for vision problems

Installation

From pip:

pip install kornia

From source:

python setup.py install

From source with symbolic links:

python setup.py install develop

From source using pip:

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

Quick Usage

import torch
import 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.