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 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 poster.

@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