Kornia
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.
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.
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 <https://github.com/arraiyopensource/kornia/tree/master/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}
}