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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.augmentation	a module to perform data augmentation in the GPU
kornia.color	a set of routines to perform color space conversions
kornia.contrib	a compilation of user contrib and experimental operators
kornia.enhance	a module to perform normalization and intensity transformations
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/kornia/kornia

Compatiblity table

torch	kornia	python
master / nightly	master	>=3.6
1.6.0	0.4.0	>=3.6
1.5.1	0.3.2	>=3.6
1.5.0	0.3.1	>=3.6
1.4.0	0.2.2	>=3.6
1.3.1	0.1.4	>=3.6
1.3.0	0.1.4	>=3.6
1.2.0	0.1.4	>=3.6

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      = {2020},
  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.

Communication

• forums: discuss implementations, research, etc. https://discuss.pytorch.org/c/vision/kornia
• GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.
• Slack: Join our workspace to keep in touch with our core contributors and be part of our community. [JOIN HERE]
• for general information, please visit our website at www.kornia.org