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    Kornia - Computer vision library for deep learning | Product Hunt

    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 existing packages, 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, 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 transformation
    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.morphology a module to perform morphological operations
    kornia.utils image to tensor utilities and metrics for vision problems

    Installation

    From pip:

    pip install kornia
    pip install kornia[x]  # to get the training API !
    Other installation options

    From source:

    python setup.py install

    From source with symbolic links:

    pip install -e .

    From source using pip:

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

    Examples

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

    🚩 Updates

    Cite

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

    @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}
    }

    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.

    Community

    • Forums: discuss implementations, research, etc. GitHub Forums
    • GitHub Issues: bug reports, feature requests, install issues, RFCs, thoughts, etc. OPEN
    • 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

    Made with contrib.rocks.

    项目简介

    Open Source Differentiable Computer Vision Library

    🚀 Github 镜像仓库 🚀

    源项目地址

    https://github.com/kornia/kornia

    发行版本 28

    Kornia 0.6.5: Image i/o module with Rust, diamond_square and plasma augmentations, new geometric metrics and gradients estimators for differentiable augmentations.

    全部发行版

    贡献者 173

    全部贡献者

    开发语言

    • Python 99.6 %
    • Dockerfile 0.2 %
    • Shell 0.1 %
    • Makefile 0.1 %
    • Jsonnet 0.0 %