Package: kornia
English |简体中文Website•Docs•Try it Now•Tutorials•Examples•Blog•CommunityKorniais a differentiable computer vision library forPyTorch.It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package usesPyTorchas 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.OverviewInspired 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:ComponentDescriptionkorniaa Differentiable Computer Vision library, with strong GPU supportkornia.augmentationa module to perform data augmentation in the GPUkornia.colora set of routines to perform color space conversionskornia.contriba compilation of user contrib and experimental operatorskornia.enhancea module to perform normalization and intensity transformationkornia.featurea module to perform feature detectionkornia.filtersa module to perform image filtering and edge detectionkornia.geometrya geometric computer vision library to perform image transformations, 3D linear algebra and conversions using different camera modelskornia.lossesa stack of loss functions to solve different vision taskskornia.morphologya module to perform morphological operationskornia.utilsimage to tensor utilities and metrics for vision problemsInstallationFrom pip:pipinstallkornia
pipinstallkornia[x]# to get the training API !Other installation optionsFrom source:pythonsetup.pyinstallFrom source with symbolic links:pipinstall-e.From source using pip:pipinstallgit+https://github.com/kornia/korniaExamplesRun our Jupyter notebookstutorialsto learn to use the library.:triangular_flag_on_post:Updates:white_check_mark:Image MatchingIntegrated toHuggingface Spaces. SeeGradio Web Demo.:white_check_mark:Face DetectionIntegrated toHuggingface Spaces. SeeGradio Web Demo.CiteIf you are using kornia in your research-related documents, it is recommended that you cite the paper. See more inCITATION.@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}}ContributingWe 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 theCONTRIBUTINGnotes. The participation in this open source project is subject toCode of Conduct.CommunityForums:discuss implementations, research, etc.GitHub ForumsGitHub Issues:bug reports, feature requests, install issues, RFCs, thoughts, etc.OPENSlack:Join our workspace to keep in touch with our core contributors and be part of our community.JOIN HEREFor general information, please visit our website atwww.kornia.orgMade withcontrib.rocks.