Repository files navigation

Kornia is a differentiable computer vision library that provides a rich set of differentiable image processing and geometric vision algorithms. Built on top ofPyTorch, Kornia integrates seamlessly into existing AI workflows, allowing you to leverage powerfulbatch transformations,auto-differentiation andGPU acceleration. Whether you’re working on image transformations, augmentations, or AI-driven image processing, Kornia equips you with the tools you need to bring your ideas to life.

1. Differentiable Image Processing
   Kornia provides a comprehensive suite of image processing operators, all differentiable and ready to integrate into deep learning pipelines.
   • Filters: Gaussian, Sobel, Median, Box Blur, etc.
   • Transformations: Affine, Homography, Perspective, etc.
   • Enhancements: Histogram Equalization, CLAHE, Gamma Correction, etc.
   • Edge Detection: Canny, Laplacian, Sobel, etc.
   • ... check ourdocs for more.
2. Advanced Augmentations
   Perform powerful data augmentation with Kornia’s built-in functions, ideal for training AI models with complex augmentation pipelines.
   • Augmentation Pipeline: AugmentationSequential, PatchSequential, VideoSequential, etc.
   • Automatic Augmentation: AutoAugment, RandAugment, TrivialAugment.
3. AI Models
   Leverage pre-trained AI models optimized for a variety of vision tasks, all within the Kornia ecosystem.
   • Face Detection: YuNet
   • Feature Matching: LoFTR, LightGlue
   • Feature Descriptor: DISK, DeDoDe, SOLD2
   • Segmentation: SAM
   • Classification: MobileViT, VisionTransformer.

Kornia is an open-source project that is developed and maintained by volunteers. Whether you're using it for research or commercial purposes, consider sponsoring or collaborating with us. Your support will help ensure Kornia's growth and ongoing innovation. Reach out to us today and be a part of shaping the future of this exciting initiative!

pip install kornia

pip install -e.

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

Kornia is not just another computer vision library — it's your gateway to effortless Computer Vision and AI.

importnumpyasnpimportkornia_rsaskrfromkornia.augmentationimportAugmentationSequential,RandomAffine,RandomBrightnessfromkornia.filtersimportStableDiffusionDissolving# Load and prepare your imageimg:np.ndarray=kr.read_image_any("img.jpeg")img=kr.resize(img, (256,256),interpolation="bilinear")# alternatively, load image with PIL# img = Image.open("img.jpeg").resize((256, 256))# img = np.array(img)img=np.stack([img]*2)# batch images# Define an augmentation pipelineaugmentation_pipeline=AugmentationSequential(RandomAffine((-45.,45.),p=1.),RandomBrightness((0.,1.),p=1.))# Leveraging StableDiffusion modelsdslv_op=StableDiffusionDissolving()img=augmentation_pipeline(img)dslv_op(img,step_number=500)dslv_op.save("Kornia-enhanced.jpg")

Are you passionate about computer vision, AI, and open-source development? Join us in shaping the future of Kornia! We are actively seeking contributors to help expand and enhance our library, making it even more powerful, accessible, and versatile. Whether you're an experienced developer or just starting, there's a place for you in our community.

We are excited to announce our latest advancement: a new initiative designed to seamlessly integrate lightweight AI models into Kornia.We aim to run any models as smooth as big models such as StableDiffusion, to support them well in many perspectives.We have already included a selection of lightweight AI models likeYuNet (Face Detection),Loftr (Feature Matching), andSAM (Segmentation). Now, we're looking for contributors to help us:

• Expand the Model Selection: Import decent models into our library. If you are a researcher, Kornia is an excellent place for you to promote your model!
• Model Optimization: Work on optimizing models to reduce their computational footprint while maintaining accuracy and performance. You may start from offering ONNX support!
• Model Documentation: Create detailed guides and examples to help users get the most out of these models in their projects.

Kornia's foundation lies in its extensive collection of classic computer vision operators, providing robust tools for image processing, feature extraction, and geometric transformations. We continuously seek for contributors to help us improve our documentation and present nice tutorials to our users.

If 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}}

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 theCONTRIBUTING notes. The participation in this open source project is subject toCode of Conduct.

• 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

Made withcontrib.rocks.

Kornia is released under the Apache 2.0 license. See theLICENSE file for more information.