Kornia is a differentiable computer vision library that provides a rich set of differentiable image processing and geometric vision algorithms. Built on top of PyTorch, Kornia integrates seamlessly into existing AI workflows, allowing you to leverage powerful batch transformations, auto-differentiation and GPU 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.
| Category | Methods/Models |
|---|---|
| Image Processing | - Color conversions (RGB, Grayscale, HSV, etc.) - Geometric transformations (Affine, Homography, Resizing, etc.) - Filtering (Gaussian blur, Median blur, etc.) - Edge detection (Sobel, Canny, etc.) - Morphological operations (Erosion, Dilation, etc.) |
| Augmentation | - Random cropping, Erasing - Random geometric transformations (Affine, flipping, Fish Eye, Perspecive, Thin plate spline, Elastic) - Random noises (Gaussian, Median, Motion, Box, Rain, Snow, Salt and Pepper) - Random color jittering (Contrast, Brightness, CLAHE, Equalize, Gamma, Hue, Invert, JPEG, Plasma, Posterize, Saturation, Sharpness, Solarize) - Random MixUp, CutMix, Mosaic, Transplantation, etc. |
| Feature Detection | - Detector (Harris, GFTT, Hessian, DoG, KeyNet, DISK and DeDoDe) - Descriptor (SIFT, HardNet, TFeat, HyNet, SOSNet, and LAFDescriptor) - Matching (nearest neighbor, mutual nearest neighbor, geometrically aware matching, AdaLAM LightGlue, and LoFTR) |
| Geometry | - Camera models and calibration - Stereo vision (epipolar geometry, disparity, etc.) - Homography estimation - Depth estimation from disparity - 3D transformations |
| Deep Learning Layers | - Custom convolution layers - Recurrent layers for vision tasks - Loss functions (e.g., SSIM, PSNR, etc.) - Vision-specific optimizers |
| Photometric Functions | - Photometric loss functions - Photometric augmentations |
| Filtering | - Bilateral filtering - DexiNed - Dissolving - Guided Blur - Laplacian - Gaussian - Non-local means - Sobel - Unsharp masking |
| Color | - Color space conversions - Brightness/contrast adjustment - Gamma correction |
| Stereo Vision | - Disparity estimation - Depth estimation - Rectification |
| Image Registration | - Affine and homography-based registration - Image alignment using feature matching |
| Pose Estimation | - Essential and Fundamental matrix estimation - PnP problem solvers - Pose refinement |
| Optical Flow | - Farneback optical flow - Dense optical flow - Sparse optical flow |
| 3D Vision | - Depth estimation - Point cloud operations - Nerf |
| Image Denoising | - Gaussian noise removal - Poisson noise removal |
| Edge Detection | - Sobel operator - Canny edge detection |
| Transformations | - Rotation - Translation - Scaling - Shearing |
| Loss Functions | - SSIM (Structural Similarity Index Measure) - PSNR (Peak Signal-to-Noise Ratio) - Cauchy - Charbonnier - Depth Smooth - Dice - Hausdorff - Tversky - Welsch |
| Morphological Operations | - Dilation - Erosion - Opening - Closing |
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.
import numpy as np
import kornia_rs as kr
from kornia.augmentation import AugmentationSequential, RandomAffine, RandomBrightness
from kornia.filters import StableDiffusionDissolving
# Load and prepare your image
img: 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 pipeline
augmentation_pipeline = AugmentationSequential(
RandomAffine((-45., 45.), p=1.),
RandomBrightness((0.,1.), p=1.)
)
# Leveraging StableDiffusion models
dslv_op = StableDiffusionDissolving()
img = augmentation_pipeline(img)
dslv_op(img, step_number=500)
dslv_op.save("Kornia-enhanced.jpg")
import numpy as np
from kornia.onnx import ONNXSequential
# Chain ONNX models from HuggingFace repo and your own local model together
onnx_seq = ONNXSequential(
"hf://operators/kornia.geometry.transform.flips.Hflip",
"hf://models/kornia.models.detection.rtdetr_r18vd_640x640", # Or you may use "YOUR_OWN_MODEL.onnx"
)
# Prepare some input data
input_data = np.random.randn(1, 3, 384, 512).astype(np.float32)
# Perform inference
outputs = onnx_seq(input_data)
# Print the model outputs
print(outputs)
# Export a new ONNX model that chains up all three models together!
onnx_seq.export("chained_model.onnx")
You can now use Kornia with TensorFlow, JAX, and NumPy. See Multi-Framework Support for more details.
import kornia
tf_kornia = kornia.to_tensorflow()
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 like YuNet (Face Detection), Loftr (Feature Matching), and SAM (Segmentation). Now, we're looking for contributors to help us:
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 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}
}
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.
Made with contrib.rocks.
Kornia is released under the Apache 2.0 license. See the LICENSE file for more information.
FAQs
Open Source Differentiable Computer Vision Library for PyTorch
We found that kornia demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.