Metadata-Version: 2.5
Name: kornia
Version: 0.9.0rc1
Summary: Open Source Differentiable Computer Vision Library for PyTorch
Project-URL: Bug Tracker, https://github.com/kornia/kornia/issues
Project-URL: Documentation, https://kornia.readthedocs.io/en/latest
Project-URL: Download, https://github.com/kornia/kornia
Project-URL: Homepage, https://www.kornia.org/
Project-URL: Issues, https://github.com/kornia/kornia/issues
Project-URL: Source Code, https://github.com/kornia/kornia
Author-email: Edgar Riba <edgar@kornia.org>
License: Apache-2.0
License-File: LICENSE
Keywords: computer vision,deep learning,pytorch
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Environment :: GPU
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.11
Requires-Dist: kornia-rs>=0.2.0
Requires-Dist: numpy>=1.21
Requires-Dist: torch>=2.5.1
Provides-Extra: dev
Requires-Dist: coverage; extra == 'dev'
Requires-Dist: onnx; extra == 'dev'
Requires-Dist: onnxruntime; extra == 'dev'
Requires-Dist: onnxscript; extra == 'dev'
Requires-Dist: pillow; extra == 'dev'
Requires-Dist: pre-commit; extra == 'dev'
Requires-Dist: pytest-timeout; extra == 'dev'
Requires-Dist: pytest>=7; extra == 'dev'
Requires-Dist: setuptools; extra == 'dev'
Provides-Extra: docs
Requires-Dist: matplotlib; extra == 'docs'
Requires-Dist: onnx; extra == 'docs'
Requires-Dist: pillow; extra == 'docs'
Requires-Dist: pydata-sphinx-theme<0.23,>=0.21; extra == 'docs'
Requires-Dist: sphinx; extra == 'docs'
Requires-Dist: sphinx-autodoc-defaultargs; extra == 'docs'
Requires-Dist: sphinx-autodoc-typehints; extra == 'docs'
Requires-Dist: sphinx-copybutton>=0.3; extra == 'docs'
Requires-Dist: sphinx-design; extra == 'docs'
Requires-Dist: sphinx-notfound-page; extra == 'docs'
Requires-Dist: sphinxcontrib-bibtex; extra == 'docs'
Requires-Dist: sphinxcontrib-gtagjs; extra == 'docs'
Requires-Dist: sphinxcontrib-youtube; extra == 'docs'
Provides-Extra: image
Requires-Dist: pillow; extra == 'image'
Provides-Extra: onnx
Requires-Dist: onnx; extra == 'onnx'
Requires-Dist: onnxruntime; extra == 'onnx'
Requires-Dist: onnxscript; extra == 'onnx'
Provides-Extra: sd
Requires-Dist: diffusers; extra == 'sd'
Requires-Dist: transformers; extra == 'sd'
Description-Content-Type: text/markdown

<div align="center">
<p align="center">
  <img width="55%" src="https://github.com/kornia/data/raw/main/kornia_banner_pixie.png" />
</p>

---

English | [简体中文](README_zh-CN.md)

<p align="center">
  <a href="https://www.kornia.org/playground/"><img alt="Try in your browser" src="https://img.shields.io/badge/%E2%96%B6%20Try%20in%20your%20browser-F5B301?style=for-the-badge&labelColor=F5B301"></a>
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<!-- prettier-ignore -->
<a href="https://kornia.readthedocs.io">Docs</a> •
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**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](https://pytorch.org), 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.

> **📢 Direction**: Kornia is becoming the **reference implementation and executable specification** for differentiable computer vision and geometry in the PyTorch ecosystem — explicit conventions, conformance tests, and honest benchmarks over API growth. Read the [Roadmap](ROADMAP.md).

## Key Components
1. **Differentiable Image Processing**<br>
  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 our [docs](https://kornia.readthedocs.io) for more.
2. **Advanced Augmentations**<br>
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**<br>
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.

<details>
<summary>See here for some of the methods that we support! (>500 ops in total !)</summary>

| **Category**               | **Methods/Models**                                                                                                   |
|----------------------------|---------------------------------------------------------------------------------------------------------------------|
| **Image Processing**        | - Color conversions (RGB, Grayscale, HSV, etc.)<br>- Geometric transformations (Affine, Homography, Resizing, etc.)<br>- Filtering (Gaussian blur, Median blur, etc.)<br>- Edge detection (Sobel, Canny, etc.)<br>- Morphological operations (Erosion, Dilation, etc.)                                 |
| **Augmentation**            | - Random cropping, Erasing<br> - Random geometric transformations (Affine, flipping, Fish Eye, Perspective, Thin plate spline, Elastic)<br>- Random noises (Gaussian, Median, Motion, Box, Rain, Snow, Salt and Pepper)<br>- Random color jittering (Contrast, Brightness, CLAHE, Equalize, Gamma, Hue, Invert, JPEG, Plasma, Posterize, Saturation, Sharpness, Solarize)<br> - Random MixUp, CutMix, Mosaic, Transplantation, etc.                  |
| **Feature Detection**       | - Detector (Harris, GFTT, Hessian, DoG, KeyNet, DISK and DeDoDe)<br> - Descriptor (SIFT, HardNet, TFeat, HyNet, SOSNet, and LAFDescriptor)<br>- Matching (nearest neighbor, mutual nearest neighbor, geometrically aware matching, AdaLAM LightGlue, and LoFTR)                    |
| **Geometry**                | - Camera models and calibration<br>- Stereo vision (epipolar geometry, disparity, etc.)<br>- Homography estimation<br>- Depth estimation from disparity<br>- 3D transformations                |
| **Deep Learning Layers**    | - Custom convolution layers<br>- Recurrent layers for vision tasks<br>- Loss functions (e.g., SSIM, PSNR, etc.)<br>- Vision-specific optimizers                                        |
| **Photometric Functions**   | - Photometric loss functions<br>- Photometric augmentations                                                                                           |
| **Filtering**               | - Bilateral filtering<br>- DexiNed<br>- Dissolving<br>- Guided Blur<br>- Laplacian<br>- Gaussian<br>- Non-local means<br>- Sobel<br>- Unsharp masking                                                                                            |
| **Color**                   | - Color space conversions<br>- Brightness/contrast adjustment<br>- Gamma correction                                                                       |
| **Stereo Vision**           | - Disparity estimation<br>- Depth estimation<br>- Rectification                                                                                           |
| **Image Registration**      | - Affine and homography-based registration<br>- Image alignment using feature matching                                                                     |
| **Pose Estimation**         | - Essential and Fundamental matrix estimation<br>- PnP problem solvers<br>- Pose refinement                                                                |
| **Optical Flow**            | - Farneback optical flow<br>- Dense optical flow<br>- Sparse optical flow                                                                                  |
| **3D Vision**               | - Depth estimation<br>- Point cloud operations<br>                                                                |
| **Image Denoising**         | - Gaussian noise removal<br>- Poisson noise removal                                                                                                        |
| **Edge Detection**          | - Sobel operator<br>- Canny edge detection                                                                                                                 |                                               |
| **Transformations**         | - Rotation<br>- Translation<br>- Scaling<br>- Shearing                                                                                                     |
| **Loss Functions**          | - SSIM (Structural Similarity Index Measure)<br>- PSNR (Peak Signal-to-Noise Ratio)<br>- Cauchy<br>- Charbonnier<br>- Depth Smooth<br>- Dice<br>- Hausdorff<br>- Tversky<br>- Welsch<br>                                   |                                                                                             |
| **Morphological Operations**| - Dilation<br>- Erosion<br>- Opening<br>- Closing                                                                                                          |

</details>

## Half-Precision Support

The status below comes from the Linux CPU half-precision CI jobs, which run the whole test suite in `float16` and in
`bfloat16` against strict known-failure manifests
([`cpu_float16.txt`](testing/half_precision_xfails/cpu_float16.txt),
[`cpu_bfloat16.txt`](testing/half_precision_xfails/cpu_bfloat16.txt)). The *known failures* column counts the manifest
entries per module; the remaining work is tracked in [#4153](https://github.com/kornia/kornia/issues/4153). No CI job
covers CUDA or MPS half precision.

| Module | float16 | bfloat16 | Known failures (fp16 / bf16) | Notes |
|--------|:-------:|:--------:|:----------------------------:|-------|
| `kornia.color` | ⚠️ | ⚠️ | 3 / 9 | float16: HLS JIT/module and RGB255 round-trip accuracy; bfloat16: Lab, Luv and RGB255 accuracy |
| `kornia.filters` | ⚠️ | ⚠️ | 6 / 8 | Accuracy misses in Canny magnitudes, the sampled Gaussian kernel and separable blur; on CPU `fft_conv` runs its FFTs in float32 |
| `kornia.enhance` | ✅ | ⚠️ | 0 / 2 | bfloat16: `DiffJPEG` and ZCA accuracy |
| `kornia.morphology` | ✅ | ✅ | 0 / 0 | |
| `kornia.augmentation` | ⚠️ | ⚠️ | 200 / 56 | float16: 108 entries are `CutmixGenerator`, whose Dirichlet sampling rejects float16 parameters; bfloat16: mostly 3D-augmentation gradient checks (28 of 56 entries are `RandomMotionBlur3D`/`RandomRotation3D` backward) |
| `kornia.geometry.transform` | ⚠️ | ⚠️ | 31 / 32 | Accuracy misses in rotation matrices, affine/perspective warps and the homography warper |
| `kornia.geometry.camera` | ⚠️ | ⚠️ | 13 / 23 | Pinhole `cam2pixel`/`pixel2cam` consistency, distortion round trips, `StereoCamera` reprojection; 12 bfloat16 entries are a test-side dtype assertion |
| `kornia.geometry.calibration` | ⚠️ | ⚠️ | 13 / 12 | `solve_pnp_dlt` rejects half inputs (float32/float64 only); `undistort_points` misses its OpenCV reference values |
| `kornia.geometry.epipolar` | ⚠️ | ⚠️ | 58 / 56 | `find_fundamental`, `find_essential`, `decompose_essential_matrix`, `motion_from_essential*` and `KRt_from_projection` raise `NotImplementedError`: CPU `lu`, `eigh` and QR have no half kernels |
| `kornia.geometry.homography` | ⚠️ | ⚠️ | 11 / 16 | The DLT solvers run (SVD is cast to float32) but miss the clean-point accuracy checks |
| `kornia.geometry.liegroup` | ⚠️ | ⚠️ | 35 / 129 | `So2`/`Se2` use complex tensors: float16 hits missing `ComplexHalf` kernels, and most bfloat16 `So2`/`Se2` tests raise (119 entries); `So3`/`Se3` nearly all pass |
| `kornia.geometry.solvers` | ⚠️ | ⚠️ | 1 / 1 | `solve_quartic` accuracy on random quartics |
| `kornia.geometry.subpix` | ⚠️ | ⚠️ | 14 / 12 | `ConvSoftArgmax3d` raises (CPU `avg_pool3d` has no half kernel); the rest are accuracy |
| `kornia.geometry.conversions` | ⚠️ | ⚠️ | 72 / 60 | Angle-axis, quaternion and rotation-matrix round trips lose accuracy |
| `kornia.geometry.ransac` | ⚠️ | ⚠️ | 4 / 4 | The essential and fundamental models raise through the epipolar solvers |
| `kornia.geometry` (other) | ⚠️ | ⚠️ | 8 / 17 | Accuracy in boxes, depth and line utilities; bfloat16 `NamedPose` construction raises |
| `kornia.image` | ⚠️ | ⚠️ | 4 / 4 | `draw_convex_polygon` fill accuracy |
| `kornia.losses` | ⚠️ | ⚠️ | 3 / 4 | Dice averaging overflows to inf/NaN in float16; mutual information range check; bfloat16 Dice weighting and total variation |
| `kornia.feature` | ✅ | ✅ | 0 / 0 | Matching uses a manual `cdist` fallback for half dtypes; LightGlue's float16 tests are skipped, so that path is unmeasured |
| `kornia.metrics` | ✅ | ⚠️ | 0 / 1 | bfloat16: `ssim3d` accuracy |
| `kornia.models` | ✅ | ⚠️ | 0 / 1 | bfloat16 RT-DETR RepVGG fusion accuracy |
| `contrib`, `core`, `io`, `onnx`, `sensors`, `tracking`, `utils` | ✅ | ⚠️ | 0 / 3 | bfloat16: histogram matching, `_torch_svd_cast`, camera-model projection |

✅ No known CPU failures &nbsp; ⚠️ Runs, with known failures (mostly accuracy; notes name the ops that raise)

**Test results:**

| Run | Passed | Failed | Skipped | Pass% | Measured |
|-----|-------:|-------:|--------:|------:|----------|
| CPU float32 *(baseline)* | 10398 | 0 | 3737 | **100.0%** | `ca5021eb`, 2026-09-14 |
| CPU float16 | 9795 | 522 | 3821 | **94.9%** | `ca5021eb`, 2026-09-14 |
| CPU bfloat16 | 9849 | 512 | 3774 | **95.1%** | `ca5021eb`, 2026-09-14 |
| CUDA float32 *(baseline)* | 12193 | 22 | 3957 | **99.8%** | `f8449854`, 2026-09-23 |
| CUDA float16 | 11738 | 451 | 3983 | **96.3%** | `f8449854`, 2026-09-23 |
| CUDA bfloat16 | 11736 | 500 | 3936 | **95.9%** | `f8449854`, 2026-09-23 |

Pass% = passed ÷ (passed + failed). The CPU rows are the scheduled `main` CI jobs (Linux x86_64, Python 3.11,
PyTorch 2.9.1, no `--runslow`). In the half jobs, *Failed* is the manifest's entry count: CI reports those tests as
strict xfails, and it fails if any of them passes or fails differently. Tests marked `xfail` in the source are
excluded from every row. Reproduce a CPU half row in that environment with
`KORNIA_TEST_OPTIMIZER= pixi run test-module tests/ --verify-known-failures --known-failure-profile=cpu-float16`
(or `cpu-bfloat16`), and the baseline with `pixi run test-f32`. `pixi run test-half` is an unseeded sweep of both
dtypes whose counts can drift slightly from the manifests. The CUDA rows are a local run, not CI (RTX 4090,
Python 3.11, PyTorch 2.14.0+cu130). The half rows run in-process with `KORNIA_TEST_IN_SUBPROCESS=1`; every failure,
in all three rows, is then re-run on its own (with `--isolate-half-precision` for the half dtypes) and counted by that
result. Eight of the float32 failures are cuDNN TF32 accuracy misses in convolutions
([#4778](https://github.com/kornia/kornia/issues/4778)); the other 14 are tests that assume CPU behavior
([#4779](https://github.com/kornia/kornia/issues/4779)). The CUDA rows deselect `TestFindHomographyDLT::test_nocrash`
and `test_nocrash_lu`, which hang on CUDA ([#4770](https://github.com/kornia/kornia/issues/4770)).

See the [full precision guide](https://kornia.readthedocs.io/en/stable/get-started/precision.html) for details.

## Sponsorship

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!

<a href="https://opencollective.com/kornia/donate" target="_blank">
  <img src="https://opencollective.com/webpack/donate/button@2x.png?color=blue" width=300 />
</a>

## Installation

[![PyPI python](https://img.shields.io/pypi/pyversions/kornia)](https://pypi.org/project/kornia)
[![pytorch](https://img.shields.io/badge/PyTorch_2.5.1+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)

### From pip

  ```bash
  pip install kornia
  ```

Some features (ONNX, Stable Diffusion dissolving) need extra packages; see [Optional extras](https://kornia.readthedocs.io/en/latest/get-started/installation.html#optional-extras).

<details>
  <summary>Other installation options</summary>

#### From source with editable mode

  ```bash
  pip install -e .
  ```

#### For development with Pixi (Recommended)

For development, Kornia uses [pixi](https://pixi.sh) for fast Python package management and environment management. The project includes a `pixi.toml` configuration file for reproducible dependency management.

  ```bash
  # Install pixi (if not already installed)
  curl -fsSL https://pixi.sh/install.sh | bash

  # Create the Pixi environment and install development dependencies
  pixi install
  pixi run -e default install

  # Run tests
  pixi run test

  # For CUDA development
  pixi run -e cuda install
  pixi run -e cuda test-cuda
  ```

These commands set up a complete development environment with all dependencies. For more details on dependency management and available tasks, see [CONTRIBUTING.md](CONTRIBUTING.md).

#### From Github url (latest version)

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

</details>

## Quick Start

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

<details>
<summary>Get started with Kornia image transformation and augmentation!</summary>

```python
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((-45.0, 45.0), p=1.0), RandomBrightness((0.0, 1.0), p=1.0))

# Leveraging StableDiffusion models
dslv_op = StableDiffusionDissolving()

img = augmentation_pipeline(img)
dslv_op(img, step_number=500)

dslv_op.save("Kornia-enhanced.jpg")
```

</details>

<details>
<summary>Find out Kornia ONNX models with ONNXSequential!</summary>

```python
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")
```
</details>

## Call For Contributors

If kornia is useful to you and you would like to help, contributions of many kinds are welcome: code, bug reports, benchmarks, documentation, questions, answers, and examples. The maintainers have limited time, so we cannot promise that every proposal or pull request will be reviewed or merged.

### Strengthen the Core (Priority)

Kornia's differentiated value is its geometry core: warping and sampling, homographies, cameras, epipolar geometry, rotations and Lie groups, and geometry-consistent augmentation. The highest-impact contributions make that core more trustworthy — see the [Roadmap](ROADMAP.md) for the full picture. Great entry points:

- **Benchmark results from your hardware**: run the benchmark suite with `--contribute` and send the JSON — CUDA numbers from diverse GPUs are especially useful.
- **Convention pinning tests and conformance vectors** for core geometry operations.
- **Corrective error messages**: upgrade bare shape assertions into errors that state what was wrong, what was expected, and which convention applies.
- **Classical vision in the core domain**: camera intrinsic calibration, fiducial markers (ArUco/ChArUco), classical tracking, dense stereo, and Hough transforms. An early design discussion can be useful for work of this size.

See the [Roadmap's contributor areas](ROADMAP.md#areas-seeking-contributors) for more project context.

### AI Models

The model zoo is currently **frozen for expansion** while maintainer bandwidth concentrates on the core. Shipped models (LoFTR, LightGlue, DISK, DeDoDe, SAM, and friends) stay available and maintained, and model work approved before the freeze (Efficient LoFTR, SANDesc) will be completed under its existing scope. New integrations — including VLM/VLA models — require a named maintainer sponsor who accepts ongoing ownership of the integration; a contributor implementation alone cannot reopen the surface. See the [Roadmap](ROADMAP.md#guiding-themes) for the reasoning and the reopen condition.

### Documentation And Tutorial Optimization

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.

## Cite

If you are using kornia in your research-related documents, it is recommended that you cite the paper. See more in [CITATION](./CITATION.md).

  ```bibtex
  @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

See [CONTRIBUTING.md](./CONTRIBUTING.md) for our social contract, development setup, and technical guidelines. Participation is subject to the [Code of Conduct](./CODE_OF_CONDUCT.md).

## Community

- **Discord:** talk with people who use and develop kornia. [Join the server](https://discord.gg/HfnywwpBnD)
- **GitHub Issues:** report bugs and propose concrete changes. [Open an issue](https://github.com/kornia/kornia/issues/new/choose)
- **GitHub Discussions:** ask questions and discuss implementations, research, and ideas. [Join a discussion](https://github.com/kornia/kornia/discussions)

<a href="https://github.com/Kornia/kornia/graphs/contributors">
  <img src="https://contrib.rocks/image?repo=Kornia/kornia" width="60%" />
</a>

Made with [contrib.rocks](https://contrib.rocks).

## License

Kornia is released under the Apache 2.0 license. See the [LICENSE](./LICENSE) file for more information.
