# kornia-rs: low level computer vision library in Rust
[](https://badge.fury.io/py/kornia-rs)
[](https://docs.rs/kornia)
[](LICENCE)
[](https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-CnydWe5fmvkcktIeRFGCEQ)
The `kornia` crate is a low level library for Computer Vision written in [Rust](https://www.rust-lang.org/) 🦀
Use the library to perform image I/O, visualisation and other low level operations in your machine learning and data-science projects in a thread-safe and efficient way.
## Getting Started
`cargo run --bin hello_world -- --image-path path/to/image.jpg`
```rust
use kornia::image::Image;
use kornia::io::functional as F;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// read the image
let image: Image<u8, 3> = F::read_image_any("tests/data/dog.jpeg")?;
println!("Hello, world! 🦀");
println!("Loaded Image size: {:?}", image.size());
println!("\nGoodbyte!");
Ok(())
}
```
```bash
Hello, world! 🦀
Loaded Image size: ImageSize { width: 258, height: 195 }
Goodbyte!
```
## Features
- 🦀The library is primarly written in [Rust](https://www.rust-lang.org/).
- 🚀 Multi-threaded and efficient image I/O, image processing and advanced computer vision operators.
- 🔢 Efficient Tensor and Image API for deep learning and scientific computing.
- 🐍 Python bindings are created with [PyO3/Maturin](https://github.com/PyO3/maturin).
- 📦 We package with support for Linux [amd64/arm64], Macos and WIndows.
- Supported Python versions are 3.7/3.8/3.9/3.10/3.11/3.12/3.13
### Supported image formats
- Read images from AVIF, BMP, DDS, Farbeld, GIF, HDR, ICO, JPEG (libjpeg-turbo), OpenEXR, PNG, PNM, TGA, TIFF, WebP.
### Image processing
- Convert images to grayscale, resize, crop, rotate, flip, pad, normalize, denormalize, and other image processing operations.
### Video processing
- Capture video frames from a camera and video writers.
## 🛠️ Installation
### >_ System dependencies
Dependeing on the features you want to use, you might need to install the following dependencies in your system:
#### turbojpeg
```bash
sudo apt-get install nasm
```
#### gstreamer
```bash
sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
```
** Check the gstreamr installation guide: <https://docs.rs/gstreamer/latest/gstreamer/#installation>
### 🦀 Rust
Add the following to your `Cargo.toml`:
```toml
[dependencies]
kornia = "v0.1.7"
```
Alternatively, you can use each sub-crate separately:
```toml
[dependencies]
kornia-core = { git = "https://github.com/kornia/kornia-rs", tag = "v0.1.7" }
kornia-io = { git = "https://github.com/kornia/kornia-rs", tag = "v0.1.7" }
kornia-image = { git = "https://github.com/kornia/kornia-rs", tag = "v0.1.7" }
kornia-imgproc = { git = "https://github.com/kornia/kornia-rs", tag = "v0.1.7" }
```
### 🐍 Python
```bash
pip install kornia-rs
```
## Examples: Image processing
The following example shows how to read an image, convert it to grayscale and resize it. The image is then logged to a [`rerun`](https://github.com/rerun-io/rerun) recording stream.
Checkout all the examples in the [`examples`](https://github.com/kornia/kornia-rs/tree/main/examples) directory to see more use cases.
```rust
use kornia::{image::{Image, ImageSize}, imgproc};
use kornia::io::functional as F;
fn main() -> Result<(), Box<dyn std::error::Error>> {
// read the image
let image: Image<u8, 3> = F::read_image_any("tests/data/dog.jpeg")?;
let image_viz = image.clone();
let image_f32: Image<f32, 3> = image.cast_and_scale::<f32>(1.0 / 255.0)?;
// convert the image to grayscale
let mut gray = Image::<f32, 1>::from_size_val(image_f32.size(), 0.0)?;
imgproc::color::gray_from_rgb(&image_f32, &mut gray)?;
// resize the image
let new_size = ImageSize {
width: 128,
height: 128,
};
let mut gray_resized = Image::<f32, 1>::from_size_val(new_size, 0.0)?;
imgproc::resize::resize_native(
&gray, &mut gray_resized,
imgproc::resize::InterpolationMode::Bilinear,
)?;
println!("gray_resize: {:?}", gray_resized.size());
// create a Rerun recording stream
let rec = rerun::RecordingStreamBuilder::new("Kornia App").connect()?;
// log the images
let _ = rec.log("image", &rerun::Image::try_from(image_viz.data)?);
let _ = rec.log("gray", &rerun::Image::try_from(gray.data)?);
let _ = rec.log("gray_resize", &rerun::Image::try_from(gray_resized.data)?);
Ok(())
}
```
## Python usage
Load an image, that is converted directly to a numpy array to ease the integration with other libraries.
```python
import kornia_rs as K
import numpy as np
# load an image with using libjpeg-turbo
img: np.ndarray = K.read_image_jpeg("dog.jpeg")
# alternatively, load other formats
# img: np.ndarray = K.read_image_any("dog.png")
assert img.shape == (195, 258, 3)
# convert to dlpack to import to torch
img_t = torch.from_dlpack(img)
assert img_t.shape == (195, 258, 3)
```
Write an image to disk
```python
import kornia_rs as K
import numpy as np
# load an image with using libjpeg-turbo
img: np.ndarray = K.read_image_jpeg("dog.jpeg")
# write the image to disk
K.write_image_jpeg("dog_copy.jpeg", img)
```
Encode or decode image streams using the `turbojpeg` backend
```python
import kornia_rs as K
# load image with kornia-rs
img = K.read_image_jpeg("dog.jpeg")
# encode the image with jpeg
image_encoder = K.ImageEncoder()
image_encoder.set_quality(95) # set the encoding quality
# get the encoded stream
img_encoded: list[int] = image_encoder.encode(img)
# decode back the image
image_decoder = K.ImageDecoder()
decoded_img: np.ndarray = image_decoder.decode(bytes(image_encoded))
```
Resize an image using the `kornia-rs` backend with SIMD acceleration
```python
import kornia_rs as K
# load image with kornia-rs
img = K.read_image_jpeg("dog.jpeg")
# resize the image
resized_img = K.resize(img, (128, 128), interpolation="bilinear")
assert resized_img.shape == (128, 128, 3)
```
## 🧑💻 Development
Pre-requisites: install `rust` and `python3` in your system.
Install rustup in your system
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
```
Install [`uv`](https://docs.astral.sh/uv/) to manage python dependencies
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
Install the [`just`](https://github.com/casey/just) command runner. This tool is used to manage the development tasks.
```bash
cargo install just
```
Clone the repository in your local directory
```bash
git clone https://github.com/kornia/kornia-rs.git
```
You can check the available commands by running `just` in the root directory of the project.
```bash
$ just
Available recipes:
check-environment # Check if the required binaries for the project are installed
clean # Clean up caches and build artifacts
clippy # Run clippy with all features
clippy-default # Run clippy with default features
fmt # Run autoformatting and linting
py-build py_version='3.9' # Create virtual environment, and build kornia-py
py-build-release py_version='3.9' # Create virtual environment, and build kornia-py for release
py-install py_version='3.9' # Create virtual environment, and install dev requirements
py-test # Test the kornia-py code with pytest
test name='' # Test the code or a specific test
```
### 🐳 Devcontainer
This project includes a development container to provide a consistent development environment.
The devcontainer is configured to include all necessary dependencies and tools required for building and testing the `kornia-rs` project. It ensures that the development environment is consistent across different machines and setups.
**How to use**
1. **Install Remote - Containers extension**: In Visual Studio Code, install the `Remote - Containers` extension from the Extensions view (`Ctrl+Shift+X`).
2. **Open the project in the container**:
- Open the `kornia-rs` project folder in Visual Studio Code.
- Press `F1` and select `Remote-Containers: Reopen in Container`.
Visual Studio Code will build the container and open the project inside it. You can now develop, build, and test the project within the containerized environment.
### 🦀 Rust
Compile the project and run the tests
```bash
just test
```
For specific tests, you can run the following command:
```bash
just test image
```
### 🐍 Python
To build the Python wheels, we use the `maturin` package. Use the following command to build the wheels:
```bash
just py-build
```
To run the tests, use the following command:
```bash
just py-test
```
## 💜 Contributing
This is a child project of [Kornia](https://github.com/kornia/kornia). Join the community to get in touch with us, or just sponsor the project: <https://opencollective.com/kornia>
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"description": "# kornia-rs: low level computer vision library in Rust\n\n\n[](https://badge.fury.io/py/kornia-rs)\n[](https://docs.rs/kornia)\n[](LICENCE)\n[](https://join.slack.com/t/kornia/shared_invite/zt-csobk21g-CnydWe5fmvkcktIeRFGCEQ)\n\nThe `kornia` crate is a low level library for Computer Vision written in [Rust](https://www.rust-lang.org/) \ud83e\udd80\n\nUse the library to perform image I/O, visualisation and other low level operations in your machine learning and data-science projects in a thread-safe and efficient way.\n\n## Getting Started\n\n`cargo run --bin hello_world -- --image-path path/to/image.jpg`\n\n```rust\nuse kornia::image::Image;\nuse kornia::io::functional as F;\n\nfn main() -> Result<(), Box<dyn std::error::Error>> {\n // read the image\n let image: Image<u8, 3> = F::read_image_any(\"tests/data/dog.jpeg\")?;\n\n println!(\"Hello, world! \ud83e\udd80\");\n println!(\"Loaded Image size: {:?}\", image.size());\n println!(\"\\nGoodbyte!\");\n\n Ok(())\n}\n```\n\n```bash\nHello, world! \ud83e\udd80\nLoaded Image size: ImageSize { width: 258, height: 195 }\n\nGoodbyte!\n```\n\n## Features\n\n- \ud83e\udd80The library is primarly written in [Rust](https://www.rust-lang.org/).\n- \ud83d\ude80 Multi-threaded and efficient image I/O, image processing and advanced computer vision operators.\n- \ud83d\udd22 Efficient Tensor and Image API for deep learning and scientific computing.\n- \ud83d\udc0d Python bindings are created with [PyO3/Maturin](https://github.com/PyO3/maturin).\n- \ud83d\udce6 We package with support for Linux [amd64/arm64], Macos and WIndows.\n- Supported Python versions are 3.7/3.8/3.9/3.10/3.11/3.12/3.13\n\n### Supported image formats\n\n- Read images from AVIF, BMP, DDS, Farbeld, GIF, HDR, ICO, JPEG (libjpeg-turbo), OpenEXR, PNG, PNM, TGA, TIFF, WebP.\n\n### Image processing\n\n- Convert images to grayscale, resize, crop, rotate, flip, pad, normalize, denormalize, and other image processing operations.\n\n### Video processing\n\n- Capture video frames from a camera and video writers.\n\n## \ud83d\udee0\ufe0f Installation\n\n### >_ System dependencies\n\nDependeing on the features you want to use, you might need to install the following dependencies in your system:\n\n#### turbojpeg\n\n```bash\nsudo apt-get install nasm\n```\n\n#### gstreamer\n\n```bash\nsudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev\n```\n\n** Check the gstreamr installation guide: <https://docs.rs/gstreamer/latest/gstreamer/#installation>\n\n### \ud83e\udd80 Rust\n\nAdd the following to your `Cargo.toml`:\n\n```toml\n[dependencies]\nkornia = \"v0.1.7\"\n```\n\nAlternatively, you can use each sub-crate separately:\n\n```toml\n[dependencies]\nkornia-core = { git = \"https://github.com/kornia/kornia-rs\", tag = \"v0.1.7\" }\nkornia-io = { git = \"https://github.com/kornia/kornia-rs\", tag = \"v0.1.7\" }\nkornia-image = { git = \"https://github.com/kornia/kornia-rs\", tag = \"v0.1.7\" }\nkornia-imgproc = { git = \"https://github.com/kornia/kornia-rs\", tag = \"v0.1.7\" }\n```\n\n### \ud83d\udc0d Python\n\n```bash\npip install kornia-rs\n```\n\n## Examples: Image processing\n\nThe following example shows how to read an image, convert it to grayscale and resize it. The image is then logged to a [`rerun`](https://github.com/rerun-io/rerun) recording stream.\n\nCheckout all the examples in the [`examples`](https://github.com/kornia/kornia-rs/tree/main/examples) directory to see more use cases.\n\n```rust\nuse kornia::{image::{Image, ImageSize}, imgproc};\nuse kornia::io::functional as F;\n\nfn main() -> Result<(), Box<dyn std::error::Error>> {\n // read the image\n let image: Image<u8, 3> = F::read_image_any(\"tests/data/dog.jpeg\")?;\n let image_viz = image.clone();\n\n let image_f32: Image<f32, 3> = image.cast_and_scale::<f32>(1.0 / 255.0)?;\n\n // convert the image to grayscale\n let mut gray = Image::<f32, 1>::from_size_val(image_f32.size(), 0.0)?;\n imgproc::color::gray_from_rgb(&image_f32, &mut gray)?;\n\n // resize the image\n let new_size = ImageSize {\n width: 128,\n height: 128,\n };\n\n let mut gray_resized = Image::<f32, 1>::from_size_val(new_size, 0.0)?;\n imgproc::resize::resize_native(\n &gray, &mut gray_resized,\n imgproc::resize::InterpolationMode::Bilinear,\n )?;\n\n println!(\"gray_resize: {:?}\", gray_resized.size());\n\n // create a Rerun recording stream\n let rec = rerun::RecordingStreamBuilder::new(\"Kornia App\").connect()?;\n\n // log the images\n let _ = rec.log(\"image\", &rerun::Image::try_from(image_viz.data)?);\n let _ = rec.log(\"gray\", &rerun::Image::try_from(gray.data)?);\n let _ = rec.log(\"gray_resize\", &rerun::Image::try_from(gray_resized.data)?);\n\n Ok(())\n}\n```\n\n\n\n## Python usage\n\nLoad an image, that is converted directly to a numpy array to ease the integration with other libraries.\n\n```python\n import kornia_rs as K\n import numpy as np\n\n # load an image with using libjpeg-turbo\n img: np.ndarray = K.read_image_jpeg(\"dog.jpeg\")\n\n # alternatively, load other formats\n # img: np.ndarray = K.read_image_any(\"dog.png\")\n\n assert img.shape == (195, 258, 3)\n\n # convert to dlpack to import to torch\n img_t = torch.from_dlpack(img)\n assert img_t.shape == (195, 258, 3)\n```\n\nWrite an image to disk\n\n```python\n import kornia_rs as K\n import numpy as np\n\n # load an image with using libjpeg-turbo\n img: np.ndarray = K.read_image_jpeg(\"dog.jpeg\")\n\n # write the image to disk\n K.write_image_jpeg(\"dog_copy.jpeg\", img)\n```\n\nEncode or decode image streams using the `turbojpeg` backend\n\n```python\nimport kornia_rs as K\n\n# load image with kornia-rs\nimg = K.read_image_jpeg(\"dog.jpeg\")\n\n# encode the image with jpeg\nimage_encoder = K.ImageEncoder()\nimage_encoder.set_quality(95) # set the encoding quality\n\n# get the encoded stream\nimg_encoded: list[int] = image_encoder.encode(img)\n\n# decode back the image\nimage_decoder = K.ImageDecoder()\n\ndecoded_img: np.ndarray = image_decoder.decode(bytes(image_encoded))\n```\n\nResize an image using the `kornia-rs` backend with SIMD acceleration\n\n```python\nimport kornia_rs as K\n\n# load image with kornia-rs\nimg = K.read_image_jpeg(\"dog.jpeg\")\n\n# resize the image\nresized_img = K.resize(img, (128, 128), interpolation=\"bilinear\")\n\nassert resized_img.shape == (128, 128, 3)\n```\n\n## \ud83e\uddd1\u200d\ud83d\udcbb Development\n\nPre-requisites: install `rust` and `python3` in your system.\n\nInstall rustup in your system\n```bash\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\n```\n\nInstall [`uv`](https://docs.astral.sh/uv/) to manage python dependencies\n```bash\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n```\n\nInstall the [`just`](https://github.com/casey/just) command runner. This tool is used to manage the development tasks.\n```bash\ncargo install just\n```\n\nClone the repository in your local directory\n```bash\ngit clone https://github.com/kornia/kornia-rs.git\n```\n\nYou can check the available commands by running `just` in the root directory of the project.\n\n```bash\n$ just\nAvailable recipes:\n check-environment # Check if the required binaries for the project are installed\n clean # Clean up caches and build artifacts\n clippy # Run clippy with all features\n clippy-default # Run clippy with default features\n fmt # Run autoformatting and linting\n py-build py_version='3.9' # Create virtual environment, and build kornia-py\n py-build-release py_version='3.9' # Create virtual environment, and build kornia-py for release\n py-install py_version='3.9' # Create virtual environment, and install dev requirements\n py-test # Test the kornia-py code with pytest\n test name='' # Test the code or a specific test\n```\n### \ud83d\udc33 Devcontainer\n\nThis project includes a development container to provide a consistent development environment.\n\nThe devcontainer is configured to include all necessary dependencies and tools required for building and testing the `kornia-rs` project. It ensures that the development environment is consistent across different machines and setups.\n\n**How to use**\n\n1. **Install Remote - Containers extension**: In Visual Studio Code, install the `Remote - Containers` extension from the Extensions view (`Ctrl+Shift+X`).\n\n2. **Open the project in the container**:\n - Open the `kornia-rs` project folder in Visual Studio Code.\n - Press `F1` and select `Remote-Containers: Reopen in Container`.\n\nVisual Studio Code will build the container and open the project inside it. You can now develop, build, and test the project within the containerized environment.\n\n### \ud83e\udd80 Rust\n\nCompile the project and run the tests\n\n```bash\njust test\n```\n\nFor specific tests, you can run the following command:\n\n```bash\njust test image\n```\n\n### \ud83d\udc0d Python\n\nTo build the Python wheels, we use the `maturin` package. Use the following command to build the wheels:\n\n```bash\njust py-build\n```\n\nTo run the tests, use the following command:\n\n```bash\njust py-test\n```\n\n## \ud83d\udc9c Contributing\n\nThis is a child project of [Kornia](https://github.com/kornia/kornia). Join the community to get in touch with us, or just sponsor the project: <https://opencollective.com/kornia>\n\n",
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