GitLocker: The Coding Marketplace

Description:

korniars 0.1.5

kornia-rs: low level computer vision library in Rust

The kornia-rs crate is a low level library for Computer Vision written in Rust 🦀
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 --example hello_world
use kornia_rs::image::Image;
use kornia_rs::io::functional as F;

fn main() -> Result<(), Box<dyn std::error::Error>> {
// read the image
let image_path = std::path::Path::new("tests/data/dog.jpeg");
let image: Image<u8, 3> = F::read_image_jpeg(image_path)?;

println!("Hello, world!");
println!("Loaded Image size: {:?}", image.size());
println!("\nGoodbyte!");

Ok(())
}

Hello, world!
Loaded Image size: ImageSize { width: 258, height: 195 }

Goodbyte!

Features

🦀The library is primarly written in Rust.
🚀 Multi-threaded and efficient image I/O, image processing and advanced computer vision operators.
🔢 The n-dimensional backend is based on the ndarray crate.
🐍 Python bindings are created with 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

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.

🛠️ Installation
>_ System dependencies
Dependeing on the features you want to use, you might need to install the following dependencies in your system:
turbojpeg
sudo apt-get install nasm

gstreamer
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:
[dependencies]
kornia-rs = { version = "0.1.2", features = ["gstreamer"] }

Alternatively, you can use the cargo command to add the dependency:
cargo add kornia-rs

🐍 Python
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 recording stream.
Checkout all the examples in the examples directory to see more use cases.
use kornia_rs::image::Image;
use kornia_rs::io::functional as F;

fn main() -> Result<(), Box<dyn std::error::Error>> {
// read the image
let image_path = std::path::Path::new("tests/data/dog.jpeg");
let image: Image<u8, 3> = F::read_image_jpeg(image_path)?;
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 gray: Image<f32, 1> = kornia_rs::color::gray_from_rgb(&image_f32)?;

let gray_resize: Image<f32, 1> = kornia_rs::resize::resize_native(
&gray,
kornia_rs::image::ImageSize {
width: 128,
height: 128,
},
kornia_rs::resize::InterpolationMode::Bilinear,
)?;

println!("gray_resize: {:?}", gray_resize.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_resize.data)?);

Ok(())
}

Python usage
Load an image, that is converted directly to a numpy array to ease the integration with other libraries.
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
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
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
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.
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

Clone the repository in your local directory
git clone https://github.com/kornia/kornia-rs.git

🦀 Rust
Compile the project and run the tests
cargo test

For specific tests, you can run the following command:
cargo test image

🐍 Python
To build the Python wheels, we use the maturin package. Use the following command to build the wheels:
make build-python

To run the tests, use the following command:
make test-python

💜 Contributing
This is a child project of Kornia. Join the community to get in touch with us, or just sponsor the project: https://opencollective.com/kornia