Iris

Documentation | API Reference | Contributing

A Rust-powered, cross-platform computer vision and deep learning library designed with a clean, modular, library-first architecture. Completely native with zero external C dependencies.

If you love Iris, make sure to give it a star!

[!NOTE] This project is still in active development. APIs and features may change before the first stable release.

• Prerequisites & Supported Platforms
• Features of Iris
• Installation
  • Build from Source
• Library Usage
• Examples
• Cargo Features
• License

Prerequisites

Before installing Iris, ensure you have:

• Rust: v1.85.0+ (Rust 2024 Edition). Install via rustup:

  curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
  
  

• Cargo: Comes bundled with Rust. Verify with cargo --version.

Supported Platforms

Iris supports a wide range of platforms and architectures:

• Windows 10+ / 11+
• Linux (Vulkan/CUDA acceleration support)
• macOS (Metal acceleration support)

Installation

Build from Source

git clone https://github.com/muhammad-fiaz/iris-cv.git

cd iris-cv

cargo build --release

Library Usage

To use iris in your Rust project, run:

cargo add iris-cv

Or add it directly to your Cargo.toml under dependencies:

[dependencies]

iris-cv = "0.0.0"

Development Version (Git)

To use the latest development branch directly from GitHub, run:

cargo add iris-cv --git https://github.com/muhammad-fiaz/iris-cv

Or add the following to your Cargo.toml:

[dependencies]

iris-cv = { git = "https://github.com/muhammad-fiaz/iris-cv" }

In your Rust code:

use iris::prelude::*;
use burn::backend::wgpu::{Wgpu, WgpuDevice};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    type Backend = Wgpu;
    let device = WgpuDevice::default();

    // 1. Open an image
    let img: Image<Backend> = Image::open("assets/images/gradient.png", &device)?;
    println!("Loaded image with shape: {:?}", img.shape());

    // 2. Convert to grayscale and apply Canny edges
    let gray = img.grayscale()?;
    let edges = gray.canny(50.0, 150.0)?;

    // 3. Draw bounding box and save
    let mut canvas = edges.to_rgb()?;
    canvas = canvas.draw_rectangle(
        Rect::new(10, 10, 100, 100),
        Scalar::new(1.0, 0.0, 0.0, 1.0),
        2
    )?;
    canvas.save("output.png")?;

    Ok(())
}

Examples

Run any example to see Iris in action. All examples require the wgpu feature:

cargo run --example image_loading --features wgpu

cargo run --example canny --features wgpu

cargo run --example filters --features wgpu

cargo run --example drawing --features wgpu

cargo run --example color_processing --features wgpu

cargo run --example image_utils --features wgpu

cargo run --example contours --features wgpu

cargo run --example morphology --features wgpu

cargo run --example threshold --features wgpu

cargo run --example optical_flow --features wgpu

cargo run --example tracking --features wgpu

cargo run --example qr_detection --features wgpu

cargo run --example face_recognition --features wgpu

cargo run --example onnx_inference --features wgpu

cargo run --example photo_processing --features wgpu

cargo run --example segmentation --features wgpu

cargo run --example kmeans_clustering --features wgpu

Cargo Features

Iris provides several features to customize compilation and backend acceleration:

Note: rayon is a required dependency for parallel processing — it is always included.

License

This project is licensed under the MIT License. See LICENSE for details.