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Rust CV

Discord Crates.io docs.rs LoC Tests Lints

Rust CV is a project to implement computer vision algorithms, abstractions, and systems in Rust. #[no_std] is supported where possible.

Documentation

Each crate has its own documentation, but the easiest way to check all of the documentation at once is to look at the docs for the cv batteries-included crate.

Check out our tutorial book here! The book source for the tutorials can be found in the tutorial directory of the repository. The example code used in the tutorial can be found in the tutorial-code directory. The resources for tutorials can be found in the site res directory.

About

This repository contains all computer vision crates for Rust CV in a mono-repo, including utilities as well as libraries. When updating libraries, all the crates in this repository should build for a PR to be accepted. Rust CV also maintains some other crates that are related to Computer Vision as well, which are located in the GitHub organization, not in this repository.

Each crate has its own associated license. Rust CV is comprised of different open source licenses, mostly MIT. See the crate directories (or their crates.io entries) for their individual licenses.

Each library was originally its own separate repository before being incorporated into the mono repo. The old repositories that are now in this repo are all archived, but still exist to find tagged versions, assocated commits, and issues. All new PRs should be made to this repository.

What is computer vision

Many people are familiar with covolutional neural networks and machine learning (ML) in computer vision, but computer vision is much more than that. Computer vision broadly encompases image processing, photogrammetry, and pattern recognition. Machine learning can be used in all of these domains (e.g. denoisers, depth map prediction, and face detection), but it is not required. Almost all of the algorithms in this repository are not based on machine learning, but that does not mean you cannot use machine learning with these tools. Please take a look at https://www.arewelearningyet.com/ for Rust ML tools. We may expand into ML more in the future for tasks at which ML outperforms statistical algorithms.

Build

Be sure to have installed rust: . The following packages will be needed on Ubuntu 20.04 (Built using Rust 1.53.0):

  • Cmake
    • sudo apt install cmake
  • build-essential
    • sudo apt-get install build-essential
  • freetype2
    • sudo apt-get install libfreetype-dev
  • libxkbcommon
    • sudo apt install libxkbcommon-dev

If you have not already done so, install Rust:

curl https://sh.rustup.rs -sSf | sh

Clone and Build

cd <directory to keep cloned repo>
git clone https://github.com/rust-cv/cv.git
cd cv
cargo build

Goals

One of the first things that Rust CV focused on was algorithms in the domain of photogrammetry. Today, Rust now has enough photogrammetry algorithms to perform SfM and visual SLAM. Weakness still exists within image processing and pattern recognition domains.

Here are some of the domains of computer vision that Rust CV intends to persue along with examples of the domain (not all algorithms below live within the Rust CV organization, and some of these may exist and are unknown to us; some things may have changed since this was last updated):

To support computer vision tooling, the following will be implemented:

Credits

TheiaSfM and all of its authors can be thanked as their abstractions are direct inspiration for this crate. In some cases, the names of some abstractions may be borrowed directly if they are consistent. You can find the TheiaSfM documentation here.

"Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age" is an excellent paper that compiles information about modern SLAM algorithms and papers.