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akaze's Introduction

Rust Computer Vision

Rust CV is a project to implement computer vision algorithms in Rust.

What is computer vision

Many people are familiar with covolutional neural networks and machine learning in computer vision, but computer vision is much more than that. One of the first things that Rust CV focused on was algorithms in the domain of Multiple-View Geometry (MVG). Today, Rust now has enough MVG algorithms to perform relatively simple camera tracking and odometry tasks. Weakness still exists within image processing and machine learning domains.

Goals

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 support computer vision tooling, the following will be implemented:

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akaze's Issues

How to adjust params for matching ?

Hi there,

Disclaimer: i'm completely new to computer vision, please forgive me for the maybe silly questions

I was trying to use feature matching with rust, and i came accross your crate rust-cv and the one from indianajohn. I had pretty good starts with the second one matching images, but yours is really much faster at computation.

However, i don't have much the results from matching i had with the other crate. I'm pretty sure it's due to the params I used, or the nature of the images. I tried running https://github.com/rust-cv/akaze/blob/master/tests/estimate_pose.rs on my sample images but it fails estimating the model.

Can you give a clue about what I can do to evaluate the model / calibrate things, or if it's really too complicated for beginners, some good resources to learn what is necessary for that ?

Thanks in advance, and bravo for making these kind of work entering the rust space

Expose separate feature detection and descriptor extraction functions

Right now, there is only extract and extract_path. This is fine for most users. Some advanced users will only want to use akaze to detect or describe features, but not both. Adding these methods will support those users. These methods should also re-expose the evolution data to the user like it used to be before the API was shrunk.

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