Hey everyone, I’m stoked to share my latest project with you – building a seamless, end-to-end computer vision pipeline using Kornia, a PyTorch framework that makes data augmentation, geometric reasoning, feature matching, and learning a breeze.
So, what is Kornia? In a nutshell, it’s a powerful framework that allows you to create a unified computer vision pipeline, with a focus on differentiability and GPU acceleration. But don’t just take my word for it – let me walk you through how to set up and use Kornia to create a modern computer vision workflow.
First things first, we need to get our environment up and running. I’ll show you how to set up a fully reproducible environment using Google Colab, including installing Kornia and its dependencies. Once we have our environment set up, we’ll be able to dive into creating a GPU-accelerated, synchronized augmentation pipeline for images, masks, and keypoints.
Next, we’ll define a set of reusable helper utilities for image conversion, visualization, safe data downloading, and artificial mask generation. These helpers will make it easy to examine augmented images, masks, and LoFTR correspondences during experimentation.
Now, let’s talk about learned feature matching with LoFTR. We’ll use LoFTR to identify dense correspondences between two images, and then apply Kornia’s RANSAC to estimate a consistent homography from these matches. Finally, we’ll warp one image into the coordinate system of the other and visualize the correspondences to ensure a seamless stitched output.
To further demonstrate the power of Kornia, we’ll apply its augmentations on-the-fly to a subset of the CIFAR-10 dataset and train a lightweight convolutional network end-to-end. We’ll show that these augmentations incur minimal overhead while improving the data range.
In conclusion, Kornia is an incredibly powerful framework that allows you to create a unified vision pipeline where data augmentation, geometric reasoning, feature matching, and learning remain differentiable and GPU-friendly within a single framework. By combining LoFTR matching, RANSAC-based homography estimation, and optimization-driven alignment with a practical training loop, we’ve demonstrated how classical computer vision and deep learning complement each other rather than competing.
So, what are you waiting for? Check out the full code on GitHub and follow me on Twitter, ML SubReddit, and Newsletter for more AI and machine learning updates. Happy learning!
