![]() Multiple matched features then helps us in the later section Feature Matching, where we try to figure out how to go from one image to the other. Using Feature Descriptors, the next section, we can compare features and know that we have found the same one. So we find a feature on a part of one image, and hopefully we can find the same feature in the other image. These points are useful because we can find them again in the other image (see the paragraph below for a greater description of this). How do I know what is identifiable if I see it again in another image? How do I know what is important to track? A 'feature point' is this, and Feature Detection finds these points. Just call some OpenCV function and magical dots appear on your image! But what are they? How do you get them? Why do we want them?įeatures are basically identifiable parts of images. "Features" can be a bit of a vague concept in computer vision, and it certainly was for me at the start. It's my explanation of my understanding, but I also have links in these explanations to the theoretical basis and better write-ups. Each section will have a brief header about the topic, and then I'll go into more detail. Here is how I have broken down my code into components, based on the above. More specifically, for each of the images, we need to find distinct features in them, create descriptors of those features so that we can potentially find that same feature elsewhere, try to match the features between the images, create a way of transforming from one image to the other based on how these matches line up, and then perform this transform for every pixel. In broad terms, we need to find what the images have in common, find a way to match those bits up, and then make sure that way works for everything else as well. Get the panorama from the two images, stitch in a third, and repeat. I'll talk specifically about the case for two pictures, but it easily generalises - or iterates. And then some magical stuff happens and la-di-da, out pops the two images made nicely into one, such that you can't even see the join (hopefully). So to get a panorama, you take a photo of something, say a mountain, and then you turn a bit and take another photo, maybe of the side of the mountain and the sunset next to it. A good dataset to work with, that was made to test panography programs, is Adobe's panorama dataset. I'll mention any tunable parameters that I think would be good to try. Another good thing to try is to tweak the parameters I mention below to 'retune' my solution, so to speak, and see how the results differ. I encourage you to read the reference texts I give, learn the theory, and perhaps try to write the algorithms yourself and compare results with what I have. But it does indeed work and I learnt a lot doing it, and I hope anyone reading this can too. I just coded it to work and be understandable. It is not optimised for efficiency or beauty or anything. The solution here is not the best solution. Specifically, I wanted to learn - really learn - what features were, how they worked, how they were used, how you transformed between images, etc, and the various 'tool' algorithms computer vision engineers use (like RANSAC and Levenberg-Marquardt). I started this project at suggestion from a colleague to learn in-depth (from scratch) more about computer vision. If you want other tutorials to work through with this one, here's a good place to start. ![]() Then step into each function or file as necessary.Įach component has a comment block that I will also explain here, along with reference texts. The best place to start after reading here is main.cpp, where each component is used in sequence. The aim of this tutorial and program is to go over all the constituent parts and theory of panorama stitching,Īs well as providing a reference solution.
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