Alright folks, let’s dive into something I’ve been tinkering with lately: the Harris corner detector, or as I affectionately call it, the ‘harris english’ project because, well, I decided to learn more about it in english. Gotta embrace the international side of things, you know?
It all started when I was messing around with image processing. I mean, who hasn’t been there? I wanted to build something that could automatically identify corners in images. Not just any corners, mind you, but the kind of corners that really stand out – the ones that scream, “Hey, look at me! I’m a corner!”.
First things first, I brushed up on the theory. I googled about the Harris corner detection algorithm. The math can get a little hairy, but the core idea is actually pretty intuitive. Basically, you’re looking for regions in an image where changes in intensity occur in multiple directions. These are your corners.
Next up, I grabbed OpenCV. It’s like the Swiss Army knife of image processing libraries. I installed it and started writing some code. I loaded up an image, converted it to grayscale and used the `cornerHarris()` function.
But here’s where things got a little tricky. The output of `cornerHarris()` isn’t exactly a list of corner coordinates. It’s more like a “cornerness” score for each pixel in the image. So, I had to figure out a way to threshold those scores and identify the pixels that were actually corners.
I started experimenting with different threshold values. Too low, and I’d get a ton of false positives. Too high, and I’d miss some of the real corners. It was a delicate balancing act. I ended up using some trial-and-error, tweaking the threshold until I got something that looked reasonable.
Once I had a decent set of corner coordinates, I wanted to visualize them. So, I drew little circles around each corner in the original image. It was super satisfying to see those circles pop up on the image, highlighting all the corners I had detected.
Of course, it wasn’t perfect. There were still some false positives and false negatives. But overall, I was pretty happy with the results. I learned a ton about the Harris corner detector, OpenCV, and the importance of choosing the right threshold value.
So what’s next? Well, I’m thinking about trying to improve the accuracy of the corner detection. Maybe I’ll look into some other corner detection algorithms, or try to implement some filtering techniques to remove noise from the image. The possibilities are endless!
Anyway, that’s my ‘harris english’ adventure in a nutshell. Hope you found it interesting. Until next time, happy coding!
