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Wails v1 (which this project uses) uses Webview for rendering it's frontend. You can read more about how Wails works here: https://wails.app/about/concepts/

Also because I'm pretty sure you're going to be the only one to see this post, do you have any feedback on the app?




I have been in search of a good GUI library for go that works well on both Linux and Windows.

What are your thoughts on Wails?

How is the learning curve for people not very familiar with Web technologies? On that subject, does it require any webdev tools to be installed (nodejs, frameworks, etc)?


I found Wails very simple to use, and would highly recommend it to anyone looking for a lightweight GUI library.

However, Wails v1 uses mshtml (basically ie11) on Windows, so some features are unavailable.

Wails uses Webpack so you need npm installed when developing your app.

You might also be interested in Tauri [1] which is a similar framework but in Rust.

[1] https://github.com/tauri-apps/tauri


Apart of very cool visual effect, the app got my attention as an interesting example of lightweight GUI use with Go. Another rabbit hole for today. Thanks!


My main concern is why the choice of a (slow) genetic algorithm vs a more traditional Delaunay triangulation algorithm?


I'm a bit unsure of what you mean, but the genetic algorithm is used to find an optimal set of points, and then a Delaunay triangulation is created from those points.


Ah, I must've misread the wiki. I thought it meant you used genetic algorithm for triangulation instead of point selection. What's your criteria for calculating the fitness for a candidate set of points?

https://github.com/RH12503/Triangula/wiki/Explanation-of-the...


I was just thinking about adding another wiki page for that, but I'll give a brief explanation here:

Firstly, a triangulation is made from the points and colors are chosen for each triangle.

Then, the variance between the triangles and the original image is calculated using Welford's online algorithm [1]. The variance is computed by iterating over the pixels of each triangle and comparing the pixel color of the original image to the color of that triangle.

Lastly, the fitness is multiplied by a weight to ensure that it covers the entire canvas.

The source code may be a bit confusing because I've applied many optimizations which make it 10-20x faster.

[1] https://en.wikipedia.org/wiki/Algorithms_for_calculating_var...




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