Cinder VS Dlib

Have you come across openFrameworks (https://openframeworks.cc/) or Cinder (https://libcinder.org/)?

CProcessing reminds me of Cinder

I only used SFML, currently making a 2D isometric game. I really like it so far overall, easy to use IMO, pretty well documented. Does what I need it to do. Heard good things about SDL2 and also Cinder++ (https://libcinder.org/) also.

What kind of game? You might be better off using a game engine unless it's more of a simple starter project. Check out https://libcinder.org/ or see lots of engines here: https://github.com/collections/game-engines

Cinder

Try Cinder (https://libcinder.org/). I have not tried it myself but it seems to have the same goals as P5 and Processing (ie. creative coding).

Kind of a shorty thing for Meta to do when Cinder is already taken by https://libcinder.org

or its C++ cousins openFrameworks and Cinder

Mid Level [Three.js (WebGL)(https://libcinder.org/) Mesh, Geometry, Material, Lighting] [Graphics Library]

dlib

The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.

I really like dlib's code https://github.com/davisking/dlib

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.

The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.

The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.

The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.

Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.