FLTK VS Dlib

Another dark horse is FLTK: https://www.fltk.org/. For utilities this might be your best choice, especially because static linking is supported. This is mostly a C++ solution but there are bindings to other solutions.

Fl_Flex (shameless plug!) is now officially in upstream FLTK since 1.4 for a "flexbox style" layout manager.

Go ahead and sell an FLTK product then. I'm sure you go to https://www.fltk.org/ and think those GUI's look awesome you delusional moron

FLTK

A few examples: Dear ImGui, SFML, FLTK. Probably even Tk could be used.

FLTK (Fast Light Toolkit) is a lightweight, cross-platform supported toolkit for building GUIs. FLTK is supported on Windows, macOS, and UNIX systems and was originally built for C++. If you use the FLTK toolkit to create a GUI application, the application looks the same on all supported operating systems.

There are a lot of options and they're not that hard to find... Qt, wxWidgets, FLTK, IUP come immediately to mind.

FLTK. It's a cross-platform C++ GUI toolkit for UNIX®/Linux® (X11), Microsoft® Windows®, and MacOS® X.

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.