terracotta VS Dlib

One can also do raster calculus between two or more COGs with Terracotta: https://github.com/DHI-GRAS/terracotta/issues/221

I am currently working on setting up a web app using leaflet that can display .tif images along with some polygons and add some filters in the web app to better interact with the data. In the process of implementing this, I came to know that I need to use a tile server because I have large amounts of data and the tile server can help to serve the tiles on the web app. Based on my research, I guess these two are the main tools as of now: 1. Geoserver and 2. Terracotta. I am confused between the two. Can anyone suggest which one would be better for my use case and the pros and cons of both if possible? In case there's a different one that's better than the two mentioned above, it would be great if you can share the tool and your experience. Would be helpful.

Terracotta: Serverless GeoTiff Tile Server\ (12 comments)

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.