Project-Lightspeed VS pigo

Depending on how you want to achieve this, you could use a combination of OBS + Restreamer or OBS + Project-Lightspeed. Another solution would be to use more specific solutions like neko

I also used to use Project-Lightspeed, which worked great. I abandoned it because I wanted to get off of FTL based on the OBS thread above. It otherwise worked for me.

https://github.com/GRVYDEV/Project-Lightspeed is a 'Twitch like' server that does FTL input from OBS. You can run OBS on the Ubuntu desktop and do a desktop capture. Viewers can then watch on the 'Project Lightspeed' host.

I recommend Project-Lightspeed for this. It uses the FTL protocol (similar to the now defunct Mixer) with WebRTC for great latency. I've used it many times for streaming games, etc. and it works well.

Special thanks to the Ryujinx Discord for helping me test and to the Lightspeed Project (https://github.com/GRVYDEV/Project-Lightspeed)!

jellyfin can certainly do what you are looking for, but it seems like you probably don't have enough bandwidth to reliably host the service at home, and may be overkill for your usage... you could set up project lightspeed (https://github.com/GRVYDEV/Project-Lightspeed) on a $5/month VPS and use OBS to stream video with sub 1 second latency, you can install teamspeak or mumble or something on the same VPS for voice chat.

Then I saw this library https://github.com/esimov/pigo but as far as I know, this library only detects if an image has face in it, but cannot really classify the face. CMIIW.

The task consists of porting a small portion of the PIGO face detection GO library (https://github.com/esimov/pigo/commit/29b25278c3e3436416440404b95cd3c18e145b3b) especially the face landmarks extraction section to WebAssembly/JavaScript or plain C depending of the programming language your are proficient with.

I tried https://github.com/ageitgey/face_recognition and https://github.com/esimov/pigo so far and they both work OK, but from there, you would need to add EXIF tags into your pictures, so they can be picked up by img-DB.

I'll use this post / opportunity to ask the HN community

(I know this is a technically complicated, and potentially sensitive subject)

I've been approached by a few people who are trying to make prescription glasses less expensive. It's a mix of (licensed) opticians, people interested in offering community health services, etc.

What is the current state of the art for PD measurement based on face / pupil tracking / detection using laptop webcams or mobile (front) cameras?

I imagine (obviously?) that there must be scholar research on the acceptable error margins for a PD measurement (depending on the type of vision condition, i.e. farsightedness, etc.)?

Would using something like Handsfree or https://github.com/esimov/pigo (which has pupil detection) be a good start, or would these be an ~order of magnitude off in terms of the necessary margins?

Thanks a lot.