pigo VS gocv

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.

- Updated Docker images with OpenCV

https://github.com/hybridgroup/gocv/releases/tag/v0.36.0

- Installing from source gives me E: Unable to locate package libdc1394-22-dev

Can anyone guess if its possible to e.g. use - https://github.com/hybridgroup/gocv - permanently monitor the network stream via address e.g. http://192.169.178.20/action/stream?subject=mjpeg&user=user&pwd=secret - Generate/Extract a snapshot as image if configured items are detected e.g.: humans, cats, cars, dogs

But if you want to have something to show: I've played around with https://gocv.io/ and was quite impressed what this library can do with the video input from your camera input.

I have just found the gocv package. I have a video analyzer program written in python, however im thinking about rewriting on golang for optimizing the execution time. Its a good idea or its better to just use the phyton one.

https://github.com/hybridgroup/gocv has openCV bindings.

I am working on a custom model, I have been through this example to apply it on my use case but the output I get has random detection from perform detection, how do I work with gocv.Mat structure and is there any example with yolo darknet model? Any help is appreciated if you can direct me to right resources where I can better understand how the output mat is to be parsed to get the box! Thanks in advance!

I think https://github.com/hybridgroup/gocv accepts tensorflow model format, why not use that?

The repo leverages gocv and can be run using CUDA(performance results here). If there's any interest I can create one for YOLO V4 as well.