imaginary VS gocv

One thing I didn't mention that AIO includes is a container called imaginary which is written in Go and processes images. I think this is mainly used here for generating image previews in NC.

Thanks. This is new to me. How is it different from https://github.com/h2non/imaginary?

There is also battle-tested imaginary tool [1], offering similar functionality and much more, also using libvips

[1] https://github.com/h2non/imaginary

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.

Love to know what do use it for in the production. Currently I'm interested in https://github.com/hybridgroup/gocv purely because OpenCV.

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.