pcopy VS AI-basketball-analysis

nopaste/pcopy, that you can selfhost. Just a single binary file.

Heyy I made a thing like that. https://github.com/binwiederhier/pcopy

And another one here:https://github.com/binwiederhier/pcopy (same, see server.go)

I host a version of it on https://nopaste.net

re 1: as fellow gopher has said, `io.CopyN` is your friend, though I have recently implemented a similar thing that may be interesting to you. I implemented a `Peak()` method that will read up to N bytes and return a reader that can be read as if it's the original reader, but you'll be able to get the `PeakedBytes` from it too: https://github.com/binwiederhier/pcopy/blob/master/util/peak.go

Since we're listing alternatives, here's the one I made: https://nopaste.net -- It's open source and available on https://github.com/binwiederhier/pcopy

curl usage is available via: "curl nopaste.net".

I have an issue on github (https://github.com/binwiederhier/pcopy/issues/20) to "modernize" the JS code a little, i.e. use "class"es, modules, strict mode, consts, exports, ...

If you want to see them implemented, see https://github.com/binwiederhier/pcopy/blob/284b6502fa736951b4ace0dfc62152ff707639ae/server.go

Source is here: https://github.com/binwiederhier/pcopy

Source code: https://github.com/chonyy/AI-basketball-analysis

I built an app to visualize and analyze basketball shots and shooting pose with machine learning.

https://github.com/chonyy/AI-basketball-analysis

The result is pretty nice. However, the only problem is the slow inference speed. I'm now refactoring the project structure and changing the model to a much faster YOLO model.

Thanks for asking! This is not a noob question.

I would say that the similar workflow could be applied to any ball-related sports. The object detection and the tracking algorithm is basically the same. Then, you could add any sport-specific feature!

For example, I have used a similar method to build AI Basketball Analysis.

https://github.com/chonyy/AI-basketball-analysis