miniCMS VS ffvideo

If you want to see the web app before considering this real and serious consulting job offer: https://github.com/bsenftner/miniCMS

I wrote an optimized C++ FFMPEG player as a video surveillance system, initially to watch my pets in my yard, and then kept going adding (human) face detection, and then a DL/ML training scaffold, then Live555 re-encoding, then an embedded web browser, then I added tons of comments and turned it into a learning demo project. It's on Github, I still use it to watch my pets: https://github.com/bsenftner/ffvideo

Are your camera's ONVIF compatible? You can confirm this by running this free open source software: https://sourceforge.net/projects/onvifdm/ If your cameras appear in this software, then they are ONVIF compatible. If they are, then you can use my free and open source windows video player to view as many stream as you want: https://github.com/bsenftner/ffvideo This player is CPU efficient, intended for use when training video based machine learning models, so it leaves processor available for machine training. Used as a pure video player, I've had 32 video windows playing at 30 fps simultaneously using it on an i9 3.2 Ghz workstation.

In my ffmpeg playback library, be aware it is optimized for computer vision; therefore any audio is ignored and if playing from a file any timing information is ignored as well. When playing real time streams, such as from an IP camera or USB camera that playback is as close to real time as possible. I seem to remember something like under 20 ms per frame latency. However, IP video services expect timing information to be honored, and because mine ignores timing a YouTube video will fly by a few hundred frames per second. Likewise, playing from a local stored video file will playback as fast as your drive delivers frames. It was designed this way to minimize overhead and delay when training algorithms with video. Here's the essential source to the playback lib: https://github.com/bsenftner/ffvideo/tree/master/ffvideolib_src

Okay, thanks to u/bsenftner I was able to figure this out by looking at his Github repository. Essentially, if you want to use a specific version of OpenGL with wxGLCanvas you have to specify the major and minor versions in the attribute list passed to the constructor of wxGLCanvas.

If you take a look at my ffvideo github project (linked in my reply above) you can search for instances of std::thread and see they are fairly self contained, with logical data fencing protecting data shared between threads. Here's an example: a video frame exporter that runs in it's own thread, enabled when the end-user wants video frames written to disk: https://github.com/bsenftner/ffvideo/blob/aed42b5a3e856e24b030e71f6d92bcbabf5d6829/ffvideolib_src/ffvideo_frameExporter.h

Try this: https://github.com/bsenftner/ffvideo

I do this in C++ here: https://github.com/bsenftner/ffvideo I think I'm using DLib rather than OpenCV, but at this level the difference between the two is negligible.

I have some code doing this in an open source C++ project here: https://github.com/bsenftner/ffvideo Towards the bottom of the README on that page you'll see an image titled "demonstrating tilted head registration" describing what you're trying to do here.