AI object detection via yolov5 didn't work out too well, in fact it was
crashing the detection threads for whatever reason. I could deep dive
why it was crashing but I think the better solution is to bring back
optical flow detection at the block level. the advantage of this over
object detection is the fact that a block doesn't need to have a whole
object in it.
potentially fixed what was apparently a long standing bug that caused
motion detection to look at just the first block. this bug was found
thanks to the stats output.
re-formed the stats output and moved it out of the motion detect
function.
block pixel diff counts will now no longer stop at the threshold at each
block. it will now count the entire block and output the results in the
stats. the code now also pick the block with the highest pixDiff instead
of stopping at the first block with a high pixDiff.
added object detection code base on yolov5 machine vision model. also
added a stat file so motion and object detection values can be monitored
in real time if used with the 'watch' command.
Broken down the code into multiple files instead having it all in
main.cpp.
Also detached recording from detection by having them now run in
separate threads instead of having motion detection inline with
recording. this will hopefully make it so there is less missed motion
events due to processing overhead.
The recording loop now take advantage of FFMPEG's "-f segment" option
instead of generating the clips implicitly in separated FFMPEG calls.
again, all in hope to reduce missed motion events.
This application have the tendency to detect motion of small insects.
to prevent this it was determined with there will need to be some means
of identifying objects via machine vision. there is an object detection
function but it doesn't currently do anything at this time. this is
something that I will be working on in the near future.
created a test branch in the repository. all early, testing code will
now go in this branch going forward. only fully tested, stable code will
be committed to master going forward.
