Fixed the crashing issue by adding tcp timeout args to ffmpeg and
having the app handle empty frames from a disconnected camera
better.
Reformed the directory structure by having live, logs and events in
seperate directories.
schLoop() no longer exists, postCmd is now handled by
detectMoInStream() to ensure motion detection is not done while the
command is running.
Completely reformed the internal workings of the application code. I
brought back multi-threaded functions so there is now 5 separate threads
for different tasks.
recLoop() - this function calls ffmpeg to begin recording footage from
the defined camera and stores the footage in hls format. It is designed
to keep running for as long as the application is running and if it does
stop for whatever reason, it will attempt to auto re-start.
upkeep() - this function does regular cleanup and enforcement of maxDays
maxLogSize and maxEvents without the need to stop recording or detecting
motion.
detectMo() - this function reads directly from recLoop's hls output and
list all footage that has motion in it. motion detection no longer has
to wait for the clip to finish recording thanks to the use of .ts
containers for the video clips. this makes the motion detection for less
cpu intensive now that it will now operate at the camera's fps (slower).
eventLoop() - this function reads the motion list from detectMo and
copies the footage pointed out by the list to an events folder, also in
hls format.
schLoop() - this function runs an optional user defined external command
every amount of seconds defined in sch_sec. this command temporary stops
motion detection without actually terminating the thread. It will also
not run the command at the scheduled time if motion was detected.
Benefits to this reform:
- far less cpu intensive operation
- multi-threaded architecture for better asynchronous operation
- it has support for live streaming now that hls is being used
- a buff_dir is no longer necessary
The fork() architecture from the previous commit is also deemed a
failure. Reverted back to v1.5.t19 code. I'll start from scratch, using
this commit as the new base.
going back to basics. removed all threading code and opted for a multi
process architecture using fork(). previous code had a bad memory leak
and doesn't handle unexpected camera disconnects and for some reason it
also didn't recover gracefully in systemctl when it crashes. Hopefully
this new re-write fixes all of those numerous issues.
moDetect() will now try multiple times to grab buffer footage before
giving up and moving on.
Found the infinite loop issue in moDetect(), turns out the frame
parameters at some point were never returning empty, hence moDetect()
would continue into perpetuity. Changed the loop structure to use a
fixed frame count instead of relying on frameFF() to return empty on
EOF.
Fixed the default webroot directory to apache's correct webroot. Also
renamed separated outDir from webRoot and made webRoot changeable on the
config file.
Added logging the recorder and detection loops to help with debugging
and troubleshooting. Just like the video clips, max log lines were added
to control the size of the data being saved to storage.
Decided to switch using opencv's builtin pixel diff motion detection via
absdiff and thresh. Doing this should increase efficiency instead of
using the home brewed pixel loops and threads.
Added a web interface of sorts by having html files output along with
the video clips. These files are designed to link together with the
assumption that the output directory is a web root like /var/www/html
that apache2 uses. The interface is crude at best but at least allow
playback of recorded footage.
Added max_clips config variable that can limit the amount of motion
events that can recorded to storage on a single day.
optical flow calculations use up a lot of processing power even at the
block level so I decided to take it back out. once again, no objection
detection is going to be used and will fall back to pixel diffs only.
also modified pixel diffs to decrement pixel diffs of no diff is
detected, going test how this works out.
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.
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.
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.
