The app is still failing with intermittent moov atom failures on trying
to open the video clips with opencv VideoCapture. I suspect it is trying
to open the files while ffmpeg is not done finalizing them. Reformed the
detection loop to spawn dedicated motion detection threads for each
video clip and only when ffmpeg confirmed finished.
Thanks the debug messages, in found a potential issue with the video
clips being pulled from the in house test cameras. Sometimes the video
clips are being pulled with incomplete meta information causing opencv
to fail to open the clips. Added "-movflags faststart" to the ffmpeg
command that should hopefully fix this and should help the app to handle
unreliable camera streams more gracefully.
max_clips now defaults to 90 instead of 30.
Added the ability change the video codec via the config file.
Changed the install script to now install the application in the /opt
directory and then symm link to /usr/bin. Doing this allowed me to
create a run script to start the application and enable the
OPENCV_VIDEOIO_DEBUG parameter for opencv. This should make it easier to
diagnose video-io issues with opencv.
Updated the README documentation with all of the changes done to the
application since v1.5.
Somewhere in the code is causing an infinite loop, root cause still
undermined. added more logging statements to help me find misbehavior.
imgDiff() will now handle empty frames on the parameters more
gracefully.
enforceMaxClips() will no longer assume all video clips are accompanied
by html and jpg files but will now instead "delete if exists."
The app is hard crashing now but I was able to determine the cause this
time. Must functions in filesystem tend to abort if the filesystem
object doesn't exists. Added protection where needed to prevent crashing
Logs are still being cutoff, I'm assuming the app is crashing but can't
locate the problem without any logs. Reformed logging to never overwrite
the logs and will instead append only. Size control will be in the form
of the byte size of the log files.
Moved logging out of it's own loop, hopefully this fixes the issue with
it not outputting all log lines. recLoop() and detectLoop() will now
update logs synchronously.
The setup.sh script will now include gstreamer and pkg-config. This
should help fix opencv video-io format support.
The error checking with ffmpeg is not working. Learned that it doesn't
always return 0 on success. Decided to remove the error checking
altogether. Instead ffmpeg failures should be checked manually using
stderr.
Dirent includes .. and . so I decided to switch to the filesystem entry
listing that should hopefully exclude those special directories.
The camera webroot was not generating .index files. those files would
only get generated if motion was detected. Copied the code that does
that onto recLoop() to execute regardless of motion.
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.
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.
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.
Added a -v command line option to display the application's current
version.
The application version is now defined in a single a const value called
APP_VER so bumping the version number now means updating this single
value in common.h.
Versioning scheme will now be major.minor.[test_rev]. test_rev will be
t1, 2, 3, etc... as updates are pushed to the test branch. all code
pushes to master shall bump major or minor and then remove test_rev.
Removed the detect loop's motion latching affect so it ONLY calls wrOut
if the video clip contains motion.
Fixed a bug in the recording loop that fail to create the needed sub-dir
before calling FFMPEG.
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.
Video clips recorded from the camera are no longer append, instead the
clips are kept as is and then linked together in a playlist file in the
output_dir. this makes it much more efficient and easier to maintain
code.
Also discovered that ffmpeg have a tendency to stall mid execution of
recording from the rtsp stream every now and then. added a work around
in the form of calling ffmpeg via the timeout command instead of
directly so it will force kill ffmpeg if it goes longer than the
expected BUF_SZ.
Increased BUF_SZ to 10 secs.
Added a clause in the recording loop that will make it write out a
second clip if motion was detected.
major changes to the motion detection scheme and re-introduced
multi-threading. this further sped up the motion detection to a point
that it can now be called in line with the recording loop without
loosing any extra camera footage due to heavy cpu usage.
pixels are now read in blocks to further increase efficiency and to
filter out movements of small objects. the footage clip size is now
hard coded to 3 seconds instead of it being external adjustable.
changed the way footage with motion is now stored. its now down to
single level files with the current date. if footage of the same date
already exists, new footage will be appended to it.
the version number shall be updated going forward.
removed all threads from the application as there is no used for them at
at this time. instead, the application will now operate on a single
event loop and now directly utilize use ffmpeg to record video footage
instead of opencv's implementation.
old code pulled tons of frames the detection stream at full speed,
wasting a lot of cpu cycles. instead it will now pull frames in a steady
speed at the new detect_fps value. doing this significantly reduced cpu
usage and can potentially further reduce cpu usage for end users by
pulling the fps value lower then the default.
The app will now count the amount of secs to record post motion
detection instead of the amount of frames. Split the main loop timer
with the motion timer in separate threads to make that happen. The
parameter was added to the config file.
Recording fps is now adjustable.
decided to change frame comparison functions again from optical flow to
a home brewed function that compares gray levels in the pixels of each
frame. significant differences in gray levels between the frames can
potentially trigger a motion event.
also moved away command line arguments to an external config file to set
app parameters.
created a README file to get this project ready for general open source
release.
all current experimentation with the code leads up to this point
for optical flow motion detection. the code as it stands will
input frames in pairs and then compare each pair of frames for
any significant changes in the optical flow distance between
points.
experiments have shown that this actual does work fairly well;
however there is significant amounts of CPU usage and video
encoding options are not flexible at all. the code still picks
up false positives but I have high confidence something that
can be adjusted through external parameters which I will
impliment in the future.
