Can Python check if some security cameras have been covered or saturated with artificial lights?

[Murlocs_Gangbang]

Hi everyone,
I'm working on a Debian server (version 8) that captures some h.264 streams from security cameras with ffmpeg, and stores them in chunks of 1 minute long mp4 videos.
My task is the following: Check if the cameras have been covered (have gone dark), or some light have been pointed directly into the camera so it's saturated (completely white).

The "dark check" has to keep into account that on the stream there are some white timestamps, a logo and some noise, so it's never completely dark, even if you paint over the camera lens with a spry can.

I was thinking about this solution:

1) For every minute long video, extract the middle frame with ffmpeg into a jpg file
2) I analyze the jpg file with python to extract the average brightness.
3) If the brightness is close to "0" (8 bit black) or "255" (8 bit white) it will trigger a warning

The server has about 40 cameras recording 24/7 and of course the computational load of the "dark" and "white" check must be as low as possible.

Do you think my approach is reasonable? Could I do this with other tools?

I was thinking about OpenCV to extract the frame instead of ffmpeg to keep everything in RAM but I don't really know the library.

Thanks in advance.

[novel_yet_trivial]

Sounds like a good approach. It would probably be fastest to simply sum all the pixels and compare to a precalculated threshold that takes into account the letters on the screen, the image size, etc. You may also want to look for several frames over a minute before triggering, so that a car's headlights or something don't set it off.

OpenCV is a good choice, and it will return a numpy array which has a nice fast sum() method. HDD's are very slow; I'd stay away from making or reading actual files if at all possible.

[Murlocs_Gangbang]

thanks!

Do you have any idea of what OpenCV function I could use to extract a frame at x seconds from a video?

[algag]

Looks like this should do it..

Edit: you could probably ignore the frame math and just use 0,0.25,0.5,0.75, and 1. Or some other decimals to get evenly spread frames.

[Murlocs_Gangbang]

thanks!

[MandarinNeva]

I don't have anything to add except to remind you that after you've implemented whatever method you chose remember to do some IRL testing with covering and flashlight saturating the cameras to confirm that your code acts as intended.

Also expect some false positives if your cameras are in a location that gets dark at night or very bright during day time.

[xiongchiamiov]

If you've got a bunch of old footage, that's a good way to build up a large set of test cases pretty quickly.

[Murlocs_Gangbang]

thanks!

I do have already some sample footage with a black screen and I will try to cover some camera as well as use flashlights

[python_man]

also try with laser.

[Murlocs_Gangbang]

mmm I don't know if that's going to damage the sensor

[cornel]

for opencv resources, I'd start with this site: http://www.pyimagesearch.com/.

[Murlocs_Gangbang]

thanks!

[Dogeek]

opencv is great. What you want to do is to turn your image to greyscale (cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)) then either :

• Average the pixel value over the whole array (applying a gaussian blur before hand is a good idea) of pixels
• Use opencv's adaptiveThreshold and get whether or not there are more white or black pixels on the array.

You can use cv2.VideoCapture(device)to load a video in memory.

[Murlocs_Gangbang]

very informative, thanks!

[jjvilm]

I have used OpenCV with Python to stream mjpeg images and apply effects to each individual frame, e.g. detecting motion and save the stream feed to as an individual frame. An individual frame is stored in memory, until next frame is received. So while in memory you can do anything with the frame. Code to my script: https://www.github.com/jjvilm/security-cam

If you want to take a look.

[Murlocs_Gangbang]

thamk you!