Opencv does not work with multiprocessing
hey i just want some help this is my code but i do not know how to display the video using imshow or anyother method. it was working fine but as soon as i put all the code into processes it is running in chunks and secondly i am not able to display video. can someone please help me ? i do not know what i am doing wrong.
I verified that the example fails on my 2.7.6 Windows 7 with PicklingError: Can't pickle <type 'thread.lock'>: it's not found as thread.lock Since the example has been removed for 3.x, a possible minimal fix would be to say that it does not work on Windows and remove the statement that implies that it does. If you can't do your work in 1 CPU then you need to either use a native library that is capable of multi-CPU work or use multiprocessing. I have to do that because sha1 is inherently CPU bound and I need to process 600-1000MB/s of data to complete the backups in a timely fashion. OpenCV is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android. My program does not close cleanly when I use the queue from the multiprocessing module (Python 2.7 on Windows) in place of Queue.Queue. Eventually I want to process the frames in imgbuffer using a multiprocessing.Process and then pull back display data using a second queue.
MultiProcessing or Multithreading with python and Opencv to detect a face Recently i working on raspberry pi 3(OS raspbian) with OPENCV + PYTHON to detect face on live camera. And I see detected face on raspberry pi3 use opencv very slow, about 4 - 5 FPS/s. Aug 07, 2015 · In that case, you can try to use loky.get_reusable_executor() to fetch a process-based, reusable executor instance that does not cause OpenCV or any other OpenMP-based library to crash. The executor of loky can be used as a drop-in replacement for concurrent.futures.ThreadPoolExecutor . I would first try a thread pool and if that doesn't help either 1) find a different, non blocking library or 2) use multiprocessing to start 20 interpreters and have them wait for a signal or define a time at which to take the photo (ie, send a message to say take a photo at time x where x is one or more seconds in the future).
Sep 16, 2019 · Hey Jon, thanks for the comment. I meant to include a note in the blog post regarding OpenCV 4 and the pip install (but I forgot about it, thanks for the reminder) — PiWheels does not currently have a pre-compiled OpenCV 4 distribution. If you can't do your work in 1 CPU then you need to either use a native library that is capable of multi-CPU work or use multiprocessing. I have to do that because sha1 is inherently CPU bound and I need to process 600-1000MB/s of data to complete the backups in a timely fashion.
Recently, I was asked about sharing large numpy arrays when using Python's multiprocessing.Pool. While not explicitly documented, this is indeed possible. I will write about this small trick in this short article. Hope it helps :) It should be noted that I am using Python 3.6. Therefore this tutorial may not work on earlier versions of Python.