Opencv: OpenCV + Python multiprocessing breaks on OSX

[PuchatekwSzortach]

I'm trying to use OpenCV with Python's multiprocessing module, but it breaks on me even with very simple code. Here is an example:

import cv2
import multiprocessing
import glob
import numpy

def job(path):

    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    return path

if __name__ == "__main__":

    main_image = cv2.imread("./image.png")
    main_image = cv2.cvtColor( main_image, cv2.COLOR_BGR2GRAY)

    paths = glob.glob("./data/*")
    pool = multiprocessing.Pool()
    result = pool.map(job, paths)

    print 'Finished'

    for value in result:
        print value

If I remove main_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) script works, but with it in there it doesn't, even though that line shouldn't affect jobs processed by the pool.
All image paths lead to simple images, about ~20 of them in total.
Funny enough code works fine if I create images in memory with numpy instead of reading them with imread().
I'm guessing OpenCV uses some shared variables behind the scene that aren't protected from race conditions.

My environment: Mac OS X 10.10, OpenCV 3.0.0, Python 2.7.
The last few lines of stack trace are:

Application Specific Information:
crashed on child side of fork pre-exec

Thread 0 Crashed:: Dispatch queue: com.apple.root.default-qos
0   libdispatch.dylib               0x00007fff8668913f dispatch_apply_f + 769
1   libopencv_core.3.0.dylib        0x000000010ccebd14 cv::parallel_for_(cv::Range const&, cv::ParallelLoopBody const&, double) + 152
2   libopencv_imgproc.3.0.dylib     0x000000010c8b782e void cv::CvtColorLoop<cv::RGB2Gray<unsigned char> >(cv::Mat const&, cv::Mat&, cv::RGB2Gray<unsigned char> const&) + 134
3   libopencv_imgproc.3.0.dylib     0x000000010c8b1fd4 cv::cvtColor(cv::_InputArray const&, cv::_OutputArray const&, int, int) + 23756
4   cv2.so                          0x000000010c0ed439 pyopencv_cv_cvtColor(_object*, _object*, _object*) + 687
5   org.python.python               0x000000010bc64968 PyEval_EvalFrameEx + 19480
6   org.python.python               0x000000010bc5fa42 PyEval_EvalCodeEx + 1538\

BTW - I got other OpenCV functions to crash when used with Python multiprocessing too, above is just the smallest example I could produce that reflects the problem.

Also, I got above algorithm (and much more complicated ones) to work in multithreaded C++ programs, using same OpenCV build on same machine, so I guess the issue lies on Python bindings side.

[berak]

can't reproduce it on win

[PuchatekwSzortach]

I can't reproduce it with Opencv 2.4.9 on Ubuntu either.

[mshabunin]

Reproduced it with latest OpenCV master on Linux. But instead of crashing, processes hung. Works well if one of cvtColor calls is commented.

[xuzhuoran0106]

I move all opencv code into the subprocess function, and it works.

So it sames when you call cv2 functions (except imread), it initializes something which can not be passed to subprocess.

[xuzhuoran0106]

so the idea is to always run opencv code in subprocess.

this is the solution for you.

import multiprocessing
import glob
import numpy

def job2(path):
    import cv2
    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    return path

def job1(path):
    import cv2
    main_image = cv2.imread(path)
    main_image = cv2.cvtColor( main_image, cv2.COLOR_BGR2GRAY)

if __name__ == "__main__":
    p = Process(target=job1, args=("./image.png"))
    p.start()
    p.join()

    paths = glob.glob("./data/*")
    pool = multiprocessing.Pool()
    result = pool.map(job2, paths)

    print 'Finished'

    for value in result:
        print value

[foozmeat]

I just came across this bug myself. In my case I'm doing a lot of work inside of RQ subprocs so moving the conversion to the main "thread" isn't an option. I've got two calls into cvtColor. The first one is a conversion from a PIL image and the second one is converting a CV image into grayscale. I can work around the first problem with this

        cv_image = np.array(image)
        cv_image = cv_image[:, :, ::-1].copy()

but I don't have a work around for the second one. Is there another way to convert an image to grayscale without cvtColor?

[mshabunin]

I'd suggest to use _concurrent.futures.ThreadPoolExecutor_ from python3.

[foozmeat]

I ended up switching celery to use eventlet to work around the problem.

[ekolve]

When I run this on El Capitan with Python 3.5 and OpenCV3 the script hangs - when I comment out the main_image lines, it finishes.

[pogam]

I have been testing the code of PuchatekwSzortach on two different ubuntu os (14.04), with 8GB and 64GB of ram respectively. I have the same problem as others when using 8 GB. However when using the 64GB it works.

This is however not sustainable with larger code. When running a code with opencv functions calls embedded in an homebrewed function parallelized in a "multiprocessing" pool, the code eventually ends up with idle processors after a number of call to the pool that can fluctuate from run to run.

[msalvato]

Seeing the same issue where the script hangs if main_image = cv2.cvtColor(...) is uncommented. OSX 10.11.1, Python 3.5. Came here because I've seen the bug in other places in my codebase as well, not just with cvtColor.

[ekolve]

Setting the start method to 'spawn' avoids the hang:

import cv2
import multiprocessing
import glob
import numpy

def job(path):

    image = cv2.imread(path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    return path

if __name__ == "__main__":

    main_image = cv2.imread("./image.png")
    main_image = cv2.cvtColor( main_image, cv2.COLOR_BGR2GRAY)

    multiprocessing.set_start_method('spawn')
    paths = glob.glob("./data/*")
    pool = multiprocessing.Pool()
    result = pool.map(job, paths)

    print('Finished')

    for value in result:
        print(value)

[pogam]

thanks ekolve. Do you know if there is an equivalent for python 2.7?

[PuchatekwSzortach]

I tried the code with OpenCV 3.1 on OSX 10.11.2 and Python 3.4 and it worked just fine.

[mfergie]

I'm having similar issues on OSX 10.11.2 (15C50), Python 3.5.1, OpenCV 2.4.12_2 installed via homebrew.

[georgwaechter]

Have the same problem after upgrading my python 2.7 code from opencv 2.4.11 to opencv 3.1. For me its is a cvtColor call which converts from BGR to HSV. Does anybody have any hints where to look in the c++ code to start fixing this annoying problem?

[georgwaechter]

I experienced the same problem (code hangs) now in another call: mySurfDetector.detectAndCompute

[berak]

@georgwaechter , there' a similar observation (c++ / multithreading/ cvtColor) here

[georgwaechter]

I'm currently testing whether this maybe originates from the thread pool used in functions like cvtColor and detectAndCompute. In my understanding forking (without an "exec" call afterwords as done in python under linux) could be a problem, but thats only a guess. I've read that forking only clones the current thread. In the case the thread pool may assume wrongly that it has more threads that it has ...

[georgwaechter]

@berak thanks for the link.

[georgwaechter]

Ok, I think i nailed down the problem to the implementation of the pthreads thread pool in https://github.com/Itseez/opencv/blob/master/modules/core/src/parallel_pthreads.cpp ..

I have recompiled OpenCV "-D BUILD_TBB=ON -D WITH_TBB=ON" and suddenly it works! Using this cmake option i use the intel threading library instead of the standard pthreads. The intel library states it has "better fork support".

@berak The problem described there is something else as this problem with python only relates to multiple processes, not multi-threading per se.

[georgwaechter]

Idea to solve the problem within the c++ code: Register a fork handler via http://pubs.opengroup.org/onlinepubs/009695399/functions/pthread_atfork.html and reset the state of the thread pool on forking the process. This has the effect that the next call to cvtColor (or any other method using the thread pool) initializes the thread pool. thus starts the threads.

At the moment it seems that the code is waiting infinitley for a condition that is never signaled because the number of threads is not as expected after forking:
https://github.com/Itseez/opencv/blob/master/modules/core/src/parallel_pthreads.cpp#L440
The "notify_complete" method only signals the condition if all of the threads completed its work ..

[georgwaechter]

This also explains why setting the start method to "spawn" solves the problem as described by @ekolve. Spawing resets the memory after forking, therefore forcing a re-inizialization of all data structures (as the thread pool) within OpenCV.

[georgwaechter]

@mshabunin Can you change the category of this issue? This is not related to the python bindings, but to the core.

Here is another library with the same problem: https://github.com/flame/blis/issues/30

I'll try to prepare a pull request for this.

[alalek]

@georgwaechter Thanks for problem digging!

Fork is very dangerous operation. In general, after fork any mutex/semaphore/other resources should be reset to initial state.
This problem is actual not for "pthread threadpool" only, but for other many states (during fork call OpenCL/CUDA can run/schedule/poll/wait for background tasks, we can capture images from camera or interact with GUI) - all these things may go into broken state with unpredictable behavior.
Also the first message of this issue comes with problem on Mac OSX where "pthreads threadpool" is not used by default - GCD is used, and it is failed.

I don't see here an easy fix.
I believe more reliable way is to detect fork() call, write error message with problem and workarounds, and die.

[georgwaechter]

Unfortunately the possibility to fork opencv processes is very important for the python-world, where multi-processing is more common than multi-threading. The problem is the Global Interpreter Lock (GIL) within the python runtime which prevents python-code from running in parallel in multiple threads.

So, producing an error in case of fork() would prevent many use-cases. In my example we are processing camera frames in near-realtime and do further costly analysis for only some selected frames in a second (and third) process.

You're right, there are many situations where fork could bring the system into an inconsistent state. But most programs fork at startup where the state can be cleaned up easily as in this case.

I'm currently testing whether resetting the thread pool works as expected. Most importantly there should be no worker thread running, so forking is only safe when no task is running at this moment. However as multithreading is not used in Python, this can not occur. In c++ this is possible but can also be detected and handled using the child-part of the fork-handler. On forking, we could automatically wait until the task is complete, so that it is safe to reset the pool state.

Other things like the background thread of the camera are more complicated - i guess. Those could throw an error on fork, but i have not investigated any time in understanding the related code.

[georgwaechter]

Sadly, on OSX this seems to be impossible. There is no fork handler i know of. Apple seems to warn about this situation and recommends to execute exec() after fork() .. https://developer.apple.com/library/ios/documentation/Performance/Reference/GCD_libdispatch_Ref/

[mshabunin]

As a workaround one can disable multithreading before forking and reenable it in each child process:

``` .py
import cv2
import multiprocessing
import glob
import numpy

def job(path):
# enable multithreading in OpenCV for child thread
cv2.setNumThreads(-1)

image = cv2.imread(path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

return path

if __name__ == "__main__":
# disable multithreading in OpenCV for main thread to avoid problems after fork
cv2.setNumThreads(0)

main_image = cv2.imread("opencv/samples/data/lena.jpg")
main_image = cv2.cvtColor( main_image, cv2.COLOR_BGR2GRAY)

paths = glob.glob("opencv/samples/data/*.png")
pool = multiprocessing.Pool()
result = pool.map(job, paths)

print 'Finished'

for value in result:
    print value

```

It works in Ubuntu (pthreads backend), but I didn't tested it on Mac (with GCD backend).

[ekolve]

On OSX El Capitan, Python 3.5.1 this workaround works as long as threads are not re-enabled in the child process. If I place the line:

cv2.setNumThreads(-1)

in the child process then the children hang.

[corentin87]

I'm actually getting a similar bug using the resize function in a subprocess. I'm using python 2.7 and opencv 2.4.11

Thread 0 Crashed:: Dispatch queue: com.apple.root.default-qos
0   libdispatch.dylib                   0x00007fff8b5d013f dispatch_apply_f + 769
1   libopencv_core.2.4.dylib            0x0000000102ea58b3 cv::parallel_for_(cv::Range const&, cv::ParallelLoopBody const&, double) + 115
2   libopencv_imgproc.2.4.dylib         0x0000000102b4b0ae void cv::resizeGeneric_<cv::HResizeLinear<unsigned char, int, short, 2048, cv::HResizeNoVec>, cv::VResizeLinear<unsigned char, int, short, cv::FixedPtCast<int, unsigned char, 22>, cv::VResizeLinearVec_32s8u> >(cv::Mat const&, cv::Mat&, int const*, void const*, int const*, void const*, int, int, int) + 590
3   libopencv_imgproc.2.4.dylib         0x0000000102b4a49f cv::resize(cv::_InputArray const&, cv::_OutputArray const&, cv::Size_<int>, double, double, int) + 8463

But it looks like the problem is a bit different because I can run resize function inside a subprocess with importing cv2 globally.
The following code is just resizing an image and works fine.

import multiprocessing
import cv2

def resize1(path):
    img = cv2.imread(path, 0)
    print "Start resizing"
    cv2.resize(img, (400, 400))
    print "Done resizing"

def main():
    path = "image1.jpg"
    p1 = multiprocessing.Process(target=resize1, args=(path,))
    p1.start()
    p1.join()

if __name__ == "__main__":
    main()

However if I change my main function to also resize an image, my subprocess crash.

def main():
    path = "image1.jpg"
    img = cv2.imread(path)
    cv2.resize(img, (400, 400))

    p1 = multiprocessing.Process(target=resize1, args=(path,))
    p1.start()
    p1.join()

The only solution which works so far it's too create a subprocess every time i need to use opencv which is pretty annoying. I also tried the cv2.setNumThreads solutions and it doesn't work.

[deter3]

@mshabunin solution works on mac 10.10.5 , opencv2.4 , python 2.7 using celery to resolve the issues at image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) .

[hjweide]

Was this ever resolved? I'm encountering the same issue using OpenCV 3.2.0-dev with Python 2.7.6 on Ubuntu 14.04.

EDIT: After following the suggestion by @georgwaechter to rebuild OpenCV with TBB, I no longer encounter this issue.

[mkassner]

I have encountered the same issue using OpenCV 3.2.0-dev and python3.6 on Ubuntu14.04 .

Compiling OpenCV with TBB solved the problem I had in my use case. I dont know if this is a decent workaround.

[tornadomeet]

any update? same here with mac os 10.12.3 with both python2 and python3, had tried build opencv with TBB, but did not solve the problem.

also tried in ubuntu(virtual machine) 16.04+python2 +opencv2.x, do not work again.

[Felix-N]

thanks a lot, I ran into the same problem, took some time to find a solution.

multiprocessing.set_start_method('spawn')

works for python 3.5, opencv 3.2 on os x

[njzhangyifei]

encountered the same issue, cvtColor causes process to hang.

multiprocessing.set_start_method('spawn')

fixes the issue for python3.5, opencv 3.2 built from source on ubuntu 16.04

[ogrisel]

We designed loky to mitigate this issue on any versions from python 2.7+ and 3.3+.

It's based on the concurrent.futures.ProcessPoolExecutor API but uses the spawn start method by default and fixes other possible deadlocks with non-picklable objects.

[terencezl]

This sounds related to the problem I have in hand. In python2.7 on MacOS Sierra I need to access the cv2.VideoCapture(0) web cam stream in a different multiprocessing.Process(), but I can't make it work without an abrupt exit, or if I have imported pygame, Fatal Python error: (pygame parachute) Segmentation Fault. I still don't know why pygame complains since I commented out all other pygame code but it is pygame that throws the Segmentation Fault...

I tried @georgwaechter 's TDD compilation, @mshabunin 's setting single thread, @corentin87 's in-proc import without global import, but none worked. @ekolve 's multiprocessing.set_start_method('spawn') in a different python3 environment gave me raise RuntimeError('context has already been set, and if I switch on force=True, there is a dark warning:

RuntimeError:
        An attempt has been made to start a new process before the
        current process has finished its bootstrapping phase.

        This probably means that you are not using fork to start your
        child processes and you have forgotten to use the proper idiom
        in the main module:

            if __name__ == '__main__':
                freeze_support()
                ...

        The "freeze_support()" line can be omitted if the program
        is not going to be frozen to produce an executable.

Any idea?

[ogrisel]

You need to launch your program under the if __name__ == '__main__' block as explained in the error message.

[vadimkantorov]

also got hit by this (in https://github.com/pytorch/pytorch/issues/1838's scenario)

[AlvarHHM]

I encounter similar error
https://stackoverflow.com/questions/46782277/opencv-sigsegv-when-applying-backgroundsubtractor-on-a-frame-that-read-from-anot

Process:               Python [16883]
Path:                  /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier:            Python
Version:               2.7.13 (2.7.13)
Code Type:             X86-64 (Native)
Parent Process:        Python [16868]
Responsible:           Python [16883]
User ID:               501

Date/Time:             2017-10-17 11:36:58.556 +0800
OS Version:            Mac OS X 10.12.5 (16F73)
Report Version:        12
Anonymous UUID:        6D22B7EB-7D25-450D-EFE7-4B5F910897A0


Time Awake Since Boot: 500000 seconds

System Integrity Protection: disabled

Crashed Thread:        0  Dispatch queue: com.apple.root.default-qos

Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
Exception Codes:       KERN_INVALID_ADDRESS at 0x0000000000000110
Exception Note:        EXC_CORPSE_NOTIFY

Termination Signal:    Segmentation fault: 11
Termination Reason:    Namespace SIGNAL, Code 0xb
Terminating Process:   exc handler [0]

VM Regions Near 0x110:
--> 
    __TEXT                 000000010c319000-000000010c31b000 [    8K] r-x/rwx SM=COW  /usr/local/Cellar/python/2.7.13_1/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python

Application Specific Information:
*** multi-threaded process forked ***
crashed on child side of fork pre-exec

Thread 0 Crashed:: Dispatch queue: com.apple.root.default-qos
0   libdispatch.dylib               0x00007fff93c6e65c dispatch_apply_f + 803
1   libopencv_core.3.3.dylib        0x00000001148935ac cv::parallel_for_(cv::Range const&, cv::ParallelLoopBody const&, double) + 454
2   libopencv_imgproc.3.3.dylib     0x0000000110a4ba17 cv::hal::resize(int, unsigned char const*, unsigned long, int, int, unsigned char*, unsigned long, int, int, double, double, int) + 2999
3   libopencv_imgproc.3.3.dylib     0x0000000110a50509 cv::resize(cv::_InputArray const&, cv::_OutputArray const&, cv::Size_<int>, double, double, int) + 6126
4   cv2.so                          0x000000010e09eb95 pyopencv_cv_resize(_object*, _object*, _object*) + 679
5   org.python.python               0x000000010c3a1c8e PyEval_EvalFrameEx + 11464
6   org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
7   org.python.python               0x000000010c345651 function_call + 330
8   org.python.python               0x000000010c327cda PyObject_Call + 97
9   org.python.python               0x000000010c3a412f PyEval_EvalFrameEx + 20841
10  org.python.python               0x000000010c3a7b84 fast_function + 196
11  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
12  org.python.python               0x000000010c3a7b84 fast_function + 196
13  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
14  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
15  org.python.python               0x000000010c345651 function_call + 330
16  org.python.python               0x000000010c327cda PyObject_Call + 97
17  org.python.python               0x000000010c3324d5 instancemethod_call + 163
18  org.python.python               0x000000010c327cda PyObject_Call + 97
19  org.python.python               0x000000010c36ddf7 slot_tp_init + 64
20  org.python.python               0x000000010c36afff type_call + 182
21  org.python.python               0x000000010c327cda PyObject_Call + 97
22  org.python.python               0x000000010c3a1b96 PyEval_EvalFrameEx + 11216
23  org.python.python               0x000000010c3a7b84 fast_function + 196
24  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
25  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
26  org.python.python               0x000000010c39f214 PyEval_EvalFrameEx + 590
27  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
28  org.python.python               0x000000010c3a7be2 fast_function + 290
29  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
30  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
31  org.python.python               0x000000010c3a7be2 fast_function + 290
32  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
33  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
34  org.python.python               0x000000010c3a7be2 fast_function + 290
35  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
36  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
37  org.python.python               0x000000010c3a7be2 fast_function + 290
38  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
39  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
40  org.python.python               0x000000010c3a7be2 fast_function + 290
41  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
42  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
43  org.python.python               0x000000010c3a7be2 fast_function + 290
44  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
45  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
46  org.python.python               0x000000010c345651 function_call + 330
47  org.python.python               0x000000010c327cda PyObject_Call + 97
48  org.python.python               0x000000010c3a412f PyEval_EvalFrameEx + 20841
49  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
50  org.python.python               0x000000010c3a7be2 fast_function + 290
51  org.python.python               0x000000010c3a1ae8 PyEval_EvalFrameEx + 11042
52  org.python.python               0x000000010c39ede5 PyEval_EvalCodeEx + 1579
53  org.python.python               0x000000010c39e7b4 PyEval_EvalCode + 32
54  org.python.python               0x000000010c3bffdf run_mod + 49
55  org.python.python               0x000000010c3c0086 PyRun_FileExFlags + 130
56  org.python.python               0x000000010c3bfc03 PyRun_SimpleFileExFlags + 697
57  org.python.python               0x000000010c3d0ee5 Py_Main + 3013
58  libdyld.dylib                   0x00007fff93c98235 start + 1

Thread 1:
0   libsystem_kernel.dylib          0x00007fff93dc6bf2 __psynch_cvwait + 10
1   libsystem_pthread.dylib         0x00007fff93eb27fa _pthread_cond_wait + 712
2   libavcodec.57.dylib             0x0000000115c42f41 0x11589b000 + 3833665
3   ???                             0x0000206000000000 0 + 35596688949248

Thread 2:
0   libsystem_kernel.dylib          0x00007fff93dc6bf2 __psynch_cvwait + 10
1   libsystem_pthread.dylib         0x00007fff93eb27fa _pthread_cond_wait + 712
2   libavcodec.57.dylib             0x0000000115c42f41 0x11589b000 + 3833665
3   ???                             0x0000206000000000 0 + 35596688949248

Thread 3:
0   libsystem_kernel.dylib          0x00007fff93dc6bf2 __psynch_cvwait + 10
1   libsystem_pthread.dylib         0x00007fff93eb27fa _pthread_cond_wait + 712
2   libavcodec.57.dylib             0x0000000115c42f41 0x11589b000 + 3833665
3   ???                             0x0000206000000000 0 + 35596688949248

Thread 4:
0   libsystem_kernel.dylib          0x00007fff93dc6bf2 __psynch_cvwait + 10
1   libsystem_pthread.dylib         0x00007fff93eb27fa _pthread_cond_wait + 712
2   libavcodec.57.dylib             0x0000000115c42f41 0x11589b000 + 3833665
3   ???                             0x0000206000000000 0 + 35596688949248

Thread 0 crashed with X86 Thread State (64-bit):
  rax: 0x00000000ffffffff  rbx: 0x0000000117412080  rcx: 0x0000000000000000  rdx: 0x0000000000ffffff
  rdi: 0x0000000117400000  rsi: 0x00000000000010ff  rbp: 0x00007fff538e0bd0  rsp: 0x00007fff538e0b30
   r8: 0x0000000117a0f9c0   r9: 0x0000000000000003  r10: 0x0000000000000000  r11: 0x00000000000018c7
  r12: 0x00007fff9cb82140  r13: 0x0000000000000100  r14: 0x0000000117412040  r15: 0x0000000000000003
  rip: 0x00007fff93c6e65c  rfl: 0x0000000000010206  cr2: 0x0000000000000110

Logical CPU:     0
Error Code:      0x00000006
Trap Number:     14


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    0x7fff923a1000 -     0x7fff923a3ffb  com.apple.loginsupport (1.0 - 1) <F3140B97-12C3-35A7-9D3D-43DA2D13C113> /System/Library/PrivateFrameworks/login.framework/Versions/A/Frameworks/loginsupport.framework/Versions/A/loginsupport
    0x7fff923f8000 -     0x7fff92413ff7  libCRFSuite.dylib (34) <F78B7F5F-0B4F-35C6-AA2F-84EE9CB22137> /usr/lib/libCRFSuite.dylib
    0x7fff92414000 -     0x7fff9241ffff  libChineseTokenizer.dylib (21) <0886E908-A825-36AF-B94B-2361FD8BC2A1> /usr/lib/libChineseTokenizer.dylib
    0x7fff924b1000 -     0x7fff924b2ff3  libDiagnosticMessagesClient.dylib (102) <84A04D24-0E60-3810-A8C0-90A65E2DF61A> /usr/lib/libDiagnosticMessagesClient.dylib
    0x7fff924b3000 -     0x7fff926c6fff  libFosl_dynamic.dylib (16.39) <E22A4243-D148-3C74-BA15-2D906A3D1F9E> /usr/lib/libFosl_dynamic.dylib
    0x7fff926e2000 -     0x7fff926e9fff  libMatch.1.dylib (27) <B30813A6-B448-34F9-AF43-7DAD96776853> /usr/lib/libMatch.1.dylib
    0x7fff926ea000 -     0x7fff926eafff  libOpenScriptingUtil.dylib (172) <90743888-C1E8-34E3-924E-1A754B2B63B9> /usr/lib/libOpenScriptingUtil.dylib
    0x7fff926eb000 -     0x7fff926efffb  libScreenReader.dylib (477.40.6) <CBE6420C-EF60-3ACD-A0B6-7CBE936BA3B8> /usr/lib/libScreenReader.dylib
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    0x7fff92789000 -     0x7fff92805fc7  libate.dylib (1.12.13) <D0767875-D02E-3377-84D8-5F174C27BEA9> /usr/lib/libate.dylib
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    0x7fff92990000 -     0x7fff92a75ff7  libcrypto.0.9.8.dylib (64.50.6) <D34E16A7-990A-37A9-933A-DFAA46554EAA> /usr/lib/libcrypto.0.9.8.dylib
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    0x7fff92cf7000 -     0x7fff92de9ff7  libiconv.2.dylib (50) <42125B35-81D7-3FC4-9475-A26DBE10884D> /usr/lib/libiconv.2.dylib
    0x7fff92dea000 -     0x7fff9300fffb  libicucore.A.dylib (57165.0.1) <422D8E4B-AABA-31DA-8F38-CDA9ECEAAFFB> /usr/lib/libicucore.A.dylib
    0x7fff93015000 -     0x7fff93016fff  liblangid.dylib (126) <2085E7A7-9A34-3735-87F4-F174EF8EABF0> /usr/lib/liblangid.dylib
    0x7fff93017000 -     0x7fff93030ffb  liblzma.5.dylib (10) <44BD0279-99DD-36B5-8A6E-C11432E2098D> /usr/lib/liblzma.5.dylib
    0x7fff93031000 -     0x7fff93047ff7  libmarisa.dylib (5) <9030D214-5D0F-30CB-AC03-902C63909362> /usr/lib/libmarisa.dylib
    0x7fff93048000 -     0x7fff932f0ff7  libmecabra.dylib (744.8) <D429FCC9-42A4-38B3-8784-44024BC859EF> /usr/lib/libmecabra.dylib
    0x7fff93323000 -     0x7fff9339dff3  libnetwork.dylib (856.60.1) <191E99F5-4723-3180-8013-02AF2F9AE4B8> /usr/lib/libnetwork.dylib
    0x7fff9339e000 -     0x7fff93770047  libobjc.A.dylib (709) <DC77AA6E-A4E4-326D-8D7F-82D63AA88F99> /usr/lib/libobjc.A.dylib
    0x7fff93773000 -     0x7fff93777fff  libpam.2.dylib (21.30.1) <71EB0D88-DE84-3C8D-A2C5-58AA282BC5BC> /usr/lib/libpam.2.dylib
    0x7fff93778000 -     0x7fff937a9fff  libpcap.A.dylib (67.60.1) <F6BC6ED6-AEE4-3520-B248-0C342636E2B0> /usr/lib/libpcap.A.dylib
    0x7fff937c6000 -     0x7fff937e2ffb  libresolv.9.dylib (64) <A244AE4C-00B0-396C-98FF-97FE4DB3DA30> /usr/lib/libresolv.9.dylib
    0x7fff937e3000 -     0x7fff9381cfff  libsandbox.1.dylib (592.60.1) <DA08FA5D-A267-31C5-9239-1A05B2E59E00> /usr/lib/libsandbox.1.dylib
    0x7fff93830000 -     0x7fff93831ff3  libspindump.dylib (231.3) <C7CEEB64-06F4-3ACA-AAC1-30ECA909501A> /usr/lib/libspindump.dylib
    0x7fff93832000 -     0x7fff9397fff7  libsqlite3.dylib (254.7) <07CD6DE3-394D-3C6A-A4B4-4CAB1054A041> /usr/lib/libsqlite3.dylib
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    0x7fff93a82000 -     0x7fff93b71ffb  libxml2.2.dylib (30.16) <D2A6861B-D9FA-3BFC-B664-830C3FCE6065> /usr/lib/libxml2.2.dylib
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    0x7fff93bcc000 -     0x7fff93bd3fff  libcompiler_rt.dylib (62) <55D47421-772A-32AB-B529-1A46C2F43B4D> /usr/lib/system/libcompiler_rt.dylib
    0x7fff93bd4000 -     0x7fff93bdcfff  libcopyfile.dylib (138) <819BEA3C-DF11-3E3D-A1A1-5A51C5BF1961> /usr/lib/system/libcopyfile.dylib
    0x7fff93bdd000 -     0x7fff93c60fdf  libcorecrypto.dylib (442.50.19) <42194A7F-53C3-3EC2-97BF-C18A8EF8F349> /usr/lib/system/libcorecrypto.dylib
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    0x7fff93c93000 -     0x7fff93c98ffb  libdyld.dylib (433.5) <EC3D88D2-3D40-3274-8E26-362C2D7352C8> /usr/lib/system/libdyld.dylib
    0x7fff93c99000 -     0x7fff93c99ffb  libkeymgr.dylib (28) <7AA011A9-DC21-3488-BF73-3B5B14D1FDD6> /usr/lib/system/libkeymgr.dylib
    0x7fff93c9a000 -     0x7fff93ca6ffb  libkxld.dylib (3789.60.24) <2FCC0970-D947-345B-B6C2-472D503A1DB3> /usr/lib/system/libkxld.dylib
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    0x7fff93cb1000 -     0x7fff93cb2ffb  libremovefile.dylib (45) <38D4CB9C-10CD-30D3-8B7B-A515EC75FE85> /usr/lib/system/libremovefile.dylib
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    0x7fff93ccd000 -     0x7fff93d5afef  libsystem_c.dylib (1158.50.2) <DE159DA6-2D73-308B-999C-03A651297F6E> /usr/lib/system/libsystem_c.dylib
    0x7fff93d5b000 -     0x7fff93d5effb  libsystem_configuration.dylib (888.60.2) <D2E06289-4FE5-3282-B50C-60A8392AD9C0> /usr/lib/system/libsystem_configuration.dylib
    0x7fff93d5f000 -     0x7fff93d62fff  libsystem_coreservices.dylib (41.4) <CC4802AE-CD6A-3A24-92EA-C7A647FB3FB2> /usr/lib/system/libsystem_coreservices.dylib
    0x7fff93d63000 -     0x7fff93d7bfff  libsystem_coretls.dylib (121.50.4) <EC6FCF07-DCFB-3A03-9CC9-6DD3709974C6> /usr/lib/system/libsystem_coretls.dylib
    0x7fff93d7c000 -     0x7fff93d82fff  libsystem_dnssd.dylib (765.50.9) <CC960215-0B1B-3822-A13A-3DDE96FA796F> /usr/lib/system/libsystem_dnssd.dylib
    0x7fff93d83000 -     0x7fff93dacff7  libsystem_info.dylib (503.50.4) <611DB84C-BF70-3F92-8702-B9F28A900920> /usr/lib/system/libsystem_info.dylib
    0x7fff93dad000 -     0x7fff93dcfff7  libsystem_kernel.dylib (3789.60.24) <6E9E485F-91F6-36B7-A125-AE91DC978BCC> /usr/lib/system/libsystem_kernel.dylib
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    0x7fff93e18000 -     0x7fff93e36ff7  libsystem_malloc.dylib (116.50.8) <47F57687-28DF-3C7C-BD1E-E7E72FCCEFC9> /usr/lib/system/libsystem_malloc.dylib
    0x7fff93e37000 -     0x7fff93e90ffb  libsystem_network.dylib (856.60.1) <369D0221-56CA-3C3E-9EDE-94B41CAE77B7> /usr/lib/system/libsystem_network.dylib
    0x7fff93e91000 -     0x7fff93e9aff3  libsystem_networkextension.dylib (563.60.2) <B021F2B3-8A75-3633-ABB0-FC012B8E9B0C> /usr/lib/system/libsystem_networkextension.dylib
    0x7fff93e9b000 -     0x7fff93ea4ff3  libsystem_notify.dylib (165.20.1) <B8160190-A069-3B3A-BDF6-2AA408221FAE> /usr/lib/system/libsystem_notify.dylib
    0x7fff93ea5000 -     0x7fff93eadfe7  libsystem_platform.dylib (126.50.8) <C55E9541-D89B-3A0B-B2D5-F3739989B224> /usr/lib/system/libsystem_platform.dylib
    0x7fff93eae000 -     0x7fff93eb8ff7  libsystem_pthread.dylib (218.60.3) <A16288DE-AF70-3076-9641-045AC3DBEDB3> /usr/lib/system/libsystem_pthread.dylib
    0x7fff93eb9000 -     0x7fff93ebcff7  libsystem_sandbox.dylib (592.60.1) <12B0E190-EA4E-3ED2-84C6-95BBAF0C6BFA> /usr/lib/system/libsystem_sandbox.dylib
    0x7fff93ebd000 -     0x7fff93ebeff3  libsystem_secinit.dylib (24.50.4) <F78B847B-3565-3E4B-98A6-F7AD40392E2D> /usr/lib/system/libsystem_secinit.dylib
    0x7fff93ebf000 -     0x7fff93ec6ffb  libsystem_symptoms.dylib (532.50.47) <3390E07C-C1CE-348F-ADBD-2C5440B45EAA> /usr/lib/system/libsystem_symptoms.dylib
    0x7fff93ec7000 -     0x7fff93edaff7  libsystem_trace.dylib (518.60.2) <1A1ED423-E3A7-3B65-A275-4EEC32BDC5CA> /usr/lib/system/libsystem_trace.dylib
    0x7fff93edb000 -     0x7fff93ee0ffb  libunwind.dylib (35.3) <3D50D8A8-C460-334D-A519-2DA841102C6B> /usr/lib/system/libunwind.dylib
    0x7fff93ee1000 -     0x7fff93f0aff7  libxpc.dylib (972.60.2) <1C9AF716-69DF-359F-85E9-7DFDE362F9A2> /usr/lib/system/libxpc.dylib

External Modification Summary:
  Calls made by other processes targeting this process:
    task_for_pid: 0
    thread_create: 0
    thread_set_state: 0
  Calls made by this process:
    task_for_pid: 0
    thread_create: 0
    thread_set_state: 0
  Calls made by all processes on this machine:
    task_for_pid: 5037783
    thread_create: 0
    thread_set_state: 0

VM Region Summary:
ReadOnly portion of Libraries: Total=436.1M resident=0K(0%) swapped_out_or_unallocated=436.1M(100%)
Writable regions: Total=129.9M written=0K(0%) resident=0K(0%) swapped_out=0K(0%) unallocated=129.9M(100%)

                                VIRTUAL   REGION 
REGION TYPE                        SIZE    COUNT (non-coalesced) 
===========                     =======  ======= 
Dispatch continuations            8192K        2 
Kernel Alloc Once                    8K        2 
MALLOC                            72.4M       30 
MALLOC guard page                   32K        7 
MALLOC_LARGE (reserved)            656K        6         reserved VM address space (unallocated)
STACK GUARD                       56.0M        6 
Stack                             14.5M        8 
Stack Guard                          8K        3 
VM_ALLOCATE                       22.5M       34 
__DATA                            46.6M      359 
__GLSLBUILTINS                    2588K        2 
__IMAGE                            528K        2 
__LINKEDIT                       126.4M      116 
__TEXT                           309.7M      340 
__UNICODE                          556K        2 
mapped file                       24.7M        2 
shared memory                       76K        8 
===========                     =======  ======= 
TOTAL                            685.2M      912 
TOTAL, minus reserved VM space   684.6M      912 

Model: iMac18,2, BootROM IM183.0145.B00, 4 processors, Intel Core i5, 3 GHz, 16 GB, SMC 2.40f0
Graphics: kHW_AMDRadeonPro555Item, Radeon Pro 555, PCIe, 2048 MB
Graphics: NVIDIA GeForce GTX 1060 6GB, NVIDIA GeForce GTX 1060 6GB, PCIe, 6143 MB
Memory Module: BANK 0/DIMM0, 8 GB, DDR4, 2400 MHz, 0x802C, 0x3841544631473634485A2D324733423220202020
Memory Module: BANK 1/DIMM0, 8 GB, DDR4, 2400 MHz, 0x802C, 0x3841544631473634485A2D324733423220202020
AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0x16E), Broadcom BCM43xx 1.0 (7.21.171.126.1a2)
Bluetooth: Version 5.0.4f18, 3 services, 27 devices, 1 incoming serial ports
Network Service: Wi-Fi, AirPort, en1
USB Device: USB 3.0 Bus
USB Device: FA GoFlex Desk
USB Device: Bluetooth USB Host Controller
USB Device: FaceTime HD Camera (Built-in)
USB Device: HID Keyboard
Thunderbolt Bus: iMac, Apple Inc., 15.10
Thunderbolt Device: AKiTiO Node, inXtron, 1, 23.1

[onlyjus]

I am having the same issue on windows 7, OpenCV 3.3.1, Python 3.6.4, from Anaconda.

cv2.setNumThreads(0) and multiprocessing.set_start_method('spawn', force=True) do not fix the issue of processes hanging. I am reading a video file in the main thread, sending frames to a Queue where those frames are then processed by multiprocessing.Process instances.

Any ideas?

[tkoch96]

I am using multiprocessing to speed up ARUCO target detection on a raspberry pi.
(Keep in mind I have gotten this working using no multiprocessing.)

Using this code:
``
workers = []
for i in range(num_workers):
workers.append(Worker_Bee(tasks,results,i))

for worker in workers:
worker.start()

for i in range(20):
tasks.put(Image_Processor(vs.read()))
time.sleep(.5)

``
The Image_Processor looks at the frame vs.read() which is an image. The image processor tries to find targets in the image.
The worker process Image_Processor hangs on the following line:
corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, self.aruco_dict, parameters=self.parameters)
where gray is the image vs.read()
I've check the arguments to aruco.detectMarkers and they seem to be correct (and as I've said before, I've ran this before without multiprocessing fine).
There is no error message, and looking at top, I see the python process spawning but then quickly going away. The parent process stays.

I've seen similar problems with openCV hanging on cvtColor or resize, but all of their problems are fixed for me (compiling with TBB options and such).

Any advice on how to get this working?

[rns4731]

I switched to eventlet to get this working.

[jaredleach]

@tkoch96 Any chance you made any progress on the issues you were seeing? I'm running into nearly exactly the same problem you are.

[tkoch96]

No, it seems the library is designed with this inherent flaw and the developers here have insinuated it probably won't be fixed. I'm not too savvy with understanding how this low-level stuff works so I did not try to fix it myself.

If I were you I'd look for alternative solutions to speeding up processing.

[jaredleach]

@tkoch96 Are you're referring to the aruco library or OpenCV?

[tkoch96]

I believe I posted this as an OpenCV issue (separate to this post) and they referred me to this post and closed my issue.
I have not posted on any aruco specific forum.

[burinov]

I had the same issue on OSX.

Thanks to @mshabunin concurrent.futures.ThreadPoolExecutor works fine for me.

[0mza987]

I have encountered the same issue on Python 2.7.10, Opencv 3.2 and Ubuntu 16.04
cv2.cvtColor(sheet_image.image, cv2.COLOR_GRAY2RGB) caused the problem, both parent process and child process hang forever, and the child process still exits after parent process gets killed.

[ogrisel]

Thanks to @mshabunin concurrent.futures.ThreadPoolExecutor works fine for me.

Using threads is fine and often very efficient but can lead to GIL-contention if a significant fraction of the time is spent in pure python code. In that case, using process-based parallelism might lead to better performance.

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.

More details: https://github.com/tomMoral/loky

[FX-HAO]

import multiprocessing as mp
mp.set_start_method('spawn')

It worked for me with python3

[alalek]

There are several related updates:

• OSX 10.13 new fork() policy
• Python 3.8 'spawn' is default on MacOS: https://github.com/python/cpython/pull/13603
• https://blog.phusion.nl/2017/10/13/why-ruby-app-servers-break-on-macos-high-sierra-and-what-can-be-done-about-it/

[PuchatekwSzortach]

@alalek Actually I got stung by this problem (6 years after I opened this issue, that must be an achievement ^^) today on OSX 10.15.5 + python 3.8 when using opencv inside celery taks. 😿

Crashed on same cv2.cvtColor with the same stack trace pointing to cv::parallel_for_ and libdispatch::dispatch_apply_f

A bit of into that I think is important - same code, same osx version, but using python 3.5 everything runs smoothly.
As far as I can see both python 3.5 and python 3.8 environments use same opencv, celery, etc libraries versions.

Note - python 3.5 fails if only opencv-python (4.2.0.34) package is installed, but with opencv-python-headless (4.2.0.34) no crashes occur. Installing opencv-python-headless on python 3.8 doesn't solve the issue.

Highlighting this since it would suggest that the issue with forking isn't something that can't be solved on OSX.

A little bit more info:
Above happens when celery runs using its default prefork method for worker pool
https://docs.celeryproject.org/en/stable/reference/celery.bin.worker.html#cmdoption-celery-worker-p

When using threads and (obviously) solo worker pool options, opencv doesn't crash.

[LifeBeyondExpectations]

I got some hint from https://stackoverflow.com/questions/54013846/pytorch-dataloader-stucked-if-using-opencv-resize-method
When I put cv2.setNumThreads(0), it works fine with me.

[kuba-lilz]

@LifeBeyondExpectations
https://docs.opencv.org/2.4/modules/core/doc/utility_and_system_functions_and_macros.html#void%20setNumThreads(int%20nthreads)
cv2.setNumThreads behavior varies between configurations. In particular documentation makes no explanation of what cv2.setNumThreads(0) actually does.

[paymog]

I found that installing opencv-python-headless fixed the problem for me with python 3.8

(venv) 12:08pm /Users/paymahn/
 ❯❯❯ python --version
Python 3.8.5
(venv) 12:08pm /Users/paymahn/
 ❮❮❮ cat requirements.txt
amqp==5.0.1
billiard==3.6.3.0
celery==5.0.0
click==7.1.2
click-didyoumean==0.0.3
click-repl==0.1.6
ffmpeg-python==0.2.0
future==0.18.2
kombu==5.0.2
numpy==1.19.1
opencv-python==4.4.0.44
prompt-toolkit==3.0.7
psycopg2==2.8.5
pytz==2020.1
redis==3.5.3
six==1.15.0
SQLAlchemy==1.3.19
vine==5.0.0
wcwidth==0.2.5

[maxvfischer]

As @xuzhuoran0106 stated above, it works for me when I import cv2 in each function that will be executed as a subprocess.

[ChrisCalculus]

I can confirm that installing opencv-python-headless==4.4.0.46 instead of opencv-python==4.4.0.46 prevents the segfault in my project (Python 3.6 / macOS Catalina 10.15.7).
I'm running this process in a Celery worker.

Code leading to the segfault (part of camelot-py package):

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

(truncated) macOS crash log:

Process:               python3.6 [81680]
Path:                  /Users/USER/*/python3.6
Identifier:            python3.6
Version:               ???
Code Type:             X86-64 (Native)
Parent Process:        python3.6 [81664]
Responsible:           pycharm [3028]
User ID:               501

Date/Time:             2020-12-03 09:41:16.155 +0100
OS Version:            Mac OS X 10.15.7 (19H2)
Report Version:        12
Bridge OS Version:     4.6 (17P6610)
Anonymous UUID:        3CF5E624-834A-4E58-B499-85BA58242AD6

Sleep/Wake UUID:       A27A7F82-8488-45BA-AAC3-8F5E6743120A

Time Awake Since Boot: 130000 seconds
Time Since Wake:       2000 seconds

System Integrity Protection: enabled

Crashed Thread:        0  Dispatch queue: com.apple.root.default-qos

Exception Type:        EXC_BAD_ACCESS (SIGSEGV)
Exception Codes:       KERN_INVALID_ADDRESS at 0x0000000000000110
Exception Note:        EXC_CORPSE_NOTIFY

VM Regions Near 0x110:
--> 
    __TEXT                 000000010569d000-0000000105865000 [ 1824K] r-x/r-x SM=COW  /Users/USER/*/*.6

Application Specific Information:
*** multi-threaded process forked ***
crashed on child side of fork pre-exec

Thread 0 Crashed:: Dispatch queue: com.apple.root.default-qos
0   libsystem_kernel.dylib          0x00007fff733c533a __pthread_kill + 10
1   libsystem_pthread.dylib         0x00007fff73481e60 pthread_kill + 430
2   libsystem_c.dylib               0x00007fff732dc93e raise + 26
3   libsystem_platform.dylib        0x00007fff734765fd _sigtramp + 29
4   ???                             0x000000015245ccf8 0 + 5675273464
5   cv2.cpython-36m-darwin.so       0x000000011ef75222 cv::parallel_for_(cv::Range const&, cv::ParallelLoopBody const&, double) + 562
6   cv2.cpython-36m-darwin.so       0x000000011f5b629b cv::hal::opt_AVX2::cvtBGRtoGray(unsigned char const*, unsigned long, unsigned char*, unsigned long, int, int, int, int, bool) + 443
7   cv2.cpython-36m-darwin.so       0x000000011f1b56f6 cv::hal::cvtBGRtoGray(unsigned char const*, unsigned long, unsigned char*, unsigned long, int, int, int, int, bool) + 646
8   cv2.cpython-36m-darwin.so       0x000000011f1bcc45 cv::cvtColorBGR2Gray(cv::_InputArray const&, cv::_OutputArray const&, bool) + 1941
9   cv2.cpython-36m-darwin.so       0x000000011f1875b8 cv::cvtColor(cv::_InputArray const&, cv::_OutputArray const&, int, int) + 2136
10  cv2.cpython-36m-darwin.so       0x000000011eb0e4e0 pyopencv_cv_cvtColor(_object*, _object*, _object*) + 832
11  python                          0x00000001056f570b _PyCFunction_FastCallDict + 475
12  python                          0x0000000105781771 call_function + 497
13  python                          0x000000010577e384 _PyEval_EvalFrameDefault + 25444
14  pydevd_frame_evaluator_darwin_36_64.cpython-36m-darwin.so