How to use shared memory in python and C/C++

Question

I am trying to modify a python program to be able to communicate with a C++ program using shared memory. The main responsibility of the python program is to read some video

Answer 1

Sorta, kinda shared memory. So not exactly what the OP wanted.

This works using memory mapped files. I do not claim high speed or efficiency in any way. These are just to show an example of it working.

 $ python --version
 Python 3.7.9

 $ g++ --version
 g++ (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0

The C++ side only monitors the values it needs. The Python side only provides the values.

Note: the file name "pods.txt" must be the same in the C++ and python code.

#include <sys/mman.h>
#include <fcntl.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
 
int main(void)
  {
  // assume file exists
  int fd = -1;
  if ((fd = open("pods.txt", O_RDWR, 0)) == -1)
     {
     printf("unable to open pods.txt\n");
     return 0;
     }
  // open the file in shared memory
  char* shared = (char*) mmap(NULL, 8, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);

  // periodically read the file contents
  while (true)
      {
      printf("0x%02X 0x%02X 0x%02X 0x%02X 0x%02X 0x%02X 0x%02X 0x%02X\n", shared[0], shared[1], shared[2], shared[3], shared[4], shared[5],           shared[6], shared[7]);
      sleep(1);
      }

   return 0;
   }

The python side:

import mmap
import os
import time
 
fname = './pods.txt'
if not os.path.isfile(fname):
    # create initial file
    with open(fname, "w+b") as fd:
         fd.write(b'\x01\x00\x00\x00\x00\x00\x00\x00')

# at this point, file exists, so memory map it
with open(fname, "r+b") as fd:
    mm = mmap.mmap(fd.fileno(), 8, access=mmap.ACCESS_WRITE, offset=0)

    # set one of the pods to true (== 0x01) all the rest to false
    posn = 0
    while True:
         print(f'writing posn:{posn}')

         # reset to the start of the file
         mm.seek(0)
 
         # write the true/false values, only one is true
         for count in range(8):
             curr = b'\x01' if count == posn else b'\x00'
             mm.write(curr)

         # admire the view
         time.sleep(2)

         # set up for the next position in the next loop
        posn = (posn + 1) % 8

    mm.close()
    fd.close()

To run it, in terminal #1:

 a.out  # or whatever you called the C++ executable
 0x00 0x00 0x00 0x00 0x01 0x00 0x00 0x00
 0x00 0x00 0x00 0x00 0x01 0x00 0x00 0x00
 0x01 0x00 0x00 0x00 0x00 0x00 0x00 0x00
 0x01 0x00 0x00 0x00 0x00 0x00 0x00 0x00
 0x00 0x01 0x00 0x00 0x00 0x00 0x00 0x00
 0x00 0x01 0x00 0x00 0x00 0x00 0x00 0x00
 0x00 0x00 0x01 0x00 0x00 0x00 0x00 0x00
 0x00 0x00 0x01 0x00 0x00 0x00 0x00 0x00
 0x00 0x00 0x00 0x01 0x00 0x00 0x00 0x00

i.e. you should see the 0x01 move one step every couple of seconds because of the sleep(2) in the C++ code.

in terminal #2:

python my.py  # or whatever you called the python file
writing posn:0
writing posn:1
writing posn:2

i.e. you should see the position change from 0 through 7 back to 0 again.

Answer 2

Perhaps shmget and shmat are not necessarily the most appropriate interfaces for you to be using. In a project I work on, we provide access to a daemon via a C and Python API using memory mapped files, which gives us a very fast way of accessing data

The order of operations goes somewhat like this:

• the client makes a door_call() to tell the daemon to create a shared memory region
• the daemon securely creates a temporary file
• the daemon open()s and then mmap()s that file
• the daemon passes the file descriptor back to the client via door_return()
• the client mmap()s the file descriptor and associates consecutively-placed variables in a structure with that fd
• the client does whatever operations it needs on those variables - when it needs to do so.
• the daemon reads from the shared region and does its own updates (in our case, writes values from that shared region to a log file).

Our clients make use of a library to handle the first 5 steps above; the library comes with Python wrappers using ctypes to expose exactly which functions and data types are needed.

For your problem space, if it's just the python app which writes to your output queue then you can track which frames have been processed just in the python app. If both your python and c++ apps are writing to the output queue then that increases your level of difficulty and perhaps refactoring the overall application architecture would be a good investment.