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python和C++如何实现共享内存传输图像

发布时间:2020-10-28 14:43:53 来源:亿速云 阅读:1053 作者:Leah 栏目:开发技术

这期内容当中小编将会给大家带来有关python和C++如何实现共享内存传输图像,文章内容丰富且以专业的角度为大家分析和叙述,阅读完这篇文章希望大家可以有所收获。

原理

python没有办法直接和c++共享内存交互,需要间接调用c++打包好的库来实现

流程

  • C++共享内存打包成库
  • python调用C++库往共享内存存图像数据
  • C++测试代码从共享内存读取图像数据

实现

1.c++打包库

创建文件

python和C++如何实现共享内存传输图像

example.cpp

#include <iostream>
#include <cassert>
#include <stdlib.h>
#include <sys/shm.h>
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/videoio.hpp"
 
#define key 650
#define image_size_max 1920*1080*3
 
using namespace std;
using namespace cv;
 
typedef struct{
int rows;
int cols;
uchar dataPointer[image_size_max];
}image_head;
 
int dump(int cam_num,int row_image, int col_image, void* block_data_image)
{
   int shm_id = shmget(key+cam_num,sizeof(image_head),IPC_CREAT);
   if(shm_id == -1)
   {
     cout<<"shmget error"<<endl;
      return -1;
   }
   cout << " shem id is  "<<shm_id<<endl;
 
   image_head *buffer_head;
   buffer_head = (image_head*) shmat(shm_id, NULL, 0);
 
   if((long)buffer_head == -1)
   {
     cout<<"Share memary can't get pointer"<<endl; 
      return -1; 
   }
    
   assert(row_image*col_image*3<=image_size_max);
   image_head image_dumper;
   image_dumper.rows=row_image;
   image_dumper.cols=col_image;
   uchar* ptr_tmp_image=(uchar*) block_data_image;
   for (int i=0;i<row_image*col_image*3;i++)
   {
      image_dumper.dataPointer[i] = *ptr_tmp_image;
      ptr_tmp_image++;
   }
   memcpy(buffer_head,&image_dumper,sizeof(image_dumper));
    
   return 1;
}
 
extern "C"
{
  int dump_(int cam_num,int row_image, int col_image, void* block_data_image)
  {
    int result=dump(cam_num,row_image, col_image, block_data_image);
    return result;
  }
}

CMakeLists.txt 

# cmake needs this line
cmake_minimum_required(VERSION 2.8)
 
# Define project name
project(opencv_example_project)
 
# Find OpenCV, you may need to set OpenCV_DIR variable
# to the absolute path to the directory containing OpenCVConfig.cmake file
# via the command line or GUI
find_package(OpenCV REQUIRED)
 
# If the package has been found, several variables will
# be set, you can find the full list with descriptions
# in the OpenCVConfig.cmake file.
# Print some message showing some of them
message(STATUS "OpenCV library status:")
message(STATUS "    version: ${OpenCV_VERSION}")
message(STATUS "    libraries: ${OpenCV_LIBS}")
message(STATUS "    include path: ${OpenCV_INCLUDE_DIRS}")
 
if(CMAKE_VERSION VERSION_LESS "2.8.11")
  # Add OpenCV headers location to your include paths
  include_directories(${OpenCV_INCLUDE_DIRS})
endif()
 
# Declare the executable target built from your sources
add_library(opencv_example  SHARED example.cpp)
add_executable(test_example test_run.cpp)
 
# Link your application with OpenCV libraries
target_link_libraries(opencv_example ${OpenCV_LIBS})
target_link_libraries(test_example ${OpenCV_LIBS})

  最后生成库

python和C++如何实现共享内存传输图像

2.python调用C++动态库进行存图

#!/usr/bin/env python
 
import sys
 
#sys.path.append("/usr/lib/python3/dist-packages")
#sys.path.append("/home/frank/Documents/215/code/parrot-groundsdk/.python/py3/lib/python3.5/site-packages")
 
import cv2
import ctypes
import numpy as np
ll = ctypes.cdll.LoadLibrary
lib = ll("./build/libopencv_example.so")
lib.dump_.restype = ctypes.c_int
 
count = 1
#path = "/home/frank/Documents/215/2020.10.24/python_ctypes/image/"
 
while count < 30:
    path = "./image/"+str(count)+".jpg"
    print(path)
    image=cv2.imread(path)
     
    #cv2.imshow("test",image)
    #cv2.waitKey(0)
 
    image_data = np.asarray(image, dtype=np.uint8)
    image_data = image_data.ctypes.data_as(ctypes.c_void_p)
 
    value = lib.dump_(0,image.shape[0], image.shape[1], image_data)
    print(value)
 
    count += 1
 
    if count == 30:
        count = 1

3.C++读取共享内存获取图像

#include <iostream>
#include <stdlib.h>
#include <sys/shm.h>
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/videoio.hpp"
 
#define key 650
#define image_size_max 1920*1080*3
 
using namespace cv;
using namespace std;
 
typedef struct{
int rows;
int cols;
uchar dataPointer[image_size_max];
}image_head;
 
int main()
{
  int count = 1;
  while(true)
  {
 
    int shm_id = shmget(key+0,sizeof(image_head) ,IPC_CREAT);
    if(shm_id == -1)
     {
        cout<<"shmget error"<<endl;
      return -1;
     }
    cout << " shem id is  "<<shm_id<<endl;
 
    image_head* buffer_head;
    buffer_head = (image_head*)shmat(shm_id, NULL, 0);
     
    if((long)buffer_head == -1)
    {
        perror("Share memary can't get pointer\n"); 
          return -1; 
    }
 
    image_head image_dumper;
    memcpy(&image_dumper, buffer_head, sizeof(image_head));
    cout<<image_dumper.rows<<"  "<<image_dumper.cols<<endl;
 
    uchar* data_raw_image=image_dumper.dataPointer;
 
    cv::Mat image(image_dumper.rows, image_dumper.cols, CV_8UC3);
    uchar* pxvec =image.ptr<uchar>(0);
    int count = 0;
    for (int row = 0; row < image_dumper.rows; row++)
    {
      pxvec = image.ptr<uchar>(row);
      for(int col = 0; col < image_dumper.cols; col++)
      {
        for(int c = 0; c < 3; c++)
        {
          pxvec[col*3+c] = data_raw_image[count];
          count++;
        }
      }
    }
 
   cv::imshow("Win",image);
   cv::waitKey(1);
 
  }
 
   return 1;
}

上述就是小编为大家分享的python和C++如何实现共享内存传输图像了,如果刚好有类似的疑惑,不妨参照上述分析进行理解。如果想知道更多相关知识,欢迎关注亿速云行业资讯频道。

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