linux 下python 调用c++的动态库的方法

Linux下python 调用c++动态库的方法

1.1 简单快速的demo

创建一个 example.cpp的源文件

cpp 复制代码

// example.cpp
extern "C" {
    void hello() {
        printf("Hello from C++!\n");
    }
}

编译产生动态库

g++ -shared -fPIC example.cpp -o libexample.so

python 调用该库

python 复制代码

import ctypes
 
# 加载动态库
lib = ctypes.CDLL('./libexample.so')
# 调用函数
lib.hello()

1.2 复杂传参数的方法

参考链接：https://zhuanlan.zhihu.com/p/466169852 指针类的传入传出

可以直接使用的demo，实现的功能为使用python传入一张图像，使用c++处理后，得到图像处理后的图像传送给python。创建一个cpp的内容如下：

cpp 复制代码

// imageprocessor.c
#include <opencv2/opencv.hpp>
#include <iostream>
#include <string>

extern "C" {
    // 函数声明，用于处理图像并返回图像数据
    void processimage(unsigned char* inputdata, int height, int width, int channels, unsigned char** outputdata, int* outputsize);
}

void processimage(unsigned char* inputdata, int height, int width, int channels, unsigned char** outputdata, int* outputsize) {
    // 从输入数据创建OpenCV图像
    cv::Mat inputimage(height, width, CV_8UC(channels), inputdata);
    std::string outputPath = "/home/ema/zzh/000000input.bmp"; 
    imwrite(outputPath,inputimage);

    // 处理图像
    cv::Mat processedimage;
    cv::cvtColor(inputimage, processedimage, cv::COLOR_BGR2GRAY); // 示例处理：转换为灰度图像

    // 分配输出数据内存
    *outputsize = processedimage.total() * processedimage.elemSize();
    *outputdata = new unsigned char[*outputsize];
    std::memcpy(*outputdata, processedimage.data, *outputsize);

}

// 注意：需要提供一个函数来释放由C代码分配的内存
extern "C" {
    void free_memory(unsigned char* data);
}

void free_memory(unsigned char* data) {
    delete[] data;
}

编译文件的内容为，使用该文件可以编译成动态库。

bash 复制代码

cmake_minimum_required(VERSION 3.0.0)
project(HalconDemo VERSION 0.1.0)
 
set(TARGET_NAME HalconDemo)
 
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_CXX_STANDARD_REQUIRED True)
set(CMAKE_CXX_EXTENSIONS OFF)

set(OpenCV_DIR "/usr/local/opencv470")  # 根据实际安装路径修改
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
 
 
# 添加头文件搜索路径  
include_directories(include)
 
link_directories(/opt/halcon/lib/aarch64-linux)
 
aux_source_directory(. SRCS )
 
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}  -static-libstdc++ -fPIC -Wl,--copy-dt-needed-entries -Wno-error=deprecated-declarations -Wno-deprecated-declarations ")
 
# 寻找./src下面所有.cpp为后缀的源文件，并且保存到SRC变量里面  
file(GLOB_RECURSE SRC ./src/*.cpp)  
  
# 编译SRC变量存储的源文件，编译生成目标文件命名为hello  
#add_executable(hello ${SRC})
add_library(hello SHARED src/HalconDemo.cpp)
target_link_libraries(hello halcon halconcpp hdevenginecpp)
target_link_libraries(hello ${OpenCV_LIBS})

python调用的程序为：

python 复制代码

import cv2
import numpy as np
from ctypes import cdll, c_char_p, c_int, POINTER
from ctypes import c_char
import ctypes
# 加载C库
imageprocessorlib = cdll.LoadLibrary('/libhello.so')


# 设置C函数的参数类型
imageprocessorlib.processimage.argtypes = [POINTER(c_char), c_int, c_int, c_int, ctypes.POINTER(ctypes.POINTER(ctypes.c_ubyte)), POINTER(c_int)]
# 设置C函数的返回类型
imageprocessorlib.processimage.restype = None

imageprocessorlib.free_memory.argtypes = [ctypes.POINTER(ctypes.c_ubyte)]
imageprocessorlib.free_memory.restype = None



def processimage(inputimage):
    height, width, channels = inputimage.shape
    inputdata = inputimage.ctypes.data_as(POINTER(c_char))
    
    # # 输出数据指针和大小
    # outputdata = POINTER(c_char)()
    # outputsize = c_int(0)

    output_data = ctypes.POINTER(ctypes.c_ubyte)()
    output_size = ctypes.c_int()
    
    # 调用C函数处理图像
    imageprocessorlib.processimage(inputdata, height, width, channels, output_data, output_size)



    if output_data and output_size.value > 0:
        # 将字节数组转换为NumPy数组
        image_array = np.ctypeslib.as_array((ctypes.c_ubyte * output_size.value).from_address(ctypes.addressof(output_data.contents)), shape=(output_size.value,))
        # 假设图像是灰度的，根据实际处理调整形状
        # height, width = 2048, 3072  # 你需要根据实际图像尺寸来设置这些值
        image = image_array.reshape((height, width))
        cv2.imwrite(r'/draw_ori_testopencv.bmp',image)

        # 释放C代码分配的内存
        imageprocessorlib.free_memory(output_data)
        return image
    else:
        return None
    
   

# 使用函数
if __name__ == '__main__':
    # 读取图像
    inputimage = cv2.imread('/images/9.bmp')
    
    # 处理图像
    processedimage = processimage(inputimage)
    
    # if processedimage is not None:
    #     # 显示图像
    #     cv2.imshow('Processed Image', processedimage)
    #     cv2.waitKey(0)
    #     cv2.destroyAllWindows()