1、编译安装pybind11

下载源代码:https://github.com/pybind/pybind11,

文档:https://pybind11.readthedocs.io/en/stable/compiling.html

解压后进入到命令行,如果有conda环境,就先进入想要的conda环境,再运行下面的指令

mkdir build

cd build

cmake ..

make -j8

sudo make install

2 编译动态链接库

新建CMakeLists.txt,如下,需要安装opencv,安装教程参考Ubuntu 18.04 安装opencv4.2.0,如果遇到IPPICV问题参考解决编译opencv时,卡在IPPICV

cmake_minimum_required(VERSION 3.4...3.18)

project(test LANGUAGES CXX)

set(CMAKE_CXX_STANDARD 11)

#使用这个命令的前提是pybind11, make install过

find_package(pybind11 REQUIRED)

find_package(OpenCV)

#添加头文件,CMAKE_SOURCE_DIR是CMakeLists.txt所在的位置

include_directories(${CMAKE_SOURCE_DIR}/ ${OpenCV_INCLUDE_DIRS})

#添加CPP文件, PROJECT_NAME = test

pybind11_add_module(${PROJECT_NAME} ${CMAKE_SOURCE_DIR}/test.cpp)

#指定动态链接库的位置

# link_directories(/home/hnty/SDK/iRaySDK/dev/bin)

#指定编译依赖库,PUBLIC是必须的参数,也可以换成其他关键字

target_link_libraries(${PROJECT_NAME} PUBLIC pthread ${OpenCV_LIBS})

#指定安装位置,表示编译的结果安装在 ${CMAKE_INSTALL_PREFIX}/site-packages 目录下,能够直接在python代码中导入

#make install 命令先编译后安装,一步到位

# set(CMAKE_INSTALL_PREFIX /home/hnty/miniconda3/envs/dev/lib/python3.7/)

# install(TARGETS ${PROJECT_NAME} DESTINATION site-packages)

新建test.cpp如下:

#include

#include

#include

namespace py = pybind11;

using namespace cv;

typedef struct test

{

/* data */

int ages;

std::string name;

cv::Mat pic;

} Person;

Person handle;

//py::array_t 表示np.ndarray

void setData(int ages, std::string &name, py::array_t& img)

{

handle.ages = ages;

handle.name = name;

//np.ndarray -> opencv mat

auto rows = img.shape(0);

auto cols = img.shape(1);

auto channels = img.shape(2);

auto type = CV_8UC3;

handle.pic = cv::Mat(rows, cols, type, (unsigned char*)img.data());

}

py::dict getData()

{

// 在cpp中可以直接使用python内置的数据类型,还是很方便的

py::dict pydata;

pydata["name"] = handle.name;

pydata["ages"] = handle.ages;

auto rows = handle.pic.rows;

auto cols = handle.pic.cols;

auto channels = handle.pic.channels();

//opencv mat -> np.ndarray

//py::array_t 表示np.ndarray

py::array_t output_img(py::buffer_info(handle.pic.data, sizeof(uint8_t), //itemsize

py::format_descriptor::format(), 3, // ndim

std::vector {rows, cols , channels}, // shape

std::vector {cols * sizeof(uint8_t)*3, sizeof(uint8_t)*3, sizeof(uint8_t)} // strides

));

pydata["pic"] = output_img;

return pydata;

}

PYBIND11_MODULE(test, m) {

m.doc() = "pybind11 example plugin"; // optional module docstring

m.def("getData", &getData, "A function which adds two numbers");

m.def("setData", &setData, "test func");

}

新建test.py:

from build import test

import cv2

import numpy as np

img = cv2.imread("oil-bin-1.png") #换成你自己的图片

ages = 23

name = "kitty"

test.setData(ages, name, img)

res = test.getData()

print(res['name'], res['ages'])

cv2.imwrite("output.png", res["pic"])

3、测试

将上述3个文件放到同一个目录,然后在命令行中进入到改目录,依次运行。会生成output.png

mkdir build

cd build

cmake ..

make

cd ..

python test.py

文件夹格式如下:

4、总结

使用pybind11相比ctypes方法ctypes实现python和c之间的数据交互-CSDN博客,更加简单、易用。但ctypes的优势在于不用额外配置库,python内置了。

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