Pybind11实现python调取C++

1、一些处理矩阵运算，图像处理算法，直接采用python实现可能速度稍微慢，效率不高，或者为了直接在python中调用其他C++第三方库。 图像，矩阵在python中通常表示为numpy.ndarray，因此如何在C++中解析numpy对象，numpy的数据如何传递到C++非常关键，解决了这些问题，就可以丝滑的在python numpy和C++中切换，互相调用。

C++代码：

#include<iostream>
#include<pybind11/pybind11.h>
#include<pybind11/numpy.h>

namespace py = pybind11;

/*
1d矩阵相加
*/
py::array_t<double> add_arrays_1d(py::array_t<double>& input1, py::array_t<double>& input2) {

    // 获取input1, input2的信息
    py::buffer_info buf1 = input1.request();
    py::buffer_info buf2 = input2.request();

    if (buf1.ndim !=1 || buf2.ndim !=1)
    {
        throw std::runtime_error("Number of dimensions must be one");
    }

    if (buf1.size !=buf2.size)
    {
        throw std::runtime_error("Input shape must match");
    }

    //申请空间
    auto result = py::array_t<double>(buf1.size);
    py::buffer_info buf3 = result.request();

    //获取numpy.ndarray 数据指针
    double* ptr1 = (double*)buf1.ptr;
    double* ptr2 = (double*)buf2.ptr;
    double* ptr3 = (double*)buf3.ptr;

    //指针访问numpy.ndarray
    for (int i = 0; i < buf1.shape[0]; i++)
    {
        ptr3[i] = ptr1[i] + ptr2[i];
    }

    return result;

}

/*
2d矩阵相加
*/
py::array_t<double> add_arrays_2d(py::array_t<double>& input1, py::array_t<double>& input2) {

    py::buffer_info buf1 = input1.request();
    py::buffer_info buf2 = input2.request();

    if (buf1.ndim != 2 || buf2.ndim != 2)
    {
        throw std::runtime_error("numpy.ndarray dims must be 2!");
    }
    if ((buf1.shape[0] != buf2.shape[0])|| (buf1.shape[1] != buf2.shape[1]))
    {
        throw std::runtime_error("two array shape must be match!");
    }

    //申请内存
    auto result = py::array_t<double>(buf1.size);
    //转换为2d矩阵
    result.resize({buf1.shape[0],buf1.shape[1]});


    py::buffer_info buf_result = result.request();

    //指针访问读写 numpy.ndarray
    double* ptr1 = (double*)buf1.ptr;
    double* ptr2 = (double*)buf2.ptr;
    double* ptr_result = (double*)buf_result.ptr;

    for (int i = 0; i < buf1.shape[0]; i++)
    {
        for (int j = 0; j < buf1.shape[1]; j++)
        {
            auto value1 = ptr1[i*buf1.shape[1] + j];
            auto value2 = ptr2[i*buf2.shape[1] + j];

            ptr_result[i*buf_result.shape[1] + j] = value1 + value2;
        }
    }

    return result;

}

//py::array_t<double> add_arrays_3d(py::array_t<double>& input1, py::array_t<double>& input2) {
//  
//  py::buffer_info buf1 = input1.request();
//  py::buffer_info buf2 = input2.request();
//
//  if (buf1.ndim != 3 || buf2.ndim != 3)
//      throw std::runtime_error("numpy array dim must is 3!");
//
//  for (int i = 0; i < buf1.ndim; i++)
//  {
//      if (buf1.shape[i]!=buf2.shape[i])
//      {
//          throw std::runtime_error("inputs shape must match!");
//      }
//  }
//
//  // 输出
//  auto result = py::array_t<double>(buf1.size);
//  result.resize({ buf1.shape[0], buf1.shape[1], buf1.shape[2] });
//  py::buffer_info buf_result = result.request();
//
//  // 指针读写numpy数据
//  double* ptr1 = (double*)buf1.ptr;
//  double* ptr2 = (double*)buf2.ptr;
//  double* ptr_result = (double*)buf_result.ptr;
//
//  for (int i = 0; i < buf1.size; i++)
//  {
//      std::cout << ptr1[i] << std::endl;
//  }
//
//  /*for (int i = 0; i < buf1.shape[0]; i++)
//  {
//      for (int j = 0; j < buf1.shape[1]; j++)
//      {
//          for (int k = 0; k < buf1.shape[2]; k++)
//          {
//
//              double value1 = ptr1[i*buf1.shape[1] * buf1.shape[2] + k];
//              double value2 = ptr2[i*buf2.shape[1] * buf2.shape[2] + k];
//
//              double value1 = ptr1[i*buf1.shape[1] * buf1.shape[2] + k];
//              double value2 = ptr2[i*buf2.shape[1] * buf2.shape[2] + k];
//
//              ptr_result[i*buf1.shape[1] * buf1.shape[2] + k] = value1 + value2;
//
//              std::cout << value1 << " ";
//
//          }
//
//          std::cout << std::endl;
//
//      }
//  }*/
//
//  return result;
//}

/*
numpy.ndarray 相加，  3d矩阵
@return 3d numpy.ndarray
*/
py::array_t<double> add_arrays_3d(py::array_t<double>& input1, py::array_t<double>& input2) {

    //unchecked<N> --------------can be non-writeable
    //mutable_unchecked<N>-------can be writeable
    auto r1 = input1.unchecked<3>();
    auto r2 = input2.unchecked<3>();

    py::array_t<double> out = py::array_t<double>(input1.size());
    out.resize({ input1.shape()[0], input1.shape()[1], input1.shape()[2] });
    auto r3 = out.mutable_unchecked<3>();

    for (int i = 0; i < input1.shape()[0]; i++)
    {
        for (int j = 0; j < input1.shape()[1]; j++)
        {
            for (int k = 0; k < input1.shape()[2]; k++)
            {
                double value1 = r1(i, j, k);
                double value2 = r2(i, j, k);

                //下标索引访问 numpy.ndarray
                r3(i, j, k) = value1 + value2;
            
            }
        }
    }

    return out;

}

PYBIND11_MODULE(numpy_demo2, m) {

    m.doc() = "Simple demo using numpy!";

    m.def("add_arrays_1d", &add_arrays_1d);
    m.def("add_arrays_2d", &add_arrays_2d);
    m.def("add_arrays_3d", &add_arrays_3d);
}

python测试代码：

import demo9.numpy_demo2 as numpy_demo2
import numpy as np


var1 = numpy_demo2.add_arrays_1d(np.array([1, 3, 5, 7, 9]),
                                 np.array([2, 4, 6, 8, 10]))
print('-'*50)
print('var1', var1)

var2 = numpy_demo2.add_arrays_2d(np.array(range(0,16)).reshape([4, 4]),
                                 np.array(range(20,36)).reshape([4, 4]))
print('-'*50)
print('var2', var2)

input1 = np.array(range(0, 48)).reshape([4, 4, 3])
input2 = np.array(range(50, 50+48)).reshape([4, 4, 3])
var3 = numpy_demo2.add_arrays_3d(input1,
                                 input2)
print('-'*50)
print('var3', var3)

结果如下：

2、python传递图像给C++

需要注意的是：这里传入的图像都是8U的，0-255数值，如果不是此类的数值需要进行修改，见后续！

#include <pybind11/numpy.h>

/*
Python->C++ Mat
*/
cv::Mat numpy_uint8_1c_to_cv_mat(py::array_t<unsigned char>& input) {

    if (input.ndim() != 2)
        throw std::runtime_error("1-channel image must be 2 dims ");

    py::buffer_info buf = input.request();

    cv::Mat mat(buf.shape[0], buf.shape[1], CV_8UC1, (unsigned char*)buf.ptr);
    
    return mat;
}

cv::Mat numpy_uint8_3c_to_cv_mat(py::array_t<unsigned char>& input) {

    if (input.ndim() != 3)
        throw std::runtime_error("3-channel image must be 3 dims ");

    py::buffer_info buf = input.request();

    cv::Mat mat(buf.shape[0], buf.shape[1], CV_8UC3, (unsigned char*)buf.ptr);

    return mat;
}


/*
C++ Mat ->numpy
*/
py::array_t<unsigned char> cv_mat_uint8_1c_to_numpy(cv::Mat& input) {

    py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols }, input.data);
    return dst;
}

py::array_t<unsigned char> cv_mat_uint8_3c_to_numpy(cv::Mat& input) {

    py::array_t<unsigned char> dst = py::array_t<unsigned char>({ input.rows,input.cols,3}, input.data);
    return dst;
}



//PYBIND11_MODULE(cv_mat_warper, m) {
//
//  m.doc() = "OpenCV Mat -> Numpy.ndarray warper";
//
//  m.def("numpy_uint8_1c_to_cv_mat", &numpy_uint8_1c_to_cv_mat);
//  m.def("numpy_uint8_1c_to_cv_mat", &numpy_uint8_1c_to_cv_mat);
//
//
//}


***如果数值不是0-255，需要进行原始数据的计算，如下：***

py::array_t<unsigned char> remove_background(py::array_t<int>& input1， py::array_t<unsigned char>& color, int max_dist)
{
    cv::Mat color_image = numpy_uint8_3c_to_cv_mat(color);
    py::buffer_info buf1 = input1.request();
    int* ptr1 = (int*)buf1.ptr;
    for (int i = 0; i < buf1.shape[0]; i++)
    {
        for (int j = 0; j < buf1.shape[1]; j++)
        {
            auto value1 = ptr1[i*buf1.shape[1] + j];
            if (value1 > max_dist)
            {
                color_image.at<Vec3b>(i, j) = Vec3b(0, 0, 0);
            }
        }
    }
}

3、python传递list给C++

例，python传递25个关节点的x，y，score给C++，C++返回x，y，score和空间的x，y，z给python