How to Convert Mat object to NDArray object using Boost Python, Numpy, OpenCV.

MatToNDArrayConverter.cpp

/*
 *  This example code is made for describe how to convert OpenCV's Mat class to Boost Numpy's ndarray class and vice versa.
 *  As far as I know, there was some library or boilerplate code about converting Mat to ndarray. But to me those methods were too expensive and difficult to understand.
 *  Recently, I have found that Boost Python has Numpy extension and it gives ndarray class properly.
 *  No other modules are used for conversion, only Boost Python, Boost Numpy, and OpenCV. (Version : Windows7, Python3.5, BoostPython 1.68, OpenCV 4.0.0)
 *  This code is designed to show you a specific case like converting 'CV_8UC3' type Mat image to 'uint8' dtype ndarray. And it contains a minimum of code to explain.
 *  I hope you get the idea of converting ndarray to fit your case properly through this example.
 */

#define BOOST_PYTHON_STATIC_LIB
#define BOOST_LIB_NAME "boost_numpy35"
//#include <boost/config/auto_link.hpp>
#include <boost/python.hpp>
#include <boost/python/numpy.hpp>
#include <iostream>
#include <opencv2/opencv.hpp>

namespace py = boost::python;
namespace np = boost::python::numpy;

void Init() {
	// set your python location.
	wchar_t str[] = L"D:\\Anaconda3\\envs\\tensorflow_vision";

	Py_SetPythonHome(str);

	Py_Initialize();
	np::initialize();
}

np::ndarray ConvertMatToNDArray(const cv::Mat& mat) {
	py::tuple shape = py::make_tuple(mat.rows, mat.cols, mat.channels());
	py::tuple stride = py::make_tuple(mat.channels() * mat.cols * sizeof(uchar), mat.channels() * sizeof(uchar), sizeof(uchar));
	np::dtype dt = np::dtype::get_builtin<uchar>();
	np::ndarray ndImg = np::from_data(mat.data, dt, shape, stride, py::object());

	return ndImg;
}

cv::Mat ConvertNDArrayToMat(const np::ndarray& ndarr) {
	//int length = ndarr.get_nd(); // get_nd() returns num of dimensions. this is used as a length, but we don't need to use in this case. because we know that image has 3 dimensions.
	const Py_intptr_t* shape = ndarr.get_shape(); // get_shape() returns Py_intptr_t* which we can get the size of n-th dimension of the ndarray.
	char* dtype_str = py::extract<char *>(py::str(ndarr.get_dtype()));

	// variables for creating Mat object
	int rows = shape[0];
	int cols = shape[1];
	int channel = shape[2];
	int depth;

	// you should find proper type for c++. in this case we use 'CV_8UC3' image, so we need to create 'uchar' type Mat.
	if (!strcmp(dtype_str, "uint8")) {
		depth = CV_8U;
	}
	else {
		std::cout << "wrong dtype error" << std::endl;
		return cv::Mat();
	}

	int type = CV_MAKETYPE(depth, channel); // CV_8UC3

	cv::Mat mat = cv::Mat(rows, cols, type);
	memcpy(mat.data, ndarr.get_data(), sizeof(uchar) * rows * cols * channel);

	return mat;
}

int main()
{
	using namespace std;

	try
	{
		// initialize boost python and numpy
		Init();
		
		// import module
		py::object main_module = py::import("__main__");
		py::object print = main_module.attr("__builtins__").attr("print"); // this is for printing python object

		// get image
		cv::Mat img;
		img = cv::imread("Lenna.jpg", cv::IMREAD_COLOR);
		if (img.empty())
		{
			std::cout << "can't getting image" << std::endl;
			return -1;
		}

		// convert Mat to NDArray
		cv::Mat cloneImg = img.clone(); // converting functions will access to same data between Mat and NDArray. so we should clone Mat object. This may important in your case.
		np::ndarray ndImg = ConvertMatToNDArray(cloneImg);
		
		// You can check if it's properly converted.
		//print(ndImg);
		
		// convert NDArray to Mat
		cv::Mat matImg = ConvertNDArrayToMat(ndImg); // also you can convert ndarray to mat.

		// add 10 brightness to converted image
		for (int i = 0; i < matImg.rows; i++) {
			for (int j = 0; j < matImg.cols; j++) {
				for (int c = 0; c < matImg.channels(); c++) {
					matImg.at<cv::Vec3b>(i, j)[c] += 10;
				}
			}
		}

		// show image
		cv::imshow("original image", img);
		cv::imshow("converted image", matImg);
		cv::waitKey(0);
		cv::destroyAllWindows();
	}
	catch (py::error_already_set&)
	{
		PyErr_Print();
		system("pause");
	}

	system("pause");
	return 0;
}

THANKS!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

This is really great!! Thanks for your hard work :)

Thank you so much!!! You saved my day!