C++ version of openvino use

I wrote an article before: Using the python version of openvino

Still use the same model, the code is as follows:

#include<iostream>
#include<opencv2/core.hpp>
#include<opencv2/highgui.hpp>
#include<opencv2/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include<inference_engine.hpp>
#include<ie_extension.h>
#include<ie_blob.h>

using namespace std;
using namespace InferenceEngine;
int main() {
	string modepayh ="C:\\ctdet_coco_dlav0_512\\ctdet_coco_dlav0_512.xml";
	string imagepath ="C:\\123.jpg";
	//read image
	auto im=cv::imread(imagepath);
	cv::Mat  image;
	cv::resize(im, image, cv::Size(512, 512));
	cout << "image loaded" << endl;	
	//read net
	Core ie;
	CNNNetwork network = ie.ReadNetwork(modepayh);

	//input info
	InputInfo::Ptr input_info = network.getInputsInfo().begin()->second;
	std::string input_name = network.getInputsInfo().begin()->first;
	input_info->getPreProcess().setResizeAlgorithm(RESIZE_BILINEAR);
	input_info->setLayout(Layout::NCHW);
	input_info->setPrecision(Precision::U8);	
	cout << input_name << endl;
	//output info
	DataPtr output_info = network.getOutputsInfo().begin()->second;
	std::string output_name = network.getOutputsInfo().begin()->first;
	output_info->setPrecision(Precision::FP32);
	//load net
	ExecutableNetwork executable_network = ie.LoadNetwork(network, "CPU");
	//create infer request
	InferRequest infer_request = executable_network.CreateInferRequest();
	//load input data
	InferenceEngine::TensorDesc tDesc(InferenceEngine::Precision::U8,
		{ 1, 3, 512, 512 },
		InferenceEngine::Layout::NCHW);
	Blob::Ptr imgBlob = InferenceEngine::make_shared_blob<uint8_t>(tDesc, image.data);
	infer_request.SetBlob(input_name, imgBlob);
	//infer
	clock_t time_start = clock();
	infer_request.Infer();
	//get output
	clock_t time_end = clock();
	cout << "infer time is:" << 1000 * (time_end - time_start) / (double)CLOCKS_PER_SEC << "ms" << endl;
	OutputsDataMap outputsInfo(network.getOutputsInfo());
	const float* output = infer_request.GetBlob(output_name)->buffer().as<PrecisionTrait<Precision::FP32>::value_type*>();		
	for (int i=0;i<5;i++) {
		cout << output[i] << endl;
	}
	return 0;
}

operation result: