一、重要说明：
本脚本参考官方代码直接整理在一个.cpp文件中完成，仅仅使用cv接口加载onnx模型完成yolov5和yolov8模型的推理过程，即只在你的c++开发环境中配置一个opencv就可以了。opencv如果要在gpu上使用，需要在编译的时候，在编译选项里面选定cuda相关的选项，配置成功即可。
二、看完别忘提示
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三、完整代码如下：

#include <fstream>
#include <vector>
#include <string>
#include <random>

// OpenCV / DNN / Inference
#include <opencv2/imgproc.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>

using namespace cv;
using namespace std;

struct Detection
{
	int class_id{ 0 };
	std::string className{};
	float confidence{ 0.0 };
	cv::Scalar color{};
	cv::Rect box{};
};

class Inference
{
public:
	Inference(const std::string &onnxModelPath, const cv::Size &modelInputShape, const std::string &classesTxtFile, const bool &runWithCuda)
	{
		modelPath = onnxModelPath;
		modelShape = modelInputShape;
		classesPath = classesTxtFile;
		cudaEnabled = runWithCuda;

		loadOnnxNetwork();
	}
private:
	std::string modelPath;
	std::string classesPath;
	bool cudaEnabled;
	cv::Size2f modelShape;
	cv::dnn::Net net;
	bool letterBoxForSquare = true;

	std::vector<std::string> classes{ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };
	float modelConfidenceThreshold{ 0.25 };
	float modelScoreThreshold{ 0.45 };
	float modelNMSThreshold{ 0.50 };


private:
	void loadOnnxNetwork()
	{
		net = cv::dnn::readNetFromONNX(modelPath);
		if (cudaEnabled)
		{
			std::cout << "\nRunning on CUDA" << std::endl;
			net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
			net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
		}
		else
		{
			std::cout << "\nRunning on CPU" << std::endl;
			net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
			net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
		}
	}
	cv::Mat formatToSquare(const cv::Mat &source)
	{
		int col = source.cols;
		int row = source.rows;
		int _max = MAX(col, row);
		cv::Mat result = cv::Mat::zeros(_max, _max, CV_8UC3);
		source.copyTo(result(cv::Rect(0, 0, col, row)));
		return result;
	}
public:
	std::vector<Detection> runInference(const cv::Mat &input)
	{
		cv::Mat modelInput = input;
		if (letterBoxForSquare && modelShape.width == modelShape.height)
			modelInput = formatToSquare(modelInput);

		cv::Mat blob;
		cv::dnn::blobFromImage(modelInput, blob, 1.0 / 255.0, modelShape, cv::Scalar(), true, false);
		net.setInput(blob);

		std::vector<cv::Mat> outputs;
		net.forward(outputs, net.getUnconnectedOutLayersNames());

		int rows = outputs[0].size[1];
		int dimensions = outputs[0].size[2];

		bool yolov8 = false;
		// yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
		// yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
		if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
		{
			yolov8 = true;
			rows = outputs[0].size[2];
			dimensions = outputs[0].size[1];

			outputs[0] = outputs[0].reshape(1, dimensions);
			cv::transpose(outputs[0], outputs[0]);
		}
		float *data = (float *)outputs[0].data;

		float x_factor = modelInput.cols / modelShape.width;
		float y_factor = modelInput.rows / modelShape.height;

		std::vector<int> class_ids;
		std::vector<float> confidences;
		std::vector<cv::Rect> boxes;

		for (int i = 0; i < rows; ++i)
		{
			if (yolov8)
			{
				float *classes_scores = data + 4;

				cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
				cv::Point class_id;
				double maxClassScore;

				minMaxLoc(scores, 0, &maxClassScore, 0, &class_id);

				if (maxClassScore > modelScoreThreshold)
				{
					confidences.push_back(maxClassScore);
					class_ids.push_back(class_id.x);

					float x = data[0];
					float y = data[1];
					float w = data[2];
					float h = data[3];

					int left = int((x - 0.5 * w) * x_factor);
					int top = int((y - 0.5 * h) * y_factor);

					int width = int(w * x_factor);
					int height = int(h * y_factor);

					boxes.push_back(cv::Rect(left, top, width, height));
				}
			}
			else // yolov5
			{
				float confidence = data[4];

				if (confidence >= modelConfidenceThreshold)
				{
					float *classes_scores = data + 5;

					cv::Mat scores(1, classes.size(), CV_32FC1, classes_scores);
					cv::Point class_id;
					double max_class_score;

					minMaxLoc(scores, 0, &max_class_score, 0, &class_id);

					if (max_class_score > modelScoreThreshold)
					{
						confidences.push_back(confidence);
						class_ids.push_back(class_id.x);

						float x = data[0];
						float y = data[1];
						float w = data[2];
						float h = data[3];

						int left = int((x - 0.5 * w) * x_factor);
						int top = int((y - 0.5 * h) * y_factor);

						int width = int(w * x_factor);
						int height = int(h * y_factor);

						boxes.push_back(cv::Rect(left, top, width, height));
					}
				}
			}

			data += dimensions;
		}

		std::vector<int> nms_result;
		cv::dnn::NMSBoxes(boxes, confidences, modelScoreThreshold, modelNMSThreshold, nms_result);

		std::vector<Detection> detections{};
		for (unsigned long i = 0; i < nms_result.size(); ++i)
		{
			int idx = nms_result[i];

			Detection result;
			result.class_id = class_ids[idx];
			result.confidence = confidences[idx];

			std::random_device rd;
			std::mt19937 gen(rd());
			std::uniform_int_distribution<int> dis(100, 255);
			result.color = cv::Scalar(dis(gen),
				dis(gen),
				dis(gen));

			result.className = classes[result.class_id];
			result.box = boxes[idx];

			detections.push_back(result);
		}

		return detections;
	}

	void loadClassesFromFile()
	{
		std::ifstream inputFile(classesPath);
		if (inputFile.is_open())
		{
			std::string classLine;
			while (std::getline(inputFile, classLine))
				classes.push_back(classLine);
			inputFile.close();
		}
	}
};

int main(int argc, char **argv)
{
	std::string projectBasePath = "E:\\mycode\\c++projects\\vs2017code\\sourcefile";
	bool runOnGPU = true;

	Inference inf(projectBasePath + "/yolov8s.onnx", cv::Size(640, 640), "classes.txt", runOnGPU);
	
	std::vector<std::string> imageNames;
	imageNames.push_back(projectBasePath + "/bus.jpg");


	for (int i = 0; i < imageNames.size(); ++i)
	{
		cv::Mat frame = cv::imread(imageNames[i]);

		// Inference starts here...
		std::vector<Detection> output = inf.runInference(frame);

		int detections = output.size();
		std::cout << "Number of detections:" << detections << std::endl;

		for (int i = 0; i < detections; ++i)
		{
			Detection detection = output[i];

			cv::Rect box = detection.box;
			cv::Scalar color = detection.color;

			// Detection box
			cv::rectangle(frame, box, color, 2);

			// Detection box text
			std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4);
			cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
			cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);

			cv::rectangle(frame, textBox, color, cv::FILLED);
			cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
		}
		// Inference ends here...

		// This is only for preview purposes
		float scale = 0.8;
		cv::resize(frame, frame, cv::Size(frame.cols*scale, frame.rows*scale));
		cv::imshow("Inference", frame);

		cv::waitKey(-1);
	}
	std::cout << "-----------------------------" << std::endl;
	std::cout << "dd"<< std::endl;
	return 0;
}