VS2019+opencv调用训练好的YOLOv5模型（3）

具体见这位老哥的博客：
代码实现
以下是我整理好的代码，实测可用：
yolo.h

#pragma once
#include 
#include 
#include 
#include 
#include 
#include 
#include 

struct Output {
	int id;//结果类别id
	float confidence;//结果置信度
	cv::Rect box;//矩形框
};

class Yolo {
public:
	//在yolo.h中的 Yolo类中添加成员函数readModel：
	bool readModel(cv::dnn::Net& net, std::string& netPath, bool isCuda);
	bool Detect(cv::Mat& SrcImg, cv::dnn::Net& net, std::vector
& output);
	void drawPred(cv::Mat& img, std::vector
 result, std::vector color);

private:
	//计算归一化函数
	float Sigmoid(float x) {
		return static_cast(1.f / (1.f + exp(-x)));
	}
	//anchors
	const float netAnchors[3][6] = { { 10.0, 13.0, 16.0, 30.0, 33.0, 23.0 },{ 30.0, 61.0, 62.0, 45.0, 59.0, 119.0 },{ 116.0, 90.0, 156.0, 198.0, 373.0, 326.0 } };
	//stride
	const float netStride[3] = { 8.0, 16.0, 32.0 };
	const int netWidth = 640; //网络模型输入大小
	const int netHeight = 640;
	float nmsThreshold = 0.45;
	float boxThreshold = 0.35;
	float classThreshold = 0.35;
	//类名
	std::vector className = { "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" };

};

yolo.cpp

//yolo.cpp中实现readModel函数
//在yolo.cpp中使用命名空间
#include "yolo.h"
using namespace std;
using namespace cv;
using namespace dnn;

bool Yolo::readModel(Net& net, string& netPath, bool isCuda = false) {
	try {
		net = readNetFromONNX(netPath);
	}
	catch (const std::exception&) {
		return false;
	}
	//cuda
	if (isCuda) {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
	}
	//cpu
	else {
		net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
		net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
	}
	return true;
}

bool Yolo::Detect(Mat& SrcImg, Net& net, vector
& output) {
	Mat blob;
	int col = SrcImg.cols;
	int row = SrcImg.rows;
	int maxLen = MAX(col, row);
	Mat netInputImg = SrcImg.clone();
	if (maxLen > 1.2 * col || maxLen > 1.2 * row) {
		Mat resizeImg = Mat::zeros(maxLen, maxLen, CV_8UC3);
		SrcImg.copyTo(resizeImg(Rect(0, 0, col, row)));
		netInputImg = resizeImg;
	}
	blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(104, 117, 123), true, false);
	//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(0, 0,0), true, false);//如果训练集未对图片进行减去均值操作，则需要设置为这句
	//blobFromImage(netInputImg, blob, 1 / 255.0, cv::Size(netWidth, netHeight), cv::Scalar(114, 114,114), true, false);
	net.setInput(blob);
	std::vector netOutputImg;
	//vector outputLayerName{"345","403", "461","output" };
	//net.forward(netOutputImg, outputLayerName[3]); //获取output的输出
	net.forward(netOutputImg, net.getUnconnectedOutLayersNames());
	std::vector classIds;//结果id数组
	std::vector confidences;//结果每个id对应置信度数组
	std::vector boxes;//每个id矩形框
	float ratio_h = (float)netInputImg.rows / netHeight;
	float ratio_w = (float)netInputImg.cols / netWidth;
	int net_width = className.size() + 5;  //输出的网络宽度是类别数+5
	float* pdata = (float*)netOutputImg[0].data;
	for (int stride = 0; stride < 3; stride++) {    //stride
		int grid_x = (int)(netWidth / netStride[stride]);
		int grid_y = (int)(netHeight / netStride[stride]);
		for (int anchor = 0; anchor < 3; anchor++) { //anchors
			const float anchor_w = netAnchors[stride][anchor * 2];
			const float anchor_h = netAnchors[stride][anchor * 2 + 1];
			for (int i = 0; i < grid_y; i++) {
				for (int j = 0; j < grid_y; j++) {
					float box_score = Sigmoid(pdata[4]);//获取每一行的box框中含有某个物体的概率
					if (box_score > boxThreshold) {
						//为了使用minMaxLoc(),将85长度数组变成Mat对象
						cv::Mat scores(1, className.size(), CV_32FC1, pdata + 5);
						Point classIdPoint;
						double max_class_socre;
						minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
						max_class_socre = Sigmoid((float)max_class_socre);
						if (max_class_socre > classThreshold) {
							//rect [x,y,w,h]
							float x = (Sigmoid(pdata[0]) * 2.f - 0.5f + j) * netStride[stride];  //x
							float y = (Sigmoid(pdata[1]) * 2.f - 0.5f + i) * netStride[stride];   //y
							float w = powf(Sigmoid(pdata[2]) * 2.f, 2.f) * anchor_w;   //w
							float h = powf(Sigmoid(pdata[3]) * 2.f, 2.f) * anchor_h;  //h
							int left = (x - 0.5 * w) * ratio_w;
							int top = (y - 0.5 * h) * ratio_h;
							classIds.push_back(classIdPoint.x);
							confidences.push_back(max_class_socre * box_score);
							boxes.push_back(Rect(left, top, int(w * ratio_w), int(h * ratio_h)));
						}
					}
					pdata += net_width;//指针移到下一行
				}
			}
		}
	}
	vector nms_result;
	NMSBoxes(boxes, confidences, classThreshold, nmsThreshold, nms_result);
	for (int i = 0; i < nms_result.size(); i++) {
		int idx = nms_result[i];
		Output result;
		result.id = classIds[idx];
		result.confidence = confidences[idx];
		result.box = boxes[idx];
		output.push_back(result);
	}

	if (output.size())
		return true;
	else
		return false;
}

void Yolo::drawPred(Mat& img, vector
 result, vector color) {
	for (int i = 0; i < result.size(); i++) {
		int left, top;
		left = result[i].box.x;
		top = result[i].box.y;
		int color_num = i;
		rectangle(img, result[i].box, color[result[i].id], 2, 8);

		string label = className[result[i].id] + ":" + to_string(result[i].confidence);

		int baseLine;
		Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
		top = max(top, labelSize.height);
		//rectangle(frame, Point(left, top - int(1.5 * labelSize.height)), Point(left + int(1.5 * labelSize.width), top + baseLine), Scalar(0, 255, 0), FILLED);
		putText(img, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1, color[result[i].id], 2);
	}
	imshow("res", img);
	//imwrite("./result.jpg", img);
	waitKey();
	//destroyAllWindows();
}


int main()
{
	cout << "Hello World" << endl;
	string img_path = "E:/OpenCV_image/test1.jpg";
	string model_path = "D:/YOLOv5/yolov5-5.0/models/yolov5s.onnx";

	Yolo test;
	Net net;
	if (test.readModel(net, model_path, true)) {
		cout << "read net ok!" << endl;
	}
	else {
		return -1;
	}

	//生成随机颜色
	vector color;
	srand(time(0));
	for (int i = 0; i < 80; i++) {
		int b = rand() % 256;
		int g = rand() % 256;
		int r = rand() % 256;
		color.push_back(Scalar(b, g, r));
	}
	vector
 result;
	Mat img = imread(img_path);
	if (test.Detect(img, net, result)) {
		test.drawPred(img, result, color);

	}
	else {
		cout << "Detect Failed!" << endl;
	}


	system("pause");
	return 0;
}