Opencv C++实现yolov5部署onnx模型完成目标检测

企业开发 2023-08-06 13:47:33 阅读次数: 0

代码分析：

头文件

#include <fstream>  //文件
#include <sstream>  //流
#include <iostream>
#include <opencv2/dnn.hpp>        //深度学习模块-仅提供推理功能
#include <opencv2/imgproc.hpp>    //图像处理模块
#include <opencv2/highgui.hpp>    //媒体的输入输出/视频捕捉/图像和视频的编码解码/图形界面的接口

命名空间

using namespace cv;
using namespace dnn;
using namespace std;

结构体 Net_config

struct Net_config{
	float confThreshold; // 置信度阈值
	float nmsThreshold;  // 非最大抑制阈值
	float objThreshold;  // 对象置信度阈值
	string modelpath;
};

里面存了三个阈值和模型地址，其中置信度，顾名思义，看检测出来的物体的精准度。以测量值为中心，在一定范围内，真值出现在该范围内的几率。

endsWith()函数 判断sub是不是s的子串

int endsWith(string s, string sub) {
	return s.rfind(sub) == (s.length() - sub.length()) ? 1 : 0;
}

anchors_640图像接收数组

const float anchors_640[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} };

根据图像大小，选择相应长度的二维数组。深度为3，每层6个位置。

YOLO类

class YOLO{
public:
	YOLO(Net_config config); //构造函数
	void detect(Mat& frame); //通过图像参数，进行目标检测
private:
	float* anchors;
	int num_stride;
	int inpWidth;
	int inpHeight;
	vector<string> class_names;
	int num_class;
	float confThreshold;
	float nmsThreshold;
	float objThreshold;
	const bool keep_ratio = true;
	Net net;
	void drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid);
	Mat resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left);
};

YOLO类构造函数的重载

YOLO::YOLO(Net_config config){
	this->confThreshold = config.confThreshold;
	this->nmsThreshold = config.nmsThreshold;
	this->objThreshold = config.objThreshold;
	this->net = readNet(config.modelpath);
	ifstream ifs("class.names"); //class.name中写入标签内容，当前只有'person'，位置与当前.cpp文件同级
	string line;
	while (getline(ifs, line)) this->class_names.push_back(line);
	this->num_class = class_names.size();
	if (endsWith(config.modelpath, "6.onnx")){ //根据onnx的输入图像格式 选择分辨率 当前为1280x1280 可灵活调整
		anchors = (float*)anchors_1280;
		this->num_stride = 4;      //深度
		this->inpHeight = 1280;    //高
		this->inpWidth = 1280;     //宽
	}
	else{                                       //当前为640x640 可以resize满足onnx需求 也可以调整数组或if中的onnx判断
		anchors = (float*)anchors_640;
		this->num_stride = 3;
		this->inpHeight = 640;
		this->inpWidth = 640;
	}
}

重新规定图像大小函数 resize_image()

Mat YOLO::resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left){//传入图像以及需要改变的参数
	int srch = srcimg.rows, srcw = srcimg.cols;
	*newh = this->inpHeight;
	*neww = this->inpWidth;
	Mat dstimg;
	if (this->keep_ratio && srch != srcw) {
		float hw_scale = (float)srch / srcw;
		if (hw_scale > 1) {
			*newh = this->inpHeight;
			*neww = int(this->inpWidth / hw_scale);
			resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
			*left = int((this->inpWidth - *neww) * 0.5);
			copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 114);
		}else {
			*newh = (int)this->inpHeight * hw_scale;
			*neww = this->inpWidth;
			resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
			*top = (int)(this->inpHeight - *newh) * 0.5;
			copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 114);
		}
	}else {
		resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
	}
	return dstimg;
}

绘制预测的边界框函数 drawPred()

void YOLO::drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid){
	//绘制一个显示边界框的矩形
	rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 2);

	//获取类名的标签及其置信度
	string label = format("%.2f", conf);
	label = this->class_names[classid] + ":" + label;

	//在边界框顶部显示标签
	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(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);
}

【核心代码】检测函数 detect()

void YOLO::detect(Mat& frame){
	int newh = 0, neww = 0, padh = 0, padw = 0;
	Mat dstimg = this->resize_image(frame, &newh, &neww, &padh, &padw);
	Mat blob = blobFromImage(dstimg, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);
	this->net.setInput(blob);
	vector<Mat> outs;
	this->net.forward(outs, this->net.getUnconnectedOutLayersNames());
	int num_proposal = outs[0].size[1];
	int nout = outs[0].size[2];
	if (outs[0].dims > 2){
		outs[0] = outs[0].reshape(0, num_proposal);
	}
	//生成提案
	vector<float> confidences;
	vector<Rect> boxes;
	vector<int> classIds;
	float ratioh = (float)frame.rows / newh, ratiow = (float)frame.cols / neww;
	int n = 0, q = 0, i = 0, j = 0, row_ind = 0;  //xmin,ymin,xamx,ymax,box_score,class_score
	float* pdata = (float*)outs[0].data;
	for (n = 0; n < this->num_stride; n++){ //特征图尺度
		const float stride = pow(2, n + 3);
		int num_grid_x = (int)ceil((this->inpWidth / stride));
		int num_grid_y = (int)ceil((this->inpHeight / stride));
		for (q = 0; q < 3; q++){
			const float anchor_w = this->anchors[n * 6 + q * 2];
			const float anchor_h = this->anchors[n * 6 + q * 2 + 1];
			for (i = 0; i < num_grid_y; i++){
				for (j = 0; j < num_grid_x; j++){
					float box_score = pdata[4];
					if (box_score > this->objThreshold){
						Mat scores = outs[0].row(row_ind).colRange(5, nout);
						Point classIdPoint;
						double max_class_socre;
						//获取最高分的值和位置
						minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
						max_class_socre *= box_score;
						if (max_class_socre > this->confThreshold){
							const int class_idx = classIdPoint.x;
							float cx = pdata[0];  //cx
							float cy = pdata[1];  //cy
							float w = pdata[2];   //w
							float h = pdata[3];   //h
							int left = int((cx - padw - 0.5 * w) * ratiow);
							int top = int((cy - padh - 0.5 * h) * ratioh);
							confidences.push_back((float)max_class_socre);
							boxes.push_back(Rect(left, top, (int)(w * ratiow), (int)(h * ratioh)));
							classIds.push_back(class_idx);
						}
					}
					row_ind++;
					pdata += nout;
				}
			}
		}
	}
	// 执行非最大抑制以消除冗余重叠框
	// 置信度较低
	vector<int> indices;
	dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);
	for (size_t i = 0; i < indices.size(); ++i){
		int idx = indices[i];
		Rect box = boxes[idx];
		this->drawPred(confidences[idx], box.x, box.y,box.x + box.width, box.y + box.height, frame, classIds[idx]);
	}
}

主函数

int main(){
        //加载onnx模型
	Net_config yolo_nets = { 0.3, 0.5, 0.3, "yolov5n_person_320.onnx" };
	YOLO yolo_model(yolo_nets);
        //加载单张图片
	string imgpath = "112.png";
	Mat srcimg = imread(imgpath);
	//开始检测
        yolo_model.detect(srcimg);
	static const string kWinName = "Deep learning object detection in OpenCV";
	namedWindow(kWinName, WINDOW_NORMAL);
	imshow(kWinName, srcimg); //显示图片
	waitKey(0);               //保持停留
	destroyAllWindows();      //关闭窗口并取消分配任何相关的内存使用
}

完整代码

#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>

using namespace cv;
using namespace dnn;
using namespace std;

struct Net_config
{
	float confThreshold; // Confidence threshold
	float nmsThreshold;  // Non-maximum suppression threshold
	float objThreshold;  //Object Confidence threshold
	string modelpath;
};

int endsWith(string s, string sub) {
	return s.rfind(sub) == (s.length() - sub.length()) ? 1 : 0;
}

const float anchors_640[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} };

const float anchors_1280[4][6] = { {19, 27, 44, 40, 38, 94},
                                   {96, 68, 86, 152, 180, 137},
                                   {140, 301, 303, 264, 238, 542},
				   {436, 615, 739, 380, 925, 792} };

class YOLO
{
public:
	YOLO(Net_config config);
	void detect(Mat& frame);
private:
	float* anchors;
	int num_stride;
	int inpWidth;
	int inpHeight;
	vector<string> class_names;
	int num_class;

	float confThreshold;
	float nmsThreshold;
	float objThreshold;
	const bool keep_ratio = true;
	Net net;
	void drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid);
	Mat resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left);
};

YOLO::YOLO(Net_config config)
{
	this->confThreshold = config.confThreshold;
	this->nmsThreshold = config.nmsThreshold;
	this->objThreshold = config.objThreshold;

	this->net = readNet(config.modelpath);
	ifstream ifs("class.names");
	string line;
	while (getline(ifs, line)) this->class_names.push_back(line);
	this->num_class = class_names.size();

	if (endsWith(config.modelpath, "6.onnx"))
	{
		anchors = (float*)anchors_1280;
		this->num_stride = 4;
		this->inpHeight = 1280;
		this->inpWidth = 1280;
	}
	else
	{
		anchors = (float*)anchors_640;
		this->num_stride = 3;
		this->inpHeight = 640;
		this->inpWidth = 640;
	}
}

Mat YOLO::resize_image(Mat srcimg, int* newh, int* neww, int* top, int* left)
{
	int srch = srcimg.rows, srcw = srcimg.cols;
	*newh = this->inpHeight;
	*neww = this->inpWidth;
	Mat dstimg;
	if (this->keep_ratio && srch != srcw) {
		float hw_scale = (float)srch / srcw;
		if (hw_scale > 1) {
			*newh = this->inpHeight;
			*neww = int(this->inpWidth / hw_scale);
			resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
			*left = int((this->inpWidth - *neww) * 0.5);
			copyMakeBorder(dstimg, dstimg, 0, 0, *left, this->inpWidth - *neww - *left, BORDER_CONSTANT, 114);
		}
		else {
			*newh = (int)this->inpHeight * hw_scale;
			*neww = this->inpWidth;
			resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
			*top = (int)(this->inpHeight - *newh) * 0.5;
			copyMakeBorder(dstimg, dstimg, *top, this->inpHeight - *newh - *top, 0, 0, BORDER_CONSTANT, 114);
		}
	}
	else {
		resize(srcimg, dstimg, Size(*neww, *newh), INTER_AREA);
	}
	return dstimg;
}

void YOLO::drawPred(float conf, int left, int top, int right, int bottom, Mat& frame, int classid)   // Draw the predicted bounding box
{
	//Draw a rectangle displaying the bounding box
	rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 2);

	//Get the label for the class name and its confidence
	string label = format("%.2f", conf);
	label = this->class_names[classid] + ":" + label;

	//Display the label at the top of the bounding box
	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(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);
}

void YOLO::detect(Mat& frame)
{
	int newh = 0, neww = 0, padh = 0, padw = 0;
	Mat dstimg = this->resize_image(frame, &newh, &neww, &padh, &padw);
	Mat blob = blobFromImage(dstimg, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);
	this->net.setInput(blob);
	vector<Mat> outs;
	this->net.forward(outs, this->net.getUnconnectedOutLayersNames());

	int num_proposal = outs[0].size[1];
	int nout = outs[0].size[2];
	if (outs[0].dims > 2)
	{
		outs[0] = outs[0].reshape(0, num_proposal);
	}
	/generate proposals
	vector<float> confidences;
	vector<Rect> boxes;
	vector<int> classIds;
	float ratioh = (float)frame.rows / newh, ratiow = (float)frame.cols / neww;
	int n = 0, q = 0, i = 0, j = 0, row_ind = 0; ///xmin,ymin,xamx,ymax,box_score,class_score
	float* pdata = (float*)outs[0].data;
	for (n = 0; n < this->num_stride; n++)   ///特征图尺度
	{
		const float stride = pow(2, n + 3);
		int num_grid_x = (int)ceil((this->inpWidth / stride));
		int num_grid_y = (int)ceil((this->inpHeight / stride));
		for (q = 0; q < 3; q++)    ///anchor
		{
			const float anchor_w = this->anchors[n * 6 + q * 2];
			const float anchor_h = this->anchors[n * 6 + q * 2 + 1];
			for (i = 0; i < num_grid_y; i++)
			{
				for (j = 0; j < num_grid_x; j++)
				{
					float box_score = pdata[4];
					if (box_score > this->objThreshold)
					{
						Mat scores = outs[0].row(row_ind).colRange(5, nout);
						Point classIdPoint;
						double max_class_socre;
						// Get the value and location of the maximum score
						minMaxLoc(scores, 0, &max_class_socre, 0, &classIdPoint);
						max_class_socre *= box_score;
						if (max_class_socre > this->confThreshold)
						{
							const int class_idx = classIdPoint.x;
							//float cx = (pdata[0] * 2.f - 0.5f + j) * stride;  ///cx
							//float cy = (pdata[1] * 2.f - 0.5f + i) * stride;   ///cy
							//float w = powf(pdata[2] * 2.f, 2.f) * anchor_w;   ///w
							//float h = powf(pdata[3] * 2.f, 2.f) * anchor_h;  ///h

							float cx = pdata[0];  ///cx
							float cy = pdata[1];   ///cy
							float w = pdata[2];   ///w
							float h = pdata[3];  ///h

							int left = int((cx - padw - 0.5 * w) * ratiow);
							int top = int((cy - padh - 0.5 * h) * ratioh);

							confidences.push_back((float)max_class_socre);
							boxes.push_back(Rect(left, top, (int)(w * ratiow), (int)(h * ratioh)));
							classIds.push_back(class_idx);
						}
					}
					row_ind++;
					pdata += nout;
				}
			}
		}
	}

	// Perform non maximum suppression to eliminate redundant overlapping boxes with
	// lower confidences
	vector<int> indices;
	dnn::NMSBoxes(boxes, confidences, this->confThreshold, this->nmsThreshold, indices);
	for (size_t i = 0; i < indices.size(); ++i)
	{
		int idx = indices[i];
		Rect box = boxes[idx];
		this->drawPred(confidences[idx], box.x, box.y,
			box.x + box.width, box.y + box.height, frame, classIds[idx]);
	}
}

int main()
{
	Net_config yolo_nets = { 0.3, 0.5, 0.3, "yolov5n_person_320.onnx" };
	YOLO yolo_model(yolo_nets);

	//string imgpath = "112.png";
	//Mat srcimg = imread(imgpath);
	//yolo_model.detect(srcimg);
        
        int n = 588;
	for (int i = 1; i <= n; i++) {
		string s = to_string(i) + ".png";
		string imgpath = "F://test//p1//yanfa2//bh//cc//" + s;
		cout << imgpath << endl;

		Mat srcimg = imread(imgpath);
		yolo_model.detect(srcimg);
		imwrite("F://test//p2//yanfa2//bh//cc//" + s, srcimg);
	}

	//static const string kWinName = "Deep learning object detection in OpenCV";
	//namedWindow(kWinName, WINDOW_NORMAL);
	//imshow(kWinName, srcimg);
	//waitKey(0);
	//destroyAllWindows();
}

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