【opencv c++】实现yolov5部署onnx模型完成目标检测
opencv安装链接
https://opencv.org/releases/
内容拆分
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url，只知道置信度是啥，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();
}
