Qt结合OpenCV部署yolov5的实现
更新时间：2022年04月07日 15:43:20 作者：SongpingWang
本文主要介绍了Qt结合OpenCV部署yolov5的实现，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧
分别使用了openvino，opencv_cuda进行加速。
关于演示视频及代码讲解请查看：
https://www.bilibili.com/video/BV13S4y1c7ea/
https://www.bilibili.com/video/BV1Dq4y1x7r6/
https://www.bilibili.com/video/BV1kT4y1S7hz/
一、新建项目 UI设计
二、代码部分 mainwindow 类
mainwindow.h
#ifndef MAINWINDOW_H
#define MAINWINDOW_H
#include <QFileDialog>
#include <QFile>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <QMainWindow>
#include <QTimer>
#include <QImage>
#include <QPixmap>
#include <QDateTime>
#include <QMutex>
#include <QMutexLocker>
#include <QMimeDatabase>
#include <iostream>
#include <yolov5.h>
#include <chrono>
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_core453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_imgcodecs453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_imgproc453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_videoio453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_objdetect453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\opencv\\lib\\opencv_dnn453.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\deployment_tools\\inference_engine\\lib\\intel64\\Release\\inference_engine.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\deployment_tools\\inference_engine\\lib\\intel64\\Release\\inference_engine_c_api.lib")
#pragma comment(lib,"C:\\Program Files (x86)\\Intel\\openvino_2021\\deployment_tools\\inference_engine\\lib\\intel64\\Release\\inference_engine_transformations.lib")
//LIBS+= -L "C:\Program Files (x86)\Intel\openvino_2021\opencv\lib\*.lib"
//LIBS+= -L "C:\Program Files (x86)\Intel\openvino_2021\deployment_tools\inference_engine\lib\intel64\Release\*.lib"
//#ifdef QT_NO_DEBUG
//#pragma comment(lib,"C:\Program Files (x86)\Intel\openvino_2021\opencv\lib\opencv_core452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgcodecs452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgproc452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgcodecs452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_video452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_videoio452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_objdetect452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_shape452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_dnn452.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_dnn_objdetect452.lib")
//#else
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_core452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgcodecs452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgproc452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_imgcodecs452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_video452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_videoio452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_objdetect452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_shape452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_dnn452d.lib")
//#pragma comment(lib,"E:/opencv_build/install/x64/vc16/lib/opencv_dnn_objdetect452d.lib")
//#endif
//#ifdef QT_NO_DEBUG
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_core452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_imgcodecs452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_imgproc452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_imgcodecs452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_video452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_videoio452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_objdetect452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_shape452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_dnn452.lib")
//#pragma comment(lib,"E:/opencv452_cuda/install/x64/vc16/lib/opencv_dnn_objdetect452.lib")
//#endif
QPixmap Mat2Image(cv::Mat src);
QT_BEGIN_NAMESPACE
namespace Ui { class MainWindow; }
QT_END_NAMESPACE
class MainWindow : public QMainWindow
{
Q_OBJECT
public:
MainWindow(QWidget *parent = nullptr);
void Init();
~MainWindow();
private slots:
void readFrame(); //自定义信号处理函数
void on_openfile_clicked();
void on_loadfile_clicked();
void on_startdetect_clicked();
void on_stopdetect_clicked();
void on_comboBox_activated(const QString &arg1);
private:
Ui::MainWindow *ui;
QTimer *timer;
cv::VideoCapture *capture;
YOLOV5 *yolov5;
NetConfig conf;
NetConfig *yolo_nets;
std::vector<cv::Rect> bboxes;
int IsDetect_ok = 0;
};
#endif // MAINWINDOW_H
mainwindow.cpp
#include "mainwindow.h"
#include "ui_mainwindow.h"
MainWindow::MainWindow(QWidget *parent)
: QMainWindow(parent)
, ui(new Ui::MainWindow)
{
ui->setupUi(this);
setWindowTitle(QStringLiteral("YoloV5目标检测软件"));
timer = new QTimer(this);
timer->setInterval(33);
connect(timer,SIGNAL(timeout()),this,SLOT(readFrame()));
ui->startdetect->setEnabled(false);
ui->stopdetect->setEnabled(false);
Init();
}
MainWindow::~MainWindow()
{
capture->release();
delete capture;
delete [] yolo_nets;
delete yolov5;
delete ui;
}
void MainWindow::Init()
{
capture = new cv::VideoCapture();
yolo_nets = new NetConfig[4]{
{0.5, 0.5, 0.5, "yolov5s"},
{0.6, 0.6, 0.6, "yolov5m"},
{0.65, 0.65, 0.65, "yolov5l"},
{0.75, 0.75, 0.75, "yolov5x"}
};
conf = yolo_nets[0];
yolov5 = new YOLOV5();
yolov5->Initialization(conf);
ui->textEditlog->append(QStringLiteral("默认模型类别：yolov5s args: %1 %2 %3")
.arg(conf.nmsThreshold)
.arg(conf.objThreshold)
.arg(conf.confThreshold));
}
void MainWindow::readFrame()
{
cv::Mat frame;
capture->read(frame);
if (frame.empty()) return;
auto start = std::chrono::steady_clock::now();
yolov5->detect(frame);
auto end = std::chrono::steady_clock::now();
std::chrono::duration<double, std::milli> elapsed = end - start;
ui->textEditlog->append(QString("cost_time: %1 ms").arg(elapsed.count()));
// double t0 = static_cast<double>(cv::getTickCount());
// yolov5->detect(frame);
// double t1 = static_cast<double>(cv::getTickCount());
// ui->textEditlog->append(QStringLiteral("cost_time: %1 ").arg((t1 - t0) / cv::getTickFrequency()));
cv::cvtColor(frame, frame, cv::COLOR_BGR2RGB);
QImage rawImage = QImage((uchar*)(frame.data),frame.cols,frame.rows,frame.step,QImage::Format_RGB888);
ui->label->setPixmap(QPixmap::fromImage(rawImage));
}
void MainWindow::on_openfile_clicked()
{
QString filename = QFileDialog::getOpenFileName(this,QStringLiteral("打开文件"),".","*.mp4 *.avi;;*.png *.jpg *.jpeg *.bmp");
if(!QFile::exists(filename)){
return;
}
ui->statusbar->showMessage(filename);
QMimeDatabase db;
QMimeType mime = db.mimeTypeForFile(filename);
if (mime.name().startsWith("image/")) {
cv::Mat src = cv::imread(filename.toLatin1().data());
if(src.empty()){
ui->statusbar->showMessage("图像不存在！");
return;
}
cv::Mat temp;
if(src.channels()==4)
cv::cvtColor(src,temp,cv::COLOR_BGRA2RGB);
else if (src.channels()==3)
cv::cvtColor(src,temp,cv::COLOR_BGR2RGB);
else
cv::cvtColor(src,temp,cv::COLOR_GRAY2RGB);
auto start = std::chrono::steady_clock::now();
yolov5->detect(temp);
auto end = std::chrono::steady_clock::now();
std::chrono::duration<double, std::milli> elapsed = end - start;
ui->textEditlog->append(QString("cost_time: %1 ms").arg(elapsed.count()));
QImage img = QImage((uchar*)(temp.data),temp.cols,temp.rows,temp.step,QImage::Format_RGB888);
ui->label->setPixmap(QPixmap::fromImage(img));
ui->label->resize(ui->label->pixmap()->size());
filename.clear();
}else if (mime.name().startsWith("video/")) {
capture->open(filename.toLatin1().data());
if (!capture->isOpened()){
ui->textEditlog->append("fail to open MP4!");
return;
}
IsDetect_ok +=1;
if (IsDetect_ok ==2)
ui->startdetect->setEnabled(true);
ui->textEditlog->append(QString::fromUtf8("Open video: %1 succesfully!").arg(filename));
//获取整个帧数QStringLiteral
long totalFrame = capture->get(cv::CAP_PROP_FRAME_COUNT);
ui->textEditlog->append(QStringLiteral("整个视频共 %1 帧").arg(totalFrame));
ui->label->resize(QSize(capture->get(cv::CAP_PROP_FRAME_WIDTH), capture->get(cv::CAP_PROP_FRAME_HEIGHT)));
//设置开始帧()
long frameToStart = 0;
capture->set(cv::CAP_PROP_POS_FRAMES, frameToStart);
ui->textEditlog->append(QStringLiteral("从第 %1 帧开始读").arg(frameToStart));
//获取帧率
double rate = capture->get(cv::CAP_PROP_FPS);
ui->textEditlog->append(QStringLiteral("帧率为: %1 ").arg(rate));
}
}
void MainWindow::on_loadfile_clicked()
{
QString onnxFile = QFileDialog::getOpenFileName(this,QStringLiteral("选择模型"),".","*.onnx");
if(!QFile::exists(onnxFile)){
return;
}
ui->statusbar->showMessage(onnxFile);
if (!yolov5->loadModel(onnxFile.toLatin1().data())){
ui->textEditlog->append(QStringLiteral("加载模型失败！"));
return;
}
IsDetect_ok +=1;
ui->textEditlog->append(QString::fromUtf8("Open onnxFile: %1 succesfully!").arg(onnxFile));
if (IsDetect_ok ==2)
ui->startdetect->setEnabled(true);
}
void MainWindow::on_startdetect_clicked()
{
timer->start();
ui->startdetect->setEnabled(false);
ui->stopdetect->setEnabled(true);
ui->openfile->setEnabled(false);
ui->loadfile->setEnabled(false);
ui->comboBox->setEnabled(false);
ui->textEditlog->append(QStringLiteral("================\n"
" 开始检测\n"
"================\n"));
}
void MainWindow::on_stopdetect_clicked()
{
ui->startdetect->setEnabled(true);
ui->stopdetect->setEnabled(false);
ui->openfile->setEnabled(true);
ui->loadfile->setEnabled(true);
ui->comboBox->setEnabled(true);
timer->stop();
ui->textEditlog->append(QStringLiteral("================\n"
" 停止检测\n"
"================\n"));
}
void MainWindow::on_comboBox_activated(const QString &arg1)
{
if (arg1.contains("s")){
conf = yolo_nets[0];
}else if (arg1.contains("m")) {
conf = yolo_nets[1];
}else if (arg1.contains("l")) {
conf = yolo_nets[2];
}else if (arg1.contains("x")) {
conf = yolo_nets[3];}
yolov5->Initialization(conf);
ui->textEditlog->append(QStringLiteral("使用模型类别：%1 args: %2 %3 %4")
.arg(arg1)
.arg(conf.nmsThreshold)
.arg(conf.objThreshold)
.arg(conf.confThreshold));
}
yolov5类
yolov5.h
#ifndef YOLOV5_H
#define YOLOV5_H
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <fstream>
#include <sstream>
#include <iostream>
#include <exception>
#include <QMessageBox>
struct NetConfig
{
float confThreshold; // class Confidence threshold
float nmsThreshold; // Non-maximum suppression threshold
float objThreshold; //Object Confidence threshold
std::string netname;
};
class YOLOV5
{
public:
YOLOV5(){}
void Initialization(NetConfig conf);
bool loadModel(const char* onnxfile);
void detect(cv::Mat& frame);
private:
const float anchors[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 stride[3] = { 8.0, 16.0, 32.0 };
std::string classes[80] = {"person", "bicycle", "car", "motorbike", "aeroplane", "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", "sofa", "pottedplant",
"bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster",
"sink", "refrigerator", "book", "clock", "vase", "scissors",
"teddy bear", "hair drier", "toothbrush"};
const int inpWidth = 640;
const int inpHeight = 640;
float confThreshold;
float nmsThreshold;
float objThreshold;
cv::Mat blob;
std::vector<cv::Mat> outs;
std::vector<int> classIds;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
cv::dnn::Net net;
void drawPred(int classId, float conf, int left, int top, int right, int bottom, cv::Mat& frame);
void sigmoid(cv::Mat* out, int length);
};
static inline float sigmoid_x(float x)
{
return static_cast<float>(1.f / (1.f + exp(-x)));
}
#endif // YOLOV5_H
yolov5.cpp
#include "yolov5.h"
using namespace std;
using namespace cv;
void YOLOV5::Initialization(NetConfig conf)
{
this->confThreshold = conf.confThreshold;
this->nmsThreshold = conf.nmsThreshold;
this->objThreshold = conf.objThreshold;
classIds.reserve(20);
confidences.reserve(20);
boxes.reserve(20);
outs.reserve(3);
}
bool YOLOV5::loadModel(const char *onnxfile)
{
try {
this->net = cv::dnn::readNetFromONNX(onnxfile);
return true;
} catch (exception& e) {
QMessageBox::critical(NULL,"Error",QStringLiteral("模型加载出错，请检查重试！\n %1").arg(e.what()),QMessageBox::Yes,QMessageBox::Yes);
return false;
}
this->net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
this->net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
// this->net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
// this->net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
// try {
// this->net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
// this->net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
// } catch (exception& e2) {
// this->net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
// this->net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
// QMessageBox::warning(NULL,"warning",QStringLiteral("正在使用CPU推理！\n %1").arg(e2.what()),QMessageBox::Yes,QMessageBox::Yes);
// return false;
// }
}
void YOLOV5::detect(cv::Mat &frame)
{
cv::dnn::blobFromImage(frame, blob, 1 / 255.0, Size(this->inpWidth, this->inpHeight), Scalar(0, 0, 0), true, false);
this->net.setInput(blob);
this->net.forward(outs, this->net.getUnconnectedOutLayersNames());
/generate proposals
classIds.clear();
confidences.clear();
boxes.clear();
float ratioh = (float)frame.rows / this->inpHeight, ratiow = (float)frame.cols / this->inpWidth;
int n = 0, q = 0, i = 0, j = 0, nout = 8 + 5, c = 0;
for (n = 0; n < 3; n++) ///尺度
{
int num_grid_x = (int)(this->inpWidth / this->stride[n]);
int num_grid_y = (int)(this->inpHeight / this->stride[n]);
int area = num_grid_x * num_grid_y;
this->sigmoid(&outs[n], 3 * nout * area);
for (q = 0; q < 3; q++) ///anchor数
{
const float anchor_w = this->anchors[n][q * 2];
const float anchor_h = this->anchors[n][q * 2 + 1];
float* pdata = (float*)outs[n].data + q * nout * area;
for (i = 0; i < num_grid_y; i++)
{
for (j = 0; j < num_grid_x; j++)
{
float box_score = pdata[4 * area + i * num_grid_x + j];
if (box_score > this->objThreshold)
{
float max_class_socre = 0, class_socre = 0;
int max_class_id = 0;
for (c = 0; c < 80; c++) get max socre
{
class_socre = pdata[(c + 5) * area + i * num_grid_x + j];
if (class_socre > max_class_socre)
{
max_class_socre = class_socre;
max_class_id = c;
}
}
if (max_class_socre > this->confThreshold)
{
float cx = (pdata[i * num_grid_x + j] * 2.f - 0.5f + j) * this->stride[n]; ///cx
float cy = (pdata[area + i * num_grid_x + j] * 2.f - 0.5f + i) * this->stride[n]; ///cy
float w = powf(pdata[2 * area + i * num_grid_x + j] * 2.f, 2.f) * anchor_w; ///w
float h = powf(pdata[3 * area + i * num_grid_x + j] * 2.f, 2.f) * anchor_h; ///h
int left = (cx - 0.5*w)*ratiow;
int top = (cy - 0.5*h)*ratioh; ///坐标还原到原图上
classIds.push_back(max_class_id);
confidences.push_back(max_class_socre);
boxes.push_back(Rect(left, top, (int)(w*ratiow), (int)(h*ratioh)));
}
}
}
}
}
}
// Perform non maximum suppression to eliminate redundant overlapping boxes with
// lower confidences
vector<int> indices;
cv::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(classIds[idx], confidences[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
}
}
void YOLOV5::drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat &frame)
{
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 0, 255), 3);
string label = format("%.2f", conf);
label = this->classes[classId] + ":" + label;
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 255, 0), 1);
}
void YOLOV5::sigmoid(Mat *out, int length)
{
float* pdata = (float*)(out->data);
int i = 0;
for (i = 0; i < length; i++)
{
pdata[i] = 1.0 / (1 + expf(-pdata[i]));
}
}
三、效果演示
到此这篇关于Qt结合OpenCV部署yolov5的实现的文章就介绍到这了,更多相关Qt OpenCV部署yolov5内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！
相关文章
这篇文章主要介绍了C++实现单链表删除倒数第k个节点的方法,结合实例形式分析了C++单链表的定义、遍历及删除相关操作技巧,需要的朋友可以参考下2017-05-05
VC编程控件类HTControl之CHTGDIManager GDI资源管理类用法解析
这篇文章主要介绍了VC编程控件类HTControl之CHTGDIManager GDI资源管理类用法解析,需要的朋友可以参考下2014-08-08
这篇文章主要介绍了C++实现LeetCode(64.最小路径和),本篇文章通过简要的案例,讲解了该项技术的了解与使用,以下就是详细内容,需要的朋友可以参考下2021-07-07
为了减少手动管理内存带来的困扰，c++提出了智能指针，可以帮助我们进行内存管理，下面小编就来和大家简单聊聊C++中智能指针的基本用法吧2023-07-07
这篇文章主要介绍了C/C++仿华容道小游戏的相关资料，模仿实现华容道游戏，感兴趣的朋友可以参考一下2016-02-02
这篇文章主要介绍了C++ 格式化日志输出实现代码,需要的朋友可以参考下2019-04-04
这篇文章主要介绍了C++中getline()的用法详解，文中通过示例代码介绍的非常详细，对大家的学习或者工作具有一定的参考学习价值，需要的朋友们下面随着小编来一起学习学习吧2020-02-02
C语言库函数是把自定义函数放到库里，是别人把一些常用到的函数编完放到一个文件里，供程序员使用，下面让我们一起来详细了解它2022-03-03
这篇文章主要为大家详细介绍了Opencv使用鼠标任意形状的抠图，具有一定的参考价值，感兴趣的小伙伴们可以参考一下2019-05-05
所谓的callable object，表示可以被某种方式调用其某些函数的对象。它可以是：一个函数、一个指向成员函数的指针、一个函数对象，该对象拥有operator()、一个lambda表达式，严格的说它是一种函数对象2022-08-08
最新评论