Qt結(jié)合OpenCV部署yolov5的實現(xiàn)
更新時間：2022年04月07日 15:43:20 作者：SongpingWang
本文主要介紹了Qt結(jié)合OpenCV部署yolov5的實現(xiàn)，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧
分別使用了openvino，opencv_cuda進行加速。
關(guān)于演示視頻及代碼講解請查看：
https://www.bilibili.com/video/BV13S4y1c7ea/
https://www.bilibili.com/video/BV1Dq4y1x7r6/
https://www.bilibili.com/video/BV1kT4y1S7hz/
一、新建項目 UI設計
二、代碼部分 mainwindow 類
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(); //自定義信號處理函數(shù)
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目標檢測軟件"));
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("默認模型類別：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("打開文件"),".","*.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("圖像不存在！");
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));
//獲取整個幀數(shù)QStringLiteral
long totalFrame = capture->get(cv::CAP_PROP_FRAME_COUNT);
ui->textEditlog->append(QStringLiteral("整個視頻共 %1 幀").arg(totalFrame));
ui->label->resize(QSize(capture->get(cv::CAP_PROP_FRAME_WIDTH), capture->get(cv::CAP_PROP_FRAME_HEIGHT)));
//設置開始幀()
long frameToStart = 0;
capture->set(cv::CAP_PROP_POS_FRAMES, frameToStart);
ui->textEditlog->append(QStringLiteral("從第 %1 幀開始讀").arg(frameToStart));
//獲取幀率
double rate = capture->get(cv::CAP_PROP_FPS);
ui->textEditlog->append(QStringLiteral("幀率為: %1 ").arg(rate));
}
}
void MainWindow::on_loadfile_clicked()
{
QString onnxFile = QFileDialog::getOpenFileName(this,QStringLiteral("選擇模型"),".","*.onnx");
if(!QFile::exists(onnxFile)){
return;
}
ui->statusbar->showMessage(onnxFile);
if (!yolov5->loadModel(onnxFile.toLatin1().data())){
ui->textEditlog->append(QStringLiteral("加載模型失??！"));
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"
" 開始檢測\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"
" 停止檢測\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("使用模型類別：%1 args: %2 %3 %4")
.arg(arg1)
.arg(conf.nmsThreshold)
.arg(conf.objThreshold)
.arg(conf.confThreshold));
}
yolov5類
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("模型加載出錯，請檢查重試！\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數(shù)
{
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; ///坐標還原到原圖上
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
到此這篇關(guān)于Qt結(jié)合OpenCV部署yolov5的實現(xiàn)的文章就介紹到這了,更多相關(guān)Qt OpenCV部署yolov5內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
相關(guān)文章
C++實現(xiàn)單鏈表刪除倒數(shù)第k個節(jié)點的方法
這篇文章主要介紹了C++實現(xiàn)單鏈表刪除倒數(shù)第k個節(jié)點的方法,結(jié)合實例形式分析了C++單鏈表的定義、遍歷及刪除相關(guān)操作技巧,需要的朋友可以參考下2017-05-05
VC編程控件類HTControl之CHTGDIManager GDI資源管理類用法解析
這篇文章主要介紹了VC編程控件類HTControl之CHTGDIManager GDI資源管理類用法解析,需要的朋友可以參考下2014-08-08
這篇文章主要介紹了C++實現(xiàn)LeetCode(64.最小路徑和),本篇文章通過簡要的案例,講解了該項技術(shù)的了解與使用,以下就是詳細內(nèi)容,需要的朋友可以參考下2021-07-07
為了減少手動管理內(nèi)存帶來的困擾，c++提出了智能指針，可以幫助我們進行內(nèi)存管理，下面小編就來和大家簡單聊聊C++中智能指針的基本用法吧2023-07-07
這篇文章主要介紹了C/C++仿華容道小游戲的相關(guān)資料，模仿實現(xiàn)華容道游戲，感興趣的朋友可以參考一下2016-02-02
這篇文章主要介紹了C++ 格式化日志輸出實現(xiàn)代碼,需要的朋友可以參考下2019-04-04
這篇文章主要介紹了C++中g(shù)etline()的用法詳解，文中通過示例代碼介紹的非常詳細，對大家的學習或者工作具有一定的參考學習價值，需要的朋友們下面隨著小編來一起學習學習吧2020-02-02
C語言庫函數(shù)是把自定義函數(shù)放到庫里，是別人把一些常用到的函數(shù)編完放到一個文件里，供程序員使用，下面讓我們一起來詳細了解它2022-03-03
這篇文章主要為大家詳細介紹了Opencv使用鼠標任意形狀的摳圖，具有一定的參考價值，感興趣的小伙伴們可以參考一下2019-05-05
C++可調(diào)用對象callable object深入分析
所謂的callable object，表示可以被某種方式調(diào)用其某些函數(shù)的對象。它可以是：一個函數(shù)、一個指向成員函數(shù)的指針、一個函數(shù)對象，該對象擁有operator()、一個lambda表達式，嚴格的說它是一種函數(shù)對象2022-08-08