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c++ visual studio 图像处理 Qt+多线程实现单幅图像高斯、灰度、边缘处理，调用opencv库导入图片

阿依古丽博客 2024-01-16 1 0

【QT】多线程+OpenCV的demo

结合最近学的知识自己做了个小demo。可以实现对输入的图片进行图像处理，高斯模糊、灰度处理以及边缘检测，三种处理同时进行。

思路：借助OpenCv库，用Mat读取图片并输出展示在界面上，多线程进行图像处理。这里采用线程池的方法。

ui界面如下

Mythread.h

#pragma once

#include "qobject.h"

#include

using namespace std;

using namespace cv;

//高斯模糊

class Gaussuianintroduce :public QObject, public QRunnable

{

Q_OBJECT

public:

Gaussuianintroduce();

~Gaussuianintroduce();

void run();

void recvmat(Mat mat);

signals:

void finish(Mat mat);

private:

Mat m_mat;

Mat mat_Gussian;

};

//灰度处理

class Grayintroduce :public QObject, public QRunnable

{

Q_OBJECT

public:

Grayintroduce();

~Grayintroduce();

void run();

void recvmat(Mat mat);

signals:

void finish(Mat mat);

private:

Mat m_mat2;

Mat mat_Gray;

};

//边缘检测

class Cannyintroduce :public QObject, public QRunnable

{

Q_OBJECT

public:

Cannyintroduce();

~Cannyintroduce();

void run();

void recvmat(Mat mat);

signals:

void finish(Mat mat);

private:

Mat m_mat3;

Mat mat_Canny;

};

Mythread.cpp

#include "Mythread.h"

#include

#pragma execution_character_set("utf-8")

#include

using namespace cv;

using namespace std;

Gaussuianintroduce::Gaussuianintroduce()

{

setAutoDelete(true);

}

Gaussuianintroduce::~Gaussuianintroduce()

{

}

//高斯模糊

void Gaussuianintroduce::run()

{

qDebug() << "高斯模糊处理的线程地址：" << QThread::currentThread();

QElapsedTimer time;

time.start();

GaussianBlur(m_mat, mat_Gussian, Size(29, 29), 0, 0);

int milsec = time.elapsed();

qDebug() << "高斯模糊处理用时" << milsec << "毫秒";

//发送完成的信号将信号中的图片传给主线程

emit finish(mat_Gussian);

}

void Gaussuianintroduce::recvmat(Mat mat)

{

m_mat = mat;

}

Grayintroduce::Grayintroduce()

{

setAutoDelete(true);

}

Grayintroduce::~Grayintroduce()

{

}

//灰度处理

void Grayintroduce::run()

{

qDebug() << "灰度处理的线程地址：" << QThread::currentThread();

QElapsedTimer time;

time.start();

cvtColor(m_mat2, mat_Gray, COLOR_BGR2GRAY);

int milsec = time.elapsed();

qDebug() << "灰度处理用时" << milsec << "毫秒";

//发送完成的信号将信号中的图片传给主线程

emit finish(mat_Gray);

}

void Grayintroduce::recvmat(Mat mat)

{

m_mat2 = mat;

}

Cannyintroduce::Cannyintroduce()

{

setAutoDelete(true);

}

Cannyintroduce::~Cannyintroduce()

{

}

//灰度处理

void Cannyintroduce::run()

{

qDebug() << "边缘检测的线程地址：" << QThread::currentThread();//返回一个指向管理当前执行线程的QThread的指针

//QElapsedTimer主要用来测量一个操作耗时多久

QElapsedTimer time;//定义一个对象

time.start();//开始计时

//相关操作

Canny(m_mat3, mat_Canny, 150, 100, 3);

//输出结果

int milsec = time.elapsed();

qDebug() << "灰度处理用时" << milsec << "毫秒";

//发送完成的信号将信号中的图片传给主线程

emit finish(mat_Canny);

}

void Cannyintroduce::recvmat(Mat mat)

{

m_mat3 = mat;

}

demo_qt_cv_thread.h

#pragma once

#include

#include "ui_demo_qt_cv_thread.h"

#include

using namespace std;

using namespace cv;

class demo_qt_cv_thread : public QMainWindow

{

Q_OBJECT

public:

demo_qt_cv_thread(QWidget *parent = Q_NULLPTR);

QImage MatToImage(Mat &mat);

signals:

void starting(Mat mat);

private:

Ui::demo_qt_cv_threadClass *ui;

Mat image;

};

demo_qt_cv_thread.cpp

#include "demo_qt_cv_thread.h"

#include

#pragma execution_character_set("utf-8")

#include

#include "Mythread.h"

#include

using namespace std;

using namespace cv;

demo_qt_cv_thread::demo_qt_cv_thread(QWidget *parent)

: QMainWindow(parent)

{

ui->setupUi(this);

this->setWindowTitle("多线程图像处理");

this->setWindowIcon(QPixmap(":/res/b2.jpg"));

connect(ui->pushButton_2, &QPushButton::clicked, [=]() {

ui->label->clear();

ui->label_2->clear();

ui->label_3->clear();

ui->label_4->clear();

//调用窗口打开图片文件

//文件对话框 QFileDialog

QString filename = QFileDialog::getOpenFileName(this,

tr("open image"), ".", tr("Image file(*.png *.jpg *.bmp)")

);

//第二个参数是弹窗的标题

//第三个参数是最初显示的目录

//第三个是筛选条件即过滤器 Image Files(*.png *.jpg *.bmp)中给出的模式匹配的文件

//将QString 转换为String 这样opencv才可读取图片

image = imread(filename.toLocal8Bit().data());

//image = imread("E:/Code/opencvcode/demo_qt_cv_thread/res/b2.jpg");

qDebug() << image.data;

if (image.empty())

{

QMessageBox::information(this, tr("提示"), tr("未成功载入图片"), QMessageBox::Ok);

}

//绘图

QPixmap pix;

pix = QPixmap::fromImage(MatToImage(image).scaled(ui->label->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation));

ui->label->setPixmap(pix);

});

//创建任务类对象

Gaussuianintroduce *th1 = new Gaussuianintroduce;

Grayintroduce *th2 = new Grayintroduce;

Cannyintroduce *th3 = new Cannyintroduce;

//主线程和子线程数据交互

connect(this, &demo_qt_cv_thread::starting, th1, &Gaussuianintroduce::recvmat);

connect(this, &demo_qt_cv_thread::starting, th2, &Grayintroduce::recvmat);

connect(this, &demo_qt_cv_thread::starting, th3, &Cannyintroduce::recvmat);

connect(ui->pushButton, &QPushButton::clicked, [=]() {

ui->label_2->clear();

ui->label_3->clear();

ui->label_4->clear();

//放入线程池启动子线程

QThreadPool::globalInstance()->start(th1);

QThreadPool::globalInstance()->start(th2);

QThreadPool::globalInstance()->start(th3);

emit starting(image);

});

//利用label绘图显示

connect(th1, &Gaussuianintroduce::finish, [=](Mat mat_Gussian) {

QPixmap pix2;

pix2 = QPixmap::fromImage(MatToImage(mat_Gussian).scaled(ui->label_2->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation));

ui->label_2->setPixmap(pix2);

});

connect(th2, &Grayintroduce::finish, [=](Mat mat_Gray) {

QPixmap pix3;

pix3 = QPixmap::fromImage(MatToImage(mat_Gray).scaled(ui->label_3->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation));

ui->label_3->setPixmap(pix3);

});

connect(th3, &Cannyintroduce::finish, [=](Mat mat_Canny) {

QPixmap pix4;

pix4 = QPixmap::fromImage(MatToImage(mat_Canny).scaled(ui->label_4->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation));

ui->label_4->setPixmap(pix4);

});

destroyAllWindows();

}

//图片转化

QImage demo_qt_cv_thread::MatToImage(Mat &mat)

{

//输入图像为三通道

if (mat.type() == CV_8UC3)

{

//复制输入的mat数据

const uchar* pSrc = (const uchar*)mat.data;

//创建与输入Mat尺寸相同的QImage

QImage image(pSrc, mat.cols, mat.rows,(mat.cols)*3, QImage::Format_RGB888);

//将RGB转换为BGR

return image.rgbSwapped();

}

//输入图像为单通道

else if (mat.type() == CV_8UC1)

{

QImage image(mat.cols, mat.rows, QImage::Format_Indexed8);

//设置颜色表（用于将颜色索引转换为qRgb值）

image.setColorCount(256);//灰度级数256

for (int i = 0; i < 256; i++)

{

image.setColor(i, qRgb(i, i, i));

}

//复制输入

uchar *pSrc = mat.data;

for (int row = 0; row < mat.rows; row++)

{

uchar *pDest = image.scanLine(row);//对图像的一行进行扫描，获取本行中个像素的内存地址

//复制src的内存的前num个字节内容给dest

memcpy(pDest, pSrc, mat.cols);

pSrc += mat.step;

}

return image;

}

//输入图像为四通道

else if (mat.type() == CV_8UC4)

{

qDebug() << "CV_8UC4";

// Copy input Mat

const uchar *pSrc = (const uchar*)mat.data;

// Create QImage with same dimensions as input Mat

QImage image(pSrc, mat.cols, mat.rows, mat.step, QImage::Format_ARGB32);

return image.copy();

}

else

{

qDebug() << "ERROR：Mat could not be converted to QImage";

return QImage();

}

运行效果如下：点击输入图片按钮，弹出一个文件夹，可选取自己想处理的图片这里我选择了一张狗头的表清包点击开始处理按钮，成功实现同时输出三种处理图像

通过调试信息可以看到

夸智网

c++ visual studio 图像处理 Qt+多线程实现单幅图像高斯、灰度、边缘处理，调用opencv库导入图片

智能机器人玩具多少元

人工智能 63 | 图像处理

发表评论取消回复

夸智网

c++ visual studio 图像处理 Qt+多线程实现单幅图像高斯、灰度、边缘处理，调用opencv库导入图片

智能机器人玩具多少元

人工智能 63 | 图像处理

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