Linux下QT配合OpenCV完成图像处理(实现基本的人脸检测)

一、环境介绍

ubuntu版本: VM虚拟机运行ubuntu18.04 64位

OpenCV版本: 3.4.9

QT版本: 5.12

OpenCV 是一个基于 BSD 许可（开源）发行的跨平台计算机视觉和机器学习软件库，可以运行在 Linux、Windows、Android 和 Mac OS 操作系统上。

OpenCV 的全称是 Open Source Computer Vision Library，是一个跨平台的计算机视觉库。OpenCV 是由英特尔公司发起并参与开发，以 BSD 许可证授权发行，可以在商业和研究领域中免费使用。OpenCV 可用于开发实时的图像处理、计算机视觉以及模式识别程序。

OpenCV 用 C++语言编写，它具有 C ++，Python，Java 和 MATLAB 接口，并支持 Windows，Linux，Android 和 Mac OS，OpenCV 主要倾向于实时视觉应用，并在可用时利用 MMX 和 SSE 指令， 如今也提供对于 C#、Ch、Ruby，GO 的支持。

二、建立QT工程加入OpenCV依赖库

下面编写例子很简单，使用OpenCV自带的分类器，检测一张图中的人脸，并圈出来。

opencv源码自带的人脸检测分类器目录：opencv-3.4.9/data/haarcascades_cuda/haarcascade_frontalface_alt2.xml

xxx.pro工程文件代码：

QT       += core gui

greaterThan(QT_MAJOR_VERSION, 4): QT += widgets

CONFIG += c++11

# The following define makes your compiler emit warnings if you use
# any Qt feature that has been marked deprecated (the exact warnings
# depend on your compiler). Please consult the documentation of the
# deprecated API in order to know how to port your code away from it.
DEFINES += QT_DEPRECATED_WARNINGS

# You can also make your code fail to compile if it uses deprecated APIs.
# In order to do so, uncomment the following line.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0

SOURCES += \
    main.cpp \
    widget.cpp

HEADERS += \
    widget.h

FORMS += \
    widget.ui

# Default rules for deployment.
qnx: target.path = /tmp/$${TARGET}/bin
else: unix:!android: target.path = /opt/$${TARGET}/bin
!isEmpty(target.path): INSTALLS += target

#linu平台的路径设置
linux {
#添加opencv头文件的路径，需要根据自己的头文件路径进行修改
INCLUDEPATH+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/include\
             /home/wbyq/work_pc/opencv-3.4.9/_install/install/include/opencv\
             /home/wbyq/work_pc/opencv-3.4.9/_install/install/include/opencv2

LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_calib3d.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_core.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_dnn.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_features2d.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_flann.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_highgui.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_imgcodecs.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_imgproc.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_ml.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_objdetect.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_photo.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_shape.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_stitching.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_superres.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_videoio.so
LIBS+=/home/wbyq/work_pc/opencv-3.4.9/_install/install/lib/libopencv_video.so
}

widget.cpp文件代码：

#include "widget.h"
#include "ui_widget.h"

Widget::Widget(QWidget *parent)
    : QWidget(parent)
    , ui(new Ui::Widget)
{
    ui->setupUi(this);
    opencv_face();
}

Widget::~Widget()
{
    delete ui;
}

//分类器的路径
#define source_xml_addr "/home/wbyq/work_pc/opencv-3.4.9/data/haarcascades_cuda/haarcascade_frontalface_alt2.xml"

//将要检测的图片路径
#define source_pix_addr "/mnt/hgfs/linux-share-dir/1.jpg"

//人脸检测代码
void Widget::opencv_face()
{
    static CvMemStorage* storage = 0;
    static CvHaarClassifierCascade* cascade = 0;

    fprintf( stderr, "start------------------------------>1 \n" );

    const char*cascade_name =source_xml_addr;

    //加载分类器
    cascade = (CvHaarClassifierCascade*)cvLoad( cascade_name, 0, 0, 0 );
    if( !cascade )
    {
        fprintf( stderr, "ERROR: Could not load classifier cascade\n" );
        return ;
    }

    //创建内存空间
    storage = cvCreateMemStorage(0);

    //加载需要检测的图片
    const char* filename =source_pix_addr;
    IplImage* img = cvLoadImage( filename, 1 );

    if(img ==nullptr )
    {
        fprintf( stderr, "jpg load error! \n" );
        return;
    }

     fprintf( stderr, "start------------------------------>2 \n" );

    double scale=1.2;
    static CvScalar colors[] = {
        {{0,0,255}},{{0,128,255}},{{0,255,255}},{{0,255,0}},
        {{255,128,0}},{{255,255,0}},{{255,0,0}},{{255,0,255}}
    };//Just some pretty colors to draw with


    IplImage* gray = cvCreateImage(cvSize(img->width,img->height),8,1);
    IplImage* small_img=cvCreateImage(cvSize(cvRound(img->width/scale),cvRound(img->height/scale)),8,1);
    cvCvtColor(img,gray, CV_BGR2GRAY);
    cvResize(gray, small_img, CV_INTER_LINEAR);

    cvEqualizeHist(small_img,small_img); //直方图均衡

    cvClearMemStorage(storage);

    double t = (double)cvGetTickCount();
    CvSeq* objects = cvHaarDetectObjects(small_img,
                                           cascade,
                                           storage,
                                           1.1,
                                           2,
                                           0/*CV_HAAR_DO_CANNY_PRUNING*/,
                                           cvSize(30,30));

       t = (double)cvGetTickCount() - t;

     fprintf( stderr, "start------------------------------>3 \n" );

    //遍历找到对象和周围画盒
    for(int i=0;i<(objects->total);++i)
    {
        CvRect* r=(CvRect*)cvGetSeqElem(objects,i);
        cvRectangle(img, cvPoint(r->x*scale,r->y*scale), cvPoint((r->x+r->width)*scale,(r->y+r->height)*scale), colors[i%8]);
    }

     fprintf( stderr, "start------------------------------>4 \n" );

    for( int i = 0; i < (objects? objects->total : 0); i++ )
    {
        CvRect* r = (CvRect*)cvGetSeqElem( objects, i );
        CvPoint center;
        int radius;
        center.x = cvRound((r->x + r->width*0.5)*scale);
        center.y = cvRound((r->y + r->height*0.5)*scale);
        radius = cvRound((r->width + r->height)*0.25*scale);
        cvCircle( img, center, radius, colors[i%8], 3, 8, 0 );
    }

    show_face(img);  //显示检测的结果

    cvReleaseImage(&gray);
    cvReleaseImage(&small_img);

    //释放图片
    cvReleaseImage( &img );
}
//显示检测的结果
void Widget::show_face(IplImage* img)
{
    /*将opecv的图片转为qimage格式*/
    uchar *imgData=(uchar *)img->imageData;

    QImage  my_image(imgData,img->width,img->height,QImage::Format_RGB888);
    my_image =my_image.rgbSwapped(); //BGR格式转RGB
    QPixmap my_pix; //创建画图类

    my_pix.convertFromImage(my_image);

    /*在控件上显示*/
    ui->label_display_face->setPixmap(my_pix);
}

widget.h文件代码：

#ifndef WIDGET_H
#define WIDGET_H

#include <QWidget>
//opencv include
#include <cv.h>
#include <cxcore.h>
#include <highgui.h>

QT_BEGIN_NAMESPACE
namespace Ui { class Widget; }
QT_END_NAMESPACE

class Widget : public QWidget
{
    Q_OBJECT

public:
    Widget(QWidget *parent = nullptr);
    void opencv_face();
    ~Widget();
    void show_face(IplImage* img);
private:
    Ui::Widget *ui;
};
#endif // WIDGET_H

运行代码检测结果如下: