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关于qt:视觉实战案例QtOpenCV实现USB摄像头监测移动物体并录制视频功能帧差法

jiezi

1、背景介绍

最近手边的零食总是莫名其妙的缩小，为了抓到一个首恶来帮我续零食，就想着应用手边的 usb 摄像头来实现一个动静物体监测和保留视频的性能，不过这里应用最简略的帧差法来实现物体的静止监测。

2、应用 OpenCV 的帧差法实现静止物体监测

  • 开发环境
    Qt5.9 + OpenCV

  • 硬件
    Logitech 摄像头

    2.1 帧差法介绍

    静止物体图像在相邻两帧间差异较大，两帧差值后进行简略的图像处理，较容易判断是否存在物体挪动，相似于剪纸动画，本例中应用帧差后判断阈值宰割后的面积来确定是否存在物体静止。帧差法用前一帧图像作为以后帧的背景模型具备较好的实时性，其背景不积攒，且更新速度快、算法简略、计算量小。算法的有余在于对环境噪声较为敏感，阈值的抉择相当要害，抉择过低不足以克制图像中的噪声，过高则疏忽了图像中有用的变动。对于比拟大的、色彩统一的静止指标，有可能在指标外部产生空洞，无奈残缺地提取静止指标。

    2.2 帧差法局部实现代码

    将以后帧图像和上一帧图像进行灰度化，而后高斯滤波后做图像差值，选定适合的二值化阈值宰割，最初对宰割解决的区域面积进行断定。

          Mat grayframePre,frameDet;
      Mat frameNow,grayframeNow;
      cvtColor(matFrame,grayframeNow,COLOR_RGB2GRAY);
      cvtColor(framePre,grayframePre,COLOR_RGB2GRAY);
      GaussianBlur(grayframeNow,grayframeNow,Size(21,21),0,0);
      GaussianBlur(grayframePre,grayframePre,Size(21,21),0,0);
      absdiff(grayframeNow,grayframePre,frameDet);
      framePre = matFrame;
      threshold(frameDet,frameDet,20,255,THRESH_BINARY);
      Mat element = getStructuringElement(0,Size(3,3));
      vector<vector<Point>> contours;
      dilate(frameDet,frameDet,element);
      findContours(frameDet,contours,RETR_TREE,CHAIN_APPROX_SIMPLE,Point());
      qDebug()<<"Num"<<contours.size();
      QString SavePath = "D:/ImgPath/" + QString::number(VideoNum) + "_track.avi";
      if(contours.size()==0)
      {if(writer.isOpened())
          {writer.release();
          }
          if(isSaveFrame)
          {
              isSaveFrame = false;
              VideoNum++;
          }
      }
      else
      {for(int i=0;i<contours.size();i++)
          {double area = contourArea(contours[i]);
              if(area < 100)continue;
              else
              {qDebug()<<"有物体静止！";
                  if(!isSaveFrame)
                  {int fourcc = writer.fourcc('M', 'J', 'P', 'G');
                      writer.open(SavePath.toStdString(),fourcc,10,Size(frameWidth,frameHeight),true);
                      isSaveFrame = true;
                  }
                  else
                  {writer.write(matFrame);
                  }
                  break;
              }
          }
      }
  }
  else
  {framePre = matFrame;}

    3、在 Qt 平台下应用 opencv 对静止物体进行监测

    widget.h

    #ifndef WIDGET_H
#define WIDGET_H

#include <QWidget>
#include "opencv2/opencv.hpp"
#include <QTimer>

using namespace cv;

namespace Ui {class Widget;}

class Widget : public QWidget
{
  Q_OBJECT

public:
  explicit Widget(QWidget *parent = 0);
  ~Widget();

private slots:
  void on_btnOpenVedio_clicked();
  void on_btnQuit_clicked();
  void readFrame();

  void on_ckb_Track_clicked(bool checked);

private:
  Ui::Widget *ui;

  bool openCam;
  bool isTrack=false;
  bool isSaveFrame = false;
  QTimer *timer;
  VideoCapture *cap;
  Mat framePre;
  int fps,frameWidth,frameHeight;
  VideoWriter writer;

  int VideoNum = 0;


  //Mat 转换 QImage
  QImage cvMat2QImage(const cv::Mat& mat);
};

#endif // WIDGET_H

    widget.cpp

    #pragma execution_character_set("utf-8")
#include "widget.h"
#include "ui_widget.h"
#include <iostream>
#include <QDebug>
using namespace std;

Widget::Widget(QWidget *parent) :
  QWidget(parent),
  ui(new Ui::Widget)
{ui->setupUi(this);
  timer = new QTimer(this);
  timer->stop();
  connect(timer,SIGNAL(timeout()),this,SLOT(readFrame()));
  openCam = true;

  cap = new VideoCapture(0);
  frameWidth = cap->get(CAP_PROP_FRAME_WIDTH);
  frameHeight = cap->get(CAP_PROP_FRAME_HEIGHT);
  fps = cap->get(CAP_PROP_FPS);
  qDebug()<<"width"<<frameWidth<<frameHeight<<fps;}

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

void Widget::on_btnOpenVedio_clicked()
{if(openCam)
  {ui->btnOpenVedio->setText("敞开摄像头");
      timer->start(30);
  }
  else {ui->btnOpenVedio->setText("关上摄像头");
      timer->stop();}
  openCam = !openCam;
}


QImage Widget::cvMat2QImage(const cv::Mat &mat)
{switch ( mat.type() )
  {
  // 8-bit  4 channel
  case CV_8UC4:
  {QImage image( (const uchar*)mat.data, mat.cols, mat.rows, static_cast<int>(mat.step), QImage::Format_RGB32 );
      return image;
  }

      // 8-bit  3 channel
  case CV_8UC3:
  {QImage image( (const uchar*)mat.data, mat.cols, mat.rows, static_cast<int>(mat.step), QImage::Format_RGB888 );
      return image.rgbSwapped();}

      // 8-bit  1 channel
  case CV_8UC1:
  {
      static QVector<QRgb>  sColorTable;
      // only create our color table once
      if (sColorTable.isEmpty() )
      {sColorTable.resize( 256);
          for (int i = 0; i < 256; ++i)
          {sColorTable[i] = qRgb(i, i, i);
          }
      }
      QImage image((const uchar*)mat.data, mat.cols, mat.rows, static_cast<int>(mat.step), QImage::Format_Indexed8 );
      image.setColorTable(sColorTable);
      return image;
  }

  default:
      qDebug("Image format is not supported: depth=%d and %d channels\n", mat.depth(), mat.channels());
      qWarning() << "cvMatToQImage - cv::Mat image type not handled in switch:" << mat.type();
      break;
  }

  return QImage();}


void Widget::on_btnQuit_clicked()
{timer->stop();
  cap->release();
  close();}

void Widget::readFrame()
{
  Mat matFrame;
  cap->read(matFrame);
  if(isTrack)
  {
      Mat grayframePre,frameDet;
      Mat frameNow,grayframeNow;
      cvtColor(matFrame,grayframeNow,COLOR_RGB2GRAY);
      cvtColor(framePre,grayframePre,COLOR_RGB2GRAY);
      GaussianBlur(grayframeNow,grayframeNow,Size(21,21),0,0);
      GaussianBlur(grayframePre,grayframePre,Size(21,21),0,0);
      absdiff(grayframeNow,grayframePre,frameDet);
      framePre = matFrame;
      threshold(frameDet,frameDet,20,255,THRESH_BINARY);
      Mat element = getStructuringElement(0,Size(3,3));
      vector<vector<Point>> contours;
      dilate(frameDet,frameDet,element);
      findContours(frameDet,contours,RETR_TREE,CHAIN_APPROX_SIMPLE,Point());
      qDebug()<<"Num"<<contours.size();
      QString SavePath = "D:/ImgPath/" + QString::number(VideoNum) + "_track.avi";
      if(contours.size()==0)
      {if(writer.isOpened())
          {writer.release();
          }

          if(isSaveFrame)
          {
              isSaveFrame = false;
              VideoNum++;
          }
      }
      else
      {for(int i=0;i<contours.size();i++)
          {double area = contourArea(contours[i]);
              if(area < 100)continue;
              else
              {qDebug()<<"有物体静止！";
                  if(!isSaveFrame)
                  {int fourcc = writer.fourcc('M', 'J', 'P', 'G');
                      writer.open(SavePath.toStdString(),fourcc,10,Size(frameWidth,frameHeight),true);
                      isSaveFrame = true;
                  }
                  else
                  {writer.write(matFrame);
                  }
                  break;
              }
          }
      }
  }
  else
  {framePre = matFrame;}
  QImage Qimg = cvMat2QImage(matFrame);
  ui->picshow->setPixmap(QPixmap::fromImage(Qimg));

}

void Widget::on_ckb_Track_clicked(bool checked)
{if(checked)
  {isTrack = true;}
  else {isTrack = false;}

}

    4、界面成果展现


    关上摄像头后，能够进行采集视频操作，勾选“关上追踪”，程序会调用帧差算法断定是否有静止物体，如果有物体静止，就保留静止时的视频。

5、总结

首先，两帧差是比拟根底的检测静止物体的办法，尽管其运算速度快，但其无奈过滤光照或渺小抖动的烦扰，而且静止指标会呈现“重影”导致呈现外部空洞。三帧差法是在相邻帧差法根底上改良的算法，在肯定水平上优化了静止物体双边，粗轮廓的景象，相比之下，三帧差法比相邻帧差法更实用于物体挪动速度较快的状况，比方路线上车辆的智能监控。

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