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_OBJECTpublic: 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_Hwidget.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。