opencv-视频处理-实时前景检测--三帧差法

假设下面的是视频流中的按时间先后顺序的任意三帧图片

依次定义它们的灰度图为：

 ，，

其中

代表的任意一点处的坐标

代表在任意一点处的坐标

代表在任意一点处的坐标

然后定义：

前两张的灰度的差为：

后两张的灰度差为：

最后做一下和“与”运算。

优点：

实时性高

缺点：

1、运动物体本身颜色相近时，会出现较大的空洞。

2、无法应对光照骤变的情况

下面为整个工程的代码：

#include<iostream>
using namespace std;
#include<opencv2\core\core.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>
using namespace cv;

const unsigned char FORE_GROUD = 255;
int thresh = 10;

int main(int argc,char*argv[])
{	
	
	VideoCapture video(argv[1]);

	//判断如果video是否可以打开
	if(!video.isOpened())
		return -1;
	
	//用于保存当前帧的图片
	Mat currentBGRFrame;
	
	//用来保存上一帧和当前帧的灰度图片
	Mat previousSecondGrayFrame;
	Mat previousFirstGrayFrame;
	Mat currentGaryFrame;

	//保存两次的帧差
	Mat previousFrameDifference;//previousFrameFirst - previousFrameSecond的差分
	Mat currentFrameDifference;//currentFrame - previousFrameFirst;

	//用来保存帧差的绝对值
	Mat absFrameDifferece;

	//用来显示前景
	Mat previousSegmentation;
	Mat currentSegmentation;
	Mat segmentation;
	

	//显示前景
	namedWindow("segmentation",1);
	createTrackbar("阈值:","segmentation",&thresh,FORE_GROUD,NULL);

	//帧数
	int numberFrame = 0;

	//形态学处理用到的算子
	Mat morphologyKernel = getStructuringElement(MORPH_RECT,Size(3,3),Point(-1,-1));
	
	for(;;)
	{
		//读取当前帧
		video >> currentBGRFrame;

		//判断当前帧是否存在
		if(!currentBGRFrame.data)
			break;
		
		numberFrame++;
		//颜色空间的转换
		cvtColor(currentBGRFrame,currentGaryFrame,COLOR_BGR2GRAY);
		
		if( numberFrame == 1)
		{
			//保存当前帧的灰度图
			previousSecondGrayFrame = currentGaryFrame.clone();
			
			//显示视频
		   imshow("video",currentBGRFrame);
			continue;
		}
		else if( numberFrame == 2)
		{
			//保存当前帧的灰度图
			previousFirstGrayFrame = currentGaryFrame.clone();

			//previousFirst - previousSecond
			subtract(previousFirstGrayFrame,previousSecondGrayFrame,previousFrameDifference,Mat(),CV_16SC1);

			//取绝对值
			absFrameDifferece = abs(previousFrameDifference);
			
			//位深的改变
			absFrameDifferece.convertTo(absFrameDifferece,CV_8UC1,1,0);

			//阈值处理
			threshold(absFrameDifferece,previousSegmentation,double(thresh),double(FORE_GROUD),THRESH_BINARY);
			
			//显示视频
		   imshow("video",currentBGRFrame);
			continue;
		}

		else
		{
			//src1-src2
			subtract(currentGaryFrame,previousFirstGrayFrame,currentFrameDifference,Mat(),CV_16SC1);
			
			//取绝对值
			absFrameDifferece = abs(currentFrameDifference);
			
			//位深的改变
			absFrameDifferece.convertTo(absFrameDifferece,CV_8UC1,1,0);

			//阈值处理
			threshold(absFrameDifferece,currentSegmentation,double(thresh),double(FORE_GROUD),THRESH_BINARY);

			//与运算
			bitwise_and(previousSegmentation,currentSegmentation,segmentation);
			
			//中值滤波
			medianBlur(segmentation,segmentation,3);

			//形态学处理(开闭运算)
			//morphologyEx(segmentation,segmentation,MORPH_OPEN,morphologyKernel,Point(-1,-1),1,BORDER_REPLICATE);
			morphologyEx(segmentation,segmentation,MORPH_CLOSE,morphologyKernel,Point(-1,-1),2,BORDER_REPLICATE);
					
			
			//找边界
			vector< vector<Point> > contours;
			vector<Vec4i> hierarchy;
			//复制segmentation
			Mat tempSegmentation = segmentation.clone();
			findContours( segmentation, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );//CV_RETR_TREE
			vector< vector<Point> > contours_poly( contours.size() );
		
			/*存储运动物体*/
			vector<Rect> boundRect;
			boundRect.clear();
			
			//画出运动物体
			for(int index = 0;index < contours.size() ;index++)
			{
				approxPolyDP( Mat(contours[index]), contours_poly[index], 3, true );
				Rect rect =  boundingRect( Mat(contours_poly[index]) );
				rectangle(currentBGRFrame,rect,Scalar(0,255,255),2);
			}

			//显示视频
		   imshow("video",currentBGRFrame);
		   	
		   //前景检测
			imshow("segmentation",segmentation);

			//保存当前帧的灰度图
			previousFirstGrayFrame = currentGaryFrame.clone();
			
			//保存当前的前景检测
			previousSegmentation = currentSegmentation.clone();
		}
		
		if(waitKey(33) == 'q')
			break;

	}
	return 0;
}

运行结果如下：

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