opencv利用帧差法背景差分实现运动目标检测

运动物体检测顾名思义就是在视频（视频文件、摄像头获取）中检测运动物体（目标）。

OpenCV中常用的运动物体检测有背景差法、帧差法、光流法，运动物体检测广泛应用于视频安全监控、车辆检测等方面。

本博文主要介绍背景差法与帧差法：

背景差法：就是用原图像减去背景模型，剩下的就是前景图像，即运动目标

帧差法：就是利用相邻的两帧或者三帧图像，利用像素之间的差异性，判断是否有运动目标

（视频就是一帧一帧图像组成的、求图像差异最基本的就是图像减法--suntract,absdiff） 

背景减法基本步骤：原图-背景------阈值处理------去除噪声（腐蚀滤波）------膨胀连通-----查找轮廓-----外接矩形（椭圆/圆）

一个摄像头：

#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv;

Mat MoveDetect(Mat background, Mat img)
{   
	//将background和img转为灰度图
	Mat result = img.clone();
	Mat gray1, gray2;
	cvtColor(background, gray1, CV_BGR2GRAY);
	cvtColor(img, gray2, CV_BGR2GRAY);

//进行canny边缘检测 
	Canny(background, background, 0, 30, 3);

	//将background和img做差；对差值图diff进行阈值化处理
	Mat diff;
	absdiff(gray1, gray2, diff);
	//imshow("absdiss", diff);
	threshold(diff, diff, 50, 255, CV_THRESH_BINARY);
	//imshow("threshold", diff);

	//腐蚀膨胀消除噪音
	/*
	Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));
	Mat element2 = getStructuringElement(MORPH_RECT, Size(15, 15));
	erode(diff, diff, element);
	//imshow("erode", diff);
	dilate(diff, diff, element2);
	//imshow("dilate", diff);
	*/

	//二值化后使用中值滤波+膨胀
	Mat element = getStructuringElement(MORPH_RECT, Size(11, 11));
	medianBlur(diff, diff, 5);//中值滤波
	//imshow("medianBlur", diff);
	dilate(diff, diff, element);
	//blur(diff, diff, Size(10, 10)); //均值滤波
	//imshow("dilate", diff);

	//查找并绘制轮廓
	vector<vector<Point>> contours;
	vector<Vec4i> hierarcy;
	findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
	vector<Rect> boundRect(contours.size()); //定义外接矩形集合
	//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

	//查找正外接矩形
	int x0 = 0, y0 = 0, w0 = 0, h0 = 0;
	double Area = 0  ,  AreaAll = 0  ;
	for (int i = 0; i<contours.size(); i++)
	{
		boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形
		x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标
		y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
		w0 = boundRect[i].width; //获得第i个外接矩形的宽度
		h0 = boundRect[i].height; //获得第i个外接矩形的高度

		//计算面积
		double Area = contourArea(contours[i]);//计算第i个轮廓的面积
		AreaAll = Area + AreaAll;
		
		//筛选
		if (w0>140 && h0>140)
		rectangle(result, Point(x0, y0), Point(x0 + w0, y0 + h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形

		//文字输出
		Point org(10, 35);
		if (i >= 1 && AreaAll>=19600)
		putText(result, "Is Blocked ", org , CV_FONT_HERSHEY_SIMPLEX,0.8f,Scalar(0, 255, 0),2);

	}
	return result;
}

void main()
{
	VideoCapture cap;
	cap.open(0);
	if (!cap.isOpened()) //检查打开是否成功
		return;
	Mat frame;
	Mat background;
	Mat result;
	int count = 0;
	while (1)
	{
		cap >> frame;
		if (!frame.empty())
		{
			count++;
			if (count == 1)
				background = frame.clone(); //提取第一帧为背景帧
			//imshow("video", frame);
			result = MoveDetect(background, frame);
			imshow("result", result);
			if (waitKey(50) == 27)
				break;
		}
		else
			continue;
	}
	cap.release();
}

两个摄像头：

#include "opencv2/opencv.hpp"
#include<iostream>
using namespace std;
using namespace cv;

Mat MoveDetect01(Mat background01, Mat img)
{
	//将background和img转为灰度图
	Mat result = img.clone();
	Mat gray1, gray2;
	cvtColor(background01, gray1, CV_BGR2GRAY);
	cvtColor(img, gray2, CV_BGR2GRAY);

	//进行canny边缘检测 
	Canny(background01, background01, 0, 30, 3);

	//将background和img做差；对差值图diff进行阈值化处理
	Mat diff;
	absdiff(gray1, gray2, diff);
	//imshow("absdiss", diff);
	threshold(diff, diff, 50, 255, CV_THRESH_BINARY);
	//imshow("threshold", diff);

	//腐蚀膨胀消除噪音
	/*
	Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));
	Mat element2 = getStructuringElement(MORPH_RECT, Size(15, 15));
	erode(diff, diff, element);
	//imshow("erode", diff);
	dilate(diff, diff, element2);
	//imshow("dilate", diff);
	*/

	//二值化后使用中值滤波+膨胀
	Mat element = getStructuringElement(MORPH_RECT, Size(11, 11));
	medianBlur(diff, diff, 5);//中值滤波
	//imshow("medianBlur", diff);
	dilate(diff, diff, element);
	//blur(diff, diff, Size(10, 10)); //均值滤波
	//imshow("dilate", diff);

	//查找并绘制轮廓
	vector<vector<Point>> contours;
	vector<Vec4i> hierarcy;
	findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
	vector<Rect> boundRect(contours.size()); //定义外接矩形集合
	//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

	//查找正外接矩形
	int x0 = 0, y0 = 0, w0 = 0, h0 = 0;
	double Area = 0, AreaAll = 0;
	for (int i = 0; i<contours.size(); i++)
	{
		boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形
		x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标
		y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
		w0 = boundRect[i].width; //获得第i个外接矩形的宽度
		h0 = boundRect[i].height; //获得第i个外接矩形的高度

		//计算面积
		double Area = contourArea(contours[i]);//计算第i个轮廓的面积
		AreaAll = Area + AreaAll;

		//筛选
		if (w0>140 && h0>140)
			rectangle(result, Point(x0, y0), Point(x0 + w0, y0 + h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形

		//文字输出
		Point org(10, 35);
		if (i >= 1 && AreaAll >= 19600)
			putText(result, "Is Blocked ", org, CV_FONT_HERSHEY_SIMPLEX, 0.8f, Scalar(0, 255, 0), 2);

	}
	return result;
}

Mat MoveDetect02(Mat background02, Mat img02)
{
	//将background和img转为灰度图
	Mat result02 = img02.clone();
	Mat gray1, gray2;
	cvtColor(background02, gray1, CV_BGR2GRAY);
	cvtColor(img02, gray2, CV_BGR2GRAY);

	//进行canny边缘检测 
	Canny(background02, background02, 0, 30, 3);

	//将background和img做差；对差值图diff进行阈值化处理
	Mat diff;
	absdiff(gray1, gray2, diff);
	//imshow("absdiss", diff);
	threshold(diff, diff, 50, 255, CV_THRESH_BINARY);
	//imshow("threshold", diff);

	//腐蚀膨胀消除噪音
	/*
	Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));
	Mat element2 = getStructuringElement(MORPH_RECT, Size(15, 15));
	erode(diff, diff, element);
	//imshow("erode", diff);
	dilate(diff, diff, element2);
	//imshow("dilate", diff);
	*/

	//二值化后使用中值滤波+膨胀
	Mat element = getStructuringElement(MORPH_RECT, Size(11, 11));
	medianBlur(diff, diff, 5);//中值滤波
	//imshow("medianBlur", diff);
	dilate(diff, diff, element);
	//blur(diff, diff, Size(10, 10)); //均值滤波
	//imshow("dilate", diff);

	//查找并绘制轮廓
	vector<vector<Point>> contours;
	vector<Vec4i> hierarcy;
	findContours(diff, contours, hierarcy, CV_RETR_EXTERNAL, CHAIN_APPROX_NONE); //查找轮廓
	vector<Rect> boundRect(contours.size()); //定义外接矩形集合
	//drawContours(img2, contours, -1, Scalar(0, 0, 255), 1, 8);  //绘制轮廓

	//查找正外接矩形
	int x0 = 0, y0 = 0, w0 = 0, h0 = 0;
	double Area = 0, AreaAll = 0;
	for (int i = 0; i<contours.size(); i++)
	{
		boundRect[i] = boundingRect((Mat)contours[i]); //查找每个轮廓的外接矩形
		x0 = boundRect[i].x;  //获得第i个外接矩形的左上角的x坐标
		y0 = boundRect[i].y; //获得第i个外接矩形的左上角的y坐标
		w0 = boundRect[i].width; //获得第i个外接矩形的宽度
		h0 = boundRect[i].height; //获得第i个外接矩形的高度

		//计算面积
		double Area = contourArea(contours[i]);//计算第i个轮廓的面积
		AreaAll = Area + AreaAll;

		//筛选
		if (w0>140 && h0>140)
			rectangle(result02, Point(x0, y0), Point(x0 + w0, y0 + h0), Scalar(0, 255, 0), 2, 8); //绘制第i个外接矩形

		//文字输出
		Point org(10, 35);
		if (i >= 1 && AreaAll >= 19600)
			putText(result02, "Is Blocked ", org, CV_FONT_HERSHEY_SIMPLEX, 0.8f, Scalar(0, 255, 0), 2);

	}
	return result02;
}


void main()
{
	VideoCapture cap01;
	VideoCapture cap02;

	cap01.open(0);
	cap02.open(1);

	if (!cap01.isOpened() && !cap02.isOpened()) //检查打开是否成功
		return;
	Mat frame01, frame02;
	Mat background01, background02;
	Mat result01, result02;
	int count01 = 0;
	int count02 = 0;
	while (1)
	{
		cap01 >> frame01;
		if (!frame01.empty())
		{
			count01++;
			if (count01 == 1)
				background01 = frame01.clone(); //提取第一帧为背景帧
			//imshow("video", frame);
			result01 = MoveDetect01(background01, frame01);
			imshow("result01", result01);
		}

		cap02 >> frame02;
		if (!frame02.empty())
		{
			count02++;
			if (count02 == 1)
				background02 = frame02.clone(); //提取第一帧为背景帧
			//imshow("video", frame);
			result02 = MoveDetect02(background02, frame02);
			imshow("result02", result02);
			if (waitKey(50) == 27)
				break;
		}

		else
			continue;
	}

	cap01.release();
	cap02.release();

}