运动物体检测——帧差法&///运动物体检测——背景减法

1、注意，使用的是opencv3,所以在cmakelists.txt加上（系统默认安装的是opencv2）

set(OpenCV_DIR /usr/local/opencv3/share/OpenCV)

2、在cmakelists.txt加上

add_executable(node1 src/node1.cpp)
target_link_libraries(node1
  ${catkin_LIBRARIES}
)


add_executable(node2 src/node2.cpp)
target_link_libraries(node2
  ${catkin_LIBRARIES}
)

运动物体检测——帧差法

///运动物体检测——帧差法
#include "opencv2/opencv.hpp"
using namespace cv;
#include 
using namespace std;
//运动物体检测函数声明
Mat MoveDetect(Mat temp, Mat frame);

int main()
{

    VideoCapture video("/home/ly/1.mp4");//定义VideoCapture类video
    if (!video.isOpened())  //对video进行异常检测
    {
        cout << "video open error!" << endl;
        return 0;
    }
    int frameCount = video.get(CV_CAP_PROP_FRAME_COUNT);//获取帧数
    double FPS = video.get(CV_CAP_PROP_FPS);//获取FPS
    Mat frame;//存储帧
    Mat temp;//存储前一帧图像
    Mat result;//存储结果图像
    for (int i = 0; i < frameCount; i++)
    {

        video >> frame;//读帧进frame
        imshow("frame", frame);
        if (frame.empty())//对帧进行异常检测
        {
            cout << "frame is empty!" << endl;
            break;
        }
        if (i == 0)//如果为第一帧（temp还为空）
        {
            result = MoveDetect(frame, frame);//调用MoveDetect()进行运动物体检测，返回值存入result
        }
        else//若不是第一帧（temp有值了）
        {
            result = MoveDetect(temp, frame);//调用MoveDetect()进行运动物体检测，返回值存入result

        }
        imshow("result", result);
        if (waitKey(1000.0 / FPS) == 27)//按原FPS显示
        {
            cout << "ESC退出!" << endl;
            break;
        }
        temp = frame.clone();
    }
    return 0;


}
Mat MoveDetect(Mat temp, Mat frame)
{
    Mat result = frame.clone();
    //1.将background和frame转为灰度图
    Mat gray1, gray2;
    cvtColor(temp, gray1, CV_BGR2GRAY);
    cvtColor(frame, gray2, CV_BGR2GRAY);
    //2.将background和frame做差
    Mat diff;
    absdiff(gray1, gray2, diff);
    imshow("diff", diff);
    //3.对差值图diff_thresh进行阈值化处理
    Mat diff_thresh;
    threshold(diff, diff_thresh, 50, 255, CV_THRESH_BINARY);
    imshow("diff_thresh", diff_thresh);
    //4.腐蚀
    Mat kernel_erode = getStructuringElement(MORPH_RECT, Size(3, 3));
    Mat kernel_dilate = getStructuringElement(MORPH_RECT, Size(18, 18));
    erode(diff_thresh, diff_thresh, kernel_erode);
    imshow("erode", diff_thresh);
    //5.膨胀
    dilate(diff_thresh, diff_thresh, kernel_dilate);
    imshow("dilate", diff_thresh);
    //6.查找轮廓并绘制轮廓
    vector<vector > contours;
    findContours(diff_thresh, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    drawContours(result, contours, -1, Scalar(0, 0, 255), 2);//在result上绘制轮廓
    //7.查找正外接矩形
    vector boundRect(contours.size());
    for (int i = 0; i < contours.size(); i++)
    {
        boundRect[i] = boundingRect(contours[i]);
        rectangle(result, boundRect[i], Scalar(0, 255, 0), 2);//在result上绘制正外接矩形
    }
    return result;//返回result
}

运动物体检测——背景减法

///运动物体检测——背景减法
/// https://blog.csdn.net/abc8730866/article/details/70170267
#include "opencv2/opencv.hpp"
using namespace cv;
#include 
using namespace std;
//运动物体检测函数声明
Mat MoveDetect(Mat background,Mat frame);

int main()
{

    VideoCapture video("/home/ly/1.mp4");//定义VideoCapture类video
    if (!video.isOpened())  //对video进行异常检测
    {
        cout << "video open error!" << endl;
        return 0;
    }
    int frameCount = video.get(CV_CAP_PROP_FRAME_COUNT);//获取帧数
    double FPS = video.get(CV_CAP_PROP_FPS);//获取FPS
    Mat frame;//存储帧
    Mat background;//存储背景图像
    Mat result;//存储结果图像
    for (int i = 0; i < frameCount; i++)
    {
        video >> frame;//读帧进frame
        imshow("frame", frame);
        if (frame.empty())//对帧进行异常检测
        {
            cout << "frame is empty!" << endl;
            break;
        }
        int framePosition = video.get(CV_CAP_PROP_POS_FRAMES);//获取帧位置（第几帧）
        cout << "framePosition: " << framePosition << endl;
        if (framePosition == 1)//将第一帧作为背景图像
            background = frame.clone();
        result = MoveDetect(background, frame);//调用MoveDetect()进行运动物体检测，返回值存入result
        imshow("result", result);
        if (waitKey(1000.0/FPS) == 27)//按原FPS显示
        {
            cout << "ESC退出!" << endl;
            break;
        }
    }
    return 0;
}
Mat MoveDetect(Mat background, Mat frame)
{
    Mat result = frame.clone();
    //1.将background和frame转为灰度图
    Mat gray1, gray2;
    cvtColor(background, gray1, CV_BGR2GRAY);
    cvtColor(frame, gray2, CV_BGR2GRAY);
    //2.将background和frame做差
    Mat diff;
    absdiff(gray1, gray2, diff);
    imshow("diff", diff);
    //3.对差值图diff_thresh进行阈值化处理
    Mat diff_thresh;
    threshold(diff, diff_thresh, 50, 255, CV_THRESH_BINARY);
    imshow("diff_thresh", diff_thresh);
    //4.腐蚀
    Mat kernel_erode = getStructuringElement(MORPH_RECT, Size(3, 3));
    Mat kernel_dilate = getStructuringElement(MORPH_RECT, Size(15, 15));
    erode(diff_thresh, diff_thresh, kernel_erode);
    imshow("erode", diff_thresh);
    //5.膨胀
    dilate(diff_thresh, diff_thresh, kernel_dilate);
    imshow("dilate", diff_thresh);
    //6.查找轮廓并绘制轮廓
    vector<vector > contours;
    findContours(diff_thresh, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
    drawContours(result, contours, -1, Scalar(0, 0, 255), 2);//在result上绘制轮廓
    //7.查找正外接矩形
    vector boundRect(contours.size());
    for (int i = 0; i < contours.size(); i++)
    {
        boundRect[i] = boundingRect(contours[i]);
        rectangle(result, boundRect[i], Scalar(0, 255, 0), 2);//在result上绘制正外接矩形
    }
    return result;//返回result
}