Meanshift and Camshift

Meanshift and Camshift

一.算法原理

1.Meanshift

Mean Shift算法,一般是指一个迭代的步骤,即先算出当前点的偏移均值,移动该点到其偏移均值,然后以此为新的起始点,继续移动,直到满足一定的条件结束.

参考博客：

https://www.cnblogs.com/xfzhang/p/7261172.html

https://www.cnblogs.com/necp-zwl/p/6517092.html

2.Camshift

Camshift它是MeanShift算法的改进，称为连续自适应的MeanShift算法，CamShift算法的全称是"Continuously Adaptive Mean-SHIFT"，它的基本思想是视频图像的所有帧作MeanShift运算，并将上一帧的结果（即Search Window的中心和大小）作为下一帧MeanShift算法的Search Window的初始值，如此迭代下去

二.代码实现

Meanshift:

函数：

C++: void calcHist(const Mat* images, int nimages, const int* channels, InputArray mask, OutputArray hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=false )

参数详解：

onst Mat* images：输入图像

int nimages：输入图像的个数

const int* channels：需要统计直方图的第几通道

InputArray mask：掩膜，，计算掩膜内的直方图 …Mat()

OutputArray hist:输出的直方图数组

int dims：需要统计直方图通道的个数

const int* histSize：指的是直方图分成多少个区间，就是 bin的个数

const float** ranges： 统计像素值得区间

bool uniform=true::是否对得到的直方图数组进行归一化处理

#include 
#include 
#include 
#include 
#include 
#include 
using namespace cv;
using namespace std;
int main(int argc, char **argv)
{
    const string about =
        "This sample demonstrates the meanshift algorithm.\n"
        "The example file can be downloaded from:\n"
        "  https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4";
    const string keys =
        "{ h help |      | print this help message }"
        "{ @image || path to image file }";
    CommandLineParser parser(argc, argv, keys);//命令行类
    parser.about(about);
    if (parser.has("help"))//判断是否需要帮助
    {
        parser.printMessage();
        return 0;
    }
    string filename = parser.get<string>("@image");//获取图片路径
    if (!parser.check())
    {
        parser.printErrors();
        return 0;
    }
    VideoCapture capture(filename);//读取视频
    if (!capture.isOpened()){
        //error in opening the video input
        cerr << "Unable to open file!" << endl;
        return 0;
    }
    Mat frame, roi, hsv_roi, mask;
    // take first frame of the video
    capture >> frame;
    // setup initial location of window
    Rect track_window(300, 200, 100, 50); // simply hardcoded the values
    // set up the ROI for tracking
    roi = frame(track_window);//感兴趣区域
    cvtColor(roi, hsv_roi, COLOR_BGR2HSV);//感兴趣区域颜色空间转换
    inRange(hsv_roi, Scalar(0, 60, 32), Scalar(180, 255, 255), mask);//根据HSV值丢弃低亮度值
    float range_[] = {0, 180};
    const float* range[] = {range_};
    Mat roi_hist;
    int histSize[] = {180};
    int channels[] = {0};
    calcHist(&hsv_roi, 1, channels, mask, roi_hist, 1, histSize, range);//计算直方图
    normalize(roi_hist, roi_hist, 0, 255, NORM_MINMAX);//标准化
    // Setup the termination criteria, either 10 iteration or move by atleast 1 pt
    TermCriteria term_crit(TermCriteria::EPS | TermCriteria::COUNT, 10, 1);
    while(true){
        Mat hsv, dst;
        capture >> frame;
        if (frame.empty())
            break;
        cvtColor(frame, hsv, COLOR_BGR2HSV);
        calcBackProject(&hsv, 1, channels, roi_hist, dst, range);
        // apply meanshift to get the new location
        meanShift(dst, track_window, term_crit);
        // Draw it on image
        rectangle(frame, track_window, 255, 2);
        imshow("img2", frame);
        int keyboard = waitKey(30);
        if (keyboard == 'q' || keyboard == 27)
            break;
    }
}

截图：

Camshift:

#include 
#include 
#include 
#include 
#include 
#include 
using namespace cv;
using namespace std;
int main(int argc, char **argv)
{
    const string about =
        "This sample demonstrates the camshift algorithm.\n"
        "The example file can be downloaded from:\n"
        "  https://www.bogotobogo.com/python/OpenCV_Python/images/mean_shift_tracking/slow_traffic_small.mp4";
    const string keys =
        "{ h help |      | print this help message }"
        "{ @image || path to image file }";
    CommandLineParser parser(argc, argv, keys);
    parser.about(about);
    if (parser.has("help"))
    {
        parser.printMessage();
        return 0;
    }
    string filename = parser.get<string>("@image");
    if (!parser.check())
    {
        parser.printErrors();
        return 0;
    }
    VideoCapture capture(filename);
    if (!capture.isOpened()){
        //error in opening the video input
        cerr << "Unable to open file!" << endl;
        return 0;
    }
    Mat frame, roi, hsv_roi, mask;
    // take first frame of the video
    capture >> frame;
    // setup initial location of window
    Rect track_window(300, 200, 100, 50); // simply hardcoded the values
    // set up the ROI for tracking
    roi = frame(track_window);
    cvtColor(roi, hsv_roi, COLOR_BGR2HSV);
    inRange(hsv_roi, Scalar(0, 60, 32), Scalar(180, 255, 255), mask);
    float range_[] = {0, 180};
    const float* range[] = {range_};
    Mat roi_hist;
    int histSize[] = {180};
    int channels[] = {0};
    calcHist(&hsv_roi, 1, channels, mask, roi_hist, 1, histSize, range);
    normalize(roi_hist, roi_hist, 0, 255, NORM_MINMAX);
    // Setup the termination criteria, either 10 iteration or move by atleast 1 pt
    TermCriteria term_crit(TermCriteria::EPS | TermCriteria::COUNT, 10, 1);
    while(true){
        Mat hsv, dst;
        capture >> frame;
        if (frame.empty())
            break;
        cvtColor(frame, hsv, COLOR_BGR2HSV);
        calcBackProject(&hsv, 1, channels, roi_hist, dst, range);
        // apply camshift to get the new location
        RotatedRect rot_rect = CamShift(dst, track_window, term_crit);
        // Draw it on image
        Point2f points[4];
        rot_rect.points(points);
        for (int i = 0; i < 4; i++)
            line(frame, points[i], points[(i+1)%4], 255, 2);
        imshow("img2", frame);
        int keyboard = waitKey(30);
        if (keyboard == 'q' || keyboard == 27)
            break;
    }
}