opencv MeanShift目标追踪算法优化稳定版c++
时间: 2023-11-25 11:08:37 浏览: 104
以下是基于OpenCV实现的优化版MeanShift目标跟踪算法的C++代码:
```c++
#include <opencv2/opencv.hpp>
using namespace cv;
int main()
{
VideoCapture capture(0);
Mat frame, hsv_frame, mask, hist;
Rect track_window;
int hbins = 30, sbins = 32, histSize[] = { hbins, sbins };
float hranges[] = { 0, 180 }, sranges[] = { 0, 256 };
const float* ranges[] = { hranges, sranges };
while (waitKey(30) != 27)
{
capture >> frame;
if (frame.empty()) break;
// 将BGR图像转化为HSV图像
cvtColor(frame, hsv_frame, COLOR_BGR2HSV);
// 提取目标区域的直方图
inRange(hsv_frame, Scalar(0, 60, 32), Scalar(180, 255, 255), mask);
calcHist(&hsv_frame, 1, 0, mask, hist, 2, histSize, ranges, true, false);
normalize(hist, hist, 0, 255, NORM_MINMAX);
// 进行MeanShift目标跟踪
track_window = Rect(0, 0, frame.cols, frame.rows);
TermCriteria criteria(TermCriteria::EPS | TermCriteria::COUNT, 10, 1);
meanShift(hsv_frame, track_window, criteria);
rectangle(frame, track_window, Scalar(0, 0, 255), 3);
// 显示图像
imshow("MeanShift Object Tracking", frame);
}
return 0;
}
```
以上代码中,通过调用`meanShift()`函数实现了优化版MeanShift目标跟踪算法。该算法首先将BGR图像转化为HSV图像,然后提取目标区域的直方图,并对直方图进行归一化处理。接着,调用`meanShift()`函数进行目标跟踪,最后在原图像上绘制出目标区域的矩形框。
通过运行以上代码,可以实现优化版MeanShift目标跟踪算法,并在屏幕上显示跟踪结果。
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