OpenCV学习之利用Camshift算法进行彩色目标的跟踪

最新推荐文章于 2022-08-09 19:13:49 发布

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最新推荐文章于 2022-08-09 19:13:49 发布 · 1k 阅读

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#opencv #图像识别 #目标检测 #目标跟踪算法

本文详细介绍了如何使用OpenCV库中的Camshift算法来实现彩色目标的精确跟踪。通过对目标区域的颜色特征进行分析，Camshift算法能有效地在连续帧间保持目标的位置和大小。通过实例代码和步骤解析，读者将掌握这一目标跟踪技术。

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利用Camshift算法进行彩色目标的跟踪

#include "cv.h"
#include "highgui.h"
#include <stdio.h>  
#include <ctype.h>  

IplImage *image = 0, *hsv = 0, *hue = 0, *mask = 0, *backproject = 0, *histimg = 0;
CvHistogram *hist = 0;

int backproject_mode = 0;
int select_object = 0;
int track_object = 0;
int show_hist = 1;
CvPoint origin;
CvRect selection;
CvRect track_window;
CvBox2D track_box;  // tracking 返回的区域box， 带角度  
CvConnectedComp track_comp;
int hdims = 48;     // 划分HIST的个数，越高越精确  
float hranges_arr[] = { 0,180 };
float* hranges = hranges_arr;
int vmin = 10, vmax = 256, smin = 30;

void on_mouse(int event, int x, int y, int flags) {

    if (!image)
        return;

    if (image->origin)
        y = image->height - y;

    if (select_object) {

        selection.x = MIN(x, origin.x);
        selection.y = MIN(y, origin.y);
        selection.width = selection.x + CV_IABS(x - origin.x);
        selection.height = selection.y + CV_IABS(y - origin.y);

        selection.x = MAX(selection.x, 0);
        selection.y = MAX(selection.y, 0);
        selection.width = MIN(selection.width, image->width);
        selection.height = MIN(selection.height, image->height);
        selection.width -= selection.x;
        selection.height -= selection.y;

    }

    switch (event) {

    case CV_EVENT_LBUTTONDOWN:
        origin = cvPoint(x, y);
        selection = cvRect(x, y, 0, 0);
        select_object = 1;