opencv学习之旅_绘制跟踪轨迹
如何将运动物体的轨迹画出来 我的想法是先；用CAMSHIFT跟踪物体，这个函数会返回一个track_box，将box的中心提取出来，然后以这个中心在另外的图像上画出来，然后将这张图像处理，提取轮廓，提取出来的轮廓就是物体的运动的序列。
示例：
//对运动物体的跟踪：
//如果背景固定,可用帧差法 然后在计算下连通域 将面积小的去掉即可
//如果背景单一,即你要跟踪的物体颜色和背景色有较大区别 可用基于颜色的跟踪 如CAMSHIFT 鲁棒性都是较好的
//如果背景复杂,如背景中有和前景一样的颜色 就需要用到一些具有预测性的算法 如卡尔曼滤波等 可以和CAMSHIFT结合
#ifdef _CH_
#pragma package <opencv>
#endif
#ifndef _EiC
#include "cv.h"
#include "highgui.h"
#include <stdio.h>
#include <ctype.h>
#endif
IplImage *image = 0, *hsv = 0, *hue = 0, *mask = 0, *backproject = 0, *histimg = 0;
IplImage *trackimg;
//用HSV中的Hue分量进行跟踪
CvHistogram *hist = 0;
//直方图类
int backproject_mode = 0;
int select_object = 0;
int track_object = 0;
int show_hist = 1;
int p = 0;
int w = 50;//每50个帧确定一点
CvSize sz;
CvPoint origin;
CvPoint center;
CvPoint lastcenter;
CvRect selection;
CvRect track_window;
CvBox2D track_box;
//Meanshift跟踪算法返回的Box类
//typedef struct CvBox2D{
//CvPoint2D32f center; /* 盒子的中心 */
//CvSize2D32f size; /* 盒子的长和宽 */
//float angle; /* 水平轴与第一个边的夹角，用弧度表示*/
//}CvBox2D;
CvConnectedComp track_comp;
//连接部件
//typedef struct CvConnectedComp{
//double area; /* 连通域的面积 */
//float value; /* 分割域的灰度缩放值 */
//CvRect rect; /* 分割域的 ROI */
//} CvConnectedComp;
int hdims = 16;
//划分直方图bins的个数，越多越精确
float hranges_arr[] = {0,180};
//像素值的范围
float* hranges = hranges_arr;
//用于初始化CvHistogram类
int vmin = 10, vmax = 256, smin = 30;
//用于设置滑动条
void on_mouse( int event, int x, int y, int flags, void* param )
//鼠标回调函数,该函数用鼠标进行跟踪目标的选择
{
if( !image )
return;
if( image->origin )
y = image->height - y;
//如果图像原点坐标在左下,则将其改为左上
if( select_object )
//select_object为1,表示在用鼠标进行目标选择
//此时对矩形类selection用当前的鼠标位置进行设置
{
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:
//鼠标按下,开始点击选择跟踪物体
origin = cvPoint(x,y);
selection = cvRect(x,y,0,0);
select_object = 1;
break;
case CV_EVENT_LBUTTONUP:
//鼠标松开,完成选择跟踪物体
select_object = 0;
if( selection.width > 0 && selection.height > 0 )
//如果选择物体有效，则打开跟踪功能
track_object = -1;
break;
}
}
CvScalar hsv2rgb( float hue )
//用于将Hue量转换成RGB量
{
int rgb[3], p, sector;
static const int sector_data[][3]=
{{0,2,1}, {1,2,0}, {1,0,2}, {2,0,1}, {2,1,0}, {0,1,2}};
hue *= 0.033333333333333333333333333333333f;
sector = cvFloor(hue);
p = cvRound(255*(hue - sector));
p ^= sector & 1 ? 255 : 0;
rgb[sector_data[sector][0]] = 255;
rgb[sector_data[sector][1]] = 0;
rgb[sector_data[sector][2]] = p;
return cvScalar(rgb[2], rgb[1], rgb[0],0);
}
int main( int argc, char** argv )
{
CvCapture* capture = 0;
if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
//打开摄像头
capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 );
else if( argc == 2 )
//打开avi
capture = cvCaptureFromAVI( argv[1] );
if( !capture )
//打开视频流失败
{
fprintf(stderr,"Could not initialize capturing...\n");
return -1;
}
printf( "Hot keys: \n"
"\tESC - quit the program\n"
"\tc - stop the tracking\n"
"\tb - switch to/from backprojection view\n"
"\th - show/hide object histogram\n"
"To initialize tracking, select the object with mouse\n" );
//打印程序功能列表
IplImage* frametmp = 0;
frametmp = cvQueryFrame( capture );
sz = cvGetSize(frametmp);
trackimg = cvCreateImage(sz,8,1);
trackimg = cvCloneImage(frametmp);
cvNamedWindow( "Histogram", 1 );
//用于显示直方图
cvNamedWindow( "CamShiftDemo", 1 );
//用于显示视频
cvNamedWindow("trackfollw",1);
//用于显示轨迹
cvSetMouseCallback( "CamShiftDemo", on_mouse, 0 );
//设置鼠标回调函数
cvCreateTrackbar( "Vmin", "CamShiftDemo", &vmin, 256, 0 );
cvCreateTrackbar( "Vmax", "CamShiftDemo", &vmax, 256, 0 );
cvCreateTrackbar( "Smin", "CamShiftDemo", &smin, 256, 0 );
//设置滑动条
for(;;)
//进入视频帧处理主循环
{
IplImage* frame = 0;
int i, bin_w, c;
frame = cvQueryFrame( capture );
if( !frame )
break;
if( !image )
//image为0,表明刚开始还未对image操作过,先建立一些缓冲区
{
image = cvCreateImage( cvGetSize(frame), 8, 3 );
image->origin = frame->origin;
hsv = cvCreateImage( cvGetSize(frame), 8, 3 );
hue = cvCreateImage( cvGetSize(frame), 8, 1 );
mask = cvCreateImage( cvGetSize(frame), 8, 1 );
//分配掩膜图像空间
backproject = cvCreateImage( cvGetSize(frame), 8, 1 );
//分配反向投影图空间,大小一样,单通道
hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 );
//分配直方图空间
histimg = cvCreateImage( cvSize(320,200), 8, 3 );
//分配用于直方图显示的空间
cvZero( histimg );
//置背景为黑色
}
cvCopy( frame, image, 0 );
cvCvtColor( image, hsv, CV_BGR2HSV );
//把图像从RGB表色系转为HSV表色系
if( track_object )
//track_object非零,表示有需要跟踪的物体
{
int _vmin = vmin, _vmax = vmax;
cvInRangeS( hsv, cvScalar(0,smin,MIN(_vmin,_vmax),0),
cvScalar(180,256,MAX(_vmin,_vmax),0), mask );
//制作掩膜板，只处理像素值为H：0~180，S：smin~256，V：vmin~vmax之间的部分
cvSplit( hsv, hue, 0, 0, 0 );
//分离H分量
if( track_object < 0 )
//如果需要跟踪的物体还没有进行属性提取，则进行选取框类的图像属性提取
{
float max_val = 0.f;
cvSetImageROI( hue, selection );
//设置原选择框为ROI
cvSetImageROI( mask, selection );
//设置掩膜板选择框为ROI
cvCalcHist( &hue, hist, 0, mask );
//得到选择框内且满足掩膜板内的直方图
cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );
cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 );
// 对直方图的数值转为0~255
cvResetImageROI( hue );
//去除ROI
cvResetImageROI( mask );
//去除ROI
track_window = selection;
track_object = 1;
//置track_object为1,表明属性提取完成
cvZero( histimg );
bin_w = histimg->width / hdims;
for( i = 0; i < hdims; i++ )
//画直方图到图像空间
{
int val = cvRound( cvGetReal1D(hist->bins,i)*histimg->height/255 );
CvScalar color = hsv2rgb(i*180.f/hdims);
cvRectangle( histimg, cvPoint(i*bin_w,histimg->height),
cvPoint((i+1)*bin_w,histimg->height - val),
color, -1, 8, 0 );
}
}
cvCalcBackProject( &hue, backproject, hist );
//计算hue的反向投影图
cvAnd( backproject, mask, backproject, 0 );
//得到掩膜内的反向投影
cvCamShift( backproject, track_window,
cvTermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ),
&track_comp, &track_box );
//使用MeanShift算法对backproject中的内容进行搜索,返回跟踪结果
track_window = track_comp.rect;
//得到跟踪结果的矩形框
if( backproject_mode )
cvCvtColor( backproject, image, CV_GRAY2BGR );
if( image->origin )
track_box.angle = -track_box.angle;
cvEllipseBox( image, track_box, CV_RGB(255,0,0), 3, CV_AA, 0 );
//画出跟踪结果的位置
// if(w%50==0){
center.x = track_box.center.x;
center.y = track_box.center.y;
cvCircle(trackimg,center, 5, CV_RGB(255,0,0),-1, 8, 0 );
/*if(p!=0)
cvLine( image, center, lastcenter, CV_RGB(255,0,0),8, 8, 0 );
p++;
lastcenter = center;
}*/
}
w++;
if( select_object && selection.width > 0 && selection.height > 0 )
//如果正处于物体选择，画出选择框
{
cvSetImageROI( image, selection );
cvXorS( image, cvScalarAll(255), image, 0 );
cvResetImageROI( image );
}
cvShowImage( "CamShiftDemo", image );
cvShowImage( "Histogram", histimg );
cvShowImage("trackfollw",trackimg);
c = cvWaitKey(10);
if( (char) c == 27 )
break;
switch( (char) c )
//按键切换功能
{
case 'b':
backproject_mode ^= 1;
break;
case 'c':
track_object = 0;
cvZero( histimg );
break;
case 'h':
show_hist ^= 1;
if( !show_hist )
cvDestroyWindow( "Histogram" );
else
cvNamedWindow( "Histogram", 1 );
break;
default:
;
}
}
cvReleaseCapture( &capture );
cvDestroyWindow("CamShiftDemo");
cvDestroyWindow("trackfollw");
return 0;
}
#ifdef _EiC
main(1,"camshiftdemo.c");
#endif
