基于Opencv的MeanShift跟踪算法实现

转载请标明出处：http://blog.csdn.net/koriya/archive/2008/11/21/3347365.aspx

#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;//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;
CvPoint origin;
CvRect selection;
CvRect track_window;
CvBox2D track_box; // Meanshift跟踪算法返回的Box类
CvConnectedComp track_comp;
int hdims = 50; // 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;

void on_mouse( int event, int x, int y, int flags,void *NotUsed)//该函数用于选择跟踪目标
{
if( !image )
return;

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

if( select_object )//如果处于选择跟踪物体阶段，则对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://开始点击选择跟踪物体
origin = cvPoint(x,y);
selection = cvRect(x,y,0,0);//坐标
select_object = 1;//表明开始进行选取
break;
case CV_EVENT_LBUTTONUP:
select_object = 0;//选取完成
if( selection.width > 0 && selection.height > 0 )
track_object = -1;//如果选择物体有效，则打开跟踪功能

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);//返回对应的颜色值
}

int main( int argc, char** argv )
{
CvCapture* capture = 0;
IplImage* frame = 0;

if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))
capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 );//打开摄像头
else if( argc == 2 )
capture = cvCaptureFromAVI( argv[1] );//打开AVI文件

if( !capture )
{
fprintf(stderr,"Could not initialize capturing...\n");//打开视频流失败处理
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\nTo initialize tracking, select the object with mouse\n" );//打印出程序功能列表
cvNamedWindow( "CamShiftDemo", 1 );//建立视频窗口
cvSetMouseCallback( "CamShiftDemo", on_mouse ); // 设置鼠标回调函数

cvCreateTrackbar( "Vmin", "CamShiftDemo", &vmin, 256, 0 );//建立滑动条
cvCreateTrackbar( "Vmax", "CamShiftDemo", &vmax, 256, 0 );
cvCreateTrackbar( "Smin", "CamShiftDemo", &smin, 256, 0 );

for(;;)//进入视频帧处理主循环
{
int i, bin_w, c;
frame = cvQueryFrame( capture );
if( !frame )
break;

if( !image )//刚开始先建立一些缓冲区
{

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 ); //分配建立直方图空间

histimg = cvCreateImage( cvSize(320,200), 8, 3 );//分配用于画直方图的空间
cvZero( histimg );//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 )// 如果当前有需要跟踪的物体

{
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 ); // 取得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
cvSetImageROI( mask, selection ); // 设置Mask的选择框

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 ); // 对直方图转为0~255
cvResetImageROI( hue ); // remove ROI
cvResetImageROI( mask );
track_window = selection;
track_object = 1;

cvZero( histimg );
bin_w = histimg->width / hdims;

for( i = 0; i < hdims; i++ )
{
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 );//画直方图到图像空间
}
}

cvCalcBackProject( &hue, backproject, hist ); // 得到hue的反向投影图

cvAnd( backproject, mask, backproject, 0 );得到反向投影图mask内的内容
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 ); // 显示模式
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( 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 );//显示视频和直方图
cvShowImage( "Histogram", histimg );

c = cvWaitKey(10);
if( c == 27 )
break;

switch( c )
{
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");

return 0;
}