mean-shift算法在目标跟踪方面的应用

mean-shift算法具体内容在学习opencv这本书上讲解的比较详细，但是怎么应用到目标跟踪方面描述的有点欠缺。大致思路如下：

将要跟踪的目标划分成hsv三个子空间，可以对三个子空间都计算，也可以只针对h空间进行计算，假设只对h空间计算。计算出h空间的颜色直方图，该直方图就用于后面的目标跟踪的漂移计算。第一次计算时，用要跟踪的目标的矩形窗口的位置作为搜索窗口，在图像中利用mean-shift算法找到和初始直方图最接近的颜色直方图的位置，即为目标位置。把每次得到的目标位置作为下一帧图像的初始搜索窗口进行搜索。书中的例子如下：



#ifdef _CH_

#pragma package <opencv>

#endif

#ifndef _EiC

#include "cv.h"

#include "highgui.h"

#include <stdio.h>

#include <ctype.h>

#endif

#pragma comment(lib, "cv.lib")

#pragma comment(lib, "cxcore.lib")

#pragma comment(lib, "highgui.lib")

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;

CvConnectedComp track_comp;

int hdims = 16;

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, void* param )

{//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);

select_object = 1;

break;

case CV_EVENT_LBUTTONUP: //选取完成

select_object = 0;

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);

}

int main( int argc, char** argv )

{

CvCapture* capture = 0;



if( argc == 1 || (argc == 2 && strlen(argv[1]) == 1 && isdigit(argv[1][0])))

capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 );

else if( argc == 2 )

capture = cvCaptureFrom***I( argv[1] );

if( !capture )

{

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" );

cvNamedWindow( "Histogram", 1 );

cvNamedWindow( "CamShiftDemo", 1 );

cvSetMouseCallback( "CamShiftDemo", on_mouse, 0 );

cvCreateTrackbar( "Vmin", "CamShiftDemo", &vmin, 256, 0 );

cvCreateTrackbar( "Vmax", "CamShiftDemo", &vmax, 256, 0 );

cvCreateTrackbar( "Smin", "CamShiftDemo", &smin, 256, 0 );

for(;;)

{

IplImage* frame = 0;

int i, bin_w, c;

frame = cvQueryFrame( capture );

if( !frame )

break;

if( !image )

{

/* allocate all the buffers */

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 );

backproject = cvCreateImage( cvGetSize(frame), 8, 1 );

hist = cvCreateHist( 1, &hdims, CV_HIST_ARRAY, &hranges, 1 );

histimg = cvCreateImage( cvSize(320,200), 8, 3 );

cvZero( histimg );

}

cvCopy( frame, image, 0 );

cvCvtColor( image, hsv, CV_BGR2HSV );

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 );//

cvSplit( hsv, hue, 0, 0, 0 );

if( track_object < 0 )

{//计算选择区域的颜色直方图

float max_val = 0.f;

cvSetImageROI( hue, selection );

cvSetImageROI( mask, selection );

cvCalcHist( &hue, hist, 0, mask );

cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 );

cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 );

cvResetImageROI( hue );

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 );

}

}

cvCalcBackProject( &hue, backproject, hist ); //用于将Hue量转换成RGB量

cvAnd( backproject, mask, backproject, 0 );

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( 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 );

c = cvWaitKey(10);

if( (char) c == 27 )

break;

switch( (char) 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;

}

#ifdef _EiC

main(1,"camshiftdemo.c");

#endif