运动目标跟踪与检测的源代码（CAMSHIFT 算法）

采用 CAMSHIFT 算法快速跟踪和检测运动目标的 C/C++ 源代码，OPENCV BETA 4.0 版本在其 SAMPLE 中给出了这个例子。算法的简单描述如下（英文）：

This application demonstrates a fast, simple color tracking algorithm that can be used to track faces, hands . The CAMSHIFT algorithm is a modification of the Meanshift algorithm which is a robust statistical method of finding the mode (top) of a probability distribution. Both CAMSHIFT and Meanshift algorithms exist in the library. While it is a very fast and simple method of tracking, because CAMSHIFT tracks the center and size of the probability distribution of an object, it is only as good as the probability distribution that you produce for the object. Typically the probability distribution is derived from color via a histogram, although it could be produced from correlation, recognition scores or bolstered by frame differencing or motion detection schemes, or joint probabilities of different colors/motions etc.

In this application, we use only the most simplistic approach: A 1-D Hue histogram is sampled from the object in an HSV color space version of the image. To produce the probability image to track, histogram "back projection" (we replace image pixels by their histogram hue value) is used.

算法的详细情况，请看论文：

http://www.assuredigit.com/incoming/camshift.pdf

关于OPENCV B4.0 库的使用方法以及相关问题，请查阅下面的相关文章：

http://forum.assuredigit.com/display_topic_threads.asp?ForumID=11&TopicID=3471

运行文件下载：

http://www.assuredigit.com/product_tech/Demo_Download_files/camshiftdemo.exe

该运行文件在VC6.0环境下编译通过，是一个 stand-alone 运行程序，不需要OPENCV的DLL库支持。在运行之前，请先连接好USB接口的摄像头。然后可以用鼠标选定欲跟踪目标。

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#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;
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;
break;
case CV_EVENT_LBUTTONUP:
select_object = 0;
if( selection.width > 0 && selection.height > 0 )
track_object = -1;
#ifdef _DEBUG
printf("\n # 鼠标的选择区域：");
printf("\n X = %d, Y = %d, Width = %d, Height = %d",
selection.x, selection.y, selection.width, selection.height);
#endif
break;
}
}

CvScalar hsv2rgb( float hue )
{
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;

#ifdef _DEBUG
printf("\n # Convert HSV to RGB：");
printf("\n HUE = %f", hue);
printf("\n R = %d, G = %d, B = %d", rgb[0],rgb[1],rgb[2]);
#endif

return cvScalar(rgb[2], rgb[1], rgb[0],0);
}

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 );
else if( argc == 2 )
capture = cvCaptureFromAVI( 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 ); // on_mouse 自定义事件
cvCreateTrackbar( "Vmin", "CamShiftDemo", &vmin, 256, 0 );
cvCreateTrackbar( "Vmax", "CamShiftDemo", &vmax, 256, 0 );
cvCreateTrackbar( "Smin", "CamShiftDemo", &smin, 256, 0 );

for(;;)
{
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 ); // 计算直方图
histimg = cvCreateImage( cvSize(320,200), 8, 3 );
cvZero( histimg );
}

cvCopy( frame, image, 0 );
cvCvtColor( image, hsv, CV_BGR2HSV ); // 彩色空间转换 BGR to HSV

if( track_object )
{
int _vmin = vmin, _vmax = vmax;

cvInRangeS( hsv, cvScalar(0,smin,MIN(_vmin,_vmax),0),
cvScalar(180,256,MAX(_vmin,_vmax),0), mask ); // 得到二值的MASK
cvSplit( hsv, hue, 0, 0, 0 ); // 只提取 HUE 分量

if( track_object < 0 )
{
float max_val = 0.f;
cvSetImageROI( hue, selection ); // 得到选择区域 for ROI
cvSetImageROI( mask, selection ); // 得到选择区域 for mask
cvCalcHist( &hue, hist, 0, mask ); // 计算直方图
cvGetMinMaxHistValue( hist, 0, &max_val, 0, 0 ); // 只找最大值
cvConvertScale( hist->bins, hist->bins, max_val ? 255. / max_val : 0., 0 ); // 缩放 bin 到区间 [0,255]
cvResetImageROI( hue ); // remove ROI
cvResetImageROI( mask );
track_window = selection;
track_object = 1;

cvZero( histimg );
bin_w = histimg->width / hdims; // hdims: 条的个数，则 bin_w 为条的宽度

// 画直方图
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 ); // 使用 back project 方法
cvAnd( backproject, mask, backproject, 0 );

// calling CAMSHIFT 算法模块
cvCamShift( backproject, track_window,
cvTermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ),
&track_comp, &track_box );
track_window = track_comp.rect;

if( backproject_mode )
cvCvtColor( backproject, image, CV_GRAY2BGR ); // 使用backproject灰度图像
if( image->origin )
track_box.angle = -track_box.angle;
cvEllipseBox( image, track_box, CV_RGB(255,0,0), 3, CV_AA, 0 );
}

if( select_object && selection.width > 0 && selection.height > 0 )
{
cvSetImageROI( image, selection );
cvXorS( image, cvScalarAll(255), image, 0 );
cvResetImageROI( image );
}

cvShowImage( "CamShiftDemo", image );
cvShowImage( "Histogram", histimg );

c = cvWaitKey(10);
if( c == 27 )
break; // exit from for-loop
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;
}

#ifdef _EiC
main(1,"camshiftdemo.c");
#endif