opencv之运动目标动作分割

本文所需要了解的库函数有：

void cvAbsDiff( const CvArr* src1, const CvArr* src2, CvArr* dst );//计算两个数组差的绝对值

void cvUpdateMotionHistory( const CvArr* silhouette, CvArr* mhi,
double timestamp, double duration );去掉影像(silhouette) 以更新运动历史图像

void cvCalcMotionGradient( const CvArr* mhi, CvArr* mask, CvArr* orientation,
double delta1, double delta2, int aperture_size=3 );计算运动历史图像的梯度方向

CvSeq* cvSegmentMotion( const CvArr* mhi, CvArr* seg_mask, CvMemStorage* storage,
double timestamp, double seg_thresh );将整个运动分割为独立的运动部分，返回独立的CvConnectedComp运动部件

double cvCalcGlobalOrientation( const CvArr* orientation, const CvArr* mask, const CvArr* mhi,
double timestamp, double duration );计算某些选择区域的全局运动方向

double cvNorm( const CvArr* arr1, const CvArr* arr2=NULL, int norm_type=CV_L2, const CvArr* mask=NULL );//计算范数

如果 arr2 为空（NULL）

norm = ||arr1||C = maxI abs(arr1(I)), 如果 normType = CV_C

norm = ||arr1||L1 = sumI abs(arr1(I)), 如果 normType = CV_L1

norm = ||arr1||L2 = sqrt( sumI arr1(I)2), 如果 normType = CV_L2

/* types of array norm */

#define CV_C 1

#define CV_L1 2

#define CV_L2 4

#define CV_NORM_MASK 7

#define CV_RELATIVE 8

#define CV_DIFF 16

#define CV_MINMAX 32

#define CV_DIFF_C (CV_DIFF | CV_C)

#define CV_DIFF_L1 (CV_DIFF | CV_L1)

#define CV_DIFF_L2 (CV_DIFF | CV_L2)

#define CV_RELATIVE_C (CV_RELATIVE | CV_C)

#define CV_RELATIVE_L1 (CV_RELATIVE | CV_L1)

#define CV_RELATIVE_L2 (CV_RELATIVE | CV_L2)

更多参考:http://www.opencv.org.cn/index.php/Cxcore%E6%95%B0%E7%BB%84%E6%93%8D%E4%BD%9C

对于上式，有个数学概念要明确：（范数）

当p取1，2，∞的时候分别是以下几种最简单的情形：
1-范数：║x║1=│x1│+│x2│+…+│xn│
2-范数：║x║2=（│x1│^2+│x2│^2+…+│xn│^2）^1/2
∞-范数：║x║∞=max（│x1│，│x2│，…，│xn│）
其中2-范数就是通常意义下的距离。

#pragma comment(lib,"cv.lib")
#pragma comment(lib,"cvaux.lib")
#pragma comment(lib,"highgui.lib")
#pragma comment(lib,"cxcore.lib")
#include<cv.h>
#include<highgui.h>
#include<stdio.h>
#include<time.h>
#include"Camer.h"

IplImage* frame;//原始图像
IplImage* dist;//最终的显示图像
IplImage* pre=NULL;//前一张图像
IplImage* gray;//灰度图
IplImage* diff;//差值图
IplImage *mhi = 0; // 运动历史图
IplImage *orient = 0; // 方向图
IplImage *mask = 0; //掩码图
IplImage *segmask = 0;
CvMemStorage* storage = 0; // 内存仓
const char* windowname= "演示";

void  Process( IplImage* img)
{
double timestamp = (double)clock()/CLOCKS_PER_SEC; // get current time in seconds
CvSize size = cvSize(img->width,img->height); // get current frame size

cvCvtColor(img,gray,CV_BGR2GRAY);//灰度转换
cvAbsDiff(gray,pre,diff);//计算差值
cvCopy( gray,pre);//拷贝数据 便于下次使用
cvThreshold( diff, diff, 30, 1, CV_THRESH_BINARY ); // 二值化
cvUpdateMotionHistory( diff, mhi, timestamp, 1 ); // 运动历史图

// 转换MHI到8U
cvCvtScale( mhi, mask, 255,(1 - timestamp)*255 );
cvZero( dist );
cvCvtPlaneToPix( mask, 0, 0, 0, dist );

// 计算运动的方向图
cvCalcMotionGradient( mhi, mask, orient, 0.5, 0.05, 3 );

//分割运动到独立的部分
cvClearMemStorage(storage);
CvSeq *seq = cvSegmentMotion( mhi, segmask, storage, timestamp, 1 );

//获取所有的运动组件
for(int i = -1; i < seq->total; i++ ) {

CvRect comp_rect;
CvScalar color;
double magnitude;
if( i < 0 ) { // 整张图片的运动
comp_rect = cvRect( 0, 0, size.width, size.height );
color = CV_RGB(255,255,255);
magnitude = 100;
}
else { //单独的运动部件
comp_rect = ((CvConnectedComp*)cvGetSeqElem( seq, i ))->rect;
if( comp_rect.width + comp_rect.height < 100 ) // reject very small components
continue;
color = CV_RGB(255,0,0);
magnitude = 30;
}

//设置ROI
cvSetImageROI( diff, comp_rect );
cvSetImageROI( mhi, comp_rect );
cvSetImageROI( orient, comp_rect );
cvSetImageROI( mask, comp_rect );

// 计算方向
double angle = cvCalcGlobalOrientation( orient, mask, mhi, timestamp, 1);
angle = 360.0 - angle;  // adjust for images with top-left origin
double count = cvNorm( diff, 0, CV_L1, 0 ); // calculate number of points within silhouette ROI

//重设ROI
cvResetImageROI( mhi );
cvResetImageROI( orient );
cvResetImageROI( mask );
cvResetImageROI( diff );

// check for the case of little motion
if( count < comp_rect.width*comp_rect.height * 0.05 )
continue;

// draw a clock with arrow indicating the direction
CvPoint center = cvPoint( (comp_rect.x + comp_rect.width/2),
(comp_rect.y + comp_rect.height/2) );

cvCircle( dist, center, cvRound(magnitude*1.2), color, 3, CV_AA, 0 );
cvLine( dist, center, cvPoint( cvRound( center.x + magnitude*cos(angle*CV_PI/180)),
cvRound( center.y - magnitude*sin(angle*CV_PI/180))), color, 3, CV_AA, 0 );
}
}

int main()
{
CCameraDS camer;
camer.OpenCamera(0,false); //打开摄像头
frame = camer.QueryFrame();
if(frame==NULL)
{
return 1;
}
//创建相应的变量
pre= cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1); //创建前一张显示图像的存储空间
dist = cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,3); //创建显示图像
gray=cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);//灰度图像
diff=cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);//差值图像
mhi = cvCreateImage( cvGetSize(frame), IPL_DEPTH_32F, 1 );
orient = cvCreateImage( cvGetSize(frame), IPL_DEPTH_32F, 1 );
segmask = cvCreateImage( cvGetSize(frame), IPL_DEPTH_32F, 1 );
mask = cvCreateImage( cvGetSize(frame), IPL_DEPTH_8U, 1 );

storage = cvCreateMemStorage(0);

cvNamedWindow(windowname,1);

while(true)
{
frame = camer.QueryFrame();
if(!frame)
break;

Process(frame);//处理图像
cvShowImage(windowname,dist);
if(cvWaitKey(10)>=0) break;
}
cvReleaseImage(&dist);
cvReleaseImage(&pre);
cvReleaseImage(&gray);
cvReleaseImage(&diff);
cvReleaseImage(&mhi);
cvReleaseImage(&orient);
cvReleaseImage(&segmask);
cvReleaseImage(&mask);
cvReleaseMemStorage(&storage);
cvDestroyWindow(windowname);
return 0;
}