javaCV之⽬标跟踪
Opencv应⽤⾮常的⼴泛，最近⼀段时间在学习它。 基于WEB开发的⼤型程序，主流是使⽤Java进⾏开发，⽽java处理底层的东西时速度不理想，于是通过Java调⽤C开发的库⽂件变得很流⾏，JavaCV就是在这个⼤的环境下产⽣了。JavaCV它实现了java和OpenCV的关联，为Java程序员提供了⼀个很好的处理图像和视频的API。JavaCV开发环境的搭配就不在这个地⽅多⼝⾆了，Google,Baidu都有。
下⾯是基于Camshift算法实现了摄像头下对运动物体的跟踪⽤JavaCV来实现:
lecode.javacpp.Pointer;
lecode.javacv.cpp.opencv_highgui.CvCapture;
import lecode.javacv.cpp.opencv_core.*;
import lecode.javacv.cpp.opencv_imgproc.*;
import lecode.javacv.cpp.opencv_highgui.*;
import lecode.javacv.cpp.opencv_video.*;
public class JavaCVCamShift{
IplImage frame, image , hsv , hue , mask , backproject , histimg ;
IplImage[] imageArray;
//⽤HSV中的Hue分量进⾏跟踪
CvHistogram hist ;
//直⽅图类
int x1=0,y1=0,x2=0,y2=0;//选取对象的坐标
int backproject_mode = 0;
int select_object = 0;
int track_object = 0;
int show_hist = 1;
CvPoint origin;
CvPoint cp1,cp2;
CvRect selection;
CvRect track_window;
CvBox2D track_box;
float[] max_val=new float[1];
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
CvConnectedComp track_comp;
public JavaCVCamShift()
{
imageArray=new IplImage[1];
CvCapture capture= cvCreateCameraCapture(0);
cvNamedWindow("imageName",CV_WINDOW_AUTOSIZE);
Pointer pointer=null;
cvSetMouseCallback("imageName",new mouseClike(),pointer);
track_comp=new CvConnectedComp();
while(true)
{
frame=cvQueryFrame(capture);
if(frame==null)break;
if( image==null )
//image为空，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 );
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
//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
}
cvCopy(frame,image);
cvCvtColor( image, hsv, CV_BGR2HSV );
if( track_object !=0)
//track_object⾮零，表⽰有需要跟踪的物体
{
double _vmin = 10.0, _vmax = 256.0,smin=30.0;
cvInRangeS( hsv, cvScalar(0.0,smin,Math.min(_vmin,_vmax),0.0), cvScalar(180.0,256.0,Math.max(_vmin,_vmax),0.0), mask ); //，只处理像素值为H：0~180，S：smin~256，V：vmin~vmax之间的部分制作掩膜板
cvSplit( hsv, hue, null, null, null );
//分离H分量
imageArray[0]=hue;
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
{
cvSetImageROI( imageArray[0],selection );
//设置原选择框为ROI
cvSetImageROI( mask,selection );
//设置掩膜板选择框为ROI
cvCalcHist( imageArray,hist,0,mask );
//得到选择框内且满⾜掩膜板内的直⽅图rectangle函数opencv
cvGetMinMaxHistValue( hist, null, max_val, null, null );
cvConvertScale( hist.bins(), hist.bins(),max_val[0]>0 ? (double)255/ max_val[0]:0.0,0 );
// 对直⽅图的数值转为0~255
cvResetImageROI( imageArray[0] );
//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
}
cvCalcBackProject( imageArray, backproject, hist );
//计算hue的反向投影图
cvAnd( backproject, mask, backproject, null );
//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 = ();
//得到跟踪结果的矩形框
cp1=cvPoint(track_window.x(),track_window.y());
cp2=cvPoint(track_window.x()+track_window.width(),track_window.y()+track_window.height());
if( igin()>0 )
track_box.angle(-track_box.angle());
cvRectangle(frame,cp1,cp2, CV_RGB(0,255,0),3,CV_AA,0);
}
if( select_object==1 && selection.width() > 0 && selection.height() > 0 )
/
/如果正处于物体选择，画出选择框
{
cvSetImageROI( frame, selection );
cvXorS(frame,cvScalarAll(255),frame,null );
cvResetImageROI( frame );
}
cvShowImage("imageName",frame);
int c=cvWaitKey(33);
if(c==27) break;
}
}
cvReleaseCapture(capture);
cvDestroyWindow("imageName");
}
public static void main(String[] args) {
new JavaCVCamShift();
}
class mouseClike extends CvMouseCallback
{
public void call(int event,int x, int y,int flags, Pointer param)
//⿏标回调函数，该函数⽤⿏标进⾏跟踪⽬标的选择
{
if( image==null )
return;
if( igin()!=0 )
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==1 )
//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(Math.min(x,origin.x()));
selection.y(Math.min(y,origin.y()));
selection.width(selection.x() + Math.abs(x - origin.x()));
selection.height(selection.y() + Math.abs(y - origin.y()));
selection.x(Math.max(selection.x(),0));
selection.y(Math.max(selection.y(),0 ));
selection.width(Math.min( selection.width(), image.width() )); selection.height(Math.min( selection.height(), image.height())); selection.width(selection.width()-selection.x());
selection.height( 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(0,0,0,0);
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;
}
}
}
}
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