camshiftdemozhushi.rar_opencv camshiftdemo_opencv 图像识别_opencv

//对运动物体的跟踪： //如果背景固定,可用帧差法 然后在计算下连通域 将面积小的去掉即可 //如果背景单一,即你要跟踪的物体颜色和背景色有较大区别 可用基于颜色的跟踪 如CAMSHIFT 鲁棒性都是较好的 //如果背景复杂,如背景中有和前景一样的颜色 就需要用到一些具有预测性的算法 如卡尔曼滤波等 可以和CAMSHIFT结合 #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; //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 //typedef struct CvBox2D{ //CvPoint2D32f center; /* 盒子的中心 */ //CvSize2D32f size; /* 盒子的长和宽 */ //float angle; /* 水平轴与第一个边的夹角，用弧度表示*/ //}CvBox2D; CvConnectedComp track_comp; //连接部件 //typedef struct CvConnectedComp{ //double area; /* 连通域的面积 */ //float value; /* 分割域的灰度缩放值 */ //CvRect rect; /* 分割域的 ROI */ //} CvConnectedComp; 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 int vmin = 10, vmax = 256, smin = 30; //用于设置滑动条 void on_mouse( int event, int x, int y, int flags, void* param ) //鼠标回调函数,该函数用鼠标进行跟踪目标的选择 { if( !image ) return; if( image->origin ) 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 ) //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 = 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: //鼠标按下,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: //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; } } 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]))) //打开摄像头 capture = cvCaptureFromCAM( argc == 2 ? argv[1][0] - '0' : 0 ); else if( argc == 2 ) //打开avi capture = cvCaptureFromAVI( argv[1] ); if( !capture ) //打开视频流失败 { 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" ); //打印程序功能列表 cvNamedWindow( "Histogram", 1 ); //用于显示直方图 cvNamedWindow( "CamShiftDemo", 1 ); //用于显示视频 cvSetMouseCallback( "CamShiftDemo", on_mouse, 0 ); //设置鼠标回调函数 cvCreateTrackbar( "Vmin", "CamShiftDemo", &vmin, 256, 0 ); cvCreateTrackbar( "Vmax", "CamShiftDemo", &vmax, 256, 0 ); cvCreateTrackbar( "Smin", "CamShiftDemo", &smin, 256, 0 ); //设置滑动条 for(;;) //进入视频帧处理主循环 { IplImage* frame = 0; int i, bin_w, c; frame = cvQueryFrame( capture ); if( !frame ) break; if( !image ) //image为0,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 ); 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 ); //分配直方图空间 histimg = cvCreateImage( cvSize(320,200), 8, 3 ); //分配用于直方图显示的空间 cvZero( histimg ); //置背景为黑色 } 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 ) //track_object非零,表示有需要跟踪的物体 { 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 ); //分离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 ); //设置原选择框为ROI cvSetImageROI( mask, selection ); //设置掩膜板选择框为ROI 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 ); // 对直方图的数值转为0~255 cvResetImageROI( hue ); //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 cvZero( histimg ); bin_w = histimg->width / hdims; for( i = 0; i < hdims; i++ ) //画直方图到图像空间 { 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(