OpenCV学习笔记非打印版本
OpenCV学习笔记非打印版本直方图
#include"opencv2/highgui/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include
#include
using namespace std;
using namespace cv;
/**@函数main*/
int main(int argc,char**argv)
{
Mat src,dst;
///装载图像
src=imread("001.jpg");
if(!src.data)
{
return-1;
}
///分割成3个单通道图像(R,G和B)
vector
split(src,rgb_planes);
///设定bin数目
int histSize=255;
///设定取值范围(R,G,B))
float range[]={0,255};
const float*histRange={range};
bool uniform=true;bool accumulate=false;
Mat r_hist,g_hist,b_hist;
///计算直方图:
calcHist(&rgb_planes[0],1,0,Mat(),r_hist,1,&histSize,&histRange,uniform,accumulate); calcHist(&rgb_planes[1],1,0,Mat(),g_hist,1,&histSize,&histRange,uniform,accumulate); calcHist(&rgb_planes[2],1,0,Mat(),b_hist,1,&histSize,&histRange,uniform,accumulate); //创建直方图画布
int hist_w=400;int hist_h=400;
int bin_w=cvRound((double)hist_w/histSize);
Mat histImage(hist_w,hist_h,CV_8UC3,Scalar(0,0,0));
///将直方图归一化到范围[0,histImage.rows]
normalize(r_hist,r_hist,0,histImage.rows,NORM_MINMAX,-1,Mat());
normalize(g_hist,g_hist,0,histImage.rows,NORM_MINMAX,-1,Mat());
normalize(b_hist,b_hist,0,histImage.rows,NORM_MINMAX,-1,Mat());
///在直方图画布上画出直方图
for(int i=1;i { line(histImage,Point(bin_w*(i-1),hist_h-cvRound(r_hist.at Scalar(0,0,255),2,8,0); line(histImage,Point(bin_w*(i-1),hist_h-cvRound(g_hist.at Scalar(0,255,0),2,8,0); line(histImage,Point(bin_w*(i-1),hist_h-cvRound(b_hist.at Scalar(255,0,0),2,8,0); } ///显示直方图 namedWindow("calcHist Demo",CV_WINDOW_AUTOSIZE); imshow("calcHist Demo",histImage); waitKey(0); return0; } 图像金字塔 #include #include #include #include #include #include using namespace cv; using namespace std; Mat src,dst,tmp; char*window_name="Pyramids Demo"; int main() { cout<<"\n Zoom In-Out demo\n"< cout<<"--------------------\n"; cout<<"*[u]->Zoom in\n"< cout<<"*[d]->Zoom out\n"< cout<<"*[ESC]->Close program\n\n"< src=imread("001.jpg"); if(!src.data) { cout<<"No data!--Exiting the program\n"< return-1; } tmp=src; dst=tmp; namedWindow(window_name,CV_WINDOW_AUTOSIZE); imshow(window_name,dst); while(true) { int c; c=waitKey(10); if((char)c==27) { break; } if((char)c=='u') { pyrUp(tmp,dst,Size(tmp.cols*2,tmp.rows*2)); printf("**Zoom In:Image x2\n"); } else if((char)c=='d') { pyrDown(tmp,dst,Size(tmp.cols/2,tmp.rows/2)); cout<<"**Zoom Out:Image/2\n"< } imshow(window_name,dst); tmp=dst; } return0; } 基于混合高斯背景建模的目标跟踪 //混合高斯背景建模的目标跟踪 #include"cv.h" #include"highgui.h" int main() { CvCapture*capture=cvCreateFileCapture("001.mp4"); IplImage*mframe,*current,*frg,*test; int*fg,*bg_bw,*rank_ind; double*w,*mean,*sd,*u_diff,*rank; int C,M,sd_init,i,j,k,m,rand_temp=0,rank_ind_temp=0,min_index=0,x=0,y=0,counter_frame =0; double D,alph,thresh,p,temp; CvRNG state; int match,height,width; mframe=cvQueryFrame(capture); frg=cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1); current=cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1); test=cvCreateImage(cvSize(mframe->width,mframe->height),IPL_DEPTH_8U,1); C=4;//number of gaussian components(typically3-5) M=4;//number of background components sd_init=6;//initial standard deviation(for new components)var=36in paper alph=0.01;//learning rate(between0and1)(from paper0.01) D=2.5;//positive deviation threshold thresh=0.25;//foreground threshold(0.25or0.75in paper) p=alph/(1/C);//initial p variable(used to update mean and sd) height=current->height;width=current->widthStep; fg=(int*)malloc(sizeof(int)*width*height);//foreground array bg_bw=(int*)malloc(sizeof(int)*width*height);//background array rank=(double*)malloc(sizeof(double)*1*C);//rank of components(w/sd) w=(double*)malloc(sizeof(double)*width*height*C);//weights array mean=(double*)malloc(sizeof(double)*width*height*C);//pixel means sd=(double*)malloc(sizeof(double)*width*height*C);//pixel standard deviations u_diff=(double*)malloc(sizeof(double)*width*height*C);//difference of each pixel from mean for(i=0;i { for(j=0;j { for(k=0;k { mean[i*width*C+j*C+k]=cvRandReal(&state)*255; w[i*width*C+j*C+k]=(double)1/C; sd[i*width*C+j*C+k]=sd_init; } } } while(1){ rank_ind=(int*)malloc(sizeof(int)*C); cvCvtColor(mframe,current,CV_BGR2GRAY); //calculate difference of pixel values from mean for(i=0;i { for(j=0;j { for(m=0;m { u_diff[i*width*C+j*C+m]=abs((uchar)current->imageData[i*width+j]-mean[i*width*C +j*C+m]); } } } //update gaussian components for each pixel for(i=0;i { for(j=0;j { match=0; temp=0; for(k=0;k { if(abs(u_diff[i*width*C+j*C+k])<=D*sd[i*width*C+j*C+k])//pixel matches component { match=1;//variable to signal component match //update weights,mean,sd,p w[i*width*C+j*C+k]=(1-alph)*w[i*width*C+j*C+k]+alph; p=alph/w[i*width*C+j*C+k]; mean[i*width*C+j*C+k]=(1-p)*mean[i*width*C+j*C+k]+ p*(uchar)current->imageData[i*width+j]; sd[i*width*C+j*C+k]=sqrt((1-p)*(sd[i*width*C+j*C+k]*sd[i*width*C+j*C +k])+p*(pow((uchar)current->imageData[i*width+j]-mean[i*width*C+j*C+k],2))); } else{ w[i*width*C+j*C+k]=(1-alph)*w[i*width*C+j*C+k];//weight slighly decreases } temp+=w[i*width*C+j*C+k]; } for(k=0;k { w[i*width*C+j*C+k]=w[i*width*C+j*C+k]/temp; } temp=w[i*width*C+j*C]; bg_bw[i*width+j]=0; for(k=0;k { bg_bw[i*width+j]=bg_bw[i*width+j]+mean[i*width*C+j*C+k]*w[i*width*C+j*C +k]; if(w[i*width*C+j*C+k]<=temp) { min_index=k; temp=w[i*width*C+j*C+k]; } rank_ind[k]=k; } test->imageData[i*width+j]=(uchar)bg_bw[i*width+j]; //if no components match,create new component if(match==0) { mean[i*width*C+j*C+min_index]=(uchar)current->imageData[i*width+j]; //printf("%d",(uchar)bg->imageData[i*width+j]); sd[i*width*C+j*C+min_index]=sd_init; } for(k=0;k { rank[k]=w[i*width*C+j*C+k]/sd[i*width*C+j*C+k]; //printf("%f",w[i*width*C+j*C+k]); } //sort rank values for(k=1;k { for(m=0;m { if(rank[k]>rank[m]) { //swap max values rand_temp=rank[m]; rank[m]=rank[k]; rank[k]=rand_temp; //swap max index values rank_ind_temp=rank_ind[m]; rank_ind[m]=rank_ind[k]; rank_ind[k]=rank_ind_temp; } } } //calculate foreground match=0;k=0; //frg->imageData[i*width+j]=0; while((match==0)&&(k if(w[i*width*C+j*C+rank_ind[k]]>=thresh) if(abs(u_diff[i*width*C+j*C+rank_ind[k]])<=D*sd[i*width*C+j*C+rank_ind[k]]){ frg->imageData[i*width+j]=0; match=1; } else frg->imageData[i*width+j]=(uchar)current->imageData[i*width+j]; k=k+1; } } } mframe=cvQueryFrame(capture); cvShowImage("fore",frg); cvShowImage("back",test); char s=cvWaitKey(33); if(s==27)break; free(rank_ind); } free(fg);free(w);free(mean);free(sd);free(u_diff);free(rank); cvNamedWindow("back",0); cvNamedWindow("fore",0); cvReleaseCapture(&capture); cvDestroyWindow("fore"); cvDestroyWindow("back"); return0; } Camshift #include"opencv2/video/tracking.hpp" #include"opencv2/imgproc/imgproc.hpp" #include"opencv2/highgui/highgui.hpp" #include #include using namespace cv; using namespace std; Mat image; bool backprojMode=false;//表示是否要进入反向投影模式，ture表示准备进入反向投影模式bool selectObject=false;//代表是否在选要跟踪的初始目标，true表示正在用鼠标选择 int trackObject=0;//代表跟踪目标数目 bool showHist=true;//是否显示直方图 Point origin;//用于保存鼠标选择第一次单击时点的位置 Rect selection;//用于保存鼠标选择的矩形框 int vmin=10,vmax=256,smin=30; void onMouse(int event,int x,int y,int,void*) { if(selectObject)//只有当鼠标左键按下去时才有效，然后通过if里面代码就可以确定所选择的矩形区域selection了{ selection.x=MIN(x,origin.x);//矩形左上角顶点坐标 selection.y=MIN(y,origin.y); selection.width=std::abs(x-origin.x);//矩形宽 selection.height=std::abs(y-origin.y);//矩形高 selection&=Rect(0,0,image.cols,image.rows);//用于确保所选的矩形区域在图片范围内} switch(event) { case CV_EVENT_LBUTTONDOWN: origin=Point(x,y); selection=Rect(x,y,0,0);//鼠标刚按下去时初始化了一个矩形区域 selectObject=true; break; case CV_EVENT_LBUTTONUP: selectObject=false; if(selection.width>0&&selection.height>0) trackObject=-1; break; } } void help() { cout<<"\nThis is a demo that shows mean-shift based tracking\n" "You select a color objects such as your face and it tracks it.\n" "This reads from video camera(0by default,or the camera number the user enters\n" "Usage:\n" "./camshiftdemo[camera number]\n"; cout<<"\n\nHot 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" "\tp-pause video\n" "To initialize tracking,select the object with mouse\n"; } const char*keys= { "{1||0|camera number}" }; int main(int argc,const char**argv) { help(); VideoCapture cap;//定义一个摄像头捕捉的类对象 Rect trackWindow; RotatedRect trackBox;//定义一个旋转的矩阵类对象 int hsize=16; float hranges[]={0,180};//hranges在后面的计算直方图函数中要用到const float*phranges=hranges; CommandLineParser parser(argc,argv,keys);//命令解析器函数 int camNum=parser.get cap.open(camNum);//直接调用成员函数打开摄像头 if(!cap.isOpened()) { help(); cout<<"***Could not initialize capturing...***\n"; cout<<"Current parameter's value:\n"; parser.printParams(); return-1; } namedWindow("Histogram",0); namedWindow("CamShift Demo",0); setMouseCallback("CamShift Demo",onMouse,0);//消息响应机制 createTrackbar("Vmin","CamShift Demo",&vmin,256,0);//createTrackbar函数的功能是在对应的窗口创建滑动条，滑动条Vmin,vmin表示滑动条的值，最大为256 createTrackbar("Vmax","CamShift Demo",&vmax,256,0);//最后一个参数为0代表没有调用滑动拖动的响应函数createTrackbar("Smin","CamShift Demo",&smin,256,0);//vmin,vmax,smin初始值分别为10,256,30 Mat frame,hsv,hue,mask,hist,histimg=Mat::zeros(200,320,CV_8UC3),backproj; bool paused=false; for(;;) { if(!paused)//没有暂停 { cap>>frame;//从摄像头抓取一帧图像并输出到frame中 if(frame.empty()) break; } frame.copyTo(image); if(!paused)//没有按暂停键 { cvtColor(image,hsv,CV_BGR2HSV);//将rgb摄像头帧转化成hsv空间的 if(trackObject)//trackObject初始化为0,或者按完键盘的'c'键后也为0，当鼠标单击松开后为-1 { int_vmin=vmin,_vmax=vmax; //inRange函数的功能是检查输入数组每个元素大小是否在2个给定数值之间，可以有多通道,mask保存0通道的最小值，也就是h分量 //这里利用了hsv的3个通道，比较h,0~180,s,smin~256,v,min(vmin,vmax),max(vmin,vmax)。如果3个通道都在对应的范围内，则 //mask对应的那个点的值全为1(0xff)，否则为0(0x00). inRange(hsv,Scalar(0,smin,MIN(_vmin,_vmax)), Scalar(180,256,MAX(_vmin,_vmax)),mask); int ch[]={0,0}; hue.create(hsv.size(),hsv.depth());//hue初始化为与hsv大小深度一样的矩阵，色调的度量是用角度表示的，红绿蓝之间相差120度，反色相差180度 mixChannels(&hsv,1,&hue,1,ch,1);//将hsv第一个通道(也就是色调)的数复制到hue中，0索引数组 if(trackObject<0)//鼠标选择区域松开后，该函数内部又将其赋值1 { //此处的构造函数roi用的是Mat hue的矩阵头，且roi的数据指针指向hue，即共用相同的数据，select 为其感兴趣的区域