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<iostream>#include<stdio.h>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<Mat>rgb_planes;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<histSize;i++){line(histImage,Point(bin_w*(i-1),hist_h-cvRound(r_hist.at<float>(i-1))), Point(bin_w*(i),hist_h-cvRound(r_hist.at<float>(i))),Scalar(0,0,255),2,8,0);line(histImage,Point(bin_w*(i-1),hist_h-cvRound(g_hist.at<float>(i-1))), Point(bin_w*(i),hist_h-cvRound(g_hist.at<float>(i))),Scalar(0,255,0),2,8,0);line(histImage,Point(bin_w*(i-1),hist_h-cvRound(b_hist.at<float>(i-1))), Point(bin_w*(i),hist_h-cvRound(b_hist.at<float>(i))),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<cv.h>#include<highgui.h>#include<iostream>#include<cmath>#include<cstdlib>#include<cstdio>using namespace cv;using namespace std;Mat src,dst,tmp;char*window_name="Pyramids Demo";int main(){cout<<"\n Zoom In-Out demo\n"<<endl;cout<<"--------------------\n";cout<<"*[u]->Zoom in\n"<<endl;cout<<"*[d]->Zoom out\n"<<endl;cout<<"*[ESC]->Close program\n\n"<<endl;src=imread("001.jpg");if(!src.data){cout<<"No data!--Exiting the program\n"<<endl;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"<<endl;}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 componentssd_init=6;//initial standard deviation(for new components)var=36in paperalph=0.01;//learning rate(between0and1)(from paper0.01)D=2.5;//positive deviation thresholdthresh=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 arraybg_bw=(int*)malloc(sizeof(int)*width*height);//background arrayrank=(double*)malloc(sizeof(double)*1*C);//rank of components(w/sd)w=(double*)malloc(sizeof(double)*width*height*C);//weights arraymean=(double*)malloc(sizeof(double)*width*height*C);//pixel meanssd=(double*)malloc(sizeof(double)*width*height*C);//pixel standard deviationsu_diff=(double*)malloc(sizeof(double)*width*height*C);//difference of each pixel from mean for(i=0;i<height;i++){for(j=0;j<width;j++){for(k=0;k<C;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 meanfor(i=0;i<height;i++){for(j=0;j<width;j++){for(m=0;m<C;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 pixelfor(i=0;i<height;i++){for(j=0;j<width;j++){match=0;temp=0;for(k=0;k<C;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,pw[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<C;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<C;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 componentif(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<C;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 valuesfor(k=1;k<C;k++){for(m=0;m<k;m++){if(rank[k]>rank[m]){//swap max valuesrand_temp=rank[m];rank[m]=rank[k];rank[k]=rand_temp;//swap max index valuesrank_ind_temp=rank_ind[m];rank_ind[m]=rank_ind[k];rank_ind[k]=rank_ind_temp;}}}//calculate foregroundmatch=0;k=0;//frg->imageData[i*width+j]=0;while((match==0)&&(k<M)){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;}elsefrg->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<iostream>#include<ctype.h>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<int>("1");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256createTrackbar("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)。