#include <opencv2/core/core.hpp> #include "opencv2/imgproc/imgproc.hpp" #include <opencv2/highgui/highgui.hpp> #include <stdio.h> #include <iostream>
using namespace cv; using namespace std;
Rect select; bool select_flag = false; bool tracking = false;//跟踪标志位 bool select_show = false; Point origin; Mat frame, hsv; int after_select_frames = 0;//选择矩形区域完后的帧计数
/****rgb空间用到的变量****/ //int hist_size[]={16,16,16};//rgb空间各维度的bin个数 //float rrange[]={0,255.0}; //float grange[]={0,255.0}; //float brange[]={0,255.0}; //const float *ranges[] ={rrange,grange,brange};//range相当于一个二维数组指针
/****hsv空间用到的变量****/ int hist_size[] = { 16, 16, 16 }; float hrange[] = { 0, 180.0 }; float srange[] = { 0, 256.0 }; float vrange[] = { 0, 256.0 };
//int hist_size[]={32,32,32}; //float hrange[]={0,359.0.0}; //float srange[]={0,1.0}; //float vrange[]={0,1.0}; const float *ranges[] = { hrange, srange, vrange };
int channels[] = { 0, 1, 2 };
/****有关粒子窗口变化用到的相关变量****/ int A1 = 2; int A2 = -1; int B0 = 1; double sigmax = 1.0; double sigmay = 0.5; double sigmas = 0.001;
/****定义使用粒子数目宏****/ #define PARTICLE_NUMBER 100 //如果这个数设定太大，经测试这个数字超过25就会报错，则在运行时将会出现错误
/****定义粒子结构体****/ typedef struct particle { int orix, oriy;//原始粒子坐标 int x, y;//当前粒子的坐标 double scale;//当前粒子窗口的尺寸 int prex, prey;//上一帧粒子的坐标 double prescale;//上一帧粒子窗口的尺寸 Rect rect;//当前粒子矩形窗口 Mat hist;//当前粒子窗口直方图特征 double weight;//当前粒子权值 }PARTICLE;
PARTICLE particles[PARTICLE_NUMBER];
/************************************************************************************************************************/ /**** 如果采用这个onMouse()函数的话，则可以画出鼠标拖动矩形框的4种情形 ****/ /************************************************************************************************************************/ void onMouse(int event, int x, int y, int, void*) { //Point origin;//不能在这个地方进行定义，因为这是基于消息响应的函数，执行完后origin就释放了，所以达不到效果。 if (select_flag) { select.x = MIN(origin.x, x);//不一定要等鼠标弹起才计算矩形框，而应该在鼠标按下开始到弹起这段时间实时计算所选矩形框 select.y = MIN(origin.y, y); select.width = abs(x - origin.x);//算矩形宽度和高度 select.height = abs(y - origin.y); select &= Rect(0, 0, frame.cols, frame.rows);//保证所选矩形框在视频显示区域之内
// rectangle(frame,select,Scalar(0,0,255),3,8,0);//显示手动选择的矩形框 } if (event == CV_EVENT_LBUTTONDOWN) { select_flag = true;//鼠标按下的标志赋真值 tracking = false; select_show = true; after_select_frames = 0;//还没开始选择，或者重新开始选择，计数为0 origin = Point(x, y);//保存下来单击是捕捉到的点 select = Rect(x, y, 0, 0);//这里一定要初始化，因为在opencv中Rect矩形框类内的点是包含左上角那个点的，但是不含右下角那个点。 } else if (event == CV_EVENT_LBUTTONUP) { select_flag = false; tracking = true; select_show = false; after_select_frames = 1;//选择完后的那一帧当做第1帧 } }
/****粒子权值降序排列函数****/ int particle_decrease(const void *p1, const void *p2) { PARTICLE* _p1 = (PARTICLE*)p1; PARTICLE* _p2 = (PARTICLE*)p2; if (_p1->weight<_p2->weight) return 1; else if (_p1->weight>_p2->weight) return -1; return 0;//相等的情况下返回0 }
int main(int argc, unsigned char* argv[]) { char c; Mat target_img, track_img; Mat target_hist, track_hist; PARTICLE *pParticle;
/***打开摄像头****/ VideoCapture cam(0); if (!cam.isOpened()) return -1;
/****读取一帧图像****/ cam >> frame; if (frame.empty()) return -1;
VideoWriter output_dst("demo.avi", CV_FOURCC('M', 'J', 'P', 'G'), 10, frame.size(), 1);
/****建立窗口****/ namedWindow("camera", 1);//显示视频原图像的窗口
/****捕捉鼠标****/ setMouseCallback("camera", onMouse, 0);
while (1) { /****读取一帧图像****/ cam >> frame; if (frame.empty()) return -1;
/****将rgb空间转换为hsv空间****/ cvtColor(frame, hsv, CV_BGR2HSV);
if (tracking) {
if (1 == after_select_frames)//选择完目标区域后 { /****计算目标模板的直方图特征****/ target_img = Mat(hsv, select);//在此之前先定义好target_img,然后这样赋值也行，要学会Mat的这个操作 calcHist(&target_img, 1, channels, Mat(), target_hist, 3, hist_size, ranges); normalize(target_hist, target_hist);
/****初始化目标粒子****/ pParticle = particles;//指针初始化指向particles数组 for (int x = 0; x<PARTICLE_NUMBER; x++) { pParticle->x = cvRound(select.x + 0.5*select.width);//选定目标矩形框中心为初始粒子窗口中心 pParticle->y = cvRound(select.y + 0.5*select.height); pParticle->orix = pParticle->x;//粒子的原始坐标为选定矩形框(即目标)的中心 pParticle->oriy = pParticle->y; pParticle->prex = pParticle->x;//更新上一次的粒子位置 pParticle->prey = pParticle->y; pParticle->rect = select; pParticle->prescale = 1; pParticle->scale = 1; pParticle->hist = target_hist; pParticle->weight = 0; pParticle++; } } else if (2 == after_select_frames)//从第二帧开始就可以开始跟踪了 { double sum = 0.0; pParticle = particles; RNG rng;//随机数产生器
/****更新粒子结构体的大部分参数****/ for (int i = 0; i<PARTICLE_NUMBER; i++) { int x, y; int xpre, ypre; double s, pres;
xpre = pParticle->x; ypre = pParticle->y; pres = pParticle->scale;
/****更新粒子的矩形区域即粒子中心****/ x = cvRound(A1*(pParticle->x - pParticle->orix) + A2*(pParticle->prex - pParticle->orix) + B0*rng.gaussian(sigmax) + pParticle->orix); pParticle->x = max(0, min(x, frame.cols - 1));
y = cvRound(A1*(pParticle->y - pParticle->oriy) + A2*(pParticle->prey - pParticle->oriy) + B0*rng.gaussian(sigmay) + pParticle->oriy); pParticle->y = max(0, min(y, frame.rows - 1));
s = A1*(pParticle->scale - 1) + A2*(pParticle->prescale - 1) + B0*(rng.gaussian(sigmas)) + 1.0; pParticle->scale = max(1.0, min(s, 3.0));
pParticle->prex = xpre; pParticle->prey = ypre; pParticle->prescale = pres; // pParticle->orix=pParticle->orix; // pParticle->oriy=pParticle->oriy;
//注意在c语言中，x-1.0，如果x是int型，则这句语法有错误,但如果前面加了cvRound(x-0.5)则是正确的 pParticle->rect.x = max(0, min(cvRound(pParticle->x - 0.5*pParticle->scale*pParticle->rect.width), frame.cols)); pParticle->rect.y = max(0, min(cvRound(pParticle->y - 0.5*pParticle->scale*pParticle->rect.height), frame.rows)); pParticle->rect.width = min(cvRound(pParticle->rect.width), frame.cols - pParticle->rect.x); pParticle->rect.height = min(cvRound(pParticle->rect.height), frame.rows - pParticle->rect.y); // pParticle->rect.width=min(cvRound(pParticle->scale*pParticle->rect.width),frame.cols-pParticle->rect.x); // pParticle->rect.height=min(cvRound(pParticle->scale*pParticle->rect.height),frame.rows-pParticle->rect.y);
/****计算粒子区域的新的直方图特征****/ track_img = Mat(hsv, pParticle->rect); calcHist(&track_img, 1, channels, Mat(), track_hist, 3, hist_size, ranges); normalize(track_hist, track_hist);
/****更新粒子的权值****/ // pParticle->weight=compareHist(target_hist,track_hist,CV_COMP_INTERSECT); //采用巴氏系数计算相似度,永远与最开始的那一目标帧相比较 pParticle->weight = 1.0 - compareHist(target_hist, track_hist, CV_COMP_BHATTACHARYYA); /****累加粒子权值****/ sum += pParticle->weight; pParticle++; }
/****归一化粒子权重****/ pParticle = particles; for (int i = 0; i<PARTICLE_NUMBER; i++) { pParticle->weight /= sum; pParticle++; }
/****根据粒子的权值降序排列****/ pParticle = particles; qsort(pParticle, PARTICLE_NUMBER, sizeof(PARTICLE), &particle_decrease);
/****根据粒子权重重采样粒子****/ PARTICLE newParticle[PARTICLE_NUMBER]; int np = 0, k = 0; for (int i = 0; i<PARTICLE_NUMBER; i++) { np = cvRound(pParticle->weight*PARTICLE_NUMBER); for (int j = 0; j<np; j++) { newParticle[k++] = particles[i]; if (k == PARTICLE_NUMBER) goto EXITOUT; } } while (k<PARTICLE_NUMBER) newParticle[k++] = particles[0]; EXITOUT: for (int i = 0; i<PARTICLE_NUMBER; i++) particles[i] = newParticle[i]; }//end else
//????????这个排序很慢，粒子数一多就卡 // qsort(pParticle,PARTICLE_NUMBER,sizeof(PARTICLE),&particle_decrease);
/****计算粒子期望，采用所有粒子位置的期望值做为跟踪结果****/ /*Rect_<double> rectTrackingTemp(0.0,0.0,0.0,0.0); pParticle=particles; for(int i=0;i<PARTICLE_NUMBER;i++) { rectTrackingTemp.x+=pParticle->rect.x*pParticle->weight; rectTrackingTemp.y+=pParticle->rect.y*pParticle->weight; rectTrackingTemp.width+=pParticle->rect.width*pParticle->weight; rectTrackingTemp.height+=pParticle->rect.height*pParticle->weight; pParticle++; }*/
/****计算最大权重目标的期望位置，作为跟踪结果****/ Rect rectTrackingTemp(0, 0, 0, 0); pParticle = particles; rectTrackingTemp.x = pParticle->x - 0.5*pParticle->rect.width; rectTrackingTemp.y = pParticle->y - 0.5*pParticle->rect.height; rectTrackingTemp.width = pParticle->rect.width; rectTrackingTemp.height = pParticle->rect.height;
/****计算最大权重目标的期望位置，采用权值最大的1/4个粒子数作为跟踪结果****/ /*Rect rectTrackingTemp(0,0,0,0); double weight_temp=0.0; pParticle=particles; for(int i=0;i<PARTICLE_NUMBER/4;i++) { weight_temp+=pParticle->weight; pParticle++; } pParticle=particles; for(int i=0;i<PARTICLE_NUMBER/4;i++) { pParticle->weight/=weight_temp; pParticle++; } pParticle=particles; for(int i=0;i<PARTICLE_NUMBER/4;i++) { rectTrackingTemp.x+=pParticle->rect.x*pParticle->weight; rectTrackingTemp.y+=pParticle->rect.y*pParticle->weight; rectTrackingTemp.width+=pParticle->rect.width*pParticle->weight; rectTrackingTemp.height+=pParticle->rect.height*pParticle->weight; pParticle++; }*/
/****计算最大权重目标的期望位置，采用所有粒子数作为跟踪结果****/ /*Rect rectTrackingTemp(0,0,0,0); pParticle=particles; for(int i=0;i<PARTICLE_NUMBER;i++) { rectTrackingTemp.x+=cvRound(pParticle->rect.x*pParticle->weight); rectTrackingTemp.y+=cvRound(pParticle->rect.y*pParticle->weight); pParticle++; } pParticle=particles; rectTrackingTemp.width = pParticle->rect.width; rectTrackingTemp.height = pParticle->rect.height;*/
//创建目标矩形区域 Rect tracking_rect(rectTrackingTemp);
pParticle = particles;
/****显示各粒子运动结果****/ for (int m = 0; m<PARTICLE_NUMBER; m++) { rectangle(frame, pParticle->rect, Scalar(255, 0, 0), 1, 8, 0); pParticle++; }
/****显示跟踪结果****/ rectangle(frame, tracking_rect, Scalar(0, 0, 255), 3, 8, 0);
after_select_frames++;//总循环每循环一次，计数加1 if (after_select_frames>2)//防止跟踪太长，after_select_frames计数溢出 after_select_frames = 2; }
if (select_show) rectangle(frame, select, Scalar(0, 0, 255), 3, 8, 0);//显示手动选择的矩形框 output_dst << frame; //显示视频图片到窗口 imshow("camera", frame);
// select.zeros(); //键盘响应 c = (char)waitKey(20); if (27 == c)//ESC键 return -1; }
return 0; }
