基于opencv的绊线检测代码

#include "stdafx.h" #include <stdio.h> #include <iostream> #include <math.h> #include "cv.h" #include "cxcore.h" #include "highgui.h" #include <string> #include <vector> #include <fstream> //用于保存监控记录 #include "GENZONG.h" using namespace std; // typedef struct // { // int flag; // int min_w; // int min_h; // int max_w; // int max_h; // }Rect; // // typedef struct // { // int flag; // int min_w; // int min_h; // int max_w; // int max_h; // float *objectHist; // vector<CvPoint> trajectory;//记录目标轨迹 // CvPoint start; // int len;//轨迹点数 // // }Object; int iniBkT=6; //初始背景掩膜判断阈值（影响背景建立速度） int thrZero=10; //初始背景建成阈值（简单背景时取10，复杂背景取100） float gx=(float)0.0008; Rect blobNow[1000] ; int numberBlobNow=0; int blobNowFlag=0; CvKalman *kalman[1000]; Object object[100]; int allObjectNum = 0; #define calc_point(p) cvPoint(cvRound(p[0]), cvRound(p[1])) Object objectPredict[1000]; #define NUM (8*8*8) # define R_BIN 8 /* 红色分量的直方图条数 */ # define G_BIN 8 /* 绿色分量的直方图条数 */ # define B_BIN 8 /* 兰色分量的直方图条数 */ # define R_SHIFT 5 /* 与上述直方图条数对应 */ # define G_SHIFT 5 /* 的R、G、B分量左移位数 */ # define B_SHIFT 5 /* log2( 256/8 )为移动位数 */ vector<CvPoint*> trajectorys; vector<int> ntrajectorys; CvRect selection; int selectionOrigin=1; //为1时，使用默认的监测区域 int select_object = 0; int detect_object = 0; //等于1时，才开始目标检测 int new_detect_object = 0; //为1时，重新分配内存 unsigned int nFrmNum = 0; //采集的帧数 IplImage *image = NULL; //保存捕捉的视频帧，用于鼠标响应函数 int In_num = 0; int Out_num = 0; /************************************************************************/ /* 二值化 t1是阈值 */ /************************************************************************/ void threshold(int t1,IplImage* image) { for (int i=0;i< image->height;i++) //行 { for (int j=0;j< image->width;j++) //列 { uchar t=((uchar*)( image->imageData + image->widthStep*i))[j]; if (t>t1) ((uchar*)( image->imageData + image->widthStep*i))[j]=255; else ((uchar*)( image->imageData + image->widthStep*i))[j]=0; } } } /************************************************************************/ /* 初始化为零 */ /************************************************************************/ void setZero(IplImage* image) { for(int i=0;i<image->height;i++) { for(int j=0;j<image->width;j++) { ((uchar*)(image->imageData + image->widthStep*i))[j] = 0 ; } } } /************************************************************************/ /* 给运动区域加框 */ /************************************************************************/ int RegionRect(IplImage* frontImgBinary,IplImage* frontImgGray, IplImage *imageRGB, IplImage *imageHue) { int i, j, n = 0; int flage=0; Rect blobTemp[1000]; //更新当前帧框位置 for(i=0;i<frontImgBinary->height;i++) for(j=0;j<frontImgBinary->width;j++) { //从左至右，从上至下扫描二值前景图，遇目标像素（即白色）则开始区域增长 if(*(uchar *)(frontImgBinary->imageData + frontImgBinary->widthStep*i+j)==255) blobTemp[n++] = RegionGrow(j,i,frontImgBinary); }//n为区域增长后的独立区域个数，增长后的矩形框满足条件，则flag=1，否则为0 int a = 0;//a为增长之后满足条件的区域的个数 for(i=0; i<n; i++) { if(blobTemp[i].flag)//把增长之后满足条件的区域，把它的边界值赋给当前帧存储目标框的变量blobNow { blobNow[a].min_w = blobTemp[i].min_w; blobNow[a].min_h = blobTemp[i].min_h; blobNow[a].max_w = blobTemp[i].max_w; blobNow[a].max_h = blobTemp[i].max_h; blobNow[a].flag = blobTemp[i].flag; a++; } } numberBlobNow = a;//本帧前景块数 for(int k=0;k<frontImgBinary->height;k++) { for(int l=0;l<frontImgBinary->width;l++) { if(((uchar*)(frontImgBinary->imageData + frontImgBinary->widthStep*k))[l] ==40 ) ((uchar*)(frontImgBinary->imageData + frontImgBinary->widthStep*k))[l] =255; } } //本帧为首次画框，则目标全部为新目标进场 if(!blobNowFlag) { for(i=0; i<numberBlobNow; i++) { if((blobNow[i].max_w-blobNow[i].min_w)>20 && (blobNow[i].max_h-blobNow[i].min_h)>20) { kalman[i]= InitializeKalman(kalman[i]); newObject(object[i], blobNow[i], kalman[i], frontImgGray, imageRGB, selection); allObjectNum++; } } } for(i=0; i<100; i++) { if(object[i].flag) { //预测值：Kalman预测的本帧中心位置，作为本帧搜索起始点 const CvMat* prediction = cvKalmanPredict( kalman[i], 0 ); CvPoint predictCenter; predictCenter = calc_point(prediction->data.fl); //限制预测中心的区域,防止预测框超出视频 int halfWidth = (object[i].max_w-object[i].min_w)/2; int halfHeight = (object[i].max_h-object[i].min_h)/2; //只要保证目标框与视频窗口有交集即可，画框时也仅画交集区 predictCenter.x = MAX(predictCenter.x, -halfWidth); predictCenter.x = MIN(predictCenter.x, frontImgBinary->width+halfWidth); predictCenter.y = MAX(predictCenter.y, -halfHeight); predictCenter.y = MIN(predictCenter.y, frontImgBinary->height+halfHeight); objectPredict[i].min_w = predictCenter.x-halfWidth; objectPredict[i].min_h = predictCenter.y-halfHeight; objectPredict[i].max_w = predictCenter.x+halfWidth; objectPredict[i].max_h = predictCenter.y+halfHeight; } } int oldAllObjectNum = allObjectNum%100; if(blobNowFlag) { campObjectRect(object, objectPredict, blobNow, 100, numberBlobNow, frontImgBinary, frontImgGray, imageRGB, imageHue, &allObjectNum, selection, kalman); } else { blobNowFlag = 1; } char strFrameNum[10]; CvFont font; int lineType=8; cvInitFont(&font, 5, 1, 1, 0, 2); CvScalar colour; for( i = 0; i<100; i++) { if(object[i].flag==1) { CvPoint pointRect1 = cvPoint(object[i].min_w+selection.x,object[i].min_h+selection.y); CvPoint pointRect2 = cvPoint(object[i].max_w+selection.x,object[i].max_h+selection.y); colour = CV_RGB(255,0,0); if(i>=oldAllObjectNum) colour = CV_RGB(0,255,0); cvRectangle(imageRGB,pointRect1, pointRect2,colour,2, lineType); itoa(i+1, strFrameNum, 10); if(pointRect1.y>10) cvPutText(imageRGB, strFrameNum, cvPoint(pointRect1.x, pointRect1.y-3), &font, colour); else cvPutText(imageRGB, strFrameNum, cvPoint(pointRect2.x-30, pointRect2.y+15), &font, colour); flage = 1; } } //------------------------------------------------------------------------------------------------------------------------ char out_number[10]; char In_number[10]; cvInitFont(&font, 5, 1, 1, 0, 2); itoa( In_num, In_number, 10 ); itoa( Out_num, out_number, 10 ); cvPutText(image, In_number, cvPoint(selection.x+selection.width, selection.y), &font, cvScalar( 255, 0, 255 )); cvPutText(image, out_number,cvPoint(selection.x+selection.width, selection.y+30), &font, cvScalar( 255, 255, 0 )); //------------------------------------------------------------------------------------------------------------------------ return flage; } int main( int argc, char** argv ) { CvCapture* pCapture = NULL; //捕捉的视频 IplImage* queryFrame = NULL; //捕捉的视频帧 IplImage* image_gray = NULL; //保存视频帧的灰度图 IplImage* pFrame = NULL; //加检测区域后的视频帧 IplImage* temp = NULL; IplImage* frameDiff = NULL; //帧差图像 IplImage* pBkImgTemp = NULL; IplImage* pFrImg_cross = NULL; IplImage* pBkImg = NULL; //背景灰度图