Bobby-D-Match.rar_bobby _match Opencv

#include <opencv2\opencv.hpp> #include"highgui.h" #include<cxcore.h> #include<cv.h> #include<iostream> #include "CHausdorff.h" #include<string> #include<Windows.h> using namespace cv; using namespace std; void delay(long t) { for(long i=0;i<t;i++) { for(long k=0;k<10000;k++) {; } } } int main(int argc,char ** argv) { char modelName[] = "mega.bmp"; //********** 变量 *********** double RowModelCen = 0.0; // 模板中心Y坐标 double ColumnModelCen = 0.0; // 模板中心X坐标 //************* 搜索图像的相关参数设定 *************** double AngleStart = -50; double AngleExtent = 50; double AngleStep = 2; //********** 获取模板图，获取中心坐标 *********** IplImage *modelColor = cvLoadImage(modelName,1); ColumnModelCen = int(modelColor->width/2)+1; RowModelCen = int(modelColor->height/2)+1; cvNamedWindow("model",1); cvShowImage ( "model", modelColor ); double modelLength = modelColor->width; //********** 模板图Canny边沿化 ********** IplImage* modelCanny=cvCreateImage(cvGetSize(modelColor),IPL_DEPTH_8U,1); cvCanny( modelColor , modelCanny , 250 , 170 , 3); cvNamedWindow("Canny",1); cvShowImage ( "Canny", modelCanny ); //*************** 声明模板缓存空间 **************** CvPoint **ModelXY;//储存边沿XY点相对中心坐标 double **ModelDisToCen;//储存轮廓点到中心的相对距离 int *ModelBlobCount;//储存每个步进角的轮廓点数 ModelXY = new CvPoint *[int((AngleExtent-AngleStart)/AngleStep)+1]; for(int i=0;i<int((AngleExtent-AngleStart)/AngleStep)+1;i++) { ModelXY[i] = new CvPoint[modelColor->width * modelColor->height]; } ModelDisToCen = new double *[int((AngleExtent-AngleStart)/AngleStep)+1]; for(int i=0;i<int((AngleExtent-AngleStart)/AngleStep)+1;i++) { ModelDisToCen[i] = new double[modelColor->width * modelColor->height]; } ModelBlobCount = new int[ int((AngleExtent-AngleStart)/AngleStep)+1]; for(int i=0;i<int((AngleExtent-AngleStart)/AngleStep)+1;i++)//边沿XY点个数 { ModelBlobCount[i] = 0; } int ModelNum = 0; CvPoint **ModelCornXY;//储存每个角间距的四边形四角 ModelCornXY = new CvPoint *[int((AngleExtent-AngleStart)/AngleStep)+1]; for(int i=0;i<int((AngleExtent-AngleStart)/AngleStep)+1;i++) { ModelCornXY[i] = new CvPoint[4]; } CvPoint *CannyXY;//储存canny后边沿XY的坐标 double *CannyDisToCen;//储存canny轮廓点到中心的相对距离 int CannyCount=0;//储存canny后的白点的数 CannyXY = new CvPoint[ modelColor->width * modelColor->height];//储存canny的所有白点坐标 CannyDisToCen = new double [modelColor->width * modelColor->height]; for(int i=0;i<modelColor->width * modelColor->height;i++) { CannyDisToCen[i] = 0; } CannyCount=0; std::vector<cv::Point2f> corners2; //角点总数 -- 质量等级 -- 容忍距离 cv::goodFeaturesToTrack(cv::Mat(modelCanny),corners2,100,0.6,10); std::vector<cv::Point2f>::const_iterator it2= corners2.begin(); while (it2!=corners2.end()) { CannyXY[CannyCount].x = it2->x; CannyXY[CannyCount].y = it2->y; CannyDisToCen[CannyCount] = sqrt(pow((CannyXY[CannyCount].x-ColumnModelCen),2) + pow((CannyXY[CannyCount].y-RowModelCen),2)); CannyCount++; //cv::circle(cv::Mat(modelColor),*it2,2,cv::Scalar(0,0,255),1); ++it2; } //cv::imshow("source1",cv::Mat(modelColor)); //************* 新建一幅临时显示图 *************** IplImage* srcExam1=cvCreateImage(cvSize(modelLength,modelLength),IPL_DEPTH_8U,1); cvZero(srcExam1); cvNamedWindow("srcExam1",1); cvShowImage ( "srcExam1", srcExam1 ); double start_t0 = GetTickCount(); //************* 各步进角的轮廓点的计算 *************** for(int k=0;k<int((AngleExtent-AngleStart)/AngleStep);k++) { IplImage *modelColor = cvLoadImage(modelName,1); double rotateAngle = AngleStart + k*AngleStep; IplImage* srcExam1=cvCreateImage(cvSize(modelLength,modelLength),IPL_DEPTH_8U,1); cvZero(srcExam1); double start_t = GetTickCount(); //-------- 计算模板4顶点的坐标 ------- for(int n=0;n<4;n++) { double Q0=0; if(n==0) ModelCornXY[k][n] = cvPoint(0,0); else if(n==1) ModelCornXY[k][n] = cvPoint(modelCanny->width-1,0); else if(n==2) ModelCornXY[k][n] = cvPoint(modelCanny->width-1,modelCanny->height-1); else if(n==3) ModelCornXY[k][n] = cvPoint(0,modelCanny->height-1); if(ModelCornXY[k][n].x > ColumnModelCen) Q0 = atan(double(RowModelCen-ModelCornXY[k][n].y)/double(ColumnModelCen-ModelCornXY[k][n].x))*180/CV_PI; else Q0 = 180 + atan(double(RowModelCen-ModelCornXY[k][n].y)/double(ColumnModelCen-ModelCornXY[k][n].x))*180/CV_PI; ModelCornXY[k][ n ].x = int(modelLength/2)*cos((rotateAngle+Q0)*CV_PI / 180); ModelCornXY[k][ n ].y = int(modelLength/2)*sin((rotateAngle+Q0)*CV_PI / 180); cvSet2D( srcExam1, ModelCornXY[k][ n ].y+int(modelLength/2), ModelCornXY[k][ n ].x+int(modelLength/2), Scalar( 255, 255, 255) ); } //----------- 获取边沿点的个数和各点的坐标 ------------ CvScalar s; ModelNum = 0; for(int h=0;h<CannyCount;h++) { cvSet2D( modelColor, CannyXY[h].y, CannyXY[h].x, Scalar( 0, 255, 255) ); //-------- 保存各步进角各点到中心坐标距离 --------- ModelDisToCen[k][ ModelNum ] = CannyDisToCen[h]; //-------- 用极坐标计算旋转后的点的坐标 --------- double Q1,x1,y1; if(CannyXY[h].x > ColumnModelCen) Q1 = atan(double(RowModelCen-CannyXY[h].y)/double(ColumnModelCen-CannyXY[h].x))*180/CV_PI; else Q1 = 180+atan(double(RowModelCen-CannyXY[h].y)/double(ColumnModelCen-CannyXY[h].x))*180/CV_PI; ModelXY[k][ ModelNum ].x = ModelDisToCen[k][ModelNum]*cos((rotateAngle+Q1)*CV_PI / 180); ModelXY[k][ ModelNum ].y = ModelDisToCen[k][ModelNum]*sin((rotateAngle+Q1)*CV_PI / 180); cvSet2D( srcExam1, ModelXY[k][ ModelNum ].y+int(modelLength/2), ModelXY[k][ ModelNum ].x+int(modelLength/2), Scalar( 255, 255, 255) ); ModelNum++; ModelBlobCount[k]++; } double end_t = GetTickCount(); //-------- 多步进角运行显示 --------- cout<<"第"<<k<<"次测试有 "<<ModelBlobCount[k] <<" 个轮廓点，当前步进角是 "<<rotateAngle <<" 耗时: "<<end_t - start_t <<endl; cvShowImage ( "model", modelColor ); cvShowImage ( "srcExam1", srcExam1 ); delay(2000); for ( ; ; ){ cvWaitKey(1)==100; break; } //--------- 析放图像缓存 --------- cvReleaseImage( &modelColor ); cvReleaseImage( &srcExam1 ); } double end_t0 = GetTickCount(); cout<<" 耗时: "<<end_t0 - start_t0 <<endl; cvWaitKey(0); //********************************************** //*************** 目标图像处理 ****************** //********************************************** //********** 获取模板图，获取中心坐标 *********** char srcName[] = "mega8.bmp"; //********** 变量 *********** double RowSrcCen = 0.0; // 目标中心Y坐标 double ColumnSrcCen = 0.0; // 目标中心X坐标 double srcGreediness = 2; // 贪心算法 bool showFlag = false; //显示使能 //*********** 记录合适范围的个数和中心坐标 ************ int searchAgSteN =0;//当前步进角的个数 CvPoint searchCenPMax = cvPoint(modelColor->width/2,modelColor->width/2);//最大值的中心 int searchNumMax = 0;//最大值的个数 int searchAngleMax = 0;//最大值的步进角 double hausDis = MAX_DISTANCE;//hausdorff距离 double dHausMixDis = MAX_DISTANCE;//hausdorff最大距离 IplImage *srcColor = cvLoadImage(srcName,1); cvNamedWindow("src",1); cvShowImage ( "src", srcColor); //********** 计算目标搜索图像的canny边沿数组 ********** IplImage* srcCanny=cvCreateImage(cvGetSize(srcColor),IPL_DEPTH_8U,1); cvCanny( srcColor , srcCanny , 250 , 170 , 3); //cvNamedWindow("srcCanny",1); //cvShowImage ( "srcCanny", srcCanny ); // 用于Hausdorff的模板和目标图数组 CvPoint *pMPoints = new CvPoint[2 * modelColor->width * modelColor->height];//模板haus CvPoint *pSPoints = new CvPoint[2 * srcColor->width * srcColor->height];//目标haus int numMod=0;//模板haus点数 int numSrc=0;//目标haus点数 CHausdorff haus(modelCanny,srcCanny,8,8); d