用opencv写的k均值聚类的VC++代码

#include "cxcore.h" #include "highgui.h" #include <iostream> #include <time.h> #include <vector> #include <iomanip> #pragma comment(lib,"cxcore.lib") #pragma comment(lib,"highgui.lib") using namespace std; struct Pattern { int x; int y; }; struct CenterPattern { float x; float y; }; int Classify(Pattern pn1,const CenterPattern CenterPatterns[],int isampleclass); void GetClusterCenter(vector<Pattern> parr, CenterPattern &NewCenterPattern1); void main(void) { //Temp Variable int i,j; //sample Class int iSampleClass; //Sample Number int iSampleNum; //Iterations int iIteration = 0; //the Origin Patterns IplImage *pImgOriPatterns = NULL; //the Image to show the Result; IplImage *pImgResult = NULL; //input the number of sample class cout<<"Please input the number of sample class(2-50): "; cin>>iSampleClass; while(iSampleClass<2 || iSampleClass > 50) { cout<<" the number of sample class ranges from 2 to 50!!!"<<endl; cout<<"Please input the number of sample class(2-50): "; cin>>iSampleClass; } //input the number of sample cout<<"Please input the number of sample(10-10000): "; cin>>iSampleNum; while(iSampleNum < 10 || iSampleNum > 10000) { cout<<" the number of sample ranges from 10 to 10000!!!"<<endl; cout<<"Please input the number of sample(10-10000) : "; cin>>iSampleNum; } //generate random patterns Pattern *patterns = new Pattern[iSampleNum]; srand( (unsigned)time( NULL ) ); for( i = 0; i < iSampleNum;i++ ) { patterns[i].x = rand()%200; patterns[i].y = rand()%200; // cout<<"patterns[i].x = " <<setw(3)<<patterns[i].x<< " patterns[i].y = " <<setw(3)<<patterns[i].y<<endl; } //Central Patterns CenterPattern *OldCenterPatterns = new CenterPattern[iSampleClass]; CenterPattern *NewCenterPatterns = new CenterPattern[iSampleClass]; //initialize the Central Patterns for (j = 0; j < iSampleClass; ++j) { OldCenterPatterns[j].x = static_cast<float>(patterns[j].x); OldCenterPatterns[j].y = static_cast<float>(patterns[j].y); } // create iSampleClass containers to store different of patterns vector<Pattern> *pArray = new vector<Pattern>[iSampleClass]; bool iterFlag = true; int index; //start to cluster while(iterFlag && (iIteration < 200) ) { ++iIteration; //empty the pArray for (j = 0; j < iSampleClass; ++j) { if (!pArray[j].empty()) { pArray[j].clear(); } } for ( i = 0; i < iSampleNum ; ++i) { // get the class index of each pattern index = Classify(patterns[i],OldCenterPatterns,iSampleClass); pArray[index].push_back(patterns[i]); } //get the new cluster center for (i = 0; i < iSampleClass; ++i) { GetClusterCenter(pArray[i],NewCenterPatterns[i]); } iterFlag = false; for (i = 0; i < iSampleClass; ++i) { if (NewCenterPatterns[i].x != OldCenterPatterns[i].x ||NewCenterPatterns[i].y != OldCenterPatterns[i].y) { //store the new Center Patterns OldCenterPatterns[i].x = NewCenterPatterns[i].x; OldCenterPatterns[i].y = NewCenterPatterns[i].y; iterFlag = true; } } } cout<<"the number of iterations is :" <<iIteration<<endl; cout<<"finish cluster!!!"<<endl; pImgOriPatterns = cvCreateImage(cvSize(600,600),IPL_DEPTH_8U,1); pImgResult = cvCreateImage(cvSize(600,600),IPL_DEPTH_8U,3); cvZero(pImgOriPatterns); cvZero(pImgResult); //paint the patterns in the original image for (i = 0; i < iSampleNum; ++i) { cvRectangle(pImgOriPatterns,cvPoint( patterns[i].x*3, patterns[i].y*3), cvPoint( patterns[i].x*3+2, patterns[i].y*3+2 ),CV_RGB(255,255,255),CV_FILLED,8,0); } //generate the color table CvScalar *pColorTable =new CvScalar[iSampleClass]; for (i = 0; i < iSampleClass; ++i) { pColorTable[i].val[0] = 255 / ( rand()%iSampleClass+1); pColorTable[i].val[1] = 255 / ( rand()%iSampleClass+1); pColorTable[i].val[2] = 255 / ( rand()%iSampleClass+1); } //paint the result of cluster for (i = 0; i < iSampleClass; ++i) { for (vector<Pattern>::iterator iter1 = pArray[i].begin(); iter1 != pArray[i].end(); ++iter1) { cvRectangle(pImgResult,cvPoint( (*iter1).x*3, (*iter1).y*3 ), cvPoint( (*iter1).x*3+2, (*iter1).y*3+2 ),pColorTable[i],CV_FILLED,8,0); } CvPoint CentralPoint; CentralPoint.x = static_cast<int>(NewCenterPatterns[i].x*3); CentralPoint.y = static_cast<int>(NewCenterPatterns[i].y*3); cvCircle(pImgResult,CentralPoint,3,CV_RGB(255,255,255),CV_FILLED,8,0); } cvNamedWindow("Origin",1); cvShowImage("Origin",pImgOriPatterns); cvNamedWindow("Result",1); cvShowImage("Result",pImgResult); cvWaitKey(0); cvDestroyAllWindows(); cvReleaseImage(&pImgOriPatterns); cvReleaseImage(&pImgResult); delete []patterns; delete []pArray; delete []OldCenterPatterns; delete []NewCenterPatterns; delete []pColorTable; } //Func // Classify() //Parameter // Pattern pn1 --the pattern to be classified // CenterPattern CenterPatterns[] --the CenterPatterns // int isampleclass --the number of the sample class //return // the class index of the pn1 int Classify(Pattern pn1,const CenterPattern CenterPatterns[],int isampleclass) { float MinDist = 1000000000; float Dist; int index1; for (int i = 0; i < isampleclass; ++i) { Dist = static_cast<float>((pn1.x - CenterPatterns[i].x)*(pn1.x - CenterPatterns[i].x) + (pn1.y - CenterPatterns[i].x)*(pn1.x - CenterPatterns[i].y)); if (MinDist > Dist) { MinDist = Dist; index1 = i; } } return index1; } //Func // GetClusterCenter() //Parameter // vector<Pattern> parr --the pattern to be classified // Pattern & NewCenterPattern1 --the CenterPatterns //Return value // NULL //Description // ******************** void GetClusterCenter(vector<Pattern> parr, CenterPattern & NewCenterPattern1) { float SumX = 0, SumY = 0; for (vector<Pattern>::iterator iter1 = parr.begin(); iter1 != parr.end(); ++iter1) { SumX += (*iter1).x; SumY += (*iter1).y; } NewCenterPattern1.x = SumX * 1.0 / parr.size(); NewCenterPattern1.y = SumY * 1.0 / parr.size(); }