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Clustering algorithm Source code_Version 2.txt
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Clustering algorithm Source code_Version 2.txt
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The main function
//
using namespace std;
using namespace cv;
int main()
{
prepoccessedRCCSTDProblem p1;
string path1 = "D:\\Forrest_chen_documents\\My papers\\Thrid paper\\Personal Data\\Test6\\";
/* char * path = new char[path1.length() + 1];
for (int i = 0; i < 17; i++)
{
strcpy(path, path1.c_str());
std::stringstream s1;
s1<<i+1;
string filenumber1 = s1.str();
char * filenumber = new char[filenumber1.length() + 1];
strcpy(filenumber, filenumber1.c_str());
//cout << path1;
strcat(path,filenumber);
strcat(path,"\\");*/
p1.readImageSet(path1);
p1.classify(path1);
//}
//waitKey(0);
getchar();
return 0;
}
-----------------------------------------------------------------------------------------------------------------------------------
prepoccessedRCCSTDProblem.h
#pragma once
#include <opencv2\opencv.hpp>
#include <opencv2\imgproc\imgproc.hpp>
//#include <vector>
using namespace cv;
class prepoccessedRCCSTDProblem
{
private:
public:
void readImageSet(String address);
int signal[187][183];
vector<vector<int> > classifyResult;
Mat orignalImage_[187];
void classify(String address);
prepoccessedRCCSTDProblem();
~prepoccessedRCCSTDProblem();
};
----------------------------------------------------------------------------------------------------------------------------------
prepoccessedRCCSTDProblem.cpp
#include "stdafx.h"
#include "prepoccessedRCCSTDProblem.h"
#include <String>
#include <algorithm>
#include <vector>
#include <stdio.h>
#include <stdlib.h>
#include <highgui.h>
#include <math.h>
#include <iomanip>
#include <set>
using namespace std;
using namespace cv;
int intialClusteringCentralIndex[11];
prepoccessedRCCSTDProblem::prepoccessedRCCSTDProblem()
{
classifyResult.resize(11);
}
void prepoccessedRCCSTDProblem::readImageSet(String address)
{
String path;
for (int i = 0; i < 187; i++)
{
for (int j = 0; j < 182; j++)
signal[i][j] = 0;
}
FILE* signalResult;
char * preadd = new char[address.length() + 1];
strcpy(preadd, address.c_str());
//const char* preadd = address.data();
cout << preadd << endl;
signalResult = fopen(strcat(preadd,"signalResult.txt"), "wb");
int beginLine, endLine;
memset(intialClusteringCentralIndex, 0, 10);
int counter = 0;
for (int i = 0; i < 187; i++)
{
beginLine ,endLine ;
bool startFlag, endFlag;
path = address;
char fragmentNumber[4],flag[1];
sprintf(fragmentNumber,"%03d",i);
path = path + fragmentNumber + ".bmp";
//cout << path << endl;
Mat tempImage = imread(path);
cvtColor(tempImage, orignalImage_[i], CV_BGR2GRAY);
for (int j = 0; j < 180; j++)
{
for (int k = 0; k < 72; k++)
{
if (orignalImage_[i].at<uchar>(j, k) < 255)
orignalImage_[i].at<uchar>(j, k) = 0;
else
orignalImage_[i].at<uchar>(j, k) = 255;
//cout << path << endl;
signal[i][j] = signal[i][j] + orignalImage_[i].at<uchar>(j, k);
}
signal[i][j] = signal[i][j] / 255;
fprintf(signalResult, "%d\t", signal[i][j]);
}
for (int j = 0; j < 50; j++)
{
beginLine = 0;
startFlag = true;
for (int k = 0; k < 180; k++)
{
beginLine = beginLine + orignalImage_[i].at<uchar>(k,j);
}
if (beginLine / 255 < 180)
startFlag = !startFlag;
if (!startFlag)
{
signal[i][180] = j+1;
fprintf(signalResult, "%d\t", signal[i][180]);
if (j > 20)
{
intialClusteringCentralIndex[counter] = i;
cout << intialClusteringCentralIndex[counter] << endl;
counter++;
}
break;
}
}
fprintf(signalResult, "\r\n");
}
fclose(signalResult);
//cv::imshow(" ", orignalImage_[0]);
//cv::waitKey(0);
}
void prepoccessedRCCSTDProblem::classify(String address)
{
//cout << "sdsdsd" << endl;
vector<vector<int> > preClassifyResult;
preClassifyResult.resize(11);
preClassifyResult = classifyResult;
/*Take me some time to thinking*/
/*-----------------------------------------------------------------------------------------------------------*/
double clusteringVectorCenter[11][180] = { 0 };
for (int i = 0; i < 11; i++)
{
for (int j = 0; j < 180;j++)
clusteringVectorCenter[i][j] = (signal[intialClusteringCentralIndex[i]][j]);
}
int count;
int origianlIndex[187];
for (int i = 0; i < 187; i++)
{
origianlIndex[i] = i;
}
vector<int> classifyIndex(220);
set_symmetric_difference(begin(intialClusteringCentralIndex), end(intialClusteringCentralIndex), begin(origianlIndex), end(origianlIndex), classifyIndex.begin());
//auto iter=set_difference(origianlIndex, origianlIndex+184, intialClusteringCentralIndex, intialClusteringCentralIndex+10, classifyIndex.begin());
int count2 = 1;
do
{
count = 0;
preClassifyResult.clear();
preClassifyResult.resize(11);
preClassifyResult = classifyResult;
//cout << "sdsdsd" << endl;
classifyResult.clear();
classifyResult.resize(11);
for (int i = 0; i < 11; i++)
{
classifyResult[i].push_back(intialClusteringCentralIndex[i]);
}
vector<int> Distance(11, 0);
/*****************Clusering the fragments**********************************/
for (int t = 0;t<176; t++)
{
int j = 0;
for (int i = 0; i < 11; i++)
{
int tempDistance = 0;
for (int k = 0; k < 180; k++)
{
tempDistance = tempDistance + (signal[classifyIndex[t]][k] - clusteringVectorCenter[i][k])*(signal[classifyIndex[t]][k] - clusteringVectorCenter[i][k]);
}
Distance[j] = sqrt(tempDistance);
j++;
}
vector<int>::iterator smallest = min_element(std::begin(Distance), std::end(Distance));
classifyResult[std::distance(std::begin(Distance), smallest)].push_back(classifyIndex[t]);
}
/****Before Recalculating the Clusering vector Centar, Reset is needed*********/
for (int i = 0; i < 11 ; i++)
for (int j = 0; j < 180; j++)
{
clusteringVectorCenter[i][j] = 0;
}
/*****************Recalculate the Clusering Vector Centar*********************/
for (int i = 0; i < 11; i++)
{
for (int t = 0; t < 180; t++)
{
for (int j = 0; j < classifyResult[i].size(); j++)
{
clusteringVectorCenter[i][t] = clusteringVectorCenter[i][t] + signal[classifyResult[i][j]][t];
}
clusteringVectorCenter[i][t] = clusteringVectorCenter[i][t] / classifyResult[i].size();
}
}
/*************************************************************************************/
/*************************************************************************************
**************************************************************************************/
for (int i = 0; i < 11; i++)
{
if (classifyResult[i] != preClassifyResult[i])
count++;
//if ()
}
cout <<"count: "<< count << endl;
count2++;
} while (count!=0||count2<13);
/*****************K-means Algorithm End*********************************/
/**************************************************************************************************/
FILE* classifyResult_txt;
char * preadd = new char[address.length() + 1];
strcpy(preadd, address.c_str());
//const char* preadd = address.data();
cout << preadd << endl;
classifyResult_txt = fopen(strcat(preadd, "classifyResult.txt"), "wb");
for (int i = 0; i < 11; i++)
{
for (int j = 0; j < classifyResult[i].size(); j++)
{
cout << classifyResult[i][j] << " ";
fprintf(classifyResult_txt, "%d\t", classifyResult[i][j]);
}
fprintf(classifyResult_txt,"num: %d\t", classifyResult[i].size());
cout << "num: " << classifyResult[i].size() << endl;
fprintf(classifyResult_txt, "\r\n");
}
fclose(classifyResult_txt);
}
prepoccessedRCCSTDProblem::~prepoccessedRCCSTDProblem()
{
}