www.gusucode.com > C++ 实现的共生矩阵-源码程序 > C++ 实现的共生矩阵-源码程序/code/aa.cpp

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 /*图像理解与分析中
           灰度共生矩阵算法
周一早上图像理解与分析课上，朱启疆老师讲了灰度共生矩阵算法，回去后我编程实现了这个算法。
内容如下：
共有 matrix.cpp    d_matrix.h   d_exept.h   mat.txt  四个文件   */
//matrix.cpp

/*
Visual C++ 6.0 
matrix
designed by bfly
*/
#include <iostream>
#include <fstream>
#include <cstdlib>
#include <cmath>
#include <vector>
#include "d_matrix.h"
template <typename T>
void outputmat(const matrix<T>& mat);
template <typename T>
int classifymat(const matrix<T>& mat);
template <typename T>
void transformmat(const matrix<T>& formermat, matrix<T>& lattermat);
template <typename T>
void probablitymat(const matrix<T>& mat,matrix<T>& probmat);
template <typename T>
void typicalarguement(const matrix<T>& mat,const matrix<T>& probmat);
using namespace std;
int main()
{
 //input matrix
 matrix<float> initMat;
 int numRows, numCols;
 int i, j;
 ifstream fin("mat.txt");
 if(!fin)
 {
  cerr << "Cannot open 'mat.txt'" << endl;
  exit(1);
 }
 fin >> numRows >> numCols;
 initMat.resize(numRows, numCols);
 for(i = 0; i < numRows; i++)
 {
  for(j = 0; j < numCols; j++)
  {
   fin >> initMat[i][j];
  }
 }
 //transform matrix to tempMat
 int counter=classifymat(initMat);
    matrix<float> tempMat;
 tempMat.resize(counter, counter);
 transformmat(initMat, tempMat);
 outputmat(tempMat);
 //transform matrix to probMat
 matrix<float> probMat;
 probMat.resize(counter, counter);
 probablitymat(tempMat, probMat);
 outputmat(probMat);
 cout << endl;
 //output the typicalarguements
 typicalarguement(tempMat, probMat);
return 0;
}
//outputmat matrix functrion
template <typename T>
void outputmat(const matrix<T>& mat)
{
 int i, j;
 for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   cout << mat[i][j] << "  ";
  }
  cout << endl;
 }
}
//classifymat matrix function
template <typename T>
int classifymat(const matrix<T>& mat)
{
 vector<T> memoryval;
 memoryval.push_back(mat[0][0]);
 int counter=1;
 bool flag = false;
 int i, j;
 for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   for(int m = 0; m < memoryval.size(); m++)
   {
    if(mat[i][j] == memoryval[m])
    flag = true;
   }
   if(!flag)
   {
    memoryval.push_back(mat[i][j]);
       counter++; 
   }
   flag = false;
  }
 }
 return counter;
}
//transformmat matrix function
template <typename T>
void transformmat(const matrix<T>& formermat, matrix<T>& lattermat)
{
 cout << "Plesase enter a and b: ";
 int a, b;
 int matval = 0;
 cin >> a >> b;
 int i, j, m, n;
 for(i = 0; i < lattermat.rows(); i++)
 {
  for(j = 0; j < lattermat.cols(); j++)
  {
   for(m = 0; m < formermat.rows(); m++)
   {
         for(n = 0; n < formermat.cols(); n++)
      {
         if(formermat[m][n]==i)
      {
       if(((m+a) < formermat.rows()) && ((n+b) < formermat.cols()))
       {
        if(formermat[m+a][n+b] == j)
        {
         matval++;
        }
       }
      }
      }
   }
      lattermat[i][j]=matval; 
   matval=0;
  }
 }
}
//probablitymat matrix function
template <typename T>
void probablitymat(const matrix<T>& mat,matrix<T>& probmat)
{
 T sum = T();
 int i, j;
 for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   sum += mat[i][j];
  }
 }
 for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   probmat[i][j] = mat[i][j]/sum;
  }
  cout << endl;
 }
}
//typicalarguements
template <typename T>
void typicalarguement(const matrix<T>& mat,const matrix<T>& probmat)
{
 T e = T(), H = T(), C = T(),I = T(), mean = T(), stdvar = T(), sum = T(), var = T();
 T M = T();
 int i, j;
//typicalargument e
 for(i = 0; i < probmat.rows(); i++)
 {
  for(j = 0; j < probmat.cols(); j++)
  {
   e += probmat[i][j]*probmat[i][j];
  }
 }
//typicalargument H
  for(i = 0; i < probmat.rows(); i++)
 {
  for(j = 0; j < probmat.cols(); j++)
  {
   H += probmat[i][j]*log(probmat[i][j])/log(10.0);
  }
 }
  H = -H;
//typicalargument sum
  for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   sum += mat[i][j];
  }
 }
//typicalargument mean
    mean = sum/(mat.rows()*mat.cols());
//typicalargument var
  for(i = 0; i < mat.rows(); i++)
 {
  for(j = 0; j < mat.cols(); j++)
  {
   var += (mat[i][j]-mean)*(mat[i][j]-mean);
  }
 }
//typicalargument stdvar
 stdvar=sqrt(var);
//typicalargument C
 for(i = 0; i < probmat.rows(); i++)
 {
  for(j = 0; j < probmat.cols(); j++)
  {
   C += (i - mean)*(j - mean)*probmat[i][j];
  }
 }
 C /= (stdvar*stdvar);
//typicalargument M
 for(i = 0; i < probmat.rows(); i++)
 {
  for(j = 0; j < probmat.cols(); j++)
  {
   M += (probmat[i][j]/ (1 + (i - j)*(i - j)));
  }
 }
//typicalargument I
 for(i = 0; i < probmat.rows(); i++)
 {
  for(j = 0; j < probmat.cols(); j++)
  {
   I += (i - j)*(i -j)*probmat[i][j];
  }
 }
//output typicalarguments
 cout<<" 能量e = "<< e <<endl;
 cout<<" 熵H = "<< H <<endl;
 cout<<" 相关性C = "<< C <<endl;
 cout<<" 局部均匀性M = "<< M <<endl;
 cout<<" 惯性I = "<< I <<endl;
 
}
 
//剩下内容请看下一页的图像理解与分析中灰度共生矩阵算法2