int main()
{
int i,k;
printf("\n------------------------------------------ input1 -----------------------------\n");
int n;
printf("First Input Text File => No. of Rows : ");
scanf("%d",&n);
int **ceps1 = (int **)malloc(sizeof(int*)*n);
for(int i=0; i < n;i++)
{
ceps1[i]= (int *)malloc(sizeof(int)*12);
}
char filename1[256];
printf("First Input Text File Name : ");
scanf("%s",filename1);
printf("\n");
FILE *file1;
file1 = fopen(filename1,"r");
if(file1==NULL)
printf("file not found!");
for(i=0; i<n; i++)
{
for(k=0; k<12; k++)
{
fscanf(file1,"%d",&ceps1[i][k]);
printf("%d ",ceps1[i][k]);
}printf("\n");
}
fclose(file1);
printf("\n------------------------------------------ input2 -----------------------------\n");
int m;
printf("First Input Text File => No. of Rows : ");
scanf("%d",&m);
int** ceps2 = (int**)malloc(sizeof(int *)*m);
for(int i= 0;i<m;i++)
{
ceps2[i]=(int*)malloc(sizeof(int)*12);
}
char filename2[256];
printf("Second Input Text File Name : ");
scanf("%s",filename2);
printf("\n");
FILE *file2;
file2 = fopen(filename2,"r");
if(file2==NULL)
printf("file not found!");
for(i=0; i<m; i++)
{
for(k=0; k<12; k++)
{
fscanf(file2,"%d",&ceps2[i][k]);
printf("%d ",ceps2[i][k]);
}printf("\n");
}
fclose(file2);
printf("\n----------------------------- Local Distance Matrix ---------------------------\n\n");
int local_distance[n][m];
for(i=0; i<n; i++)
{
for(k=0; k<m; k++)
{
local_distance[i][k] = distance_measure(ceps1[i],ceps2[k]);
printf("%d\t ",local_distance[i][k]);
}
printf("\n");
}
printf("-------------------------------------------------------------------------------\n\n");
printf("----------------------------- Global Distance Matrix --------------------------\n\n");
int global_distance[n][m];
global_distance[0][0] = local_distance[0][0];
printf("%d\t ",global_distance[0][0]);
for(i=1; i<n; i++) // generating first row element of global_distance matrix
global_distance[i][0] = local_distance[i][0] + global_distance[i-1][0];
for(k=1; k<m; k++) // generating first column element of global_distance matrix
{
global_distance[0][k] = local_distance[0][k] + global_distance[0][k-1];
printf("%d\t ",global_distance[0][k]);
}
printf("\n");
for(i=1; i<n; i++)
{
printf("%d\t ",global_distance[i][0]);
for(k=1; k<m; k++)
{
global_distance[i][k] = local_distance[i][k] + min(global_distance[i-1][k],global_distance[i-1][k-1],global_distance[i][k-1]);
printf("%d\t ",global_distance[i][k]);
}
printf("\n");
}
printf("-------------------------------------------------------------------------------\n\n");
printf("Optimal Warping Path : (0,0) ");
i=0;
k=0;
while((i!=n-1)||(k!=m-1))
{
if(i==n-1) // if you reached to the last row (n-1) then go only one step forward in last row
k=k+1;
else if(k==m-1) //if you have reached to the last column (m-1) then go only one step upward in last column
i=i+1;
else
{
int global_minm = min(global_distance[i+1][k],global_distance[i+1][k+1],global_distance[i][k+1]);
if(global_distance[i+1][k] == global_minm)
{
i=i+1;
}
else if(global_distance[i+1][k+1] == global_minm)
{
i=i+1;
k=k+1;
}
else//(global_distance[i][k+1] == global_minm)
{
k=k+1;
}
}
printf("(%d,%d) ",i,k);
}
printf("\nOptimal Warping Path Cost : %d\n", global_distance[n-1][m-1]);
printf("\n-------------------------------------------------------------------------------\n\n");
return 0;
}
// Get the vector distance (x, y, z) of the mean distance between pairs of closest points
// - For each point, find it's closest neighbour and save the distance
// - Take the mean of all the values to obtain the mean spacing between points
//
//
vector<float> get_mean_point_distance(vector<int> & x, vector<int> & y, vector<int> & z)
{
vector<float> dist (3, 0);
// distance pairs
vector<int> sx;
vector<int> sy;
vector<int> sz;
long dmeas;
long dist1;
long nn;
for (int m=0; m<x.size(); m++)
{
dmeas = numeric_limits<long>::max();
for (int n=0; n<x.size(); n++)
{
// Skip comparing point to itself
if (n==m)
continue;
// Calculate the squared distance between each point and every other
dist1 = distance_measure(x[m], y[m], z[m], x[n], y[n], z[n]);
if (dist1 < dmeas)
{
// If closer then save the indices for that point
dmeas = dist1;
nn=n;
}
}
// Save the closest distance for that point
sx.push_back(abs(x[m] - x[nn]));
sy.push_back(abs(y[m] - y[nn]));
sz.push_back(abs(z[m] - z[nn]));
}
float d0m = 0;
float d1m = 0;
float d2m = 0;
for (int r=0; r<sx.size(); r++)
{
d0m += sx.at(r);
d1m += sy.at(r);
d2m += sz.at(r);
}
// Mean distance instead
dist[0] = d0m/sx.size();
dist[1] = d1m/sy.size();
dist[2] = d2m/sz.size();
// Find the maximum distance between two points in the region and use this for the supervoxel extraction
// dist[0] = *max_element(sx.begin(), sx.end());
// dist[1] = *max_element(sy.begin(), sy.end());
// dist[2] = *max_element(sz.begin(), sz.end());
return dist;
}
