//! apply k-means classify to volume
void apply_hard_classify(simple_volume<float> & input,
minc_byte_volume &mask,
std::vector<double> mu,
minc_byte_volume& cls
)
{
int number_of_classes=mu.size();
int i,j,k;
if(cls.size()!=mask.size())
REPORT_ERROR("Mask is wrong size");
//calculate all the classes
for(j=0;j<input.c_buf_size();j++)
{
if(mask.c_buf()[j])
{
int best_k=0;
double best_dist=0;
for(k=0;k<number_of_classes;k++)
{
double dist=fabs(input.c_buf()[j]-mu[k]);
if(dist<best_dist|| best_k==0)
{
best_dist=dist;
best_k=k+1;
}
}
cls.c_buf()[j]=best_k;
} else {
cls.c_buf()[j]=0;//unclassified?
}
}
}
void masked_mul(minc_grid_volume &v2,const minc_grid_volume& v1,const minc_byte_volume &mask)
{
if(v1.size()!=v2.size())
REPORT_ERROR("Volume size mismatch");
if(v1.size()!=mask.size())
REPORT_ERROR("Volume size mismatch");
for(int i=0;i<v2.c_buf_size();i++)
if(mask.c_buf()[i])
v2.c_buf()[i]*=v1.c_buf()[i];
else
v2.c_buf()[i]=IDX<float>(0,0,0);
}
double sum2(const minc_grid_volume& v,const minc_byte_volume& mask)
{
if(v.size()!=mask.size())
REPORT_ERROR("Volume size mismatch");
double r=0;
for(size_t i=0;i<v.c_buf_size();i++)
if(mask.c_buf()[i])
{
double t=v.c_buf()[i].mod2();
r+=t;
}
return r;
}