Refactoring lbpm_uCT_pp.cpp
This commit is contained in:
Mark Berrill
parent
7a6f35bd45
commit
1e8ca14d85
@ -405,6 +405,28 @@ std::vector<int> defDim( int fid, const std::vector<std::string>& names, const s
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return dimid;
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}
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template<class TYPE>
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int nc_put_vars_TYPE( int, int, const size_t*, const size_t*, const ptrdiff_t*, const TYPE* );
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template<>
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int nc_put_vars_TYPE<short>( int fid, int varid, const size_t* start, const size_t* count, const ptrdiff_t* stride, const short* data )
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{
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return nc_put_vars_short( fid, varid, start, count, stride, data );
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}
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template<>
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int nc_put_vars_TYPE<int>( int fid, int varid, const size_t* start, const size_t* count, const ptrdiff_t* stride, const int* data )
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{
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return nc_put_vars_int( fid, varid, start, count, stride, data );
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}
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template<>
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int nc_put_vars_TYPE<float>( int fid, int varid, const size_t* start, const size_t* count, const ptrdiff_t* stride, const float* data )
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{
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return nc_put_vars_float( fid, varid, start, count, stride, data );
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}
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template<>
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int nc_put_vars_TYPE<double>( int fid, int varid, const size_t* start, const size_t* count, const ptrdiff_t* stride, const double* data )
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{
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return nc_put_vars_double( fid, varid, start, count, stride, data );
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}
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template<class TYPE>
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void write( int fid, const std::string& var, const std::vector<int>& dimids,
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const Array<TYPE>& data, const std::vector<size_t>& start,
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const std::vector<size_t>& count, const std::vector<size_t>& stride )
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@ -421,12 +443,20 @@ void write( int fid, const std::string& var, const std::vector<int>& dimids,
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CHECK_NC_ERR( err );
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// parallel write: each process writes its subarray to the file
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auto x = data.reverseDim();
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nc_put_vars_float( fid, varid, start.data(), count.data(), (const ptrdiff_t*) stride.data(), x.data() );
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nc_put_vars_TYPE<TYPE>( fid, varid, start.data(), count.data(), (const ptrdiff_t*) stride.data(), x.data() );
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}
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template
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void write<float>( int fid, const std::string& var, const std::vector<int>& dimids,
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template void write<short>( int fid, const std::string& var, const std::vector<int>& dimids,
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const Array<short>& data, const std::vector<size_t>& start,
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const std::vector<size_t>& count, const std::vector<size_t>& stride );
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template void write<int>( int fid, const std::string& var, const std::vector<int>& dimids,
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const Array<int>& data, const std::vector<size_t>& start,
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const std::vector<size_t>& count, const std::vector<size_t>& stride );
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template void write<float>( int fid, const std::string& var, const std::vector<int>& dimids,
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const Array<float>& data, const std::vector<size_t>& start,
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const std::vector<size_t>& count, const std::vector<size_t>& stride );
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template void write<double>( int fid, const std::string& var, const std::vector<int>& dimids,
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const Array<double>& data, const std::vector<size_t>& start,
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const std::vector<size_t>& count, const std::vector<size_t>& stride );
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@ -21,7 +21,7 @@
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* @param[in] timesteps Maximum number of timesteps to process
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* @return Returns the global variation
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*/
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inline float Eikonal3D( Array<float> &Distance, const Array<char> &ID, const Domain &Dm, const int timesteps);
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float Eikonal3D( Array<float> &Distance, const Array<char> &ID, const Domain &Dm, const int timesteps);
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/*!
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@ -34,7 +34,7 @@ inline float Eikonal3D( Array<float> &Distance, const Array<char> &ID, const Dom
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* @param[in] DM Domain information
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* @return Returns the global variation
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*/
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inline void CalcDist3D( Array<float> &Distance, const Array<char> &ID, const Domain &Dm );
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void CalcDist3D( Array<float> &Distance, const Array<char> &ID, const Domain &Dm );
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/*!
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@ -47,7 +47,7 @@ inline void CalcDist3D( Array<float> &Distance, const Array<char> &ID, const Dom
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* @param[in] DM Domain information
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* @return Returns the global variation
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*/
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inline void CalcDistMultiLevel( Array<float> &Distance, const Array<char> &ID, const Domain &Dm );
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void CalcDistMultiLevel( Array<float> &Distance, const Array<char> &ID, const Domain &Dm );
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@ -2,6 +2,7 @@
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#define Eikonal_HPP_INC
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#include "analysis/eikonal.h"
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#include "common/imfilter.h"
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149
analysis/filters.cpp
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149
analysis/filters.cpp
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@ -0,0 +1,149 @@
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#include "analysis/filters.h"
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#include "ProfilerApp.h"
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void Med3D( const Array<float> &Input, Array<float> &Output )
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{
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PROFILE_START("Med3D");
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// Perform a 3D Median filter on Input array with specified width
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int i,j,k,ii,jj,kk;
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int imin,jmin,kmin,imax,jmax,kmax;
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float *List;
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List=new float[27];
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int Nx = int(Input.size(0));
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int Ny = int(Input.size(1));
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int Nz = int(Input.size(2));
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for (k=1; k<Nz-1; k++){
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for (j=1; j<Ny-1; j++){
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for (i=1; i<Nx-1; i++){
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// Just use a 3x3x3 window (hit recursively if needed)
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imin = i-1;
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jmin = j-1;
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kmin = k-1;
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imax = i+2;
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jmax = j+2;
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kmax = k+2;
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// Populate the list with values in the window
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int Number=0;
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for (kk=kmin; kk<kmax; kk++){
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for (jj=jmin; jj<jmax; jj++){
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for (ii=imin; ii<imax; ii++){
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List[Number++] = Input(ii,jj,kk);
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}
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}
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}
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// Sort the first 5 entries and return the median
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for (ii=0; ii<14; ii++){
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for (jj=ii+1; jj<27; jj++){
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if (List[jj] < List[ii]){
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float tmp = List[ii];
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List[ii] = List[jj];
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List[jj] = tmp;
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}
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}
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}
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// Return the median
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Output(i,j,k) = List[13];
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}
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}
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}
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PROFILE_STOP("Med3D");
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}
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int NLM3D( const Array<float> &Input, Array<float> &Mean,
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const Array<float> &Distance, Array<float> &Output, const int d, const float h)
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{
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PROFILE_START("NLM3D");
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// Implemenation of 3D non-local means filter
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// d determines the width of the search volume
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// h is a free parameter for non-local means (i.e. 1/sigma^2)
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// Distance is the signed distance function
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// If Distance(i,j,k) > THRESHOLD_DIST then don't compute NLM
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float THRESHOLD_DIST = float(d);
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float weight, sum;
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int i,j,k,ii,jj,kk;
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int imin,jmin,kmin,imax,jmax,kmax;
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int returnCount=0;
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int Nx = int(Input.size(0));
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int Ny = int(Input.size(1));
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int Nz = int(Input.size(2));
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// Compute the local means
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for (k=1; k<Nz-1; k++){
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for (j=1; j<Ny-1; j++){
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for (i=1; i<Nx-1; i++){
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imin = std::max(0,i-d);
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jmin = std::max(0,j-d);
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kmin = std::max(0,k-d);
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imax = std::min(Nx-1,i+d);
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jmax = std::min(Ny-1,j+d);
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kmax = std::min(Nz-1,k+d);
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// Populate the list with values in the window
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sum = 0; weight=0;
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for (kk=kmin; kk<kmax; kk++){
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for (jj=jmin; jj<jmax; jj++){
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for (ii=imin; ii<imax; ii++){
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sum += Input(ii,jj,kk);
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weight++;
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}
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}
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}
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Mean(i,j,k) = sum / weight;
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}
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}
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}
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// Compute the non-local means
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for (k=1; k<Nz-1; k++){
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for (j=1; j<Ny-1; j++){
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for (i=1; i<Nx-1; i++){
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if (fabs(Distance(i,j,k)) < THRESHOLD_DIST){
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// compute the expensive non-local means
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sum = 0; weight=0;
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imin = std::max(0,i-d);
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jmin = std::max(0,j-d);
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kmin = std::max(0,k-d);
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imax = std::min(Nx-1,i+d);
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jmax = std::min(Ny-1,j+d);
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kmax = std::min(Nz-1,k+d);
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for (kk=kmin; kk<kmax; kk++){
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for (jj=jmin; jj<jmax; jj++){
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for (ii=imin; ii<imax; ii++){
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float tmp = Mean(i,j,k) - Mean(ii,jj,kk);
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sum += exp(-tmp*tmp*h)*Input(ii,jj,kk);
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weight += exp(-tmp*tmp*h);
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}
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}
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}
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returnCount++;
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//Output(i,j,k) = Mean(i,j,k);
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Output(i,j,k) = sum / weight;
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}
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else{
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// Just return the mean
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Output(i,j,k) = Mean(i,j,k);
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}
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}
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}
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}
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// Return the number of sites where NLM was applied
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PROFILE_STOP("NLM3D");
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return returnCount;
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}
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28
analysis/filters.h
Normal file
28
analysis/filters.h
Normal file
@ -0,0 +1,28 @@
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#ifndef Filters_H_INC
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#define Filters_H_INC
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#include "common/Array.h"
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/*!
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* @brief Filter image
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* @details This routine performs a median filter
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* @param[in] Input Input image
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* @param[out] Output Output image
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*/
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void Med3D( const Array<float> &Input, Array<float> &Output );
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/*!
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* @brief Filter image
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* @details This routine performs a non-linear local means filter
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* @param[in] Input Input image
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* @param[in] Mean Mean value
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* @param[out] Output Output image
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*/
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int NLM3D( const Array<float> &Input, Array<float> &Mean,
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const Array<float> &Distance, Array<float> &Output, const int d, const float h);
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#endif
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395
analysis/uCT.cpp
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395
analysis/uCT.cpp
Normal file
@ -0,0 +1,395 @@
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#include "analysis/uCT.h"
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#include "analysis/analysis.h"
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#include "analysis/eikonal.h"
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#include "analysis/filters.h"
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#include "analysis/uCT.h"
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#include "common/imfilter.h"
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template<class T>
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inline int sign( T x )
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{
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if ( x==0 )
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return 0;
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return x>0 ? 1:-1;
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}
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inline float trilinear( float dx, float dy, float dz, float f1, float f2,
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float f3, float f4, float f5, float f6, float f7, float f8 )
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{
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double f, dx2, dy2, dz2, h0, h1;
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dx2 = 1.0 - dx;
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dy2 = 1.0 - dy;
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dz2 = 1.0 - dz;
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h0 = ( dx * f2 + dx2 * f1 ) * dy2 + ( dx * f4 + dx2 * f3 ) * dy;
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h1 = ( dx * f6 + dx2 * f5 ) * dy2 + ( dx * f8 + dx2 * f7 ) * dy;
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f = h0 * dz2 + h1 * dz;
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return ( f );
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}
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void InterpolateMesh( const Array<float> &Coarse, Array<float> &Fine )
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{
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PROFILE_START("InterpolateMesh");
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// Interpolate values from a Coarse mesh to a fine one
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// This routine assumes cell-centered meshes with 1 ghost cell
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// Fine mesh
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int Nx = int(Fine.size(0))-2;
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int Ny = int(Fine.size(1))-2;
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int Nz = int(Fine.size(2))-2;
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// Coarse mesh
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int nx = int(Coarse.size(0))-2;
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int ny = int(Coarse.size(1))-2;
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int nz = int(Coarse.size(2))-2;
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// compute the stride
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int hx = Nx/nx;
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int hy = Ny/ny;
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int hz = Nz/nz;
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ASSERT(nx*hx==Nx);
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ASSERT(ny*hy==Ny);
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ASSERT(nz*hz==Nz);
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// value to map distance between meshes (since distance is in voxels)
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// usually hx=hy=hz (or something very close)
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// the mapping is not exact
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// however, it's assumed the coarse solution will be refined
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// a good guess is the goal here!
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float mapvalue = sqrt(hx*hx+hy*hy+hz*hz);
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// Interpolate to the fine mesh
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for (int k=-1; k<Nz+1; k++){
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int k0 = floor((k-0.5*hz)/hz);
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int k1 = k0+1;
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int k2 = k0+2;
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float dz = ( (k+0.5) - (k0+0.5)*hz ) / hz;
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ASSERT(k0>=-1&&k0<nz+1&&dz>=0&&dz<=1);
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for (int j=-1; j<Ny+1; j++){
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int j0 = floor((j-0.5*hy)/hy);
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int j1 = j0+1;
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int j2 = j0+2;
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float dy = ( (j+0.5) - (j0+0.5)*hy ) / hy;
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ASSERT(j0>=-1&&j0<ny+1&&dy>=0&&dy<=1);
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for (int i=-1; i<Nx+1; i++){
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int i0 = floor((i-0.5*hx)/hx);
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int i1 = i0+1;
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int i2 = i0+2;
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float dx = ( (i+0.5) - (i0+0.5)*hx ) / hx;
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ASSERT(i0>=-1&&i0<nx+1&&dx>=0&&dx<=1);
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float val = trilinear( dx, dy, dz,
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Coarse(i1,j1,k1), Coarse(i2,j1,k1), Coarse(i1,j2,k1), Coarse(i2,j2,k1),
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Coarse(i1,j1,k2), Coarse(i2,j1,k2), Coarse(i1,j2,k2), Coarse(i2,j2,k2) );
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Fine(i+1,j+1,k+1) = mapvalue*val;
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}
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}
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}
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PROFILE_STOP("InterpolateMesh");
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}
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// Smooth the data using the distance
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void smooth( const Array<float>& VOL, const Array<float>& Dist, float sigma, Array<float>& MultiScaleSmooth, fillHalo<float>& fillFloat )
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{
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for (size_t i=0; i<VOL.length(); i++) {
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// use exponential weight based on the distance
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float dst = Dist(i);
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float tmp = exp(-(dst*dst)/(sigma*sigma));
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float value = dst>0 ? -1:1;
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MultiScaleSmooth(i) = tmp*VOL(i) + (1-tmp)*value;
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}
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fillFloat.fill(MultiScaleSmooth);
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}
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// Segment the data
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void segment( const Array<float>& data, Array<char>& ID, float tol )
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{
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ASSERT(data.size()==ID.size());
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for (size_t i=0; i<data.length(); i++) {
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if ( data(i) > tol )
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ID(i) = 0;
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else
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ID(i) = 1;
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}
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}
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// Remove disconnected phases
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void removeDisconnected( Array<char>& ID, const Domain& Dm )
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{
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// Run blob identification to remove disconnected volumes
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BlobIDArray GlobalBlobID;
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DoubleArray SignDist(ID.size());
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DoubleArray Phase(ID.size());
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for (size_t i=0; i<ID.length(); i++) {
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SignDist(i) = (2*ID(i)-1);
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Phase(i) = 1;
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}
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ComputeGlobalBlobIDs( ID.size(0)-2, ID.size(1)-2, ID.size(2)-2,
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Dm.rank_info, Phase, SignDist, 0, 0, GlobalBlobID, Dm.Comm );
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for (size_t i=0; i<ID.length(); i++) {
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if ( GlobalBlobID(i) > 0 )
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ID(i) = 0;
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ID(i) = GlobalBlobID(i);
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}
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}
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// Solve a level (without any coarse level information)
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void solve( const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
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Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
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fillHalo<float>& fillFloat, const Domain& Dm, int nprocx )
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{
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PROFILE_SCOPED(timer,"solve");
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// Compute the median filter on the sparse array
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Med3D( VOL, Mean );
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fillFloat.fill( Mean );
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segment( Mean, ID, 0.01 );
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// Compute the distance using the segmented volume
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Eikonal3D( Dist, ID, Dm, ID.size(0)*nprocx );
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fillFloat.fill(Dist);
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smooth( VOL, Dist, 2.0, MultiScaleSmooth, fillFloat );
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// Compute non-local mean
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int depth = 5;
|
||||
float sigsq=0.1;
|
||||
int nlm_count = NLM3D( MultiScaleSmooth, Mean, Dist, NonLocalMean, depth, sigsq);
|
||||
fillFloat.fill(NonLocalMean);
|
||||
}
|
||||
|
||||
|
||||
// Refine a solution from a coarse grid to a fine grid
|
||||
void refine( const Array<float>& Dist_coarse,
|
||||
const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
|
||||
Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm, int nprocx, int level )
|
||||
{
|
||||
PROFILE_SCOPED(timer,"refine");
|
||||
int ratio[3] = { int(Dist.size(0)/Dist_coarse.size(0)),
|
||||
int(Dist.size(1)/Dist_coarse.size(1)),
|
||||
int(Dist.size(2)/Dist_coarse.size(2)) };
|
||||
// Interpolate the distance from the coarse to fine grid
|
||||
InterpolateMesh( Dist_coarse, Dist );
|
||||
// Compute the median filter on the array and segment
|
||||
Med3D( VOL, Mean );
|
||||
fillFloat.fill( Mean );
|
||||
segment( Mean, ID, 0.01 );
|
||||
// If the ID has the wrong distance, set the distance to 0 and run a simple filter to set neighbors to 0
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
char id = Dist(i)>0 ? 1:0;
|
||||
if ( id != ID(i) )
|
||||
Dist(i) = 0;
|
||||
}
|
||||
fillFloat.fill( Dist );
|
||||
std::function<float(int,const float*)> filter_1D = []( int N, const float* data )
|
||||
{
|
||||
bool zero = data[0]==0 || data[2]==0;
|
||||
return zero ? data[1]*1e-12 : data[1];
|
||||
};
|
||||
std::vector<imfilter::BC> BC(3,imfilter::BC::replicate);
|
||||
std::vector<std::function<float(int,const float*)>> filter_set(3,filter_1D);
|
||||
Dist = imfilter::imfilter_separable<float>( Dist, {1,1,1}, filter_set, BC );
|
||||
fillFloat.fill( Dist );
|
||||
// Smooth the volume data
|
||||
float lambda = 2*sqrt(double(ratio[0]*ratio[0]+ratio[1]*ratio[1]+ratio[2]*ratio[2]));
|
||||
smooth( VOL, Dist, lambda, MultiScaleSmooth, fillFloat );
|
||||
// Compute non-local mean
|
||||
int depth = 3;
|
||||
float sigsq = 0.1;
|
||||
int nlm_count = NLM3D( MultiScaleSmooth, Mean, Dist, NonLocalMean, depth, sigsq);
|
||||
fillFloat.fill(NonLocalMean);
|
||||
segment( NonLocalMean, ID, 0.001 );
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
char id = Dist(i)>0 ? 1:0;
|
||||
if ( id!=ID(i) || fabs(Dist(i))<1 )
|
||||
Dist(i) = 2.0*ID(i)-1.0;
|
||||
}
|
||||
// Remove disconnected domains
|
||||
//removeDisconnected( ID, Dm );
|
||||
// Compute the distance using the segmented volume
|
||||
if ( level > 0 ) {
|
||||
//Eikonal3D( Dist, ID, Dm, ID.size(0)*nprocx );
|
||||
//CalcDist3D( Dist, ID, Dm );
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Remove regions that are likely noise by shrinking the volumes by dx,
|
||||
// removing all values that are more than dx+delta from the surface, and then
|
||||
// growing by dx+delta and intersecting with the original data
|
||||
void filter_final( Array<char>& ID, Array<float>& Dist,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm,
|
||||
Array<float>& Mean, Array<float>& Dist1, Array<float>& Dist2 )
|
||||
{
|
||||
PROFILE_SCOPED(timer,"filter_final");
|
||||
int rank;
|
||||
MPI_Comm_rank(Dm.Comm,&rank);
|
||||
int Nx = Dm.Nx-2;
|
||||
int Ny = Dm.Ny-2;
|
||||
int Nz = Dm.Nz-2;
|
||||
// Calculate the distance
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
// Compute the range to shrink the volume based on the L2 norm of the distance
|
||||
Array<float> Dist0(Nx,Ny,Nz);
|
||||
fillFloat.copy(Dist,Dist0);
|
||||
float tmp = 0;
|
||||
for (size_t i=0; i<Dist0.length(); i++)
|
||||
tmp += Dist0(i)*Dist0(i);
|
||||
tmp = sqrt( sumReduce(Dm.Comm,tmp) / sumReduce(Dm.Comm,(float)Dist0.length()) );
|
||||
const float dx1 = 0.3*tmp;
|
||||
const float dx2 = 1.05*dx1;
|
||||
if (rank==0)
|
||||
printf(" %0.1f %0.1f %0.1f\n",tmp,dx1,dx2);
|
||||
// Update the IDs/Distance removing regions that are < dx of the range
|
||||
Dist1 = Dist;
|
||||
Dist2 = Dist;
|
||||
Array<char> ID1 = ID;
|
||||
Array<char> ID2 = ID;
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
ID1(i) = Dist(i)<-dx1 ? 1:0;
|
||||
ID2(i) = Dist(i)> dx1 ? 1:0;
|
||||
}
|
||||
//Array<float> Dist1 = Dist;
|
||||
//Array<float> Dist2 = Dist;
|
||||
CalcDistMultiLevel( Dist1, ID1, Dm );
|
||||
CalcDistMultiLevel( Dist2, ID2, Dm );
|
||||
fillFloat.fill(Dist1);
|
||||
fillFloat.fill(Dist2);
|
||||
// Keep those regions that are within dx2 of the new volumes
|
||||
Mean = Dist;
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
if ( Dist1(i)+dx2>0 && ID(i)<=0 ) {
|
||||
Mean(i) = -1;
|
||||
} else if ( Dist2(i)+dx2>0 && ID(i)>0 ) {
|
||||
Mean(i) = 1;
|
||||
} else {
|
||||
Mean(i) = Dist(i)>0 ? 0.5:-0.5;
|
||||
}
|
||||
}
|
||||
// Find regions of uncertainty that are entirely contained within another region
|
||||
fillHalo<double> fillDouble(Dm.Comm,Dm.rank_info,Nx,Ny,Nz,1,1,1,0,1);
|
||||
fillHalo<BlobIDType> fillInt(Dm.Comm,Dm.rank_info,Nx,Ny,Nz,1,1,1,0,1);
|
||||
BlobIDArray GlobalBlobID;
|
||||
DoubleArray SignDist(ID.size());
|
||||
for (size_t i=0; i<ID.length(); i++)
|
||||
SignDist(i) = fabs(Mean(i))==1 ? -1:1;
|
||||
fillDouble.fill(SignDist);
|
||||
DoubleArray Phase(ID.size());
|
||||
Phase.fill(1);
|
||||
ComputeGlobalBlobIDs( Nx, Ny, Nz, Dm.rank_info, Phase, SignDist, 0, 0, GlobalBlobID, Dm.Comm );
|
||||
fillInt.fill(GlobalBlobID);
|
||||
int N_blobs = maxReduce(Dm.Comm,GlobalBlobID.max()+1);
|
||||
std::vector<float> mean(N_blobs,0);
|
||||
std::vector<int> count(N_blobs,0);
|
||||
for (int k=1; k<=Nz; k++) {
|
||||
for (int j=1; j<=Ny; j++) {
|
||||
for (int i=1; i<=Nx; i++) {
|
||||
int id = GlobalBlobID(i,j,k);
|
||||
if ( id >= 0 ) {
|
||||
if ( GlobalBlobID(i-1,j,k)<0 ) {
|
||||
mean[id] += Mean(i-1,j,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i+1,j,k)<0 ) {
|
||||
mean[id] += Mean(i+1,j,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j-1,k)<0 ) {
|
||||
mean[id] += Mean(i,j-1,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j+1,k)<0 ) {
|
||||
mean[id] += Mean(i,j+1,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j,k-1)<0 ) {
|
||||
mean[id] += Mean(i,j,k-1);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j,k+1)<0 ) {
|
||||
mean[id] += Mean(i,j,k+1);
|
||||
count[id]++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
mean = sumReduce(Dm.Comm,mean);
|
||||
count = sumReduce(Dm.Comm,count);
|
||||
for (size_t i=0; i<mean.size(); i++)
|
||||
mean[i] /= count[i];
|
||||
/*if (rank==0) {
|
||||
for (size_t i=0; i<mean.size(); i++)
|
||||
printf("%i %0.4f\n",i,mean[i]);
|
||||
}*/
|
||||
for (size_t i=0; i<Mean.length(); i++) {
|
||||
int id = GlobalBlobID(i);
|
||||
if ( id >= 0 ) {
|
||||
if ( fabs(mean[id]) > 0.95 ) {
|
||||
// Isolated domain surrounded by one domain
|
||||
GlobalBlobID(i) = -2;
|
||||
Mean(i) = sign(mean[id]);
|
||||
} else {
|
||||
// Boarder volume, set to liquid
|
||||
Mean(i) = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// Perform the final segmentation and update the distance
|
||||
fillFloat.fill(Mean);
|
||||
segment( Mean, ID, 0.01 );
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Filter the original data
|
||||
void filter_src( const Domain& Dm, Array<float>& src )
|
||||
{
|
||||
PROFILE_START("Filter source data");
|
||||
int Nx = Dm.Nx-2;
|
||||
int Ny = Dm.Ny-2;
|
||||
int Nz = Dm.Nz-2;
|
||||
fillHalo<float> fillFloat(Dm.Comm,Dm.rank_info,Nx,Ny,Nz,1,1,1,0,1);
|
||||
// Perform a hot-spot filter on the data
|
||||
std::vector<imfilter::BC> BC = { imfilter::BC::replicate, imfilter::BC::replicate, imfilter::BC::replicate };
|
||||
std::function<float(const Array<float>&)> filter_3D = []( const Array<float>& data )
|
||||
{
|
||||
float min1 = std::min(data(0,1,1),data(2,1,1));
|
||||
float min2 = std::min(data(1,0,1),data(1,2,1));
|
||||
float min3 = std::min(data(1,1,0),data(1,1,2));
|
||||
float max1 = std::max(data(0,1,1),data(2,1,1));
|
||||
float max2 = std::max(data(1,0,1),data(1,2,1));
|
||||
float max3 = std::max(data(1,1,0),data(1,1,2));
|
||||
float min = std::min(min1,std::min(min2,min3));
|
||||
float max = std::max(max1,std::max(max2,max3));
|
||||
return std::max(std::min(data(1,1,1),max),min);
|
||||
};
|
||||
std::function<float(const Array<float>&)> filter_1D = []( const Array<float>& data )
|
||||
{
|
||||
float min = std::min(data(0),data(2));
|
||||
float max = std::max(data(0),data(2));
|
||||
return std::max(std::min(data(1),max),min);
|
||||
};
|
||||
//LOCVOL[0] = imfilter::imfilter<float>( LOCVOL[0], {1,1,1}, filter_3D, BC );
|
||||
std::vector<std::function<float(const Array<float>&)>> filter_set(3,filter_1D);
|
||||
src = imfilter::imfilter_separable<float>( src, {1,1,1}, filter_set, BC );
|
||||
fillFloat.fill( src );
|
||||
// Perform a gaussian filter on the data
|
||||
int Nh[3] = { 2, 2, 2 };
|
||||
float sigma[3] = { 1.0, 1.0, 1.0 };
|
||||
std::vector<Array<float>> H(3);
|
||||
H[0] = imfilter::create_filter<float>( { Nh[0] }, "gaussian", &sigma[0] );
|
||||
H[1] = imfilter::create_filter<float>( { Nh[1] }, "gaussian", &sigma[1] );
|
||||
H[2] = imfilter::create_filter<float>( { Nh[2] }, "gaussian", &sigma[2] );
|
||||
src = imfilter::imfilter_separable( src, H, BC );
|
||||
fillFloat.fill( src );
|
||||
PROFILE_STOP("Filter source data");
|
||||
}
|
||||
56
analysis/uCT.h
Normal file
56
analysis/uCT.h
Normal file
@ -0,0 +1,56 @@
|
||||
#ifndef uCT_H_INC
|
||||
#define uCT_H_INC
|
||||
|
||||
#include "common/Array.h"
|
||||
#include "common/Domain.h"
|
||||
#include "common/Communication.h"
|
||||
|
||||
|
||||
|
||||
/*!
|
||||
* @brief Interpolate between meshes
|
||||
* @details This routine interpolates from a coarse to a fine mesh
|
||||
* @param[in] Coarse Coarse mesh solution
|
||||
* @param[out] Fine Fine mesh solution
|
||||
*/
|
||||
void InterpolateMesh( const Array<float> &Coarse, Array<float> &Fine );
|
||||
|
||||
|
||||
// Smooth the data using the distance
|
||||
void smooth( const Array<float>& VOL, const Array<float>& Dist, float sigma, Array<float>& MultiScaleSmooth, fillHalo<float>& fillFloat );
|
||||
|
||||
|
||||
// Segment the data
|
||||
void segment( const Array<float>& data, Array<char>& ID, float tol );
|
||||
|
||||
|
||||
// Remove disconnected phases
|
||||
void removeDisconnected( Array<char>& ID, const Domain& Dm );
|
||||
|
||||
|
||||
// Solve a level (without any coarse level information)
|
||||
void solve( const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
|
||||
Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm, int nprocx );
|
||||
|
||||
|
||||
// Refine a solution from a coarse grid to a fine grid
|
||||
void refine( const Array<float>& Dist_coarse,
|
||||
const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
|
||||
Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm, int nprocx, int level );
|
||||
|
||||
|
||||
// Remove regions that are likely noise by shrinking the volumes by dx,
|
||||
// removing all values that are more than dx+delta from the surface, and then
|
||||
// growing by dx+delta and intersecting with the original data
|
||||
void filter_final( Array<char>& ID, Array<float>& Dist,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm,
|
||||
Array<float>& Mean, Array<float>& Dist1, Array<float>& Dist2 );
|
||||
|
||||
|
||||
// Filter the original data
|
||||
void filter_src( const Domain& Dm, Array<float>& src );
|
||||
|
||||
|
||||
#endif
|
||||
@ -20,6 +20,43 @@ static int MAX_BLOB_COUNT=50;
|
||||
using namespace std;
|
||||
|
||||
|
||||
|
||||
// Reading the domain information file
|
||||
void read_domain( int rank, int nprocs, MPI_Comm comm,
|
||||
int& nprocx, int& nprocy, int& nprocz, int& nx, int& ny, int& nz,
|
||||
int& nspheres, double& Lx, double& Ly, double& Lz )
|
||||
{
|
||||
if (rank==0){
|
||||
ifstream domain("Domain.in");
|
||||
domain >> nprocx;
|
||||
domain >> nprocy;
|
||||
domain >> nprocz;
|
||||
domain >> nx;
|
||||
domain >> ny;
|
||||
domain >> nz;
|
||||
domain >> nspheres;
|
||||
domain >> Lx;
|
||||
domain >> Ly;
|
||||
domain >> Lz;
|
||||
|
||||
}
|
||||
MPI_Barrier(comm);
|
||||
// Computational domain
|
||||
//.................................................
|
||||
MPI_Bcast(&nx,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&ny,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nz,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocx,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocy,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocz,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nspheres,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&Lx,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Bcast(&Ly,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Bcast(&Lz,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Barrier(comm);
|
||||
}
|
||||
|
||||
|
||||
/********************************************************
|
||||
* Constructor/Destructor *
|
||||
********************************************************/
|
||||
|
||||
@ -18,6 +18,14 @@
|
||||
|
||||
using namespace std;
|
||||
|
||||
|
||||
//! Read the domain information file
|
||||
void read_domain( int rank, int nprocs, MPI_Comm comm,
|
||||
int& nprocx, int& nprocy, int& nprocz, int& nx, int& ny, int& nz,
|
||||
int& nspheres, double& Lx, double& Ly, double& Lz );
|
||||
|
||||
|
||||
//! Class to hold domain info
|
||||
struct Domain{
|
||||
// Default constructor
|
||||
Domain(int nx, int ny, int nz, int rnk, int npx, int npy, int npz,
|
||||
|
||||
@ -15,541 +15,18 @@
|
||||
#include "common/Domain.h"
|
||||
#include "common/Communication.h"
|
||||
#include "common/MPI_Helpers.h"
|
||||
#include "common/imfilter.h"
|
||||
#include "IO/MeshDatabase.h"
|
||||
#include "IO/Mesh.h"
|
||||
#include "IO/Writer.h"
|
||||
#include "IO/netcdf.h"
|
||||
#include "analysis/analysis.h"
|
||||
#include "analysis/eikonal.h"
|
||||
#include "analysis/filters.h"
|
||||
#include "analysis/uCT.h"
|
||||
|
||||
#include "ProfilerApp.h"
|
||||
|
||||
|
||||
template<class T>
|
||||
inline int sign( T x )
|
||||
{
|
||||
if ( x==0 )
|
||||
return 0;
|
||||
return x>0 ? 1:-1;
|
||||
}
|
||||
|
||||
|
||||
inline void Med3D( const Array<float> &Input, Array<float> &Output )
|
||||
{
|
||||
PROFILE_START("Med3D");
|
||||
// Perform a 3D Median filter on Input array with specified width
|
||||
int i,j,k,ii,jj,kk;
|
||||
int imin,jmin,kmin,imax,jmax,kmax;
|
||||
|
||||
float *List;
|
||||
List=new float[27];
|
||||
|
||||
int Nx = int(Input.size(0));
|
||||
int Ny = int(Input.size(1));
|
||||
int Nz = int(Input.size(2));
|
||||
|
||||
for (k=1; k<Nz-1; k++){
|
||||
for (j=1; j<Ny-1; j++){
|
||||
for (i=1; i<Nx-1; i++){
|
||||
|
||||
// Just use a 3x3x3 window (hit recursively if needed)
|
||||
imin = i-1;
|
||||
jmin = j-1;
|
||||
kmin = k-1;
|
||||
imax = i+2;
|
||||
jmax = j+2;
|
||||
kmax = k+2;
|
||||
|
||||
// Populate the list with values in the window
|
||||
int Number=0;
|
||||
for (kk=kmin; kk<kmax; kk++){
|
||||
for (jj=jmin; jj<jmax; jj++){
|
||||
for (ii=imin; ii<imax; ii++){
|
||||
List[Number++] = Input(ii,jj,kk);
|
||||
}
|
||||
}
|
||||
}
|
||||
// Sort the first 5 entries and return the median
|
||||
for (ii=0; ii<14; ii++){
|
||||
for (jj=ii+1; jj<27; jj++){
|
||||
if (List[jj] < List[ii]){
|
||||
float tmp = List[ii];
|
||||
List[ii] = List[jj];
|
||||
List[jj] = tmp;
|
||||
}
|
||||
}
|
||||
}
|
||||
// Return the median
|
||||
Output(i,j,k) = List[13];
|
||||
}
|
||||
}
|
||||
}
|
||||
PROFILE_STOP("Med3D");
|
||||
}
|
||||
|
||||
|
||||
inline float trilinear( float dx, float dy, float dz, float f1, float f2,
|
||||
float f3, float f4, float f5, float f6, float f7, float f8 )
|
||||
{
|
||||
double f, dx2, dy2, dz2, h0, h1;
|
||||
dx2 = 1.0 - dx;
|
||||
dy2 = 1.0 - dy;
|
||||
dz2 = 1.0 - dz;
|
||||
h0 = ( dx * f2 + dx2 * f1 ) * dy2 + ( dx * f4 + dx2 * f3 ) * dy;
|
||||
h1 = ( dx * f6 + dx2 * f5 ) * dy2 + ( dx * f8 + dx2 * f7 ) * dy;
|
||||
f = h0 * dz2 + h1 * dz;
|
||||
return ( f );
|
||||
}
|
||||
inline void InterpolateMesh( const Array<float> &Coarse, Array<float> &Fine )
|
||||
{
|
||||
PROFILE_START("InterpolateMesh");
|
||||
|
||||
// Interpolate values from a Coarse mesh to a fine one
|
||||
// This routine assumes cell-centered meshes with 1 ghost cell
|
||||
|
||||
// Fine mesh
|
||||
int Nx = int(Fine.size(0))-2;
|
||||
int Ny = int(Fine.size(1))-2;
|
||||
int Nz = int(Fine.size(2))-2;
|
||||
|
||||
// Coarse mesh
|
||||
int nx = int(Coarse.size(0))-2;
|
||||
int ny = int(Coarse.size(1))-2;
|
||||
int nz = int(Coarse.size(2))-2;
|
||||
|
||||
// compute the stride
|
||||
int hx = Nx/nx;
|
||||
int hy = Ny/ny;
|
||||
int hz = Nz/nz;
|
||||
ASSERT(nx*hx==Nx);
|
||||
ASSERT(ny*hy==Ny);
|
||||
ASSERT(nz*hz==Nz);
|
||||
|
||||
// value to map distance between meshes (since distance is in voxels)
|
||||
// usually hx=hy=hz (or something very close)
|
||||
// the mapping is not exact
|
||||
// however, it's assumed the coarse solution will be refined
|
||||
// a good guess is the goal here!
|
||||
float mapvalue = sqrt(hx*hx+hy*hy+hz*hz);
|
||||
|
||||
// Interpolate to the fine mesh
|
||||
for (int k=-1; k<Nz+1; k++){
|
||||
int k0 = floor((k-0.5*hz)/hz);
|
||||
int k1 = k0+1;
|
||||
int k2 = k0+2;
|
||||
float dz = ( (k+0.5) - (k0+0.5)*hz ) / hz;
|
||||
ASSERT(k0>=-1&&k0<nz+1&&dz>=0&&dz<=1);
|
||||
for (int j=-1; j<Ny+1; j++){
|
||||
int j0 = floor((j-0.5*hy)/hy);
|
||||
int j1 = j0+1;
|
||||
int j2 = j0+2;
|
||||
float dy = ( (j+0.5) - (j0+0.5)*hy ) / hy;
|
||||
ASSERT(j0>=-1&&j0<ny+1&&dy>=0&&dy<=1);
|
||||
for (int i=-1; i<Nx+1; i++){
|
||||
int i0 = floor((i-0.5*hx)/hx);
|
||||
int i1 = i0+1;
|
||||
int i2 = i0+2;
|
||||
float dx = ( (i+0.5) - (i0+0.5)*hx ) / hx;
|
||||
ASSERT(i0>=-1&&i0<nx+1&&dx>=0&&dx<=1);
|
||||
float val = trilinear( dx, dy, dz,
|
||||
Coarse(i1,j1,k1), Coarse(i2,j1,k1), Coarse(i1,j2,k1), Coarse(i2,j2,k1),
|
||||
Coarse(i1,j1,k2), Coarse(i2,j1,k2), Coarse(i1,j2,k2), Coarse(i2,j2,k2) );
|
||||
Fine(i+1,j+1,k+1) = mapvalue*val;
|
||||
}
|
||||
}
|
||||
}
|
||||
PROFILE_STOP("InterpolateMesh");
|
||||
}
|
||||
|
||||
|
||||
inline int NLM3D( const Array<float> &Input, Array<float> &Mean,
|
||||
const Array<float> &Distance, Array<float> &Output, const int d, const float h)
|
||||
{
|
||||
PROFILE_START("NLM3D");
|
||||
// Implemenation of 3D non-local means filter
|
||||
// d determines the width of the search volume
|
||||
// h is a free parameter for non-local means (i.e. 1/sigma^2)
|
||||
// Distance is the signed distance function
|
||||
// If Distance(i,j,k) > THRESHOLD_DIST then don't compute NLM
|
||||
|
||||
float THRESHOLD_DIST = float(d);
|
||||
float weight, sum;
|
||||
int i,j,k,ii,jj,kk;
|
||||
int imin,jmin,kmin,imax,jmax,kmax;
|
||||
int returnCount=0;
|
||||
|
||||
int Nx = int(Input.size(0));
|
||||
int Ny = int(Input.size(1));
|
||||
int Nz = int(Input.size(2));
|
||||
|
||||
// Compute the local means
|
||||
for (k=1; k<Nz-1; k++){
|
||||
for (j=1; j<Ny-1; j++){
|
||||
for (i=1; i<Nx-1; i++){
|
||||
|
||||
imin = max(0,i-d);
|
||||
jmin = max(0,j-d);
|
||||
kmin = max(0,k-d);
|
||||
imax = min(Nx-1,i+d);
|
||||
jmax = min(Ny-1,j+d);
|
||||
kmax = min(Nz-1,k+d);
|
||||
|
||||
// Populate the list with values in the window
|
||||
sum = 0; weight=0;
|
||||
for (kk=kmin; kk<kmax; kk++){
|
||||
for (jj=jmin; jj<jmax; jj++){
|
||||
for (ii=imin; ii<imax; ii++){
|
||||
sum += Input(ii,jj,kk);
|
||||
weight++;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Mean(i,j,k) = sum / weight;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Compute the non-local means
|
||||
for (k=1; k<Nz-1; k++){
|
||||
for (j=1; j<Ny-1; j++){
|
||||
for (i=1; i<Nx-1; i++){
|
||||
|
||||
|
||||
if (fabs(Distance(i,j,k)) < THRESHOLD_DIST){
|
||||
// compute the expensive non-local means
|
||||
sum = 0; weight=0;
|
||||
|
||||
imin = max(0,i-d);
|
||||
jmin = max(0,j-d);
|
||||
kmin = max(0,k-d);
|
||||
imax = min(Nx-1,i+d);
|
||||
jmax = min(Ny-1,j+d);
|
||||
kmax = min(Nz-1,k+d);
|
||||
|
||||
for (kk=kmin; kk<kmax; kk++){
|
||||
for (jj=jmin; jj<jmax; jj++){
|
||||
for (ii=imin; ii<imax; ii++){
|
||||
float tmp = Mean(i,j,k) - Mean(ii,jj,kk);
|
||||
sum += exp(-tmp*tmp*h)*Input(ii,jj,kk);
|
||||
weight += exp(-tmp*tmp*h);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
returnCount++;
|
||||
//Output(i,j,k) = Mean(i,j,k);
|
||||
Output(i,j,k) = sum / weight;
|
||||
}
|
||||
else{
|
||||
// Just return the mean
|
||||
Output(i,j,k) = Mean(i,j,k);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// Return the number of sites where NLM was applied
|
||||
PROFILE_STOP("NLM3D");
|
||||
return returnCount;
|
||||
}
|
||||
|
||||
|
||||
// Reading the domain information file
|
||||
void read_domain( int rank, int nprocs, MPI_Comm comm,
|
||||
int& nprocx, int& nprocy, int& nprocz, int& nx, int& ny, int& nz,
|
||||
int& nspheres, double& Lx, double& Ly, double& Lz )
|
||||
{
|
||||
if (rank==0){
|
||||
ifstream domain("Domain.in");
|
||||
domain >> nprocx;
|
||||
domain >> nprocy;
|
||||
domain >> nprocz;
|
||||
domain >> nx;
|
||||
domain >> ny;
|
||||
domain >> nz;
|
||||
domain >> nspheres;
|
||||
domain >> Lx;
|
||||
domain >> Ly;
|
||||
domain >> Lz;
|
||||
|
||||
}
|
||||
MPI_Barrier(comm);
|
||||
// Computational domain
|
||||
//.................................................
|
||||
MPI_Bcast(&nx,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&ny,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nz,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocx,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocy,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nprocz,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&nspheres,1,MPI_INT,0,comm);
|
||||
MPI_Bcast(&Lx,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Bcast(&Ly,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Bcast(&Lz,1,MPI_DOUBLE,0,comm);
|
||||
MPI_Barrier(comm);
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Smooth the data using the distance
|
||||
void smooth( const Array<float>& VOL, const Array<float>& Dist, float sigma, Array<float>& MultiScaleSmooth, fillHalo<float>& fillFloat )
|
||||
{
|
||||
for (size_t i=0; i<VOL.length(); i++) {
|
||||
// use exponential weight based on the distance
|
||||
float dst = Dist(i);
|
||||
float tmp = exp(-(dst*dst)/(sigma*sigma));
|
||||
float value = dst>0 ? -1:1;
|
||||
MultiScaleSmooth(i) = tmp*VOL(i) + (1-tmp)*value;
|
||||
}
|
||||
fillFloat.fill(MultiScaleSmooth);
|
||||
}
|
||||
|
||||
|
||||
// Segment the data
|
||||
void segment( const Array<float>& data, Array<char>& ID, float tol )
|
||||
{
|
||||
ASSERT(data.size()==ID.size());
|
||||
for (size_t i=0; i<data.length(); i++) {
|
||||
if ( data(i) > tol )
|
||||
ID(i) = 0;
|
||||
else
|
||||
ID(i) = 1;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Remove disconnected phases
|
||||
void removeDisconnected( Array<char>& ID, const Domain& Dm )
|
||||
{
|
||||
// Run blob identification to remove disconnected volumes
|
||||
BlobIDArray GlobalBlobID;
|
||||
DoubleArray SignDist(ID.size());
|
||||
DoubleArray Phase(ID.size());
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
SignDist(i) = (2*ID(i)-1);
|
||||
Phase(i) = 1;
|
||||
}
|
||||
ComputeGlobalBlobIDs( ID.size(0)-2, ID.size(1)-2, ID.size(2)-2,
|
||||
Dm.rank_info, Phase, SignDist, 0, 0, GlobalBlobID, Dm.Comm );
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
if ( GlobalBlobID(i) > 0 )
|
||||
ID(i) = 0;
|
||||
ID(i) = GlobalBlobID(i);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Solve a level (without any coarse level information)
|
||||
void solve( const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
|
||||
Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm, int nprocx )
|
||||
{
|
||||
PROFILE_SCOPED(timer,"solve");
|
||||
// Compute the median filter on the sparse array
|
||||
Med3D( VOL, Mean );
|
||||
fillFloat.fill( Mean );
|
||||
segment( Mean, ID, 0.01 );
|
||||
// Compute the distance using the segmented volume
|
||||
Eikonal3D( Dist, ID, Dm, ID.size(0)*nprocx );
|
||||
fillFloat.fill(Dist);
|
||||
smooth( VOL, Dist, 2.0, MultiScaleSmooth, fillFloat );
|
||||
// Compute non-local mean
|
||||
int depth = 5;
|
||||
float sigsq=0.1;
|
||||
int nlm_count = NLM3D( MultiScaleSmooth, Mean, Dist, NonLocalMean, depth, sigsq);
|
||||
fillFloat.fill(NonLocalMean);
|
||||
}
|
||||
|
||||
|
||||
// Refine a solution from a coarse grid to a fine grid
|
||||
void refine( const Array<float>& Dist_coarse,
|
||||
const Array<float>& VOL, Array<float>& Mean, Array<char>& ID,
|
||||
Array<float>& Dist, Array<float>& MultiScaleSmooth, Array<float>& NonLocalMean,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm, int nprocx, int level )
|
||||
{
|
||||
PROFILE_SCOPED(timer,"refine");
|
||||
int ratio[3] = { int(Dist.size(0)/Dist_coarse.size(0)),
|
||||
int(Dist.size(1)/Dist_coarse.size(1)),
|
||||
int(Dist.size(2)/Dist_coarse.size(2)) };
|
||||
// Interpolate the distance from the coarse to fine grid
|
||||
InterpolateMesh( Dist_coarse, Dist );
|
||||
// Compute the median filter on the array and segment
|
||||
Med3D( VOL, Mean );
|
||||
fillFloat.fill( Mean );
|
||||
segment( Mean, ID, 0.01 );
|
||||
// If the ID has the wrong distance, set the distance to 0 and run a simple filter to set neighbors to 0
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
char id = Dist(i)>0 ? 1:0;
|
||||
if ( id != ID(i) )
|
||||
Dist(i) = 0;
|
||||
}
|
||||
fillFloat.fill( Dist );
|
||||
std::function<float(int,const float*)> filter_1D = []( int N, const float* data )
|
||||
{
|
||||
bool zero = data[0]==0 || data[2]==0;
|
||||
return zero ? data[1]*1e-12 : data[1];
|
||||
};
|
||||
std::vector<imfilter::BC> BC(3,imfilter::BC::replicate);
|
||||
std::vector<std::function<float(int,const float*)>> filter_set(3,filter_1D);
|
||||
Dist = imfilter::imfilter_separable<float>( Dist, {1,1,1}, filter_set, BC );
|
||||
fillFloat.fill( Dist );
|
||||
// Smooth the volume data
|
||||
float lambda = 2*sqrt(double(ratio[0]*ratio[0]+ratio[1]*ratio[1]+ratio[2]*ratio[2]));
|
||||
smooth( VOL, Dist, lambda, MultiScaleSmooth, fillFloat );
|
||||
// Compute non-local mean
|
||||
int depth = 3;
|
||||
float sigsq = 0.1;
|
||||
int nlm_count = NLM3D( MultiScaleSmooth, Mean, Dist, NonLocalMean, depth, sigsq);
|
||||
fillFloat.fill(NonLocalMean);
|
||||
segment( NonLocalMean, ID, 0.001 );
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
char id = Dist(i)>0 ? 1:0;
|
||||
if ( id!=ID(i) || fabs(Dist(i))<1 )
|
||||
Dist(i) = 2.0*ID(i)-1.0;
|
||||
}
|
||||
// Remove disconnected domains
|
||||
//removeDisconnected( ID, Dm );
|
||||
// Compute the distance using the segmented volume
|
||||
if ( level > 0 ) {
|
||||
//Eikonal3D( Dist, ID, Dm, ID.size(0)*nprocx );
|
||||
//CalcDist3D( Dist, ID, Dm );
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
// Remove regions that are likely noise by shrinking the volumes by dx,
|
||||
// removing all values that are more than dx+delta from the surface, and then
|
||||
// growing by dx+delta and intersecting with the original data
|
||||
void filter_final( Array<char>& ID, Array<float>& Dist,
|
||||
fillHalo<float>& fillFloat, const Domain& Dm,
|
||||
Array<float>& Mean, Array<float>& Dist1, Array<float>& Dist2 )
|
||||
{
|
||||
PROFILE_SCOPED(timer,"filter_final");
|
||||
int rank;
|
||||
MPI_Comm_rank(Dm.Comm,&rank);
|
||||
int Nx = Dm.Nx-2;
|
||||
int Ny = Dm.Ny-2;
|
||||
int Nz = Dm.Nz-2;
|
||||
// Calculate the distance
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
// Compute the range to shrink the volume based on the L2 norm of the distance
|
||||
Array<float> Dist0(Nx,Ny,Nz);
|
||||
fillFloat.copy(Dist,Dist0);
|
||||
float tmp = 0;
|
||||
for (size_t i=0; i<Dist0.length(); i++)
|
||||
tmp += Dist0(i)*Dist0(i);
|
||||
tmp = sqrt( sumReduce(Dm.Comm,tmp) / sumReduce(Dm.Comm,(float)Dist0.length()) );
|
||||
const float dx1 = 0.3*tmp;
|
||||
const float dx2 = 1.05*dx1;
|
||||
if (rank==0)
|
||||
printf(" %0.1f %0.1f %0.1f\n",tmp,dx1,dx2);
|
||||
// Update the IDs/Distance removing regions that are < dx of the range
|
||||
Dist1 = Dist;
|
||||
Dist2 = Dist;
|
||||
Array<char> ID1 = ID;
|
||||
Array<char> ID2 = ID;
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
ID1(i) = Dist(i)<-dx1 ? 1:0;
|
||||
ID2(i) = Dist(i)> dx1 ? 1:0;
|
||||
}
|
||||
//Array<float> Dist1 = Dist;
|
||||
//Array<float> Dist2 = Dist;
|
||||
CalcDistMultiLevel( Dist1, ID1, Dm );
|
||||
CalcDistMultiLevel( Dist2, ID2, Dm );
|
||||
fillFloat.fill(Dist1);
|
||||
fillFloat.fill(Dist2);
|
||||
// Keep those regions that are within dx2 of the new volumes
|
||||
Mean = Dist;
|
||||
for (size_t i=0; i<ID.length(); i++) {
|
||||
if ( Dist1(i)+dx2>0 && ID(i)<=0 ) {
|
||||
Mean(i) = -1;
|
||||
} else if ( Dist2(i)+dx2>0 && ID(i)>0 ) {
|
||||
Mean(i) = 1;
|
||||
} else {
|
||||
Mean(i) = Dist(i)>0 ? 0.5:-0.5;
|
||||
}
|
||||
}
|
||||
// Find regions of uncertainty that are entirely contained within another region
|
||||
fillHalo<double> fillDouble(Dm.Comm,Dm.rank_info,Nx,Ny,Nz,1,1,1,0,1);
|
||||
fillHalo<BlobIDType> fillInt(Dm.Comm,Dm.rank_info,Nx,Ny,Nz,1,1,1,0,1);
|
||||
BlobIDArray GlobalBlobID;
|
||||
DoubleArray SignDist(ID.size());
|
||||
for (size_t i=0; i<ID.length(); i++)
|
||||
SignDist(i) = fabs(Mean(i))==1 ? -1:1;
|
||||
fillDouble.fill(SignDist);
|
||||
DoubleArray Phase(ID.size());
|
||||
Phase.fill(1);
|
||||
ComputeGlobalBlobIDs( Nx, Ny, Nz, Dm.rank_info, Phase, SignDist, 0, 0, GlobalBlobID, Dm.Comm );
|
||||
fillInt.fill(GlobalBlobID);
|
||||
int N_blobs = maxReduce(Dm.Comm,GlobalBlobID.max()+1);
|
||||
std::vector<float> mean(N_blobs,0);
|
||||
std::vector<int> count(N_blobs,0);
|
||||
for (int k=1; k<=Nz; k++) {
|
||||
for (int j=1; j<=Ny; j++) {
|
||||
for (int i=1; i<=Nx; i++) {
|
||||
int id = GlobalBlobID(i,j,k);
|
||||
if ( id >= 0 ) {
|
||||
if ( GlobalBlobID(i-1,j,k)<0 ) {
|
||||
mean[id] += Mean(i-1,j,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i+1,j,k)<0 ) {
|
||||
mean[id] += Mean(i+1,j,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j-1,k)<0 ) {
|
||||
mean[id] += Mean(i,j-1,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j+1,k)<0 ) {
|
||||
mean[id] += Mean(i,j+1,k);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j,k-1)<0 ) {
|
||||
mean[id] += Mean(i,j,k-1);
|
||||
count[id]++;
|
||||
}
|
||||
if ( GlobalBlobID(i,j,k+1)<0 ) {
|
||||
mean[id] += Mean(i,j,k+1);
|
||||
count[id]++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
mean = sumReduce(Dm.Comm,mean);
|
||||
count = sumReduce(Dm.Comm,count);
|
||||
for (size_t i=0; i<mean.size(); i++)
|
||||
mean[i] /= count[i];
|
||||
/*if (rank==0) {
|
||||
for (size_t i=0; i<mean.size(); i++)
|
||||
printf("%i %0.4f\n",i,mean[i]);
|
||||
}*/
|
||||
for (size_t i=0; i<Mean.length(); i++) {
|
||||
int id = GlobalBlobID(i);
|
||||
if ( id >= 0 ) {
|
||||
if ( fabs(mean[id]) > 0.95 ) {
|
||||
// Isolated domain surrounded by one domain
|
||||
GlobalBlobID(i) = -2;
|
||||
Mean(i) = sign(mean[id]);
|
||||
} else {
|
||||
// Boarder volume, set to liquid
|
||||
Mean(i) = 1;
|
||||
}
|
||||
}
|
||||
}
|
||||
// Perform the final segmentation and update the distance
|
||||
fillFloat.fill(Mean);
|
||||
segment( Mean, ID, 0.01 );
|
||||
CalcDistMultiLevel( Dist, ID, Dm );
|
||||
fillFloat.fill(Dist);
|
||||
}
|
||||
|
||||
|
||||
int main(int argc, char **argv)
|
||||
{
|
||||
|
||||
@ -615,7 +92,7 @@ int main(int argc, char **argv)
|
||||
}
|
||||
|
||||
// array containing a distance mask
|
||||
Array<float> MASK(Nx[i]+2,Ny[i]+2,Nz[i]+2);
|
||||
Array<float> MASK(Nx[0]+2,Ny[0]+2,Nz[0]+2);
|
||||
|
||||
// Create the level data
|
||||
std::vector<Array<char>> ID(N_levels);
|
||||
@ -675,50 +152,14 @@ int main(int argc, char **argv)
|
||||
|
||||
|
||||
// Filter the original data
|
||||
PROFILE_START("Filter source data");
|
||||
{
|
||||
// Perform a hot-spot filter on the data
|
||||
std::vector<imfilter::BC> BC = { imfilter::BC::replicate, imfilter::BC::replicate, imfilter::BC::replicate };
|
||||
std::function<float(const Array<float>&)> filter_3D = []( const Array<float>& data )
|
||||
{
|
||||
float min1 = std::min(data(0,1,1),data(2,1,1));
|
||||
float min2 = std::min(data(1,0,1),data(1,2,1));
|
||||
float min3 = std::min(data(1,1,0),data(1,1,2));
|
||||
float max1 = std::max(data(0,1,1),data(2,1,1));
|
||||
float max2 = std::max(data(1,0,1),data(1,2,1));
|
||||
float max3 = std::max(data(1,1,0),data(1,1,2));
|
||||
float min = std::min(min1,std::min(min2,min3));
|
||||
float max = std::max(max1,std::max(max2,max3));
|
||||
return std::max(std::min(data(1,1,1),max),min);
|
||||
};
|
||||
std::function<float(const Array<float>&)> filter_1D = []( const Array<float>& data )
|
||||
{
|
||||
float min = std::min(data(0),data(2));
|
||||
float max = std::max(data(0),data(2));
|
||||
return std::max(std::min(data(1),max),min);
|
||||
};
|
||||
//LOCVOL[0] = imfilter::imfilter<float>( LOCVOL[0], {1,1,1}, filter_3D, BC );
|
||||
std::vector<std::function<float(const Array<float>&)>> filter_set(3,filter_1D);
|
||||
LOCVOL[0] = imfilter::imfilter_separable<float>( LOCVOL[0], {1,1,1}, filter_set, BC );
|
||||
fillFloat[0]->fill( LOCVOL[0] );
|
||||
// Perform a gaussian filter on the data
|
||||
int Nh[3] = { 2, 2, 2 };
|
||||
float sigma[3] = { 1.0, 1.0, 1.0 };
|
||||
std::vector<Array<float>> H(3);
|
||||
H[0] = imfilter::create_filter<float>( { Nh[0] }, "gaussian", &sigma[0] );
|
||||
H[1] = imfilter::create_filter<float>( { Nh[1] }, "gaussian", &sigma[1] );
|
||||
H[2] = imfilter::create_filter<float>( { Nh[2] }, "gaussian", &sigma[2] );
|
||||
LOCVOL[0] = imfilter::imfilter_separable( LOCVOL[0], H, BC );
|
||||
fillFloat[0]->fill( LOCVOL[0] );
|
||||
}
|
||||
PROFILE_STOP("Filter source data");
|
||||
filter_src( *Dm[0], LOCVOL[0] );
|
||||
|
||||
|
||||
// Set up the mask to be distance to cylinder (crop outside cylinder)
|
||||
float CylRad=900;
|
||||
for (int k=0;k<Nz[0]+2;k++) {
|
||||
for (int j=0;j<Ny[0]+2;j++) {
|
||||
for (int i=0;i<Nx[0]+1;2++) {
|
||||
for (int i=0;i<Nx[0]+1;i++) {
|
||||
|
||||
int iproc = Dm[0]->iproc;
|
||||
int jproc = Dm[0]->jproc;
|
||||
@ -824,7 +265,23 @@ int main(int argc, char **argv)
|
||||
filter_final( ID[0], Dist[0], *fillFloat[0], *Dm[0], filter_Mean, filter_Dist1, filter_Dist2 );
|
||||
PROFILE_STOP("Filtering final domains");
|
||||
|
||||
//removeDisconnected( ID[0], *Dm[0] );
|
||||
//removeDisconnected( ID[0], *Dm[0] );
|
||||
|
||||
|
||||
// Write the distance function to a netcdf file
|
||||
const char* netcdf_filename = "Distance.nc";
|
||||
{
|
||||
RankInfoStruct info( rank, nprocx, nprocy, nprocz );
|
||||
size_t x = info.ix*nx;
|
||||
size_t y = info.jy*ny;
|
||||
size_t z = info.kz*nz;
|
||||
std::vector<int> dim = { Nx[0]*nprocx, Ny[0]*nprocy, Nz[0]*nprocz };
|
||||
int fid = netcdf::open( netcdf_filename, netcdf::CREATE, MPI_COMM_WORLD );
|
||||
auto dims = netcdf::defDim( fid, {"X", "Y", "Z"}, dim );
|
||||
netcdf::write( fid, "Distance", dims, Dist[0], {x,y,z}, Dist[0].size(), {1,1,1} );
|
||||
netcdf::close( fid );
|
||||
}
|
||||
|
||||
|
||||
// Write the results to visit
|
||||
if (rank==0) printf("Writing output \n");
|
||||
|
||||
Loading…
Reference in New Issue
Block a user