Refactoring lbpm_uCT_pp.cpp

analysis/filters.cpp

@@ -0,0 +1,149 @@
+#include "analysis/filters.h"
+
+#include "ProfilerApp.h"
+
+
+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");
+}
+
+
+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 = std::max(0,i-d);
+				jmin = std::max(0,j-d);
+				kmin = std::max(0,k-d);
+				imax = std::min(Nx-1,i+d);
+				jmax = std::min(Ny-1,j+d);
+				kmax = std::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 = std::max(0,i-d);
+					jmin = std::max(0,j-d);
+					kmin = std::max(0,k-d);
+					imax = std::min(Nx-1,i+d);
+					jmax = std::min(Ny-1,j+d);
+					kmax = std::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;
+}