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Clang format (#55)

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Run clang-format on modules of code
This commit is contained in:
Thomas Ramstad
2021-11-08 22:58:37 +01:00
committed by GitHub
parent f29ae0b0bc
commit 23189f5577
104 changed files with 56746 additions and 49196 deletions
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304
analysis/filters.cpp
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@@ -18,175 +18,181 @@
#include "math.h"
#include "ProfilerApp.h"
void Mean3D( const Array<double> &Input, Array<double> &Output )
{
PROFILE_START("Mean3D");
// Perform a 3D Mean filter on Input array
int i,j,k;
void Mean3D(const Array<double> &Input, Array<double> &Output) {
PROFILE_START("Mean3D");
// Perform a 3D Mean filter on Input array
int i, j, k;
int Nx = int(Input.size(0));
int Ny = int(Input.size(1));
int Nz = int(Input.size(2));
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++){
double MeanValue = Input(i,j,k);
// next neighbors
MeanValue += Input(i+1,j,k)+Input(i,j+1,k)+Input(i,j,k+1)+Input(i-1,j,k)+Input(i,j-1,k)+Input(i,j,k-1);
MeanValue += Input(i+1,j+1,k)+Input(i-1,j+1,k)+Input(i+1,j-1,k)+Input(i-1,j-1,k);
MeanValue += Input(i+1,j,k+1)+Input(i-1,j,k+1)+Input(i+1,j,k-1)+Input(i-1,j,k-1);
MeanValue += Input(i,j+1,k+1)+Input(i,j-1,k+1)+Input(i,j+1,k-1)+Input(i,j-1,k-1);
MeanValue += Input(i+1,j+1,k+1)+Input(i-1,j+1,k+1)+Input(i+1,j-1,k+1)+Input(i-1,j-1,k+1);
MeanValue += Input(i+1,j+1,k-1)+Input(i-1,j+1,k-1)+Input(i+1,j-1,k-1)+Input(i-1,j-1,k-1);
Output(i,j,k) = MeanValue/27.0;
}
}
}
PROFILE_STOP("Mean3D");
for (k = 1; k < Nz - 1; k++) {
for (j = 1; j < Ny - 1; j++) {
for (i = 1; i < Nx - 1; i++) {
double MeanValue = Input(i, j, k);
// next neighbors
MeanValue += Input(i + 1, j, k) + Input(i, j + 1, k) +
Input(i, j, k + 1) + Input(i - 1, j, k) +
Input(i, j - 1, k) + Input(i, j, k - 1);
MeanValue += Input(i + 1, j + 1, k) + Input(i - 1, j + 1, k) +
Input(i + 1, j - 1, k) + Input(i - 1, j - 1, k);
MeanValue += Input(i + 1, j, k + 1) + Input(i - 1, j, k + 1) +
Input(i + 1, j, k - 1) + Input(i - 1, j, k - 1);
MeanValue += Input(i, j + 1, k + 1) + Input(i, j - 1, k + 1) +
Input(i, j + 1, k - 1) + Input(i, j - 1, k - 1);
MeanValue +=
Input(i + 1, j + 1, k + 1) + Input(i - 1, j + 1, k + 1) +
Input(i + 1, j - 1, k + 1) + Input(i - 1, j - 1, k + 1);
MeanValue +=
Input(i + 1, j + 1, k - 1) + Input(i - 1, j + 1, k - 1) +
Input(i + 1, j - 1, k - 1) + Input(i - 1, j - 1, k - 1);
Output(i, j, k) = MeanValue / 27.0;
}
}
}
PROFILE_STOP("Mean3D");
}
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;
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];
float *List;
List = new float[27];
int Nx = int(Input.size(0));
int Ny = int(Input.size(1));
int Nz = int(Input.size(2));
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++){
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;
// 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");
// 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
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;
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));
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++) {
// 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);
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++;
}
}
}
// 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;
}
}
}
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++) {
// 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);
if (fabs(Distance(i,j,k)) < THRESHOLD_DIST){
// compute the expensive non-local means
sum = 0; weight=0;
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);
}
}
}
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;
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;
}