cythonised nltv and updated demo, readme, bash run added

9 files changed, 27 insertions, 561 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 2cbdb56..54b8bac 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -136,21 +136,20 @@ figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)');
 % figure; imshow(u_diff4_g, [0 1]); title('Diffusion 4thO denoised image (GPU)');
 %%
 fprintf('Weights pre-calculation for Non-local TV (takes time on CPU) \n');
-SearchingWindow = 9;
-PatchWindow = 3;
+SearchingWindow = 7;
+PatchWindow = 2;
 NeighboursNumber = 15; % the number of neibours to include
-h = 0.25; % edge related parameter for NLM
+h = 0.23; % edge related parameter for NLM
 [H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, NeighboursNumber, h);
 %%
 fprintf('Denoise using Non-local Total Variation (CPU) \n');
-iter_nltv = 3; % number of nltv iterations
+iter_nltv = 2; % number of nltv iterations
 lambda_nltv = 0.085; % regularisation parameter for nltv
 tic; u_nltv = Nonlocal_TV(single(u0), H_i, H_j, 0, Weights, lambda_nltv, iter_nltv); toc; 
 rmse_nltv = (RMSE(u_nltv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for Non-local Total Variation is:', rmse_nltv);
 figure; imshow(u_nltv, [0 1]); title('Non-local Total Variation denoised image (CPU)');
 %%
-
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 
 fprintf('Denoise using the FGP-dTV model (CPU) \n');
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
index 49b5dfd..72a828e 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
@@ -72,6 +72,7 @@ mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c CFLAGS="\
 movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
 
 delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* LLT_ROF_core* CCPiDefines.h
+delete PatchSelect_core* Nonlocal_TV_core*
 delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
 fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>');
 
diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
index 1b59dc2..6f7541c 100644
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
+++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
@@ -60,6 +60,12 @@ fprintf('%s \n', 'Compiling ROF-LLT...');
 mex LLT_ROF.c LLT_ROF_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('LLT_ROF.mex*',Pathmove);
 
+fprintf('%s \n', 'Compiling NonLocal-TV...');
+mex PatchSelect.c PatchSelect_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+mex Nonlocal_TV.c Nonlocal_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('Nonlocal_TV.mex*',Pathmove);
+movefile('PatchSelect.mex*',Pathmove);
+
 fprintf('%s \n', 'Compiling additional tools...');
 mex TV_energy.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('TV_energy.mex*',Pathmove);
@@ -73,9 +79,7 @@ fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...');
 mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
 movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
 
-delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h
-delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
-fprintf('%s \n', 'Regularisers successfully compiled!');
+
 %%
 %%% The second approach to compile using TDM-GCC which follows this
 %%% discussion:
@@ -105,15 +109,24 @@ fprintf('%s \n', 'Regularisers successfully compiled!');
 % movefile('TGV.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" LLT_ROF.c LLT_ROF_core.c utils.c
 % movefile('LLT_ROF.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" PatchSelect.c PatchSelect_core.c utils.c
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" Nonlocal_TV.c Nonlocal_TV_core.c utils.c
+% movefile('Nonlocal_TV.mex*',Pathmove);
+% movefile('PatchSelect.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TV_energy.c utils.c
 % movefile('TV_energy.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c
 % movefile('NonlDiff_Inp.mex*',Pathmove);
 % mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c
 % movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
-% delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h
-% delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
-% fprintf('%s \n', 'Regularisers successfully compiled!');
+
+
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h
+delete PatchSelect_core* Nonlocal_TV_core*
+delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
+fprintf('%s \n', 'Regularisers successfully compiled!');
+
+
 %%
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
index dea343c..014c0a0 100644
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
@@ -68,7 +68,6 @@ void mexFunction(
     lambda = (float) mxGetScalar(prhs[5]); /* regularisation parameter */
     IterNumb = (int) mxGetScalar(prhs[6]); /* the number of iterations */
  
-    lambda = 1.0f/lambda;       
     dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];   
          
     /*****2D INPUT *****/
@@ -81,9 +80,9 @@ void mexFunction(
     /****************************************************/
     if (number_of_dims == 3) {
         NumNeighb = dim_array2[3];
-        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));               
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     }
     
     /* run the main function here */
-    Nonlocal_TV_CPU_main(A_orig, Output, H_i, H_j, H_k, Weights, dimX, dimY, dimZ, NumNeighb, lambda, IterNumb);        
+    Nonlocal_TV_CPU_main(A_orig, Output, H_i, H_j, H_k, Weights, dimX, dimY, dimZ, NumNeighb, lambda, IterNumb);
 }
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.c
deleted file mode 100644
index d327dd5..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.c
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * This work is part of the Core Imaging Library developed by
- * Visual Analytics and Imaging System Group of the Science Technology
- * Facilities Council, STFC and Diamond Light Source Ltd. 
- *
- * Copyright 2017 Daniil Kazantsev
- * Copyright 2017 Srikanth Nagella, Edoardo Pasca
- * Copyright 2018 Diamond Light Source Ltd. 
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- * http://www.apache.org/licenses/LICENSE-2.0
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "Nonlocal_TV_core.h"
-
-/* C-OMP implementation of non-local regulariser
- * Weights and associated indices must be given as an input.
- * Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
- * goes in pre-calculation of weights and selection of patches
- *
- *
- * Input Parameters:
- * 1. 2D/3D grayscale image/volume
- * 2. AR_i - indeces of i neighbours
- * 3. AR_j - indeces of j neighbours
- * 4. AR_k - indeces of k neighbours (0 - for 2D case)
- * 5. Weights_ij(k) - associated weights 
- * 6. regularisation parameter
- * 7. iterations number 
- 
- * Output:
- * 1. denoised image/volume 	
- * Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans. Image Processing 17, no. 7 (2008): 1047-1060.
- 
- */
-/*****************************************************************************/
-
-float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambda, int IterNumb)
-{
-
-    long i, j, k;
-    int iter;
-         
-    /*****2D INPUT *****/
-    if (dimZ == 0) {
-	  copyIm(A_orig, Output, (long)(dimX), (long)(dimY), 1l);       
-    /* for each pixel store indeces of the most similar neighbours (patches) */
-     for(iter=0; iter<IterNumb; iter++) {    
-#pragma omp parallel for shared (A_orig, Output, Weights, H_i, H_j, iter) private(i,j)
-      for(i=0; i<(long)(dimX); i++) {
-            for(j=0; j<(long)(dimY); j++) {              
-            /* NLM_H1_2D(Output, A_orig, H_i, H_j, Weights, i, j, dimX, dimY, NumNeighb, lambda); */ /* NLM - H1 penalty */
-            NLM_TV_2D(Output, A_orig, H_i, H_j, Weights, i, j, (long)(dimX), (long)(dimY), NumNeighb, lambda);   /* NLM - TV penalty */     
-           }}          
-          }
-    }  
-    else {
-     /*****3D INPUT *****/
-        copyIm(A_orig, Output, (long)(dimX), (long)(dimY), (long)(dimZ));       
-    /* for each pixel store indeces of the most similar neighbours (patches) */
-     for(iter=0; iter<IterNumb; iter++) {    
-#pragma omp parallel for shared (A_orig, Output, Weights, H_i, H_j, H_k, iter) private(i,j,k)
-      for(i=0; i<(long)(dimX); i++) {
-            for(j=0; j<(long)(dimY); j++) {              
-               for(k=0; k<(long)(dimZ); k++) {
-            /* NLM_H1_3D(Output, A_orig, H_i, H_j, H_k, Weights, i, j, k, dimX, dimY, dimZ, NumNeighb, lambda); */ /* NLM - H1 penalty */
-            NLM_TV_3D(Output, A_orig, H_i, H_j, H_k, Weights, i, j, k, (long)(dimX), (long)(dimY), (long)(dimZ), NumNeighb, lambda);   /* NLM - TV penalty */     
-           }}}          
-          }          
-    }
-    return *Output;
-}
-
-/***********<<<<Main Function for NLM - H1 penalty>>>>**********/
-float NLM_H1_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambda)
-{
-	long x, i1, j1, index; 
-	float value = 0.0f, normweight  = 0.0f;
-	
-	for(x=0; x < NumNeighb; x++) {
-	index =  (dimX*dimY*x) + j*dimX+i;
-		i1 = H_i[index];
-		j1 = H_j[index];
-		value += A[j1*dimX+i1]*Weights[index];
-		normweight += Weights[index];
-	}	
-    A[j*dimX+i] = (lambda*A_orig[j*dimX+i] + value)/(lambda + normweight);
-    return *A;
-}
-/*3D version*/
-float NLM_H1_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambda)
-{
-	long x, i1, j1, k1, index; 
-	float value = 0.0f, normweight  = 0.0f;
-	
-	for(x=0; x < NumNeighb; x++) {
-	index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
-		i1 = H_i[index];
-		j1 = H_j[index];
-		k1 = H_k[index];
-		value += A[(dimX*dimY*k1) + j1*dimX+i1]*Weights[index];
-		normweight += Weights[index];
-	}	
-    A[(dimX*dimY*k) + j*dimX+i] = (lambda*A_orig[(dimX*dimY*k) + j*dimX+i] + value)/(lambda + normweight);
-    return *A;
-}
-
-
-/***********<<<<Main Function for NLM - TV penalty>>>>**********/
-float NLM_TV_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambda)
-{
-	long x, i1, j1, index; 
-	float value = 0.0f, normweight  = 0.0f, NLgrad_magn = 0.0f, NLCoeff;
-	
-	for(x=0; x < NumNeighb; x++) {
-		index =  (dimX*dimY*x) + j*dimX+i;
-		i1 = H_i[index];
-		j1 = H_j[index];
-	        NLgrad_magn += powf((A[j1*dimX+i1] - A[j*dimX+i]),2)*Weights[index];
-	}
-  
-    NLgrad_magn = sqrtf(NLgrad_magn); /*Non Local Gradients Magnitude */
-    NLCoeff = 2.0f*(1.0f/(NLgrad_magn + EPS));
-    		
-    for(x=0; x < NumNeighb; x++) {
-	index =  (dimX*dimY*x) + j*dimX+i;
-	i1 = H_i[index];
-	j1 = H_j[index];
-        value += A[j1*dimX+i1]*NLCoeff*Weights[index];
-        normweight += Weights[index]*NLCoeff;
-    }   		
-    A[j*dimX+i] = (lambda*A_orig[j*dimX+i] + value)/(lambda + normweight);
-    return *A;
-}
-/*3D version*/
-float NLM_TV_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambda)
-{
-	long x, i1, j1, k1, index; 
-	float value = 0.0f, normweight  = 0.0f, NLgrad_magn = 0.0f, NLCoeff;
-	
-	for(x=0; x < NumNeighb; x++) {
-	index =  dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
-		i1 = H_i[index];
-		j1 = H_j[index];
-		k1 = H_k[index];
-	        NLgrad_magn += powf((A[(dimX*dimY*k1) + j1*dimX+i1] - A[(dimX*dimY*k1) + j*dimX+i]),2)*Weights[index];
-	}
-  
-    NLgrad_magn = sqrtf(NLgrad_magn); /*Non Local Gradients Magnitude */
-    NLCoeff = 2.0f*(1.0f/(NLgrad_magn + EPS));
-    		
-    for(x=0; x < NumNeighb; x++) {
-	index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
-	i1 = H_i[index];
-	j1 = H_j[index];
-	k1 = H_k[index];
-        value += A[(dimX*dimY*k1) + j1*dimX+i1]*NLCoeff*Weights[index];
-        normweight += Weights[index]*NLCoeff;
-    }   		
-    A[(dimX*dimY*k) + j*dimX+i] = (lambda*A_orig[(dimX*dimY*k) + j*dimX+i] + value)/(lambda + normweight);
-    return *A;
-}
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.h b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.h
deleted file mode 100644
index 5b6963e..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV_core.h
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * This work is part of the Core Imaging Library developed by
- * Visual Analytics and Imaging System Group of the Science Technology
- * Facilities Council, STFC and Diamond Light Source Ltd. 
- *
- * Copyright 2017 Daniil Kazantsev
- * Copyright 2017 Srikanth Nagella, Edoardo Pasca
- * Copyright 2018 Diamond Light Source Ltd. 
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- * http://www.apache.org/licenses/LICENSE-2.0
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <math.h>
-#include <stdlib.h>
-#include <memory.h>
-#include <stdio.h>
-#include "omp.h"
-#include "utils.h"
-#include "CCPiDefines.h"
-
-#define EPS 1.0000e-9
-
-/* C-OMP implementation of non-local regulariser
- * Weights and associated indices must be given as an input.
- * Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
- * goes in pre-calculation of weights and selection of patches
- *
- *
- * Input Parameters:
- * 1. 2D/3D grayscale image/volume
- * 2. AR_i - indeces of i neighbours
- * 3. AR_j - indeces of j neighbours
- * 4. AR_k - indeces of k neighbours (0 - for 2D case)
- * 5. Weights_ij(k) - associated weights 
- * 6. regularisation parameter
- * 7. iterations number 
- 
- * Output:
- * 1. denoised image/volume 	
- * Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans.   Image Processing 17, no. 7 (2008): 1047-1060. 
- */
- 
-#ifdef __cplusplus
-extern "C" {
-#endif
-CCPI_EXPORT float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambda, int IterNumb);
-CCPI_EXPORT float NLM_H1_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambda);
-CCPI_EXPORT float NLM_TV_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambda);
-CCPI_EXPORT float NLM_H1_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambda);
-CCPI_EXPORT float NLM_TV_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambda);
-#ifdef __cplusplus
-}
-#endif
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
index fdd9a97..f942539 100644
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
+++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
@@ -87,6 +87,6 @@ void mexFunction(
         Weights = (float*)mxGetPr(plhs[3] = mxCreateNumericArray(4, dim_array3, mxSINGLE_CLASS, mxREAL));        
     }
     
-    PatchSelect_CPU_main(A, H_i, H_j, H_k, Weights, (long)(dimX), (long)(dimY), (long)(dimZ), SearchWindow, SimilarWin, NumNeighb, h); 
+    PatchSelect_CPU_main(A, H_i, H_j, H_k, Weights, (long)(dimX), (long)(dimY), (long)(dimZ), SearchWindow, SimilarWin, NumNeighb, h, 0); 
     
  }
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.c
deleted file mode 100644
index efc5498..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.c
+++ /dev/null
@@ -1,254 +0,0 @@
-/*
- * This work is part of the Core Imaging Library developed by
- * Visual Analytics and Imaging System Group of the Science Technology
- * Facilities Council, STFC and Diamond Light Source Ltd. 
- *
- * Copyright 2017 Daniil Kazantsev
- * Copyright 2017 Srikanth Nagella, Edoardo Pasca
- * Copyright 2018 Diamond Light Source Ltd. 
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- * http://www.apache.org/licenses/LICENSE-2.0
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include "PatchSelect_core.h"
-
-/* C-OMP implementation of non-local weight pre-calculation for non-local priors
- * Weights and associated indices are stored into pre-allocated arrays and passed
- * to the regulariser
- *
- *
- * Input Parameters:
- * 1. 2D/3D grayscale image/volume
- * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
- * 3. Similarity window (half-size of the patch window, e.g. 2)
- * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
- * 5. noise-related parameter to calculate non-local weights
- *
- * Output [2D]:
- * 1. AR_i - indeces of i neighbours
- * 2. AR_j - indeces of j neighbours
- * 3. Weights_ij - associated weights
- *
- * Output [3D]:
- * 1. AR_i - indeces of i neighbours
- * 2. AR_j - indeces of j neighbours
- * 3. AR_k - indeces of j neighbours
- * 4. Weights_ijk - associated weights
- */
-
-/**************************************************/
-
-float PatchSelect_CPU_main(float *A, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long dimX, long dimY, long dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h)
-{
-    int counterG;
-    long i, j, k;
-    float *Eucl_Vec, h2;
-    h2 = h*h;   
-   
-    /****************2D INPUT ***************/
-    if (dimZ == 0) {       
-        /* generate a 2D Gaussian kernel for NLM procedure */
-        Eucl_Vec = (float*) calloc ((2*SimilarWin+1)*(2*SimilarWin+1),sizeof(float));
-        counterG = 0;
-        for(i=-SimilarWin; i<=SimilarWin; i++) {
-            for(j=-SimilarWin; j<=SimilarWin; j++) {
-                Eucl_Vec[counterG] = (float)exp(-(pow(((float) i), 2) + pow(((float) j), 2))/(2*SimilarWin*SimilarWin));
-                counterG++;
-            }} /*main neighb loop */
-              
-        /* for each pixel store indeces of the most similar neighbours (patches) */
-#pragma omp parallel for shared (A, Weights, H_i, H_j) private(i,j)
-        for(i=0; i<dimX; i++) {
-            for(j=0; j<dimY; j++) {
-                Indeces2D(A, H_i, H_j, Weights, (i), (j), (dimX), (dimY), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
-            }}
-    }
-    else {
-    /****************3D INPUT ***************/       
-        /* generate a 3D Gaussian kernel for NLM procedure */
-        Eucl_Vec = (float*) calloc ((2*SimilarWin+1)*(2*SimilarWin+1)*(2*SimilarWin+1),sizeof(float));
-        counterG = 0;
-        for(i=-SimilarWin; i<=SimilarWin; i++) {
-            for(j=-SimilarWin; j<=SimilarWin; j++) {
-                for(k=-SimilarWin; k<=SimilarWin; k++) {
-                    Eucl_Vec[counterG] = (float)exp(-(pow(((float) i), 2) + pow(((float) j), 2) + pow(((float) k), 2))/(2*SimilarWin*SimilarWin*SimilarWin));
-                    counterG++;
-                }}} /*main neighb loop */     
-        
-        /* for each voxel store indeces of the most similar neighbours (patches) */
-#pragma omp parallel for shared (A, Weights, H_i, H_j, H_k) private(i,j,k)
-        for(i=0; i<dimX; i++) {
-            for(j=0; j<dimY; j++) {
-                for(k=0; k<dimZ; k++) {
-                    Indeces3D(A, H_i, H_j, H_k, Weights, (i), (j), (k), (dimX), (dimY), (dimZ), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
-                }}}
-    }
-    free(Eucl_Vec);
-    return 1;
-}
-
-float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimY, long dimX, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2)
-{
-    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, index, sizeWin_tot, counterG;
-    float *Weight_Vec, normsum, temp;
-    unsigned short *ind_i, *ind_j, temp_i, temp_j;
-    
-    sizeWin_tot = (2*SearchWindow + 1)*(2*SearchWindow + 1);
-    
-    Weight_Vec = (float*) calloc(sizeWin_tot, sizeof(float));
-    ind_i = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
-    ind_j = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
-    
-    counter = 0;
-    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
-        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
-            i1 = i+i_m;
-            j1 = j+j_m;
-            if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) {
-                normsum = 0.0f; counterG = 0;
-                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
-                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
-                        i2 = i1 + i_c;
-                        j2 = j1 + j_c;
-                        i3 = i + i_c;
-                        j3 = j + j_c;
-                        if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY))) {
-                            if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY))) {
-                                normsum += Eucl_Vec[counterG]*pow(Aorig[j3*dimX + (i3)] - Aorig[j2*dimX + (i2)], 2);
-                                counterG++;
-                            }}
-                        
-                    }}
-                /* writing temporarily into vectors */
-                if (normsum > EPS) {
-                    Weight_Vec[counter] = expf(-normsum/h2);
-                    ind_i[counter] = i1;
-                    ind_j[counter] = j1;
-                    counter++;
-                }
-            }
-        }}
-    /* do sorting to choose the most prominent weights [HIGH to LOW] */
-    /* and re-arrange indeces accordingly */
-    for (x = 0; x < counter; x++)  {
-        for (y = 0; y < counter; y++)  {
-            if (Weight_Vec[y] < Weight_Vec[x]) {
-                temp = Weight_Vec[y+1];
-                temp_i = ind_i[y+1];
-                temp_j = ind_j[y+1];
-                Weight_Vec[y+1] = Weight_Vec[y];
-                Weight_Vec[y] = temp;
-                ind_i[y+1] = ind_i[y];
-                ind_i[y] = temp_i;
-                ind_j[y+1] = ind_j[y];
-                ind_j[y] = temp_j;
-            }}}
-    /*sorting loop finished*/
-    
-    /*now select the NumNeighb more prominent weights and store into arrays */
-    for(x=0; x < NumNeighb; x++) {
-        index = (dimX*dimY*x) + j*dimX+i;
-        H_i[index] = ind_i[x];
-        H_j[index] = ind_j[x];
-        Weights[index] = Weight_Vec[x];
-    }
-    
-    free(ind_i);
-    free(ind_j);
-    free(Weight_Vec);
-    return 1;
-}
-
-float Indeces3D(float *Aorig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimY, long dimX, long dimZ, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2)
-{
-    long i1, j1, k1, i_m, j_m, k_m, i_c, j_c, k_c, i2, j2, k2, i3, j3, k3, counter, x, y, index, sizeWin_tot, counterG;
-    float *Weight_Vec, normsum, temp;
-    unsigned short *ind_i, *ind_j, *ind_k, temp_i, temp_j, temp_k;
-    
-    sizeWin_tot = (2*SearchWindow + 1)*(2*SearchWindow + 1)*(2*SearchWindow + 1);
-    
-    Weight_Vec = (float*) calloc(sizeWin_tot, sizeof(float));
-    ind_i = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
-    ind_j = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
-    ind_k = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
-    
-    counter = 0l;
-    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
-        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
-            for(k_m=-SearchWindow; k_m<=SearchWindow; k_m++) {
-                k1 = k+k_m;
-                i1 = i+i_m;
-                j1 = j+j_m;
-                if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY)) && ((k1 >= 0) && (k1 < dimZ))) {
-                    normsum = 0.0f; counterG = 0l;
-                    for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
-                        for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
-                            for(k_c=-SimilarWin; k_c<=SimilarWin; k_c++) {
-                                i2 = i1 + i_c;
-                                j2 = j1 + j_c;
-                                k2 = k1 + k_c;
-                                i3 = i + i_c;
-                                j3 = j + j_c;
-                                k3 = k + k_c;
-                                if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY)) && ((k2 >= 0) && (k2 < dimZ))) {
-                                    if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY)) && ((k3 >= 0) && (k3 < dimZ))) {
-                                        normsum += Eucl_Vec[counterG]*pow(Aorig[(dimX*dimY*k3) + j3*dimX + (i3)] - Aorig[(dimX*dimY*k2) + j2*dimX + (i2)], 2);
-                                        counterG++;
-                                    }}
-                            }}}
-                    /* writing temporarily into vectors */
-                    if (normsum > EPS) {
-                        Weight_Vec[counter] = expf(-normsum/h2);
-                        ind_i[counter] = i1;
-                        ind_j[counter] = j1;
-                        ind_k[counter] = k1;
-                        counter ++;
-                    }
-                }
-            }}}
-    /* do sorting to choose the most prominent weights [HIGH to LOW] */
-    /* and re-arrange indeces accordingly */
-    for (x = 0; x < counter; x++)  {
-        for (y = 0; y < counter; y++)  {
-            if (Weight_Vec[y] < Weight_Vec[x]) {
-                temp = Weight_Vec[y+1];
-                temp_i = ind_i[y+1];
-                temp_j = ind_j[y+1];
-                temp_k = ind_k[y+1];
-                Weight_Vec[y+1] = Weight_Vec[y];
-                Weight_Vec[y] = temp;
-                ind_i[y+1] = ind_i[y];
-                ind_i[y] = temp_i;
-                ind_j[y+1] = ind_j[y];
-                ind_j[y] = temp_j;
-                ind_k[y+1] = ind_k[y];
-                ind_k[y] = temp_k;
-            }}}
-    /*sorting loop finished*/
-    
-    /*now select the NumNeighb more prominent weights and store into arrays */
-    for(x=0; x < NumNeighb; x++) {
-        index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
-        
-        H_i[index] = ind_i[x];
-        H_j[index] = ind_j[x];
-        H_k[index] = ind_k[x];
-        
-        Weights[index] = Weight_Vec[x];
-    }
-    
-    free(ind_i);
-    free(ind_j);
-    free(ind_k);
-    free(Weight_Vec);
-    return 1;
-}
-
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.h b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.h
deleted file mode 100644
index 43fce87..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect_core.h
+++ /dev/null
@@ -1,62 +0,0 @@
-/*
- * This work is part of the Core Imaging Library developed by
- * Visual Analytics and Imaging System Group of the Science Technology
- * Facilities Council, STFC and Diamond Light Source Ltd. 
- *
- * Copyright 2017 Daniil Kazantsev
- * Copyright 2017 Srikanth Nagella, Edoardo Pasca
- * Copyright 2018 Diamond Light Source Ltd. 
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- * http://www.apache.org/licenses/LICENSE-2.0
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#include <math.h>
-#include <stdlib.h>
-#include <memory.h>
-#include <stdio.h>
-#include "omp.h"
-#include "utils.h"
-#include "CCPiDefines.h"
-#define EPS 1.0000e-12
-
-/* C-OMP implementation of non-local weight pre-calculation for non-local priors
- * Weights and associated indices are stored into pre-allocated arrays and passed
- * to the regulariser
- *
- *
- * Input Parameters:
- * 1. 2D/3D grayscale image/volume
- * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
- * 3. Similarity window (half-size of the patch window, e.g. 2)
- * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
- * 5. noise-related parameter to calculate non-local weights
- *
- * Output [2D]:
- * 1. AR_i - indeces of i neighbours
- * 2. AR_j - indeces of j neighbours
- * 3. Weights_ij - associated weights
- *
- * Output [3D]:
- * 1. AR_i - indeces of i neighbours
- * 2. AR_j - indeces of j neighbours
- * 3. AR_k - indeces of j neighbours
- * 4. Weights_ijk - associated weights
- */
-/*****************************************************************************/
-#ifdef __cplusplus
-extern "C" {
-#endif
-CCPI_EXPORT float PatchSelect_CPU_main(float *A, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long dimX, long dimY, long dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h);
-CCPI_EXPORT float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimY, long dimX, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2);
-CCPI_EXPORT float Indeces3D(float *Aorig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimY, long dimX, long dimZ, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2);
-#ifdef __cplusplus
-}
-#endif