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-rw-r--r--src/Matlab/mex_compile/compileCPU_mex_Linux.m2
-rw-r--r--src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m6
-rw-r--r--src/Matlab/mex_compile/compileGPU_mex.m8
-rw-r--r--src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c30
-rw-r--r--src/Matlab/mex_compile/regularisers_GPU/PatchSelect_GPU.cpp94
-rw-r--r--src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp27
6 files changed, 150 insertions, 17 deletions
diff --git a/src/Matlab/mex_compile/compileCPU_mex_Linux.m b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
index 19fb1a5..2ed7ea6 100644
--- a/src/Matlab/mex_compile/compileCPU_mex_Linux.m
+++ b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
@@ -77,5 +77,5 @@ delete PatchSelect_core* Nonlocal_TV_core*
delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>');
-pathA2 = sprintf(['..' fsep '..' fsep '..' fsep '..' fsep 'demos'], 1i);
+pathA2 = sprintf(['..' fsep '..' fsep '..' fsep '..' fsep 'demos' fsep 'Matlab_demos'], 1i);
cd(pathA2); \ No newline at end of file
diff --git a/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
index 3a9e2af..7d0917f 100644
--- a/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
+++ b/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
@@ -7,7 +7,11 @@
% Here I present two ways how software can be compiled, if you have some
% other suggestions/remarks please contact me at dkazanc@hotmail.com
% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
-
+% cuda related messgaes can be solved in Linux:
+%export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
+%export CUDA_HOME=/usr/local/cuda
+%export LD_PRELOAD=/home/algol/anaconda3/lib/libstdc++.so.6.0.24
+% >>
fsep = '/';
pathcopyFrom = sprintf(['..' fsep '..' fsep 'Core' fsep 'regularisers_CPU'], 1i);
diff --git a/src/Matlab/mex_compile/compileGPU_mex.m b/src/Matlab/mex_compile/compileGPU_mex.m
index 7e15233..56fcd38 100644
--- a/src/Matlab/mex_compile/compileGPU_mex.m
+++ b/src/Matlab/mex_compile/compileGPU_mex.m
@@ -66,10 +66,14 @@ fprintf('%s \n', 'Compiling ROF-LLT...');
mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o
movefile('LLT_ROF_GPU.mex*',Pathmove);
+fprintf('%s \n', 'Compiling PatchSelect...');
+!/usr/local/cuda/bin/nvcc -O0 -c PatchSelect_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu PatchSelect_GPU.cpp PatchSelect_GPU_core.o
+movefile('PatchSelect_GPU.mex*',Pathmove);
delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h
-delete PatchSelect_core* Nonlocal_TV_core* shared.h
+delete PatchSelect_GPU_core* Nonlocal_TV_core* shared.h
fprintf('%s \n', 'All successfully compiled!');
-pathA2 = sprintf(['..' fsep '..' fsep '..' fsep '..' fsep 'demos'], 1i);
+pathA2 = sprintf(['..' fsep '..' fsep '..' fsep '..' fsep 'demos' fsep 'Matlab_demos'], 1i);
cd(pathA2);
diff --git a/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
index ffe7b91..d6bdaa6 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
@@ -26,7 +26,7 @@
*
* Input Parameters:
* 1. Noisy image/volume [REQUIRED]
- * 2. lambda - regularization parameter [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED] scalar or the same size as 1
* 3. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED]
* 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
* 5. eplsilon: tolerance constant [REQUIRED]
@@ -38,7 +38,7 @@
* This function is based on the paper by
* [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
*
- * D. Kazantsev, 2016-18
+ * D. Kazantsev, 2016-19
*/
void mexFunction(
@@ -49,18 +49,29 @@ void mexFunction(
int number_of_dims, iter_numb;
mwSize dimX, dimY, dimZ;
const mwSize *dim_array_i;
- float *Input, *Output=NULL, lambda, tau, epsil;
+ float *Input, *Output=NULL, lambda_scalar, tau, epsil;
float *infovec=NULL;
+ float *lambda;
dim_array_i = mxGetDimensions(prhs[0]);
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
/*Handling Matlab input data*/
Input = (float *) mxGetData(prhs[0]);
- lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */
+ /*special check to find the input scalar or an array*/
+ int mrows = mxGetM(prhs[1]);
+ int ncols = mxGetN(prhs[1]);
+ if (mrows==1 && ncols==1) {
+ lambda = (float*) calloc (1 ,sizeof(float));
+ lambda_scalar = (float) mxGetScalar(prhs[1]); /* regularization parameter */
+ lambda[0] = lambda_scalar;
+ }
+ else {
+ lambda = (float *) mxGetData(prhs[1]); /* regularization parameter */
+ }
iter_numb = (int) mxGetScalar(prhs[2]); /* iterations number */
tau = (float) mxGetScalar(prhs[3]); /* marching step parameter */
- epsil = (float) mxGetScalar(prhs[4]); /* tolerance */
+ epsil = (float) mxGetScalar(prhs[4]); /* tolerance */
if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
if(nrhs != 5) mexErrMsgTxt("Four inputs reqired: Image(2D,3D), regularization parameter, iterations number, marching step constant, tolerance");
@@ -80,6 +91,11 @@ void mexFunction(
mwSize vecdim[1];
vecdim[0] = 2;
infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
-
- TV_ROF_CPU_main(Input, Output, infovec, lambda, iter_numb, tau, epsil, dimX, dimY, dimZ);
+
+ if (mrows==1 && ncols==1) {
+ TV_ROF_CPU_main(Input, Output, infovec, lambda, 0, iter_numb, tau, epsil, dimX, dimY, dimZ);
+ free(lambda);
+ }
+ else TV_ROF_CPU_main(Input, Output, infovec, lambda, 1, iter_numb, tau, epsil, dimX, dimY, dimZ);
+
}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/PatchSelect_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/PatchSelect_GPU.cpp
new file mode 100644
index 0000000..13319fa
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/PatchSelect_GPU.cpp
@@ -0,0 +1,94 @@
+/*
+ * 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 "matrix.h"
+#include "mex.h"
+#include "PatchSelect_GPU_core.h"
+
+/* CUDA 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
+ */
+/**************************************************/
+void mexFunction(
+ int nlhs, mxArray *plhs[],
+ int nrhs, const mxArray *prhs[])
+{
+ int number_of_dims, SearchWindow, SimilarWin, NumNeighb;
+ mwSize dimX, dimY, dimZ;
+ const mwSize *dim_array;
+ unsigned short *H_i=NULL, *H_j=NULL, *H_k=NULL;
+ float *A, *Weights = NULL, h;
+ mwSize dim_array2[3]; /* for 2D data */
+ mwSize dim_array3[4]; /* for 3D data */
+
+ dim_array = mxGetDimensions(prhs[0]);
+ number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+
+ /*Handling Matlab input data*/
+ A = (float *) mxGetData(prhs[0]); /* a 2D or 3D image/volume */
+ SearchWindow = (int) mxGetScalar(prhs[1]); /* Large Searching window */
+ SimilarWin = (int) mxGetScalar(prhs[2]); /* Similarity window (patch-search)*/
+ NumNeighb = (int) mxGetScalar(prhs[3]); /* the total number of neighbours to take */
+ h = (float) mxGetScalar(prhs[4]); /* NLM parameter */
+
+ dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+ dim_array2[0] = dimX; dim_array2[1] = dimY; dim_array2[2] = NumNeighb; /* 2D case */
+ dim_array3[0] = dimX; dim_array3[1] = dimY; dim_array3[2] = dimZ; dim_array3[3] = NumNeighb; /* 3D case */
+
+ /****************2D INPUT ***************/
+ if (number_of_dims == 2) {
+ dimZ = 0;
+ H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
+ H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
+ Weights = (float*)mxGetPr(plhs[2] = mxCreateNumericArray(3, dim_array2, mxSINGLE_CLASS, mxREAL));
+ }
+ /****************3D INPUT ***************/
+ if (number_of_dims == 3) {
+ /*
+ H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+ H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+ H_k = (unsigned short*)mxGetPr(plhs[2] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+ Weights = (float*)mxGetPr(plhs[3] = mxCreateNumericArray(4, dim_array3, mxSINGLE_CLASS, mxREAL));
+ */
+ mexErrMsgTxt("3D version is not yet supported");
+ }
+
+ PatchSelect_GPU_main(A, H_i, H_j, Weights, (long)(dimX), (long)(dimY), SearchWindow, SimilarWin, NumNeighb, h);
+ }
diff --git a/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
index d9b7e83..2cd4469 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
@@ -25,7 +25,7 @@
*
* Input Parameters:
* 1. Noisy image/volume [REQUIRED]
- * 2. lambda - regularization parameter [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED], scalar or the same size as 1
* 3. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED]
* 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
* 5. eplsilon: tolerance constant [REQUIRED]
@@ -37,7 +37,7 @@
* This function is based on the paper by
* [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
*
- * D. Kazantsev, 2016-18
+ * D. Kazantsev, 2016-19
*/
void mexFunction(
int nlhs, mxArray *plhs[],
@@ -47,15 +47,26 @@ void mexFunction(
int number_of_dims, iter_numb;
mwSize dimX, dimY, dimZ;
const mwSize *dim_array_i;
- float *Input, *Output=NULL, lambda, tau, epsil;
+ float *Input, *Output=NULL, lambda_scalar, tau, epsil;
float *infovec=NULL;
-
+ float *lambda;
+
dim_array_i = mxGetDimensions(prhs[0]);
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
/*Handling Matlab input data*/
Input = (float *) mxGetData(prhs[0]);
- lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */
+ /*special check to find the input scalar or an array*/
+ int mrows = mxGetM(prhs[1]);
+ int ncols = mxGetN(prhs[1]);
+ if (mrows==1 && ncols==1) {
+ lambda = (float*) calloc (1 ,sizeof(float));
+ lambda_scalar = (float) mxGetScalar(prhs[1]); /* regularization parameter */
+ lambda[0] = lambda_scalar;
+ }
+ else {
+ lambda = (float *) mxGetData(prhs[1]); /* regularization parameter */
+ }
iter_numb = (int) mxGetScalar(prhs[2]); /* iterations number */
tau = (float) mxGetScalar(prhs[3]); /* marching step parameter */
epsil = (float) mxGetScalar(prhs[4]); /* tolerance */
@@ -79,5 +90,9 @@ void mexFunction(
vecdim[0] = 2;
infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
- TV_ROF_GPU_main(Input, Output, infovec, lambda, iter_numb, tau, epsil, dimX, dimY, dimZ);
+ if (mrows==1 && ncols==1) {
+ TV_ROF_GPU_main(Input, Output, infovec, lambda, 0, iter_numb, tau, epsil, dimX, dimY, dimZ);
+ free(lambda);
+ }
+ else TV_ROF_GPU_main(Input, Output, infovec, lambda, 1, iter_numb, tau, epsil, dimX, dimY, dimZ);
}
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-08 15:07:46 +0000