From d6ee5585e696f855d1c687d34efa04328729e94c Mon Sep 17 00:00:00 2001 From: dkazanc Date: Tue, 9 Apr 2019 16:44:39 +0100 Subject: 2D CPU version for constrained diffusion --- build/run.sh | 18 +-- src/Core/CMakeLists.txt | 1 + src/Core/regularisers_CPU/DiffusionMASK_core.c | 214 +++++++++++++++++++++++++ src/Core/regularisers_CPU/DiffusionMASK_core.h | 62 +++++++ src/Python/ccpi/filters/regularisers.py | 27 +++- src/Python/src/cpu_regularisers.pyx | 37 +++++ 6 files changed, 349 insertions(+), 10 deletions(-) create mode 100644 src/Core/regularisers_CPU/DiffusionMASK_core.c create mode 100644 src/Core/regularisers_CPU/DiffusionMASK_core.h diff --git a/build/run.sh b/build/run.sh index e6f171e..b40d222 100755 --- a/build/run.sh +++ b/build/run.sh @@ -1,14 +1,14 @@ #!/bin/bash echo "Building CCPi-regularisation Toolkit using CMake" -rm -r build_proj +rm -r ../build_proj # Requires Cython, install it first: # pip install cython -mkdir build_proj -cd build_proj/ +mkdir ../build_proj +cd ../build_proj/ #make clean -export CIL_VERSION=19.03 +export CIL_VERSION=19.04 # install Python modules without CUDA -# cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install +cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install # install Python modules with CUDA # cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install # install Matlab modules without CUDA @@ -18,12 +18,12 @@ export CIL_VERSION=19.03 # cmake ../ -DBUILD_PYTHON_WRAPPER=OFF -DMatlab_ROOT_DIR=/home/algol/SOFT/MATLAB9/ -DBUILD_MATLAB_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install make install ############### Python(linux)############### -#cp install/lib/libcilreg.so install/python/ccpi/filters -# cd install/python -# export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib +cp install/lib/libcilreg.so install/python/ccpi/filters +cd install/python +export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib # spyder ############### Matlab(linux)############### -export LD_PRELOAD=/home/algol/anaconda3/lib/libstdc++.so.6 # if there is libstdc error in matlab +# export LD_PRELOAD=/home/algol/anaconda3/lib/libstdc++.so.6 # if there is libstdc error in matlab # PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" matlab # PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/matlab/:$PATH" LD_LIBRARY_PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/lib:$LD_LIBRARY_PATH" matlab #PATH="/home/algol/Documents/DEV/CCPi-Regularisation-Toolkit/build_proj/install/matlab/:$PATH" LD_LIBRARY_PATH="/home/algol/Documents/DEV/CCPi-Regularisation-Toolkit/build_proj/install/lib:$LD_LIBRARY_PATH" /home/algol/SOFT/MATLAB9/bin/matlab diff --git a/src/Core/CMakeLists.txt b/src/Core/CMakeLists.txt index b3c0dfb..6975a89 100644 --- a/src/Core/CMakeLists.txt +++ b/src/Core/CMakeLists.txt @@ -68,6 +68,7 @@ add_library(cilreg SHARED ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/SB_TV_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TGV_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffusion_core.c + ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/DiffusionMASK_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffus4th_order_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/LLT_ROF_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ROF_TV_core.c diff --git a/src/Core/regularisers_CPU/DiffusionMASK_core.c b/src/Core/regularisers_CPU/DiffusionMASK_core.c new file mode 100644 index 0000000..eef173d --- /dev/null +++ b/src/Core/regularisers_CPU/DiffusionMASK_core.c @@ -0,0 +1,214 @@ +/* + * This work is part of the Core Imaging Library developed by + * Visual Analytics and Imaging System Group of the Science Technology + * Facilities Council, STFC + * + * Copyright 2017 Daniil Kazantsev + * Copyright 2017 Srikanth Nagella, Edoardo Pasca + * + * 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 "DiffusionMASK_core.h" +#include "utils.h" + +#define EPS 1.0e-5 +#define MAX(x, y) (((x) > (y)) ? (x) : (y)) +#define MIN(x, y) (((x) < (y)) ? (x) : (y)) + +/*sign function*/ +int signNDF_m(float x) { + return (x > 0) - (x < 0); +} + +/* C-OMP implementation of linear and nonlinear diffusion [1,2] which is constrained by the provided MASK. + * The minimisation is performed using explicit scheme. + * Implementation using the diffusivity window to increase the coverage area of the diffusivity + * + * Input Parameters: + * 1. Noisy image/volume + * 2. MASK (in unsigned char format) + * 3. Diffusivity window (half-size of the searching window, e.g. 3) + * 4. lambda - regularization parameter + * 5. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion + * 6. Number of iterations, for explicit scheme >= 150 is recommended + * 7. tau - time-marching step for explicit scheme + * 8. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * 9. eplsilon - tolerance constant + + * Output: + * [1] Filtered/regularized image/volume + * [2] Information vector which contains [iteration no., reached tolerance] + * + * This function is based on the paper by + * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639. + * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432. + */ + +float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, float *Output, float *infovector, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ) +{ + int i,j,k,counterG; + float sigmaPar2, *Output_prev=NULL, *Eucl_Vec; + int DiffusWindow_tot; + sigmaPar2 = sigmaPar/sqrt(2.0f); + long DimTotal; + float re, re1; + re = 0.0f; re1 = 0.0f; + int count = 0; + DimTotal = (long)(dimX*dimY*dimZ); + + if (dimZ == 1) { + DiffusWindow_tot = (2*DiffusWindow + 1)*(2*DiffusWindow + 1); + /* generate a 2D Gaussian kernel for NLM procedure */ + Eucl_Vec = (float*) calloc (DiffusWindow_tot,sizeof(float)); + counterG = 0; + for(i=-DiffusWindow; i<=DiffusWindow; i++) { + for(j=-DiffusWindow; j<=DiffusWindow; j++) { + Eucl_Vec[counterG] = (float)expf(-(powf(((float) i), 2) + powf(((float) j), 2))/(2.0f*DiffusWindow*DiffusWindow)); + counterG++; + }} /*main neighb loop */ + } + else { + DiffusWindow_tot = (2*DiffusWindow + 1)*(2*DiffusWindow + 1)*(2*DiffusWindow + 1); + Eucl_Vec = (float*) calloc (DiffusWindow_tot,sizeof(float)); + counterG = 0; + for(i=-DiffusWindow; i<=DiffusWindow; i++) { + for(j=-DiffusWindow; j<=DiffusWindow; j++) { + for(k=-DiffusWindow; k<=DiffusWindow; k++) { + Eucl_Vec[counterG] = (float)expf(-(powf(((float) i), 2) + powf(((float) j), 2) + powf(((float) k), 2))/(2*DiffusWindow*DiffusWindow*DiffusWindow)); + counterG++; + }}} /*main neighb loop */ + } + + if (epsil != 0.0f) Output_prev = calloc(DimTotal, sizeof(float)); + + /* copy into output */ + copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ)); + + for(i=0; i < iterationsNumb; i++) { + + if ((epsil != 0.0f) && (i % 5 == 0)) copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ)); + if (dimZ == 1) { + /* running 2D diffusion iterations */ + if (sigmaPar == 0.0f) LinearDiff_MASK2D(Input, MASK, Output, Eucl_Vec, DiffusWindow, lambdaPar, tau, (long)(dimX), (long)(dimY)); /* constrained linear diffusion */ + else NonLinearDiff_MASK2D(Input, MASK, Output, Eucl_Vec, DiffusWindow, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY)); /* constrained nonlinear diffusion */ + } + else { + /* running 3D diffusion iterations */ + //if (sigmaPar == 0.0f) LinearDiff3D(Input, Output, lambdaPar, tau, (long)(dimX), (long)(dimY), (long)(dimZ)); +// else NonLinearDiff3D(Input, Output, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY), (long)(dimZ)); + } + /* check early stopping criteria if epsilon not equal zero */ + if ((epsil != 0.0f) && (i % 5 == 0)) { + re = 0.0f; re1 = 0.0f; + for(j=0; j 3) break; + } + } + + free(Output_prev); + /*adding info into info_vector */ + infovector[0] = (float)(i); /*iterations number (if stopped earlier based on tolerance)*/ + infovector[1] = re; /* reached tolerance */ + return 0; +} + + +/********************************************************************/ +/***************************2D Functions*****************************/ +/********************************************************************/ +/* MASKED-constrained 2D linear diffusion (PDE heat equation) */ +float LinearDiff_MASK2D(float *Input, unsigned char *MASK, float *Output, float *Eucl_Vec, int DiffusWindow, float lambdaPar, float tau, long dimX, long dimY) +{ + +long i,j,i1,j1,i_m,j_m,index,indexneighb,counter; +unsigned char class_c, class_n; +float diffVal; + +#pragma omp parallel for shared(Input) private(index,i,j,i1,j1,i_m,j_m,counter,diffVal,indexneighb,class_c,class_n) + for(i=0; i= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) { + indexneighb = j1*dimX+i1; /* neighbour pixel index */ + class_c = MASK[index]; /* current class value */ + class_n = MASK[indexneighb]; /* neighbour class value */ + + /* perform diffusion only within the same class (given by MASK) */ + if (class_n == class_c) diffVal += Output[indexneighb] - Output[index]; + } + counter++; + }} + Output[index] += tau*(lambdaPar*(diffVal) - (Output[index] - Input[index])); + }} + return *Output; +} + +/* MASKED-constrained 2D nonlinear diffusion */ +float NonLinearDiff_MASK2D(float *Input, unsigned char *MASK, float *Output, float *Eucl_Vec, int DiffusWindow, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY) +{ + long i,j,i1,j1,i_m,j_m,index,indexneighb,counter; + unsigned char class_c, class_n; + float diffVal, funcVal; + +#pragma omp parallel for shared(Input) private(index,i,j,i1,j1,i_m,j_m,counter,diffVal,funcVal,indexneighb,class_c,class_n) + for(i=0; i= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) { + indexneighb = j1*dimX+i1; /* neighbour pixel index */ + class_c = MASK[index]; /* current class value */ + class_n = MASK[indexneighb]; /* neighbour class value */ + + /* perform diffusion only within the same class (given by MASK) */ + if (class_n == class_c) { + diffVal = Output[indexneighb] - Output[index]; + if (penaltytype == 1) { + /* Huber penalty */ + if (fabs(diffVal) > sigmaPar) funcVal += signNDF_m(diffVal); + else funcVal += diffVal/sigmaPar; } + else if (penaltytype == 2) { + /* Perona-Malik */ + funcVal += (diffVal)/(1.0f + powf((diffVal/sigmaPar),2)); } + else if (penaltytype == 3) { + /* Tukey Biweight */ + if (fabs(diffVal) <= sigmaPar) funcVal += diffVal*powf((1.0f - powf((diffVal/sigmaPar),2)), 2); } + else { + printf("%s \n", "No penalty function selected! Use Huber,2 or 3."); + break; } + } + } + counter++; + }} + Output[index] += tau*(lambdaPar*(funcVal) - (Output[index] - Input[index])); + }} + return *Output; +} +/********************************************************************/ +/***************************3D Functions*****************************/ +/********************************************************************/ diff --git a/src/Core/regularisers_CPU/DiffusionMASK_core.h b/src/Core/regularisers_CPU/DiffusionMASK_core.h new file mode 100644 index 0000000..8890c73 --- /dev/null +++ b/src/Core/regularisers_CPU/DiffusionMASK_core.h @@ -0,0 +1,62 @@ +/* +This work is part of the Core Imaging Library developed by +Visual Analytics and Imaging System Group of the Science Technology +Facilities Council, STFC + +Copyright 2017 Daniil Kazantsev +Copyright 2017 Srikanth Nagella, Edoardo Pasca + +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 +#include +#include +#include +#include "omp.h" +#include "utils.h" +#include "CCPiDefines.h" + + +/* C-OMP implementation of linear and nonlinear diffusion [1,2] which is constrained by the provided MASK. + * The minimisation is performed using explicit scheme. + * Implementation using the Diffusivity window to increase the coverage area of the diffusivity + * + * Input Parameters: + * 1. Noisy image/volume + * 2. MASK (in unsigned short format) + * 3. Diffusivity window (half-size of the searching window, e.g. 3) + * 4. lambda - regularization parameter + * 5. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion + * 6. Number of iterations, for explicit scheme >= 150 is recommended + * 7. tau - time-marching step for explicit scheme + * 8. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * 9. eplsilon - tolerance constant + + * Output: + * [1] Filtered/regularized image/volume + * [2] Information vector which contains [iteration no., reached tolerance] + * + * This function is based on the paper by + * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639. + * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432. + */ + + +#ifdef __cplusplus +extern "C" { +#endif +CCPI_EXPORT float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, float *Output, float *infovector, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); +CCPI_EXPORT float LinearDiff_MASK2D(float *Input, unsigned char *MASK, float *Output, float *Eucl_Vec, int DiffusWindow, float lambdaPar, float tau, long dimX, long dimY); +CCPI_EXPORT float NonLinearDiff_MASK2D(float *Input, unsigned char *MASK, float *Output, float *Eucl_Vec, int DiffusWindow, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY); +#ifdef __cplusplus +} +#endif diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index 398e11c..1e427bf 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -2,7 +2,7 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU +from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_MASK_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU try: from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU gpu_enabled = True @@ -127,6 +127,31 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, raise ValueError ('GPU is not available') raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def NDF_MASK(inputData, diffuswindow, regularisation_parameter, edge_parameter, iterations, + time_marching_parameter, penalty_type, tolerance_param, device='cpu'): + if device == 'cpu': + return NDF_MASK_CPU(inputData, + diffuswindow, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type, + tolerance_param) + elif device == 'gpu' and gpu_enabled: + return NDF_MASK_CPU(inputData, + diffuswindow, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type, + tolerance_param) + else: + if not gpu_enabled and device == 'gpu': + raise ValueError ('GPU is not available') + raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ + .format(device)) def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, tolerance_param, device='cpu'): if device == 'cpu': diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx index add641b..305ee1f 100644 --- a/src/Python/src/cpu_regularisers.pyx +++ b/src/Python/src/cpu_regularisers.pyx @@ -24,6 +24,7 @@ cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector, cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ); cdef extern float TGV_main(float *Input, float *Output, float *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ); cdef extern float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); +cdef extern float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, float *Output, float *infovector, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ); cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int dimX, int dimY, int dimZ); cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ); @@ -379,6 +380,42 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, tolerance_param, dims[2], dims[1], dims[0]) return (outputData,infovec) + +#****************************************************************# +#********Constrained Nonlinear(Isotropic) Diffusion**************# +#****************************************************************# +def NDF_MASK_CPU(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type, tolerance_param): + if inputData.ndim == 2: + return NDF_MASK_2D(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param) + elif inputData.ndim == 3: + return 0 + +def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + int diffuswindow, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type, + float tolerance_param): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \ + np.zeros([2], dtype='float32') + + # Run constrained nonlinear diffusion iterations for 2D data + DiffusionMASK_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &infovec[0], + diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, + time_marching_parameter, penalty_type, + tolerance_param, + dims[1], dims[0], 1) + return (outputData,infovec) + #****************************************************************# #*************Anisotropic Fourth-Order diffusion*****************# #****************************************************************# -- cgit v1.2.3