diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 34 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 33 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex.m | 5 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileGPU_mex.m | 16 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c | 89 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp | 89 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 23 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 103 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py | 90 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 102 | ||||
| -rw-r--r-- | Wrappers/Python/setup-regularisers.py.in | 1 | ||||
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 58 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 75 |
13 files changed, 673 insertions, 45 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index dc49d9c..fb55097 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -14,35 +14,47 @@ end vol3D(vol3D < 0) = 0; figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image'); + +lambda_reg = 0.03; % regularsation parameter for all methods %% fprintf('Denoise a volume using the ROF-TV model (CPU) \n'); -lambda_rof = 0.03; % regularisation parameter tau_rof = 0.0025; % time-marching constant iter_rof = 300; % number of ROF iterations -tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; +tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the ROF-TV model (GPU) \n'); -% lambda_rof = 0.03; % regularisation parameter % tau_rof = 0.0025; % time-marching constant % iter_rof = 300; % number of ROF iterations -% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; +% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; % figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)'); %% fprintf('Denoise a volume using the FGP-TV model (CPU) \n'); -lambda_fgp = 0.03; % regularisation parameter iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance -tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; +tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)'); %% % fprintf('Denoise a volume using the FGP-TV model (GPU) \n'); -% lambda_fgp = 0.03; % regularisation parameter % iter_fgp = 300; % number of FGP iterations % epsil_tol = 1.0e-05; % tolerance -% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; +% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)'); %% +fprintf('Denoise a volume using the SB-TV model (CPU) \n'); +iter_sb = 150; % number of SB iterations +epsil_tol = 1.0e-05; % tolerance +tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; +figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the SB-TV model (GPU) \n'); +% iter_sb = 150; % number of SB iterations +% epsil_tol = 1.0e-05; % tolerance +% tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; +% figure; imshow(u_sbG(:,:,15), [0 1]); title('SB-TV denoised volume (GPU)'); +%% + +%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % fprintf('Denoise a volume using the FGP-dTV model (CPU) \n'); % create another volume (reference) with slightly less amount of noise @@ -53,11 +65,10 @@ end vol3D_ref(vol3D_ref < 0) = 0; % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) -lambda_fgp = 0.03; % regularisation parameter iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance eta = 0.2; % Reference image gradient smoothing constant -tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; +tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; figure; imshow(u_fgp_dtv(:,:,15), [0 1]); title('FGP-dTV denoised volume (CPU)'); %% fprintf('Denoise a volume using the FGP-dTV model (GPU) \n'); @@ -70,10 +81,9 @@ end vol3D_ref(vol3D_ref < 0) = 0; % vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) -lambda_fgp = 0.03; % regularisation parameter iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance eta = 0.2; % Reference image gradient smoothing constant -tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; +tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; figure; imshow(u_fgp_dtv_g(:,:,15), [0 1]); title('FGP-dTV denoised volume (GPU)'); %%
\ No newline at end of file diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index 145f2ff..129bedc 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -8,45 +8,55 @@ Im = double(imread('lena_gray_512.tif'))/255; % loading image u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; figure; imshow(u0, [0 1]); title('Noisy image'); +lambda_reg = 0.03; % regularsation parameter for all methods %% fprintf('Denoise using the ROF-TV model (CPU) \n'); -lambda_rof = 0.03; % regularisation parameter tau_rof = 0.0025; % time-marching constant iter_rof = 2000; % number of ROF iterations -tic; u_rof = ROF_TV(single(u0), lambda_rof, iter_rof, tau_rof); toc; +tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); %% % fprintf('Denoise using the ROF-TV model (GPU) \n'); -% lambda_rof = 0.03; % regularisation parameter % tau_rof = 0.0025; % time-marching constant % iter_rof = 2000; % number of ROF iterations -% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_rof, iter_rof, tau_rof); toc; +% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof); toc; % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)'); %% fprintf('Denoise using the FGP-TV model (CPU) \n'); -lambda_fgp = 0.03; % regularisation parameter iter_fgp = 1000; % number of FGP iterations epsil_tol = 1.0e-06; % tolerance -tic; u_fgp = FGP_TV(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; +tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% % fprintf('Denoise using the FGP-TV model (GPU) \n'); -% lambda_fgp = 0.03; % regularisation parameter % iter_fgp = 1000; % number of FGP iterations % epsil_tol = 1.0e-05; % tolerance -% tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; +% tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)'); %% +fprintf('Denoise using the SB-TV model (CPU) \n'); +iter_sb = 150; % number of SB iterations +epsil_tol = 1.0e-06; % tolerance +tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; +figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)'); +%% +% fprintf('Denoise using the SB-TV model (GPU) \n'); +% iter_sb = 150; % number of SB iterations +% epsil_tol = 1.0e-06; % tolerance +% tic; u_sbG = SB_TV_GPU(single(u0), lambda_reg, iter_sb, epsil_tol); toc; +% figure; imshow(u_sbG, [0 1]); title('SB-TV denoised image (GPU)'); +%% +%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % + fprintf('Denoise using the FGP-dTV model (CPU) \n'); % create another image (reference) with slightly less amount of noise u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0; % u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) -lambda_fgp = 0.03; % regularisation parameter iter_fgp = 1000; % number of FGP iterations epsil_tol = 1.0e-06; % tolerance eta = 0.2; % Reference image gradient smoothing constant -tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; +tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)'); %% % fprintf('Denoise using the FGP-dTV model (GPU) \n'); @@ -54,10 +64,9 @@ figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)'); % u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0; % % u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) % -% lambda_fgp = 0.03; % regularisation parameter % iter_fgp = 1000; % number of FGP iterations % epsil_tol = 1.0e-06; % tolerance % eta = 0.2; % Reference image gradient smoothing constant -% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_fgp, iter_fgp, epsil_tol, eta); toc; +% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; % figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)'); %% diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m index 71f345a..c3c82ff 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m @@ -11,10 +11,13 @@ movefile ROF_TV.mex* ../installed/ mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile FGP_TV.mex* ../installed/ +mex SB_TV.c SB_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile SB_TV.mex* ../installed/ + mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile FGP_dTV.mex* ../installed/ -delete ROF_TV_core* FGP_TV_core* FGP_dTV_core* utils* CCPiDefines.h +delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* utils* CCPiDefines.h fprintf('%s \n', 'All successfully compiled!'); diff --git a/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m index f58e9bc..3dbeb8a 100644 --- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m @@ -1,13 +1,13 @@ % execute this mex file in Matlab once -%>>>>>>>>>>>>>>Important<<<<<<<<<<<<<<<<<<< +%>>>>>>>>>>>>>>>>>Important<<<<<<<<<<<<<<<<<<< % In order to compile CUDA modules one needs to have nvcc-compiler -% installed (see CUDA SDK) -% check it under MATLAB with !nvcc --version -% In the code bellow we provide a full path to nvcc compiler +% installed (see CUDA SDK), check it under MATLAB with !nvcc --version + +% In the code bellow we provide a full explicit path to nvcc compiler % ! paths to matlab and CUDA sdk can be different, modify accordingly ! -% tested on Ubuntu 16.04/MATLAB 2016b +% tested on Ubuntu 16.04/MATLAB 2016b/cuda7.5/gcc4.9 copyfile ../../../Core/regularisers_GPU/ regularisers_GPU/ copyfile ../../../Core/CCPiDefines.h regularisers_GPU/ @@ -23,11 +23,15 @@ movefile ROF_TV_GPU.mex* ../installed/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o movefile FGP_TV_GPU.mex* ../installed/ +!/usr/local/cuda/bin/nvcc -O0 -c TV_SB_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ +mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o +movefile SB_TV_GPU.mex* ../installed/ + !/usr/local/cuda/bin/nvcc -O0 -c dTV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/ mex -g -I/usr/local/cuda-7.5/include -L/usr/local/cuda-7.5/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o movefile FGP_dTV_GPU.mex* ../installed/ -delete TV_ROF_GPU_core* TV_FGP_GPU_core* dTV_FGP_GPU_core* CCPiDefines.h +delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* CCPiDefines.h fprintf('%s \n', 'All successfully compiled!'); cd ../../ diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c new file mode 100644 index 0000000..d284cac --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c @@ -0,0 +1,89 @@ +/* + * 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 "matrix.h" +#include "mex.h" +#include "SB_TV_core.h" + +/* C-OMP implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1] +* +* Input Parameters: +* 1. Noisy image/volume +* 2. lambda - regularisation parameter +* 3. Number of iterations [OPTIONAL parameter] +* 4. eplsilon - tolerance constant [OPTIONAL parameter] +* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] +* 6. print information: 0 (off) or 1 (on) [OPTIONAL parameter] +* +* Output: +* 1. Filtered/regularized image +* +* This function is based on the Matlab's code and paper by +* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343. +*/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, methTV, printswitch; + const int *dim_array; + float *Input, *Output=NULL, lambda, epsil; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), print switch"); + + Input = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */ + lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */ + iter = 100; /* default iterations number */ + epsil = 0.0001; /* default tolerance constant */ + methTV = 0; /* default isotropic TV penalty */ + printswitch = 0; /*default print is switched, off - 0 */ + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + + if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */ + if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */ + if ((nrhs == 5) || (nrhs == 6)) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ + if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); + if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ + mxFree(penalty_type); + } + if (nrhs == 6) { + printswitch = (int) mxGetScalar(prhs[5]); + if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0"); + } + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + /* running the function */ + SB_TV_CPU_main(Input, Output, lambda, iter, epsil, methTV, printswitch, dimX, dimY, dimZ); +} diff --git a/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp new file mode 100644 index 0000000..60847d9 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp @@ -0,0 +1,89 @@ +/* + * 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 "matrix.h" +#include "mex.h" +#include "TV_SB_GPU_core.h" + +/* CUDA mex-file for implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1] +* +* Input Parameters: +* 1. Noisy image/volume +* 2. lambda - regularisation parameter +* 3. Number of iterations [OPTIONAL parameter] +* 4. eplsilon - tolerance constant [OPTIONAL parameter] +* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter] +* 6. print information: 0 (off) or 1 (on) [OPTIONAL parameter] +* +* Output: +* 1. Filtered/regularized image +* +* This function is based on the Matlab's code and paper by +* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343. +*/ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter, dimX, dimY, dimZ, methTV, printswitch; + const int *dim_array; + float *Input, *Output=NULL, lambda, epsil; + + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + dim_array = mxGetDimensions(prhs[0]); + + /*Handling Matlab input data*/ + if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), print switch"); + + Input = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */ + lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */ + iter = 100; /* default iterations number */ + epsil = 0.0001; /* default tolerance constant */ + methTV = 0; /* default isotropic TV penalty */ + printswitch = 0; /*default print is switched, off - 0 */ + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + + if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */ + if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */ + if ((nrhs == 5) || (nrhs == 6)) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */ + if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',"); + if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */ + mxFree(penalty_type); + } + if (nrhs == 6) { + printswitch = (int) mxGetScalar(prhs[5]); + if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0"); + } + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + + /* running the function */ + TV_SB_GPU_main(Input, Output, lambda, iter, epsil, methTV, printswitch, dimX, dimY, dimZ); +} diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index 376cc9c..50c4374 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,8 +2,8 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, dTV_FGP_CPU -from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, dTV_FGP_GPU +from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU +from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU def ROF_TV(inputData, regularisation_parameter, iterations, time_marching_parameter,device='cpu'): @@ -42,6 +42,25 @@ def FGP_TV(inputData, regularisation_parameter,iterations, else: raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def SB_TV(inputData, regularisation_parameter, iterations, + tolerance_param, methodTV, printM, device='cpu'): + if device == 'cpu': + return TV_SB_CPU(inputData, + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM) + elif device == 'gpu': + return TV_SB_GPU(inputData, + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM) + else: + raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ + .format(device)) def FGP_dTV(inputData, refdata, regularisation_parameter, iterations, tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'): if device == 'cpu': diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 00beb0b..0e4355b 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -141,13 +141,60 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_cpu, cmap="gray") plt.title('{}'.format('CPU results')) +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________SB-TV (2D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of SB-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : SB_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :150 ,\ + 'tolerance_constant':1e-06,\ + 'methodTV': 0 ,\ + 'printingOut': 0 + } + +print ("#############SB TV CPU####################") +start_time = timeit.default_timer() +sb_cpu = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'cpu') + + +rms = rmse(Im, sb_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_____________FGP-dTV (2D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure(4) plt.suptitle('Performance of FGP-dTV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -223,7 +270,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure(5) plt.suptitle('Performance of ROF-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy 15th slice of a volume') @@ -263,7 +310,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure(6) plt.suptitle('Performance of FGP-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -307,13 +354,59 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the CPU using FGP-TV')) +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________SB-TV (3D)_________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(7) +plt.suptitle('Performance of SB-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") + +# set parameters +pars = {'algorithm' : SB_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :150 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'printingOut': 0 + } + +print ("#############SB TV CPU####################") +start_time = timeit.default_timer() +sb_cpu3D = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'cpu') + +rms = rmse(idealVol, sb_cpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the CPU using SB-TV')) + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-dTV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure(8) plt.suptitle('Performance of FGP-dTV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py index 310cf75..d8e2da7 100644 --- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -218,13 +218,99 @@ if (diff_im.sum() > 1): else: print ("Arrays match") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________SB-TV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations') +a=fig.add_subplot(1,4,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : SB_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :150 ,\ + 'tolerance_constant':1e-05,\ + 'methodTV': 0 ,\ + 'printingOut': 0 + } + +print ("#############SB-TV CPU####################") +start_time = timeit.default_timer() +sb_cpu = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'cpu') + + +rms = rmse(Im, sb_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + + +print ("##############SB TV GPU##################") +start_time = timeit.default_timer() +sb_gpu = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'gpu') + +rms = rmse(Im, sb_gpu) +pars['rmse'] = rms +pars['algorithm'] = SB_TV +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,3) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + +print ("--------Compare the results--------") +tolerance = 1e-05 +diff_im = np.zeros(np.shape(rof_cpu)) +diff_im = abs(sb_cpu - sb_gpu) +diff_im[diff_im > tolerance] = 1 +a=fig.add_subplot(1,4,4) +imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray") +plt.title('{}'.format('Pixels larger threshold difference')) +if (diff_im.sum() > 1): + print ("Arrays do not match!") +else: + print ("Arrays match") + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________FGP-dTV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure(4) plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index 24a3c88..25d8d85 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -139,12 +139,59 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_gpu, cmap="gray") plt.title('{}'.format('GPU results')) + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________FGP-dTV bench___________________") +print ("____________SB-TV bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(3) +plt.suptitle('Performance of the SB-TV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : SB_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :150 ,\ + 'tolerance_constant':1e-06,\ + 'methodTV': 0 ,\ + 'printingOut': 0 + } + +print ("##############SB TV GPU##################") +start_time = timeit.default_timer() +sb_gpu = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'gpu') + +rms = rmse(Im, sb_gpu) +pars['rmse'] = rms +pars['algorithm'] = SB_TV +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________FGP-dTV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(4) plt.suptitle('Performance of the FGP-dTV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -219,7 +266,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure(5) plt.suptitle('Performance of ROF-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy 15th slice of a volume') @@ -259,7 +306,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure(6) plt.suptitle('Performance of FGP-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -302,13 +349,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the GPU using FGP-TV')) +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________SB-TV (3D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(7) +plt.suptitle('Performance of SB-TV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") + +# set parameters +pars = {'algorithm' : SB_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :100 ,\ + 'tolerance_constant':1e-05,\ + 'methodTV': 0 ,\ + 'printingOut': 0 + } + +print ("#############SB TV GPU####################") +start_time = timeit.default_timer() +sb_gpu3D = SB_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['printingOut'],'gpu') + +rms = rmse(idealVol, sb_gpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the GPU using SB-TV')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-dTV (3D)________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure(8) plt.suptitle('Performance of FGP-dTV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in index c7ebb5c..0681cc4 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -36,6 +36,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"), os.path.join(".." , ".." , "Core", "regularisers_CPU"), os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_ROF" ) , + os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_SB" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "dTV_FGP" ) , "."] diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 1661375..417670d 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -20,6 +20,7 @@ cimport numpy as np cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ); cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); +cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ); cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); @@ -125,6 +126,63 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, printM, dims[2], dims[1], dims[0]) return outputData + +#***************************************************************# +#********************** Total-variation SB *********************# +#***************************************************************# +#*************** Total-variation Split Bregman (SB)*************# +def TV_SB_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM): + if inputData.ndim == 2: + return TV_SB_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM) + elif inputData.ndim == 3: + return TV_SB_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM) + +def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + int iterationsNumb, + float tolerance_param, + int methodTV, + int printM): + + 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') + + #/* Run SB-TV iterations for 2D data */ + SB_TV_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, + iterationsNumb, + tolerance_param, + methodTV, + printM, + dims[0], dims[1], 1) + + return outputData + +def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularisation_parameter, + int iterationsNumb, + float tolerance_param, + int methodTV, + int printM): + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0], dims[1], dims[2]], dtype='float32') + + #/* Run SB-TV iterations for 3D data */ + SB_TV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, + iterationsNumb, + tolerance_param, + methodTV, + printM, + dims[2], dims[1], dims[0]) + return outputData #****************************************************************# #**************Directional Total-variation FGP ******************# #****************************************************************# diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index 18efdcd..36eec95 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -20,6 +20,7 @@ cimport numpy as np cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z); cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z); +cdef extern void TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z); cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z); # Total-variation Rudin-Osher-Fatemi (ROF) @@ -62,6 +63,27 @@ def TV_FGP_GPU(inputData, methodTV, nonneg, printM) +# Total-variation Split Bregman (SB) +def TV_SB_GPU(inputData, + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM): + if inputData.ndim == 2: + return SBTV2D(inputData, + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM) + elif inputData.ndim == 3: + return SBTV3D(inputData, + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM) # Directional Total-variation Fast-Gradient-Projection (FGP) def dTV_FGP_GPU(inputData, refdata, @@ -197,7 +219,60 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, dims[2], dims[1], dims[0]); return outputData +#***************************************************************# +#********************** Total-variation SB *********************# +#***************************************************************# +#*************** Total-variation Split Bregman (SB)*************# +def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + int iterations, + float tolerance_param, + int methodTV, + int printM): + + 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') + + # Running CUDA code here + TV_SB_GPU_main(&inputData[0,0], &outputData[0,0], + regularisation_parameter, + iterations, + tolerance_param, + methodTV, + printM, + dims[0], dims[1], 1); + + return outputData +def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularisation_parameter, + int iterations, + float tolerance_param, + int methodTV, + int printM): + + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Running CUDA code here + TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0], + regularisation_parameter , + iterations, + tolerance_param, + methodTV, + printM, + dims[2], dims[1], dims[0]); + + return outputData #****************************************************************# #**************Directional Total-variation FGP ******************# #****************************************************************# |