diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 16 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m | 7 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m | 25 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c | 88 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c | 92 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 27 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 69 | ||||
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 90 |
8 files changed, 404 insertions, 10 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index d11bc63..54b8bac 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -135,7 +135,21 @@ figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)'); % tic; u_diff4_g = Diffusion_4thO_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; % 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 = 7; +PatchWindow = 2; +NeighboursNumber = 15; % the number of neibours to include +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 = 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 767d811..72a828e 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m @@ -52,6 +52,12 @@ fprintf('%s \n', 'Compiling ROF-LLT...'); mex LLT_ROF.c LLT_ROF_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('LLT_ROF.mex*',Pathmove); +fprintf('%s \n', 'Compiling NonLocal-TV...'); +mex PatchSelect.c PatchSelect_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +mex Nonlocal_TV.c Nonlocal_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile('Nonlocal_TV.mex*',Pathmove); +movefile('PatchSelect.mex*',Pathmove); + fprintf('%s \n', 'Compiling additional tools...'); mex TV_energy.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('TV_energy.mex*',Pathmove); @@ -66,6 +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 new file mode 100644 index 0000000..014c0a0 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c @@ -0,0 +1,88 @@ +/* + * 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 "Nonlocal_TV_core.h" + +#define EPS 1.0000e-9 + +/* Matlab wrapper for 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. + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) +{ + long number_of_dims, dimX, dimY, dimZ; + int IterNumb, NumNeighb = 0; + unsigned short *H_i, *H_j, *H_k; + const int *dim_array; + const int *dim_array2; + float *A_orig, *Output=NULL, *Weights, lambda; + + dim_array = mxGetDimensions(prhs[0]); + dim_array2 = mxGetDimensions(prhs[1]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + /*Handling Matlab input data*/ + A_orig = (float *) mxGetData(prhs[0]); /* a 2D image or a set of 2D images (3D stack) */ + H_i = (unsigned short *) mxGetData(prhs[1]); /* indeces of i neighbours */ + H_j = (unsigned short *) mxGetData(prhs[2]); /* indeces of j neighbours */ + H_k = (unsigned short *) mxGetData(prhs[3]); /* indeces of k neighbours */ + Weights = (float *) mxGetData(prhs[4]); /* weights for patches */ + lambda = (float) mxGetScalar(prhs[5]); /* regularisation parameter */ + IterNumb = (int) mxGetScalar(prhs[6]); /* the number of iterations */ + + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /*****2D INPUT *****/ + if (number_of_dims == 2) { + dimZ = 0; + NumNeighb = dim_array2[2]; + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + /*****3D INPUT *****/ + /****************************************************/ + if (number_of_dims == 3) { + NumNeighb = dim_array2[3]; + 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); +} diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c new file mode 100644 index 0000000..f942539 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c @@ -0,0 +1,92 @@ +/* + * 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_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 + */ +/**************************************************/ +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) +{ + int number_of_dims, SearchWindow, SimilarWin, NumNeighb; + mwSize dimX, dimY, dimZ; + unsigned short *H_i=NULL, *H_j=NULL, *H_k=NULL; + const int *dim_array; + float *A, *Weights = NULL, h; + int dim_array2[3]; /* for 2D data */ + int 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)); + } + + 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/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index c7ae808..bf7e23c 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/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 +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 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 gpu_enabled = True @@ -144,6 +144,31 @@ def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations, raise ValueError ('GPU is not available') raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) + +def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter, device='cpu'): + if device == 'cpu': + return PATCHSEL_CPU(inputData, + searchwindow, + patchwindow, + neighbours, + edge_parameter) + elif device == 'gpu' and gpu_enabled: + return 1 + 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 NLTV(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations): + return NLTV_CPU(inputData, + H_i, + H_j, + H_k, + Weights, + regularisation_parameter, + iterations) + def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst, device='cpu'): if device == 'cpu': diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index e99b271..31e4cad 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -13,6 +13,7 @@ import numpy as np import os import timeit from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, DIFF4th +from ccpi.filters.regularisers import PatchSelect, NLTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -350,7 +351,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(ndf_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("___Anisotropic Diffusion 4th Order (2D)____") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -395,7 +396,71 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(diff4_cpu, cmap="gray") plt.title('{}'.format('CPU results')) +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("___Nonlocal patches pre-calculation____") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +# set parameters +pars = {'algorithm' : PatchSelect, \ + 'input' : u0,\ + 'searchwindow': 7, \ + 'patchwindow': 2,\ + 'neighbours' : 15 ,\ + 'edge_parameter':0.23} + +H_i, H_j, Weights = PatchSelect(pars['input'], + pars['searchwindow'], + pars['patchwindow'], + pars['neighbours'], + pars['edge_parameter'],'cpu') + +#%% +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("___Nonlocal Total Variation penalty____") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +## plot +fig = plt.figure() +plt.suptitle('Performance of NLTV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +pars2 = {'algorithm' : NLTV, \ + 'input' : u0,\ + 'H_i': H_i, \ + 'H_j': H_j,\ + 'H_k' : 0,\ + 'Weights' : Weights,\ + 'regularisation_parameter': 0.085,\ + 'iterations': 2 + } +#%% +start_time = timeit.default_timer() +nltv_cpu = NLTV(pars2['input'], + pars2['H_i'], + pars2['H_j'], + pars2['H_k'], + pars2['Weights'], + pars2['regularisation_parameter'], + pars2['iterations']) + +rms = rmse(Im, nltv_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(nltv_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_____________FGP-dTV (2D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -447,7 +512,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("__________Total nuclear Variation__________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index bf9c861..e51e6d8 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -27,6 +27,8 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, 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); 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); +cdef extern float PatchSelect_CPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM); +cdef extern 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 lambdaReg, int IterNumb); cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ); @@ -446,6 +448,94 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, Diffus4th_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0]) return outputData + +#****************************************************************# +#***************Patch-based weights calculation******************# +#****************************************************************# +def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter): + if inputData.ndim == 2: + return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter) + elif inputData.ndim == 3: + return PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter) +def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + int searchwindow, + int patchwindow, + int neighbours, + float edge_parameter): + cdef long dims[3] + dims[0] = neighbours + dims[1] = inputData.shape[0] + dims[2] = inputData.shape[1] + + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \ + np.zeros([dims[0], dims[1],dims[2]], dtype='float32') + + cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \ + np.zeros([dims[0], dims[1],dims[2]], dtype='uint16') + + cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \ + np.zeros([dims[0], dims[1],dims[2]], dtype='uint16') + + # Run patch-based weight selection function + PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow, neighbours, edge_parameter, 1) + return H_i, H_j, Weights + +def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + int searchwindow, + int patchwindow, + int neighbours, + float edge_parameter): + cdef long dims[4] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + dims[3] = neighbours + + cdef np.ndarray[np.float32_t, ndim=4, mode="c"] Weights = \ + np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='float32') + + cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_i = \ + np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16') + + cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_j = \ + np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16') + + cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_k = \ + np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16') + + # Run patch-based weight selection function + PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow, neighbours, edge_parameter, 1) + return H_i, H_j, H_k, Weights + + +#****************************************************************# +#***************Non-local Total Variation******************# +#****************************************************************# +def NLTV_CPU(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations): + if inputData.ndim == 2: + return NLTV_2D(inputData, H_i, H_j, Weights, regularisation_parameter, iterations) + elif inputData.ndim == 3: + return 1 +def NLTV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i, + np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j, + np.ndarray[np.float32_t, ndim=3, mode="c"] Weights, + float regularisation_parameter, + int iterations): + + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + neighbours = H_i.shape[0] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Run nonlocal TV regularisation + Nonlocal_TV_CPU_main(&inputData[0,0], &outputData[0,0], &H_i[0,0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[1], dims[0], 0, neighbours, regularisation_parameter, iterations) + return outputData + #*********************Inpainting WITH****************************# #***************Nonlinear (Isotropic) Diffusion******************# #****************************************************************# |