diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 17 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 16 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileCPU_mex.m | 5 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/compileGPU_mex.m | 6 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c | 87 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp | 90 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp | 1 | ||||
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 23 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 106 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py | 91 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 114 | ||||
| -rw-r--r-- | Wrappers/Python/setup-regularisers.py.in | 1 | ||||
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 46 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 67 |
14 files changed, 646 insertions, 24 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index fb55097..973d060 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -53,6 +53,23 @@ figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)'); % 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)'); %% +%% +fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n'); +iter_diff = 300; % number of diffusion iterations +lambda_regDiff = 0.06; % regularisation for the diffusivity +sigmaPar = 0.04; % edge-preserving parameter +tau_param = 0.025; % time-marching constant +tic; u_diff = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n'); +% iter_diff = 300; % number of diffusion iterations +% lambda_regDiff = 0.06; % regularisation for the diffusivity +% sigmaPar = 0.04; % edge-preserving parameter +% tau_param = 0.025; % time-marching constant +% tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +% figure; imshow(u_diff_g(:,:,15), [0 1]); title('Diffusion denoised volume (GPU)'); +%% %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % fprintf('Denoise a volume using the FGP-dTV model (CPU) \n'); diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index dab98dc..4a0a19a 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -46,6 +46,22 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)'); % 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)'); %% +fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n'); +iter_diff = 800; % number of diffusion iterations +lambda_regDiff = 0.06; % regularisation for the diffusivity +sigmaPar = 0.04; % edge-preserving parameter +tau_param = 0.025; % time-marching constant +tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)'); +%% +% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n'); +% iter_diff = 800; % number of diffusion iterations +% lambda_regDiff = 0.06; % regularisation for the diffusivity +% sigmaPar = 0.04; % edge-preserving parameter +% tau_param = 0.025; % time-marching constant +% tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +% figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)'); +%% %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % fprintf('Denoise using the FGP-dTV model (CPU) \n'); diff --git a/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m index 9892d73..ec799bd 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m @@ -20,7 +20,10 @@ movefile FGP_dTV.mex* ../installed/ mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile TNV.mex* ../installed/ -delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* CCPiDefines.h +mex NonlDiff.c Diffusion_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile NonlDiff.mex* ../installed/ + +delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* 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 3dbeb8a..55b51eb 100644 --- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileGPU_mex.m @@ -31,7 +31,11 @@ movefile SB_TV_GPU.mex* ../installed/ 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* TV_SB_GPU_core* dTV_FGP_GPU_core* CCPiDefines.h +!/usr/local/cuda/bin/nvcc -O0 -c NonlDiff_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 NonlDiff_GPU.cpp NonlDiff_GPU_core.o +movefile NonlDiff_GPU.mex* ../installed/ + +delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* CCPiDefines.h fprintf('%s \n', 'All successfully compiled!'); cd ../../ diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c new file mode 100644 index 0000000..e05f5d4 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c @@ -0,0 +1,87 @@ +/* + * 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 "Diffusion_core.h" + +/* C-OMP implementation of linear and nonlinear diffusion with the regularisation model [1] (2D/3D case) + * The minimisation is performed using explicit scheme. + * + * Input Parameters: + * 1. Noisy image/volume + * 2. lambda - regularization parameter + * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion + * 4. Number of iterations, for explicit scheme >= 150 is recommended [OPTIONAL parameter] + * 5. tau - time-marching step for explicit scheme [OPTIONAL parameter] + * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight [OPTIONAL parameter] + * + * Output: + * [1] Regularized image/volume + * + * 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. + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter_numb, dimX, dimY, dimZ, penaltytype; + const int *dim_array; + float *Input, *Output=NULL, lambda, tau, sigma; + + dim_array = 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 */ + sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */ + iter_numb = 300; /* iterations number */ + tau = 0.025; /* marching step parameter */ + penaltytype = 1; /* Huber penalty by default */ + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if ((nrhs < 3) || (nrhs > 6)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey"); + if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6)) iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */ + if ((nrhs == 5) || (nrhs == 6)) tau = (float) mxGetScalar(prhs[4]); /* marching step parameter */ + if (nrhs == 6) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[5]); /* Huber, PM or Tukey 'Huber' is the default */ + if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',"); + if (strcmp(penalty_type, "Huber") == 0) penaltytype = 1; /* enable 'Huber' penalty */ + if (strcmp(penalty_type, "PM") == 0) penaltytype = 2; /* enable Perona-Malik penalty */ + if (strcmp(penalty_type, "Tukey") == 0) penaltytype = 3; /* enable Tikey Biweight penalty */ + mxFree(penalty_type); + } + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + 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)); + + Diffusion_CPU_main(Input, Output, lambda, sigma, iter_numb, tau, penaltytype, dimX, dimY, dimZ); +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp new file mode 100644 index 0000000..bfba9ea --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp @@ -0,0 +1,90 @@ +/* + * 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 <stdio.h> +#include <string.h> +#include "NonlDiff_GPU_core.h" + +/* CUDA implementation of linear and nonlinear diffusion with the regularisation model [1,2] (2D/3D case) + * The minimisation is performed using explicit scheme. + * + * Input Parameters: + * 1. Noisy image/volume + * 2. lambda - regularization parameter + * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion + * 4. Number of iterations, for explicit scheme >= 150 is recommended + * 5. tau - time-marching step for explicit scheme + * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * + * Output: + * [1] Regularized image/volume + * + * 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. + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter_numb, dimX, dimY, dimZ, penaltytype; + const int *dim_array; + float *Input, *Output=NULL, lambda, tau, sigma; + + dim_array = 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 */ + sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */ + iter_numb = 300; /* iterations number */ + tau = 0.025; /* marching step parameter */ + penaltytype = 1; /* Huber penalty by default */ + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if ((nrhs < 3) || (nrhs > 6)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey"); + if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6)) iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */ + if ((nrhs == 5) || (nrhs == 6)) tau = (float) mxGetScalar(prhs[4]); /* marching step parameter */ + if (nrhs == 6) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[5]); /* Huber, PM or Tukey 'Huber' is the default */ + if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',"); + if (strcmp(penalty_type, "Huber") == 0) penaltytype = 1; /* enable 'Huber' penalty */ + if (strcmp(penalty_type, "PM") == 0) penaltytype = 2; /* enable Perona-Malik penalty */ + if (strcmp(penalty_type, "Tukey") == 0) penaltytype = 3; /* enable Tikey Biweight penalty */ + mxFree(penalty_type); + } + + /*Handling Matlab output data*/ + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + 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)); + + NonlDiff_GPU_main(Input, Output, lambda, sigma, iter_numb, tau, penaltytype, dimX, dimY, dimZ); +}
\ No newline at end of file diff --git a/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp index 7bbe3af..f60ba7b 100644 --- a/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp +++ b/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp @@ -37,7 +37,6 @@ * * D. Kazantsev, 2016-18 */ - void mexFunction( int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index e6814e8..eec8c4d 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, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU -from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU +from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU +from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU def ROF_TV(inputData, regularisation_parameter, iterations, time_marching_parameter,device='cpu'): @@ -91,3 +91,22 @@ def TNV(inputData, regularisation_parameter, iterations, tolerance_param): regularisation_parameter, iterations, tolerance_param) +def NDF(inputData, regularisation_parameter, edge_parameter, iterations, + time_marching_parameter, penalty_type, device='cpu'): + if device == 'cpu': + return NDF_CPU(inputData, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type) + elif device == 'gpu': + return NDF_GPU(inputData, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type) + else: + raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ + .format(device)) diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 7443b83..3567f91 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, SB_TV, FGP_dTV, TNV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, TNV, NDF from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -190,11 +190,58 @@ plt.title('{}'.format('CPU results')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_____________FGP-dTV (2D)__________________") +print ("________________NDF (2D)___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(4) +plt.suptitle('Performance of NDF 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' : NDF, \ + 'input' : u0,\ + 'regularisation_parameter':0.06, \ + 'edge_parameter':0.04,\ + 'number_of_iterations' :1000 ,\ + 'time_marching_parameter':0.025,\ + 'penalty_type':1 + } + +print ("#############NDF CPU################") +start_time = timeit.default_timer() +ndf_cpu = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type'],'cpu') + +rms = rmse(Im, ndf_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(ndf_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_____________FGP-dTV (2D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(5) plt.suptitle('Performance of FGP-dTV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -247,7 +294,7 @@ print ("__________Total nuclear Variation__________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure(6) plt.suptitle('Performance of TNV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -321,7 +368,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure(7) 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') @@ -361,7 +408,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(7) +fig = plt.figure(8) plt.suptitle('Performance of FGP-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -410,7 +457,7 @@ print ("_______________SB-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(8) +fig = plt.figure(9) plt.suptitle('Performance of SB-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -451,13 +498,58 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the CPU using SB-TV')) +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("________________NDF (3D)___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(10) +plt.suptitle('Performance of NDF regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy volume') +imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") + +# set parameters +pars = {'algorithm' : NDF, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.06, \ + 'edge_parameter':0.04,\ + 'number_of_iterations' :1000 ,\ + 'time_marching_parameter':0.025,\ + 'penalty_type': 1 + } + +print ("#############NDF CPU################") +start_time = timeit.default_timer() +ndf_cpu3D = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type']) + +rms = rmse(idealVol, ndf_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(ndf_cpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the CPU using NDF iterations')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-dTV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(9) +fig = plt.figure(11) 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 d8e2da7..05db23e 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, SB_TV, FGP_dTV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, NDF from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -306,11 +306,98 @@ else: print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________FGP-dTV bench___________________") +print ("_______________NDF bench___________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(4) +plt.suptitle('Comparison of NDF 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' : NDF, \ + 'input' : u0,\ + 'regularisation_parameter':0.06, \ + 'edge_parameter':0.04,\ + 'number_of_iterations' :1000 ,\ + 'time_marching_parameter':0.025,\ + 'penalty_type': 1 + } + +print ("#############NDF CPU####################") +start_time = timeit.default_timer() +ndf_cpu = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type'],'cpu') + +rms = rmse(Im, ndf_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(ndf_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + + +print ("##############NDF GPU##################") +start_time = timeit.default_timer() +ndf_gpu = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type'],'gpu') + +rms = rmse(Im, ndf_gpu) +pars['rmse'] = rms +pars['algorithm'] = NDF +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(ndf_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(ndf_cpu - ndf_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(5) 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 25d8d85..b873700 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, SB_TV, FGP_dTV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, FGP_dTV, NDF from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -50,7 +50,7 @@ u0 = u0.astype('float32') u_ref = u_ref.astype('float32') print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________ROF-TV bench___________________") +print ("____________ROF-TV regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot @@ -92,7 +92,7 @@ plt.title('{}'.format('GPU results')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________FGP-TV bench___________________") +print ("____________FGP-TV regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot @@ -141,7 +141,7 @@ plt.title('{}'.format('GPU results')) print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________SB-TV bench___________________") +print ("____________SB-TV regulariser______________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot @@ -186,12 +186,60 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(sb_gpu, cmap="gray") plt.title('{}'.format('GPU results')) + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________FGP-dTV bench___________________") +print ("_______________NDF regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(4) +plt.suptitle('Performance of the NDF 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' : NDF, \ + 'input' : u0,\ + 'regularisation_parameter':0.06, \ + 'edge_parameter':0.04,\ + 'number_of_iterations' :1000 ,\ + 'time_marching_parameter':0.025,\ + 'penalty_type': 1 + } + +print ("##############NDF GPU##################") +start_time = timeit.default_timer() +ndf_gpu = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type'],'gpu') + +rms = rmse(Im, ndf_gpu) +pars['rmse'] = rms +pars['algorithm'] = NDF +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(ndf_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________FGP-dTV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(5) plt.suptitle('Performance of the FGP-dTV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -266,7 +314,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(5) +fig = plt.figure(6) 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') @@ -306,7 +354,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(6) +fig = plt.figure(7) plt.suptitle('Performance of FGP-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -354,7 +402,7 @@ print ("_______________SB-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(7) +fig = plt.figure(8) plt.suptitle('Performance of SB-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -395,12 +443,60 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray") plt.title('{}'.format('Recovered volume on the GPU using SB-TV')) + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________NDF-TV (3D)_________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(9) +plt.suptitle('Performance of NDF 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' : NDF, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.06, \ + 'edge_parameter':0.04,\ + 'number_of_iterations' :1000 ,\ + 'time_marching_parameter':0.025,\ + 'penalty_type': 1 + } + +print ("#############NDF GPU####################") +start_time = timeit.default_timer() +ndf_gpu3D = NDF(pars['input'], + pars['regularisation_parameter'], + pars['edge_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'], + pars['penalty_type'],'gpu') + +rms = rmse(idealVol, ndf_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(ndf_gpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the GPU using NDF')) + + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________FGP-dTV (3D)________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(8) +fig = plt.figure(10) 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 0681cc4..b900efe 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -37,6 +37,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"), 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" , "NDF" ) , 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 abbf3b0..7ed8fa1 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -21,10 +21,10 @@ 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 Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, 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); - #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -275,3 +275,47 @@ def TNV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, # Run TNV iterations for 3D (X,Y,Channels) data TNV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, tolerance_param, dims[2], dims[1], dims[0]) return outputData +#****************************************************************# +#***************Nonlinear (Isotropic) Diffusion******************# +#****************************************************************# +def NDF_CPU(inputData, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type): + if inputData.ndim == 2: + return NDF_2D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + elif inputData.ndim == 3: + return NDF_3D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + +def NDF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + 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 Nonlinear Diffusion iterations for 2D data + Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + return outputData + +def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + 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 Nonlinear Diffusion iterations for 3D data + Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + + return outputData diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index 36eec95..b0775054 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -21,6 +21,7 @@ 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 NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, 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) @@ -114,6 +115,27 @@ def dTV_FGP_GPU(inputData, methodTV, nonneg, printM) +# Nonlocal Isotropic Diffusion (NDF) +def NDF_GPU(inputData, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type): + if inputData.ndim == 2: + return NDF_GPU_2D(inputData, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type) + elif inputData.ndim == 3: + return NDF_GPU_3D(inputData, + regularisation_parameter, + edge_parameter, + iterations, + time_marching_parameter, + penalty_type) #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -336,3 +358,48 @@ def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, printM, dims[2], dims[1], dims[0]); return outputData + +#****************************************************************# +#***************Nonlinear (Isotropic) Diffusion******************# +#****************************************************************# +def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + 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') + + #rangecheck = penalty_type < 1 and penalty_type > 3 + #if not rangecheck: +# raise ValueError('Choose penalty type as 1 for Huber, 2 - Perona-Malik, 3 - Tukey Biweight') + + # Run Nonlinear Diffusion iterations for 2D data + # Running CUDA code here + NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + return outputData + +def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + 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 Nonlinear Diffusion iterations for 3D data + # Running CUDA code here + NonlDiff_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + + return outputData |