From 4505a79103e98adb33bfb4c10391319e56ae7031 Mon Sep 17 00:00:00 2001 From: Tomas Kulhanek Date: Fri, 22 Feb 2019 06:44:53 -0500 Subject: UPDATE: docs -> demos and update paths in m and py demos --- docs/data/SinoInpaint.mat | Bin 3335061 -> 0 bytes docs/data/lena_gray_512.tif | Bin 262598 -> 0 bytes docs/demos/demoMatlab_3Ddenoise.m | 178 ------- docs/demos/demoMatlab_denoise.m | 189 ------- docs/demos/demoMatlab_inpaint.m | 35 -- docs/demos/demo_cpu_inpainters.py | 192 ------- docs/demos/demo_cpu_regularisers.py | 572 --------------------- docs/demos/demo_cpu_regularisers3D.py | 458 ----------------- docs/demos/demo_cpu_vs_gpu_regularisers.py | 790 ----------------------------- docs/demos/demo_gpu_regularisers.py | 518 ------------------- docs/demos/demo_gpu_regularisers3D.py | 460 ----------------- docs/demos/qualitymetrics.py | 18 - docs/images/TV_vs_NLTV.jpg | Bin 111273 -> 0 bytes docs/images/probl.pdf | Bin 62326 -> 0 bytes docs/images/probl.png | Bin 38161 -> 0 bytes docs/images/reg_penalties.jpg | Bin 237455 -> 0 bytes docs/installation.txt | 11 - 17 files changed, 3421 deletions(-) delete mode 100644 docs/data/SinoInpaint.mat delete mode 100644 docs/data/lena_gray_512.tif delete mode 100644 docs/demos/demoMatlab_3Ddenoise.m delete mode 100644 docs/demos/demoMatlab_denoise.m delete mode 100644 docs/demos/demoMatlab_inpaint.m delete mode 100644 docs/demos/demo_cpu_inpainters.py delete mode 100644 docs/demos/demo_cpu_regularisers.py delete mode 100644 docs/demos/demo_cpu_regularisers3D.py delete mode 100644 docs/demos/demo_cpu_vs_gpu_regularisers.py delete mode 100644 docs/demos/demo_gpu_regularisers.py delete mode 100644 docs/demos/demo_gpu_regularisers3D.py delete mode 100644 docs/demos/qualitymetrics.py delete mode 100644 docs/images/TV_vs_NLTV.jpg delete mode 100644 docs/images/probl.pdf delete mode 100644 docs/images/probl.png delete mode 100644 docs/images/reg_penalties.jpg delete mode 100644 docs/installation.txt (limited to 'docs') diff --git a/docs/data/SinoInpaint.mat b/docs/data/SinoInpaint.mat deleted file mode 100644 index d748fb4..0000000 Binary files a/docs/data/SinoInpaint.mat and /dev/null differ diff --git a/docs/data/lena_gray_512.tif b/docs/data/lena_gray_512.tif deleted file mode 100644 index f80cafc..0000000 Binary files a/docs/data/lena_gray_512.tif and /dev/null differ diff --git a/docs/demos/demoMatlab_3Ddenoise.m b/docs/demos/demoMatlab_3Ddenoise.m deleted file mode 100644 index 0c331a4..0000000 --- a/docs/demos/demoMatlab_3Ddenoise.m +++ /dev/null @@ -1,178 +0,0 @@ -% Volume (3D) denoising demo using CCPi-RGL -clear; close all -Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); -Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); -Path3 = sprintf(['..' filesep 'supp'], 1i); -addpath(Path1); -addpath(Path2); -addpath(Path3); - -N = 512; -slices = 7; -vol3D = zeros(N,N,slices, 'single'); -Ideal3D = zeros(N,N,slices, 'single'); -Im = double(imread('lena_gray_512.tif'))/255; % loading image -for i = 1:slices -vol3D(:,:,i) = Im + .05*randn(size(Im)); -Ideal3D(:,:,i) = Im; -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'); -tau_rof = 0.0025; % time-marching constant -iter_rof = 300; % number of ROF iterations -tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; -energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1); % get energy function value -rmse_rof = (RMSE(Ideal3D(:),u_rof(:))); -fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof); -figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)'); -%% -% fprintf('Denoise a volume using the ROF-TV model (GPU) \n'); -% tau_rof = 0.0025; % time-marching constant -% iter_rof = 300; % number of ROF iterations -% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; -% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:))); -% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG); -% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)'); -%% -fprintf('Denoise a volume using the FGP-TV model (CPU) \n'); -iter_fgp = 300; % number of FGP iterations -epsil_tol = 1.0e-05; % tolerance -tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; -energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value -rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:))); -fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp); -figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)'); -%% -% fprintf('Denoise a volume using the FGP-TV model (GPU) \n'); -% iter_fgp = 300; % number of FGP iterations -% epsil_tol = 1.0e-05; % tolerance -% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; -% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:))); -% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG); -% figure; imshow(u_fgpG(:,:,7), [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; -energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1); % get energy function value -rmse_sb = (RMSE(Ideal3D(:),u_sb(:))); -fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb); -figure; imshow(u_sb(:,:,7), [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; -% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:))); -% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG); -% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)'); -%% -fprintf('Denoise a volume using the ROF-LLT model (CPU) \n'); -lambda_ROF = lambda_reg; % ROF regularisation parameter -lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter -iter_LLT = 300; % iterations -tau_rof_llt = 0.0025; % time-marching constant -tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; -rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:))); -fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); -figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)'); -%% -% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n'); -% lambda_ROF = lambda_reg; % ROF regularisation parameter -% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter -% iter_LLT = 300; % iterations -% tau_rof_llt = 0.0025; % time-marching constant -% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; -% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:))); -% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); -% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)'); -%% -fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n'); -iter_diff = 300; % number of diffusion iterations -lambda_regDiff = 0.025; % regularisation for the diffusivity -sigmaPar = 0.015; % 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; -rmse_diff = (RMSE(Ideal3D(:),u_diff(:))); -fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); -figure; imshow(u_diff(:,:,7), [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 = 0.025; % regularisation for the diffusivity -% sigmaPar = 0.015; % 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; -% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:))); -% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); -% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)'); -%% -fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n'); -iter_diff = 300; % number of diffusion iterations -lambda_regDiff = 3.5; % regularisation for the diffusivity -sigmaPar = 0.02; % edge-preserving parameter -tau_param = 0.0015; % time-marching constant -tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:))); -fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4); -figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)'); -%% -% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); -% iter_diff = 300; % number of diffusion iterations -% lambda_regDiff = 3.5; % regularisation for the diffusivity -% sigmaPar = 0.02; % edge-preserving parameter -% tau_param = 0.0015; % time-marching constant -% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:))); -% fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4); -% figure; imshow(u_diff4_g(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (GPU)'); -%% -fprintf('Denoise using the TGV model (CPU) \n'); -lambda_TGV = 0.03; % regularisation parameter -alpha1 = 1.0; % parameter to control the first-order term -alpha0 = 2.0; % parameter to control the second-order term -iter_TGV = 500; % number of Primal-Dual iterations for TGV -tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc; -rmseTGV = RMSE(Ideal3D(:),u_tgv(:)); -fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV); -figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)'); -%% -%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< % -fprintf('Denoise a volume using the FGP-dTV model (CPU) \n'); - -% create another volume (reference) with slightly less amount of noise -vol3D_ref = zeros(N,N,slices, 'single'); -for i = 1:slices -vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); -end -vol3D_ref(vol3D_ref < 0) = 0; -% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) - -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_reg, iter_fgp, epsil_tol, eta); toc; -figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)'); -%% -fprintf('Denoise a volume using the FGP-dTV model (GPU) \n'); - -% create another volume (reference) with slightly less amount of noise -vol3D_ref = zeros(N,N,slices, 'single'); -for i = 1:slices -vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); -end -vol3D_ref(vol3D_ref < 0) = 0; -% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) - -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_reg, iter_fgp, epsil_tol, eta); toc; -figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)'); -%% diff --git a/docs/demos/demoMatlab_denoise.m b/docs/demos/demoMatlab_denoise.m deleted file mode 100644 index 14d3096..0000000 --- a/docs/demos/demoMatlab_denoise.m +++ /dev/null @@ -1,189 +0,0 @@ -% Image (2D) denoising demo using CCPi-RGL -clear; close all -fsep = '/'; - -Path1 = sprintf(['..' fsep 'mex_compile' fsep 'installed'], 1i); -Path2 = sprintf(['..' fsep '..' fsep '..' fsep 'data' fsep], 1i); -Path3 = sprintf(['..' fsep 'supp'], 1i); -addpath(Path1); addpath(Path2); addpath(Path3); - -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'); -tau_rof = 0.0025; % time-marching constant -iter_rof = 750; % number of ROF iterations -tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; -energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value -rmseROF = (RMSE(u_rof(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); -figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); -%% -% fprintf('Denoise using the ROF-TV model (GPU) \n'); -% tau_rof = 0.0025; % time-marching constant -% iter_rof = 750; % number of ROF iterations -% 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'); -iter_fgp = 1000; % number of FGP iterations -epsil_tol = 1.0e-06; % tolerance -tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; -energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value -rmseFGP = (RMSE(u_fgp(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmseFGP); -figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); - -%% -% fprintf('Denoise using the FGP-TV model (GPU) \n'); -% iter_fgp = 1000; % number of FGP iterations -% epsil_tol = 1.0e-05; % tolerance -% 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; -energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1); % get energy function value -rmseSB = (RMSE(u_sb(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmseSB); -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)'); -%% -fprintf('Denoise using the TGV model (CPU) \n'); -lambda_TGV = 0.045; % regularisation parameter -alpha1 = 1.0; % parameter to control the first-order term -alpha0 = 2.0; % parameter to control the second-order term -iter_TGV = 2000; % number of Primal-Dual iterations for TGV -tic; u_tgv = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; -rmseTGV = (RMSE(u_tgv(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV); -figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)'); -%% -% fprintf('Denoise using the TGV model (GPU) \n'); -% lambda_TGV = 0.045; % regularisation parameter -% alpha1 = 1.0; % parameter to control the first-order term -% alpha0 = 2.0; % parameter to control the second-order term -% iter_TGV = 2000; % number of Primal-Dual iterations for TGV -% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; -% rmseTGV_gpu = (RMSE(u_tgv_gpu(:),Im(:))); -% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV_gpu); -% figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)'); -%% -fprintf('Denoise using the ROF-LLT model (CPU) \n'); -lambda_ROF = lambda_reg; % ROF regularisation parameter -lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter -iter_LLT = 1; % iterations -tau_rof_llt = 0.0025; % time-marching constant -tic; u_rof_llt = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; -rmseROFLLT = (RMSE(u_rof_llt(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT); -figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)'); -%% -% fprintf('Denoise using the ROF-LLT model (GPU) \n'); -% lambda_ROF = lambda_reg; % ROF regularisation parameter -% lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter -% iter_LLT = 500; % iterations -% tau_rof_llt = 0.0025; % time-marching constant -% tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; -% rmseROFLLT_g = (RMSE(u_rof_llt_g(:),Im(:))); -% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT_g); -% figure; imshow(u_rof_llt_g, [0 1]); title('ROF-LLT denoised image (GPU)'); -%% -fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n'); -iter_diff = 800; % number of diffusion iterations -lambda_regDiff = 0.025; % regularisation for the diffusivity -sigmaPar = 0.015; % 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; -rmseDiffus = (RMSE(u_diff(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus); -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 = 0.025; % regularisation for the diffusivity -% sigmaPar = 0.015; % 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)'); -%% -fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n'); -iter_diff = 800; % number of diffusion iterations -lambda_regDiff = 3.5; % regularisation for the diffusivity -sigmaPar = 0.02; % edge-preserving parameter -tau_param = 0.0015; % time-marching constant -tic; u_diff4 = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -rmseDiffHO = (RMSE(u_diff4(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO); -figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)'); -%% -% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); -% iter_diff = 800; % number of diffusion iterations -% lambda_regDiff = 3.5; % regularisation for the diffusivity -% sigmaPar = 0.02; % edge-preserving parameter -% tau_param = 0.0015; % time-marching constant -% 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 = 20; % the number of neibours to include -h = 0.23; % edge related parameter for NLM -tic; [H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, NeighboursNumber, h); toc; -%% -fprintf('Denoise using Non-local Total Variation (CPU) \n'); -iter_nltv = 3; % number of nltv iterations -lambda_nltv = 0.05; % 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; imagesc(u_nltv, [0 1]); colormap(gray); daspect([1 1 1]); title('Non-local Total Variation denoised image (CPU)'); -%% -%>>>>>>>>>>>>>> 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) - -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_reg, iter_fgp, epsil_tol, eta); toc; -rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:))); -fprintf('%s %f \n', 'RMSE error for Directional Total Variation (dTV) is:', rmse_dTV); -figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)'); -%% -% fprintf('Denoise using the FGP-dTV model (GPU) \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) -% -% 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_reg, iter_fgp, epsil_tol, eta); toc; -% figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)'); -%% -fprintf('Denoise using the TNV prior (CPU) \n'); -slices = 5; N = 512; -vol3D = zeros(N,N,slices, 'single'); -for i = 1:slices -vol3D(:,:,i) = Im + .05*randn(size(Im)); -end -vol3D(vol3D < 0) = 0; - -iter_tnv = 200; % number of TNV iterations -tic; u_tnv = TNV(single(vol3D), lambda_reg, iter_tnv); toc; -figure; imshow(u_tnv(:,:,3), [0 1]); title('TNV denoised stack of channels (CPU)'); diff --git a/docs/demos/demoMatlab_inpaint.m b/docs/demos/demoMatlab_inpaint.m deleted file mode 100644 index 66f9c15..0000000 --- a/docs/demos/demoMatlab_inpaint.m +++ /dev/null @@ -1,35 +0,0 @@ -% Image (2D) inpainting demo using CCPi-RGL -clear; close all -Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); -Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); -addpath(Path1); -addpath(Path2); - -load('SinoInpaint.mat'); -Sinogram = Sinogram./max(Sinogram(:)); -Sino_mask = Sinogram.*(1-single(Mask)); -figure; -subplot(1,2,1); imshow(Sino_mask, [0 1]); title('Missing data sinogram'); -subplot(1,2,2); imshow(Mask, [0 1]); title('Mask'); -%% -fprintf('Inpaint using Linear-Diffusion model (CPU) \n'); -iter_diff = 5000; % number of diffusion iterations -lambda_regDiff = 6000; % regularisation for the diffusivity -sigmaPar = 0.0; % edge-preserving parameter -tau_param = 0.000075; % time-marching constant -tic; u_diff = NonlDiff_Inp(single(Sino_mask), Mask, lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; -figure; imshow(u_diff, [0 1]); title('Linear-Diffusion inpainted sinogram (CPU)'); -%% -fprintf('Inpaint using Nonlinear-Diffusion model (CPU) \n'); -iter_diff = 1500; % number of diffusion iterations -lambda_regDiff = 80; % regularisation for the diffusivity -sigmaPar = 0.00009; % edge-preserving parameter -tau_param = 0.000008; % time-marching constant -tic; u_diff = NonlDiff_Inp(single(Sino_mask), Mask, lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; -figure; imshow(u_diff, [0 1]); title('Non-Linear Diffusion inpainted sinogram (CPU)'); -%% -fprintf('Inpaint using Nonlocal Vertical Marching model (CPU) \n'); -Increment = 1; % linear increment for the searching window -tic; [u_nom,maskupd] = NonlocalMarching_Inpaint(single(Sino_mask), Mask, Increment); toc; -figure; imshow(u_nom, [0 1]); title('NVM inpainted sinogram (CPU)'); -%% \ No newline at end of file diff --git a/docs/demos/demo_cpu_inpainters.py b/docs/demos/demo_cpu_inpainters.py deleted file mode 100644 index 3b4191b..0000000 --- a/docs/demos/demo_cpu_inpainters.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Demonstration of CPU inpainters -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import timeit -from scipy import io -from ccpi.filters.regularisers import NDF_INP, NVM_INP -from qualitymetrics import rmse -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'maskData': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### - -# read sinogram and the mask -filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat") -sino = io.loadmat(filename) -sino_full = sino.get('Sinogram') -Mask = sino.get('Mask') -[angles_dim,detectors_dim] = sino_full.shape -sino_full = sino_full/np.max(sino_full) -#apply mask to sinogram -sino_cut = sino_full*(1-Mask) -#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32') -#sino_cut_new = sino_cut.copy(order='c') -#sino_cut_new[:] = sino_cut[:] -sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32); -#mask = np.zeros((angles_dim,detectors_dim),'uint8') -#mask =Mask.copy(order='c') -#mask[:] = Mask[:] -mask = np.ascontiguousarray(Mask, dtype=np.uint8); - -plt.figure(1) -plt.subplot(121) -plt.imshow(sino_cut_new,vmin=0.0, vmax=1) -plt.title('Missing Data sinogram') -plt.subplot(122) -plt.imshow(mask) -plt.title('Mask') -plt.show() -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("___Inpainting using linear diffusion (2D)__") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure(2) -plt.suptitle('Performance of linear inpainting using the CPU') -a=fig.add_subplot(1,2,1) -a.set_title('Missing data sinogram') -imgplot = plt.imshow(sino_cut_new,cmap="gray") - -# set parameters -pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut_new,\ - 'maskData' : mask,\ - 'regularisation_parameter':5000,\ - 'edge_parameter':0,\ - 'number_of_iterations' :5000 ,\ - 'time_marching_parameter':0.000075,\ - 'penalty_type':0 - } - -start_time = timeit.default_timer() -ndf_inp_linear = NDF_INP(pars['input'], - pars['maskData'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], - pars['penalty_type']) - -rms = rmse(sino_full, ndf_inp_linear) -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_inp_linear, cmap="gray") -plt.title('{}'.format('Linear diffusion inpainting results')) -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_Inpainting using nonlinear diffusion (2D)_") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure(3) -plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU') -a=fig.add_subplot(1,2,1) -a.set_title('Missing data sinogram') -imgplot = plt.imshow(sino_cut_new,cmap="gray") - -# set parameters -pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut_new,\ - 'maskData' : mask,\ - 'regularisation_parameter':80,\ - 'edge_parameter':0.00009,\ - 'number_of_iterations' :1500 ,\ - 'time_marching_parameter':0.000008,\ - 'penalty_type':1 - } - -start_time = timeit.default_timer() -ndf_inp_nonlinear = NDF_INP(pars['input'], - pars['maskData'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], - pars['penalty_type']) - -rms = rmse(sino_full, ndf_inp_nonlinear) -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_inp_nonlinear, cmap="gray") -plt.title('{}'.format('Nonlinear diffusion inpainting results')) -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("Inpainting using nonlocal vertical marching") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure(4) -plt.suptitle('Performance of NVM inpainting using the CPU') -a=fig.add_subplot(1,2,1) -a.set_title('Missing data sinogram') -imgplot = plt.imshow(sino_cut,cmap="gray") - -# set parameters -pars = {'algorithm' : NVM_INP, \ - 'input' : sino_cut_new,\ - 'maskData' : mask,\ - 'SW_increment': 1,\ - 'number_of_iterations' : 150 - } - -start_time = timeit.default_timer() -(nvm_inp, mask_upd) = NVM_INP(pars['input'], - pars['maskData'], - pars['SW_increment'], - pars['number_of_iterations']) - -rms = rmse(sino_full, nvm_inp) -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(nvm_inp, cmap="gray") -plt.title('{}'.format('Nonlocal Vertical Marching inpainting results')) -#%% diff --git a/docs/demos/demo_cpu_regularisers.py b/docs/demos/demo_cpu_regularisers.py deleted file mode 100644 index e6befa9..0000000 --- a/docs/demos/demo_cpu_regularisers.py +++ /dev/null @@ -1,572 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 22 11:39:43 2018 - -Demonstration of CPU regularisers - -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -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): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### -#%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - -# read image -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255.0 -perc = 0.05 -u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) -u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) -(N,M) = np.shape(u0) -# map the u0 u0->u0>0 -# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = u0.astype('float32') -u_ref = u_ref.astype('float32') - -# change dims to check that modules work with non-squared images -""" -M = M-100 -u_ref2 = np.zeros([N,M],dtype='float32') -u_ref2[:,0:M] = u_ref[:,0:M] -u_ref = u_ref2 -del u_ref2 - -u02 = np.zeros([N,M],dtype='float32') -u02[:,0:M] = u0[:,0:M] -u0 = u02 -del u02 - -Im2 = np.zeros([N,M],dtype='float32') -Im2[:,0:M] = Im[:,0:M] -Im = Im2 -del Im2 -""" -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________ROF-TV (2D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of ROF-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': ROF_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 1200,\ - 'time_marching_parameter': 0.0025 - } -print ("#############ROF TV CPU####################") -start_time = timeit.default_timer() -rof_cpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') -rms = rmse(Im, rof_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(rof_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________FGP-TV (2D)__________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-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' : FGP_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :2000 ,\ - 'tolerance_constant':1e-06,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP TV CPU####################") -start_time = timeit.default_timer() -fgp_cpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - -rms = rmse(Im, fgp_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(fgp_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________SB-TV (2D)__________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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 ("_____Total Generalised Variation (2D)______") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of TGV 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' : TGV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :1350 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV CPU####################") -start_time = timeit.default_timer() -tgv_cpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - - -rms = rmse(Im, tgv_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(tgv_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -#%% - -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("______________LLT- ROF (2D)________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of LLT-ROF 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' : LLT_ROF, \ - 'input' : u0,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.01, \ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter' :0.0025 ,\ - } - -print ("#############LLT- ROF CPU####################") -start_time = timeit.default_timer() -lltrof_cpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -rms = rmse(Im, lltrof_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(lltrof_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -#%% - - -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("________________NDF (2D)___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - '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 ("___Anisotropic Diffusion 4th Order (2D)____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of Diff4th 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' : Diff4th, \ - 'input' : u0,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.0015 - } - -print ("#############Diff4th CPU################") -start_time = timeit.default_timer() -diff4_cpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -rms = rmse(Im, diff4_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(diff4_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("___Nonlocal patches pre-calculation____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -start_time = timeit.default_timer() -# set parameters -pars = {'algorithm' : PatchSelect, \ - 'input' : u0,\ - 'searchwindow': 7, \ - 'patchwindow': 2,\ - 'neighbours' : 15 ,\ - 'edge_parameter':0.18} - -H_i, H_j, Weights = PatchSelect(pars['input'], - pars['searchwindow'], - pars['patchwindow'], - pars['neighbours'], - pars['edge_parameter'],'cpu') - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -""" -plt.figure() -plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1) -plt.show() -""" -#%% -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': 0.04,\ - 'iterations': 3 - } -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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-dTV 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' : FGP_dTV, \ - 'input' : u0,\ - 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :2000 ,\ - 'tolerance_constant':1e-06,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP dTV CPU####################") -start_time = timeit.default_timer() -fgp_dtv_cpu = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - -rms = rmse(Im, fgp_dtv_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(fgp_dtv_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("__________Total nuclear Variation__________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of TNV regulariser using the CPU') -a=fig.add_subplot(1,2,1) -a.set_title('Noisy Image') -imgplot = plt.imshow(u0,cmap="gray") - -channelsNo = 5 -noisyVol = np.zeros((channelsNo,N,M),dtype='float32') -idealVol = np.zeros((channelsNo,N,M),dtype='float32') - -for i in range (channelsNo): - noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) - idealVol[i,:,:] = Im - -# set parameters -pars = {'algorithm' : TNV, \ - 'input' : noisyVol,\ - 'regularisation_parameter': 0.04, \ - 'number_of_iterations' : 200 ,\ - 'tolerance_constant':1e-05 - } - -print ("#############TNV CPU#################") -start_time = timeit.default_timer() -tnv_cpu = TNV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant']) - -rms = rmse(idealVol, tnv_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(tnv_cpu[3,:,:], cmap="gray") -plt.title('{}'.format('CPU results')) diff --git a/docs/demos/demo_cpu_regularisers3D.py b/docs/demos/demo_cpu_regularisers3D.py deleted file mode 100644 index 2d2fc22..0000000 --- a/docs/demos/demo_cpu_regularisers3D.py +++ /dev/null @@ -1,458 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 22 11:39:43 2018 - -Demonstration of 3D CPU regularisers - -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th -from qualitymetrics import rmse -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### -#%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - -# read image -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 -perc = 0.05 -u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) -u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) -(N,M) = np.shape(u0) -# map the u0 u0->u0>0 -# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = u0.astype('float32') -u_ref = u_ref.astype('float32') - -# change dims to check that modules work with non-squared images -""" -M = M-100 -u_ref2 = np.zeros([N,M],dtype='float32') -u_ref2[:,0:M] = u_ref[:,0:M] -u_ref = u_ref2 -del u_ref2 - -u02 = np.zeros([N,M],dtype='float32') -u02[:,0:M] = u0[:,0:M] -u0 = u02 -del u02 - -Im2 = np.zeros([N,M],dtype='float32') -Im2[:,0:M] = Im[:,0:M] -Im = Im2 -del Im2 -""" -slices = 15 - -noisyVol = np.zeros((slices,N,M),dtype='float32') -noisyRef = np.zeros((slices,N,M),dtype='float32') -idealVol = np.zeros((slices,N,M),dtype='float32') - -for i in range (slices): - noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) - noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) - idealVol[i,:,:] = Im - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________ROF-TV (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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') -imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") - -# set parameters -pars = {'algorithm': ROF_TV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 500,\ - 'time_marching_parameter': 0.0025 - } -print ("#############ROF TV CPU####################") -start_time = timeit.default_timer() -rof_cpu3D = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') -rms = rmse(idealVol, rof_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(rof_cpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the CPU using ROF-TV')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________FGP-TV (3D)__________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-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' : FGP_TV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP TV CPU####################") -start_time = timeit.default_timer() -fgp_cpu3D = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - -rms = rmse(idealVol, fgp_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(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() -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 ("_______________LLT-ROF (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of LLT-ROF 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' : LLT_ROF, \ - 'input' : noisyVol,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.015, \ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter' :0.0025 ,\ - } - -print ("#############LLT ROF CPU####################") -start_time = timeit.default_timer() -lltrof_cpu3D = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -rms = rmse(idealVol, lltrof_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(lltrof_cpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________TGV (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of TGV 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' : TGV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :250 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV CPU####################") -start_time = timeit.default_timer() -tgv_cpu3D = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - - -rms = rmse(idealVol, tgv_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(tgv_cpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the CPU using TGV')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("________________NDF (3D)___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - '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 ("___Anisotropic Diffusion 4th Order (2D)____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of Diff4th 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' : Diff4th, \ - 'input' : noisyVol,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter':0.0015 - } - -print ("#############Diff4th CPU################") -start_time = timeit.default_timer() -diff4th_cpu3D = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter']) - -rms = rmse(idealVol, diff4th_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(diff4th_cpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the CPU using DIFF4th iterations')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________FGP-dTV (3D)__________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-dTV 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' : FGP_dTV,\ - 'input' : noisyVol,\ - 'refdata' : noisyRef,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP dTV CPU####################") -start_time = timeit.default_timer() -fgp_dTV_cpu3D = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - -rms = rmse(idealVol, fgp_dTV_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(fgp_dTV_cpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV')) -#%% diff --git a/docs/demos/demo_cpu_vs_gpu_regularisers.py b/docs/demos/demo_cpu_vs_gpu_regularisers.py deleted file mode 100644 index 230a761..0000000 --- a/docs/demos/demo_cpu_vs_gpu_regularisers.py +++ /dev/null @@ -1,790 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 22 11:39:43 2018 - -Demonstration of CPU implementation against the GPU one - -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th -from ccpi.filters.regularisers import PatchSelect -from qualitymetrics import rmse -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### - -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - -# read image -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 -perc = 0.05 -u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) -u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) - -# map the u0 u0->u0>0 -# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = u0.astype('float32') -u_ref = u_ref.astype('float32') - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________ROF-TV bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of ROF-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': ROF_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 4500,\ - 'time_marching_parameter': 0.00002 - } -print ("#############ROF TV CPU####################") -start_time = timeit.default_timer() -rof_cpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') -rms = rmse(Im, rof_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(rof_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -print ("##############ROF TV GPU##################") -start_time = timeit.default_timer() -rof_gpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(Im, rof_gpu) -pars['rmse'] = rms -pars['algorithm'] = ROF_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(rof_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(rof_cpu - rof_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-TV bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of FGP-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' : FGP_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1200 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP TV CPU####################") -start_time = timeit.default_timer() -fgp_cpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - -rms = rmse(Im, fgp_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(fgp_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - - -print ("##############FGP TV GPU##################") -start_time = timeit.default_timer() -fgp_gpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(Im, fgp_gpu) -pars['rmse'] = rms -pars['algorithm'] = FGP_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(fgp_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(fgp_cpu)) -diff_im = abs(fgp_cpu - fgp_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 ("____________SB-TV bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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(sb_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 ("____________TGV bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of TGV 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' : TGV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :400 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV CPU####################") -start_time = timeit.default_timer() -tgv_cpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'cpu') - -rms = rmse(Im, tgv_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(tgv_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -print ("##############TGV GPU##################") -start_time = timeit.default_timer() -tgv_gpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') - -rms = rmse(Im, tgv_gpu) -pars['rmse'] = rms -pars['algorithm'] = TGV -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(tgv_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(tgv_gpu)) -diff_im = abs(tgv_cpu - tgv_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 ("____________LLT-ROF bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of LLT-ROF 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' : LLT_ROF, \ - 'input' : u0,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.01, \ - 'number_of_iterations' :4500 ,\ - 'time_marching_parameter' :0.00002 ,\ - } - -print ("#############LLT- ROF CPU####################") -start_time = timeit.default_timer() -lltrof_cpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -rms = rmse(Im, lltrof_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(lltrof_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -print ("#############LLT- ROF GPU####################") -start_time = timeit.default_timer() -lltrof_gpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(Im, lltrof_gpu) -pars['rmse'] = rms -pars['algorithm'] = LLT_ROF -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(lltrof_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(lltrof_gpu)) -diff_im = abs(lltrof_cpu - lltrof_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 ("_______________NDF bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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(ndf_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 ("___Anisotropic Diffusion 4th Order (2D)____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of Diff4th 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' : Diff4th, \ - 'input' : u0,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.001 - } - -print ("#############Diff4th CPU####################") -start_time = timeit.default_timer() -diff4th_cpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'cpu') - -rms = rmse(Im, diff4th_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(diff4th_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -print ("##############Diff4th GPU##################") -start_time = timeit.default_timer() -diff4th_gpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], 'gpu') - -rms = rmse(Im, diff4th_gpu) -pars['rmse'] = rms -pars['algorithm'] = Diff4th -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(diff4th_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(diff4th_cpu)) -diff_im = abs(diff4th_cpu - diff4th_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() -plt.suptitle('Comparison of FGP-dTV 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' : FGP_dTV, \ - 'input' : u0,\ - 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1000 ,\ - 'tolerance_constant':1e-07,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP dTV CPU####################") -start_time = timeit.default_timer() -fgp_dtv_cpu = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'cpu') - - -rms = rmse(Im, fgp_dtv_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(fgp_dtv_cpu, cmap="gray") -plt.title('{}'.format('CPU results')) - -print ("##############FGP dTV GPU##################") -start_time = timeit.default_timer() -fgp_dtv_gpu = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') -rms = rmse(Im, fgp_dtv_gpu) -pars['rmse'] = rms -pars['algorithm'] = FGP_dTV -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(fgp_dtv_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(fgp_dtv_cpu)) -diff_im = abs(fgp_dtv_cpu - fgp_dtv_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 ("____Non-local regularisation bench_________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Comparison of Nonlocal TV regulariser using CPU and GPU implementations') -a=fig.add_subplot(1,2,1) -a.set_title('Noisy Image') -imgplot = plt.imshow(u0,cmap="gray") - -pars = {'algorithm' : PatchSelect, \ - 'input' : u0,\ - 'searchwindow': 7, \ - 'patchwindow': 2,\ - 'neighbours' : 15 ,\ - 'edge_parameter':0.18} - -print ("############## Nonlocal Patches on CPU##################") -start_time = timeit.default_timer() -H_i, H_j, WeightsCPU = PatchSelect(pars['input'], - pars['searchwindow'], - pars['patchwindow'], - pars['neighbours'], - pars['edge_parameter'],'cpu') -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - -print ("############## Nonlocal Patches on GPU##################") -start_time = timeit.default_timer() -start_time = timeit.default_timer() -H_i, H_j, WeightsGPU = PatchSelect(pars['input'], - pars['searchwindow'], - pars['patchwindow'], - pars['neighbours'], - pars['edge_parameter'],'gpu') -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - -print ("--------Compare the results--------") -tolerance = 1e-05 -diff_im = np.zeros(np.shape(u0)) -diff_im = abs(WeightsCPU[0,:,:] - WeightsGPU[0,:,:]) -diff_im[diff_im > tolerance] = 1 -a=fig.add_subplot(1,2,2) -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") -#%% \ No newline at end of file diff --git a/docs/demos/demo_gpu_regularisers.py b/docs/demos/demo_gpu_regularisers.py deleted file mode 100644 index e1c6575..0000000 --- a/docs/demos/demo_gpu_regularisers.py +++ /dev/null @@ -1,518 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 22 11:39:43 2018 - -Demonstration of GPU regularisers - -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th -from ccpi.filters.regularisers import PatchSelect, NLTV -from qualitymetrics import rmse -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### -#%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - -# read image -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 -perc = 0.05 -u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) -u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) -(N,M) = np.shape(u0) -# map the u0 u0->u0>0 -# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = u0.astype('float32') -u_ref = u_ref.astype('float32') -""" -M = M-100 -u_ref2 = np.zeros([N,M],dtype='float32') -u_ref2[:,0:M] = u_ref[:,0:M] -u_ref = u_ref2 -del u_ref2 - -u02 = np.zeros([N,M],dtype='float32') -u02[:,0:M] = u0[:,0:M] -u0 = u02 -del u02 - -Im2 = np.zeros([N,M],dtype='float32') -Im2[:,0:M] = Im[:,0:M] -Im = Im2 -del Im2 -""" -#%% - -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________ROF-TV regulariser_____________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of the ROF-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': ROF_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 1200,\ - 'time_marching_parameter': 0.0025 - } -print ("##############ROF TV GPU##################") -start_time = timeit.default_timer() -rof_gpu = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(Im, rof_gpu) -pars['rmse'] = rms -pars['algorithm'] = ROF_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(rof_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________FGP-TV regulariser_____________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of the FGP-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' : FGP_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1200 ,\ - 'tolerance_constant':1e-06,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("##############FGP TV GPU##################") -start_time = timeit.default_timer() -fgp_gpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(Im, fgp_gpu) -pars['rmse'] = rms -pars['algorithm'] = FGP_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(fgp_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("____________SB-TV regulariser______________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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 ("_____Total Generalised Variation (2D)______") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of TGV 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' : TGV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :1250 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV CPU####################") -start_time = timeit.default_timer() -tgv_gpu = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') - - -rms = rmse(Im, tgv_gpu) -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(tgv_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% - -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("______________LLT- ROF (2D)________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of LLT-ROF 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' : LLT_ROF, \ - 'input' : u0,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.01, \ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter' :0.0025 ,\ - } - -print ("#############LLT- ROF GPU####################") -start_time = timeit.default_timer() -lltrof_gpu = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - - -rms = rmse(Im, lltrof_gpu) -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(lltrof_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________NDF regulariser_____________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - '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 ("___Anisotropic Diffusion 4th Order (2D)____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of Diff4th 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' : Diff4th, \ - 'input' : u0,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :500 ,\ - 'time_marching_parameter':0.0015 - } - -print ("#############DIFF4th CPU################") -start_time = timeit.default_timer() -diff4_gpu = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(Im, diff4_gpu) -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(diff4_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("___Nonlocal patches pre-calculation____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -start_time = timeit.default_timer() -# set parameters -pars = {'algorithm' : PatchSelect, \ - 'input' : u0,\ - 'searchwindow': 7, \ - 'patchwindow': 2,\ - 'neighbours' : 15 ,\ - 'edge_parameter':0.18} - -H_i, H_j, Weights = PatchSelect(pars['input'], - pars['searchwindow'], - pars['patchwindow'], - pars['neighbours'], - pars['edge_parameter'],'gpu') - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -""" -plt.figure() -plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1) -plt.show() -""" -#%% -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': 0.02,\ - 'iterations': 3 - } -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 bench___________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of the FGP-dTV 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' : FGP_dTV, \ - 'input' : u0,\ - 'refdata' : u_ref,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :2000 ,\ - 'tolerance_constant':1e-06,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("##############FGP dTV GPU##################") -start_time = timeit.default_timer() -fgp_dtv_gpu = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(Im, fgp_dtv_gpu) -pars['rmse'] = rms -pars['algorithm'] = FGP_dTV -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(fgp_dtv_gpu, cmap="gray") -plt.title('{}'.format('GPU results')) diff --git a/docs/demos/demo_gpu_regularisers3D.py b/docs/demos/demo_gpu_regularisers3D.py deleted file mode 100644 index b6058d2..0000000 --- a/docs/demos/demo_gpu_regularisers3D.py +++ /dev/null @@ -1,460 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Thu Feb 22 11:39:43 2018 - -Demonstration of GPU regularisers - -@authors: Daniil Kazantsev, Edoardo Pasca -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th -from qualitymetrics import rmse -############################################################################### -def printParametersToString(pars): - txt = r'' - for key, value in pars.items(): - if key== 'algorithm' : - txt += "{0} = {1}".format(key, value.__name__) - elif key == 'input': - txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': - txt += "{0} = {1}".format(key, np.shape(value)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### -#%% -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") - -# read image -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 -perc = 0.05 -u0 = Im + np.random.normal(loc = 0 , - scale = perc * Im , - size = np.shape(Im)) -u_ref = Im + np.random.normal(loc = 0 , - scale = 0.01 * Im , - size = np.shape(Im)) -(N,M) = np.shape(u0) -# map the u0 u0->u0>0 -# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = u0.astype('float32') -u_ref = u_ref.astype('float32') -""" -M = M-100 -u_ref2 = np.zeros([N,M],dtype='float32') -u_ref2[:,0:M] = u_ref[:,0:M] -u_ref = u_ref2 -del u_ref2 - -u02 = np.zeros([N,M],dtype='float32') -u02[:,0:M] = u0[:,0:M] -u0 = u02 -del u02 - -Im2 = np.zeros([N,M],dtype='float32') -Im2[:,0:M] = Im[:,0:M] -Im = Im2 -del Im2 -""" - - -slices = 20 - -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 -perc = 0.05 - -noisyVol = np.zeros((slices,N,N),dtype='float32') -noisyRef = np.zeros((slices,N,N),dtype='float32') -idealVol = np.zeros((slices,N,N),dtype='float32') - -for i in range (slices): - noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) - noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) - idealVol[i,:,:] = Im - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________ROF-TV (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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') -imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray") - -# set parameters -pars = {'algorithm': ROF_TV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04,\ - 'number_of_iterations': 500,\ - 'time_marching_parameter': 0.0025 - } -print ("#############ROF TV GPU####################") -start_time = timeit.default_timer() -rof_gpu3D = ROF_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') -rms = rmse(idealVol, rof_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(rof_gpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the GPU using ROF-TV')) -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________FGP-TV (3D)__________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-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' : FGP_TV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP TV GPU####################") -start_time = timeit.default_timer() -fgp_gpu3D = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(idealVol, fgp_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(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() -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 ("_______________LLT-ROF (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of LLT-ROF 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' : LLT_ROF, \ - 'input' : noisyVol,\ - 'regularisation_parameterROF':0.04, \ - 'regularisation_parameterLLT':0.015, \ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter' :0.0025 ,\ - } - -print ("#############LLT ROF CPU####################") -start_time = timeit.default_timer() -lltrof_gpu3D = LLT_ROF(pars['input'], - pars['regularisation_parameterROF'], - pars['regularisation_parameterLLT'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(idealVol, lltrof_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(lltrof_gpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________TGV (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of TGV 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' : TGV, \ - 'input' : noisyVol,\ - 'regularisation_parameter':0.04, \ - 'alpha1':1.0,\ - 'alpha0':2.0,\ - 'number_of_iterations' :600 ,\ - 'LipshitzConstant' :12 ,\ - } - -print ("#############TGV GPU####################") -start_time = timeit.default_timer() -tgv_gpu3D = TGV(pars['input'], - pars['regularisation_parameter'], - pars['alpha1'], - pars['alpha0'], - pars['number_of_iterations'], - pars['LipshitzConstant'],'gpu') - - -rms = rmse(idealVol, tgv_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(tgv_gpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the GPU using TGV')) -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________NDF-TV (3D)_________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -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':0.025, \ - 'edge_parameter':0.015,\ - 'number_of_iterations' :500 ,\ - '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 ("___Anisotropic Diffusion 4th Order (3D)____") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of DIFF4th 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' : Diff4th, \ - 'input' : noisyVol,\ - 'regularisation_parameter':3.5, \ - 'edge_parameter':0.02,\ - 'number_of_iterations' :300 ,\ - 'time_marching_parameter':0.0015 - } - -print ("#############DIFF4th CPU################") -start_time = timeit.default_timer() -diff4_gpu3D = Diff4th(pars['input'], - pars['regularisation_parameter'], - pars['edge_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(idealVol, diff4_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(diff4_gpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('GPU results')) - -#%% -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") -print ("_______________FGP-dTV (3D)________________") -print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") - -## plot -fig = plt.figure() -plt.suptitle('Performance of FGP-dTV 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' : FGP_dTV, \ - 'input' : noisyVol,\ - 'refdata' : noisyRef,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ - 'eta_const':0.2,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -print ("#############FGP TV GPU####################") -start_time = timeit.default_timer() -fgp_dTV_gpu3D = FGP_dTV(pars['input'], - pars['refdata'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['eta_const'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(idealVol, fgp_dTV_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(fgp_dTV_gpu3D[10,:,:], cmap="gray") -plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV')) -#%% diff --git a/docs/demos/qualitymetrics.py b/docs/demos/qualitymetrics.py deleted file mode 100644 index 850829e..0000000 --- a/docs/demos/qualitymetrics.py +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Feb 21 13:34:32 2018 -# quality metrics -@authors: Daniil Kazantsev, Edoardo Pasca -""" -import numpy as np - -def nrmse(im1, im2): - rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size)) - max_val = max(np.max(im1), np.max(im2)) - min_val = min(np.min(im1), np.min(im2)) - return 1 - (rmse / (max_val - min_val)) - -def rmse(im1, im2): - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) - return rmse diff --git a/docs/images/TV_vs_NLTV.jpg b/docs/images/TV_vs_NLTV.jpg deleted file mode 100644 index e976512..0000000 Binary files a/docs/images/TV_vs_NLTV.jpg and /dev/null differ diff --git a/docs/images/probl.pdf b/docs/images/probl.pdf deleted file mode 100644 index 6a06021..0000000 Binary files a/docs/images/probl.pdf and /dev/null differ diff --git a/docs/images/probl.png b/docs/images/probl.png deleted file mode 100644 index af0e852..0000000 Binary files a/docs/images/probl.png and /dev/null differ diff --git a/docs/images/reg_penalties.jpg b/docs/images/reg_penalties.jpg deleted file mode 100644 index 923d5c4..0000000 Binary files a/docs/images/reg_penalties.jpg and /dev/null differ diff --git a/docs/installation.txt b/docs/installation.txt deleted file mode 100644 index f6db38c..0000000 --- a/docs/installation.txt +++ /dev/null @@ -1,11 +0,0 @@ -One can install CCPi-RGL toolkit using cmake: - - -cmake ../CCPi-Regularisation-Toolkit/ -DBUILD_MATLAB_WRAPPERS=ON -DBUILD_PYTHON_WRAPPERS=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=install -DMatlab_ROOT_DIR= -DBUILD_CUDA=OFF - -make - -make install - -Running Matlab from Linux do: -PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" ./matlab -nosplash & -- cgit v1.2.3