From 09eb48ffbb4ad699e2eefd25678e10dc59d6a177 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Tue, 1 May 2018 09:44:07 +0100 Subject: new inpainters --- Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 9 +- Wrappers/Matlab/demos/demoMatlab_denoise.m | 9 +- Wrappers/Matlab/demos/demoMatlab_inpaint.m | 35 +++++++ Wrappers/Matlab/mex_compile/compileCPU_mex.m | 16 +++- .../mex_compile/regularisers_CPU/NonlDiff_Inp.c | 101 +++++++++++++++++++++ .../regularisers_CPU/NonlocalMarching_Inpaint.c | 82 +++++++++++++++++ Wrappers/Python/ccpi/filters/regularisers.py | 7 +- Wrappers/Python/setup-regularisers.py.in | 3 +- Wrappers/Python/src/cpu_regularisers.pyx | 50 ++++++++++ 9 files changed, 299 insertions(+), 13 deletions(-) create mode 100644 Wrappers/Matlab/demos/demoMatlab_inpaint.m create mode 100644 Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c create mode 100644 Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c (limited to 'Wrappers') diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index 5a54d18..c087433 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -1,8 +1,9 @@ % Volume (3D) denoising demo using CCPi-RGL -clear -close all -addpath('../mex_compile/installed'); -addpath('../../../data/'); +clear; close all +Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); +Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); +addpath(Path1); +addpath(Path2); N = 512; slices = 30; diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index 151a604..d93f477 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -1,8 +1,9 @@ % Image (2D) denoising demo using CCPi-RGL -clear -close all -addpath('../mex_compile/installed'); -addpath('../../../data/'); +clear; close all +Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); +Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); +addpath(Path1); +addpath(Path2); Im = double(imread('lena_gray_512.tif'))/255; % loading image u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; diff --git a/Wrappers/Matlab/demos/demoMatlab_inpaint.m b/Wrappers/Matlab/demos/demoMatlab_inpaint.m new file mode 100644 index 0000000..66f9c15 --- /dev/null +++ b/Wrappers/Matlab/demos/demoMatlab_inpaint.m @@ -0,0 +1,35 @@ +% 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/Wrappers/Matlab/mex_compile/compileCPU_mex.m b/Wrappers/Matlab/mex_compile/compileCPU_mex.m index ee0d99e..b232f33 100644 --- a/Wrappers/Matlab/mex_compile/compileCPU_mex.m +++ b/Wrappers/Matlab/mex_compile/compileCPU_mex.m @@ -2,9 +2,11 @@ pathcopyFrom = sprintf(['..' filesep '..' filesep '..' filesep 'Core' filesep 'regularisers_CPU'], 1i); pathcopyFrom1 = sprintf(['..' filesep '..' filesep '..' filesep 'Core' filesep 'CCPiDefines.h'], 1i); +pathcopyFrom2 = sprintf(['..' filesep '..' filesep '..' filesep 'Core' filesep 'inpainters_CPU'], 1i); copyfile(pathcopyFrom, 'regularisers_CPU'); copyfile(pathcopyFrom1, 'regularisers_CPU'); +copyfile(pathcopyFrom2, 'regularisers_CPU'); cd regularisers_CPU @@ -32,9 +34,17 @@ movefile('NonlDiff.mex*',Pathmove); mex TV_energy.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" movefile('TV_energy.mex*',Pathmove); +%############Inpainters##############% +mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile('NonlDiff_Inp.mex*',Pathmove); + +mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp" +movefile('NonlocalMarching_Inpaint.mex*',Pathmove); + delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* CCPiDefines.h -fprintf('%s \n', 'All successfully compiled!'); +delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core* +fprintf('%s \n', 'Regularisers successfully compiled!'); -pathA = sprintf(['..' filesep '..' filesep], 1i); -cd(pathA); +pathA2 = sprintf(['..' filesep '..' filesep], 1i); +cd(pathA2); cd demos diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c new file mode 100644 index 0000000..eaab4a7 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c @@ -0,0 +1,101 @@ +/* + * This work is part of the Core Imaging Library developed by + * Visual Analytics and Imaging System Group of the Science Technology + * Facilities Council, STFC + * + * Copyright 2017 Daniil Kazantsev + * Copyright 2017 Srikanth Nagella, Edoardo Pasca + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * http://www.apache.org/licenses/LICENSE-2.0 + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ +#include "matrix.h" +#include "mex.h" +#include "Diffusion_Inpaint_core.h" + +/* C-OMP implementation of linear and nonlinear diffusion [1,2] for inpainting task (2D/3D case) + * The minimisation is performed using explicit scheme. + * + * Input Parameters: + * 1. Image/volume to inpaint + * 2. Inpainting Mask of the same size as (1) in 'unsigned char' format (ones mark the region to inpaint, zeros belong to the data) + * 3. lambda - regularization parameter + * 4. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion + * 5. Number of iterations, for explicit scheme >= 150 is recommended + * 6. tau - time-marching step for explicit scheme + * 7. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight + * + * Output: + * [1] Inpainted image/volume + * + * This function is based on the paper by + * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639. + * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432. + */ + +void mexFunction( + int nlhs, mxArray *plhs[], + int nrhs, const mxArray *prhs[]) + +{ + int number_of_dims, iter_numb, dimX, dimY, dimZ, penaltytype, i, inpaint_elements; + const int *dim_array; + const int *dim_array2; + float *Input, *Output=NULL, lambda, tau, sigma; + unsigned char *Mask; + + dim_array = mxGetDimensions(prhs[0]); + dim_array2 = mxGetDimensions(prhs[1]); + number_of_dims = mxGetNumberOfDimensions(prhs[0]); + + /*Handling Matlab input data*/ + Input = (float *) mxGetData(prhs[0]); + Mask = (unsigned char *) mxGetData(prhs[1]); /* MASK */ + lambda = (float) mxGetScalar(prhs[2]); /* regularization parameter */ + sigma = (float) mxGetScalar(prhs[3]); /* Edge-preserving parameter */ + iter_numb = 300; /* iterations number */ + tau = 0.025; /* marching step parameter */ + penaltytype = 1; /* Huber penalty by default */ + + if ((nrhs < 4) || (nrhs > 7)) mexErrMsgTxt("At least 4 parameters is required, all parameters are: Image(2D/3D), Mask(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey"); + if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7)) iter_numb = (int) mxGetScalar(prhs[4]); /* iterations number */ + if ((nrhs == 6) || (nrhs == 7)) tau = (float) mxGetScalar(prhs[5]); /* marching step parameter */ + if (nrhs == 7) { + char *penalty_type; + penalty_type = mxArrayToString(prhs[6]); /* Huber, PM or Tukey 'Huber' is the default */ + if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',"); + if (strcmp(penalty_type, "Huber") == 0) penaltytype = 1; /* enable 'Huber' penalty */ + if (strcmp(penalty_type, "PM") == 0) penaltytype = 2; /* enable Perona-Malik penalty */ + if (strcmp(penalty_type, "Tukey") == 0) penaltytype = 3; /* enable Tikey Biweight penalty */ + mxFree(penalty_type); + } + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if (mxGetClassID(prhs[1]) != mxUINT8_CLASS) {mexErrMsgTxt("The mask must be in uint8 precision");} + + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!"); + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + } + if (number_of_dims == 3) { + if ((dimX != dim_array2[0]) || (dimY != dim_array2[1]) || (dimZ != dim_array2[2])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!"); + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL)); + } + + inpaint_elements = 0; + for (i=0; i 4)) mexErrMsgTxt("At least 4 parameters is required, all parameters are: Image(2D/3D), Mask(2D/3D), Linear increment, Iterations number"); + if ((nrhs == 3) || (nrhs == 4)) SW_increment = (int) mxGetScalar(prhs[2]); /* linear increment */ + if ((nrhs == 4)) iterations = (int) mxGetScalar(prhs[3]); /* iterations number */ + + if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); } + if (mxGetClassID(prhs[1]) != mxUINT8_CLASS) {mexErrMsgTxt("The mask must be in uint8 precision");} + + dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2]; + + /* output arrays*/ + if (number_of_dims == 2) { + dimZ = 1; /*2D case*/ + /* output image/volume */ + if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!"); + Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL)); + Mask_upd = (unsigned char*)mxGetPr(plhs[1] = mxCreateNumericArray(2, dim_array, mxUINT8_CLASS, mxREAL)); + } + if (number_of_dims == 3) { + mexErrMsgTxt("Currently 2D supported only"); + } + NonlocalMarching_Inpaint_main(Input, Mask, Output, Mask_upd, SW_increment, iterations, dimX, dimY, dimZ); +} \ No newline at end of file diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index eec8c4d..e62c020 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,7 +2,7 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU +from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_CPU from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU def ROF_TV(inputData, regularisation_parameter, iterations, @@ -110,3 +110,8 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, else: raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations, + time_marching_parameter, penalty_type): + return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, + edge_parameter, iterationsNumb, + time_marching_parameter, penalty_type) diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in index b900efe..f55c6fe 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -34,6 +34,7 @@ extra_libraries = ['cilreg'] extra_include_dirs += [os.path.join(".." , ".." , "Core"), os.path.join(".." , ".." , "Core", "regularisers_CPU"), + os.path.join(".." , ".." , "Core", "inpainters_CPU"), os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_ROF" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_SB" ) , @@ -52,7 +53,7 @@ setup( description='CCPi Core Imaging Library - Image regularisers', version=cil_version, cmdclass = {'build_ext': build_ext}, - ext_modules = [Extension("ccpi.filters.cpu_regularisers_cython", + ext_modules = [Extension("ccpi.filters.cpu_regularisers", sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ], include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 7ed8fa1..3625106 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -25,6 +25,9 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ); cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); +cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); +#cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ); + #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -319,3 +322,50 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) return outputData + +#*********************Inpainting WITH****************************# +#***************Nonlinear (Isotropic) Diffusion******************# +#****************************************************************# +def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type): + if inputData.ndim == 2: + return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + elif inputData.ndim == 3: + return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + +def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 2D data + Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + return outputData + +def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 3D data + Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + + return outputData -- cgit v1.2.3 From fa47bdc29ba4178254531174c02f790a9d10a187 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Tue, 1 May 2018 10:03:16 +0100 Subject: make updates --- Wrappers/Python/CMakeLists.txt | 5 ----- 1 file changed, 5 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/CMakeLists.txt b/Wrappers/Python/CMakeLists.txt index fb00706..7833b54 100644 --- a/Wrappers/Python/CMakeLists.txt +++ b/Wrappers/Python/CMakeLists.txt @@ -81,8 +81,3 @@ else() endif() configure_file("setup-regularisers.py.in" "setup-regularisers.py") - - -#add_executable(regulariser_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regulariser.cpp) - -#target_link_libraries (regulariser_test LINK_PUBLIC regularisers_lib) -- cgit v1.2.3 From 5ef87da22a31868fd88c7f0ab4c2201e816e92ed Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Tue, 1 May 2018 12:07:30 +0100 Subject: inpaint demo --- Wrappers/Python/demos/demo_cpu_inpainters.py | 143 +++++++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 Wrappers/Python/demos/demo_cpu_inpainters.py (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py new file mode 100644 index 0000000..a022bc8 --- /dev/null +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -0,0 +1,143 @@ +#!/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 +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 +############################################################################### +#%% +# read sinogram and the mask +filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat") +sino = io.loadmat(filename) +sino_no_cut = sino.get('Sinogram') +Mask = sino.get('Mask') +[angles_dim,detectors_dim] = sino_no_cut.shape +sinogram = sino_no_cut/np.max(sino_no_cut) +#apply mask to sinogram +sino_cut = sinogram*(1-Mask) + +plt.figure(1) +plt.subplot(121) +plt.imshow(sino_cut,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,cmap="gray") + +# set parameters +pars = {'algorithm' : NDF_INP, \ + 'input' : sino_cut,\ + 'maskData' : Mask,\ + 'regularisation_parameter':6000,\ + 'edge_parameter':0.0,\ + 'number_of_iterations' :5000 ,\ + 'time_marching_parameter':0.000075,\ + 'penalty_type':1 + } + +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(sinogram, 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,cmap="gray") + +# set parameters +pars = {'algorithm' : NDF_INP, \ + 'input' : sino_cut,\ + '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(sinogram, 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')) +#%% \ No newline at end of file -- cgit v1.2.3 From 033c2030a05c7aa4c832e7a5e9fd13346d05e33d Mon Sep 17 00:00:00 2001 From: algol Date: Tue, 1 May 2018 14:48:45 +0100 Subject: some correction --- Wrappers/Python/ccpi/filters/regularisers.py | 3 +-- Wrappers/Python/demos/demo_cpu_inpainters.py | 40 ++++++++++++++++------------ 2 files changed, 24 insertions(+), 19 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index e62c020..8120f72 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -113,5 +113,4 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, penalty_type): return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, - edge_parameter, iterationsNumb, - time_marching_parameter, penalty_type) + edge_parameter, iterations, time_marching_parameter, penalty_type) diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index a022bc8..b067b11 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -20,30 +20,36 @@ def printParametersToString(pars): txt += "{0} = {1}".format(key, value.__name__) elif key == 'input': txt += "{0} = {1}".format(key, np.shape(value)) - elif key == 'refdata': + 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_no_cut = sino.get('Sinogram') +sino_full = sino.get('Sinogram') Mask = sino.get('Mask') -[angles_dim,detectors_dim] = sino_no_cut.shape -sinogram = sino_no_cut/np.max(sino_no_cut) +[angles_dim,detectors_dim] = sino_full.shape +sino_full = sino_full/np.max(sino_full) #apply mask to sinogram -sino_cut = sinogram*(1-Mask) +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[:] +mask = np.zeros((angles_dim,detectors_dim),'uint8') +#mask =Mask.copy(order='c') +mask[:] = Mask[:] plt.figure(1) plt.subplot(121) -plt.imshow(sino_cut,vmin=0.0, vmax=1) +plt.imshow(sino_cut_new,vmin=0.0, vmax=1) plt.title('Missing Data sinogram') plt.subplot(122) -plt.imshow(Mask) +plt.imshow(mask) plt.title('Mask') plt.show() #%% @@ -56,15 +62,15 @@ 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,cmap="gray") +imgplot = plt.imshow(sino_cut_new,cmap="gray") # set parameters pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut,\ - 'maskData' : Mask,\ - 'regularisation_parameter':6000,\ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ + 'regularisation_parameter':1000,\ 'edge_parameter':0.0,\ - 'number_of_iterations' :5000 ,\ + 'number_of_iterations' :1000 ,\ 'time_marching_parameter':0.000075,\ 'penalty_type':1 } @@ -78,7 +84,7 @@ ndf_inp_linear = NDF_INP(pars['input'], pars['time_marching_parameter'], pars['penalty_type']) -rms = rmse(sinogram, ndf_inp_linear) +rms = rmse(sino_full, ndf_inp_linear) pars['rmse'] = rms txtstr = printParametersToString(pars) @@ -107,8 +113,8 @@ imgplot = plt.imshow(sino_cut,cmap="gray") # set parameters pars = {'algorithm' : NDF_INP, \ - 'input' : sino_cut,\ - 'maskData' : Mask,\ + 'input' : sino_cut_new,\ + 'maskData' : mask,\ 'regularisation_parameter':80,\ 'edge_parameter':0.00009,\ 'number_of_iterations' :1500 ,\ @@ -125,7 +131,7 @@ ndf_inp_nonlinear = NDF_INP(pars['input'], pars['time_marching_parameter'], pars['penalty_type']) -rms = rmse(sinogram, ndf_inp_nonlinear) +rms = rmse(sino_full, ndf_inp_nonlinear) pars['rmse'] = rms txtstr = printParametersToString(pars) -- cgit v1.2.3 From 42a10faa06bd56bff3f0f1804ddcdf1a3e1283cd Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Tue, 1 May 2018 15:16:49 +0100 Subject: inpaint NVM added --- Wrappers/Python/ccpi/filters/regularisers.py | 5 +++- Wrappers/Python/demos/demo_cpu_inpainters.py | 45 ++++++++++++++++++++++++++-- Wrappers/Python/src/cpu_regularisers.pyx | 31 ++++++++++++++++++- 3 files changed, 77 insertions(+), 4 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index 8120f72..a07b39a 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,7 +2,7 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_CPU +from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_CPU, NVM_INPAINT_CPU from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU def ROF_TV(inputData, regularisation_parameter, iterations, @@ -114,3 +114,6 @@ def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, itera time_marching_parameter, penalty_type): return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, penalty_type) + +def NVM_INP(inputData, maskData, SW_increment, iterations): + return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations) diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index b067b11..ab7ed2f 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -10,7 +10,7 @@ import numpy as np import os import timeit from scipy import io -from ccpi.filters.regularisers import NDF_INP +from ccpi.filters.regularisers import NDF_INP, NVM_INP from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -146,4 +146,45 @@ 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')) -#%% \ No newline at end of file +#%% +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' :20 + } + +start_time = timeit.default_timer() +nvm_inp = 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/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 3625106..19dd707 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -26,7 +26,7 @@ cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxI cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); -#cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ); +cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ); #****************************************************************# #********************** Total-variation ROF *********************# @@ -368,4 +368,33 @@ def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, # Run Inpaiting by Diffusion iterations for 3D data Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + return outputData +#*********************Inpainting WITH****************************# +#***************Nonlocal Vertical Marching method****************# +#****************************************************************# +def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations): + if inputData.ndim == 2: + return NVM_INP_2D(inputData, maskData, SW_increment, iterations) + elif inputData.ndim == 3: + return + +def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + int SW_increment, + int iterationsNumb): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData_upd = \ + np.zeros([dims[0],dims[1]], dtype='uint8') + + # Run Inpaiting by Nonlocal vertical marching method for 2D data + NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &maskData_upd[0,0], + SW_increment, iterationsNumb, + dims[0], dims[1], 1) + return outputData -- cgit v1.2.3 From c4f50db4f5b318aad785ae577908d37fe05f53d2 Mon Sep 17 00:00:00 2001 From: algol Date: Tue, 1 May 2018 15:30:28 +0100 Subject: some updates in demo --- Wrappers/Python/demos/demo_cpu_inpainters.py | 2 +- Wrappers/Python/src/cpu_regularisers.pyx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index ab7ed2f..9a677c4 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -167,7 +167,7 @@ pars = {'algorithm' : NVM_INP, \ } start_time = timeit.default_timer() -nvm_inp = NVM_INP(pars['input'], +(nvm_inp, mask_upd) = NVM_INP(pars['input'], pars['maskData'], pars['SW_increment'], pars['number_of_iterations']) diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 19dd707..52befd7 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -397,4 +397,4 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, SW_increment, iterationsNumb, dims[0], dims[1], 1) - return outputData + return (outputData, maskData_upd) -- cgit v1.2.3 From 73965b6b80c49a2867d54e4a42f3069fe35d9cc6 Mon Sep 17 00:00:00 2001 From: algol Date: Wed, 2 May 2018 09:47:58 +0100 Subject: corrections to dimens issues --- Wrappers/Python/demos/demo_cpu_inpainters.py | 18 ++++++++++-------- Wrappers/Python/demos/demo_cpu_regularisers.py | 17 +++++++++++++++++ Wrappers/Python/demos/demo_gpu_regularisers.py | 16 ++++++++++++++++ Wrappers/Python/src/cpu_regularisers.pyx | 10 ++++++---- 4 files changed, 49 insertions(+), 12 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index 9a677c4..348d235 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -37,12 +37,14 @@ Mask = sino.get('Mask') 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 = np.zeros((angles_dim,detectors_dim),'float32') #sino_cut_new = sino_cut.copy(order='c') -sino_cut_new[:] = sino_cut[:] -mask = np.zeros((angles_dim,detectors_dim),'uint8') +#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[:] = Mask[:] +mask = np.ascontiguousarray(Mask, dtype=np.uint8); plt.figure(1) plt.subplot(121) @@ -68,11 +70,11 @@ imgplot = plt.imshow(sino_cut_new,cmap="gray") pars = {'algorithm' : NDF_INP, \ 'input' : sino_cut_new,\ 'maskData' : mask,\ - 'regularisation_parameter':1000,\ - 'edge_parameter':0.0,\ + 'regularisation_parameter':5000,\ + 'edge_parameter':0,\ 'number_of_iterations' :1000 ,\ 'time_marching_parameter':0.000075,\ - 'penalty_type':1 + 'penalty_type':0 } start_time = timeit.default_timer() @@ -163,7 +165,7 @@ pars = {'algorithm' : NVM_INP, \ 'input' : sino_cut_new,\ 'maskData' : mask,\ 'SW_increment': 1,\ - 'number_of_iterations' :20 + 'number_of_iterations' :0 } start_time = timeit.default_timer() diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 3567f91..f803870 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -50,7 +50,24 @@ u_ref = Im + np.random.normal(loc = 0 , u0 = u0.astype('float32') u_ref = u_ref.astype('float32') +# change dims to check that modules work with non-squared images +(N,M) = np.shape(u0) +u_ref2 = np.zeros([N,M-100],dtype='float32') +u_ref2[:,0:M-100] = u_ref[:,0:M-100] +u_ref = u_ref2 +del u_ref2 + +u02 = np.zeros([N,M-100],dtype='float32') +u02[:,0:M-100] = u0[:,0:M-100] +u0 = u02 +del u02 + +Im2 = np.zeros([N,M-100],dtype='float32') +Im2[:,0:M-100] = Im[:,0:M-100] +Im = Im2 +del Im2 +#%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (2D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index b873700..dfdceee 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -49,6 +49,22 @@ u_ref = Im + np.random.normal(loc = 0 , u0 = u0.astype('float32') u_ref = u_ref.astype('float32') +(N,M) = np.shape(u0) +u_ref2 = np.zeros([N,M-100],dtype='float32') +u_ref2[:,0:M-100] = u_ref[:,0:M-100] +u_ref = u_ref2 +del u_ref2 + +u02 = np.zeros([N,M-100],dtype='float32') +u02[:,0:M-100] = u0[:,0:M-100] +u0 = u02 +del u02 + +Im2 = np.zeros([N,M-100],dtype='float32') +Im2[:,0:M-100] = Im[:,0:M-100] +Im = Im2 +del Im2 + print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV regulariser_____________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 52befd7..21a1a00 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -49,7 +49,7 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Run ROF iterations for 2D data - TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[0], dims[1], 1) + TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1) return outputData @@ -333,18 +333,20 @@ def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_paramete return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, - np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, float regularisation_parameter, float edge_parameter, int iterationsNumb, float time_marching_parameter, int penalty_type): + cdef long dims[2] dims[0] = inputData.shape[0] dims[1] = inputData.shape[1] - + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ - np.zeros([dims[0],dims[1]], dtype='float32') + np.zeros([dims[0],dims[1]], dtype='float32') # Run Inpaiting by Diffusion iterations for 2D data Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) -- cgit v1.2.3 From a64fe4d083173cc67dd7585c3160a94ea24bca80 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Wed, 2 May 2018 10:06:38 +0100 Subject: cyth corr --- Wrappers/Python/src/cpu_regularisers.pyx | 13 ++++++------- Wrappers/Python/src/gpu_regularisers.pyx | 10 +++++----- 2 files changed, 11 insertions(+), 12 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 21a1a00..7c06c28 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -102,7 +102,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1) + dims[1],dims[0],1) return outputData @@ -161,7 +161,7 @@ def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, tolerance_param, methodTV, printM, - dims[0], dims[1], 1) + dims[1],dims[0],1) return outputData @@ -222,7 +222,7 @@ def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1) + dims[1], dims[0], 1) return outputData @@ -301,7 +301,7 @@ def NDF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Run Nonlinear Diffusion iterations for 2D data - Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) return outputData def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, @@ -349,7 +349,7 @@ def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') # Run Inpaiting by Diffusion iterations for 2D data - Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) return outputData def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, @@ -396,7 +396,6 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Run Inpaiting by Nonlocal vertical marching method for 2D data NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &maskData_upd[0,0], - SW_increment, iterationsNumb, - dims[0], dims[1], 1) + SW_increment, iterationsNumb,dims[1], dims[0], 1) return (outputData, maskData_upd) diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index b0775054..7eab5d5 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -157,7 +157,7 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, regularisation_parameter, iterations , time_marching_parameter, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -210,7 +210,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -266,7 +266,7 @@ def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, tolerance_param, methodTV, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -325,7 +325,7 @@ def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, methodTV, nonneg, printM, - dims[0], dims[1], 1); + dims[1], dims[0], 1); return outputData @@ -381,7 +381,7 @@ def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Run Nonlinear Diffusion iterations for 2D data # Running CUDA code here - NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1) return outputData def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, -- cgit v1.2.3 From 985fee04ac1abef2aaa69f282ae6c207e438b4af Mon Sep 17 00:00:00 2001 From: algol Date: Wed, 2 May 2018 11:01:57 +0100 Subject: bugs in cython files --- Wrappers/Python/demos/demo_cpu_inpainters.py | 2 +- Wrappers/Python/demos/demo_cpu_regularisers.py | 40 +++++++++++--------------- Wrappers/Python/demos/demo_gpu_regularisers.py | 18 ++++++------ 3 files changed, 28 insertions(+), 32 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index 348d235..7f452c1 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -72,7 +72,7 @@ pars = {'algorithm' : NDF_INP, \ 'maskData' : mask,\ 'regularisation_parameter':5000,\ 'edge_parameter':0,\ - 'number_of_iterations' :1000 ,\ + 'number_of_iterations' :5000 ,\ 'time_marching_parameter':0.000075,\ 'penalty_type':0 } diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index f803870..986e3e9 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -44,29 +44,30 @@ u0 = Im + np.random.normal(loc = 0 , 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 -(N,M) = np.shape(u0) -u_ref2 = np.zeros([N,M-100],dtype='float32') -u_ref2[:,0:M-100] = u_ref[:,0:M-100] +""" +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-100],dtype='float32') -u02[:,0:M-100] = u0[:,0:M-100] +u02 = np.zeros([N,M],dtype='float32') +u02[:,0:M] = u0[:,0:M] u0 = u02 del u02 -Im2 = np.zeros([N,M-100],dtype='float32') -Im2[:,0:M-100] = Im[:,0:M-100] +Im2 = np.zeros([N,M],dtype='float32') +Im2[:,0:M] = Im[:,0:M] Im = Im2 del Im2 - +""" #%% print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("_______________ROF-TV (2D)_________________") @@ -305,7 +306,6 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray") plt.title('{}'.format('CPU results')) - print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("__________Total nuclear Variation__________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -318,9 +318,8 @@ a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") channelsNo = 5 -N = 512 -noisyVol = np.zeros((channelsNo,N,N),dtype='float32') -idealVol = np.zeros((channelsNo,N,N),dtype='float32') +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)) @@ -361,25 +360,19 @@ plt.title('{}'.format('CPU results')) # Uncomment to test 3D regularisation performance #%% """ -N = 512 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') +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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -420,6 +413,7 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, 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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index dfdceee..f3ed50c 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -44,26 +44,28 @@ u0 = Im + np.random.normal(loc = 0 , 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') - -(N,M) = np.shape(u0) -u_ref2 = np.zeros([N,M-100],dtype='float32') -u_ref2[:,0:M-100] = u_ref[:,0:M-100] +""" +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-100],dtype='float32') -u02[:,0:M-100] = u0[:,0:M-100] +u02 = np.zeros([N,M],dtype='float32') +u02[:,0:M] = u0[:,0:M] u0 = u02 del u02 -Im2 = np.zeros([N,M-100],dtype='float32') -Im2[:,0:M-100] = Im[:,0:M-100] +Im2 = np.zeros([N,M],dtype='float32') +Im2[:,0:M] = Im[:,0:M] Im = Im2 del Im2 +""" print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV regulariser_____________") -- cgit v1.2.3 From 14edd18d07c871c0a355d70e68350a899014dbc7 Mon Sep 17 00:00:00 2001 From: algol Date: Wed, 2 May 2018 13:11:50 +0100 Subject: bugs in NVM fixed --- Wrappers/Python/demos/demo_cpu_inpainters.py | 6 ++++-- Wrappers/Python/src/cpu_regularisers.pyx | 12 ++++++------ 2 files changed, 10 insertions(+), 8 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index 7f452c1..9197e91 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -55,6 +55,7 @@ plt.imshow(mask) plt.title('Mask') plt.show() #%% +""" print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("___Inpainting using linear diffusion (2D)__") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -111,7 +112,7 @@ 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,cmap="gray") +imgplot = plt.imshow(sino_cut_new,cmap="gray") # set parameters pars = {'algorithm' : NDF_INP, \ @@ -148,6 +149,7 @@ 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") @@ -165,7 +167,7 @@ pars = {'algorithm' : NVM_INP, \ 'input' : sino_cut_new,\ 'maskData' : mask,\ 'SW_increment': 1,\ - 'number_of_iterations' :0 + 'number_of_iterations' : 150 } start_time = timeit.default_timer() diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 7c06c28..732b4cb 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -27,7 +27,6 @@ cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ); - #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -374,14 +373,14 @@ def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, #*********************Inpainting WITH****************************# #***************Nonlocal Vertical Marching method****************# #****************************************************************# -def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations): +def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterationsNumb): if inputData.ndim == 2: - return NVM_INP_2D(inputData, maskData, SW_increment, iterations) + return NVM_INP_2D(inputData, maskData, SW_increment, iterationsNumb) elif inputData.ndim == 3: return def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, - np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, int SW_increment, int iterationsNumb): cdef long dims[2] @@ -395,7 +394,8 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='uint8') # Run Inpaiting by Nonlocal vertical marching method for 2D data - NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &maskData_upd[0,0], - SW_increment, iterationsNumb,dims[1], dims[0], 1) + NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], + &maskData_upd[0,0], + SW_increment, iterationsNumb, dims[1], dims[0], 1) return (outputData, maskData_upd) -- cgit v1.2.3 From 37ae2bdb0a15298f312e9f6545a465d4d20c57f1 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Wed, 2 May 2018 15:47:19 +0100 Subject: bugs of NVM are fixed --- .../Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c | 2 +- Wrappers/Python/src/cpu_regularisers.pyx | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c index 5e4ab1f..36cf05c 100644 --- a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c +++ b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c @@ -78,5 +78,5 @@ void mexFunction( if (number_of_dims == 3) { mexErrMsgTxt("Currently 2D supported only"); } - NonlocalMarching_Inpaint_main(Input, Mask, Output, Mask_upd, SW_increment, iterations, dimX, dimY, dimZ); + NonlocalMarching_Inpaint_main(Input, Mask, Output, Mask_upd, SW_increment, iterations, 0, dimX, dimY, dimZ); } \ No newline at end of file diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 732b4cb..c934f1d 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -26,7 +26,7 @@ cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxI cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ); -cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ); +cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ); #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -396,6 +396,6 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Run Inpaiting by Nonlocal vertical marching method for 2D data NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], &maskData_upd[0,0], - SW_increment, iterationsNumb, dims[1], dims[0], 1) + SW_increment, iterationsNumb, 1, dims[1], dims[0], 1) return (outputData, maskData_upd) -- cgit v1.2.3 From 6e285c109938a43b5f8a84b7a48afaeb6b058c90 Mon Sep 17 00:00:00 2001 From: algol Date: Wed, 2 May 2018 15:43:55 +0100 Subject: demo inpainters fixed for python --- Wrappers/Python/demos/demo_cpu_inpainters.py | 2 -- 1 file changed, 2 deletions(-) (limited to 'Wrappers') diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py index 9197e91..3b4191b 100644 --- a/Wrappers/Python/demos/demo_cpu_inpainters.py +++ b/Wrappers/Python/demos/demo_cpu_inpainters.py @@ -55,7 +55,6 @@ plt.imshow(mask) plt.title('Mask') plt.show() #%% -""" print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("___Inpainting using linear diffusion (2D)__") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -149,7 +148,6 @@ 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") -- cgit v1.2.3