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/Python/ccpi/filters/regularisers.py | 7 +++- Wrappers/Python/setup-regularisers.py.in | 3 +- Wrappers/Python/src/cpu_regularisers.pyx | 50 ++++++++++++++++++++++++++++ 3 files changed, 58 insertions(+), 2 deletions(-) (limited to 'Wrappers/Python') 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 --- Core/CMakeLists.txt | 19 +++---------------- Wrappers/Python/CMakeLists.txt | 5 ----- 2 files changed, 3 insertions(+), 21 deletions(-) (limited to 'Wrappers/Python') diff --git a/Core/CMakeLists.txt b/Core/CMakeLists.txt index 322732b..86d69b2 100644 --- a/Core/CMakeLists.txt +++ b/Core/CMakeLists.txt @@ -24,15 +24,6 @@ if (PYTHONINTERP_FOUND) endif() endif() -#find_package(Boost REQUIRED -# COMPONENTS ${BOOST_PYTHON} ${BOOST_NUMPY}) -# -#if (Boost_FOUND) -# message("Boost version " ${Boost_VERSION}) -# message("Boost include dir " ${Boost_INCLUDE_DIRS}) -# message("Boost library dir " ${Boost_LIBRARY_DIRS}) -# message("Boost libraries " ${Boost_LIBRARIES}) -#endif() find_package(OpenMP) if (OPENMP_FOUND) @@ -85,7 +76,7 @@ message("Adding regularisers as a shared library") add_library(cilreg SHARED ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/FGP_TV_core.c ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/SB_TV_core.c - ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffusion_core.c + ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffusion_core.c #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/LLT_model_core.c #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/PatchBased_Regul_core.c #${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TGV_PD_core.c @@ -100,7 +91,8 @@ target_link_libraries(cilreg ${EXTRA_LIBRARIES} ) include_directories(cilreg PUBLIC ${LIBRARY_INC}/include ${CMAKE_CURRENT_SOURCE_DIR} - ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ ) + ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ + ${CMAKE_CURRENT_SOURCE_DIR}/inpainters_CPU/ ) #GENERATE_EXPORT_HEADER( cilreg # BASE_NAME cilreg # EXPORT_MACRO_NAME CCPiCore_EXPORTS @@ -155,8 +147,3 @@ if (CUDA_FOUND) else() message("CUDA NOT FOUND") endif() - - -#add_executable(regulariser_test ${CMAKE_CURRENT_SOURCE_DIR}/test/test_regulariser.cpp) - -#target_link_libraries (regulariser_test LINK_PUBLIC regularisers_lib) 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/Python') 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 --- Core/inpainters_CPU/Diffusion_Inpaint_core.c | 9 ++++++- Wrappers/Python/ccpi/filters/regularisers.py | 3 +-- Wrappers/Python/demos/demo_cpu_inpainters.py | 40 ++++++++++++++++------------ 3 files changed, 32 insertions(+), 20 deletions(-) (limited to 'Wrappers/Python') diff --git a/Core/inpainters_CPU/Diffusion_Inpaint_core.c b/Core/inpainters_CPU/Diffusion_Inpaint_core.c index e6972ab..56e0421 100644 --- a/Core/inpainters_CPU/Diffusion_Inpaint_core.c +++ b/Core/inpainters_CPU/Diffusion_Inpaint_core.c @@ -47,13 +47,19 @@ int signNDF_inc(float x) { 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) { - int i; + int i,pointsone; float sigmaPar2; sigmaPar2 = sigmaPar/sqrt(2.0f); /* copy into output */ copyIm(Input, Output, dimX, dimY, dimZ); + pointsone = 0; + for (i=0; i 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/Python') 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/Python') 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/Python') 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/Python') 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 --- Core/regularisers_CPU/TNV_core.c | 36 +++++++++++----------- Wrappers/Python/demos/demo_cpu_inpainters.py | 2 +- Wrappers/Python/demos/demo_cpu_regularisers.py | 40 +++++++++++-------------- Wrappers/Python/demos/demo_gpu_regularisers.py | 18 ++++++----- data/SinoInpaint.mat | Bin 3584100 -> 3335061 bytes 5 files changed, 47 insertions(+), 49 deletions(-) (limited to 'Wrappers/Python') diff --git a/Core/regularisers_CPU/TNV_core.c b/Core/regularisers_CPU/TNV_core.c index 8e61869..c51d6cd 100755 --- a/Core/regularisers_CPU/TNV_core.c +++ b/Core/regularisers_CPU/TNV_core.c @@ -251,18 +251,19 @@ float gradient(float *u_upd, float *gradx_upd, float *grady_upd, int dimX, int d // Compute discrete gradient using forward differences #pragma omp parallel for shared(gradx_upd,grady_upd,u_upd) private(i, j, k, l) for(k = 0; k < dimZ; k++) { - for(j = 0; j < dimX; j++) { - l = j * dimY; - for(i = 0; i < dimY; i++) { + for(j = 0; j < dimY; j++) { + l = j * dimX; + for(i = 0; i < dimX; i++) { // Derivatives in the x-direction - if(i != dimY-1) + if(i != dimX-1) gradx_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+1+l] - u_upd[(dimX*dimY)*k + i+l]; else gradx_upd[(dimX*dimY)*k + i+l] = 0.0f; // Derivatives in the y-direction - if(j != dimX-1) - grady_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+dimY+l] -u_upd[(dimX*dimY)*k + i+l]; + if(j != dimY-1) + //grady_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+dimY+l] -u_upd[(dimX*dimY)*k + i+l]; + grady_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+(j+1)*dimX] -u_upd[(dimX*dimY)*k + i+l]; else grady_upd[(dimX*dimY)*k + i+l] = 0.0f; }}} @@ -301,8 +302,8 @@ float proxF(float *gx, float *gy, float *vx, float *vy, float sigma, int p, int for(k = 0; k < dimZ; k++) { - valuex = vx[(dimX*dimY)*k + (i)*dimY + (j)]; - valuey = vy[(dimX*dimY)*k + (i)*dimY + (j)]; + valuex = vx[(dimX*dimY)*k + (j)*dimX + (i)]; + valuey = vy[(dimX*dimY)*k + (j)*dimX + (i)]; M1 += (valuex * valuex); M2 += (valuex * valuey); @@ -412,9 +413,9 @@ float proxF(float *gx, float *gy, float *vx, float *vy, float sigma, int p, int for(k = 0; k < dimZ; k++) { - gx[(dimX*dimY)*k + (i)*dimY + (j)] = vx[(dimX*dimY)*k + (i)*dimY + (j)] * t1 + vy[(dimX*dimY)*k + (i)*dimY + (j)] * t2; - gy[(dimX*dimY)*k + (i)*dimY + (j)] = vx[(dimX*dimY)*k + (i)*dimY + (j)] * t2 + vy[(dimX*dimY)*k + (i)*dimY + (j)] * t3; - } + gx[(dimX*dimY)*k + j*dimX + i] = vx[(dimX*dimY)*k + j*dimX + i] * t1 + vy[(dimX*dimY)*k + j*dimX + i] * t2; + gy[(dimX*dimY)*k + j*dimX + i] = vx[(dimX*dimY)*k + j*dimX + i] * t2 + vy[(dimX*dimY)*k + j*dimX + i] * t3; + } // Delete allocated memory free(proj); @@ -428,20 +429,21 @@ float divergence(float *qx_upd, float *qy_upd, float *div_upd, int dimX, int dim int i, j, k, l; #pragma omp parallel for shared(qx_upd,qy_upd,div_upd) private(i, j, k, l) for(k = 0; k < dimZ; k++) { - for(j = 0; j < dimX; j++) { - l = j * dimY; - for(i = 0; i < dimY; i++) { - if(i != dimY-1) + for(j = 0; j < dimY; j++) { + l = j * dimX; + for(i = 0; i < dimX; i++) { + if(i != dimX-1) { // ux[k][i+l] = u[k][i+1+l] - u[k][i+l] div_upd[(dimX*dimY)*k + i+1+l] -= qx_upd[(dimX*dimY)*k + i+l]; div_upd[(dimX*dimY)*k + i+l] += qx_upd[(dimX*dimY)*k + i+l]; } - if(j != dimX-1) + if(j != dimY-1) { // uy[k][i+l] = u[k][i+width+l] - u[k][i+l] - div_upd[(dimX*dimY)*k + i+dimY+l] -= qy_upd[(dimX*dimY)*k + i+l]; + //div_upd[(dimX*dimY)*k + i+dimY+l] -= qy_upd[(dimX*dimY)*k + i+l]; + div_upd[(dimX*dimY)*k + i+(j+1)*dimX] -= qy_upd[(dimX*dimY)*k + i+l]; div_upd[(dimX*dimY)*k + i+l] += qy_upd[(dimX*dimY)*k + i+l]; } } 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_____________") diff --git a/data/SinoInpaint.mat b/data/SinoInpaint.mat index 58174a9..d748fb4 100644 Binary files a/data/SinoInpaint.mat and b/data/SinoInpaint.mat differ -- 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 --- .../inpainters_CPU/NonlocalMarching_Inpaint_core.c | 48 +++++++++++++++++----- Wrappers/Python/demos/demo_cpu_inpainters.py | 6 ++- Wrappers/Python/src/cpu_regularisers.pyx | 12 +++--- 3 files changed, 48 insertions(+), 18 deletions(-) (limited to 'Wrappers/Python') diff --git a/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c b/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c index 3a846bb..a197375 100644 --- a/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c +++ b/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c @@ -42,7 +42,7 @@ float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Outpu { int i, j, i_m, j_m, counter, iter, iterations_number, W_fullsize, switchmask, switchcurr, counterElements; float *Gauss_weights; - + /* copying M to M_upd */ copyIm_unchar(M, M_upd, dimX, dimY, 1); @@ -54,11 +54,11 @@ float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Outpu iterations_number = 0; for (i=0; i dimX) iterations_number = dimX; } - else iterations_number = (int)(iterationsNumb/dimX); - if (iterations_number > dimX) iterations_number = dimX; - + else iterations_number = iterationsNumb; + if (iterations_number == 0) printf("%s \n", "Nothing to inpaint, zero mask!"); else { @@ -81,11 +81,38 @@ float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Outpu counter++; } } + + if (trigger == 0) { + /*Matlab*/ + #pragma omp parallel for shared(Output, M_upd, Gauss_weights) private(i, j, switchmask, switchcurr) + for(j=0; j Date: Wed, 2 May 2018 15:47:19 +0100 Subject: bugs of NVM are fixed --- .../inpainters_CPU/NonlocalMarching_Inpaint_core.c | 100 ++++++++++----------- .../inpainters_CPU/NonlocalMarching_Inpaint_core.h | 2 +- .../regularisers_CPU/NonlocalMarching_Inpaint.c | 2 +- Wrappers/Python/src/cpu_regularisers.pyx | 4 +- 4 files changed, 54 insertions(+), 54 deletions(-) (limited to 'Wrappers/Python') diff --git a/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c b/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c index a197375..66be15c 100644 --- a/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c +++ b/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c @@ -30,7 +30,7 @@ * 1. 2D image or sinogram with horizontal or inclined regions of missing data * 2. Mask of the same size as A in 'unsigned char' format (ones mark the region to inpaint, zeros belong to the data) * 3. Linear increment to increase searching window size in iterations, values from 1-3 is a good choice - + * * Output: * 1. Inpainted image or a sinogram * 2. updated mask @@ -38,11 +38,11 @@ * Reference: TBA */ -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) +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) { int i, j, i_m, j_m, counter, iter, iterations_number, W_fullsize, switchmask, switchcurr, counterElements; float *Gauss_weights; - + /* copying M to M_upd */ copyIm_unchar(M, M_upd, dimX, dimY, 1); @@ -54,11 +54,11 @@ float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Outpu iterations_number = 0; for (i=0; i dimX) iterations_number = dimX; + } + if ((int)(iterations_number/dimY) > dimX) iterations_number = dimX; } - else iterations_number = iterationsNumb; - + else iterations_number = iterationsNumb; + if (iterations_number == 0) printf("%s \n", "Nothing to inpaint, zero mask!"); else { @@ -83,54 +83,54 @@ float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Outpu } if (trigger == 0) { - /*Matlab*/ - #pragma omp parallel for shared(Output, M_upd, Gauss_weights) private(i, j, switchmask, switchcurr) - for(j=0; j 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/Python') 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