From b9fafd363d1d181a4a8b42ea4038924097207913 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Mon, 9 Apr 2018 13:41:06 +0100 Subject: major renaming and new 3D demos for Matlab --- Wrappers/Python/demo/test_cpu_regularisers.py | 323 +++++++++++++++++ Wrappers/Python/demo/test_cpu_regularizers.py | 488 -------------------------- 2 files changed, 323 insertions(+), 488 deletions(-) create mode 100644 Wrappers/Python/demo/test_cpu_regularisers.py delete mode 100644 Wrappers/Python/demo/test_cpu_regularizers.py (limited to 'Wrappers/Python/demo') diff --git a/Wrappers/Python/demo/test_cpu_regularisers.py b/Wrappers/Python/demo/test_cpu_regularisers.py new file mode 100644 index 0000000..4e4a2dd --- /dev/null +++ b/Wrappers/Python/demo/test_cpu_regularisers.py @@ -0,0 +1,323 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 4 11:10:05 2017 + +@author: ofn77899 +""" + +import matplotlib.pyplot as plt +import numpy as np +import os +from enum import Enum +import timeit +from ccpi.filters.regularisers import ROF_TV, FGP_TV +############################################################################### +def nrmse(im1, im2): + a, b = im1.shape + rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b)) + max_val = max(np.max(im1), np.max(im2)) + min_val = min(np.min(im1), np.min(im2)) + return 1 - (rmse / (max_val - min_val)) + +def rmse(im1, im2): + a, b = im1.shape + rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) + return 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)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### +# +# 2D Regularizers +# +############################################################################### +# assumes the script is launched from the test directory +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") + +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +Im = Im/255 + +perc = 0.075 +u0 = Im + np.random.normal(loc = Im , + scale = perc * Im , + size = np.shape(Im)) +# map the u0 u0->u0>0 +f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +u0 = f(u0).astype('float32') + +## plot +fig = plt.figure() + +a=fig.add_subplot(2,4,1) +a.set_title('noise') +imgplot = plt.imshow(u0,cmap="gray" + ) + +reg_output = [] +############################################################################## +# Call regularisers + +###################### FGP_TV ######################################### + +start_time = timeit.default_timer() +pars = {'algorithm' : FGP_TV , \ + 'input' : u0,\ + 'regularisation_parameter':0.07, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +fgp = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'cpu') + +rms = rmse(Im, fgp) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) + + +a=fig.add_subplot(2,4,3) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +# place a text box in upper left in axes coords +imgplot = plt.imshow(fgp, \ + cmap="gray" + ) +# place a text box in upper left in axes coords +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) + +# ###################### ROF_TV ######################################### + +start_time = timeit.default_timer() + +pars = {'algorithm': ROF_TV , \ + 'input' : u0,\ + 'regularisation_parameter':0.07,\ + 'marching_step': 0.0025,\ + 'number_of_iterations': 300 + } +rof = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['marching_step'], 'cpu') + +rms = rmse(Im, rof) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(2,4,7) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +# place a text box in upper left in axes coords +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(rof, cmap="gray") + +plt.show() + +################################################################################ +## +## 3D Regularizers +## +################################################################################ +##Example: +## figure; +## Im = double(imread('lena_gray_256.tif'))/255; % loading image +## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; +## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +# +##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha" +#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha" +# +#reader = vtk.vtkMetaImageReader() +#reader.SetFileName(os.path.normpath(filename)) +#reader.Update() +##vtk returns 3D images, let's take just the one slice there is as 2D +#Im = Converter.vtk2numpy(reader.GetOutput()) +#Im = Im.astype('float32') +##imgplot = plt.imshow(Im) +#perc = 0.05 +#u0 = Im + (perc* np.random.normal(size=np.shape(Im))) +## map the u0 u0->u0>0 +#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +#u0 = f(u0).astype('float32') +#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(), +# reader.GetOutput().GetOrigin()) +#converter.Update() +#writer = vtk.vtkMetaImageWriter() +#writer.SetInputData(converter.GetOutput()) +#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha") +##writer.Write() +# +# +### plot +#fig3D = plt.figure() +##a=fig.add_subplot(3,3,1) +##a.set_title('Original') +##imgplot = plt.imshow(Im) +#sliceNo = 32 +# +#a=fig3D.add_subplot(2,4,1) +#a.set_title('noise') +#imgplot = plt.imshow(u0.T[sliceNo]) +# +#reg_output3d = [] +# +############################################################################### +## Call regularizer +# +######################## SplitBregman_TV ##################################### +## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +# +##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) +# +##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30, +## #tolerance_constant=1e-4, +## TV_Penalty=Regularizer.TotalVariationPenalty.l1) +# +#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30, +# tolerance_constant=1e-4, +# TV_Penalty=Regularizer.TotalVariationPenalty.l1) +# +# +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,4,2) +# +# +#textstr = out2[-1] +# +# +## these are matplotlib.patch.Patch properties +#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +## place a text box in upper left in axes coords +#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +# verticalalignment='top', bbox=props) +#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) +# +####################### FGP_TV ######################################### +## u = FGP_TV(single(u0), 0.05, 100, 1e-04); +#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005, +# number_of_iterations=200) +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,4,2) +# +# +#textstr = out2[-1] +# +# +## these are matplotlib.patch.Patch properties +#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +## place a text box in upper left in axes coords +#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +# verticalalignment='top', bbox=props) +#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) +# +####################### LLT_model ######################################### +## * u0 = Im + .03*randn(size(Im)); % adding noise +## [Den] = LLT_model(single(u0), 10, 0.1, 1); +##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); +##input, regularization_parameter , time_step, number_of_iterations, +## tolerance_constant, restrictive_Z_smoothing=0 +#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25, +# time_step=0.0003, +# tolerance_constant=0.0001, +# number_of_iterations=300) +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,4,2) +# +# +#textstr = out2[-1] +# +# +## these are matplotlib.patch.Patch properties +#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +## place a text box in upper left in axes coords +#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +# verticalalignment='top', bbox=props) +#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) +# +####################### PatchBased_Regul ######################################### +## Quick 2D denoising example in Matlab: +## Im = double(imread('lena_gray_256.tif'))/255; % loading image +## u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise +## ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); +# +#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05, +# searching_window_ratio=3, +# similarity_window_ratio=1, +# PB_filtering_parameter=0.08) +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,4,2) +# +# +#textstr = out2[-1] +# +# +## these are matplotlib.patch.Patch properties +#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +## place a text box in upper left in axes coords +#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +# verticalalignment='top', bbox=props) +#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) +# + +###################### TGV_PD ######################################### +# Quick 2D denoising example in Matlab: +# Im = double(imread('lena_gray_256.tif'))/255; % loading image +# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise +# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); + + +#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05, +# first_order_term=1.3, +# second_order_term=1, +# number_of_iterations=550) +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,4,2) +# +# +#textstr = out2[-1] +# +# +## these are matplotlib.patch.Patch properties +#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) +## place a text box in upper left in axes coords +#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +# verticalalignment='top', bbox=props) +#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py deleted file mode 100644 index 76b9de7..0000000 --- a/Wrappers/Python/demo/test_cpu_regularizers.py +++ /dev/null @@ -1,488 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Aug 4 11:10:05 2017 - -@author: ofn77899 -""" - - -import matplotlib.pyplot as plt -import numpy as np -import os -from enum import Enum -import timeit -from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV, LLT_model, PatchBased_Regul, TGV_PD -from ccpi.filters.regularizers import ROF_TV, FGP_TV -############################################################################### -#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956 -#NRMSE a normalization of the root of the mean squared error -#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum -# intensity from the two images being compared, and respectively the same for -# minval. RMSE is given by the square root of MSE: -# sqrt[(sum(A - B) ** 2) / |A|], -# where |A| means the number of elements in A. By doing this, the maximum value -# given by RMSE is maxval. - -def nrmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b)) - max_val = max(np.max(im1), np.max(im2)) - min_val = min(np.min(im1), np.min(im2)) - return 1 - (rmse / (max_val - min_val)) - -def rmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) - return 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)) - else: - txt += "{0} = {1}".format(key, value) - txt += '\n' - return txt -############################################################################### -# -# 2D Regularizers -# -############################################################################### -#Example: -# figure; -# Im = double(imread('lena_gray_256.tif'))/255; % loading image -# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; -# u = SplitBregman_TV(single(u0), 10, 30, 1e-04); - -# assumes the script is launched from the test directory -filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") -#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif" -#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif" -#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif' - -Im = plt.imread(filename) -Im = np.asarray(Im, dtype='float32') - -Im = Im/255 - -perc = 0.075 -u0 = Im + np.random.normal(loc = Im , - scale = perc * Im , - size = np.shape(Im)) -# map the u0 u0->u0>0 -f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = f(u0).astype('float32') - -## plot -fig = plt.figure() - -a=fig.add_subplot(2,4,1) -a.set_title('noise') -imgplot = plt.imshow(u0,cmap="gray" - ) - -reg_output = [] -############################################################################## -# Call regularizer - -####################### SplitBregman_TV ##################################### -# u = SplitBregman_TV(single(u0), 10, 30, 1e-04); - -start_time = timeit.default_timer() -pars = {'algorithm' : SplitBregman_TV , \ - 'input' : u0, - 'regularization_parameter':15. , \ - 'number_of_iterations' :40 ,\ - 'tolerance_constant':0.0001 , \ - 'TV_penalty': 0 -} - -out = SplitBregman_TV (pars['input'], pars['regularization_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['TV_penalty']) -splitbregman = out[0] -rms = rmse(Im, splitbregman) -pars['rmse'] = rms -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - -a=fig.add_subplot(2,4,2) - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(splitbregman,\ - cmap="gray" - ) - -###################### FGP_TV ######################################### - -start_time = timeit.default_timer() -pars = {'algorithm' : FGP_TV , \ - 'input' : u0,\ - 'regularization_parameter':0.07, \ - 'number_of_iterations' :300 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -fgp = FGP_TV(pars['input'], - pars['regularization_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'], 'cpu') - -rms = rmse(Im, fgp) -pars['rmse'] = rms - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - - -a=fig.add_subplot(2,4,3) - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -imgplot = plt.imshow(fgp, \ - cmap="gray" - ) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) - - -###################### LLT_model ######################################### -start_time = timeit.default_timer() - -pars = {'algorithm': LLT_model , \ - 'input' : u0, - 'regularization_parameter': 5,\ - 'time_step':0.00035, \ - 'number_of_iterations' :350,\ - 'tolerance_constant':0.0001,\ - 'restrictive_Z_smoothing': 0 -} -out = LLT_model(pars['input'], - pars['regularization_parameter'], - pars['time_step'] , - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['restrictive_Z_smoothing'] ) - -llt = out[0] -rms = rmse(Im, out[0]) -pars['rmse'] = rms - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -a=fig.add_subplot(2,4,4) - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(llt,\ - cmap="gray" - ) -# ###################### PatchBased_Regul ######################################### -# # Quick 2D denoising example in Matlab: -# # Im = double(imread('lena_gray_256.tif'))/255; % loading image -# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise -# # ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); - -start_time = timeit.default_timer() - -pars = {'algorithm': PatchBased_Regul , \ - 'input' : u0, - 'regularization_parameter': 0.05,\ - 'searching_window_ratio':3, \ - 'similarity_window_ratio':1,\ - 'PB_filtering_parameter': 0.06 -} -out = PatchBased_Regul(pars['input'], - pars['regularization_parameter'], - pars['searching_window_ratio'] , - pars['similarity_window_ratio'] , - pars['PB_filtering_parameter']) -pbr = out[0] -rms = rmse(Im, out[0]) -pars['rmse'] = rms - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - -a=fig.add_subplot(2,4,5) - - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(pbr ,cmap="gray") - - -# ###################### TGV_PD ######################################### -# # Quick 2D denoising example in Matlab: -# # Im = double(imread('lena_gray_256.tif'))/255; % loading image -# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise -# # u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); - -start_time = timeit.default_timer() - -pars = {'algorithm': TGV_PD , \ - 'input' : u0,\ - 'regularization_parameter':0.07,\ - 'first_order_term': 1.3,\ - 'second_order_term': 1, \ - 'number_of_iterations': 550 - } -out = TGV_PD(pars['input'], - pars['regularization_parameter'], - pars['first_order_term'] , - pars['second_order_term'] , - pars['number_of_iterations']) -tgv = out[0] -rms = rmse(Im, out[0]) -pars['rmse'] = rms - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -a=fig.add_subplot(2,4,6) - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(tgv, cmap="gray") - -# ###################### ROF_TV ######################################### - -start_time = timeit.default_timer() - -pars = {'algorithm': ROF_TV , \ - 'input' : u0,\ - 'regularization_parameter':0.07,\ - 'marching_step': 0.0025,\ - 'number_of_iterations': 300 - } -rof = ROF_TV(pars['input'], - pars['regularization_parameter'], - pars['number_of_iterations'], - pars['marching_step'], 'cpu') - -rms = rmse(Im, rof) -pars['rmse'] = rms - -txtstr = printParametersToString(pars) -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -a=fig.add_subplot(2,4,7) - -# these are matplotlib.patch.Patch properties -props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -# place a text box in upper left in axes coords -a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(rof, cmap="gray") - -plt.show() - -################################################################################ -## -## 3D Regularizers -## -################################################################################ -##Example: -## figure; -## Im = double(imread('lena_gray_256.tif'))/255; % loading image -## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; -## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); -# -##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha" -#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha" -# -#reader = vtk.vtkMetaImageReader() -#reader.SetFileName(os.path.normpath(filename)) -#reader.Update() -##vtk returns 3D images, let's take just the one slice there is as 2D -#Im = Converter.vtk2numpy(reader.GetOutput()) -#Im = Im.astype('float32') -##imgplot = plt.imshow(Im) -#perc = 0.05 -#u0 = Im + (perc* np.random.normal(size=np.shape(Im))) -## map the u0 u0->u0>0 -#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -#u0 = f(u0).astype('float32') -#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(), -# reader.GetOutput().GetOrigin()) -#converter.Update() -#writer = vtk.vtkMetaImageWriter() -#writer.SetInputData(converter.GetOutput()) -#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha") -##writer.Write() -# -# -### plot -#fig3D = plt.figure() -##a=fig.add_subplot(3,3,1) -##a.set_title('Original') -##imgplot = plt.imshow(Im) -#sliceNo = 32 -# -#a=fig3D.add_subplot(2,4,1) -#a.set_title('noise') -#imgplot = plt.imshow(u0.T[sliceNo]) -# -#reg_output3d = [] -# -############################################################################### -## Call regularizer -# -######################## SplitBregman_TV ##################################### -## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); -# -##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) -# -##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30, -## #tolerance_constant=1e-4, -## TV_Penalty=Regularizer.TotalVariationPenalty.l1) -# -#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30, -# tolerance_constant=1e-4, -# TV_Penalty=Regularizer.TotalVariationPenalty.l1) -# -# -#pars = out2[-2] -#reg_output3d.append(out2) -# -#a=fig3D.add_subplot(2,4,2) -# -# -#textstr = out2[-1] -# -# -## these are matplotlib.patch.Patch properties -#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -## place a text box in upper left in axes coords -#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, -# verticalalignment='top', bbox=props) -#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) -# -####################### FGP_TV ######################################### -## u = FGP_TV(single(u0), 0.05, 100, 1e-04); -#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005, -# number_of_iterations=200) -#pars = out2[-2] -#reg_output3d.append(out2) -# -#a=fig3D.add_subplot(2,4,2) -# -# -#textstr = out2[-1] -# -# -## these are matplotlib.patch.Patch properties -#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -## place a text box in upper left in axes coords -#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, -# verticalalignment='top', bbox=props) -#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) -# -####################### LLT_model ######################################### -## * u0 = Im + .03*randn(size(Im)); % adding noise -## [Den] = LLT_model(single(u0), 10, 0.1, 1); -##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); -##input, regularization_parameter , time_step, number_of_iterations, -## tolerance_constant, restrictive_Z_smoothing=0 -#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25, -# time_step=0.0003, -# tolerance_constant=0.0001, -# number_of_iterations=300) -#pars = out2[-2] -#reg_output3d.append(out2) -# -#a=fig3D.add_subplot(2,4,2) -# -# -#textstr = out2[-1] -# -# -## these are matplotlib.patch.Patch properties -#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -## place a text box in upper left in axes coords -#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, -# verticalalignment='top', bbox=props) -#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) -# -####################### PatchBased_Regul ######################################### -## Quick 2D denoising example in Matlab: -## Im = double(imread('lena_gray_256.tif'))/255; % loading image -## u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise -## ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); -# -#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05, -# searching_window_ratio=3, -# similarity_window_ratio=1, -# PB_filtering_parameter=0.08) -#pars = out2[-2] -#reg_output3d.append(out2) -# -#a=fig3D.add_subplot(2,4,2) -# -# -#textstr = out2[-1] -# -# -## these are matplotlib.patch.Patch properties -#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -## place a text box in upper left in axes coords -#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, -# verticalalignment='top', bbox=props) -#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) -# - -###################### TGV_PD ######################################### -# Quick 2D denoising example in Matlab: -# Im = double(imread('lena_gray_256.tif'))/255; % loading image -# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise -# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); - - -#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05, -# first_order_term=1.3, -# second_order_term=1, -# number_of_iterations=550) -#pars = out2[-2] -#reg_output3d.append(out2) -# -#a=fig3D.add_subplot(2,4,2) -# -# -#textstr = out2[-1] -# -# -## these are matplotlib.patch.Patch properties -#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) -## place a text box in upper left in axes coords -#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, -# verticalalignment='top', bbox=props) -#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo]) -- cgit v1.2.3