diff options
Diffstat (limited to 'Wrappers/Python/test')
| -rw-r--r-- | Wrappers/Python/test/test_cpu_regularisers.py (renamed from Wrappers/Python/test/test_cpu_regularizers.py) | 0 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py (renamed from Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py) | 18 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_gpu_regularisers.py (renamed from Wrappers/Python/test/test_gpu_regularizers.py) | 110 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_regularisers_3d.py (renamed from Wrappers/Python/test/test_regularizers_3d.py) | 0 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_regularizers.py | 395 |
5 files changed, 16 insertions, 507 deletions
diff --git a/Wrappers/Python/test/test_cpu_regularizers.py b/Wrappers/Python/test/test_cpu_regularisers.py index 9713baa..9713baa 100644 --- a/Wrappers/Python/test/test_cpu_regularizers.py +++ b/Wrappers/Python/test/test_cpu_regularisers.py diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py b/Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py index 63be1a0..15e9042 100644 --- a/Wrappers/Python/test/test_cpu_vs_gpu_regularizers.py +++ b/Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularizers import ROF_TV, FGP_TV +from ccpi.filters.regularisers import ROF_TV, FGP_TV ############################################################################### def printParametersToString(pars): @@ -54,7 +54,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(1) -plt.suptitle('Comparison of ROF-TV regularizer using CPU and GPU implementations') +plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") @@ -62,14 +62,14 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm': ROF_TV, \ 'input' : u0,\ - 'regularization_parameter':0.04,\ + 'regularisation_parameter':0.04,\ 'number_of_iterations': 1200,\ 'time_marching_parameter': 0.0025 } print ("#############ROF TV CPU####################") start_time = timeit.default_timer() rof_cpu = ROF_TV(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'],'cpu') rms = rmse(Im, rof_cpu) @@ -92,7 +92,7 @@ plt.title('{}'.format('CPU results')) print ("##############ROF TV GPU##################") start_time = timeit.default_timer() rof_gpu = ROF_TV(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'],'gpu') @@ -132,7 +132,7 @@ print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot fig = plt.figure(2) -plt.suptitle('Comparison of FGP-TV regularizer using CPU and GPU implementations') +plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations') a=fig.add_subplot(1,4,1) a.set_title('Noisy Image') imgplot = plt.imshow(u0,cmap="gray") @@ -140,7 +140,7 @@ imgplot = plt.imshow(u0,cmap="gray") # set parameters pars = {'algorithm' : FGP_TV, \ 'input' : u0,\ - 'regularization_parameter':0.04, \ + 'regularisation_parameter':0.04, \ 'number_of_iterations' :1200 ,\ 'tolerance_constant':0.00001,\ 'methodTV': 0 ,\ @@ -151,7 +151,7 @@ pars = {'algorithm' : FGP_TV, \ print ("#############FGP TV CPU####################") start_time = timeit.default_timer() fgp_cpu = FGP_TV(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['methodTV'], @@ -179,7 +179,7 @@ plt.title('{}'.format('CPU results')) print ("##############FGP TV GPU##################") start_time = timeit.default_timer() fgp_gpu = FGP_TV(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['methodTV'], diff --git a/Wrappers/Python/test/test_gpu_regularizers.py b/Wrappers/Python/test/test_gpu_regularisers.py index 640b3f9..2103c0e 100644 --- a/Wrappers/Python/test/test_gpu_regularizers.py +++ b/Wrappers/Python/test/test_gpu_regularisers.py @@ -11,8 +11,7 @@ import numpy as np import os from enum import Enum import timeit -from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML -from ccpi.filters.regularizers import ROF_TV, FGP_TV +from ccpi.filters.regularisers import ROF_TV, FGP_TV ############################################################################### def printParametersToString(pars): txt = r'' @@ -32,9 +31,6 @@ def rmse(im1, im2): return rmse 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') @@ -56,112 +52,20 @@ a.set_title('noise') imgplot = plt.imshow(u0,cmap="gray") -## Diff4thHajiaboli -start_time = timeit.default_timer() -pars = { -'algorithm' : Diff4thHajiaboli , \ - 'input' : u0, - 'edge_preserv_parameter':0.1 , \ -'number_of_iterations' :250 ,\ -'time_marching_parameter':0.03 ,\ -'regularization_parameter':0.7 -} - - -d4h = Diff4thHajiaboli(pars['input'], - pars['edge_preserv_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'], - pars['regularization_parameter']) -rms = rmse(Im, d4h) -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=0.75) -# place a text box in upper left in axes coords -a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(d4h, cmap="gray") - -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, 'd4h - u0', transform=a.transAxes, fontsize=12, - verticalalignment='top', bbox=props) -imgplot = plt.imshow((d4h - u0)**2, vmin=0, vmax=0.03, cmap="gray") -plt.colorbar(ticks=[0, 0.03], orientation='vertical') - - -## Patch Based Regul NML -start_time = timeit.default_timer() -""" -pars = {'algorithm' : NML , \ - 'input' : u0, - 'SearchW_real':3 , \ -'SimilW' :1,\ -'h':0.05 ,# -'lambda' : 0.08 -} -""" -pars = { -'algorithm' : NML , \ - 'input' : u0, - 'regularization_parameter': 0.01,\ - 'searching_window_ratio':3, \ - 'similarity_window_ratio':1,\ - 'PB_filtering_parameter': 0.2 -} - -nml = NML(pars['input'], - pars['searching_window_ratio'], - pars['similarity_window_ratio'], - pars['PB_filtering_parameter'], - pars['regularization_parameter']) -rms = rmse(Im, nml) -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=0.75) -# place a text box in upper left in axes coords -a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=12, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(nml, cmap="gray") - -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, 'nml - u0', transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow((nml - u0)**2, vmin=0, vmax=0.03, cmap="gray") -plt.colorbar(ticks=[0, 0.03], orientation='vertical') - - -## Rudin-Osher-Fatemi (ROF) TV regularization +## Rudin-Osher-Fatemi (ROF) TV regularisation start_time = timeit.default_timer() pars = { 'algorithm' : ROF_TV , \ 'input' : u0, - 'regularization_parameter': 0.04,\ + 'regularisation_parameter': 0.04,\ 'number_of_iterations':300,\ 'time_marching_parameter': 0.0025 } rof_tv = TV_ROF_GPU(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['time_marching_parameter'],'gpu') @@ -190,13 +94,13 @@ imgplot = plt.imshow((rof_tv - u0)**2, vmin=0, vmax=0.03, cmap="gray") plt.colorbar(ticks=[0, 0.03], orientation='vertical') plt.show() -## Fast-Gradient Projection TV regularization +## Fast-Gradient Projection TV regularisation """ start_time = timeit.default_timer() pars = {'algorithm' : FGP_TV, \ 'input' : u0,\ - 'regularization_parameter':0.04, \ + 'regularisation_parameter':0.04, \ 'number_of_iterations' :1200 ,\ 'tolerance_constant':0.00001,\ 'methodTV': 0 ,\ @@ -205,7 +109,7 @@ pars = {'algorithm' : FGP_TV, \ } fgp_gpu = FGP_TV(pars['input'], - pars['regularization_parameter'], + pars['regularisation_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], pars['methodTV'], diff --git a/Wrappers/Python/test/test_regularizers_3d.py b/Wrappers/Python/test/test_regularisers_3d.py index 2d11a7e..2d11a7e 100644 --- a/Wrappers/Python/test/test_regularizers_3d.py +++ b/Wrappers/Python/test/test_regularisers_3d.py diff --git a/Wrappers/Python/test/test_regularizers.py b/Wrappers/Python/test/test_regularizers.py deleted file mode 100644 index cf5da2b..0000000 --- a/Wrappers/Python/test/test_regularizers.py +++ /dev/null @@ -1,395 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Aug 4 11:10:05 2017 - -@author: ofn77899 -""" - -#from ccpi.viewer.CILViewer2D import Converter -#import vtk - -import matplotlib.pyplot as plt -import numpy as np -import os -from enum import Enum -import timeit -#from PIL import Image -#from Regularizer import Regularizer -from ccpi.filters.Regularizer import Regularizer - -############################################################################### -#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)) -############################################################################### - -############################################################################### -# -# 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); - - -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' - -#reader = vtk.vtkTIFFReader() -#reader.SetFileName(os.path.normpath(filename)) -#reader.Update() -Im = plt.imread(filename) -#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255 -#img.show() -Im = np.asarray(Im, dtype='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') - -## plot -fig = plt.figure() -#a=fig.add_subplot(3,3,1) -#a.set_title('Original') -#imgplot = plt.imshow(Im) - -a=fig.add_subplot(2,3,1) -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() -reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) -print (reg.pars) -reg.setParameter(input=u0) -reg.setParameter(regularization_parameter=10.) -# or -# reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30, - #tolerance_constant=1e-4, - #TV_Penalty=Regularizer.TotalVariationPenalty.l1) -plotme = reg(output_all=True) [0] -pars = reg.pars -txtstr = reg.printParametersToString() -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - - -a=fig.add_subplot(2,3,2) - - -# 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(plotme,cmap="gray") - -###################### FGP_TV ######################################### -# u = FGP_TV(single(u0), 0.05, 100, 1e-04); -start_time = timeit.default_timer() -reg = Regularizer(Regularizer.Algorithm.FGP_TV) -out2 = reg(input=u0, regularization_parameter=5e-4, - number_of_iterations=10) -txtstr = reg.printParametersToString() -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - - -a=fig.add_subplot(2,3,3) - -# 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(out2,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 ######################################### -# * 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 - -del out2 -start_time = timeit.default_timer() -reg = Regularizer(Regularizer.Algorithm.LLT_model) -out2 = reg(input=u0, regularization_parameter=25, - time_step=0.0003, - tolerance_constant=0.001, - number_of_iterations=300) -txtstr = reg.printParametersToString() -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -a=fig.add_subplot(2,3,4) - -# 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(out2,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() -reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul) -out2 = reg(input=u0, regularization_parameter=0.05, - searching_window_ratio=3, - similarity_window_ratio=1, - PB_filtering_parameter=0.08) -txtstr = reg.printParametersToString() -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - -a=fig.add_subplot(2,3,5) - - -# 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(out2,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() -reg = Regularizer(Regularizer.Algorithm.TGV_PD) -out2 = reg(input=u0, regularization_parameter=0.05, - first_order_term=1.3, - second_order_term=1, - number_of_iterations=550) -txtstr = reg.printParametersToString() -txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) -a=fig.add_subplot(2,3,6) - -# 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(out2,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(2,3,1) -#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(2,3,2) -# -# -#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(2,3,2) -# -# -#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(2,3,2) -# -# -#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(2,3,2) -# -# -#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(2,3,2) -# -# -#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]) |