diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 14 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 4 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt | 0 | ||||
| -rw-r--r-- | Wrappers/Python/demo/test_cpu_regularisers.py | 323 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 243 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py (renamed from Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py) | 20 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 247 | ||||
| -rw-r--r-- | Wrappers/Python/demos/qualitymetrics.py (renamed from Wrappers/Python/test/metrics.py) | 4 | ||||
| -rw-r--r-- | Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc | bin | 0 -> 823 bytes | |||
| -rw-r--r-- | Wrappers/Python/test/test_cpu_regularisers.py | 442 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_gpu_regularisers.py | 143 | ||||
| -rw-r--r-- | Wrappers/Python/test/test_regularisers_3d.py | 425 | ||||
| -rw-r--r-- | Wrappers/Python/test/view_result.py | 12 |
13 files changed, 509 insertions, 1368 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index f5c3ad1..71082e7 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -3,10 +3,10 @@ addpath('../mex_compile/installed'); addpath('../../../data/'); -N = 256; +N = 512; slices = 30; vol3D = zeros(N,N,slices, 'single'); -Im = double(imread('lena_gray_256.tif'))/255; % loading image +Im = double(imread('lena_gray_512.tif'))/255; % loading image for i = 1:slices vol3D(:,:,i) = Im + .05*randn(size(Im)); end @@ -17,28 +17,28 @@ figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image'); fprintf('Denoise using ROF-TV model (CPU) \n'); lambda_rof = 0.03; % regularisation parameter tau_rof = 0.0025; % time-marching constant -iter_rof = 1000; % number of ROF iterations +iter_rof = 300; % number of ROF iterations tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)'); %% % fprintf('Denoise using ROF-TV model (GPU) \n'); % lambda_rof = 0.03; % regularisation parameter % tau_rof = 0.0025; % time-marching constant -% iter_rof = 1000; % number of ROF iterations +% iter_rof = 300; % number of ROF iterations % tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; % figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)'); %% fprintf('Denoise using FGP-TV model (CPU) \n'); lambda_fgp = 0.03; % regularisation parameter -iter_fgp = 500; % number of FGP iterations +iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)'); %% % fprintf('Denoise using FGP-TV model (GPU) \n'); % lambda_fgp = 0.03; % regularisation parameter -% iter_fgp = 500; % number of FGP iterations +% iter_fgp = 300; % number of FGP iterations % epsil_tol = 1.0e-05; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)'); -%%
\ No newline at end of file +%% diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index ab4e95d..7f87fbb 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -3,7 +3,7 @@ addpath('../mex_compile/installed'); addpath('../../../data/'); -Im = double(imread('lena_gray_256.tif'))/255; % loading image +Im = double(imread('lena_gray_512.tif'))/255; % loading image u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; figure; imshow(u0, [0 1]); title('Noisy image'); @@ -35,4 +35,4 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); % epsil_tol = 1.0e-05; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)'); -%%
\ No newline at end of file +%% diff --git a/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt b/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt diff --git a/Wrappers/Python/demo/test_cpu_regularisers.py b/Wrappers/Python/demo/test_cpu_regularisers.py deleted file mode 100644 index 4e4a2dd..0000000 --- a/Wrappers/Python/demo/test_cpu_regularisers.py +++ /dev/null @@ -1,323 +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.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/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py new file mode 100644 index 0000000..84d86c0 --- /dev/null +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -0,0 +1,243 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 22 11:39:43 2018 + +Demonstration of CPU regularisers + +@authors: Daniil Kazantsev, Edoardo Pasca +""" + +import matplotlib.pyplot as plt +import numpy as np +import os +import timeit +from ccpi.filters.regularisers import ROF_TV, FGP_TV +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)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### + +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") + +# read image +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +Im = Im/255 +perc = 0.05 +u0 = Im + np.random.normal(loc = 0 , + scale = perc * Im , + size = np.shape(Im)) +# map the u0 u0->u0>0 +# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +u0 = u0.astype('float32') + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________ROF-TV (2D)_________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(1) +plt.suptitle('Performance of ROF-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm': ROF_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04,\ + 'number_of_iterations': 1200,\ + 'time_marching_parameter': 0.0025 + } +print ("#############ROF TV CPU####################") +start_time = timeit.default_timer() +rof_cpu = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'],'cpu') +rms = rmse(Im, rof_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(rof_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________FGP-TV (2D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(2) +plt.suptitle('Performance of FGP-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :1200 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP TV CPU####################") +start_time = timeit.default_timer() +fgp_cpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'cpu') + + +rms = rmse(Im, fgp_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(fgp_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + +# Uncomment to test 3D regularisation performance +""" +N = 512 +slices = 20 + +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +Im = Im/255 +perc = 0.05 + +noisyVol = np.zeros((N,N,slices),dtype='float32') +idealVol = np.zeros((N,N,slices),dtype='float32') + +for i in range (slices): + noisyVol[:,:,i] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + idealVol[:,:,i] = Im + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________ROF-TV (3D)_________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of ROF-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy 15th slice of a volume') +imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") + +# set parameters +pars = {'algorithm': ROF_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04,\ + 'number_of_iterations': 500,\ + 'time_marching_parameter': 0.0025 + } +print ("#############ROF TV CPU####################") +start_time = timeit.default_timer() +rof_cpu3D = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'],'cpu') +rms = rmse(idealVol, rof_cpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(rof_cpu3D[:,:,10], cmap="gray") +plt.title('{}'.format('Recovered volume on the CPU using ROF-TV')) + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________FGP-TV (3D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(4) +plt.suptitle('Performance of FGP-TV regulariser using the CPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP TV CPU####################") +start_time = timeit.default_timer() +fgp_cpu3D = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'cpu') + + +rms = rmse(idealVol, fgp_cpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(fgp_cpu3D[:,:,10], cmap="gray") +plt.title('{}'.format('Recovered volume on the CPU using FGP-TV')) +"""
\ No newline at end of file diff --git a/Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py index 15e9042..cfe2e7d 100644 --- a/Wrappers/Python/test/test_cpu_vs_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py @@ -3,9 +3,9 @@ """ Created on Thu Feb 22 11:39:43 2018 -Testing CPU implementation against the GPU one +Demonstration of CPU implementation against the GPU one -@author: Daniil Kazantsev +@authors: Daniil Kazantsev, Edoardo Pasca """ import matplotlib.pyplot as plt @@ -13,7 +13,7 @@ import numpy as np import os import timeit from ccpi.filters.regularisers import ROF_TV, FGP_TV - +from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): txt = r'' @@ -27,11 +27,7 @@ def printParametersToString(pars): txt += '\n' return txt ############################################################################### -def rmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) - return rmse - + filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") # read image @@ -39,13 +35,13 @@ Im = plt.imread(filename) Im = np.asarray(Im, dtype='float32') Im = Im/255 -perc = 0.075 -u0 = Im + np.random.normal(loc = Im , +perc = 0.05 +u0 = Im + np.random.normal(loc = 0 , 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') +# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +u0 = u0.astype('float32') print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py new file mode 100644 index 0000000..fd7b32c --- /dev/null +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -0,0 +1,247 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 22 11:39:43 2018 + +Demonstration of GPU regularisers + +@authors: Daniil Kazantsev, Edoardo Pasca +""" + +import matplotlib.pyplot as plt +import numpy as np +import os +import timeit +from ccpi.filters.regularisers import ROF_TV, FGP_TV +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)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### + +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") + +# read image +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +Im = Im/255 +perc = 0.05 +u0 = Im + np.random.normal(loc = 0 , + scale = perc * Im , + size = np.shape(Im)) +# map the u0 u0->u0>0 +# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +u0 = u0.astype('float32') + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________ROF-TV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(1) +plt.suptitle('Performance of the ROF-TV regulariser using the GPU') +a=fig.add_subplot(1,4,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm': ROF_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04,\ + 'number_of_iterations': 1200,\ + 'time_marching_parameter': 0.0025 + } +print ("##############ROF TV GPU##################") +start_time = timeit.default_timer() +rof_gpu = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'],'gpu') + +rms = rmse(Im, rof_gpu) +pars['rmse'] = rms +pars['algorithm'] = ROF_TV +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,3) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(rof_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________FGP-TV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(2) +plt.suptitle('Performance of the FGP-TV regulariser using the GPU') +a=fig.add_subplot(1,4,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_TV, \ + 'input' : u0,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :1200 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("##############FGP TV GPU##################") +start_time = timeit.default_timer() +fgp_gpu = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'gpu') + +rms = rmse(Im, fgp_gpu) +pars['rmse'] = rms +pars['algorithm'] = FGP_TV +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,3) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(fgp_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + + +# Uncomment to test 3D regularisation performance +""" +N = 512 +slices = 20 + +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +Im = Im/255 +perc = 0.05 + +noisyVol = np.zeros((N,N,slices),dtype='float32') +idealVol = np.zeros((N,N,slices),dtype='float32') + +for i in range (slices): + noisyVol[:,:,i] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + idealVol[:,:,i] = Im + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________ROF-TV (3D)_________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of ROF-TV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy 15th slice of a volume') +imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") + +# set parameters +pars = {'algorithm': ROF_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04,\ + 'number_of_iterations': 500,\ + 'time_marching_parameter': 0.0025 + } +print ("#############ROF TV CPU####################") +start_time = timeit.default_timer() +rof_gpu3D = ROF_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['time_marching_parameter'],'gpu') +rms = rmse(idealVol, rof_gpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(rof_gpu3D[:,:,10], cmap="gray") +plt.title('{}'.format('Recovered volume on the GPU using ROF-TV')) + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________FGP-TV (3D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(4) +plt.suptitle('Performance of FGP-TV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(noisyVol[:,:,10],cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_TV, \ + 'input' : noisyVol,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP TV CPU####################") +start_time = timeit.default_timer() +fgp_gpu3D = FGP_TV(pars['input'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'gpu') + + +rms = rmse(idealVol, fgp_gpu3D) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,2,2) + +# these are matplotlib.patch.Patch properties +props = dict(boxstyle='round', facecolor='wheat', alpha=0.75) +# place a text box in upper left in axes coords +a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(fgp_gpu3D[:,:,10], cmap="gray") +plt.title('{}'.format('Recovered volume on the GPU using FGP-TV')) + +""" + + diff --git a/Wrappers/Python/test/metrics.py b/Wrappers/Python/demos/qualitymetrics.py index 53f68fb..32fa479 100644 --- a/Wrappers/Python/test/metrics.py +++ b/Wrappers/Python/demos/qualitymetrics.py @@ -3,7 +3,7 @@ """ Created on Wed Feb 21 13:34:32 2018 # quality metrics -@author: algol +@authors: Daniil Kazantsev, Edoardo Pasca """ import numpy as np @@ -17,4 +17,4 @@ def nrmse(im1, im2): def rmse(im1, im2): a, b = im1.shape rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) - return rmse
\ No newline at end of file + return rmse diff --git a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc b/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc Binary files differnew file mode 100644 index 0000000..2196a53 --- /dev/null +++ b/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc diff --git a/Wrappers/Python/test/test_cpu_regularisers.py b/Wrappers/Python/test/test_cpu_regularisers.py deleted file mode 100644 index 9713baa..0000000 --- a/Wrappers/Python/test/test_cpu_regularisers.py +++ /dev/null @@ -1,442 +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 , FGP_TV ,\ - LLT_model, PatchBased_Regul ,\ - TGV_PD - -############################################################################### -#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 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') - -perc = 0.15 -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(2,3,1) -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() -pars = {'algorithm' : SplitBregman_TV , \ - 'input' : u0, - 'regularization_parameter':10. , \ -'number_of_iterations' :35 ,\ -'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] -txtstr = printParametersToString(pars) -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(splitbregman,\ - #cmap="gray" - ) - -###################### FGP_TV ######################################### -# u = FGP_TV(single(u0), 0.05, 100, 1e-04); -start_time = timeit.default_timer() -pars = {'algorithm' : FGP_TV , \ - 'input' : u0, - 'regularization_parameter':5e-4, \ - 'number_of_iterations' :10 ,\ - 'tolerance_constant':0.001,\ - 'TV_penalty': 0 -} - -out = FGP_TV (pars['input'], - pars['regularization_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['TV_penalty']) - -fgp = out[0] -txtstr = printParametersToString(pars) -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(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': 25,\ - 'time_step':0.0003, \ - 'number_of_iterations' :300,\ - 'tolerance_constant':0.001,\ - '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] -txtstr = printParametersToString(pars) -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(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': 0.08 -} -out = PatchBased_Regul(pars['input'], - pars['regularization_parameter'], - pars['searching_window_ratio'] , - pars['similarity_window_ratio'] , - pars['PB_filtering_parameter']) -pbr = out[0] -txtstr = printParametersToString(pars) -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(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':0.05,\ - '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] -txtstr = printParametersToString(pars) -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(tgv #, 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]) diff --git a/Wrappers/Python/test/test_gpu_regularisers.py b/Wrappers/Python/test/test_gpu_regularisers.py deleted file mode 100644 index 2103c0e..0000000 --- a/Wrappers/Python/test/test_gpu_regularisers.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue Jan 30 10:24:26 2018 - -@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 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 -############################################################################### -def rmse(im1, im2): - a, b = im1.shape - rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(a * b)) - return rmse - -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") - - - -## Rudin-Osher-Fatemi (ROF) TV regularisation -start_time = timeit.default_timer() -pars = { -'algorithm' : ROF_TV , \ - 'input' : u0, - 'regularisation_parameter': 0.04,\ - 'number_of_iterations':300,\ - 'time_marching_parameter': 0.0025 - - } - -rof_tv = TV_ROF_GPU(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['time_marching_parameter'],'gpu') - -rms = rmse(Im, rof_tv) -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=0.75) -# 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(rof_tv, cmap="gray") - -a=fig.add_subplot(2,4,8) - -# 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, 'rof_tv - u0', transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -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 regularisation -""" -start_time = timeit.default_timer() - -pars = {'algorithm' : FGP_TV, \ - 'input' : u0,\ - 'regularisation_parameter':0.04, \ - 'number_of_iterations' :1200 ,\ - 'tolerance_constant':0.00001,\ - 'methodTV': 0 ,\ - 'nonneg': 0 ,\ - 'printingOut': 0 - } - -fgp_gpu = FGP_TV(pars['input'], - pars['regularisation_parameter'], - pars['number_of_iterations'], - pars['tolerance_constant'], - pars['methodTV'], - pars['nonneg'], - pars['printingOut'],'gpu') - -rms = rmse(Im, fgp_gpu) -pars['rmse'] = rms -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=0.75) -# 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(fgp_gpu, cmap="gray") - -a=fig.add_subplot(2,4,8) - -# 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, 'fgp_gpu - u0', transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow((fgp_gpu - u0)**2, vmin=0, vmax=0.03, cmap="gray") -plt.colorbar(ticks=[0, 0.03], orientation='vertical') -plt.show() -""" diff --git a/Wrappers/Python/test/test_regularisers_3d.py b/Wrappers/Python/test/test_regularisers_3d.py deleted file mode 100644 index 2d11a7e..0000000 --- a/Wrappers/Python/test/test_regularisers_3d.py +++ /dev/null @@ -1,425 +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.imaging.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') - -# create a 3D image by stacking N of this images - - -#imgplot = plt.imshow(Im) -perc = 0.05 -u_n = Im + (perc* np.random.normal(size=np.shape(Im))) -y,z = np.shape(u_n) -u_n = np.reshape(u_n , (1,y,z)) - -u0 = u_n.copy() -for i in range (19): - u_n = Im + (perc* np.random.normal(size=np.shape(Im))) - u_n = np.reshape(u_n , (1,y,z)) - - u0 = np.vstack ( (u0, u_n) ) - -# map the u0 u0->u0>0 -f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) -u0 = f(u0).astype('float32') - -print ("Passed image shape {0}".format(np.shape(u0))) - -## plot -fig = plt.figure() -#a=fig.add_subplot(3,3,1) -#a.set_title('Original') -#imgplot = plt.imshow(Im) -sliceno = 10 - -a=fig.add_subplot(2,3,1) -a.set_title('noise') -imgplot = plt.imshow(u0[sliceno],cmap="gray") - -reg_output = [] -############################################################################## -# Call regularizer - -####################### SplitBregman_TV ##################################### -# u = SplitBregman_TV(single(u0), 10, 30, 1e-04); - -use_object = True -if use_object: - 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() [0] - pars = reg.pars - textstr = reg.printParametersToString() - - #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) - -else: - out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. ) - pars = out2[2] - reg_output.append(out2) - plotme = reg_output[-1][0] - textstr = out2[-1] - -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, textstr, transform=a.transAxes, fontsize=14, - verticalalignment='top', bbox=props) -imgplot = plt.imshow(plotme[sliceno],cmap="gray") - -###################### FGP_TV ######################################### -# u = FGP_TV(single(u0), 0.05, 100, 1e-04); -out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005, - number_of_iterations=50) -pars = out2[-2] - -reg_output.append(out2) - -a=fig.add_subplot(2,3,3) - -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_output[-1][0][sliceno]) -# 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_output[-1][0][sliceno],cmap="gray") - -###################### 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_output.append(out2) - -a=fig.add_subplot(2,3,4) - -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_output[-1][0][sliceno],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); - -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_output.append(out2) - -a=fig.add_subplot(2,3,5) - - -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_output[-1][0][sliceno],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); - - -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_output.append(out2) - -a=fig.add_subplot(2,3,6) - - -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_output[-1][0][sliceno],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]) diff --git a/Wrappers/Python/test/view_result.py b/Wrappers/Python/test/view_result.py deleted file mode 100644 index f89a90c..0000000 --- a/Wrappers/Python/test/view_result.py +++ /dev/null @@ -1,12 +0,0 @@ -import numpy -from ccpi.viewer.CILViewer2D import * -import sys -#reader = vtk.vtkMetaImageReader() -#reader.SetFileName("X_out_os_s.mhd") -#reader.Update() - -X = numpy.load(sys.argv[1]) - -v = CILViewer2D() -v.setInputAsNumpy(X) -v.startRenderLoop() |