tol work

3 files changed, 75 insertions, 15 deletions

diff --git a/demos/SoftwareX_supp/Demo_VolumeDenoise.py b/demos/SoftwareX_supp/Demo_VolumeDenoise.py
index 2387e94..17cdf4d 100644
--- a/demos/SoftwareX_supp/Demo_VolumeDenoise.py
+++ b/demos/SoftwareX_supp/Demo_VolumeDenoise.py
@@ -60,13 +60,14 @@ plt.title('3D Phantom, sagittal view')
 plt.show()
 #%%
 print ("____________________Applying regularisers_______________________")
+print ("________________________________________________________________")
 
 print ("#############ROF TV CPU####################")
 # set parameters
 pars = {'algorithm': ROF_TV, \
         'input' : phantom_noise,\
         'regularisation_parameter':0.04,\
-        'number_of_iterations': 100,\
+        'number_of_iterations': 600,\
         'time_marching_parameter': 0.0025
         }
 
@@ -116,6 +117,65 @@ win2d = win * (win.T)
 ssim_rof = Qtools.ssim(win2d)
 
 print("ROF-TV (gpu) ____ RMSE: {}, MMSIM: {}, run time: {} sec".format(RMSE_rof,ssim_rof[0],Run_time_rof))
+#%%
+print ("#############FGP TV CPU####################")
+# set parameters
+pars = {'algorithm':FGP_TV, \
+        'input' : phantom_noise,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 300,\
+        'time_marching_parameter': 0.0025,\
+        'tolerance_constant':1e-05,\
+        }
+
+tic=timeit.default_timer()
+fgp_cpu3D = FGP_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+toc=timeit.default_timer()
+
+Run_time_fgp = toc - tic
+Qtools = QualityTools(phantom_tm, fgp_cpu3D)
+RMSE_rof = Qtools.rmse()
+
+# SSIM measure
+Qtools = QualityTools(phantom_tm[128,:,:]*255, fgp_cpu3D[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_fgp = Qtools.ssim(win2d)
+
+print("FGP-TV (cpu) ____ RMSE: {}, MMSIM: {}, run time: {} sec".format(RMSE_rof,ssim_fgp[0],Run_time_fgp))
+#%%
+print ("#############FGP TV GPU####################")
+# set parameters
+pars = {'algorithm':FGP_TV, \
+        'input' : phantom_noise,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 300,\
+        'time_marching_parameter': 0.0025,\
+        'tolerance_constant':1e-05,\
+        }
 
+tic=timeit.default_timer()
+fgp_gpu3D = FGP_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'gpu')
+toc=timeit.default_timer()
+
+Run_time_fgp = toc - tic
+Qtools = QualityTools(phantom_tm, fgp_gpu3D)
+RMSE_rof = Qtools.rmse()
+
+# SSIM measure
+Qtools = QualityTools(phantom_tm[128,:,:]*255, fgp_gpu3D[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_fgp = Qtools.ssim(win2d)
+
+print("FGP-TV (gpu) ____ RMSE: {}, MMSIM: {}, run time: {} sec".format(RMSE_rof,ssim_fgp[0],Run_time_fgp))
 #%%
 
+
+
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m
index 5e92ee1..fa81f6d 100644
--- a/demos/demoMatlab_denoise.m
+++ b/demos/demoMatlab_denoise.m
@@ -2,9 +2,11 @@
 clear; close all
 fsep = '/';
 
-Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
-Path2 = sprintf([ data' fsep], 1i);
-Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i);
+%Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
+Path1 = ('/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/src/Matlab/mex_compile/installed');
+Path2 = sprintf(['data' fsep], 1i);
+%Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i);
+Path3 = '/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/src/Matlab/supp';
 addpath(Path1);
 addpath(Path2);
 addpath(Path3);
@@ -34,8 +36,8 @@ figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 %%
 fprintf('Denoise using the FGP-TV model (CPU) \n');
 lambda_reg = 0.033;
-iter_fgp = 300; % number of FGP iterations
-epsil_tol =  1.0e-09; % tolerance
+iter_fgp = 200; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
 tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
 energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value
 rmseFGP = (RMSE(u_fgp(:),Im(:)));
diff --git a/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py
index d34607a..e58ed2f 100644
--- a/demos/demo_cpu_regularisers.py
+++ b/demos/demo_cpu_regularisers.py
@@ -30,8 +30,9 @@ def printParametersToString(pars):
             txt += '\n'
         return txt
 ###############################################################################
-#%%
-filename = os.path.join( "data" ,"lena_gray_512.tif")
+
+# filename = os.path.join( "data" ,"lena_gray_512.tif")
+filename = "/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/test/lena_gray_512.tif"
 
 # read image
 Im = plt.imread(filename)
@@ -127,23 +128,20 @@ imgplot = plt.imshow(u0,cmap="gray")
 pars = {'algorithm' : FGP_TV, \
         'input' : u0,\
         'regularisation_parameter':0.04, \
-        'number_of_iterations' :2000 ,\
+        'number_of_iterations' :200 ,\
         'tolerance_constant':1e-06,\
         'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
+        'nonneg': 0
         }
         
 print ("#############FGP TV CPU####################")
 start_time = timeit.default_timer()
-fgp_cpu = FGP_TV(pars['input'], 
+fgp_cpu,info_vec = FGP_TV(pars['input'], 
               pars['regularisation_parameter'],
               pars['number_of_iterations'],
               pars['tolerance_constant'], 
               pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')  
-             
+              pars['nonneg'],'cpu')
 
 Qtools = QualityTools(Im, fgp_cpu)
 pars['rmse'] = Qtools.rmse()