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-rw-r--r--demos/SoftwareX_supp/Demo_VolumeDenoise.py62
-rw-r--r--demos/demoMatlab_denoise.m12
-rw-r--r--demos/demo_cpu_regularisers.py16
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()
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-07 21:53:59 +0000