diff options
Diffstat (limited to 'Wrappers/Python/demos')
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 127 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py | 101 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 123 |
3 files changed, 336 insertions, 15 deletions
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 929f0af..fd3050c 100644 --- a/Wrappers/Python/demos/demo_cpu_regularisers.py +++ b/Wrappers/Python/demos/demo_cpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -39,9 +39,14 @@ perc = 0.05 u0 = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) +u_ref = Im + np.random.normal(loc = 0 , + scale = 0.01 * 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') +u_ref = u_ref.astype('float32') print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -134,9 +139,64 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_cpu, cmap="gray") plt.title('{}'.format('CPU results')) + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_____________FGP-dTV (2D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of FGP-dTV 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_dTV, \ + 'input' : u0,\ + 'refdata' : u_ref,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :2000 ,\ + 'tolerance_constant':1e-06,\ + 'eta_const':0.2,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP dTV CPU####################") +start_time = timeit.default_timer() +fgp_dtv_cpu = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'cpu') + +rms = rmse(Im, fgp_dtv_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_dtv_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + + + # Uncomment to test 3D regularisation performance #%% -""" + N = 512 slices = 20 @@ -148,10 +208,12 @@ Im = Im/255 perc = 0.05 noisyVol = np.zeros((slices,N,N),dtype='float32') +noisyRef = np.zeros((slices,N,N),dtype='float32') idealVol = np.zeros((slices,N,N),dtype='float32') for i in range (slices): noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) idealVol[i,:,:] = Im print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -159,7 +221,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure(4) 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') @@ -199,7 +261,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure(5) plt.suptitle('Performance of FGP-TV regulariser using the CPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -242,5 +304,58 @@ 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 + + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________FGP-dTV (3D)__________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(6) +plt.suptitle('Performance of FGP-dTV 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_dTV, \ + 'input' : noisyVol,\ + 'refdata' : noisyRef,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'eta_const':0.2,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP dTV CPU####################") +start_time = timeit.default_timer() +fgp_dTV_cpu3D = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'cpu') + + +rms = rmse(idealVol, fgp_dTV_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_dTV_cpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV')) +#%% diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py index cfe2e7d..aa1f865 100644 --- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_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.regularisers import ROF_TV, FGP_TV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -39,10 +39,14 @@ perc = 0.05 u0 = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) +u_ref = Im + np.random.normal(loc = 0 , + scale = 0.01 * 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') - +u_ref = u_ref.astype('float32') print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV bench___________________") @@ -213,3 +217,96 @@ else: print ("Arrays match") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________FGP-dTV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations') +a=fig.add_subplot(1,4,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_dTV, \ + 'input' : u0,\ + 'refdata' : u_ref,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :2000 ,\ + 'tolerance_constant':1e-06,\ + 'eta_const':0.2,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP dTV CPU####################") +start_time = timeit.default_timer() +fgp_dtv_cpu = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'cpu') + + +rms = rmse(Im, fgp_dtv_cpu) +pars['rmse'] = rms + +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(1,4,2) + +# 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=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray") +plt.title('{}'.format('CPU results')) + +print ("##############FGP dTV GPU##################") +start_time = timeit.default_timer() +fgp_dtv_gpu = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'gpu') +rms = rmse(Im, fgp_dtv_gpu) +pars['rmse'] = rms +pars['algorithm'] = FGP_dTV +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_dtv_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + + +print ("--------Compare the results--------") +tolerance = 1e-05 +diff_im = np.zeros(np.shape(rof_cpu)) +diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu) +diff_im[diff_im > tolerance] = 1 +a=fig.add_subplot(1,4,4) +imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray") +plt.title('{}'.format('Pixels larger threshold difference')) +if (diff_im.sum() > 1): + print ("Arrays do not match!") +else: + print ("Arrays match") diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py index c496e1c..4759cc3 100644 --- a/Wrappers/Python/demos/demo_gpu_regularisers.py +++ b/Wrappers/Python/demos/demo_gpu_regularisers.py @@ -12,7 +12,7 @@ import matplotlib.pyplot as plt import numpy as np import os import timeit -from ccpi.filters.regularisers import ROF_TV, FGP_TV +from ccpi.filters.regularisers import ROF_TV, FGP_TV, FGP_dTV from qualitymetrics import rmse ############################################################################### def printParametersToString(pars): @@ -39,10 +39,13 @@ perc = 0.05 u0 = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) +u_ref = Im + np.random.normal(loc = 0 , + scale = 0.01 * 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') - +u_ref = u_ref.astype('float32') print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") print ("____________ROF-TV bench___________________") @@ -134,10 +137,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14, imgplot = plt.imshow(fgp_gpu, cmap="gray") plt.title('{}'.format('GPU results')) +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("____________FGP-dTV bench___________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(3) +plt.suptitle('Performance of the FGP-dTV regulariser using the GPU') +a=fig.add_subplot(1,2,1) +a.set_title('Noisy Image') +imgplot = plt.imshow(u0,cmap="gray") + +# set parameters +pars = {'algorithm' : FGP_dTV, \ + 'input' : u0,\ + 'refdata' : u_ref,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :2000 ,\ + 'tolerance_constant':1e-06,\ + 'eta_const':0.2,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("##############FGP dTV GPU##################") +start_time = timeit.default_timer() +fgp_dtv_gpu = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'gpu') + +rms = rmse(Im, fgp_dtv_gpu) +pars['rmse'] = rms +pars['algorithm'] = FGP_dTV +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_dtv_gpu, cmap="gray") +plt.title('{}'.format('GPU results')) + # Uncomment to test 3D regularisation performance #%% -""" + N = 512 slices = 20 @@ -149,10 +204,12 @@ Im = Im/255 perc = 0.05 noisyVol = np.zeros((slices,N,N),dtype='float32') +noisyRef = np.zeros((slices,N,N),dtype='float32') idealVol = np.zeros((slices,N,N),dtype='float32') for i in range (slices): noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im)) + noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im)) idealVol[i,:,:] = Im print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -160,7 +217,7 @@ print ("_______________ROF-TV (3D)_________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(3) +fig = plt.figure(4) 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') @@ -200,7 +257,7 @@ print ("_______________FGP-TV (3D)__________________") print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") ## plot -fig = plt.figure(4) +fig = plt.figure(5) plt.suptitle('Performance of FGP-TV regulariser using the GPU') a=fig.add_subplot(1,2,1) a.set_title('Noisy Image') @@ -242,6 +299,58 @@ 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')) -#%% -""" + +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") +print ("_______________FGP-dTV (3D)________________") +print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") + +## plot +fig = plt.figure(6) +plt.suptitle('Performance of FGP-dTV 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_dTV, \ + 'input' : noisyVol,\ + 'refdata' : noisyRef,\ + 'regularisation_parameter':0.04, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'eta_const':0.2,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } + +print ("#############FGP TV GPU####################") +start_time = timeit.default_timer() +fgp_dTV_gpu3D = FGP_dTV(pars['input'], + pars['refdata'], + pars['regularisation_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['eta_const'], + pars['methodTV'], + pars['nonneg'], + pars['printingOut'],'gpu') + +rms = rmse(idealVol, fgp_dTV_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_dTV_gpu3D[10,:,:], cmap="gray") +plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV')) + +#%% |