diff options
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/ccpi/filters/regularisers.py | 29 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/run_test.py.in (renamed from Wrappers/Python/test/run_test.py) | 17 | ||||
| -rw-r--r-- | Wrappers/Python/conda-recipe/testLena.npy | bin | 0 -> 1048656 bytes | |||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_regularisers.py | 126 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py | 103 | ||||
| -rw-r--r-- | Wrappers/Python/demos/demo_gpu_regularisers.py | 123 | ||||
| -rw-r--r-- | Wrappers/Python/setup-regularisers.py.in | 1 | ||||
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 71 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularisers.pyx | 101 | ||||
| -rw-r--r-- | Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc | bin | 823 -> 0 bytes |
10 files changed, 542 insertions, 29 deletions
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py index 039daab..376cc9c 100644 --- a/Wrappers/Python/ccpi/filters/regularisers.py +++ b/Wrappers/Python/ccpi/filters/regularisers.py @@ -2,8 +2,8 @@ script which assigns a proper device core function based on a flag ('cpu' or 'gpu') """ -from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU -from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU +from ccpi.filters.cpu_regularisers_cython import TV_ROF_CPU, TV_FGP_CPU, dTV_FGP_CPU +from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, dTV_FGP_GPU def ROF_TV(inputData, regularisation_parameter, iterations, time_marching_parameter,device='cpu'): @@ -42,3 +42,28 @@ def FGP_TV(inputData, regularisation_parameter,iterations, else: raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) +def FGP_dTV(inputData, refdata, regularisation_parameter, iterations, + tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'): + if device == 'cpu': + return dTV_FGP_CPU(inputData, + refdata, + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM) + elif device == 'gpu': + return dTV_FGP_GPU(inputData, + refdata, + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM) + else: + raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ + .format(device)) diff --git a/Wrappers/Python/test/run_test.py b/Wrappers/Python/conda-recipe/run_test.py.in index 04bbd40..9a6f4de 100644 --- a/Wrappers/Python/test/run_test.py +++ b/Wrappers/Python/conda-recipe/run_test.py.in @@ -1,8 +1,6 @@ import unittest import numpy as np -import os from ccpi.filters.regularisers import ROF_TV, FGP_TV -import matplotlib.pyplot as plt def rmse(im1, im2): rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size)) @@ -14,13 +12,16 @@ class TestRegularisers(unittest.TestCase): pass def test_cpu_regularisers(self): - filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") + #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") + Im = np.load('testLena.npy'); + """ # read noiseless image Im = plt.imread(filename) Im = np.asarray(Im, dtype='float32') Im = Im/255 + """ tolerance = 1e-05 rms_rof_exp = 0.006812507 #expected value for ROF model rms_fgp_exp = 0.019152347 #expected value for FGP model @@ -80,13 +81,11 @@ class TestRegularisers(unittest.TestCase): """ self.assertTrue(res) def test_gpu_regularisers(self): - filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") + #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy") - # read noiseless image - Im = plt.imread(filename) - Im = np.asarray(Im, dtype='float32') + Im = np.load('testLena.npy'); - Im = Im/255 + #Im = Im/255 tolerance = 1e-05 rms_rof_exp = 0.006812507 #expected value for ROF model rms_fgp_exp = 0.019152347 #expected value for FGP model @@ -146,4 +145,4 @@ class TestRegularisers(unittest.TestCase): """ self.assertTrue(res) if __name__ == '__main__': - unittest.main()
\ No newline at end of file + unittest.main() diff --git a/Wrappers/Python/conda-recipe/testLena.npy b/Wrappers/Python/conda-recipe/testLena.npy Binary files differnew file mode 100644 index 0000000..14bc0e3 --- /dev/null +++ b/Wrappers/Python/conda-recipe/testLena.npy diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py index 929f0af..00beb0b 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): @@ -22,6 +22,8 @@ def printParametersToString(pars): txt += "{0} = {1}".format(key, value.__name__) elif key == 'input': txt += "{0} = {1}".format(key, np.shape(value)) + elif key == 'refdata': + txt += "{0} = {1}".format(key, np.shape(value)) else: txt += "{0} = {1}".format(key, value) txt += '\n' @@ -39,9 +41,14 @@ 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 ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%") @@ -134,6 +141,61 @@ 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 #%% """ @@ -148,10 +210,12 @@ 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 +223,7 @@ 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 +263,7 @@ 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 +306,59 @@ 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')) + + +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')) """ -#%%
\ No newline at end of file +#%% diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py index cfe2e7d..310cf75 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): @@ -22,6 +22,8 @@ def printParametersToString(pars): txt += "{0} = {1}".format(key, value.__name__) elif key == 'input': txt += "{0} = {1}".format(key, np.shape(value)) + elif key == 'refdata': + txt += "{0} = {1}".format(key, np.shape(value)) else: txt += "{0} = {1}".format(key, value) txt += '\n' @@ -39,10 +41,14 @@ 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 +219,96 @@ 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..24a3c88 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): @@ -22,6 +22,8 @@ def printParametersToString(pars): txt += "{0} = {1}".format(key, value.__name__) elif key == 'input': txt += "{0} = {1}".format(key, np.shape(value)) + elif key == 'refdata': + txt += "{0} = {1}".format(key, np.shape(value)) else: txt += "{0} = {1}".format(key, value) txt += '\n' @@ -39,10 +41,13 @@ 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___________________") @@ -134,6 +139,58 @@ 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 #%% @@ -149,10 +206,12 @@ 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 +219,7 @@ 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 +259,7 @@ 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 +301,58 @@ 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')) +""" +#%% diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in index a1c1ab6..c7ebb5c 100644 --- a/Wrappers/Python/setup-regularisers.py.in +++ b/Wrappers/Python/setup-regularisers.py.in @@ -36,6 +36,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"), os.path.join(".." , ".." , "Core", "regularisers_CPU"), os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_FGP" ) , os.path.join(".." , ".." , "Core", "regularisers_GPU" , "TV_ROF" ) , + os.path.join(".." , ".." , "Core", "regularisers_GPU" , "dTV_FGP" ) , "."] if platform.system() == 'Windows': diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 0f08f7f..1661375 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -20,6 +20,7 @@ cimport numpy as np cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ); cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); +cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); #****************************************************************# @@ -89,7 +90,7 @@ def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ np.zeros([dims[0],dims[1]], dtype='float32') - #/* Run ROF iterations for 2D data */ + #/* Run FGP-TV iterations for 2D data */ TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, tolerance_param, @@ -115,7 +116,7 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ np.zeros([dims[0], dims[1], dims[2]], dtype='float32') - #/* Run ROF iterations for 3D data */ + #/* Run FGP-TV iterations for 3D data */ TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, tolerance_param, @@ -124,3 +125,69 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, printM, dims[2], dims[1], dims[0]) return outputData +#****************************************************************# +#**************Directional Total-variation FGP ******************# +#****************************************************************# +#******** Directional TV Fast-Gradient-Projection (FGP)*********# +def dTV_FGP_CPU(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM): + if inputData.ndim == 2: + return dTV_FGP_2D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM) + elif inputData.ndim == 3: + return dTV_FGP_3D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM) + +def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=2, mode="c"] refdata, + float regularisation_parameter, + int iterationsNumb, + float tolerance_param, + float eta_const, + int methodTV, + int nonneg, + int printM): + + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + #/* Run FGP-dTV iterations for 2D data */ + dTV_FGP_CPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0], regularisation_parameter, + iterationsNumb, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM, + dims[0], dims[1], 1) + + return outputData + +def dTV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=3, mode="c"] refdata, + float regularisation_parameter, + int iterationsNumb, + float tolerance_param, + float eta_const, + int methodTV, + int nonneg, + int printM): + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0], dims[1], dims[2]], dtype='float32') + + #/* Run FGP-dTV iterations for 3D data */ + dTV_FGP_CPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0], regularisation_parameter, + iterationsNumb, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM, + dims[2], dims[1], dims[0]) + return outputData diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx index ea746d3..18efdcd 100644 --- a/Wrappers/Python/src/gpu_regularisers.pyx +++ b/Wrappers/Python/src/gpu_regularisers.pyx @@ -20,6 +20,7 @@ cimport numpy as np cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z); cdef extern void TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z); +cdef extern void dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z); # Total-variation Rudin-Osher-Fatemi (ROF) def TV_ROF_GPU(inputData, @@ -61,7 +62,36 @@ def TV_FGP_GPU(inputData, methodTV, nonneg, printM) - +# Directional Total-variation Fast-Gradient-Projection (FGP) +def dTV_FGP_GPU(inputData, + refdata, + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM): + if inputData.ndim == 2: + return FGPdTV2D(inputData, + refdata, + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM) + elif inputData.ndim == 3: + return FGPdTV3D(inputData, + refdata, + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM) #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -157,8 +187,7 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, np.zeros([dims[0],dims[1],dims[2]], dtype='float32') # Running CUDA code here - TV_FGP_GPU_main( - &inputData[0,0,0], &outputData[0,0,0], + TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter , iterations, tolerance_param, @@ -167,4 +196,68 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, printM, dims[2], dims[1], dims[0]); - return outputData + return outputData + +#****************************************************************# +#**************Directional Total-variation FGP ******************# +#****************************************************************# +#******** Directional TV Fast-Gradient-Projection (FGP)*********# +def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=2, mode="c"] refdata, + float regularisation_parameter, + int iterations, + float tolerance_param, + float eta_const, + int methodTV, + int nonneg, + int printM): + + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Running CUDA code here + dTV_FGP_GPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0], + regularisation_parameter, + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM, + dims[0], dims[1], 1); + + return outputData + +def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.float32_t, ndim=3, mode="c"] refdata, + float regularisation_parameter, + int iterations, + float tolerance_param, + float eta_const, + int methodTV, + int nonneg, + int printM): + + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Running CUDA code here + dTV_FGP_GPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0], + regularisation_parameter , + iterations, + tolerance_param, + eta_const, + methodTV, + nonneg, + printM, + dims[2], dims[1], dims[0]); + return outputData diff --git a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc b/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc Binary files differdeleted file mode 100644 index 2196a53..0000000 --- a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc +++ /dev/null |