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-rw-r--r--Wrappers/Python/ccpi/filters/regularisers.py29
-rw-r--r--Wrappers/Python/conda-recipe/run_test.py (renamed from Wrappers/Python/test/run_test.py)0
-rw-r--r--Wrappers/Python/demos/demo_cpu_regularisers.py127
-rw-r--r--Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py101
-rw-r--r--Wrappers/Python/demos/demo_gpu_regularisers.py123
-rw-r--r--Wrappers/Python/setup-regularisers.py.in1
-rw-r--r--Wrappers/Python/src/cpu_regularisers.pyx71
-rw-r--r--Wrappers/Python/src/gpu_regularisers.pyx98
-rw-r--r--Wrappers/Python/test/__pycache__/metrics.cpython-35.pycbin823 -> 0 bytes
9 files changed, 529 insertions, 21 deletions
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 039daab..c6723fa 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
index 04bbd40..04bbd40 100644
--- a/Wrappers/Python/test/run_test.py
+++ b/Wrappers/Python/conda-recipe/run_test.py
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'))
+
+#%%
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..8f9185a 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,
+ methodTV,
+ eta_const,
+ 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..4a14f69 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_CPU_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 *********************#
#****************************************************************#
@@ -167,4 +197,68 @@ 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=3, 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
deleted file mode 100644
index 2196a53..0000000
--- a/Wrappers/Python/test/__pycache__/metrics.cpython-35.pyc
+++ /dev/null
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generated by cgit v1.2.3 (git 2.25.1) at 2024-11-06 00:19:39 +0000