TGV for CPU and GPU added with demos

7 files changed, 306 insertions, 30 deletions

diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 0b79dac..0e435a6 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 import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU
-from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU
+from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU
 from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
@@ -128,6 +128,26 @@ def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations,
     else:
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
+def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
+                     LipshitzConst, device='cpu'):
+    if device == 'cpu':
+        return TGV_CPU(inputData, 
+					regularisation_parameter, 
+					alpha1, 
+					alpha0, 
+					iterations,
+                    LipshitzConst)
+    elif device == 'gpu':
+        return TGV_GPU(inputData, 
+					regularisation_parameter, 
+					alpha1, 
+					alpha0, 
+					iterations,
+                    LipshitzConst)
+    else:
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+                         
 def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
                      time_marching_parameter, penalty_type):
         return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, 
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
index ff500ae..5c20244 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, SB_TV, FGP_dTV, TNV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, TNV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -74,7 +74,7 @@ print ("_______________ROF-TV (2D)_________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(1)
+fig = plt.figure()
 plt.suptitle('Performance of ROF-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -109,13 +109,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(rof_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________FGP-TV (2D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(2)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -159,12 +159,13 @@ 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 ("_______________SB-TV (2D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure()
 plt.suptitle('Performance of SB-TV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -205,14 +206,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(sb_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV 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' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':0.7,\
+        'number_of_iterations' :250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+             
+rms = rmse(Im, tgv_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(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("________________NDF (2D)___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure()
 plt.suptitle('Performance of NDF regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -259,7 +308,7 @@ print ("___Anisotropic Diffusion 4th Order (2D)____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure()
 plt.suptitle('Performance of DIFF4th regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -304,7 +353,7 @@ print ("_____________FGP-dTV (2D)__________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure()
 plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -356,7 +405,7 @@ print ("__________Total nuclear Variation__________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(7)
+fig = plt.figure()
 plt.suptitle('Performance of TNV regulariser using the CPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
index 4611522..46b8ffc 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, SB_TV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -50,12 +50,13 @@ u_ref = Im + np.random.normal(loc = 0 ,
 u0 = u0.astype('float32')
 u_ref = u_ref.astype('float32')
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________ROF-TV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(1)
+fig = plt.figure()
 plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -90,7 +91,6 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(rof_cpu, cmap="gray")
 plt.title('{}'.format('CPU results'))
 
-
 print ("##############ROF TV GPU##################")
 start_time = timeit.default_timer()
 rof_gpu = ROF_TV(pars['input'], 
@@ -128,12 +128,13 @@ if (diff_im.sum() > 1):
 else:
     print ("Arrays match")
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________FGP-TV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(2)
+fig = plt.figure()
 plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -218,12 +219,13 @@ if (diff_im.sum() > 1):
 else:
     print ("Arrays match")
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________SB-TV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure()
 plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -303,14 +305,98 @@ if (diff_im.sum() > 1):
     print ("Arrays do not match!")
 else:
     print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________TGV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of TGV 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' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':0.7,\
+        'number_of_iterations' :250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+rms = rmse(Im, tgv_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(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
 
+print ("##############SB TV GPU##################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+                                   
+rms = rmse(Im, tgv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = TGV
+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(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
 
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(tgv_gpu))
+diff_im = abs(tgv_cpu - tgv_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")
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure()
 plt.suptitle('Comparison of NDF regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -390,13 +476,13 @@ if (diff_im.sum() > 1):
 else:
     print ("Arrays match")
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Anisotropic Diffusion 4th Order (2D)____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure()
 plt.suptitle('Comparison of Diff4th regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -472,12 +558,13 @@ if (diff_im.sum() > 1):
 else:
     print ("Arrays match")
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________FGP-dTV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure()
 plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations')
 a=fig.add_subplot(1,4,1)
 a.set_title('Noisy Image')
@@ -565,3 +652,4 @@ if (diff_im.sum() > 1):
     print ("Arrays do not match!")
 else:
     print ("Arrays match")
+#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
index 3179428..792a019 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, SB_TV, FGP_dTV, NDF, DIFF4th
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, FGP_dTV, NDF, DIFF4th
 from qualitymetrics import rmse
 ###############################################################################
 def printParametersToString(pars):
@@ -66,13 +66,14 @@ Im2[:,0:M] = Im[:,0:M]
 Im = Im2
 del Im2
 """
+#%%
 
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________ROF-TV regulariser_____________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(1)
+fig = plt.figure()
 plt.suptitle('Performance of the ROF-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -108,13 +109,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(rof_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________FGP-TV regulariser_____________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(2)
+fig = plt.figure()
 plt.suptitle('Performance of the FGP-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -157,13 +158,13 @@ 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 ("____________SB-TV regulariser______________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(3)
+fig = plt.figure()
 plt.suptitle('Performance of the SB-TV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -203,14 +204,62 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
          verticalalignment='top', bbox=props)
 imgplot = plt.imshow(sb_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
+#%%
 
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV 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' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':0.7,\
+        'number_of_iterations' :250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')  
+             
+             
+rms = rmse(Im, tgv_gpu)
+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(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("_______________NDF regulariser_____________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(4)
+fig = plt.figure()
 plt.suptitle('Performance of the NDF regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -251,13 +300,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(ndf_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 
-
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("___Anisotropic Diffusion 4th Order (2D)____")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(5)
+fig = plt.figure()
 plt.suptitle('Performance of DIFF4th regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
@@ -296,12 +345,13 @@ a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(diff4_gpu, cmap="gray")
 plt.title('{}'.format('GPU results'))
 
+#%%
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 print ("____________FGP-dTV bench___________________")
 print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
 
 ## plot 
-fig = plt.figure(6)
+fig = plt.figure()
 plt.suptitle('Performance of the FGP-dTV regulariser using the GPU')
 a=fig.add_subplot(1,2,1)
 a.set_title('Noisy Image')
diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in
index 76dfecf..89ebaf9 100644
--- a/Wrappers/Python/setup-regularisers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -38,6 +38,7 @@ extra_include_dirs += [os.path.join(".." , ".." , "Core"),
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_SB" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TGV" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "NDF" ) ,
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "dTV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "DIFF4th" ) , 
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index bdb1eff..cf81bec 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,6 +21,7 @@ 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 SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
 cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
 cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
 cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
@@ -189,6 +190,39 @@ def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        printM,
                        dims[2], dims[1], dims[0])
     return outputData 
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+    if inputData.ndim == 2:
+        return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+    elif inputData.ndim == 3:
+        return 0
+
+def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    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 TGV iterations for 2D data */
+    TGV_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[1],dims[0])                           
+    return outputData
+    
 #****************************************************************#
 #**************Directional Total-variation FGP ******************#
 #****************************************************************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index b67e62b..4a202d7 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -21,6 +21,7 @@ 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 TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
+cdef extern void TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
 cdef extern void NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, 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);
 cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
@@ -86,6 +87,12 @@ def TV_SB_GPU(inputData,
                      tolerance_param,
                      methodTV,
                      printM)
+# Total Generilised Variation (TGV)
+def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+    if inputData.ndim == 2:
+        return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+    elif inputData.ndim == 3:
+        return 0
 # Directional Total-variation Fast-Gradient-Projection (FGP)
 def dTV_FGP_GPU(inputData,
                      refdata,
@@ -315,6 +322,33 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
                        dims[2], dims[1], dims[0]);
      
     return outputData 
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    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 TGV iterations for 2D data */
+    TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[1],dims[0])
+    return outputData
+
 #****************************************************************#
 #**************Directional Total-variation FGP ******************#
 #****************************************************************#