Merge pull request #87 from vais-ral/fix_TGV

Fix tgv

3 files changed, 23 insertions, 12 deletions

diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index 0a65590..588ea32 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -24,7 +24,7 @@ def ROF_TV(inputData, regularisation_parameter, iterations,
                      time_marching_parameter)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 
@@ -48,7 +48,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
                      printM)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def SB_TV(inputData, regularisation_parameter, iterations,
@@ -69,7 +69,7 @@ def SB_TV(inputData, regularisation_parameter, iterations,
                      printM)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
@@ -96,7 +96,7 @@ def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
                      printM)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
@@ -125,7 +125,7 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
     	    raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
-def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations,
+def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations,
                      time_marching_parameter, device='cpu'):
     if device == 'cpu':
         return Diff4th_CPU(inputData,
@@ -141,7 +141,7 @@ def DIFF4th(inputData, regularisation_parameter, edge_parameter, iterations,
                      time_marching_parameter)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
         
@@ -160,7 +160,7 @@ def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter
                      edge_parameter)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 
@@ -191,7 +191,7 @@ def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
                     LipshitzConst)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
@@ -202,7 +202,7 @@ def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT,
         return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
     else:
         if not gpu_enabled and device == 'gpu':
-    	    raise ValueError ('GPU is not available')
+            raise ValueError ('GPU is not available')
         raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
                          .format(device))
 def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 4aa3251..7d57ed1 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -199,9 +199,15 @@ def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 #***************************************************************#
 def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
     if inputData.ndim == 2:
-        return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+        return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, 
+                      iterations, LipshitzConst)
     elif inputData.ndim == 3:
-        return 0
+        shape = inputData.shape
+        out = inputData.copy()
+        for i in range(shape[0]):
+            out[i,:,:] = TGV_2D(inputData[i,:,:], regularisation_parameter, 
+               alpha1, alpha0, iterations, LipshitzConst)
+        return out
 
 def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      float regularisation_parameter,
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 2b97865..47a6149 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -102,7 +102,12 @@ def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, Lip
     if inputData.ndim == 2:
         return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
     elif inputData.ndim == 3:
-        return 0
+        shape = inputData.shape
+        out = inputData.copy()
+        for i in range(shape[0]):
+            out[i,:,:] = TGV2D(inputData[i,:,:], regularisation_parameter, 
+               alpha1, alpha0, iterations, LipshitzConst)
+        return out
 # Directional Total-variation Fast-Gradient-Projection (FGP)
 def dTV_FGP_GPU(inputData,
                      refdata,