3 files changed, 58 insertions, 2 deletions
diff --git a/Wrappers/Python/ccpi/filters/regularisers.py b/Wrappers/Python/ccpi/filters/regularisers.py
index eec8c4d..e62c020 100644
--- a/Wrappers/Python/ccpi/filters/regularisers.py
+++ b/Wrappers/Python/ccpi/filters/regularisers.py
@@ -2,7 +2,7 @@
 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, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, NDF_INPAINT_CPU
 from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU
 
 def ROF_TV(inputData, regularisation_parameter, iterations,
@@ -110,3 +110,8 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
     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, 
+        edge_parameter, iterationsNumb, 
+        time_marching_parameter, penalty_type)
diff --git a/Wrappers/Python/setup-regularisers.py.in b/Wrappers/Python/setup-regularisers.py.in
index b900efe..f55c6fe 100644
--- a/Wrappers/Python/setup-regularisers.py.in
+++ b/Wrappers/Python/setup-regularisers.py.in
@@ -34,6 +34,7 @@ extra_libraries = ['cilreg']
 
 extra_include_dirs += [os.path.join(".." , ".." , "Core"),
                        os.path.join(".." , ".." , "Core",  "regularisers_CPU"),
+                       os.path.join(".." , ".." , "Core",  "inpainters_CPU"),
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_FGP" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
                        os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_SB" ) ,
@@ -52,7 +53,7 @@ setup(
 	description='CCPi Core Imaging Library - Image regularisers',
 	version=cil_version,
     cmdclass = {'build_ext': build_ext},
-    ext_modules = [Extension("ccpi.filters.cpu_regularisers_cython",
+    ext_modules = [Extension("ccpi.filters.cpu_regularisers",
                              sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ],
                              include_dirs=extra_include_dirs, 
 							 library_dirs=extra_library_dirs, 
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 7ed8fa1..3625106 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -25,6 +25,9 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa
 cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, 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);
 
+cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
+#cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int dimX, int dimY, int dimZ);
+
 #****************************************************************#
 #********************** Total-variation ROF *********************#
 #****************************************************************#
@@ -319,3 +322,50 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
 
     return outputData
+
+#*********************Inpainting WITH****************************#
+#***************Nonlinear (Isotropic) Diffusion******************#
+#****************************************************************#
+def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type):
+    if inputData.ndim == 2:
+        return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+    elif inputData.ndim == 3:
+        return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+
+def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+               np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+    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 Inpaiting by Diffusion iterations for 2D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1)    
+    return outputData
+            
+def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+    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 Inpaiting by Diffusion iterations for 3D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
+
+    return outputData