cythonised nltv and updated demo, readme, bash run added

1 files changed, 66 insertions, 14 deletions

diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index b056bba..e51e6d8 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -27,10 +27,9 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa
 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);
 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 PatchSelect_CPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long dimX, long dimY, long dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h);
+cdef extern float PatchSelect_CPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM);
 cdef extern float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambdaReg, int IterNumb);
 
-
 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 trigger, int dimX, int dimY, int dimZ);
 cdef extern float TV_energy2D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY);
@@ -450,7 +449,6 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
 
     return outputData
 
-
 #****************************************************************#
 #***************Patch-based weights calculation******************#
 #****************************************************************#
@@ -458,31 +456,85 @@ def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_paramete
     if inputData.ndim == 2:
         return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
     elif inputData.ndim == 3:
-        return 1
-# PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
-def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+        return PatchSel_3D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
                      int searchwindow,
                      int patchwindow,
                      int neighbours,
                      float edge_parameter):
     cdef long dims[3]
-    dims[0] = inputData.shape[0]
-    dims[1] = inputData.shape[1]
-    dims[2] = neighbours
+    dims[0] = neighbours
+    dims[1] = inputData.shape[0]
+    dims[2] = inputData.shape[1]
+    
     
     cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
-            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+            np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
     
     cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
-            np.zeros([dims[0],dims[1],dims[2]], dtype='uint16 ')
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
             
     cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
-            np.zeros([dims[0],dims[1],dims[2]], dtype='uint16 ')
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
 
     # Run patch-based weight selection function
-    PatchSelect_CPU_main(&inputData[0,0], &H_i[0,0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[1], dims[0], 1, searchwindow, patchwindow,  neighbours,  edge_parameter)
+    PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow,  neighbours,  edge_parameter, 1)
     return H_i, H_j, Weights
-          
+
+def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+                     int searchwindow,
+                     int patchwindow,
+                     int neighbours,
+                     float edge_parameter):
+    cdef long dims[4]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    dims[3] = neighbours
+    
+    cdef np.ndarray[np.float32_t, ndim=4, mode="c"] Weights = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='float32')
+    
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_i = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_j = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_k = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+
+    # Run patch-based weight selection function
+    PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow,  neighbours, edge_parameter, 1)
+    return H_i, H_j, H_k, Weights
+
+
+#****************************************************************#
+#***************Non-local Total Variation******************#
+#****************************************************************#
+def NLTV_CPU(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+    if inputData.ndim == 2:
+        return NLTV_2D(inputData, H_i, H_j, Weights, regularisation_parameter, iterations)
+    elif inputData.ndim == 3:
+        return 1
+def NLTV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                     np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i,
+                     np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j,
+                     np.ndarray[np.float32_t, ndim=3, mode="c"] Weights,
+                     float regularisation_parameter,
+                     int iterations):
+
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    neighbours = H_i.shape[0]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    # Run nonlocal TV regularisation
+    Nonlocal_TV_CPU_main(&inputData[0,0], &outputData[0,0], &H_i[0,0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[1], dims[0], 0, neighbours, regularisation_parameter, iterations)
+    return outputData
 
 #*********************Inpainting WITH****************************#
 #***************Nonlinear (Isotropic) Diffusion******************#