added NML but commented out

1 files changed, 149 insertions, 43 deletions

diff --git a/Wrappers/Python/src/fista_module_gpu.pyx b/Wrappers/Python/src/fista_module_gpu.pyx
index 41cf4a6..f18181b 100644
--- a/Wrappers/Python/src/fista_module_gpu.pyx
+++ b/Wrappers/Python/src/fista_module_gpu.pyx
@@ -26,6 +26,10 @@ cdef extern void NLM_GPU_kernel(float *A, float* B, float *Eucl_Vec,
                                 int N, int M,  int Z, int dimension, 
                                 int SearchW, int SimilW, 
                                 int SearchW_real, float denh2, float lambdaf);
+cdef extern float pad_crop(float *A, float *Ap, 
+                           int OldSizeX, int OldSizeY, int OldSizeZ, 
+                           int NewSizeX, int NewSizeY, int NewSizeZ, 
+                           int padXY, int switchpad_crop);
 
 def Diff4thHajiaboli(inputData, 
                      regularization_parameter, 
@@ -152,46 +156,148 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
     return B
 
 		
-def NML(inputData, 
-                     regularization_parameter, 
-                     iterations, 
-                     edge_preserving_parameter):
-    if inputData.ndim == 2:
-        return NML2D(inputData,  
-                     regularization_parameter, 
-                     iterations, 
-                     edge_preserving_parameter)
-    elif inputData.ndim == 3:
-        return NML3D(inputData,  
-                     regularization_parameter, 
-                     iterations, 
-                     edge_preserving_parameter)
-
-    #SearchW_real  = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
-    #SimilW =  (int) mxGetScalar(prhs[2]);  /* the similarity window ratio */
-    #h =  (float) mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
-    #lambda = (float) mxGetScalar(prhs[4]);
-
-def NML2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
-                     SearchW_real, 
-                     SimilW, 
-                     h,
-                     lambdaf):    
-    N, M = inputData.shape       
-    if h < 0:
-        raise ValueError('Parameter for the PB filtering function must be > 0') 
-             
-    SearchW = SearchW_real + 2*SimilW;
-    
-    SearchW_full = 2*SearchW + 1; #/* the full searching window  size */
-    SimilW_full = 2*SimilW + 1;   #/* the full similarity window  size */
-    h2 = h*h;
-    
-    padXY = SearchW + 2*SimilW; #/* padding sizes */
-    newsizeX = N + 2*(padXY); #/* the X size of the padded array */
-    newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
-    newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
-    
-    B = np.zeros((N,M), dtype=np.float )
-    
-    
\ No newline at end of file
+#def NML(inputData, 
+#                     regularization_parameter, 
+#                     iterations, 
+#                     edge_preserving_parameter):
+#    if inputData.ndim == 2:
+#        return NML2D(inputData,  
+#                     regularization_parameter, 
+#                     iterations, 
+#                     edge_preserving_parameter)
+#    elif inputData.ndim == 3:
+#        return NML3D(inputData,  
+#                     regularization_parameter, 
+#                     iterations, 
+#                     edge_preserving_parameter)
+#
+#    #SearchW_real  = (int) mxGetScalar(prhs[1]); /* the searching window ratio */
+#    #SimilW =  (int) mxGetScalar(prhs[2]);  /* the similarity window ratio */
+#    #h =  (float) mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
+#    #lambda = (float) mxGetScalar(prhs[4]);
+#
+#def NML2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+#                     SearchW_real, 
+#                     SimilW, 
+#                     h,
+#                     lambdaf):    
+#    N = inputData.shape[0]
+#    M = inputData.shape[1]      
+#    Z = 0
+#    numdims = inputData.ndim
+#     
+#    if h < 0:
+#        raise ValueError('Parameter for the PB filtering function must be > 0') 
+#             
+#    SearchW = SearchW_real + 2*SimilW;
+#    
+#    SearchW_full = 2*SearchW + 1; #/* the full searching window  size */
+#    SimilW_full = 2*SimilW + 1;   #/* the full similarity window  size */
+#    h2 = h*h;
+#    
+#    padXY = SearchW + 2*SimilW; #/* padding sizes */
+#    newsizeX = N + 2*(padXY); #/* the X size of the padded array */
+#    newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
+#    #newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
+#    
+#    #output
+#    B = np.zeros((N,M), dtype=np.float )
+#    #/*allocating memory for the padded arrays */
+#    
+#    Ap = np.zeros((newsizeX, newsizeY), dtype=np.float)    
+#    Bp = np.zeros((newsizeX, newsizeY), dtype=np.float)
+#    Eucl_Vec = np.zeros((SimilW_full*SimilW_full), dtype=np.float)
+#    
+#    #/*Gaussian kernel */
+#    cdef int count, i_n, j_n;
+#    cdef float val;
+#    count = 0
+#    for i_n in range (-SimilW, SimilW +1):
+#        for j_n in range(-SimilW, SimilW +1):
+#            val = (float)(i_n*i_n + j_n*j_n)/(2*SimilW*SimilW)
+#            Eucl_Vec[count] = np.exp(-val)
+#            count = count + 1
+#    
+#    #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+#    switchpad_crop = 0; # /*padding*/
+#    pad_crop(&inputData[0,0], &Ap[0,0], M, N, 0, newsizeY, newsizeX, 0, padXY,
+#             switchpad_crop);
+#    
+#    #/* Do PB regularization with the padded array  */
+#    NLM_GPU_kernel(&Ap[0,0], &Bp[0,0], &Eucl_Vec[0,0], newsizeY, newsizeX, 0,
+#                   numdims, SearchW, SimilW, SearchW_real, 
+#                   h2, lambdaf);
+#    
+#    switchpad_crop = 1; #/*cropping*/
+#    pad_crop(&Bp[0,0], &B[0,0], M, N, 0, newsizeX, newsizeY, 0, padXY, 
+#             switchpad_crop)
+#    
+#    return B
+#
+#def NML3D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+#                     SearchW_real, 
+#                     SimilW, 
+#                     h,
+#                     lambdaf):    
+#    N = inputData.shape[0]
+#    M = inputData.shape[1]      
+#    Z = inputData.shape[2]     
+#    numdims = inputData.ndim
+#
+#    if h < 0:
+#        raise ValueError('Parameter for the PB filtering function must be > 0') 
+#             
+#    SearchW = SearchW_real + 2*SimilW;
+#    
+#    SearchW_full = 2*SearchW + 1; #/* the full searching window  size */
+#    SimilW_full = 2*SimilW + 1;   #/* the full similarity window  size */
+#    h2 = h*h;
+#    
+#    padXY = SearchW + 2*SimilW; #/* padding sizes */
+#    newsizeX = N + 2*(padXY); #/* the X size of the padded array */
+#    newsizeY = M + 2*(padXY); #/* the Y size of the padded array */
+#    newsizeZ = Z + 2*(padXY); #/* the Z size of the padded array */
+#    
+#    #output
+#    B = np.zeros((N,M,Z), dtype=np.float )
+#    #/*allocating memory for the padded arrays */
+#    
+#    Ap = np.zeros((newsizeX, newsizeY, newsizeZ), dtype=np.float)    
+#    Bp = np.zeros((newsizeX, newsizeY, newsizeZ), dtype=np.float)
+#    Eucl_Vec = np.zeros((SimilW_full*SimilW_full*SimilW_full), dtype=np.float)
+#    
+#    #/*Gaussian kernel */
+#    cdef int count, i_n, j_n, k_n;
+#    cdef float val;
+#    count = 0
+#    for i_n in range (-SimilW, SimilW +1):
+#        for j_n in range(-SimilW, SimilW +1):
+#            for k_n in range(-SimilW, SimilW+1):
+#                val = (i_n*i_n + j_n*j_n + k_n*k_n)/(2*SimilW*SimilW*SimilW)
+#                Eucl_Vec[count] = np.exp(-val)
+#                count = count + 1
+#    
+#    #/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+#    switchpad_crop = 0; # /*padding*/
+#    pad_crop(&inputData[0,0,0], &Ap[0,0,0], 
+#             M, N, Z, 
+#             newsizeY, newsizeX, newsizeZ, 
+#             padXY,
+#             switchpad_crop);
+#    
+#    #/* Do PB regularization with the padded array  */
+#    NLM_GPU_kernel(&Ap[0,0,0], &Bp[0,0,0], &Eucl_Vec[0,0,0], 
+#                   newsizeY, newsizeX, newsizeZ,
+#                   numdims, SearchW, SimilW, SearchW_real, 
+#                   h2, lambdaf);
+#        
+#    switchpad_crop = 1; #/*cropping*/
+#    pad_crop(&Bp[0,0,0], &B[0,0,0],
+#             M, N, Z, 
+#             newsizeX, newsizeY, newsizeZ,
+#             padXY, 
+#             switchpad_crop)
+#    
+#    return B
+#        
+#    
\ No newline at end of file