diff options
Diffstat (limited to 'Wrappers/Python')
| -rw-r--r-- | Wrappers/Python/src/fista_module_gpu.pyx | 75 |
1 files changed, 59 insertions, 16 deletions
diff --git a/Wrappers/Python/src/fista_module_gpu.pyx b/Wrappers/Python/src/fista_module_gpu.pyx index da86c0a..41cf4a6 100644 --- a/Wrappers/Python/src/fista_module_gpu.pyx +++ b/Wrappers/Python/src/fista_module_gpu.pyx @@ -74,14 +74,14 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Running CUDA code here #Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda); -# Diff4th_GPU_kernel( -# #<float*> A_L.data, <float*> B_L.data, -# &A_L[0,0], &B_L[0,0], -# N, M, 0, -# edge_preserving_parameter, -# iterations , -# tau, -# regularization_parameter) + Diff4th_GPU_kernel( + #<float*> A_L.data, <float*> B_L.data, + &A_L[0,0], &B_L[0,0], + N, M, 0, + edge_preserving_parameter, + iterations , + tau, + regularization_parameter) # copy the processed B_L to a smaller B for i in range(N): for j in range(M): @@ -131,14 +131,14 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, # Running CUDA code here #Diff4th_GPU_kernel(A_L, B_L, N, M, Z, (float)sigma, iter, (float)tau, lambda); -# Diff4th_GPU_kernel( -# #<float*> A_L.data, <float*> B_L.data, -# &A_L[0,0,0], &B_L[0,0,0], -# N, M, Z, -# edge_preserving_parameter, -# iterations , -# tau, -# regularization_parameter) + Diff4th_GPU_kernel( + #<float*> A_L.data, <float*> B_L.data, + &A_L[0,0,0], &B_L[0,0,0], + N, M, Z, + edge_preserving_parameter, + iterations , + tau, + regularization_parameter) # copy the processed B_L to a smaller B for i in range(N): for j in range(M): @@ -152,3 +152,46 @@ 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 |