diff options
Diffstat (limited to 'Wrappers/Python/src')
| -rw-r--r-- | Wrappers/Python/src/gpu_regularizers.pyx | 38 |
1 files changed, 19 insertions, 19 deletions
diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx index dc625c3..84fc30a 100644 --- a/Wrappers/Python/src/gpu_regularizers.pyx +++ b/Wrappers/Python/src/gpu_regularizers.pyx @@ -31,19 +31,22 @@ cdef extern float pad_crop(float *A, float *Ap, int padXY, int switchpad_crop); def Diff4thHajiaboli(inputData, - regularization_parameter, + edge_preserv_parameter, iterations, - edge_preserving_parameter): + time_marching_parameter, + regularization_parameter): if inputData.ndim == 2: return Diff4thHajiaboli2D(inputData, - regularization_parameter, + edge_preserv_parameter, iterations, - edge_preserving_parameter) + time_marching_parameter, + regularization_parameter) elif inputData.ndim == 3: return Diff4thHajiaboli3D(inputData, - regularization_parameter, + edge_preserv_parameter, iterations, - edge_preserving_parameter) + time_marching_parameter, + regularization_parameter) def NML(inputData, SearchW_real, @@ -64,9 +67,10 @@ def NML(inputData, lambdaf) def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, - float regularization_parameter, + float edge_preserv_parameter, int iterations, - float edge_preserving_parameter): + float time_marching_parameter, + float regularization_parameter): cdef long dims[2] dims[0] = inputData.shape[0] @@ -74,9 +78,6 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, N = dims[0] + 2; M = dims[1] + 2; - #time step is sufficiently small for an explicit methods - tau = 0.01 - #A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL)); #B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL)); cdef np.ndarray[np.float32_t, ndim=2, mode="c"] A_L = \ @@ -102,9 +103,9 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, #<float*> A_L.data, <float*> B_L.data, &A_L[0,0], &B_L[0,0], N, M, 0, - edge_preserving_parameter, + edge_preserv_parameter, iterations , - tau, + time_marching_parameter, regularization_parameter); # copy the processed B_L to a smaller B for i in range(N): @@ -120,9 +121,10 @@ def Diff4thHajiaboli2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, return B def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, - float regularization_parameter, + float edge_preserv_parameter, int iterations, - float edge_preserving_parameter): + float time_marching_parameter, + float regularization_parameter): cdef long dims[3] dims[0] = inputData.shape[0] @@ -132,8 +134,6 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, M = dims[1] + 2 Z = dims[2] + 2 - # Time Step is small for an explicit methods - tau = 0.0007; #A_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL)); #B_L = (float*)mxGetData(mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL)); @@ -162,9 +162,9 @@ def Diff4thHajiaboli3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, #<float*> A_L.data, <float*> B_L.data, &A_L[0,0,0], &B_L[0,0,0], N, M, Z, - edge_preserving_parameter, + edge_preserv_parameter, iterations , - tau, + time_marching_parameter, regularization_parameter); # copy the processed B_L to a smaller B for i in range(N): |