diff options
Diffstat (limited to 'Wrappers/Python/src')
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 38 |
1 files changed, 38 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index bf9c861..b056bba 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -27,6 +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 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); @@ -446,6 +449,41 @@ def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, Diffus4th_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0]) return outputData + + +#****************************************************************# +#***************Patch-based weights calculation******************# +#****************************************************************# +def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter): + 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, + 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 + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \ + 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 ') + + cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \ + 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) + return H_i, H_j, Weights + + #*********************Inpainting WITH****************************# #***************Nonlinear (Isotropic) Diffusion******************# #****************************************************************# |