diff options
Diffstat (limited to 'Wrappers/Python/src')
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 50 |
1 files changed, 50 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 7ed8fa1..3625106 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -25,6 +25,9 @@ cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPa 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 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 dimX, int dimY, int dimZ); + #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -319,3 +322,50 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) return outputData + +#*********************Inpainting WITH****************************# +#***************Nonlinear (Isotropic) Diffusion******************# +#****************************************************************# +def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type): + if inputData.ndim == 2: + return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + elif inputData.ndim == 3: + return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type) + +def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 2D data + Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[0], dims[1], 1) + return outputData + +def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData, + float regularisation_parameter, + float edge_parameter, + int iterationsNumb, + float time_marching_parameter, + int penalty_type): + cdef long dims[3] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + dims[2] = inputData.shape[2] + + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + # Run Inpaiting by Diffusion iterations for 3D data + Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0]) + + return outputData |