diff options
| -rw-r--r-- | Wrappers/Python/src/cpu_regularisers.pyx | 44 |
1 files changed, 44 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx index 549b046..bb55df5 100644 --- a/Wrappers/Python/src/cpu_regularisers.pyx +++ b/Wrappers/Python/src/cpu_regularisers.pyx @@ -28,6 +28,8 @@ cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, 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); +cdef extern float TV_energy2D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY); +cdef extern float TV_energy3D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY, int dimZ); #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# @@ -442,3 +444,45 @@ def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, SW_increment, iterationsNumb, 1, dims[1], dims[0], 1) return (outputData, maskData_upd) + + +#****************************************************************# +#***************Calculation of TV-energy functional**************# +#****************************************************************# +def TV_ENERGY(inputData, regularisation_parameter, typeFunctional): + if inputData.ndim == 2: + return TV_ENERGY_2D(inputData, regularisation_parameter, typeFunctional) + elif inputData.ndim == 3: + return TV_ENERGY_3D(inputData, regularisation_parameter, typeFunctional) + +def TV_ENERGY_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularisation_parameter, + int typeFunctional): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=1, mode="c"] outputData = \ + np.zeros([1], dtype='float32') + + # run function + TV_energy2D(&inputData[0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[1], dims[0]) + + return outputData + +def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularisation_parameter, + int typeFunctional): + + 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=1, mode="c"] outputData = \ + np.zeros([1], dtype='float32') + + # Run function + TV_energy3D(&inputData[0,0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[2], dims[1], dims[0]) + + return outputData |