diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/src/cpu_regularizers.pyx | 84 |
1 files changed, 84 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx index a8f8c8f..b22e603 100644 --- a/Wrappers/Python/src/cpu_regularizers.pyx +++ b/Wrappers/Python/src/cpu_regularizers.pyx @@ -17,3 +17,87 @@ Author: Edoardo Pasca import cython import numpy as np cimport numpy as np + +cdef extern float TV_main(float *D1, float *D2, float *D3, float *B, float *A, + float lambda, float tau, int dimY, int dimX, int dimZ); +cdef extern float D1_func(float *A, float *D1, int dimY, int dimX, int dimZ); +cdef extern float D2_func(float *A, float *D2, int dimY, int dimX, int dimZ); +cdef extern float D3_func(float *A, float *D3, int dimY, int dimX, int dimZ); +cdef extern void copyIm (float *A, float *U, int dimX, int dimY, int dimZ); + + +def ROF_TV(inputData, iterations, regularization_parameter, marching_step_parameter): + if inputData.ndim == 2: + return ROF_TV_2D(inputData, iterations, regularization_parameter, + marching_step_parameter) + elif inputData.ndim == 3: + return ROF_TV_3D(inputData, iterations, regularization_parameter, + marching_step_parameter) + +def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + int iterations, + float regularization_parameter + float marching_step_parameter + ): + cdef long dims[2] + dims[0] = inputData.shape[0] + dims[1] = inputData.shape[1] + + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] B = \ + np.zeros([dims[0],dims[1]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] D1 = \ + np.zeros([dims[0],dims[1]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=2, mode="c"] D2 = \ + np.zeros([dims[0],dims[1]], dtype='float32') + + copyIm(&inputData[0,0], &B[0,0], dims[0], dims[1], 1); + #/* start TV iterations */ + cdef int i = 0; + for i in range(iterations): + + #/* calculate differences */ + D1_func(&B[0,0], &D1[0,0], dims[0], dims[1], 1); + D2_func(&B[0,0], &D2[0,0], dims[0], dims[1], 1); + + #/* calculate divergence and image update*/ + TV_main(&D1[0,0], &D2[0,0], &D2[0,0], &B[0,0], &A[0,0], + regularization_parameter, marching_step_parameter, + dims[0], dims[1], 1) + return B + + +def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + int iterations, + float regularization_parameter + float marching_step_parameter + ): + cdef long dims[2] + 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"] B = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D1 = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D2 = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=3, mode="c"] D3 = \ + np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + + copyIm(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2]); + #/* start TV iterations */ + cdef int i = 0; + for i in range(iterations): + + #/* calculate differences */ + D1_func(&B[0,0,0], &D1[0,0,0], dims[0], dims[1], dims[2]); + D2_func(&B[0,0,0], &D2[0,0,0], dims[0], dims[1], dims[2]); + D3_func(&B[0,0,0], &D3[0,0,0], dims[0], dims[1], dims[2]); + + #/* calculate divergence and image update*/ + TV_main(&D1[0,0,0], &D2[0,0,0], &D3[0,0,0], &B[0,0,0], &A[0,0,0], + regularization_parameter, marching_step_parameter, + dims[0], dims[1], dims[2]) + return B +
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