diff options
Diffstat (limited to 'Wrappers')
| -rw-r--r-- | Wrappers/Python/src/cpu_regularizers.pyx | 77 | ||||
| -rw-r--r-- | Wrappers/Python/src/gpu_regularizers.pyx | 2 |
2 files changed, 68 insertions, 11 deletions
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx index e7ff78c..b8089a8 100644 --- a/Wrappers/Python/src/cpu_regularizers.pyx +++ b/Wrappers/Python/src/cpu_regularizers.pyx @@ -18,20 +18,20 @@ import cython import numpy as np cimport numpy as np -cdef extern float TV_ROF(float *A, float *B, int dimX, int dimY, int dimZ, - int iterationsNumb, float tau, float flambda); +cdef extern float TV_ROF_CPU(float *Input, float *Output, int dimX, int dimY, int dimZ, int iterationsNumb, float tau, float flambda); +cdef extern float TV_FGP_CPU(float *Input, float *Output, float lambda, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ); - -def ROF_TV(inputData, iterations, regularization_parameter, +# Can we use the same name here in "def" as the C function? +def TV_ROF_CPU(inputData, iterations, regularization_parameter, marching_step_parameter): if inputData.ndim == 2: - return ROF_TV_2D(inputData, iterations, regularization_parameter, + return TV_ROF_2D(inputData, iterations, regularization_parameter, marching_step_parameter) elif inputData.ndim == 3: - return ROF_TV_3D(inputData, iterations, regularization_parameter, + return TV_ROF_3D(inputData, iterations, regularization_parameter, marching_step_parameter) -def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, +def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, int iterations, float regularization_parameter, float marching_step_parameter @@ -45,13 +45,13 @@ def ROF_TV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.zeros([dims[0],dims[1]], dtype='float32') #/* Run ROF iterations for 2D data */ - TV_ROF(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations, + TV_ROF_CPU(&inputData[0,0], &B[0,0], dims[0], dims[1], 1, iterations, marching_step_parameter, regularization_parameter) return B -def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, +def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, int iterations, float regularization_parameter, float marching_step_parameter @@ -65,8 +65,65 @@ def ROF_TV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, np.zeros([dims[0],dims[1],dims[2]], dtype='float32') #/* Run ROF iterations for 3D data */ - TV_ROF(&inputData[0,0, 0], &B[0,0, 0], dims[0], dims[1], dims[2], iterations, + TV_FGP_CPU(&inputData[0,0,0], &B[0,0,0], dims[0], dims[1], dims[2], iterations, marching_step_parameter, regularization_parameter) return B +def TV_FGP_CPU(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM): + if inputData.ndim == 2: + return TV_FGP_2D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM) + elif inputData.ndim == 3: + return TV_FGP_3D(inputData, regularization_parameter, iterations, tolerance_param, methodTV, nonneg, printM) + +def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, + float regularization_parameter, + int iterations, + float tolerance_param, + int methodTV, + int nonneg, + int printM): + + 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') + + #/* Run ROF iterations for 2D data */ + TV_FGP_CPU(&inputData[0,0], &B[0,0], regularization_parameter, + iterations, + tolerance_param, + methodTV, + nonneg, + printM, + dims[0], dims[1], 1) + + return B + + +def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, + float regularization_parameter, + int iterations, + float tolerance_param, + int methodTV, + int nonneg, + int printM): + 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') + + #/* Run ROF iterations for 3D data */ + TV_FGP_CPU(&inputData[0,0, 0], &B[0,0, 0], iterations, + tolerance_param, + methodTV, + nonneg, + printM, + dims[0], dims[1], dims[2]) + + return B diff --git a/Wrappers/Python/src/gpu_regularizers.pyx b/Wrappers/Python/src/gpu_regularizers.pyx index 263fa4a..a14a20d 100644 --- a/Wrappers/Python/src/gpu_regularizers.pyx +++ b/Wrappers/Python/src/gpu_regularizers.pyx @@ -396,7 +396,7 @@ def TVFGP2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Running CUDA code here TV_FGP_GPU( &inputData[0,0], &B[0,0], - regularization_parameter , + regularization_parameter, iterations, tolerance_param, methodTV, |