diff options
Diffstat (limited to 'src/Python/src/gpu_regularisers.pyx')
| -rw-r--r-- | src/Python/src/gpu_regularisers.pyx | 16 |
1 files changed, 6 insertions, 10 deletions
diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx index b22d15e..8a4568e 100644 --- a/src/Python/src/gpu_regularisers.pyx +++ b/src/Python/src/gpu_regularisers.pyx @@ -22,7 +22,7 @@ CUDAErrorMessage = 'CUDA error' cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float *infovector, float *lambdaPar, int lambda_is_arr, int iter, float tau, float epsil, int N, int M, int Z); cdef extern int TV_FGP_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int N, int M, int Z); -cdef extern int TV_PD_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ); +cdef extern int TV_PD_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ); cdef extern int TV_SB_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, int methodTV, int N, int M, int Z); cdef extern int LLT_ROF_GPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int N, int M, int Z); cdef extern int TGV_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ); @@ -72,11 +72,11 @@ def TV_FGP_GPU(inputData, methodTV, nonneg) # Total-variation Primal-Dual (PD) -def TV_PD_GPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau): +def TV_PD_GPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const): if inputData.ndim == 2: - return TVPD2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau) + return TVPD2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const) elif inputData.ndim == 3: - return TVPD3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau) + return TVPD3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const) def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, @@ -84,8 +84,7 @@ def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float tolerance_param, int methodTV, int nonneg, - float lipschitz_const, - float tau): + float lipschitz_const): cdef long dims[2] dims[0] = inputData.shape[0] @@ -103,7 +102,6 @@ def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, lipschitz_const, methodTV, nonneg, - tau, dims[1],dims[0], 1) ==0): return (outputData,infovec) else: @@ -115,8 +113,7 @@ def TVPD3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, float tolerance_param, int methodTV, int nonneg, - float lipschitz_const, - float tau): + float lipschitz_const): cdef long dims[3] dims[0] = inputData.shape[0] @@ -134,7 +131,6 @@ def TVPD3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, lipschitz_const, methodTV, nonneg, - tau, dims[2], dims[1], dims[0]) ==0): return (outputData,infovec) else: |