From cfcc4be4413f65a0b9c4ef197687e3a167eff0e8 Mon Sep 17 00:00:00 2001 From: Daniil Kazantsev Date: Wed, 6 Mar 2019 23:34:55 +0000 Subject: cont1 --- src/Python/ccpi/filters/regularisers.py | 5 +++-- src/Python/src/cpu_regularisers.pyx | 28 +++++++++++++++++----------- 2 files changed, 20 insertions(+), 13 deletions(-) (limited to 'src/Python') diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index fb2c999..67f432b 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -11,12 +11,13 @@ except ImportError: from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU def ROF_TV(inputData, regularisation_parameter, iterations, - time_marching_parameter,device='cpu'): + time_marching_parameter,tolerance_param,device='cpu'): if device == 'cpu': return TV_ROF_CPU(inputData, regularisation_parameter, iterations, - time_marching_parameter) + time_marching_parameter, + tolerance_param) elif device == 'gpu' and gpu_enabled: return TV_ROF_GPU(inputData, regularisation_parameter, diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx index 49cdf94..aeca141 100644 --- a/src/Python/src/cpu_regularisers.pyx +++ b/src/Python/src/cpu_regularisers.pyx @@ -18,7 +18,7 @@ import cython import numpy as np cimport numpy as np -cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ); +cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ); cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int dimX, int dimY, int dimZ); cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ); cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ); @@ -37,32 +37,36 @@ cdef extern float TV_energy3D(float *U, float *U0, float *E_val, float lambdaPar #****************************************************************# #********************** Total-variation ROF *********************# #****************************************************************# -def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter): +def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param): if inputData.ndim == 2: - return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter) + return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param) elif inputData.ndim == 3: - return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter) + return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param) def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, int iterationsNumb, - float marching_step_parameter): + float marching_step_parameter, + float tolerance_param): 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') - + cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \ + np.ones([2], dtype='float32') + # Run ROF iterations for 2D data - TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1) + TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1) - return outputData + return (outputData,infovec) def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, float regularisation_parameter, int iterationsNumb, - float marching_step_parameter): + float marching_step_parameter, + float tolerance_param): cdef long dims[3] dims[0] = inputData.shape[0] dims[1] = inputData.shape[1] @@ -70,11 +74,13 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \ np.zeros([dims[0],dims[1],dims[2]], dtype='float32') + cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \ + np.ones([2], dtype='float32') # Run ROF iterations for 3D data - TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[2], dims[1], dims[0]) + TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0]) - return outputData + return (outputData,infovec) #****************************************************************# #********************** Total-variation FGP *********************# -- cgit v1.2.3