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| author | dkazanc <dkazanc@hotmail.com> | 2019-11-27 18:38:59 +0000 |
|---|---|---|
| committer | dkazanc <dkazanc@hotmail.com> | 2019-11-27 18:38:59 +0000 |
| commit | cdef6a981f1772ed04fe44bbe2b8251983a4ba7a (patch) | |
| tree | 8a839450703c9b2c3284cea7c5298e86fa91267f /src/Python | |
| parent | ccd5ef48846c613d29c6f3a33d99aa69d636a47c (diff) | |
| download | regularization-cdef6a981f1772ed04fe44bbe2b8251983a4ba7a.tar.gz regularization-cdef6a981f1772ed04fe44bbe2b8251983a4ba7a.tar.bz2 regularization-cdef6a981f1772ed04fe44bbe2b8251983a4ba7a.tar.xz regularization-cdef6a981f1772ed04fe44bbe2b8251983a4ba7a.zip | |
modifications in pdtv
Diffstat (limited to 'src/Python')
| -rw-r--r-- | src/Python/ccpi/filters/regularisers.py | 8 | ||||
| -rw-r--r-- | src/Python/src/cpu_regularisers.pyx | 18 |
2 files changed, 16 insertions, 10 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index d65c0b9..bc745fe 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -53,7 +53,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations, .format(device)) def PD_TV(inputData, regularisation_parameter, iterations, - tolerance_param, methodTV, nonneg, lipschitz_const, device='cpu'): + tolerance_param, methodTV, nonneg, lipschitz_const, tau, device='cpu'): if device == 'cpu': return TV_PD_CPU(inputData, regularisation_parameter, @@ -61,7 +61,8 @@ def PD_TV(inputData, regularisation_parameter, iterations, tolerance_param, methodTV, nonneg, - lipschitz_const) + lipschitz_const, + tau) elif device == 'gpu' and gpu_enabled: return TV_PD_CPU(inputData, regularisation_parameter, @@ -69,7 +70,8 @@ def PD_TV(inputData, regularisation_parameter, iterations, tolerance_param, methodTV, nonneg, - lipschitz_const) + lipschitz_const, + tau) else: if not gpu_enabled and device == 'gpu': raise ValueError ('GPU is not available') diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx index 08e247c..8de6aea 100644 --- a/src/Python/src/cpu_regularisers.pyx +++ b/src/Python/src/cpu_regularisers.pyx @@ -20,7 +20,7 @@ cimport numpy as np cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float *infovector, float *lambdaPar, int lambda_is_arr, 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 PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ); +cdef extern float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ); cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector, float mu, int iter, float epsil, int methodTV, int dimX, int dimY, int dimZ); cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ); cdef extern float TGV_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); @@ -159,11 +159,11 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, #****************************************************************# #****************** Total-variation Primal-dual *****************# #****************************************************************# -def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const): +def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau): if inputData.ndim == 2: - return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const) + return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau) elif inputData.ndim == 3: - return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const) + return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau) def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, @@ -171,7 +171,8 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float tolerance_param, int methodTV, int nonneg, - float lipschitz_const): + float lipschitz_const, + float tau): cdef long dims[2] dims[0] = inputData.shape[0] @@ -190,6 +191,7 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, lipschitz_const, methodTV, nonneg, + tau, dims[1],dims[0], 1) return (outputData,infovec) @@ -198,8 +200,9 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, int iterationsNumb, float tolerance_param, int methodTV, - int nonneg, - float lipschitz_const): + int nonneg, + float lipschitz_const, + float tau): cdef long dims[3] dims[0] = inputData.shape[0] @@ -218,6 +221,7 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, lipschitz_const, methodTV, nonneg, + tau, dims[2], dims[1], dims[0]) return (outputData,infovec) |