diff options
Diffstat (limited to 'src/Python')
| -rw-r--r-- | src/Python/ccpi/filters/regularisers.py | 7 | ||||
| -rw-r--r-- | src/Python/setup-regularisers.py.in | 2 | ||||
| -rw-r--r-- | src/Python/src/cpu_regularisers.pyx | 38 |
3 files changed, 24 insertions, 23 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index 588ea32..fb2c999 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -29,15 +29,14 @@ def ROF_TV(inputData, regularisation_parameter, iterations, .format(device)) def FGP_TV(inputData, regularisation_parameter,iterations, - tolerance_param, methodTV, nonneg, printM, device='cpu'): + tolerance_param, methodTV, nonneg, device='cpu'): if device == 'cpu': return TV_FGP_CPU(inputData, regularisation_parameter, iterations, tolerance_param, methodTV, - nonneg, - printM) + nonneg) elif device == 'gpu' and gpu_enabled: return TV_FGP_GPU(inputData, regularisation_parameter, @@ -45,7 +44,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations, tolerance_param, methodTV, nonneg, - printM) + 1) else: if not gpu_enabled and device == 'gpu': raise ValueError ('GPU is not available') diff --git a/src/Python/setup-regularisers.py.in b/src/Python/setup-regularisers.py.in index 82d9f9f..39b820a 100644 --- a/src/Python/setup-regularisers.py.in +++ b/src/Python/setup-regularisers.py.in @@ -44,7 +44,7 @@ extra_include_dirs += [os.path.join(".." , "Core"), os.path.join(".." , "Core", "regularisers_GPU" , "LLTROF" ) , os.path.join(".." , "Core", "regularisers_GPU" , "NDF" ) , os.path.join(".." , "Core", "regularisers_GPU" , "dTV_FGP" ) , - os.path.join(".." , "Core", "regularisers_GPU" , "DIFF4th" ) , + os.path.join(".." , "Core", "regularisers_GPU" , "Diff4th" ) , os.path.join(".." , "Core", "regularisers_GPU" , "PatchSelect" ) , "."] diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx index 11a0617..b7d029d 100644 --- a/src/Python/src/cpu_regularisers.pyx +++ b/src/Python/src/cpu_regularisers.pyx @@ -19,7 +19,7 @@ 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_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, 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); cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ); @@ -45,7 +45,7 @@ def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_ste def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, - int iterationsNumb, + int iterationsNumb, float marching_step_parameter): cdef long dims[2] dims[0] = inputData.shape[0] @@ -80,45 +80,46 @@ def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, #********************** Total-variation FGP *********************# #****************************************************************# #******** Total-variation Fast-Gradient-Projection (FGP)*********# -def TV_FGP_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM): +def TV_FGP_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg): if inputData.ndim == 2: - return TV_FGP_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM) + return TV_FGP_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg) elif inputData.ndim == 3: - return TV_FGP_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM) + return TV_FGP_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg) def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, int iterationsNumb, float tolerance_param, int methodTV, - int nonneg, - int printM): - + int nonneg): + 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=2, mode="c"] infovec = \ + np.ones([dims[0],dims[1]], dtype='float32') + #/* Run FGP-TV iterations for 2D data */ - TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, + TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0,0], regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, - printM, dims[1],dims[0],1) - return outputData + return (outputData,infovec) def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, float regularisation_parameter, int iterationsNumb, float tolerance_param, int methodTV, - int nonneg, - int printM): + int nonneg): + cdef long dims[3] dims[0] = inputData.shape[0] dims[1] = inputData.shape[1] @@ -126,16 +127,17 @@ def TV_FGP_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.zeros([2], dtype='float32') + #/* Run FGP-TV iterations for 3D data */ - TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, + TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, - printM, dims[2], dims[1], dims[0]) - return outputData + return (outputData,infovec) #***************************************************************# #********************** Total-variation SB *********************# |