diff options
Diffstat (limited to 'src/Python')
| -rw-r--r-- | src/Python/ccpi/filters/regularisers.py | 6 | ||||
| -rw-r--r-- | src/Python/src/cpu_regularisers.pyx | 15 |
2 files changed, 14 insertions, 7 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index 00afcc2..610907d 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -127,11 +127,13 @@ def NDF(inputData, regularisation_parameter, edge_parameter, iterations, raise ValueError ('GPU is not available') raise ValueError('Unknown device {0}. Expecting gpu or cpu'\ .format(device)) -def NDF_MASK(inputData, maskdata, diffuswindow, regularisation_parameter, edge_parameter, iterations, +def NDF_MASK(inputData, maskdata, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterations, time_marching_parameter, penalty_type, tolerance_param, device='cpu'): if device == 'cpu': return NDF_MASK_CPU(inputData, maskdata, + select_classes, + total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, @@ -142,6 +144,8 @@ def NDF_MASK(inputData, maskdata, diffuswindow, regularisation_parameter, edge_p elif device == 'gpu' and gpu_enabled: return NDF_MASK_CPU(inputData, maskdata, + select_classes, + total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx index ca402c1..78c46e2 100644 --- a/src/Python/src/cpu_regularisers.pyx +++ b/src/Python/src/cpu_regularisers.pyx @@ -24,7 +24,7 @@ cdef extern float SB_TV_CPU_main(float *Input, float *Output, float *infovector, 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); cdef extern float Diffusion_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); -cdef extern float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, unsigned char *MASK_upd, float *Output, float *infovector, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); +cdef extern float DiffusionMASK_CPU_main(float *Input, unsigned char *MASK, unsigned char *MASK_upd, unsigned char *SelClassesList, int SelClassesList_length, float *Output, float *infovector, int classesNumb, int DiffusWindow, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, float epsil, int dimX, int dimY, int dimZ); cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, float epsil, int dimX, int dimY, int dimZ); cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int dimX, int dimY, int dimZ); cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ); @@ -384,14 +384,16 @@ def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, #****************************************************************# #********Constrained Nonlinear(Isotropic) Diffusion**************# #****************************************************************# -def NDF_MASK_CPU(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type, tolerance_param): +def NDF_MASK_CPU(inputData, maskData, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type, tolerance_param): if inputData.ndim == 2: - return NDF_MASK_2D(inputData, maskData, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param) + return NDF_MASK_2D(inputData, maskData, select_classes, total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param) elif inputData.ndim == 3: return 0 def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData, + np.ndarray[np.uint8_t, ndim=1, mode="c"] select_classes, + int total_classesNum, int diffuswindow, float regularisation_parameter, float edge_parameter, @@ -403,7 +405,8 @@ def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, dims[0] = inputData.shape[0] dims[1] = inputData.shape[1] - + select_classes_length = select_classes.shape[0] + cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] mask_upd = \ np.zeros([dims[0],dims[1]], dtype='uint8') cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \ @@ -413,8 +416,8 @@ def NDF_MASK_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, # Run constrained nonlinear diffusion iterations for 2D data - DiffusionMASK_CPU_main(&inputData[0,0], &maskData[0,0], &mask_upd[0,0], &outputData[0,0], &infovec[0], - diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, + DiffusionMASK_CPU_main(&inputData[0,0], &maskData[0,0], &mask_upd[0,0], &select_classes[0], select_classes_length, &outputData[0,0], &infovec[0], + total_classesNum, diffuswindow, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, tolerance_param, dims[1], dims[0], 1) |