From 47693d15132130513f8d0f74fd4831a3bbf69159 Mon Sep 17 00:00:00 2001 From: dkazanc Date: Thu, 7 Mar 2019 17:52:57 +0000 Subject: matlab cmake fixed, rof tv eps --- src/Python/ccpi/filters/regularisers.py | 6 +-- src/Python/setup-regularisers.py.in | 2 +- src/Python/src/gpu_regularisers.pyx | 72 ++++++++++++++++++--------------- 3 files changed, 44 insertions(+), 36 deletions(-) (limited to 'src/Python') diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py index 67f432b..05120af 100644 --- a/src/Python/ccpi/filters/regularisers.py +++ b/src/Python/ccpi/filters/regularisers.py @@ -22,7 +22,8 @@ def ROF_TV(inputData, regularisation_parameter, iterations, return TV_ROF_GPU(inputData, regularisation_parameter, iterations, - time_marching_parameter) + time_marching_parameter, + tolerance_param) else: if not gpu_enabled and device == 'gpu': raise ValueError ('GPU is not available') @@ -44,8 +45,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations, iterations, tolerance_param, methodTV, - nonneg, - 1) + nonneg) 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 39b820a..4c578e3 100644 --- a/src/Python/setup-regularisers.py.in +++ b/src/Python/setup-regularisers.py.in @@ -68,7 +68,7 @@ setup( ], zip_safe = False, - packages = {'ccpi','ccpi.filters', 'ccpi.supp'}, + packages = {'ccpi', 'ccpi.filters', 'ccpi.supp'}, ) diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx index b52f669..db4fa67 100644 --- a/src/Python/src/gpu_regularisers.pyx +++ b/src/Python/src/gpu_regularisers.pyx @@ -20,8 +20,8 @@ cimport numpy as np CUDAErrorMessage = 'CUDA error' -cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z); -cdef extern int TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z); +cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float *infovector, float lambdaPar, 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_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z); cdef extern int TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ); cdef extern int LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z); @@ -34,17 +34,20 @@ cdef extern int PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned def TV_ROF_GPU(inputData, regularisation_parameter, iterations, - time_marching_parameter): + time_marching_parameter, + tolerance_param): if inputData.ndim == 2: return ROFTV2D(inputData, regularisation_parameter, iterations, - time_marching_parameter) + time_marching_parameter, + tolerance_param) elif inputData.ndim == 3: return ROFTV3D(inputData, regularisation_parameter, iterations, - time_marching_parameter) + time_marching_parameter, + tolerance_param) # Total-variation Fast-Gradient-Projection (FGP) def TV_FGP_GPU(inputData, @@ -52,24 +55,21 @@ def TV_FGP_GPU(inputData, iterations, tolerance_param, methodTV, - nonneg, - printM): + nonneg): if inputData.ndim == 2: return FGPTV2D(inputData, regularisation_parameter, iterations, tolerance_param, methodTV, - nonneg, - printM) + nonneg) elif inputData.ndim == 3: return FGPTV3D(inputData, regularisation_parameter, iterations, tolerance_param, methodTV, - nonneg, - printM) + nonneg) # Total-variation Split Bregman (SB) def TV_SB_GPU(inputData, regularisation_parameter, @@ -179,7 +179,8 @@ def Diff4th_GPU(inputData, def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, float regularisation_parameter, int iterations, - float time_marching_parameter): + float time_marching_parameter, + float tolerance_param): cdef long dims[2] dims[0] = inputData.shape[0] @@ -187,22 +188,26 @@ def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 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') # Running CUDA code here if (TV_ROF_GPU_main( - &inputData[0,0], &outputData[0,0], + &inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, - iterations , + iterations, time_marching_parameter, + tolerance_param, dims[1], dims[0], 1)==0): - return outputData; + return (outputData,infovec) else: raise ValueError(CUDAErrorMessage); def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, float regularisation_parameter, int iterations, - float time_marching_parameter): + float time_marching_parameter, + float tolerance_param): cdef long dims[3] dims[0] = inputData.shape[0] @@ -211,15 +216,18 @@ def ROFTV3D(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') # Running CUDA code here if (TV_ROF_GPU_main( - &inputData[0,0,0], &outputData[0,0,0], + &inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, - iterations , + iterations, time_marching_parameter, + tolerance_param, dims[2], dims[1], dims[0])==0): - return outputData; + return (outputData,infovec) else: raise ValueError(CUDAErrorMessage); #****************************************************************# @@ -231,8 +239,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, int iterations, float tolerance_param, int methodTV, - int nonneg, - int printM): + int nonneg): cdef long dims[2] dims[0] = inputData.shape[0] @@ -240,47 +247,48 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 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') # Running CUDA code here - if (TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0], + if (TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, - iterations, + iterations, tolerance_param, methodTV, nonneg, - printM, dims[1], dims[0], 1)==0): - return outputData; + return (outputData,infovec) else: raise ValueError(CUDAErrorMessage); - def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, float regularisation_parameter, int iterations, 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] dims[2] = inputData.shape[2] + 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') # Running CUDA code here - if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0], - regularisation_parameter , + if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], + regularisation_parameter, iterations, tolerance_param, methodTV, nonneg, - printM, dims[2], dims[1], dims[0])==0): - return outputData; + return (outputData,infovec) else: raise ValueError(CUDAErrorMessage); -- cgit v1.2.3