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-rw-r--r--src/Python/ccpi/filters/regularisers.py30
-rw-r--r--src/Python/src/cpu_regularisers.pyx132
-rw-r--r--src/Python/src/gpu_regularisers.pyx72
3 files changed, 127 insertions, 107 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 05120af..09b465a 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -52,21 +52,30 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
.format(device))
def SB_TV(inputData, regularisation_parameter, iterations,
- tolerance_param, methodTV, printM, device='cpu'):
+ tolerance_param, methodTV, device='cpu'):
if device == 'cpu':
return TV_SB_CPU(inputData,
regularisation_parameter,
iterations,
tolerance_param,
- methodTV,
- printM)
+ methodTV)
elif device == 'gpu' and gpu_enabled:
return TV_SB_GPU(inputData,
regularisation_parameter,
iterations,
tolerance_param,
- methodTV,
- printM)
+ methodTV)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
+ time_marching_parameter, tolerance_param, device='cpu'):
+ if device == 'cpu':
+ return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
+ elif device == 'gpu' and gpu_enabled:
+ return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
else:
if not gpu_enabled and device == 'gpu':
raise ValueError ('GPU is not available')
@@ -194,17 +203,6 @@ def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
raise ValueError ('GPU is not available')
raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
.format(device))
-def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
- time_marching_parameter, device='cpu'):
- if device == 'cpu':
- return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
- elif device == 'gpu' and gpu_enabled:
- return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
- else:
- if not gpu_enabled and device == 'gpu':
- raise ValueError ('GPU is not available')
- raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
- .format(device))
def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
time_marching_parameter, penalty_type):
return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter,
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index aeca141..f2276bb 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -20,8 +20,8 @@ cimport numpy as np
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);
+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 lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
@@ -149,18 +149,17 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
#********************** Total-variation SB *********************#
#***************************************************************#
#*************** Total-variation Split Bregman (SB)*************#
-def TV_SB_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM):
+def TV_SB_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV):
if inputData.ndim == 2:
- return TV_SB_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+ return TV_SB_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV)
elif inputData.ndim == 3:
- return TV_SB_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+ return TV_SB_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV)
def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
int iterationsNumb,
float tolerance_param,
- int methodTV,
- int printM):
+ int methodTV):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -168,23 +167,24 @@ def TV_SB_2D(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.zeros([2], dtype='float32')
#/* Run SB-TV iterations for 2D data */
- SB_TV_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ SB_TV_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0],
+ regularisation_parameter,
iterationsNumb,
tolerance_param,
methodTV,
- printM,
- dims[1],dims[0],1)
+ dims[1],dims[0], 1)
- return outputData
+ return (outputData,infovec)
def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
int iterationsNumb,
float tolerance_param,
- int methodTV,
- int printM):
+ int methodTV):
cdef long dims[3]
dims[0] = inputData.shape[0]
dims[1] = inputData.shape[1]
@@ -192,16 +192,71 @@ def TV_SB_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 SB-TV iterations for 3D data */
- SB_TV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+ SB_TV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0],
+ regularisation_parameter,
iterationsNumb,
tolerance_param,
methodTV,
- printM,
dims[2], dims[1], dims[0])
- return outputData
+ return (outputData,infovec)
+#***************************************************************#
+#******************* ROF - LLT regularisation ******************#
+#***************************************************************#
+def LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param):
+ if inputData.ndim == 2:
+ return LLT_ROF_2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
+ elif inputData.ndim == 3:
+ return LLT_ROF_3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
+def LLT_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameterROF,
+ float regularisation_parameterLLT,
+ int iterations,
+ float time_marching_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.zeros([2], dtype='float32')
+
+ #/* Run ROF-LLT iterations for 2D data */
+ LLT_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter,
+ tolerance_param,
+ dims[1],dims[0],1)
+ return (outputData,infovec)
+
+def LLT_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ float regularisation_parameterROF,
+ float regularisation_parameterLLT,
+ int iterations,
+ float time_marching_parameter,
+ float tolerance_param):
+
+ 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.zeros([2], dtype='float32')
+
+ #/* Run ROF-LLT iterations for 3D data */
+ LLT_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameterROF, regularisation_parameterLLT, iterations,
+ time_marching_parameter,
+ tolerance_param,
+ dims[2], dims[1], dims[0])
+ return (outputData,infovec)
#***************************************************************#
#***************** Total Generalised Variation *****************#
#***************************************************************#
@@ -259,49 +314,6 @@ def TGV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
dims[2], dims[1], dims[0])
return outputData
-#***************************************************************#
-#******************* ROF - LLT regularisation ******************#
-#***************************************************************#
-def LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
- if inputData.ndim == 2:
- return LLT_ROF_2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
- elif inputData.ndim == 3:
- return LLT_ROF_3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
-
-def LLT_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
- float regularisation_parameterROF,
- float regularisation_parameterLLT,
- int iterations,
- float time_marching_parameter):
-
- 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')
-
- #/* Run ROF-LLT iterations for 2D data */
- LLT_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)
- return outputData
-
-def LLT_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
- float regularisation_parameterROF,
- float regularisation_parameterLLT,
- int iterations,
- float time_marching_parameter):
-
- 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')
-
- #/* Run ROF-LLT iterations for 3D data */
- LLT_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])
- return outputData
#****************************************************************#
#**************Directional Total-variation FGP ******************#
diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx
index db4fa67..6fc4135 100644
--- a/src/Python/src/gpu_regularisers.pyx
+++ b/src/Python/src/gpu_regularisers.pyx
@@ -22,9 +22,9 @@ CUDAErrorMessage = 'CUDA error'
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 TV_SB_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, int methodTV, int N, int M, int Z);
+cdef extern int LLT_ROF_GPU_main(float *Input, float *Output, float *infovector, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, float epsil, 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);
cdef extern int NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
cdef extern int dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
cdef extern int Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
@@ -75,28 +75,25 @@ def TV_SB_GPU(inputData,
regularisation_parameter,
iterations,
tolerance_param,
- methodTV,
- printM):
+ methodTV):
if inputData.ndim == 2:
return SBTV2D(inputData,
regularisation_parameter,
iterations,
tolerance_param,
- methodTV,
- printM)
+ methodTV)
elif inputData.ndim == 3:
return SBTV3D(inputData,
regularisation_parameter,
iterations,
tolerance_param,
- methodTV,
- printM)
+ methodTV)
# LLT-ROF model
-def LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+def LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param):
if inputData.ndim == 2:
- return LLT_ROF_GPU2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+ return LLT_ROF_GPU2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
elif inputData.ndim == 3:
- return LLT_ROF_GPU3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+ return LLT_ROF_GPU3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, tolerance_param)
# Total Generilised Variation (TGV)
def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
if inputData.ndim == 2:
@@ -300,8 +297,7 @@ def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
int iterations,
float tolerance_param,
- int methodTV,
- int printM):
+ int methodTV):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -309,16 +305,17 @@ def SBTV2D(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_SB_GPU_main(&inputData[0,0], &outputData[0,0],
+ # Running CUDA code here
+ if (TV_SB_GPU_main(&inputData[0,0], &outputData[0,0],&infovec[0],
regularisation_parameter,
iterations,
tolerance_param,
methodTV,
- printM,
dims[1], dims[0], 1)==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
@@ -327,8 +324,7 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
int iterations,
float tolerance_param,
- int methodTV,
- int printM):
+ int methodTV):
cdef long dims[3]
dims[0] = inputData.shape[0]
@@ -337,16 +333,17 @@ def SBTV3D(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_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+ if (TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],&infovec[0],
regularisation_parameter ,
iterations,
tolerance_param,
methodTV,
- printM,
dims[2], dims[1], dims[0])==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
@@ -359,7 +356,8 @@ def LLT_ROF_GPU2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameterROF,
float regularisation_parameterLLT,
int iterations,
- float time_marching_parameter):
+ float time_marching_parameter,
+ float tolerance_param):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -367,10 +365,15 @@ def LLT_ROF_GPU2D(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 (LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)==0):
- return outputData;
+ if (LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],&infovec[0],regularisation_parameterROF, regularisation_parameterLLT, iterations,
+ time_marching_parameter,
+ tolerance_param,
+ dims[1],dims[0],1)==0):
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
@@ -379,7 +382,8 @@ def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameterROF,
float regularisation_parameterLLT,
int iterations,
- float time_marching_parameter):
+ float time_marching_parameter,
+ float tolerance_param):
cdef long dims[3]
dims[0] = inputData.shape[0]
@@ -388,10 +392,16 @@ def LLT_ROF_GPU3D(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 (LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])==0):
- return outputData;
+ # Running CUDA code here
+ if (LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameterROF, regularisation_parameterLLT,
+ iterations,
+ time_marching_parameter,
+ tolerance_param,
+ dims[2], dims[1], dims[0])==0):
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 06:38:57 +0000