From 80c5a5e5de2aca8d5c7b96f0adc91b5738cc9025 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Mon, 16 Apr 2018 13:38:40 +0100
Subject: SB TV method CPU/GPU added
---
Wrappers/Python/src/cpu_regularisers.pyx | 58 ++++++++++++++++++++++++
Wrappers/Python/src/gpu_regularisers.pyx | 75 ++++++++++++++++++++++++++++++++
2 files changed, 133 insertions(+)
(limited to 'Wrappers/Python/src')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index 1661375..b8d2523 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -20,6 +20,7 @@ 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_SB_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 dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
@@ -125,6 +126,63 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
printM,
dims[2], dims[1], dims[0])
return outputData
+
+#***************************************************************#
+#********************** Total-variation SB *********************#
+#***************************************************************#
+#*************** Total-variation Split Bregman (SB)*************#
+def TV_SB_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM):
+ if inputData.ndim == 2:
+ return TV_SB_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+ elif inputData.ndim == 3:
+ return TV_SB_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+
+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):
+
+ 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 SB-TV iterations for 2D data */
+ TV_SB_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ iterationsNumb,
+ tolerance_param,
+ methodTV,
+ printM,
+ dims[0], dims[1], 1)
+
+ return outputData
+
+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):
+ 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 SB-TV iterations for 3D data */
+ TV_SB_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+ iterationsNumb,
+ tolerance_param,
+ methodTV,
+ printM,
+ dims[2], dims[1], dims[0])
+ return outputData
#****************************************************************#
#**************Directional Total-variation FGP ******************#
#****************************************************************#
diff --git a/Wrappers/Python/src/gpu_regularisers.pyx b/Wrappers/Python/src/gpu_regularisers.pyx
index 18efdcd..36eec95 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -20,6 +20,7 @@ cimport numpy as np
cdef extern void TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
cdef extern void 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 void 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 void 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);
# Total-variation Rudin-Osher-Fatemi (ROF)
@@ -62,6 +63,27 @@ def TV_FGP_GPU(inputData,
methodTV,
nonneg,
printM)
+# Total-variation Split Bregman (SB)
+def TV_SB_GPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM):
+ if inputData.ndim == 2:
+ return SBTV2D(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM)
+ elif inputData.ndim == 3:
+ return SBTV3D(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM)
# Directional Total-variation Fast-Gradient-Projection (FGP)
def dTV_FGP_GPU(inputData,
refdata,
@@ -197,7 +219,60 @@ def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
dims[2], dims[1], dims[0]);
return outputData
+#***************************************************************#
+#********************** Total-variation SB *********************#
+#***************************************************************#
+#*************** Total-variation Split Bregman (SB)*************#
+def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameter,
+ int iterations,
+ float tolerance_param,
+ int methodTV,
+ int printM):
+
+ 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')
+
+ # Running CUDA code here
+ TV_SB_GPU_main(&inputData[0,0], &outputData[0,0],
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM,
+ dims[0], dims[1], 1);
+
+ return outputData
+def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+ float regularisation_parameter,
+ int iterations,
+ float tolerance_param,
+ int methodTV,
+ int printM):
+
+ 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')
+
+ # Running CUDA code here
+ TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+ regularisation_parameter ,
+ iterations,
+ tolerance_param,
+ methodTV,
+ printM,
+ dims[2], dims[1], dims[0]);
+
+ return outputData
#****************************************************************#
#**************Directional Total-variation FGP ******************#
#****************************************************************#
--
cgit v1.2.3
From 57727584760e6b1a980071587e1f1e8910c7d6a3 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Mon, 16 Apr 2018 15:16:32 +0100
Subject: SB TV demos all work
---
Wrappers/Python/src/cpu_regularisers.pyx | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
(limited to 'Wrappers/Python/src')
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index b8d2523..417670d 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/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 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_SB_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 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 dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
@@ -152,7 +152,7 @@ def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
np.zeros([dims[0],dims[1]], dtype='float32')
#/* Run SB-TV iterations for 2D data */
- TV_SB_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ SB_TV_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
iterationsNumb,
tolerance_param,
methodTV,
@@ -176,7 +176,7 @@ def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
#/* Run SB-TV iterations for 3D data */
- TV_SB_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+ SB_TV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
iterationsNumb,
tolerance_param,
methodTV,
--
cgit v1.2.3