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authorDaniil Kazantsev <dkazanc@hotmail.com>2018-05-23 15:41:35 +0100
committerDaniil Kazantsev <dkazanc@hotmail.com>2018-05-23 15:41:35 +0100
commite53d631a2d0c34915459028e3db64153c3a936c3 (patch)
tree86601c3ccf2c3b21a307e484b9cf35f1bd364fed /Wrappers/Python/src
parent601cd64a26786cf27a4ea1083bca146094909799 (diff)
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TGV for CPU and GPU added with demos
Diffstat (limited to 'Wrappers/Python/src')
-rw-r--r--Wrappers/Python/src/cpu_regularisers.pyx34
-rw-r--r--Wrappers/Python/src/gpu_regularisers.pyx34
2 files changed, 68 insertions, 0 deletions
diff --git a/Wrappers/Python/src/cpu_regularisers.pyx b/Wrappers/Python/src/cpu_regularisers.pyx
index bdb1eff..cf81bec 100644
--- a/Wrappers/Python/src/cpu_regularisers.pyx
+++ b/Wrappers/Python/src/cpu_regularisers.pyx
@@ -21,6 +21,7 @@ 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 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 TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
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);
cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
@@ -189,6 +190,39 @@ def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
printM,
dims[2], dims[1], dims[0])
return outputData
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+ if inputData.ndim == 2:
+ return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+ elif inputData.ndim == 3:
+ return 0
+
+def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameter,
+ float alpha1,
+ float alpha0,
+ int iterationsNumb,
+ float LipshitzConst):
+
+ 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 TGV iterations for 2D data */
+ TGV_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ alpha1,
+ alpha0,
+ iterationsNumb,
+ LipshitzConst,
+ 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 b67e62b..4a202d7 100644
--- a/Wrappers/Python/src/gpu_regularisers.pyx
+++ b/Wrappers/Python/src/gpu_regularisers.pyx
@@ -21,6 +21,7 @@ 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 TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY);
cdef extern void 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 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);
cdef extern void Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
@@ -86,6 +87,12 @@ def TV_SB_GPU(inputData,
tolerance_param,
methodTV,
printM)
+# Total Generilised Variation (TGV)
+def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+ if inputData.ndim == 2:
+ return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+ elif inputData.ndim == 3:
+ return 0
# Directional Total-variation Fast-Gradient-Projection (FGP)
def dTV_FGP_GPU(inputData,
refdata,
@@ -315,6 +322,33 @@ def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
dims[2], dims[1], dims[0]);
return outputData
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameter,
+ float alpha1,
+ float alpha0,
+ int iterationsNumb,
+ float LipshitzConst):
+
+ 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 TGV iterations for 2D data */
+ TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+ alpha1,
+ alpha0,
+ iterationsNumb,
+ LipshitzConst,
+ dims[1],dims[0])
+ return outputData
+
#****************************************************************#
#**************Directional Total-variation FGP ******************#
#****************************************************************#