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-rw-r--r--src/Python/ccpi/filters/regularisers.py8
-rw-r--r--src/Python/src/cpu_regularisers.pyx18
-rw-r--r--src/Python/src/gpu_regularisers.pyx16
3 files changed, 16 insertions, 26 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 5f4001a..e3a984e 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -53,7 +53,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
.format(device))
def PD_TV(inputData, regularisation_parameter, iterations,
- tolerance_param, methodTV, nonneg, lipschitz_const, tau, device='cpu'):
+ tolerance_param, methodTV, nonneg, lipschitz_const, device='cpu'):
if device == 'cpu':
return TV_PD_CPU(inputData,
regularisation_parameter,
@@ -61,8 +61,7 @@ def PD_TV(inputData, regularisation_parameter, iterations,
tolerance_param,
methodTV,
nonneg,
- lipschitz_const,
- tau)
+ lipschitz_const)
elif device == 'gpu' and gpu_enabled:
return TV_PD_GPU(inputData,
regularisation_parameter,
@@ -70,8 +69,7 @@ def PD_TV(inputData, regularisation_parameter, iterations,
tolerance_param,
methodTV,
nonneg,
- lipschitz_const,
- tau)
+ lipschitz_const)
else:
if not gpu_enabled and device == 'gpu':
raise ValueError ('GPU is not available')
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index 8de6aea..08e247c 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/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 *infovector, float *lambdaPar, int lambda_is_arr, 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 PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ);
+cdef extern float PDTV_CPU_main(float *Input, float *U, float *infovector, float lambdaPar, int iterationsNumb, float epsil, float lipschitz_const, int methodTV, int nonneg, 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 *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ);
@@ -159,11 +159,11 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
#****************************************************************#
#****************** Total-variation Primal-dual *****************#
#****************************************************************#
-def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau):
+def TV_PD_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const):
if inputData.ndim == 2:
- return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
+ return TV_PD_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
elif inputData.ndim == 3:
- return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
+ return TV_PD_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
@@ -171,8 +171,7 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float tolerance_param,
int methodTV,
int nonneg,
- float lipschitz_const,
- float tau):
+ float lipschitz_const):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -191,7 +190,6 @@ def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
lipschitz_const,
methodTV,
nonneg,
- tau,
dims[1],dims[0], 1)
return (outputData,infovec)
@@ -200,9 +198,8 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
int iterationsNumb,
float tolerance_param,
int methodTV,
- int nonneg,
- float lipschitz_const,
- float tau):
+ int nonneg,
+ float lipschitz_const):
cdef long dims[3]
dims[0] = inputData.shape[0]
@@ -221,7 +218,6 @@ def TV_PD_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
lipschitz_const,
methodTV,
nonneg,
- tau,
dims[2], dims[1], dims[0])
return (outputData,infovec)
diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx
index b22d15e..8a4568e 100644
--- a/src/Python/src/gpu_regularisers.pyx
+++ b/src/Python/src/gpu_regularisers.pyx
@@ -22,7 +22,7 @@ CUDAErrorMessage = 'CUDA error'
cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float *infovector, float *lambdaPar, int lambda_is_arr, 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_PD_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, float lipschitz_const, int methodTV, int nonneg, float tau, int dimX, int dimY, int dimZ);
+cdef extern int TV_PD_GPU_main(float *Input, float *Output, float *infovector, float lambdaPar, int iter, float epsil, float lipschitz_const, int methodTV, int nonneg, int dimX, int dimY, int dimZ);
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 *infovector, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, float epsil, int dimX, int dimY, int dimZ);
@@ -72,11 +72,11 @@ def TV_FGP_GPU(inputData,
methodTV,
nonneg)
# Total-variation Primal-Dual (PD)
-def TV_PD_GPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau):
+def TV_PD_GPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const):
if inputData.ndim == 2:
- return TVPD2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
+ return TVPD2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
elif inputData.ndim == 3:
- return TVPD3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const, tau)
+ return TVPD3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, lipschitz_const)
def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
@@ -84,8 +84,7 @@ def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float tolerance_param,
int methodTV,
int nonneg,
- float lipschitz_const,
- float tau):
+ float lipschitz_const):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -103,7 +102,6 @@ def TVPD2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
lipschitz_const,
methodTV,
nonneg,
- tau,
dims[1],dims[0], 1) ==0):
return (outputData,infovec)
else:
@@ -115,8 +113,7 @@ def TVPD3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float tolerance_param,
int methodTV,
int nonneg,
- float lipschitz_const,
- float tau):
+ float lipschitz_const):
cdef long dims[3]
dims[0] = inputData.shape[0]
@@ -134,7 +131,6 @@ def TVPD3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
lipschitz_const,
methodTV,
nonneg,
- tau,
dims[2], dims[1], dims[0]) ==0):
return (outputData,infovec)
else:
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 06:38:59 +0000