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-rw-r--r--demos/demo_cpu_regularisers.py8
-rw-r--r--demos/demo_cpu_regularisers3D.py6
-rw-r--r--demos/demo_cpu_vs_gpu_regularisers.py12
-rw-r--r--demos/demo_gpu_regularisers.py6
-rw-r--r--demos/demo_gpu_regularisers3D.py6
-rw-r--r--src/Core/regularisers_CPU/PD_TV_core.c6
-rw-r--r--src/Core/regularisers_CPU/PD_TV_core.h2
-rw-r--r--src/Core/regularisers_GPU/TV_PD_GPU_core.cu13
-rw-r--r--src/Core/regularisers_GPU/TV_PD_GPU_core.h2
-rw-r--r--src/Matlab/mex_compile/regularisers_CPU/PD_TV.c50
-rw-r--r--src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp49
-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
-rw-r--r--test/test_CPU_regularisers.py2
-rwxr-xr-xtest/test_run_test.py9
16 files changed, 90 insertions, 123 deletions
diff --git a/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py
index 7d66b7f..50a5065 100644
--- a/demos/demo_cpu_regularisers.py
+++ b/demos/demo_cpu_regularisers.py
@@ -176,11 +176,10 @@ pars = {'algorithm' : PD_TV, \
'input' : u0,\
'regularisation_parameter':0.02, \
'number_of_iterations' :1500 ,\
- 'tolerance_constant':1e-08,\
+ 'tolerance_constant':1e-06,\
'methodTV': 0 ,\
'nonneg': 1,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV CPU####################")
start_time = timeit.default_timer()
@@ -190,8 +189,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'cpu')
+ pars['lipschitz_const'],'cpu')
Qtools = QualityTools(Im, pd_cpu)
pars['rmse'] = Qtools.rmse()
diff --git a/demos/demo_cpu_regularisers3D.py b/demos/demo_cpu_regularisers3D.py
index cfdd2d4..0e7e9be 100644
--- a/demos/demo_cpu_regularisers3D.py
+++ b/demos/demo_cpu_regularisers3D.py
@@ -188,8 +188,7 @@ pars = {'algorithm' : PD_TV, \
'tolerance_constant':1e-06,\
'methodTV': 0 ,\
'nonneg': 0,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############FGP TV GPU####################")
start_time = timeit.default_timer()
@@ -199,8 +198,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'cpu')
+ pars['lipschitz_const'],'cpu')
Qtools = QualityTools(idealVol, pd_cpu3D)
pars['rmse'] = Qtools.rmse()
diff --git a/demos/demo_cpu_vs_gpu_regularisers.py b/demos/demo_cpu_vs_gpu_regularisers.py
index 015dfc6..a34bc19 100644
--- a/demos/demo_cpu_vs_gpu_regularisers.py
+++ b/demos/demo_cpu_vs_gpu_regularisers.py
@@ -241,8 +241,7 @@ pars = {'algorithm' : PD_TV, \
'tolerance_constant':0.0,\
'methodTV': 0 ,\
'nonneg': 0,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV CPU####################")
start_time = timeit.default_timer()
@@ -252,8 +251,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'cpu')
+ pars['lipschitz_const'],'cpu')
Qtools = QualityTools(Im, pd_cpu)
pars['rmse'] = Qtools.rmse()
@@ -279,8 +277,7 @@ pars = {'algorithm' : PD_TV, \
'tolerance_constant':0.0,\
'methodTV': 0 ,\
'nonneg': 0,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV CPU####################")
start_time = timeit.default_timer()
@@ -290,8 +287,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'gpu')
+ pars['lipschitz_const'],'gpu')
Qtools = QualityTools(Im, pd_gpu)
pars['rmse'] = Qtools.rmse()
diff --git a/demos/demo_gpu_regularisers.py b/demos/demo_gpu_regularisers.py
index 5131847..c6114db 100644
--- a/demos/demo_gpu_regularisers.py
+++ b/demos/demo_gpu_regularisers.py
@@ -176,8 +176,7 @@ pars = {'algorithm' : PD_TV, \
'tolerance_constant':1e-06,\
'methodTV': 0 ,\
'nonneg': 1,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV CPU####################")
start_time = timeit.default_timer()
@@ -187,8 +186,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'gpu')
+ pars['lipschitz_const'],'gpu')
Qtools = QualityTools(Im, pd_gpu)
pars['rmse'] = Qtools.rmse()
diff --git a/demos/demo_gpu_regularisers3D.py b/demos/demo_gpu_regularisers3D.py
index 2c25d01..18d97e5 100644
--- a/demos/demo_gpu_regularisers3D.py
+++ b/demos/demo_gpu_regularisers3D.py
@@ -192,8 +192,7 @@ pars = {'algorithm' : PD_TV, \
'tolerance_constant':1e-06,\
'methodTV': 0 ,\
'nonneg': 0,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV GPU####################")
start_time = timeit.default_timer()
@@ -203,8 +202,7 @@ start_time = timeit.default_timer()
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'gpu')
+ pars['lipschitz_const'],'gpu')
Qtools = QualityTools(idealVol, pd_gpu3D)
pars['rmse'] = Qtools.rmse()
diff --git a/src/Core/regularisers_CPU/PD_TV_core.c b/src/Core/regularisers_CPU/PD_TV_core.c
index 534091b..c1b21e7 100644
--- a/src/Core/regularisers_CPU/PD_TV_core.c
+++ b/src/Core/regularisers_CPU/PD_TV_core.c
@@ -29,7 +29,6 @@
* 5. lipschitz_const: convergence related parameter
* 6. TV-type: methodTV - 'iso' (0) or 'l1' (1)
* 7. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
- * 8. tau: time marching parameter
* Output:
* [1] TV - Filtered/regularized image/volume
@@ -38,16 +37,17 @@
* [1] Antonin Chambolle, Thomas Pock. "A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging", 2010
*/
-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)
+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)
{
int ll;
long j, DimTotal;
- float re, re1, sigma, theta, lt;
+ float re, re1, sigma, theta, lt, tau;
re = 0.0f; re1 = 0.0f;
int count = 0;
//tau = 1.0/powf(lipschitz_const,0.5);
//sigma = 1.0/powf(lipschitz_const,0.5);
+ tau = lambdaPar*0.1f;
sigma = 1.0/(lipschitz_const*tau);
theta = 1.0f;
lt = tau/lambdaPar;
diff --git a/src/Core/regularisers_CPU/PD_TV_core.h b/src/Core/regularisers_CPU/PD_TV_core.h
index 97edc05..294e75c 100644
--- a/src/Core/regularisers_CPU/PD_TV_core.h
+++ b/src/Core/regularisers_CPU/PD_TV_core.h
@@ -47,7 +47,7 @@ limitations under the License.
#ifdef __cplusplus
extern "C" {
#endif
-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);
+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);
CCPI_EXPORT float DualP2D(float *U, float *P1, float *P2, long dimX, long dimY, float sigma);
CCPI_EXPORT float DivProj2D(float *U, float *Input, float *P1, float *P2, long dimX, long dimY, float lt, float tau);
diff --git a/src/Core/regularisers_GPU/TV_PD_GPU_core.cu b/src/Core/regularisers_GPU/TV_PD_GPU_core.cu
index e57020a..01e0ab5 100644
--- a/src/Core/regularisers_GPU/TV_PD_GPU_core.cu
+++ b/src/Core/regularisers_GPU/TV_PD_GPU_core.cu
@@ -33,7 +33,6 @@ limitations under the License.
* 5. lipschitz_const: convergence related parameter
* 6. TV-type: methodTV - 'iso' (0) or 'l1' (1)
* 7. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
- * 8. tau: time marching parameter
* Output:
* [1] TV - Filtered/regularized image/volume
@@ -42,8 +41,8 @@ limitations under the License.
* [1] Antonin Chambolle, Thomas Pock. "A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging", 2010
*/
-#define BLKXSIZE2D 8
-#define BLKYSIZE2D 8
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
#define BLKXSIZE 8
#define BLKYSIZE 8
@@ -322,12 +321,9 @@ __global__ void PDResidCalc3D_kernel(float *Input1, float *Input2, float* Output
Output[index] = Input1[index] - Input2[index];
}
}
-
-
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
-
////////////MAIN HOST FUNCTION ///////////////
-extern "C" 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)
+extern "C" 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)
{
int deviceCount = -1; // number of devices
cudaGetDeviceCount(&deviceCount);
@@ -336,9 +332,10 @@ extern "C" int TV_PD_GPU_main(float *Input, float *Output, float *infovector, fl
return -1;
}
int count = 0, i;
- float re, sigma, theta, lt;
+ float re, sigma, theta, lt, tau;
re = 0.0f;
+ tau = lambdaPar*0.1f;
sigma = 1.0/(lipschitz_const*tau);
theta = 1.0f;
lt = tau/lambdaPar;
diff --git a/src/Core/regularisers_GPU/TV_PD_GPU_core.h b/src/Core/regularisers_GPU/TV_PD_GPU_core.h
index 2b123d9..48e353e 100644
--- a/src/Core/regularisers_GPU/TV_PD_GPU_core.h
+++ b/src/Core/regularisers_GPU/TV_PD_GPU_core.h
@@ -4,6 +4,6 @@
#include "CCPiDefines.h"
#include <memory.h>
-extern "C" CCPI_EXPORT 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);
+extern "C" CCPI_EXPORT 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);
#endif
diff --git a/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c b/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c
index e5ab1e4..f8f5272 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/PD_TV.c
@@ -30,8 +30,7 @@
* 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
* 6. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
* 7. lipschitz_const: convergence related parameter
- * 8. tau: convergence related parameter
-
+
* Output:
* [1] TV - Filtered/regularized image/volume
* [2] Information vector which contains [iteration no., reached tolerance]
@@ -41,19 +40,19 @@
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
-
+
{
int number_of_dims, iter, methTV, nonneg;
mwSize dimX, dimY, dimZ;
const mwSize *dim_array;
- float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const, tau;
-
+ float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const;
+
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
dim_array = mxGetDimensions(prhs[0]);
-
+
/*Handling Matlab input data*/
- if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
-
+ if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
+
Input = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */
iter = 500; /* default iterations number */
@@ -61,40 +60,39 @@ void mexFunction(
methTV = 0; /* default isotropic TV penalty */
nonneg = 0; /* default nonnegativity switch, off - 0 */
lipschitz_const = 8.0; /* lipschitz_const */
- tau = 0.0025; /* tau convergence const */
-
+
+
if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
-
- if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */
- if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */
- if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) {
+
+ if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+ if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */
+ if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7)) {
char *penalty_type;
penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */
mxFree(penalty_type);
}
- if ((nrhs == 6) || (nrhs == 7) || (nrhs == 8)) {
+ if ((nrhs == 6) || (nrhs == 7)) {
nonneg = (int) mxGetScalar(prhs[5]);
if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
}
- if ((nrhs == 7) || (nrhs == 8)) lipschitz_const = (float) mxGetScalar(prhs[6]);
- if (nrhs == 8) tau = (float) mxGetScalar(prhs[7]);
-
+ if (nrhs == 7) lipschitz_const = (float) mxGetScalar(prhs[6]);
+
/*Handling Matlab output data*/
- dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
-
+ dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+
if (number_of_dims == 2) {
dimZ = 1; /*2D case*/
- Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+ Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
}
- if (number_of_dims == 3) {
+ if (number_of_dims == 3) {
Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- }
+ }
mwSize vecdim[1];
vecdim[0] = 2;
infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
-
- /* running the function */
- PDTV_CPU_main(Input, Output, infovec, lambda, iter, epsil, lipschitz_const, methTV, nonneg, tau, dimX, dimY, dimZ);
+
+ /* running the function */
+ PDTV_CPU_main(Input, Output, infovec, lambda, iter, epsil, lipschitz_const, methTV, nonneg, dimX, dimY, dimZ);
}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp
index e853dd3..2c037a5 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/PD_TV_GPU.cpp
@@ -30,8 +30,7 @@
* 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
* 6. nonneg: 'nonnegativity (0 is OFF by default, 1 is ON)
* 7. lipschitz_const: convergence related parameter
- * 8. tau: convergence related parameter
-
+
* Output:
* [1] TV - Filtered/regularized image/volume
* [2] Information vector which contains [iteration no., reached tolerance]
@@ -42,19 +41,19 @@
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
-
+
{
int number_of_dims, iter, methTV, nonneg;
mwSize dimX, dimY, dimZ;
const mwSize *dim_array;
- float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const, tau;
-
+ float *Input, *infovec=NULL, *Output=NULL, lambda, epsil, lipschitz_const;
+
number_of_dims = mxGetNumberOfDimensions(prhs[0]);
dim_array = mxGetDimensions(prhs[0]);
-
+
/*Handling Matlab input data*/
- if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
-
+ if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, lipschitz_const");
+
Input = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
lambda = (float) mxGetScalar(prhs[1]); /* regularization parameter */
iter = 500; /* default iterations number */
@@ -62,40 +61,38 @@ void mexFunction(
methTV = 0; /* default isotropic TV penalty */
nonneg = 0; /* default nonnegativity switch, off - 0 */
lipschitz_const = 8.0; /* lipschitz_const */
- tau = 0.0025; /* tau convergence const */
-
+
if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
-
- if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */
- if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */
- if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8)) {
+
+ if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7)) iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+ if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7)) epsil = (float) mxGetScalar(prhs[3]); /* tolerance constant */
+ if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7)) {
char *penalty_type;
penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
if (strcmp(penalty_type, "l1") == 0) methTV = 1; /* enable 'l1' penalty */
mxFree(penalty_type);
}
- if ((nrhs == 6) || (nrhs == 7) || (nrhs == 8)) {
+ if ((nrhs == 6) || (nrhs == 7)) {
nonneg = (int) mxGetScalar(prhs[5]);
if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
}
- if ((nrhs == 7) || (nrhs == 8)) lipschitz_const = (float) mxGetScalar(prhs[6]);
- if (nrhs == 8) tau = (float) mxGetScalar(prhs[7]);
-
+ if (nrhs == 7) lipschitz_const = (float) mxGetScalar(prhs[6]);
+
/*Handling Matlab output data*/
- dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
-
+ dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+
if (number_of_dims == 2) {
dimZ = 1; /*2D case*/
- Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+ Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
}
- if (number_of_dims == 3) {
+ if (number_of_dims == 3) {
Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
- }
+ }
mwSize vecdim[1];
vecdim[0] = 2;
infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
-
- /* running the function */
- TV_PD_GPU_main(Input, Output, infovec, lambda, iter, epsil, lipschitz_const, methTV, nonneg, tau, dimX, dimY, dimZ);
+
+ /* running the function */
+ TV_PD_GPU_main(Input, Output, infovec, lambda, iter, epsil, lipschitz_const, methTV, nonneg, dimX, dimY, dimZ);
}
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:
diff --git a/test/test_CPU_regularisers.py b/test/test_CPU_regularisers.py
index 266ca8a..1eba479 100644
--- a/test/test_CPU_regularisers.py
+++ b/test/test_CPU_regularisers.py
@@ -42,7 +42,7 @@ class TestRegularisers(unittest.TestCase):
def test_PD_TV_CPU(self):
Im,input,ref = self.getPars()
- pd_cpu,info = PD_TV(input, 0.02, 300, 0.0, 0, 0, 8, 0.0025, 'cpu');
+ pd_cpu,info = PD_TV(input, 0.02, 300, 0.0, 0, 0, 8, 'cpu');
rms = rmse(Im, pd_cpu)
diff --git a/test/test_run_test.py b/test/test_run_test.py
index 1707aec..f562593 100755
--- a/test/test_run_test.py
+++ b/test/test_run_test.py
@@ -200,8 +200,7 @@ class TestRegularisers(unittest.TestCase):
'tolerance_constant':0.0,\
'methodTV': 0 ,\
'nonneg': 0,
- 'lipschitz_const' : 8,
- 'tau' : 0.0025}
+ 'lipschitz_const' : 8}
print ("#############PD TV CPU####################")
start_time = timeit.default_timer()
@@ -211,8 +210,7 @@ class TestRegularisers(unittest.TestCase):
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'cpu')
+ pars['lipschitz_const'],'cpu')
rms = rmse(Im, pd_cpu)
pars['rmse'] = rms
@@ -230,8 +228,7 @@ class TestRegularisers(unittest.TestCase):
pars['tolerance_constant'],
pars['methodTV'],
pars['nonneg'],
- pars['lipschitz_const'],
- pars['tau'],'gpu')
+ pars['lipschitz_const'],'gpu')
except ValueError as ve:
self.skipTest("Results not comparable. GPU computing error.")
generated by cgit v1.2.3 (git 2.39.1) at 2025-11-04 11:49:32 +0000