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-rw-r--r--src/Python/ccpi/filters/regularisers.py6
-rw-r--r--src/Python/setup-regularisers.py.in2
-rw-r--r--src/Python/src/gpu_regularisers.pyx72
3 files changed, 44 insertions, 36 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 67f432b..05120af 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -22,7 +22,8 @@ def ROF_TV(inputData, regularisation_parameter, iterations,
return TV_ROF_GPU(inputData,
regularisation_parameter,
iterations,
- time_marching_parameter)
+ time_marching_parameter,
+ tolerance_param)
else:
if not gpu_enabled and device == 'gpu':
raise ValueError ('GPU is not available')
@@ -44,8 +45,7 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
iterations,
tolerance_param,
methodTV,
- nonneg,
- 1)
+ nonneg)
else:
if not gpu_enabled and device == 'gpu':
raise ValueError ('GPU is not available')
diff --git a/src/Python/setup-regularisers.py.in b/src/Python/setup-regularisers.py.in
index 39b820a..4c578e3 100644
--- a/src/Python/setup-regularisers.py.in
+++ b/src/Python/setup-regularisers.py.in
@@ -68,7 +68,7 @@ setup(
],
zip_safe = False,
- packages = {'ccpi','ccpi.filters', 'ccpi.supp'},
+ packages = {'ccpi', 'ccpi.filters', 'ccpi.supp'},
)
diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx
index b52f669..db4fa67 100644
--- a/src/Python/src/gpu_regularisers.pyx
+++ b/src/Python/src/gpu_regularisers.pyx
@@ -20,8 +20,8 @@ cimport numpy as np
CUDAErrorMessage = 'CUDA error'
-cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
-cdef extern int 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 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 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);
@@ -34,17 +34,20 @@ cdef extern int PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned
def TV_ROF_GPU(inputData,
regularisation_parameter,
iterations,
- time_marching_parameter):
+ time_marching_parameter,
+ tolerance_param):
if inputData.ndim == 2:
return ROFTV2D(inputData,
regularisation_parameter,
iterations,
- time_marching_parameter)
+ time_marching_parameter,
+ tolerance_param)
elif inputData.ndim == 3:
return ROFTV3D(inputData,
regularisation_parameter,
iterations,
- time_marching_parameter)
+ time_marching_parameter,
+ tolerance_param)
# Total-variation Fast-Gradient-Projection (FGP)
def TV_FGP_GPU(inputData,
@@ -52,24 +55,21 @@ def TV_FGP_GPU(inputData,
iterations,
tolerance_param,
methodTV,
- nonneg,
- printM):
+ nonneg):
if inputData.ndim == 2:
return FGPTV2D(inputData,
regularisation_parameter,
iterations,
tolerance_param,
methodTV,
- nonneg,
- printM)
+ nonneg)
elif inputData.ndim == 3:
return FGPTV3D(inputData,
regularisation_parameter,
iterations,
tolerance_param,
methodTV,
- nonneg,
- printM)
+ nonneg)
# Total-variation Split Bregman (SB)
def TV_SB_GPU(inputData,
regularisation_parameter,
@@ -179,7 +179,8 @@ def Diff4th_GPU(inputData,
def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
int iterations,
- float time_marching_parameter):
+ float time_marching_parameter,
+ float tolerance_param):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -187,22 +188,26 @@ def ROFTV2D(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_ROF_GPU_main(
- &inputData[0,0], &outputData[0,0],
+ &inputData[0,0], &outputData[0,0], &infovec[0],
regularisation_parameter,
- iterations ,
+ iterations,
time_marching_parameter,
+ tolerance_param,
dims[1], dims[0], 1)==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
int iterations,
- float time_marching_parameter):
+ float time_marching_parameter,
+ float tolerance_param):
cdef long dims[3]
dims[0] = inputData.shape[0]
@@ -211,15 +216,18 @@ def ROFTV3D(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_ROF_GPU_main(
- &inputData[0,0,0], &outputData[0,0,0],
+ &inputData[0,0,0], &outputData[0,0,0], &infovec[0],
regularisation_parameter,
- iterations ,
+ iterations,
time_marching_parameter,
+ tolerance_param,
dims[2], dims[1], dims[0])==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
#****************************************************************#
@@ -231,8 +239,7 @@ def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
int iterations,
float tolerance_param,
int methodTV,
- int nonneg,
- int printM):
+ int nonneg):
cdef long dims[2]
dims[0] = inputData.shape[0]
@@ -240,47 +247,48 @@ def FGPTV2D(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_FGP_GPU_main(&inputData[0,0], &outputData[0,0],
+ if (TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0], &infovec[0],
regularisation_parameter,
- iterations,
+ iterations,
tolerance_param,
methodTV,
nonneg,
- printM,
dims[1], dims[0], 1)==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
-
def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
int iterations,
float tolerance_param,
int methodTV,
- int nonneg,
- int printM):
+ int nonneg):
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.ones([2], dtype='float32')
# Running CUDA code here
- if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
- regularisation_parameter ,
+ if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0],
+ regularisation_parameter,
iterations,
tolerance_param,
methodTV,
nonneg,
- printM,
dims[2], dims[1], dims[0])==0):
- return outputData;
+ return (outputData,infovec)
else:
raise ValueError(CUDAErrorMessage);
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 06:38:59 +0000