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-rw-r--r--src/Python/ccpi/filters/regularisers.py28
-rw-r--r--src/Python/setup-regularisers.py.in25
-rw-r--r--src/Python/src/cpu_regularisers.pyx42
3 files changed, 78 insertions, 17 deletions
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index 0b5b2ee..d65c0b9 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -2,7 +2,7 @@
script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
"""
-from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_PD_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
try:
from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU
gpu_enabled = True
@@ -51,6 +51,31 @@ def FGP_TV(inputData, regularisation_parameter,iterations,
raise ValueError ('GPU is not available')
raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
.format(device))
+
+def PD_TV(inputData, regularisation_parameter, iterations,
+ tolerance_param, methodTV, nonneg, lipschitz_const, device='cpu'):
+ if device == 'cpu':
+ return TV_PD_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ nonneg,
+ lipschitz_const)
+ elif device == 'gpu' and gpu_enabled:
+ return TV_PD_CPU(inputData,
+ regularisation_parameter,
+ iterations,
+ tolerance_param,
+ methodTV,
+ nonneg,
+ lipschitz_const)
+ else:
+ if not gpu_enabled and device == 'gpu':
+ raise ValueError ('GPU is not available')
+ raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+ .format(device))
+
def SB_TV(inputData, regularisation_parameter, iterations,
tolerance_param, methodTV, device='cpu'):
if device == 'cpu':
@@ -212,4 +237,3 @@ def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, itera
def NVM_INP(inputData, maskData, SW_increment, iterations):
return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations)
-
diff --git a/src/Python/setup-regularisers.py.in b/src/Python/setup-regularisers.py.in
index 4c578e3..9bcd46d 100644
--- a/src/Python/setup-regularisers.py.in
+++ b/src/Python/setup-regularisers.py.in
@@ -8,13 +8,13 @@ from Cython.Distutils import build_ext
import os
import sys
import numpy
-import platform
+import platform
cil_version=os.environ['CIL_VERSION']
if cil_version == '':
print("Please set the environmental variable CIL_VERSION")
sys.exit(1)
-
+
library_include_path = ""
library_lib_path = ""
try:
@@ -23,7 +23,7 @@ try:
except:
library_include_path = os.environ['PREFIX']+'/include'
pass
-
+
extra_include_dirs = [numpy.get_include(), library_include_path]
#extra_library_dirs = [os.path.join(library_include_path, "..", "lib")]
extra_compile_args = []
@@ -38,6 +38,7 @@ extra_include_dirs += [os.path.join(".." , "Core"),
os.path.join(".." , "Core", "regularisers_CPU"),
os.path.join(".." , "Core", "inpainters_CPU"),
os.path.join(".." , "Core", "regularisers_GPU" , "TV_FGP" ) ,
+ os.path.join(".." , "Core", "regularisers_GPU" , "TV_PD" ) ,
os.path.join(".." , "Core", "regularisers_GPU" , "TV_ROF" ) ,
os.path.join(".." , "Core", "regularisers_GPU" , "TV_SB" ) ,
os.path.join(".." , "Core", "regularisers_GPU" , "TGV" ) ,
@@ -48,12 +49,12 @@ extra_include_dirs += [os.path.join(".." , "Core"),
os.path.join(".." , "Core", "regularisers_GPU" , "PatchSelect" ) ,
"."]
-if platform.system() == 'Windows':
- extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]
+if platform.system() == 'Windows':
+ extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]
else:
extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
extra_libraries += [@EXTRA_OMP_LIB@]
-
+
setup(
name='ccpi',
description='CCPi Core Imaging Library - Image regularisers',
@@ -61,13 +62,13 @@ setup(
cmdclass = {'build_ext': build_ext},
ext_modules = [Extension("ccpi.filters.cpu_regularisers",
sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ],
- include_dirs=extra_include_dirs,
- library_dirs=extra_library_dirs,
- extra_compile_args=extra_compile_args,
- libraries=extra_libraries ),
-
+ include_dirs=extra_include_dirs,
+ library_dirs=extra_library_dirs,
+ extra_compile_args=extra_compile_args,
+ libraries=extra_libraries ),
+
],
- zip_safe = False,
+ zip_safe = False,
packages = {'ccpi', 'ccpi.filters', 'ccpi.supp'},
)
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index 4917d06..724634b 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/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 *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, 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);
@@ -58,9 +59,6 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \
np.ones([2], dtype='float32')
- # Run ROF iterations for 2D data
- # TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
- # Run ROF iterations for 2D data
if isinstance (regularisation_parameter, np.ndarray):
reg = regularisation_parameter.copy()
TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], &reg[0,0], 1, iterationsNumb, marching_step_parameter, tolerance_param, dims[1], dims[0], 1)
@@ -158,6 +156,44 @@ def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
dims[2], dims[1], dims[0])
return (outputData,infovec)
+#****************************************************************#
+#****************** Total-variation Primal-dual *****************#
+#****************************************************************#
+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)
+ elif inputData.ndim == 3:
+ return 0
+
+def TV_PD_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+ float regularisation_parameter,
+ int iterationsNumb,
+ float tolerance_param,
+ int methodTV,
+ int nonneg,
+ float lipschitz_const):
+
+ 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')
+
+ cdef np.ndarray[np.float32_t, ndim=1, mode="c"] infovec = \
+ np.ones([2], dtype='float32')
+
+ #/* Run FGP-TV iterations for 2D data */
+ PDTV_CPU_main(&inputData[0,0], &outputData[0,0], &infovec[0], regularisation_parameter,
+ iterationsNumb,
+ tolerance_param,
+ lipschitz_const,
+ methodTV,
+ nonneg,
+ dims[1],dims[0], 1)
+
+ return (outputData,infovec)
+
#***************************************************************#
#********************** Total-variation SB *********************#
#***************************************************************#
generated by cgit v1.2.3 (git 2.25.1) at 2025-11-04 06:39:01 +0000