From cfcc4be4413f65a0b9c4ef197687e3a167eff0e8 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Wed, 6 Mar 2019 23:34:55 +0000
Subject: cont1
---
src/Python/ccpi/filters/regularisers.py | 5 +++--
src/Python/src/cpu_regularisers.pyx | 28 +++++++++++++++++-----------
2 files changed, 20 insertions(+), 13 deletions(-)
(limited to 'src/Python')
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
index fb2c999..67f432b 100644
--- a/src/Python/ccpi/filters/regularisers.py
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -11,12 +11,13 @@ except ImportError:
from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
def ROF_TV(inputData, regularisation_parameter, iterations,
- time_marching_parameter,device='cpu'):
+ time_marching_parameter,tolerance_param,device='cpu'):
if device == 'cpu':
return TV_ROF_CPU(inputData,
regularisation_parameter,
iterations,
- time_marching_parameter)
+ time_marching_parameter,
+ tolerance_param)
elif device == 'gpu' and gpu_enabled:
return TV_ROF_GPU(inputData,
regularisation_parameter,
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
index 49cdf94..aeca141 100644
--- a/src/Python/src/cpu_regularisers.pyx
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -18,7 +18,7 @@ import cython
import numpy as np
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_ROF_CPU_main(float *Input, float *Output, float *infovector, float lambdaPar, 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 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 LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
@@ -37,32 +37,36 @@ cdef extern float TV_energy3D(float *U, float *U0, float *E_val, float lambdaPar
#****************************************************************#
#********************** Total-variation ROF *********************#
#****************************************************************#
-def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter):
+def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param):
if inputData.ndim == 2:
- return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
+ return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param)
elif inputData.ndim == 3:
- return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
+ return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter,tolerance_param)
def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
float regularisation_parameter,
int iterationsNumb,
- float marching_step_parameter):
+ float marching_step_parameter,
+ float tolerance_param):
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 ROF iterations for 2D data
- TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1)
+ 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)
- return outputData
+ return (outputData,infovec)
def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
float regularisation_parameter,
int iterationsNumb,
- float marching_step_parameter):
+ float marching_step_parameter,
+ float tolerance_param):
cdef long dims[3]
dims[0] = inputData.shape[0]
dims[1] = inputData.shape[1]
@@ -70,11 +74,13 @@ def TV_ROF_3D(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')
# Run ROF iterations for 3D data
- TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[2], dims[1], dims[0])
+ TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], &infovec[0], regularisation_parameter, iterationsNumb, marching_step_parameter, tolerance_param, dims[2], dims[1], dims[0])
- return outputData
+ return (outputData,infovec)
#****************************************************************#
#********************** Total-variation FGP *********************#
--
cgit v1.2.3