From 8d310478254f3cda63f3663729b416f425ad70b6 Mon Sep 17 00:00:00 2001
From: algol <dkazanc@hotmail.com>
Date: Tue, 6 Mar 2018 11:45:53 +0000
Subject: work on FGP intergration

---
 Wrappers/Python/demo/test_cpu_regularizers.py | 17 +++++++++--------
 Wrappers/Python/src/cpu_regularizers.pyx      | 14 +++++---------
 2 files changed, 14 insertions(+), 17 deletions(-)

(limited to 'Wrappers/Python')

diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py
index 53b8538..f1eb3c3 100644
--- a/Wrappers/Python/demo/test_cpu_regularizers.py
+++ b/Wrappers/Python/demo/test_cpu_regularizers.py
@@ -131,9 +131,9 @@ imgplot = plt.imshow(splitbregman,\
 start_time = timeit.default_timer()
 pars = {'algorithm' : TV_FGP_CPU , \
         'input' : u0,
-        'regularization_parameter':0.05, \
-        'number_of_iterations' :200 ,\
-        'tolerance_constant':1e-5,\
+        'regularization_parameter':0.07, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
         'methodTV': 0 ,\
         'nonneg': 0 ,\
         'printingOut': 0
@@ -156,7 +156,7 @@ txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
 print (txtstr)       
 
 
-a=fig.add_subplot(2,4,3)
+a=fig.add_subplot(2,4,4)
 
 # these are matplotlib.patch.Patch properties
 props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
@@ -168,8 +168,9 @@ imgplot = plt.imshow(fgp, \
 a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
         verticalalignment='top', bbox=props)
 
-###################### LLT_model #########################################
 
+###################### LLT_model #########################################
+"""
 start_time = timeit.default_timer()
 
 pars = {'algorithm': LLT_model , \
@@ -204,7 +205,7 @@ a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
 imgplot = plt.imshow(llt,\
                      cmap="gray"
                      )
-
+"""
 
 # ###################### PatchBased_Regul #########################################
 # # Quick 2D denoising example in Matlab:   
@@ -292,8 +293,8 @@ pars = {'algorithm': TV_ROF_CPU , \
         'number_of_iterations': 300
         }
 rof = TV_ROF_CPU(pars['input'],
-             pars['number_of_iterations'],
-             pars['regularization_parameter'],
+			 pars['regularization_parameter'],
+             pars['number_of_iterations'],             
              pars['marching_step'] 
              )
 #tgv = out
diff --git a/Wrappers/Python/src/cpu_regularizers.pyx b/Wrappers/Python/src/cpu_regularizers.pyx
index 2654831..d62ca59 100644
--- a/Wrappers/Python/src/cpu_regularizers.pyx
+++ b/Wrappers/Python/src/cpu_regularizers.pyx
@@ -21,14 +21,11 @@ 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_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
 
-def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb
-           marching_step_parameter):
+def TV_ROF_CPU(inputData, regularization_parameter, iterationsNumb, marching_step_parameter):
     if inputData.ndim == 2:
-        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb
-                         marching_step_parameter)
+        return TV_ROF_2D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
     elif inputData.ndim == 3:
-        return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb
-                         marching_step_parameter)
+        return TV_ROF_3D(inputData, regularization_parameter, iterationsNumb, marching_step_parameter)
 
 def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
                      float regularization_parameter,
@@ -47,10 +44,9 @@ def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
     return outputData
             
 def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
-                     int iterations,
+                     int iterationsNumb,
                      float regularization_parameter,
-                     float marching_step_parameter
-                     ):
+                     float marching_step_parameter):
     cdef long dims[3]
     dims[0] = inputData.shape[0]
     dims[1] = inputData.shape[1]
-- 
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