inpaint demo

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diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
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+++ b/Wrappers/Python/demos/demo_cpu_inpainters.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Demonstration of CPU inpainters
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from scipy import io
+from ccpi.filters.regularisers import NDF_INP
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+# read sinogram and the mask
+filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat")
+sino = io.loadmat(filename)
+sino_no_cut = sino.get('Sinogram')
+Mask = sino.get('Mask')
+[angles_dim,detectors_dim] = sino_no_cut.shape
+sinogram = sino_no_cut/np.max(sino_no_cut)
+#apply mask to sinogram
+sino_cut = sinogram*(1-Mask)
+
+plt.figure(1)
+plt.subplot(121)
+plt.imshow(sino_cut,vmin=0.0, vmax=1)
+plt.title('Missing Data sinogram')
+plt.subplot(122)
+plt.imshow(Mask)
+plt.title('Mask')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Inpainting using linear diffusion (2D)__")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(2)
+plt.suptitle('Performance of linear inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut,\
+        'maskData' : Mask,\
+        'regularisation_parameter':6000,\
+        'edge_parameter':0.0,\
+        'number_of_iterations' :5000 ,\
+        'time_marching_parameter':0.000075,\
+        'penalty_type':1
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_linear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+             
+rms = rmse(sinogram, ndf_inp_linear)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_linear, cmap="gray")
+plt.title('{}'.format('Linear diffusion inpainting results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_Inpainting using nonlinear diffusion (2D)_")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut,\
+        'maskData' : Mask,\
+        'regularisation_parameter':80,\
+        'edge_parameter':0.00009,\
+        'number_of_iterations' :1500 ,\
+        'time_marching_parameter':0.000008,\
+        'penalty_type':1
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_nonlinear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+             
+rms = rmse(sinogram, ndf_inp_nonlinear)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_nonlinear, cmap="gray")
+plt.title('{}'.format('Nonlinear diffusion inpainting results'))
+#%%
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