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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 22 11:39:43 2018
Demonstration of CPU regularisers
@authors: Daniil Kazantsev, Edoardo Pasca
"""
import matplotlib.pyplot as plt
import numpy as np
import os
import timeit
from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, Diff4th
from ccpi.filters.regularisers import PatchSelect, NLTV
from ccpi.supp.qualitymetrics import QualityTools
from ccpi.plugins.regularisers import TNV as TNV_new
###############################################################################
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
###############################################################################
filename = 'lena_gray_512.tif'
# read image
Im = plt.imread(filename)
Im = np.asarray(Im, dtype='float32')
Im = Im/255.0
perc = 0.05
u0 = Im + np.random.normal(loc = 0 ,
scale = perc * Im ,
size = np.shape(Im))
u_ref = Im + np.random.normal(loc = 0 ,
scale = 0.01 * Im ,
size = np.shape(Im))
(N,M) = np.shape(u0)
# map the u0 u0->u0>0
# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
u0 = u0.astype('float32')
u_ref = u_ref.astype('float32')
# change dims to check that modules work with non-squared images
"""
M = M-100
u_ref2 = np.zeros([N,M],dtype='float32')
u_ref2[:,0:M] = u_ref[:,0:M]
u_ref = u_ref2
del u_ref2
u02 = np.zeros([N,M],dtype='float32')
u02[:,0:M] = u0[:,0:M]
u0 = u02
del u02
Im2 = np.zeros([N,M],dtype='float32')
Im2[:,0:M] = Im[:,0:M]
Im = Im2
del Im2
"""
#%%
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
print ("__________Total nuclear Variation__________")
print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
## plot
fig = plt.figure()
plt.suptitle('Performance of TNV regulariser using the CPU')
a=fig.add_subplot(1,2,1)
a.set_title('Noisy Image')
imgplot = plt.imshow(u0,cmap="gray")
channelsNo = 5
noisyVol = np.zeros((channelsNo,N,M),dtype='float32')
idealVol = np.zeros((channelsNo,N,M),dtype='float32')
for i in range (channelsNo):
noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
idealVol[i,:,:] = Im
# set parameters
pars = {'algorithm' : TNV, \
'input' : noisyVol,\
'regularisation_parameter': 0.04, \
'number_of_iterations' : 200 ,\
'tolerance_constant':1e-05
}
print ("#############TNV CPU#################")
start_time = timeit.default_timer()
tnv_cpu = TNV(pars['input'],
pars['regularisation_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'])
Qtools = QualityTools(idealVol, tnv_cpu)
pars['rmse'] = Qtools.rmse()
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(tnv_cpu[3,:,:], cmap="gray")
plt.title('{}'.format('CPU results'))
#%%
from ccpi.framework import ImageData
g = TNV_new(0.04, 200, 1e-5)
sol = g.proximal(ImageData(noisyVol), 1)
#%%
plt.figure(figsize=(10,10))
plt.subplot(3,1,1)
plt.imshow(sol.as_array()[2])
plt.colorbar()
plt.subplot(3,1,2)
plt.imshow(tnv_cpu[2])
plt.colorbar()
plt.subplot(3,1,3)
plt.imshow(np.abs(tnv_cpu[2] - sol.as_array()[2]))
plt.colorbar()
plt.show()
#plt.imshow(sol.as_array())
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