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-rw-r--r--Wrappers/Python/test/test_cpu_regularizers.py98
1 files changed, 47 insertions, 51 deletions
diff --git a/Wrappers/Python/test/test_cpu_regularizers.py b/Wrappers/Python/test/test_cpu_regularizers.py
index ac595e3..6c97875 100644
--- a/Wrappers/Python/test/test_cpu_regularizers.py
+++ b/Wrappers/Python/test/test_cpu_regularizers.py
@@ -5,17 +5,12 @@ Created on Fri Aug 4 11:10:05 2017
@author: ofn77899
"""
-#from ccpi.viewer.CILViewer2D import Converter
-#import vtk
import matplotlib.pyplot as plt
import numpy as np
import os
from enum import Enum
import timeit
-#from PIL import Image
-#from Regularizer import Regularizer
-#from ccpi.filters.Regularizer import Regularizer
from ccpi.filters.cpu_regularizers import SplitBregman_TV , FGP_TV , LLT_model, \
PatchBased_Regul , TGV_PD
@@ -67,8 +62,10 @@ filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
Im = plt.imread(filename)
Im = np.asarray(Im, dtype='float32')
-perc = 0.05
-u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+perc = 0.15
+u0 = Im + np.random.normal(loc = Im ,
+ scale = perc * Im ,
+ size = np.shape(Im))
# map the u0 u0->u0>0
f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
u0 = f(u0).astype('float32')
@@ -78,7 +75,8 @@ fig = plt.figure()
a=fig.add_subplot(2,3,1)
a.set_title('noise')
-imgplot = plt.imshow(u0,cmap="gray")
+imgplot = plt.imshow(u0#,cmap="gray"
+ )
reg_output = []
##############################################################################
@@ -100,7 +98,7 @@ out = SplitBregman_TV (pars['input'], pars['regularization_parameter'],
pars['number_of_iterations'],
pars['tolerance_constant'],
pars['TV_penalty'])
-plotme = out[0]
+splitbregman = out[0]
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
@@ -114,7 +112,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme,\
+imgplot = plt.imshow(splitbregman,\
#cmap="gray"
)
@@ -124,16 +122,18 @@ start_time = timeit.default_timer()
pars = {'algorithm' : FGP_TV , \
'input' : u0,
'regularization_parameter':5e-4, \
-'number_of_iterations' :10 ,\
-'tolerance_constant':0.001,\
-'TV_penalty': 0
+ 'number_of_iterations' :10 ,\
+ 'tolerance_constant':0.001,\
+ 'TV_penalty': 0
}
-out = FGP_TV (pars['input'], pars['regularization_parameter'],
- pars['number_of_iterations'],
- pars['tolerance_constant'],
- pars['TV_penalty'])
-plotme = out[0]
+out = FGP_TV (pars['input'],
+ pars['regularization_parameter'],
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['TV_penalty'])
+
+fgp = out[0]
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
@@ -144,7 +144,7 @@ a=fig.add_subplot(2,3,3)
# these are matplotlib.patch.Patch properties
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
-imgplot = plt.imshow(plotme, \
+imgplot = plt.imshow(fgp, \
#cmap="gray"
)
# place a text box in upper left in axes coords
@@ -152,11 +152,6 @@ a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
###################### LLT_model #########################################
-# * u0 = Im + .03*randn(size(Im)); % adding noise
-# [Den] = LLT_model(single(u0), 10, 0.1, 1);
-#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0);
-#input, regularization_parameter , time_step, number_of_iterations,
-# tolerance_constant, restrictive_Z_smoothing=0
start_time = timeit.default_timer()
@@ -164,18 +159,18 @@ pars = {'algorithm': LLT_model , \
'input' : u0,
'regularization_parameter': 25,\
'time_step':0.0003, \
-'number_of_iterations' :300,\
-'tolerance_constant':0.001,\
-'restrictive_Z_smoothing': 0
+ 'number_of_iterations' :300,\
+ 'tolerance_constant':0.001,\
+ 'restrictive_Z_smoothing': 0
}
out = LLT_model(pars['input'],
- pars['regularization_parameter'],
- pars['time_step'] ,
- pars['number_of_iterations'],
- pars['tolerance_constant'],
- pars['restrictive_Z_smoothing'] )
+ pars['regularization_parameter'],
+ pars['time_step'] ,
+ pars['number_of_iterations'],
+ pars['tolerance_constant'],
+ pars['restrictive_Z_smoothing'] )
-plotme = out[0]
+llt = out[0]
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
@@ -186,7 +181,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme,\
+imgplot = plt.imshow(llt,\
#cmap="gray"
)
@@ -202,15 +197,15 @@ pars = {'algorithm': PatchBased_Regul , \
'input' : u0,
'regularization_parameter': 0.05,\
'searching_window_ratio':3, \
-'similarity_window_ratio':1,\
-'PB_filtering_parameter': 0.08
+ 'similarity_window_ratio':1,\
+ 'PB_filtering_parameter': 0.08
}
-out = PatchBased_Regul(
- pars['input'], pars['regularization_parameter'],
- pars['searching_window_ratio'] ,
- pars['similarity_window_ratio'] ,
- pars['PB_filtering_parameter'])
-plotme = out[0]
+out = PatchBased_Regul(pars['input'],
+ pars['regularization_parameter'],
+ pars['searching_window_ratio'] ,
+ pars['similarity_window_ratio'] ,
+ pars['PB_filtering_parameter'])
+pbr = out[0]
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
@@ -223,7 +218,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme #,cmap="gray"
+imgplot = plt.imshow(pbr #,cmap="gray"
)
@@ -240,13 +235,14 @@ pars = {'algorithm': TGV_PD , \
'regularization_parameter':0.05,\
'first_order_term': 1.3,\
'second_order_term': 1, \
-'number_of_iterations': 550
-}
-out = TGV_PD(pars['input'], pars['regularization_parameter'],
- pars['first_order_term'] ,
- pars['second_order_term'] ,
- pars['number_of_iterations'])
-plotme = out[0]
+ 'number_of_iterations': 550
+ }
+out = TGV_PD(pars['input'],
+ pars['regularization_parameter'],
+ pars['first_order_term'] ,
+ pars['second_order_term'] ,
+ pars['number_of_iterations'])
+tgv = out[0]
txtstr = printParametersToString(pars)
txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
print (txtstr)
@@ -257,7 +253,7 @@ props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# place a text box in upper left in axes coords
a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
-imgplot = plt.imshow(plotme #, cmap="gray")
+imgplot = plt.imshow(tgv #, cmap="gray")
)
generated by cgit v1.2.3 (git 2.25.1) at 2024-11-16 04:51:06 +0000