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| -rw-r--r-- | Wrappers/Python/test/test_gpu_regularizers.py | 131 |
1 files changed, 131 insertions, 0 deletions
diff --git a/Wrappers/Python/test/test_gpu_regularizers.py b/Wrappers/Python/test/test_gpu_regularizers.py new file mode 100644 index 0000000..1a78132 --- /dev/null +++ b/Wrappers/Python/test/test_gpu_regularizers.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jan 30 10:24:26 2018 + +@author: ofn77899 +""" + + + +import matplotlib.pyplot as plt +import numpy as np +import os +from enum import Enum +import timeit +from ccpi.filters.gpu_regularizers import Diff4thHajiaboli, NML +############################################################################### +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)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### + +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif" +#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif" +#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif' + +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +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') + +## plot +fig = plt.figure() + +a=fig.add_subplot(2,3,1) +a.set_title('noise') +imgplot = plt.imshow(u0#,cmap="gray" + ) + + +## Diff4thHajiaboli +start_time = timeit.default_timer() +pars = {'algorithm' : Diff4thHajiaboli , \ + 'input' : u0, + 'regularization_parameter':0.02 , \ +'number_of_iterations' :150 ,\ +'edge_preserving_parameter':0.001 +} +d4h = Diff4thHajiaboli(pars['input'], + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['edge_preserving_parameter']) +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(2,3,2) + +# 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 +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(d4h #, cmap="gray" + ) + +a=fig.add_subplot(2,3,5) + +# 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 +a.text(0.05, 0.95, 'd4h - u0', transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(d4h - u0 #, cmap="gray" + ) + + +## Patch Based Regul NML +start_time = timeit.default_timer() + +pars = {'algorithm' : NML , \ + 'input' : u0, + 'SearchW_real':3 , \ +'SimilW' :1,\ +'h':0.05 ,# +'lambda' : 0.08 +} +nml = NML(pars['input'], + pars['SearchW_real'], + pars['SimilW'], + pars['h'], + pars['lambda']) +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +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 +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(nml #, cmap="gray" + ) + +a=fig.add_subplot(2,3,6) + +# 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 +a.text(0.05, 0.95, 'nml - u0', transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(nml - u0 #, cmap="gray" + ) + +plt.show() +
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