diff options
Diffstat (limited to 'Wrappers/Python/demo')
| -rw-r--r-- | Wrappers/Python/demo/test_cpu_regularizers.py | 55 |
1 files changed, 24 insertions, 31 deletions
diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py index 5908c3c..76b9de7 100644 --- a/Wrappers/Python/demo/test_cpu_regularizers.py +++ b/Wrappers/Python/demo/test_cpu_regularizers.py @@ -11,11 +11,8 @@ import numpy as np import os from enum import Enum import timeit -from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV , FGP_TV ,\ - LLT_model, PatchBased_Regul ,\ - TGV_PD -from ccpi.filters.cpu_regularizers_cython import ROF_TV - +from ccpi.filters.cpu_regularizers_boost import SplitBregman_TV, LLT_model, PatchBased_Regul, TGV_PD +from ccpi.filters.regularizers import ROF_TV, FGP_TV ############################################################################### #https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956 #NRMSE a normalization of the root of the mean squared error @@ -112,12 +109,10 @@ rms = rmse(Im, splitbregman) pars['rmse'] = rms txtstr = printParametersToString(pars) txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) -print (txtstr) - +print (txtstr) a=fig.add_subplot(2,4,2) - # 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 @@ -128,23 +123,26 @@ imgplot = plt.imshow(splitbregman,\ ) ###################### FGP_TV ######################################### -# u = FGP_TV(single(u0), 0.05, 100, 1e-04); + start_time = timeit.default_timer() pars = {'algorithm' : FGP_TV , \ - 'input' : u0, - 'regularization_parameter':0.05, \ - 'number_of_iterations' :200 ,\ - 'tolerance_constant':1e-4,\ - 'TV_penalty': 0 -} + 'input' : u0,\ + 'regularization_parameter':0.07, \ + 'number_of_iterations' :300 ,\ + 'tolerance_constant':0.00001,\ + 'methodTV': 0 ,\ + 'nonneg': 0 ,\ + 'printingOut': 0 + } -out = FGP_TV (pars['input'], +fgp = FGP_TV(pars['input'], pars['regularization_parameter'], pars['number_of_iterations'], pars['tolerance_constant'], - pars['TV_penalty']) + pars['methodTV'], + pars['nonneg'], + pars['printingOut'], 'cpu') -fgp = out[0] rms = rmse(Im, fgp) pars['rmse'] = rms @@ -165,8 +163,8 @@ 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 , \ @@ -201,8 +199,6 @@ 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: # # Im = double(imread('lena_gray_256.tif'))/255; % loading image @@ -284,16 +280,15 @@ start_time = timeit.default_timer() pars = {'algorithm': ROF_TV , \ 'input' : u0,\ - 'regularization_parameter':1,\ - 'marching_step': 0.003,\ + 'regularization_parameter':0.07,\ + 'marching_step': 0.0025,\ 'number_of_iterations': 300 } rof = ROF_TV(pars['input'], - pars['number_of_iterations'], - pars['regularization_parameter'], - pars['marching_step'] - ) -#tgv = out + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['marching_step'], 'cpu') + rms = rmse(Im, rof) pars['rmse'] = rms @@ -307,9 +302,7 @@ 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(tgv, cmap="gray") - - +imgplot = plt.imshow(rof, cmap="gray") plt.show() |