diff options
| -rw-r--r-- | src/Python/test_regularizers.py | 66 |
1 files changed, 33 insertions, 33 deletions
diff --git a/src/Python/test_regularizers.py b/src/Python/test_regularizers.py index 755804a..5804897 100644 --- a/src/Python/test_regularizers.py +++ b/src/Python/test_regularizers.py @@ -163,52 +163,52 @@ imgplot = plt.imshow(reg_output[-1][0]) # # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise # # ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); -# out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05, - # searching_window_ratio=3, - # similarity_window_ratio=1, - # PB_filtering_parameter=0.08) -# pars = out2[-2] -# reg_output.append(out2) +out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05, + searching_window_ratio=3, + similarity_window_ratio=1, + PB_filtering_parameter=0.08) +pars = out2[-2] +reg_output.append(out2) -# a=fig.add_subplot(2,3,5) +a=fig.add_subplot(2,3,5) -# textstr = out2[-1] +textstr = out2[-1] -# # 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, textstr, transform=a.transAxes, fontsize=14, - # verticalalignment='top', bbox=props) -# imgplot = plt.imshow(reg_output[-1][0]) +# 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, textstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(reg_output[-1][0]) -# ###################### TGV_PD ######################################### -# # Quick 2D denoising example in Matlab: -# # Im = double(imread('lena_gray_256.tif'))/255; % loading image -# # u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise -# # u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); +###################### TGV_PD ######################################### +# Quick 2D denoising example in Matlab: +# Im = double(imread('lena_gray_256.tif'))/255; % loading image +# u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise +# u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550); -# out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05, - # first_order_term=1.3, - # second_order_term=1, - # number_of_iterations=550) -# pars = out2[-2] -# reg_output.append(out2) +out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05, + first_order_term=1.3, + second_order_term=1, + number_of_iterations=550) +pars = out2[-2] +reg_output.append(out2) -# a=fig.add_subplot(2,3,6) +a=fig.add_subplot(2,3,6) -# textstr = out2[-1] +textstr = out2[-1] -# # 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, textstr, transform=a.transAxes, fontsize=14, - # verticalalignment='top', bbox=props) -# imgplot = plt.imshow(reg_output[-1][0]) +# 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, textstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(reg_output[-1][0]) plt.show() |