diff options
Diffstat (limited to 'Wrappers/Python/test')
| -rw-r--r-- | Wrappers/Python/test/test_regularizers.py | 126 |
1 files changed, 48 insertions, 78 deletions
diff --git a/Wrappers/Python/test/test_regularizers.py b/Wrappers/Python/test/test_regularizers.py index 3c756f0..cf5da2b 100644 --- a/Wrappers/Python/test/test_regularizers.py +++ b/Wrappers/Python/test/test_regularizers.py @@ -86,34 +86,21 @@ reg_output = [] ####################### SplitBregman_TV ##################################### # u = SplitBregman_TV(single(u0), 10, 30, 1e-04); -use_object = True -if use_object: - reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) - print (reg.pars) - reg.setParameter(input=u0) - reg.setParameter(regularization_parameter=10.) - # or - # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30, - #tolerance_constant=1e-4, - #TV_Penalty=Regularizer.TotalVariationPenalty.l1) - plotme = reg(output_all=True) [0] - pars = reg.pars - textstr = reg.printParametersToString() +start_time = timeit.default_timer() +reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) +print (reg.pars) +reg.setParameter(input=u0) +reg.setParameter(regularization_parameter=10.) +# or +# reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30, + #tolerance_constant=1e-4, + #TV_Penalty=Regularizer.TotalVariationPenalty.l1) +plotme = reg(output_all=True) [0] +pars = reg.pars +txtstr = reg.printParametersToString() +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) - #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30, - #tolerance_constant=1e-4, - # TV_Penalty=Regularizer.TotalVariationPenalty.l1) - -#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30, -# tolerance_constant=1e-4, -# TV_Penalty=Regularizer.TotalVariationPenalty.l1) - -else: - out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. ) - pars = out2[2] - reg_output.append(out2) - plotme = reg_output[-1][0] - textstr = out2[-1] a=fig.add_subplot(2,3,2) @@ -121,32 +108,30 @@ 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, textstr, transform=a.transAxes, fontsize=14, +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) imgplot = plt.imshow(plotme,cmap="gray") ###################### FGP_TV ######################################### # u = FGP_TV(single(u0), 0.05, 100, 1e-04); -out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=5e-4, - number_of_iterations=10, output_all=True) -pars = out2[-2] +start_time = timeit.default_timer() +reg = Regularizer(Regularizer.Algorithm.FGP_TV) +out2 = reg(input=u0, regularization_parameter=5e-4, + number_of_iterations=10) +txtstr = reg.printParametersToString() +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) -reg_output.append(out2) a=fig.add_subplot(2,3,3) -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]) +imgplot = plt.imshow(out2,cmap="gray") # place a text box in upper left in axes coords -a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14, +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(reg_output[-1][0],cmap="gray") ###################### LLT_model ######################################### # * u0 = Im + .03*randn(size(Im)); % adding noise @@ -156,35 +141,23 @@ imgplot = plt.imshow(reg_output[-1][0],cmap="gray") # tolerance_constant, restrictive_Z_smoothing=0 del out2 -out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25, +start_time = timeit.default_timer() +reg = Regularizer(Regularizer.Algorithm.LLT_model) +out2 = reg(input=u0, regularization_parameter=25, time_step=0.0003, tolerance_constant=0.001, number_of_iterations=300) -print ("call ended??") - -i = 0 -while(i < len(out2)): - shape = " not applicable" - if type (out2[i]) == np.ndarray: - shape = out2[i].shape - print ("len out2[{0}] type {1} shape {2}".format(i, type(out2[i]) , shape)) - i += 1 - -#print ("out2", out2) -pars = out2[-2] - -reg_output.append(out2) - +txtstr = reg.printParametersToString() +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) a=fig.add_subplot(2,3,4) -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, +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(reg_output[-1][0],cmap="gray") +imgplot = plt.imshow(out2,cmap="gray") # ###################### PatchBased_Regul ######################################### @@ -192,25 +165,25 @@ imgplot = plt.imshow(reg_output[-1][0],cmap="gray") # # Im = double(imread('lena_gray_256.tif'))/255; % loading image # # 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, +start_time = timeit.default_timer() +reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul) +out2 = reg(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) +txtstr = reg.printParametersToString() +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) a=fig.add_subplot(2,3,5) -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, +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(reg_output[-1][0],cmap="gray") +imgplot = plt.imshow(out2,cmap="gray") # ###################### TGV_PD ######################################### @@ -219,26 +192,23 @@ imgplot = plt.imshow(reg_output[-1][0],cmap="gray") # # 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, +start_time = timeit.default_timer() +reg = Regularizer(Regularizer.Algorithm.TGV_PD) +out2 = reg(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) - +txtstr = reg.printParametersToString() +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) a=fig.add_subplot(2,3,6) - -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, +a.text(0.05, 0.95, txtstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(reg_output[-1][0],cmap="gray") +imgplot = plt.imshow(out2,cmap="gray") plt.show() |