fixed test_regularizers.py

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()