diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/Python/setup.py | 1 | ||||
| -rw-r--r-- | src/Python/test_regularizers.py | 195 |
2 files changed, 168 insertions, 28 deletions
diff --git a/src/Python/setup.py b/src/Python/setup.py index a4eed14..0468722 100644 --- a/src/Python/setup.py +++ b/src/Python/setup.py @@ -53,6 +53,7 @@ setup( "..\\..\\main_func\\regularizers_CPU\\SplitBregman_TV_core.c", "..\\..\\main_func\\regularizers_CPU\\LLT_model_core.c", "..\\..\\main_func\\regularizers_CPU\\PatchBased_Regul_core.c", + "..\\..\\main_func\\regularizers_CPU\\TGV_PD_core.c", "..\\..\\main_func\\regularizers_CPU\\utils.c" ], include_dirs=extra_include_dirs, library_dirs=extra_library_dirs, extra_compile_args=extra_compile_args, libraries=extra_libraries ), diff --git a/src/Python/test_regularizers.py b/src/Python/test_regularizers.py index 6abfba4..6a34749 100644 --- a/src/Python/test_regularizers.py +++ b/src/Python/test_regularizers.py @@ -47,6 +47,8 @@ class Regularizer(): SplitBregman_TV = regularizers.SplitBregman_TV FGP_TV = regularizers.FGP_TV LLT_model = regularizers.LLT_model + PatchBased_Regul = regularizers.PatchBased_Regul + TGV_PD = regularizers.TGV_PD # Algorithm class TotalVariationPenalty(Enum): @@ -55,13 +57,17 @@ class Regularizer(): # TotalVariationPenalty def __init__(self , algorithm): - + self.setAlgorithm ( algorithm ) + # __init__ + + def setAlgorithm(self, algorithm): self.algorithm = algorithm self.pars = self.parsForAlgorithm(algorithm) - # __init__ + # setAlgorithm def parsForAlgorithm(self, algorithm): pars = dict() + if algorithm == Regularizer.Algorithm.SplitBregman_TV : pars['algorithm'] = algorithm pars['input'] = None @@ -69,6 +75,7 @@ class Regularizer(): pars['number_of_iterations'] = 35 pars['tolerance_constant'] = 0.0001 pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic + elif algorithm == Regularizer.Algorithm.FGP_TV : pars['algorithm'] = algorithm pars['input'] = None @@ -76,6 +83,7 @@ class Regularizer(): pars['number_of_iterations'] = 50 pars['tolerance_constant'] = 0.001 pars['TV_penalty'] = Regularizer.TotalVariationPenalty.isotropic + elif algorithm == Regularizer.Algorithm.LLT_model: pars['algorithm'] = algorithm pars['input'] = None @@ -85,6 +93,24 @@ class Regularizer(): pars['tolerance_constant'] = None pars['restrictive_Z_smoothing'] = 0 + elif algorithm == Regularizer.Algorithm.PatchBased_Regul: + pars['algorithm'] = algorithm + pars['input'] = None + pars['searching_window_ratio'] = None + pars['similarity_window_ratio'] = None + pars['PB_filtering_parameter'] = None + pars['regularization_parameter'] = None + + elif algorithm == Regularizer.Algorithm.TGV_PD: + pars['algorithm'] = algorithm + pars['input'] = None + pars['first_order_term'] = None + pars['second_order_term'] = None + pars['number_of_iterations'] = None + pars['regularization_parameter'] = None + + + return pars # parsForAlgorithm @@ -98,6 +124,8 @@ class Regularizer(): self.pars['regularization_parameter'] = regularization_parameter #for key, value in self.pars.items(): # print("{0} = {1}".format(key, value)) + if None in self.pars: + raise Exception("Not all parameters have been provided") if self.algorithm == Regularizer.Algorithm.SplitBregman_TV : return self.algorithm(input, regularization_parameter, @@ -112,15 +140,27 @@ class Regularizer(): elif self.algorithm == Regularizer.Algorithm.LLT_model : #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) # no default - if None in self.pars: - raise Exception("Not all parameters have been provided") - else: - return self.algorithm(input, - regularization_parameter, - self.pars['time_step'] , - self.pars['number_of_iterations'], - self.pars['tolerance_constant'], - self.pars['restrictive_Z_smoothing'] ) + return self.algorithm(input, + regularization_parameter, + self.pars['time_step'] , + self.pars['number_of_iterations'], + self.pars['tolerance_constant'], + self.pars['restrictive_Z_smoothing'] ) + elif self.algorithm == Regularizer.Algorithm.PatchBased_Regul : + #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) + # no default + return self.algorithm(input, regularization_parameter, + self.pars['searching_window_ratio'] , + self.pars['similarity_window_ratio'] , + self.pars['PB_filtering_parameter']) + elif self.algorithm == Regularizer.Algorithm.TGV_PD : + #LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) + # no default + return self.algorithm(input, regularization_parameter, + self.pars['first_order_term'] , + self.pars['second_order_term'] , + self.pars['number_of_iterations']) + # __call__ @@ -142,13 +182,40 @@ class Regularizer(): @staticmethod def LLT_model(input, regularization_parameter , time_step, number_of_iterations, tolerance_constant, restrictive_Z_smoothing=0): - reg = Regularizer(Regularizer.Algorithm.FGP_TV) + reg = Regularizer(Regularizer.Algorithm.LLT_model) out = list( reg(input, regularization_parameter, time_step=time_step, number_of_iterations=number_of_iterations, tolerance_constant=tolerance_constant, restrictive_Z_smoothing=restrictive_Z_smoothing) ) out.append(reg.pars) return out + + @staticmethod + def PatchBased_Regul(input, regularization_parameter, + searching_window_ratio, + similarity_window_ratio, + PB_filtering_parameter): + reg = Regularizer(Regularizer.Algorithm.PatchBased_Regul) + out = list( reg(input, + regularization_parameter, + searching_window_ratio=searching_window_ratio, + similarity_window_ratio=similarity_window_ratio, + PB_filtering_parameter=PB_filtering_parameter ) + ) + out.append(reg.pars) + return out + + @staticmethod + def TGV_PD(input, regularization_parameter , first_order_term, + second_order_term, number_of_iterations): + + reg = Regularizer(Regularizer.Algorithm.TGV_PD) + out = list( reg(input, regularization_parameter, + first_order_term=first_order_term, + second_order_term=second_order_term, + number_of_iterations=number_of_iterations) ) + out.append(reg.pars) + return out #Example: @@ -171,17 +238,17 @@ u0 = Im + (perc* np.random.normal(size=np.shape(Im))) f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) u0 = f(u0).astype('float32') -# plot +## plot fig = plt.figure() -a=fig.add_subplot(2,3,1) -a.set_title('Original') -imgplot = plt.imshow(Im) +#a=fig.add_subplot(3,3,1) +#a.set_title('Original') +#imgplot = plt.imshow(Im) -a=fig.add_subplot(2,3,2) +a=fig.add_subplot(2,3,1) a.set_title('noise') imgplot = plt.imshow(u0) - +reg_output = [] ############################################################################## # Call regularizer @@ -199,8 +266,9 @@ out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., numbe TV_Penalty=Regularizer.TotalVariationPenalty.l1) out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. ) pars = out2[2] +reg_output.append(out2) -a=fig.add_subplot(2,3,3) +a=fig.add_subplot(2,3,2) a.set_title('SplitBregman_TV') textstr = 'regularization_parameter=%.2f\niterations=%d\ntolerance=%.2e\npenalty=%s' textstr = textstr % (pars['regularization_parameter'], @@ -213,7 +281,7 @@ 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, textstr, transform=a.transAxes, fontsize=14, verticalalignment='top', bbox=props) -imgplot = plt.imshow(out2[0]) +imgplot = plt.imshow(reg_output[-1][0]) ###################### FGP_TV ######################################### # u = FGP_TV(single(u0), 0.05, 100, 1e-04); @@ -221,7 +289,9 @@ out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.05, number_of_iterations=10) pars = out2[-1] -a=fig.add_subplot(2,3,4) +reg_output.append(out2) + +a=fig.add_subplot(2,3,3) a.set_title('FGP_TV') textstr = 'regularization_parameter=%.2f\niterations=%d\ntolerance=%.2e\npenalty=%s' textstr = textstr % (pars['regularization_parameter'], @@ -234,18 +304,23 @@ 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(out2[0]) +imgplot = plt.imshow(reg_output[-1][0]) ###################### LLT_model ######################################### # * u0 = Im + .03*randn(size(Im)); % adding noise # [Den] = LLT_model(single(u0), 10, 0.1, 1); -out2 = Regularizer.LLT_model(input=u0, regularization_parameter=10., - time_step=0.1, - tolerance_constant=1e-4, - number_of_iterations=10) +#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); +#input, regularization_parameter , time_step, number_of_iterations, +# tolerance_constant, restrictive_Z_smoothing=0 +out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25, + time_step=0.0003, + tolerance_constant=0.0001, + number_of_iterations=300) pars = out2[-1] -a=fig.add_subplot(2,3,5) +reg_output.append(out2) + +a=fig.add_subplot(2,3,4) a.set_title('LLT_model') textstr = 'regularization_parameter=%.2f\niterations=%d\ntolerance=%.2e\ntime-step=%f' textstr = textstr % (pars['regularization_parameter'], @@ -259,7 +334,71 @@ 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(out2[0]) +imgplot = plt.imshow(reg_output[-1][0]) + +###################### PatchBased_Regul ######################################### +# 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 +# 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[-1] +reg_output.append(out2) + +a=fig.add_subplot(2,3,5) +a.set_title('PatchBased_Regul') +textstr = 'regularization_parameter=%.2f\nsearching_window_ratio=%d\nsimilarity_window_ratio=%.2e\nPB_filtering_parameter=%f' +textstr = textstr % (pars['regularization_parameter'], + pars['searching_window_ratio'], + pars['similarity_window_ratio'], + pars['PB_filtering_parameter']) + + + + +# 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); + + +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[-1] +reg_output.append(out2) + +a=fig.add_subplot(2,3,6) +a.set_title('TGV_PD') +textstr = 'regularization_parameter=%.2f\nfirst_order_term=%.2f\nsecond_order_term=%.2f\nnumber_of_iterations=%d' +textstr = textstr % (pars['regularization_parameter'], + pars['first_order_term'], + pars['second_order_term'], + pars['number_of_iterations']) + + + + +# 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]) |