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| -rw-r--r-- | Wrappers/Python/demo/test_cpu_regularizers.py | 442 |
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diff --git a/Wrappers/Python/demo/test_cpu_regularizers.py b/Wrappers/Python/demo/test_cpu_regularizers.py new file mode 100644 index 0000000..9713baa --- /dev/null +++ b/Wrappers/Python/demo/test_cpu_regularizers.py @@ -0,0 +1,442 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 4 11:10:05 2017 + +@author: ofn77899 +""" + + +import matplotlib.pyplot as plt +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 + +############################################################################### +#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 +#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum +# intensity from the two images being compared, and respectively the same for +# minval. RMSE is given by the square root of MSE: +# sqrt[(sum(A - B) ** 2) / |A|], +# where |A| means the number of elements in A. By doing this, the maximum value +# given by RMSE is maxval. + +def nrmse(im1, im2): + a, b = im1.shape + rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b)) + max_val = max(np.max(im1), np.max(im2)) + min_val = min(np.min(im1), np.min(im2)) + return 1 - (rmse / (max_val - min_val)) +############################################################################### +def printParametersToString(pars): + txt = r'' + for key, value in pars.items(): + if key== 'algorithm' : + txt += "{0} = {1}".format(key, value.__name__) + elif key == 'input': + txt += "{0} = {1}".format(key, np.shape(value)) + else: + txt += "{0} = {1}".format(key, value) + txt += '\n' + return txt +############################################################################### +# +# 2D Regularizers +# +############################################################################### +#Example: +# figure; +# Im = double(imread('lena_gray_256.tif'))/255; % loading image +# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; +# u = SplitBregman_TV(single(u0), 10, 30, 1e-04); + +# assumes the script is launched from the test directory +filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif") +#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif" +#filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif" +#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif' + +Im = plt.imread(filename) +Im = np.asarray(Im, dtype='float32') + +perc = 0.15 +u0 = Im + np.random.normal(loc = Im , + scale = perc * Im , + size = np.shape(Im)) +# map the u0 u0->u0>0 +f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +u0 = f(u0).astype('float32') + +## plot +fig = plt.figure() + +a=fig.add_subplot(2,3,1) +a.set_title('noise') +imgplot = plt.imshow(u0#,cmap="gray" + ) + +reg_output = [] +############################################################################## +# Call regularizer + +####################### SplitBregman_TV ##################################### +# u = SplitBregman_TV(single(u0), 10, 30, 1e-04); + +start_time = timeit.default_timer() +pars = {'algorithm' : SplitBregman_TV , \ + 'input' : u0, + 'regularization_parameter':10. , \ +'number_of_iterations' :35 ,\ +'tolerance_constant':0.0001 , \ +'TV_penalty': 0 +} + +out = SplitBregman_TV (pars['input'], pars['regularization_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['TV_penalty']) +splitbregman = out[0] +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) + + +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, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(splitbregman,\ + #cmap="gray" + ) + +###################### 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':5e-4, \ + 'number_of_iterations' :10 ,\ + 'tolerance_constant':0.001,\ + 'TV_penalty': 0 +} + +out = FGP_TV (pars['input'], + pars['regularization_parameter'], + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['TV_penalty']) + +fgp = out[0] +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) + + +a=fig.add_subplot(2,3,3) + +# 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 +imgplot = plt.imshow(fgp, \ + #cmap="gray" + ) +# 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) + +###################### LLT_model ######################################### + +start_time = timeit.default_timer() + +pars = {'algorithm': LLT_model , \ + 'input' : u0, + 'regularization_parameter': 25,\ + 'time_step':0.0003, \ + 'number_of_iterations' :300,\ + 'tolerance_constant':0.001,\ + 'restrictive_Z_smoothing': 0 +} +out = LLT_model(pars['input'], + pars['regularization_parameter'], + pars['time_step'] , + pars['number_of_iterations'], + pars['tolerance_constant'], + pars['restrictive_Z_smoothing'] ) + +llt = out[0] +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(2,3,4) + +# 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, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(llt,\ + #cmap="gray" + ) + + +# ###################### 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); +start_time = timeit.default_timer() + +pars = {'algorithm': PatchBased_Regul , \ + 'input' : u0, + 'regularization_parameter': 0.05,\ + 'searching_window_ratio':3, \ + 'similarity_window_ratio':1,\ + 'PB_filtering_parameter': 0.08 +} +out = PatchBased_Regul(pars['input'], + pars['regularization_parameter'], + pars['searching_window_ratio'] , + pars['similarity_window_ratio'] , + pars['PB_filtering_parameter']) +pbr = out[0] +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) + +a=fig.add_subplot(2,3,5) + + +# 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, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(pbr #,cmap="gray" + ) + + +# ###################### 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); + +start_time = timeit.default_timer() + +pars = {'algorithm': TGV_PD , \ + 'input' : u0,\ + 'regularization_parameter':0.05,\ + 'first_order_term': 1.3,\ + 'second_order_term': 1, \ + 'number_of_iterations': 550 + } +out = TGV_PD(pars['input'], + pars['regularization_parameter'], + pars['first_order_term'] , + pars['second_order_term'] , + pars['number_of_iterations']) +tgv = out[0] +txtstr = printParametersToString(pars) +txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time) +print (txtstr) +a=fig.add_subplot(2,3,6) + +# 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, txtstr, transform=a.transAxes, fontsize=14, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(tgv #, cmap="gray") + ) + + +plt.show() + +################################################################################ +## +## 3D Regularizers +## +################################################################################ +##Example: +## figure; +## Im = double(imread('lena_gray_256.tif'))/255; % loading image +## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; +## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +# +##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha" +#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha" +# +#reader = vtk.vtkMetaImageReader() +#reader.SetFileName(os.path.normpath(filename)) +#reader.Update() +##vtk returns 3D images, let's take just the one slice there is as 2D +#Im = Converter.vtk2numpy(reader.GetOutput()) +#Im = Im.astype('float32') +##imgplot = plt.imshow(Im) +#perc = 0.05 +#u0 = Im + (perc* np.random.normal(size=np.shape(Im))) +## map the u0 u0->u0>0 +#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1) +#u0 = f(u0).astype('float32') +#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(), +# reader.GetOutput().GetOrigin()) +#converter.Update() +#writer = vtk.vtkMetaImageWriter() +#writer.SetInputData(converter.GetOutput()) +#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha") +##writer.Write() +# +# +### plot +#fig3D = plt.figure() +##a=fig.add_subplot(3,3,1) +##a.set_title('Original') +##imgplot = plt.imshow(Im) +#sliceNo = 32 +# +#a=fig3D.add_subplot(2,3,1) +#a.set_title('noise') +#imgplot = plt.imshow(u0.T[sliceNo]) +# +#reg_output3d = [] +# +############################################################################### +## Call regularizer +# +######################## SplitBregman_TV ##################################### +## u = SplitBregman_TV(single(u0), 10, 30, 1e-04); +# +##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV) +# +##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) +# +# +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,3,2) +# +# +#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_output3d[-1][0].T[sliceNo]) +# +####################### FGP_TV ######################################### +## u = FGP_TV(single(u0), 0.05, 100, 1e-04); +#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005, +# number_of_iterations=200) +#pars = out2[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,3,2) +# +# +#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_output3d[-1][0].T[sliceNo]) +# +####################### LLT_model ######################################### +## * u0 = Im + .03*randn(size(Im)); % adding noise +## [Den] = LLT_model(single(u0), 10, 0.1, 1); +##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[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,3,2) +# +# +#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_output3d[-1][0].T[sliceNo]) +# +####################### 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[-2] +#reg_output3d.append(out2) +# +#a=fig3D.add_subplot(2,3,2) +# +# +#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_output3d[-1][0].T[sliceNo]) +# + +###################### 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_output3d.append(out2) +# +#a=fig3D.add_subplot(2,3,2) +# +# +#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_output3d[-1][0].T[sliceNo]) |