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| -rw-r--r-- | src/Python/test/test_regularizers.py | 412 |
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diff --git a/src/Python/test/test_regularizers.py b/src/Python/test/test_regularizers.py new file mode 100644 index 0000000..27e4ed3 --- /dev/null +++ b/src/Python/test/test_regularizers.py @@ -0,0 +1,412 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Aug 4 11:10:05 2017 + +@author: ofn77899 +""" + +#from ccpi.viewer.CILViewer2D import Converter +#import vtk + +import matplotlib.pyplot as plt +import numpy as np +import os +from enum import Enum +import timeit +#from PIL import Image +#from Regularizer import Regularizer +from ccpi.imaging.Regularizer import Regularizer + +############################################################################### +#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)) +############################################################################### + +############################################################################### +# +# 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); + + +#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' + +#reader = vtk.vtkTIFFReader() +#reader.SetFileName(os.path.normpath(filename)) +#reader.Update() +Im = plt.imread(filename) +#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255 +#img.show() +Im = np.asarray(Im, dtype='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') + +## plot +fig = plt.figure() +#a=fig.add_subplot(3,3,1) +#a.set_title('Original') +#imgplot = plt.imshow(Im) + +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); + +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() [0] + pars = reg.pars + textstr = reg.printParametersToString() + + #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) + + +# 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(plotme,cmap="gray") + +###################### FGP_TV ######################################### +# u = FGP_TV(single(u0), 0.05, 100, 1e-04); +out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005, + number_of_iterations=50) +pars = out2[-2] + +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]) +# 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],cmap="gray") + +###################### 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_output.append(out2) + +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, + verticalalignment='top', bbox=props) +imgplot = plt.imshow(reg_output[-1][0],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); + +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) + + +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],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); + + +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) + + +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],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]) |