Merge branch 'pythonize' of https://github.com/vais-ral/CCPi-FISTA_Reconstruction into pythonize

1 files changed, 380 insertions, 0 deletions

diff --git a/src/Python/test/test_regularizers_3d.py b/src/Python/test/test_regularizers_3d.py
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+++ b/src/Python/test/test_regularizers_3d.py
@@ -0,0 +1,380 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+from ccpi.viewer.CILViewer2D import Converter
+import vtk
+
+import regularizers
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+
+from 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"
+#reader = vtk.vtkTIFFReader()
+#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()).T[0]/255
+#
+##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)
+#
+#reg_output = []
+###############################################################################
+## 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)
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+#pars = out2[2]
+#reg_output.append(out2)
+#
+#a=fig.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_output[-1][0])
+#
+####################### 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_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])
+#
+####################### 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])
+#
+####################### 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])
+#
+#
+####################### 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])
+#
+
+###############################################################################
+#
+#  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(figsize=(20,16))
+
+#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(2,3,3)
+
+
+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_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(2,3,4)
+
+
+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_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])
+fig3D.savefig('test\\3d.png')
+plt.close(fig3D)
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