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| -rw-r--r-- | Wrappers/Python/wip/CGLS_tikhonov.py | 182 |
1 files changed, 182 insertions, 0 deletions
diff --git a/Wrappers/Python/wip/CGLS_tikhonov.py b/Wrappers/Python/wip/CGLS_tikhonov.py new file mode 100644 index 0000000..7178510 --- /dev/null +++ b/Wrappers/Python/wip/CGLS_tikhonov.py @@ -0,0 +1,182 @@ +from ccpi.optimisation.algorithms import CGLS + +from ccpi.plugins.ops import CCPiProjectorSimple +from ccpi.optimisation.ops import PowerMethodNonsquare +from ccpi.optimisation.ops import TomoIdentity +from ccpi.optimisation.funcs import Norm2sq, Norm1 +from ccpi.framework import ImageGeometry, AcquisitionGeometry, ImageData, AcquisitionData +from ccpi.optimisation.algorithms import GradientDescent +#from ccpi.optimisation.algorithms import CGLS +import matplotlib.pyplot as plt +import numpy +from ccpi.framework import BlockDataContainer +from ccpi.optimisation.operators import BlockOperator +from ccpi.optimisation.operators.BlockOperator import BlockLinearOperator + +# Set up phantom size N x N x vert by creating ImageGeometry, initialising the +# ImageData object with this geometry and empty array and finally put some +# data into its array, and display one slice as image. + +# Image parameters +N = 128 +vert = 4 + +# Set up image geometry +ig = ImageGeometry(voxel_num_x=N, + voxel_num_y=N, + voxel_num_z=vert) + +# Set up empty image data +Phantom = ImageData(geometry=ig, + dimension_labels=['horizontal_x', + 'horizontal_y', + 'vertical']) +Phantom += 0.05 +# Populate image data by looping over and filling slices +i = 0 +while i < vert: + if vert > 1: + x = Phantom.subset(vertical=i).array + else: + x = Phantom.array + x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5 + x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 0.94 + if vert > 1 : + Phantom.fill(x, vertical=i) + i += 1 + + +perc = 0.02 +# Set up empty image data +noise = ImageData(numpy.random.normal(loc = 0.04 , + scale = perc , + size = Phantom.shape), geometry=ig, + dimension_labels=['horizontal_x', + 'horizontal_y', + 'vertical']) +Phantom += noise + +# Set up AcquisitionGeometry object to hold the parameters of the measurement +# setup geometry: # Number of angles, the actual angles from 0 to +# pi for parallel beam, set the width of a detector +# pixel relative to an object pixe and the number of detector pixels. +angles_num = 20 +det_w = 1.0 +det_num = N + +angles = numpy.linspace(0,numpy.pi,angles_num,endpoint=False,dtype=numpy.float32)*\ + 180/numpy.pi + +# Inputs: Geometry, 2D or 3D, angles, horz detector pixel count, +# horz detector pixel size, vert detector pixel count, +# vert detector pixel size. +ag = AcquisitionGeometry('parallel', + '3D', + angles, + N, + det_w, + vert, + det_w) + +# Set up Operator object combining the ImageGeometry and AcquisitionGeometry +# wrapping calls to CCPi projector. +A = CCPiProjectorSimple(ig, ag) + +# Forward and backprojection are available as methods direct and adjoint. Here +# generate test data b and some noise + +b = A.direct(Phantom) + + +#z = A.adjoint(b) + + +# Using the test data b, different reconstruction methods can now be set up as +# demonstrated in the rest of this file. In general all methods need an initial +# guess and some algorithm options to be set. Note that 100 iterations for +# some of the methods is a very low number and 1000 or 10000 iterations may be +# needed if one wants to obtain a converged solution. +x_init = ImageData(geometry=ig, + dimension_labels=['horizontal_x','horizontal_y','vertical']) +X_init = BlockDataContainer(x_init) +B = BlockDataContainer(b, + ImageData(geometry=ig, dimension_labels=['horizontal_x','horizontal_y','vertical'])) + +# setup a tomo identity +Ibig = 1e5 * TomoIdentity(geometry=ig) +Ismall = 1e-5 * TomoIdentity(geometry=ig) + +# composite operator +Kbig = BlockOperator(A, Ibig, shape=(2,1)) +Ksmall = BlockOperator(A, Ismall, shape=(2,1)) + +#out = K.direct(X_init) + +f = Norm2sq(Kbig,B) +f.L = 0.00003 + +fsmall = Norm2sq(Ksmall,B) +f.L = 0.00003 + +simplef = Norm2sq(A, b) +simplef.L = 0.00003 + +gd = GradientDescent( x_init=x_init, objective_function=simplef, + rate=simplef.L) +gd.max_iteration = 10 + +cg = CGLS() +cg.set_up(X_init, Kbig, B ) +cg.max_iteration = 1 + +cgsmall = CGLS() +cgsmall.set_up(X_init, Ksmall, B ) +cgsmall.max_iteration = 1 + + +cgs = CGLS() +cgs.set_up(x_init, A, b ) +cgs.max_iteration = 6 +# # +#out.__isub__(B) +#out2 = K.adjoint(out) + +#(2.0*self.c)*self.A.adjoint( self.A.direct(x) - self.b ) + +for _ in gd: + print ("iteration {} {}".format(gd.iteration, gd.get_current_loss())) + +cg.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) + +cgs.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) + +cgsmall.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) +cgsmall.run(10, lambda it,val: print ("iteration {} objective {}".format(it,val))) +# # for _ in cg: +# print ("iteration {} {}".format(cg.iteration, cg.get_current_loss())) +# # +# # fig = plt.figure() +# # plt.imshow(cg.get_output().get_item(0,0).subset(vertical=0).as_array()) +# # plt.title('Composite CGLS') +# # plt.show() +# # +# # for _ in cgs: +# print ("iteration {} {}".format(cgs.iteration, cgs.get_current_loss())) +# # +fig = plt.figure() +plt.subplot(1,5,1) +plt.imshow(Phantom.subset(vertical=0).as_array()) +plt.title('Simulated Phantom') +plt.subplot(1,5,2) +plt.imshow(gd.get_output().subset(vertical=0).as_array()) +plt.title('Simple Gradient Descent') +plt.subplot(1,5,3) +plt.imshow(cgs.get_output().subset(vertical=0).as_array()) +plt.title('Simple CGLS') +plt.subplot(1,5,4) +plt.imshow(cg.get_output().get_item(0,0).subset(vertical=0).as_array()) +plt.title('Composite CGLS\nbig lambda') +plt.subplot(1,5,5) +plt.imshow(cgsmall.get_output().get_item(0,0).subset(vertical=0).as_array()) +plt.title('Composite CGLS\nsmall lambda') +plt.show() |