From f082d54dfdef5335524806b703d630985afb247c Mon Sep 17 00:00:00 2001 From: Edoardo Pasca Date: Wed, 21 Mar 2018 17:28:09 +0000 Subject: initial revision --- Wrappers/Python/wip/test_reader_reconstr.py | 198 ++++++++++++++++++++++++++++ 1 file changed, 198 insertions(+) create mode 100755 Wrappers/Python/wip/test_reader_reconstr.py (limited to 'Wrappers') diff --git a/Wrappers/Python/wip/test_reader_reconstr.py b/Wrappers/Python/wip/test_reader_reconstr.py new file mode 100755 index 0000000..33ab461 --- /dev/null +++ b/Wrappers/Python/wip/test_reader_reconstr.py @@ -0,0 +1,198 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Mar 21 14:26:21 2018 + +@author: ofn77899 +""" + +from ccpi.framework import ImageData , AcquisitionData, ImageGeometry, AcquisitionGeometry +from ccpi.reconstruction.algs import FISTA, FBPD, CGLS +from ccpi.reconstruction.funcs import Norm2sq, Norm1 +from ccpi.reconstruction.ops import CCPiProjectorSimple +from ccpi.reconstruction.parallelbeam import alg as pbalg +from ccpi.processors import CCPiForwardProjector, CCPiBackwardProjector , \ +Normalizer , CenterOfRotationFinder , AcquisitionDataPadder + +from ccpi.io.reader import NexusReader + +import numpy +import matplotlib.pyplot as plt + +import os + +def add_dimension(data, fill_with, axis, start=True): + delta = data.shape[data.get_dimension_axis(axis)] - fill_with.shape[fill_with.get_dimension_axis(axis)] + command = 'data.array[' + i = 0 + for k,v in data.dimension_labels.items(): + if axis == v: + if start: + command = command + str(delta) + ":" + else: + l = data.get_dimension_size(axis) - delta + command = command + "0:" + str(l) + else: + command = command + ":" + + if i < data.number_of_dimensions -1: + command = command + ',' + i += 1 + command = command + "] = fill_with.array[:]" + #print (command) + exec(command) + #return command + + +def avg_img(image): + shape = list(numpy.shape(image)) + l = shape.pop(0) + avg = numpy.zeros(shape) + for i in range(l): + avg += image[i] / l + return avg + +def setupCCPiGeometries(voxel_num_x, voxel_num_y, voxel_num_z, angles, counter): + vg = ImageGeometry(voxel_num_x=voxel_num_x,voxel_num_y=voxel_num_y, voxel_num_z=voxel_num_z) + Phantom_ccpi = ImageData(geometry=vg, + dimension_labels=['horizontal_x','horizontal_y','vertical']) + #.subset(['horizontal_x','horizontal_y','vertical']) + # ask the ccpi code what dimensions it would like + + voxel_per_pixel = 1 + geoms = pbalg.pb_setup_geometry_from_image(Phantom_ccpi.as_array(), + angles, + voxel_per_pixel ) + + pg = AcquisitionGeometry('parallel', + '3D', + angles, + geoms['n_h'], 1, + geoms['n_v'], 1 #2D in 3D is a slice 1 pixel thick + ) + + center_of_rotation = Phantom_ccpi.get_dimension_size('horizontal_x') / 2 + ad = AcquisitionData(geometry=pg,dimension_labels=['angle','vertical','horizontal']) + geoms_i = pbalg.pb_setup_geometry_from_acquisition(ad.as_array(), + angles, + center_of_rotation, + voxel_per_pixel ) + + #print (counter) + counter+=1 + #print (geoms , geoms_i) + if counter < 4: + if (not ( geoms_i == geoms )): + print ("not equal and {0}".format(counter)) + X = max(geoms['output_volume_x'], geoms_i['output_volume_x']) + Y = max(geoms['output_volume_y'], geoms_i['output_volume_y']) + Z = max(geoms['output_volume_z'], geoms_i['output_volume_z']) + return setupCCPiGeometries(X,Y,Z,angles, counter) + else: + print ("return geoms {0}".format(geoms)) + return geoms + else: + print ("return geoms_i {0}".format(geoms_i)) + return geoms_i + +reader = NexusReader(os.path.join(".." ,".." ,".." , "data" , "24737_fd.nxs" )) + +dims = reader.get_projection_dimensions() +print (dims) + +flat = avg_img(reader.load_flat()) +dark = avg_img(reader.load_dark()) + +data = reader.getAcquisitionData() +norm = Normalizer(flat_field=flat, dark_field=dark) + +norm.set_input(reader.getAcquisitionData()) + +cor = CenterOfRotationFinder() +cor.set_input(norm.get_output()) +center_of_rotation = cor.get_output() +voxel_per_pixel = 1 + +padder = AcquisitionDataPadder(center_of_rotation=center_of_rotation) +padder.set_input(norm.get_output()) +padded_data = padder.get_output() + +pg = padded_data.geometry +geoms = pbalg.pb_setup_geometry_from_acquisition(padded_data.as_array(), + pg.angles, + center_of_rotation, + voxel_per_pixel ) +vg = ImageGeometry(voxel_num_x=geoms['output_volume_x'], + voxel_num_y=geoms['output_volume_y'], + voxel_num_z=geoms['output_volume_z']) +#data = numpy.reshape(reader.getAcquisitionData()) +print ("define projector") +Cop = CCPiProjectorSimple(vg, pg) +# Create least squares object instance with projector and data. +print ("Create least squares object instance with projector and data.") +f = Norm2sq(Cop,padded_data,c=0.5) +print ("Initial guess") +# Initial guess +x_init = ImageData(geometry=vg, dimension_labels=['horizontal_x','horizontal_y','vertical']) +#invL = 0.5 +#g = f.grad(x_init) +#print (g) +#u = x_init - invL*f.grad(x_init) + +#%% +print ("run FISTA") +# Run FISTA for least squares without regularization +opt = {'tol': 1e-4, 'iter': 10} +x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt=opt) + +plt.imshow(x_fista0.subset(horizontal_x=80).array) +plt.title('FISTA0') +plt.show() + +# Now least squares plus 1-norm regularization +lam = 0.1 +g0 = Norm1(lam) + +# Run FISTA for least squares plus 1-norm function. +x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0,opt=opt) + +plt.imshow(x_fista0.subset(horizontal_x=80).array) +plt.title('FISTA1') +plt.show() + +plt.semilogy(criter1) +plt.show() + +# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm +x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt) + +plt.imshow(x_fbpd1.subset(horizontal_x=80).array) +plt.title('FBPD1') +plt.show() + +plt.semilogy(criter_fbpd1) +plt.show() + +# Now FBPD for least squares plus TV +#lamtv = 1 +#gtv = TV2D(lamtv) + +#x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt) + +#plt.imshow(x_fbpdtv.subset(vertical=0).array) +#plt.show() + +#plt.semilogy(criter_fbpdtv) +#plt.show() + + +# Run CGLS, which should agree with the FISTA0 +x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Cop, padded_data, opt=opt) + +plt.imshow(x_CGLS.subset(horizontal_x=80).array) +plt.title('CGLS') +plt.title('CGLS recon, compare FISTA0') +plt.show() + +plt.semilogy(criter_CGLS) +plt.title('CGLS criterion') +plt.show() \ No newline at end of file -- cgit v1.2.3