def CT(load_path, dset, sc, ang_freq, Exp_bin, bin_param, nTrain, nTD, nVal,
nVD, bpath):
dataset = ddf.load_and_preprocess_real_data(load_path, dset, sc)
meta = ddf.load_meta(load_path + dset + '/', sc)
pix_size = meta['pix_size']
src_rad = meta['s2o']
det_rad = meta['o2d']
data_obj = ddf.real_data(dataset,
pix_size,
src_rad,
det_rad,
ang_freq,
zoom=False)
CT_obj = ddf.CCB_CT(data_obj)
CT_obj.init_algo()
spf_space, Exp_op = ddf.support_functions.ExpOp_builder(
bin_param, CT_obj.filter_space, interp=Exp_bin)
# Create the FDK binned operator
# CT_obj.FDK_bin_nn = CT_obj.FDK_op * Exp_op
# Create the NN-FDK object
CT_obj.NNFDK = nn.NNFDK_class(CT_obj,
nTrain,
nTD,
nVal,
nVD,
Exp_bin,
Exp_op,
bin_param,
dset=dset,
base_path=bpath)
CT_obj.rec_methods += [CT_obj.NNFDK]
return CT_obj
def CT(load_path, dset, sc, ang_freq):
dataset = ddf.load_and_preprocess_real_data(load_path, dset, sc)
meta = ddf.load_meta(load_path + dset + '/', sc)
pix_size = meta['pix_size']
src_rad = meta['s2o']
det_rad = meta['o2d']
data_obj = ddf.real_data(dataset, pix_size, src_rad, det_rad, ang_freq,
zoom=False)
CT_obj = ddf.CCB_CT(data_obj)
CT_obj.init_algo()
return CT_obj
def CT(load_path, dset, sc, ang_freq, Exp_bin, bin_param, offset):
dataset = ddf.load_and_preprocess_real_data(load_path, dset, sc)
meta = ddf.load_meta(load_path + dset + '/', sc)
pix_size = meta['pix_size']
src_rad = meta['s2o']
det_rad = meta['o2d']
data_obj = ddf.real_data(dataset,
pix_size,
src_rad,
det_rad,
ang_freq,
zoom=True,
offset=offset)
CT_obj = ddf.CCB_CT(data_obj)
CT_obj.init_algo()
CT_obj.init_DDF_FDK()
return CT_obj
freq = 2 * np.arange(len(filt)) / (rs_detu)
filt = filt * (np.cos(freq * np.pi / 2)**2) / 2 / w_detu
# filt = filt / 2 / w_detu
return filt
# %%
path = '/bigstore/lagerwer/data/FleXray/pomegranate1_02MAR/'
#path = '/export/scratch2/lagerwer/data/FleXray/walnuts_10MAY/walnut_01/'
dset = 'noisy'
#dset2 = 'good'
pd = 'processed_data/'
sc = 1
ang_freq = 1
redo = False
dataset = ddf.load_and_preprocess_real_data(path, dset, sc, redo=redo)
meta = ddf.load_meta(path + dset + '/', sc)
pix_size = meta['pix_size']
src_rad = meta['s2o']
det_rad = meta['o2d']
#dataset = {'g': }
# Specifics for the expansion operator
Exp_bin = 'linear'
bin_param = 2
# %% Create a test phantom
# Create a data object
t2 = time.time()
offset = 1.26 / 360 * 2 * np.pi
def load_and_preprocess(path, dset, sc, redo):
dataset = ddf.load_and_preprocess_real_data(path, dset, sc, redo=redo)
meta = ddf.load_meta(path + dset + '/', sc)
return dataset, meta