def main():
options = get_parser().parse_args()
# load data
dataset = load_standard_dataset(options.dataset)
test_data = dataset.get_data_pairs('test', 100)
test_data = list(test_data)[options.start:options.start + options.count]
obs = list([item[0] for item in test_data])
gt = list([item[1] for item in test_data])
test_data = DataPairs(obs, gt, name='test')
# load reconstructor
fbp = fbp_reconstructor(options.dataset)
tv = tvadam_reconstructor(options.dataset, name='TV')
diptv = diptv_reconstructor(options.dataset)
learnedgd = learnedgd_reconstructor(options.dataset, size_part=1.0)
fbpunet = fbpunet_reconstructor(options.dataset, size_part=1.0)
iradonmap = iradonmap_reconstructor(options.dataset, size_part=1.0)
learnedpd = learnedpd_reconstructor(options.dataset, size_part=1.0)
# compute and plot reconstructions
plot_reconstructors_tests(
[fbp, tv, diptv, iradonmap, fbpunet, learnedgd, learnedpd],
ray_trafo=dataset.ray_trafo,
test_data=test_data,
save_name='{}-all-{}'.format(options.dataset, options.start),
fig_size=(9, 6.5),
cmap=options.cmap)
def main():
options = get_parser().parse_args()
# load data
dataset = load_standard_dataset(options.dataset)
test_data = dataset.get_data_pairs('test', 3000)
# index = [0, 2, 68]
index = range(options.start, options.start + options.count)
obs = [test_data[i][0] for i in index]
gt = [test_data[i][1] for i in index]
test_data = DataPairs(obs, gt, name='test')
data_size = options.size_part
# load reconstructors
diptv = diptv_reconstructor(options.dataset)
learnedpd = learnedpd_reconstructor(options.dataset, data_size)
learnedpd_dip = learnedpd_dip_reconstructor(options.dataset, data_size)
# compute example reconstructions
plot_reconstructors_tests([diptv, learnedpd, learnedpd_dip],
dataset.ray_trafo,
test_data,
save_name='{}-learnedpd-dip-{}-{}'.format(
options.dataset, data_size, options.start),
fig_size=(9, 3),
cmap=options.cmap)
def main():
options = get_parser().parse_args()
# load data
dataset = load_standard_dataset(options.dataset, ordered=False)
test_data = dataset.get_data_pairs('test', 1000)
sizes = [0.0001, 0.01, 1.00]
reconstructors = []
for size_part in sizes:
reconstructors.append(
get_reconstructor(options.method,
dataset=options.dataset,
size_part=size_part,
pretrained=True))
for i in range(options.start, options.count):
obs, gt = test_data[i]
test_data = DataPairs([obs], [gt], name='test')
# compute and plot reconstructions
plot_reconstructors_tests(reconstructors,
dataset.ray_trafo,
test_data,
save_name='{}-{}-test-{}'.format(
options.dataset, options.method, i),
fig_size=(9, 3),
cmap='bone')
def main():
options = get_parser().parse_args()
# load data
dataset = load_standard_dataset(options.dataset)
test_data = dataset.get_data_pairs('test', 100)
test_data = list(test_data)[options.start:options.start + options.count]
obs = list([item[0] for item in test_data])
gt = list([item[1] for item in test_data])
test_data = DataPairs(obs, gt, name='test')
# load reconstructor
fbp = fbp_reconstructor(options.dataset)
tv = tv_reconstructor(options.dataset, name='TV')
tvadam = tvadam_reconstructor(options.dataset, name='TV-Adam')
# compute and plot reconstructions
plot_reconstructors_tests([fbp, tv, tvadam],
ray_trafo=dataset.ray_trafo,
test_data=test_data,
save_name='{}-tv-tvadam-{}'.format(
options.dataset, options.start),
fig_size=(9, 3),
cmap=options.cmap)