def test_inverse_mapping(self):
reader = get_label_reader()
data_param = MOD_LABEL_DATA
sampler = GridSampler(reader=reader,
window_sizes=data_param,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='label',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
interp_order=0)
more_batch = True
with self.test_session() as sess:
sampler.set_num_threads(2)
while more_batch:
out = sess.run(sampler.pop_batch_op())
more_batch = aggregator.decode_batch(out['label'],
out['label_location'])
output_filename = '{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, [256, 168, 256, 1, 1])
sampler.close_all()
output_data = nib.load(output_file).get_data()[..., 0, 0]
expected_data = nib.load(
'testing_data/T1_1023_NeuroMorph_Parcellation.nii.gz').get_data()
self.assertAllClose(output_data, expected_data)
def test_25d_init(self):
reader = get_25d_reader()
sampler = GridSampler(reader=reader,
window_sizes=SINGLE_25D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
interp_order=0)
more_batch = True
with self.test_session() as sess:
sampler.set_num_threads(2)
while more_batch:
out = sess.run(sampler.pop_batch_op())
more_batch = aggregator.decode_batch(out['image'],
out['image_location'])
output_filename = '{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, [256, 168, 256, 1, 1],
rtol=1e-03,
atol=1e-03)
sampler.close_all()
def test_init_2d_mo_bidimcsv(self):
reader = get_2d_reader()
sampler = GridSampler(reader=reader,
window_sizes=MOD_2D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
interp_order=0)
more_batch = True
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
out = sess.run(sampler.pop_batch_op())
out_flatten = np.reshape(np.asarray(out['image']), [10, -1])
min_val = np.sum(
np.reshape(np.asarray(out['image']), [10, -1]), 1)
stats_val = np.concatenate([
np.min(out_flatten, 1, keepdims=True),
np.max(out_flatten, 1, keepdims=True),
np.sum(out_flatten, 1, keepdims=True)
], 1)
stats_val = np.expand_dims(stats_val, 1)
stats_val = np.concatenate([stats_val, stats_val], axis=1)
more_batch = aggregator.decode_batch(
{
'window_image': out['image'],
'csv_sum': min_val,
'csv_stats_2d': stats_val
}, out['image_location'])
output_filename = 'window_image_{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
sum_filename = os.path.join(
'testing_data', 'aggregated', 'csv_sum_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
stats_filename = os.path.join(
'testing_data', 'aggregated',
'csv_stats_2d_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, (128, 128))
min_pd = pd.read_csv(sum_filename)
self.assertAllClose(min_pd.shape, [10, 9])
stats_pd = pd.read_csv(stats_filename)
self.assertAllClose(stats_pd.shape, [10, 14])
sampler.close_all()
def test_dynamic_window_initialising(self):
sampler = GridSampler(reader=get_dynamic_window_reader(),
window_sizes=DYNAMIC_MOD_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(0, 0, 0),
queue_length=10)
with self.cached_session() as sess:
sampler.set_num_threads(1)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, (10, 8, 2, 256, 2))
sampler.close_all()
def test_25d_initialising(self):
sampler = GridSampler(reader=get_3d_reader(),
window_sizes=MULTI_MOD_DATA,
batch_size=10,
spatial_window_size=(1, 20, 15),
window_border=(0, 0, 0),
queue_length=10)
with self.cached_session() as sess:
sampler.set_num_threads(2)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, (10, 20, 15, 2))
sampler.close_all()
def test_2d_initialising(self):
sampler = GridSampler(reader=get_2d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(0, 0, 0),
queue_length=10)
with self.test_session() as sess:
sampler.set_num_threads(1)
out = sess.run(sampler.pop_batch_op())
self.assertAllClose(out['image'].shape, (10, 10, 7, 1))
sampler.close_all()
def test_3d_init_mo(self):
reader = get_3d_reader()
sampler = GridSampler(reader=reader,
window_sizes=MULTI_MOD_DATA,
batch_size=10,
spatial_window_size=None,
window_border=(3, 4, 5),
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='image',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=(3, 4, 5),
interp_order=0)
more_batch = True
with self.cached_session() as sess:
sampler.set_num_threads(2)
while more_batch:
out = sess.run(sampler.pop_batch_op())
out_flatten = np.reshape(np.asarray(out['image']), [10, -1])
min_val = np.sum(
np.reshape(np.asarray(out['image']), [10, -1]), 1)
more_batch = aggregator.decode_batch(
{
'window_image': out['image'],
'csv_sum': min_val
}, out['image_location'])
output_filename = 'window_image_{}_niftynet_out.nii.gz'.format(
sampler.reader.get_subject_id(0))
sum_filename = os.path.join(
'testing_data', 'aggregated', 'csv_sum_{}_niftynet_out.csv'.format(
sampler.reader.get_subject_id(0)))
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
self.assertAllClose(nib.load(output_file).shape, (256, 168, 256, 1, 2))
min_pd = pd.read_csv(sum_filename)
self.assertAllClose(min_pd.shape, [420, 9])
sampler.close_all()