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_filling(self):
reader = get_nonnormalising_label_reader()
test_constant = 0.5731
postfix = '_niftynet_out_background'
test_border = (10, 7, 8)
data_param = MOD_LABEL_DATA
sampler = GridSampler(reader=reader,
window_sizes=data_param,
batch_size=10,
spatial_window_size=None,
window_border=test_border,
queue_length=50)
aggregator = GridSamplesAggregator(image_reader=reader,
name='label',
output_path=os.path.join(
'testing_data', 'aggregated'),
window_border=test_border,
interp_order=0,
postfix=postfix,
fill_constant=test_constant)
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 = '{}{}.nii.gz'.format(
sampler.reader.get_subject_id(0), postfix)
output_file = os.path.join('testing_data', 'aggregated',
output_filename)
output_data = nib.load(output_file).get_data()[..., 0, 0]
output_shape = output_data.shape
for i in range(3):
def _test_background(idcs):
extract = output_data[idcs]
self.assertTrue(
(extract == test_constant).sum() == extract.size)
extract_idcs = [slice(None)] * 3
extract_idcs[i] = slice(0, test_border[i])
_test_background(tuple(extract_idcs))
extract_idcs[i] = slice(output_shape[i] - test_border[i],
output_shape[i])
_test_background(tuple(extract_idcs))
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()
spatial_window_size,
windows_per_image=100)
next_window = uniform_sampler.pop_batch_op()
coords = []
with tf.Session() as sess:
for _ in range(20):
uniform_windows = sess.run(next_window)
coords.append(uniform_windows['MR_location'])
coords = np.concatenate(coords, axis=0)
vis_coordinates(image_2d, coords, 'output/uniform.png')
###
# create & show all grid samples
###
grid_sampler = GridSampler(reader, spatial_window_size, window_border=border)
next_grid = grid_sampler.pop_batch_op()
coords = []
with tf.Session() as sess:
while True:
window = sess.run(next_grid)
if window['MR_location'][0, 0] == -1:
break
coords.append(window['MR_location'])
coords = np.concatenate(coords, axis=0)
vis_coordinates(image_2d, coords, 'output/grid.png')
###
# create & show cropped grid samples (in aggregator)
###
n_window = coords.shape[0]
dummy_window = np.zeros((n_window, 800, 800, 1, 1))