def test_close_early(self):
sampler = WeightedSampler(reader=get_2d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
sampler.close_all()
def test_dynamic_init(self):
sampler = WeightedSampler(reader=get_dynamic_window_reader(),
window_sizes=DYNAMIC_MOD_DATA,
batch_size=2,
windows_per_image=10,
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[1:], (8, 2, 256, 2))
def test_ill_init(self):
with self.assertRaisesRegexp(ValueError, ""):
sampler = WeightedSampler(reader=get_3d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
queue_length=10)
def get_sampler(image_reader,
patch_size,
phase,
windows_per_image=None,
window_border=None):
if phase in ('training', 'validation'):
if windows_per_image:
sampler = WeightedSampler(image_reader,
window_sizes=patch_size,
windows_per_image=windows_per_image)
else:
raise Exception('Invalid windows per image!')
elif phase == 'inference':
if window_border:
sampler = GridSampler(image_reader,
window_sizes=patch_size,
window_border=window_border)
else:
raise Exception('Invalid window border!')
else:
raise Exception('Invalid phase choice: {}'.format(
{'phase': ['training', 'validation', 'inference']}))
return sampler
def initialise_weighted_sampler(self):
self.sampler = [[WeightedSampler(
reader=reader,
window_sizes=self.data_param,
batch_size=self.net_param.batch_size,
windows_per_image=self.action_param.sample_per_volume,
queue_length=self.net_param.queue_length) for reader in
self.readers]]
def test_2d_init(self):
sampler = WeightedSampler(reader=get_2d_reader(),
window_sizes=MOD_2D_DATA,
batch_size=2,
windows_per_image=10,
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, (2, 10, 9, 1))
sampler.close_all()
# 'filename_contains': ['middleSlice','mhd'], 'pixdim': (1,1), 'axcodes': ['L','P','S'],'interp_order': 3},
# 'sampler': {'path_to_search': './sample_images', 'spatial_window_size': (48,48,1),
# 'filename_contains': ['middleLabels','mhd'],'pixdim': (1,1), 'axcodes': ['L','P','S'],'interp_order': 0},
# }
#Create image reader and add padding layer with 'constant' value 0
#This will ensure that samples are taken even at the beginning and ending slice
reader = ImageReader().initialise(data_param)
reader.add_preprocessing_layers(
PadLayer(image_name=['MR', 'sampler'],
border=(20, 20, 20),
mode='constant'))
_, img, _ = reader(idx=0)
#Create samplers with window_size
weighted_sampler = WeightedSampler(reader, window_sizes=(48, 48, 48))
balanced_sampler = BalancedSampler(reader, window_sizes=(48, 48, 48))
uniform_sampler = UniformSampler(reader, window_sizes=(48, 48, 48))
#Generate N samples for each type
N = 30
import tensorflow as tf
# adding the tensorflow tensors
next_window = weighted_sampler.pop_batch_op()
# run the tensors
with tf.Session() as sess:
weighted_sampler.run_threads(sess) #initialise the iterator
w_coords = []
for _ in range(N):