def test_flip(self):
with tf.Session() as sess:
image_3d = self.constant_3d_image()
image_3d_np = sess.run(image_3d)
for flip_axis in [0, 1, 2]:
image_3d_flip, _ = data_aug_lib.maybe_flip(
image_3d, tf.zeros_like(image_3d), flip_axis, 0.0)
image_3d_flip_np = sess.run(image_3d_flip)
self.assertAllClose(image_3d_flip_np, image_3d_np)
image_3d_flip = image_3d
for flip_axis in [0, 1, 2]:
if flip_axis == 0:
image_3d_np = image_3d_np[::-1, ...]
elif flip_axis == 1:
image_3d_np = image_3d_np[:, ::-1, :]
else:
image_3d_np = image_3d_np[..., ::-1]
image_3d_flip, _ = data_aug_lib.maybe_flip(
image_3d_flip, tf.zeros_like(image_3d_flip), flip_axis, 1.0)
image_3d_flip_np = sess.run(image_3d_flip)
self.assertAllClose(image_3d_flip_np, image_3d_np)
def _parser_fn(serialized_example):
"""Parses a single tf.Example into image and label tensors."""
features = {}
features['image/ct_image'] = tf.FixedLenFeature([], tf.string)
features['image/label'] = tf.FixedLenFeature([], tf.string)
parsed = tf.parse_single_example(serialized_example,
features=features)
spatial_dims = [FLAGS.ct_resolution] * 3
if FLAGS.sampled_2d_slices:
noise_shape = [FLAGS.ct_resolution] * 2 + [FLAGS.image_c]
else:
noise_shape = [FLAGS.ct_resolution] * 3
image = tf.decode_raw(parsed['image/ct_image'], tf.float32)
label = tf.decode_raw(parsed['image/label'], tf.float32)
if dataset_str != 'train':
# Preprocess intensity, clip to 0 ~ 1.
# The training set is already preprocessed.
image = tf.clip_by_value(image / 1024.0 + 0.5, 0, 1)
image = tf.reshape(image, spatial_dims)
label = tf.reshape(label, spatial_dims)
if dataset_str == 'eval' and FLAGS.sampled_2d_slices:
return _get_stacked_2d_slices(image, label)
if FLAGS.sampled_2d_slices:
# Take random slices of images and label
begin_idx = tf.random_uniform(shape=[],
minval=0,
maxval=FLAGS.ct_resolution -
FLAGS.image_c + 1,
dtype=tf.int32)
slice_begin = [0, 0, begin_idx]
slice_size = [
FLAGS.ct_resolution, FLAGS.ct_resolution, FLAGS.image_c
]
image = tf.slice(image, slice_begin, slice_size)
label = tf.slice(label, slice_begin, slice_size)
if dataset_str == 'train':
for flip_axis in [0, 1, 2]:
image, label = data_aug_lib.maybe_flip(
image, label, flip_axis)
image, label = data_aug_lib.maybe_rot180(image,
label,
static_axis=2)
image = data_aug_lib.intensity_shift(
image, label, FLAGS.per_class_intensity_scale,
FLAGS.per_class_intensity_shift)
image = data_aug_lib.image_corruption(
image, label, FLAGS.ct_resolution,
FLAGS.image_corrupt_ratio_mean,
FLAGS.image_corrupt_ratio_stddev)
image = data_aug_lib.maybe_add_noise(
image, noise_shape, 1, 4, FLAGS.image_noise_probability,
FLAGS.image_noise_ratio)
image, label = data_aug_lib.projective_transform(
image, label, FLAGS.ct_resolution,
FLAGS.image_translate_ratio, FLAGS.image_transform_ratio,
FLAGS.sampled_2d_slices)
if FLAGS.sampled_2d_slices:
# Only get the center slice of label.
label = tf.slice(label, [0, 0, FLAGS.image_c // 2],
[FLAGS.ct_resolution, FLAGS.ct_resolution, 1])
spatial_dims_w_blocks = [
FLAGS.image_nx_block,
FLAGS.ct_resolution // FLAGS.image_nx_block,
FLAGS.image_ny_block,
FLAGS.ct_resolution // FLAGS.image_ny_block
]
if not FLAGS.sampled_2d_slices:
spatial_dims_w_blocks += [FLAGS.ct_resolution]
image = tf.reshape(image, spatial_dims_w_blocks + [FLAGS.image_c])
label = tf.reshape(label, spatial_dims_w_blocks)
label = tf.cast(label, tf.int32)
label = tf.one_hot(label, FLAGS.label_c)
data_dtype = tf.as_dtype(FLAGS.mtf_dtype)
image = tf.cast(image, data_dtype)
label = tf.cast(label, data_dtype)
return image, label
