def apply_random_transform_scalar_labels(features, labels):
"""Apply a random rigid transformation to `features`.
Features are interpolated trilinearly, and labels are unchanged because they are
scalars.
"""
if len(features.shape) != 3:
raise ValueError("features must be rank 3")
if len(labels.shape) != 1:
raise ValueError("labels must be rank 1")
# Rotate -180 degrees to 180 degrees in three dimensions.
rotation = tf.random.uniform(shape=[3],
minval=-np.pi,
maxval=np.pi,
dtype=tf.float32)
# Translate at most 5% in any direction, so there's less chance of
# important data going out of view.
maxval = 0.05 * features.shape[0]
translation = tf.random.uniform(shape=[3],
minval=-maxval,
maxval=maxval,
dtype=tf.float32)
volume_shape = np.asarray(features.shape)
matrix = get_affine(volume_shape=volume_shape,
rotation=rotation,
translation=translation)
return warp_features_labels(features=features,
labels=labels,
matrix=matrix,
scalar_label=True)
def apply_random_transform(features, labels):
"""Apply a random rigid transformation to `features` and `labels`.
The same transformation is applied to features and labels. Features are
interpolated trilinearly, and labels are interpolated with nearest
neighbors.
"""
if len(features.shape) != 3 or len(labels.shape) != 3:
raise ValueError("features and labels must be rank 3")
if features.shape != labels.shape:
raise ValueError("shape of features and labels must be the same.")
# Rotate -180 degrees to 180 degrees in three dimensions.
rotation = tf.random.uniform(shape=[3],
minval=-np.pi,
maxval=np.pi,
dtype=tf.float32)
# Translate at most 5% in any direction, so there's less chance of
# important data going out of view.
maxval = 0.05 * features.shape[0]
translation = tf.random.uniform(shape=[3],
minval=-maxval,
maxval=maxval,
dtype=tf.float32)
volume_shape = np.asarray(features.shape)
matrix = get_affine(volume_shape=volume_shape,
rotation=rotation,
translation=translation)
return warp_features_labels(features=features,
labels=labels,
matrix=matrix)