def augment_spatial_peaks(data,
seg,
patch_size,
patch_center_dist_from_border=30,
do_elastic_deform=True,
alpha=(0., 1000.),
sigma=(10., 13.),
do_rotation=True,
angle_x=(0, 2 * np.pi),
angle_y=(0, 2 * np.pi),
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='nearest',
border_cval_data=0,
order_data=3,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=0,
random_crop=True,
p_el_per_sample=1,
p_scale_per_sample=1,
p_rot_per_sample=1,
slice_dir=None):
dim = len(patch_size)
seg_result = None
if seg is not None:
if dim == 2:
seg_result = np.zeros(
(seg.shape[0], seg.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
seg_result = np.zeros((seg.shape[0], seg.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if dim == 2:
data_result = np.zeros(
(data.shape[0], data.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
data_result = np.zeros((data.shape[0], data.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if not isinstance(patch_center_dist_from_border,
(list, tuple, np.ndarray)):
patch_center_dist_from_border = dim * [patch_center_dist_from_border]
for sample_id in range(data.shape[0]):
coords = create_zero_centered_coordinate_mesh(patch_size)
modified_coords = False
if np.random.uniform() < p_el_per_sample and do_elastic_deform:
a = np.random.uniform(alpha[0], alpha[1])
s = np.random.uniform(sigma[0], sigma[1])
coords = elastic_deform_coordinates(coords, a, s)
modified_coords = True
# NEW: initialize all because all needed for rotate_multiple_peaks (even if only rotating along one axis)
a_x = 0
a_y = 0
a_z = 0
if np.random.uniform() < p_rot_per_sample and do_rotation:
if angle_x[0] == angle_x[1]:
a_x = angle_x[0]
else:
a_x = np.random.uniform(angle_x[0], angle_x[1])
if dim == 3:
if angle_y[0] == angle_y[1]:
a_y = angle_y[0]
else:
a_y = np.random.uniform(angle_y[0], angle_y[1])
if angle_z[0] == angle_z[1]:
a_z = angle_z[0]
else:
a_z = np.random.uniform(angle_z[0], angle_z[1])
coords = rotate_coords_3d(coords, a_x, a_y, a_z)
else:
coords = rotate_coords_2d(coords, a_x)
modified_coords = True
if np.random.uniform() < p_scale_per_sample and do_scale:
if np.random.random() < 0.5 and scale[0] < 1:
sc = np.random.uniform(scale[0], 1)
else:
sc = np.random.uniform(max(scale[0], 1), scale[1])
coords = scale_coords(coords, sc)
modified_coords = True
# now find a nice center location
if modified_coords:
for d in range(dim):
if random_crop:
ctr = np.random.uniform(
patch_center_dist_from_border[d],
data.shape[d + 2] - patch_center_dist_from_border[d])
else:
ctr = int(np.round(data.shape[d + 2] / 2.))
coords[d] += ctr
for channel_id in range(data.shape[1]):
data_result[sample_id, channel_id] = interpolate_img(
data[sample_id, channel_id],
coords,
order_data,
border_mode_data,
cval=border_cval_data)
if seg is not None:
for channel_id in range(seg.shape[1]):
seg_result[sample_id, channel_id] = interpolate_img(
seg[sample_id, channel_id],
coords,
order_seg,
border_mode_seg,
cval=border_cval_seg,
is_seg=True)
else:
if seg is None:
s = None
else:
s = seg[sample_id:sample_id + 1]
if random_crop:
margin = [
patch_center_dist_from_border[d] - patch_size[d] // 2
for d in range(dim)
]
d, s = random_crop_aug(data[sample_id:sample_id + 1], s,
patch_size, margin)
else:
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size, s)
data_result[sample_id] = d[0]
if seg is not None:
seg_result[sample_id] = s[0]
# NEW: Rotate Peaks / Tensors
if dim > 2:
raise ValueError(
"augment_spatial_peaks only supports 2D at the moment")
sampled_2D_angle = a_x # if 2D angle will always be a_x even if rotating other axis
a_x = 0
a_y = 0
a_z = 0
if slice_dir == 0:
a_x = sampled_2D_angle
elif slice_dir == 1:
a_y = sampled_2D_angle
elif slice_dir == 2:
# Somehow we have to invert rotation direction for z to make align properly with rotated voxels.
# Unclear why this is the case. Maybe some different conventions for peaks and voxels??
a_z = sampled_2D_angle * -1
else:
raise ValueError("invalid slice_dir passed as argument")
data_aug = data_result[sample_id]
if data_aug.shape[0] == 9:
data_result[sample_id] = rotate_multiple_peaks(
data_aug, a_x, a_y, a_z)
elif data_aug.shape[0] == 18:
data_result[sample_id] = rotate_multiple_tensors(
data_aug, a_x, a_y, a_z)
else:
raise ValueError("Incorrect number of channels (expected 9 or 18)")
return data_result, seg_result
def augment_spatial(data,
seg,
patch_size,
patch_center_dist_from_border=30,
do_elastic_deform=True,
alpha=(0., 1000.),
sigma=(10., 13.),
do_rotation=True,
angle_x=(0, 2 * np.pi),
angle_y=(0, 2 * np.pi),
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='nearest',
border_cval_data=0,
order_data=3,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=0,
random_crop=True,
p_el_per_sample=1,
p_scale_per_sample=1,
p_rot_per_sample=1,
independent_scale_for_each_axis=False,
p_rot_per_axis: float = 1,
p_independent_scale_per_axis: int = 1):
dim = len(patch_size)
seg_result = None
if seg is not None:
if dim == 2:
seg_result = np.zeros(
(seg.shape[0], seg.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
seg_result = np.zeros((seg.shape[0], seg.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if dim == 2:
data_result = np.zeros(
(data.shape[0], data.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
data_result = np.zeros((data.shape[0], data.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if not isinstance(patch_center_dist_from_border,
(list, tuple, np.ndarray)):
patch_center_dist_from_border = dim * [patch_center_dist_from_border]
for sample_id in range(data.shape[0]):
coords = create_zero_centered_coordinate_mesh(patch_size)
modified_coords = False
if do_elastic_deform and np.random.uniform() < p_el_per_sample:
a = np.random.uniform(alpha[0], alpha[1])
s = np.random.uniform(sigma[0], sigma[1])
coords = elastic_deform_coordinates(coords, a, s)
modified_coords = True
if do_rotation and np.random.uniform() < p_rot_per_sample:
if np.random.uniform() <= p_rot_per_axis:
a_x = np.random.uniform(angle_x[0], angle_x[1])
else:
a_x = 0
if dim == 3:
if np.random.uniform() <= p_rot_per_axis:
a_y = np.random.uniform(angle_y[0], angle_y[1])
else:
a_y = 0
if np.random.uniform() <= p_rot_per_axis:
a_z = np.random.uniform(angle_z[0], angle_z[1])
else:
a_z = 0
coords = rotate_coords_3d(coords, a_x, a_y, a_z)
else:
coords = rotate_coords_2d(coords, a_x)
modified_coords = True
if do_scale and np.random.uniform() < p_scale_per_sample:
if independent_scale_for_each_axis and np.random.uniform(
) < p_independent_scale_per_axis:
sc = []
for _ in range(dim):
if np.random.random() < 0.5 and scale[0] < 1:
sc.append(np.random.uniform(scale[0], 1))
else:
sc.append(np.random.uniform(max(scale[0], 1),
scale[1]))
else:
if np.random.random() < 0.5 and scale[0] < 1:
sc = np.random.uniform(scale[0], 1)
else:
sc = np.random.uniform(max(scale[0], 1), scale[1])
coords = scale_coords(coords, sc)
modified_coords = True
# now find a nice center location
if modified_coords:
for d in range(dim):
if random_crop:
ctr = np.random.uniform(
patch_center_dist_from_border[d],
data.shape[d + 2] - patch_center_dist_from_border[d])
else:
ctr = int(np.round(data.shape[d + 2] / 2.))
coords[d] += ctr
for channel_id in range(data.shape[1]):
data_result[sample_id, channel_id] = interpolate_img(
data[sample_id, channel_id],
coords,
order_data,
border_mode_data,
cval=border_cval_data)
if seg is not None:
for channel_id in range(seg.shape[1]):
seg_result[sample_id, channel_id] = interpolate_img(
seg[sample_id, channel_id],
coords,
order_seg,
border_mode_seg,
cval=border_cval_seg,
is_seg=True)
else:
if seg is None:
s = None
else:
s = seg[sample_id:sample_id + 1]
if random_crop:
margin = [
patch_center_dist_from_border[d] - patch_size[d] // 2
for d in range(dim)
]
d, s = random_crop_aug(data[sample_id:sample_id + 1], s,
patch_size, margin)
else:
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size, s)
data_result[sample_id] = d[0]
if seg is not None:
seg_result[sample_id] = s[0]
return data_result, seg_result
def augment_spatial_2(data,
seg,
patch_size,
patch_center_dist_from_border=30,
do_elastic_deform=True,
deformation_scale=(0, 0.25),
do_rotation=True,
angle_x=(0, 2 * np.pi),
angle_y=(0, 2 * np.pi),
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.75, 1.25),
border_mode_data='nearest',
border_cval_data=0,
order_data=3,
border_mode_seg='constant',
border_cval_seg=0,
order_seg=0,
random_crop=True,
p_el_per_sample=1,
p_scale_per_sample=1,
p_rot_per_sample=1,
independent_scale_for_each_axis=False,
p_rot_per_axis: float = 1,
p_independent_scale_per_axis: int = 1):
"""
:param data:
:param seg:
:param patch_size:
:param patch_center_dist_from_border:
:param do_elastic_deform:
:param magnitude: this determines how large the magnitude of the deformation is relative to the patch_size.
0.125 = 12.5%% of the patch size (in each dimension).
:param sigma: this determines the scale of the deformation. small values = local deformations,
large values = large deformations.
:param do_rotation:
:param angle_x:
:param angle_y:
:param angle_z:
:param do_scale:
:param scale:
:param border_mode_data:
:param border_cval_data:
:param order_data:
:param border_mode_seg:
:param border_cval_seg:
:param order_seg:
:param random_crop:
:param p_el_per_sample:
:param p_scale_per_sample:
:param p_rot_per_sample:
:param clip_to_safe_magnitude:
:return:
"""
dim = len(patch_size)
seg_result = None
if seg is not None:
if dim == 2:
seg_result = np.zeros(
(seg.shape[0], seg.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
seg_result = np.zeros((seg.shape[0], seg.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if dim == 2:
data_result = np.zeros(
(data.shape[0], data.shape[1], patch_size[0], patch_size[1]),
dtype=np.float32)
else:
data_result = np.zeros((data.shape[0], data.shape[1], patch_size[0],
patch_size[1], patch_size[2]),
dtype=np.float32)
if not isinstance(patch_center_dist_from_border,
(list, tuple, np.ndarray)):
patch_center_dist_from_border = dim * [patch_center_dist_from_border]
for sample_id in range(data.shape[0]):
coords = create_zero_centered_coordinate_mesh(patch_size)
modified_coords = False
if np.random.uniform() < p_el_per_sample and do_elastic_deform:
mag = []
sigmas = []
# one scale per case, scale is in percent of patch_size
def_scale = np.random.uniform(deformation_scale[0],
deformation_scale[1])
for d in range(len(data[sample_id].shape) - 1):
# transform relative def_scale in pixels
sigmas.append(def_scale * patch_size[d])
# define max magnitude and min_magnitude
max_magnitude = sigmas[-1] * (1 / 2)
min_magnitude = sigmas[-1] * (1 / 8)
# the magnitude needs to depend on the scale, otherwise not much is going to happen most of the time.
# we want the magnitude to be high, but not higher than max_magnitude (otherwise the deformations
# become very ugly). Let's sample mag_real with a gaussian
# mag_real = np.random.normal(max_magnitude * (2 / 3), scale=max_magnitude / 3)
# clip to make sure we stay reasonable
# mag_real = np.clip(mag_real, 0, max_magnitude)
mag_real = np.random.uniform(min_magnitude, max_magnitude)
mag.append(mag_real)
# print(np.round(sigmas, decimals=3), np.round(mag, decimals=3))
coords = elastic_deform_coordinates_2(coords, sigmas, mag)
modified_coords = True
if do_rotation and np.random.uniform() < p_rot_per_sample:
if np.random.uniform() <= p_rot_per_axis:
a_x = np.random.uniform(angle_x[0], angle_x[1])
else:
a_x = 0
if dim == 3:
if np.random.uniform() <= p_rot_per_axis:
a_y = np.random.uniform(angle_y[0], angle_y[1])
else:
a_y = 0
if np.random.uniform() <= p_rot_per_axis:
a_z = np.random.uniform(angle_z[0], angle_z[1])
else:
a_z = 0
coords = rotate_coords_3d(coords, a_x, a_y, a_z)
else:
coords = rotate_coords_2d(coords, a_x)
modified_coords = True
if do_scale and np.random.uniform() < p_scale_per_sample:
if independent_scale_for_each_axis and np.random.uniform(
) < p_independent_scale_per_axis:
sc = []
for _ in range(dim):
if np.random.random() < 0.5 and scale[0] < 1:
sc.append(np.random.uniform(scale[0], 1))
else:
sc.append(np.random.uniform(max(scale[0], 1),
scale[1]))
else:
if np.random.random() < 0.5 and scale[0] < 1:
sc = np.random.uniform(scale[0], 1)
else:
sc = np.random.uniform(max(scale[0], 1), scale[1])
coords = scale_coords(coords, sc)
modified_coords = True
# now find a nice center location
if modified_coords:
# recenter coordinates
coords_mean = coords.mean(axis=tuple(range(1, len(coords.shape))),
keepdims=True)
coords -= coords_mean
for d in range(dim):
if random_crop:
ctr = np.random.uniform(
patch_center_dist_from_border[d],
data.shape[d + 2] - patch_center_dist_from_border[d])
else:
ctr = int(np.round(data.shape[d + 2] / 2.))
coords[d] += ctr
for channel_id in range(data.shape[1]):
data_result[sample_id, channel_id] = interpolate_img(
data[sample_id, channel_id],
coords,
order_data,
border_mode_data,
cval=border_cval_data)
if seg is not None:
for channel_id in range(seg.shape[1]):
seg_result[sample_id, channel_id] = interpolate_img(
seg[sample_id, channel_id],
coords,
order_seg,
border_mode_seg,
cval=border_cval_seg,
is_seg=True)
else:
if seg is None:
s = None
else:
s = seg[sample_id:sample_id + 1]
if random_crop:
margin = [
patch_center_dist_from_border[d] - patch_size[d] // 2
for d in range(dim)
]
d, s = random_crop_aug(data[sample_id:sample_id + 1], s,
patch_size, margin)
else:
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size, s)
data_result[sample_id] = d[0]
if seg is not None:
seg_result[sample_id] = s[0]
return data_result, seg_result
def augment_spatial_transform(data, seg=None, patch_size=None, patch_center_dist_from_border=None,
do_elastic_deform=True, alpha=(0., 900.), sigma=(9., 13.), do_rotation=True,
angle_x=default_3D_augmentation_params.get("rotation_x"),
angle_y=default_3D_augmentation_params.get("rotation_y"),
angle_z=default_3D_augmentation_params.get("rotation_z"),
do_scale=True, scale=(0.85, 1.25),
border_mode_data='constant', border_cval_data=0, order_data=3,
border_mode_seg="constant", border_cval_seg=-1, order_seg=1,
random_crop=False, p_el_per_sample=1,
p_scale_per_sample=1, p_rot_per_sample=1):
if len(data.shape) == 3:
print("Attention: data shape must be (C, D, H, W). I will transform it.")
data = data[None]
if seg is not None and len(seg.shape) == 3:
seg = seg[None]
if patch_size is None:
patch_size = (data.shape[1], data.shape[2], data.shape[3])
dim = len(patch_size)
seg_result = None
if seg is not None:
if dim == 2:
seg_result = np.zeros((seg.shape[0], patch_size[0], patch_size[1]), dtype=np.float32)
else:
seg_result = np.zeros((seg.shape[0], patch_size[0], patch_size[1], patch_size[2]),
dtype=np.float32)
if dim == 2:
data_result = np.zeros((data.shape[0], patch_size[0], patch_size[1]), dtype=np.float32)
else:
data_result = np.zeros((data.shape[0], patch_size[0], patch_size[1], patch_size[2]),
dtype=np.float32)
if not isinstance(patch_center_dist_from_border, (list, tuple, np.ndarray)):
patch_center_dist_from_border = dim * [patch_center_dist_from_border]
coords = create_zero_centered_coordinate_mesh(patch_size)
modified_coords = False
if np.random.uniform() < p_el_per_sample and do_elastic_deform:
a = np.random.uniform(alpha[0], alpha[1])
s = np.random.uniform(sigma[0], sigma[1])
coords = elastic_deform_coordinates(coords, a, s)
modified_coords = True
if np.random.uniform() < p_rot_per_sample and do_rotation:
if angle_x[0] == angle_x[1]:
a_x = angle_x[0]
else:
a_x = np.random.uniform(angle_x[0], angle_x[1])
if dim == 3:
if angle_y[0] == angle_y[1]:
a_y = angle_y[0]
else:
a_y = np.random.uniform(angle_y[0], angle_y[1])
if angle_z[0] == angle_z[1]:
a_z = angle_z[0]
else:
a_z = np.random.uniform(angle_z[0], angle_z[1])
coords = rotate_coords_3d(coords, a_x, a_y, a_z)
else:
coords = rotate_coords_2d(coords, a_x)
modified_coords = True
if np.random.uniform() < p_scale_per_sample and do_scale:
if np.random.random() < 0.5 and scale[0] < 1:
sc = np.random.uniform(scale[0], 1)
else:
sc = np.random.uniform(max(scale[0], 1), scale[1])
coords = scale_coords(coords, sc)
modified_coords = True
# now find a nice center location
if modified_coords:
for d in range(dim):
if random_crop:
ctr = np.random.uniform(patch_center_dist_from_border[d],
data.shape[d + 1] - patch_center_dist_from_border[d])
else:
ctr = int(np.around(data.shape[d + 1] / 2.))
coords[d] += ctr
for channel_id in range(data.shape[0]):
data_result[channel_id] = interpolate_img(data[channel_id], coords, order_data,
border_mode_data, cval=border_cval_data)
if seg is not None:
for channel_id in range(seg.shape[0]):
seg_result[channel_id] = interpolate_img(seg[channel_id], coords, order_seg,
border_mode_seg, cval=border_cval_seg, is_seg=True)
else:
if seg is None:
s = None
else:
s = seg[None]
if random_crop:
margin = [patch_center_dist_from_border[d] - patch_size[d] // 2 for d in range(dim)]
d, s = random_crop_aug(data[None], s, patch_size, margin)
else:
d, s = center_crop_aug(data[None], patch_size, s)
data_result = d[0]
if seg is not None:
seg_result = s[0]
return data_result, seg_result
