def rotate(self, data_dict):
data = data_dict["data"]
do_seg = False
seg = None
if "seg" in list(data_dict.keys()):
seg = data_dict["seg"]
do_seg = True
shape = np.array(data.shape[2:])
dim = len(shape)
for sample_id in range(data.shape[0]):
coords = create_zero_centered_coordinate_mesh(shape)
if dim == 3:
coords = rotate_coords_3d(coords,
self.rand_params['ax'][sample_id],
self.rand_params['ay'][sample_id],
self.rand_params['az'][sample_id])
else:
coords = rotate_coords_2d(coords, self.rand_params['ax'][sample_id])
coords = uncenter_coords(coords)
for channel_id in range(data.shape[1]):
data[sample_id, channel_id] = interpolate_img(data[sample_id, channel_id], coords,
self.params['order_data'], self.params['bmode_data'],
cval=self.params['bcval_data'])
if do_seg:
for channel_id in range(seg.shape[1]):
seg[sample_id, channel_id] = interpolate_img(seg[sample_id, channel_id], coords,
self.params['order_seg'], self.params['bmode_seg'],
cval=self.params['bcval_seg'], is_seg=True)
return {'data': data, 'seg': seg}
def batch_deform_3d(vol, coords, bspline_order, border_mode, constant_val):
data_result = np.zeros_like(vol, dtype=np.float32)
for sample_id in range(vol.shape[0]):
for channel_id in range(vol.shape[1]):
data_result[sample_id,
channel_id] = interpolate_img(vol[sample_id,
channel_id],
coords,
order=bspline_order,
mode=border_mode,
cval=constant_val)
return data_result
def augment_spatial(self, data, coords, is_label=False):
if is_label:
order = self.order_seg
border_mode = self.border_mode_seg
border_cval = self.border_cval_seg
else:
order = self.order_data
border_mode = self.border_mode_data
border_cval = self.border_cval_data
data = interpolate_img(data,
coords,
order,
border_mode,
cval=border_cval)
return data
def augment_spatial(data,
seg,
patch_size,
labels_extra=None,
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):
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)
# shuailin: extra segs
seg_extra_outputs = []
if labels_extra is not None:
for _ in range(len(labels_extra)):
if dim == 2:
seg_extra_outputs.append(
np.zeros((seg.shape[0], seg.shape[1], patch_size[0],
patch_size[1]),
dtype=np.float32))
else:
seg_extra_outputs.append(
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 not independent_scale_for_each_axis:
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])
else:
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]))
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)
# shuailin: extra segs transformation
if labels_extra is not None:
for ith, seg_arr in enumerate(labels_extra):
for channel_id in range(seg_arr.shape[1]):
seg_extra_outputs[ith][sample_id,
channel_id] = interpolate_img(
seg_arr[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]
# shuailin extension
if labels_extra is not None:
labels_extra_sample = []
for ith in range(len(labels_extra)):
labels_extra_sample.append(
labels_extra[ith][sample_id:sample_id + 1])
else:
labels_extra_sample = None
if random_crop:
margin = [
patch_center_dist_from_border[d] - patch_size[d] // 2
for d in range(dim)
]
d, s, seg_extra_output = random_crop_aug(
data[sample_id:sample_id + 1],
s,
patch_size,
margin,
labels_extra=labels_extra_sample)
else:
d, s, seg_extra_output = center_crop_aug(
data[sample_id:sample_id + 1],
patch_size,
s,
labels_extra=labels_extra_sample)
data_result[sample_id] = d[0]
if seg is not None:
seg_result[sample_id] = s[0]
# shuailin extension
if labels_extra is not None:
for ith in range(len(labels_extra)):
seg_extra_outputs[ith][sample_id] = seg_extra_output[ith]
return data_result, seg_result, seg_extra_outputs
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_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
def cc_augment(config_task,
data,
seg,
patch_type,
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='constant',
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,
tag=''):
# patch_center_dist_from_border should be no more than 1/2 patch size. otherwise code not available.
# data: [n,c,d,h,w]
# seg: [n,c,d,h,w]
dim = len(patch_size)
seg_result = None
if seg is not None:
seg_result = np.zeros([seg.shape[0], seg.shape[1]] + patch_size,
dtype=np.float32)
data_result = np.zeros([data.shape[0], data.shape[1]] + patch_size,
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-loop for dim[0]
augs = list()
for sample_id in range(data.shape[0]):
coords = create_zero_centered_coordinate_mesh(patch_size)
# now find a nice center location and extract patch
if seg is None:
patch_type = 'any'
handler = 0
n = 0
while handler == 0:
# augmentation
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
augs.append('elastic')
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
augs.append('rotation')
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
augs.append('scale')
# find candidate area for center, the area is cand_point_coord +/- patch_size
if patch_type in ['fore', 'small'] and seg is not None:
if seg.shape[1] > 1:
logger.error('TBD for seg with multiple channels')
if patch_type == 'fore':
lab_coords = np.where(
seg[sample_id, 0, ...] > 0) # lab_coords: tuple
elif patch_type == 'small':
if config_task.task == 'Task05_Prostate':
lab_coords = np.where(seg[sample_id, 0, ...] == 1)
else:
lab_coords = np.where(
seg[sample_id, 0,
...] == config_task.num_class - 1)
if len(lab_coords[0]) > 0: # 0 means no such label exists
idx = np.random.choice(len(lab_coords[0]))
cand_point_coord = [
coords[idx] for coords in lab_coords
] # coords for one random point from 'fore' ground
else:
cand_point_coord = None
if patch_type in ['fore', 'small'] and cand_point_coord is None:
ctr_list = None
handler = 1
data_result = None
seg_result = None
augs = None
else:
ctr_list = list() # coords of the patch center
for d in range(dim):
if random_crop:
if patch_type in ['fore', 'small'] and seg is not None:
low = max(
patch_center_dist_from_border[d] - 1,
cand_point_coord[d] - (patch_size[d] / 2 - 1))
low = int(low)
upper = min(
cand_point_coord[d] + (patch_size[d] / 2 - 1),
data.shape[d + 2] -
(patch_center_dist_from_border[d] - 1)
) # +/- patch_size[d] is better but computation costly
upper = int(upper)
if low == upper:
ctr = int(low)
elif low < upper:
ctr = int(np.random.randint(low, upper))
# if n > 1:
# logger.info('n:{}; [low,upper]:{}, ctr:{}'.format(n, str([low, upper]), ctr))
else:
logger.error(
'(low:{} should be <= upper:{}). patch_type:{}, patch_center_dist_from_border:{}, cand_point_coord:{}, cand point seg value:{}, data.shape:{}, ctr_list:{}'
.format(
low, upper, str(patch_type),
str(patch_center_dist_from_border),
str(cand_point_coord),
seg[sample_id, 0] + cand_point_coord,
str(data.shape), str(ctr_list)))
elif patch_type == 'any':
if patch_center_dist_from_border[d] == data.shape[
d + 2] - patch_center_dist_from_border[d]:
ctr = int(patch_center_dist_from_border[d])
elif patch_center_dist_from_border[d] < data.shape[
d + 2] - patch_center_dist_from_border[d]:
ctr = int(
np.random.randint(
patch_center_dist_from_border[d],
data.shape[d + 2] -
patch_center_dist_from_border[d]))
else:
logger.error(
'low should be <= upper. patch_type:{}, patch_center_dist_from_border:{}, data.shape:{}, ctr_list:{}'
.format(str(patch_type),
str(patch_center_dist_from_border),
str(data.shape), str(ctr_list)))
else: # center crop
ctr = int(np.round(data.shape[d + 2] / 2.))
ctr_list.append(ctr)
# extracting patch
if n < 10 and modified_coords:
for d in range(dim):
coords[d] += ctr_list[d]
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:
augs = list()
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)
d_tmps = list()
for channel_id in range(data.shape[1]):
d_tmp = utils.extract_roi_from_volume(
data[sample_id, channel_id],
ctr_list,
patch_size,
fill="zero")
d_tmps.append(d_tmp)
d = np.asarray(d_tmps)
if seg is not None:
s_tmps = list()
for channel_id in range(seg.shape[1]):
s_tmp = utils.extract_roi_from_volume(
seg[sample_id, channel_id],
ctr_list,
patch_size,
fill="zero")
s_tmps.append(s_tmp)
s = np.asarray(s_tmps)
else:
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size, s)
# data_result[sample_id] = d[0]
data_result[sample_id] = d
if seg is not None:
# seg_result[sample_id] = s[0]
seg_result[sample_id] = s
## check patch
if patch_type in [
'fore'
]: # cancer could be very very small. so use opproximate method (i.e. use 'fore').
if np.any(seg_result > 0) and np.any(data_result != 0):
handler = 1
else:
handler = 0
elif patch_type in ['small']:
if config_task.task == 'Task05_Prostate':
if np.any(seg_result == 1) and np.any(
data_result != 0):
handler = 1
else:
handler = 0
else:
if np.any(seg_result == config_task.num_class -
1) and np.any(data_result != 0):
handler = 1
else:
handler = 0
else:
if np.any(data_result != 0):
handler = 1
else:
handler = 0
n += 1
if n > 5:
logger.info(
'tag:{}, patch_type: {}; handler: {}; times: {}; cand point:{}; cand point seg value:{}; ctr_list:{}; data.shape:{}; np.unique(seg_result):{}; np.sum(data_result):{}'
.format(
tag, patch_type, handler, n, str(cand_point_coord),
seg[sample_id, 0, cand_point_coord[0],
cand_point_coord[1], cand_point_coord[2]],
str(ctr_list), str(data.shape),
np.unique(seg_result, return_counts=True),
np.sum(data_result)))
return data_result, seg_result, augs
def affine_transformation(vol, radius, translate, scale, bspline_order,
border_mode, constant_val, is_reverse):
"""
forward: scale -> rotation -> translation
backward: translation -> rotation -> scale
:param vol: np.ndarray or torch.tensor(batch, channel, h, w, d)
:param radius: tuple
:param translate: tuple
:param scale: tuple
:param bspline_order: 0~5 0:nearest, 1:bilinear, 2~5 bspline, 3 is common used.
:param border_mode: "constant", "nearest"
:param constant_val: contant value
:param is_reverse: bool
:return:
"""
tensor_flag = False
_device = torch.device("cpu")
if isinstance(vol, torch.Tensor):
_device = vol.device
vol = vol.cpu().detach().numpy()
tensor_flag = True
shape = vol.shape[2:]
dim = len(vol.shape) - 2
center = tuple((i - 1) / 2. for i in shape)
tmp = tuple([np.arange(i) for i in shape])
coords = np.array(np.meshgrid(*tmp, indexing='ij')).astype(float)
for d in range(len(shape)):
coords[d] -= center[d] # centered coords
if is_reverse:
# translation first
for i in range(dim):
coords[i] -= translate[i]
# rotation
rot_matrix = np.identity(len(coords))
rot_matrix = create_matrix_rotation_z_3d(radius[2], rot_matrix)
rot_matrix = create_matrix_rotation_y_3d(radius[1], rot_matrix)
rot_matrix = create_matrix_rotation_x_3d(radius[0], rot_matrix)
coords = np.dot(
coords.reshape(len(coords), -1).transpose(),
rot_matrix).transpose().reshape(coords.shape)
# scale
if isinstance(scale,
(tuple, list, np.ndarray)): # scale axis individual
assert len(scale) == len(coords)
for i in range(len(scale)):
coords[i] *= scale[i]
else:
coords *= scale # scale axis both
else:
# scale first
if isinstance(scale,
(tuple, list, np.ndarray)): # scale axis individual
assert len(scale) == len(coords)
for i in range(len(scale)):
coords[i] *= scale[i]
else:
coords *= scale # scale axis both
# rotation
rot_matrix = np.identity(len(coords))
rot_matrix = create_matrix_rotation_x_3d(radius[0], rot_matrix)
rot_matrix = create_matrix_rotation_y_3d(radius[1], rot_matrix)
rot_matrix = create_matrix_rotation_z_3d(radius[2], rot_matrix)
coords = np.dot(
coords.reshape(len(coords), -1).transpose(),
rot_matrix).transpose().reshape(coords.shape)
# translate
for i in range(dim):
coords[i] -= translate[i]
for i in range(dim):
coords[i] += center[i]
data_result = np.zeros_like(vol, dtype=np.float32)
for sample_id in range(vol.shape[0]):
for channel_id in range(vol.shape[1]):
data_result[sample_id,
channel_id] = interpolate_img(vol[sample_id,
channel_id],
coords,
order=bspline_order,
mode=border_mode,
cval=constant_val)
if tensor_flag:
data_result = torch.from_numpy(data_result)
data_result = data_result.to(_device)
return data_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):
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)
if 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)
if 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)
if 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)
# now find a nice center location
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)
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):
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 not independent_scale_for_each_axis:
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])
else:
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]))
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:
# valid values: constant/edge/median/minimum/reflect/symmetric/etc
if border_mode_data == 'nearest':
border_pad_mode_data = 'edge'
pad_kwargs_data = {}
elif border_mode_data == 'constant':
border_pad_mode_data = border_mode_data
pad_kwargs_data = {'constant_values': border_cval_data}
if border_mode_seg == 'nearest':
border_pad_mode_seg = 'edge'
pad_kwargs_seg = {}
elif border_mode_seg == 'constant':
border_pad_mode_seg = border_mode_seg
pad_kwargs_seg = {'constant_values': border_cval_seg}
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,
pad_mode=border_pad_mode_data,
pad_kwargs=pad_kwargs_data,
pad_mode_seg=border_pad_mode_seg,
pad_kwargs_seg=pad_kwargs_seg)
else:
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size,
s,
pad_mode=border_pad_mode_data,
pad_kwargs=pad_kwargs_data,
pad_mode_seg=border_pad_mode_seg,
pad_kwargs_seg=pad_kwargs_seg)
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(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,
crop_mode='random',
p_el_per_sample=1,
p_scale_per_sample=1,
p_rot_per_sample=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 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:
if crop_mode == 'roi':
lbs = get_lbls_for_roi_crop(patch_size, data.shape,
seg[sample_id, 0])
for d in range(dim):
if crop_mode == 'roi':
ctr = int(lbs[d] + np.round(patch_size[d] / 2.))
elif crop_mode == 'center':
ctr = int(np.round(data.shape[d + 2] / 2.))
else:
ctr = np.random.uniform(
patch_center_dist_from_border[d],
data.shape[d + 2] - patch_center_dist_from_border[d])
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 crop_mode == 'roi':
d, s = crop(data[sample_id:sample_id + 1],
s,
patch_size,
crop_type='roi')
elif crop_mode == 'center':
d, s = center_crop_aug(data[sample_id:sample_id + 1],
patch_size, s)
else: #crop_mode == 'random'
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)
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(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=1,
border_mode_seg='constant', border_cval_seg=0, order_seg=0, fraction=0.0, spacing=(1,1,1)):
dim = len(patch_size)
seg_result = None
while len(data.shape) <5:
data = data[np.newaxis,:]
while len(seg.shape) <5:
seg = seg[np.newaxis, :]
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)
if do_elastic_deform:
a_base = np.repeat(np.random.uniform(alpha[0], alpha[1]),3)
a = a_base / np.array(spacing)
s = np.repeat(np.random.uniform(sigma[0], sigma[1]), 3)
coords = elastic_deform_coordinates(coords, a, s)
if 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)
if 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)
# now find a nice center location
ctr_obj = np.where(seg[0,1:] > 0)[1:]
ctr_obj = np.array([np.mean(i) for i in ctr_obj])
ctr_obj = np.random.uniform(0.8, 1.2) * ctr_obj
ctr_obj = np.clip(ctr_obj, patch_size, data.shape[-3:] - patch_size)
rval = np.random.uniform(0.0,1.0)
for d in range(dim):
if rval > fraction:
ctr = np.random.uniform(patch_center_dist_from_border[d],
data.shape[d + 2] - patch_center_dist_from_border[d])
#elif crop > 'center':
# ctr = int(np.round(data.shape[d + 2] / 2.))
elif rval < fraction:
ctr = ctr_obj[d]
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)
return data_result[0], seg_result[0], rval
