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'''
Utilities for augmentation. Partly credit to Dr. Jo Schlemper
'''
from os.path import join
import torch
import numpy as np
import torchvision.transforms as deftfx
import dataloaders.image_transforms as myit
import copy
from util.consts import IMG_SIZE
import time
import functools
def get_sabs_aug(input_size, use_3d=False):
sabs_aug = {
# turn flipping off as medical data has fixed orientations
'flip': {'v': False, 'h': False, 't': False, 'p': 0.25},
'affine': {
'rotate': 5,
'shift': (5, 5),
'shear': 5,
'scale': (0.9, 1.2),
},
'elastic': {'alpha': 10, 'sigma': 5},
'patch': input_size,
'reduce_2d': True,
'3d': use_3d,
'gamma_range': (0.5, 1.5)
}
return sabs_aug
def get_sabs_augv3(input_size):
sabs_augv3 = {
'flip': {'v': False, 'h': False, 't': False, 'p': 0.25},
'affine': {
'rotate': 30,
'shift': (30, 30),
'shear': 30,
'scale': (0.8, 1.3),
},
'elastic': {'alpha': 20, 'sigma': 5},
'patch': input_size,
'reduce_2d': True,
'gamma_range': (0.2, 1.8)
}
return sabs_augv3
def get_aug(which_aug, input_size):
if which_aug == 'sabs_aug':
return get_sabs_aug(input_size)
elif which_aug == 'aug_v3':
return get_sabs_augv3(input_size)
else:
raise NotImplementedError
# augs = {
# 'sabs_aug': get_sabs_aug,
# 'aug_v3': get_sabs_augv3, # more aggresive
# }
def get_geometric_transformer(aug, order=3):
"""order: interpolation degree. Select order=0 for augmenting segmentation """
affine = aug['aug'].get('affine', 0)
alpha = aug['aug'].get('elastic', {'alpha': 0})['alpha']
sigma = aug['aug'].get('elastic', {'sigma': 0})['sigma']
flip = aug['aug'].get(
'flip', {'v': True, 'h': True, 't': True, 'p': 0.125})
tfx = []
if 'flip' in aug['aug']:
tfx.append(myit.RandomFlip3D(**flip))
if 'affine' in aug['aug']:
tfx.append(myit.RandomAffine(affine.get('rotate'),
affine.get('shift'),
affine.get('shear'),
affine.get('scale'),
affine.get('scale_iso', True),
order=order))
if 'elastic' in aug['aug']:
tfx.append(myit.ElasticTransform(alpha, sigma))
input_transform = deftfx.Compose(tfx)
return input_transform
def get_geometric_transformer_3d(aug, order=3):
"""order: interpolation degree. Select order=0 for augmenting segmentation """
affine = aug['aug'].get('affine', 0)
alpha = aug['aug'].get('elastic', {'alpha': 0})['alpha']
sigma = aug['aug'].get('elastic', {'sigma': 0})['sigma']
flip = aug['aug'].get(
'flip', {'v': True, 'h': True, 't': True, 'p': 0.125})
tfx = []
if 'flip' in aug['aug']:
tfx.append(myit.RandomFlip3D(**flip))
if 'affine' in aug['aug']:
tfx.append(myit.RandomAffine(affine.get('rotate'),
affine.get('shift'),
affine.get('shear'),
affine.get('scale'),
affine.get('scale_iso', True),
order=order,
use_3d=True))
if 'elastic' in aug['aug']:
tfx.append(myit.ElasticTransform(alpha, sigma))
input_transform = deftfx.Compose(tfx)
return input_transform
def gamma_transform(img, aug):
gamma_range = aug['aug']['gamma_range']
if isinstance(gamma_range, tuple):
gamma = np.random.rand() * \
(gamma_range[1] - gamma_range[0]) + gamma_range[0]
cmin = img.min()
irange = (img.max() - cmin + 1e-5)
img = img - cmin + 1e-5
img = irange * np.power(img * 1.0 / irange, gamma)
img = img + cmin
elif gamma_range == False:
pass
else:
raise ValueError(
"Cannot identify gamma transform range {}".format(gamma_range))
return img
def get_intensity_transformer(aug):
"""some basic intensity transforms"""
return functools.partial(gamma_transform, aug=aug)
def transform_with_label(aug):
"""
Doing image geometric transform
Proposed image to have the following configurations
[H x W x C + CL]
Where CL is the number of channels for the label. It is NOT in one-hot form
"""
geometric_tfx = get_geometric_transformer(aug)
intensity_tfx = get_intensity_transformer(aug)
def transform(comp, c_label, c_img, use_onehot, nclass, **kwargs):
"""
Args
comp: a numpy array with shape [H x W x C + c_label]
c_label: number of channels for a compact label. Note that the current version only supports 1 slice (H x W x 1)
nc_onehot: -1 for not using one-hot representation of mask. otherwise, specify number of classes in the label
"""
comp = copy.deepcopy(comp)
if (use_onehot is True) and (c_label != 1):
raise NotImplementedError(
"Only allow compact label, also the label can only be 2d")
assert c_img + 1 == comp.shape[-1], "only allow single slice 2D label"
# geometric transform
_label = comp[..., c_img]
_h_label = np.float32(np.arange(nclass) == (_label[..., None]))
# _h_label = np.float32(_label[..., None])
comp = np.concatenate([comp[..., :c_img], _h_label], -1)
comp = geometric_tfx(comp)
# round one_hot labels to 0 or 1
t_label_h = comp[..., c_img:]
t_label_h = np.rint(t_label_h)
assert t_label_h.max() <= 1
t_img = comp[..., 0: c_img]
# intensity transform
t_img = intensity_tfx(t_img)
if use_onehot is True:
t_label = t_label_h
else:
t_label = np.expand_dims(np.argmax(t_label_h, axis=-1), -1)
return t_img, t_label
return transform
def transform(scan, label, nclass, geometric_tfx, intensity_tfx):
"""
Args
scan: a numpy array with shape [D x H x W x C]
label: a numpy array with shape [D x H x W x 1]
"""
assert len(scan.shape) == 4, "Input scan must be 4D"
if len(label.shape) == 3:
label = np.expand_dims(label, -1)
# geometric transform
comp = copy.deepcopy(np.concatenate(
[scan, label], -1)) # [D x H x W x C + 1]
_label = comp[..., -1]
_h_label = np.float32(np.arange(nclass) == (_label[..., None]))
comp = np.concatenate([comp[..., :-1], _h_label], -1)
# change comp to be H x W x D x C + 1
comp = np.transpose(comp, (1, 2, 0, 3))
comp = geometric_tfx(comp)
t_label_h = comp[..., 1:]
t_label_h = np.rint(t_label_h)
assert t_label_h.max() <= 1
t_img = comp[..., 0:1]
# intensity transform
t_img = intensity_tfx(t_img)
return t_img, t_label_h
def transform_wrapper(scan, label, nclass, geometric_tfx, intensity_tfx):
return transform(scan, label, nclass, geometric_tfx, intensity_tfx)
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