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Utilities for image transforms, part of the code base credits to Dr. Jo Schlemper
"""
from os.path import join
import torch
import numpy as np
import torchvision.transforms as deftfx
from . import image_transforms as myit
import copy
import math
from torchvision.transforms.functional import rotate as torchrotate
from torchvision.transforms.functional import InterpolationMode
my_augv = {
'flip' : { 'v':False, 'h':False, 't': False, 'p':0.25 },
'affine' : {
'rotate':20,
'shift':(15,15),
'shear': 20,
'scale':(0.5, 1.5),
},
'elastic' : {'alpha':0,'sigma':0}, # medium {'alpha':20,'sigma':5},
'reduce_2d': True,
'gamma_range': (1.0, 1.0 ), #(0.2, 1.8),
'noise' : {
'noise_std': 0, # 0.15
'clip_pm1': False
},
'bright_contrast': {
'contrast': (1.0, 1.0), #(0.60, 1.5),
'bright': (0, 0)#(-10, 10)
}
}
tr_aug = {
'aug': my_augv
}
def get_contrast_example(image, random_angle=0, flip=0):
if flip == [3]:
flip = [1, 2]
# [..., H, W]
image_rotate = torchrotate(image, random_angle,
interpolation=InterpolationMode.BILINEAR) # Bilinear
image_rotate = torch.flip(image_rotate, flip)
return image_rotate
def get_geometric_transformer(aug, order=3):
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_intensity_transformer(aug):
def gamma_tansform(img):
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 brightness_contrast(img):
'''
Chaitanya,K. et al. Semi-Supervised and Task-Driven data augmentation,864in: International Conference on Information Processing in Medical Imaging,865Springer. pp. 29–41.
'''
cmin, cmax = aug['aug']['bright_contrast']['contrast']
bmin, bmax = aug['aug']['bright_contrast']['bright']
c = np.random.rand() * (cmax - cmin) + cmin
b = np.random.rand() * (bmax - bmin) + bmin
img_mean = img.mean()
img = (img - img_mean) * c + img_mean + b
return img
def zm_gaussian_noise(img):
"""
zero-mean gaussian noise
"""
noise_sigma = aug['aug']['noise']['noise_std']
noise_vol = np.random.randn(*img.shape) * noise_sigma
img = img + noise_vol
if aug['aug']['noise']['clip_pm1']: # if clip to plus-minus 1
img = np.clip(img, -1.0, 1.0)
return img
def compile_transform(img):
# bright contrast
if 'bright_contrast' in aug['aug'].keys():
img = brightness_contrast(img)
# gamma
if 'gamma_range' in aug['aug'].keys():
img = gamma_tansform(img)
# additive noise
if 'noise' in aug['aug'].keys():
img = zm_gaussian_noise(img)
return img
return compile_transform
def transform_with_label(aug, add_pseudolabel = False):
"""
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 a one-hot thing
"""
geometric_tfx = get_geometric_transformer(aug)
intensity_tfx = get_intensity_transformer(aug)
def transform(comp, c_label, c_img, c_sam, nclass, is_train, use_onehot = False):
"""
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
is_train: whether this is the training set or not. If not, do not perform the geometric transform
"""
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 + c_sam + c_label == comp.shape[-1], "only allow single slice 2D label"
if is_train is True:
_label = comp[..., c_img ]
_sam = np.expand_dims(comp[..., c_img+c_label], axis=-1)
# compact to onehot
_h_label = np.float32(np.arange( nclass ) == (_label[..., None]) )
# print("h_label=", _h_label.shape)
# print("_sam=", _sam.shape)
comp = np.concatenate( [comp[..., :c_img ], _h_label, _sam], -1 )
comp = geometric_tfx(comp)
# round one_hot labels to 0 or 1
t_label_h = comp[..., c_img : -c_sam]
t_label_h = np.rint(t_label_h)
t_img = comp[..., 0 : c_img ]
t_sam = np.rint(comp[..., -c_sam:])
# 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, t_sam
return transform
def gamma_concern(img, gamma):
mean = np.mean(img)
img = (img - mean) * gamma
img = img + mean
img = np.clip(img, 0, 1)
return img
def gamma_power(img, gamma, direction=0):
if direction == 1:
img = 1 - img
img = np.power(img, gamma)
img = img / np.max(img)
if direction == 1:
img = 1 - img
return img
def gamma_exp(img, gamma, direction=0):
if direction == 1:
img = 1 - img
img = np.exp(img * gamma)
img = img / np.max(img)
if direction == 1:
img = 1 - img
return img
def GammaInterference(img):
# Shape Span
gamma = np.random.random() * 1.5 + 0.25 # 0.25 ~ 1.75
# gamma = np.random.random() * 1.75 + 0.25 # 0.25 ~ 1.75
img = gamma_concern(img, gamma) # concerntrate
# Shape Tilt
choose = np.random.randint(0, 2)
direction = np.random.randint(0, 2)
if choose == 0:
gamma = 0.2 + np.random.random() * 2.3 # 2.5
img = gamma_power(img, gamma, direction)
else:
gamma = np.random.random() * 2.3 + 0.6 # 1.5 center
img = gamma_exp(img, gamma, direction)
return img
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