def test_projection_translate_plus_minus_1():
trf = slt.RandomProjection(affine_transforms=slc.Stream([
slt.RandomTranslate(range_x=(1, 1), range_y=(1, 1), p=1),
slt.RandomTranslate(range_x=(-1, -1), range_y=(-1, -1), p=1),
]),
p=1)
trf.sample_transform()
assert np.array_equal(trf.state_dict['transform_matrix'], np.eye(3))
def test_translate_forward_backward_sampling():
stream = slc.Stream([
slt.RandomTranslate(range_x=(1, 1), range_y=(1, 1), p=1),
slt.RandomTranslate(range_x=(-1, -1), range_y=(-1, -1), p=1),
])
trf = stream.optimize_stack(stream.transforms)[0]
assert 1 == trf.state_dict[
'translate_x'] # The settings will be overrided by the first transform
assert 1 == trf.state_dict[
'translate_y'] # The settings will be overrided by the first transform
assert np.array_equal(trf.state_dict['transform_matrix'], np.eye(3))
def init_transforms(nc=1):
if nc == 1:
norm_mean_std = Normalize((0.1307, ), (0.3081, ))
elif nc == 3:
norm_mean_std = Normalize((0.4914, 0.4822, 0.4465),
(0.247, 0.243, 0.261))
else:
raise ValueError("Not support channels of {}".format(nc))
train_trf = Compose([
wrap2solt,
slc.Stream([
# slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'),
slt.RandomScale(range_x=(0.95, 1.05), same=False, p=0.5),
# slt.RandomShear(range_x=(-0.05, 0.05), p=0.5),
slt.RandomRotate(rotation_range=(-10, 10), p=0.5),
slt.RandomFlip(p=0.5, axis=1),
# slt.RandomRotate(rotation_range=(-5, 5), p=0.5),
slt.RandomTranslate(range_x=3, range_y=3),
# slt.PadTransform(pad_to=34),
# slt.CropTransform(crop_size=32, crop_mode='r'),
# slt.ImageAdditiveGaussianNoise(p=1.0)
]),
unpack_solt,
ApplyTransform(norm_mean_std)
])
test_trf = Compose([
wrap2solt,
slt.ResizeTransform(resize_to=(32, 32), interpolation='bilinear'),
unpack_solt,
ApplyTransform(norm_mean_std)
])
def custom_augment(img):
tr = Compose([
wrap2solt,
slc.Stream([
slt.ResizeTransform(resize_to=(32, 32),
interpolation='bilinear'),
slt.RandomScale(range_x=(0.9, 1.1), same=False, p=0.5),
slt.RandomShear(range_x=(-0.05, 0.05), p=0.5),
slt.RandomRotate(rotation_range=(-10, 10), p=0.5),
# slt.RandomRotate(rotation_range=(-5, 5), p=0.5),
slt.PadTransform(pad_to=36),
slt.CropTransform(crop_size=32, crop_mode='r'),
slt.ImageAdditiveGaussianNoise(p=1.0)
]),
unpack_solt,
ApplyTransform(norm_mean_std)
])
img_tr, _ = tr((img, 0))
return img_tr
return train_trf, test_trf, custom_augment
def test_scale_when_range_x_is_none(translate_x, translate_y, expected):
trf = slt.RandomTranslate(range_x=translate_x, range_y=translate_y, p=1)
trf.sample_transform()
assert (trf.state_dict['translate_x'],
trf.state_dict['translate_y']) == expected
def test_translate_range_from_number(translate, expected):
trf = slt.RandomTranslate(range_x=translate, range_y=translate)
assert trf.translate_range_x == expected
assert trf.translate_range_y == expected
def train_test_transforms(conf, mean=None, std=None):
trf = conf['training']
prob = trf['transform_probability']
crop_size = tuple(trf['crop_size'])
# Training transforms
if trf['uCT']:
train_transforms = [
slt.RandomProjection(slc.Stream([
slt.RandomRotate(rotation_range=tuple(trf['rotation_range']),
p=prob),
slt.RandomScale(range_x=tuple(trf['scale_range']),
range_y=tuple(trf['scale_range']),
same=False,
p=prob),
slt.RandomShear(range_x=tuple(trf['shear_range']),
range_y=tuple(trf['shear_range']),
p=prob),
slt.RandomTranslate(range_x=trf['translation_range'],
range_y=trf['translation_range'],
p=prob)
]),
v_range=tuple(trf['v_range'])),
# Spatial
slt.RandomFlip(p=prob),
slt.PadTransform(pad_to=crop_size),
slt.CropTransform(crop_mode='r', crop_size=crop_size),
# Intensity
#slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']), p=prob),
#slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']),
# s_range=tuple(trf['hsv_range']),
# v_range=tuple(trf['hsv_range']), p=prob),
# Brightness/contrast
slc.SelectiveStream([
slt.ImageRandomBrightness(brightness_range=tuple(
trf['brightness_range']),
p=prob),
slt.ImageRandomContrast(contrast_range=trf['contrast_range'],
p=prob)
]),
# Noise
slc.SelectiveStream([
slt.ImageSaltAndPepper(p=prob,
gain_range=trf['gain_range_sp']),
slt.ImageAdditiveGaussianNoise(
p=prob, gain_range=trf['gain_range_gn']),
slc.SelectiveStream([
slt.ImageBlur(p=prob,
blur_type='g',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma'])),
slt.ImageBlur(p=prob,
blur_type='m',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma']))
])
])
]
else:
train_transforms = [
# Projection
slt.RandomProjection(
slc.Stream([
slt.RandomRotate(rotation_range=tuple(
trf['rotation_range']),
p=prob),
slt.RandomScale(range_x=tuple(trf['scale_range']),
range_y=tuple(trf['scale_range']),
same=False,
p=prob),
#slt.RandomShear(range_x=tuple(trf['shear_range']),
# range_y=tuple(trf['shear_range']), p=prob),
#slt.RandomTranslate(range_x=trf['translation_range'], range_y=trf['translation_range'], p=prob)
]),
v_range=tuple(trf['v_range'])),
# Spatial
slt.RandomFlip(p=prob),
slt.PadTransform(pad_to=crop_size[1]),
slt.CropTransform(crop_mode='r', crop_size=crop_size),
# Intensity
# Add an empty stream
#slc.SelectiveStream([]),
slc.SelectiveStream([
slt.ImageGammaCorrection(gamma_range=tuple(trf['gamma_range']),
p=prob),
slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']),
s_range=tuple(trf['hsv_range']),
v_range=tuple(trf['hsv_range']),
p=prob)
]),
slc.SelectiveStream([
slt.ImageRandomBrightness(brightness_range=tuple(
trf['brightness_range']),
p=prob),
slt.ImageRandomContrast(contrast_range=trf['contrast_range'],
p=prob)
]),
slc.SelectiveStream([
slt.ImageSaltAndPepper(p=prob,
gain_range=trf['gain_range_sp']),
slt.ImageAdditiveGaussianNoise(
p=prob, gain_range=trf['gain_range_gn']),
slc.SelectiveStream([
slt.ImageBlur(p=prob,
blur_type='g',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma'])),
slt.ImageBlur(p=prob,
blur_type='m',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma']))
])
])
]
train_trf = [
wrap_solt,
#slc.Stream(train_transforms),
slc.Stream([
slt.PadTransform(pad_to=crop_size[1]),
slt.CropTransform(crop_mode='r', crop_size=crop_size)
]),
unwrap_solt,
ApplyTransform(numpy2tens, (0, 1, 2))
]
# Validation transforms
val_trf = [
wrap_solt,
slc.Stream([
slt.PadTransform(pad_to=crop_size[1]),
slt.CropTransform(crop_mode='r', crop_size=crop_size)
]), unwrap_solt,
ApplyTransform(numpy2tens, idx=(0, 1, 2))
]
# Test transforms
test_trf = [unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2))]
# Use normalize_channel_wise if mean and std not calculated
if mean is not None and std is not None:
train_trf.append(
ApplyTransform(partial(normalize_channel_wise, mean=mean,
std=std)))
if mean is not None and std is not None:
val_trf.append(
ApplyTransform(partial(normalize_channel_wise, mean=mean,
std=std)))
# Compose transforms
train_trf_cmp = Compose(train_trf)
val_trf_cmp = Compose(val_trf)
test_trf_cmp = Compose(test_trf)
return {
'train': train_trf_cmp,
'val': val_trf_cmp,
'test': test_trf_cmp,
'train_list': train_trf,
'val_list': val_trf,
'test_list': test_trf
}
def train_test_transforms(conf, mean=None, std=None, crop_size=(512, 1024)):
"""
Compiles the different image augmentations that are used for input images.
:param conf: Transformation parameters
:param mean: Dataset image mean
:param std: Dataset image std
:param crop_size: Image size for the segmentation model
:return: Compiled transformation objects, and lists of the used transforms
"""
trf = conf['training']
prob = trf['transform_probability']
# Training transforms
# 3D transforms
if trf['experiment'] == '3D':
train_transforms = [
slc.SelectiveStream([
slc.Stream([
slt.RandomProjection(
slc.Stream([
slt.RandomRotate(rotation_range=tuple(
trf['rotation_range']),
p=prob),
slt.RandomScale(range_x=tuple(trf['scale_range']),
range_y=tuple(trf['scale_range']),
same=False,
p=prob),
#slt.RandomShear(range_x=tuple(trf['shear_range']),
# range_y=tuple(trf['shear_range']), p=prob),
slt.RandomTranslate(
range_x=trf['translation_range'],
range_y=trf['translation_range'],
p=prob)
]),
v_range=None #tuple(trf['v_range'])
),
# Spatial
slt.RandomFlip(p=prob),
slt.PadTransform(pad_to=crop_size),
slt.CropTransform(crop_mode='r', crop_size=crop_size),
# Intensity
# Brightness/contrast
slc.SelectiveStream([
slt.ImageRandomBrightness(brightness_range=tuple(
trf['brightness_range']),
p=prob),
slt.ImageRandomContrast(
contrast_range=trf['contrast_range'], p=prob)
]),
# Noise
slc.SelectiveStream([
slt.ImageSaltAndPepper(
p=prob, gain_range=trf['gain_range_sp']),
slt.ImageAdditiveGaussianNoise(
p=prob, gain_range=trf['gain_range_gn']),
slc.SelectiveStream([
slt.ImageBlur(p=prob,
blur_type='g',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma'])),
slt.ImageBlur(p=prob,
blur_type='m',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma']))
])
])
]),
# Empty stream
slc.Stream([
slt.PadTransform(pad_to=crop_size),
slt.CropTransform(crop_mode='r', crop_size=crop_size)
])
])
]
# 2D transforms
else:
train_transforms = [
slc.SelectiveStream([
slc.Stream([
# Projection
slt.RandomProjection(
slc.Stream([
slt.RandomRotate(rotation_range=tuple(
trf['rotation_range']),
p=prob),
slt.RandomScale(range_x=tuple(trf['scale_range']),
range_y=tuple(trf['scale_range']),
same=False,
p=prob),
# slt.RandomShear(range_x=tuple(trf['shear_range']),
# range_y=tuple(trf['shear_range']), p=prob),
slt.RandomTranslate(
range_x=trf['translation_range'],
range_y=trf['translation_range'],
p=prob)
]),
v_range=tuple(trf['v_range'])),
# Spatial
slt.RandomFlip(p=prob),
slt.PadTransform(pad_to=crop_size),
slt.CropTransform(crop_mode='r', crop_size=crop_size),
# Intensity
slc.SelectiveStream([
slt.ImageGammaCorrection(gamma_range=tuple(
trf['gamma_range']),
p=prob),
slt.ImageRandomHSV(h_range=tuple(trf['hsv_range']),
s_range=tuple(trf['hsv_range']),
v_range=tuple(trf['hsv_range']),
p=prob)
]),
slc.SelectiveStream([
slt.ImageRandomBrightness(brightness_range=tuple(
trf['brightness_range']),
p=prob),
slt.ImageRandomContrast(
contrast_range=trf['contrast_range'], p=prob)
]),
slc.SelectiveStream([
slt.ImageSaltAndPepper(
p=prob, gain_range=trf['gain_range_sp']),
slt.ImageAdditiveGaussianNoise(
p=prob, gain_range=trf['gain_range_gn']),
slc.SelectiveStream([
slt.ImageBlur(p=prob,
blur_type='g',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma'])),
slt.ImageBlur(p=prob,
blur_type='m',
k_size=(3, 7, 11),
gaussian_sigma=tuple(trf['sigma']))
])
])
]),
# Empty stream
slc.Stream([
slt.PadTransform(pad_to=crop_size),
slt.CropTransform(crop_mode='r', crop_size=crop_size)
])
])
]
# Compile training transforms
train_trf = [
# Move to SOLT format
wrap_solt,
# Transforms
slc.Stream(train_transforms),
# Extract image
unwrap_solt,
# Move to tensor
ApplyTransform(numpy2tens, (0, 1, 2))
]
# Validation transforms
val_trf = [
wrap_solt,
slc.Stream([
slt.PadTransform(pad_to=crop_size[1]),
slt.CropTransform(crop_mode='r', crop_size=crop_size)
]), unwrap_solt,
ApplyTransform(numpy2tens, idx=(0, 1, 2))
]
# Test transforms
test_trf = [unwrap_solt, ApplyTransform(numpy2tens, idx=(0, 1, 2))]
# Normalize train and val images if mean and std are given
if mean is not None and std is not None:
train_trf.append(
ApplyTransform(partial(normalize_channel_wise, mean=mean,
std=std)))
if mean is not None and std is not None:
val_trf.append(
ApplyTransform(partial(normalize_channel_wise, mean=mean,
std=std)))
# Compose transforms
train_trf_cmp = Compose(train_trf)
val_trf_cmp = Compose(val_trf)
test_trf_cmp = Compose(test_trf)
return {
'train': train_trf_cmp,
'val': val_trf_cmp,
'test': test_trf_cmp,
'train_list': train_trf,
'val_list': val_trf,
'test_list': test_trf
}