def test_transforms(self):
landmarks_dict = dict(
t1=np.linspace(0, 100, 13),
t2=np.linspace(0, 100, 13),
)
elastic = torchio.RandomElasticDeformation(max_displacement=1)
transforms = (
torchio.CropOrPad((9, 21, 30)),
torchio.ToCanonical(),
torchio.Resample((1, 1.1, 1.25)),
torchio.RandomFlip(axes=(0, 1, 2), flip_probability=1),
torchio.RandomMotion(),
torchio.RandomGhosting(axes=(0, 1, 2)),
torchio.RandomSpike(),
torchio.RandomNoise(),
torchio.RandomBlur(),
torchio.RandomSwap(patch_size=2, num_iterations=5),
torchio.Lambda(lambda x: 2 * x, types_to_apply=torchio.INTENSITY),
torchio.RandomBiasField(),
torchio.RescaleIntensity((0, 1)),
torchio.ZNormalization(masking_method='label'),
torchio.HistogramStandardization(landmarks_dict=landmarks_dict),
elastic,
torchio.RandomAffine(),
torchio.OneOf({
torchio.RandomAffine(): 3,
elastic: 1
}),
torchio.Pad((1, 2, 3, 0, 5, 6), padding_mode='constant', fill=3),
torchio.Crop((3, 2, 8, 0, 1, 4)),
)
transform = torchio.Compose(transforms)
transform(self.sample)
def get_transform(self, channels, is_3d=True, labels=True):
landmarks_dict = {
channel: np.linspace(0, 100, 13)
for channel in channels
}
disp = 1 if is_3d else (1, 1, 0.01)
elastic = tio.RandomElasticDeformation(max_displacement=disp)
cp_args = (9, 21, 30) if is_3d else (21, 30, 1)
resize_args = (10, 20, 30) if is_3d else (10, 20, 1)
flip_axes = axes_downsample = (0, 1, 2) if is_3d else (0, 1)
swap_patch = (2, 3, 4) if is_3d else (3, 4, 1)
pad_args = (1, 2, 3, 0, 5, 6) if is_3d else (0, 0, 3, 0, 5, 6)
crop_args = (3, 2, 8, 0, 1, 4) if is_3d else (0, 0, 8, 0, 1, 4)
remapping = {1: 2, 2: 1, 3: 20, 4: 25}
transforms = [
tio.CropOrPad(cp_args),
tio.EnsureShapeMultiple(2, method='crop'),
tio.Resize(resize_args),
tio.ToCanonical(),
tio.RandomAnisotropy(downsampling=(1.75, 2), axes=axes_downsample),
tio.CopyAffine(channels[0]),
tio.Resample((1, 1.1, 1.25)),
tio.RandomFlip(axes=flip_axes, flip_probability=1),
tio.RandomMotion(),
tio.RandomGhosting(axes=(0, 1, 2)),
tio.RandomSpike(),
tio.RandomNoise(),
tio.RandomBlur(),
tio.RandomSwap(patch_size=swap_patch, num_iterations=5),
tio.Lambda(lambda x: 2 * x, types_to_apply=tio.INTENSITY),
tio.RandomBiasField(),
tio.RescaleIntensity(out_min_max=(0, 1)),
tio.ZNormalization(),
tio.HistogramStandardization(landmarks_dict),
elastic,
tio.RandomAffine(),
tio.OneOf({
tio.RandomAffine(): 3,
elastic: 1,
}),
tio.RemapLabels(remapping=remapping, masking_method='Left'),
tio.RemoveLabels([1, 3]),
tio.SequentialLabels(),
tio.Pad(pad_args, padding_mode=3),
tio.Crop(crop_args),
]
if labels:
transforms.append(tio.RandomLabelsToImage(label_key='label'))
return tio.Compose(transforms)
def get_transform(self, channels, is_3d=True, labels=True):
landmarks_dict = {
channel: np.linspace(0, 100, 13)
for channel in channels
}
disp = 1 if is_3d else (1, 1, 0.01)
elastic = torchio.RandomElasticDeformation(max_displacement=disp)
cp_args = (9, 21, 30) if is_3d else (21, 30, 1)
flip_axes = axes_downsample = (0, 1, 2) if is_3d else (0, 1)
swap_patch = (2, 3, 4) if is_3d else (3, 4, 1)
pad_args = (1, 2, 3, 0, 5, 6) if is_3d else (0, 0, 3, 0, 5, 6)
crop_args = (3, 2, 8, 0, 1, 4) if is_3d else (0, 0, 8, 0, 1, 4)
transforms = [
torchio.CropOrPad(cp_args),
torchio.ToCanonical(),
torchio.RandomDownsample(downsampling=(1.75, 2),
axes=axes_downsample),
torchio.Resample((1, 1.1, 1.25)),
torchio.RandomFlip(axes=flip_axes, flip_probability=1),
torchio.RandomMotion(),
torchio.RandomGhosting(axes=(0, 1, 2)),
torchio.RandomSpike(),
torchio.RandomNoise(),
torchio.RandomBlur(),
torchio.RandomSwap(patch_size=swap_patch, num_iterations=5),
torchio.Lambda(lambda x: 2 * x, types_to_apply=torchio.INTENSITY),
torchio.RandomBiasField(),
torchio.RescaleIntensity((0, 1)),
torchio.ZNormalization(),
torchio.HistogramStandardization(landmarks_dict),
elastic,
torchio.RandomAffine(),
torchio.OneOf({
torchio.RandomAffine(): 3,
elastic: 1,
}),
torchio.Pad(pad_args, padding_mode=3),
torchio.Crop(crop_args),
]
if labels:
transforms.append(torchio.RandomLabelsToImage(label_key='label'))
return torchio.Compose(transforms)
def swap_image(filename):
""" Image manipulation method - swap patches of image for context restoration """
image = tio.ScalarImage(filename)
swap = tio.RandomSwap()
swapped = swap(image)
return swapped
def dataloader(handles, mode = 'train'):
# If pickle exists, load it
try:
with open('../inputs/flpickles/' + mode + '.pickle', 'rb') as f:
images = pickle.load(f)
except:
images = {}
images['Image'] = []
images['Label'] = []
images['Gap'] = []
images['ID'] = []
# Data augmentations
random_flip = tio.RandomFlip(axes=1)
random_flip2 = tio.RandomFlip(axes=2)
random_affine = tio.RandomAffine(seed=0, scales=(3, 3))
random_elastic = tio.RandomElasticDeformation(
max_displacement=(0, 20, 40),
num_control_points=20,
seed=0,
)
rescale = tio.RescaleIntensity((-1, 1), percentiles=(1, 99))
standardize_foreground = tio.ZNormalization(masking_method=lambda x: x > x.mean())
blur = tio.RandomBlur(seed=0)
standardize = tio.ZNormalization()
add_noise = tio.RandomNoise(std=0.5, seed=42)
add_spike = tio.RandomSpike(seed=42)
add_ghosts = tio.RandomGhosting(intensity=1.5, seed=42)
add_motion = tio.RandomMotion(num_transforms=6, image_interpolation='nearest', seed=42)
swap = tio.RandomSwap(patch_size = 7)
# For each image
for idx, row in handles.iterrows():
im_aug = []
lb_aug = []
gap_aug = []
imgs = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32) # change patch shape if necessary
lbs = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32)
gaps = np.zeros(shape=(1, 1,7,1024, 1024), dtype=np.float32)
im = io.imread(row['Image'])
im = im / 255 # Normalization
im = np.expand_dims(im, axis=0)
imgs[0] = im
im_aug.append(imgs)
images['ID'].append(row['ID'])
if mode == 'train':
im_flip1 = random_flip(im)
imgs[0] = im_flip1
im_aug.append(imgs)
im_flip2 = random_flip2(im)
imgs[0] = im_flip2
im_aug.append(imgs)
im_affine = random_affine(im)
imgs[0] = im_affine
im_aug.append(imgs)
im_elastic = random_elastic(im)
imgs[0] = im_elastic
im_aug.append(imgs)
im_rescale = rescale(im)
imgs[0] = im_rescale
im_aug.append(imgs)
im_standard = standardize_foreground(im)
imgs[0] = im_standard
im_aug.append(imgs)
im_blur = blur(im)
imgs[0] = im_blur
im_aug.append(imgs)
im_noisy = add_noise(standardize(im))
imgs[0] = im_noisy
im_aug.append(imgs)
im_spike = add_spike(im)
imgs[0] = im_spike
im_aug.append(imgs)
im_ghost = add_ghosts(im)
imgs[0] = im_ghost
im_aug.append(imgs)
im_motion = add_motion(im)
imgs[0] = im_motion
im_aug.append(imgs)
im_swap = swap(im)
imgs[0] = im_swap
im_aug.append(imgs)
images['Image'].append(np.array(im_aug))
if mode != 'test':
lb = io.imread(row['Label'])
lb = label_converter(lb)
lb = np.expand_dims(lb, axis=0)
lbs[0] = lb
lb_aug.append(lbs)
gap = io.imread(row['Gap'])
gap = np.expand_dims(gap, axis = 0)
gaps[0] = gap
gap_aug.append(gaps)
if mode == 'train':
lb_flip1 = random_flip(lb)
lbs[0] = lb_flip1
lb_aug.append(lbs)
lb_flip2 = random_flip2(lb)
lbs[0] = lb_flip2
lb_aug.append(lbs)
lb_affine = random_affine(lb)
lbs[0] = lb_affine
lb_aug.append(lbs)
lb_elastic = random_elastic(lb)
lbs[0] = lb_elastic
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
lbs[0] = lb
lb_aug.append(lbs)
gap_flip1 = random_flip(gap)
gaps[0] = gap_flip1
gap_aug.append(gaps)
gap_flip2 = random_flip2(gap)
gaps[0] = gap_flip2
gap_aug.append(gaps)
gap_affine = random_affine(gap)
gaps[0] = gap_affine
gap_aug.append(gaps)
gap_elastic = random_elastic(gap)
gaps[0] = gap_elastic
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
gaps[0] = gap
gap_aug.append(gaps)
images['Label'].append(np.array(lb_aug))
images['Gap'].append(np.array(gap_aug))
# Save images
with open("../inputs/flpickles/" + mode + '.pickle', 'wb') as f:
pickle.dump(images, f)
with open('../inputs/flpickles/' + mode + '.pickle', 'rb') as f:
images = pickle.load(f)
return images
