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transformations.py
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transformations.py
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from typing import List, Callable, Tuple
import numpy as np
import albumentations as A
from sklearn.externals._pilutil import bytescale
from skimage.util import crop
def normalize_01(inp: np.ndarray):
"""Squash image input to the value range [0, 1] (no clipping)"""
inp_out = (inp - np.min(inp)) / np.ptp(inp)
return inp_out
def normalize(inp: np.ndarray, mean: float, std: float):
"""Normalize based on mean and standard deviation."""
inp_out = (inp - mean) / std
return inp_out
def create_dense_target(tar: np.ndarray):
classes = np.unique(tar)
dummy = np.zeros_like(tar)
for idx, value in enumerate(classes):
mask = np.where(tar == value)
dummy[mask] = idx
return dummy
def center_crop_to_size(x: np.ndarray,
size: Tuple,
copy: bool = False,
) -> np.ndarray:
"""
Center crops a given array x to the size passed in the function.
Expects even spatial dimensions!
"""
x_shape = np.array(x.shape)
size = np.array(size)
params_list = ((x_shape - size) / 2).astype(np.int).tolist()
params_tuple = tuple([(i, i) for i in params_list])
cropped_image = crop(x, crop_width=params_tuple, copy=copy)
return cropped_image
def re_normalize(inp: np.ndarray,
low: int = 0,
high: int = 255
):
"""Normalize the data to a certain range. Default: [0-255]"""
inp_out = bytescale(inp, low=low, high=high)
return inp_out
def random_flip(inp: np.ndarray, tar: np.ndarray, ndim_spatial: int):
flip_dims = [np.random.randint(low=0, high=2) for dim in range(ndim_spatial)]
flip_dims_inp = tuple([i + 1 for i, element in enumerate(flip_dims) if element == 1])
flip_dims_tar = tuple([i for i, element in enumerate(flip_dims) if element == 1])
inp_flipped = np.flip(inp, axis=flip_dims_inp)
tar_flipped = np.flip(tar, axis=flip_dims_tar)
return inp_flipped, tar_flipped
class Repr:
"""Evaluable string representation of an object"""
def __repr__(self): return f'{self.__class__.__name__}: {self.__dict__}'
class FunctionWrapperSingle(Repr):
"""A function wrapper that returns a partial for input only."""
def __init__(self, function: Callable, *args, **kwargs):
from functools import partial
self.function = partial(function, *args, **kwargs)
def __call__(self, inp: np.ndarray): return self.function(inp)
class FunctionWrapperDouble(Repr):
"""A function wrapper that returns a partial for an input-target pair."""
def __init__(self, function: Callable, input: bool = True, target: bool = False, *args, **kwargs):
from functools import partial
self.function = partial(function, *args, **kwargs)
self.input = input
self.target = target
def __call__(self, inp: np.ndarray, tar: dict):
if self.input: inp = self.function(inp)
if self.target: tar = self.function(tar)
return inp, tar
class Compose:
"""Baseclass - composes several transforms together."""
def __init__(self, transforms: List[Callable]):
self.transforms = transforms
def __repr__(self): return str([transform for transform in self.transforms])
class ComposeDouble(Compose):
"""Composes transforms for input-target pairs."""
def __call__(self, inp: np.ndarray, target: dict):
for t in self.transforms:
inp, target = t(inp, target)
return inp, target
class ComposeSingle(Compose):
"""Composes transforms for input only."""
def __call__(self, inp: np.ndarray):
for t in self.transforms:
inp = t(inp)
return inp
class AlbuSeg2d(Repr):
"""
Wrapper for albumentations' segmentation-compatible 2D augmentations.
Wraps an augmentation so it can be used within the provided transform pipeline.
See https://github.com/albu/albumentations for more information.
Expected input: (C, spatial_dims)
Expected target: (spatial_dims) -> No (C)hannel dimension
"""
def __init__(self, albumentation: Callable):
self.albumentation = albumentation
def __call__(self, inp: np.ndarray, tar: np.ndarray):
# input, target
out_dict = self.albumentation(image=inp, mask=tar)
input_out = out_dict['image']
target_out = out_dict['mask']
return input_out, target_out
class AlbuSeg3d(Repr):
"""
Wrapper for albumentations' segmentation-compatible 2D augmentations.
Wraps an augmentation so it can be used within the provided transform pipeline.
See https://github.com/albu/albumentations for more information.
Expected input: (spatial_dims) -> No (C)hannel dimension
Expected target: (spatial_dims) -> No (C)hannel dimension
Iterates over the slices of a input-target pair stack and performs the same albumentation function.
"""
def __init__(self, albumentation: Callable):
self.albumentation = A.ReplayCompose([albumentation])
def __call__(self, inp: np.ndarray, tar: np.ndarray):
# input, target
tar = tar.astype(np.uint8) # target has to be in uint8
input_copy = np.copy(inp)
target_copy = np.copy(tar)
replay_dict = self.albumentation(image=inp[0])['replay'] # perform an albu on one slice and access the replay dict
# TODO: consider cases with RGB 3D or multimodal 3D input
# only if input_shape == target_shape
for index, (input_slice, target_slice) in enumerate(zip(inp, tar)):
result = A.ReplayCompose.replay(replay_dict, image=input_slice, mask=target_slice)
input_copy[index] = result['image']
target_copy[index] = result['mask']
return input_copy, target_copy