import numpy as np
import os
import collections
from skimage.io import imread
from skimage.transform import resize
from delira.utils.decorators import make_deprecated
def norm_range(mode):
"""
Closure function for range normalization
Parameters
----------
mode : str
'-1,1' normalizes data to range [-1, 1], while '0,1'
normalizes data to range [0, 1]
Returns
-------
callable
normalization function
"""
def norm_fn(data):
"""
Returns the input data normalized to the range
Parameters
----------
data : np.ndarray
data which should be normalized
Returns
-------
np.ndarary
normalized data
"""
norm = data - data.min()
norm = norm / norm.max()
if mode == '-1,1':
norm = norm - 0.5
norm = norm * 2
elif mode == '0,1':
pass
else:
raise ValueError('{mode} not supported.')
return norm
return norm_fn
def norm_zero_mean_unit_std(data):
"""
Return normalized data with mean 0, standard deviation 1
Parameters
----------
data : np.nadarray
Returns
-------
np.ndarray
normalized data
"""
return (data - np.mean(data)) / np.std(data)
@make_deprecated
def _is_valid_image_file(fname, img_extensions, gt_extensions):
"""
Helper Function to check wheter file is image file and has at least
one label file
Parameters
----------
fname : str
filename of image path
Returns
-------
bool
is valid data sample
"""
is_valid_file = False
for ext in img_extensions:
if fname.endswith(ext):
is_valid_file = True
has_label = not gt_extensions
for ext in gt_extensions:
label_file = fname.rsplit(".", maxsplit=1)[0] + ext
if os.path.isfile(label_file):
has_label = True
return is_valid_file and has_label
[docs]@make_deprecated("Will be replaced by LoadSample.")
def default_load_fn_2d(img_file, *label_files, img_shape, n_channels=1):
"""
loading single 2d sample with arbitrary number of samples
Parameters
----------
img_file : string
path to image file
label_files : list of strings
paths to label files
img_shape : iterable
shape of image
n_channels : int
number of image channels
Returns
-------
numpy.ndarray
image
Any:
labels
"""
img = imread(img_file, n_channels == 1)
labels = [np.loadtxt(_file).reshape(1).astype(np.float32) for _file in
label_files]
img = resize(img, img_shape, mode='reflect', anti_aliasing=True)
img = np.reshape(img, (*img_shape, n_channels))
img = img.transpose((len(img_shape), *range(len(img_shape))))
img = img.astype(np.float32)
# return img, labels
result_dict = {"data": img}
for idx, label in enumerate(labels):
result_dict["label_%d" % idx] = label
return result_dict
class LoadSample:
def __init__(self,
sample_ext: dict,
sample_fn: collections.abc.Callable,
dtype={}, normalize=(), norm_fn=norm_range('-1,1'),
**kwargs):
"""
Provides a callable to load a single sample from multiple files in a
folder
Parameters
----------
sample_ext : dict of iterable
Defines the data _sample_ext. The dict key defines the position of
the sample inside the returned data dict, while the list defines
the the files which should be loaded inside the data dict.
sample_fn : callable
function to load a single sample
dtype : dict
defines the data type which should be used for the respective key
normalize : iterable of hashable
list of hashable which should be normalized. Can contain
entire keys of extension (normalizes each element individually)
or provide the file name which should be normalized
norm_fn : callable
callable to normalize input. Default: normalize range to [-1, 1]
kwargs :
variable number of keyword arguments passed to load function
Examples
--------
Simple loading function which returns a dict with `data`
>>> from delira.data_loading.nii import load_nii
>>> load_fn = LoadSample({'data:': ['data.nii']}, load_nii)
Loading function for data (casted to float32 and normalized) and
segmentation (casted to unit8)
>>> from delira.data_loading.nii import load_nii
>>> load_fn = LoadSample({'data:': ['data.nii'], 'seg': ['seg.nii']},
>>> load_nii, dtype={'data': 'float32',
>>> 'seg': 'uint8'},
>>> normalize=('data',))
"""
self._sample_ext = sample_ext
self._sample_fn = sample_fn
self._dtype = dtype
self._normalize = normalize
self._norm_fn = norm_fn
self._kwargs = kwargs
def __call__(self, path):
"""
Load sample from multiple files
Parameters
----------
path : str
defines patch to folder which contain the _sample_ext
Returns
-------
dict
dict with data defines by _sample_ext
"""
sample_dict = {}
for key, item in self._sample_ext.items():
data_list = []
for f in item:
data = self._sample_fn(os.path.join(path, f), **self._kwargs)
# _normalize data if necessary
if (key in self._normalize) or (f in self._normalize):
data = self._norm_fn(data)
# cast data to type
if key in self._dtype:
data = data.astype(self._dtype[key])
# append data
data_list.append(data)
if len(data_list) == 1:
sample_dict[key] = data_list[0][np.newaxis]
else:
sample_dict[key] = np.stack(data_list)
return sample_dict
class LoadSampleLabel(LoadSample):
def __init__(self,
sample_ext: dict,
sample_fn: collections.abc.Callable,
label_ext: collections.abc.Iterable,
label_fn: collections.abc.Callable,
sample_kwargs={}, **kwargs):
"""
Load sample and label from folder
Parameters
----------
sample_ext : dict of list
Defines the data _sample_ext. The dict key defines the position of
the sample inside the returned data dict, while the list defines
the the files which should be loaded inside the data dict.
Passed to LoadSample.
sample_fn : callable
function to load a single sample
Passed to LoadSample.
label_ext : str
extension for label
label_fn: function
functions which returns the label inside a dict
args :
variable number of positional arguments passed to LoadSample
sample_kwargs :
additional keyword arguments passed to LoadSample
kwargs :
variable number of keyword arguments passed to _label_fn
See Also
--------
:class: `LoadSample`
"""
super().__init__(sample_ext, sample_fn, **sample_kwargs)
self._label_ext = label_ext
self._label_fn = label_fn
self._label_kwargs = kwargs
def __call__(self, path):
"""
Loads a sample and a label
Parameters
----------
path : str
Returns
-------
dict
dict with data and label
"""
sample_dict = super(LoadSampleLabel, self).__call__(path)
label_dict = self._label_fn(os.path.join(path, self._label_ext),
**self._label_kwargs)
sample_dict.update(label_dict)
return sample_dict