Newer
Older
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
# ============================================================================================= #
# Author: Filip Lux #
# Copyright: Filip Lux lux.filip@gmail.com #
# #
# MIT License. #
# #
# Permission is hereby granted, free of charge, to any person obtaining a copy #
# of this software and associated documentation files (the "Software"), to deal #
# in the Software without restriction, including without limitation the rights #
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell #
# copies of the Software, and to permit persons to whom the Software is #
# furnished to do so, subject to the following conditions: #
# #
# The above copyright notice and this permission notice shall be included in all #
# copies or substantial portions of the Software. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE #
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER #
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, #
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE #
# SOFTWARE. #
# ============================================================================================= #
from typing import Sequence, Union
from ..typing import TypeSextetFloat, TypeTripletFloat, TypePairFloat
import numpy as np
import SimpleITK as sitk
from collections.abc import Iterable
def parse_limits(input_limit: Union[float, TypePairFloat, TypeTripletFloat, TypeSextetFloat],
identity_element: float = 1) -> TypeSextetFloat:
# input_limit = None
# returns (ie, ie, ie, ie, ie, ie)
if input_limit is None:
return tuple((identity_element, ) * 6)
# input_limit = x : float
# returns (ie-x, ie+x, ie-x, ie+x, ie-x, ie+x)
elif len(input_limit) == 1:
return tuple((identity_element - input_limit, identity_element + input_limit) * 3)
# input_limit = (a, b) : TypePairFloat
# returns (a, b, a, b, a, b)
elif len(input_limit) == 2:
a, b = input_limit
return a, b, a, b, a, b
# input_limit = (a, b, c) : TypeTripletFloat
# returns (ie-a, ie+a, ie-b, ie+b, ie-c, ie+c)
elif len(input_limit) == 3:
res = []
for item in input_limit:
# input_limit = ((a, b), (c, d), (e, f))
# return (a, b, c, d, e, f)
if isinstance(item, Iterable):
for val in item:
res.append(float(val))
# input_limit = (a, b, c)
# return (ie-a, ie+a, ie-b, ie+b, ie-c, ie+c)
else:
res.append(float(identity_element - item))
res.append(float(identity_element + item))
return tuple(res)
# input_limit = (a, b, c, d, e, f)
# returns (a, b, c, d, e, f)
elif len(input_limit) == 6:
return input_limit
def parse_coefs(coefs: Union[float, TypeTripletFloat],
identity_element: float = 1) -> TypeTripletFloat:
# input_limit = None
# return (ie, ie, ie)
if coefs is None:
return tuple((identity_element, ) * 3)
# return (a, a, a)
elif isinstance(coefs, (int, float)):
return coefs, coefs, coefs
# return (a, b, c)
elif len(coefs) == 3:
return coefs
def get_image_center(image: np.array,
spacing: TypeTripletFloat = (1., 1., 1.),
lps: bool = False) -> TypeTripletFloat:
shape = np.array(image.shape)
if len(shape) == 3:
center = (shape - 1) / 2
elif len(shape) == 4:
center = (shape[1:4] - 1) / 2
else:
center = np.array((0, 0, 0))
if lps:
center = ras_to_lps(center)
return center * np.array(spacing)
def to_tuple(param, low=None, bias=None):
"""Convert input argument to min-max tuple
Args:
param (scalar, tuple or list of 2+ elements): Input value.
If value is scalar, return value would be (offset - value, offset + value).
If value is tuple, return value would be value + offset (broadcasted).
low: Second element of tuple can be passed as optional argument
bias: An offset factor added to each element
"""
if low is not None and bias is not None:
raise ValueError("Arguments low and bias are mutually exclusive")
if param is None:
return param
if isinstance(param, (int, float)):
if low is None:
param = -param, +param
else:
param = (low, param) if low < param else (param, low)
elif isinstance(param, Sequence):
param = tuple(param)
else:
raise ValueError("Argument param must be either scalar (int, float) or tuple")
if bias is not None:
return tuple(bias + x for x in param)
return tuple(param)
# Simple ITK uses LPS coordinates format
def ras_to_lps(triplet: Sequence[float]):
return np.array((-1, -1, 1), dtype=float) * np.asarray(triplet)
def np_to_sitk(img: np.array) -> sitk.Image:
# image in format (c, s1, s2, s3, [t])
assert len(img.shape) == 5
channels, w, h, d, frames = img.shape
sample = np.moveaxis(img, 0, 3)
sample = sample.reshape((w, h, d, channels * frames))
# TODO: rather swap axis of parameters than data
sample = np.swapaxes(sample, 0, 2)
return sitk.GetImageFromArray(sample)
def sitk_to_np(sitk_img: sitk.Image,
channels,
frames=1) -> np.array:
# shape (d, w, h, c*f)
img = sitk.GetArrayFromImage(sitk_img)
if len(img.shape) == 3:
img = np.expand_dims(img, 3)
assert channels * frames == img.shape[-1], (f'Number of channels ({channels}) and frames ({frames})'
f'do not correspond to the sitk vector size {img.shape[-1]}')
# split channels and frames
w, h, d = img.shape[:3]
img = img.reshape((w, h, d, channels, frames))
img = np.swapaxes(img, 0, 2)
img = np.moveaxis(img, 3, 0)
# shape (c, w, h, d, f)
return img