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
|
import warnings
import numpy as np
def normalize(src, min_value=None, max_value=None, return_minmax=False):
"""Normalize image.
Parameters
----------
src: numpy.ndarray, (H, W) or (H, W, C), float
Input image.
min_value: float
Minimum value.
max_value: float
Maximum value.
return_minmax: bool
Flag to return min_value and max_value.
Returns
-------
dst: numpy.ndarray, float
Normalized image in [0, 1].
"""
if src.ndim == 2:
D = 1
else:
assert src.ndim == 3, "src ndim must be 2 or 3"
D = src.shape[2]
if min_value is None:
min_value = np.nanmin(src, axis=(0, 1))
min_value = np.atleast_1d(min_value).astype(float)
assert min_value.shape == (D,)
if max_value is None:
max_value = np.nanmax(src, axis=(0, 1))
max_value = np.atleast_1d(max_value).astype(float)
assert max_value.shape == (D,)
if np.isinf(min_value).any() or np.isinf(max_value).any():
warnings.warn("some of min or max values are inf.")
eps = np.finfo(src.dtype).eps
issame = max_value == min_value
min_value[issame] -= eps
max_value[issame] += eps
dst = np.zeros(src.shape, dtype=float)
if src.ndim == 2:
isnan = np.isnan(src)
else:
isnan = np.isnan(src).any(axis=2)
dst[~isnan] = 1.0 * (src[~isnan] - min_value) / (max_value - min_value)
dst[isnan] = np.nan
if return_minmax:
return dst, min_value, max_value
else:
return dst
|
