Scipy 杂项字节缩放方法的替代方案

Question

我有一段旧代码，上面说它已经被弃用了。有什么可能的替代方案来实现这行代码的目的？

img = misc.bytescale(img, high=16, low=0)

Answer 1

我处理了同样的问题，但没有找到解决方案。我在这里复制了一个稍微修改过的源代码版本(只是numpy导入(：

import numpy as np
# Returns a byte-scaled image
def bytescale(data, cmin=None, cmax=None, high=255, low=0):
"""
Byte scales an array (image).
Byte scaling means converting the input image to uint8 dtype and scaling
the range to ``(low, high)`` (default 0-255).
If the input image already has dtype uint8, no scaling is done.
Parameters
----------
data : ndarray
PIL image data array.
cmin : scalar, optional
Bias scaling of small values. Default is ``data.min()``.
cmax : scalar, optional
Bias scaling of large values. Default is ``data.max()``.
high : scalar, optional
Scale max value to `high`.  Default is 255.
low : scalar, optional
Scale min value to `low`.  Default is 0.
Returns
-------
img_array : uint8 ndarray
The byte-scaled array.
Examples
--------
>>> img = array([[ 91.06794177,   3.39058326,  84.4221549 ],
[ 73.88003259,  80.91433048,   4.88878881],
[ 51.53875334,  34.45808177,  27.5873488 ]])
>>> bytescale(img)
array([[255,   0, 236],
[205, 225,   4],
[140,  90,  70]], dtype=uint8)
>>> bytescale(img, high=200, low=100)
array([[200, 100, 192],
[180, 188, 102],
[155, 135, 128]], dtype=uint8)
>>> bytescale(img, cmin=0, cmax=255)
array([[91,  3, 84],
[74, 81,  5],
[52, 34, 28]], dtype=uint8)
"""
if data.dtype == np.uint8:
return data
if high < low:
raise ValueError("`high` should be larger than `low`.")
if cmin is None:
cmin = data.min()
if cmax is None:
cmax = data.max()
cscale = cmax - cmin
if cscale < 0:
raise ValueError("`cmax` should be larger than `cmin`.")
elif cscale == 0:
cscale = 1
scale = float(high - low) / cscale
bytedata = (data * 1.0 - cmin) * scale + 0.4999
bytedata[bytedata > high] = high
bytedata[bytedata < 0] = 0
return np.cast[np.uint8](bytedata) + np.cast[np.uint8](low)
#example
img = np.array([[ 91.06794177,   3.39058326,  84.4221549 ],
[ 73.88003259,  80.91433048,   4.88878881],
[ 51.53875334,  34.45808177,  27.5873488 ]])
print(img)
print(bytescale(img))

这会返回

[[91.06794177  3.39058326 84.4221549 ]
[73.88003259 80.91433048  4.88878881]
[51.53875334 34.45808177 27.5873488 ]]
[[255   0 236]
[205 225   4]
[140  90  70]]

Answer 2

def bytescale(arr, low=None, high=None, a=0, b=255):
"""Linear rescale of array. Defaults to bytescale"""

if low or high:
arr = np.clip(arr, low, high)

return (b-a) * ((arr - np.min(arr)) / (np.max(arr) - np.min(arr))) + a