AttributeError: 模块 'scipy.misc' 没有属性 'toimage' [英] AttributeError: module 'scipy.misc' has no attribute 'toimage'
问题描述
执行以下代码时:
scipy.misc.toimage(output * 255, high=255, low=0, cmin=0, cmax=255).save(
params.result_dir + 'final/%5d_00_%d_out.png' % (test_id, ratio))
我收到以下错误:
AttributeError: 模块 'scipy.misc' 没有属性 'toimage'
AttributeError: module 'scipy.misc' has no attribute 'toimage'
我尝试按照此处所述安装 Pillow:scipy.misc 模块没有属性imread? 但同样的错误仍然存在.请帮忙.谢谢.
I tried installing Pillow as mentioned here: scipy.misc module has no attribute imread? But the same error persisted. Please help. Thanks.
推荐答案
scipy.misc.toimage() 函数在 Scipy 1.0.0 中被弃用，并在 1.3.0 版本中被完全删除.来自 1.3.0发行说明:
The scipy.misc.toimage() function was deprecated in Scipy 1.0.0, and was completely removed in version 1.3.0. From the 1.3.0 release notes:
来自 scipy.interpolate 的函数(spleval、spline、splmake 和 spltopp) 和来自 scipy.misc 的函数(bytescale、fromimage、imfilter、imread、imresize、imrotate、imsave、imshow、toimage)移除.前者自 v0.19.0 起已弃用，后者自 v1.0.0 起已弃用.
Funtions from
scipy.interpolate(spleval,spline,splmake, andspltopp) and functions fromscipy.misc(bytescale,fromimage,imfilter,imread,imresize,imrotate,imsave,imshow,toimage) have been removed. The former set has been deprecated since v0.19.0 and the latter has been deprecated since v1.0.0.
指向 v1.1.0 文档的说明链接，显示了要使用的内容；来自 scipy.misc.toimage() v1.1.0 的文档:
The notes link to the v1.1.0 documentation that shows what to use instead; from the scipy.misc.toimage() documentation for v1.1.0:
直接使用 Pillow 的 Image.fromarray 代替.
Use Pillow’s
Image.fromarraydirectly instead.
然而，该函数所做的工作比 Image.fromarray 所能做的更多.您可以移植原始函数:
The function does more work than just Image.fromarray could do, however. You could port the original function:
import numpy as np
from PIL import Image
_errstr = "Mode is unknown or incompatible with input array shape."
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.
This function is only available if Python Imaging Library (PIL) is installed.
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
--------
>>> from scipy.misc import bytescale
>>> img = np.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 > 255:
raise ValueError("`high` should be less than or equal to 255.")
if low < 0:
raise ValueError("`low` should be greater than or equal to 0.")
if high < low:
raise ValueError("`high` should be greater than or equal to `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 - cmin) * scale + low
return (bytedata.clip(low, high) + 0.5).astype(np.uint8)
def toimage(arr, high=255, low=0, cmin=None, cmax=None, pal=None,
mode=None, channel_axis=None):
"""Takes a numpy array and returns a PIL image.
This function is only available if Python Imaging Library (PIL) is installed.
The mode of the PIL image depends on the array shape and the `pal` and
`mode` keywords.
For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values
(from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode
is given as 'F' or 'I' in which case a float and/or integer array is made.
.. warning::
This function uses `bytescale` under the hood to rescale images to use
the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``.
It will also cast data for 2-D images to ``uint32`` for ``mode=None``
(which is the default).
Notes
-----
For 3-D arrays, the `channel_axis` argument tells which dimension of the
array holds the channel data.
For 3-D arrays if one of the dimensions is 3, the mode is 'RGB'
by default or 'YCbCr' if selected.
The numpy array must be either 2 dimensional or 3 dimensional.
"""
data = np.asarray(arr)
if np.iscomplexobj(data):
raise ValueError("Cannot convert a complex-valued array.")
shape = list(data.shape)
valid = len(shape) == 2 or ((len(shape) == 3) and
((3 in shape) or (4 in shape)))
if not valid:
raise ValueError("'arr' does not have a suitable array shape for "
"any mode.")
if len(shape) == 2:
shape = (shape[1], shape[0]) # columns show up first
if mode == 'F':
data32 = data.astype(np.float32)
image = Image.frombytes(mode, shape, data32.tostring())
return image
if mode in [None, 'L', 'P']:
bytedata = bytescale(data, high=high, low=low,
cmin=cmin, cmax=cmax)
image = Image.frombytes('L', shape, bytedata.tostring())
if pal is not None:
image.putpalette(np.asarray(pal, dtype=np.uint8).tostring())
# Becomes a mode='P' automagically.
elif mode == 'P': # default gray-scale
pal = (np.arange(0, 256, 1, dtype=np.uint8)[:, np.newaxis] *
np.ones((3,), dtype=np.uint8)[np.newaxis, :])
image.putpalette(np.asarray(pal, dtype=np.uint8).tostring())
return image
if mode == '1': # high input gives threshold for 1
bytedata = (data > high)
image = Image.frombytes('1', shape, bytedata.tostring())
return image
if cmin is None:
cmin = np.amin(np.ravel(data))
if cmax is None:
cmax = np.amax(np.ravel(data))
data = (data*1.0 - cmin)*(high - low)/(cmax - cmin) + low
if mode == 'I':
data32 = data.astype(np.uint32)
image = Image.frombytes(mode, shape, data32.tostring())
else:
raise ValueError(_errstr)
return image
# if here then 3-d array with a 3 or a 4 in the shape length.
# Check for 3 in datacube shape --- 'RGB' or 'YCbCr'
if channel_axis is None:
if (3 in shape):
ca = np.flatnonzero(np.asarray(shape) == 3)[0]
else:
ca = np.flatnonzero(np.asarray(shape) == 4)
if len(ca):
ca = ca[0]
else:
raise ValueError("Could not find channel dimension.")
else:
ca = channel_axis
numch = shape[ca]
if numch not in [3, 4]:
raise ValueError("Channel axis dimension is not valid.")
bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax)
if ca == 2:
strdata = bytedata.tostring()
shape = (shape[1], shape[0])
elif ca == 1:
strdata = np.transpose(bytedata, (0, 2, 1)).tostring()
shape = (shape[2], shape[0])
elif ca == 0:
strdata = np.transpose(bytedata, (1, 2, 0)).tostring()
shape = (shape[2], shape[1])
if mode is None:
if numch == 3:
mode = 'RGB'
else:
mode = 'RGBA'
if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']:
raise ValueError(_errstr)
if mode in ['RGB', 'YCbCr']:
if numch != 3:
raise ValueError("Invalid array shape for mode.")
if mode in ['RGBA', 'CMYK']:
if numch != 4:
raise ValueError("Invalid array shape for mode.")
# Here we know data and mode is correct
image = Image.frombytes(mode, shape, strdata)
return image
这可以根据使用的实际参数进一步简化；例如，您的示例代码不使用 pal 参数.
This could be further simplified based on the actual arguments used; your sample code doesn't use the pal argument, for example.
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