AttributeError：模块“ scipy.misc”没有属性“ toimage”

AttributeError：模块“ scipy.misc”没有属性“ toimage”

该

scipy.misc.toimage()

功能在Scipy
1.0.0中已弃用，在1.3.0版中已完全删除。从1.3.0发行说明中：

从Funtions

scipy.interpolate

（

spleval

，

spline

，

splmake

，和

spltopp

），并从功能

scipy.misc

（

bytescale

，

fromimage

，

imfilter

，

imread

，

imresize

，

imrotate

，

imsave

，

imshow

，

toimage

）已被删除。从v0.19.0开始不推荐使用前一组，而从v1.0.0开始不推荐使用后者。

注释链接到v1.1.0文档，该文档显示了替代方法。从

scipy.misc.toimage()

v1.1.0的文档中：

Image.fromarray

直接使用枕头的。

但是，该功能

Image.fromarray

所要做的工作远远超过了所能做的。您可以移植原始功能：

import numpy as npfrom 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

可以根据使用的实际参数进一步简化此 *** 作；例如，您的示例代码不使用

pal

参数。