AttributeError: 模块 'scipy.misc' 没有属性 'toimage'

Question

在执行以下代码时：

 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’

我尝试按照此处所述安装 Pillow： scipy.misc module has no attribute imread? 但同样的错误仍然存在。请帮忙。谢谢。

原文由 KCK 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

scipy.misc.toimage() 函数在 Scipy 1.0.0 中被弃用，并在 1.3.0 版中被完全删除。从 1.3.0 发行说明：

Funtions from scipy.interpolate ( spleval , spline , splmake , and spltopp ) and functions from scipy.misc ( bytescale , fromimage , imfilter , imread , imresize , imrotate , imsave , imshow , toimage ) 已被删除。前一组自 v0.19.0 起已弃用，后者自 v1.0.0 起已弃用。

注释链接到 v1.1.0 文档，该文档显示了要使用的内容；来自 scipy.misc.toimage() v1.1.0 的文档：

直接使用 Pillow 的 Image.fromarray 代替。

然而，该函数所做的工作比 Image.fromarray 所能做的更多。您可以 移植原始功能：

 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

这可以根据实际使用的参数进一步简化；例如，您的示例代码不使用 pal 参数。

原文由 Martijn Pieters 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 2

@Martijn Pieters 为我工作，但我也找到了另一种可能更适合某些人的解决方案。您还可以使用下面的代码导入 keras.preprocessing.image、array_to_img 而不是 scipy.misc.toimage，正如@Martijn Pieters 已经提到的那样，它在 Scipy 1.0.0 中已弃用。

因此，作为使用 keras API 处理转换图像的示例：

 # example of converting an image with the Keras API
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.preprocessing.image import array_to_img

# load the image
img = load_img('image.jpg')
print(type(img))

# convert to numpy array
img_array = img_to_array(img)
print(img_array.dtype)
print(img_array.shape)

# convert back to image
img_pil = array_to_img(img_array)
print(type(img_pil))

# show image
fig = plt.figure()
ax = fig.add_subplot()
ax.imshow(img_pil)

并使用 keras 保存图像：

 from keras.preprocessing.image import save_img
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array

# load image
img = load_img('image.jpg')

# convert image to a numpy array
img_array = img_to_array(img)

# save the image with a new filename
save_img('image_save.jpg', img_array)

# load the image to confirm it was saved correctly
img = load_img('image_save.jpg')

print(type(img))
print(img.format)
print(img.mode)
print(img.size)

原文由 recurseuntilfor 发布，翻译遵循 CC BY-SA 4.0 许可协议