update

.CondaPkg/env/Lib/site-packages/skimage/measure/block.py

@@ -1,90 +0,0 @@
-import numpy as np
-from ..util import view_as_blocks
-
-
-def block_reduce(image, block_size=2, func=np.sum, cval=0, func_kwargs=None):
-    """Downsample image by applying function `func` to local blocks.
-
-    This function is useful for max and mean pooling, for example.
-
-    Parameters
-    ----------
-    image : ndarray
-        N-dimensional input image.
-    block_size : array_like or int
-        Array containing down-sampling integer factor along each axis.
-        Default block_size is 2.
-    func : callable
-        Function object which is used to calculate the return value for each
-        local block. This function must implement an ``axis`` parameter.
-        Primary functions are ``numpy.sum``, ``numpy.min``, ``numpy.max``,
-        ``numpy.mean`` and ``numpy.median``.  See also `func_kwargs`.
-    cval : float
-        Constant padding value if image is not perfectly divisible by the
-        block size.
-    func_kwargs : dict
-        Keyword arguments passed to `func`. Notably useful for passing dtype
-        argument to ``np.mean``. Takes dictionary of inputs, e.g.:
-        ``func_kwargs={'dtype': np.float16})``.
-
-    Returns
-    -------
-    image : ndarray
-        Down-sampled image with same number of dimensions as input image.
-
-    Examples
-    --------
-    >>> from skimage.measure import block_reduce
-    >>> image = np.arange(3*3*4).reshape(3, 3, 4)
-    >>> image # doctest: +NORMALIZE_WHITESPACE
-    array([[[ 0,  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]]])
-    >>> block_reduce(image, block_size=(3, 3, 1), func=np.mean)
-    array([[[16., 17., 18., 19.]]])
-    >>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=np.max)
-    >>> image_max1 # doctest: +NORMALIZE_WHITESPACE
-    array([[[11]],
-           [[23]],
-           [[35]]])
-    >>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max)
-    >>> image_max2 # doctest: +NORMALIZE_WHITESPACE
-    array([[[27],
-            [31],
-            [35]]])
-    """
-
-    if np.isscalar(block_size):
-        block_size = (block_size,) * image.ndim
-    elif len(block_size) != image.ndim:
-        raise ValueError("`block_size` must be a scalar or have "
-                         "the same length as `image.shape`")
-
-    if func_kwargs is None:
-        func_kwargs = {}
-
-    pad_width = []
-    for i in range(len(block_size)):
-        if block_size[i] < 1:
-            raise ValueError("Down-sampling factors must be >= 1. Use "
-                             "`skimage.transform.resize` to up-sample an "
-                             "image.")
-        if image.shape[i] % block_size[i] != 0:
-            after_width = block_size[i] - (image.shape[i] % block_size[i])
-        else:
-            after_width = 0
-        pad_width.append((0, after_width))
-
-    image = np.pad(image, pad_width=pad_width, mode='constant',
-                   constant_values=cval)
-
-    blocked = view_as_blocks(image, block_size)
-
-    return func(blocked, axis=tuple(range(image.ndim, blocked.ndim)),
-                **func_kwargs)