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import numpy as np
from ..util import view_as_blocks
def block_reduce(image, block_size, func=np.sum, cval=0):
"""Down-sample image by applying function to local blocks.
Parameters
----------
image : ndarray
N-dimensional input image.
block_size : array_like
Array containing down-sampling integer factor along each axis.
func : callable
Function object which is used to calculate the return value for each
local block. This function must implement an ``axis`` parameter such
as ``numpy.sum`` or ``numpy.min``.
cval : float
Constant padding value if image is not perfectly divisible by the
block size.
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 len(block_size) != image.ndim:
raise ValueError("`block_size` must have the same length "
"as `image.shape`.")
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)
out = view_as_blocks(image, block_size)
for i in range(len(out.shape) // 2):
out = func(out, axis=-1)
return out