forked from 170010011/fr
88 lines
3.0 KiB
Python
88 lines
3.0 KiB
Python
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import numpy as np
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from ..util import view_as_blocks
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def block_reduce(image, block_size, func=np.sum, cval=0, func_kwargs=None):
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"""Downsample image by applying function `func` to local blocks.
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This function is useful for max and mean pooling, for example.
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Parameters
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----------
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image : ndarray
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N-dimensional input image.
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block_size : array_like
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Array containing down-sampling integer factor along each axis.
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func : callable
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Function object which is used to calculate the return value for each
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local block. This function must implement an ``axis`` parameter.
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Primary functions are ``numpy.sum``, ``numpy.min``, ``numpy.max``,
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``numpy.mean`` and ``numpy.median``. See also `func_kwargs`.
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cval : float
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Constant padding value if image is not perfectly divisible by the
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block size.
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func_kwargs : dict
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Keyword arguments passed to `func`. Notably useful for passing dtype
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argument to ``np.mean``. Takes dictionary of inputs, e.g.:
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``func_kwargs={'dtype': np.float16})``.
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Returns
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-------
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image : ndarray
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Down-sampled image with same number of dimensions as input image.
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Examples
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--------
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>>> from skimage.measure import block_reduce
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>>> image = np.arange(3*3*4).reshape(3, 3, 4)
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>>> image # doctest: +NORMALIZE_WHITESPACE
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array([[[ 0, 1, 2, 3],
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[ 4, 5, 6, 7],
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[ 8, 9, 10, 11]],
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[[12, 13, 14, 15],
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[16, 17, 18, 19],
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[20, 21, 22, 23]],
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[[24, 25, 26, 27],
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[28, 29, 30, 31],
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[32, 33, 34, 35]]])
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>>> block_reduce(image, block_size=(3, 3, 1), func=np.mean)
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array([[[16., 17., 18., 19.]]])
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>>> image_max1 = block_reduce(image, block_size=(1, 3, 4), func=np.max)
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>>> image_max1 # doctest: +NORMALIZE_WHITESPACE
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array([[[11]],
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[[23]],
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[[35]]])
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>>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max)
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>>> image_max2 # doctest: +NORMALIZE_WHITESPACE
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array([[[27],
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[31],
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[35]]])
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"""
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if len(block_size) != image.ndim:
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raise ValueError("`block_size` must have the same length "
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"as `image.shape`.")
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if func_kwargs is None:
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func_kwargs = {}
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pad_width = []
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for i in range(len(block_size)):
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if block_size[i] < 1:
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raise ValueError("Down-sampling factors must be >= 1. Use "
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"`skimage.transform.resize` to up-sample an "
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"image.")
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if image.shape[i] % block_size[i] != 0:
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after_width = block_size[i] - (image.shape[i] % block_size[i])
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else:
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after_width = 0
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pad_width.append((0, after_width))
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image = np.pad(image, pad_width=pad_width, mode='constant',
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constant_values=cval)
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blocked = view_as_blocks(image, block_size)
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return func(blocked, axis=tuple(range(image.ndim, blocked.ndim)),
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**func_kwargs)
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