view-as-blocks

view_as_blocks

skimage.util.view_as_blocks(arr_in, block_shape) [source]

Block view of the input n-dimensional array (using re-striding).

Blocks are non-overlapping views of the input array.

Parameters:	arr_in : ndarray N-d input array. block_shape : tuple The shape of the block. Each dimension must divide evenly into the corresponding dimensions of arr_in.
Returns:	arr_out : ndarray Block view of the input array. If arr_in is non-contiguous, a copy is made.

Examples

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>>> import numpy as np >>> from skimage.util.shape import view_as_blocks >>> A = np.arange(4*4).reshape(4,4) >>> A array([[ 0,  1,  2,  3],        [ 4,  5,  6,  7],        [ 8,  9, 10, 11],        [12, 13, 14, 15]]) >>> B = view_as_blocks(A, block_shape=(2, 2)) >>> B[0, 0] array([[0, 1],        [4, 5]]) >>> B[0, 1] array([[2, 3],        [6, 7]]) >>> B[1, 0, 1, 1] 13

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>>> A = np.arange(4*4*6).reshape(4,4,6) >>> A  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],         [36, 37, 38, 39, 40, 41],         [42, 43, 44, 45, 46, 47]],        [[48, 49, 50, 51, 52, 53],         [54, 55, 56, 57, 58, 59],         [60, 61, 62, 63, 64, 65],         [66, 67, 68, 69, 70, 71]],        [[72, 73, 74, 75, 76, 77],         [78, 79, 80, 81, 82, 83],         [84, 85, 86, 87, 88, 89],         [90, 91, 92, 93, 94, 95]]]) >>> B = view_as_blocks(A, block_shape=(1, 2, 2)) >>> B.shape (4, 2, 3, 1, 2, 2) >>> B[2:, 0, 2]  array([[[[52, 53],          [58, 59]]],        [[[76, 77],          [82, 83]]]])