block-reduce

block_reduce

skimage.measure.block_reduce(image, block_size, func=, cval=0) [source]

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

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>>> from skimage.measure import block_reduce >>> image = np.arange(3*3*4).reshape(3, 3, 4) >>> image 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 array([[[11]],        [[23]],        [[35]]]) >>> image_max2 = block_reduce(image, block_size=(3, 1, 4), func=np.max) >>> image_max2 array([[[27],         [31],         [35]]])