downscale_local_mean(image, factors, cval=0, clip=True)

The image is padded with :None:None:`cval` if it is not perfectly divisible by the integer factors.

In contrast to interpolation in skimage.transform.resize and skimage.transform.rescale this function calculates the local mean of elements in each block of size :None:None:`factors` in the input image.

Parameters

image : ndarray

N-dimensional input image.

factors : array_like

Array containing down-sampling integer factor along each axis.

cval : float, optional

Constant padding value if image is not perfectly divisible by the integer factors.

clip : bool, optional

Unused, but kept here for API consistency with the other transforms in this module. (The local mean will never fall outside the range of values in the input image, assuming the provided :None:None:`cval` also falls within that range.)

Returns

image : ndarray

Down-sampled image with same number of dimensions as input image. For integer inputs, the output dtype will be float64 . See numpy.mean for details.

Down-sample N-dimensional image by local averaging.

Examples

This example is valid syntax, but we were not able to check execution

>>> a = np.arange(15).reshape(3, 5)
... a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

This example is valid syntax, but we were not able to check execution

>>> downscale_local_mean(a, (2, 3))
array([[3.5, 4. ],
       [5.5, 4.5]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

skimage.transform._warps.downscale_local_mean skimage.transform._warps.resize skimage.feature.orb.ORB.extract skimage.data.stereo_motorcycle skimage.transform._warps.rescale

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