resize(image, output_shape, order=None, mode='reflect', cval=0, clip=True, preserve_range=False, anti_aliasing=None, anti_aliasing_sigma=None)

Performs interpolation to up-size or down-size N-dimensional images. Note that anti-aliasing should be enabled when down-sizing images to avoid aliasing artifacts. For down-sampling with an integer factor also see skimage.transform.downscale_local_mean .

Notes

Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].

Other Parameters

order : int, optional

The order of the spline interpolation, default is 0 if image.dtype is bool and 1 otherwise. The order has to be in the range 0-5. See skimage.transform.warp for detail.

mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional

Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad .

cval : float, optional

Used in conjunction with mode 'constant', the value outside the image boundaries.

clip : bool, optional

Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.

preserve_range : bool, optional

Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float . Also see https://scikit-image.org/docs/dev/user_guide/data_types.html

anti_aliasing : bool, optional

Whether to apply a Gaussian filter to smooth the image prior to down-scaling. It is crucial to filter when down-sampling the image to avoid aliasing artifacts. If input image data type is bool, no anti-aliasing is applied.

anti_aliasing_sigma : {float, tuple of floats}, optional

Standard deviation for Gaussian filtering to avoid aliasing artifacts. By default, this value is chosen as (s - 1) / 2 where s is the down-scaling factor, where s > 1. For the up-size case, s < 1, no anti-aliasing is performed prior to rescaling.

Parameters

image : ndarray

Input image.

output_shape : tuple or ndarray

Size of the generated output image :None:None:`(rows, cols[, ...][, dim])`. If :None:None:`dim` is not provided, the number of channels is preserved. In case the number of input channels does not equal the number of output channels a n-dimensional interpolation is applied.

Returns

resized : ndarray

Resized version of the input.

Resize image to match a certain size.

Examples

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

>>> from skimage import data
... from skimage.transform import resize
... image = data.camera()
... resize(image, (100, 100)).shape
(100, 100)

See :

Back References

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

skimage.transform._warps.resize skimage.transform._warps.downscale_local_mean

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