resize

resize

skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source]

Resize image to match a certain size.

Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively.

Parameters:	image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output image (rows, cols[, dim]). If 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 3-dimensional interpolation is applied.
Returns:	resized : ndarray Resized version of the input.
Other Parameters:
	order : int, optional The order of the spline interpolation, default is 1. 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.

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].

Examples

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