`skimage.transform._warps:resize_local_mean`

source: /dev/scikit-image/src/skimage/transform/_warps.py :1290

Signature

   def     resize_local_mean (    image    ,    output_shape    ,    grid_mode    =  True   ,    preserve_range    =  False   ,  * ,     channel_axis    =  None     )

Summary

Resize an array with the local mean / bilinear scaling.

Parameters

image : ndarray

Input image. If this is a multichannel image, the axis corresponding to channels should be specified using channel_axis.

output_shape : iterable

Size of the generated output image. When channel_axis is not None, the channel_axis should either be omitted from output_shape or the output_shape[channel_axis] must match image.shape[channel_axis]. If the length of output_shape exceeds image.ndim, additional singleton dimensions will be appended to the input image as needed.

grid_mode : bool, optional

Defines image pixels position: if True, pixels are assumed to be at grid intersections, otherwise at cell centers. As a consequence, for example, a 1d signal of length 5 is considered to have length 4 when grid_mode is False, but length 5 when grid_mode is True. See the following visual illustration:

| pixel 1 | pixel 2 | pixel 3 | pixel 4 | pixel 5 |
     |<-------------------------------------->|
                        vs.
|<----------------------------------------------->|

The starting point of the arrow in the diagram above corresponds to coordinate location 0 in each mode.

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

Returns

resized : ndarray: Resized version of the input.

Notes

This method is sometimes referred to as "area-based" interpolation or "pixel mixing" interpolation ^[1]. When grid_mode is True, it is equivalent to using OpenCV's resize with INTER_AREA interpolation mode. It is commonly used for image downsizing. If the downsizing factors are integers, then downscale_local_mean should be preferred instead.

Examples

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

✓

Aliases

skimage.transform.resize_local_mean