"""
==============================
Rescale, resize, and downscale
==============================

`Rescale` operation resizes an image by a given scaling factor. The scaling
factor can either be a single floating point value, or multiple values - one
along each axis.

`Resize` serves the same purpose, but allows to specify an output image shape
instead of a scaling factor.

Note that when down-sampling an image, `resize` and `rescale` should perform
Gaussian smoothing to avoid aliasing artifacts. See the `anti_aliasing` and
`anti_aliasing_sigma` arguments to these functions.

`Downscale` serves the purpose of down-sampling an n-dimensional image by
integer factors using the local mean on the elements of each block of the size
factors given as a parameter to the function.

"""

import matplotlib.pyplot as plt

from skimage import data, color
from skimage.transform import rescale, resize, downscale_local_mean

image = color.rgb2gray(data.astronaut())

image_rescaled = rescale(image, 0.25, anti_aliasing=False)
image_resized = resize(
    image, (image.shape[0] // 4, image.shape[1] // 4), anti_aliasing=True
)
image_downscaled = downscale_local_mean(image, (4, 3))

fig, axes = plt.subplots(nrows=2, ncols=2)

ax = axes.ravel()

ax[0].imshow(image, cmap='gray')
ax[0].set_title("Original image")

ax[1].imshow(image_rescaled, cmap='gray')
ax[1].set_title("Rescaled image (aliasing)")

ax[2].imshow(image_resized, cmap='gray')
ax[2].set_title("Resized image (no aliasing)")

ax[3].imshow(image_downscaled, cmap='gray')
ax[3].set_title("Downscaled image (no aliasing)")

ax[0].set_xlim(0, 512)
ax[0].set_ylim(512, 0)
plt.tight_layout()
plt.show()