scikit-image库-- 图像块视图（十四）

此示例说明了如何使用skimage.util（）中的view_as_blocks。 当想要在非重叠图像块上执行本地操作时，块视图非常有用。
我们使用来自skimage.data的宇航员并将其“切片”成方块。 然后，在每个块上，我们或者汇集该块的平均值，最大值或中值。 结果一起显示，以及原始宇航员图像的3阶重新缩放的样条插值。

import numpy as np
from scipy import ndimage as ndi
from matplotlib import pyplot as plt
import matplotlib.cm as cm
%matplotlib inline
from skimage import data
from skimage import color
from skimage.util import view_as_blocks


# get astronaut from skimage.data in grayscale
l = color.rgb2gray(data.astronaut())

# size of blocks
block_shape = (4, 4)

# see astronaut as a matrix of blocks (of shape block_shape)
view = view_as_blocks(l, block_shape)

# collapse the last two dimensions in one
flatten_view = view.reshape(view.shape[0], view.shape[1], -1)

# resampling the image by taking either the `mean`,
# the `max` or the `median` value of each blocks.
mean_view = np.mean(flatten_view, axis=2)# np对数组图像操作
max_view = np.max(flatten_view, axis=2)
median_view = np.median(flatten_view, axis=2)

# display resampled images
fig, axes = plt.subplots(2, 2, figsize=(18, 13), sharex=True, sharey=True)
ax = axes.ravel()

l_resized = ndi.zoom(l, 2, order=3)  #放大函数， 2代表放大2倍 3代表【三次样条插值】
ax[0].set_title("Original rescaled with\n spline interpolation (order=3)")
ax[0].imshow(l_resized, extent=(0, 128, 128, 0), interpolation='nearest',
             cmap=cm.Greys_r)

ax[1].set_title("Block view with\n local mean pooling")
ax[1].imshow(mean_view, interpolation='nearest', cmap=cm.Greys_r)

ax[2].set_title("Block view with\n local max pooling")
ax[2].imshow(max_view, interpolation='nearest', cmap=cm.Greys_r)

ax[3].set_title("Block view with\n local median pooling")
ax[3].imshow(median_view, interpolation='nearest', cmap=cm.Greys_r)

for a in ax:
    a.set_axis_off()

fig.tight_layout()
plt.show()