[英]Extracting 2d patches from 3d arrays
scikit-learn's extract_patches_2d
can be used to reshape a 2D image into a collection of patches. scikit-learn 的
extract_patches_2d
可用于将 2D 图像重塑为补丁集合。 extract_patches
is the generalized form that uses NumPy's as_strided
. extract_patches
是使用 NumPy 的as_strided
的通用形式。
import numpy as np
from sklearn.feature_extraction import image
ex = np.arange(3 * 3).reshape(3, 3)
image.extract_patches_2d(ex, patch_size=(2, 2))
[[[0 1]
[3 4]]
[[1 2]
[4 5]]
[[3 4]
[6 7]]
[[4 5]
[7 8]]]
I have a three-dimensional array a
and would like to extract 2d patches from each "innermost" 2d array, then find the (axis-agnostic) mean of each 2d patch.我有一个三维数组
a
并且想从每个“最里面”的二维数组中提取二维补丁,然后找到每个二维补丁的(轴不可知)平均值。
a = np.arange(2 * 3 * 3).reshape(2, 3, 3)
In this case, I effectively want to first call extract_patches_2d
on each (3, 3) inner array.在这种情况下,我实际上想首先在每个 (3, 3) 内部数组上调用
extract_patches_2d
。
patches = np.array([image.extract_patches_2d(i, patch_size=(2, 2)) for i in a])
and then find the mean of each innermost 2d array (each patch):然后找到每个最里面的二维数组(每个补丁)的平均值:
means = patches.reshape(*patches.shape[:-2], -1).mean(axis=-1)
print(means)
[[ 2. 3. 5. 6.]
[ 11. 12. 14. 15.]]
How can I vectorize this and get rid of the for-loop above?如何矢量化它并摆脱上面的 for 循环? The important thing here is that the size of the first dimension of
means
is equal to the size of a
's first dimension.这里重要的是,
means
的第一个维度的大小等于a
的第一个维度的大小。
You can use scikit-image as view_as_windows
to get those patches
as a view
into input array -您可以使用scikit-image 作为
view_as_windows
将这些patches
作为输入数组的view
-
from skimage.util.shape import view_as_windows
size = 2 # patch size
patches = view_as_windows(a, (1,size,size))[...,0,:,:]
This gives us a 5D
array as patches
, on which we can use mean
reduction along the last two axes for a 3D
output -这为我们提供了一个
5D
数组作为patches
,我们可以使用沿最后两个轴的mean
减少来进行3D
输出 -
out = patches.mean((-2,-1))
If the final output is needed as a 2D
one, reshape to merge last two axes -如果最终输出需要作为
2D
输出,请重塑以合并最后两个轴 -
out.reshape(a.shape[0],-1)
This can also utilize sklearn
's extract_patches
:这也可以利用
sklearn
的extract_patches
:
def inner_means(arr_3d, patch_size):
"""Axis-agnostic mean of each 2d patch.
Maintains the first dimension of `arr_3d`.
patch_size: tuple
Same syntax as the parameter passed to extract_patches_2d
"""
shape = (1,) + patch_size
patches = image.extract_patches(arr_3d, shape)[..., 0, :, :].mean((-2, -1))
return patches.reshape(*patches.shape[:-2], -1)
a = np.arange(2 * 3 * 3).reshape(2, 3, 3)
print(inner_means(a, patch_size=(2, 2)))
[[ 2. 3. 5. 6.]
[ 11. 12. 14. 15.]]
Alternatively, to directly get to the blocky average values, we can use one of those convolution tools from Scipy.或者,为了直接获得块状平均值,我们可以使用 Scipy 的卷积工具之一。 So with
fftconvolve
-所以使用
fftconvolve
-
from scipy.signal import fftconvolve
out = fftconvolve(a, np.ones((1,size,size)),mode='valid')/size**2
Or use scipy.signal.convolve
or scipy.ndimage.filters.uniform_filter
there without the division.或者在没有除法的情况下使用
scipy.signal.convolve
或scipy.ndimage.filters.uniform_filter
。
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