问题描述
我想以pythonic的方式将3D numpy数组拆分为3D块.我正在处理有点大阵列(1000X1200X1600)的图像序列，因此我需要将它们分成多个部分进行处理.
I would like to split a 3D numpy array into 3D blocks in a 'pythonic' way. I am working with image sequences that are somewhat large arrays (1000X1200X1600), so I need to split them into pieces to do my processing.
我已经编写了函数来执行此操作，但是我想知道是否有本机的numpy方法来完成此操作-numpy.split似乎无法满足我对3D数组的要求(但也许我不了解它的功能) )
I have written functions to do this, but I am wondering if there is a native numpy way to accomplish this - numpy.split does not seem to do what I want for 3D arrays (but perhaps I don't understand its functionality)
要清楚:下面的代码完成了我的任务，但是我正在寻找一种更快的方法.
To be clear: the code below accomplishes my task, but I am seeking a faster way to do it.
def make_blocks(x,t):
#x should be a yXmXn matrix, and t should even divides m,n
#returns a list of 3D blocks of size yXtXt
down = range(0,x.shape[1],t)
across = range(0,x.shape[2],t)
reshaped = []
for d in down:
for a in across:
reshaped.append(x[:,d:d+t,a:a+t])
return reshaped
def unmake_blocks(x,d,m,n):
#this takes a list of matrix blocks of size dXd that is m*n/d^2 long
#returns a 2D array of size mXn
rows = []
for i in range(0,int(m/d)):
rows.append(np.hstack(x[i*int(n/d):(i+1)*int(n/d)]))
return np.vstack(rows)
推荐答案
以下是这些循环实现的矢量化版本，结合使用了置换暗淡与 np.transpose 和 reshaping -
Here are vectorized versions of those loopy implementations using a combination of permuting dims with np.transpose and reshaping -
def make_blocks_vectorized(x,d):
p,m,n = x.shape
return x.reshape(-1,m//d,d,n//d,d).transpose(1,3,0,2,4).reshape(-1,p,d,d)
def unmake_blocks_vectorized(x,d,m,n):
return np.concatenate(x).reshape(m//d,n//d,d,d).transpose(0,2,1,3).reshape(m,n)
make_blocks的样品运行-
Sample run for make_blocks -
In [120]: x = np.random.randint(0,9,(2,4,4))
In [121]: make_blocks(x,2)
Out[121]:
[array([[[4, 7],
[8, 3]],
[[0, 5],
[3, 2]]]), array([[[5, 7],
[4, 0]],
[[7, 3],
[5, 7]]]), ... and so on.
In [122]: make_blocks_vectorized(x,2)
Out[122]:
array([[[[4, 7],
[8, 3]],
[[0, 5],
[3, 2]]],
[[[5, 7],
[4, 0]],
[[7, 3],
[5, 7]]], ... and so on.
unmake_blocks的样品运行-
In [135]: A = [np.random.randint(0,9,(3,3)) for i in range(6)]
In [136]: d = 3
In [137]: m,n = 6,9
In [138]: unmake_blocks(A,d,m,n)
Out[138]:
array([[6, 6, 7, 8, 6, 4, 5, 4, 8],
[8, 8, 3, 2, 7, 6, 8, 5, 1],
[5, 2, 2, 7, 1, 2, 3, 1, 5],
[6, 7, 8, 2, 2, 1, 6, 8, 4],
[8, 3, 0, 4, 4, 8, 8, 6, 3],
[5, 5, 4, 8, 5, 2, 2, 2, 3]])
In [139]: unmake_blocks_vectorized(A,d,m,n)
Out[139]:
array([[6, 6, 7, 8, 6, 4, 5, 4, 8],
[8, 8, 3, 2, 7, 6, 8, 5, 1],
[5, 2, 2, 7, 1, 2, 3, 1, 5],
[6, 7, 8, 2, 2, 1, 6, 8, 4],
[8, 3, 0, 4, 4, 8, 8, 6, 3],
[5, 5, 4, 8, 5, 2, 2, 2, 3]])
使用 make_blocks > -
Alternative to make_blocks with view_as_blocks -
from skimage.util.shape import view_as_blocks
def make_blocks_vectorized_v2(x,d):
return view_as_blocks(x,(x.shape[0],d,d))
运行时测试
1)make_blocks使用原始方法和基于view_as_blocks的方法-
1) make_blocks with original and view_as_blocks based approaches -
In [213]: x = np.random.randint(0,9,(100,160,120)) # scaled down by 10
In [214]: %timeit make_blocks(x,10)
1000 loops, best of 3: 198 µs per loop
In [215]: %timeit view_as_blocks(x,(x.shape[0],10,10))
10000 loops, best of 3: 85.4 µs per loop
2)unmake_blocks使用基于原始方法和基于transpose+reshape的方法-
2) unmake_blocks with original and transpose+reshape based approaches -
In [237]: A = [np.random.randint(0,9,(10,10)) for i in range(600)]
In [238]: d = 10
In [239]: m,n = 10*20,10*30
In [240]: %timeit unmake_blocks(A,d,m,n)
100 loops, best of 3: 2.03 ms per loop
In [241]: %timeit unmake_blocks_vectorized(A,d,m,n)
1000 loops, best of 3: 511 µs per loop
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