我想编写一个可以拍摄小图像并以块为单位返回它们的排列的函数。
基本上我想把这个转过来:
对此:
Is there a function in Python that shuffle data by data blocks?中有一个很好的答案,它帮助我编写了一个解决方案。但是,对于约50,000张28x28图像,这需要很长时间才能运行。
# blocks of 7x7 shuffling
range1 = np.arange(4)
range2 = np.arange(4)
block_size = int(28 / 4)
print([[x[i*block_size:(i+1)*block_size].shape] for i in range1])
for x in x1:
np.random.shuffle(range1)
x[:] = np.block([[x[i*block_size:(i+1)*block_size]] for i in range1])
for a in x:
np.random.shuffle(range2)
a[:] = np.block([a[i*block_size:(i+1)*block_size] for i in range2])
print("x1", time.time() - begin)
begin = time.time()
答案 0 :(得分:2)
这是一种基于this post
的方法-
def randomize_tiles_3D(x1, H, W):
# W,H are width and height of blocks
m,n,p = x1.shape
l1,l2 = n//H,p//W
combs = np.random.rand(m,l1*l2).argsort(axis=1)
r,c = np.unravel_index(combs,(l1,l2))
x1cr = x1.reshape(-1,l1,H,l2,W)
out = x1cr[np.arange(m)[:,None],r,:,c]
return out.reshape(-1,l1,l2,H,W).swapaxes(2,3).reshape(-1,n,p)
样品运行-
In [46]: x1
Out[46]:
array([[[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11],
[12, 13, 14, 15, 16, 17],
[18, 19, 20, 21, 22, 23],
[24, 25, 26, 27, 28, 29],
[30, 31, 32, 33, 34, 35]],
[[36, 37, 38, 39, 40, 41],
[42, 43, 44, 45, 46, 47],
[48, 49, 50, 51, 52, 53],
[54, 55, 56, 57, 58, 59],
[60, 61, 62, 63, 64, 65],
[66, 67, 68, 69, 70, 71]]])
In [47]: np.random.seed(0)
In [48]: randomize_tiles_3D(x1, H=3, W=3)
Out[48]:
array([[[21, 22, 23, 0, 1, 2],
[27, 28, 29, 6, 7, 8],
[33, 34, 35, 12, 13, 14],
[18, 19, 20, 3, 4, 5],
[24, 25, 26, 9, 10, 11],
[30, 31, 32, 15, 16, 17]],
[[36, 37, 38, 54, 55, 56],
[42, 43, 44, 60, 61, 62],
[48, 49, 50, 66, 67, 68],
[39, 40, 41, 57, 58, 59],
[45, 46, 47, 63, 64, 65],
[51, 52, 53, 69, 70, 71]]])
答案 1 :(得分:0)
我已经找到了运行速度更快的解决方案。我觉得很傻,因为我真的不需要double for循环,只需要两个单独的shuffle索引。万一有人想以numpy的方式按块随机播放图像,请将此解决方案留在这里。
如果有人想出另一个好的解决方案,请告诉我。
# blocks of 7x7 shuffling
range1 = np.arange(4)
range2 = np.arange(4)
block_size = int(28 / 4)
for x in x1:
np.random.shuffle(range1)
np.random.shuffle(range2)
x[:] = np.block([[x[i*block_size:(i+1)*block_size]] for i in range1])
x[:] = np.block([x[:,i*block_size:(i+1)*block_size] for i in range2])