如何更快地将图像堆叠为Numpy数组？

Question

我经常需要把二维的numpy数组（比如tiff格式的图片）叠加在一起。为此，我通常先把它们放进一个列表里，然后用np.dstack来处理。这样做似乎是把图片叠成三维数组最快的方法。不过，有没有更快或者更省内存的方法呢？

from time import time
import numpy as np

# Create 100 images of the same dimention 256x512 (8-bit). 
# In reality, each image comes from a different file
img = np.random.randint(0,255,(256, 512, 100))

t0 = time()
temp = []
for n in range(100):
    temp.append(img[:,:,n])
stacked = np.dstack(temp)
#stacked = np.array(temp)  # much slower 3.5 s for 100

print time()-t0  # 0.58 s for 100 frames
print stacked.shape

# dstack in each loop is slower
t0 = time()
temp = img[:,:,0]
for n in range(1, 100):
    temp = np.dstack((temp, img[:,:,n]))
print time()-t0  # 3.13 s for 100 frames
print temp.shape

# counter-intuitive but preallocation is slightly slower
stacked = np.empty((256, 512, 100))
t0 = time()
for n in range(100):
    stacked[:,:,n] = img[:,:,n]
print time()-t0  # 0.651 s for 100 frames
print stacked.shape

# (Edit) As in the accepted answer, re-arranging axis to mainly use 
# the first axis to access data improved the speed significantly.
img = np.random.randint(0,255,(100, 256, 512))

stacked = np.empty((100, 256, 512))
t0 = time()
for n in range(100):
    stacked[n,:,:] = img[n,:,:]
print time()-t0  # 0.08 s for 100 frames
print stacked.shape

Answer 1

经过和otterb的一些共同努力，我们得出结论，预先分配数组是个好主意。显然，导致性能下降的瓶颈是数组的布局，其中图像编号（n）是变化最快的索引。如果我们把n放在数组的第一个索引位置（这会默认采用"C"的排列方式：第一个索引变化最慢，最后一个索引变化最快），这样可以获得最佳性能：

from time import time
import numpy as np

# Create 100 images of the same dimention 256x512 (8-bit). 
# In reality, each image comes from a different file
img = np.random.randint(0,255,(100, 256, 512))

# counter-intuitive but preallocation is slightly slower
stacked = np.empty((100, 256, 512))
t0 = time()
for n in range(100):
    stacked[n] = img[n]
print time()-t0  
print stacked.shape