我经常需要堆叠 2d numpy 数组(tiff 图像)。为此,我首先将它们附加到列表中并使用 np.dstack。这似乎是获得 3D 阵列堆叠图像的最快方法。但是,是否有更快/内存效率更高的方法?
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
最佳答案
在与 otterb 共同努力后,我们得出结论,预分配数组是可行的方法。显然,性能瓶颈是数组布局,其中图像编号 (n) 是变化最快的索引。如果我们将 n 作为数组的第一个索引(默认为“C”顺序:第一个索引最慢,最后一个索引变化最快)我们将获得最佳性能:
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
关于python - 将图像堆叠为 numpy 数组更快(比预分配)?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/23416888/