我正在尝试使用python和numpy计算一个特征计数矩阵的summed area table。目前我正在使用以下代码:
def summed_area_table(img):
table = np.zeros_like(img).astype(int)
for row in range(img.shape[0]):
for col in range(img.shape[1]):
if (row > 0) and (col > 0):
table[row, col] = (img[row, col] +
table[row, col - 1] +
table[row - 1, col] -
table[row - 1, col - 1])
elif row > 0:
table[row, col] = img[row, col] + table[row - 1, col]
elif col > 0:
table[row, col] = img[row, col] + table[row, col - 1]
else:
table[row, col] = img[row, col]
return table
上述代码大约需要35秒才能在3200 x 1400阵列上执行计算。有没有办法使用Numpy技巧来加速计算?我意识到基本的速度问题在于嵌套的python循环,但我不知道如何避免它们。
答案 0 :(得分:7)
累积金额有NumPy函数cumsum
。将它应用两次会产生所需的表格:
import numpy as np
A = np.random.randint(0, 10, (3, 4))
print A
print A.cumsum(axis=0).cumsum(axis=1)
输出:
[[7 4 7 2]
[6 9 9 5]
[6 6 7 6]]
[[ 7 11 18 20]
[13 26 42 49]
[19 38 61 74]]
效果分析:(https://stackoverflow.com/a/25351344/3419103)
import numpy as np
import time
A = np.random.randint(0, 10, (3200, 1400))
t = time.time()
S = A.cumsum(axis=0).cumsum(axis=1)
print np.round_(time.time() - t, 3), 'sec elapsed'
输出:
0.15 sec elapsed