有没有一种方便的方法可以将查找表应用于 numpy 中的大型数组? [英] Is there a convenient way to apply a lookup table to a large array in numpy?

问题描述

我已将一张图像读入 numpy，结果数组中有相当多的像素.

I’ve got an image read into numpy with quite a few pixels in my resulting array.

我计算了一个包含 256 个值的查找表.现在我想做以下事情:

I calculated a lookup table with 256 values. Now I want to do the following:

for i in image.rows:
    for j in image.cols:
        mapped_image[i,j] = lut[image[i,j]]

是的，这基本上就是 lut 所做的.
唯一的问题是:我想高效地完成它，在 python 中调用该循环会让我等待几秒钟才能完成.

Yep, that’s basically what a lut does.
Only problem is: I want to do it efficient and calling that loop in python will have me waiting for some seconds for it to finish.

我知道numpy.vectorize()，它只是一个调用相同python代码的便捷函数.

I know of numpy.vectorize(), it’s simply a convenience function that calls the same python code.

推荐答案

如果 lut 是一维.
这是在 NumPy 中建立索引的入门:
http://www.scipy.org/Tentative_NumPy_Tutorial8604c88c88608c8c8c8c8c8c8c8c8c8c8c8c8c8c8c8c8c8c5c8c4d

You can just use image to index into lut if lut is 1D.
Here's a starter on indexing in NumPy:
http://www.scipy.org/Tentative_NumPy_Tutorial#head-864862d3f2bb4c32f04260fac61eb4ef34788c4c

In [54]: lut = np.arange(10) * 10

In [55]: img = np.random.randint(0,9,size=(3,3))

In [56]: lut
Out[56]: array([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])

In [57]: img
Out[57]: 
array([[2, 2, 4],
       [1, 3, 0],
       [4, 3, 1]])

In [58]: lut[img]
Out[58]: 
array([[20, 20, 40],
       [10, 30,  0],
       [40, 30, 10]])

请注意索引从 0

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