替换 Python NumPy 数组中大于某个值的所有元素 [英] Replace all elements of Python NumPy Array that are greater than some value

问题描述

我有一个 2D NumPy 数组，我想用 255.0.0 替换其中大于或等于阈值 T 的所有值.据我所知，最基本的方法是:

I have a 2D NumPy array and would like to replace all values in it greater than or equal to a threshold T with 255.0. To my knowledge, the most fundamental way would be:

shape = arr.shape
result = np.zeros(shape)
for x in range(0, shape[0]):
    for y in range(0, shape[1]):
        if arr[x, y] >= T:
            result[x, y] = 255

1. 最简洁和 Pythonic 的方法是什么?

1. What is the most concise and pythonic way to do this?

有没有更快(可能不那么简洁和/或不那么 Pythonic)的方法来做到这一点?

Is there a faster (possibly less concise and/or less pythonic) way to do this?

这将是用于人体头部 MRI 扫描的窗口/水平调整子程序的一部分.二维 numpy 数组是图像像素数据.

This will be part of a window/level adjustment subroutine for MRI scans of the human head. The 2D numpy array is the image pixel data.

推荐答案

我认为最快和最简洁的方法是使用 NumPy 的内置 Fancy 索引.如果您有一个名为 arr 的 ndarray，则可以将所有元素 >255 替换为值 x，如下所示:

I think both the fastest and most concise way to do this is to use NumPy's built-in Fancy indexing. If you have an ndarray named arr, you can replace all elements >255 with a value x as follows:

arr[arr > 255] = x

我在我的机器上用一个 500 x 500 的随机矩阵运行它，用 5 替换所有 >0.5 的值，平均耗时 7.59 毫秒.

I ran this on my machine with a 500 x 500 random matrix, replacing all values >0.5 with 5, and it took an average of 7.59ms.

In [1]: import numpy as np
In [2]: A = np.random.rand(500, 500)
In [3]: timeit A[A > 0.5] = 5
100 loops, best of 3: 7.59 ms per loop

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