如何将 NumPy 数组规范化到某个范围内? [英] How to normalize a NumPy array to within a certain range?

问题描述

对音频或图像数组进行一些处理后，需要在一个范围内进行归一化，然后才能将其写回文件.可以这样做:

After doing some processing on an audio or image array, it needs to be normalized within a range before it can be written back to a file. This can be done like so:

# Normalize audio channels to between -1.0 and +1.0
audio[:,0] = audio[:,0]/abs(audio[:,0]).max()
audio[:,1] = audio[:,1]/abs(audio[:,1]).max()

# Normalize image to between 0 and 255
image = image/(image.max()/255.0)

有没有一种不那么冗长、方便的函数方式来做到这一点?matplotlib.colors.Normalize() 似乎没有关系.

Is there a less verbose, convenience function way to do this? matplotlib.colors.Normalize() doesn't seem to be related.

推荐答案

audio /= np.max(np.abs(audio),axis=0)
image *= (255.0/image.max())

使用 /= 和 *= 可以消除中间临时数组，从而节省一些内存.乘法比除法便宜，所以

Using /= and *= allows you to eliminate an intermediate temporary array, thus saving some memory. Multiplication is less expensive than division, so

image *= 255.0/image.max()    # Uses 1 division and image.size multiplications

比

image /= image.max()/255.0    # Uses 1+image.size divisions

由于我们在这里使用基本的 numpy 方法，我认为这是 numpy 中最有效的解决方案.

Since we are using basic numpy methods here, I think this is about as efficient a solution in numpy as can be.

就地操作不会改变容器数组的数据类型.由于所需的归一化值是浮点数，audio 和 image 数组在执行就地操作之前需要具有浮点数据类型.如果它们还不是浮点 dtype，则需要使用 astype 转换它们.例如，

In-place operations do not change the dtype of the container array. Since the desired normalized values are floats, the audio and image arrays need to have floating-point point dtype before the in-place operations are performed. If they are not already of floating-point dtype, you'll need to convert them using astype. For example,

image = image.astype('float64')

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