如何将dtype = float的numpy NxM数组显示为纯灰度图像? [英] How to show numpy NxM array with dtype=float as plain gray scale image?

问题描述

使用dtype=float创建numpy数组时，使用matplotlib.pyplot.imshow的表示方法似乎取决于这些值，因此0.50的值不仅是50％的灰度.

When creating an numpy array with dtype=float, the the presentation method using matplotlib.pyplot.imshow appears to be dependent on the values, so a value of 0.50 is not just 50% gray.

使用此代码模板:

import numpy as np
import matplotlib.pyplot as plt
img = np.empty([2, 2])
img[:] = {image}
plt.imshow(img, cmap='gray')
plt.show()

然后图像[[1.0, 1.0], [0.0, 0.5]]显示为:

但是图像[[1.0, 1.0], [1.0, 0.5]](仅将0.0更改为1.0)显示为:

But the image [[1.0, 1.0], [1.0, 0.5]], where only 0.0 is changed to 1.0, shows as:

我本来希望在两种情况下0.5(右下角)都显示为50％的灰色，但是由于某种原因，表示方法发生了变化，所以0.5的解释不同，具体取决于数组中其他元素的值.

I had expected that the 0.5 (at lower right) would show as 50% gray in both cases, but for some reason the presentation method changes, so 0.5 is interpreted differently, depending on the value of other elements in the array.

那么，如何将dtype = float的numpy NxM数组显示为纯灰度图像?

So, how to show numpy NxM array with dtype=float as plain gray scale image?

推荐答案

您必须确定色标的限制:

You have to fix the limits of the color-scale:

plt.imshow(img, cmap='gray',clim=(0,1))

要对正在发生的事情有一个很好的感觉，您可以包括一个色条，该色条可视化颜色和数值之间的转换；例如使用以下代码:

To get a good feeling of what is going on you could include a colorbar which visualizes the conversion between colors and numerical values; for example using the following code:

fig,ax = plt.subplots()
cax = plt.imshow(img, cmap='gray')
cbar = fig.colorbar(cax)
plt.show()

立即针对两个示例执行此操作，可以清楚地看到matplotlib.pyplot将色标的范围更新为数据.因此，两种情况下颜色和数值之间的转换是不同的.

Doing this for the two examples immediately makes clear that matplotlib.pyplot updates the range of the color-scale to the data. Consequently the conversion betweens colors and numerical values is different for the two cases.

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