在保留分辨率的同时保存类似imshow的图像 [英] Saving an imshow-like image while preserving resolution

问题描述

我有一个一直用matplotlib.pyplot.imshow可视化的(n，m)数组.我想将此数据保存在某种类型的光栅图形文件(例如png)中，以便:

I have an (n, m) array that I've been visualizing with matplotlib.pyplot.imshow. I'd like to save this data in some type of raster graphics file (e.g. a png) so that:

1. 颜色是用imshow
显示的颜色
2. 基础数组的每个元素恰好是保存的图像中的一个像素-意味着如果基础数组是(n，m)个元素，则图像为NxM像素. (我对imshow中的interpolation='nearest'不感兴趣.)
3. 在保存的图像中，除了与数组中的数据相对应的像素以外，没有任何其他内容. (即，边缘，轴等周围没有空格)

1. The colors are the ones shown with imshow
2. Each element of the underlying array is exactly one pixel in the saved image -- meaning that if the underlying array is (n, m) elements, the image is NxM pixels. (I'm not interested in interpolation='nearest' in imshow.)
3. There is nothing in the saved image except for the pixels corresponding to the data in the array. (I.e. there's no white space around the edges, axes, etc.)

我该怎么做?

我已经看到一些代码可以通过使用interpolation='nearest'并强制matplotlib(勉强地)关闭轴，空格等来实现此目的.但是，必须有某种方法可以更直接地做到这一点-也许与PIL?毕竟，我有基础数据.如果可以为基础数组的每个元素获取RGB值，则可以使用PIL保存它.有什么方法可以从imshow中提取RGB数据吗?我可以编写自己的代码以将数组值映射为RGB值，但是我不想重新发明轮子，因为该功能已经存在于matplotlib中.

I've seen some code that can kind of do this by using interpolation='nearest' and forcing matplotlib to (grudgingly) turn off axes, whitespace, etc. However, there must be some way to do this more directly -- maybe with PIL? After all, I have the underlying data. If I can get an RGB value for each element of the underlying array, then I can save it with PIL. Is there some way to extract the RGB data from imshow? I can write my own code to map the array values to RGB values, but I don't want to reinvent the wheel, since that functionality already exists in matplotlib.

推荐答案

您已经猜到不需要创建图形了.您基本上需要三个步骤.标准化您的数据，应用颜色图，保存图像. matplotlib提供所有必要的功能:

As you already guessed there is no need to create a figure. You basically need three steps. Normalize your data, apply the colormap, save the image. matplotlib provides all the necessary functionality:

import numpy as np
import matplotlib.pyplot as plt

# some data (512x512)
import scipy.misc
data = scipy.misc.lena()

# a colormap and a normalization instance
cmap = plt.cm.jet
norm = plt.Normalize(vmin=data.min(), vmax=data.max())

# map the normalized data to colors
# image is now RGBA (512x512x4) 
image = cmap(norm(data))

# save the image
plt.imsave('test.png', image)

虽然上面的代码解释了单个步骤，但是您也可以让imsave执行所有三个步骤(类似于imshow):

While the code above explains the single steps, you can also let imsave do all three steps (similar to imshow):

plt.imsave('test.png', data, cmap=cmap)

结果(test.png):

Result (test.png):

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