如何使用 matplotlib/numpy 将数组保存为灰度图像？

Question

I am trying to save a numpy array of dimensions 128x128 pixels into a grayscale image. I simply thought that the pyplot.imsave function would do the job but it's not, it somehow converts my array into an RGB image. I tried to force the colormap to Gray during conversion but eventhough the saved image appears in grayscale, it still has a 128x128x4 dimension. Here is a code sample I wrote to show the behaviour :

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mplimg
from matplotlib import cm

x_tot = 10e-3
nx = 128

x = np.arange(-x_tot/2, x_tot/2, x_tot/nx)

[X, Y] = np.meshgrid(x,x)
R = np.sqrt(X**2 + Y**2)

diam = 5e-3
I = np.exp(-2*(2*R/diam)**4)

plt.figure()
plt.imshow(I, extent = [-x_tot/2, x_tot/2, -x_tot/2, x_tot/2])

print I.shape

plt.imsave('image.png', I)
I2 = plt.imread('image.png')
print I2.shape

mplimg.imsave('image2.png',np.uint8(I), cmap = cm.gray)
testImg = plt.imread('image2.png')
print testImg.shape

In both cases the results of the "print" function are (128,128,4).

Can anyone explain why the imsave function is creating those dimensions eventhough my input array is of a luminance type? And of course, does anyone have a solution to save the array into a standard grayscale format?

Thanks!

Answer 1

With PIL it should work like this

import Image

I8 = (((I - I.min()) / (I.max() - I.min())) * 255.9).astype(np.uint8)

img = Image.fromarray(I8)
img.save("file.png")

Answer 2

There is also an alternative of using imageio . It provides an easy and convenient API and it is bundled with Anaconda. It can save grayscale images as a single color channel file.

Quoting the documentation

>>> import imageio
>>> im = imageio.imread('imageio:astronaut.png')
>>> im.shape  # im is a numpy array
(512, 512, 3)
>>> imageio.imwrite('astronaut-gray.jpg', im[:, :, 0])

Answer 3

I didn't want to use PIL in my code and as noted in the question I ran into the same problem with pyplot, where even in grayscale, the file is saved in MxNx3 matrix.

Since the actual image on disk wasn't important to me, I ended up writing the matrix as is and reading it back "as-is" using numpy's save and load methods:

np.save("filename", image_matrix)

And:

np.load("filename.npy")

Answer 4

There is also a possibility to use scikit-image , then there is no need to convert numpy array into a PIL object.

from skimage import io
io.imsave('output.tiff', I.astype(np.uint16))