Turning A Large Matrix Into A Grayscale Image

I have a NumPy array of 3,076,568 binary values (1s and 0s). I would like to convert this to a matrix, and then to a grayscale image in Python. However, when I try to reshape the a

Solution 1:

Your array of "binary values" is an array of bytes?

If so, you can do (using Pillow) after resizing it:

from PIL importImageim= Image.fromarray(arr)

And then im.show() to see it.

If your array has only 0's and 1's (1-bit depth or b/w) you may have to multiply it to 255

im = Image.fromarray(arr * 255)

Here an example:

>>>arr = numpy.random.randint(0,256, 100*100) #example of a 1-D array>>>arr.resize((100,100))>>>im = Image.fromarray(arr)>>>im.show()

Edit (2018):

This question was written in 2011 and Pillow changed ever since requiring to use the mode='L' parameter when loading with fromarray.

Also on comments below it was said arr.astype(np.uint8) was needed as well, but I have not tested it

Solution 2:

Using PIL is not really needed, you can plot the array directly with pyplot (see below). To save to a file, you could use plt.imsave('fname.png', im).

Code below.

import numpy as np
import matplotlib.pyplot as plt

x = (np.random.rand(1754**2) < 0.5).astype(int)

im = x.reshape(1754, 1754)
plt.gray()
plt.imshow(im)

You can also use plt.show(im) to display image in new window.

Solution 3:

You can do so with scipy.misc.toimage and im.save("foobar.png"):

#!/usr/bin/env python# your data is "array" - I just made this for testing
width, height = 512, 100
import numpy as np
array = (np.random.rand(width*height) < 0.5).astype(int)
array = array.reshape(height, width)

# what you needfrom scipy.misc import toimage

im = toimage(array)
im.save("foobar.png")

which gives

Solution 4:

If you have as example a txt file in your PC with some data (an image), in order to visualize such data as gray scale image you can use this:

with open("example.txt", "r") as f:
data = [i.strip("\n").split() for i in f.readlines()]
data1 = np.array(data, dtype=float)
plt.figure(1)
plt.gray()
plt.imshow(data1)
plt.show()