Extract images from .idx3-ubyte file or GZIP via Python

Question

I have created a simple function for facerecognition by using the facerecognizer from OpenCV. It works all fine with images from people.

Now I would like to make a test by using handwritten characters instead of people. I came across MNIST dataset, but they store images in a weird file which I have never seen before.

I simply need to extract a few images from:

train-images.idx3-ubyte

and save them in a folder as .gif

Or am I missunderstand this MNIST thing. If yes where could I get such a dataset?

EDIT

I also have the gzip file:

train-images-idx3-ubyte.gz

I am trying to read the content, but show() does not work and if I read() I see random symbols.

images = gzip.open("train-images-idx3-ubyte.gz", 'rb')
print images.read()

EDIT

Managed to get some usefull output by using:

with gzip.open('train-images-idx3-ubyte.gz','r') as fin:
    for line in fin:
        print('got line', line)

Somehow I have to convert this now to an image, output:

Answer 1

Download the training/test images and labels:

• train-images-idx3-ubyte.gz: training set images
• train-labels-idx1-ubyte.gz: training set labels
• t10k-images-idx3-ubyte.gz: test set images
• t10k-labels-idx1-ubyte.gz: test set labels

And uncompress them in a workdir, say samples/.

Get the python-mnist package from PyPi:

pip install python-mnist

Import the mnist package and read the training/test images:

from mnist import MNIST

mndata = MNIST('samples')

images, labels = mndata.load_training()
# or
images, labels = mndata.load_testing()

To display an image to the console:

index = random.randrange(0, len(images))  # choose an index ;-)
print(mndata.display(images[index]))

You'll get something like this:

............................
............................
............................
............................
............................
.................@@.........
..............@@@@@.........
............@@@@............
..........@@................
..........@.................
...........@................
...........@................
...........@...@............
...........@@@@@.@..........
...........@@@...@@.........
...........@@.....@.........
..................@.........
..................@@........
..................@@........
..................@.........
.................@@.........
...........@.....@..........
...........@....@@..........
............@@@@............
.............@..............
............................
............................
............................

Explanation:

• Each image of the images list is a Python list of unsigned bytes.
• The labels is an Python array of unsigned bytes.