How to convert image into array?

Question

When I import the keras dataset mnist, I get x_train elements like the following:

from keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()

How can I take any image and force it into these same dimensions (28x28) with grayscale intensity?

I've tried

from PIL import Image
from numpy import asarray
img = Image. open(r'<my_dir>\test.jpg')
resized_img = img. resize((28,28))
x = asarray(resized_img)
x

But that doesn't appears to get the shape (28, 28, 3) and I'm looking for a shape (28,28).

Answer 1

I was able to use the following code to accomplish this:

from PIL import ImageFilter
from PIL import Image
import matplotlib.pyplot as plt

def imageprepare(argv):
    im = Image.open(argv).convert('L')
    width = float(im.size[0])
    height = float(im.size[1])
    newImage = Image.new('L',(28,28),(255))
    
    if width > height:
        nheight = int(round((28.0 / width * height),0))
        if (nheight == 0):
            nheight = 1
        img = im.resize((28,28), Image.ANTIALIAS).filter(ImageFilter.SHRPEN)
        wtop = 0
        newImage.paste(img,(0,wtop))
    else:
        nwidth = int(round((28.0/height*width),0))
        if (nwidth == 0):
            nwidth = 1
        img = im.resize((28,28), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
        wleft = 0
        newImage.paste(img,(wleft,0))
    tv = list(newImage.getdata())
    
    tva = tv #[(255 - x) * 1.0 / 255.0 for x in tv]
    return tva

x=[imageprepare(r'<my_dir>\test.jpg')]

newArr=[[0 for d in range(28)] for y in range(28)]
k = 0
for i in range(28):
    for j in range(28):
        newArr[i][j]=x[0][k]
        k = k+1
        
plt.imshow(newArr,interpolation='nearest')
plt.show()