%tensorflow_version 1.x
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
tf.reset_default_graph()
from PIL import Image, ImageFilter
def imageprepare(argv):
"""
This function returns the pixel values.
The imput is a png file location.
"""
im = Image.open(argv).convert(r'C:\Users\DELL\Desktop\devnagari digits\1.png')
width = float(im.size[0])
height = float(im.size[1])
newImage = Image.new(r'C:\Users\DELL\Desktop\devnagari digits\1.png', (28, 28), (255)) # creates white canvas of 28x28 pixels
if width > height: # check which dimension is bigger
# Width is bigger. Width becomes 20 pixels.
nheight = int(round((20.0 / width * height), 0)) # resize height according to ratio width
if (nheight == 0): # rare case but minimum is 1 pixel
nheight = 1
# resize and sharpen
img = im.resize((20, nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wtop = int(round(((28 - nheight) / 2), 0)) # calculate horizontal position
newImage.paste(img, (4, wtop)) # paste resized image on white canvas
else:
# Height is bigger. Heigth becomes 20 pixels.
nwidth = int(round((20.0 / height * width), 0)) # resize width according to ratio height
if (nwidth == 0): # rare case but minimum is 1 pixel
nwidth = 1
# resize and sharpen
img = im.resize((nwidth, 20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)
wleft = int(round(((28 - nwidth) / 2), 0)) # caculate vertical pozition
newImage.paste(img, (wleft, 4)) # paste resized image on white canvas
# newImage.save("sample.png
tv = list(newImage.getdata()) # get pixel values
# normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
tva = [(255 - x) * 1.0 / 255.0 for x in tv]
print(tva)
return tva
x=imageprepare(r'C:\Users\DELL\Desktop\devnagari digits\2.png')
print(len(x))# mnist IMAGES are 28x28=784 pixels