"""
this module converts an image into a (1, 784) numpy array
"""

import numpy as np
from PIL import Image, ImageFilter


def image_prepare(argv):
    """
    This function returns the pixel values.
    The input is a png file location.
    :param argv: image
    :return: list of pixel values
    """

    im = Image.open(argv).convert('L')
    width = float(im.size[0])
    height = float(im.size[1])
    newImage = Image.new('L', (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. Height 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))  # calculate vertical position
        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


image3 = np.asarray(image_prepare('../digit3.png')).reshape(784, 1)  # file path here


