Image preprocessing methodology

The first and foremost step towards implementation of such a network is to preprocess the data or images in this case. The following code snippet shows some quick utilities to preprocess and postprocess images for size and channel adjustments:

import numpy as np
from keras.applications import vgg16
from keras.preprocessing.image import load_img, img_to_array


def preprocess_image(image_path, height=None, width=None):
    height = 400 if not height else height
    width = width if width else int(width * height / height)
    img = load_img(image_path, target_size=(height, width))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg16.preprocess_input(img)
    return img

def deprocess_image(x):
    # Remove zero-center by mean pixel
    x[:, :, 0] += 103.939
    x[:, :, 1] += 116.779
    x[:, :, 2] += 123.68
    # 'BGR'->&apos...