input_images.py

# Output folder for the images.
OUTPUT_DIR = 'output/'
# Style image to use.
STYLE_IMAGE = 'images/muse.jpg'
# Content image to use.
CONTENT_IMAGE = 'images/trojan_shrine.jpg'
# Image dimensions constants. 
IMAGE_WIDTH = 640
IMAGE_HEIGHT = 480
COLOR_CHANNELS = 3

def load_image(path):
    image_raw = scipy.misc.imread(path)
    # Resize the image for convnet input and add an extra dimension
    image_raw = scipy.misc.imresize(image_raw, (IMAGE_HEIGHT, IMAGE_WIDTH))
    # Input to the VGG model expects the mean to be subtracted.
    image = (image_raw - MEAN_VALUES)

    return [image_raw, image]

def recover_image(image):
    image_raw = image + MEAN_VALUES
    image_raw = np.clip(image_raw, 0, 255).astype('uint8')

    return image_raw

def save_image(path, image):
    # Output should add back the mean.
    image = recover_image(image)
    scipy.misc.imsave(path, image)