Go over a folder of images and reduce the quality and/or add noise to some of them (Useful for making a Pix2Pix model more resilient)

crapify_images.py

# crapify_images.py
# Go over a folder of images and reduce the quality and/or add noise to some of them (Useful for making a Pix2Pix model more resilient) 
# @Norod78

import skimage
import skimage.io
import skimage.io._plugins.pil_plugin as pp
import numpy as np

from PIL import Image
import glob
import os

number_of_gen_cycles_for_noise = 10
number_of_gen_cycles_for_resize = 9
index = 0

image_output_size = (1024, 1024)

input_folder= "./train_data/ffhq"
#input_folder= "./test_data/ffhq"
input_image_files = glob.glob(os.path.join(input_folder, "*.jpg"))
for input_image_file in input_image_files:
    index = index + 1
    imagename_w_ext = os.path.basename(input_image_file)
    image_name, image_extension = os.path.splitext(imagename_w_ext)
    if ((index % number_of_gen_cycles_for_resize) == 0):
        inputImage = Image.open(input_image_file)
        im96x96 = inputImage.resize((96, 96), resample=Image.NEAREST)
        inputImage = im96x96.resize(image_output_size, resample=Image.NEAREST)
        inputImage.save(input_image_file)
        print("Crapified (resize) \"" + image_name + "\"")
    if ((index % number_of_gen_cycles_for_noise) == 0):
        inputImage = Image.open(input_image_file)
        inputImage = pp.ndarray_to_pil(skimage.util.random_noise(pp.pil_to_ndarray(inputImage), mode="poisson"))
        inputImage.save(input_image_file)
        print("Crapified (poisson noise) \"" + image_name + "\"")