Function to scale any image to the pixel values of [-1, 1] for GAN input

scale_images.py

"""
scale_images.py
Function to scale any image to the pixel values of [-1, 1] for GAN input.

Author: liuhh02 https://machinelearningtutorials.weebly.com/
"""
from PIL import Image
import numpy as np
from os import listdir

def normalize(arr):
    ''' Function to scale an input array to [-1, 1] '''
    arr_min = arr.min()
    arr_max = arr.max()
    # Check the original min and max values
    print('Min: %.3f, Max: %.3f' % (arr_min, arr_max))
	arr_range = arr_max - arr_min
	scaled = np.array((arr-arr_min) / float(arr_range), dtype='f')
	arr_new = -1 + (scaled * 2)
    # Make sure min value is -1 and max value is 1
    print('Min: %.3f, Max: %.3f' % (arr_new.min(), arr_new.max()))
    return arr_new

# path to folder containing images
path = './directory/to/image/folder/'

# loop through all files in the directory
for filename in listdir(path):
	# load image
	image = Image.open(path + filename)
	# convert to numpy array
	image = np.array(image)
	# scale to [-1,1]
	image = normalize(image)

@JohanRaniseth Amazing work! Thank you for sharing it here :)

Hello @liuhh02, would it be possible to identify in your code what parameters I would need to change should I require a different scale of values other than [-1 1].

Hello @liuhh02, would it be possible to identify in your code what parameters I would need to change should I require a different scale of values other than [-1 1].

Sure! This line of code scales the values to [0, 1]:
scaled = np.array((arr-arr_min) / float(arr_range), dtype='f')

So you may modify
arr_new = -1 + (scaled * 2)
accordingly to scale it to the range you require.

@liuhh02 Nice approach, thank you for your input

Thanks to @liuhh02 and @JohanRaniseth for the work. For anyone visiting in the future: you have to normalize based on the min and max of your whole (training) dataset, not every image individually like in the code provided above (see #950). If you normalize individually, you will lose information and be unable to reverse the process later.

I also had to make another change to the tensor2im function in util.py by changing this line to just return image_numpy.