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)

So an update for people who might come looking here. Got the PyTorch pix2pix implementation to work with a few changes. I ended up doing all image pre-processing and post-processing outside the PyTorch implementation so that I just inserted my [-1,1] normalized images and got out [-1,1] normalized output images. You can probably do this inside the pix2pix code if you want.

In util.py the function tensor2im transforms a tensor to an image array BUT it also rescales your image since it assumes your images only have values from 0-255 which is usually not the case for tifs. So change the line image_numpy = (np.transpose(image_numpy, (1, 2, 0))+1) / 2.0 * 255.0 to image_numpy = (np.transpose(image_numpy, (1, 2, 0))) OR put in your own rescaling/post-processing. Also remove the lines

#if image_numpy.shape[0] == 1:  # grayscale to RGB
#    image_numpy = np.tile(image_numpy, (3, 1, 1))

I did the rescaling afterwards in a separate .py script which does the reverse of the normalization from @liuhh02

In the save_image function change #Original to #Changed

    # Original:
    # image_pil = Image.fromarray(image_numpy)
    # h, w, _ = image_numpy.shape

    # Changed
    image_numpy = np.squeeze(image_numpy) #Remove empty dimension
    image_pil = Image.fromarray(image_numpy, mode='F')
    h, w = image_numpy.shape
    image_path = image_path.replace(".png", ".tif") # Replace .png ending with .tif ending

In aligned_dataset.py your input image is put into a tensor and several transforms are applied to it.
Here you only want to change the way the image is loaded since you dont want it to convert into RGB.

AB = Image.open(AB_path).convert('RGB')
Changed to
AB = Image.open(AB_path)

Lastly in the base_dataset.py you want to change the function get_transform.
You want to remove if grayscale:. The Github page for the transforms.Grayscale is currently down while writing this, but if I remember correctly it tries to scale an [0,255] range image to [0,1] grayscale. And if you have already normalized to [-1,1] this transformation just reduced all your values to 0.

#if grayscale:
#       transform_list.append(transforms.Grayscale(1))

You also want to make changes to if convert:. If your images are already normalized you need to remove the normalization here. OR insert the normalization code from @liuhh02

    if convert:
        transform_list += [transforms.ToTensor()]
        if grayscale:
            transform_list += [transforms.Normalize((0.5,), (0.5,))]
        else:
            transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]

Should be changed to

    if convert:
        transform_list += [transforms.ToTensor()]

I think these were all the changes I made.

Best,
Johan

@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.