def show(image_index, threshold=0.000000001):
    imgs = [trans(Image.open(image_list[image_index]))]
    for i in range(1, 4):
        aged_face = trans(Image.open('FaceAging/evaluation/imgs/0-{}-morph-ProgressiveGAN-{}.png'.format(i, image_names[image_index])))
        if i == 1:
            j = 0
        elif i == 2:
            j = 1
        elif i == 3:
            j = 3
        aging_effct = aged_face - imgs[j]
        aging_effct[torch.abs(aging_effct) < threshold] = 0
#         aging_effct = torch.mean(aging_effct, dim=0).unsqueeze(0).repeat(3, 1, 1)
        aging_effct = torch.mean(aging_effct, dim=0)
#         aging_effct = torch.mean(torch.abs(aging_effct), dim=0)
#         aging_effct[aging_effct < threshold] = 0
        aging_effct = aging_effct.numpy()
#         pmin = np.min(aging_effct)
#         pmax = np.max(aging_effct)
#         aging_effct = ((aging_effct - pmin) / (pmax - pmin))
        aging_effct = Image.fromarray(np.uint8(cm.Greys(aging_effct) * 255)).convert('RGB')
        aging_effct = trans(aging_effct)
        aging_effct = aging_effct.float()
        imgs.extend([aged_face, aging_effct])
    imgs = torchvision.utils.make_grid(imgs, nrow=7, pad_value=1, padding=0)
    imgs = torchvision.transforms.ToPILImage()(imgs)
    return imgs