spaces/1-13-am/neural-style-transfer/utils.py

from skimage.exposure import match_histograms
from skimage import io
import os
from PIL import Image
import torch
import torchvision
import torchvision.transforms as transforms

def normalize():
  MEAN = [0.485, 0.456, 0.406]
  STD = [0.229, 0.224, 0.225]
  return transforms.Normalize(mean = MEAN, std = STD)

def denormalize():
  # out = (x - mean) / std
  MEAN = [0.485, 0.456, 0.406]
  STD = [0.229, 0.224, 0.225]
  MEAN = [-mean/std for mean, std in zip(MEAN, STD)]
  STD = [1/std for std in STD]
  return transforms.Normalize(mean=MEAN, std=STD)

def transformer(imsize = None, cropsize = None):
  transformer = []
  if imsize:
        transformer.append(transforms.Resize(imsize))
  if cropsize:
        transformer.append(transforms.RandomCrop(cropsize))

  transformer.append(transforms.ToTensor())
  transformer.append(normalize())
  return transforms.Compose(transformer)

def load_img(path, imsize = None, cropsize = None):
  transform = transformer(imsize = imsize, cropsize = cropsize)
  # torchvision.transforms supports PIL Images
  return transform(Image.open(path).convert("RGB")).unsqueeze(0)

def tensor_to_img(tensor):
  denormalizer = denormalize()
  if tensor.device == "cuda":
    tensor = tensor.cpu()
  #
  tensor = torchvision.utils.make_grid(denormalizer(tensor.squeeze()))
  image = transforms.functional.to_pil_image(tensor.clamp_(0., 1.))
  return image

def save_img(tensor, path):
  pass

def histogram_matching(image, reference):
  """
  img: style image
  reference: original img
  output: style image that resembles original img's color histogram
  """
  device = image.device
  reference = reference.cpu().permute(1, 2, 0).numpy()
  image = image.cpu().permute(1, 2, 0).numpy()
  output = match_histograms(image, reference, multichannel = True)
  return torch.Tensor(output).permute(2, 0, 1).to(device)

def batch_histogram_matching(images, reference):
  """
  images of shape BxCxHxW
  reference of shape 1xCxHxW
  """
  reference = reference.squeeze()
  output = torch.zeros_like(images, dtype = images.dtype)
  B = images.shape[0]
  for i in range(B):
    output[i] = histogram_matching(images[i], reference)
  return output

def statistics(f, inverse = False, eps = 1e-10):
  c, h, w = f.shape
  f_mean = torch.mean(f.view(c, h*w), dim=1, keepdim=True)
  f_zeromean = f.view(c, h*w) - f_mean
  f_cov = torch.mm(f_zeromean, f_zeromean.t())

  u, s, v = torch.svd(f_cov)

  k = c
  for i in range(c):
    if s[i] < eps:
        k = i
        break
  if inverse:
        p = -0.5
  else:
        p = 0.5

  f_covsqrt = torch.mm(torch.mm(u[:, 0:k], torch.diag(s[0:k].pow(p))), v[:, 0:k].t())
  return f_mean, f_covsqrt

def whitening(f):
  c, h, w = f.shape
  f_mean, f_inv_covsqrt = statistics(f, inverse = True)
  whitened_f = torch.mm(f_inv_covsqrt, f.view(c, h*w) - f_mean)
  return whitened_f.view(c, h, w)

def batch_whitening(f):
  b, c, h, w = f.shape
  whitened_f = torch.zeros(size = (b, c, h, w), dtype = f.dtype, device = f.device)
  for i in range(b):
    whitened_f[i] = whitening(f[i])
  return whitened_f

def coloring(style, content):
  s_c, s_h, s_w = style.shape
  c_mean, c_covsqrt = statistics(content, inverse = False)
  colored_s = torch.mm(c_covsqrt, whitening(style).view(s_c, s_h * s_w)) + c_mean
  return colored_s.view(s_c, s_h, s_w)

def batch_coloring(styles, content):
  colored_styles = torch.zeros_like(styles, dtype = styles.dtype, device = styles.device)
  for i, style in enumerate(styles):
    colored_styles[i] = coloring(style, content[i])

  return colored_styles

def batch_wct(styles, content):
  whitened_styles = batch_whitening(styles)
  return batch_coloring(whitened_styles, content)

class Image_Set(torch.utils.data.Dataset):
  def __init__(self, root_path, imsize, cropsize):
    super(Image_Set, self).__init__()
    self.root_path = root_path
    self.files = sorted(os.listdir(self.root_path))
    self.transformer = transformer(imsize, cropsize)

  def __len__(self):
    return len(self.file_names)

  def __getitem__(self, index):
    image = Image.open(os.path.join(self.root_path + self.file_names[index])).convert("RGB")
    return self.transformer(image)