# ------------------------------------------------------------------------------
# Copyright (c) Tencent
# Licensed under the GPLv3 License.
# Created by Kai Ma (makai0324@gmail.com)
# ------------------------------------------------------------------------------

import torch
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp


def gaussian(window_size, sigma):
  gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
  return gauss / gauss.sum()


def create_window(window_size, channel):
  _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
  _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
  window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
  return window


def _ssim(img1, img2, window, window_size, channel, size_average=True):
  mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
  mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

  mu1_sq = mu1.pow(2)
  mu2_sq = mu2.pow(2)
  mu1_mu2 = mu1 * mu2

  sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
  sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
  sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2

  C1 = 0.01 ** 2
  C2 = 0.03 ** 2

  ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))

  if size_average:
    return ssim_map.mean()
  else:
    return ssim_map.mean(1).mean(1).mean(1)


class SSIM(torch.nn.Module):
  def __init__(self, window_size=11, size_average=True):
    super(SSIM, self).__init__()
    self.window_size = window_size
    self.size_average = size_average
    self.channel = 1
    self.window = create_window(window_size, self.channel)

  def forward(self, img1, img2):
    (_, channel, _, _) = img1.size()

    if channel == self.channel and self.window.data.type() == img1.data.type():
      window = self.window
    else:
      window = create_window(self.window_size, channel)

      if img1.is_cuda:
        window = window.cuda(img1.get_device())
      window = window.type_as(img1)

      self.window = window
      self.channel = channel

    return _ssim(img1, img2, window, self.window_size, channel, self.size_average)


def ssim(img1, img2, window_size=11, size_average=True):
  (_, channel, _, _) = img1.size()
  window = create_window(window_size, channel)

  if img1.is_cuda:
    window = window.cuda(img1.get_device())
  window = window.type_as(img1)

  return _ssim(img1, img2, window, window_size, channel, size_average)


if __name__ == '__main__':
#   import cv2

  # img2 = img1.copy()
  # img1 = torch.from_numpy(img1).unsqueeze(0).permute(0,3,1,2).float()
  # img2 = img1.clone()
  img1 = torch.rand(3, 1, 256, 256)
  img2 = torch.rand(3, 1, 256, 256)

  print(ssim(img1, img2, size_average=False))
