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

"""
# ============================================
# SSIM loss
# https://github.com/Po-Hsun-Su/pytorch-ssim
# ============================================
"""


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 SSIMLoss(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True):
        super(SSIMLoss, 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
    from torch import optim
    from skimage import io
    npImg1 = cv2.imread("einstein.png")

    img1 = torch.from_numpy(
        np.rollaxis(
            npImg1,
            2)).float().unsqueeze(0) / 255.0
    img2 = torch.rand(img1.size())

    if torch.cuda.is_available():
        img1 = img1.cuda()
        img2 = img2.cuda()

    img1 = Variable(img1, requires_grad=False)
    img2 = Variable(img2, requires_grad=True)

    ssim_value = ssim(img1, img2).item()
    print("Initial ssim:", ssim_value)

    ssim_loss = SSIMLoss()
    optimizer = optim.Adam([img2], lr=0.01)

    while ssim_value < 0.99:
        optimizer.zero_grad()
        ssim_out = -ssim_loss(img1, img2)
        ssim_value = -ssim_out.item()
        print('{:<4.4f}'.format(ssim_value))
        ssim_out.backward()
        optimizer.step()
    img = np.transpose(
        img2.detach().cpu().squeeze().float().numpy(), (1, 2, 0))
    io.imshow(np.uint8(np.clip(img * 255, 0, 255)))
