import torch
import numpy as np
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import matplotlib as mpl
from matplotlib import cm
import cv2
import os
from datetime import datetime
import shutil
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp
import lpips

lpips_alex = lpips.LPIPS(net="alex")  # best forward scores
lpips_vgg = lpips.LPIPS(
    net="vgg"
)  # closer to "traditional" perceptual loss, when used for optimization

HUGE_NUMBER = 1e10
TINY_NUMBER = 1e-6  # float32 only has 7 decimal digits precision

img_HWC2CHW = lambda x: x.permute(2, 0, 1)
gray2rgb = lambda x: x.unsqueeze(2).repeat(1, 1, 3)


to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
mse2psnr = lambda x: -10.0 * np.log(x + TINY_NUMBER) / np.log(10.0)


def save_current_code(outdir):
    now = datetime.now()  # current date and time
    date_time = now.strftime("%m_%d-%H:%M:%S")
    src_dir = "."
    dst_dir = os.path.join(outdir, "code_{}".format(date_time))
    shutil.copytree(
        src_dir,
        dst_dir,
        ignore=shutil.ignore_patterns(
            "data*",
            "pretrained*",
            "logs*",
            "out*",
            "*.png",
            "*.mp4",
            "*__pycache__*",
            "*.git*",
            "*.idea*",
            "*.zip",
            "*.jpg",
        ),
    )


def img2mse(x, y, mask=None):
    """
    :param x: img 1, [(...), 3]
    :param y: img 2, [(...), 3]
    :param mask: optional, [(...)]
    :return: mse score
    """
    if mask is None:
        return torch.mean((x - y) * (x - y))
    else:
        return torch.sum((x - y) * (x - y) * mask.unsqueeze(-1)) / (
            torch.sum(mask) * x.shape[-1] + TINY_NUMBER
        )


def img2psnr(x, y, mask=None):
    return mse2psnr(img2mse(x, y, mask).item())


def cycle(iterable):
    while True:
        for x in iterable:
            yield x


def get_vertical_colorbar(h, vmin, vmax, cmap_name="jet", label=None, cbar_precision=2):
    """
    :param w: pixels
    :param h: pixels
    :param vmin: min value
    :param vmax: max value
    :param cmap_name:
    :param label
    :return:
    """
    fig = Figure(figsize=(2, 8), dpi=100)
    fig.subplots_adjust(right=1.5)
    canvas = FigureCanvasAgg(fig)

    # Do some plotting.
    ax = fig.add_subplot(111)
    cmap = cm.get_cmap(cmap_name)
    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)

    tick_cnt = 6
    tick_loc = np.linspace(vmin, vmax, tick_cnt)
    cb1 = mpl.colorbar.ColorbarBase(
        ax, cmap=cmap, norm=norm, ticks=tick_loc, orientation="vertical"
    )

    tick_label = [str(np.round(x, cbar_precision)) for x in tick_loc]
    if cbar_precision == 0:
        tick_label = [x[:-2] for x in tick_label]

    cb1.set_ticklabels(tick_label)

    cb1.ax.tick_params(labelsize=18, rotation=0)

    if label is not None:
        cb1.set_label(label)

    fig.tight_layout()

    canvas.draw()
    s, (width, height) = canvas.print_to_buffer()

    im = np.frombuffer(s, np.uint8).reshape((height, width, 4))

    im = im[:, :, :3].astype(np.float32) / 255.0
    if h != im.shape[0]:
        w = int(im.shape[1] / im.shape[0] * h)
        im = cv2.resize(im, (w, h), interpolation=cv2.INTER_AREA)

    return im


def colorize_np(
    x,
    cmap_name="jet",
    mask=None,
    range=None,
    append_cbar=False,
    cbar_in_image=False,
    cbar_precision=2,
):
    """
    turn a grayscale image into a color image
    :param x: input grayscale, [H, W]
    :param cmap_name: the colorization method
    :param mask: the mask image, [H, W]
    :param range: the range for scaling, automatic if None, [min, max]
    :param append_cbar: if append the color bar
    :param cbar_in_image: put the color bar inside the image to keep the output image the same size as the input image
    :return: colorized image, [H, W]
    """
    if range is not None:
        vmin, vmax = range
    elif mask is not None:
        # vmin, vmax = np.percentile(x[mask], (2, 100))
        vmin = np.min(x[mask][np.nonzero(x[mask])])
        vmax = np.max(x[mask])
        # vmin = vmin - np.abs(vmin) * 0.01
        x[np.logical_not(mask)] = vmin
        # print(vmin, vmax)
    else:
        vmin, vmax = np.percentile(x, (1, 100))
        vmax += TINY_NUMBER

    x = np.clip(x, vmin, vmax)
    x = (x - vmin) / (vmax - vmin)
    # x = np.clip(x, 0., 1.)

    cmap = cm.get_cmap(cmap_name)
    x_new = cmap(x)[:, :, :3]

    if mask is not None:
        mask = np.float32(mask[:, :, np.newaxis])
        x_new = x_new * mask + np.ones_like(x_new) * (1.0 - mask)

    cbar = get_vertical_colorbar(
        h=x.shape[0], vmin=vmin, vmax=vmax, cmap_name=cmap_name, cbar_precision=cbar_precision
    )

    if append_cbar:
        if cbar_in_image:
            x_new[:, -cbar.shape[1] :, :] = cbar
        else:
            x_new = np.concatenate((x_new, np.zeros_like(x_new[:, :5, :]), cbar), axis=1)
        return x_new
    else:
        return x_new


# tensor
def colorize(x, cmap_name="jet", mask=None, range=None, append_cbar=False, cbar_in_image=False):
    device = x.device
    x = x.cpu().numpy()
    if mask is not None:
        mask = mask.cpu().numpy() > 0.99
        kernel = np.ones((3, 3), np.uint8)
        mask = cv2.erode(mask.astype(np.uint8), kernel, iterations=1).astype(bool)

    x = colorize_np(x, cmap_name, mask, range, append_cbar, cbar_in_image)
    x = torch.from_numpy(x).to(device)
    return x


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

    if img1.is_cuda:
        window = window.to(img1.device)
    window = window.type_as(img1)

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


def ssim(img1, img2, window_size=11, size_average=True, format="NCHW"):
    if format == "HWC":
        img1 = img1.permute([2, 0, 1])[None, ...]
        img2 = img2.permute([2, 0, 1])[None, ...]
    elif format == "NHWC":
        img1 = img1.permute([0, 3, 1, 2])
        img2 = img2.permute([0, 3, 1, 2])

    return ssim_utils(img1, img2, window_size, size_average)


def lpips(img1, img2, net="vgg", format="NCHW"):
    if format == "HWC":
        img1 = img1.permute([2, 0, 1])[None, ...]
        img2 = img2.permute([2, 0, 1])[None, ...]
    elif format == "NHWC":
        img1 = img1.permute([0, 3, 1, 2])
        img2 = img2.permute([0, 3, 1, 2])

    if net == "alex":
        return lpips_alex(img1, img2)
    elif net == "vgg":
        return lpips_vgg(img1, img2)


def concat_images(img0,img1,vert=False):
    if not vert:
        h0,h1=img0.shape[0],img1.shape[0],
        if h0<h1: img0=cv2.copyMakeBorder(img0,0,h1-h0,0,0,borderType=cv2.BORDER_CONSTANT,value=0)
        if h1<h0: img1=cv2.copyMakeBorder(img1,0,h0-h1,0,0,borderType=cv2.BORDER_CONSTANT,value=0)
        img = np.concatenate([img0, img1], axis=1)
    else:
        w0,w1=img0.shape[1],img1.shape[1]
        if w0<w1: img0=cv2.copyMakeBorder(img0,0,0,0,w1-w0,borderType=cv2.BORDER_CONSTANT,value=0)
        if w1<w0: img1=cv2.copyMakeBorder(img1,0,0,0,w0-w1,borderType=cv2.BORDER_CONSTANT,value=0)
        img = np.concatenate([img0, img1], axis=0)

    return img

def concat_images_list(*args,vert=False):
    if len(args)==1: return args[0]
    img_out=args[0]
    for img in args[1:]:
        img_out=concat_images(img_out,img,vert)
    return img_out