import os

import imageio
import numpy as np
import torch
import torchvision
import cv2
from einops import rearrange
from PIL import Image


def color_transfer(sc, dc):
    """
    Transfer color distribution from of sc, referred to dc.

    Args:
        sc (numpy.ndarray): input image to be transfered.
        dc (numpy.ndarray): reference image

    Returns:
        numpy.ndarray: Transferred color distribution on the sc.
    """

    def get_mean_and_std(img):
        x_mean, x_std = cv2.meanStdDev(img)
        x_mean = np.hstack(np.around(x_mean, 2))
        x_std = np.hstack(np.around(x_std, 2))
        return x_mean, x_std

    sc = cv2.cvtColor(sc, cv2.COLOR_RGB2LAB)
    s_mean, s_std = get_mean_and_std(sc)
    dc = cv2.cvtColor(dc, cv2.COLOR_RGB2LAB)
    t_mean, t_std = get_mean_and_std(dc)
    img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
    np.putmask(img_n, img_n > 255, 255)
    np.putmask(img_n, img_n < 0, 0)
    dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2RGB)
    return dst

def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=12, imageio_backend=True, color_transfer_post_process=False):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        outputs.append(Image.fromarray(x))

    if color_transfer_post_process:
        for i in range(1, len(outputs)):
            outputs[i] = Image.fromarray(color_transfer(np.uint8(outputs[i]), np.uint8(outputs[0])))

    os.makedirs(os.path.dirname(path), exist_ok=True)
    if imageio_backend:
        if path.endswith("mp4"):
            imageio.mimsave(path, outputs, fps=fps)
        else:
            imageio.mimsave(path, outputs, duration=(1000 * 1/fps))
    else:
        if path.endswith("mp4"):
            path = path.replace('.mp4', '.gif')
        outputs[0].save(path, format='GIF', append_images=outputs, save_all=True, duration=100, loop=0)