src/utils/util.py

import importlib
import os
import os.path as osp
import shutil
import sys
from pathlib import Path

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

def seed_everything(seed):
    import random
    import numpy as np

    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed % (2**32))
    random.seed(seed)


def save_videos_from_pil(pil_images, path, fps=8):
    save_fmt = Path(path).suffix
    os.makedirs(os.path.dirname(path), exist_ok=True)

    if save_fmt == ".mp4":
        with imageio.get_writer(path, fps=fps) as writer:
            for img in pil_images:
                img_array = np.array(img)  # Convert PIL Image to numpy array
                writer.append_data(img_array)

    elif save_fmt == ".gif":
        pil_images[0].save(
            fp=path,
            format="GIF",
            append_images=pil_images[1:],
            save_all=True,
            duration=(1 / fps * 1000),
            loop=0,
            optimize=False,
            lossless=True
        )
    else:
        raise ValueError("Unsupported file type. Use .mp4 or .gif.")


def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    height, width = videos.shape[-2:]
    outputs = []

    for i, x in enumerate(videos):
        x = torchvision.utils.make_grid(x, nrow=n_rows)  # (c h w)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)  # (h w c)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = (x * 255).numpy().astype(np.uint8)
        x = Image.fromarray(x)
        outputs.append(x)

    os.makedirs(os.path.dirname(path), exist_ok=True)

    save_videos_from_pil(outputs, path, fps)