wan/utils/utils.py

# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import argparse
import binascii
import os
import os.path as osp
import cv2

import imageio
import torch
import torchvision
from PIL import Image
import librosa
import soundfile as sf
import subprocess
from decord import VideoReader, cpu
import gc

__all__ = ['cache_video', 'cache_image', 'str2bool']


def rand_name(length=8, suffix=''):
    name = binascii.b2a_hex(os.urandom(length)).decode('utf-8')
    if suffix:
        if not suffix.startswith('.'):
            suffix = '.' + suffix
        name += suffix
    return name



def str2bool(v):
    """
    Convert a string to a boolean.

    Supported true values: 'yes', 'true', 't', 'y', '1'
    Supported false values: 'no', 'false', 'f', 'n', '0'

    Args:
        v (str): String to convert.

    Returns:
        bool: Converted boolean value.

    Raises:
        argparse.ArgumentTypeError: If the value cannot be converted to boolean.
    """
    if isinstance(v, bool):
        return v
    v_lower = v.lower()
    if v_lower in ('yes', 'true', 't', 'y', '1'):
        return True
    elif v_lower in ('no', 'false', 'f', 'n', '0'):
        return False
    else:
        raise argparse.ArgumentTypeError('Boolean value expected (True/False)')
    
def cache_video(tensor,
                save_file=None,
                fps=30,
                suffix='.mp4',
                nrow=8,
                normalize=True,
                value_range=(-1, 1),
                retry=5):
    # cache file
    cache_file = osp.join('/tmp', rand_name(
        suffix=suffix)) if save_file is None else save_file

    # save to cache
    error = None
    for _ in range(retry):
        try:
            # preprocess
            tensor = tensor.clamp(min(value_range), max(value_range))
            tensor = torch.stack([
                torchvision.utils.make_grid(
                    u, nrow=nrow, normalize=normalize, value_range=value_range)
                for u in tensor.unbind(2)
            ],
                                 dim=1).permute(1, 2, 3, 0)
            tensor = (tensor * 255).type(torch.uint8).cpu()

            # write video
            writer = imageio.get_writer(
                cache_file, fps=fps, codec='libx264', quality=8)
            for frame in tensor.numpy():
                writer.append_data(frame)
            writer.close()
            return cache_file
        except Exception as e:
            error = e
            continue
    else:
        print(f'cache_video failed, error: {error}', flush=True)
        return None


def cache_image(tensor,
                save_file,
                nrow=8,
                normalize=True,
                value_range=(-1, 1),
                retry=5):
    # cache file
    suffix = osp.splitext(save_file)[1]
    if suffix.lower() not in [
            '.jpg', '.jpeg', '.png', '.tiff', '.gif', '.webp'
    ]:
        suffix = '.png'

    # save to cache
    error = None
    for _ in range(retry):
        try:
            tensor = tensor.clamp(min(value_range), max(value_range))
            torchvision.utils.save_image(
                tensor,
                save_file,
                nrow=nrow,
                normalize=normalize,
                value_range=value_range)
            return save_file
        except Exception as e:
            error = e
            continue

def convert_video_to_h264(input_video_path, output_video_path):
    subprocess.run(
        ['ffmpeg', '-i', input_video_path, '-c:v', 'libx264', '-c:a', 'copy', output_video_path],
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )


def is_video(path):
    video_exts = ['.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.webm', '.mpeg', '.mpg']
    return os.path.splitext(path)[1].lower() in video_exts


def extract_specific_frames(video_path, frame_id):
    if is_video(video_path):
        vr = VideoReader(video_path, ctx=cpu(0))
        if frame_id < vr._num_frame:
            frame = vr[frame_id].asnumpy()  # RGB
        else:
            frame = vr[-1].asnumpy()
        del vr
        gc.collect()
        frame = Image.fromarray(frame)
    else:
        frame = Image.open(video_path).convert("RGB")
    return frame

def get_video_codec(video_path):
    result = subprocess.run(
        ['ffprobe', '-v', 'error', '-select_streams', 'v:0',
         '-show_entries', 'stream=codec_name', '-of', 'default=nw=1:nk=1', video_path],
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    codec = result.stdout.decode().strip()
    return codec



def split_wav_librosa(wav_path, segments, save_dir):
    y, sr = librosa.load(wav_path, sr=None)
    filename = wav_path.split('/')[-1].split('.')[0]
    save_list = []
    for idx, (start, end) in enumerate(segments):
        start_sample = int(start * sr)
        end_sample = int(end * sr)
        segment = y[start_sample:end_sample]
        out_path = os.path.join(save_dir, filename + str(start) + '_' + str(end) + '.wav')
        sf.write(out_path, segment, sr)
        print(f"Saved {out_path}: {start}s to {end}s")
        save_list.append(out_path)
    return save_list