mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
import os
|
|
import cv2
|
|
import torch
|
|
import numpy as np
|
|
import imageio
|
|
import torchvision
|
|
from einops import rearrange
|
|
|
|
|
|
def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8, quality=8):
|
|
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 = torch.clamp(x,0,1)
|
|
x = (x * 255).numpy().astype(np.uint8)
|
|
outputs.append(x)
|
|
|
|
os.makedirs(os.path.dirname(path), exist_ok=True)
|
|
imageio.mimsave(path, outputs, fps=fps, quality=quality)
|
|
|
|
def pad_image(crop_img, size, color=(255, 255, 255), resize_ratio=1):
|
|
crop_h, crop_w = crop_img.shape[:2]
|
|
target_w, target_h = size
|
|
scale_h, scale_w = target_h / crop_h, target_w / crop_w
|
|
if scale_w > scale_h:
|
|
resize_h = int(target_h*resize_ratio)
|
|
resize_w = int(crop_w / crop_h * resize_h)
|
|
else:
|
|
resize_w = int(target_w*resize_ratio)
|
|
resize_h = int(crop_h / crop_w * resize_w)
|
|
crop_img = cv2.resize(crop_img, (resize_w, resize_h))
|
|
pad_left = (target_w - resize_w) // 2
|
|
pad_top = (target_h - resize_h) // 2
|
|
pad_right = target_w - resize_w - pad_left
|
|
pad_bottom = target_h - resize_h - pad_top
|
|
crop_img = cv2.copyMakeBorder(crop_img, pad_top, pad_bottom, pad_left, pad_right, cv2.BORDER_CONSTANT, value=color)
|
|
return crop_img |