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| import os | |
| import PIL | |
| import cv2 | |
| import math | |
| import numpy as np | |
| import torch | |
| import torchvision | |
| import imageio | |
| from einops import rearrange | |
| def save_videos_grid(videos, path=None, rescale=True, n_rows=4, fps=8, discardN=0): | |
| videos = rearrange(videos, "b c t h w -> t b c h w").cpu() | |
| 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 / 2.0 + 0.5).clamp(0, 1) # -1,1 -> 0,1 | |
| x = (x * 255).numpy().astype(np.uint8) | |
| #x = adjust_gamma(x, 0.5) | |
| outputs.append(x) | |
| outputs = outputs[discardN:] | |
| if path is not None: | |
| #os.makedirs(os.path.dirname(path), exist_ok=True) | |
| imageio.mimsave(path, outputs, duration=1000/fps, loop=0) | |
| return outputs | |
| def convert_image_to_fn(img_type, minsize, image, eps=0.02): | |
| width, height = image.size | |
| if min(width, height) < minsize: | |
| scale = minsize/min(width, height) + eps | |
| image = image.resize((math.ceil(width*scale), math.ceil(height*scale))) | |
| if image.mode != img_type: | |
| return image.convert(img_type) | |
| return image |