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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)
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