65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
import os
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import imageio
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import numpy as np
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import torch
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import torchvision
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import cv2
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from einops import rearrange
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from PIL import Image
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def color_transfer(sc, dc):
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"""
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Transfer color distribution from of sc, referred to dc.
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Args:
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sc (numpy.ndarray): input image to be transfered.
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dc (numpy.ndarray): reference image
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Returns:
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numpy.ndarray: Transferred color distribution on the sc.
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"""
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def get_mean_and_std(img):
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x_mean, x_std = cv2.meanStdDev(img)
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x_mean = np.hstack(np.around(x_mean, 2))
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x_std = np.hstack(np.around(x_std, 2))
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return x_mean, x_std
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sc = cv2.cvtColor(sc, cv2.COLOR_RGB2LAB)
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s_mean, s_std = get_mean_and_std(sc)
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dc = cv2.cvtColor(dc, cv2.COLOR_RGB2LAB)
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t_mean, t_std = get_mean_and_std(dc)
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img_n = ((sc - s_mean) * (t_std / s_std)) + t_mean
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np.putmask(img_n, img_n > 255, 255)
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np.putmask(img_n, img_n < 0, 0)
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dst = cv2.cvtColor(cv2.convertScaleAbs(img_n), cv2.COLOR_LAB2RGB)
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return dst
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def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=12, imageio_backend=True, color_transfer_post_process=False):
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videos = rearrange(videos, "b c t h w -> t b c h w")
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outputs = []
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for x in videos:
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x = torchvision.utils.make_grid(x, nrow=n_rows)
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x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
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if rescale:
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x = (x + 1.0) / 2.0 # -1,1 -> 0,1
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x = (x * 255).numpy().astype(np.uint8)
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outputs.append(Image.fromarray(x))
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if color_transfer_post_process:
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for i in range(1, len(outputs)):
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outputs[i] = Image.fromarray(color_transfer(np.uint8(outputs[i]), np.uint8(outputs[0])))
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os.makedirs(os.path.dirname(path), exist_ok=True)
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if imageio_backend:
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if path.endswith("mp4"):
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imageio.mimsave(path, outputs, fps=fps)
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else:
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imageio.mimsave(path, outputs, duration=(1000 * 1/fps))
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else:
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if path.endswith("mp4"):
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path = path.replace('.mp4', '.gif')
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outputs[0].save(path, format='GIF', append_images=outputs, save_all=True, duration=100, loop=0)
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