From 73fa4be48fb495a1b8360c26eec628a9e53d60db Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Sat, 21 Sep 2024 16:37:54 +0300 Subject: [PATCH] Cleanup unused dependencies --- cogvideox_fun/utils.py | 77 +----------------------------------------- 1 file changed, 1 insertion(+), 76 deletions(-) diff --git a/cogvideox_fun/utils.py b/cogvideox_fun/utils.py index e9c5cc7..0b71f55 100644 --- a/cogvideox_fun/utils.py +++ b/cogvideox_fun/utils.py @@ -1,20 +1,10 @@ import os import gc -import imageio import numpy as np import torch -import torchvision -import cv2 -from einops import rearrange from PIL import Image # Copyright (c) OpenMMLab. All rights reserved. -import os -import cv2 -import numpy as np -import torch -from PIL import Image - def tensor2pil(image): return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8)) @@ -73,60 +63,6 @@ def get_width_and_height_from_image_and_base_resolution(image, base_resolution): height_slider = round(original_height * ratio) return height_slider, width_slider -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) - def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size): if validation_image_start is not None and validation_image_end is not None: if type(validation_image_start) is str and os.path.isfile(validation_image_start): @@ -224,18 +160,7 @@ def get_image_to_video_latent(validation_image_start, validation_image_end, vide return input_video, input_video_mask, clip_image def get_video_to_video_latent(input_video_path, video_length, sample_size): - if type(input_video_path) is str: - cap = cv2.VideoCapture(input_video_path) - input_video = [] - while True: - ret, frame = cap.read() - if not ret: - break - frame = cv2.resize(frame, (sample_size[1], sample_size[0])) - input_video.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) - cap.release() - else: - input_video = input_video_path + input_video = input_video_path input_video = torch.from_numpy(np.array(input_video))[:video_length] input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255