| import os | |
| import imageio | |
| import cv2 | |
| import numpy as np | |
| from tqdm import tqdm | |
| MAX_H, MAX_W = 512, 768 | |
| exp_name = "360_v2_glo" | |
| keys = ["color", "distance_mean"] | |
| # keys = ["color"] | |
| os.makedirs('assets', exist_ok=True) | |
| root = os.path.join("exp", exp_name) | |
| scenes = sorted(os.listdir(root)) | |
| video_files = [[os.path.join(root, scene, "render", | |
| f"{scene}_{exp_name}_path_renders_step_25000_{k}.mp4") | |
| for k in keys] for scene in scenes] | |
| video_files = [f for f in video_files if os.path.exists(f[0])] | |
| with imageio.get_writer(os.path.join("assets", exp_name+'.mp4'), fps=30) as writer: | |
| for scene_videos in tqdm(video_files): | |
| readers = [imageio.get_reader(f) for f in scene_videos] | |
| for data in zip(*readers): | |
| data = np.concatenate([cv2.resize(img, (MAX_W, MAX_H)) for img in data], axis=1) | |
| writer.append_data(data) | |