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multimodalart HF Staff
Learn2Splat ZeroGPU demo: app, vendored optgs, prebuilt sm_120 wheels
9196862 verified | import argparse | |
| import os | |
| from glob import glob | |
| from pathlib import Path | |
| import torch | |
| from tqdm import tqdm | |
| from optgs.scripts.convert_dl3dv_utils import is_image_shape_matched, Example, get_size, load_images, load_metadata | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--input_dir", type=str, help="original dataset directory") | |
| parser.add_argument("--output_dir", type=str, help="processed dataset directory") | |
| parser.add_argument( | |
| "--img_subdir", | |
| type=str, | |
| default="images_8", | |
| help="image directory name", | |
| choices=[ | |
| "images_4", | |
| "images_8", | |
| ], | |
| ) | |
| parser.add_argument("--n_test", type=int, default=8, help="test skip") | |
| parser.add_argument("--which_stage", type=str, default=None, help="dataset directory") | |
| parser.add_argument("--detect_overlap", action="store_true") | |
| args = parser.parse_args() | |
| INPUT_DIR = Path(args.input_dir) | |
| OUTPUT_DIR = Path(args.output_dir) | |
| # Target 200 MB per chunk. | |
| TARGET_BYTES_PER_CHUNK = int(1e8) | |
| def legal_check_for_all_scenes(root_dir, target_shape, img_dir): | |
| valid_folders = [] | |
| sub_folders = sorted(glob(os.path.join(root_dir, "*/nerfstudio"))) | |
| # NOTE that | |
| # 07d9f9724ca854fae07cb4c57d7ea22bf667d5decd4058f547728922f909956b | |
| # images_4 folder has resolution 270x480 | |
| for sub_folder in tqdm(sub_folders, desc="checking scenes..."): | |
| # Use a per-scene variable; reusing img_dir here would clobber the parameter and | |
| # build a wrong path for every scene after the first. | |
| scene_img_dir = os.path.join(sub_folder, img_dir) # e.g. <scene>/nerfstudio/images_4 | |
| if not is_image_shape_matched(Path(scene_img_dir), target_shape): | |
| print(f"image shape does not match for {sub_folder}") | |
| continue | |
| pose_file = os.path.join(sub_folder, "transforms.json") | |
| if not os.path.isfile(pose_file): | |
| print(f"cannot find pose file for {sub_folder}") | |
| continue | |
| valid_folders.append(sub_folder) | |
| return valid_folders | |
| if __name__ == "__main__": | |
| if "images_8" in args.img_subdir: | |
| target_shape = (270, 480) # (h, w) | |
| elif "images_4" in args.img_subdir: | |
| target_shape = (540, 960) | |
| else: | |
| raise ValueError | |
| print("checking all scenes...") | |
| valid_scenes = legal_check_for_all_scenes(INPUT_DIR, target_shape, args.img_subdir) | |
| print("valid scenes:", len(valid_scenes)) | |
| # assert False | |
| for stage in ["test"]: | |
| error_logs = [] | |
| image_dirs = valid_scenes | |
| chunk_size = 0 | |
| chunk_index = 0 | |
| chunk: list[Example] = [] | |
| def save_chunk(): | |
| global chunk_size | |
| global chunk_index | |
| global chunk | |
| chunk_key = f"{chunk_index:0>6}" | |
| dir = OUTPUT_DIR / stage | |
| dir.mkdir(exist_ok=True, parents=True) | |
| torch.save(chunk, dir / f"{chunk_key}.torch") | |
| # Reset the chunk. | |
| chunk_size = 0 | |
| chunk_index += 1 | |
| chunk = [] | |
| for image_dir in tqdm(image_dirs, desc=f"Processing {stage}"): | |
| key = os.path.basename(os.path.dirname(image_dir.strip("/"))) | |
| image_dir = Path(image_dir) / args.img_subdir # 270x480 | |
| # image_dir = Path(image_dir) / 'images_4' # 540x960 | |
| num_bytes = get_size(image_dir) | |
| # Read images and metadata. | |
| try: | |
| images = load_images(image_dir) | |
| except: | |
| print("image loading error") | |
| continue | |
| meta_path = image_dir.parent / "transforms.json" | |
| if not meta_path.is_file(): | |
| error_msg = f"---------> [ERROR] no meta file in {key}, skip." | |
| print(error_msg) | |
| error_logs.append(error_msg) | |
| continue | |
| example = load_metadata(meta_path) | |
| # Merge the images into the example. | |
| try: | |
| example["images"] = [ | |
| images[timestamp.item()] for timestamp in example["timestamps"] | |
| ] | |
| except: | |
| error_msg = f"---------> [ERROR] Some images missing in {key}, skip." | |
| print(error_msg) | |
| error_logs.append(error_msg) | |
| continue | |
| # Add the key to the example. | |
| example["key"] = key | |
| chunk.append(example) | |
| chunk_size += num_bytes | |
| if chunk_size >= TARGET_BYTES_PER_CHUNK: | |
| save_chunk() | |
| if chunk_size > 0: | |
| save_chunk() | |