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| # ------------------------------------------------------------------------------ | |
| # Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # | |
| # This work is made available under the Nvidia Source Code License. | |
| # To view a copy of this license, visit | |
| # https://github.com/NVlabs/ODISE/blob/main/LICENSE | |
| # | |
| # Written by Jiarui Xu | |
| # ------------------------------------------------------------------------------ | |
| import os | |
| from pathlib import Path | |
| import shutil | |
| import numpy as np | |
| import tqdm | |
| from PIL import Image | |
| import multiprocessing as mp | |
| import functools | |
| from detail import Detail | |
| # fmt: off | |
| _mapping = np.sort( | |
| np.array([ | |
| 0, 2, 259, 260, 415, 324, 9, 258, 144, 18, 19, 22, 23, 397, 25, 284, | |
| 158, 159, 416, 33, 162, 420, 454, 295, 296, 427, 44, 45, 46, 308, 59, | |
| 440, 445, 31, 232, 65, 354, 424, 68, 326, 72, 458, 34, 207, 80, 355, | |
| 85, 347, 220, 349, 360, 98, 187, 104, 105, 366, 189, 368, 113, 115 | |
| ])) | |
| # fmt: on | |
| _key = np.array(range(len(_mapping))).astype("uint8") | |
| def generate_labels(img_info, detail_api, out_dir): | |
| def _class_to_index(mask, _mapping, _key): | |
| # assert the values | |
| values = np.unique(mask) | |
| for i in range(len(values)): | |
| assert values[i] in _mapping | |
| index = np.digitize(mask.ravel(), _mapping, right=True) | |
| return _key[index].reshape(mask.shape) | |
| sem_seg = _class_to_index(detail_api.getMask(img_info), _mapping=_mapping, _key=_key) | |
| sem_seg = sem_seg - 1 # 0 (ignore) becomes 255. others are shifted by 1 | |
| filename = img_info["file_name"] | |
| Image.fromarray(sem_seg).save(out_dir / filename.replace("jpg", "png")) | |
| def copy_images(img_info, img_dir, out_dir): | |
| filename = img_info["file_name"] | |
| shutil.copy2(img_dir / filename, out_dir / filename) | |
| if __name__ == "__main__": | |
| dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "pascal_ctx_d2" | |
| voc_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "VOCdevkit/VOC2010" | |
| for split in ["training", "validation"]: | |
| img_dir = voc_dir / "JPEGImages" | |
| if split == "training": | |
| detail_api = Detail(voc_dir / "trainval_merged.json", img_dir, "train") | |
| else: | |
| detail_api = Detail(voc_dir / "trainval_merged.json", img_dir, "val") | |
| img_infos = detail_api.getImgs() | |
| output_img_dir = dataset_dir / "images" / split | |
| output_ann_dir = dataset_dir / "annotations_ctx59" / split | |
| output_img_dir.mkdir(parents=True, exist_ok=True) | |
| output_ann_dir.mkdir(parents=True, exist_ok=True) | |
| pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) | |
| pool.map( | |
| functools.partial(copy_images, img_dir=img_dir, out_dir=output_img_dir), | |
| tqdm.tqdm(img_infos, desc=f"Writing {split} images to {output_img_dir} ..."), | |
| chunksize=100, | |
| ) | |
| pool.map( | |
| functools.partial(generate_labels, detail_api=detail_api, out_dir=output_ann_dir), | |
| tqdm.tqdm(img_infos, desc=f"Writing {split} images to {output_ann_dir} ..."), | |
| chunksize=100, | |
| ) |