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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import functools | |
| import json | |
| import multiprocessing as mp | |
| import numpy as np | |
| import os | |
| import time | |
| from fvcore.common.download import download | |
| from panopticapi.utils import rgb2id | |
| from PIL import Image | |
| from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES | |
| def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): | |
| panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) | |
| panoptic = rgb2id(panoptic) | |
| output = np.zeros_like(panoptic, dtype=np.uint8) + 255 | |
| for seg in segments: | |
| cat_id = seg["category_id"] | |
| new_cat_id = id_map[cat_id] | |
| output[panoptic == seg["id"]] = new_cat_id | |
| Image.fromarray(output).save(output_semantic) | |
| def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): | |
| """ | |
| Create semantic segmentation annotations from panoptic segmentation | |
| annotations, to be used by PanopticFPN. | |
| It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. | |
| It maps all stuff categories to contiguous ids starting from 1. | |
| Args: | |
| panoptic_json (str): path to the panoptic json file, in COCO's format. | |
| panoptic_root (str): a directory with panoptic annotation files, in COCO's format. | |
| sem_seg_root (str): a directory to output semantic annotation files | |
| categories (list[dict]): category metadata. Each dict needs to have: | |
| "id": corresponds to the "category_id" in the json annotations | |
| "isthing": 0 or 1 | |
| """ | |
| os.makedirs(sem_seg_root, exist_ok=True) | |
| id_map = {} # map from category id to id in the output semantic annotation | |
| assert len(categories) <= 254 | |
| for i, k in enumerate(categories): | |
| id_map[k["id"]] = i | |
| # what is id = 0? | |
| # id_map[0] = 255 | |
| print(id_map) | |
| with open(panoptic_json) as f: | |
| obj = json.load(f) | |
| pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) | |
| def iter_annotations(): | |
| for anno in obj["annotations"]: | |
| file_name = anno["file_name"] | |
| segments = anno["segments_info"] | |
| input = os.path.join(panoptic_root, file_name) | |
| output = os.path.join(sem_seg_root, file_name) | |
| yield input, output, segments | |
| print("Start writing to {} ...".format(sem_seg_root)) | |
| start = time.time() | |
| pool.starmap( | |
| functools.partial(_process_panoptic_to_semantic, id_map=id_map), | |
| iter_annotations(), | |
| chunksize=100, | |
| ) | |
| print("Finished. time: {:.2f}s".format(time.time() - start)) | |
| if __name__ == "__main__": | |
| dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") | |
| for s in ["val2017", "train2017"]: | |
| separate_coco_semantic_from_panoptic( | |
| os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), | |
| os.path.join(dataset_dir, "panoptic_{}".format(s)), | |
| os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)), | |
| COCO_CATEGORIES, | |
| ) | |