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| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import glob | |
| import json | |
| import os | |
| from collections import Counter | |
| import numpy as np | |
| import tqdm | |
| from panopticapi.utils import IdGenerator, save_json | |
| from PIL import Image | |
| import pycocotools.mask as mask_util | |
| if __name__ == "__main__": | |
| dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets") | |
| for name, dirname in [("train", "training"), ("val", "validation")]: | |
| image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/") | |
| instance_dir = os.path.join( | |
| dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/" | |
| ) | |
| # img_id = 0 | |
| ann_id = 1 | |
| # json | |
| out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_instance_{name}.json") | |
| # json config | |
| instance_config_file = "datasets/ade20k_instance_imgCatIds.json" | |
| with open(instance_config_file) as f: | |
| category_dict = json.load(f)["categories"] | |
| # load catid mapping | |
| # it is important to share category id for both instance and panoptic annotations | |
| mapping_file = "datasets/ade20k_instance_catid_mapping.txt" | |
| with open(mapping_file) as f: | |
| map_id = {} | |
| for i, line in enumerate(f.readlines()): | |
| if i == 0: | |
| continue | |
| ins_id, sem_id, _ = line.strip().split() | |
| # shift id by 1 because we want it to start from 0! | |
| # ignore_label becomes 255 | |
| map_id[int(ins_id)] = int(sem_id) - 1 | |
| for cat in category_dict: | |
| cat["id"] = map_id[cat["id"]] | |
| filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg"))) | |
| ann_dict = {} | |
| images = [] | |
| annotations = [] | |
| for idx, filename in enumerate(tqdm.tqdm(filenames)): | |
| image = {} | |
| image_id = os.path.basename(filename).split(".")[0] | |
| image["id"] = image_id | |
| image["file_name"] = os.path.basename(filename) | |
| original_format = np.array(Image.open(filename)) | |
| image["width"] = original_format.shape[1] | |
| image["height"] = original_format.shape[0] | |
| images.append(image) | |
| filename_instance = os.path.join(instance_dir, image_id + ".png") | |
| ins_seg = np.asarray(Image.open(filename_instance)) | |
| assert ins_seg.dtype == np.uint8 | |
| instance_cat_ids = ins_seg[..., 0] | |
| # instance id starts from 1! | |
| # because 0 is reserved as VOID label | |
| instance_ins_ids = ins_seg[..., 1] | |
| # process things | |
| for thing_id in np.unique(instance_ins_ids): | |
| if thing_id == 0: | |
| continue | |
| mask = instance_ins_ids == thing_id | |
| instance_cat_id = np.unique(instance_cat_ids[mask]) | |
| assert len(instance_cat_id) == 1 | |
| anno = {} | |
| anno['id'] = ann_id | |
| ann_id += 1 | |
| anno['image_id'] = image['id'] | |
| anno["iscrowd"] = int(0) | |
| anno["category_id"] = int(map_id[instance_cat_id[0]]) | |
| inds = np.nonzero(mask) | |
| ymin, ymax = inds[0].min(), inds[0].max() | |
| xmin, xmax = inds[1].min(), inds[1].max() | |
| anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)] | |
| # if xmax <= xmin or ymax <= ymin: | |
| # continue | |
| rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0] | |
| rle["counts"] = rle["counts"].decode("utf-8") | |
| anno["segmentation"] = rle | |
| anno["area"] = int(mask_util.area(rle)) | |
| annotations.append(anno) | |
| # save this | |
| ann_dict['images'] = images | |
| ann_dict['categories'] = category_dict | |
| ann_dict['annotations'] = annotations | |
| save_json(ann_dict, out_file) | |