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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import os
from pathlib import Path
import numpy as np
import pycocotools.mask as mask_util
from mmengine.utils import ProgressBar, mkdir_or_exist
from panopticapi.utils import IdGenerator, save_json
from PIL import Image
from mmdet.datasets.ade20k import ADE20KPanopticDataset
ORIGINAL_CATEGORIES = [
'wall', 'building', 'sky', 'floor', 'tree', 'ceiling', 'road, route',
'bed', 'window', 'grass', 'cabinet', 'sidewalk, pavement', 'person',
'earth, ground', 'door', 'table', 'mountain, mount', 'plant', 'curtain',
'chair', 'car', 'water', 'painting, picture', 'sofa', 'shelf', 'house',
'sea', 'mirror', 'rug', 'field', 'armchair', 'seat', 'fence', 'desk',
'rock, stone', 'wardrobe, closet, press', 'lamp', 'tub', 'rail', 'cushion',
'base, pedestal, stand', 'box', 'column, pillar', 'signboard, sign',
'chest of drawers, chest, bureau, dresser', 'counter', 'sand', 'sink',
'skyscraper', 'fireplace', 'refrigerator, icebox',
'grandstand, covered stand', 'path', 'stairs', 'runway',
'case, display case, showcase, vitrine',
'pool table, billiard table, snooker table', 'pillow',
'screen door, screen', 'stairway, staircase', 'river', 'bridge, span',
'bookcase', 'blind, screen', 'coffee table',
'toilet, can, commode, crapper, pot, potty, stool, throne', 'flower',
'book', 'hill', 'bench', 'countertop', 'stove', 'palm, palm tree',
'kitchen island', 'computer', 'swivel chair', 'boat', 'bar',
'arcade machine', 'hovel, hut, hutch, shack, shanty', 'bus', 'towel',
'light', 'truck', 'tower', 'chandelier', 'awning, sunshade, sunblind',
'street lamp', 'booth', 'tv', 'airplane', 'dirt track', 'clothes', 'pole',
'land, ground, soil',
'bannister, banister, balustrade, balusters, handrail',
'escalator, moving staircase, moving stairway',
'ottoman, pouf, pouffe, puff, hassock', 'bottle',
'buffet, counter, sideboard',
'poster, posting, placard, notice, bill, card', 'stage', 'van', 'ship',
'fountain',
'conveyer belt, conveyor belt, conveyer, conveyor, transporter', 'canopy',
'washer, automatic washer, washing machine', 'plaything, toy', 'pool',
'stool', 'barrel, cask', 'basket, handbasket', 'falls', 'tent', 'bag',
'minibike, motorbike', 'cradle', 'oven', 'ball', 'food, solid food',
'step, stair', 'tank, storage tank', 'trade name', 'microwave', 'pot',
'animal', 'bicycle', 'lake', 'dishwasher', 'screen', 'blanket, cover',
'sculpture', 'hood, exhaust hood', 'sconce', 'vase', 'traffic light',
'tray', 'trash can', 'fan', 'pier', 'crt screen', 'plate', 'monitor',
'bulletin board', 'shower', 'radiator', 'glass, drinking glass', 'clock',
'flag'
]
def parse_args():
parser = argparse.ArgumentParser(
description='Convert ADE20K annotations to COCO format')
parser.add_argument('src', help='ade20k data path')
parser.add_argument('--task', help='task name', default='panoptic')
args = parser.parse_args()
return args
def prepare_instance_annotations(dataset_dir: str):
dataset_dir = Path(dataset_dir)
for name, dirname in [('train', 'training'), ('val', 'validation')]:
image_dir = dataset_dir / 'images' / dirname
instance_dir = dataset_dir / 'annotations_instance' / dirname
ann_id = 0
# json
out_file = dataset_dir / f'ade20k_instance_{name}.json'
# json config
instance_config_file = dataset_dir / 'imgCatIds.json'
with open(instance_config_file, 'r') as f:
category_dict = json.load(f)['categories']
# catid mapping
mapping_file = dataset_dir / 'categoryMapping.txt'
with open(mapping_file, 'r') as f:
map_id = {}
for i, line in enumerate(f.readlines()):
if i == 0:
continue
ins_id, sem_id, _ = line.strip().split()
map_id[int(ins_id)] = int(sem_id) - 1
for cat in category_dict:
cat['id'] = map_id[cat['id']]
filenames = sorted(list(image_dir.iterdir()))
ann_dict = {}
images = []
annotations = []
progressbar = ProgressBar(len(filenames))
for filename in filenames:
image = {}
image_id = filename.stem
image['id'] = image_id
image['file_name'] = filename.name
original_format = np.array(Image.open(filename))
image['height'] = original_format.shape[0]
image['width'] = original_format.shape[1]
images.append(image)
instance_file = instance_dir / f'{image_id}.png'
ins_seg = np.array(Image.open(instance_file))
assert ins_seg.dtype == np.uint8
instance_cat_ids = ins_seg[..., 0]
instance_ins_ids = ins_seg[..., 1]
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)
]
rle = mask_util.encode(
np.array(mask[:, :, np.newaxis], 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)
progressbar.update()
ann_dict['images'] = images
ann_dict['categories'] = category_dict
ann_dict['annotations'] = annotations
save_json(ann_dict, out_file)
def prepare_panoptic_annotations(dataset_dir: str):
dataset_dir = Path(dataset_dir)
for name, dirname in [('train', 'training'), ('val', 'validation')]:
image_dir = dataset_dir / 'images' / dirname
semantic_dir = dataset_dir / 'annotations' / dirname
instance_dir = dataset_dir / 'annotations_instance' / dirname
# folder to store panoptic PNGs
out_folder = dataset_dir / f'ade20k_panoptic_{name}'
# json with segmentations information
out_file = dataset_dir / f'ade20k_panoptic_{name}.json'
mkdir_or_exist(out_folder)
# catid mapping
neworder_categories = []
all_classes = ORIGINAL_CATEGORIES
thing_classes = ADE20KPanopticDataset.METAINFO['thing_classes']
stuff_classes = ADE20KPanopticDataset.METAINFO['stuff_classes']
palette = ADE20KPanopticDataset.METAINFO['palette']
old_2_new_mapping = {}
new_2_old_mapping = {}
for i, t in enumerate(thing_classes):
j = list(all_classes).index(t)
old_2_new_mapping[j] = i
new_2_old_mapping[i] = j
for i, t in enumerate(stuff_classes):
j = list(all_classes).index(t)
old_2_new_mapping[j] = i + len(thing_classes)
new_2_old_mapping[i + len(thing_classes)] = j
for old, new in old_2_new_mapping.items():
neworder_categories.append({
'id': new,
'name': all_classes[old],
'isthing': int(new < len(thing_classes)),
'color': palette[new]
})
categories_dict = {cat['id']: cat for cat in neworder_categories}
panoptic_json_categories = neworder_categories[:]
panoptic_json_images = []
panoptic_json_annotations = []
filenames = sorted(list(image_dir.iterdir()))
progressbar = ProgressBar(len(filenames))
for filename in filenames:
panoptic_json_image = {}
image_id = filename.stem
panoptic_json_image['id'] = image_id
panoptic_json_image['file_name'] = filename.name
original_format = np.array(Image.open(filename))
panoptic_json_image['height'] = original_format.shape[0]
panoptic_json_image['width'] = original_format.shape[1]
pan_seg = np.zeros(
(original_format.shape[0], original_format.shape[1], 3),
dtype=np.uint8)
id_generator = IdGenerator(categories_dict)
filename_semantic = semantic_dir / f'{image_id}.png'
filename_instance = instance_dir / f'{image_id}.png'
sem_seg = np.array(Image.open(filename_semantic))
ins_seg = np.array(Image.open(filename_instance))
assert sem_seg.dtype == np.uint8
assert ins_seg.dtype == np.uint8
semantic_cat_ids = sem_seg - 1
instance_cat_ids = ins_seg[..., 0] - 1
# instance id starts from 1!
# because 0 is reserved as VOID label
instance_ins_ids = ins_seg[..., 1]
segm_info = []
# process stuffs
for semantic_cat_id in np.unique(semantic_cat_ids):
if semantic_cat_id == 255:
continue
if categories_dict[old_2_new_mapping[int(
semantic_cat_id)]]['isthing'] == 1:
continue
mask = semantic_cat_ids == semantic_cat_id
# should not have any overlap
assert pan_seg[mask].sum() == 0
segment_id, color = id_generator.get_id_and_color(
old_2_new_mapping[int(semantic_cat_id)])
pan_seg[mask] = color
area = np.sum(mask)
# bbox computation for a segment
hor = np.sum(mask, axis=0)
hor_idx = np.nonzero(hor)[0]
x = hor_idx[0]
width = hor_idx[-1] - x + 1
vert = np.sum(mask, axis=1)
vert_idx = np.nonzero(vert)[0]
y = vert_idx[0]
height = vert_idx[-1] - y + 1
bbox = [int(x), int(y), int(width), int(height)]
segm_info.append({
'id':
int(segment_id),
'category_id':
old_2_new_mapping[int(semantic_cat_id)],
'area':
int(area),
'bbox':
bbox,
'iscrowd':
0
})
# 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
segment_id, color = id_generator.get_id_and_color(
instance_cat_id[0])
pan_seg[mask] = color
area = np.sum(mask)
# bbox computation for a segment
hor = np.sum(mask, axis=0)
hor_idx = np.nonzero(hor)[0]
x = hor_idx[-1] - x + 1
width = hor_idx[-1] - x + 1
vert = np.sum(mask, axis=1)
vert_idx = np.nonzero(vert)[0]
y = vert_idx[0]
height = vert_idx[-1] - y + 1
bbox = [int(x), int(y), int(width), int(height)]
segm_info.append({
'id': int(segment_id),
'category_id': int(instance_cat_id[0]),
'area': int(area),
'bbox': bbox,
'iscrowd': 0
})
panoptic_json_annotation = {
'image_id': image_id,
'file_name': image_id + '.png',
'segments_info': segm_info
}
Image.fromarray(pan_seg).save(out_folder / f'{image_id}.png')
panoptic_json_images.append(panoptic_json_image)
panoptic_json_annotations.append(panoptic_json_annotation)
progressbar.update()
panoptic_json = {
'images': panoptic_json_images,
'annotations': panoptic_json_annotations,
'categories': panoptic_json_categories
}
save_json(panoptic_json, out_file)
def main():
args = parse_args()
assert args.task in ['panoptic', 'instance']
src = args.src
if args.task == 'panoptic':
annotation_train_path = f'{src}/ade20k_panoptic_train'
annotation_val_path = f'{src}/ade20k_panoptic_val'
print('Preparing ADE20K panoptic annotations ...')
print(
f'Creating panoptic annotations to {annotation_train_path} and {annotation_val_path} ...' # noqa
)
if os.path.exists(annotation_train_path) or os.path.exists(
annotation_val_path):
raise RuntimeError('Panoptic annotations already exist.')
prepare_panoptic_annotations(src)
print('Done.')
else:
annotation_train_path = f'{src}/ade20k_instance_train'
annotation_val_path = f'{src}/ade20k_instance_val'
print('Preparing ADE20K instance annotations ...')
print(
f'Creating instance annotations to {annotation_train_path} and {annotation_val_path} ...' # noqa
)
if os.path.exists(annotation_train_path) or os.path.exists(
annotation_val_path):
raise RuntimeError('Instance annotations already exist.')
prepare_instance_annotations(src)
print('Done.')
if __name__ == '__main__':
main()
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