|
import copy |
|
import json |
|
import logging |
|
import os |
|
from collections import defaultdict |
|
from typing import Dict, TypedDict |
|
|
|
import datasets as ds |
|
|
|
logger = logging.getLogger(__name__) |
|
|
|
_CITATION = """\ |
|
@INPROCEEDINGS{caesar2018cvpr, |
|
title={COCO-Stuff: Thing and stuff classes in context}, |
|
author={Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio}, |
|
booktitle={Computer vision and pattern recognition (CVPR), 2018 IEEE conference on}, |
|
organization={IEEE}, |
|
year={2018} |
|
} |
|
""" |
|
|
|
_DESCRIPTION = """\ |
|
COCO-Stuff augments all 164K images of the popular COCO dataset with pixel-level stuff annotations. These annotations can be used for scene understanding tasks like semantic segmentation, object detection and image captioning. |
|
""" |
|
|
|
_HOMEPAGE = "https://github.com/nightrome/cocostuff" |
|
|
|
_LICENSE = """\ |
|
COCO-Stuff is a derivative work of the COCO dataset. The authors of COCO do not in any form endorse this work. Different licenses apply: |
|
- COCO images: Flickr Terms of use |
|
- COCO annotations: Creative Commons Attribution 4.0 License |
|
- COCO-Stuff annotations & code: Creative Commons Attribution 4.0 License |
|
""" |
|
|
|
|
|
class URLs(TypedDict): |
|
train: str |
|
val: str |
|
stuffthingmaps_trainval: str |
|
stuff_trainval: str |
|
labels: str |
|
|
|
|
|
_URLS: URLs = { |
|
"train": "http://images.cocodataset.org/zips/train2017.zip", |
|
"val": "http://images.cocodataset.org/zips/val2017.zip", |
|
"stuffthingmaps_trainval": "http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip", |
|
"stuff_trainval": "http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuff_trainval2017.zip", |
|
"labels": "https://raw.githubusercontent.com/nightrome/cocostuff/master/labels.txt", |
|
} |
|
|
|
|
|
class GenerateExamplesArguments(TypedDict): |
|
image_dirpath: str |
|
stuff_dirpath: str |
|
stuff_thing_maps_dirpath: str |
|
labels_path: str |
|
split: str |
|
|
|
|
|
def _load_json(json_path: str): |
|
logger.info(f"Load json from {json_path}") |
|
with open(json_path, "r") as rf: |
|
json_data = json.load(rf) |
|
return json_data |
|
|
|
|
|
def _load_labels(labels_path: str) -> Dict[int, str]: |
|
label_id_to_label_name: Dict[int, str] = {} |
|
|
|
logger.info(f"Load labels from {labels_path}") |
|
with open(labels_path, "r") as rf: |
|
for line in rf: |
|
label_id, label_name = line.strip().split(": ") |
|
label_id_to_label_name[int(label_id)] = label_name |
|
return label_id_to_label_name |
|
|
|
|
|
class CocoStuffDataset(ds.GeneratorBasedBuilder): |
|
|
|
VERSION = ds.Version("1.0.0") |
|
|
|
BUILDER_CONFIGS = [ |
|
ds.BuilderConfig( |
|
name="stuff-thing", |
|
version=VERSION, |
|
description="Stuff+thing PNG-style annotations on COCO 2017 trainval", |
|
), |
|
ds.BuilderConfig( |
|
name="stuff-only", |
|
version=VERSION, |
|
description="Stuff-only COCO-style annotations on COCO 2017 trainval", |
|
), |
|
] |
|
|
|
def _info(self) -> ds.DatasetInfo: |
|
if self.config.name == "stuff-thing": |
|
features = ds.Features( |
|
{ |
|
"image": ds.Image(), |
|
"image_id": ds.Value("int32"), |
|
"image_filename": ds.Value("string"), |
|
"width": ds.Value("int32"), |
|
"height": ds.Value("int32"), |
|
"stuff_map": ds.Image(), |
|
"objects": [ |
|
{ |
|
"object_id": ds.Value("string"), |
|
"x": ds.Value("int32"), |
|
"y": ds.Value("int32"), |
|
"w": ds.Value("int32"), |
|
"h": ds.Value("int32"), |
|
"name": ds.Value("string"), |
|
} |
|
], |
|
} |
|
) |
|
elif self.config.name == "stuff-only": |
|
features = ds.Features( |
|
{ |
|
"image": ds.Image(), |
|
"image_id": ds.Value("int32"), |
|
"image_filename": ds.Value("string"), |
|
"width": ds.Value("int32"), |
|
"height": ds.Value("int32"), |
|
"objects": [ |
|
{ |
|
"object_id": ds.Value("int32"), |
|
"x": ds.Value("int32"), |
|
"y": ds.Value("int32"), |
|
"w": ds.Value("int32"), |
|
"h": ds.Value("int32"), |
|
"name": ds.Value("string"), |
|
} |
|
], |
|
} |
|
) |
|
else: |
|
raise ValueError(f"Invalid dataset name: {self.config.name}") |
|
|
|
return ds.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=features, |
|
homepage=_HOMEPAGE, |
|
license=_LICENSE, |
|
citation=_CITATION, |
|
) |
|
|
|
def load_stuff_json(self, stuff_dirpath: str, split: str): |
|
return _load_json( |
|
json_path=os.path.join(stuff_dirpath, f"stuff_{split}2017.json") |
|
) |
|
|
|
def get_image_id_to_image_infos(self, images): |
|
|
|
image_id_to_image_infos = {} |
|
for img_dict in images: |
|
image_id = img_dict.pop("id") |
|
image_id_to_image_infos[image_id] = img_dict |
|
|
|
image_id_to_image_infos = dict(sorted(image_id_to_image_infos.items())) |
|
return image_id_to_image_infos |
|
|
|
def get_image_id_to_annotations(self, annotations): |
|
|
|
image_id_to_annotations = defaultdict(list) |
|
for ann_dict in annotations: |
|
image_id = ann_dict.pop("image_id") |
|
image_id_to_annotations[image_id].append(ann_dict) |
|
|
|
image_id_to_annotations = dict(sorted(image_id_to_annotations.items())) |
|
return image_id_to_annotations |
|
|
|
def _split_generators(self, dl_manager: ds.DownloadManager): |
|
downloaded_files = dl_manager.download_and_extract(_URLS) |
|
|
|
tng_image_dirpath = os.path.join(downloaded_files["train"], "train2017") |
|
val_image_dirpath = os.path.join(downloaded_files["val"], "val2017") |
|
|
|
stuff_dirpath = downloaded_files["stuff_trainval"] |
|
stuff_things_maps_dirpath = downloaded_files["stuffthingmaps_trainval"] |
|
labels_path = downloaded_files["labels"] |
|
|
|
tng_gen_kwargs: GenerateExamplesArguments = { |
|
"image_dirpath": tng_image_dirpath, |
|
"stuff_dirpath": stuff_dirpath, |
|
"stuff_thing_maps_dirpath": stuff_things_maps_dirpath, |
|
"labels_path": labels_path, |
|
"split": "train", |
|
} |
|
val_gen_kwargs: GenerateExamplesArguments = { |
|
"image_dirpath": val_image_dirpath, |
|
"stuff_dirpath": stuff_dirpath, |
|
"stuff_thing_maps_dirpath": stuff_things_maps_dirpath, |
|
"labels_path": labels_path, |
|
"split": "val", |
|
} |
|
return [ |
|
ds.SplitGenerator( |
|
name=ds.Split.TRAIN, |
|
gen_kwargs=tng_gen_kwargs, |
|
), |
|
ds.SplitGenerator( |
|
name=ds.Split.VALIDATION, |
|
gen_kwargs=val_gen_kwargs, |
|
), |
|
] |
|
|
|
def _generate_examples_for_stuff_thing( |
|
self, |
|
image_dirpath: str, |
|
stuff_dirpath: str, |
|
stuff_thing_maps_dirpath: str, |
|
labels_path: str, |
|
split: str, |
|
): |
|
id_to_label = _load_labels(labels_path=labels_path) |
|
stuff_json = self.load_stuff_json(stuff_dirpath=stuff_dirpath, split=split) |
|
|
|
image_id_to_image_infos = self.get_image_id_to_image_infos( |
|
images=copy.deepcopy(stuff_json["images"]) |
|
) |
|
image_id_to_stuff_annotations = self.get_image_id_to_annotations( |
|
annotations=copy.deepcopy(stuff_json["annotations"]) |
|
) |
|
|
|
assert len(image_id_to_image_infos.keys()) >= len( |
|
image_id_to_stuff_annotations.keys() |
|
) |
|
|
|
for image_id in image_id_to_stuff_annotations.keys(): |
|
|
|
img_info = image_id_to_image_infos[image_id] |
|
image_filename = img_info["file_name"] |
|
image_filepath = os.path.join(image_dirpath, image_filename) |
|
img_example_dict = { |
|
"image": image_filepath, |
|
"image_id": image_id, |
|
"image_filename": image_filename, |
|
"width": img_info["width"], |
|
"height": img_info["height"], |
|
} |
|
|
|
img_anns = image_id_to_stuff_annotations[image_id] |
|
bboxes = [list(map(int, ann["bbox"])) for ann in img_anns] |
|
category_ids = [ann["category_id"] for ann in img_anns] |
|
category_labels = list( |
|
map( |
|
lambda cat_id: id_to_label.get(cat_id, f"unknown-{cat_id}"), |
|
category_ids, |
|
) |
|
) |
|
assert len(bboxes) == len(category_ids) == len(category_labels) |
|
zip_it = zip(bboxes, category_ids, category_labels) |
|
objects_example = [ |
|
{ |
|
"object_id": category_id, |
|
"x": bbox[0], |
|
"y": bbox[1], |
|
"w": bbox[2], |
|
"h": bbox[3], |
|
"name": category_label, |
|
} |
|
for bbox, category_id, category_label in zip_it |
|
] |
|
|
|
root, _ = os.path.splitext(img_example_dict["image_filename"]) |
|
stuff_map_filepath = os.path.join( |
|
stuff_thing_maps_dirpath, f"{split}2017", f"{root}.png" |
|
) |
|
|
|
example_dict = { |
|
**img_example_dict, |
|
"objects": objects_example, |
|
"stuff_map": stuff_map_filepath, |
|
} |
|
yield image_id, example_dict |
|
|
|
def _generate_examples_for_stuff_only( |
|
self, |
|
image_dirpath: str, |
|
stuff_dirpath: str, |
|
labels_path: str, |
|
split: str, |
|
): |
|
id_to_label = _load_labels(labels_path=labels_path) |
|
stuff_json = self.load_stuff_json(stuff_dirpath=stuff_dirpath, split=split) |
|
|
|
image_id_to_image_infos = self.get_image_id_to_image_infos( |
|
images=copy.deepcopy(stuff_json["images"]) |
|
) |
|
image_id_to_stuff_annotations = self.get_image_id_to_annotations( |
|
annotations=copy.deepcopy(stuff_json["annotations"]) |
|
) |
|
|
|
assert len(image_id_to_image_infos.keys()) >= len( |
|
image_id_to_stuff_annotations.keys() |
|
) |
|
|
|
for image_id in image_id_to_stuff_annotations.keys(): |
|
|
|
img_info = image_id_to_image_infos[image_id] |
|
image_filename = img_info["file_name"] |
|
image_filepath = os.path.join(image_dirpath, image_filename) |
|
img_example_dict = { |
|
"image": image_filepath, |
|
"image_id": image_id, |
|
"image_filename": image_filename, |
|
"width": img_info["width"], |
|
"height": img_info["height"], |
|
} |
|
|
|
img_anns = image_id_to_stuff_annotations[image_id] |
|
bboxes = [list(map(int, ann["bbox"])) for ann in img_anns] |
|
category_ids = [ann["category_id"] for ann in img_anns] |
|
category_labels = list( |
|
map( |
|
lambda cat_id: id_to_label.get(cat_id, f"unknown-{cat_id}"), |
|
category_ids, |
|
) |
|
) |
|
assert len(bboxes) == len(category_ids) == len(category_labels) |
|
zip_it = zip(bboxes, category_ids, category_labels) |
|
objects_example = [ |
|
{ |
|
"object_id": category_id, |
|
"x": bbox[0], |
|
"y": bbox[1], |
|
"w": bbox[2], |
|
"h": bbox[3], |
|
"name": category_label, |
|
} |
|
for bbox, category_id, category_label in zip_it |
|
] |
|
|
|
example_dict = { |
|
**img_example_dict, |
|
"objects": objects_example, |
|
} |
|
yield image_id, example_dict |
|
|
|
def _generate_examples( |
|
self, |
|
image_dirpath: str, |
|
stuff_dirpath: str, |
|
stuff_thing_maps_dirpath: str, |
|
labels_path: str, |
|
split: str, |
|
): |
|
logger.info(f"Generating examples for {split}.") |
|
|
|
if "stuff-thing" in self.config.name: |
|
return self._generate_examples_for_stuff_thing( |
|
image_dirpath=image_dirpath, |
|
stuff_dirpath=stuff_dirpath, |
|
stuff_thing_maps_dirpath=stuff_thing_maps_dirpath, |
|
labels_path=labels_path, |
|
split=split, |
|
) |
|
elif "stuff-only" in self.config.name: |
|
return self._generate_examples_for_stuff_only( |
|
image_dirpath=image_dirpath, |
|
stuff_dirpath=stuff_dirpath, |
|
labels_path=labels_path, |
|
split=split, |
|
) |
|
else: |
|
raise ValueError(f"Invalid dataset name: {self.config.name}") |
|
|
