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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_str, label_name = line.strip().split(": ")
label_id = int(label_id_str)
# correspondence between .png annotation & category_id 路 Issue #17 路 nightrome/cocostuff https://github.com/nightrome/cocostuff/issues/17
# Label matching, 182 or 183 labels? 路 Issue #8 路 nightrome/cocostuff https://github.com/nightrome/cocostuff/issues/8
if label_id == 0:
# for unlabeled class
assert label_name == "unlabeled", label_name
label_id_to_label_name[183] = label_name
else:
label_id_to_label_name[label_id] = label_name
assert len(label_id_to_label_name) == 183
return label_id_to_label_name
class CocoStuffDataset(ds.GeneratorBasedBuilder):
VERSION = ds.Version("1.0.0") # type: ignore
BUILDER_CONFIGS = [
ds.BuilderConfig(
name="stuff-thing",
version=VERSION, # type: ignore
description="Stuff+thing PNG-style annotations on COCO 2017 trainval",
),
ds.BuilderConfig(
name="stuff-only",
version=VERSION, # type: ignore
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, # type: ignore
gen_kwargs=tng_gen_kwargs, # type: ignore
),
ds.SplitGenerator(
name=ds.Split.VALIDATION, # type: ignore
gen_kwargs=val_gen_kwargs, # type: ignore
),
]
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 cid: id_to_label[cid], 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 cid: id_to_label[cid], 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( # type: ignore
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}")
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