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import copy |
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import json |
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import logging |
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import os |
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from collections import defaultdict |
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from typing import Dict, TypedDict |
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import datasets as ds |
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logger = logging.getLogger(__name__) |
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_CITATION = """\ |
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@INPROCEEDINGS{caesar2018cvpr, |
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title={COCO-Stuff: Thing and stuff classes in context}, |
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author={Caesar, Holger and Uijlings, Jasper and Ferrari, Vittorio}, |
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booktitle={Computer vision and pattern recognition (CVPR), 2018 IEEE conference on}, |
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organization={IEEE}, |
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year={2018} |
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} |
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""" |
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_DESCRIPTION = """\ |
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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. |
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""" |
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_HOMEPAGE = "https://github.com/nightrome/cocostuff" |
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_LICENSE = """\ |
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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: |
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- COCO images: Flickr Terms of use |
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- COCO annotations: Creative Commons Attribution 4.0 License |
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- COCO-Stuff annotations & code: Creative Commons Attribution 4.0 License |
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""" |
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class URLs(TypedDict): |
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train: str |
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val: str |
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stuffthingmaps_trainval: str |
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stuff_trainval: str |
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labels: str |
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_URLS: URLs = { |
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"train": "http://images.cocodataset.org/zips/train2017.zip", |
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"val": "http://images.cocodataset.org/zips/val2017.zip", |
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"stuffthingmaps_trainval": "http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip", |
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"stuff_trainval": "http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuff_trainval2017.zip", |
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"labels": "https://raw.githubusercontent.com/nightrome/cocostuff/master/labels.txt", |
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} |
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class GenerateExamplesArguments(TypedDict): |
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image_dirpath: str |
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stuff_dirpath: str |
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stuff_thing_maps_dirpath: str |
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labels_path: str |
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split: str |
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def _load_json(json_path: str): |
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logger.info(f"Load json from {json_path}") |
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with open(json_path, "r") as rf: |
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json_data = json.load(rf) |
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return json_data |
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def _load_labels(labels_path: str) -> Dict[int, str]: |
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label_id_to_label_name: Dict[int, str] = {} |
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logger.info(f"Load labels from {labels_path}") |
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with open(labels_path, "r") as rf: |
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for line in rf: |
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label_id_str, label_name = line.strip().split(": ") |
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label_id = int(label_id_str) |
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if label_id == 0: |
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assert label_name == "unlabeled", label_name |
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label_id_to_label_name[183] = label_name |
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else: |
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label_id_to_label_name[label_id] = label_name |
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assert len(label_id_to_label_name) == 183 |
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return label_id_to_label_name |
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class CocoStuffDataset(ds.GeneratorBasedBuilder): |
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VERSION = ds.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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ds.BuilderConfig( |
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name="stuff-thing", |
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version=VERSION, |
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description="Stuff+thing PNG-style annotations on COCO 2017 trainval", |
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), |
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ds.BuilderConfig( |
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name="stuff-only", |
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version=VERSION, |
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description="Stuff-only COCO-style annotations on COCO 2017 trainval", |
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), |
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] |
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def _info(self) -> ds.DatasetInfo: |
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if self.config.name == "stuff-thing": |
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features = ds.Features( |
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{ |
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"image": ds.Image(), |
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"image_id": ds.Value("int32"), |
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"image_filename": ds.Value("string"), |
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"width": ds.Value("int32"), |
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"height": ds.Value("int32"), |
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"stuff_map": ds.Image(), |
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"objects": [ |
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{ |
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"object_id": ds.Value("string"), |
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"x": ds.Value("int32"), |
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"y": ds.Value("int32"), |
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"w": ds.Value("int32"), |
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"h": ds.Value("int32"), |
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"name": ds.Value("string"), |
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} |
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], |
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} |
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) |
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elif self.config.name == "stuff-only": |
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features = ds.Features( |
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{ |
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"image": ds.Image(), |
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"image_id": ds.Value("int32"), |
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"image_filename": ds.Value("string"), |
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"width": ds.Value("int32"), |
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"height": ds.Value("int32"), |
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"objects": [ |
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{ |
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"object_id": ds.Value("int32"), |
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"x": ds.Value("int32"), |
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"y": ds.Value("int32"), |
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"w": ds.Value("int32"), |
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"h": ds.Value("int32"), |
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"name": ds.Value("string"), |
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} |
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], |
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} |
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) |
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else: |
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raise ValueError(f"Invalid dataset name: {self.config.name}") |
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return ds.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def load_stuff_json(self, stuff_dirpath: str, split: str): |
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return _load_json( |
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json_path=os.path.join(stuff_dirpath, f"stuff_{split}2017.json") |
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) |
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def get_image_id_to_image_infos(self, images): |
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image_id_to_image_infos = {} |
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for img_dict in images: |
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image_id = img_dict.pop("id") |
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image_id_to_image_infos[image_id] = img_dict |
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image_id_to_image_infos = dict(sorted(image_id_to_image_infos.items())) |
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return image_id_to_image_infos |
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def get_image_id_to_annotations(self, annotations): |
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image_id_to_annotations = defaultdict(list) |
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for ann_dict in annotations: |
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image_id = ann_dict.pop("image_id") |
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image_id_to_annotations[image_id].append(ann_dict) |
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image_id_to_annotations = dict(sorted(image_id_to_annotations.items())) |
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return image_id_to_annotations |
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def _split_generators(self, dl_manager: ds.DownloadManager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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tng_image_dirpath = os.path.join(downloaded_files["train"], "train2017") |
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val_image_dirpath = os.path.join(downloaded_files["val"], "val2017") |
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stuff_dirpath = downloaded_files["stuff_trainval"] |
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stuff_things_maps_dirpath = downloaded_files["stuffthingmaps_trainval"] |
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labels_path = downloaded_files["labels"] |
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tng_gen_kwargs: GenerateExamplesArguments = { |
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"image_dirpath": tng_image_dirpath, |
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"stuff_dirpath": stuff_dirpath, |
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"stuff_thing_maps_dirpath": stuff_things_maps_dirpath, |
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"labels_path": labels_path, |
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"split": "train", |
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} |
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val_gen_kwargs: GenerateExamplesArguments = { |
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"image_dirpath": val_image_dirpath, |
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"stuff_dirpath": stuff_dirpath, |
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"stuff_thing_maps_dirpath": stuff_things_maps_dirpath, |
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"labels_path": labels_path, |
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"split": "val", |
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} |
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return [ |
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ds.SplitGenerator( |
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name=ds.Split.TRAIN, |
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gen_kwargs=tng_gen_kwargs, |
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), |
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ds.SplitGenerator( |
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name=ds.Split.VALIDATION, |
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gen_kwargs=val_gen_kwargs, |
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), |
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] |
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def _generate_examples_for_stuff_thing( |
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self, |
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image_dirpath: str, |
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stuff_dirpath: str, |
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stuff_thing_maps_dirpath: str, |
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labels_path: str, |
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split: str, |
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): |
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id_to_label = _load_labels(labels_path=labels_path) |
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stuff_json = self.load_stuff_json(stuff_dirpath=stuff_dirpath, split=split) |
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image_id_to_image_infos = self.get_image_id_to_image_infos( |
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images=copy.deepcopy(stuff_json["images"]) |
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) |
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image_id_to_stuff_annotations = self.get_image_id_to_annotations( |
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annotations=copy.deepcopy(stuff_json["annotations"]) |
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) |
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assert len(image_id_to_image_infos.keys()) >= len( |
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image_id_to_stuff_annotations.keys() |
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) |
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for image_id in image_id_to_stuff_annotations.keys(): |
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img_info = image_id_to_image_infos[image_id] |
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image_filename = img_info["file_name"] |
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image_filepath = os.path.join(image_dirpath, image_filename) |
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img_example_dict = { |
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"image": image_filepath, |
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"image_id": image_id, |
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"image_filename": image_filename, |
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"width": img_info["width"], |
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"height": img_info["height"], |
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} |
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img_anns = image_id_to_stuff_annotations[image_id] |
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bboxes = [list(map(int, ann["bbox"])) for ann in img_anns] |
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category_ids = [ann["category_id"] for ann in img_anns] |
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category_labels = list(map(lambda cid: id_to_label[cid], category_ids)) |
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assert len(bboxes) == len(category_ids) == len(category_labels) |
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zip_it = zip(bboxes, category_ids, category_labels) |
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objects_example = [ |
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{ |
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"object_id": category_id, |
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"x": bbox[0], |
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"y": bbox[1], |
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"w": bbox[2], |
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"h": bbox[3], |
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"name": category_label, |
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} |
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for bbox, category_id, category_label in zip_it |
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] |
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root, _ = os.path.splitext(img_example_dict["image_filename"]) |
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stuff_map_filepath = os.path.join( |
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stuff_thing_maps_dirpath, f"{split}2017", f"{root}.png" |
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) |
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example_dict = { |
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**img_example_dict, |
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"objects": objects_example, |
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"stuff_map": stuff_map_filepath, |
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} |
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yield image_id, example_dict |
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def _generate_examples_for_stuff_only( |
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self, |
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image_dirpath: str, |
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stuff_dirpath: str, |
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labels_path: str, |
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split: str, |
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): |
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id_to_label = _load_labels(labels_path=labels_path) |
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stuff_json = self.load_stuff_json(stuff_dirpath=stuff_dirpath, split=split) |
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image_id_to_image_infos = self.get_image_id_to_image_infos( |
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images=copy.deepcopy(stuff_json["images"]) |
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) |
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image_id_to_stuff_annotations = self.get_image_id_to_annotations( |
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annotations=copy.deepcopy(stuff_json["annotations"]) |
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) |
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assert len(image_id_to_image_infos.keys()) >= len( |
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image_id_to_stuff_annotations.keys() |
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) |
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for image_id in image_id_to_stuff_annotations.keys(): |
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img_info = image_id_to_image_infos[image_id] |
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image_filename = img_info["file_name"] |
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image_filepath = os.path.join(image_dirpath, image_filename) |
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img_example_dict = { |
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"image": image_filepath, |
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"image_id": image_id, |
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"image_filename": image_filename, |
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"width": img_info["width"], |
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"height": img_info["height"], |
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} |
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img_anns = image_id_to_stuff_annotations[image_id] |
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bboxes = [list(map(int, ann["bbox"])) for ann in img_anns] |
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category_ids = [ann["category_id"] for ann in img_anns] |
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category_labels = list(map(lambda cid: id_to_label[cid], category_ids)) |
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assert len(bboxes) == len(category_ids) == len(category_labels) |
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zip_it = zip(bboxes, category_ids, category_labels) |
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objects_example = [ |
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{ |
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"object_id": category_id, |
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"x": bbox[0], |
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"y": bbox[1], |
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"w": bbox[2], |
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"h": bbox[3], |
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"name": category_label, |
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} |
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for bbox, category_id, category_label in zip_it |
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] |
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example_dict = { |
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**img_example_dict, |
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"objects": objects_example, |
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} |
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yield image_id, example_dict |
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def _generate_examples( |
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self, |
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image_dirpath: str, |
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stuff_dirpath: str, |
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stuff_thing_maps_dirpath: str, |
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labels_path: str, |
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split: str, |
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): |
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logger.info(f"Generating examples for {split}.") |
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if "stuff-thing" in self.config.name: |
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return self._generate_examples_for_stuff_thing( |
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image_dirpath=image_dirpath, |
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stuff_dirpath=stuff_dirpath, |
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stuff_thing_maps_dirpath=stuff_thing_maps_dirpath, |
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labels_path=labels_path, |
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split=split, |
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) |
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elif "stuff-only" in self.config.name: |
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return self._generate_examples_for_stuff_only( |
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image_dirpath=image_dirpath, |
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stuff_dirpath=stuff_dirpath, |
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labels_path=labels_path, |
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split=split, |
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) |
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else: |
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raise ValueError(f"Invalid dataset name: {self.config.name}") |
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