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 · Issue #17 · nightrome/cocostuff https://github.com/nightrome/cocostuff/issues/17 # Label matching, 182 or 183 labels? · Issue #8 · 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}")