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| import json |
| from pathlib import Path |
| from typing import Any, Dict, Iterator, List, Optional, Tuple, Union |
|
|
| import datasets |
| from datasets.data_files import DataFilesDict |
| from datasets.download.download_manager import ArchiveIterable, DownloadManager |
| from datasets.features import Features |
| from datasets.info import DatasetInfo |
|
|
| |
| _TYPING_BOX = Tuple[float, float, float, float] |
|
|
| _CITATION = """\ |
| @article{DBLP:journals/corr/LinMBHPRDZ14, |
| author = {Tsung{-}Yi Lin and |
| Michael Maire and |
| Serge J. Belongie and |
| Lubomir D. Bourdev and |
| Ross B. Girshick and |
| James Hays and |
| Pietro Perona and |
| Deva Ramanan and |
| Piotr Doll{\'{a}}r and |
| C. Lawrence Zitnick}, |
| title = {Microsoft {COCO:} Common Objects in Context}, |
| journal = {CoRR}, |
| volume = {abs/1405.0312}, |
| year = {2014}, |
| url = {http://arxiv.org/abs/1405.0312}, |
| archivePrefix = {arXiv}, |
| eprint = {1405.0312}, |
| timestamp = {Mon, 13 Aug 2018 16:48:13 +0200}, |
| biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| """ |
|
|
| _DESCRIPTION = """\ |
| This dataset contains all COCO 2017 images and annotations split in training (118287 images) \ |
| and validation (5000 images). |
| """ |
|
|
| _HOMEPAGE = "https://cocodataset.org" |
|
|
| _URLS = { |
| "annotations": "http://images.cocodataset.org/annotations/annotations_trainval2017.zip", |
| "train": "http://images.cocodataset.org/zips/train2017.zip", |
| "val": "http://images.cocodataset.org/zips/val2017.zip", |
| } |
|
|
| _SPLITS = ["train", "val"] |
|
|
| _PATHS = { |
| "annotations": { |
| "train": Path("annotations/instances_train2017.json"), |
| "val": Path("annotations/instances_val2017.json"), |
| }, |
| "images": { |
| "train": Path("train2017"), |
| "val": Path("val2017"), |
| }, |
| } |
|
|
| _CLASSES = [ |
| "None", |
| "person", |
| "bicycle", |
| "car", |
| "motorcycle", |
| "airplane", |
| "bus", |
| "train", |
| "truck", |
| "boat", |
| "traffic light", |
| "fire hydrant", |
| "street sign", |
| "stop sign", |
| "parking meter", |
| "bench", |
| "bird", |
| "cat", |
| "dog", |
| "horse", |
| "sheep", |
| "cow", |
| "elephant", |
| "bear", |
| "zebra", |
| "giraffe", |
| "hat", |
| "backpack", |
| "umbrella", |
| "shoe", |
| "eye glasses", |
| "handbag", |
| "tie", |
| "suitcase", |
| "frisbee", |
| "skis", |
| "snowboard", |
| "sports ball", |
| "kite", |
| "baseball bat", |
| "baseball glove", |
| "skateboard", |
| "surfboard", |
| "tennis racket", |
| "bottle", |
| "plate", |
| "wine glass", |
| "cup", |
| "fork", |
| "knife", |
| "spoon", |
| "bowl", |
| "banana", |
| "apple", |
| "sandwich", |
| "orange", |
| "broccoli", |
| "carrot", |
| "hot dog", |
| "pizza", |
| "donut", |
| "cake", |
| "chair", |
| "couch", |
| "potted plant", |
| "bed", |
| "mirror", |
| "dining table", |
| "window", |
| "desk", |
| "toilet", |
| "door", |
| "tv", |
| "laptop", |
| "mouse", |
| "remote", |
| "keyboard", |
| "cell phone", |
| "microwave", |
| "oven", |
| "toaster", |
| "sink", |
| "refrigerator", |
| "blender", |
| "book", |
| "clock", |
| "vase", |
| "scissors", |
| "teddy bear", |
| "hair drier", |
| "toothbrush", |
| "hair brush", |
| ] |
|
|
| def round_box_values(box, decimals=2): |
| return [round(val, decimals) for val in box] |
|
|
| class COCOHelper: |
| """Helper class to load COCO annotations""" |
|
|
| def __init__(self, annotation_path: Path, images_dir: Path) -> None: |
| with open(annotation_path, "r") as file: |
| data = json.load(file) |
| self.data = data |
| |
| dict_id2annot: Dict[int, Any] = {} |
| for annot in self.annotations: |
| dict_id2annot.setdefault(annot["image_id"], []).append(annot) |
|
|
| |
| dict_id2annot = { |
| k: list(sorted(v, key=lambda a: a["id"])) for k, v in dict_id2annot.items() |
| } |
|
|
| self.dict_path2annot: Dict[str, Any] = {} |
| self.dict_path2id: Dict[str, Any] = {} |
| for img in self.images: |
| path_img = images_dir / str(img["file_name"]) |
| path_img_str = str(path_img) |
| idx = int(img["id"]) |
| annot = dict_id2annot.get(idx, []) |
| self.dict_path2annot[path_img_str] = annot |
| self.dict_path2id[path_img_str] = img["id"] |
|
|
|
|
| def __len__(self) -> int: |
| return len(self.data["images"]) |
|
|
| @property |
| def info(self) -> Dict[str, Union[str, int]]: |
| return self.data["info"] |
|
|
| @property |
| def licenses(self) -> List[Dict[str, Union[str, int]]]: |
| return self.data["licenses"] |
|
|
| @property |
| def images(self) -> List[Dict[str, Union[str, int]]]: |
| return self.data["images"] |
|
|
| @property |
| def annotations(self) -> List[Any]: |
| return self.data["annotations"] |
|
|
| @property |
| def categories(self) -> List[Dict[str, Union[str, int]]]: |
| return self.data["categories"] |
|
|
| def get_annotations(self, image_path: str) -> List[Any]: |
| return self.dict_path2annot.get(image_path, []) |
|
|
| def get_image_id(self, image_path: str) -> int: |
| return self.dict_path2id.get(image_path, -1) |
|
|
|
|
| class COCO2017(datasets.GeneratorBasedBuilder): |
| """COCO 2017 dataset.""" |
|
|
| VERSION = datasets.Version("1.0.1") |
| |
| def _info(self) -> datasets.DatasetInfo: |
| """ |
| Returns the dataset metadata and features. |
| |
| Returns: |
| DatasetInfo: Metadata and features of the dataset. |
| """ |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "image": datasets.Image(), |
| "image_id": datasets.Value("int64"), |
| "objects": datasets.Sequence( |
| { |
| "id": datasets.Value("int64"), |
| "area": datasets.Value("float64"), |
| "bbox": datasets.Sequence( |
| datasets.Value("float32"), length=4 |
| ), |
| "label": datasets.ClassLabel(names=_CLASSES), |
| "iscrowd": datasets.Value("bool"), |
| } |
| ), |
| } |
| ), |
| homepage=_HOMEPAGE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators( |
| self, dl_manager: DownloadManager |
| ) -> List[datasets.SplitGenerator]: |
| """ |
| Provides the split information and downloads the data. |
| |
| Args: |
| dl_manager (DownloadManager): The DownloadManager to use for downloading and |
| extracting data. |
| |
| Returns: |
| List[SplitGenerator]: List of SplitGenerator objects representing the data splits. |
| """ |
| archive_annots = dl_manager.download_and_extract(_URLS["annotations"]) |
|
|
| splits = [] |
| for split in _SPLITS: |
| archive_split = dl_manager.download(_URLS[split]) |
| annotation_path = Path(archive_annots) / _PATHS["annotations"][split] |
| images = dl_manager.iter_archive(archive_split) |
| splits.append( |
| datasets.SplitGenerator( |
| name=datasets.Split(split), |
| gen_kwargs={ |
| "annotation_path": annotation_path, |
| "images_dir": _PATHS["images"][split], |
| "images": images, |
| }, |
| ) |
| ) |
| return splits |
| |
| def _generate_examples( |
| self, annotation_path: Path, images_dir: Path, images: ArchiveIterable |
| ) -> Iterator: |
| """ |
| Generates examples for the dataset. |
| |
| Args: |
| annotation_path (Path): The path to the annotation file. |
| images_dir (Path): The path to the directory containing the images. |
| images: (ArchiveIterable): An iterable containing the images. |
| |
| Yields: |
| Dict[str, Union[str, Image]]: A dictionary containing the generated examples. |
| """ |
| coco_annotation = COCOHelper(annotation_path, images_dir) |
| |
| for image_path, f in images: |
| annotations = coco_annotation.get_annotations(image_path) |
| ret = { |
| "image": {"path": image_path, "bytes": f.read()}, |
| "image_id": coco_annotation.get_image_id(image_path), |
| "objects": [ |
| { |
| "id": annot["id"], |
| "area": annot["area"], |
| "bbox": round_box_values(annot["bbox"], 2), |
| "label": annot["category_id"], |
| "iscrowd": bool(annot["iscrowd"]), |
| } |
| for annot in annotations |
| ], |
| } |
|
|
| yield image_path, ret |
|
|
