coco_detection_dataset_script.py

import json
import os

import datasets


class COCOBuilderConfig(datasets.BuilderConfig):
 def __init__(self, name, splits, **kwargs):
 super().__init__(name, **kwargs)
 self.splits = splits


# Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_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}
}
"""

# Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
COCO is a large-scale object detection, segmentation, and captioning dataset.
"""

# Add a link to an official homepage for the dataset here
_HOMEPAGE = "http://cocodataset.org/#home"

# Add the licence for the dataset here if you can find it
_LICENSE = ""

# Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)

# This script is supposed to work with local (downloaded) COCO dataset.
_URLs = {}


# Name of the dataset usually match the script name with CamelCase instead of snake_case
class COCODataset(datasets.GeneratorBasedBuilder):
 """An example dataset script to work with the local (downloaded) COCO dataset"""

 VERSION = datasets.Version("0.0.0")

 BUILDER_CONFIG_CLASS = COCOBuilderConfig
 BUILDER_CONFIGS = [
 COCOBuilderConfig(name="2017", splits=["train", "val"]),
 ]
 DEFAULT_CONFIG_NAME = "2017"

 def _info(self):
 # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset

 feature_dict = {
 "id": datasets.Value("int64"),
 "objects": {
 "bbox_id": datasets.Sequence(datasets.Value("int64")),
 "category_id": datasets.Sequence(
 datasets.ClassLabel(
 names=[
 "N/A",
 "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",
 ]
 )
 ),
 "bbox": datasets.Sequence(
 datasets.Sequence(datasets.Value("float64"), length=4)
 ),
 "iscrowd": datasets.Sequence(datasets.Value("int64")),
 "area": datasets.Sequence(datasets.Value("float64")),
 },
 "height": datasets.Value("int64"),
 "width": datasets.Value("int64"),
 "file_name": datasets.Value("string"),
 "coco_url": datasets.Value("string"),
 "image_path": datasets.Value("string"),
 }

 features = datasets.Features(feature_dict)

 return datasets.DatasetInfo(
 # This is the description that will appear on the datasets page.
 description=_DESCRIPTION,
 # This defines the different columns of the dataset and their types
 features=features, # Here we define them above because they are different between the two configurations
 # If there's a common (input, target) tuple from the features,
 # specify them here. They'll be used if as_supervised=True in
 # builder.as_dataset.
 supervised_keys=None,
 # Homepage of the dataset for documentation
 homepage=_HOMEPAGE,
 # License for the dataset if available
 license=_LICENSE,
 # Citation for the dataset
 citation=_CITATION,
 )

 def _split_generators(self, dl_manager):
 """Returns SplitGenerators."""
 # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
 # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

 data_dir = self.config.data_dir
 if not data_dir:
 raise ValueError(
 "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
 )

 _DL_URLS = {
 "train": os.path.join(data_dir, "train2017.zip"),
 "val": os.path.join(data_dir, "val2017.zip"),
 "annotations_trainval": os.path.join(
 data_dir, "annotations_trainval2017.zip"
 ),
 }
 archive_path = dl_manager.download_and_extract(_DL_URLS)

 splits = []
 for split in self.config.splits:
 if split == "train":
 dataset = datasets.SplitGenerator(
 name=datasets.Split.TRAIN,
 # These kwargs will be passed to _generate_examples
 gen_kwargs={
 "json_path": os.path.join(
 archive_path["annotations_trainval"],
 "annotations",
 "instances_train2017.json",
 ),
 "image_dir": os.path.join(archive_path["train"], "train2017"),
 "split": "train",
 },
 )
 elif split in ["val", "valid", "validation", "dev"]:
 dataset = datasets.SplitGenerator(
 name=datasets.Split.VALIDATION,
 # These kwargs will be passed to _generate_examples
 gen_kwargs={
 "json_path": os.path.join(
 archive_path["annotations_trainval"],
 "annotations",
 "instances_val2017.json",
 ),
 "image_dir": os.path.join(archive_path["val"], "val2017"),
 "split": "valid",
 },
 )
 else:
 continue

 splits.append(dataset)

 return splits

 def _generate_examples(
 # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
 self,
 json_path,
 image_dir,
 split,
 ):
 """Yields examples as (key, example) tuples."""
 # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
 # The `key` is here for legacy reason (tfds) and is not important in itself.

 features = [
 "id",
 "objects",
 "height",
 "width",
 "file_name",
 "coco_url",
 "image_path",
 ]
 object_features = [
 "bbox_id",
 "category_id",
 "bbox",
 "iscrowd",
 "area",
 ]

 with open(json_path, "r", encoding="UTF-8") as fp:
 data = json.load(fp)

 images = data["images"]
 images_entry = {image["id"]: image for image in images}
 for image_id, image_entry in images_entry.items():
 image_entry["image_path"] = os.path.join(
 image_dir, image_entry["file_name"]
 )
 image_entry["objects"] = []

 objects = data["annotations"]
 for id_, object_entry in enumerate(objects):
 image_id = object_entry["image_id"]

 entry = {k: v for k, v in object_entry.items() if k in object_features}
 entry["bbox_id"] = object_entry["id"]
 if entry["iscrowd"]:
 continue
 images_entry[image_id]["objects"].append(entry)

 for id_, entry in images_entry.items():
 entry = {k: v for k, v in entry.items() if k in features}
 # collate objects
 objects = entry.pop("objects")
 if not objects:
 continue
 entry["objects"] = {
 object_feature: [obj[object_feature] for obj in objects]
 for object_feature in object_features
 }

 yield str(entry["id"]), entry