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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
_TYPING_BOX = Tuple[float, float, float, float]
_DESCRIPTION = """\
This dataset contains all THIENVIET products images and annotations split in training
and validation.
"""
_URLS = {
"train": "https://huggingface.co./datasets/chanelcolgate/yenthienviet/resolve/main/data/coco/train.zip",
"val": "https://huggingface.co./datasets/chanelcolgate/yenthienviet/resolve/main/data/coco/val.zip",
"test": "https://huggingface.co./datasets/chanelcolgate/yenthienviet/resolve/main/data/coco/test.zip",
"annotations": "https://huggingface.co./datasets/chanelcolgate/yenthienviet/resolve/main/data/coco/annotations.zip",
}
_SPLITS = ["train", "val", "test"]
_PATHS = {
"annotations": {
"train": Path("_annotations.coco.train.json"),
"val": Path("_annotaions.coco.val.json"),
"test": Path("_annotations.coco.test.json"),
},
"images": {
"train": Path("train"),
"val": Path("val"),
"test": Path("test"),
},
}
_CLASSES = [
"hop_dln",
"hop_jn",
"hop_vtg",
"hop_ytv",
"lo_kids",
"lo_ytv",
"loc_ytv",
"loc_kids",
"loc_dln",
"bot_dln",
"loc_jn",
]
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)
# Sort by id
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 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 COCOThienviet(datasets.GeneratorBasedBuilder):
"""COCO Thienviet dataset."""
VERSION = datasets.Version("1.0.1")
def _info(self) -> datasets.DatasetInfo:
"""
Return 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"),
}
),
}
),
)
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
), # [x, y, w, h]
"label": annot["category_id"],
"iscrowd": bool(annot["iscrowd"]),
}
for annot in annotations
],
}
yield image_path, ret
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