pigs-detection-dataset / pigs-detection-dataset.py
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import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {pigs-detection-dataset},
author = {TrainingDataPro},
year = {2023}
}
"""
_DESCRIPTION = """\
The dataset is a collection of images along with corresponding bounding box annotations
that are specifically curated for **detecting pigs' heads** in images. The dataset
covers different *pig breeds, sizes, and orientations*, providing a comprehensive
representation of pig appearances.
The pig detection dataset provides a valuable resource for researchers working on pig
detection tasks. It offers a diverse collection of annotated images, allowing for
comprehensive algorithm development, evaluation, and benchmarking, ultimately aiding in
the development of accurate and robust models.
"""
_NAME = "pigs-detection-dataset"
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class PigsDetectionDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"image": datasets.Image(),
"mask": datasets.Image(),
"bboxes": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images = dl_manager.download(f"{_DATA}images.tar.gz")
masks = dl_manager.download(f"{_DATA}boxes.tar.gz")
annotations = dl_manager.download(f"{_DATA}{_NAME}.csv")
images = dl_manager.iter_archive(images)
masks = dl_manager.iter_archive(masks)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": images,
"masks": masks,
"annotations": annotations,
},
),
]
def _generate_examples(self, images, masks, annotations):
annotations_df = pd.read_csv(annotations)
for idx, ((image_path, image), (mask_path, mask)) in enumerate(
zip(images, masks)
):
yield idx, {
"id": annotations_df["image_id"].iloc[idx],
"image": {"path": image_path, "bytes": image.read()},
"mask": {"path": mask_path, "bytes": mask.read()},
"bboxes": annotations_df["annotations"].iloc[idx],
}