File size: 2,782 Bytes
5abef98 b4eccae 5abef98 b4eccae 5abef98 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
import datasets
import pandas as pd
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {cows-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 cows** in images. The dataset covers
different *cows breeds, sizes, and orientations*, providing a comprehensive
representation of cows appearances and positions. Additionally, the visibility of each
cow is presented in the .xml file.
The cow detection dataset provides a valuable resource for researchers working on
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 = "cows-detection-dataset"
_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
_LICENSE = ""
_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
class CowsDetectionDataset(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],
}
|