facial_keypoint_detection.py

import datasets
import pandas as pd

_CITATION = """\
@InProceedings{huggingface:dataset,
title = {facial_keypoint_detection},
author = {TrainingDataPro},
year = {2023}
}
"""

_DESCRIPTION = """\
The dataset is designed for computer vision and machine learning tasks
involving the identification and analysis of key points on a human face.
It consists of images of human faces, each accompanied by key point
annotations in XML format.
"""
_NAME = 'facial_keypoint_detection'

_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"

_LICENSE = "cc-by-nc-nd-4.0"

_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"


class FacialKeypointDetection(datasets.GeneratorBasedBuilder):

    def _info(self):
        return datasets.DatasetInfo(description=_DESCRIPTION,
                                    features=datasets.Features({
                                        'image_id': datasets.Value('uint32'),
                                        'image': datasets.Image(),
                                        'mask': datasets.Image(),
                                        'key_points': datasets.Value('string')
                                    }),
                                    supervised_keys=None,
                                    homepage=_HOMEPAGE,
                                    citation=_CITATION,
                                    license=_LICENSE)

    def _split_generators(self, dl_manager):
        images = dl_manager.download(f"{_DATA}images.tar.gz")
        masks = dl_manager.download(f"{_DATA}masks.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, sep=',')
        for idx, ((image_path, image),
                  (mask_path, mask)) in enumerate(zip(images, masks)):
            yield idx, {
                'image_id': annotations_df['image_id'].iloc[idx],
                "image": {
                    "path": image_path,
                    "bytes": image.read()
                },
                "mask": {
                    "path": mask_path,
                    "bytes": mask.read()
                },
                'key_points': annotations_df['key_points'].iloc[idx]
            }