miners-detection.py

from xml.etree import ElementTree as ET

import datasets

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

_DESCRIPTION = """\
The dataset consists of of photos captured within various mines, focusing on **miners**
engaged in their work. Each photo is annotated with bounding box detection of the
miners, an attribute highlights whether each miner is sitting or standing in the photo.

The dataset's diverse applications such as computer vision, safety assessment and others
make it a valuable resource for *researchers, employers, and policymakers in the mining
industry*.
"""

_NAME = "miners-detection"

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

_LICENSE = ""

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

_LABELS = ["Miner"]


class MinersDetection(datasets.GeneratorBasedBuilder):
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("int32"),
                    "name": datasets.Value("string"),
                    "image": datasets.Image(),
                    "mask": datasets.Image(),
                    "width": datasets.Value("uint16"),
                    "height": datasets.Value("uint16"),
                    "shapes": datasets.Sequence(
                        {
                            "label": datasets.ClassLabel(
                                num_classes=len(_LABELS),
                                names=_LABELS,
                            ),
                            "type": datasets.Value("string"),
                            "points": datasets.Sequence(
                                datasets.Sequence(
                                    datasets.Value("float"),
                                ),
                            ),
                            "rotation": datasets.Value("float"),
                            "occluded": datasets.Value("uint8"),
                            "attributes": datasets.Sequence(
                                {
                                    "name": datasets.Value("string"),
                                    "text": 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}annotations.xml")
        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,
                },
            ),
        ]

    @staticmethod
    def parse_shape(shape: ET.Element) -> dict:
        label = shape.get("label")
        shape_type = shape.tag
        rotation = shape.get("rotation", 0.0)
        occluded = shape.get("occluded", 0)

        points = None

        if shape_type == "points":
            points = tuple(map(float, shape.get("points").split(",")))

        elif shape_type == "box":
            points = [
                (float(shape.get("xtl")), float(shape.get("ytl"))),
                (float(shape.get("xbr")), float(shape.get("ybr"))),
            ]

        elif shape_type == "polygon":
            points = [
                tuple(map(float, point.split(",")))
                for point in shape.get("points").split(";")
            ]

        attributes = []

        for attr in shape:
            attr_name = attr.get("name")
            attr_text = attr.text
            attributes.append({"name": attr_name, "text": attr_text})

        shape_data = {
            "label": label,
            "type": shape_type,
            "points": points,
            "rotation": rotation,
            "occluded": occluded,
            "attributes": attributes,
        }

        return shape_data

    def _generate_examples(self, images, masks, annotations):
        tree = ET.parse(annotations)
        root = tree.getroot()

        for idx, (
            (image_path, image),
            (mask_path, mask),
        ) in enumerate(zip(images, masks)):
            image_name = image_path.split("/")[-1]
            img = root.find(f"./image[@name='images/{image_name}']")

            image_id = img.get("id")
            name = img.get("name")
            width = img.get("width")
            height = img.get("height")
            shapes = [self.parse_shape(shape) for shape in img]

            yield idx, {
                "id": image_id,
                "name": name,
                "image": {"path": image_path, "bytes": image.read()},
                "mask": {"path": mask_path, "bytes": mask.read()},
                "width": width,
                "height": height,
                "shapes": shapes,
            }