ripe-strawberries-detection.py

from xml.etree import ElementTree as ET

import datasets

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

_DESCRIPTION = """\
The dataset consists of photos of strawberries for the identification and recognition of
ripe berries. 
The images are annotated with **bounding boxes** that accurately demarcate the location
of the ripe strawberries within the image.
Each image in the dataset showcases a strawberry plantation, and includes a diverse
range of backgrounds, lighting conditions, and orientations. The photos are captured
from various angles and distances, providing a realistic representation of strawberries.
The dataset can be utilised for enabling advancements in strawberry production, quality
control, and greater precision in agricultural practices.

"""
_NAME = "ripe-strawberries-detection"

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

_LICENSE = ""

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

_LABELS = ["strawberry"]


class BotoxInjectionsBeforeAndAfter(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"),
                            "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)

        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,
            "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='{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,
            }